zaenalium/the-stack-v2-r-code · Datasets at Hugging Face

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#service_data_new_data_eda.csv dataset <- read.csv(file.choose()) View(dataset) str(dataset) # 범주형 vs 범주형을 가지고 데이터의 분포를 확인한다면? # 1. resident2, gender2를 범주형으로 변환 dataset$resident2 <- factor(dataset$resident2) dataset$gender2 <- factor(dataset$gender2) levels(dataset$resident2) levels(dataset$gender2) # 2. 두 변수를 table()함수를 이용하여 분포를 확인해보자 resident_gender <- table(dataset$resident2, dataset$gender2) #막대 가로로 horiz이용 barplot(resident_gender,horiz = T) #한 막대로 누적되어있는거를 멀티바로 나열 barplot(resident_gender,horiz = T,beside = T) #범례 추가 barplot(resident_gender,horiz = T,beside = T, legend = row.names(resident_gender)) #색 지정 barplot(resident_gender,horiz = T,beside = T, legend = row.names(resident_gender), col = rainbow(5)) ###위 그래프랑 비슷하게 ggplot이용 resident_gender_df <- data.frame(resident_gender) ###그룹은 박스플롯할때 특정그룹으로 묶어지는데 여기서는 fill자체에서 그룹화되어짐 ggplot(data = resident_gender_df, aes(x=Freq, y = Var2, fill = Var1, group = Var1)) + geom_bar(stat = "identity", position = 'dodge') #모자이크플롯 mosaicplot(resident_gender, col = rainbow(2)) # 직업유형(job2) vs 나이(age2) dataset$job2 <- factor(dataset$job2) dataset$age2 <- factor(dataset$age2) jobage <- table(dataset$job2, dataset$age2) barplot(jobage, beside = T,legend = row.names(jobage), col = rainbow(3)) jobage_df <- data.frame(jobage) ggplot(data = jobage_df, aes(x=Freq, y=Var2, fill = Var1, group = Var1)) + geom_bar(stat = "identity", position = "dodge") + coord_flip() # 숫자형 vs 범주형 # 직업유형에 따른 나이 비율 # 카테고리 유형별 시각화 install.packages("lattice") library(lattice) ?densityplot #density는 x축값 먼저정의하고 데이터정의, 근데 팩터형이어야함 densityplot(dataset$age2, dataset) str(dataset) #auto.key = T 로 범례 설정 densityplot(dataset$age, dataset, group = dataset$job2, auto.key = T) ggplot(data = dataset, aes(x= age, fill = job2)) + geom_bar(width = .5, position = "dodge") ##실습3!!! # 데이터 프레임의 복사본 생성하기 library(ggplot2) midwest midwest_raw <- data.frame(midwest) midwest_new <- midwest_raw str(midwest_new) head(midwest_new) # [문제] # poptotal(전체인구) 변수를 total로, # popasian(아시안 인구) 변수를 asian으로 수정하세요. midwest_new <- rename(midwest_new, "total" = "poptotal") midwest_new <- rename(midwest_new, "asian" = "popasian") library(dplyr) # [문제] # total, asian 변수를 이용해 '전체 인구 대비 아시아 인구 백분율' percasian 파생변수를 만들고, # 히스토그램을 만들어 도시들이 어떻게 분포하는지 살펴보세요. midwest_new$"percasian2" <- (midwest_new$asian / midwest_new$total)100 hist(midwest_new$percasian2) # [문제] # 아시아 인구 백분율 전체 평균을 구하고, # 평균을 초과하면 "large", # 그 외에는 "small"을 부여하는 mean 파생변수를 만들어 보세요. mean(midwest_new$percasian2) midwest_new$mean_percasian2 <- ifelse(midwest_new$percasian2 > mean(midwest_new$percasian2),"large","small") # [문제] # "large"와 "small"에 해당하는 지역이 얼마나 되는지 빈도표와 빈도 막대 그래프를 만들어 확인해 보세요. densityplot(midwest_new$percasian2, midwest_new, group = midwest_new$country, auto.key = T) ggplot(data = midwest_new, aes(x=percasian2,y=county)) + geom_bar(stat = "identity",width = .1, position = "dodge") + geom_point() teacher <- as.data.frame(table(midwest_new$mean_percasian2)) ggplot(teacher, aes(x=Var1, y=Freq)) + geom_bar(stat = "identity", width = .2) # ggplot2의 midwest 데이터를 사용하여 데이터 분석을 실습하는 문제 입니다. # popadults는 해당 지역의 성인 인구, poptotal은 전체 인구를 나타냅니다. # 1번 문제 # midwest 데이터에 '전체 인구 대비 미성년 인구 백분율' 변수를 추가하세요. midwest_new$"전체 인구 대비 미성년 인구 백분율" <- ((midwest_new$total - midwest_new$popadults) / midwest_new$total)100 ##강사님 답 midwest_new <- midwest_new %>% mutate(percyouth = (poptotal - popadults)/poptotal * 100) # 2번 문제 # 미성년 인구 백분율이 가장 높은 상위 5개 county(지역)의 미성년 인구 백분율을 출력하시오. tail(sort(midwest_new$"전체 인구 대비 미성년 인구 백분율"), n=5) ##강사님 답 midwest_new %>% arrange(desc(percyouth)) %>% dplyr::select(county, percyouth) %>% head(5) # 3번 문제 # 다음과 같은 분류표의 기준에 따라 미성년 비율 등급 변수를 추가하고, 각 등급에 몇 개의 지역이 있는지 알아보세요. # 분류 기준 # large 40%이상 # middle 30 ~ 40미만 # small 30미만 midwest_new$"미성년 비율 등급" <- ifelse("전체 인구 대비 미성년 인구 백분율" >= 40,"large",ifelse("전체 인구 대비 미성년 인구 백분율" >= 30,"middle","small")) ##강사님 답 midwest_new <- midwest_new %>% mutate(gradeyouth = ifelse(percyouth >= 40 ,"large", ifelse(percyouth >= 30,"middle","small"))) table(midwest_new$gradeyouth) # 4번 문제 # popasian은 해당 지역의 아시아인 인구를 나타냅니다. # '전체 인구 대비 아시아인 인구 백분율' 변수를 추가하고 하위 10개 지역의 state(주), county(지역), 아시아인 인구 백분율을 출력하세요. midwest_new$"전체 인구 대비 아시아인 인구 백분율" <- (midwest_new$asian / midwest_new$total)100 head(sort(midwest_new$state), n=10) head(sort(midwest_new$county), n=10) head(sort(midwest_new$"전체 인구 대비 아시아인 인구 백분율"), n=10) ##강사님 답 midwest_new <- midwest_new %>% mutate(ratio_asian = popasian/poptotal100) midwest_new %>% arrange(ratio_asian) %>% dplyr::select(state, county, ratio_asian) %>% head(10) # 시계열(time series) # 변수간의 상관성 # iris 시계열 데이터 만들기 iris seq <- as.integer( rownames(iris) ) ?cbind irisDF <- cbind(seq = as.integer(rownames(iris)),iris) # x축은 seq # y축은 -Species #랜덤하게 4색 추출 colsColor <- topo.colors(4,alpha = .4) #2열부터 5열까지 이름부여 names(colsColor) <- names(irisDF)[2:5] #melt함수 이용해서 기준seq, species #나머지 컬럼을 variable해서 wide -> long irisDF library(reshape2) iris_melt <- melt(irisDF, id = c("seq","Species")) ##버젼떄문에 오류가 나면 id대신 id.vars로 표현 library(ggplot2) g <- ggplot(data = iris_melt, aes(x=seq, y=value, col = variable)) + geom_line(cex = 0.8, show.legend = T) #추가적으로 선의 색상과 범례 라벨링 g <- g + scale_color_manual( name = "", values = colsColor[iris_melt$variable], labels = c("꽃받침 길이", "꽃받침 너비", "꽃잎 길이", "꽃잎 너비") ) # 날짜 # 문자변수를 날짜변수 변환 # R의 날짜 데이터 타입 "POSIXct" # as.POSIXct() str_date <- "200730 13:40" as.POSIXct(str_date, format = "%y%m%d %H:%M") #2020이라Y 20이면y str_date <- "2020-07-30 13:40:01 PM" as.POSIXct(str_date, format = "%Y-%m-%d %H:%M:%S") str_date <- "07/30/20 13:40:01" as.POSIXct(str_date, format = "%m/%d/%y %H:%M:%S") cospi <- read.csv(file.choose()) #날짜별 주가 만들기(시간이 있어서 따로 시계열 만들필요 없음) cospi_test <- cospi cospi_melt <- melt(cospi_test, id = c("Date","Volume")) ggplot(data = cospi_melt, aes(x= Date,y=value,col = variable, group = variable)) + geom_line(cex = 0.5, show.legend = T) ##실습4!!! spanish_train <- read.csv(file.choose()) str(spanish_train) # 1. # 데이터 내에 결측치 여부를 확인한다. # NA값이 310681개 있는 것을 확인할 수 있다. #결측치 확인방법 spanish_train[!complete.cases(spanish_train),] str(spanish_train[!complete.cases(spanish_train),]) #결측치 제거 test_renfe2 <- spanish_train[complete.cases(spanish_train),] str(test_renfe2) # 2. # filter와 !is.na함수를 통해 결측치를 모두 제거했다. # 3. # 마드리드 출발 # 마드리드에서 출발하는 열차 데이터만을 떼어내 madrid_origin이라는 변수로 저장하고 # 우선, 마드리드에서 출발하는 열차 데이터만을 이용해 비교해보기로 한다. madrid_origin <- filter(test_renfe2, test_renfe2$origin == "MADRID") str(madrid_origin) # 4. # summary함수를 통해 일반적 데이터 정보를 다시 확인한다. summary(test_renfe2) # 5. # 마드리드 출발 열차의 빈도 수 # 마드리드를 출발하는 기차의 도착 도시별 운행빈도 수를 바형태로 나타내보자 ggplot(data = madrid_origin, aes(x=destination,fill=origin)) + geom_bar(width = .5, position = "dodge") # 6. # 마드리드발 도착지별 가격 박스플롯으로 티켓가격의 높은 순을 확인해보자 boxplot(price ~ destination, data=madrid_origin) str(madrid_origin) # 7. # AVE의좌석 등급별 가격박스플롯이 시각화 # 똑같은 열차와 똑같은 좌석등급, 똑같은 도착지라 하더라도 가격이 차이가 나는 것을 확인할 수 있다. AVE_MADRID <- filter(madrid_origin, madrid_origin$train_type == "AVE") boxplot(price ~ destination,data= AVE_MADRID) # 8. # 이 차이를 이해하고 싶어 시계열로 데이터를 만들어보았다. # 9. #날짜 데이터 변환. as.POSIXct는 factor형식의 날짜 사용가능 # 10. # 컬럼이름지정 # colnames(a_b) <- c("preferente","Turista") # 11. # 도착지별, 트레인 클래스별로 가격을 박스플롯형태로 나타낼 수도 있다.	/eighth_class.R	no_license	Lucidhomme/R	R	false	false	10,122	r	#service_data_new_data_eda.csv dataset <- read.csv(file.choose()) View(dataset) str(dataset) # 범주형 vs 범주형을 가지고 데이터의 분포를 확인한다면? # 1. resident2, gender2를 범주형으로 변환 dataset$resident2 <- factor(dataset$resident2) dataset$gender2 <- factor(dataset$gender2) levels(dataset$resident2) levels(dataset$gender2) # 2. 두 변수를 table()함수를 이용하여 분포를 확인해보자 resident_gender <- table(dataset$resident2, dataset$gender2) #막대 가로로 horiz이용 barplot(resident_gender,horiz = T) #한 막대로 누적되어있는거를 멀티바로 나열 barplot(resident_gender,horiz = T,beside = T) #범례 추가 barplot(resident_gender,horiz = T,beside = T, legend = row.names(resident_gender)) #색 지정 barplot(resident_gender,horiz = T,beside = T, legend = row.names(resident_gender), col = rainbow(5)) ###위 그래프랑 비슷하게 ggplot이용 resident_gender_df <- data.frame(resident_gender) ###그룹은 박스플롯할때 특정그룹으로 묶어지는데 여기서는 fill자체에서 그룹화되어짐 ggplot(data = resident_gender_df, aes(x=Freq, y = Var2, fill = Var1, group = Var1)) + geom_bar(stat = "identity", position = 'dodge') #모자이크플롯 mosaicplot(resident_gender, col = rainbow(2)) # 직업유형(job2) vs 나이(age2) dataset$job2 <- factor(dataset$job2) dataset$age2 <- factor(dataset$age2) jobage <- table(dataset$job2, dataset$age2) barplot(jobage, beside = T,legend = row.names(jobage), col = rainbow(3)) jobage_df <- data.frame(jobage) ggplot(data = jobage_df, aes(x=Freq, y=Var2, fill = Var1, group = Var1)) + geom_bar(stat = "identity", position = "dodge") + coord_flip() # 숫자형 vs 범주형 # 직업유형에 따른 나이 비율 # 카테고리 유형별 시각화 install.packages("lattice") library(lattice) ?densityplot #density는 x축값 먼저정의하고 데이터정의, 근데 팩터형이어야함 densityplot(dataset$age2, dataset) str(dataset) #auto.key = T 로 범례 설정 densityplot(dataset$age, dataset, group = dataset$job2, auto.key = T) ggplot(data = dataset, aes(x= age, fill = job2)) + geom_bar(width = .5, position = "dodge") ##실습3!!! # 데이터 프레임의 복사본 생성하기 library(ggplot2) midwest midwest_raw <- data.frame(midwest) midwest_new <- midwest_raw str(midwest_new) head(midwest_new) # [문제] # poptotal(전체인구) 변수를 total로, # popasian(아시안 인구) 변수를 asian으로 수정하세요. midwest_new <- rename(midwest_new, "total" = "poptotal") midwest_new <- rename(midwest_new, "asian" = "popasian") library(dplyr) # [문제] # total, asian 변수를 이용해 '전체 인구 대비 아시아 인구 백분율' percasian 파생변수를 만들고, # 히스토그램을 만들어 도시들이 어떻게 분포하는지 살펴보세요. midwest_new$"percasian2" <- (midwest_new$asian / midwest_new$total)100 hist(midwest_new$percasian2) # [문제] # 아시아 인구 백분율 전체 평균을 구하고, # 평균을 초과하면 "large", # 그 외에는 "small"을 부여하는 mean 파생변수를 만들어 보세요. mean(midwest_new$percasian2) midwest_new$mean_percasian2 <- ifelse(midwest_new$percasian2 > mean(midwest_new$percasian2),"large","small") # [문제] # "large"와 "small"에 해당하는 지역이 얼마나 되는지 빈도표와 빈도 막대 그래프를 만들어 확인해 보세요. densityplot(midwest_new$percasian2, midwest_new, group = midwest_new$country, auto.key = T) ggplot(data = midwest_new, aes(x=percasian2,y=county)) + geom_bar(stat = "identity",width = .1, position = "dodge") + geom_point() teacher <- as.data.frame(table(midwest_new$mean_percasian2)) ggplot(teacher, aes(x=Var1, y=Freq)) + geom_bar(stat = "identity", width = .2) # ggplot2의 midwest 데이터를 사용하여 데이터 분석을 실습하는 문제 입니다. # popadults는 해당 지역의 성인 인구, poptotal은 전체 인구를 나타냅니다. # 1번 문제 # midwest 데이터에 '전체 인구 대비 미성년 인구 백분율' 변수를 추가하세요. midwest_new$"전체 인구 대비 미성년 인구 백분율" <- ((midwest_new$total - midwest_new$popadults) / midwest_new$total)100 ##강사님 답 midwest_new <- midwest_new %>% mutate(percyouth = (poptotal - popadults)/poptotal * 100) # 2번 문제 # 미성년 인구 백분율이 가장 높은 상위 5개 county(지역)의 미성년 인구 백분율을 출력하시오. tail(sort(midwest_new$"전체 인구 대비 미성년 인구 백분율"), n=5) ##강사님 답 midwest_new %>% arrange(desc(percyouth)) %>% dplyr::select(county, percyouth) %>% head(5) # 3번 문제 # 다음과 같은 분류표의 기준에 따라 미성년 비율 등급 변수를 추가하고, 각 등급에 몇 개의 지역이 있는지 알아보세요. # 분류 기준 # large 40%이상 # middle 30 ~ 40미만 # small 30미만 midwest_new$"미성년 비율 등급" <- ifelse("전체 인구 대비 미성년 인구 백분율" >= 40,"large",ifelse("전체 인구 대비 미성년 인구 백분율" >= 30,"middle","small")) ##강사님 답 midwest_new <- midwest_new %>% mutate(gradeyouth = ifelse(percyouth >= 40 ,"large", ifelse(percyouth >= 30,"middle","small"))) table(midwest_new$gradeyouth) # 4번 문제 # popasian은 해당 지역의 아시아인 인구를 나타냅니다. # '전체 인구 대비 아시아인 인구 백분율' 변수를 추가하고 하위 10개 지역의 state(주), county(지역), 아시아인 인구 백분율을 출력하세요. midwest_new$"전체 인구 대비 아시아인 인구 백분율" <- (midwest_new$asian / midwest_new$total)100 head(sort(midwest_new$state), n=10) head(sort(midwest_new$county), n=10) head(sort(midwest_new$"전체 인구 대비 아시아인 인구 백분율"), n=10) ##강사님 답 midwest_new <- midwest_new %>% mutate(ratio_asian = popasian/poptotal100) midwest_new %>% arrange(ratio_asian) %>% dplyr::select(state, county, ratio_asian) %>% head(10) # 시계열(time series) # 변수간의 상관성 # iris 시계열 데이터 만들기 iris seq <- as.integer( rownames(iris) ) ?cbind irisDF <- cbind(seq = as.integer(rownames(iris)),iris) # x축은 seq # y축은 -Species #랜덤하게 4색 추출 colsColor <- topo.colors(4,alpha = .4) #2열부터 5열까지 이름부여 names(colsColor) <- names(irisDF)[2:5] #melt함수 이용해서 기준seq, species #나머지 컬럼을 variable해서 wide -> long irisDF library(reshape2) iris_melt <- melt(irisDF, id = c("seq","Species")) ##버젼떄문에 오류가 나면 id대신 id.vars로 표현 library(ggplot2) g <- ggplot(data = iris_melt, aes(x=seq, y=value, col = variable)) + geom_line(cex = 0.8, show.legend = T) #추가적으로 선의 색상과 범례 라벨링 g <- g + scale_color_manual( name = "", values = colsColor[iris_melt$variable], labels = c("꽃받침 길이", "꽃받침 너비", "꽃잎 길이", "꽃잎 너비") ) # 날짜 # 문자변수를 날짜변수 변환 # R의 날짜 데이터 타입 "POSIXct" # as.POSIXct() str_date <- "200730 13:40" as.POSIXct(str_date, format = "%y%m%d %H:%M") #2020이라Y 20이면y str_date <- "2020-07-30 13:40:01 PM" as.POSIXct(str_date, format = "%Y-%m-%d %H:%M:%S") str_date <- "07/30/20 13:40:01" as.POSIXct(str_date, format = "%m/%d/%y %H:%M:%S") cospi <- read.csv(file.choose()) #날짜별 주가 만들기(시간이 있어서 따로 시계열 만들필요 없음) cospi_test <- cospi cospi_melt <- melt(cospi_test, id = c("Date","Volume")) ggplot(data = cospi_melt, aes(x= Date,y=value,col = variable, group = variable)) + geom_line(cex = 0.5, show.legend = T) ##실습4!!! spanish_train <- read.csv(file.choose()) str(spanish_train) # 1. # 데이터 내에 결측치 여부를 확인한다. # NA값이 310681개 있는 것을 확인할 수 있다. #결측치 확인방법 spanish_train[!complete.cases(spanish_train),] str(spanish_train[!complete.cases(spanish_train),]) #결측치 제거 test_renfe2 <- spanish_train[complete.cases(spanish_train),] str(test_renfe2) # 2. # filter와 !is.na함수를 통해 결측치를 모두 제거했다. # 3. # 마드리드 출발 # 마드리드에서 출발하는 열차 데이터만을 떼어내 madrid_origin이라는 변수로 저장하고 # 우선, 마드리드에서 출발하는 열차 데이터만을 이용해 비교해보기로 한다. madrid_origin <- filter(test_renfe2, test_renfe2$origin == "MADRID") str(madrid_origin) # 4. # summary함수를 통해 일반적 데이터 정보를 다시 확인한다. summary(test_renfe2) # 5. # 마드리드 출발 열차의 빈도 수 # 마드리드를 출발하는 기차의 도착 도시별 운행빈도 수를 바형태로 나타내보자 ggplot(data = madrid_origin, aes(x=destination,fill=origin)) + geom_bar(width = .5, position = "dodge") # 6. # 마드리드발 도착지별 가격 박스플롯으로 티켓가격의 높은 순을 확인해보자 boxplot(price ~ destination, data=madrid_origin) str(madrid_origin) # 7. # AVE의좌석 등급별 가격박스플롯이 시각화 # 똑같은 열차와 똑같은 좌석등급, 똑같은 도착지라 하더라도 가격이 차이가 나는 것을 확인할 수 있다. AVE_MADRID <- filter(madrid_origin, madrid_origin$train_type == "AVE") boxplot(price ~ destination,data= AVE_MADRID) # 8. # 이 차이를 이해하고 싶어 시계열로 데이터를 만들어보았다. # 9. #날짜 데이터 변환. as.POSIXct는 factor형식의 날짜 사용가능 # 10. # 컬럼이름지정 # colnames(a_b) <- c("preferente","Turista") # 11. # 도착지별, 트레인 클래스별로 가격을 박스플롯형태로 나타낼 수도 있다.
#Access data from the openFEC api using R wrapper from Stephen Holzman #https://stephenholzman.github.io/tidyusafec/articles/intro.html library(tidyverse) library(tidyusafec) library(tigris) library(leaflet) #signup for api key at https://api.open.fec.gov/developers/. Save a one line file called "data.gov.key" in the root project folder, that one line assigning the key to a variable like the next line: save_datagov_apikey(key = "n3BB27dCbHpsI0BAIyYi5i4nMa3xJk9AXF7cG2Hc", install = TRUE) #I want to do the opposite of in, later as part of my filter, so I'm definining the %notin% function here `%notin%` <- Negate(`%in%`) #select all candidates running for Senate, unnest the data, deliver it in a df, and make sure they raised money #For North Carolina races #senate <- search_candidates(state = "NC", election_year = "2020", office = "S", candidate_status = "C" , has_raised_funds = TRUE, unnest_committees = TRUE ) %>% #for alaska races (this is useful because the file is somewhat small..... See my sampling script at the bottom for a way to take a radom sample to make testing much speedier) #senate <- search_candidates(state = "AK", election_year = "2020", office = "S", candidate_status = "C" , has_raised_funds = TRUE, unnest_committees = TRUE ) %>% #for colorado races #senate <- search_candidates(state = "CO", election_year = "2020", office = "S", candidate_status = "C" , has_raised_funds = TRUE, unnest_committees = TRUE ) %>% #for arizona races #### Here's the queries I've been using for counties #senate <- search_candidates(name = c("MCSALLY, MARTHA"), election_year = "2020", office = "S", candidate_status = "C" , has_raised_funds = TRUE, unnest_committees = TRUE ) %>% #I've split these into two queries, because my key currently only allows for 1,000 queries in an hour. This is the one for Kelly. When we make the maps, we can address this. senate <- search_candidates(name = c("KELLY, MARK"), election_year = "2020", office = "S", candidate_status = "C" , has_raised_funds = TRUE, unnest_committees = TRUE ) %>% #get all their itemized contributions get_itemized_contributions(data_structure = "tidy") %>% #unnests the pesky committee column and creates unique columns for each of the nested list items. #here's a great tutorial on how to get rid of nested lists https://cfss.uchicago.edu/notes/simplify-nested-lists/ Thanks Andrew Tran for pointing me to this #something else he suggested. I didn't need it, but am putting it here for safe keeping https://jennybc.github.io/purrr-tutorial/ls00_inspect-explore.html unnest_wider(committee, names_repair = "unique") %>% #select only the fields I want. select(schedule_type_full, report_type, line_number, line_number_label, contributor_state, is_individual, report_year, contributor_city, contribution_receipt_date, contributor_aggregate_ytd, two_year_transaction_period, contribution_receipt_amount, contributor_zip, contributor_name, state, party, committee_type_full, state_full, name, fec_election_type_desc, memo_code) %>% # itemized individual contributions are recorded on line 11ai & we want to get the contributions given in the last 2-year cycle. We also want to exclude a double-count that happens with win red and act blue contributions. By excluding the x memo_code, we can eliminate those double-counts filter(report_year > 2018 & line_number %in% "11AI" & memo_code %notin% "X" ) #preserve the zeroes senate$contributor_zip <- as.character(senate$contributor_zip) #Rural/urban codes were downloaded from https://www.ers.usda.gov/data-products/rural-urban-continuum-codes.aspx #fec data accessed from https://classic.fec.gov/disclosurep/PDownload.do #subsets the first 5 numbers of the zipcodes row https://www.rdocumentation.org/packages/base/versions/3.6.0/topics/substr senate$contributor_zip <- substr(senate$contributor_zip, 1, 5) #FYI, if you want the last 5, use https://www.rdocumentation.org/packages/FedData/versions/1.1.0/topics/substrRight ########## #let's see what and where these senators are getting their funding. This query is just for Tillis donations... and I need to go back and make sure there's no pacs in it. Right now it's set up just to make the map work. totals <- senate %>% #this filter changes based on the race group_by(contributor_zip) %>% summarise( total_raised = sum(contribution_receipt_amount)) #returns tigris query file as a shapefile (if not, it's a weird list that doesn't join) options(tigris_class = "sf") #Download a Zip Code Tabulation Area (ZCTA) shapefile into R #read in our shapefile options(tigris_use_cache = TRUE) #code_shapefile <- zctas(cb = FALSE, year = 2010, state = "NC") #code_shapefile <- zctas(cb = FALSE, year = 2010, state = "AK") #code_shapefile <- zctas(cb = FALSE, year = 2010, state = "CO") code_shapefile <- zctas(cb = FALSE, year = 2010, state = "AZ") #join democratic fundraising numbers with shapefile #make sure the zip column is numeric and then join the two, so we can make the shapefile and contributions data work together like happy friends in leaflet cong_tot <- left_join(code_shapefile, totals , by = c("ZCTA5CE10"= "contributor_zip" )) #and there's two counties that have a total of 0 (their populations are a total of 27k residents), so we're going to replace those values with zeroes.We'll create a new column just in case cong_tot <- cong_tot %>% mutate(total_raised_no_na = replace_na(total_raised, 0)) ########## #########works, but is so slow because of all the shapefiles to plot. #ggplot(cong_tot) + # geom_sf(data = cong_tot) + # aes(fill= cut(total_raised,breaks = c(-1, 10000, 30000, 60000, 120000), labels= c("0-9k", "10k-29k", "30k-59k", "60k-120k") )) + # geom_sf(color="black") + # scale_fill_manual(values = c("#FFFFFF", "#C9C5DB", "#938CB8", "#3E386D")) + # theme_void() + # labs(title="Funds raised by Thom Tillis in NC zip codes", caption="Source: Federal Elections Commission", color='legend', fill='legend title') #leaflet works much faster and it gives us a more interactive graphic, which i'm partial to. bins <- c(0, 100, 1000, 10000, 100000, 200000, Inf) pal1 <- colorBin(palette = c("#FFFFFF", "#C9C5DB","#05B69C", "#F9A51A", "#C73D49"), domain = cong_tot$total_raised_no_na, bins = bins) map <- leaflet(cong_tot) %>% addTiles() state_popup1 <- paste0("<strong> District: </strong>", cong_tot$ZCTA5CE10, "<br><strong>Total Raised: </strong>", cong_tot$total_raised_no_na) leaflet(data = cong_tot) %>% addProviderTiles("CartoDB.Positron") %>% addPolygons(fillColor = ~pal1(total_raised_no_na), fillOpacity = 0.8, color = "#BDBDC3", weight = 1, popup = state_popup1) %>% addLegend("bottomright", pal = pal1, values = ~total_raised_no_na, title = "Total raised", labFormat = labelFormat(prefix = " ")) #helpful for running code in a smaller subset of data for testing purposes dt <- #df %>% #slices off number of rows from 1:xx in df #slice(1:4) #slices off random number of rows from data sample_n(senate, 10) #from zip code and noncensus packages. Was used in original code. And Supposedly cleans up zip codes, gets me FIPS codes. Cran has been rudely pulled down the orphan packages which I found rude. It's useful. But if it's gone, it's gone. #library(zipcode) #library(noncensus) #senate$contributor_zip <- clean.zipcodes(senate$contributor_zip) #conjures up the zip_codes table from the noncensus library. Which is a great resource for this project. Because, unlike the zip codes data(zipcodes) table. It has fips codes. #data(zip_codes) #however, there was a lack of zeroes in the fips code fields. And since the clean.zipcodes puts zeroes in front of some four-digit I'm using it here to match up the Census database. #zip_codes$fips <- clean.zipcodes(zip_codes$fips) #############This will be useful if we need to group by county. Not just by zip. Until then, it shall be noted and sit idle. #counties <- read_csv("ZIP_COUNTY.csv") #props to Dhmontgomery in newsnerdery. to helping me eliminate the excess dupes by using a combination of rank and filter...basically what top_n does. But with the added ability of adding the ties.method element. #I was able to select the highest ratios and then, in the case of ties, R randomly chose one. #nodupes <- counties %>% # group_by(zip) %>% # mutate(rank = rank(tot_ratio, ties.method = "random")) %>% # filter(rank < 2) ###################### #USEFUL DOCUMENTATION # #https://s3.amazonaws.com/ire16/campaign-finance/MiningFECData.pdf	/old or unused scripts/vulnerable_senators_zips.R	no_license	ChrisCioffi/districts	R	false	false	8,643	r	#Access data from the openFEC api using R wrapper from Stephen Holzman #https://stephenholzman.github.io/tidyusafec/articles/intro.html library(tidyverse) library(tidyusafec) library(tigris) library(leaflet) #signup for api key at https://api.open.fec.gov/developers/. Save a one line file called "data.gov.key" in the root project folder, that one line assigning the key to a variable like the next line: save_datagov_apikey(key = "n3BB27dCbHpsI0BAIyYi5i4nMa3xJk9AXF7cG2Hc", install = TRUE) #I want to do the opposite of in, later as part of my filter, so I'm definining the %notin% function here `%notin%` <- Negate(`%in%`) #select all candidates running for Senate, unnest the data, deliver it in a df, and make sure they raised money #For North Carolina races #senate <- search_candidates(state = "NC", election_year = "2020", office = "S", candidate_status = "C" , has_raised_funds = TRUE, unnest_committees = TRUE ) %>% #for alaska races (this is useful because the file is somewhat small..... See my sampling script at the bottom for a way to take a radom sample to make testing much speedier) #senate <- search_candidates(state = "AK", election_year = "2020", office = "S", candidate_status = "C" , has_raised_funds = TRUE, unnest_committees = TRUE ) %>% #for colorado races #senate <- search_candidates(state = "CO", election_year = "2020", office = "S", candidate_status = "C" , has_raised_funds = TRUE, unnest_committees = TRUE ) %>% #for arizona races #### Here's the queries I've been using for counties #senate <- search_candidates(name = c("MCSALLY, MARTHA"), election_year = "2020", office = "S", candidate_status = "C" , has_raised_funds = TRUE, unnest_committees = TRUE ) %>% #I've split these into two queries, because my key currently only allows for 1,000 queries in an hour. This is the one for Kelly. When we make the maps, we can address this. senate <- search_candidates(name = c("KELLY, MARK"), election_year = "2020", office = "S", candidate_status = "C" , has_raised_funds = TRUE, unnest_committees = TRUE ) %>% #get all their itemized contributions get_itemized_contributions(data_structure = "tidy") %>% #unnests the pesky committee column and creates unique columns for each of the nested list items. #here's a great tutorial on how to get rid of nested lists https://cfss.uchicago.edu/notes/simplify-nested-lists/ Thanks Andrew Tran for pointing me to this #something else he suggested. I didn't need it, but am putting it here for safe keeping https://jennybc.github.io/purrr-tutorial/ls00_inspect-explore.html unnest_wider(committee, names_repair = "unique") %>% #select only the fields I want. select(schedule_type_full, report_type, line_number, line_number_label, contributor_state, is_individual, report_year, contributor_city, contribution_receipt_date, contributor_aggregate_ytd, two_year_transaction_period, contribution_receipt_amount, contributor_zip, contributor_name, state, party, committee_type_full, state_full, name, fec_election_type_desc, memo_code) %>% # itemized individual contributions are recorded on line 11ai & we want to get the contributions given in the last 2-year cycle. We also want to exclude a double-count that happens with win red and act blue contributions. By excluding the x memo_code, we can eliminate those double-counts filter(report_year > 2018 & line_number %in% "11AI" & memo_code %notin% "X" ) #preserve the zeroes senate$contributor_zip <- as.character(senate$contributor_zip) #Rural/urban codes were downloaded from https://www.ers.usda.gov/data-products/rural-urban-continuum-codes.aspx #fec data accessed from https://classic.fec.gov/disclosurep/PDownload.do #subsets the first 5 numbers of the zipcodes row https://www.rdocumentation.org/packages/base/versions/3.6.0/topics/substr senate$contributor_zip <- substr(senate$contributor_zip, 1, 5) #FYI, if you want the last 5, use https://www.rdocumentation.org/packages/FedData/versions/1.1.0/topics/substrRight ########## #let's see what and where these senators are getting their funding. This query is just for Tillis donations... and I need to go back and make sure there's no pacs in it. Right now it's set up just to make the map work. totals <- senate %>% #this filter changes based on the race group_by(contributor_zip) %>% summarise( total_raised = sum(contribution_receipt_amount)) #returns tigris query file as a shapefile (if not, it's a weird list that doesn't join) options(tigris_class = "sf") #Download a Zip Code Tabulation Area (ZCTA) shapefile into R #read in our shapefile options(tigris_use_cache = TRUE) #code_shapefile <- zctas(cb = FALSE, year = 2010, state = "NC") #code_shapefile <- zctas(cb = FALSE, year = 2010, state = "AK") #code_shapefile <- zctas(cb = FALSE, year = 2010, state = "CO") code_shapefile <- zctas(cb = FALSE, year = 2010, state = "AZ") #join democratic fundraising numbers with shapefile #make sure the zip column is numeric and then join the two, so we can make the shapefile and contributions data work together like happy friends in leaflet cong_tot <- left_join(code_shapefile, totals , by = c("ZCTA5CE10"= "contributor_zip" )) #and there's two counties that have a total of 0 (their populations are a total of 27k residents), so we're going to replace those values with zeroes.We'll create a new column just in case cong_tot <- cong_tot %>% mutate(total_raised_no_na = replace_na(total_raised, 0)) ########## #########works, but is so slow because of all the shapefiles to plot. #ggplot(cong_tot) + # geom_sf(data = cong_tot) + # aes(fill= cut(total_raised,breaks = c(-1, 10000, 30000, 60000, 120000), labels= c("0-9k", "10k-29k", "30k-59k", "60k-120k") )) + # geom_sf(color="black") + # scale_fill_manual(values = c("#FFFFFF", "#C9C5DB", "#938CB8", "#3E386D")) + # theme_void() + # labs(title="Funds raised by Thom Tillis in NC zip codes", caption="Source: Federal Elections Commission", color='legend', fill='legend title') #leaflet works much faster and it gives us a more interactive graphic, which i'm partial to. bins <- c(0, 100, 1000, 10000, 100000, 200000, Inf) pal1 <- colorBin(palette = c("#FFFFFF", "#C9C5DB","#05B69C", "#F9A51A", "#C73D49"), domain = cong_tot$total_raised_no_na, bins = bins) map <- leaflet(cong_tot) %>% addTiles() state_popup1 <- paste0("<strong> District: </strong>", cong_tot$ZCTA5CE10, "<br><strong>Total Raised: </strong>", cong_tot$total_raised_no_na) leaflet(data = cong_tot) %>% addProviderTiles("CartoDB.Positron") %>% addPolygons(fillColor = ~pal1(total_raised_no_na), fillOpacity = 0.8, color = "#BDBDC3", weight = 1, popup = state_popup1) %>% addLegend("bottomright", pal = pal1, values = ~total_raised_no_na, title = "Total raised", labFormat = labelFormat(prefix = " ")) #helpful for running code in a smaller subset of data for testing purposes dt <- #df %>% #slices off number of rows from 1:xx in df #slice(1:4) #slices off random number of rows from data sample_n(senate, 10) #from zip code and noncensus packages. Was used in original code. And Supposedly cleans up zip codes, gets me FIPS codes. Cran has been rudely pulled down the orphan packages which I found rude. It's useful. But if it's gone, it's gone. #library(zipcode) #library(noncensus) #senate$contributor_zip <- clean.zipcodes(senate$contributor_zip) #conjures up the zip_codes table from the noncensus library. Which is a great resource for this project. Because, unlike the zip codes data(zipcodes) table. It has fips codes. #data(zip_codes) #however, there was a lack of zeroes in the fips code fields. And since the clean.zipcodes puts zeroes in front of some four-digit I'm using it here to match up the Census database. #zip_codes$fips <- clean.zipcodes(zip_codes$fips) #############This will be useful if we need to group by county. Not just by zip. Until then, it shall be noted and sit idle. #counties <- read_csv("ZIP_COUNTY.csv") #props to Dhmontgomery in newsnerdery. to helping me eliminate the excess dupes by using a combination of rank and filter...basically what top_n does. But with the added ability of adding the ties.method element. #I was able to select the highest ratios and then, in the case of ties, R randomly chose one. #nodupes <- counties %>% # group_by(zip) %>% # mutate(rank = rank(tot_ratio, ties.method = "random")) %>% # filter(rank < 2) ###################### #USEFUL DOCUMENTATION # #https://s3.amazonaws.com/ire16/campaign-finance/MiningFECData.pdf
# load necessary libraries library("shiny") # intro page intro <- mainPanel( h1("Reproductive Health and Resources"), strong("Purpose and Importance of the Project"), p("As a group, we believe that women should have reproductive rights. We think this would serve as an opportunity to delve into learning which reproductive resources are provided in the United States and the differences between in each geographic region. We also find it interesting how there are many different sub-categories that fall under reproductive health such as teen health, resources, infant health, abortion, etc."), strong("Origin of Sources"), p( "The", a("Guttmacher Institute", href = "https://data.guttmacher.org/states/table?dataset=data&state=AL+AK+AZ+AR+CA+CO+CT+DE+DC+FL+GA+HI+ID+IL+IN+IA+KS+KY+LA+ME+MD+MA+MI+MN+MS+MO+MT+NE+NV+NH+NJ+NM+NY+NC+ND+OH+OK+OR+PA+RI+SC+SD+TN+TX+UT+VT+VA+WA+WV+WI+WY&topics=57+283+65"), " dataframe has information on number of abortions, number of abortion clinics, percent of women taking contraceptive, etc. The", a("Kaggle", href = "https://www.kaggle.com/omer2040/usa-states-to-region"), " dataframe labels each of the United States into the four different geographic regions. We then joined these dataframes to create our new dataset that's grouped by regions." ), img("We support women's reproductive health!", src = "https://www.plannedparenthood.org/uploads/filer_public_thumbnails/filer_public/cb/c4/cbc45653-b657-4b15-97fd-490aeefcd262/damore_ppla_da_7867.jpg__1200x675_q75_crop_subsampling-2.jpg") ) # Interactive page one interactive_one <- sidebarLayout( sidebarPanel( h4("Choose which region you want to highlight"), selectInput( inputId = "chart_regions", label = "Pick a geographic region below:", choices = region_grouped$Region ) ), mainPanel( h1("Number of Abortion Clinics Per Region"), plotlyOutput("bar_graph"), tags$p( id = "graph_two_paragraph", "This bar graph shows the number of abortion clinics in each region. It attempts to show the general differences while attempting to highlight specific regions upon user selection on the left. A bar graph is ideal because the numerical data points aren’t closely related. As shown, the West region has the most abortion clinics - more than 250, with the midwest region having the least - less than 100. When hovering with the cursor over each bar, the specific number of abortion clinics is displayed." ) ) ) # Interactive page two interactive_two <- sidebarLayout(# creates a side bar and provides a wdiget to adjust Y axis sidebarPanel( h4("Zoom in or out on the Abortion Rates Axis"), sliderInput( inputId = "Max_y", "Pick a Max y Value", min = 0, max = 300, value = 300 ), sliderInput( inputId = "Min_y", "Pick a Min y Value", min = 0, max = 300, value = 80 ) ), mainPanel( h1("Abortion Clinics x Abortion Rates"), # page title plotlyOutput("clin_rates"), # ID tags$p( id = "graph_two_paragraph", # page paragraph definition "This point chart attempts to show the relationship between the total number of abortion clinics and the total abortion rate in each region. I used a scatterplot to show this correlation, however from the results it demostrates there is a small correlation between the two. However, the results disprove our hypothesis that the more amount of abortion clinics the more about of abortion rates. The graph displays (with one outlier) that the more abortion clinics the less total abortion rates in that region. This implies that less people need abortions in regions that have more abortion clinics available. This could be due to the fact that abortion clinics also provide other health care resources such as contraceptive, family planning, Plan B, health education and more, which can decrease the need for an unwanted pregnancy and therefore lessens the need for an abortion. " ) ) ) # Interactive page three interactive_three <- sidebarLayout( sidebarPanel( h4("Zoom in or out on the Percent of Contraceptives Axis"), sliderInput( inputId = "Max", "Pick a Max y Value", min = 75, max = 115, value = 80 ), sliderInput( inputId = "Min", "Pick a Min y Value", min = 20, max = 60, value = 60 ) ), mainPanel( h1("Abortion Clinics x Contraceptives"), plotlyOutput("clin_con"), tags$p( id = "graph_three_paragraph", "This bubble chart attempts to show the relationship, if any, between the number of abortion clinics in a region and the percentage of women aged 18 to 49 that use contraceptives. If there is a relationship then one could argue that abortion clincs being around might sway the number of women on contraceptives. As you can see this chart shows little to no correlation between the number of abortion clincs and the percentage of women on contraceptives. This implies that it doesn't matter if you have an easy option or not to abort, because either way most women are going to be on contraceptives." ) ) ) # Conclusion Page summary <- mainPanel( h1("Summary Information and Data"), h3("Takeaway #1"), h4("Table"), tableOutput("table_1"), h4("Explanation"), tags$p( id = "Expanation_1", "When looking at the total number of abortion clinics throughout the four different regions of the United States we found that the West had the most recorded number of clinics - 277 clinics - and the least being the Midwest - 91 clinics. We can see that the West had over three times the number of abortion clinics compared to Midwest. This could imply that there's a lower need for demand in the Midwest than in the other regions, hence the lower number of clinics. Higher population regions such as the West and Northeast may have more clinics to serve more people. Beliefs and feeling towards abortion in specific regions may also be a large contributor to the significant difference in how many clinics that are opened." ), h3("Takeaway #2"), h4("Table"), tableOutput("table_clin_rates"), h4("Explanation"), tags$p( id = "Explanation_2", "As we took a look at the total number of abortion clinics to the total abortion rate in each of the four regions we found that there was no clear correlations in the data. While the West had the most number of abortion clinics, it had the 2nd lowest abortion rate. However the South, which had the 2nd least number of clinics, surprisingly had the highest recorded abortion rates. Meanwhile the Midwest had the lowest number of abortion clinics and rates. These insights inform us that the abortion rate can vary region to region regardless of the number of clinics provided." ), h3("Takeaway #3"), h4("Table"), tableOutput("table_clin_con"), h4("Explanation"), tags$p( id = "Explanation_3", "As we pulled and analyzed the information on the total abortion clinics and the percentage of contraceptive usage in each region, we found that data for contraceptive was fairly the same falling within the bracket of 71.86% - 68.68 %. This is very interesting to observe as that tells us the rate of contraceptive usage isn't directly impacted by the total number of clinics provided in the region." ) ) # UI ui <- fluidPage( includeCSS("style.css"), navbarPage( "Womens Reproductive Resources", tabPanel("Overview", intro), tabPanel("Abortion Clinics/Region", interactive_one), tabPanel("Abortion Clinics x Abortion Rates", interactive_two), tabPanel("Abortion Clinics x Contraceptives", interactive_three), tabPanel("Summary Information", summary) ) )	/Shiny Website/app_ui.R	permissive	info-201a-sp20/final-project-achenn98	R	false	false	8,461	r	# load necessary libraries library("shiny") # intro page intro <- mainPanel( h1("Reproductive Health and Resources"), strong("Purpose and Importance of the Project"), p("As a group, we believe that women should have reproductive rights. We think this would serve as an opportunity to delve into learning which reproductive resources are provided in the United States and the differences between in each geographic region. We also find it interesting how there are many different sub-categories that fall under reproductive health such as teen health, resources, infant health, abortion, etc."), strong("Origin of Sources"), p( "The", a("Guttmacher Institute", href = "https://data.guttmacher.org/states/table?dataset=data&state=AL+AK+AZ+AR+CA+CO+CT+DE+DC+FL+GA+HI+ID+IL+IN+IA+KS+KY+LA+ME+MD+MA+MI+MN+MS+MO+MT+NE+NV+NH+NJ+NM+NY+NC+ND+OH+OK+OR+PA+RI+SC+SD+TN+TX+UT+VT+VA+WA+WV+WI+WY&topics=57+283+65"), " dataframe has information on number of abortions, number of abortion clinics, percent of women taking contraceptive, etc. The", a("Kaggle", href = "https://www.kaggle.com/omer2040/usa-states-to-region"), " dataframe labels each of the United States into the four different geographic regions. We then joined these dataframes to create our new dataset that's grouped by regions." ), img("We support women's reproductive health!", src = "https://www.plannedparenthood.org/uploads/filer_public_thumbnails/filer_public/cb/c4/cbc45653-b657-4b15-97fd-490aeefcd262/damore_ppla_da_7867.jpg__1200x675_q75_crop_subsampling-2.jpg") ) # Interactive page one interactive_one <- sidebarLayout( sidebarPanel( h4("Choose which region you want to highlight"), selectInput( inputId = "chart_regions", label = "Pick a geographic region below:", choices = region_grouped$Region ) ), mainPanel( h1("Number of Abortion Clinics Per Region"), plotlyOutput("bar_graph"), tags$p( id = "graph_two_paragraph", "This bar graph shows the number of abortion clinics in each region. It attempts to show the general differences while attempting to highlight specific regions upon user selection on the left. A bar graph is ideal because the numerical data points aren’t closely related. As shown, the West region has the most abortion clinics - more than 250, with the midwest region having the least - less than 100. When hovering with the cursor over each bar, the specific number of abortion clinics is displayed." ) ) ) # Interactive page two interactive_two <- sidebarLayout(# creates a side bar and provides a wdiget to adjust Y axis sidebarPanel( h4("Zoom in or out on the Abortion Rates Axis"), sliderInput( inputId = "Max_y", "Pick a Max y Value", min = 0, max = 300, value = 300 ), sliderInput( inputId = "Min_y", "Pick a Min y Value", min = 0, max = 300, value = 80 ) ), mainPanel( h1("Abortion Clinics x Abortion Rates"), # page title plotlyOutput("clin_rates"), # ID tags$p( id = "graph_two_paragraph", # page paragraph definition "This point chart attempts to show the relationship between the total number of abortion clinics and the total abortion rate in each region. I used a scatterplot to show this correlation, however from the results it demostrates there is a small correlation between the two. However, the results disprove our hypothesis that the more amount of abortion clinics the more about of abortion rates. The graph displays (with one outlier) that the more abortion clinics the less total abortion rates in that region. This implies that less people need abortions in regions that have more abortion clinics available. This could be due to the fact that abortion clinics also provide other health care resources such as contraceptive, family planning, Plan B, health education and more, which can decrease the need for an unwanted pregnancy and therefore lessens the need for an abortion. " ) ) ) # Interactive page three interactive_three <- sidebarLayout( sidebarPanel( h4("Zoom in or out on the Percent of Contraceptives Axis"), sliderInput( inputId = "Max", "Pick a Max y Value", min = 75, max = 115, value = 80 ), sliderInput( inputId = "Min", "Pick a Min y Value", min = 20, max = 60, value = 60 ) ), mainPanel( h1("Abortion Clinics x Contraceptives"), plotlyOutput("clin_con"), tags$p( id = "graph_three_paragraph", "This bubble chart attempts to show the relationship, if any, between the number of abortion clinics in a region and the percentage of women aged 18 to 49 that use contraceptives. If there is a relationship then one could argue that abortion clincs being around might sway the number of women on contraceptives. As you can see this chart shows little to no correlation between the number of abortion clincs and the percentage of women on contraceptives. This implies that it doesn't matter if you have an easy option or not to abort, because either way most women are going to be on contraceptives." ) ) ) # Conclusion Page summary <- mainPanel( h1("Summary Information and Data"), h3("Takeaway #1"), h4("Table"), tableOutput("table_1"), h4("Explanation"), tags$p( id = "Expanation_1", "When looking at the total number of abortion clinics throughout the four different regions of the United States we found that the West had the most recorded number of clinics - 277 clinics - and the least being the Midwest - 91 clinics. We can see that the West had over three times the number of abortion clinics compared to Midwest. This could imply that there's a lower need for demand in the Midwest than in the other regions, hence the lower number of clinics. Higher population regions such as the West and Northeast may have more clinics to serve more people. Beliefs and feeling towards abortion in specific regions may also be a large contributor to the significant difference in how many clinics that are opened." ), h3("Takeaway #2"), h4("Table"), tableOutput("table_clin_rates"), h4("Explanation"), tags$p( id = "Explanation_2", "As we took a look at the total number of abortion clinics to the total abortion rate in each of the four regions we found that there was no clear correlations in the data. While the West had the most number of abortion clinics, it had the 2nd lowest abortion rate. However the South, which had the 2nd least number of clinics, surprisingly had the highest recorded abortion rates. Meanwhile the Midwest had the lowest number of abortion clinics and rates. These insights inform us that the abortion rate can vary region to region regardless of the number of clinics provided." ), h3("Takeaway #3"), h4("Table"), tableOutput("table_clin_con"), h4("Explanation"), tags$p( id = "Explanation_3", "As we pulled and analyzed the information on the total abortion clinics and the percentage of contraceptive usage in each region, we found that data for contraceptive was fairly the same falling within the bracket of 71.86% - 68.68 %. This is very interesting to observe as that tells us the rate of contraceptive usage isn't directly impacted by the total number of clinics provided in the region." ) ) # UI ui <- fluidPage( includeCSS("style.css"), navbarPage( "Womens Reproductive Resources", tabPanel("Overview", intro), tabPanel("Abortion Clinics/Region", interactive_one), tabPanel("Abortion Clinics x Abortion Rates", interactive_two), tabPanel("Abortion Clinics x Contraceptives", interactive_three), tabPanel("Summary Information", summary) ) )
#' Base class #' #' @export SimpleQuestion <- R6::R6Class( "SimpleQuestion", public = list( type = "abstract", quiz = NULL, initialize = function(text, data = quote({}), hidden_data = quote({}), seed = NULL, hidden_seed = NULL, feedback = NULL, answer = NULL, tags = NULL, header = NULL) { private$.text <- text private$.data <- data private$.hidden_data <- hidden_data private$.seed <- seed private$.hidden_seed <- hidden_seed private$.answer <- answer private$.feedback <- feedback private$.tags <- tags private$.header <- header # Default placeholders private$placeholders <- list( TITLE = "get_title", REC_DATA_CHUNK = "get_rec_data_chunk", DATA_CHUNK = "get_data_chunk", ANSWER_INFO = "get_answer_info", INST_TEXT = "get_inst_text", SEED_CHUNK = "get_seed_chunk", EVALUATED_ANSWER = "get_evaluated_answer", FEEDBACK = "get_feedback", FEEDBACK_ANSWER = "get_feedback_answer", ANSWER_STRING = "get_answer_string") # Specific placeholders for XML export private$xml_placeholders <- list( TYPE = "get_type", TITLE = "get_title", XML_QUESTION_TEXT = "get_XML_question_text", XML_GENERALFEEDBACK = "get_XML_generalfeedback", XML_ANSWER = "get_XML_answer" ) }, instantiate_placeholders = function(template, placeholders, opts, info) { placeholders_regex <- paste0("@", names(placeholders), "@", collapse = "\|") repeat { match <- stringi::stri_locate_first_regex(template, placeholders_regex) if(any(is.na(match))) # breaking if no match break if(nrow(match) == 0) break # Computing replacement string id <- substring(template, match[1] + 1, match[2] - 1) funcname <- placeholders[[id]] if (is.null(funcname)) stop("Unable to find corresponding function for ", sQuote(id)) replacement <- self[[funcname]](opts, info) # If replacement is NULL, try to replace whole line if (is.null(replacement)) { template <- stringi::stri_replace_first_regex(template, paste0("^ @", id, "@ \n"), "", opts_regex = list(multiline = TRUE)) template <- stringi::stri_replace_first_regex(template, paste0("@", id, "@"), "") } else { # Escape backslash and dollar in replacement string replacement <- gsub("\\\\", "\\\\\\\\", replacement) replacement <- gsub("\\$", "\\\\$", replacement) template <- stringi::stri_replace_first_regex(template, paste0("@", id, "@"), replacement) } } if(nchar(template) == 0) NULL else template }, update_quiz = function(quiz) { self$quiz <- quiz self$invalidate_hidden_data_list() }, get_data_and_environment = function() { if(is.null(self$quiz)) data <- self$recursive_instantiated_data() else data <- self$quiz$recursive_instantiated_data() env <- empty_env() eval(data, env) list(data = data, env = env) }, validate_data = function(parent_data = NULL) {}, get_markdown_from_template = function(template, opts = list(), info = list()) { self$instantiate_placeholders(template, private$placeholders, opts, info) }, to_markdown = function(opts = list(), info = list()) { opts <- update_list(private$default_options, opts) opts$export <- "markdown" private$validate_options(opts, info) template <- "@INST_TEXT@\n\n@FEEDBACK@\n" self$instantiate_placeholders(template, private$placeholders, opts, info) }, #' Feedback options if none is provided get_default_feedback = function() { list(text = self$answer) }, #' Sanitize provided feedback get_feedback_from_field = function(feedback) { feedback <- if(is.null(feedback)) { self$get_default_feedback() } else if(is.character(feedback)) { list(text = feedback) } else if(is.numeric(feedback)) { list(text = feedback) } else if(is.language(feedback)) { list(text = feedback) } else if(is.list(feedback)) { feedback } update_list(private$default_feedback_options, feedback) }, #' Template for full feedback get_feedback = function(opts, info) { # No feedback if (!is.null(opts$feedback) && !opts$feedback) return(NULL) "@ANSWER_INFO@\n\n@ANSWER_STRING@@EVALUATED_ANSWER@\n\n@FEEDBACK_ANSWER@\n" }, get_answer_string = function(opts, info) { if(is.null(info$answer_string)) "R\u00E9ponse : " else info$answer_string }, #' Feedback itself giving the right answer get_feedback_answer = function(opts, info) { # Get feedback as a proper named list with default arguments feedback_opts <- self$instantiated_feedback # Check for spurious arguments in feedback_options unknown_opts <- setdiff(names(feedback_opts), names(private$default_feedback_options)) if (length(unknown_opts) > 0) { stop("Unknown options: ", paste0(unknown_opts)) } # Override feedback_options with upstream opts feedback_opts <- update_list(feedback_opts, opts) # Check that options are coherent private$validate_feedback_options(feedback_opts, info) feedback <- if (feedback_opts$eval) { feedback_opts$text } else { if (is.null(feedback_opts$noeval_text)) feedback_opts$text else feedback_opts$noeval_text } feedback <- if (is.character(feedback)) { feedback } else if (is.language(feedback)) sprintf("```{r}\n%s\n```\n", answerstr(feedback)) else if (is.numeric(feedback)) sprintf("```{r}\n%s\n```\n", answerstr(feedback)) else stop("Unsupported feedback type: ", sQuote(feedback)) # Maybe indent the feedback if(is.null(opts$indent)) feedback else add_spaces_left(feedback, 4) }, get_XML_question_text = function(opts, info) { # md_question <- self$get_XML_question_markdown(opts, info) md_question <- self$get_inst_text(opts, info) HTML_question <- render_HTML(md_question, opts, info) trimws(HTML_question) # pandoc seems to add some leading newlines }, # Return XML "generalfeedback" node with feedback in HTML as CDATA get_XML_generalfeedback = function(opts, info) { if(!opts$feedback) return(NULL) # Generate HTML for feedback placeholders <- update_list(private$placeholders, private$xml_placeholders) tmpl <- self$get_feedback(opts, info) md_feedback <- self$instantiate_placeholders(tmpl, placeholders, opts, info) HTML_feedback <- render_HTML(md_feedback, opts, info) HTML_feedback0 <- trimws(HTML_feedback) # pandoc seems to add some leading newlines # Return XML with inner HTML tmpl <-"<generalfeedback format=\"html\"> <text><![CDATA[%s]]></text> </generalfeedback>" tmpl0 <- add_spaces_left(tmpl, opts$indent) sprintf(tmpl0, HTML_feedback0) }, get_XML_answer = function(opts, info) { as.character(self$get_evaluated_answer2(opts, info)) }, #' Export Question as XML to_XML = function(opts = NULL, info = NULL) { # Set up environment for evaluating data if not already if (is.null(info$env)) { info0 <- self$get_data_and_environment() info <- update_list(info, info0) } # Setting options default_opts <- update_list(private$default_options, private$xml_default_options) opts <- update_list(default_opts, opts) opts$export <- "xml" private$validate_options(opts, info) template <- add_spaces_left(private$xml_question_template, opts$indent) placeholders <- update_list(private$placeholders, private$xml_placeholders) # Answers might modify datasets stored in info$env self$instantiate_placeholders(template, placeholders, opts, info) }, invalidate_all = function() { self$invalidate_text() self$invalidate_answer() self$invalidate_feedback() self$invalidate_hidden_data() self$invalidate_data() self$invalidate_hidden_data_list() }, invalidate_ancestor = function() { if (is.null(self$ancestor)) self$invalidate_hidden_data() else self$ancestor$invalidate_hidden_data() }, invalidate_data = function() { private$is_data_available <- FALSE self$invalidate_inst_data() }, invalidate_hidden_data = function() { private$is_hidden_data_available <- FALSE self$invalidate_inst_text() self$invalidate_inst_answer() self$invalidate_inst_feedback() self$invalidate_inst_data() self$invalidate_hidden_data_list() }, invalidate_hidden_data_list = function() { private$is_hidden_data_list_available <- FALSE self$invalidate_inst_text() self$invalidate_inst_answer() self$invalidate_inst_feedback() self$invalidate_inst_data() }, invalidate_inst_data = function() { private$is_data_instantiated <- FALSE }, invalidate_inst_text = function() { private$is_text_instantiated <- FALSE }, invalidate_inst_answer = function() { private$is_answer_instantiated <- FALSE }, invalidate_inst_feedback = function() { private$is_feedback_instantiated <- FALSE }, invalidate_header = function() { self$invalidate_text() }, invalidate_text = function() { private$is_text_available <- FALSE self$invalidate_inst_text() }, invalidate_answer = function() { private$is_answer_available <- FALSE self$invalidate_inst_answer() }, invalidate_feedback = function() { private$is_feedback_available <- FALSE self$invalidate_inst_feedback() }, recursive_instantiated_data = function(seed_init = FALSE) { if (!seed_init & !is_empty_language(self$data) & is.null(self$seed)) stop("Some data but no seed to initialize") if(is.null(self$seed)) self$instantiated_data else merge_languages( instantiate_data_list( bquote(set.seed(.(self$seed))), self$hidden_data_list), self$instantiated_data) }, get_type = function(opts, info) { self$type }, get_title = function(opts, info) { self$title }, get_data_chunk = function(opts, info) { l <- sanitize_language(self$local_instantiated_data) if (is.null(l)) NULL else sprintf("```{r include = FALSE}\n%s\n```", answerstr(l)) }, get_rec_data_chunk = function(opts, info) { if (is.null(self$quiz)) stop("No defined Quiz in question ", sQuote(self$title)) l <- sanitize_language(self$quiz$recursive_instantiated_data()) if (is.null(l)) NULL else sprintf("```{r include = FALSE}\n%s\n```", answerstr(l)) }, # Data chunk for formatting answer get_answer_info = function(opts, info) { sprintf("```{r, include = FALSE}\n%s\nanswer <- {\n%s}\n```", answerstr(quote(cquote <- function(s) { if (is.character(s)) "`" else if (is.numeric(s)) "$" else stop("Argument is not character or numeric ", sQuote(s)) })), answerstr(self$instantiated_answer)) }, get_evaluated_answer = function(opts, info) { "`r cquote(answer)``r answer``r cquote(answer)`" }, get_evaluated_answer2 = function(opts, info) { # Set up environment for evaluating data if not already if (is.null(info$env)) { info0 <- self$get_data_and_environment() info <- update_list(info, info0) } eval(self$instantiated_answer, info$env) }, get_seed_chunk = function(opts, info) { if (is.null(self$seed)) NULL else sprintf("```{r}\nset.seed(%d)\n```", self$seed) }, get_text = function(opts, info) { self$text }, get_inst_text = function(opts, info) { if (!is.null(opts$numbered) && opts$numbered) { paste0("Question ", info$num, " : ", self$instantiated_text) } else self$instantiated_text }, get_inst_cookie = function(opts, info) { stop("Abstract method") }, get_inst_text_and_cookie = function(opts, info) { sprintf("%s\n\n%s", self$get_inst_text(opts, info), self$get_inst_cookie(opts, info)) }, get_inst_text_and_number = function(opts, info) { stopifnot(is.numeric(info$index)) sprintf("%s (%d)", self$get_inst_text(opts, info), info$index) }, get_guess = function() { }, get_is_correct_icon = function() { }, instantiate_hidden_data_list = function(var_list = NULL, seed_init = FALSE) { if (!seed_init & !is_empty_language(self$hidden_data) & is.null(self$hidden_seed)) stop("Some hidden data but no seed to instantiate them") # VAR_LIST must be a named list for list2env to work if (is.null(var_list)) var_list <- list() env <- list2env(var_list, envir = empty_env()) # Set seed if any and eval hidden data if (!is.null(self$hidden_seed)) eval(bquote(set.seed(.(self$hidden_seed))), envir = env) eval(self$hidden_data, envir = env) # Update variables in VAR_LIST with ENV new_var_list <- update_list(var_list, as.list(env, all.names = TRUE)) self$hidden_data_list <- new_var_list ## private$.hidden_data_list <- new_var_list ## private$is_hidden_data_list_available <- TRUE }, instantiate_feedback_list = function(feedback, var_list) { text <- instantiate_object(feedback$text, self$hidden_data_list) noeval_text <- instantiate_object(feedback$noeval_text, self$hidden_data_list) feedback$text <- text feedback$noeval_text <- noeval_text feedback }, hidden_data_names = function() { env <- empty_env() if (!is.null(self$hidden_seed)) eval(bquote(set.seed(.(self$hidden_seed))), envir = env) eval(self$hidden_data, envir = env) ls(envir = env, all.names = TRUE) }, rename = function(prefix, names = self$hidden_data_names()) { self$rename_text(prefix, names) self$rename_header(prefix, names) self$rename_answer(prefix, names) self$rename_feedback(prefix, names) self$rename_data(prefix, names) self$rename_hidden_data(prefix, names) self$invalidate_text() self$invalidate_answer() self$invalidate_hidden_data() self$invalidate_hidden_data_list() self$invalidate_feedback() self$invalidate_data() self }, rename_header = function(prefix, names = self$hidden_data_names()) { self$header <- prefix_object(prefix, names, self$header) self }, rename_text = function(prefix, names = self$hidden_data_names()) { self$text <- prefix_object(prefix, names, private$.text) self$invalidate_text() self }, rename_answer = function(prefix, names = self$hidden_data_names()) { self$answer <- prefix_object(prefix, names, self$answer) self$invalidate_answer() self }, rename_feedback = function(prefix, names = self$hidden_data_names()) { feedback <- self$feedback text <- prefix_object(prefix, names, feedback$text) noeval_text <- prefix_object(prefix, names, feedback$noeval_text) if (!is.null(text)) feedback$text <- text if (!is.null(noeval_text)) feedback$noeval_text <- noeval_text self$feedback <- feedback self$invalidate_feedback() self }, rename_data = function(prefix, names = self$hidden_data_names()) { self$data <- prefix_object(prefix, names, self$data) self$invalidate_data() self }, rename_hidden_data = function(prefix, names = self$hidden_data_names()) { self$hidden_data <- prefix_object(prefix, names, self$hidden_data) self$invalidate_hidden_data() self }, copy = function() { Question(self$text, type = self$type, seed = self$seed, hidden_seed = self$hidden_seed, hidden_data = self$hidden_data, data = self$data, answer = self$answer, feedback = self$feedback) } ), active = list( title = function(title) { if (missing(title)) { if (is.null(private$.title)) { if (nchar(self$instantiated_text) > 60) paste0(substr(self$instantiated_text, 0, 57), "...") else self$instantiated_text } else private$.title } else { private$.title <- title } }, header = function(header) { if (missing(header)) { private$.header } else { private$.header <- header self$invalidate_header() private$.header } }, ancestor = function(ancestor) { if (missing(ancestor)) { private$.ancestor } else { private$.ancestor <- ancestor self$invalidate_ancestor() private$.ancestor } }, text = function(text) { if (missing(text)) { paste(c(trimws(self$header), trimws(private$.text)), collapse = "\n\n") } else { private$.text <- text self$invalidate_inst_text() } }, instantiated_text = function() { if (private$is_text_instantiated) private$.instantiated_text else { private$.instantiated_text <- instantiate_text_list( self$text, self$hidden_data_list) private$is_text_instantiated <- TRUE private$.instantiated_text } }, answer = function(answer) { if (missing(answer)) { private$.answer } else { private$.answer <- answer self$invalidate_inst_answer() } }, instantiated_answer = function() { if (private$is_answer_instantiated) private$.instantiated_answer else { private$.instantiated_answer <- instantiate_object( self$answer, self$hidden_data_list) private$is_answer_instantiated <- TRUE private$.instantiated_answer } }, feedback = function(feedback) { if (missing(feedback)) { if (private$is_feedback_available) private$.feedback else { private$.feedback <- self$get_feedback_from_field(private$.feedback) private$is_feedback_available <- TRUE private$.feedback } } else { private$.feedback <- self$get_feedback_from_field(feedback) self$invalidate_inst_feedback() private$is_feedback_available <- TRUE } }, instantiated_feedback = function() { if (private$is_feedback_instantiated) private$.instantiated_feedback else { inst_feedback <- self$instantiate_feedback_list(self$feedback, self$hidden_data_list) private$is_feedback_instantiated <- TRUE private$.instantiated_feedback <- inst_feedback } }, hidden_seed = function(seed) { if (missing(seed)) private$.hidden_seed else { private$.hidden_seed <- seed self$invalidate_ancestor() } }, hidden_data = function(hidden_data) { if (missing(hidden_data)) { private$.hidden_data } else { private$.hidden_data <- hidden_data self$invalidate_ancestor() } }, hidden_data_list = function(hidden_data_list) { if (missing(hidden_data_list)) { if (private$is_hidden_data_list_available) private$.hidden_data_list else { root <- self while (!is.null(root$ancestor)) root <- root$ancestor root$instantiate_hidden_data_list() private$is_hidden_data_list_available <- TRUE private$.hidden_data_list } } else { private$is_hidden_data_list_available <- TRUE private$.hidden_data_list <- hidden_data_list ## Need to be recomputed self$invalidate_inst_text() self$invalidate_inst_answer() self$invalidate_inst_feedback() self$invalidate_inst_data() } }, seed = function(seed) { if (missing(seed)) private$.seed else { private$.seed <- seed } }, data = function(data) { if (missing(data)) { private$.data } else { private$.data <- data self$invalidate_inst_data() } }, instantiated_data = function() { if (private$is_data_instantiated) private$.instantiated_data else { private$.instantiated_data <- instantiate_data_list( private$.data, self$hidden_data_list) private$is_data_instantiated <- TRUE private$.instantiated_data } }, local_instantiated_data = function() { if(is.null(self$seed)) self$instantiated_data else merge_languages( instantiate_data_list( bquote(set.seed(.(self$seed))), self$hidden_data_list), self$instantiated_data) }, tags = function(tags) { if (missing(tags)) { private$.tags } else { private$.tags <- tags } } ), private = list( .title = NULL, .header = NULL, .ancestor = NULL, .text = NULL, is_text_available = FALSE, .answer = NULL, is_answer_available = FALSE, .feedback = NULL, is_feedback_available = FALSE, .data = NULL, is_data_available = FALSE, .seed = NULL, .hidden_seed = NULL, .hidden_data = NULL, is_hidden_data_available = FALSE, .hidden_data_list = NULL, is_hidden_data_list_available = FALSE, .instantiated_text = NULL, is_text_instantiated = FALSE, .instantiated_answer = NULL, is_answer_instantiated = FALSE, .instantiated_feedback = NULL, is_feedback_instantiated = FALSE, .instantiated_data = NULL, is_data_instantiated = FALSE, .tags = NULL, default_feedback_options = list(text = "", noeval_text = NULL, eval = TRUE, indent = 2), default_options = list(numbered = TRUE, export = "markdown", feedback = TRUE, quiet = FALSE), xml_default_options = list( numbered = FALSE, indent = 0 ), feedback_options = NULL, placeholders = NULL, placeholders_regex = NULL, validate_options = function(opts, info) { if (!is.null(opts$numbered) && opts$numbered && is.null(info$num)) stop("`numbered` option is enabled but no `num` available") }, validate_feedback_options = function(opts, info) { ## super$validate_feedback_options(opts, info) }, xml_question_template = trimws(" <question type=\"@TYPE@\"> <name> <text><![CDATA[@TITLE@]]></text> </name> <questiontext format=\"html\"> <text><![CDATA[@XML_QUESTION_TEXT@]]></text> </questiontext> <answer fraction=\"100\" format=\"plain_text\"> <text>@XML_ANSWER@</text> </answer> @XML_GENERALFEEDBACK@ <hidden>0</hidden> </question> "), xml_placeholders = NULL))	/R/simple_question.R	no_license	thisirs/quizR	R	false	false	27,951	r	#' Base class #' #' @export SimpleQuestion <- R6::R6Class( "SimpleQuestion", public = list( type = "abstract", quiz = NULL, initialize = function(text, data = quote({}), hidden_data = quote({}), seed = NULL, hidden_seed = NULL, feedback = NULL, answer = NULL, tags = NULL, header = NULL) { private$.text <- text private$.data <- data private$.hidden_data <- hidden_data private$.seed <- seed private$.hidden_seed <- hidden_seed private$.answer <- answer private$.feedback <- feedback private$.tags <- tags private$.header <- header # Default placeholders private$placeholders <- list( TITLE = "get_title", REC_DATA_CHUNK = "get_rec_data_chunk", DATA_CHUNK = "get_data_chunk", ANSWER_INFO = "get_answer_info", INST_TEXT = "get_inst_text", SEED_CHUNK = "get_seed_chunk", EVALUATED_ANSWER = "get_evaluated_answer", FEEDBACK = "get_feedback", FEEDBACK_ANSWER = "get_feedback_answer", ANSWER_STRING = "get_answer_string") # Specific placeholders for XML export private$xml_placeholders <- list( TYPE = "get_type", TITLE = "get_title", XML_QUESTION_TEXT = "get_XML_question_text", XML_GENERALFEEDBACK = "get_XML_generalfeedback", XML_ANSWER = "get_XML_answer" ) }, instantiate_placeholders = function(template, placeholders, opts, info) { placeholders_regex <- paste0("@", names(placeholders), "@", collapse = "\|") repeat { match <- stringi::stri_locate_first_regex(template, placeholders_regex) if(any(is.na(match))) # breaking if no match break if(nrow(match) == 0) break # Computing replacement string id <- substring(template, match[1] + 1, match[2] - 1) funcname <- placeholders[[id]] if (is.null(funcname)) stop("Unable to find corresponding function for ", sQuote(id)) replacement <- self[[funcname]](opts, info) # If replacement is NULL, try to replace whole line if (is.null(replacement)) { template <- stringi::stri_replace_first_regex(template, paste0("^ @", id, "@ \n"), "", opts_regex = list(multiline = TRUE)) template <- stringi::stri_replace_first_regex(template, paste0("@", id, "@"), "") } else { # Escape backslash and dollar in replacement string replacement <- gsub("\\\\", "\\\\\\\\", replacement) replacement <- gsub("\\$", "\\\\$", replacement) template <- stringi::stri_replace_first_regex(template, paste0("@", id, "@"), replacement) } } if(nchar(template) == 0) NULL else template }, update_quiz = function(quiz) { self$quiz <- quiz self$invalidate_hidden_data_list() }, get_data_and_environment = function() { if(is.null(self$quiz)) data <- self$recursive_instantiated_data() else data <- self$quiz$recursive_instantiated_data() env <- empty_env() eval(data, env) list(data = data, env = env) }, validate_data = function(parent_data = NULL) {}, get_markdown_from_template = function(template, opts = list(), info = list()) { self$instantiate_placeholders(template, private$placeholders, opts, info) }, to_markdown = function(opts = list(), info = list()) { opts <- update_list(private$default_options, opts) opts$export <- "markdown" private$validate_options(opts, info) template <- "@INST_TEXT@\n\n@FEEDBACK@\n" self$instantiate_placeholders(template, private$placeholders, opts, info) }, #' Feedback options if none is provided get_default_feedback = function() { list(text = self$answer) }, #' Sanitize provided feedback get_feedback_from_field = function(feedback) { feedback <- if(is.null(feedback)) { self$get_default_feedback() } else if(is.character(feedback)) { list(text = feedback) } else if(is.numeric(feedback)) { list(text = feedback) } else if(is.language(feedback)) { list(text = feedback) } else if(is.list(feedback)) { feedback } update_list(private$default_feedback_options, feedback) }, #' Template for full feedback get_feedback = function(opts, info) { # No feedback if (!is.null(opts$feedback) && !opts$feedback) return(NULL) "@ANSWER_INFO@\n\n@ANSWER_STRING@@EVALUATED_ANSWER@\n\n@FEEDBACK_ANSWER@\n" }, get_answer_string = function(opts, info) { if(is.null(info$answer_string)) "R\u00E9ponse : " else info$answer_string }, #' Feedback itself giving the right answer get_feedback_answer = function(opts, info) { # Get feedback as a proper named list with default arguments feedback_opts <- self$instantiated_feedback # Check for spurious arguments in feedback_options unknown_opts <- setdiff(names(feedback_opts), names(private$default_feedback_options)) if (length(unknown_opts) > 0) { stop("Unknown options: ", paste0(unknown_opts)) } # Override feedback_options with upstream opts feedback_opts <- update_list(feedback_opts, opts) # Check that options are coherent private$validate_feedback_options(feedback_opts, info) feedback <- if (feedback_opts$eval) { feedback_opts$text } else { if (is.null(feedback_opts$noeval_text)) feedback_opts$text else feedback_opts$noeval_text } feedback <- if (is.character(feedback)) { feedback } else if (is.language(feedback)) sprintf("```{r}\n%s\n```\n", answerstr(feedback)) else if (is.numeric(feedback)) sprintf("```{r}\n%s\n```\n", answerstr(feedback)) else stop("Unsupported feedback type: ", sQuote(feedback)) # Maybe indent the feedback if(is.null(opts$indent)) feedback else add_spaces_left(feedback, 4) }, get_XML_question_text = function(opts, info) { # md_question <- self$get_XML_question_markdown(opts, info) md_question <- self$get_inst_text(opts, info) HTML_question <- render_HTML(md_question, opts, info) trimws(HTML_question) # pandoc seems to add some leading newlines }, # Return XML "generalfeedback" node with feedback in HTML as CDATA get_XML_generalfeedback = function(opts, info) { if(!opts$feedback) return(NULL) # Generate HTML for feedback placeholders <- update_list(private$placeholders, private$xml_placeholders) tmpl <- self$get_feedback(opts, info) md_feedback <- self$instantiate_placeholders(tmpl, placeholders, opts, info) HTML_feedback <- render_HTML(md_feedback, opts, info) HTML_feedback0 <- trimws(HTML_feedback) # pandoc seems to add some leading newlines # Return XML with inner HTML tmpl <-"<generalfeedback format=\"html\"> <text><![CDATA[%s]]></text> </generalfeedback>" tmpl0 <- add_spaces_left(tmpl, opts$indent) sprintf(tmpl0, HTML_feedback0) }, get_XML_answer = function(opts, info) { as.character(self$get_evaluated_answer2(opts, info)) }, #' Export Question as XML to_XML = function(opts = NULL, info = NULL) { # Set up environment for evaluating data if not already if (is.null(info$env)) { info0 <- self$get_data_and_environment() info <- update_list(info, info0) } # Setting options default_opts <- update_list(private$default_options, private$xml_default_options) opts <- update_list(default_opts, opts) opts$export <- "xml" private$validate_options(opts, info) template <- add_spaces_left(private$xml_question_template, opts$indent) placeholders <- update_list(private$placeholders, private$xml_placeholders) # Answers might modify datasets stored in info$env self$instantiate_placeholders(template, placeholders, opts, info) }, invalidate_all = function() { self$invalidate_text() self$invalidate_answer() self$invalidate_feedback() self$invalidate_hidden_data() self$invalidate_data() self$invalidate_hidden_data_list() }, invalidate_ancestor = function() { if (is.null(self$ancestor)) self$invalidate_hidden_data() else self$ancestor$invalidate_hidden_data() }, invalidate_data = function() { private$is_data_available <- FALSE self$invalidate_inst_data() }, invalidate_hidden_data = function() { private$is_hidden_data_available <- FALSE self$invalidate_inst_text() self$invalidate_inst_answer() self$invalidate_inst_feedback() self$invalidate_inst_data() self$invalidate_hidden_data_list() }, invalidate_hidden_data_list = function() { private$is_hidden_data_list_available <- FALSE self$invalidate_inst_text() self$invalidate_inst_answer() self$invalidate_inst_feedback() self$invalidate_inst_data() }, invalidate_inst_data = function() { private$is_data_instantiated <- FALSE }, invalidate_inst_text = function() { private$is_text_instantiated <- FALSE }, invalidate_inst_answer = function() { private$is_answer_instantiated <- FALSE }, invalidate_inst_feedback = function() { private$is_feedback_instantiated <- FALSE }, invalidate_header = function() { self$invalidate_text() }, invalidate_text = function() { private$is_text_available <- FALSE self$invalidate_inst_text() }, invalidate_answer = function() { private$is_answer_available <- FALSE self$invalidate_inst_answer() }, invalidate_feedback = function() { private$is_feedback_available <- FALSE self$invalidate_inst_feedback() }, recursive_instantiated_data = function(seed_init = FALSE) { if (!seed_init & !is_empty_language(self$data) & is.null(self$seed)) stop("Some data but no seed to initialize") if(is.null(self$seed)) self$instantiated_data else merge_languages( instantiate_data_list( bquote(set.seed(.(self$seed))), self$hidden_data_list), self$instantiated_data) }, get_type = function(opts, info) { self$type }, get_title = function(opts, info) { self$title }, get_data_chunk = function(opts, info) { l <- sanitize_language(self$local_instantiated_data) if (is.null(l)) NULL else sprintf("```{r include = FALSE}\n%s\n```", answerstr(l)) }, get_rec_data_chunk = function(opts, info) { if (is.null(self$quiz)) stop("No defined Quiz in question ", sQuote(self$title)) l <- sanitize_language(self$quiz$recursive_instantiated_data()) if (is.null(l)) NULL else sprintf("```{r include = FALSE}\n%s\n```", answerstr(l)) }, # Data chunk for formatting answer get_answer_info = function(opts, info) { sprintf("```{r, include = FALSE}\n%s\nanswer <- {\n%s}\n```", answerstr(quote(cquote <- function(s) { if (is.character(s)) "`" else if (is.numeric(s)) "$" else stop("Argument is not character or numeric ", sQuote(s)) })), answerstr(self$instantiated_answer)) }, get_evaluated_answer = function(opts, info) { "`r cquote(answer)``r answer``r cquote(answer)`" }, get_evaluated_answer2 = function(opts, info) { # Set up environment for evaluating data if not already if (is.null(info$env)) { info0 <- self$get_data_and_environment() info <- update_list(info, info0) } eval(self$instantiated_answer, info$env) }, get_seed_chunk = function(opts, info) { if (is.null(self$seed)) NULL else sprintf("```{r}\nset.seed(%d)\n```", self$seed) }, get_text = function(opts, info) { self$text }, get_inst_text = function(opts, info) { if (!is.null(opts$numbered) && opts$numbered) { paste0("Question ", info$num, " : ", self$instantiated_text) } else self$instantiated_text }, get_inst_cookie = function(opts, info) { stop("Abstract method") }, get_inst_text_and_cookie = function(opts, info) { sprintf("%s\n\n%s", self$get_inst_text(opts, info), self$get_inst_cookie(opts, info)) }, get_inst_text_and_number = function(opts, info) { stopifnot(is.numeric(info$index)) sprintf("%s (%d)", self$get_inst_text(opts, info), info$index) }, get_guess = function() { }, get_is_correct_icon = function() { }, instantiate_hidden_data_list = function(var_list = NULL, seed_init = FALSE) { if (!seed_init & !is_empty_language(self$hidden_data) & is.null(self$hidden_seed)) stop("Some hidden data but no seed to instantiate them") # VAR_LIST must be a named list for list2env to work if (is.null(var_list)) var_list <- list() env <- list2env(var_list, envir = empty_env()) # Set seed if any and eval hidden data if (!is.null(self$hidden_seed)) eval(bquote(set.seed(.(self$hidden_seed))), envir = env) eval(self$hidden_data, envir = env) # Update variables in VAR_LIST with ENV new_var_list <- update_list(var_list, as.list(env, all.names = TRUE)) self$hidden_data_list <- new_var_list ## private$.hidden_data_list <- new_var_list ## private$is_hidden_data_list_available <- TRUE }, instantiate_feedback_list = function(feedback, var_list) { text <- instantiate_object(feedback$text, self$hidden_data_list) noeval_text <- instantiate_object(feedback$noeval_text, self$hidden_data_list) feedback$text <- text feedback$noeval_text <- noeval_text feedback }, hidden_data_names = function() { env <- empty_env() if (!is.null(self$hidden_seed)) eval(bquote(set.seed(.(self$hidden_seed))), envir = env) eval(self$hidden_data, envir = env) ls(envir = env, all.names = TRUE) }, rename = function(prefix, names = self$hidden_data_names()) { self$rename_text(prefix, names) self$rename_header(prefix, names) self$rename_answer(prefix, names) self$rename_feedback(prefix, names) self$rename_data(prefix, names) self$rename_hidden_data(prefix, names) self$invalidate_text() self$invalidate_answer() self$invalidate_hidden_data() self$invalidate_hidden_data_list() self$invalidate_feedback() self$invalidate_data() self }, rename_header = function(prefix, names = self$hidden_data_names()) { self$header <- prefix_object(prefix, names, self$header) self }, rename_text = function(prefix, names = self$hidden_data_names()) { self$text <- prefix_object(prefix, names, private$.text) self$invalidate_text() self }, rename_answer = function(prefix, names = self$hidden_data_names()) { self$answer <- prefix_object(prefix, names, self$answer) self$invalidate_answer() self }, rename_feedback = function(prefix, names = self$hidden_data_names()) { feedback <- self$feedback text <- prefix_object(prefix, names, feedback$text) noeval_text <- prefix_object(prefix, names, feedback$noeval_text) if (!is.null(text)) feedback$text <- text if (!is.null(noeval_text)) feedback$noeval_text <- noeval_text self$feedback <- feedback self$invalidate_feedback() self }, rename_data = function(prefix, names = self$hidden_data_names()) { self$data <- prefix_object(prefix, names, self$data) self$invalidate_data() self }, rename_hidden_data = function(prefix, names = self$hidden_data_names()) { self$hidden_data <- prefix_object(prefix, names, self$hidden_data) self$invalidate_hidden_data() self }, copy = function() { Question(self$text, type = self$type, seed = self$seed, hidden_seed = self$hidden_seed, hidden_data = self$hidden_data, data = self$data, answer = self$answer, feedback = self$feedback) } ), active = list( title = function(title) { if (missing(title)) { if (is.null(private$.title)) { if (nchar(self$instantiated_text) > 60) paste0(substr(self$instantiated_text, 0, 57), "...") else self$instantiated_text } else private$.title } else { private$.title <- title } }, header = function(header) { if (missing(header)) { private$.header } else { private$.header <- header self$invalidate_header() private$.header } }, ancestor = function(ancestor) { if (missing(ancestor)) { private$.ancestor } else { private$.ancestor <- ancestor self$invalidate_ancestor() private$.ancestor } }, text = function(text) { if (missing(text)) { paste(c(trimws(self$header), trimws(private$.text)), collapse = "\n\n") } else { private$.text <- text self$invalidate_inst_text() } }, instantiated_text = function() { if (private$is_text_instantiated) private$.instantiated_text else { private$.instantiated_text <- instantiate_text_list( self$text, self$hidden_data_list) private$is_text_instantiated <- TRUE private$.instantiated_text } }, answer = function(answer) { if (missing(answer)) { private$.answer } else { private$.answer <- answer self$invalidate_inst_answer() } }, instantiated_answer = function() { if (private$is_answer_instantiated) private$.instantiated_answer else { private$.instantiated_answer <- instantiate_object( self$answer, self$hidden_data_list) private$is_answer_instantiated <- TRUE private$.instantiated_answer } }, feedback = function(feedback) { if (missing(feedback)) { if (private$is_feedback_available) private$.feedback else { private$.feedback <- self$get_feedback_from_field(private$.feedback) private$is_feedback_available <- TRUE private$.feedback } } else { private$.feedback <- self$get_feedback_from_field(feedback) self$invalidate_inst_feedback() private$is_feedback_available <- TRUE } }, instantiated_feedback = function() { if (private$is_feedback_instantiated) private$.instantiated_feedback else { inst_feedback <- self$instantiate_feedback_list(self$feedback, self$hidden_data_list) private$is_feedback_instantiated <- TRUE private$.instantiated_feedback <- inst_feedback } }, hidden_seed = function(seed) { if (missing(seed)) private$.hidden_seed else { private$.hidden_seed <- seed self$invalidate_ancestor() } }, hidden_data = function(hidden_data) { if (missing(hidden_data)) { private$.hidden_data } else { private$.hidden_data <- hidden_data self$invalidate_ancestor() } }, hidden_data_list = function(hidden_data_list) { if (missing(hidden_data_list)) { if (private$is_hidden_data_list_available) private$.hidden_data_list else { root <- self while (!is.null(root$ancestor)) root <- root$ancestor root$instantiate_hidden_data_list() private$is_hidden_data_list_available <- TRUE private$.hidden_data_list } } else { private$is_hidden_data_list_available <- TRUE private$.hidden_data_list <- hidden_data_list ## Need to be recomputed self$invalidate_inst_text() self$invalidate_inst_answer() self$invalidate_inst_feedback() self$invalidate_inst_data() } }, seed = function(seed) { if (missing(seed)) private$.seed else { private$.seed <- seed } }, data = function(data) { if (missing(data)) { private$.data } else { private$.data <- data self$invalidate_inst_data() } }, instantiated_data = function() { if (private$is_data_instantiated) private$.instantiated_data else { private$.instantiated_data <- instantiate_data_list( private$.data, self$hidden_data_list) private$is_data_instantiated <- TRUE private$.instantiated_data } }, local_instantiated_data = function() { if(is.null(self$seed)) self$instantiated_data else merge_languages( instantiate_data_list( bquote(set.seed(.(self$seed))), self$hidden_data_list), self$instantiated_data) }, tags = function(tags) { if (missing(tags)) { private$.tags } else { private$.tags <- tags } } ), private = list( .title = NULL, .header = NULL, .ancestor = NULL, .text = NULL, is_text_available = FALSE, .answer = NULL, is_answer_available = FALSE, .feedback = NULL, is_feedback_available = FALSE, .data = NULL, is_data_available = FALSE, .seed = NULL, .hidden_seed = NULL, .hidden_data = NULL, is_hidden_data_available = FALSE, .hidden_data_list = NULL, is_hidden_data_list_available = FALSE, .instantiated_text = NULL, is_text_instantiated = FALSE, .instantiated_answer = NULL, is_answer_instantiated = FALSE, .instantiated_feedback = NULL, is_feedback_instantiated = FALSE, .instantiated_data = NULL, is_data_instantiated = FALSE, .tags = NULL, default_feedback_options = list(text = "", noeval_text = NULL, eval = TRUE, indent = 2), default_options = list(numbered = TRUE, export = "markdown", feedback = TRUE, quiet = FALSE), xml_default_options = list( numbered = FALSE, indent = 0 ), feedback_options = NULL, placeholders = NULL, placeholders_regex = NULL, validate_options = function(opts, info) { if (!is.null(opts$numbered) && opts$numbered && is.null(info$num)) stop("`numbered` option is enabled but no `num` available") }, validate_feedback_options = function(opts, info) { ## super$validate_feedback_options(opts, info) }, xml_question_template = trimws(" <question type=\"@TYPE@\"> <name> <text><![CDATA[@TITLE@]]></text> </name> <questiontext format=\"html\"> <text><![CDATA[@XML_QUESTION_TEXT@]]></text> </questiontext> <answer fraction=\"100\" format=\"plain_text\"> <text>@XML_ANSWER@</text> </answer> @XML_GENERALFEEDBACK@ <hidden>0</hidden> </question> "), xml_placeholders = NULL))
# 30DayChartChallenge Day 27 - Educational # load packages library(tidyverse) library(gganimate) # load data datasaurus <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-10-13/datasaurus.csv') # compute summary statistics dino_stats <- datasaurus %>% #datasauRus::datasaurus_dozen %>% group_by(dataset) %>% summarize( mean_x = mean(x), mean_y = mean(y), std_dev_x = sd(x), std_dev_y = sd(y), corr_x_y = cor(x, y) ) # merge raw data & summary stats d <- datasaurus %>% #datasauRus::datasaurus_dozen %>% left_join(dino_stats, by = "dataset") # plot data d %>% filter(dataset %in% c("dino", "star", "circle", "x_shape")) %>% ggplot(aes(x = x, y = y, colour = dataset)) + geom_point(size = 2) + xlim(0,100) + ylim(0,100) + coord_fixed(clip = "off") + scale_colour_manual(values = c("#298fca", "#4C5F28", "#ffe12b", "#E3242B")) + labs(title = "Same stats, different graph", subtitle = "The importance of visually inspecting your data", caption = "Source: datasauRus package / TidyTuesday", x = "", y = "") + geom_text(x = 10, y = 98, label = paste("r = ", signif(d$corr_x_y[1], 3), sep = ""), colour = "black", hjust = 0.5, size = 6) + theme_bw() + theme(legend.position = "none", plot.title = element_text(size = 22, hjust = 0.5, face = "bold"), plot.subtitle = element_text(size = 16, hjust = 0.5), plot.caption = element_text(size = 12, face = "italic"), plot.background = element_rect(fill = "#ffffff"), panel.background = element_rect(fill = "#ffffff"), axis.text = element_text(size = 16)) + transition_states(dataset, state_length = 1) # save gif anim_save("Day27_Educational/Day27.gif")	/Day27_Educational/Day27_Educational.R	no_license	timschoof/30DayChartChallenge	R	false	false	1,793	r	# 30DayChartChallenge Day 27 - Educational # load packages library(tidyverse) library(gganimate) # load data datasaurus <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-10-13/datasaurus.csv') # compute summary statistics dino_stats <- datasaurus %>% #datasauRus::datasaurus_dozen %>% group_by(dataset) %>% summarize( mean_x = mean(x), mean_y = mean(y), std_dev_x = sd(x), std_dev_y = sd(y), corr_x_y = cor(x, y) ) # merge raw data & summary stats d <- datasaurus %>% #datasauRus::datasaurus_dozen %>% left_join(dino_stats, by = "dataset") # plot data d %>% filter(dataset %in% c("dino", "star", "circle", "x_shape")) %>% ggplot(aes(x = x, y = y, colour = dataset)) + geom_point(size = 2) + xlim(0,100) + ylim(0,100) + coord_fixed(clip = "off") + scale_colour_manual(values = c("#298fca", "#4C5F28", "#ffe12b", "#E3242B")) + labs(title = "Same stats, different graph", subtitle = "The importance of visually inspecting your data", caption = "Source: datasauRus package / TidyTuesday", x = "", y = "") + geom_text(x = 10, y = 98, label = paste("r = ", signif(d$corr_x_y[1], 3), sep = ""), colour = "black", hjust = 0.5, size = 6) + theme_bw() + theme(legend.position = "none", plot.title = element_text(size = 22, hjust = 0.5, face = "bold"), plot.subtitle = element_text(size = 16, hjust = 0.5), plot.caption = element_text(size = 12, face = "italic"), plot.background = element_rect(fill = "#ffffff"), panel.background = element_rect(fill = "#ffffff"), axis.text = element_text(size = 16)) + transition_states(dataset, state_length = 1) # save gif anim_save("Day27_Educational/Day27.gif")
library(sfsmisc) sapply(list.files(pattern="[.]R$", path="R/", full.names=TRUE), source); filenames <- list.files("test/steve", pattern="autism*", full.names=TRUE) matrices = list() #for (i in 1:3) { for (i in 1:length(filenames)) { matrices[[i]] <- as.matrix(read.table(filenames[[i]], skip=1)) } images = new("IMaGES", matrices = matrices, penalty=1.5)	/call_gies.R	no_license	noahfl/pcalg	R	false	false	362	r	library(sfsmisc) sapply(list.files(pattern="[.]R$", path="R/", full.names=TRUE), source); filenames <- list.files("test/steve", pattern="autism*", full.names=TRUE) matrices = list() #for (i in 1:3) { for (i in 1:length(filenames)) { matrices[[i]] <- as.matrix(read.table(filenames[[i]], skip=1)) } images = new("IMaGES", matrices = matrices, penalty=1.5)
% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tally.R \name{prop} \alias{count} \alias{perc} \alias{prop} \alias{prop1} \title{Compute proportions, percents, or counts for a single level} \usage{ prop(x, data = parent.frame(), useNA = "no", ..., level = NULL, long.names = TRUE, sep = ".", format = "proportion", quiet = TRUE, pval.adjust = FALSE) prop1(..., pval.adjust = TRUE) count(x, data = parent.frame(), ..., format = "count") perc(x, data = parent.frame(), ..., format = "percent") } \arguments{ \item{x}{an R object, usually a formula} \item{data}{a data frame in which \code{x} is to be evaluated} \item{useNA}{an indication of how NA's should be handled. By default, they are ignored.} \item{level}{the level for which counts, proportions or percents are calculated} \item{long.names}{a logical indicating whether long names should be when there is a conditioning variable} \item{sep}{a character used to separate portions of long names} \item{format}{one of \code{proportion}, \code{percent}, or \code{count}, possibly abbrevaited} \item{quiet}{a logical indicating whether messages regarding the target level should be supressed.} \item{pval.adjust}{a logical indicating whether the "p-value" adjustment should be applied. This adjustment adds 1 to the numerator and denominator counts.} \item{\dots}{arguments passed through to \code{\link{tally}}} } \description{ Compute proportions, percents, or counts for a single level } \details{ \code{prop1} is intended for the computation of p-values from randomization distributions and differs from \code{prop} only in its default value of \code{pval.adjust}. } \note{ For 0-1 data, level is set to 1 by default since that a standard coding scheme for success and failure. } \examples{ if (require(mosaicData)) { prop( ~sex, data=HELPrct) prop( ~sex, data=HELPrct, level='male') count( ~sex \| substance, data=HELPrct) prop( ~sex \| substance, data=HELPrct) perc( ~sex \| substance, data=HELPrct) } }	/man/prop.Rd	no_license	aaronbaggett/mosaic	R	false	true	2,014	rd	% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tally.R \name{prop} \alias{count} \alias{perc} \alias{prop} \alias{prop1} \title{Compute proportions, percents, or counts for a single level} \usage{ prop(x, data = parent.frame(), useNA = "no", ..., level = NULL, long.names = TRUE, sep = ".", format = "proportion", quiet = TRUE, pval.adjust = FALSE) prop1(..., pval.adjust = TRUE) count(x, data = parent.frame(), ..., format = "count") perc(x, data = parent.frame(), ..., format = "percent") } \arguments{ \item{x}{an R object, usually a formula} \item{data}{a data frame in which \code{x} is to be evaluated} \item{useNA}{an indication of how NA's should be handled. By default, they are ignored.} \item{level}{the level for which counts, proportions or percents are calculated} \item{long.names}{a logical indicating whether long names should be when there is a conditioning variable} \item{sep}{a character used to separate portions of long names} \item{format}{one of \code{proportion}, \code{percent}, or \code{count}, possibly abbrevaited} \item{quiet}{a logical indicating whether messages regarding the target level should be supressed.} \item{pval.adjust}{a logical indicating whether the "p-value" adjustment should be applied. This adjustment adds 1 to the numerator and denominator counts.} \item{\dots}{arguments passed through to \code{\link{tally}}} } \description{ Compute proportions, percents, or counts for a single level } \details{ \code{prop1} is intended for the computation of p-values from randomization distributions and differs from \code{prop} only in its default value of \code{pval.adjust}. } \note{ For 0-1 data, level is set to 1 by default since that a standard coding scheme for success and failure. } \examples{ if (require(mosaicData)) { prop( ~sex, data=HELPrct) prop( ~sex, data=HELPrct, level='male') count( ~sex \| substance, data=HELPrct) prop( ~sex \| substance, data=HELPrct) perc( ~sex \| substance, data=HELPrct) } }
# A242310: Ilya Lopatin and Juri-Stepan Gerasimov, May 10 2014 # A000056: _N. J. A. Sloane_ # A051419: Paul L. Chessin (pchess(AT)ix.netcom.com) # A000045: _N. J. A. Sloane, 1964_ # A169890: _David Applegate_, _Marc LeBrun_ and _N. J. A. Sloane_, Jul 06 2010 # A169888: _N. J. A. Sloane_, Jul 07 2010, based on a letter from _Jean-Claude # Babois_. id <- "A169890" test_that("OEIS sequence A169890 has three authors", { testthat::expect_equal(id %>% OEIS_author %>% length, 3) })	/tests/testthat/test_OEIS_author.R	permissive	EnriquePH/OEIS.R	R	false	false	539	r	# A242310: Ilya Lopatin and Juri-Stepan Gerasimov, May 10 2014 # A000056: _N. J. A. Sloane_ # A051419: Paul L. Chessin (pchess(AT)ix.netcom.com) # A000045: _N. J. A. Sloane, 1964_ # A169890: _David Applegate_, _Marc LeBrun_ and _N. J. A. Sloane_, Jul 06 2010 # A169888: _N. J. A. Sloane_, Jul 07 2010, based on a letter from _Jean-Claude # Babois_. id <- "A169890" test_that("OEIS sequence A169890 has three authors", { testthat::expect_equal(id %>% OEIS_author %>% length, 3) })
## This following function takes a square invertible matrix ## and return a list of functions to: ## 1.- Set the matrix. ## 2.- Get the matrix. ## 3.- Set the inverse of matrix. ## 4.- Get the inverse of matrix. ## Finally, this is used as input in cacheSolve function. makeCacheMatrix <- function(x = matrix()) { m <- NULL ## Set the matrix set <- function(y){ x <<- y m <<- NULL } ## Get the matrix get <- function() x ## Set the inverse of matrix. setinverse <- function(solve) m <<- solve ## Get the inverse of matrix. getinverse <- function() m ## Return the list of functions. list(set = set, get = get, setinverse = setinverse, getinverse = getinverse) } ## In cacheSolve function, we take the matrix and ## calculate the inverse. ## Finally we return it. cacheSolve <- function(x, ...) { ## Get m <- x$getinverse() if(!is.null(m)) { ## If the value already exists, returns m. message("getting cached data") return(m) } ## Else data <- x$get() m <- solve(data, ...) x$setinverse(m) return(m) } ## Running Example # > matriz <- matrix(runif(9,1,100),3,3) # Show "matriz" # > matriz # [,1] [,2] [,3] # [1,] 90.15773 26.488523 72.41762 # [2,] 60.19124 9.766337 91.25337 # [3,] 96.55614 47.140896 67.54101 # Generating the cache matrix # > matrixExample <- makeCacheMatrix(matriz) # Finally calculate or retrieve the value of # Inverted matrix using cacheSolve: # > cacheSolve(matrixExample) # [,1] [,2] [,3] # [1,] -0.162284059 -0.12840965 0.22513611 # [2,] 0.194560788 -0.05779459 -0.08093354 # [3,] 0.004915707 0.20579781 -0.04886617	/LexicalScoping.R	no_license	fsaavedraolmos/RProgramming	R	false	false	1,706	r	## This following function takes a square invertible matrix ## and return a list of functions to: ## 1.- Set the matrix. ## 2.- Get the matrix. ## 3.- Set the inverse of matrix. ## 4.- Get the inverse of matrix. ## Finally, this is used as input in cacheSolve function. makeCacheMatrix <- function(x = matrix()) { m <- NULL ## Set the matrix set <- function(y){ x <<- y m <<- NULL } ## Get the matrix get <- function() x ## Set the inverse of matrix. setinverse <- function(solve) m <<- solve ## Get the inverse of matrix. getinverse <- function() m ## Return the list of functions. list(set = set, get = get, setinverse = setinverse, getinverse = getinverse) } ## In cacheSolve function, we take the matrix and ## calculate the inverse. ## Finally we return it. cacheSolve <- function(x, ...) { ## Get m <- x$getinverse() if(!is.null(m)) { ## If the value already exists, returns m. message("getting cached data") return(m) } ## Else data <- x$get() m <- solve(data, ...) x$setinverse(m) return(m) } ## Running Example # > matriz <- matrix(runif(9,1,100),3,3) # Show "matriz" # > matriz # [,1] [,2] [,3] # [1,] 90.15773 26.488523 72.41762 # [2,] 60.19124 9.766337 91.25337 # [3,] 96.55614 47.140896 67.54101 # Generating the cache matrix # > matrixExample <- makeCacheMatrix(matriz) # Finally calculate or retrieve the value of # Inverted matrix using cacheSolve: # > cacheSolve(matrixExample) # [,1] [,2] [,3] # [1,] -0.162284059 -0.12840965 0.22513611 # [2,] 0.194560788 -0.05779459 -0.08093354 # [3,] 0.004915707 0.20579781 -0.04886617
# # IKI Bangladesh (MIOASI): S6 Exceedence Probabilities # # Make netcdf of estimates of mean, and low and upper credible intervals of mean gust speed. # This process fits a GAM to the ensemble data, estimates a posterior distribution, # and samples them to find the mean and credible interval based on a set of quantiles. # # # Author: HS # Created: Jan 2020 # QA: TE 17/3/20 library(RNetCDF) library(abind) library(doParallel) registerDoParallel(cores = 10) # Define Bangladesh tropical cyclon categories in knots converted to m/s # WINDTHRESHOLDS <- c(17, 22, 28, 34, 48, 64, 120) * 0.514444 WINDTHRESHOLDS <- seq(0, 100, 1) INDIR <- "" VAR <- 'fg.T1Hmax' RES <- '4p4' # Load base netcdf to get lat/lon variables filename <- paste('fp.', VAR, '.AILA.', RES, 'km.nc', sep = '') stormsnc <- open.nc(paste(INDIR, filename, sep = '/')) # Extract netcdf variables lat <- var.get.nc(stormsnc, 'latitude') lon <- var.get.nc(stormsnc, 'longitude') nLon <- length(lon) nLat <- length(lat) nCells <- nLon*nLat # Make lon-lat-ens grid lonlat <- expand.grid(lon = lon, lat = lat) # Load predictions data from RData object predsfile <- "grand_fp_preds.Rdata" load(predsfile) # Calculate posterior stats from preds # Use the simulated values at each gridpoint to calculate exceedend probabilities # Do in parallel with %dopar% and write out to list qlist plist <- foreach(i = seq_along(WINDTHRESHOLDS)) %dopar% { # Make bool array of T/F values based on wind thresholds bool <- preds >= WINDTHRESHOLDS[i] # Find threshold probabilities based on total True values / total number of preds -> mean # N.B. Here the mean is the Monte Carlo approximation to the integral -- so unrelated to the mean of any distribution p <- apply(bool, 2, mean) matrix(p, nrow = nLon, ncol = nLat) } # Convert list to 3D matrix exceedm <- abind(plist, along=0) # Write out netCDF file outfile <- paste('pgrand.exceedance_tmp.', VAR, '.', RES, 'km.nc', sep = '') outnc <- create.nc(outfile, large=T) # Dimensions dim.def.nc(outnc, 'latitude', nLat) dim.def.nc(outnc, 'longitude', nLon) dim.def.nc(outnc, 'gust_threshold', length(WINDTHRESHOLDS)) # Variables var.def.nc(outnc, 'latitude', 'NC_FLOAT', 'latitude') var.def.nc(outnc, 'longitude', 'NC_FLOAT', 'longitude') var.def.nc(outnc, 'gust_threshold', 'NC_INT', 'gust_threshold') var.def.nc(outnc, 'latitude_longitude', 'NC_CHAR', NA) var.def.nc(outnc, 'probability_of_exceedance', 'NC_FLOAT', c('gust_threshold', 'longitude', 'latitude')) # Define some attributes att.put.nc(outnc, "latitude", "axis", "NC_CHAR", "Y") att.put.nc(outnc, "latitude", "units", "NC_CHAR", "degrees_north") att.put.nc(outnc, "latitude", "standard_name", "NC_CHAR", "latitude") att.put.nc(outnc, "longitude", "axis", "NC_CHAR", "X") att.put.nc(outnc, "longitude", "units", "NC_CHAR", "degrees_east") att.put.nc(outnc, "longitude", "standard_name", "NC_CHAR", "longitude") att.put.nc(outnc, "gust_threshold", "long_name", "NC_CHAR", "Gust speed classification threshold") att.put.nc(outnc, "gust_threshold", "units", "NC_CHAR", "m s-1") att.put.nc(outnc, "probability_of_exceedance", "long_name", "NC_CHAR", "posterior wind_speed_of_gust probability_of_exceedance") att.put.nc(outnc, "probability_of_exceedance", "units", "NC_INT", 1) att.put.nc(outnc, "probability_of_exceedance", "grid_mapping", "NC_CHAR", "latitude_longitude") att.put.nc(outnc, "latitude_longitude", "grid_mapping_name", "NC_CHAR", "latitude_longitude") att.put.nc(outnc, "latitude_longitude", "longitude_of_prime_meridian", "NC_INT", 0) att.put.nc(outnc, "latitude_longitude", "earth_radius", "NC_INT", 6371229.) att.put.nc(outnc, "latitude_longitude", "proj4", "NC_CHAR", "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs") att.put.nc(outnc, "NC_GLOBAL", "comment", "NC_CHAR", "Supported by the International Climate Initiative (IKI) and the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety, based on a decision of the Germany Bundestag") att.put.nc(outnc, "NC_GLOBAL", "contact", "NC_CHAR", "enquiries@metoffice.gov.uk") att.put.nc(outnc, "NC_GLOBAL", "data_type", "NC_CHAR", "grid") att.put.nc(outnc, "NC_GLOBAL", "date_created", "NC_CHAR", format(Sys.time(), "%Y%m%dT%H:%M:%S")) att.put.nc(outnc, "NC_GLOBAL", "geospatial_lat_max", "NC_FLOAT", max(lat)) att.put.nc(outnc, "NC_GLOBAL", "geospatial_lat_min", "NC_FLOAT", min(lat)) att.put.nc(outnc, "NC_GLOBAL", "geospatial_lat_resolution", "NC_FLOAT", 0.04) att.put.nc(outnc, "NC_GLOBAL", "geospatial_lat_units", "NC_CHAR", "degrees_north") att.put.nc(outnc, "NC_GLOBAL", "geospatial_lon_max", "NC_FLOAT", max(lon)) att.put.nc(outnc, "NC_GLOBAL", "geospatial_lon_min", "NC_FLOAT", min(lon)) att.put.nc(outnc, "NC_GLOBAL", "geospatial_lon_resolution", "NC_FLOAT", 0.04) att.put.nc(outnc, "NC_GLOBAL", "geospatial_lon_units", "NC_CHAR", "degrees_east") att.put.nc(outnc, "NC_GLOBAL", "history", "NC_CHAR", "(1.0) Initial release") att.put.nc(outnc, "NC_GLOBAL", "id", "NC_CHAR", paste("fpgrandw.", VAR, ".", RES, "km.nc", sep="")) att.put.nc(outnc, "NC_GLOBAL", "institution", "NC_CHAR", "Met Office, UK") att.put.nc(outnc, "NC_GLOBAL", "licence", "NC_CHAR", "Creative Commons Attribution 4.0 International (CC BY 4.0)") att.put.nc(outnc, "NC_GLOBAL", "product_version", "NC_CHAR", "v1.0") att.put.nc(outnc, "NC_GLOBAL", "project", "NC_CHAR", "Oasis Platform for Climate and Catastrophe Risk Assessment – Asia") att.put.nc(outnc, "NC_GLOBAL", "institution", "NC_CHAR", "Met Office, UK") att.put.nc(outnc, "NC_GLOBAL", "keywords", "NC_CHAR", "Bangladesh, footprint, quantiles, Met Office") att.put.nc(outnc, "NC_GLOBAL", "source", "NC_CHAR", "Met Office UM RA2T CON") att.put.nc(outnc, "NC_GLOBAL", "spatial_resolution", "NC_CHAR", "4.4km") att.put.nc(outnc, "NC_GLOBAL", "summary", "NC_CHAR", "Tropical cyclone gust speed threshold exceedence probabilities over Bangladesh") att.put.nc(outnc, "NC_GLOBAL", "keywords", "NC_CHAR", "Bangladesh, footprint, posterior, exceedence probabilities, Met Office") # Write in data var.put.nc(outnc, 'latitude', lat) var.put.nc(outnc, 'longitude', lon) var.put.nc(outnc, 'gust_threshold', WINDTHRESHOLDS) var.put.nc(outnc, 'probability_of_exceedance', exceedm) close.nc(outnc)	/r/s6_exceedence_probability.R	permissive	MetOffice/IKI-Oasis-Bangladesh	R	false	false	6,191	r	# # IKI Bangladesh (MIOASI): S6 Exceedence Probabilities # # Make netcdf of estimates of mean, and low and upper credible intervals of mean gust speed. # This process fits a GAM to the ensemble data, estimates a posterior distribution, # and samples them to find the mean and credible interval based on a set of quantiles. # # # Author: HS # Created: Jan 2020 # QA: TE 17/3/20 library(RNetCDF) library(abind) library(doParallel) registerDoParallel(cores = 10) # Define Bangladesh tropical cyclon categories in knots converted to m/s # WINDTHRESHOLDS <- c(17, 22, 28, 34, 48, 64, 120) * 0.514444 WINDTHRESHOLDS <- seq(0, 100, 1) INDIR <- "" VAR <- 'fg.T1Hmax' RES <- '4p4' # Load base netcdf to get lat/lon variables filename <- paste('fp.', VAR, '.AILA.', RES, 'km.nc', sep = '') stormsnc <- open.nc(paste(INDIR, filename, sep = '/')) # Extract netcdf variables lat <- var.get.nc(stormsnc, 'latitude') lon <- var.get.nc(stormsnc, 'longitude') nLon <- length(lon) nLat <- length(lat) nCells <- nLon*nLat # Make lon-lat-ens grid lonlat <- expand.grid(lon = lon, lat = lat) # Load predictions data from RData object predsfile <- "grand_fp_preds.Rdata" load(predsfile) # Calculate posterior stats from preds # Use the simulated values at each gridpoint to calculate exceedend probabilities # Do in parallel with %dopar% and write out to list qlist plist <- foreach(i = seq_along(WINDTHRESHOLDS)) %dopar% { # Make bool array of T/F values based on wind thresholds bool <- preds >= WINDTHRESHOLDS[i] # Find threshold probabilities based on total True values / total number of preds -> mean # N.B. Here the mean is the Monte Carlo approximation to the integral -- so unrelated to the mean of any distribution p <- apply(bool, 2, mean) matrix(p, nrow = nLon, ncol = nLat) } # Convert list to 3D matrix exceedm <- abind(plist, along=0) # Write out netCDF file outfile <- paste('pgrand.exceedance_tmp.', VAR, '.', RES, 'km.nc', sep = '') outnc <- create.nc(outfile, large=T) # Dimensions dim.def.nc(outnc, 'latitude', nLat) dim.def.nc(outnc, 'longitude', nLon) dim.def.nc(outnc, 'gust_threshold', length(WINDTHRESHOLDS)) # Variables var.def.nc(outnc, 'latitude', 'NC_FLOAT', 'latitude') var.def.nc(outnc, 'longitude', 'NC_FLOAT', 'longitude') var.def.nc(outnc, 'gust_threshold', 'NC_INT', 'gust_threshold') var.def.nc(outnc, 'latitude_longitude', 'NC_CHAR', NA) var.def.nc(outnc, 'probability_of_exceedance', 'NC_FLOAT', c('gust_threshold', 'longitude', 'latitude')) # Define some attributes att.put.nc(outnc, "latitude", "axis", "NC_CHAR", "Y") att.put.nc(outnc, "latitude", "units", "NC_CHAR", "degrees_north") att.put.nc(outnc, "latitude", "standard_name", "NC_CHAR", "latitude") att.put.nc(outnc, "longitude", "axis", "NC_CHAR", "X") att.put.nc(outnc, "longitude", "units", "NC_CHAR", "degrees_east") att.put.nc(outnc, "longitude", "standard_name", "NC_CHAR", "longitude") att.put.nc(outnc, "gust_threshold", "long_name", "NC_CHAR", "Gust speed classification threshold") att.put.nc(outnc, "gust_threshold", "units", "NC_CHAR", "m s-1") att.put.nc(outnc, "probability_of_exceedance", "long_name", "NC_CHAR", "posterior wind_speed_of_gust probability_of_exceedance") att.put.nc(outnc, "probability_of_exceedance", "units", "NC_INT", 1) att.put.nc(outnc, "probability_of_exceedance", "grid_mapping", "NC_CHAR", "latitude_longitude") att.put.nc(outnc, "latitude_longitude", "grid_mapping_name", "NC_CHAR", "latitude_longitude") att.put.nc(outnc, "latitude_longitude", "longitude_of_prime_meridian", "NC_INT", 0) att.put.nc(outnc, "latitude_longitude", "earth_radius", "NC_INT", 6371229.) att.put.nc(outnc, "latitude_longitude", "proj4", "NC_CHAR", "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs") att.put.nc(outnc, "NC_GLOBAL", "comment", "NC_CHAR", "Supported by the International Climate Initiative (IKI) and the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety, based on a decision of the Germany Bundestag") att.put.nc(outnc, "NC_GLOBAL", "contact", "NC_CHAR", "enquiries@metoffice.gov.uk") att.put.nc(outnc, "NC_GLOBAL", "data_type", "NC_CHAR", "grid") att.put.nc(outnc, "NC_GLOBAL", "date_created", "NC_CHAR", format(Sys.time(), "%Y%m%dT%H:%M:%S")) att.put.nc(outnc, "NC_GLOBAL", "geospatial_lat_max", "NC_FLOAT", max(lat)) att.put.nc(outnc, "NC_GLOBAL", "geospatial_lat_min", "NC_FLOAT", min(lat)) att.put.nc(outnc, "NC_GLOBAL", "geospatial_lat_resolution", "NC_FLOAT", 0.04) att.put.nc(outnc, "NC_GLOBAL", "geospatial_lat_units", "NC_CHAR", "degrees_north") att.put.nc(outnc, "NC_GLOBAL", "geospatial_lon_max", "NC_FLOAT", max(lon)) att.put.nc(outnc, "NC_GLOBAL", "geospatial_lon_min", "NC_FLOAT", min(lon)) att.put.nc(outnc, "NC_GLOBAL", "geospatial_lon_resolution", "NC_FLOAT", 0.04) att.put.nc(outnc, "NC_GLOBAL", "geospatial_lon_units", "NC_CHAR", "degrees_east") att.put.nc(outnc, "NC_GLOBAL", "history", "NC_CHAR", "(1.0) Initial release") att.put.nc(outnc, "NC_GLOBAL", "id", "NC_CHAR", paste("fpgrandw.", VAR, ".", RES, "km.nc", sep="")) att.put.nc(outnc, "NC_GLOBAL", "institution", "NC_CHAR", "Met Office, UK") att.put.nc(outnc, "NC_GLOBAL", "licence", "NC_CHAR", "Creative Commons Attribution 4.0 International (CC BY 4.0)") att.put.nc(outnc, "NC_GLOBAL", "product_version", "NC_CHAR", "v1.0") att.put.nc(outnc, "NC_GLOBAL", "project", "NC_CHAR", "Oasis Platform for Climate and Catastrophe Risk Assessment – Asia") att.put.nc(outnc, "NC_GLOBAL", "institution", "NC_CHAR", "Met Office, UK") att.put.nc(outnc, "NC_GLOBAL", "keywords", "NC_CHAR", "Bangladesh, footprint, quantiles, Met Office") att.put.nc(outnc, "NC_GLOBAL", "source", "NC_CHAR", "Met Office UM RA2T CON") att.put.nc(outnc, "NC_GLOBAL", "spatial_resolution", "NC_CHAR", "4.4km") att.put.nc(outnc, "NC_GLOBAL", "summary", "NC_CHAR", "Tropical cyclone gust speed threshold exceedence probabilities over Bangladesh") att.put.nc(outnc, "NC_GLOBAL", "keywords", "NC_CHAR", "Bangladesh, footprint, posterior, exceedence probabilities, Met Office") # Write in data var.put.nc(outnc, 'latitude', lat) var.put.nc(outnc, 'longitude', lon) var.put.nc(outnc, 'gust_threshold', WINDTHRESHOLDS) var.put.nc(outnc, 'probability_of_exceedance', exceedm) close.nc(outnc)
#' Returns information on how to partition y_data #' #' This function returns information on how to partition (randomly or paired) y_data #' importFrom magrittr "%>%" #' @param data_object argument is the output produced by as.MLinput, which contains a single x data frame or a list of x data frames, a y data frames and attributes #' @param partition_style one of 'random' or 'paired' (character string) indicating type of partition #' @param folds integer of k for k-fold cross validation #' @param repeats integer of number of iterations to repeat cross validation #' @param holdout_perc numeric between 0 and 1 indicating the percentage of data to withold for the holdout validation set #' @export dataPartitioning = function(data_object, partition_style = "random", folds = 4, repeats = 100, holdout_perc = 0.25) { # extract y_data from data_object and cnames y_data = data_object$Y group_identifier = attr(data_object, "cnames")$outcome_cname pair_identifier = attr(data_object, "cnames")$pair_cname # initial checks if (!(partition_style %in% c("random", "paired"))) stop("'partition_style' must be one of, 'random' or 'paired'") if (!inherits(y_data, "data.frame")) stop("'y_data' must be of class 'data.frame'") if (partition_style == "paired" & is.null(pair_identifier)) stop("'pair_identifier' is required for a 'paired' partition_style") repeats_new = repeats * 1.5 repeats_extra = repeats_new - repeats if (partition_style == "random") { partition_result = pureRandomTestingTraining(data = y_data, group_identifier = group_identifier, folds = folds, repeats = repeats, holdout_perc = holdout_perc) # rearrrange partition_result data into test data and training data train_list = lapply(partition_result, function(item) { item$train }) test_list = lapply(partition_result, function(item) { item$test }) # form fold and repeat lists fld = rep(1:folds, repeats) fld = as.list(fld) reps = as.list(1:repeats) reps = lapply(reps, rep, folds) reps = unlist(reps) reps = as.list(reps) # form folds repeats data frames train_df = mapply(fold_rep_train, train_list, fld, reps) train_df = as.data.frame(train_df) train_df = lapply(train_df, as.data.frame, stringsAsFactors = F) train_df = do.call(rbind, train_df) test_df = mapply(fold_rep_test, test_list, fld, reps) test_df = as.data.frame(test_df) test_df = lapply(test_df, as.data.frame, stringsAsFactors = F) test_df = do.call(rbind, test_df) final_result = list(train = train_df, test = test_df) final_result = lapply(final_result, function(x) { split(data.table::as.data.table(x), by = c("Fold", "Rep")) }) ########## partition data for repeats_extra########## ########## partition_result_extra = pureRandomTestingTraining(data = y_data, group_identifier = group_identifier, folds = folds, repeats = repeats_extra, holdout_perc = holdout_perc) # rearrrange partition_result data into test data and training data train_list = lapply(partition_result_extra, function(item) { item$train }) test_list = lapply(partition_result_extra, function(item) { item$test }) # form fold and repeat lists fld = rep(1:folds, repeats_extra) fld = as.list(fld) reps = as.list(1:repeats_extra) reps = lapply(reps, rep, folds) reps = unlist(reps) reps = as.list(reps) # form folds repeats data frames train_df = mapply(fold_rep_train, train_list, fld, reps) train_df = as.data.frame(train_df) train_df = lapply(train_df, as.data.frame, stringsAsFactors = F) train_df = do.call(rbind, train_df) test_df = mapply(fold_rep_test, test_list, fld, reps) test_df = as.data.frame(test_df) test_df = lapply(test_df, as.data.frame, stringsAsFactors = F) test_df = do.call(rbind, test_df) final_result_extra = list(train = train_df, test = test_df) final_result_extra = lapply(final_result_extra, function(x) { split(data.table::as.data.table(x), by = c("Fold", "Rep")) }) } else if (partition_style == "paired") { partition_result = pairedCaseControlTestingTraining(data = y_data, group_identifier = group_identifier, pair_identifier = pair_identifier, folds = folds, repeats = repeats) # rearrrange partition_result data into two tibble data frames, one for test data one for train data train_list = lapply(partition_result, function(item) { item$train }) test_list = lapply(partition_result, function(item) { item$test }) # form fold and repeat lists fld = rep(1:folds, repeats) fld = as.list(fld) reps = as.list(1:repeats) reps = lapply(reps, rep, folds) reps = unlist(reps) reps = as.list(reps) # form folds repeats data frames train_df = mapply(fold_rep_train, train_list, fld, reps) train_df = as.data.frame(train_df) train_df = lapply(train_df, as.data.frame, stringsAsFactors = F) train_df = do.call(rbind, train_df) test_df = mapply(fold_rep_test, test_list, fld, reps) test_df = as.data.frame(test_df) test_df = lapply(test_df, as.data.frame, stringsAsFactors = F) test_df = do.call(rbind, test_df) final_result = list(train = train_df, test = test_df) final_result = lapply(final_result, function(x) { split(data.table::as.data.table(x), by = c("Fold", "Rep")) }) ########## partition data for repeats_extra########## ########## partition_result_extra = pairedCaseControlTestingTraining(data = y_data, group_identifier = group_identifier, pair_identifier = pair_identifier, folds = folds, repeats = repeats) # rearrrange partition_result data into two tibble data frames, one for test data one for train data train_list = lapply(partition_result_extra, function(item) { item$train }) test_list = lapply(partition_result_extra, function(item) { item$test }) # form fold and repeat lists fld = rep(1:folds, repeats_extra) fld = as.list(fld) reps = as.list(1:repeats_extra) reps = lapply(reps, rep, folds) reps = unlist(reps) reps = as.list(reps) # form folds repeats data frames train_df = mapply(fold_rep_train, train_list, fld, reps) train_df = as.data.frame(train_df) train_df = lapply(train_df, as.data.frame, stringsAsFactors = F) train_df = do.call(rbind, train_df) test_df = mapply(fold_rep_test, test_list, fld, reps) test_df = as.data.frame(test_df) test_df = lapply(test_df, as.data.frame, stringsAsFactors = F) test_df = do.call(rbind, test_df) final_result_extra = list(train = train_df, test = test_df) final_result_extra = lapply(final_result_extra, function(x) { split(data.table::as.data.table(x), by = c("Fold", "Rep")) }) } attr(data_object, "partition_info") = final_result attr(data_object, "extra_partitions") = final_result_extra attr(data_object, "foldrep") = list(folds = folds, repeats = repeats) return(data_object) } pureRandomTestingTraining <- function(data, group_identifier, folds = 4, repeats = 100, holdout_perc = 0.25) { groups <- data[, group_identifier] seed <- 42 #------ determine training indeces ----------# # returns a k x repeats size list of training indeces set seed for reproducability purposes set.seed(42) training_ind_list <- caret::createMultiFolds(y = groups, k = folds, times = repeats) #------ diff data and train to return testing indeces -----# # returns a k x repeats size list of grouped testing and training indeces ind_list <- lapply(training_ind_list, function(training_inds) { partition_list <- list() partition_list$train <- training_inds partition_list$test <- setdiff(1:nrow(data), training_inds) return(partition_list) }) return(ind_list) } pairedCaseControlTestingTraining <- function(data, group_identifier, pair_identifier, folds = 4, repeats = 100, holdout_set = NULL) { tuning_set <- FALSE seed <- 42 #------- create a map to keep orignal indexing in tact -----# data$old_index <- 1:nrow(data) #------- remove the holdout set if there is one ---------# if (length(holdout_set) > 0) { data <- data[-which(data[, pair_identifier] %in% holdout_set), ] } #------- use map to find unique pairs -----# singled_pair_subset <- data %>% dplyr::arrange_(group_identifier) %>% dplyr::distinct_(pair_identifier, .keep_all = TRUE) #keep only unique identifiers for training subset singled_pair_subset[1:nrow(singled_pair_subset)/2, group_identifier] <- 1 # convert half the labes to zeros for caret's balancing act index_map <- data.frame(old = singled_pair_subset$old_index, new = 1:nrow(singled_pair_subset), pair = singled_pair_subset[, pair_identifier]) rownames(index_map) <- index_map$new #------ vector of group labels to split ------# group <- singled_pair_subset[, group_identifier] #------ determine training indeces ----------# # returns a k x repeats size list of training indeces training_ind_list <- caret::createMultiFolds(y = group, k = folds, times = repeats) #------- match case-control pairs --------# ind_list <- lapply(training_ind_list, function(x) { pairs <- index_map[x, "pair"] partition_list <- list() partition_list$train <- data[which(data[, pair_identifier] %in% pairs), "old_index"] partition_list$test <- data[which(!data[, pair_identifier] %in% pairs), "old_index"] return(partition_list) }) #------ diff data and train to return testing indeces -----# # returns a k x repeats size list of grouped testing and training indeces ind_list <- lapply(training_ind_list_mapped, # function(training_inds){ pairs <- index_map[x, 'pair'] partition_list <- list() partition_list$train <- training_inds # partition_list$test <- data[which(!data[,pair_identifier] %in% pairs), 'old_index'] return(partition_list) }) } # folds repeats helper functions for training data and test data fold_rep_train = function(train_vec, fold, repp) { result = data.frame(Fold = rep(fold, length(train_vec)), Rep = rep(repp, length(train_vec)), Train = train_vec) return(result) } fold_rep_test = function(train_vec, fold, repp) { result = data.frame(Fold = rep(fold, length(train_vec)), Rep = rep(repp, length(train_vec)), Test = train_vec) return(result) }	/R/dataPartitioning.R	permissive	pmartR/peppuR	R	false	false	11,323	r	#' Returns information on how to partition y_data #' #' This function returns information on how to partition (randomly or paired) y_data #' importFrom magrittr "%>%" #' @param data_object argument is the output produced by as.MLinput, which contains a single x data frame or a list of x data frames, a y data frames and attributes #' @param partition_style one of 'random' or 'paired' (character string) indicating type of partition #' @param folds integer of k for k-fold cross validation #' @param repeats integer of number of iterations to repeat cross validation #' @param holdout_perc numeric between 0 and 1 indicating the percentage of data to withold for the holdout validation set #' @export dataPartitioning = function(data_object, partition_style = "random", folds = 4, repeats = 100, holdout_perc = 0.25) { # extract y_data from data_object and cnames y_data = data_object$Y group_identifier = attr(data_object, "cnames")$outcome_cname pair_identifier = attr(data_object, "cnames")$pair_cname # initial checks if (!(partition_style %in% c("random", "paired"))) stop("'partition_style' must be one of, 'random' or 'paired'") if (!inherits(y_data, "data.frame")) stop("'y_data' must be of class 'data.frame'") if (partition_style == "paired" & is.null(pair_identifier)) stop("'pair_identifier' is required for a 'paired' partition_style") repeats_new = repeats * 1.5 repeats_extra = repeats_new - repeats if (partition_style == "random") { partition_result = pureRandomTestingTraining(data = y_data, group_identifier = group_identifier, folds = folds, repeats = repeats, holdout_perc = holdout_perc) # rearrrange partition_result data into test data and training data train_list = lapply(partition_result, function(item) { item$train }) test_list = lapply(partition_result, function(item) { item$test }) # form fold and repeat lists fld = rep(1:folds, repeats) fld = as.list(fld) reps = as.list(1:repeats) reps = lapply(reps, rep, folds) reps = unlist(reps) reps = as.list(reps) # form folds repeats data frames train_df = mapply(fold_rep_train, train_list, fld, reps) train_df = as.data.frame(train_df) train_df = lapply(train_df, as.data.frame, stringsAsFactors = F) train_df = do.call(rbind, train_df) test_df = mapply(fold_rep_test, test_list, fld, reps) test_df = as.data.frame(test_df) test_df = lapply(test_df, as.data.frame, stringsAsFactors = F) test_df = do.call(rbind, test_df) final_result = list(train = train_df, test = test_df) final_result = lapply(final_result, function(x) { split(data.table::as.data.table(x), by = c("Fold", "Rep")) }) ########## partition data for repeats_extra########## ########## partition_result_extra = pureRandomTestingTraining(data = y_data, group_identifier = group_identifier, folds = folds, repeats = repeats_extra, holdout_perc = holdout_perc) # rearrrange partition_result data into test data and training data train_list = lapply(partition_result_extra, function(item) { item$train }) test_list = lapply(partition_result_extra, function(item) { item$test }) # form fold and repeat lists fld = rep(1:folds, repeats_extra) fld = as.list(fld) reps = as.list(1:repeats_extra) reps = lapply(reps, rep, folds) reps = unlist(reps) reps = as.list(reps) # form folds repeats data frames train_df = mapply(fold_rep_train, train_list, fld, reps) train_df = as.data.frame(train_df) train_df = lapply(train_df, as.data.frame, stringsAsFactors = F) train_df = do.call(rbind, train_df) test_df = mapply(fold_rep_test, test_list, fld, reps) test_df = as.data.frame(test_df) test_df = lapply(test_df, as.data.frame, stringsAsFactors = F) test_df = do.call(rbind, test_df) final_result_extra = list(train = train_df, test = test_df) final_result_extra = lapply(final_result_extra, function(x) { split(data.table::as.data.table(x), by = c("Fold", "Rep")) }) } else if (partition_style == "paired") { partition_result = pairedCaseControlTestingTraining(data = y_data, group_identifier = group_identifier, pair_identifier = pair_identifier, folds = folds, repeats = repeats) # rearrrange partition_result data into two tibble data frames, one for test data one for train data train_list = lapply(partition_result, function(item) { item$train }) test_list = lapply(partition_result, function(item) { item$test }) # form fold and repeat lists fld = rep(1:folds, repeats) fld = as.list(fld) reps = as.list(1:repeats) reps = lapply(reps, rep, folds) reps = unlist(reps) reps = as.list(reps) # form folds repeats data frames train_df = mapply(fold_rep_train, train_list, fld, reps) train_df = as.data.frame(train_df) train_df = lapply(train_df, as.data.frame, stringsAsFactors = F) train_df = do.call(rbind, train_df) test_df = mapply(fold_rep_test, test_list, fld, reps) test_df = as.data.frame(test_df) test_df = lapply(test_df, as.data.frame, stringsAsFactors = F) test_df = do.call(rbind, test_df) final_result = list(train = train_df, test = test_df) final_result = lapply(final_result, function(x) { split(data.table::as.data.table(x), by = c("Fold", "Rep")) }) ########## partition data for repeats_extra########## ########## partition_result_extra = pairedCaseControlTestingTraining(data = y_data, group_identifier = group_identifier, pair_identifier = pair_identifier, folds = folds, repeats = repeats) # rearrrange partition_result data into two tibble data frames, one for test data one for train data train_list = lapply(partition_result_extra, function(item) { item$train }) test_list = lapply(partition_result_extra, function(item) { item$test }) # form fold and repeat lists fld = rep(1:folds, repeats_extra) fld = as.list(fld) reps = as.list(1:repeats_extra) reps = lapply(reps, rep, folds) reps = unlist(reps) reps = as.list(reps) # form folds repeats data frames train_df = mapply(fold_rep_train, train_list, fld, reps) train_df = as.data.frame(train_df) train_df = lapply(train_df, as.data.frame, stringsAsFactors = F) train_df = do.call(rbind, train_df) test_df = mapply(fold_rep_test, test_list, fld, reps) test_df = as.data.frame(test_df) test_df = lapply(test_df, as.data.frame, stringsAsFactors = F) test_df = do.call(rbind, test_df) final_result_extra = list(train = train_df, test = test_df) final_result_extra = lapply(final_result_extra, function(x) { split(data.table::as.data.table(x), by = c("Fold", "Rep")) }) } attr(data_object, "partition_info") = final_result attr(data_object, "extra_partitions") = final_result_extra attr(data_object, "foldrep") = list(folds = folds, repeats = repeats) return(data_object) } pureRandomTestingTraining <- function(data, group_identifier, folds = 4, repeats = 100, holdout_perc = 0.25) { groups <- data[, group_identifier] seed <- 42 #------ determine training indeces ----------# # returns a k x repeats size list of training indeces set seed for reproducability purposes set.seed(42) training_ind_list <- caret::createMultiFolds(y = groups, k = folds, times = repeats) #------ diff data and train to return testing indeces -----# # returns a k x repeats size list of grouped testing and training indeces ind_list <- lapply(training_ind_list, function(training_inds) { partition_list <- list() partition_list$train <- training_inds partition_list$test <- setdiff(1:nrow(data), training_inds) return(partition_list) }) return(ind_list) } pairedCaseControlTestingTraining <- function(data, group_identifier, pair_identifier, folds = 4, repeats = 100, holdout_set = NULL) { tuning_set <- FALSE seed <- 42 #------- create a map to keep orignal indexing in tact -----# data$old_index <- 1:nrow(data) #------- remove the holdout set if there is one ---------# if (length(holdout_set) > 0) { data <- data[-which(data[, pair_identifier] %in% holdout_set), ] } #------- use map to find unique pairs -----# singled_pair_subset <- data %>% dplyr::arrange_(group_identifier) %>% dplyr::distinct_(pair_identifier, .keep_all = TRUE) #keep only unique identifiers for training subset singled_pair_subset[1:nrow(singled_pair_subset)/2, group_identifier] <- 1 # convert half the labes to zeros for caret's balancing act index_map <- data.frame(old = singled_pair_subset$old_index, new = 1:nrow(singled_pair_subset), pair = singled_pair_subset[, pair_identifier]) rownames(index_map) <- index_map$new #------ vector of group labels to split ------# group <- singled_pair_subset[, group_identifier] #------ determine training indeces ----------# # returns a k x repeats size list of training indeces training_ind_list <- caret::createMultiFolds(y = group, k = folds, times = repeats) #------- match case-control pairs --------# ind_list <- lapply(training_ind_list, function(x) { pairs <- index_map[x, "pair"] partition_list <- list() partition_list$train <- data[which(data[, pair_identifier] %in% pairs), "old_index"] partition_list$test <- data[which(!data[, pair_identifier] %in% pairs), "old_index"] return(partition_list) }) #------ diff data and train to return testing indeces -----# # returns a k x repeats size list of grouped testing and training indeces ind_list <- lapply(training_ind_list_mapped, # function(training_inds){ pairs <- index_map[x, 'pair'] partition_list <- list() partition_list$train <- training_inds # partition_list$test <- data[which(!data[,pair_identifier] %in% pairs), 'old_index'] return(partition_list) }) } # folds repeats helper functions for training data and test data fold_rep_train = function(train_vec, fold, repp) { result = data.frame(Fold = rep(fold, length(train_vec)), Rep = rep(repp, length(train_vec)), Train = train_vec) return(result) } fold_rep_test = function(train_vec, fold, repp) { result = data.frame(Fold = rep(fold, length(train_vec)), Rep = rep(repp, length(train_vec)), Test = train_vec) return(result) }
library(Hmisc) library(caret) library(dplyr) library(ggplot2) library(data.table) # 파일 불러오기 test <- read.csv("test.csv") train <- read.csv("train.csv") train <- train %>% filter(!is.na(Upc)) # 데이터 확인 head(train) describe(train) str(train) table(train$TripType) table(train$Upc) table(train$DepartmentDescription) summary(train) boxplot(train$ScanCount) table(train$ScanCount) hist(train$VisitNumber) table(train$VisitNumber) train %>% filter(TripType == 39) %>% select(VisitNumber) train %>% filter(ScanCount < -4) train %>% filter(is.na(Upc)) train %>% filter(VisitNumber == 8) train %>% filter(DepartmentDescription == 'PHARMACY RX') train %>% filter(ScanCount > 19) %>% arrange(TripType, VisitNumber, ScanCount) train %>% filter(FinelineNumber == 9998) train %>% filter(TripType == 999) train %>% filter(FinelineNumber == 1000) train %>% filter(floor(log10(train$Upc))+1 == 12) # DepartmentDescription과 Weekday와의 관계 확인 week_depart <- list() wd_data <- tapply(train$DepartmentDescription, train$Weekday, table) for(i in 1 : NROW(unique(train$Weekday))){ week_depart <- c(week_depart, list(sort(wd_data[[i]], decreasing = T))) } names(week_depart) <- names(wd_data) sort_week_depart <- c(week_depart['Monday'], week_depart['Tuesday'], week_depart['Wednesday'], week_depart['Thursday'], week_depart['Friday'], week_depart['Saturday'], week_depart['Sunday']) # DepartmentDescription과 TripType과의 관계 triptype_depart <- list() trde_data <- tapply(train$DepartmentDescription, train$TripType, table) for(i in 1 : NROW(unique(train$TripType))){ triptype_depart <- c(triptype_depart, list(sort(trde_data[[i]], decreasing = T))) } names(triptype_depart) <- names(trde_data) trde_top10 <- list() for(i in 1 : NROW(unique(train$TripType))){ trde_top10 <- c(trde_top10, list(triptype_depart[[i]][1 : 10])) } names(trde_top10) <- names(trde_data) trde_top10_plot_data <- train %>% group_by(TripType, DepartmentDescription) %>% summarise(n = n()) %>% arrange(TripType, desc(n)) trde_top5_plot_result <- c() for(i in unique(trde_top10_plot_data$TripType)){ trde_top5_plot_result <- rbind(trde_top5_plot_result, trde_top10_plot_data %>% filter(TripType == i) %>% head(5)) } mosaicplot(~ DepartmentDescription + TripType, data = trde_top5_plot_result, las = 1) # Read Data File test <- read.csv("test.csv") train <- read.csv("train.csv") train <- train %>% filter(!is.na(Upc)) options(scipen = 100) # UPC 12자리 채우기 # First : 11자리까지 채우기(0으로) Upc12 <- matrix(0, nrow = nrow(train), ncol = 1) ZeroFillFunc <- function(x, y){ zeros <- c() zeroc <- 11 - (floor(log10(x)) + 1) for(j in 1 : zeroc){ zeros <- paste0(zeros, '0') } return (paste0(zeros, x)) } for(i in 1 : nrow(train)){ Upc12[i] <- ifelse(floor(log10(train$Upc[i])) > 9, train$Upc[i], ZeroFillFunc(train$Upc[i])) } Upc12 <- as.vector(Upc12) # Second : CheckSumDigit 만들기 MakeCheckSumDigitFunc <- function(x){ odds_sum <- 0 evens_sum <- 0 digit11 <- "" split_upc <- strsplit(x, "")[[1]] for(i in 1 : 11){ digit11 <- paste0(digit11, split_upc[i]) if (i %% 2 == 1) { odds_sum <- odds_sum + as.numeric(split_upc[i]) }else{ evens_sum <- evens_sum + as.numeric(split_upc[i]) } } total_sum <- odds_sum * 3 + evens_sum sum_result <- ifelse(total_sum %% 10 == 0, 0, (10 - (total_sum %% 10))) return (paste0(digit11, sum_result)) } Upc12 <- sapply(Upc12, MakeCheckSumDigitFunc) names(Upc12) <- NULL # Final : Train데이터와 합치기 train <- data.frame(train, Upc12) head(train, 20) colnames(train)[8] <- "UPC" train$Upc <- NULL # train Copy / Divide Company Code / Divide Product Code cptrain <- train MakeCompanyCodeFunc <- function(x){ ccode <- "" upc <- strsplit(as.character(x), "")[[1]] for(i in 1 : 6){ ccode <- paste0(ccode, upc[i]) } return (ccode) } Upc6 <- sapply(cptrain$UPC, MakeCompanyCodeFunc) cptrain <- data.frame(cptrain, Upc6) colnames(cptrain)[8] <- "CompanyCode" cptrain$UPC <- NULL cptrain$CompanyCode <- as.character(cptrain$CompanyCode) write.csv(cptrain, "htrain.csv", row.names = F) pUpc <- matrix(0, nrow = nrow(cptrain), ncol = 5) for(i in 1 : 5){ pUpc[, i] <- sapply(cptrain$UPC, function(x){ pcode <- "" upc <- strsplit(as.character(x), "")[[1]] for(j in (7 + (i - 1)) : 11){ pcode <- paste0(pcode, upc[j]) } return (pcode) }) } cptrain <- data.frame(cptrain, pUpc) colnames(cptrain)[8 : 12] <- c("ProductCode5", "ProductCode4", "ProductCode3", "ProductCode2", "ProductCode1") for(i in 8 : 12){ cptrain[, i] <- as.character(cptrain[, i]) } write.csv(cptrain, "ptrain.csv", row.names = F) write.table(cptrain, "ptrain.txt", sep = ",", col.names = T) tt <- fread("ptrain.csv") str(cptrain) head(tt) # Read Refined Data & Set Company Code len 6 library(data.table) library(dplyr) library(caret) rtrain <- fread("htrain.csv", stringsAsFactors = T) rtrain <- as.data.frame(rtrain) rtrain <- rtrain %>% filter(TripType %in% c(3 : 20)) rtrain$TripType <- as.factor(rtrain$TripType) rtrain <- rtrain %>% arrange(DepartmentDescription, FinelineNumber) t <- rtrain %>% select(DepartmentDescription, FinelineNumber) %>% arrange(DepartmentDescription, FinelineNumber) %>% distinct() t$defi <- 1 : nrow(t) rtrain <- merge(rtrain, t, by = c("DepartmentDescription", "FinelineNumber"), all.x = T) # Divide Train / Validation sample_idx <- rtrain %>% sample_frac(0.7) %>% rownames() %>% as.numeric() rtrain_train <- rtrain[sample_idx, ] rtrain_val <- rtrain[-sample_idx, ] # Multinomial Logistic Regression library(nnet) mlr_model <- multinom(TripType ~ ., data = rtrain_train) str(rtrain) summary(mlr_model) table(fitted(mlr_model)) # Decision Tree library(party) tree_control <- ctree_control(mincriterion = 0.9, minsplit = 10, maxdepth = 6) tree_model <- ctree(TripType ~ ., data = rtrain_train) plot(tree_model) tree_pred <- predict(tree_model, newdata = rtrain_val) confusionMatrix(tree_pred, rtrain_val$TripType_3) # RandomForest library(randomForest) rf_model <- randomForest(TripType ~ ScanCount, data = rtrain_train) rf_pred <- predict(rf_model, newdata = rtrain_val) confusionMatrix(rf_pred, rtrain_val$TripType) rf_model <- randomForest(TripType ~ VisitNumber, data = rtrain_train, ntree = 200, importance = TRUE, do.trace = TRUE) rf_pred <- predict(rf_model, newdata = rtrain_val) rf_cfm <- confusionMatrix(rf_pred, rtrain_val$TripType) rf_imp <- importance(rf_model) plot(rf_model) rf_model1 <- randomForest(TripType ~ VisitNumber + FinelineNumber, data = rtrain_train, ntree = 200, importance = T, do.trace = T) rf_pred1 <- predict(rf_model1, newdata = rtrain_val) rf_cfm1 <- confusionMatrix(rf_pred1, rtrain_val$TripType) rf_imp <- importance(rf_model1) varImpPlot(rf_model1) rf_model2 <- randomForest(TripType ~ VisitNumber + Weekday, data = rtrain_train, ntree = 100, importance = T, do.trace = T) rf_pred2 <- predict(rf_model2, newdata = rtrain_val) rf_cfm <- confusionMatrix(rf_pred2, rtrain_val$TripType) levels(rtrain_train$DepartmentDescription) <- c(1 : 68) table(rtrain_train$DepartmentDescription) rtrain_train$DepartmentDescription <- as.numeric(rtrain_train$DepartmentDescription) levels(rtrain_val$DepartmentDescription) <- c(1 : 68) table(rtrain_val$DepartmentDescription) rtrain_val$DepartmentDescription <- as.numeric(rtrain_val$DepartmentDescription) rf_model3 <- randomForest(TripType ~ VisitNumber + DepartmentDescription, data = rtrain_train, ntree = 100, importance = T, do.trace = T) rf_pred3 <- predict(rf_model3, newdata = rtrain_val) rf_cfm3 <- confusionMatrix(rf_pred3, rtrain_val$TripType) rf_model4 <- randomForest(TripType ~ DepartmentDescription, data = rtrain_train, ntree = 100, importance = T, do.trace = T) rf_pred4 <- predict(rf_model4, rtrain_val) rf_cfm <- confusionMatrix(rf_pred4, rtrain_val$TripType) rtrain_train <- rtrain_train %>% arrange(DepartmentDescription, FinelineNumber) t <- rtrain_train %>% select(DepartmentDescription, FinelineNumber) %>% group_by(DepartmentDescription, FinelineNumber) %>% arrange(DepartmentDescription, FinelineNumber) %>% unique() rtrain_train <- merge(rtrain_train, t, by = c("DepartmentDescription", "FinelineNumber"), all.x = T) rtrain_val <- merge(rtrain_val, t, by = c("DepartmentDescription", "FinelineNumber"), all.x = T) rf_model5 <- randomForest(TripType ~ VisitNumber + defi, data = rtrain_train, ntree = 200, importance = T, do.trace = T) rf_pred5 <- predict(rf_model5, newdata = rtrain_val) rf_cfm5 <- confusionMatrix(rf_pred5, rtrain_val$TripType) rf_imp5 <- importance(rf_model5) varImpPlot(rf_model5) t1 <- rtrain_train %>% select(defi, CompanyCode) %>% group_by(defi, CompanyCode) %>% arrange(defi, CompanyCode) %>% unique() rtrain_train %>% filter(TripType %in% c(7, 8)) %>% head(20) t1$CompanyCode <- as.character(t1$CompanyCode) t1 <- t1 %>% arrange(CompanyCode, defi) t1$defico <- 1 : nrow(t1) rtrain_train <- merge(rtrain_train, t1, by = c("defi", "CompanyCode"), all.x = T) rtrain_val <- merge(rtrain_val, t1, by = c("defi", "CompanyCode"), all.x = T) rf_model6 <- randomForest(TripType ~ VisitNumber + defico, data = rtrain_train, ntree = 200, importance = T, do.trace = T) rf_pred6 <- predict(rf_model6, newdata = rtrain_val) rf_cfm6 <- confusionMatrix(rf_pred6, rtrain_val$TripType) # Make New DataSet head(train) sumscancount <- train %>% filter(ScanCount > 0) %>% group_by(VisitNumber) %>% summarise(sumscancount = sum(ScanCount)) minuscancount <- train %>% filter(ScanCount < 0) %>% group_by(VisitNumber) %>% summarise(n = n()) colnames(minuscancount) <- c("id", "RefundCount") maxperdesc <- train %>% group_by(VisitNumber, DepartmentDescription) %>% summarise(n = n()) maxperdesc1 <- train %>% group_by(VisitNumber) %>% summarise(n = n()) maxperdescs <- merge(maxperdesc, maxperdesc1, by = "VisitNumber", all.x = T) maxperdesc <- maxperdescs %>% group_by(VisitNumber) %>% summarise(maxdesc = max(n.x), totaldesc = mean(n.y), per = round(maxdesc / totaldesc, 3)) newdata <- read.csv("newdata.csv") newdata <- merge(newdata, minuscancount, by = "id", all.x = T) newdata$RefundCount <- ifelse(is.na(newdata$RefundCount), 0, newdata$RefundCount) colnames(maxperdesc)[1] <- "id" newdata <- merge(newdata, maxperdesc, by = "id", all.x = T) head(newdata) colnames(newdata)[5 : 7] <- c("MaxDescCount", "TotalDescCount", "max_prodrate") write.csv(newdata, "newdata.csv", row.names = F) library(dummies) tt <- dummy(train$DepartmentDescription) train <- data.frame(train, tt) train$DepartmentDescription <- NULL colnames(train) head(tt) library(dplyr) # 데이터 읽어오기 & NA 지우기 train <- read.csv("train.csv") train <- train %>% filter(!is.na(Upc)) # VisitNumber와 DepartmentDescription 그룹으로 묶어서 # 각 그룹에 대한 총 갯수 데이터 셋 만들기 -- ㉠ descdummy <- train %>% group_by(VisitNumber, DepartmentDescription) %>% summarise(sumcategory = n()) # DepartmentDescription 더미변수를 담을 데이터프레임 공간 만들기 # column 이름 변경해주기 departdummy <- data.frame(unique(train$VisitNumber), matrix(0, nrow = length(unique(train$VisitNumber)), ncol = length(levels(train$DepartmentDescription)))) colnames(departdummy)[1] <- "id" colnames(departdummy)[2 : ncol(departdummy)] <- levels(train$DepartmentDescription) # 이 전처리 방식의 핵심은 DepartmentDescription의 레벨을 1부터 69까지로 변경 후에 # 그 숫자 + 1에 해당하는 인덱스에 위의 ㉠부분에서 구한 총 갯수를 넣어주는 것 # 또한, factor형식은 사칙연산에 대해서 의미가 없기 때문에 # 이를 numeric으로 변경 후에 연산을 진행 levels(descdummy$DepartmentDescription) <- 1 : length(levels(descdummy$DepartmentDescription)) descdummy$DepartmentDescription <- as.numeric(descdummy$DepartmentDescription) departindex <- 1 # departdummy(데이터를 저장해야할 변수)의 행의 인덱스 descindex <- 1 # descdummy(㉠의 그룹에 대한 총 갯수가 담긴 변수)의 행의 인덱스 repeat{ # 서로의 VisitNumber를 비교, # departdummy의 id가 VisitNumber(열이름만 위에서 바꾼것) # why? 밑에서 데이터 셋과 조인하기위해 이름을 id로 변경 해준것. if(descdummy$VisitNumber[descindex] == departdummy$id[departindex]){ # 위에서 간단히 설명한대로 VisitNumber가 같다면 총 갯수 데이터를 # departmentdescription의 값 + 1의 인덱스에 삽입 # 그리고 나서 총 갯수 데이터가 담긴 descindex의 값을 1증가하여 아래 행으로 이동 departdummy[departindex, descdummy$DepartmentDescription[descindex] + 1] <- descdummy$sumcategory[descindex] descindex <- descindex + 1 }else{ # 만약 VisitNumber가 다르다면 우리가 담고자 했던 departdummy의 인덱스를 1 증가 departindex <- departindex + 1 } print(paste(departindex, descindex)) if(descindex == (nrow(descdummy) + 1)){ break } } # 데이터 셋 불러온 후 더미변수와 조인시킨후에 저장 newdata <- read.csv("newdata.csv") newdata <- merge(newdata, departdummy, by = "id", all.x = T) write.csv(newdata, "newdata.csv", row.names = F) colnames(newdata) # TripType - a categorical id representing the type of shopping trip the customer made. # This is the ground truth that you are predicting. # TripType_999 is an "other" category. # VisitNumber - an id corresponding to a single trip by a single customer # Weekday - the weekday of the trip # Upc - the UPC number of the product purchased # ScanCount - the number of the given item that was purchased. # A negative value indicates a product return. # DepartmentDescription - a high-level description of the item's department # FinelineNumber - a more refined category for each of the products, created by Walmart # upc 설명 : https://www.kaggle.com/c/walmart-recruiting-trip-type-classification/discussion/18158 # https://www.kaggle.com/c/walmart-recruiting-trip-type-classification/discussion/18163 # https://www.kaggle.com/c/walmart-recruiting-trip-type-classification/discussion/30345 # https://en.wikipedia.org/wiki/Check_digit	/Walmart.R	no_license	SubAkBa/Kaggle_Walmart	R	false	false	14,666	r	library(Hmisc) library(caret) library(dplyr) library(ggplot2) library(data.table) # 파일 불러오기 test <- read.csv("test.csv") train <- read.csv("train.csv") train <- train %>% filter(!is.na(Upc)) # 데이터 확인 head(train) describe(train) str(train) table(train$TripType) table(train$Upc) table(train$DepartmentDescription) summary(train) boxplot(train$ScanCount) table(train$ScanCount) hist(train$VisitNumber) table(train$VisitNumber) train %>% filter(TripType == 39) %>% select(VisitNumber) train %>% filter(ScanCount < -4) train %>% filter(is.na(Upc)) train %>% filter(VisitNumber == 8) train %>% filter(DepartmentDescription == 'PHARMACY RX') train %>% filter(ScanCount > 19) %>% arrange(TripType, VisitNumber, ScanCount) train %>% filter(FinelineNumber == 9998) train %>% filter(TripType == 999) train %>% filter(FinelineNumber == 1000) train %>% filter(floor(log10(train$Upc))+1 == 12) # DepartmentDescription과 Weekday와의 관계 확인 week_depart <- list() wd_data <- tapply(train$DepartmentDescription, train$Weekday, table) for(i in 1 : NROW(unique(train$Weekday))){ week_depart <- c(week_depart, list(sort(wd_data[[i]], decreasing = T))) } names(week_depart) <- names(wd_data) sort_week_depart <- c(week_depart['Monday'], week_depart['Tuesday'], week_depart['Wednesday'], week_depart['Thursday'], week_depart['Friday'], week_depart['Saturday'], week_depart['Sunday']) # DepartmentDescription과 TripType과의 관계 triptype_depart <- list() trde_data <- tapply(train$DepartmentDescription, train$TripType, table) for(i in 1 : NROW(unique(train$TripType))){ triptype_depart <- c(triptype_depart, list(sort(trde_data[[i]], decreasing = T))) } names(triptype_depart) <- names(trde_data) trde_top10 <- list() for(i in 1 : NROW(unique(train$TripType))){ trde_top10 <- c(trde_top10, list(triptype_depart[[i]][1 : 10])) } names(trde_top10) <- names(trde_data) trde_top10_plot_data <- train %>% group_by(TripType, DepartmentDescription) %>% summarise(n = n()) %>% arrange(TripType, desc(n)) trde_top5_plot_result <- c() for(i in unique(trde_top10_plot_data$TripType)){ trde_top5_plot_result <- rbind(trde_top5_plot_result, trde_top10_plot_data %>% filter(TripType == i) %>% head(5)) } mosaicplot(~ DepartmentDescription + TripType, data = trde_top5_plot_result, las = 1) # Read Data File test <- read.csv("test.csv") train <- read.csv("train.csv") train <- train %>% filter(!is.na(Upc)) options(scipen = 100) # UPC 12자리 채우기 # First : 11자리까지 채우기(0으로) Upc12 <- matrix(0, nrow = nrow(train), ncol = 1) ZeroFillFunc <- function(x, y){ zeros <- c() zeroc <- 11 - (floor(log10(x)) + 1) for(j in 1 : zeroc){ zeros <- paste0(zeros, '0') } return (paste0(zeros, x)) } for(i in 1 : nrow(train)){ Upc12[i] <- ifelse(floor(log10(train$Upc[i])) > 9, train$Upc[i], ZeroFillFunc(train$Upc[i])) } Upc12 <- as.vector(Upc12) # Second : CheckSumDigit 만들기 MakeCheckSumDigitFunc <- function(x){ odds_sum <- 0 evens_sum <- 0 digit11 <- "" split_upc <- strsplit(x, "")[[1]] for(i in 1 : 11){ digit11 <- paste0(digit11, split_upc[i]) if (i %% 2 == 1) { odds_sum <- odds_sum + as.numeric(split_upc[i]) }else{ evens_sum <- evens_sum + as.numeric(split_upc[i]) } } total_sum <- odds_sum * 3 + evens_sum sum_result <- ifelse(total_sum %% 10 == 0, 0, (10 - (total_sum %% 10))) return (paste0(digit11, sum_result)) } Upc12 <- sapply(Upc12, MakeCheckSumDigitFunc) names(Upc12) <- NULL # Final : Train데이터와 합치기 train <- data.frame(train, Upc12) head(train, 20) colnames(train)[8] <- "UPC" train$Upc <- NULL # train Copy / Divide Company Code / Divide Product Code cptrain <- train MakeCompanyCodeFunc <- function(x){ ccode <- "" upc <- strsplit(as.character(x), "")[[1]] for(i in 1 : 6){ ccode <- paste0(ccode, upc[i]) } return (ccode) } Upc6 <- sapply(cptrain$UPC, MakeCompanyCodeFunc) cptrain <- data.frame(cptrain, Upc6) colnames(cptrain)[8] <- "CompanyCode" cptrain$UPC <- NULL cptrain$CompanyCode <- as.character(cptrain$CompanyCode) write.csv(cptrain, "htrain.csv", row.names = F) pUpc <- matrix(0, nrow = nrow(cptrain), ncol = 5) for(i in 1 : 5){ pUpc[, i] <- sapply(cptrain$UPC, function(x){ pcode <- "" upc <- strsplit(as.character(x), "")[[1]] for(j in (7 + (i - 1)) : 11){ pcode <- paste0(pcode, upc[j]) } return (pcode) }) } cptrain <- data.frame(cptrain, pUpc) colnames(cptrain)[8 : 12] <- c("ProductCode5", "ProductCode4", "ProductCode3", "ProductCode2", "ProductCode1") for(i in 8 : 12){ cptrain[, i] <- as.character(cptrain[, i]) } write.csv(cptrain, "ptrain.csv", row.names = F) write.table(cptrain, "ptrain.txt", sep = ",", col.names = T) tt <- fread("ptrain.csv") str(cptrain) head(tt) # Read Refined Data & Set Company Code len 6 library(data.table) library(dplyr) library(caret) rtrain <- fread("htrain.csv", stringsAsFactors = T) rtrain <- as.data.frame(rtrain) rtrain <- rtrain %>% filter(TripType %in% c(3 : 20)) rtrain$TripType <- as.factor(rtrain$TripType) rtrain <- rtrain %>% arrange(DepartmentDescription, FinelineNumber) t <- rtrain %>% select(DepartmentDescription, FinelineNumber) %>% arrange(DepartmentDescription, FinelineNumber) %>% distinct() t$defi <- 1 : nrow(t) rtrain <- merge(rtrain, t, by = c("DepartmentDescription", "FinelineNumber"), all.x = T) # Divide Train / Validation sample_idx <- rtrain %>% sample_frac(0.7) %>% rownames() %>% as.numeric() rtrain_train <- rtrain[sample_idx, ] rtrain_val <- rtrain[-sample_idx, ] # Multinomial Logistic Regression library(nnet) mlr_model <- multinom(TripType ~ ., data = rtrain_train) str(rtrain) summary(mlr_model) table(fitted(mlr_model)) # Decision Tree library(party) tree_control <- ctree_control(mincriterion = 0.9, minsplit = 10, maxdepth = 6) tree_model <- ctree(TripType ~ ., data = rtrain_train) plot(tree_model) tree_pred <- predict(tree_model, newdata = rtrain_val) confusionMatrix(tree_pred, rtrain_val$TripType_3) # RandomForest library(randomForest) rf_model <- randomForest(TripType ~ ScanCount, data = rtrain_train) rf_pred <- predict(rf_model, newdata = rtrain_val) confusionMatrix(rf_pred, rtrain_val$TripType) rf_model <- randomForest(TripType ~ VisitNumber, data = rtrain_train, ntree = 200, importance = TRUE, do.trace = TRUE) rf_pred <- predict(rf_model, newdata = rtrain_val) rf_cfm <- confusionMatrix(rf_pred, rtrain_val$TripType) rf_imp <- importance(rf_model) plot(rf_model) rf_model1 <- randomForest(TripType ~ VisitNumber + FinelineNumber, data = rtrain_train, ntree = 200, importance = T, do.trace = T) rf_pred1 <- predict(rf_model1, newdata = rtrain_val) rf_cfm1 <- confusionMatrix(rf_pred1, rtrain_val$TripType) rf_imp <- importance(rf_model1) varImpPlot(rf_model1) rf_model2 <- randomForest(TripType ~ VisitNumber + Weekday, data = rtrain_train, ntree = 100, importance = T, do.trace = T) rf_pred2 <- predict(rf_model2, newdata = rtrain_val) rf_cfm <- confusionMatrix(rf_pred2, rtrain_val$TripType) levels(rtrain_train$DepartmentDescription) <- c(1 : 68) table(rtrain_train$DepartmentDescription) rtrain_train$DepartmentDescription <- as.numeric(rtrain_train$DepartmentDescription) levels(rtrain_val$DepartmentDescription) <- c(1 : 68) table(rtrain_val$DepartmentDescription) rtrain_val$DepartmentDescription <- as.numeric(rtrain_val$DepartmentDescription) rf_model3 <- randomForest(TripType ~ VisitNumber + DepartmentDescription, data = rtrain_train, ntree = 100, importance = T, do.trace = T) rf_pred3 <- predict(rf_model3, newdata = rtrain_val) rf_cfm3 <- confusionMatrix(rf_pred3, rtrain_val$TripType) rf_model4 <- randomForest(TripType ~ DepartmentDescription, data = rtrain_train, ntree = 100, importance = T, do.trace = T) rf_pred4 <- predict(rf_model4, rtrain_val) rf_cfm <- confusionMatrix(rf_pred4, rtrain_val$TripType) rtrain_train <- rtrain_train %>% arrange(DepartmentDescription, FinelineNumber) t <- rtrain_train %>% select(DepartmentDescription, FinelineNumber) %>% group_by(DepartmentDescription, FinelineNumber) %>% arrange(DepartmentDescription, FinelineNumber) %>% unique() rtrain_train <- merge(rtrain_train, t, by = c("DepartmentDescription", "FinelineNumber"), all.x = T) rtrain_val <- merge(rtrain_val, t, by = c("DepartmentDescription", "FinelineNumber"), all.x = T) rf_model5 <- randomForest(TripType ~ VisitNumber + defi, data = rtrain_train, ntree = 200, importance = T, do.trace = T) rf_pred5 <- predict(rf_model5, newdata = rtrain_val) rf_cfm5 <- confusionMatrix(rf_pred5, rtrain_val$TripType) rf_imp5 <- importance(rf_model5) varImpPlot(rf_model5) t1 <- rtrain_train %>% select(defi, CompanyCode) %>% group_by(defi, CompanyCode) %>% arrange(defi, CompanyCode) %>% unique() rtrain_train %>% filter(TripType %in% c(7, 8)) %>% head(20) t1$CompanyCode <- as.character(t1$CompanyCode) t1 <- t1 %>% arrange(CompanyCode, defi) t1$defico <- 1 : nrow(t1) rtrain_train <- merge(rtrain_train, t1, by = c("defi", "CompanyCode"), all.x = T) rtrain_val <- merge(rtrain_val, t1, by = c("defi", "CompanyCode"), all.x = T) rf_model6 <- randomForest(TripType ~ VisitNumber + defico, data = rtrain_train, ntree = 200, importance = T, do.trace = T) rf_pred6 <- predict(rf_model6, newdata = rtrain_val) rf_cfm6 <- confusionMatrix(rf_pred6, rtrain_val$TripType) # Make New DataSet head(train) sumscancount <- train %>% filter(ScanCount > 0) %>% group_by(VisitNumber) %>% summarise(sumscancount = sum(ScanCount)) minuscancount <- train %>% filter(ScanCount < 0) %>% group_by(VisitNumber) %>% summarise(n = n()) colnames(minuscancount) <- c("id", "RefundCount") maxperdesc <- train %>% group_by(VisitNumber, DepartmentDescription) %>% summarise(n = n()) maxperdesc1 <- train %>% group_by(VisitNumber) %>% summarise(n = n()) maxperdescs <- merge(maxperdesc, maxperdesc1, by = "VisitNumber", all.x = T) maxperdesc <- maxperdescs %>% group_by(VisitNumber) %>% summarise(maxdesc = max(n.x), totaldesc = mean(n.y), per = round(maxdesc / totaldesc, 3)) newdata <- read.csv("newdata.csv") newdata <- merge(newdata, minuscancount, by = "id", all.x = T) newdata$RefundCount <- ifelse(is.na(newdata$RefundCount), 0, newdata$RefundCount) colnames(maxperdesc)[1] <- "id" newdata <- merge(newdata, maxperdesc, by = "id", all.x = T) head(newdata) colnames(newdata)[5 : 7] <- c("MaxDescCount", "TotalDescCount", "max_prodrate") write.csv(newdata, "newdata.csv", row.names = F) library(dummies) tt <- dummy(train$DepartmentDescription) train <- data.frame(train, tt) train$DepartmentDescription <- NULL colnames(train) head(tt) library(dplyr) # 데이터 읽어오기 & NA 지우기 train <- read.csv("train.csv") train <- train %>% filter(!is.na(Upc)) # VisitNumber와 DepartmentDescription 그룹으로 묶어서 # 각 그룹에 대한 총 갯수 데이터 셋 만들기 -- ㉠ descdummy <- train %>% group_by(VisitNumber, DepartmentDescription) %>% summarise(sumcategory = n()) # DepartmentDescription 더미변수를 담을 데이터프레임 공간 만들기 # column 이름 변경해주기 departdummy <- data.frame(unique(train$VisitNumber), matrix(0, nrow = length(unique(train$VisitNumber)), ncol = length(levels(train$DepartmentDescription)))) colnames(departdummy)[1] <- "id" colnames(departdummy)[2 : ncol(departdummy)] <- levels(train$DepartmentDescription) # 이 전처리 방식의 핵심은 DepartmentDescription의 레벨을 1부터 69까지로 변경 후에 # 그 숫자 + 1에 해당하는 인덱스에 위의 ㉠부분에서 구한 총 갯수를 넣어주는 것 # 또한, factor형식은 사칙연산에 대해서 의미가 없기 때문에 # 이를 numeric으로 변경 후에 연산을 진행 levels(descdummy$DepartmentDescription) <- 1 : length(levels(descdummy$DepartmentDescription)) descdummy$DepartmentDescription <- as.numeric(descdummy$DepartmentDescription) departindex <- 1 # departdummy(데이터를 저장해야할 변수)의 행의 인덱스 descindex <- 1 # descdummy(㉠의 그룹에 대한 총 갯수가 담긴 변수)의 행의 인덱스 repeat{ # 서로의 VisitNumber를 비교, # departdummy의 id가 VisitNumber(열이름만 위에서 바꾼것) # why? 밑에서 데이터 셋과 조인하기위해 이름을 id로 변경 해준것. if(descdummy$VisitNumber[descindex] == departdummy$id[departindex]){ # 위에서 간단히 설명한대로 VisitNumber가 같다면 총 갯수 데이터를 # departmentdescription의 값 + 1의 인덱스에 삽입 # 그리고 나서 총 갯수 데이터가 담긴 descindex의 값을 1증가하여 아래 행으로 이동 departdummy[departindex, descdummy$DepartmentDescription[descindex] + 1] <- descdummy$sumcategory[descindex] descindex <- descindex + 1 }else{ # 만약 VisitNumber가 다르다면 우리가 담고자 했던 departdummy의 인덱스를 1 증가 departindex <- departindex + 1 } print(paste(departindex, descindex)) if(descindex == (nrow(descdummy) + 1)){ break } } # 데이터 셋 불러온 후 더미변수와 조인시킨후에 저장 newdata <- read.csv("newdata.csv") newdata <- merge(newdata, departdummy, by = "id", all.x = T) write.csv(newdata, "newdata.csv", row.names = F) colnames(newdata) # TripType - a categorical id representing the type of shopping trip the customer made. # This is the ground truth that you are predicting. # TripType_999 is an "other" category. # VisitNumber - an id corresponding to a single trip by a single customer # Weekday - the weekday of the trip # Upc - the UPC number of the product purchased # ScanCount - the number of the given item that was purchased. # A negative value indicates a product return. # DepartmentDescription - a high-level description of the item's department # FinelineNumber - a more refined category for each of the products, created by Walmart # upc 설명 : https://www.kaggle.com/c/walmart-recruiting-trip-type-classification/discussion/18158 # https://www.kaggle.com/c/walmart-recruiting-trip-type-classification/discussion/18163 # https://www.kaggle.com/c/walmart-recruiting-trip-type-classification/discussion/30345 # https://en.wikipedia.org/wiki/Check_digit
% Generated by roxygen2: do not edit by hand % Please edit documentation in R/1_score.R \name{log_loss} \alias{log_loss} \title{Logarithmic Loss} \usage{ log_loss(act, pred, allow_inf = FALSE) } \arguments{ \item{act}{actual results} \item{pred}{predicted probabilties} \item{allow_inf}{pass through infinite loss? \code{FALSE} replaces Inf with large but finite penalties at the extremes. Default \code{FALSE}.} } \value{ numeric. } \description{ \code{log_loss()} returns the logarithmic loss obtained from a given prediction and known result } \details{ The allow_inf parameter controls whether infinite loss is allowed (default is FALSE) Setting allow_inf to FALSE will cause large but finite penalties at the extremes }	/man/log_loss.Rd	no_license	deepfriar/corsicaUtils	R	false	true	727	rd	% Generated by roxygen2: do not edit by hand % Please edit documentation in R/1_score.R \name{log_loss} \alias{log_loss} \title{Logarithmic Loss} \usage{ log_loss(act, pred, allow_inf = FALSE) } \arguments{ \item{act}{actual results} \item{pred}{predicted probabilties} \item{allow_inf}{pass through infinite loss? \code{FALSE} replaces Inf with large but finite penalties at the extremes. Default \code{FALSE}.} } \value{ numeric. } \description{ \code{log_loss()} returns the logarithmic loss obtained from a given prediction and known result } \details{ The allow_inf parameter controls whether infinite loss is allowed (default is FALSE) Setting allow_inf to FALSE will cause large but finite penalties at the extremes }
■ R 을 활용한 머신러닝 11장 - 모델성능개선 5. random forest - 자동튜닝 library(caret) library(C50) library(irr) data(iris) head(iris) #0.shuffle 을 먼저 합니다. set.seed(123) iris_shuffle <- sample(1:150, 150) iris_shuffle iris2 <- iris[iris_shuffle,] iris2 set.seed(123) in_train <- createDataPartition(iris2$Species, p = 0.75, list = FALSE) iris_train <- iris2[in_train, ] # 훈련 데이터 구성 iris_test <- iris2[-in_train, ] # 테스트 데이터 구성 m <- train( Species~ . , data=iris_train, method="rf" ) # 랜덤포레스트: 의사결정트리 + 앙상블 기법 m # 튜닝한 결과를 확인할 수 있다. p <- predict( m , iris_test ) table(p, iris_test$Species) library(gmodels) y <- CrossTable(iris_test$Species ,p) sum(y$prop.tbl * diag(3))	/[R] R 을 활용한 머신러닝 11장 - 모델성능개선 5. random forest - 자동튜닝.R	no_license	data-githu/R-MACHINE-LEARNING	R	false	false	830	r	■ R 을 활용한 머신러닝 11장 - 모델성능개선 5. random forest - 자동튜닝 library(caret) library(C50) library(irr) data(iris) head(iris) #0.shuffle 을 먼저 합니다. set.seed(123) iris_shuffle <- sample(1:150, 150) iris_shuffle iris2 <- iris[iris_shuffle,] iris2 set.seed(123) in_train <- createDataPartition(iris2$Species, p = 0.75, list = FALSE) iris_train <- iris2[in_train, ] # 훈련 데이터 구성 iris_test <- iris2[-in_train, ] # 테스트 데이터 구성 m <- train( Species~ . , data=iris_train, method="rf" ) # 랜덤포레스트: 의사결정트리 + 앙상블 기법 m # 튜닝한 결과를 확인할 수 있다. p <- predict( m , iris_test ) table(p, iris_test$Species) library(gmodels) y <- CrossTable(iris_test$Species ,p) sum(y$prop.tbl * diag(3))
\name{get.ISFrag.results} \alias{get.ISFrag.results} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Summarizes ISFrag Results } \description{ Creates a list containing ISFrag parent-fragment trees and feature table containing parent-fragment relationship information. } \usage{ get.ISFrag.results(ISF_List, featureTable) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{ISF_List}{ List of tables generated by find.level1() function. } \item{featureTable}{ featureTable generated by feature.annotaion() function } } \details{ %% ~~ If necessary, more details than the description above ~~ } \value{ Returns a list containing [1]: a list of tree objects where each tree contains ISF relationship of each parent feature, [2]: a dataframe of the original feature table containing additional columns denoting each feature's ISF annotation/relationship. } \references{ %% ~put references to the literature/web site here ~ } \author{ Sam Shen, Jian Guo, Tao Huan } \note{ %% ~~further notes~~ } %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ %% ~~objects to See Also as \code{\link{help}}, ~~~ } \examples{ library(ISFrag) MS1directory <- "X:/Users/Sam_Shen/ISFtest20210127/HILIC(+)/HILIC(+)3/fullscan" MS2directory <- "X:/Users/Sam_Shen/ISFtest20210127/HILIC(+)/HILIC(+)3/DDA" type <- "single" lib_directory <- "E:/SAM" lib_name <- "convertedLibraryPos.msp" featureTable <- generate.featuretable(MS1directory = MS1directory, type = type, ppm=10, peakwidth=c(10,120), mzdiff = 0.01, snthresh = 6, integrate = 1, prefilter = c(3,100), noise = 100, bw = 5, mzwid = 0.015, max = 100, CAMERA = F) featureTable <- ms2.tofeaturetable(MS2directory = MS2directory, featureTable = featureTable) featureTable <- feature.annotation(featureTable = featureTable, lib_directory = lib_directory, lib_name = lib_name, dp = 0.7) level3 <- find.level3(MS1directory = MS1directory, MS1.files = MS1.files, featureTable = featureTable, type = type) level2 <- find.level2(ISFtable = level3) level1 <- find.level1(ISF_putative = level2) results <- get.ISFrag.results(ISF_List = level1, featureTable = featureTable) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory (show via RShowDoc("KEYWORDS")): % \keyword{ ~kwd1 } % \keyword{ ~kwd2 } % Use only one keyword per line. % For non-standard keywords, use \concept instead of \keyword: % \concept{ ~cpt1 } % \concept{ ~cpt2 } % Use only one concept per line.	/man/get.ISFrag.results.Rd	no_license	shen420/ISFrag	R	false	false	2,593	rd	\name{get.ISFrag.results} \alias{get.ISFrag.results} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Summarizes ISFrag Results } \description{ Creates a list containing ISFrag parent-fragment trees and feature table containing parent-fragment relationship information. } \usage{ get.ISFrag.results(ISF_List, featureTable) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{ISF_List}{ List of tables generated by find.level1() function. } \item{featureTable}{ featureTable generated by feature.annotaion() function } } \details{ %% ~~ If necessary, more details than the description above ~~ } \value{ Returns a list containing [1]: a list of tree objects where each tree contains ISF relationship of each parent feature, [2]: a dataframe of the original feature table containing additional columns denoting each feature's ISF annotation/relationship. } \references{ %% ~put references to the literature/web site here ~ } \author{ Sam Shen, Jian Guo, Tao Huan } \note{ %% ~~further notes~~ } %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ %% ~~objects to See Also as \code{\link{help}}, ~~~ } \examples{ library(ISFrag) MS1directory <- "X:/Users/Sam_Shen/ISFtest20210127/HILIC(+)/HILIC(+)3/fullscan" MS2directory <- "X:/Users/Sam_Shen/ISFtest20210127/HILIC(+)/HILIC(+)3/DDA" type <- "single" lib_directory <- "E:/SAM" lib_name <- "convertedLibraryPos.msp" featureTable <- generate.featuretable(MS1directory = MS1directory, type = type, ppm=10, peakwidth=c(10,120), mzdiff = 0.01, snthresh = 6, integrate = 1, prefilter = c(3,100), noise = 100, bw = 5, mzwid = 0.015, max = 100, CAMERA = F) featureTable <- ms2.tofeaturetable(MS2directory = MS2directory, featureTable = featureTable) featureTable <- feature.annotation(featureTable = featureTable, lib_directory = lib_directory, lib_name = lib_name, dp = 0.7) level3 <- find.level3(MS1directory = MS1directory, MS1.files = MS1.files, featureTable = featureTable, type = type) level2 <- find.level2(ISFtable = level3) level1 <- find.level1(ISF_putative = level2) results <- get.ISFrag.results(ISF_List = level1, featureTable = featureTable) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory (show via RShowDoc("KEYWORDS")): % \keyword{ ~kwd1 } % \keyword{ ~kwd2 } % Use only one keyword per line. % For non-standard keywords, use \concept instead of \keyword: % \concept{ ~cpt1 } % \concept{ ~cpt2 } % Use only one concept per line.
# samples that did not sequence well source("scripts/lab_helpers.R") # library(ggplot2) lab <- read_db("Laboratory") # # get the list of missing samples from amphiprion that have been edited to just Ligation number in text editor using the find: (APCL_\d+)(L\d+) and the replace: \2 and the find: (APCL_)(L\d+) and the replace: \2 # fails <- read.csv("data/lowDP.indv.csv", header = F) # names(fails) <- "ligation_id" # # # find sample_ids and extract ids # lab <- read_db("Laboratory") # ligs <- lab %>% # tbl("ligation") %>% # collect() %>% # filter(ligation_id %in% fails$ligation_id) %>% # select(ligation_id, digest_id, total_reads, retained) # # digs <- lab %>% # tbl("digest") %>% # collect() %>% # filter(digest_id %in% ligs$digest_id) %>% # select(digest_id, extraction_id, quant) %>% # rename(dig_quant = quant) # # digs <- left_join(ligs, digs, by = "digest_id") # rm(ligs) # # sample <- lab %>% # tbl("extraction") %>% # collect() %>% # filter(extraction_id %in% digs$extraction_id) %>% # select(extraction_id, sample_id, quant, gel) %>% # rename(extr_quant = quant) # # sample <- left_join(digs, sample, by = "extraction_id") # # sample <- sample %>% # select(sample_id, extraction_id, extr_quant, gel, digest_id, dig_quant, ligation_id, total_reads, retained) # # # write.csv(sample, "data/problem_samples.csv", row.names = F) # pull in successful ligations from Rdata ligs <- readRDS("data/passed_ligs.Rdata") temp <- lab %>% tbl("ligation") %>% filter(ligation_id %in% ligs$ligation_id) %>% select(ligation_id, digest_id, DNA, retained) %>% collect() ligs <- left_join(ligs, temp, by = "ligation_id") failed_ligs <- lab %>% tbl("ligation") %>% filter(!ligation_id %in% ligs$ligation_id) %>% select(ligation_id, digest_id, DNA, retained) %>% collect() temp <- lab %>% tbl("digest") %>% filter(digest_id %in% ligs$digest_id) %>% select(digest_id, extraction_id, quant) %>% collect() ligs <- left_join(ligs, temp, by = "digest_id") %>% rename(dig_quant = quant) temp <- lab %>% tbl("digest") %>% filter(digest_id %in% failed_ligs$digest_id) %>% select(digest_id, extraction_id, quant) %>% collect() failed_ligs <- left_join(failed_ligs, temp, by = "digest_id") %>% rename(dig_quant = quant) temp <- lab %>% tbl("extraction") %>% filter(extraction_id %in% ligs$extraction_id) %>% select(extraction_id, quant, sample_id) %>% collect() ligs <- left_join(ligs, temp, by = "extraction_id") %>% rename(extr_quant = quant) temp <- lab %>% tbl("extraction") %>% filter(extraction_id %in% failed_ligs$extraction_id) %>% select(extraction_id, quant, sample_id) %>% collect() failed_ligs <- left_join(failed_ligs, temp, by = "extraction_id") %>% rename(extr_quant = quant) #1343 # remove any sample_ids from the failed_ligs that are also on the ligs failed_ligs <- anti_join(failed_ligs, ligs, by = "sample_id") %>% #1052 select(sample_id, extraction_id, extr_quant, digest_id, dig_quant, ligation_id,DNA, retained) %>% arrange(sample_id) # remove samples that are not clarkii removed <- failed_ligs %>% filter(!grepl("APCL", sample_id)) failed_ligs <- failed_ligs %>% filter(grepl("APCL", sample_id)) # separate out into low conc digest and regular digest # brks<-seq(0,800,50) # plot concentration of failed seqs hist(x = ligs$quant,col="blue",breaks=brks,ylim=c(0,150)) hist(x = sample$extr_quant,col="red",breaks=brks,ylim=c(0,150), add = T) # how many digests with a quant < 10 led to successful sequences? low_dig_succ <- ligs %>% filter(ligs$dig_quant < 10) low_dig_fail <- sample %>% filter(sample$dig_quant < 10) # rm(digs) # # # make a list of samples that need to be re-processed # sample <- sample %>% # select(sample_id, extraction_id, extr_quant, digest_id, dig_quant, ligation_id) %>% # arrange(sample_id) # # # inform the leyte database that these samples failed sequencing # nam <- sample %>% # distinct(sample_id) # # leyte <- write_db("Leyte") # clownfish <- dbReadTable(leyte, "clownfish") # change <- clownfish %>% # filter(sample_id %in% nam$sample_id) # # test <- anti_join(nam, change, by = "sample_id") # # # find out if these extractions were ligated successfully # digs <- lab %>% # tbl("digest") %>% # filter(extraction_id %in% sample$extraction_id) %>% # select(digest_id, extraction_id) %>% # collect() # # ligs <- lab %>% # tbl("ligation") %>% # filter(digest_id %in% digs$digest_id, # !ligation_id %in% sample$ligation_id, # notes != "PCR failed", # ligation_id != "L0548" # ) %>% # select(ligation_id, digest_id, notes) %>% # collect() # # ligs <- left_join(ligs, digs, by = "digest_id") # rm(digs) # # # remove successful ligations from the samples # sample <- anti_join(sample, ligs, by = "extraction_id") %>% # arrange(extr_quant, sample_id) # # # write.csv(sample, paste("data/samples_to_retry", Sys.Date(), ".csv", sep = ""), row.names = F) # # # plot a histogram of extr_quants # ggplot(data = sample) + # geom_histogram(mapping = aes(x=extr_quant), binwidth = 10) # # # based on this histogram, maybe do a low concentration enzyme digest with any samples with a quant lower than 10? # # low_conc <- sample %>% # filter(extr_quant < 10) # # # ggplot(data = low_conc) + # geom_histogram(mapping = aes(x = extr_quant), binwidth = 0.5) # # # in order to make a plate of low concentration samples to digest, need to import locations from db # low_conc <- low_conc %>% # distinct(extraction_id) # # low_conc_plate <- lab %>% # tbl("extraction") %>% # filter(extraction_id %in% low_conc$extraction_id) %>% # select(extraction_id, sample_id, quant, well, plate, notes) %>% # collect() %>% # filter(!grepl("empty", notes)) # remove empty wells # # # # get a list of plate names and the count of samples from each plate # counts <- low_conc_plate %>% # group_by(plate) %>% # summarise(samples = n()) %>% # count the number of samples to be digested on each plate # arrange(plate) # # # add a digest plate name # low_conc_plate <- low_conc_plate %>% # mutate(dig_plate = "low_conc_plate") %>% # arrange(extraction_id) # sort by extraction_id # # plate_master <- data.frame( row = rep(LETTERS[1:8], 12), col = unlist(lapply(1:12, rep, 8))) # plate_master <- plate_master %>% # mutate(dig_well = paste(row, col, sep = "")) # plate_master <- plate_master[1:nrow(low_conc_plate), ] # low_conc_plate <- cbind(low_conc_plate, plate_master) # low_conc_plate <- low_conc_plate %>% # mutate(col = formatC(as.numeric(col), width = 2, format = "d", flag = "0"))%>% # arrange(col, row) # low_conc_plate <- low_conc_plate %>% # mutate(digest_id = 1:nrow(low_conc_plate)) # # # # # #	/scripts/problem_samples.R	no_license	mstuart1/laboratory_old	R	false	false	6,824	r	# samples that did not sequence well source("scripts/lab_helpers.R") # library(ggplot2) lab <- read_db("Laboratory") # # get the list of missing samples from amphiprion that have been edited to just Ligation number in text editor using the find: (APCL_\d+)(L\d+) and the replace: \2 and the find: (APCL_)(L\d+) and the replace: \2 # fails <- read.csv("data/lowDP.indv.csv", header = F) # names(fails) <- "ligation_id" # # # find sample_ids and extract ids # lab <- read_db("Laboratory") # ligs <- lab %>% # tbl("ligation") %>% # collect() %>% # filter(ligation_id %in% fails$ligation_id) %>% # select(ligation_id, digest_id, total_reads, retained) # # digs <- lab %>% # tbl("digest") %>% # collect() %>% # filter(digest_id %in% ligs$digest_id) %>% # select(digest_id, extraction_id, quant) %>% # rename(dig_quant = quant) # # digs <- left_join(ligs, digs, by = "digest_id") # rm(ligs) # # sample <- lab %>% # tbl("extraction") %>% # collect() %>% # filter(extraction_id %in% digs$extraction_id) %>% # select(extraction_id, sample_id, quant, gel) %>% # rename(extr_quant = quant) # # sample <- left_join(digs, sample, by = "extraction_id") # # sample <- sample %>% # select(sample_id, extraction_id, extr_quant, gel, digest_id, dig_quant, ligation_id, total_reads, retained) # # # write.csv(sample, "data/problem_samples.csv", row.names = F) # pull in successful ligations from Rdata ligs <- readRDS("data/passed_ligs.Rdata") temp <- lab %>% tbl("ligation") %>% filter(ligation_id %in% ligs$ligation_id) %>% select(ligation_id, digest_id, DNA, retained) %>% collect() ligs <- left_join(ligs, temp, by = "ligation_id") failed_ligs <- lab %>% tbl("ligation") %>% filter(!ligation_id %in% ligs$ligation_id) %>% select(ligation_id, digest_id, DNA, retained) %>% collect() temp <- lab %>% tbl("digest") %>% filter(digest_id %in% ligs$digest_id) %>% select(digest_id, extraction_id, quant) %>% collect() ligs <- left_join(ligs, temp, by = "digest_id") %>% rename(dig_quant = quant) temp <- lab %>% tbl("digest") %>% filter(digest_id %in% failed_ligs$digest_id) %>% select(digest_id, extraction_id, quant) %>% collect() failed_ligs <- left_join(failed_ligs, temp, by = "digest_id") %>% rename(dig_quant = quant) temp <- lab %>% tbl("extraction") %>% filter(extraction_id %in% ligs$extraction_id) %>% select(extraction_id, quant, sample_id) %>% collect() ligs <- left_join(ligs, temp, by = "extraction_id") %>% rename(extr_quant = quant) temp <- lab %>% tbl("extraction") %>% filter(extraction_id %in% failed_ligs$extraction_id) %>% select(extraction_id, quant, sample_id) %>% collect() failed_ligs <- left_join(failed_ligs, temp, by = "extraction_id") %>% rename(extr_quant = quant) #1343 # remove any sample_ids from the failed_ligs that are also on the ligs failed_ligs <- anti_join(failed_ligs, ligs, by = "sample_id") %>% #1052 select(sample_id, extraction_id, extr_quant, digest_id, dig_quant, ligation_id,DNA, retained) %>% arrange(sample_id) # remove samples that are not clarkii removed <- failed_ligs %>% filter(!grepl("APCL", sample_id)) failed_ligs <- failed_ligs %>% filter(grepl("APCL", sample_id)) # separate out into low conc digest and regular digest # brks<-seq(0,800,50) # plot concentration of failed seqs hist(x = ligs$quant,col="blue",breaks=brks,ylim=c(0,150)) hist(x = sample$extr_quant,col="red",breaks=brks,ylim=c(0,150), add = T) # how many digests with a quant < 10 led to successful sequences? low_dig_succ <- ligs %>% filter(ligs$dig_quant < 10) low_dig_fail <- sample %>% filter(sample$dig_quant < 10) # rm(digs) # # # make a list of samples that need to be re-processed # sample <- sample %>% # select(sample_id, extraction_id, extr_quant, digest_id, dig_quant, ligation_id) %>% # arrange(sample_id) # # # inform the leyte database that these samples failed sequencing # nam <- sample %>% # distinct(sample_id) # # leyte <- write_db("Leyte") # clownfish <- dbReadTable(leyte, "clownfish") # change <- clownfish %>% # filter(sample_id %in% nam$sample_id) # # test <- anti_join(nam, change, by = "sample_id") # # # find out if these extractions were ligated successfully # digs <- lab %>% # tbl("digest") %>% # filter(extraction_id %in% sample$extraction_id) %>% # select(digest_id, extraction_id) %>% # collect() # # ligs <- lab %>% # tbl("ligation") %>% # filter(digest_id %in% digs$digest_id, # !ligation_id %in% sample$ligation_id, # notes != "PCR failed", # ligation_id != "L0548" # ) %>% # select(ligation_id, digest_id, notes) %>% # collect() # # ligs <- left_join(ligs, digs, by = "digest_id") # rm(digs) # # # remove successful ligations from the samples # sample <- anti_join(sample, ligs, by = "extraction_id") %>% # arrange(extr_quant, sample_id) # # # write.csv(sample, paste("data/samples_to_retry", Sys.Date(), ".csv", sep = ""), row.names = F) # # # plot a histogram of extr_quants # ggplot(data = sample) + # geom_histogram(mapping = aes(x=extr_quant), binwidth = 10) # # # based on this histogram, maybe do a low concentration enzyme digest with any samples with a quant lower than 10? # # low_conc <- sample %>% # filter(extr_quant < 10) # # # ggplot(data = low_conc) + # geom_histogram(mapping = aes(x = extr_quant), binwidth = 0.5) # # # in order to make a plate of low concentration samples to digest, need to import locations from db # low_conc <- low_conc %>% # distinct(extraction_id) # # low_conc_plate <- lab %>% # tbl("extraction") %>% # filter(extraction_id %in% low_conc$extraction_id) %>% # select(extraction_id, sample_id, quant, well, plate, notes) %>% # collect() %>% # filter(!grepl("empty", notes)) # remove empty wells # # # # get a list of plate names and the count of samples from each plate # counts <- low_conc_plate %>% # group_by(plate) %>% # summarise(samples = n()) %>% # count the number of samples to be digested on each plate # arrange(plate) # # # add a digest plate name # low_conc_plate <- low_conc_plate %>% # mutate(dig_plate = "low_conc_plate") %>% # arrange(extraction_id) # sort by extraction_id # # plate_master <- data.frame( row = rep(LETTERS[1:8], 12), col = unlist(lapply(1:12, rep, 8))) # plate_master <- plate_master %>% # mutate(dig_well = paste(row, col, sep = "")) # plate_master <- plate_master[1:nrow(low_conc_plate), ] # low_conc_plate <- cbind(low_conc_plate, plate_master) # low_conc_plate <- low_conc_plate %>% # mutate(col = formatC(as.numeric(col), width = 2, format = "d", flag = "0"))%>% # arrange(col, row) # low_conc_plate <- low_conc_plate %>% # mutate(digest_id = 1:nrow(low_conc_plate)) # # # # # #
# put your ASV table and taxonomy table in Database/Tax4Fun2 folder... library(Tax4Fun2) library(seqinr) setwd("~/R/Database/Tax4Fun2") ASV.table <- read.table(file="ASV_table.txt",header=T,row.names=1) ASV <- ASV.table [,1:(ncol(ASV.table)-6)] # I've changed 7--> 6 ASV <- cbind (rownames(ASV),ASV) ASV <- rbind (colnames(ASV),ASV) ASV[1,1] <- "ID" rownames(ASV) <- NULL colnames(ASV) <- NULL write.table(ASV, "ASV.txt",sep="\t",col.names = F, row.names = F,quote=F) taxonomy <- read.table(file="taxonomy.txt",header=T, row.names=1) tax <- subset(taxonomy, Family != "Mitochondria" & Class != "Chloroplast" & Kingdom != "NA") write.fasta (sequences = as.list(rownames(tax)),names = rownames(ASV.table),file.out="seqs.fasta") dir.create("Tax4Fun2") #Step 2: Generate your own reference datasets #1. Extracting SSU seqeunces (16S rRNA and 18S rRNA) extractSSU(genome_file = "OneProkaryoticGenome.fasta", file_extension = "fasta", path_to_reference_data ="Tax4Fun2_ReferenceData_v2") #2. Assigning functions to prokayotic genomes assignFunction(genome_file = "OneProkaryoticGenome.fasta", file_extension = "fasta", path_to_reference_data = "Tax4Fun2_ReferenceData_v2", num_of_threads = 1, fast = TRUE) #3. Generate the reference data generateUserData(path_to_reference_data ="Tax4Fun2_ReferenceData_v2", path_to_user_data = ".", name_of_user_data = "User_Ref0", SSU_file_extension = "_16SrRNA.ffn", KEGG_file_extension = "_funPro.txt") #Step 3: Making functional predictions #1. Making functional predictions using the default reference data only #1. Run the reference blast runRefBlast(path_to_otus = "seqs.fasta" , path_to_reference_data ="Tax4Fun2_ReferenceData_v2", path_to_temp_folder = "Tax4Fun2", database_mode = "Ref99NR", use_force = T, num_threads = 6) # 2) Predicting functional profiles # Remove the first row's # and the second row's # of "17_otu_97_table_taxonomy.txt" --> "17_otu_97_table_taxonomy_tax4fun.txt" makeFunctionalPrediction(path_to_otu_table = "ASV.txt", path_to_reference_data = "Tax4Fun2_ReferenceData_v2", path_to_temp_folder = "Tax4Fun2", database_mode = "Ref99NR", normalize_by_copy_number = TRUE, min_identity_to_reference = 0.97, normalize_pathways = FALSE) # note. normalize_pathways = FALSE will affiliate the rel. abundance of each KO to each pathway it belongs to. By setting it to true, the rel. abundance is equally distributed to all pathways it was assigned to.) #Step 4: Calculating (multi-)functional redundancy indices (experimental) calculateFunctionalRedundancy(path_to_otu_table = "ASV.txt", path_to_reference_data = "Tax4Fun2_ReferenceData_v2", path_to_temp_folder = "Tax4Fun2", database_mode = "Ref99NR", min_identity_to_reference = 0.97) # Don't forget to move the output file (Tax4Fun2) for your project file	/tax4fun2_from_dada2.R	no_license	HaihuaWang-hub/NGS-Data-Processing	R	false	false	2,862	r	# put your ASV table and taxonomy table in Database/Tax4Fun2 folder... library(Tax4Fun2) library(seqinr) setwd("~/R/Database/Tax4Fun2") ASV.table <- read.table(file="ASV_table.txt",header=T,row.names=1) ASV <- ASV.table [,1:(ncol(ASV.table)-6)] # I've changed 7--> 6 ASV <- cbind (rownames(ASV),ASV) ASV <- rbind (colnames(ASV),ASV) ASV[1,1] <- "ID" rownames(ASV) <- NULL colnames(ASV) <- NULL write.table(ASV, "ASV.txt",sep="\t",col.names = F, row.names = F,quote=F) taxonomy <- read.table(file="taxonomy.txt",header=T, row.names=1) tax <- subset(taxonomy, Family != "Mitochondria" & Class != "Chloroplast" & Kingdom != "NA") write.fasta (sequences = as.list(rownames(tax)),names = rownames(ASV.table),file.out="seqs.fasta") dir.create("Tax4Fun2") #Step 2: Generate your own reference datasets #1. Extracting SSU seqeunces (16S rRNA and 18S rRNA) extractSSU(genome_file = "OneProkaryoticGenome.fasta", file_extension = "fasta", path_to_reference_data ="Tax4Fun2_ReferenceData_v2") #2. Assigning functions to prokayotic genomes assignFunction(genome_file = "OneProkaryoticGenome.fasta", file_extension = "fasta", path_to_reference_data = "Tax4Fun2_ReferenceData_v2", num_of_threads = 1, fast = TRUE) #3. Generate the reference data generateUserData(path_to_reference_data ="Tax4Fun2_ReferenceData_v2", path_to_user_data = ".", name_of_user_data = "User_Ref0", SSU_file_extension = "_16SrRNA.ffn", KEGG_file_extension = "_funPro.txt") #Step 3: Making functional predictions #1. Making functional predictions using the default reference data only #1. Run the reference blast runRefBlast(path_to_otus = "seqs.fasta" , path_to_reference_data ="Tax4Fun2_ReferenceData_v2", path_to_temp_folder = "Tax4Fun2", database_mode = "Ref99NR", use_force = T, num_threads = 6) # 2) Predicting functional profiles # Remove the first row's # and the second row's # of "17_otu_97_table_taxonomy.txt" --> "17_otu_97_table_taxonomy_tax4fun.txt" makeFunctionalPrediction(path_to_otu_table = "ASV.txt", path_to_reference_data = "Tax4Fun2_ReferenceData_v2", path_to_temp_folder = "Tax4Fun2", database_mode = "Ref99NR", normalize_by_copy_number = TRUE, min_identity_to_reference = 0.97, normalize_pathways = FALSE) # note. normalize_pathways = FALSE will affiliate the rel. abundance of each KO to each pathway it belongs to. By setting it to true, the rel. abundance is equally distributed to all pathways it was assigned to.) #Step 4: Calculating (multi-)functional redundancy indices (experimental) calculateFunctionalRedundancy(path_to_otu_table = "ASV.txt", path_to_reference_data = "Tax4Fun2_ReferenceData_v2", path_to_temp_folder = "Tax4Fun2", database_mode = "Ref99NR", min_identity_to_reference = 0.97) # Don't forget to move the output file (Tax4Fun2) for your project file
setwd("~/Dropbox/umich_2017_winter/stats503/project") library(stringr) library(rvest) ######################################################## # players drafted start_year = 2010 end_year = 2015 seasons <- seq(start_year, end_year, 1) # scrape player names and ranking from nbadraft.net draftplayer_read <- function(){ urls <- sapply(seasons, function(e) str_c("http://www.nbadraft.net/nba_final_draft/",e)) page <- lapply(urls, read_html) draftees <- lapply(1:length(seasons), function(e) html_table(html_nodes(page[[e]], "table"))[[1]]) return(draftees) } player_list <- do.call(rbind, draftplayer_read()) names(player_list) # convert wide format to long format player_list <- rbind(player_list[,1:8], player_list[,9:16]) names(player_list) # select only useful columns player_list <- player_list[,c("#", "Player", "H", "P")] sapply(draftplayer_read(), nrow) # include draft_class lable draft_class = as.numeric(rep(str_c("201", 0:5), each = 60)) # complete 360 rows player_list_df <- cbind(player_list, draft_class) # incomplete player_name <- player_list_df$Player player_name <- str_replace_all(player_name, " ", "-") # to lower player_name <- tolower(player_name) # create list container player_html <- vector("list", length(player_name)) # load each player's college stat into each element in the list for (i in 1:length(player_name)){ player_html[[i]] <- tryCatch(read_html(str_c("http://www.sports-reference.com/cbb/players/", player_name[i], "-1.html")), error = function(w){print(str_c("error!", i, player_name[i]))}, warning = function(e){print(str_c("warning!",i, player_name[i]))} ) } player_html_orginal <- player_html # save(player_html_orginal, file = "player_html_orginal.rda") # leave only draf players whose college stats available ind_original <- unlist(lapply(player_html_orginal, function(e){length(e) == 2})) player_list_df <- player_list_df[ind_original, ] head(player_list_df) # save(player_list_df, file = "player_list_df") load("player_list_df.rda") # find college statistics # convert html table to dataframes player_ind <- player_html[ind_original] playercollege_stats <- lapply(1:length(player_ind), function(e) html_table(html_nodes(player_ind[[e]], "table"))[[1]]) # save(playercollege_stats, file = "playercollege_stats.rda") load("playercollege_stats.rda") # player names playername <- player_list_df$Player # check each player's stat's dimension a <- sapply(1:length(playername), function(e) ncol(playercollege_stats[[e]])) min(a) # 24 max(a) # 26 player_stats <- matrix(rep(NA, n = min(a)149), ncol = min(a), nrow = length(playername)) player_stats <- as.data.frame(player_stats) for(i in 1:length(playercollege_stats)){ player_stats[i,1:24]<- tail(playercollege_stats[[i]][,1:24], n = 1) } names(player_stats) <- names(playercollege_stats[[1]][,1:24]) # remove conference variable player_stats <- player_stats[,-3] rownames(player_stats) <- playername head(player_stats) # combine draft list and college stats player_stats_original <- cbind(player_stats[, -c(1,2)], player_list_df[, c("#", "H", "P", "draft_class")]) x = player_stats_original$H a <- str_replace_all(x, "-[0-9]+", "") a <- cm(as.numeric(a)12) b <- str_replace_all(x, "[0-9]+-", "") b <- cm(as.numeric(b)) player_stats_original$H <- a + b # save(player_stats_original, file = "player_stats_original.rda") load("player_stats_original.rda") head(player_stats_original) nrow(player_stats_original) ############################################################################## ############################################################################## ############################################################################## ############################################################################## ############################################################################## ############################################################################## ############################################################################## ############################################################################## ######################################################## # players drafted start_year = 2009 end_year = 2015 seasons <- seq(start_year, end_year, 1) # scrape player names and ranking from nbadraft.net draftplayer_read_1 <- function(){ urls <- sapply(seasons, function(e) str_c("http://www.nbadraft.net/nba_final_draft/",e)) page <- lapply(urls, read_html) draftees <- lapply(1:length(seasons), function(e) html_table(html_nodes(page[[e]], "table"))[[1]]) return(draftees) } player_list_1 <- do.call(rbind, draftplayer_read_1()) player_list_1 <- rbind(player_list_1[,1:8], player_list_1[,9:16]) names(player_list_1) draft_class = as.numeric(rep(c("2009",str_c("201", 0:5)), each = 60)) player_list_1 <- cbind(player_list_1, draft_class) player_list_1 <- player_list_1[,c("#", "Player", "H", "P", "draft_class")] names(player_list_1) # players drafted start_year = 2000 end_year = 2008 seasons <- seq(start_year, end_year, 1) # scrape player names and ranking from nbadraft.net draftplayer_read_2 <- function(){ urls <- sapply(seasons, function(e) str_c("http://www.nbadraft.net/nba_draft_history/",e, ".html")) page <- lapply(urls, read_html) draftees <- lapply(1:length(seasons), function(e) html_table(html_nodes(page[[e]], "table"))[[1]]) draftees return(draftees) } ll <- draftplayer_read_2() for(i in 1:9){ ll[[i]] <- cbind(ll[[i]], draft_class = rep(str_c("200", i-1), each = nrow(ll[[i]]))) x <- ll[[i]] x <- x[,-c(2, 6)] names(x) <- c("#", "Team", "info", "#", "Team", "info", "draft_class") player_list_21 <- cbind(x[,1:3], draft_class = x[,7]) player_list_22 <- cbind(x[,4:6], draft_class = x[,7]) ll[[i]] <- rbind(player_list_21, player_list_22) } player_list_2 <- do.call(rbind, ll) # extract player names x4 <- player_list_2$info # x4 <- x4[-c(47, 82, 145)] x4 <- str_replace_all(x4, "\\n", " ") x4 <- str_replace_all(x4, "[0-9]-[0-9][[:print:]]$", "") x4 <- str_replace_all(x4, "[[:blank:]]$", "") x4 <- str_replace_all(x4, "\\", "") # extract player height hei <- player_list_2$info hei <- str_replace_all(hei, "\\n", " ") hei <- as.vector(str_extract_all(hei, "[0-9]-[0-9]", "")) # extract player position pos <- player_list_2$info pos <- str_replace_all(pos, "\\n", " ") pos <- str_replace_all(pos, "^[[:print:]][0-9]-[0-9]", "") pos <- str_replace_all(pos, "^[[:blank:]]+", "") pos <- str_replace_all(pos, "[[:digit:]]", "") pos <- str_replace_all(pos, "^[[:blank:]]+", "") pos <- str_replace_all(pos, "[[:punct:]]+", "") pos <- str_replace_all(pos, "^[[:blank:]]+", "") pos <- strsplit(pos, " ") pos <- unlist(lapply(pos, function(e) e[1])) remov <- str_which(x4, "[[:punct:]]") x4 <- x4[-remov] # remove everything irregular hei <- hei[-remov] pos <- pos[-remov] draft_class <- player_list_2$draft_class draft_class <- draft_class[-remov] player_list_2 <- player_list_2[-remov,] player_list_2 <- data.frame(player_list_2$`#`, x4, hei, pos, draft_class) names(player_list_2) <- c("#", "Player", "H", "P", "draft_class") # combine 2000-2015 player_list_tot <- rbind(player_list_1, player_list_2) # save(player_list_tot, file = "player_list_tot.rda") ######################################################## load("player_list_tot.rda") # webscraping format: # add hyphen between first and last name dt <- player_list_tot player_name <- dt$Player player_name <- str_replace_all(player_name, " ", "-") # to lower player_name <- tolower(player_name) # create list container player_html <- vector("list", length(player_name)) # load each player's college stat into each element in the list for (i in 1:684){ player_html[[i]] <- tryCatch(read_html(str_c("http://www.sports-reference.com/cbb/players/", player_name[i], "-1.html")), error = function(w){print(str_c("error!", i, player_name[i]))}, warning = function(e){print(str_c("warning!",i, player_name[i]))} ) } for (i in 685:length(player_name)){ player_html[[i]] <- tryCatch(read_html(str_c("http://www.sports-reference.com/cbb/players/", player_name[i], "-1.html")), error = function(w){print(str_c("error!", i, player_name[i]))}, warning = function(e){print(str_c("warning!",i, player_name[i]))} ) } # leave only players whose college stats available ind_tot <- unlist(lapply(player_html, function(e){length(e) == 2})) # save(ind_tot, file = "ind_tot.rda") load("ind_tot.rda") player_list_tot <- player_list_tot[ind_tot, ] head(player_list_tot) # save(player_list_tot, file = "player_list_tot.rda") player_ind <- player_html[ind_tot] ################################################################################ # convert html table to dataframes playercollege_stats_tot <- lapply(1:length(player_ind), function(e) html_table(html_nodes(player_ind[[e]], "table"))[[1]]) # save(playercollege_stats_tot, file = "playercollege_stats_tot.rda") load("playercollege_stats_tot.rda") load("player_list_tot.rda") playername <- player_list_tot$Player length(playername) a <- sapply(1:length(playername), function(e) ncol(playercollege_stats_tot[[e]])) min(a) # 24 max(a) # 26 player_stats <- matrix(rep(NA, n = min(a)length(playername)), ncol = min(a), nrow = length(playername)) dim(player_stats) player_stats <- as.data.frame(player_stats) for(i in 1:length(playercollege_stats_tot)){ player_stats[i,1:24]<- tail(playercollege_stats_tot[[i]][,1:24], n = 1) } names(player_stats) <- names(playercollege_stats_tot[[1]][,1:24]) # remove conference variable player_stats <- player_stats[,-3] # rownames dupl <- which(duplicated(playername) == TRUE) dup <- which(playername == playername[dupl]) rownames(player_stats)[1:dup[1]] <- playername[1:dup[1]] rownames(player_stats)[dup[1]] <- "Marcus Williams.1" rownames(player_stats)[(dup[1]+1):dup[2]] <- playername[(dup[1]+1):dup[2]] rownames(player_stats)[dup[2]] <- "Marcus Williams.2" rownames(player_stats)[(dup[2]+1):length(playername)] <- playername[(dup[2]+1):length(playername)] head(player_stats) # combine draft list and college stats player_stats <- cbind(player_stats[, -c(1,2)], player_list_tot[, c("#", "H", "P", "draft_class")]) player_stats_tot <- player_stats x = player_stats_tot$H a <- str_replace_all(x, "-[0-9]+", "") a <- cm(as.numeric(a)*12) b <- str_replace_all(x, "[0-9]+-", "") b <- cm(as.numeric(b)) player_stats_tot$H <- a + b head(player_stats_tot) # save(player_stats_tot, file = "player_stats_tot.rda") load("player_stats_tot.rda") ############################################################################## # original 2010 - 2015 load("player_stats_original.rda") head(player_stats_original) nrow(player_stats_original) ############################################################################## # expanded 2000 - 2015 load("player_stats_tot.rda") head(player_stats_tot) nrow(player_stats_tot)	/data_scraping.R	no_license	lizeyuyuz/NBA_Clustering	R	false	false	11,406	r	setwd("~/Dropbox/umich_2017_winter/stats503/project") library(stringr) library(rvest) ######################################################## # players drafted start_year = 2010 end_year = 2015 seasons <- seq(start_year, end_year, 1) # scrape player names and ranking from nbadraft.net draftplayer_read <- function(){ urls <- sapply(seasons, function(e) str_c("http://www.nbadraft.net/nba_final_draft/",e)) page <- lapply(urls, read_html) draftees <- lapply(1:length(seasons), function(e) html_table(html_nodes(page[[e]], "table"))[[1]]) return(draftees) } player_list <- do.call(rbind, draftplayer_read()) names(player_list) # convert wide format to long format player_list <- rbind(player_list[,1:8], player_list[,9:16]) names(player_list) # select only useful columns player_list <- player_list[,c("#", "Player", "H", "P")] sapply(draftplayer_read(), nrow) # include draft_class lable draft_class = as.numeric(rep(str_c("201", 0:5), each = 60)) # complete 360 rows player_list_df <- cbind(player_list, draft_class) # incomplete player_name <- player_list_df$Player player_name <- str_replace_all(player_name, " ", "-") # to lower player_name <- tolower(player_name) # create list container player_html <- vector("list", length(player_name)) # load each player's college stat into each element in the list for (i in 1:length(player_name)){ player_html[[i]] <- tryCatch(read_html(str_c("http://www.sports-reference.com/cbb/players/", player_name[i], "-1.html")), error = function(w){print(str_c("error!", i, player_name[i]))}, warning = function(e){print(str_c("warning!",i, player_name[i]))} ) } player_html_orginal <- player_html # save(player_html_orginal, file = "player_html_orginal.rda") # leave only draf players whose college stats available ind_original <- unlist(lapply(player_html_orginal, function(e){length(e) == 2})) player_list_df <- player_list_df[ind_original, ] head(player_list_df) # save(player_list_df, file = "player_list_df") load("player_list_df.rda") # find college statistics # convert html table to dataframes player_ind <- player_html[ind_original] playercollege_stats <- lapply(1:length(player_ind), function(e) html_table(html_nodes(player_ind[[e]], "table"))[[1]]) # save(playercollege_stats, file = "playercollege_stats.rda") load("playercollege_stats.rda") # player names playername <- player_list_df$Player # check each player's stat's dimension a <- sapply(1:length(playername), function(e) ncol(playercollege_stats[[e]])) min(a) # 24 max(a) # 26 player_stats <- matrix(rep(NA, n = min(a)149), ncol = min(a), nrow = length(playername)) player_stats <- as.data.frame(player_stats) for(i in 1:length(playercollege_stats)){ player_stats[i,1:24]<- tail(playercollege_stats[[i]][,1:24], n = 1) } names(player_stats) <- names(playercollege_stats[[1]][,1:24]) # remove conference variable player_stats <- player_stats[,-3] rownames(player_stats) <- playername head(player_stats) # combine draft list and college stats player_stats_original <- cbind(player_stats[, -c(1,2)], player_list_df[, c("#", "H", "P", "draft_class")]) x = player_stats_original$H a <- str_replace_all(x, "-[0-9]+", "") a <- cm(as.numeric(a)12) b <- str_replace_all(x, "[0-9]+-", "") b <- cm(as.numeric(b)) player_stats_original$H <- a + b # save(player_stats_original, file = "player_stats_original.rda") load("player_stats_original.rda") head(player_stats_original) nrow(player_stats_original) ############################################################################## ############################################################################## ############################################################################## ############################################################################## ############################################################################## ############################################################################## ############################################################################## ############################################################################## ######################################################## # players drafted start_year = 2009 end_year = 2015 seasons <- seq(start_year, end_year, 1) # scrape player names and ranking from nbadraft.net draftplayer_read_1 <- function(){ urls <- sapply(seasons, function(e) str_c("http://www.nbadraft.net/nba_final_draft/",e)) page <- lapply(urls, read_html) draftees <- lapply(1:length(seasons), function(e) html_table(html_nodes(page[[e]], "table"))[[1]]) return(draftees) } player_list_1 <- do.call(rbind, draftplayer_read_1()) player_list_1 <- rbind(player_list_1[,1:8], player_list_1[,9:16]) names(player_list_1) draft_class = as.numeric(rep(c("2009",str_c("201", 0:5)), each = 60)) player_list_1 <- cbind(player_list_1, draft_class) player_list_1 <- player_list_1[,c("#", "Player", "H", "P", "draft_class")] names(player_list_1) # players drafted start_year = 2000 end_year = 2008 seasons <- seq(start_year, end_year, 1) # scrape player names and ranking from nbadraft.net draftplayer_read_2 <- function(){ urls <- sapply(seasons, function(e) str_c("http://www.nbadraft.net/nba_draft_history/",e, ".html")) page <- lapply(urls, read_html) draftees <- lapply(1:length(seasons), function(e) html_table(html_nodes(page[[e]], "table"))[[1]]) draftees return(draftees) } ll <- draftplayer_read_2() for(i in 1:9){ ll[[i]] <- cbind(ll[[i]], draft_class = rep(str_c("200", i-1), each = nrow(ll[[i]]))) x <- ll[[i]] x <- x[,-c(2, 6)] names(x) <- c("#", "Team", "info", "#", "Team", "info", "draft_class") player_list_21 <- cbind(x[,1:3], draft_class = x[,7]) player_list_22 <- cbind(x[,4:6], draft_class = x[,7]) ll[[i]] <- rbind(player_list_21, player_list_22) } player_list_2 <- do.call(rbind, ll) # extract player names x4 <- player_list_2$info # x4 <- x4[-c(47, 82, 145)] x4 <- str_replace_all(x4, "\\n", " ") x4 <- str_replace_all(x4, "[0-9]-[0-9][[:print:]]$", "") x4 <- str_replace_all(x4, "[[:blank:]]$", "") x4 <- str_replace_all(x4, "\\", "") # extract player height hei <- player_list_2$info hei <- str_replace_all(hei, "\\n", " ") hei <- as.vector(str_extract_all(hei, "[0-9]-[0-9]", "")) # extract player position pos <- player_list_2$info pos <- str_replace_all(pos, "\\n", " ") pos <- str_replace_all(pos, "^[[:print:]][0-9]-[0-9]", "") pos <- str_replace_all(pos, "^[[:blank:]]+", "") pos <- str_replace_all(pos, "[[:digit:]]", "") pos <- str_replace_all(pos, "^[[:blank:]]+", "") pos <- str_replace_all(pos, "[[:punct:]]+", "") pos <- str_replace_all(pos, "^[[:blank:]]+", "") pos <- strsplit(pos, " ") pos <- unlist(lapply(pos, function(e) e[1])) remov <- str_which(x4, "[[:punct:]]") x4 <- x4[-remov] # remove everything irregular hei <- hei[-remov] pos <- pos[-remov] draft_class <- player_list_2$draft_class draft_class <- draft_class[-remov] player_list_2 <- player_list_2[-remov,] player_list_2 <- data.frame(player_list_2$`#`, x4, hei, pos, draft_class) names(player_list_2) <- c("#", "Player", "H", "P", "draft_class") # combine 2000-2015 player_list_tot <- rbind(player_list_1, player_list_2) # save(player_list_tot, file = "player_list_tot.rda") ######################################################## load("player_list_tot.rda") # webscraping format: # add hyphen between first and last name dt <- player_list_tot player_name <- dt$Player player_name <- str_replace_all(player_name, " ", "-") # to lower player_name <- tolower(player_name) # create list container player_html <- vector("list", length(player_name)) # load each player's college stat into each element in the list for (i in 1:684){ player_html[[i]] <- tryCatch(read_html(str_c("http://www.sports-reference.com/cbb/players/", player_name[i], "-1.html")), error = function(w){print(str_c("error!", i, player_name[i]))}, warning = function(e){print(str_c("warning!",i, player_name[i]))} ) } for (i in 685:length(player_name)){ player_html[[i]] <- tryCatch(read_html(str_c("http://www.sports-reference.com/cbb/players/", player_name[i], "-1.html")), error = function(w){print(str_c("error!", i, player_name[i]))}, warning = function(e){print(str_c("warning!",i, player_name[i]))} ) } # leave only players whose college stats available ind_tot <- unlist(lapply(player_html, function(e){length(e) == 2})) # save(ind_tot, file = "ind_tot.rda") load("ind_tot.rda") player_list_tot <- player_list_tot[ind_tot, ] head(player_list_tot) # save(player_list_tot, file = "player_list_tot.rda") player_ind <- player_html[ind_tot] ################################################################################ # convert html table to dataframes playercollege_stats_tot <- lapply(1:length(player_ind), function(e) html_table(html_nodes(player_ind[[e]], "table"))[[1]]) # save(playercollege_stats_tot, file = "playercollege_stats_tot.rda") load("playercollege_stats_tot.rda") load("player_list_tot.rda") playername <- player_list_tot$Player length(playername) a <- sapply(1:length(playername), function(e) ncol(playercollege_stats_tot[[e]])) min(a) # 24 max(a) # 26 player_stats <- matrix(rep(NA, n = min(a)length(playername)), ncol = min(a), nrow = length(playername)) dim(player_stats) player_stats <- as.data.frame(player_stats) for(i in 1:length(playercollege_stats_tot)){ player_stats[i,1:24]<- tail(playercollege_stats_tot[[i]][,1:24], n = 1) } names(player_stats) <- names(playercollege_stats_tot[[1]][,1:24]) # remove conference variable player_stats <- player_stats[,-3] # rownames dupl <- which(duplicated(playername) == TRUE) dup <- which(playername == playername[dupl]) rownames(player_stats)[1:dup[1]] <- playername[1:dup[1]] rownames(player_stats)[dup[1]] <- "Marcus Williams.1" rownames(player_stats)[(dup[1]+1):dup[2]] <- playername[(dup[1]+1):dup[2]] rownames(player_stats)[dup[2]] <- "Marcus Williams.2" rownames(player_stats)[(dup[2]+1):length(playername)] <- playername[(dup[2]+1):length(playername)] head(player_stats) # combine draft list and college stats player_stats <- cbind(player_stats[, -c(1,2)], player_list_tot[, c("#", "H", "P", "draft_class")]) player_stats_tot <- player_stats x = player_stats_tot$H a <- str_replace_all(x, "-[0-9]+", "") a <- cm(as.numeric(a)*12) b <- str_replace_all(x, "[0-9]+-", "") b <- cm(as.numeric(b)) player_stats_tot$H <- a + b head(player_stats_tot) # save(player_stats_tot, file = "player_stats_tot.rda") load("player_stats_tot.rda") ############################################################################## # original 2010 - 2015 load("player_stats_original.rda") head(player_stats_original) nrow(player_stats_original) ############################################################################## # expanded 2000 - 2015 load("player_stats_tot.rda") head(player_stats_tot) nrow(player_stats_tot)
% Generated by roxygen2: do not edit by hand % Please edit documentation in R/vis.R \name{print.vis} \alias{print.vis} \title{Print method for a vis object} \usage{ \method{print}{vis}(x, min.prob = 0.3, print.full.model = FALSE, ...) } \arguments{ \item{x}{a \code{vis} object, the result of \code{\link{vis}}} \item{min.prob}{a lower bound on the probability of selection before the result is printed} \item{print.full.model}{logical, determines if the full model gets printed or not. Default=\code{FALSE}.} \item{...}{further arguments (currently unused)} } \description{ Prints basic output of the bootstrap results of an vis object. }	/man/print.vis.Rd	no_license	garthtarr/mplot	R	false	true	644	rd	% Generated by roxygen2: do not edit by hand % Please edit documentation in R/vis.R \name{print.vis} \alias{print.vis} \title{Print method for a vis object} \usage{ \method{print}{vis}(x, min.prob = 0.3, print.full.model = FALSE, ...) } \arguments{ \item{x}{a \code{vis} object, the result of \code{\link{vis}}} \item{min.prob}{a lower bound on the probability of selection before the result is printed} \item{print.full.model}{logical, determines if the full model gets printed or not. Default=\code{FALSE}.} \item{...}{further arguments (currently unused)} } \description{ Prints basic output of the bootstrap results of an vis object. }
df<-data.frame( sleep=c(7,8,9,6), college=c("Saga","Cendana","Cendana","Saga") ) aggregate(sleep~college, data=df, mean) #we want to permute the data #we will use SAMPLE set.seed(123456789) sample(c("a","b","c")) #create a new data frame with only college as the permuted variable df_p<-df df_p$college<-sample(df$college) aggregate(sleep~college, data=df_p, mean) #simulating permutations in R data(faithful) cor(faithful$eruptions,faithful$waiting) #0.90 trials<-10000 responses<-numeric(trials) cor(sample(faithful$eruptions), faithful$waiting) #0.04 for(i in 1:trials) responses[i]<-cor(sample(faithful$eruptions), faithful$waiting) hist(responses, xlab="Correlation") #IN-CLASS PREP titanic<-read.csv("titanic.csv", stringsAsFactors = F) trials<-20 d<-5 res<-matrix(NA, trials, d) for(i in 1:trials) res[i,]<-sample(1:10, 5, replace=F) res[2,] res[,5] #IN-CLASS WORK #Coin flipping simulation: Is Tim right? p_val<-seq(0.1, 0.9, by=0.1) p_dim<-length(p_val) #number of coin tosses n<-10 #number of trials trials<-10000 outcomes<-c("H","T") results<-matrix(NA, trials, p_dim) for(i in 1:p_dim){ for(j in 1:trials){ sim_coin<-sample(outcomes, size=n, replace=T, prob=c(p_val[i], 1-p_val[i])) results[j,i]<-sum(sim_coin=="H")/n } } par(mfrow=c(3,3)) for(i in 1:p_dim){ p<-hist(results[,i], probability = T, breaks=seq(0,1,by=0.1), xlim=c(0,1), main=paste("p=",p_val[i]), xaxt="n") } #TITANIC DATA titanic<-read.csv("titanic.csv", stringsAsFactors = F) freq_tables<-function(cat_data){ tab<-table(cat_data) #create a table from the data that's been read in print(tab) print(tab/length(cat_data)) } freq_tables(titanic$gender) freq_tables(titanic$class) freq_tables(titanic$survived) freq_tables(titanic$age>=18) table(titanic$survived, titanic$gender) table(titanic$survived, titanic$gender) / nrow(titanic)100 freq_tables(titanic$survived[titanic$gender=="Male"])100 freq_tables(titanic$survived[titanic$gender=="Female"])*100 #simulation solution response<-function(dying_people){ death_rate_males<- 1- mean(dying_people$survived[dying_people$gender=="Male"]) death_rate_females<- 1- mean(dying_people$survived[dying_people$gender=="Female"]) death_rate_males/death_rate_females #finding the ratio of death rate between males and females } response(titanic) permute<-function(){ tmp<-titanic #permute gender tmp$gender<-sample(tmp$gender) response(tmp) } permute() #is our observed difference of 3 actually statistacally significant?? trials<-10000 # results<-numeric(trials) for(i in 1:trials){ results[i]<-permute() } par(mfrow=c(1,1)) hist(results) titanic$total_price<-titanic$ticket_pound + titanic$ticket_shilling/20 +titanic$ticket_penny/240 tit<-titanic[titanic$total_price!=0 & titanic$gender=="Male" & titanic$class=="2nd",] model<-lm(log(tit$total_price)~tit$age, data=tit) plot(log(tit$total_price)~tit$age, col="blue") abline(model, lwd=3) coef(model) slope<-model$coefficients[2] #is the slope significant trials<-10000 results<-numeric(trials) for(i in 1:trials){ results[i]<-lm(log(total_price)~sample(age), data=tit)$coefficients[2] } hist(results) mean(results<=slope \| results>=-slope) #p=0.06	/PERMUTATIONS/Permutations.R	no_license	stefanroata/quantitative-reasoning	R	false	false	3,248	r	df<-data.frame( sleep=c(7,8,9,6), college=c("Saga","Cendana","Cendana","Saga") ) aggregate(sleep~college, data=df, mean) #we want to permute the data #we will use SAMPLE set.seed(123456789) sample(c("a","b","c")) #create a new data frame with only college as the permuted variable df_p<-df df_p$college<-sample(df$college) aggregate(sleep~college, data=df_p, mean) #simulating permutations in R data(faithful) cor(faithful$eruptions,faithful$waiting) #0.90 trials<-10000 responses<-numeric(trials) cor(sample(faithful$eruptions), faithful$waiting) #0.04 for(i in 1:trials) responses[i]<-cor(sample(faithful$eruptions), faithful$waiting) hist(responses, xlab="Correlation") #IN-CLASS PREP titanic<-read.csv("titanic.csv", stringsAsFactors = F) trials<-20 d<-5 res<-matrix(NA, trials, d) for(i in 1:trials) res[i,]<-sample(1:10, 5, replace=F) res[2,] res[,5] #IN-CLASS WORK #Coin flipping simulation: Is Tim right? p_val<-seq(0.1, 0.9, by=0.1) p_dim<-length(p_val) #number of coin tosses n<-10 #number of trials trials<-10000 outcomes<-c("H","T") results<-matrix(NA, trials, p_dim) for(i in 1:p_dim){ for(j in 1:trials){ sim_coin<-sample(outcomes, size=n, replace=T, prob=c(p_val[i], 1-p_val[i])) results[j,i]<-sum(sim_coin=="H")/n } } par(mfrow=c(3,3)) for(i in 1:p_dim){ p<-hist(results[,i], probability = T, breaks=seq(0,1,by=0.1), xlim=c(0,1), main=paste("p=",p_val[i]), xaxt="n") } #TITANIC DATA titanic<-read.csv("titanic.csv", stringsAsFactors = F) freq_tables<-function(cat_data){ tab<-table(cat_data) #create a table from the data that's been read in print(tab) print(tab/length(cat_data)) } freq_tables(titanic$gender) freq_tables(titanic$class) freq_tables(titanic$survived) freq_tables(titanic$age>=18) table(titanic$survived, titanic$gender) table(titanic$survived, titanic$gender) / nrow(titanic)100 freq_tables(titanic$survived[titanic$gender=="Male"])100 freq_tables(titanic$survived[titanic$gender=="Female"])*100 #simulation solution response<-function(dying_people){ death_rate_males<- 1- mean(dying_people$survived[dying_people$gender=="Male"]) death_rate_females<- 1- mean(dying_people$survived[dying_people$gender=="Female"]) death_rate_males/death_rate_females #finding the ratio of death rate between males and females } response(titanic) permute<-function(){ tmp<-titanic #permute gender tmp$gender<-sample(tmp$gender) response(tmp) } permute() #is our observed difference of 3 actually statistacally significant?? trials<-10000 # results<-numeric(trials) for(i in 1:trials){ results[i]<-permute() } par(mfrow=c(1,1)) hist(results) titanic$total_price<-titanic$ticket_pound + titanic$ticket_shilling/20 +titanic$ticket_penny/240 tit<-titanic[titanic$total_price!=0 & titanic$gender=="Male" & titanic$class=="2nd",] model<-lm(log(tit$total_price)~tit$age, data=tit) plot(log(tit$total_price)~tit$age, col="blue") abline(model, lwd=3) coef(model) slope<-model$coefficients[2] #is the slope significant trials<-10000 results<-numeric(trials) for(i in 1:trials){ results[i]<-lm(log(total_price)~sample(age), data=tit)$coefficients[2] } hist(results) mean(results<=slope \| results>=-slope) #p=0.06
I am done	/third_party/virtualbox/src/VBox/ExtPacks/VBoxDTrace/onnv/cmd/dtrace/test/tst/common/dtraceUtil/tst.ZeroProviderProbes.d.ksh.out	permissive	thalium/icebox	R	false	false	10	out	I am done
library(OpenMx) # Simulate some data sampleSize <- 250 x=rnorm(sampleSize, mean=0, sd=1) y= 0.5*x + rnorm(sampleSize, mean=0, sd=1) tmpFrame <- data.frame(x, y) tmpNames <- names(tmpFrame) # Create a model that includes an expected covariance matrix, # an expectation function, a fit function, and an observed covariance matrix data <- mxData(cov(tmpFrame), type="cov", numObs = sampleSize) expCov <- mxMatrix(type="Symm", nrow=2, ncol=2, values=c(.2,.1,.2), free=TRUE, name="expCov") expFunction <- mxExpectationNormal(covariance="expCov", dimnames=tmpNames) fitFunction <- mxFitFunctionML() testModel <- mxModel(model="testModel", expCov, data, expFunction, fitFunction) #Use mxRun to optimize the free parameters in the expected covariance matrix modelOut <- mxRun(testModel, checkpoint = TRUE) modelOut$expCov # Save the ending state of modelOut in a checkpoint file mxSave(modelOut) modelRestored <- mxRestore(testModel) omxCheckCloseEnough(modelRestored$expCov$values, modelOut$expCov$values, 1e-5)	/inst/models/passing/mxSave.R	permissive	falkcarl/OpenMx	R	false	false	1,012	r	library(OpenMx) # Simulate some data sampleSize <- 250 x=rnorm(sampleSize, mean=0, sd=1) y= 0.5*x + rnorm(sampleSize, mean=0, sd=1) tmpFrame <- data.frame(x, y) tmpNames <- names(tmpFrame) # Create a model that includes an expected covariance matrix, # an expectation function, a fit function, and an observed covariance matrix data <- mxData(cov(tmpFrame), type="cov", numObs = sampleSize) expCov <- mxMatrix(type="Symm", nrow=2, ncol=2, values=c(.2,.1,.2), free=TRUE, name="expCov") expFunction <- mxExpectationNormal(covariance="expCov", dimnames=tmpNames) fitFunction <- mxFitFunctionML() testModel <- mxModel(model="testModel", expCov, data, expFunction, fitFunction) #Use mxRun to optimize the free parameters in the expected covariance matrix modelOut <- mxRun(testModel, checkpoint = TRUE) modelOut$expCov # Save the ending state of modelOut in a checkpoint file mxSave(modelOut) modelRestored <- mxRestore(testModel) omxCheckCloseEnough(modelRestored$expCov$values, modelOut$expCov$values, 1e-5)
context("comparing get_kgram_freqs() and get_kgram_freqs_fast()") test_that("coincidence on long char vector", { freqs <- get_kgram_freqs(twitter_train[1:10000], 3, twitter_dict) freqs_fast <- get_kgram_freqs_fast(twitter_train[1:10000], 3, twitter_dict) transform <- . %>% arrange(across(starts_with("w"))) freqs_attr_bckp <- attributes(freqs) freqs %<>% lapply(transform) attributes(freqs) <- freqs_attr_bckp freqs_fast_attr_bckp <- attributes(freqs_fast) freqs_fast %<>% lapply(transform) attributes(freqs_fast) <- freqs_fast_attr_bckp attr(freqs_fast, ".preprocess") <- attr(freqs, ".preprocess") expect_identical(freqs, freqs_fast) })	/tests/testthat/test-get_kgram_freqs_comparisons.R	no_license	minghao2016/sbo	R	false	false	758	r	context("comparing get_kgram_freqs() and get_kgram_freqs_fast()") test_that("coincidence on long char vector", { freqs <- get_kgram_freqs(twitter_train[1:10000], 3, twitter_dict) freqs_fast <- get_kgram_freqs_fast(twitter_train[1:10000], 3, twitter_dict) transform <- . %>% arrange(across(starts_with("w"))) freqs_attr_bckp <- attributes(freqs) freqs %<>% lapply(transform) attributes(freqs) <- freqs_attr_bckp freqs_fast_attr_bckp <- attributes(freqs_fast) freqs_fast %<>% lapply(transform) attributes(freqs_fast) <- freqs_fast_attr_bckp attr(freqs_fast, ".preprocess") <- attr(freqs, ".preprocess") expect_identical(freqs, freqs_fast) })
#' @title #' Create a New Game. #' #' @description #' `create_game()` randomly assigns a specified number of both goats and cars to the #' number #' #' @details #' This funtion creates a new game for the monty hall problem. There are three doors: #' two goats and one car. this simulation gives you the opportunity to test the #' probabilities! #' #' #' @param x Numeric vector #' #' @param values must be whole integers greater than zero #' #' @return #' Returns a length 3 charater vector showing the positions of the goats and the car #' #' @examples #' create_game() #' #' #' @export create_game <- function() { a.game <- sample( x=c("goat","goat","car"), size=3, replace=F ) return( a.game ) } #' @title #' Select Door. #' #' @description #' `select_door()` A door is randomly selected as the contestant choice. #' #' @details #' This function assignes a random door as the contestant's first choice #' of door before anything is revealed. The contestant can only choose between door 1, 2, or 3. #' #' @param x Numeric vector #' #' @return #' an integer that represents the number of the door that the participant selects. #' #' @examples #' select_door() #' #' @export select_door <- function( ) { doors <- c(1,2,3) a.pick <- sample( doors, size=1 ) return( a.pick ) # number between 1 and 3 } #' @title #' Open the Goat Door. #' #' @description #' `open_goat_door()` opens a door with a goat behind #' it and that is also not the same door the contestant #' chose. #' #' @details #' The opened door must have a goat behind it, #' meaning that the door must not have a car behind it. #' #' @param ... the arguments are `game` and `a.pick` #' #' @return #' This returns a number representing the number of #' the goat door that the host opened. #' #' @example #' open_goat_door(game = newGame, a.pick = firstDoor #' #' @export open_goat_door <- function( game, a.pick ) { doors <- c(1,2,3) # if contestant selected car, # randomly select one of two goats if( game[ a.pick ] == "car" ) { goat.doors <- doors[ game != "car" ] opened.door <- sample( goat.doors, size=1 ) } if( game[ a.pick ] == "goat" ) { opened.door <- doors[ game != "car" & doors != a.pick ] } return( opened.door ) # number between 1 and 3 } #' @title #' Changing Door Function. #' #' @description #' `change_door()` the contestant is asked if they would like to change their #' selection to a different door or if they would like to keep their first choice. #' #' @details #' a door is generated that represents the door that the #' contestant chose, whether they switched or stayed. #' #' @param ... If the argument is `TRUE` it means that the contestant stayed #' and `FALSE` means that the contestant switched doors. #' #' @return #' This function returns a number representing the number of the final door choice for the contestant. #' #' @examples #' change_door(stay = F, opened.door = 2, a.pick = 1) #' #' @export change_door <- function( stay=T, opened.door, a.pick ) { doors <- c(1,2,3) if( stay ) { final.pick <- a.pick } if( ! stay ) { final.pick <- doors[ doors != opened.door & doors != a.pick ] } return( final.pick ) # number between 1 and 3 } #' @title #' Determine Winner. #' #' @description #' `determine_winner()` returns the item behind the final door chosen, and assigns #' choosing the car as winning, and choosing the goat as losing. #' #' @details #' Provides final door and determines whether they won or lost. #' #' @param ... `final.pick` and `game` are the arguments #' #' @return #' the function returns a "WIN" or "LOSE" based on whether or not the game was won. #' #' @examples #' determine_winner(final.pick = finalDoor, game = newGame) #' #' @export determine_winner <- function( final.pick, game ) { if( game[ final.pick ] == "car" ) { return( "WIN" ) } if( game[ final.pick ] == "goat" ) { return( "LOSE" ) } } #' @title #' Play the Game #' #' @description #' `play_game()` combines the five functions above into this one function. #' #' @details #' This function lists the corresponding outcomes for each strategy, which will vary each time. #' #' @param ... No parameters #' #' @return #' This returns a dataframe with columns `strategy` and `outcome` #' strategy can be either stay or switch #' outcome can be either win or lose #' #' @examples #' play_game() #' #' @export play_game <- function( ) { new.game <- create_game() first.pick <- select_door() opened.door <- open_goat_door( new.game, first.pick ) final.pick.stay <- change_door( stay=T, opened.door, first.pick ) final.pick.switch <- change_door( stay=F, opened.door, first.pick ) outcome.stay <- determine_winner( final.pick.stay, new.game ) outcome.switch <- determine_winner( final.pick.switch, new.game ) strategy <- c("stay","switch") outcome <- c(outcome.stay,outcome.switch) game.results <- data.frame( strategy, outcome, stringsAsFactors=F ) return( game.results )} #' @title #' Play the Game n times (by default, n=1000). #' #' @description #' creates the game structure on a loop to examine the outcomes #' of running the game a multitude of times. Gives probability by #' playing the game as many times as needed using `play_n_games()`. #' #' @details #' The default for the game is 1000 times, but it can be played n time #' Corresponding outcomes are listed for each game. #' #' @param ... "n", or the number of times to run the loop #' #' @return #' This function creates a dataframe that combines the outcomes of total games and lists then out of 1 #' #' @examples #' play_n_games(n=1000) #' #' @export play_n_games <- function( n=1000 ) { library( dplyr ) results.list <- list() # collector loop.count <- 1 for( i in 1:n ) # iterator { game.outcome <- play_game() results.list[[ loop.count ]] <- game.outcome loop.count <- loop.count + 1 } results.df <- dplyr::bind_rows( results.list ) table( results.df ) %>% prop.table( margin=1 ) %>% # row proportions round( 2 ) %>% print() return( results.df ) }	/R/monty-hall-problem.R	no_license	brittanyharb/montyhall	R	false	false	6,207	r	#' @title #' Create a New Game. #' #' @description #' `create_game()` randomly assigns a specified number of both goats and cars to the #' number #' #' @details #' This funtion creates a new game for the monty hall problem. There are three doors: #' two goats and one car. this simulation gives you the opportunity to test the #' probabilities! #' #' #' @param x Numeric vector #' #' @param values must be whole integers greater than zero #' #' @return #' Returns a length 3 charater vector showing the positions of the goats and the car #' #' @examples #' create_game() #' #' #' @export create_game <- function() { a.game <- sample( x=c("goat","goat","car"), size=3, replace=F ) return( a.game ) } #' @title #' Select Door. #' #' @description #' `select_door()` A door is randomly selected as the contestant choice. #' #' @details #' This function assignes a random door as the contestant's first choice #' of door before anything is revealed. The contestant can only choose between door 1, 2, or 3. #' #' @param x Numeric vector #' #' @return #' an integer that represents the number of the door that the participant selects. #' #' @examples #' select_door() #' #' @export select_door <- function( ) { doors <- c(1,2,3) a.pick <- sample( doors, size=1 ) return( a.pick ) # number between 1 and 3 } #' @title #' Open the Goat Door. #' #' @description #' `open_goat_door()` opens a door with a goat behind #' it and that is also not the same door the contestant #' chose. #' #' @details #' The opened door must have a goat behind it, #' meaning that the door must not have a car behind it. #' #' @param ... the arguments are `game` and `a.pick` #' #' @return #' This returns a number representing the number of #' the goat door that the host opened. #' #' @example #' open_goat_door(game = newGame, a.pick = firstDoor #' #' @export open_goat_door <- function( game, a.pick ) { doors <- c(1,2,3) # if contestant selected car, # randomly select one of two goats if( game[ a.pick ] == "car" ) { goat.doors <- doors[ game != "car" ] opened.door <- sample( goat.doors, size=1 ) } if( game[ a.pick ] == "goat" ) { opened.door <- doors[ game != "car" & doors != a.pick ] } return( opened.door ) # number between 1 and 3 } #' @title #' Changing Door Function. #' #' @description #' `change_door()` the contestant is asked if they would like to change their #' selection to a different door or if they would like to keep their first choice. #' #' @details #' a door is generated that represents the door that the #' contestant chose, whether they switched or stayed. #' #' @param ... If the argument is `TRUE` it means that the contestant stayed #' and `FALSE` means that the contestant switched doors. #' #' @return #' This function returns a number representing the number of the final door choice for the contestant. #' #' @examples #' change_door(stay = F, opened.door = 2, a.pick = 1) #' #' @export change_door <- function( stay=T, opened.door, a.pick ) { doors <- c(1,2,3) if( stay ) { final.pick <- a.pick } if( ! stay ) { final.pick <- doors[ doors != opened.door & doors != a.pick ] } return( final.pick ) # number between 1 and 3 } #' @title #' Determine Winner. #' #' @description #' `determine_winner()` returns the item behind the final door chosen, and assigns #' choosing the car as winning, and choosing the goat as losing. #' #' @details #' Provides final door and determines whether they won or lost. #' #' @param ... `final.pick` and `game` are the arguments #' #' @return #' the function returns a "WIN" or "LOSE" based on whether or not the game was won. #' #' @examples #' determine_winner(final.pick = finalDoor, game = newGame) #' #' @export determine_winner <- function( final.pick, game ) { if( game[ final.pick ] == "car" ) { return( "WIN" ) } if( game[ final.pick ] == "goat" ) { return( "LOSE" ) } } #' @title #' Play the Game #' #' @description #' `play_game()` combines the five functions above into this one function. #' #' @details #' This function lists the corresponding outcomes for each strategy, which will vary each time. #' #' @param ... No parameters #' #' @return #' This returns a dataframe with columns `strategy` and `outcome` #' strategy can be either stay or switch #' outcome can be either win or lose #' #' @examples #' play_game() #' #' @export play_game <- function( ) { new.game <- create_game() first.pick <- select_door() opened.door <- open_goat_door( new.game, first.pick ) final.pick.stay <- change_door( stay=T, opened.door, first.pick ) final.pick.switch <- change_door( stay=F, opened.door, first.pick ) outcome.stay <- determine_winner( final.pick.stay, new.game ) outcome.switch <- determine_winner( final.pick.switch, new.game ) strategy <- c("stay","switch") outcome <- c(outcome.stay,outcome.switch) game.results <- data.frame( strategy, outcome, stringsAsFactors=F ) return( game.results )} #' @title #' Play the Game n times (by default, n=1000). #' #' @description #' creates the game structure on a loop to examine the outcomes #' of running the game a multitude of times. Gives probability by #' playing the game as many times as needed using `play_n_games()`. #' #' @details #' The default for the game is 1000 times, but it can be played n time #' Corresponding outcomes are listed for each game. #' #' @param ... "n", or the number of times to run the loop #' #' @return #' This function creates a dataframe that combines the outcomes of total games and lists then out of 1 #' #' @examples #' play_n_games(n=1000) #' #' @export play_n_games <- function( n=1000 ) { library( dplyr ) results.list <- list() # collector loop.count <- 1 for( i in 1:n ) # iterator { game.outcome <- play_game() results.list[[ loop.count ]] <- game.outcome loop.count <- loop.count + 1 } results.df <- dplyr::bind_rows( results.list ) table( results.df ) %>% prop.table( margin=1 ) %>% # row proportions round( 2 ) %>% print() return( results.df ) }
\name{recreate.olddata} \alias{recreate.olddata} \title{Convert data stored in msm object to old format} \description{ Converts the \code{data} element of msm objects to the old format. } \usage{ recreate.olddata(x) } \arguments{ \item{x}{Object returned by the \code{\link{msm}} function, representing a fitted multi-state model.} } \value{ A list of vectors and matrices in the undocumented ad-hoc format used for the \code{data} component of \code{msm} objects in \pkg{msm} versions 1.3.1 and earlier. } \details{ This is just provided for convenience and to illustrate the changes. It is not guaranteed to be complete, and is liable to be withdrawn. Users who were relying on the previous undocumented format are advised to upgrade their code to use the new format, which uses model frames and model design matrices in the standard format used in version 1.4, based on \code{\link{model.frame}} and \code{\link{model.matrix}}. }	/man/recreate.olddata.Rd	no_license	cran/msm	R	false	false	963	rd	\name{recreate.olddata} \alias{recreate.olddata} \title{Convert data stored in msm object to old format} \description{ Converts the \code{data} element of msm objects to the old format. } \usage{ recreate.olddata(x) } \arguments{ \item{x}{Object returned by the \code{\link{msm}} function, representing a fitted multi-state model.} } \value{ A list of vectors and matrices in the undocumented ad-hoc format used for the \code{data} component of \code{msm} objects in \pkg{msm} versions 1.3.1 and earlier. } \details{ This is just provided for convenience and to illustrate the changes. It is not guaranteed to be complete, and is liable to be withdrawn. Users who were relying on the previous undocumented format are advised to upgrade their code to use the new format, which uses model frames and model design matrices in the standard format used in version 1.4, based on \code{\link{model.frame}} and \code{\link{model.matrix}}. }
#' Correction Coefficient of Travel Speed in 2+1 Lane Road #' #' It correct travel speed in 2+1 lane road. #' This function follows <Table 7-15> in KHCM(2013), p.188. #' @param v Traffic volume in 2+1 lane road(pcphpl). #' @keywords 2+1 lane road correction coefficient TDR total delay ratio #' @export fs_pl_2lp1 Correction coefficient of travel speed in 2+1 lane road Section(f_pl) #' @examples #' fs_pl_2lp1(v = 1391) #' fs_pl_2lp1(999) fs_pl_2lp1 <- function(v = NULL){ if (v > 0 & v <= 100){f <- 1.025} else if (v > 100 & v <= 200){f <- 1.034} else if (v > 200 & v <= 300){f <- 1.042} else if (v > 300 & v <= 400){f <- 1.050} else if (v > 400 & v <= 500){f <- 1.057} else if (v > 500 & v <= 600){f <- 1.063} else if (v > 600 & v <= 700){f <- 1.069} else if (v > 700 & v <= 800){f <- 1.074} else if (v > 800 & v <= 900){f <- 1.078} else if (v > 900 & v <= 1000){f <- 1.081} else if (v > 1000 & v <= 1100){f <- 1.084} else if (v > 1100 & v <= 1200){f <- 1.086} else if (v > 1200 & v <= 1300){f <- 1.088} else if (v > 1300 & v <= 1400){f <- 1.089} else if (v > 1400 & v <= 1500){f <- 1.089} else if (v > 1500 & v <= 1600){f <- 1.088} else if (v > 1600 & v <= 1700){f <- 1.087} else {f <- 'Error : [v] must be >= 0 and <= 1700(pcphpl). Please check that.'} f }	/R/fs_pl_2lp1.R	no_license	regenesis90/KHCMinR	R	false	false	1,304	r	#' Correction Coefficient of Travel Speed in 2+1 Lane Road #' #' It correct travel speed in 2+1 lane road. #' This function follows <Table 7-15> in KHCM(2013), p.188. #' @param v Traffic volume in 2+1 lane road(pcphpl). #' @keywords 2+1 lane road correction coefficient TDR total delay ratio #' @export fs_pl_2lp1 Correction coefficient of travel speed in 2+1 lane road Section(f_pl) #' @examples #' fs_pl_2lp1(v = 1391) #' fs_pl_2lp1(999) fs_pl_2lp1 <- function(v = NULL){ if (v > 0 & v <= 100){f <- 1.025} else if (v > 100 & v <= 200){f <- 1.034} else if (v > 200 & v <= 300){f <- 1.042} else if (v > 300 & v <= 400){f <- 1.050} else if (v > 400 & v <= 500){f <- 1.057} else if (v > 500 & v <= 600){f <- 1.063} else if (v > 600 & v <= 700){f <- 1.069} else if (v > 700 & v <= 800){f <- 1.074} else if (v > 800 & v <= 900){f <- 1.078} else if (v > 900 & v <= 1000){f <- 1.081} else if (v > 1000 & v <= 1100){f <- 1.084} else if (v > 1100 & v <= 1200){f <- 1.086} else if (v > 1200 & v <= 1300){f <- 1.088} else if (v > 1300 & v <= 1400){f <- 1.089} else if (v > 1400 & v <= 1500){f <- 1.089} else if (v > 1500 & v <= 1600){f <- 1.088} else if (v > 1600 & v <= 1700){f <- 1.087} else {f <- 'Error : [v] must be >= 0 and <= 1700(pcphpl). Please check that.'} f }
# discretises a list of vectors # for each column we get a list of (discretised and breaks) discretise <- function( inp=NULL, # an input list with cols 'A', 'B' etc. levels=5, # number of discrete levels method='mltools' ){ whoami=paste0(match.call()[[1]],'()',collapse='') cnames = names(inp) ncnames = length(cnames) ret=vector("list", ncnames) names(ret) <- cnames for(acolname in cnames){ ret[[acolname]] = discretise_vector( x=inp[[acolname]], levels=levels, method=method ) } return(ret) } # discretises a single vector # returned is a list of (discretised and breaks) discretise_vector <- function( x=NULL, # an input vector levels=5, # number of discrete levels method='mltools' ){ whoami=paste0(match.call()[[1]],'()',collapse='') if( method == 'arules' ){ library(arules) thecuts=arules::discretize( x=x, method="frequency", categories=levels, onlycuts = T ) if( is.null(thecuts) ){ cat(whoami, " : call to arules::discretize() has failed.\n") return(NULL) } } else if( method == 'mltools' ){ library(mltools) library(stringr) dummy <- mltools::bin_data( x=x, bins=levels, binType='quantile', returnDT=TRUE ) if( is.null(dummy) ){ cat(whoami, " : call to mltools::bin_data() has failed.\n") return(NULL) } badR = levels(dummy$Bin) # these are text levels!!!! thecuts=c() for(alevel in badR){ regres <- str_match(alevel,"[\\[\\(](.?),\\s(.*?)[\\]\\)]$") thecuts=append(thecuts, as.numeric(regres[2])) } thecuts=append(thecuts, as.numeric(regres[3])) } else { cat(whoami, " : method '", method, "' is not recognised.\n") return(NULL) } # extract unique breaks! unique_cuts = c() for(aval in thecuts){ if( ! (aval %in% unique_cuts) ){ unique_cuts=append(unique_cuts, aval) } } # NA's are introduced at the boundaries so extends a bit the breaks unique_cuts[1] = unique_cuts[1]-0.1 unique_cuts[length(unique_cuts)] = unique_cuts[length(unique_cuts)]+0.1 discre_data <- as.numeric(cut(x, breaks=unique_cuts)) ret=list() ret[['discretised']] = discre_data ret[['breaks']] = unique_cuts ret[['frequencies']] = table(cut(x,unique_cuts, include.lowest=TRUE)) return(ret) } histo <- function( inp=NULL, # input list unique_vals=NULL # optional unique_vals list ){ whoami=paste0(match.call()[[1]],'()',collapse='') cnames = names(inp) ncnames = length(cnames) ret=vector("list", ncnames) names(ret) <- cnames for(acolname in cnames){ ret[[acolname]] = histo_vector( x=inp[[acolname]], breaks=unique_vals[['histograms']][[acolname]]$breaks ) } return(ret) } histo_vector <- function( x=NULL, # input vector breaks=NULL # optional breaks ){ whoami=paste0(match.call()[[1]],'()',collapse='') ahist = NULL if( is.null(breaks) ){ ahist <- hist(x=x, plot=F) } else { ahist <- hist(x=x,breaks=breaks, plot=F) } ncounts = length(ahist$counts) ret=matrix(ncol=2,nrow=ncounts) colnames(ret) <- c('value', 'count') for(i in 1:ncounts){ ret[i, 'value'] = ahist$breaks[i]+(ahist$breaks[i+1]-ahist$breaks[i])/2 ret[i, 'count'] = ahist$counts[i] } return(ret) } unique_values <- function( inp=NULL ){ whoami=paste0(match.call()[[1]],'()',collapse='') cnames = names(inp) ncnames = length(cnames) unix=vector("list", ncnames) names(unix) <- cnames num_unix=matrix(nrow=1,ncol=ncnames) histos=vector("list", ncnames) colnames(num_unix) <- cnames for(acolname in cnames){ unix[[acolname]] = unique(inp[[acolname]]) num_unix[,acolname] = length(unix[[acolname]]) histos[[acolname]] = hist(inp[[acolname]], breaks=unix[[acolname]], plot=F) } ret=list() ret[['unique_values']] = unix ret[['num_unique_values']] = num_unix ret[['histograms']] = histos return(ret) } unique_values_vector <- function( x=NULL ){ whoami=paste0(match.call()[[1]],'()',collapse='') ret=list() ret[['unique_values']] = unique(x) ret[['num_unique_values']] = length(ret[['unique_values']]) ret[['histograms']] = hist(x=x, breaks=ret[['unique_values']], plot=F) return(ret) }	/lib/DATA.R	no_license	hadjiprocopis/DUTC	R	false	false	4,032	r	# discretises a list of vectors # for each column we get a list of (discretised and breaks) discretise <- function( inp=NULL, # an input list with cols 'A', 'B' etc. levels=5, # number of discrete levels method='mltools' ){ whoami=paste0(match.call()[[1]],'()',collapse='') cnames = names(inp) ncnames = length(cnames) ret=vector("list", ncnames) names(ret) <- cnames for(acolname in cnames){ ret[[acolname]] = discretise_vector( x=inp[[acolname]], levels=levels, method=method ) } return(ret) } # discretises a single vector # returned is a list of (discretised and breaks) discretise_vector <- function( x=NULL, # an input vector levels=5, # number of discrete levels method='mltools' ){ whoami=paste0(match.call()[[1]],'()',collapse='') if( method == 'arules' ){ library(arules) thecuts=arules::discretize( x=x, method="frequency", categories=levels, onlycuts = T ) if( is.null(thecuts) ){ cat(whoami, " : call to arules::discretize() has failed.\n") return(NULL) } } else if( method == 'mltools' ){ library(mltools) library(stringr) dummy <- mltools::bin_data( x=x, bins=levels, binType='quantile', returnDT=TRUE ) if( is.null(dummy) ){ cat(whoami, " : call to mltools::bin_data() has failed.\n") return(NULL) } badR = levels(dummy$Bin) # these are text levels!!!! thecuts=c() for(alevel in badR){ regres <- str_match(alevel,"[\\[\\(](.?),\\s(.*?)[\\]\\)]$") thecuts=append(thecuts, as.numeric(regres[2])) } thecuts=append(thecuts, as.numeric(regres[3])) } else { cat(whoami, " : method '", method, "' is not recognised.\n") return(NULL) } # extract unique breaks! unique_cuts = c() for(aval in thecuts){ if( ! (aval %in% unique_cuts) ){ unique_cuts=append(unique_cuts, aval) } } # NA's are introduced at the boundaries so extends a bit the breaks unique_cuts[1] = unique_cuts[1]-0.1 unique_cuts[length(unique_cuts)] = unique_cuts[length(unique_cuts)]+0.1 discre_data <- as.numeric(cut(x, breaks=unique_cuts)) ret=list() ret[['discretised']] = discre_data ret[['breaks']] = unique_cuts ret[['frequencies']] = table(cut(x,unique_cuts, include.lowest=TRUE)) return(ret) } histo <- function( inp=NULL, # input list unique_vals=NULL # optional unique_vals list ){ whoami=paste0(match.call()[[1]],'()',collapse='') cnames = names(inp) ncnames = length(cnames) ret=vector("list", ncnames) names(ret) <- cnames for(acolname in cnames){ ret[[acolname]] = histo_vector( x=inp[[acolname]], breaks=unique_vals[['histograms']][[acolname]]$breaks ) } return(ret) } histo_vector <- function( x=NULL, # input vector breaks=NULL # optional breaks ){ whoami=paste0(match.call()[[1]],'()',collapse='') ahist = NULL if( is.null(breaks) ){ ahist <- hist(x=x, plot=F) } else { ahist <- hist(x=x,breaks=breaks, plot=F) } ncounts = length(ahist$counts) ret=matrix(ncol=2,nrow=ncounts) colnames(ret) <- c('value', 'count') for(i in 1:ncounts){ ret[i, 'value'] = ahist$breaks[i]+(ahist$breaks[i+1]-ahist$breaks[i])/2 ret[i, 'count'] = ahist$counts[i] } return(ret) } unique_values <- function( inp=NULL ){ whoami=paste0(match.call()[[1]],'()',collapse='') cnames = names(inp) ncnames = length(cnames) unix=vector("list", ncnames) names(unix) <- cnames num_unix=matrix(nrow=1,ncol=ncnames) histos=vector("list", ncnames) colnames(num_unix) <- cnames for(acolname in cnames){ unix[[acolname]] = unique(inp[[acolname]]) num_unix[,acolname] = length(unix[[acolname]]) histos[[acolname]] = hist(inp[[acolname]], breaks=unix[[acolname]], plot=F) } ret=list() ret[['unique_values']] = unix ret[['num_unique_values']] = num_unix ret[['histograms']] = histos return(ret) } unique_values_vector <- function( x=NULL ){ whoami=paste0(match.call()[[1]],'()',collapse='') ret=list() ret[['unique_values']] = unique(x) ret[['num_unique_values']] = length(ret[['unique_values']]) ret[['histograms']] = hist(x=x, breaks=ret[['unique_values']], plot=F) return(ret) }
% Generated by roxygen2: do not edit by hand % Please edit documentation in R/Collection of Functions.R \name{B_T_with_Qua_u_fhome} \alias{B_T_with_Qua_u_fhome} \title{Bradely-Terry Model Weighted Likelihood function with fixed home parameter, Lagrangian and Quadratic Penalty} \usage{ B_T_with_Qua_u_fhome(df, ability, i0, theta, n, v, uij, u, home) } \description{ Bradely-Terry Model Weighted Likelihood function with fixed home parameter, Lagrangian and Quadratic Penalty }	/man/B_T_with_Qua_u_fhome.Rd	no_license	heilokchow/MWLE-Lasso	R	false	true	478	rd	% Generated by roxygen2: do not edit by hand % Please edit documentation in R/Collection of Functions.R \name{B_T_with_Qua_u_fhome} \alias{B_T_with_Qua_u_fhome} \title{Bradely-Terry Model Weighted Likelihood function with fixed home parameter, Lagrangian and Quadratic Penalty} \usage{ B_T_with_Qua_u_fhome(df, ability, i0, theta, n, v, uij, u, home) } \description{ Bradely-Terry Model Weighted Likelihood function with fixed home parameter, Lagrangian and Quadratic Penalty }
############################################################## ### Simulating Moris'chance of beating the record of Babe Ruth ##############################################################	/codeSnippets/record-breaker-Ruth.R	no_license	Amitabh-G/rUtility	R	false	false	188	r	############################################################## ### Simulating Moris'chance of beating the record of Babe Ruth ##############################################################
# Computes the standard normal quantile function of the vector x, # 0<x<1. Phiinv <- function(x) { val <- sqrt(2)erfinv(2x-1) return(val) }	/Témata/09 Tobit model/R/Support/Phiinv.R	no_license	JanMelicharik/baan_python	R	false	false	158	r	# Computes the standard normal quantile function of the vector x, # 0<x<1. Phiinv <- function(x) { val <- sqrt(2)erfinv(2x-1) return(val) }
\name{apprPower} \alias{apprPower} \title{ Approximate power (any rejection!) for many-to-one comparison of binomial proportions } \description{ Approximative power to reject the hypothesis that all of the k differences of proportions of treatment groups vs. control group are zero, i.e.: probability to reject any H0[i]: p[i]-p[0] = 0, For a given setting of n[i], and p[i] assumed under the alternative. } \usage{ apprPower(n, pH1, alpha = 0.05, alternative = "greater", method = "Add4") } \arguments{ \item{n}{ vector of integers specifying the number of observations in each group, where the first value is taken as sample size of control group} \item{pH1}{ numeric vector with values between 0 and 1, specifying the proportions of success under the alternative hypothesis, should have the same length as n } \item{alpha}{ pre-specified type-I-error } \item{alternative}{ character string defining the alternative hypothesis, take care, that it fits to the parameters settings specified in pH1 } \item{method}{ character sring defining the confidence interval method to be used, one of "Add4", "Add2", "Wald" } } \details{ This function uses approximative calculation of any-pair-power of a maximum test as described in Bretz and Hothorn (2002) for a Wald test of multiple contrasts of binary data. Differing from Bretz and Hothorn (2002), unpooled variance estimators are used in the present function. In case of "Add4" and "Add2"-method, the Wald expectation and variance are replaced by that of add-4 and add-2. Since the approximate calculation assumes normality, this function can give misleading results, if sample size is small and/or proportions of success are extreme. The present function only calcualtes power for the test adjusting via the multivariate-normal-distribution. For Bonferroni-adjusted or unadjusted tests, one can make use of well-known formulas for power and sample size for binary data. The use of the function simPower in this package will result in power estimation closer to the true performance of the methods but is less convenient. } \value{ a single numeric value: the approximate any-pair power } \references{Bretz,F and Hothorn, LA (2002): Detecting dose-response using contrasts: asymptotic power and sample size determination for binomial data. Statistics in Medicine 21, 3325-3335. } \author{ Frank Schaarschmidt } \note{ The results of this functions are roughly checked by comparison with results of power simualtion, which indicate that the approximations are reasonable for at least moderate n and not too extreme proportions. The performance of a corresponding test using the add-4 or add-2 adjustment is not described. } \seealso{ simPower } \examples{ # Recalculate the power of the Dunnett-contrast # for the first setting in Bretz and Hothorn (2002, Table III), # using a balanced design and the allocation rule n0/ni=sqrt(k) # of Dunnett(1955), desiring a power of 80 percent. # Note that differing from Bretz and Hothorn (2002) # in the present function unpooled variance estimators # are used, what might lead to different results. apprPower(n=c(196, 196, 196, 196, 196), pH1=c(0.45, 0.45, 0.5, 0.5, 0.6), alpha=0.05, alternative="greater", method="Wald") apprPower(n=c(294, 147, 147, 147, 147 ), pH1=c(0.45, 0.45, 0.5, 0.5, 0.6), alpha=0.05, alternative="greater", method="Wald") } \keyword{ htest }	/man/apprPower.rd	no_license	cran/binMto	R	false	false	3,462	rd	\name{apprPower} \alias{apprPower} \title{ Approximate power (any rejection!) for many-to-one comparison of binomial proportions } \description{ Approximative power to reject the hypothesis that all of the k differences of proportions of treatment groups vs. control group are zero, i.e.: probability to reject any H0[i]: p[i]-p[0] = 0, For a given setting of n[i], and p[i] assumed under the alternative. } \usage{ apprPower(n, pH1, alpha = 0.05, alternative = "greater", method = "Add4") } \arguments{ \item{n}{ vector of integers specifying the number of observations in each group, where the first value is taken as sample size of control group} \item{pH1}{ numeric vector with values between 0 and 1, specifying the proportions of success under the alternative hypothesis, should have the same length as n } \item{alpha}{ pre-specified type-I-error } \item{alternative}{ character string defining the alternative hypothesis, take care, that it fits to the parameters settings specified in pH1 } \item{method}{ character sring defining the confidence interval method to be used, one of "Add4", "Add2", "Wald" } } \details{ This function uses approximative calculation of any-pair-power of a maximum test as described in Bretz and Hothorn (2002) for a Wald test of multiple contrasts of binary data. Differing from Bretz and Hothorn (2002), unpooled variance estimators are used in the present function. In case of "Add4" and "Add2"-method, the Wald expectation and variance are replaced by that of add-4 and add-2. Since the approximate calculation assumes normality, this function can give misleading results, if sample size is small and/or proportions of success are extreme. The present function only calcualtes power for the test adjusting via the multivariate-normal-distribution. For Bonferroni-adjusted or unadjusted tests, one can make use of well-known formulas for power and sample size for binary data. The use of the function simPower in this package will result in power estimation closer to the true performance of the methods but is less convenient. } \value{ a single numeric value: the approximate any-pair power } \references{Bretz,F and Hothorn, LA (2002): Detecting dose-response using contrasts: asymptotic power and sample size determination for binomial data. Statistics in Medicine 21, 3325-3335. } \author{ Frank Schaarschmidt } \note{ The results of this functions are roughly checked by comparison with results of power simualtion, which indicate that the approximations are reasonable for at least moderate n and not too extreme proportions. The performance of a corresponding test using the add-4 or add-2 adjustment is not described. } \seealso{ simPower } \examples{ # Recalculate the power of the Dunnett-contrast # for the first setting in Bretz and Hothorn (2002, Table III), # using a balanced design and the allocation rule n0/ni=sqrt(k) # of Dunnett(1955), desiring a power of 80 percent. # Note that differing from Bretz and Hothorn (2002) # in the present function unpooled variance estimators # are used, what might lead to different results. apprPower(n=c(196, 196, 196, 196, 196), pH1=c(0.45, 0.45, 0.5, 0.5, 0.6), alpha=0.05, alternative="greater", method="Wald") apprPower(n=c(294, 147, 147, 147, 147 ), pH1=c(0.45, 0.45, 0.5, 0.5, 0.6), alpha=0.05, alternative="greater", method="Wald") } \keyword{ htest }
testlist <- list(A = structure(c(2.31584307392677e+77, 9.53818252170339e+295, 4.94661240579261e+173, 4.12396251261199e-221, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(5L, 7L)), B = structure(0, .Dim = c(1L, 1L))) result <- do.call(multivariance:::match_rows,testlist) str(result)	/multivariance/inst/testfiles/match_rows/AFL_match_rows/match_rows_valgrind_files/1613102680-test.R	no_license	akhikolla/updatedatatype-list3	R	false	false	343	r	testlist <- list(A = structure(c(2.31584307392677e+77, 9.53818252170339e+295, 4.94661240579261e+173, 4.12396251261199e-221, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(5L, 7L)), B = structure(0, .Dim = c(1L, 1L))) result <- do.call(multivariance:::match_rows,testlist) str(result)
ResizeEtcDialog <- function() { initializeDialog(title=gettextRcmdr("Resize Panels")) resizeFrame <- tkframe(top) c.listVar <- tclVar("") condlevelsNameVar <- tclVar("") x.sameVar <- tclVar("") y.sameVar <- tclVar("") layoutVar <- tclVar("") strip.valuesVar <- tclVar("") strip.left.valuesVar <- tclVar("") strip.parVar <- tclVar("") strip.left.parVar <- tclVar("") resize.heightVar <- tclVar("") resize.widthVar <- tclVar("") mainVar <- tclVar("") mainMiddleVar <- tclVar("") c.listEntry <- tkentry(resizeFrame, width="48", textvariable=c.listVar) condlevelsNameEntry <- tkentry(resizeFrame, width="48", textvariable=condlevelsNameVar) x.sameEntry <- tkentry(resizeFrame, width="48", textvariable=x.sameVar) y.sameEntry <- tkentry(resizeFrame, width="48", textvariable=y.sameVar) layoutEntry <- tkentry(resizeFrame, width="48", textvariable=layoutVar) strip.valuesEntry <- tkentry(resizeFrame, width="48", textvariable=strip.valuesVar) strip.left.valuesEntry <- tkentry(resizeFrame, width="48", textvariable=strip.left.valuesVar) strip.parEntry <- tkentry(resizeFrame, width="48", textvariable=strip.parVar) strip.left.parEntry <- tkentry(resizeFrame, width="48", textvariable=strip.left.parVar) resize.heightEntry <- tkentry(resizeFrame, width="48", textvariable=resize.heightVar) resize.widthEntry <- tkentry(resizeFrame, width="48", textvariable=resize.widthVar) mainEntry <- tkentry(resizeFrame, width="48", textvariable=mainVar) mainMiddleEntry <- tkentry(resizeFrame, width="48", textvariable=mainMiddleVar) onOK <- function() { #on.exit(recover()) c.listValue <- tclvalue(c.listVar) condlevelsNameValue <- tclvalue(condlevelsNameVar) x.sameValue <- tclvalue(x.sameVar) y.sameValue <- tclvalue(y.sameVar) layoutValue <- tclvalue(layoutVar) strip.valuesValue <- tclvalue(strip.valuesVar) strip.left.valuesValue <- tclvalue(strip.left.valuesVar) strip.parValue <- tclvalue(strip.parVar) strip.left.parValue <- tclvalue(strip.left.parVar) resize.heightValue <- tclvalue(resize.heightVar) resize.widthValue <- tclvalue(resize.widthVar) mainValue <- tclvalue(mainVar) mainMiddleValue <- tclvalue(mainMiddleVar) closeDialog() if (nchar(c.listValue) == 0) { errorCondition(recall=ResizeEtcDialog, message=gettextRcmdr("c.list must be specified.")) return() } ##command.xmiddle <- "x.middle <- diff(current.panel.limits()$xlim)/2" ##doItAndPrint(command.xmiddle) if (nchar(mainMiddleValue)==0) mainMiddleValue <- ".5" command <- paste("ResizeEtc(", paste( "c.list=", c.listValue, sep=""), if (nchar(condlevelsNameValue ) != 0) paste(", condlevelsName='", condlevelsNameValue , "'", sep=""), if (nchar(x.sameValue ) != 0) paste(", x.same=", x.sameValue , sep=""), if (nchar(y.sameValue ) != 0) paste(", y.same=", y.sameValue , sep=""), if (nchar(layoutValue ) != 0) paste(", layout=", layoutValue , sep=""), if (nchar(strip.valuesValue ) != 0) paste(", strip.values=", strip.valuesValue , sep=""), if (nchar(strip.left.valuesValue) != 0) paste(", strip.left.values=", strip.left.valuesValue, sep=""), if (nchar(strip.parValue ) != 0) paste(", strip.par=", strip.parValue , sep=""), if (nchar(strip.left.parValue ) != 0) paste(", strip.left.par=", strip.left.parValue , sep=""), if (nchar(resize.heightValue ) != 0) paste(", resize.height=", resize.heightValue , sep=""), if (nchar(resize.widthValue ) != 0) paste(", resize.width=", resize.widthValue , sep=""), if (nchar(mainValue ) != 0) paste(", main='", mainValue , "'", sep=""), if (nchar(mainMiddleValue ) != 0) paste(", main.middle=", mainMiddleValue , sep=""), ")", sep="") doItAndPrint(command) activateMenus() tkfocus(CommanderWindow()) if (version$os == "mingw32") justDoIt("bringToTop()") } OKCancelHelp(helpSubject="ResizeEtcDialog") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("c.list:")), c.listEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("condlevelsName:")), condlevelsNameEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("x.same:")), x.sameEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("y.same:")), y.sameEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("layout:")), layoutEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("strip.values:")), strip.valuesEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("strip.left.values:")), strip.left.valuesEntry, sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("strip.par:")), strip.parEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("strip.left.par:")), strip.left.parEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("resize.height:")), resize.heightEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("resize.width:")), resize.widthEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("main:")), mainEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("mainMiddle:")), mainMiddleEntry , sticky="w") tkgrid(resizeFrame, sticky="w") tkgrid(buttonsFrame, columnspan=2, sticky="w") dialogSuffix(rows=13, columns=2) } listAllTrellisObjects <- function (envir = .GlobalEnv, ...) { objects <- ls(envir = envir, ...) if (length(objects) == 0) return(NULL) objects[sapply(objects, function(.x) { "trellis" %in% class(get(.x, envir = envir)) })] } ## source("c:/HOME/rmh/HH-R.package/RcmdrPlugin.HH2/R/ResizeEtcDialog.R")	/R/ResizeEtcDialog.R	no_license	cran/RcmdrPlugin.HH	R	false	false	6,893	r	ResizeEtcDialog <- function() { initializeDialog(title=gettextRcmdr("Resize Panels")) resizeFrame <- tkframe(top) c.listVar <- tclVar("") condlevelsNameVar <- tclVar("") x.sameVar <- tclVar("") y.sameVar <- tclVar("") layoutVar <- tclVar("") strip.valuesVar <- tclVar("") strip.left.valuesVar <- tclVar("") strip.parVar <- tclVar("") strip.left.parVar <- tclVar("") resize.heightVar <- tclVar("") resize.widthVar <- tclVar("") mainVar <- tclVar("") mainMiddleVar <- tclVar("") c.listEntry <- tkentry(resizeFrame, width="48", textvariable=c.listVar) condlevelsNameEntry <- tkentry(resizeFrame, width="48", textvariable=condlevelsNameVar) x.sameEntry <- tkentry(resizeFrame, width="48", textvariable=x.sameVar) y.sameEntry <- tkentry(resizeFrame, width="48", textvariable=y.sameVar) layoutEntry <- tkentry(resizeFrame, width="48", textvariable=layoutVar) strip.valuesEntry <- tkentry(resizeFrame, width="48", textvariable=strip.valuesVar) strip.left.valuesEntry <- tkentry(resizeFrame, width="48", textvariable=strip.left.valuesVar) strip.parEntry <- tkentry(resizeFrame, width="48", textvariable=strip.parVar) strip.left.parEntry <- tkentry(resizeFrame, width="48", textvariable=strip.left.parVar) resize.heightEntry <- tkentry(resizeFrame, width="48", textvariable=resize.heightVar) resize.widthEntry <- tkentry(resizeFrame, width="48", textvariable=resize.widthVar) mainEntry <- tkentry(resizeFrame, width="48", textvariable=mainVar) mainMiddleEntry <- tkentry(resizeFrame, width="48", textvariable=mainMiddleVar) onOK <- function() { #on.exit(recover()) c.listValue <- tclvalue(c.listVar) condlevelsNameValue <- tclvalue(condlevelsNameVar) x.sameValue <- tclvalue(x.sameVar) y.sameValue <- tclvalue(y.sameVar) layoutValue <- tclvalue(layoutVar) strip.valuesValue <- tclvalue(strip.valuesVar) strip.left.valuesValue <- tclvalue(strip.left.valuesVar) strip.parValue <- tclvalue(strip.parVar) strip.left.parValue <- tclvalue(strip.left.parVar) resize.heightValue <- tclvalue(resize.heightVar) resize.widthValue <- tclvalue(resize.widthVar) mainValue <- tclvalue(mainVar) mainMiddleValue <- tclvalue(mainMiddleVar) closeDialog() if (nchar(c.listValue) == 0) { errorCondition(recall=ResizeEtcDialog, message=gettextRcmdr("c.list must be specified.")) return() } ##command.xmiddle <- "x.middle <- diff(current.panel.limits()$xlim)/2" ##doItAndPrint(command.xmiddle) if (nchar(mainMiddleValue)==0) mainMiddleValue <- ".5" command <- paste("ResizeEtc(", paste( "c.list=", c.listValue, sep=""), if (nchar(condlevelsNameValue ) != 0) paste(", condlevelsName='", condlevelsNameValue , "'", sep=""), if (nchar(x.sameValue ) != 0) paste(", x.same=", x.sameValue , sep=""), if (nchar(y.sameValue ) != 0) paste(", y.same=", y.sameValue , sep=""), if (nchar(layoutValue ) != 0) paste(", layout=", layoutValue , sep=""), if (nchar(strip.valuesValue ) != 0) paste(", strip.values=", strip.valuesValue , sep=""), if (nchar(strip.left.valuesValue) != 0) paste(", strip.left.values=", strip.left.valuesValue, sep=""), if (nchar(strip.parValue ) != 0) paste(", strip.par=", strip.parValue , sep=""), if (nchar(strip.left.parValue ) != 0) paste(", strip.left.par=", strip.left.parValue , sep=""), if (nchar(resize.heightValue ) != 0) paste(", resize.height=", resize.heightValue , sep=""), if (nchar(resize.widthValue ) != 0) paste(", resize.width=", resize.widthValue , sep=""), if (nchar(mainValue ) != 0) paste(", main='", mainValue , "'", sep=""), if (nchar(mainMiddleValue ) != 0) paste(", main.middle=", mainMiddleValue , sep=""), ")", sep="") doItAndPrint(command) activateMenus() tkfocus(CommanderWindow()) if (version$os == "mingw32") justDoIt("bringToTop()") } OKCancelHelp(helpSubject="ResizeEtcDialog") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("c.list:")), c.listEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("condlevelsName:")), condlevelsNameEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("x.same:")), x.sameEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("y.same:")), y.sameEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("layout:")), layoutEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("strip.values:")), strip.valuesEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("strip.left.values:")), strip.left.valuesEntry, sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("strip.par:")), strip.parEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("strip.left.par:")), strip.left.parEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("resize.height:")), resize.heightEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("resize.width:")), resize.widthEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("main:")), mainEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("mainMiddle:")), mainMiddleEntry , sticky="w") tkgrid(resizeFrame, sticky="w") tkgrid(buttonsFrame, columnspan=2, sticky="w") dialogSuffix(rows=13, columns=2) } listAllTrellisObjects <- function (envir = .GlobalEnv, ...) { objects <- ls(envir = envir, ...) if (length(objects) == 0) return(NULL) objects[sapply(objects, function(.x) { "trellis" %in% class(get(.x, envir = envir)) })] } ## source("c:/HOME/rmh/HH-R.package/RcmdrPlugin.HH2/R/ResizeEtcDialog.R")
## These two functions cache the inverse of a matrix. ## Function 1 - makeCacheMatrix creates a special "matrix" object that can cache its inverse. makeCacheMatrix <- function(x = matrix()) { inv <- NULL set <- function(y) { x <<- y inv <<- NULL } get <- function() x setInverse <- function(solveMatrix) inv <<- solveMatrix getInverse <- function() inv list(set = set, get = get, setInverse = setInverse, getInverse = getInverse) } ## Function 2 - cacheSolve computes the inverse of the special "matrix" returned by makeCacheMatrix. If the inverse has already been calculated (and the matrix has not changed), then the cachesolve should retrieve the inverse from the cache. cacheSolve <- function(x, ...) { ## Return a matrix that is the inverse of 'x' inv <- x$getInverse() if(!is.null(inv)) { message("getting cached data") return(inv) } data <- x$get() inv <- solve(data, ...) x$setInverse(inv) inv }	/cachematrix.R	no_license	terryg40/ProgrammingAssignment2	R	false	false	985	r	## These two functions cache the inverse of a matrix. ## Function 1 - makeCacheMatrix creates a special "matrix" object that can cache its inverse. makeCacheMatrix <- function(x = matrix()) { inv <- NULL set <- function(y) { x <<- y inv <<- NULL } get <- function() x setInverse <- function(solveMatrix) inv <<- solveMatrix getInverse <- function() inv list(set = set, get = get, setInverse = setInverse, getInverse = getInverse) } ## Function 2 - cacheSolve computes the inverse of the special "matrix" returned by makeCacheMatrix. If the inverse has already been calculated (and the matrix has not changed), then the cachesolve should retrieve the inverse from the cache. cacheSolve <- function(x, ...) { ## Return a matrix that is the inverse of 'x' inv <- x$getInverse() if(!is.null(inv)) { message("getting cached data") return(inv) } data <- x$get() inv <- solve(data, ...) x$setInverse(inv) inv }
# Generic makeAinv <- function(pedigree, f = NULL, ggroups = NULL, fuzz = NULL, gOnTop = FALSE, det = FALSE, ...){ if(is(fuzz, "matrix") \| is(fuzz, "Matrix")) class(fuzz) <- "fuzzy" UseMethod("makeAinv", fuzz) } ############################################################################### # Methods: makeAinv.default <- function(pedigree, f = NULL, ggroups = NULL, fuzz = NULL, gOnTop = FALSE, det = FALSE, ...){ if(is.null(ggroups)){ ptype <- "O" renPed <- order(genAssign(pedigree), pedigree[, 2], pedigree[, 3], na.last = FALSE) nPed <- numPed(pedigree[renPed, ]) groupRows <- NULL nggroups <- 0 } else { if(length(ggroups) == dim(pedigree)[1]){ stop("length(ggroups) should either be:\n == 1 and a numeric indicating the number of genetic groups (type 'A')\n == length(unique(ggroups)) and a character vector indicating the names of each unique genetic goup (type 'D')") } if(!is.null(fuzz)) stop("fuzzy genetic groups not yet implemented: fuzz must be NULL") zerNA <- union(which(pedigree[, 2] == "0"), which(pedigree[, 3] == "0")) astNA <- union(which(pedigree[, 2] == ""), which(pedigree[, 3] == "")) naNA <- union(which(is.na(pedigree[, 2])), which(is.na(pedigree[, 3]))) naPed <- union(union(zerNA, astNA), naNA) #### pedigree type "D" #### if(!is.numeric(ggroups) && length(ggroups) == length(unique(ggroups))){ ptype <- "D" if(is.null(fuzz) && length(naPed) > 0){ stop("When supplying a vector of unique genetic group names in the 'ggroups' argument, all individuals in the pedigree must have a genetic group when a parent is unknown (<NA>, '0' and '' are considered unknown parents)")# or be identified as a phantom parent in 'fuzz'") } if(any(is.na(ggroups))) ggroups <- na.omit(ggroups) pedalt <- data.frame(id = c(ggroups, as.character(pedigree[, 1])), dam = c(rep(NA, length(ggroups)), as.character(pedigree[, 2])), sire = c(rep(NA, length(ggroups)), as.character(pedigree[, 3]))) #TODO: write method for numPed to handle genetic group pedigree ## same genetic group for dam and sire won't throw warning about selfing nPed <- suppressWarnings(numPed(pedalt)) # FIXME: remove suppressWarnings() throughout file ## return command below as attribute groupRows <- nPed[which(nPed[, 2] == -998), 1] } #### pedigree type "A" #### if(length(ggroups) == 1){ ptype <- "A" if(length(naPed) == ggroups){ nPed <- suppressWarnings(numPed(pedigree)) groupRows <- naPed } else { stop("Only rows identifying genetic groups should have missing parents.\n All individuals in the pedigree must have a genetic group when a parent is unknown")# or be identified as a phantom parent in 'fuzz'") } } nggroups <- length(groupRows) renPed <- order(suppressWarnings(genAssign(nPed)), nPed[, 2], nPed[, 3]) nPed <- numPed(ronPed(nPed, renPed)) } #### N <- nrow(nPed) eN <- N - nggroups dnmiss <- which(nPed[, 2] != -998) snmiss <- which(nPed[, 3] != -998) Tinv.row <- c(nPed[, 1][dnmiss], nPed[, 1][snmiss], 1:N) Tinv.col <- c(nPed[, 2][dnmiss], nPed[, 3][snmiss], 1:N) el.order <- order(Tinv.col + Tinv.row/(N + 1), decreasing = FALSE) if(is.null(ggroups)){ sTinv <- sparseMatrix(i = as.integer(Tinv.row[el.order] - 1), p = as.integer(c(match(1:N, Tinv.col[el.order]), length(el.order) + 1) - 1), index1 = FALSE, dims = c(N, N), symmetric = FALSE, dimnames = list(as.character(nPed[, 1]), NULL)) } else { sTinv <- sparseMatrix(i = as.integer(Tinv.row[el.order] - 1), p = as.integer(c(match(1:N, Tinv.col[el.order]), length(el.order) + 1) - 1), index1 = FALSE, dims = c(N, N), symmetric = FALSE, dimnames = list(as.character(nPed[, 1]), NULL))[-groupRows, ] } Ainv <- t(crossprod(sTinv)) # transpose gives lower triangle # 1: Adds Ainv elements in same for loop as calculation of f # 2: First checks to see if individual k has same dam and sire as k-1, if so then just assigns k-1's f # 3: simplifies the calculation of the addition to the Ainv element (instead of alphai 0.25 - defines alphai=alphai0.25). nPed[nPed == -998] <- N + 1 f <- c(rep(-1, nggroups), rep(0, eN), -1) Cout <- .C("ainvml", as.integer(nPed[, 2] - 1), #dam as.integer(nPed[, 3] - 1), #sire as.double(f), #f as.double(rep(0, N)), #dii as.integer(N), #n as.integer(nggroups), #g as.double(rep(0, length(Ainv@i))), #xA as.integer(Ainv@i), #iA as.integer(Ainv@p), #pA as.integer(length(Ainv@i))) #nzmaxA Ainv <- as(Ainv, "dsCMatrix") Ainv@x <- Cout[[7]] fsOrd <- as(as.integer(renPed), "pMatrix") Ainv <- as(t(fsOrd) %% Ainv %% fsOrd, "dgCMatrix") if(ptype == "D"){ Ainv@Dimnames <- list(as.character(pedalt[, 1]), NULL) f <- Cout[[3]][t(fsOrd)@perm][-seq(nggroups)] } else { Ainv@Dimnames <- list(as.character(pedigree[, 1]), NULL) f <- c(rep(0, nggroups), Cout[[3]][t(fsOrd)@perm][(nggroups+1):(nggroups + eN)]) } if(!is.null(ggroups) && !gOnTop){ permute <- as(as.integer(c(seq(eN+1, N, 1), seq(eN))), "pMatrix") Ainv <- t(permute) %% Ainv %% permute } if(det) logDet <- -1determinant(Ainv, logarithm = TRUE)$modulus[1] else logDet <- NULL return(list(Ainv = Ainv, listAinv = sm2list(Ainv, rownames = rownames(Ainv), colnames = c("row", "column", "Ainv")), f = f, logDet = logDet)) } ############################################################################### ############################################################################### makeAinv.fuzzy <- function(pedigree, f = NULL, ggroups = NULL, fuzz, gOnTop = FALSE, det = FALSE, ...){ if(!is.null(ggroups)){ stop("when 'fuzz' is non-NULL, 'ggroups' should not have any arguments (i.e., 'ggroups==NULL") } naPed2 <- which(pedigree[, 2] == "0") naPed3 <- which(pedigree[, 3] == "0") naPed2 <- union(naPed2, which(pedigree[, 2] == "")) naPed3 <- union(naPed3, which(pedigree[, 3] == "")) naPed2 <- union(naPed2, which(is.na(pedigree[, 2]))) naPed3 <- union(naPed3, which(is.na(pedigree[, 3]))) # checks on fuzzy classification matrix and pedigree consistency: ## 'fuzz' is a type of matrix if(!is(fuzz, "matrix") && !is(fuzz, "Matrix")){ cat("'fuzz' of class", class(fuzz), "\n") stop("class of 'fuzz' must be either 'matrix' or 'Matrix'") } ## rows of 'fuzz' add up to 1 if(any(rowSums(fuzz) != 1)){ cat("rows:", which(rowSums(fuzz) != 1), "\ndo not equal 1\n") stop("all rowSums(fuzz) must equal 1\n(check for possible rounding errors, e.g., 30.33333 != 1)") } ## fuzz has dimnames if(is.null(dimnames(fuzz)[[1]]) \| is.null(dimnames(fuzz)[[2]])){ stop("'fuzz' must have row and column names") } ## pedigree does not have genetic groups if(any(colnames(fuzz) %in% pedigree[, 1])){ cat("colnames:", which(colnames(fuzz) %in% pedigree[, 1]), "\nare in 'pedigree'\n") stop("colnames of 'fuzz' (genetic groups) must NOT be identities in the first column of 'pedigree'") } ## pedigree has phantom parents in 'fuzz' if(!all(rownames(fuzz) %in% pedigree[, 1])){ cat("rownames:", which(!rownames(fuzz) %in% pedigree[, 1]), "\nnot in 'pedigree'\n") stop("rownames of 'fuzz' (phantom parents) must all be identities in the first column of 'pedigree'. See the `prepPed()` function to help prepare the pedigree") } ## individuals can only have both parents missing in 'pedigree' or none if(length(naPed2) != length(naPed3) \| any(!naPed2 %in% naPed3)){ stop("Individuals must have either two or zero missing parents in 'pedigree'") } ## IDs with missing parents (if passed above check, naPed2==naPed3) in 'pedigree' are phantom parents in 'fuzz' if(!all(pedigree[naPed2, 1] %in% rownames(fuzz))){ cat("IDs for 'pedigree' rows:", naPed2[which(!pedigree[naPed2, 1] %in% rownames(fuzz))], "\nare not rownames in 'fuzz'\n") stop("Individuals with missing parents (phantom individuals) must have a rowname in 'fuzz'") } # order of genetic groups in pedalt/A^-1 is same as column order of 'fuzz' ggroups <- colnames(fuzz) nggroups <- length(ggroups) # No. genetic groups p <- nrow(fuzz) # No. phantom parents eN <- nrow(pedigree) - p # No. observed IDs N <- nggroups + eN # No. GGs + observed IDs # order of phantom parents in pedalt is same as row order in 'fuzz' phantomPars <- seq(p) + nggroups groupRows <- seq(nggroups) # order pedigree: generations, order phantom parents in fuzz, dam, & sire ## genetic groups first renPed <- c(groupRows, nggroups + order(genAssign(pedigree), match(pedigree[, 1], rownames(fuzz), nomatch = p+1), pedigree[, 2], pedigree[, 3])) pedalt <- data.frame(id = c(ggroups, as.character(pedigree[, 1])), dam = c(rep(NA, nggroups), as.character(pedigree[, 2])), sire = c(rep(NA, nggroups), as.character(pedigree[, 3])))[renPed, ] nPed <- numPed(pedalt) phantomless <- cbind(numPed(nPed[-phantomPars, ], check = FALSE), nPed[-phantomPars, -1]) if(!all(which(phantomless[, 4] == -998) == groupRows)){ stop("Something wicked happened with the dams in phantomless numeric pedigree:\n Contact package maintainer: <matthewwolak@gmail.com>\n or raise an issue on: <https://github.com/matthewwolak/nadiv/issues>") } if(!all(which(phantomless[, 5] == -998) == groupRows)){ stop("Something wicked happened with the sires in phantomless numeric pedigree:\n Contact package maintainer: <matthewwolak@gmail.com>\n or raise an issue on: <https://github.com/matthewwolak/nadiv/issues>") } groupFuzz <- Diagonal(x = 1, n = nggroups) groupFuzz@Dimnames <- list(as.character(ggroups), as.character(ggroups)) fuzzmat <- rBind(groupFuzz, as(fuzz, "sparseMatrix")) # predict non-zero elements of Astar ## make H from Quaas 1988: ## H = [-Pb Qb : Tinv] ## Astar = H' %% D^-1 %% H ### sTinv: n x n observed IDs (i.e., no GGs or phantom parent IDs) dnmiss <- which(phantomless[, 2] != -998) snmiss <- which(phantomless[, 3] != -998) Tinv.row <- c(c(phantomless[, 1][dnmiss], phantomless[, 1][snmiss]) - nggroups, 1:eN) Tinv.col <- c(c(phantomless[, 2][dnmiss], phantomless[, 3][snmiss]) - nggroups, 1:eN) el.order <- order(Tinv.col + Tinv.row/(eN + 1), decreasing = FALSE) sTinv <- sparseMatrix(i = as.integer(Tinv.row[el.order] - 1), p = as.integer(c(match(1:eN, Tinv.col[el.order]), length(el.order) + 1) - 1), index1 = FALSE, dims = c(eN, eN), symmetric = FALSE, dimnames = list(as.character(phantomless[-groupRows, 1]), NULL)) ### Pb: n x p version of sTinv pdnmiss <- which(phantomless[, 4] %in% phantomPars) psnmiss <- which(phantomless[, 5] %in% phantomPars) Pb.row <- c(phantomless[, 1][pdnmiss], phantomless[, 1][psnmiss]) - nggroups Pb.col <- c(phantomless[, 4][pdnmiss], phantomless[, 5][psnmiss]) - nggroups el.order <- order(Pb.col + Pb.row/(p + 1), decreasing = FALSE) sPb <- sparseMatrix(i = as.integer(Pb.row[el.order] - 1), p = as.integer(c(match(1:p, Pb.col[el.order]), length(el.order) + 1) - 1), index1 = FALSE, dims = c(eN, p), symmetric = FALSE, dimnames = list(NULL, as.character(pedalt[phantomPars, 1]))) ### Qb is the fuzzy classification matrix ('fuzz') Qb <- as(fuzzmat[-groupRows, ][match(rownames(fuzzmat)[-groupRows], colnames(sPb)), ], "sparseMatrix") sQb <- sparseMatrix(i = Qb@i, p = Qb@p, index1 = FALSE, dims = Qb@Dim, symmetric = FALSE, dimnames = Qb@Dimnames) ## sH = [-(sPb %% sQb) : sTinv] sH <- cBind((sPb %% sQb), sTinv) Ainv <- t(crossprod(sH)) # transpose stores lower triangle phantomless[phantomless == -998] <- N + 1 # for now, phantom parents cannot be inbred (just like genetic groups) f <- c(rep(-1, nggroups), rep(0, eN), -1) Cout <- .C("ainvfuzz", as.integer(phantomless[, 2] - 1), #dam as.integer(phantomless[, 3] - 1), #sire as.integer(phantomless[, 4] - 1), #phantom dam as.integer(phantomless[, 5] - 1), #phantom sire as.double(f), #f as.double(rep(0, N)), #dii as.integer(N), #n as.integer(nggroups), #g as.double(fuzzmat@x), #xF as.integer(fuzzmat@i), #iF as.integer(fuzzmat@p), #pF as.double(rep(0, length(Ainv@i))), #xA as.integer(Ainv@i), #iA as.integer(Ainv@p)) #pA Ainv <- as(Ainv, "dsCMatrix") Ainv@x <- Cout[[12]] fsOrd1 <- as(as(as.integer(renPed), "pMatrix")[, -c(naPed2 + nggroups)], "CsparseMatrix") fsOrd <- as(as(fsOrd1 %% matrix(seq(N), nrow = N), "sparseMatrix")@x, "pMatrix") Ainv <- as(t(fsOrd) %% Ainv %% fsOrd, "dgCMatrix") Ainv@Dimnames <- list(as.character(pedalt[(t(fsOrd1) %% matrix(seq(N+p), ncol = 1))@x, 1]), NULL) f <- (fsOrd1 %% Cout[[5]][-c(N+1)])@x[-groupRows] if(!gOnTop){ permute <- as(as.integer(c(seq(eN+1, N, 1), seq(eN))), "pMatrix") Ainv <- t(permute) %% Ainv %% permute } if(det) logDet <- -1*determinant(Ainv, logarithm = TRUE)$modulus[1] else logDet <- NULL return(list(Ainv = Ainv, listAinv = sm2list(Ainv, rownames = rownames(Ainv), colnames = c("row", "column", "Ainv")), f = f, logDet = logDet)) }	/nadiv/R/makeAinv.R	no_license	ingted/R-Examples	R	false	false	13,381	r	# Generic makeAinv <- function(pedigree, f = NULL, ggroups = NULL, fuzz = NULL, gOnTop = FALSE, det = FALSE, ...){ if(is(fuzz, "matrix") \| is(fuzz, "Matrix")) class(fuzz) <- "fuzzy" UseMethod("makeAinv", fuzz) } ############################################################################### # Methods: makeAinv.default <- function(pedigree, f = NULL, ggroups = NULL, fuzz = NULL, gOnTop = FALSE, det = FALSE, ...){ if(is.null(ggroups)){ ptype <- "O" renPed <- order(genAssign(pedigree), pedigree[, 2], pedigree[, 3], na.last = FALSE) nPed <- numPed(pedigree[renPed, ]) groupRows <- NULL nggroups <- 0 } else { if(length(ggroups) == dim(pedigree)[1]){ stop("length(ggroups) should either be:\n == 1 and a numeric indicating the number of genetic groups (type 'A')\n == length(unique(ggroups)) and a character vector indicating the names of each unique genetic goup (type 'D')") } if(!is.null(fuzz)) stop("fuzzy genetic groups not yet implemented: fuzz must be NULL") zerNA <- union(which(pedigree[, 2] == "0"), which(pedigree[, 3] == "0")) astNA <- union(which(pedigree[, 2] == ""), which(pedigree[, 3] == "")) naNA <- union(which(is.na(pedigree[, 2])), which(is.na(pedigree[, 3]))) naPed <- union(union(zerNA, astNA), naNA) #### pedigree type "D" #### if(!is.numeric(ggroups) && length(ggroups) == length(unique(ggroups))){ ptype <- "D" if(is.null(fuzz) && length(naPed) > 0){ stop("When supplying a vector of unique genetic group names in the 'ggroups' argument, all individuals in the pedigree must have a genetic group when a parent is unknown (<NA>, '0' and '' are considered unknown parents)")# or be identified as a phantom parent in 'fuzz'") } if(any(is.na(ggroups))) ggroups <- na.omit(ggroups) pedalt <- data.frame(id = c(ggroups, as.character(pedigree[, 1])), dam = c(rep(NA, length(ggroups)), as.character(pedigree[, 2])), sire = c(rep(NA, length(ggroups)), as.character(pedigree[, 3]))) #TODO: write method for numPed to handle genetic group pedigree ## same genetic group for dam and sire won't throw warning about selfing nPed <- suppressWarnings(numPed(pedalt)) # FIXME: remove suppressWarnings() throughout file ## return command below as attribute groupRows <- nPed[which(nPed[, 2] == -998), 1] } #### pedigree type "A" #### if(length(ggroups) == 1){ ptype <- "A" if(length(naPed) == ggroups){ nPed <- suppressWarnings(numPed(pedigree)) groupRows <- naPed } else { stop("Only rows identifying genetic groups should have missing parents.\n All individuals in the pedigree must have a genetic group when a parent is unknown")# or be identified as a phantom parent in 'fuzz'") } } nggroups <- length(groupRows) renPed <- order(suppressWarnings(genAssign(nPed)), nPed[, 2], nPed[, 3]) nPed <- numPed(ronPed(nPed, renPed)) } #### N <- nrow(nPed) eN <- N - nggroups dnmiss <- which(nPed[, 2] != -998) snmiss <- which(nPed[, 3] != -998) Tinv.row <- c(nPed[, 1][dnmiss], nPed[, 1][snmiss], 1:N) Tinv.col <- c(nPed[, 2][dnmiss], nPed[, 3][snmiss], 1:N) el.order <- order(Tinv.col + Tinv.row/(N + 1), decreasing = FALSE) if(is.null(ggroups)){ sTinv <- sparseMatrix(i = as.integer(Tinv.row[el.order] - 1), p = as.integer(c(match(1:N, Tinv.col[el.order]), length(el.order) + 1) - 1), index1 = FALSE, dims = c(N, N), symmetric = FALSE, dimnames = list(as.character(nPed[, 1]), NULL)) } else { sTinv <- sparseMatrix(i = as.integer(Tinv.row[el.order] - 1), p = as.integer(c(match(1:N, Tinv.col[el.order]), length(el.order) + 1) - 1), index1 = FALSE, dims = c(N, N), symmetric = FALSE, dimnames = list(as.character(nPed[, 1]), NULL))[-groupRows, ] } Ainv <- t(crossprod(sTinv)) # transpose gives lower triangle # 1: Adds Ainv elements in same for loop as calculation of f # 2: First checks to see if individual k has same dam and sire as k-1, if so then just assigns k-1's f # 3: simplifies the calculation of the addition to the Ainv element (instead of alphai 0.25 - defines alphai=alphai0.25). nPed[nPed == -998] <- N + 1 f <- c(rep(-1, nggroups), rep(0, eN), -1) Cout <- .C("ainvml", as.integer(nPed[, 2] - 1), #dam as.integer(nPed[, 3] - 1), #sire as.double(f), #f as.double(rep(0, N)), #dii as.integer(N), #n as.integer(nggroups), #g as.double(rep(0, length(Ainv@i))), #xA as.integer(Ainv@i), #iA as.integer(Ainv@p), #pA as.integer(length(Ainv@i))) #nzmaxA Ainv <- as(Ainv, "dsCMatrix") Ainv@x <- Cout[[7]] fsOrd <- as(as.integer(renPed), "pMatrix") Ainv <- as(t(fsOrd) %% Ainv %% fsOrd, "dgCMatrix") if(ptype == "D"){ Ainv@Dimnames <- list(as.character(pedalt[, 1]), NULL) f <- Cout[[3]][t(fsOrd)@perm][-seq(nggroups)] } else { Ainv@Dimnames <- list(as.character(pedigree[, 1]), NULL) f <- c(rep(0, nggroups), Cout[[3]][t(fsOrd)@perm][(nggroups+1):(nggroups + eN)]) } if(!is.null(ggroups) && !gOnTop){ permute <- as(as.integer(c(seq(eN+1, N, 1), seq(eN))), "pMatrix") Ainv <- t(permute) %% Ainv %% permute } if(det) logDet <- -1determinant(Ainv, logarithm = TRUE)$modulus[1] else logDet <- NULL return(list(Ainv = Ainv, listAinv = sm2list(Ainv, rownames = rownames(Ainv), colnames = c("row", "column", "Ainv")), f = f, logDet = logDet)) } ############################################################################### ############################################################################### makeAinv.fuzzy <- function(pedigree, f = NULL, ggroups = NULL, fuzz, gOnTop = FALSE, det = FALSE, ...){ if(!is.null(ggroups)){ stop("when 'fuzz' is non-NULL, 'ggroups' should not have any arguments (i.e., 'ggroups==NULL") } naPed2 <- which(pedigree[, 2] == "0") naPed3 <- which(pedigree[, 3] == "0") naPed2 <- union(naPed2, which(pedigree[, 2] == "")) naPed3 <- union(naPed3, which(pedigree[, 3] == "")) naPed2 <- union(naPed2, which(is.na(pedigree[, 2]))) naPed3 <- union(naPed3, which(is.na(pedigree[, 3]))) # checks on fuzzy classification matrix and pedigree consistency: ## 'fuzz' is a type of matrix if(!is(fuzz, "matrix") && !is(fuzz, "Matrix")){ cat("'fuzz' of class", class(fuzz), "\n") stop("class of 'fuzz' must be either 'matrix' or 'Matrix'") } ## rows of 'fuzz' add up to 1 if(any(rowSums(fuzz) != 1)){ cat("rows:", which(rowSums(fuzz) != 1), "\ndo not equal 1\n") stop("all rowSums(fuzz) must equal 1\n(check for possible rounding errors, e.g., 30.33333 != 1)") } ## fuzz has dimnames if(is.null(dimnames(fuzz)[[1]]) \| is.null(dimnames(fuzz)[[2]])){ stop("'fuzz' must have row and column names") } ## pedigree does not have genetic groups if(any(colnames(fuzz) %in% pedigree[, 1])){ cat("colnames:", which(colnames(fuzz) %in% pedigree[, 1]), "\nare in 'pedigree'\n") stop("colnames of 'fuzz' (genetic groups) must NOT be identities in the first column of 'pedigree'") } ## pedigree has phantom parents in 'fuzz' if(!all(rownames(fuzz) %in% pedigree[, 1])){ cat("rownames:", which(!rownames(fuzz) %in% pedigree[, 1]), "\nnot in 'pedigree'\n") stop("rownames of 'fuzz' (phantom parents) must all be identities in the first column of 'pedigree'. See the `prepPed()` function to help prepare the pedigree") } ## individuals can only have both parents missing in 'pedigree' or none if(length(naPed2) != length(naPed3) \| any(!naPed2 %in% naPed3)){ stop("Individuals must have either two or zero missing parents in 'pedigree'") } ## IDs with missing parents (if passed above check, naPed2==naPed3) in 'pedigree' are phantom parents in 'fuzz' if(!all(pedigree[naPed2, 1] %in% rownames(fuzz))){ cat("IDs for 'pedigree' rows:", naPed2[which(!pedigree[naPed2, 1] %in% rownames(fuzz))], "\nare not rownames in 'fuzz'\n") stop("Individuals with missing parents (phantom individuals) must have a rowname in 'fuzz'") } # order of genetic groups in pedalt/A^-1 is same as column order of 'fuzz' ggroups <- colnames(fuzz) nggroups <- length(ggroups) # No. genetic groups p <- nrow(fuzz) # No. phantom parents eN <- nrow(pedigree) - p # No. observed IDs N <- nggroups + eN # No. GGs + observed IDs # order of phantom parents in pedalt is same as row order in 'fuzz' phantomPars <- seq(p) + nggroups groupRows <- seq(nggroups) # order pedigree: generations, order phantom parents in fuzz, dam, & sire ## genetic groups first renPed <- c(groupRows, nggroups + order(genAssign(pedigree), match(pedigree[, 1], rownames(fuzz), nomatch = p+1), pedigree[, 2], pedigree[, 3])) pedalt <- data.frame(id = c(ggroups, as.character(pedigree[, 1])), dam = c(rep(NA, nggroups), as.character(pedigree[, 2])), sire = c(rep(NA, nggroups), as.character(pedigree[, 3])))[renPed, ] nPed <- numPed(pedalt) phantomless <- cbind(numPed(nPed[-phantomPars, ], check = FALSE), nPed[-phantomPars, -1]) if(!all(which(phantomless[, 4] == -998) == groupRows)){ stop("Something wicked happened with the dams in phantomless numeric pedigree:\n Contact package maintainer: <matthewwolak@gmail.com>\n or raise an issue on: <https://github.com/matthewwolak/nadiv/issues>") } if(!all(which(phantomless[, 5] == -998) == groupRows)){ stop("Something wicked happened with the sires in phantomless numeric pedigree:\n Contact package maintainer: <matthewwolak@gmail.com>\n or raise an issue on: <https://github.com/matthewwolak/nadiv/issues>") } groupFuzz <- Diagonal(x = 1, n = nggroups) groupFuzz@Dimnames <- list(as.character(ggroups), as.character(ggroups)) fuzzmat <- rBind(groupFuzz, as(fuzz, "sparseMatrix")) # predict non-zero elements of Astar ## make H from Quaas 1988: ## H = [-Pb Qb : Tinv] ## Astar = H' %% D^-1 %% H ### sTinv: n x n observed IDs (i.e., no GGs or phantom parent IDs) dnmiss <- which(phantomless[, 2] != -998) snmiss <- which(phantomless[, 3] != -998) Tinv.row <- c(c(phantomless[, 1][dnmiss], phantomless[, 1][snmiss]) - nggroups, 1:eN) Tinv.col <- c(c(phantomless[, 2][dnmiss], phantomless[, 3][snmiss]) - nggroups, 1:eN) el.order <- order(Tinv.col + Tinv.row/(eN + 1), decreasing = FALSE) sTinv <- sparseMatrix(i = as.integer(Tinv.row[el.order] - 1), p = as.integer(c(match(1:eN, Tinv.col[el.order]), length(el.order) + 1) - 1), index1 = FALSE, dims = c(eN, eN), symmetric = FALSE, dimnames = list(as.character(phantomless[-groupRows, 1]), NULL)) ### Pb: n x p version of sTinv pdnmiss <- which(phantomless[, 4] %in% phantomPars) psnmiss <- which(phantomless[, 5] %in% phantomPars) Pb.row <- c(phantomless[, 1][pdnmiss], phantomless[, 1][psnmiss]) - nggroups Pb.col <- c(phantomless[, 4][pdnmiss], phantomless[, 5][psnmiss]) - nggroups el.order <- order(Pb.col + Pb.row/(p + 1), decreasing = FALSE) sPb <- sparseMatrix(i = as.integer(Pb.row[el.order] - 1), p = as.integer(c(match(1:p, Pb.col[el.order]), length(el.order) + 1) - 1), index1 = FALSE, dims = c(eN, p), symmetric = FALSE, dimnames = list(NULL, as.character(pedalt[phantomPars, 1]))) ### Qb is the fuzzy classification matrix ('fuzz') Qb <- as(fuzzmat[-groupRows, ][match(rownames(fuzzmat)[-groupRows], colnames(sPb)), ], "sparseMatrix") sQb <- sparseMatrix(i = Qb@i, p = Qb@p, index1 = FALSE, dims = Qb@Dim, symmetric = FALSE, dimnames = Qb@Dimnames) ## sH = [-(sPb %% sQb) : sTinv] sH <- cBind((sPb %% sQb), sTinv) Ainv <- t(crossprod(sH)) # transpose stores lower triangle phantomless[phantomless == -998] <- N + 1 # for now, phantom parents cannot be inbred (just like genetic groups) f <- c(rep(-1, nggroups), rep(0, eN), -1) Cout <- .C("ainvfuzz", as.integer(phantomless[, 2] - 1), #dam as.integer(phantomless[, 3] - 1), #sire as.integer(phantomless[, 4] - 1), #phantom dam as.integer(phantomless[, 5] - 1), #phantom sire as.double(f), #f as.double(rep(0, N)), #dii as.integer(N), #n as.integer(nggroups), #g as.double(fuzzmat@x), #xF as.integer(fuzzmat@i), #iF as.integer(fuzzmat@p), #pF as.double(rep(0, length(Ainv@i))), #xA as.integer(Ainv@i), #iA as.integer(Ainv@p)) #pA Ainv <- as(Ainv, "dsCMatrix") Ainv@x <- Cout[[12]] fsOrd1 <- as(as(as.integer(renPed), "pMatrix")[, -c(naPed2 + nggroups)], "CsparseMatrix") fsOrd <- as(as(fsOrd1 %% matrix(seq(N), nrow = N), "sparseMatrix")@x, "pMatrix") Ainv <- as(t(fsOrd) %% Ainv %% fsOrd, "dgCMatrix") Ainv@Dimnames <- list(as.character(pedalt[(t(fsOrd1) %% matrix(seq(N+p), ncol = 1))@x, 1]), NULL) f <- (fsOrd1 %% Cout[[5]][-c(N+1)])@x[-groupRows] if(!gOnTop){ permute <- as(as.integer(c(seq(eN+1, N, 1), seq(eN))), "pMatrix") Ainv <- t(permute) %% Ainv %% permute } if(det) logDet <- -1*determinant(Ainv, logarithm = TRUE)$modulus[1] else logDet <- NULL return(list(Ainv = Ainv, listAinv = sm2list(Ainv, rownames = rownames(Ainv), colnames = c("row", "column", "Ainv")), f = f, logDet = logDet)) }
############################# DNN Model ################################################## ## Function DNN.test.training ## Parameters: ## list: data input ## epochs: number of epochs ## unitsi (i from 1 to 6): Number of neurons in each hidden layer library(keras) library(yardstick) library(dplyr) DNN.test.training<-function(list,epochs,units1,units2,units3,units4,units5,units6){ ## Empty dataframes to save the results. According to the number of epochs results.epochs.loss<-matrix(ncol = 10,nrow = epochs) #ncol number of folds, nrow = number of epochs results.epochs.accuracy<-matrix(ncol = 10,nrow = epochs) #ncol number of folds, nrow = number of epochs results.epochs.val_loss<-matrix(ncol = 10,nrow = epochs) results.epochs.val_accuracy<-matrix(ncol = 10,nrow = epochs) results.predictions<-list() results.training<-list() results.metrics<-setNames(data.frame(matrix(ncol = 7,nrow = 10)),c("Acurracy Test","Acurracy Training","AUC","PR AUC","Precision","Recall","F1")) results.times<-setNames(data.frame(matrix(ncol = 3,nrow = 10)),c("start","end","end_time")) for(i in 1:length(list)){ results.times[i,1]<-Sys.time() ### Prepare data ###### df<-list[[i]] df$DEFAULT<-as.numeric(as.character(df$DEFAULT)) #Training df.training<-df%>%filter(SET.IS == "TRAINING")%>%select(c(LIMIT_BAL:DEFAULT)) df.training.targer<-as.matrix(df.training%>%select(DEFAULT)) df.training<-df.training%>%select(LIMIT_BAL:PAY_AMT6) df.training<-as.matrix(df.training) dimnames(df.training)<-NULL #Validation df.validation<-df%>%filter(SET.IS == "TEST")%>%select(c(LIMIT_BAL:DEFAULT)) df.validation.targer<-as.matrix(df.validation%>%select(DEFAULT)) df.validation<-df.validation%>%select(LIMIT_BAL:PAY_AMT6) df.validation<-as.matrix(df.validation) dimnames(df.validation)<-NULL ### DNN Model ######## use_session_with_seed(1) # same seed options(keras.view_metrics = FALSE) # show the loss and gain chart model<-keras_model_sequential()%>% layer_flatten(input_shape = dim(df.training)[2])%>% #input layer layer_dense(units = units1, activation = "relu", use_bias = TRUE)%>% #hidden laye layer_dense(units = units2, activation = "relu", use_bias = TRUE)%>% #hidden laye layer_dense(units = units3, activation = "relu", use_bias = TRUE)%>% #hidden laye layer_dense(units = units4, activation = "relu", use_bias = TRUE)%>% #hidden laye layer_dense(units = units5, activation = "relu", use_bias = TRUE)%>% #hidden laye layer_dense(units = units6, activation = "relu", use_bias = TRUE)%>% #hidden laye layer_dense(1,activation = "sigmoid", use_bias = TRUE) #output layer model %>% compile(loss = "binary_crossentropy",optimizer = "adam",metrics = "accuracy") history<-model %>% fit( df.training, df.training.targer, epochs = epochs, batch_size = 32, verbose = 0, # Print the epochs validation_split = 0, validation_data = list(df.validation, df.validation.targer)) results.epochs.loss[,i]<-history$metrics$loss # loss per epoch training results.epochs.accuracy[,i]<-history$metrics$acc # accuracy per epoch training results.epochs.val_loss[,i]<-history$metrics$val_loss # loss per epoch validation results.epochs.val_accuracy[,i]<-history$metrics$val_acc #accuracy per epoch validation predictions<-model%>%predict_classes(df.validation) # Predictions predictions.prob<-model%>%predict_proba(df.validation) %>% as.vector() # Prob Predictions predictions.training<-model%>%predict_classes(df.training) # Predictions Training results.predictions[[i]]<-tibble::tibble( Real = as.factor(df.validation.targer), Estimate = as.factor(predictions), Prob = predictions.prob) results.training[[i]]<-tibble::tibble( Real.training = as.factor(df.training.targer), Estimate.training = as.factor(predictions.training)) options(yardstick.event_first = FALSE) results.metrics[i,1]<-data.frame(results.predictions[[i]] %>% yardstick::metrics(Real, Estimate))[1,3] #accuracy test results.metrics[i,2]<-data.frame(results.training[[i]] %>% yardstick::metrics(Real.training, Estimate.training))[1,3] #accuracy training results.metrics[i,3]<-data.frame(results.predictions[[i]] %>% yardstick::roc_auc(Real, Prob))[,3] # ROC AUC results.metrics[i,4]<-data.frame(results.predictions[[i]] %>% mutate(Estimate=as.numeric(as.character(Estimate)))%>%yardstick::pr_auc(Real,Estimate))[3] # PR AUC results.metrics[i,5]<-data.frame(results.predictions[[i]] %>% yardstick::precision(Real, Estimate))[,3] #Precision results.metrics[i,6]<-data.frame(results.predictions[[i]] %>% yardstick::recall(Real, Estimate))[,3] # Recall results.metrics[i,7]<-data.frame(results.predictions[[i]] %>% yardstick::f_meas(Real, Estimate, beta = 1))[,3] # F1 results.times[i,2]<-Sys.time() #Time results.times[i,3]<-(results.times[i,2]-results.times[i,1])/60 #Time } return(list(results.epochs.loss,results.epochs.accuracy,results.epochs.val_loss,results.epochs.val_accuracy,results.predictions,results.metrics,results.times)) } ############### 2 hidden layers ###################################### results.dnn.targetmean.2h.1000<-DNN.test.training(kfold.targetMean.scale,1000,23,23) results.dnn.frequency.2h.1000<-DNN.test.training(kfold.frequencyEncoding.scale,1000,23,23) results.dnn.onehot.2h.1000<-DNN.test.training(kfold.onehotEncoding.sale,1000,27,27) ############### 4 Hidden layers ####################################### results.dnn.targetmean.4h.1000<-DNN.test.training(kfold.targetMean.scale,1000,23,23,23,23) results.dnn.frequency.4h.1000<-DNN.test.training(kfold.frequencyEncoding.scale,1000,23,23,23,23) results.dnn.onehot.4h.1000<-DNN.test.training(kfold.onehotEncoding.sale,1000,27,27,27,27) ############### 6 Hidden layers ####################################### results.dnn.targetmean.6h.1800<-DNN.test.training(kfold.targetMean.scale,1800,23,23,23,23,23,23) results.dnn.frequency.6h.1800<-DNN.test.training(kfold.frequencyEncoding.scale,1800,23,23,23,23,23,23) results.dnn.onehot.6h.1800<-DNN.test.training(kfold.onehotEncoding.sale,1800,27,27,27,27,27,27)	/DNN Model.R	no_license	oordenesg/deep_learning_research_project	R	false	false	6,383	r	############################# DNN Model ################################################## ## Function DNN.test.training ## Parameters: ## list: data input ## epochs: number of epochs ## unitsi (i from 1 to 6): Number of neurons in each hidden layer library(keras) library(yardstick) library(dplyr) DNN.test.training<-function(list,epochs,units1,units2,units3,units4,units5,units6){ ## Empty dataframes to save the results. According to the number of epochs results.epochs.loss<-matrix(ncol = 10,nrow = epochs) #ncol number of folds, nrow = number of epochs results.epochs.accuracy<-matrix(ncol = 10,nrow = epochs) #ncol number of folds, nrow = number of epochs results.epochs.val_loss<-matrix(ncol = 10,nrow = epochs) results.epochs.val_accuracy<-matrix(ncol = 10,nrow = epochs) results.predictions<-list() results.training<-list() results.metrics<-setNames(data.frame(matrix(ncol = 7,nrow = 10)),c("Acurracy Test","Acurracy Training","AUC","PR AUC","Precision","Recall","F1")) results.times<-setNames(data.frame(matrix(ncol = 3,nrow = 10)),c("start","end","end_time")) for(i in 1:length(list)){ results.times[i,1]<-Sys.time() ### Prepare data ###### df<-list[[i]] df$DEFAULT<-as.numeric(as.character(df$DEFAULT)) #Training df.training<-df%>%filter(SET.IS == "TRAINING")%>%select(c(LIMIT_BAL:DEFAULT)) df.training.targer<-as.matrix(df.training%>%select(DEFAULT)) df.training<-df.training%>%select(LIMIT_BAL:PAY_AMT6) df.training<-as.matrix(df.training) dimnames(df.training)<-NULL #Validation df.validation<-df%>%filter(SET.IS == "TEST")%>%select(c(LIMIT_BAL:DEFAULT)) df.validation.targer<-as.matrix(df.validation%>%select(DEFAULT)) df.validation<-df.validation%>%select(LIMIT_BAL:PAY_AMT6) df.validation<-as.matrix(df.validation) dimnames(df.validation)<-NULL ### DNN Model ######## use_session_with_seed(1) # same seed options(keras.view_metrics = FALSE) # show the loss and gain chart model<-keras_model_sequential()%>% layer_flatten(input_shape = dim(df.training)[2])%>% #input layer layer_dense(units = units1, activation = "relu", use_bias = TRUE)%>% #hidden laye layer_dense(units = units2, activation = "relu", use_bias = TRUE)%>% #hidden laye layer_dense(units = units3, activation = "relu", use_bias = TRUE)%>% #hidden laye layer_dense(units = units4, activation = "relu", use_bias = TRUE)%>% #hidden laye layer_dense(units = units5, activation = "relu", use_bias = TRUE)%>% #hidden laye layer_dense(units = units6, activation = "relu", use_bias = TRUE)%>% #hidden laye layer_dense(1,activation = "sigmoid", use_bias = TRUE) #output layer model %>% compile(loss = "binary_crossentropy",optimizer = "adam",metrics = "accuracy") history<-model %>% fit( df.training, df.training.targer, epochs = epochs, batch_size = 32, verbose = 0, # Print the epochs validation_split = 0, validation_data = list(df.validation, df.validation.targer)) results.epochs.loss[,i]<-history$metrics$loss # loss per epoch training results.epochs.accuracy[,i]<-history$metrics$acc # accuracy per epoch training results.epochs.val_loss[,i]<-history$metrics$val_loss # loss per epoch validation results.epochs.val_accuracy[,i]<-history$metrics$val_acc #accuracy per epoch validation predictions<-model%>%predict_classes(df.validation) # Predictions predictions.prob<-model%>%predict_proba(df.validation) %>% as.vector() # Prob Predictions predictions.training<-model%>%predict_classes(df.training) # Predictions Training results.predictions[[i]]<-tibble::tibble( Real = as.factor(df.validation.targer), Estimate = as.factor(predictions), Prob = predictions.prob) results.training[[i]]<-tibble::tibble( Real.training = as.factor(df.training.targer), Estimate.training = as.factor(predictions.training)) options(yardstick.event_first = FALSE) results.metrics[i,1]<-data.frame(results.predictions[[i]] %>% yardstick::metrics(Real, Estimate))[1,3] #accuracy test results.metrics[i,2]<-data.frame(results.training[[i]] %>% yardstick::metrics(Real.training, Estimate.training))[1,3] #accuracy training results.metrics[i,3]<-data.frame(results.predictions[[i]] %>% yardstick::roc_auc(Real, Prob))[,3] # ROC AUC results.metrics[i,4]<-data.frame(results.predictions[[i]] %>% mutate(Estimate=as.numeric(as.character(Estimate)))%>%yardstick::pr_auc(Real,Estimate))[3] # PR AUC results.metrics[i,5]<-data.frame(results.predictions[[i]] %>% yardstick::precision(Real, Estimate))[,3] #Precision results.metrics[i,6]<-data.frame(results.predictions[[i]] %>% yardstick::recall(Real, Estimate))[,3] # Recall results.metrics[i,7]<-data.frame(results.predictions[[i]] %>% yardstick::f_meas(Real, Estimate, beta = 1))[,3] # F1 results.times[i,2]<-Sys.time() #Time results.times[i,3]<-(results.times[i,2]-results.times[i,1])/60 #Time } return(list(results.epochs.loss,results.epochs.accuracy,results.epochs.val_loss,results.epochs.val_accuracy,results.predictions,results.metrics,results.times)) } ############### 2 hidden layers ###################################### results.dnn.targetmean.2h.1000<-DNN.test.training(kfold.targetMean.scale,1000,23,23) results.dnn.frequency.2h.1000<-DNN.test.training(kfold.frequencyEncoding.scale,1000,23,23) results.dnn.onehot.2h.1000<-DNN.test.training(kfold.onehotEncoding.sale,1000,27,27) ############### 4 Hidden layers ####################################### results.dnn.targetmean.4h.1000<-DNN.test.training(kfold.targetMean.scale,1000,23,23,23,23) results.dnn.frequency.4h.1000<-DNN.test.training(kfold.frequencyEncoding.scale,1000,23,23,23,23) results.dnn.onehot.4h.1000<-DNN.test.training(kfold.onehotEncoding.sale,1000,27,27,27,27) ############### 6 Hidden layers ####################################### results.dnn.targetmean.6h.1800<-DNN.test.training(kfold.targetMean.scale,1800,23,23,23,23,23,23) results.dnn.frequency.6h.1800<-DNN.test.training(kfold.frequencyEncoding.scale,1800,23,23,23,23,23,23) results.dnn.onehot.6h.1800<-DNN.test.training(kfold.onehotEncoding.sale,1800,27,27,27,27,27,27)
## upload data onto wfu library(RPostgreSQL) # set up the connection drv <- dbDriver("PostgreSQL") con <- dbConnect(drv, user='postgres', password='postgres', dbname='U01') ## PRACTICE FOR CODE SESSION 06/10/2020 upload_practice <- Olivier_Cocaine_df_sql %>% distinct(rfid, exp, .keep_all = T) ## temporary dbWriteTable(con, c("u01_olivier_george_cocaine","olivier_rewards"), value = upload_practice, row.names = FALSE) dbExecute(con,"ALTER TABLE u01_olivier_george_cocaine.olivier_rewards ADD PRIMARY KEY(rfid,exp)") # troubleshoot dbExecute(con,"drop table if exists u01_olivier_george_cocaine.olivier_rewards") # write data frame to database ## to add dataframes (by cohort) ## write to a temporary table in the database with dbWriteTable # then issue an SQL statement to insert into your table as you would within the db environment drv <- dbDriver("PostgreSQL") con <- dbConnect(drv, user='postgres', password='postgres', dbname='U01') # update_sql_db <- function(df, df2){ # df_to_add <- anti_join(df2, df) # # } test1 <- Olivier_Cocaine_df_sql %>% distinct() %>% subset(cohort != "C11") test2 <- Olivier_Cocaine_df_sql %>% distinct() %>% subset(cohort == "C11") dbWriteTable(con, c("u01_olivier_george_cocaine","olivier_rewards"), value = test1, row.names = FALSE) dbWriteTable(con, c("u01_olivier_george_cocaine","olivier_rewards_temp"), value = test2, row.names = FALSE) dbExecute(con,"ALTER TABLE u01_olivier_george_cocaine.olivier_rewards ADD PRIMARY KEY(rfid,exp)") dbExecute(con,"insert into u01_olivier_george_cocaine.olivier_rewards select * from u01_olivier_george_cocaine.olivier_rewards_temp") dbExecute(con,"drop table if exists u01_olivier_george_cocaine.olivier_rewards_temp") # for troubleshooting dbExecute(con,"drop table if exists u01_olivier_george_cocaine.olivier_rewards") # disconnect dbDisconnect(con) ## in terminal cd /tmp sudo su postgres pg_dump -d U01 -t u01_olivier_george_cocaine.olivier_rewards > olivier_rewards.sql exit cp olivier_rewards.sql /home/bonnie/Dropbox\ \(Palmer\ Lab\)/PalmerLab_Datasets/u01_george_oliviercocaine/database ## operation not permitted # To move a file or directory, you need to have write permissions on both SOURCE and DESTINATION. Otherwise, you will receive a permission denied error. ## mkdir {C01,C02,C03,C04,C05,C06,C07,C08,C09,C10,C11}	/CREATE/R_sql_pgdump.R	no_license	BeverlyPeng/Olivier_U01Cocaine	R	false	false	2,337	r	## upload data onto wfu library(RPostgreSQL) # set up the connection drv <- dbDriver("PostgreSQL") con <- dbConnect(drv, user='postgres', password='postgres', dbname='U01') ## PRACTICE FOR CODE SESSION 06/10/2020 upload_practice <- Olivier_Cocaine_df_sql %>% distinct(rfid, exp, .keep_all = T) ## temporary dbWriteTable(con, c("u01_olivier_george_cocaine","olivier_rewards"), value = upload_practice, row.names = FALSE) dbExecute(con,"ALTER TABLE u01_olivier_george_cocaine.olivier_rewards ADD PRIMARY KEY(rfid,exp)") # troubleshoot dbExecute(con,"drop table if exists u01_olivier_george_cocaine.olivier_rewards") # write data frame to database ## to add dataframes (by cohort) ## write to a temporary table in the database with dbWriteTable # then issue an SQL statement to insert into your table as you would within the db environment drv <- dbDriver("PostgreSQL") con <- dbConnect(drv, user='postgres', password='postgres', dbname='U01') # update_sql_db <- function(df, df2){ # df_to_add <- anti_join(df2, df) # # } test1 <- Olivier_Cocaine_df_sql %>% distinct() %>% subset(cohort != "C11") test2 <- Olivier_Cocaine_df_sql %>% distinct() %>% subset(cohort == "C11") dbWriteTable(con, c("u01_olivier_george_cocaine","olivier_rewards"), value = test1, row.names = FALSE) dbWriteTable(con, c("u01_olivier_george_cocaine","olivier_rewards_temp"), value = test2, row.names = FALSE) dbExecute(con,"ALTER TABLE u01_olivier_george_cocaine.olivier_rewards ADD PRIMARY KEY(rfid,exp)") dbExecute(con,"insert into u01_olivier_george_cocaine.olivier_rewards select * from u01_olivier_george_cocaine.olivier_rewards_temp") dbExecute(con,"drop table if exists u01_olivier_george_cocaine.olivier_rewards_temp") # for troubleshooting dbExecute(con,"drop table if exists u01_olivier_george_cocaine.olivier_rewards") # disconnect dbDisconnect(con) ## in terminal cd /tmp sudo su postgres pg_dump -d U01 -t u01_olivier_george_cocaine.olivier_rewards > olivier_rewards.sql exit cp olivier_rewards.sql /home/bonnie/Dropbox\ \(Palmer\ Lab\)/PalmerLab_Datasets/u01_george_oliviercocaine/database ## operation not permitted # To move a file or directory, you need to have write permissions on both SOURCE and DESTINATION. Otherwise, you will receive a permission denied error. ## mkdir {C01,C02,C03,C04,C05,C06,C07,C08,C09,C10,C11}
load("family.rda") # After loading the dataset above, you will have the following # objects: # # > ls() # [1] "fage" "fgender" "fheight" "fnames" "fweight" # (1 point) Create a numeric vector fbmi # A person's body mass index (BMI) should be calculated as their # weight divided by their squared heights multiplied by 703 fbmi <- fweight / fheight^2 * 703 # (1 point) Create a logical vector foverWt # A person should be considered overweight if their BMI is greater than 25 foverWt <- fbmi > 25 # (1 point) Create a dataframe family # The dataframe should contain fnames, fgender, fage, fheight, fweight, fbmi, # and foverWt. The names should be as follows: # # > names(family) # [1] "name" "gender" "age" "height" "weight" "bmi" "overWt" family <- data.frame(fnames, fgender, fage, fheight, fweight, fbmi, foverWt) names(family) <- c("name", "gender", "age", "height", "weight", "bmi", "overWt") # (5 points) Plot age vs bmi # For each individual in the family dataframe, plot their age (x-axis) # against their bmi (y-axis). Males should be represented with 'red' # circles and females with 'blue' circles. You may need to set pch to # 'o'. The x-axis should range from 23 to 80 and your y-axis from 16 to # 31. Finally put a legend in the 'topright' corner of the plot with a # 'red' circle in front the label 'male' and a 'blue' circle in front the # label 'female'. You may want to look at the documentation for R's 'plot' # function family.male <- family[family$gender == 'm', ] family.female <- family[family$gender == 'f', ] plot(x = family.female$age, y=family.female$bmi, xlim=c(23,80), ylim=c(16,31), col='blue', pch='o', xlab="bmi", ylab="age") points(x = family.male$age, y=family.male$bmi, xlim=c(23,80), ylim=c(16,31), col='red', pch='o', xlab="bmi", ylab="age") legend("topright", c("male", "female"), pch = c('o', 'o'), col=c('red', 'blue'))	/midterm/ex1.r	no_license	jhkim81691/stat133	R	false	false	1,881	r	load("family.rda") # After loading the dataset above, you will have the following # objects: # # > ls() # [1] "fage" "fgender" "fheight" "fnames" "fweight" # (1 point) Create a numeric vector fbmi # A person's body mass index (BMI) should be calculated as their # weight divided by their squared heights multiplied by 703 fbmi <- fweight / fheight^2 * 703 # (1 point) Create a logical vector foverWt # A person should be considered overweight if their BMI is greater than 25 foverWt <- fbmi > 25 # (1 point) Create a dataframe family # The dataframe should contain fnames, fgender, fage, fheight, fweight, fbmi, # and foverWt. The names should be as follows: # # > names(family) # [1] "name" "gender" "age" "height" "weight" "bmi" "overWt" family <- data.frame(fnames, fgender, fage, fheight, fweight, fbmi, foverWt) names(family) <- c("name", "gender", "age", "height", "weight", "bmi", "overWt") # (5 points) Plot age vs bmi # For each individual in the family dataframe, plot their age (x-axis) # against their bmi (y-axis). Males should be represented with 'red' # circles and females with 'blue' circles. You may need to set pch to # 'o'. The x-axis should range from 23 to 80 and your y-axis from 16 to # 31. Finally put a legend in the 'topright' corner of the plot with a # 'red' circle in front the label 'male' and a 'blue' circle in front the # label 'female'. You may want to look at the documentation for R's 'plot' # function family.male <- family[family$gender == 'm', ] family.female <- family[family$gender == 'f', ] plot(x = family.female$age, y=family.female$bmi, xlim=c(23,80), ylim=c(16,31), col='blue', pch='o', xlab="bmi", ylab="age") points(x = family.male$age, y=family.male$bmi, xlim=c(23,80), ylim=c(16,31), col='red', pch='o', xlab="bmi", ylab="age") legend("topright", c("male", "female"), pch = c('o', 'o'), col=c('red', 'blue'))
## 24th May 2017 ## BISCUIT postprocessing ## ## Code author SP ## ### ### if(num_gene_batches ==1){ source("BISCUIT_parallel_impute_onegenebatch.R") source("BISCUIT_extras_onegenebatch_1.R") source("Per_marker_histogram.R") }else{ source("BISCUIT_parallel_impute.R") source("BISCUIT_extras.R") }	/superpixel_MIBI_python/BISCUIT_post_process.R	no_license	sandhya212/CSI_MIBI_Model	R	false	false	333	r	## 24th May 2017 ## BISCUIT postprocessing ## ## Code author SP ## ### ### if(num_gene_batches ==1){ source("BISCUIT_parallel_impute_onegenebatch.R") source("BISCUIT_extras_onegenebatch_1.R") source("Per_marker_histogram.R") }else{ source("BISCUIT_parallel_impute.R") source("BISCUIT_extras.R") }
# Copyright 2020 Observational Health Data Sciences and Informatics # # This file is part of Epi786FirstRound # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. .createCohorts <- function(connection, cdmDatabaseSchema, vocabularyDatabaseSchema = cdmDatabaseSchema, cohortDatabaseSchema, cohortTable, oracleTempSchema, outputFolder) { # Create study cohort table structure: sql <- SqlRender::loadRenderTranslateSql(sqlFilename = "CreateCohortTable.sql", packageName = "Epi786FirstRound", dbms = attr(connection, "dbms"), oracleTempSchema = oracleTempSchema, cohort_database_schema = cohortDatabaseSchema, cohort_table = cohortTable) DatabaseConnector::executeSql(connection, sql, progressBar = FALSE, reportOverallTime = FALSE) # Instantiate cohorts: pathToCsv <- system.file("settings", "CohortsToCreate.csv", package = "Epi786FirstRound") cohortsToCreate <- read.csv(pathToCsv) for (i in 1:nrow(cohortsToCreate)) { writeLines(paste("Creating cohort:", cohortsToCreate$name[i])) sql <- SqlRender::loadRenderTranslateSql(sqlFilename = paste0(cohortsToCreate$name[i], ".sql"), packageName = "Epi786FirstRound", dbms = attr(connection, "dbms"), oracleTempSchema = oracleTempSchema, cdm_database_schema = cdmDatabaseSchema, vocabulary_database_schema = vocabularyDatabaseSchema, target_database_schema = cohortDatabaseSchema, target_cohort_table = cohortTable, target_cohort_id = cohortsToCreate$cohortId[i]) DatabaseConnector::executeSql(connection, sql) } # Fetch cohort counts: sql <- "SELECT cohort_definition_id, COUNT(*) AS count FROM @cohort_database_schema.@cohort_table GROUP BY cohort_definition_id" sql <- SqlRender::render(sql, cohort_database_schema = cohortDatabaseSchema, cohort_table = cohortTable) sql <- SqlRender::translate(sql, targetDialect = attr(connection, "dbms")) counts <- DatabaseConnector::querySql(connection, sql) names(counts) <- SqlRender::snakeCaseToCamelCase(names(counts)) counts <- merge(counts, data.frame(cohortDefinitionId = cohortsToCreate$cohortId, cohortName = cohortsToCreate$name)) write.csv(counts, file.path(outputFolder, "CohortCounts.csv")) }	/Code/R/CreateCohorts.R	permissive	ChenyuL/Covid19EstimationFamotidine	R	false	false	3,534	r	# Copyright 2020 Observational Health Data Sciences and Informatics # # This file is part of Epi786FirstRound # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. .createCohorts <- function(connection, cdmDatabaseSchema, vocabularyDatabaseSchema = cdmDatabaseSchema, cohortDatabaseSchema, cohortTable, oracleTempSchema, outputFolder) { # Create study cohort table structure: sql <- SqlRender::loadRenderTranslateSql(sqlFilename = "CreateCohortTable.sql", packageName = "Epi786FirstRound", dbms = attr(connection, "dbms"), oracleTempSchema = oracleTempSchema, cohort_database_schema = cohortDatabaseSchema, cohort_table = cohortTable) DatabaseConnector::executeSql(connection, sql, progressBar = FALSE, reportOverallTime = FALSE) # Instantiate cohorts: pathToCsv <- system.file("settings", "CohortsToCreate.csv", package = "Epi786FirstRound") cohortsToCreate <- read.csv(pathToCsv) for (i in 1:nrow(cohortsToCreate)) { writeLines(paste("Creating cohort:", cohortsToCreate$name[i])) sql <- SqlRender::loadRenderTranslateSql(sqlFilename = paste0(cohortsToCreate$name[i], ".sql"), packageName = "Epi786FirstRound", dbms = attr(connection, "dbms"), oracleTempSchema = oracleTempSchema, cdm_database_schema = cdmDatabaseSchema, vocabulary_database_schema = vocabularyDatabaseSchema, target_database_schema = cohortDatabaseSchema, target_cohort_table = cohortTable, target_cohort_id = cohortsToCreate$cohortId[i]) DatabaseConnector::executeSql(connection, sql) } # Fetch cohort counts: sql <- "SELECT cohort_definition_id, COUNT(*) AS count FROM @cohort_database_schema.@cohort_table GROUP BY cohort_definition_id" sql <- SqlRender::render(sql, cohort_database_schema = cohortDatabaseSchema, cohort_table = cohortTable) sql <- SqlRender::translate(sql, targetDialect = attr(connection, "dbms")) counts <- DatabaseConnector::querySql(connection, sql) names(counts) <- SqlRender::snakeCaseToCamelCase(names(counts)) counts <- merge(counts, data.frame(cohortDefinitionId = cohortsToCreate$cohortId, cohortName = cohortsToCreate$name)) write.csv(counts, file.path(outputFolder, "CohortCounts.csv")) }
//////////////////////////////////////////////////////////// #Create Map Graph library (ggmap) library (osmar) library(prettymapr) #Import data on Greater London src <- osmsource_api () bb <- center_bbox(-0.129390, 51.532401, 300, 300) #bb <- osmar::corner_bbox(-0.1430397, 51.5265924, -0.1214227, 51.5400746) ua <- osmar::get_osm(bb, source = src) #The osmdata contains an osmar object, which has all the 'nodes' and 'ways' associated with the data. #Two ways of visualizing osmar::plot_nodes(ua) osmar::plot_ways(ua) #Subset the data such that we only have data associated with the roads hways_data <- subset(ua, way_ids = find(ua, way(tags(k == "highway")))) hways <- find(hways_data, way(tags(k == "name"))) hways <- find_down(ua, way(hways)) hways_data <- subset(ua, ids = hways) plot(hways_data) #visualization par(bg = "black") osmar::plot_ways(hways_data, col="blue", bg ='green') osmar::plot_nodes(hways_data, pch=19, cex=0.1, add=T, col="red") #convert to igraph hways_graph = as_igraph(hways_data) hways_graph = as.undirected(hways_graph) plot(hways_graph, vertex.label=NA, vertex.size=4, edge.color="blue") //////////////////////////////////////////////////////////// #Let's get the trace data library(plotKML) library(urltools) library(rgdal) url = "http://api.openstreetmap.org/api/0.6/trackpoints?bbox=12&page=pagenumber" mat = matrix(bb)[,1] val = capture.output(mat[]) url <- param_set(url, key = "bbox", value = "-0.1430397,51.5265924,-0.1214227,51.5400746") for (i in 0:20){ url <- param_set(url, key = "page", value = i) filename <- paste(i,"trace_data.gpx") download.file(url, destfile=filename) } #Read in GPX files that are located in the working directory files <- dir(pattern = "\\.gpx") #route <- readOGR(files[2],"tracks") #coordinates = coordinates(route)[[1]][[1]] #Set up the three vectors to hold your coordinate and path data index <- c() latitude <- c() longitude <- c() for (i in 1:length(files)) { route <- readOGR(files[i], "tracks") coordinates = coordinates(route)[[1]][[1]] index_el <- 0 : nrow(coordinates) index <- c(index, index_el) latitude <- c(latitude, coordinates[,2]) longitude <- c(longitude, coordinates[,1]) } routes <- data.frame(cbind(index, latitude, longitude)) #Get the map of Amsterdam from Google london_map <- qmap('kings cross station', zoom =18, color = 'bw') #Plot the map and the routes on top of that london_map + geom_path(aes(x = longitude, y = latitude, group = factor(index)), colour="#5F35D8", data = routes, alpha=0.3)	/OSM_ver/CW_GPS.R	no_license	soma11soma11/GPS_trace	R	false	false	2,559	r	//////////////////////////////////////////////////////////// #Create Map Graph library (ggmap) library (osmar) library(prettymapr) #Import data on Greater London src <- osmsource_api () bb <- center_bbox(-0.129390, 51.532401, 300, 300) #bb <- osmar::corner_bbox(-0.1430397, 51.5265924, -0.1214227, 51.5400746) ua <- osmar::get_osm(bb, source = src) #The osmdata contains an osmar object, which has all the 'nodes' and 'ways' associated with the data. #Two ways of visualizing osmar::plot_nodes(ua) osmar::plot_ways(ua) #Subset the data such that we only have data associated with the roads hways_data <- subset(ua, way_ids = find(ua, way(tags(k == "highway")))) hways <- find(hways_data, way(tags(k == "name"))) hways <- find_down(ua, way(hways)) hways_data <- subset(ua, ids = hways) plot(hways_data) #visualization par(bg = "black") osmar::plot_ways(hways_data, col="blue", bg ='green') osmar::plot_nodes(hways_data, pch=19, cex=0.1, add=T, col="red") #convert to igraph hways_graph = as_igraph(hways_data) hways_graph = as.undirected(hways_graph) plot(hways_graph, vertex.label=NA, vertex.size=4, edge.color="blue") //////////////////////////////////////////////////////////// #Let's get the trace data library(plotKML) library(urltools) library(rgdal) url = "http://api.openstreetmap.org/api/0.6/trackpoints?bbox=12&page=pagenumber" mat = matrix(bb)[,1] val = capture.output(mat[]) url <- param_set(url, key = "bbox", value = "-0.1430397,51.5265924,-0.1214227,51.5400746") for (i in 0:20){ url <- param_set(url, key = "page", value = i) filename <- paste(i,"trace_data.gpx") download.file(url, destfile=filename) } #Read in GPX files that are located in the working directory files <- dir(pattern = "\\.gpx") #route <- readOGR(files[2],"tracks") #coordinates = coordinates(route)[[1]][[1]] #Set up the three vectors to hold your coordinate and path data index <- c() latitude <- c() longitude <- c() for (i in 1:length(files)) { route <- readOGR(files[i], "tracks") coordinates = coordinates(route)[[1]][[1]] index_el <- 0 : nrow(coordinates) index <- c(index, index_el) latitude <- c(latitude, coordinates[,2]) longitude <- c(longitude, coordinates[,1]) } routes <- data.frame(cbind(index, latitude, longitude)) #Get the map of Amsterdam from Google london_map <- qmap('kings cross station', zoom =18, color = 'bw') #Plot the map and the routes on top of that london_map + geom_path(aes(x = longitude, y = latitude, group = factor(index)), colour="#5F35D8", data = routes, alpha=0.3)
################################################################################## ##### 1 - INITIALIZE SCRIPT PARAMETERS ################################################################################## ## Load the needed libraries and functions library(gdata) library(tm) library(SnowballC) ## Set script parameters set.seed(12345) # for repeatability of random numbers #datafileName<-"c:\\idm\\Step4ReducedNatGeoFileAP1.xlsx" # source file datafileName<-"c:\\idm\\Step4ReducedNatGeoFileAPminusModelA.xlsx" # source file outputFile<-"c:\\idm\\term.csv" ################################################################################## ##### 2 - READ DATA FILE INTO R AND PERFORM DATA PREPROCESSING ################################################################################## ## Read file into R df<-read.xls(datafileName,sheet=1,header=TRUE) numSuppliers<-length(unique(df$Supplier.ID)) ## Create blank dataframe supplier<-sort(unique(df$Supplier.ID),decreasing=FALSE) # supplierIDs in asc order description<-c(rep("",numSuppliers)) procdf<-data.frame(supplier,description) procdf$description<-as.character(procdf$description) ## populate dataframe with text from each supplier from original file ## Loop below operates once for each unique supplier referenced in both ## supplier and procdf variables for (ctr in 1:numSuppliers){ # Loop is running for supplier referenced by supplier[ctr] # Retrieve rows from original dataframe that have # suppliers matching with supplier[ctr] idx<-which(df$Supplier.ID==supplier[ctr]) # how many rows have this supplier? l<-length(idx) # initialize working variable workingText<-c("") # Loop through the matching records and concatenate # text of interest into workingText variable for (j in 1:l){ # get row reference rowidx<-idx[j] # retrieve and concatenate text workingText<-paste0(workingText,df[rowidx,3]) } # perform stemming on the text workingText<-wordStem(workingText,language='porter') # remove duplicate words from the text post stemming and assign # back into procdf procdf[ctr,2]<- vapply(lapply(strsplit(workingText, " "), unique), paste, character(1L), collapse = " ") } ################################################################################## ##### 3 - CREATE DTM AND TFXIDF MATRICES ################################################################################## ## Create corpus object and remove english stop words corpus<-Corpus(VectorSource(procdf$description)) cleancorpus<-tm_map(corpus,removeWords,stopwords("english")) cleancorpus<-tm_map(cleancorpus,removeNumbers) cleancorpus<-tm_map(cleancorpus,removePunctuation) ## Create document term matrix dtm<-DocumentTermMatrix(cleancorpus) ## Create term frequency - Inverse Document frequency matrix dtm_tfxidf<-weightTfIdf(dtm) m<-as.matrix(dtm_tfxidf) # convert into matrix object rownames(m) <- 1:nrow(m) # give appropriate row names (?) test<-rowSums(m) erridx<-which(test==0) zerov<-rep(0,ncol(m)) ################################################################################## ##### 4 - RUN K-MEANS CLUSTERING ON DATA ################################################################################## norm_eucl <- function(m) m/apply(m, MARGIN=1, FUN=function(x) sum(x^2)^.5) m_norm <- norm_eucl(m) # we have four rows that are full of zeroes cl <- kmeans(m_norm, 4) ################################################################################## ##### 5 - PREPARE DATAFRAME TO INSPECT TERMS MANUALLY ################################################################################## tdm<-TermDocumentMatrix(cleancorpus) workingm<-as.matrix(tdm) v <- sort(rowSums(workingm),decreasing=TRUE) termsFreqdf <- data.frame(word = names(v),freq=v) terms<-as.character(tdm[[6]][1]) termofInterest<-c("royalties") container<-subset(workingm,rownames(workingm)==termofInterest) docsWithTerm<-which(container>0) qualifyingSuppliers<-supplier[docsWithTerm] write.csv(qualifyingSuppliers,outputFile)	/R1.3/TextClusteringonNatGeoforClassification.R	no_license	nandatascientist/XeevaIDM	R	false	false	4,305	r	################################################################################## ##### 1 - INITIALIZE SCRIPT PARAMETERS ################################################################################## ## Load the needed libraries and functions library(gdata) library(tm) library(SnowballC) ## Set script parameters set.seed(12345) # for repeatability of random numbers #datafileName<-"c:\\idm\\Step4ReducedNatGeoFileAP1.xlsx" # source file datafileName<-"c:\\idm\\Step4ReducedNatGeoFileAPminusModelA.xlsx" # source file outputFile<-"c:\\idm\\term.csv" ################################################################################## ##### 2 - READ DATA FILE INTO R AND PERFORM DATA PREPROCESSING ################################################################################## ## Read file into R df<-read.xls(datafileName,sheet=1,header=TRUE) numSuppliers<-length(unique(df$Supplier.ID)) ## Create blank dataframe supplier<-sort(unique(df$Supplier.ID),decreasing=FALSE) # supplierIDs in asc order description<-c(rep("",numSuppliers)) procdf<-data.frame(supplier,description) procdf$description<-as.character(procdf$description) ## populate dataframe with text from each supplier from original file ## Loop below operates once for each unique supplier referenced in both ## supplier and procdf variables for (ctr in 1:numSuppliers){ # Loop is running for supplier referenced by supplier[ctr] # Retrieve rows from original dataframe that have # suppliers matching with supplier[ctr] idx<-which(df$Supplier.ID==supplier[ctr]) # how many rows have this supplier? l<-length(idx) # initialize working variable workingText<-c("") # Loop through the matching records and concatenate # text of interest into workingText variable for (j in 1:l){ # get row reference rowidx<-idx[j] # retrieve and concatenate text workingText<-paste0(workingText,df[rowidx,3]) } # perform stemming on the text workingText<-wordStem(workingText,language='porter') # remove duplicate words from the text post stemming and assign # back into procdf procdf[ctr,2]<- vapply(lapply(strsplit(workingText, " "), unique), paste, character(1L), collapse = " ") } ################################################################################## ##### 3 - CREATE DTM AND TFXIDF MATRICES ################################################################################## ## Create corpus object and remove english stop words corpus<-Corpus(VectorSource(procdf$description)) cleancorpus<-tm_map(corpus,removeWords,stopwords("english")) cleancorpus<-tm_map(cleancorpus,removeNumbers) cleancorpus<-tm_map(cleancorpus,removePunctuation) ## Create document term matrix dtm<-DocumentTermMatrix(cleancorpus) ## Create term frequency - Inverse Document frequency matrix dtm_tfxidf<-weightTfIdf(dtm) m<-as.matrix(dtm_tfxidf) # convert into matrix object rownames(m) <- 1:nrow(m) # give appropriate row names (?) test<-rowSums(m) erridx<-which(test==0) zerov<-rep(0,ncol(m)) ################################################################################## ##### 4 - RUN K-MEANS CLUSTERING ON DATA ################################################################################## norm_eucl <- function(m) m/apply(m, MARGIN=1, FUN=function(x) sum(x^2)^.5) m_norm <- norm_eucl(m) # we have four rows that are full of zeroes cl <- kmeans(m_norm, 4) ################################################################################## ##### 5 - PREPARE DATAFRAME TO INSPECT TERMS MANUALLY ################################################################################## tdm<-TermDocumentMatrix(cleancorpus) workingm<-as.matrix(tdm) v <- sort(rowSums(workingm),decreasing=TRUE) termsFreqdf <- data.frame(word = names(v),freq=v) terms<-as.character(tdm[[6]][1]) termofInterest<-c("royalties") container<-subset(workingm,rownames(workingm)==termofInterest) docsWithTerm<-which(container>0) qualifyingSuppliers<-supplier[docsWithTerm] write.csv(qualifyingSuppliers,outputFile)
setwd("/media/vivekkulkarni/362480E21B181E95/Fodder/src") d = read.csv('../real_data/ANMO_Zchannel.csv') d = as.numeric(unlist(d)) plot(as.ts(d)) datas = as.ts(d[1:50000]) plot(datas) a = acf(datas,100) p = pacf(datas,100) d = diff(d) plot(d)	/src/fit_anmodata.R	permissive	vivekkulkarni1/signal_detection	R	false	false	245	r	setwd("/media/vivekkulkarni/362480E21B181E95/Fodder/src") d = read.csv('../real_data/ANMO_Zchannel.csv') d = as.numeric(unlist(d)) plot(as.ts(d)) datas = as.ts(d[1:50000]) plot(datas) a = acf(datas,100) p = pacf(datas,100) d = diff(d) plot(d)
library(openxlsx) library(ggplot2) library(reshape2) #source("~/documents/lab/workspace/Classification_scripts/multiplot.R") source("~/workspace/classification_scripts/multiplot.R") library(zoo) library(gplots) library(RColorBrewer) library(abind) library(gridGraphics) library(grid) library(gridExtra) library(R.matlab) saveAsPng <- T ######### nhp_id <- '504' if (nhp_id == '0059'){condensed <- readRDS('0059_total_condensed.rds') }else if (nhp_id == '504'){condensed <- readRDS('504_total_condensed.rds')} r0 <- which(condensed[,4] == 0) r1 <- which(condensed[,4] == 1) r2 <- which(condensed[,4] == 2) r3 <- which(condensed[,4] == 3) p0 <- which(condensed[,5] == 0) p1 <- which(condensed[,5] == 1) p2 <- which(condensed[,5] == 2) p3 <- which(condensed[,5] == 3) v_3 <- which(condensed[,7] == -3) v_2 <- which(condensed[,7] == -2) v_1 <- which(condensed[,7] == -1) v0 <- which(condensed[,7] == 0) v1 <- which(condensed[,7] == 1) v2 <- which(condensed[,7] == 2) v3 <- which(condensed[,7] == 3) m0 <- which(condensed[,8] == 0) m1 <- which(condensed[,8] == 1) m2 <- which(condensed[,8] == 2) m3 <- which(condensed[,8] == 3) m4 <- which(condensed[,8] == 4) m5 <- which(condensed[,8] == 5) m6 <- which(condensed[,8] == 6) res0 <- which(condensed[,6] == 0) res1 <- which(condensed[,6] == 1) r0_fail <- res0[which(res0 %in% r0)] r1_fail <- res0[which(res0 %in% r1)] r2_fail <- res0[which(res0 %in% r2)] r3_fail <- res0[which(res0 %in% r3)] r0_succ <- res1[which(res1 %in% r0)] r1_succ <- res1[which(res1 %in% r1)] r2_succ <- res1[which(res1 %in% r2)] r3_succ <- res1[which(res1 %in% r3)] p0_fail <- res0[which(res0 %in% p0)] p1_fail <- res0[which(res0 %in% p1)] p2_fail <- res0[which(res0 %in% p2)] p3_fail <- res0[which(res0 %in% p3)] p0_succ <- res1[which(res1 %in% p0)] p1_succ <- res1[which(res1 %in% p1)] p2_succ <- res1[which(res1 %in% p2)] p3_succ <- res1[which(res1 %in% p3)] #reward png(paste("time_", nhp_id,"_reward.png",sep=""),width=8,height=6,units="in",res=500) ravgs <- data.frame(r_values=c(0,1,2,3),reach_time = c(mean(condensed[r0,9]),mean(condensed[r1,9]),mean(condensed[r2,9]),mean(condensed[r3,9])),intertrial = c(mean(condensed[r0,10]),mean(condensed[r1,10]),mean(condensed[r2,10]),mean(condensed[r3,10]))) rstds <- data.frame(r_values=c(0,1,2,3),reach_time = c(sd(condensed[r0,9]),sd(condensed[r1,9]),sd(condensed[r2,9]),sd(condensed[r3,9])),intertrial = c(sd(condensed[r0,10]),sd(condensed[r1,10]),sd(condensed[r2,10]),sd(condensed[r3,10]))) avg_melt <- melt(ravgs,id="r_values",variable.name='type',value.name='avg') std_melt <- melt(rstds,id="r_values",variable.name='type',value.name='std') test <- merge(std_melt,avg_melt,row.names='r_values') test[is.na(test)] <- 0 plt <- ggplot(data=test,aes(x=r_values,y=avg,ymax=avg+std,ymin=avg-std,fill=type)) + geom_bar(position="dodge",stat="identity") + geom_errorbar(position=position_dodge(width=0.9),color="gray32",width=0.25) plt <- plt + scale_fill_manual(values=c("royalblue","seagreen")) + geom_text(aes(y=0.75),size=3,label=sprintf("%0.2f", round(test$avg, digits = 2)),position=position_dodge(width=0.9)) + theme_classic() + labs(title="Average Times",y="Time (s)",x="Reward",fill="") plot(plt) graphics.off() #punishment png(paste("time_", nhp_id,"_punishment.png",sep=""),width=8,height=6,units="in",res=500) pavgs <- data.frame(p_values=c(0,1,2,3),reach_time = c(mean(condensed[p0,9]),mean(condensed[p1,9]),mean(condensed[p2,9]),mean(condensed[p3,9])),intertrial = c(mean(condensed[p0,10]),mean(condensed[p1,10]),mean(condensed[p2,10]),mean(condensed[p3,10]))) pstds <- data.frame(p_values=c(0,1,2,3),reach_time = c(sd(condensed[p0,9]),sd(condensed[p1,9]),sd(condensed[p2,9]),sd(condensed[p3,9])),intertrial = c(sd(condensed[p0,10]),sd(condensed[p1,10]),sd(condensed[p2,10]),sd(condensed[p3,10]))) avg_melt <- melt(pavgs,id="p_values",variable.name='type',value.name='avg') std_melt <- melt(pstds,id="p_values",variable.name='type',value.name='std') test <- merge(std_melt,avg_melt,row.names='p_values') test[is.na(test)] <- 0 plt <- ggplot(data=test,aes(x=p_values,y=avg,ymax=avg+std,ymin=avg-std,fill=type)) + geom_bar(position="dodge",stat="identity") + geom_errorbar(position=position_dodge(width=0.9),color="gray32",width=0.25) plt <- plt + scale_fill_manual(values=c("royalblue","seagreen")) + geom_text(aes(y=0.75),size=3,label=sprintf("%0.2f", round(test$avg, digits = 2)),position=position_dodge(width=0.9)) + theme_classic() + labs(title="Average Times",y="Time (s)",x="Punishment",fill="") plot(plt) graphics.off() #result png(paste("time_", nhp_id,"_result.png",sep=""),width=8,height=6,units="in",res=500) resavgs <- data.frame(res_values=c(0,1),reach_time = c(mean(condensed[res0,9]),mean(condensed[res1,9])),intertrial = c(mean(condensed[res0,10]),mean(condensed[res1,10]))) resstds <- data.frame(res_values=c(0,1),reach_time = c(sd(condensed[res0,9]),sd(condensed[res1,9])),intertrial = c(sd(condensed[res0,10]),sd(condensed[res1,10]))) avg_melt <- melt(resavgs,id="res_values",variable.name='type',value.name='avg') std_melt <- melt(resstds,id="res_values",variable.name='type',value.name='std') test <- merge(std_melt,avg_melt,row.names='res_values') test[is.na(test)] <- 0 plt <- ggplot(data=test,aes(x=res_values,y=avg,ymax=avg+std,ymin=avg-std,fill=type)) + geom_bar(position="dodge",stat="identity") + geom_errorbar(position=position_dodge(width=0.9),color="gray32",width=0.25) plt <- plt + scale_fill_manual(values=c("royalblue","seagreen")) + geom_text(aes(y=0.75),size=3,label=sprintf("%0.2f", round(test$avg, digits = 2)),position=position_dodge(width=0.9)) + theme_classic() + labs(title="Average Times",y="Time (s)",x="Result",fill="") + scale_x_discrete(limits=0:1,labels=c("fail","succ")) plot(plt) graphics.off() #value png(paste("time_", nhp_id,"_value.png",sep=""),width=8,height=6,units="in",res=500) vavgs <- data.frame(v_values=c(-3,-2,-1,0,1,2,3),reach_time = c(mean(condensed[v_3,9]),mean(condensed[v_2,9]),mean(condensed[v_1,9]),mean(condensed[v0,9]),mean(condensed[v1,9]),mean(condensed[v2,9]),mean(condensed[v3,9])),intertrial = c(mean(condensed[v_3,10]),mean(condensed[v_2,10]),mean(condensed[v_1,10]),mean(condensed[v0,10]),mean(condensed[v1,10]),mean(condensed[v2,10]),mean(condensed[v3,10]))) vstds <- data.frame(v_values=c(-3,-2,-1,0,1,2,3),reach_time = c(sd(condensed[v_3,9]),sd(condensed[v_2,9]),sd(condensed[v_1,9]),sd(condensed[v0,9]),sd(condensed[v1,9]),sd(condensed[v2,9]),sd(condensed[v3,9])),intertrial = c(sd(condensed[v_3,10]),sd(condensed[v_2,10]),sd(condensed[v_1,10]),sd(condensed[v0,10]),sd(condensed[v1,10]),sd(condensed[v2,10]),sd(condensed[v3,10]))) avg_melt <- melt(vavgs,id="v_values",variable.name='type',value.name='avg') std_melt <- melt(vstds,id="v_values",variable.name='type',value.name='std') test <- merge(std_melt,avg_melt,row.names='v_values') test[is.na(test)] <- 0 plt <- ggplot(data=test,aes(x=v_values,y=avg,ymax=avg+std,ymin=avg-std,fill=type)) + geom_bar(position="dodge",stat="identity") + geom_errorbar(position=position_dodge(width=0.9),color="gray32",width=0.25) plt <- plt + scale_fill_manual(values=c("royalblue","seagreen")) + geom_text(aes(y=0.75),size=3,label=sprintf("%0.2f", round(test$avg, digits = 2)),position=position_dodge(width=0.9)) + theme_classic() + labs(title="Average Times",y="Time (s)",x="Value",fill="") plot(plt) graphics.off() #motivation png(paste("time_", nhp_id,"_motivation.png",sep=""),width=8,height=6,units="in",res=500) mavgs <- data.frame(m_values=c(0,1,2,3,4,5,6),reach_time = c(mean(condensed[m0,9]),mean(condensed[m1,9]),mean(condensed[m2,9]),mean(condensed[m3,9]),mean(condensed[m4,9]),mean(condensed[m5,9]),mean(condensed[m6,9])),intertrial = c(mean(condensed[m0,10]),mean(condensed[m1,10]),mean(condensed[m2,10]),mean(condensed[m3,10]),mean(condensed[m4,10]),mean(condensed[m5,10]),mean(condensed[m6,10]))) mstds <- data.frame(m_values=c(0,1,2,3,4,5,6),reach_time = c(sd(condensed[m0,9]),sd(condensed[m1,9]),sd(condensed[m2,9]),sd(condensed[m3,9]),sd(condensed[m4,9]),sd(condensed[m5,9]),sd(condensed[m6,9])),intertrial = c(sd(condensed[m0,10]),sd(condensed[m1,10]),sd(condensed[m2,10]),sd(condensed[m3,10]),sd(condensed[m4,10]),sd(condensed[m5,10]),sd(condensed[m6,10]))) avg_melt <- melt(mavgs,id="m_values",variable.name='type',value.name='avg') std_melt <- melt(mstds,id="m_values",variable.name='type',value.name='std') test <- merge(std_melt,avg_melt,row.names='m_values') test[is.na(test)] <- 0 plt <- ggplot(data=test,aes(x=m_values,y=avg,ymax=avg+std,ymin=avg-std,fill=type)) + geom_bar(position="dodge",stat="identity") + geom_errorbar(position=position_dodge(width=0.9),color="gray32",width=0.25) plt <- plt + scale_fill_manual(values=c("royalblue","seagreen")) + geom_text(aes(y=0.75),size=3,label=sprintf("%0.2f", round(test$avg, digits = 2)),position=position_dodge(width=0.9)) + theme_classic() + labs(title="Average Times",y="Time (s)",x="Motivation",fill="") plot(plt) graphics.off() ########## ########## #reward sf png(paste("time_", nhp_id,"_reward_sf.png",sep=""),width=8,height=6,units="in",res=500) r_s_avgs <- data.frame(r_values=c(0,1,2,3),reach_time_s = c(mean(condensed[r0_succ,9]),mean(condensed[r1_succ,9]),mean(condensed[r2_succ,9]),mean(condensed[r3_succ,9])),intertrial_s = c(mean(condensed[r0_succ,10]),mean(condensed[r1_succ,10]),mean(condensed[r2_succ,10]),mean(condensed[r3_succ,10]))) r_s_stds <- data.frame(r_values=c(0,1,2,3),reach_time_s = c(sd(condensed[r0_succ,9]),sd(condensed[r1_succ,9]),sd(condensed[r2_succ,9]),sd(condensed[r3_succ,9])),intertrial_s = c(sd(condensed[r0_succ,10]),sd(condensed[r1_succ,10]),sd(condensed[r2_succ,10]),sd(condensed[r3_succ,10]))) r_f_avgs <- data.frame(r_values=c(0,1,2,3),reach_time_f = c(mean(condensed[r0_fail,9]),mean(condensed[r1_fail,9]),mean(condensed[r2_fail,9]),mean(condensed[r3_fail,9])),intertrial_f = c(mean(condensed[r0_fail,10]),mean(condensed[r1_fail,10]),mean(condensed[r2_fail,10]),mean(condensed[r3_fail,10]))) r_f_stds <- data.frame(r_values=c(0,1,2,3),reach_time_f = c(sd(condensed[r0_fail,9]),sd(condensed[r1_fail,9]),sd(condensed[r2_fail,9]),sd(condensed[r3_fail,9])),intertrial_f = c(sd(condensed[r0_fail,10]),sd(condensed[r1_fail,10]),sd(condensed[r2_fail,10]),sd(condensed[r3_fail,10]))) avg_s_melt <- melt(r_s_avgs,id="r_values",variable.name='type',value.name='avg') std_s_melt <- melt(r_s_stds,id="r_values",variable.name='type',value.name='std') avg_f_melt <- melt(r_f_avgs,id="r_values",variable.name='type',value.name='avg') std_f_melt <- melt(r_f_stds,id="r_values",variable.name='type',value.name='std') test_s <- merge(std_s_melt,avg_s_melt,row.names='r_values') test_f <- merge(std_f_melt,avg_f_melt,row.names='r_values') test <- rbind(test_s,test_f) test[is.na(test)] <- 0 plt <- ggplot(data=test,aes(x=r_values,y=avg,ymax=avg+std,ymin=avg-std,fill=type)) + geom_bar(position="dodge",stat="identity") + geom_errorbar(position=position_dodge(width=0.9),color="gray32",width=0.25) plt <- plt + scale_fill_manual(values=c("royalblue","seagreen","paleturquoise","lightgreen")) + theme_classic() + labs(title="Average Times",y="Time (s)",x="Reward",fill="") # + geom_text(aes(y=0.75),size=3,label=sprintf("%0.2f", round(test$avg, digits = 2)),position=position_dodge(width=0.9)) plot(plt) graphics.off() #punishment sf png(paste("time_", nhp_id,"_punishment_sf.png",sep=""),width=8,height=6,units="in",res=500) p_s_avgs <- data.frame(p_values=c(0,1,2,3),reach_time_s = c(mean(condensed[p0_succ,9]),mean(condensed[p1_succ,9]),mean(condensed[p2_succ,9]),mean(condensed[p3_succ,9])),intertrial_s = c(mean(condensed[p0_succ,10]),mean(condensed[p1_succ,10]),mean(condensed[p2_succ,10]),mean(condensed[p3_succ,10]))) p_s_stds <- data.frame(p_values=c(0,1,2,3),reach_time_s = c(sd(condensed[p0_succ,9]),sd(condensed[p1_succ,9]),sd(condensed[p2_succ,9]),sd(condensed[p3_succ,9])),intertrial_s = c(sd(condensed[p0_succ,10]),sd(condensed[p1_succ,10]),sd(condensed[p2_succ,10]),sd(condensed[p3_succ,10]))) p_f_avgs <- data.frame(p_values=c(0,1,2,3),reach_time_f = c(mean(condensed[p0_fail,9]),mean(condensed[p1_fail,9]),mean(condensed[p2_fail,9]),mean(condensed[p3_fail,9])),intertrial_f = c(mean(condensed[p0_fail,10]),mean(condensed[p1_fail,10]),mean(condensed[p2_fail,10]),mean(condensed[p3_fail,10]))) p_f_stds <- data.frame(p_values=c(0,1,2,3),reach_time_f = c(sd(condensed[p0_fail,9]),sd(condensed[p1_fail,9]),sd(condensed[p2_fail,9]),sd(condensed[p3_fail,9])),intertrial_f = c(sd(condensed[p0_fail,10]),sd(condensed[p1_fail,10]),sd(condensed[p2_fail,10]),sd(condensed[p3_fail,10]))) avg_s_melt <- melt(p_s_avgs,id="p_values",variable.name='type',value.name='avg') std_s_melt <- melt(p_s_stds,id="p_values",variable.name='type',value.name='std') avg_f_melt <- melt(p_f_avgs,id="p_values",variable.name='type',value.name='avg') std_f_melt <- melt(p_f_stds,id="p_values",variable.name='type',value.name='std') test_s <- merge(std_s_melt,avg_s_melt,row.names='p_values') test_f <- merge(std_f_melt,avg_f_melt,row.names='p_values') test <- rbind(test_s,test_f) test[is.na(test)] <- 0 plt <- ggplot(data=test,aes(x=p_values,y=avg,ymax=avg+std,ymin=avg-std,fill=type)) + geom_bar(position="dodge",stat="identity") + geom_errorbar(position=position_dodge(width=0.9),color="gray32",width=0.25) plt <- plt + scale_fill_manual(values=c("royalblue","seagreen","paleturquoise","lightgreen")) + theme_classic() + labs(title="Average Times",y="Time (s)",x="Punishment",fill="") # + geom_text(aes(y=0.75),size=3,label=sprintf("%0.2f", round(test$avg, digits = 2)),position=position_dodge(width=0.9)) plot(plt) graphics.off() rm(list=ls())	/timing_plots_all.R	no_license	jhess90/classification_scripts	R	false	false	13,598	r	library(openxlsx) library(ggplot2) library(reshape2) #source("~/documents/lab/workspace/Classification_scripts/multiplot.R") source("~/workspace/classification_scripts/multiplot.R") library(zoo) library(gplots) library(RColorBrewer) library(abind) library(gridGraphics) library(grid) library(gridExtra) library(R.matlab) saveAsPng <- T ######### nhp_id <- '504' if (nhp_id == '0059'){condensed <- readRDS('0059_total_condensed.rds') }else if (nhp_id == '504'){condensed <- readRDS('504_total_condensed.rds')} r0 <- which(condensed[,4] == 0) r1 <- which(condensed[,4] == 1) r2 <- which(condensed[,4] == 2) r3 <- which(condensed[,4] == 3) p0 <- which(condensed[,5] == 0) p1 <- which(condensed[,5] == 1) p2 <- which(condensed[,5] == 2) p3 <- which(condensed[,5] == 3) v_3 <- which(condensed[,7] == -3) v_2 <- which(condensed[,7] == -2) v_1 <- which(condensed[,7] == -1) v0 <- which(condensed[,7] == 0) v1 <- which(condensed[,7] == 1) v2 <- which(condensed[,7] == 2) v3 <- which(condensed[,7] == 3) m0 <- which(condensed[,8] == 0) m1 <- which(condensed[,8] == 1) m2 <- which(condensed[,8] == 2) m3 <- which(condensed[,8] == 3) m4 <- which(condensed[,8] == 4) m5 <- which(condensed[,8] == 5) m6 <- which(condensed[,8] == 6) res0 <- which(condensed[,6] == 0) res1 <- which(condensed[,6] == 1) r0_fail <- res0[which(res0 %in% r0)] r1_fail <- res0[which(res0 %in% r1)] r2_fail <- res0[which(res0 %in% r2)] r3_fail <- res0[which(res0 %in% r3)] r0_succ <- res1[which(res1 %in% r0)] r1_succ <- res1[which(res1 %in% r1)] r2_succ <- res1[which(res1 %in% r2)] r3_succ <- res1[which(res1 %in% r3)] p0_fail <- res0[which(res0 %in% p0)] p1_fail <- res0[which(res0 %in% p1)] p2_fail <- res0[which(res0 %in% p2)] p3_fail <- res0[which(res0 %in% p3)] p0_succ <- res1[which(res1 %in% p0)] p1_succ <- res1[which(res1 %in% p1)] p2_succ <- res1[which(res1 %in% p2)] p3_succ <- res1[which(res1 %in% p3)] #reward png(paste("time_", nhp_id,"_reward.png",sep=""),width=8,height=6,units="in",res=500) ravgs <- data.frame(r_values=c(0,1,2,3),reach_time = c(mean(condensed[r0,9]),mean(condensed[r1,9]),mean(condensed[r2,9]),mean(condensed[r3,9])),intertrial = c(mean(condensed[r0,10]),mean(condensed[r1,10]),mean(condensed[r2,10]),mean(condensed[r3,10]))) rstds <- data.frame(r_values=c(0,1,2,3),reach_time = c(sd(condensed[r0,9]),sd(condensed[r1,9]),sd(condensed[r2,9]),sd(condensed[r3,9])),intertrial = c(sd(condensed[r0,10]),sd(condensed[r1,10]),sd(condensed[r2,10]),sd(condensed[r3,10]))) avg_melt <- melt(ravgs,id="r_values",variable.name='type',value.name='avg') std_melt <- melt(rstds,id="r_values",variable.name='type',value.name='std') test <- merge(std_melt,avg_melt,row.names='r_values') test[is.na(test)] <- 0 plt <- ggplot(data=test,aes(x=r_values,y=avg,ymax=avg+std,ymin=avg-std,fill=type)) + geom_bar(position="dodge",stat="identity") + geom_errorbar(position=position_dodge(width=0.9),color="gray32",width=0.25) plt <- plt + scale_fill_manual(values=c("royalblue","seagreen")) + geom_text(aes(y=0.75),size=3,label=sprintf("%0.2f", round(test$avg, digits = 2)),position=position_dodge(width=0.9)) + theme_classic() + labs(title="Average Times",y="Time (s)",x="Reward",fill="") plot(plt) graphics.off() #punishment png(paste("time_", nhp_id,"_punishment.png",sep=""),width=8,height=6,units="in",res=500) pavgs <- data.frame(p_values=c(0,1,2,3),reach_time = c(mean(condensed[p0,9]),mean(condensed[p1,9]),mean(condensed[p2,9]),mean(condensed[p3,9])),intertrial = c(mean(condensed[p0,10]),mean(condensed[p1,10]),mean(condensed[p2,10]),mean(condensed[p3,10]))) pstds <- data.frame(p_values=c(0,1,2,3),reach_time = c(sd(condensed[p0,9]),sd(condensed[p1,9]),sd(condensed[p2,9]),sd(condensed[p3,9])),intertrial = c(sd(condensed[p0,10]),sd(condensed[p1,10]),sd(condensed[p2,10]),sd(condensed[p3,10]))) avg_melt <- melt(pavgs,id="p_values",variable.name='type',value.name='avg') std_melt <- melt(pstds,id="p_values",variable.name='type',value.name='std') test <- merge(std_melt,avg_melt,row.names='p_values') test[is.na(test)] <- 0 plt <- ggplot(data=test,aes(x=p_values,y=avg,ymax=avg+std,ymin=avg-std,fill=type)) + geom_bar(position="dodge",stat="identity") + geom_errorbar(position=position_dodge(width=0.9),color="gray32",width=0.25) plt <- plt + scale_fill_manual(values=c("royalblue","seagreen")) + geom_text(aes(y=0.75),size=3,label=sprintf("%0.2f", round(test$avg, digits = 2)),position=position_dodge(width=0.9)) + theme_classic() + labs(title="Average Times",y="Time (s)",x="Punishment",fill="") plot(plt) graphics.off() #result png(paste("time_", nhp_id,"_result.png",sep=""),width=8,height=6,units="in",res=500) resavgs <- data.frame(res_values=c(0,1),reach_time = c(mean(condensed[res0,9]),mean(condensed[res1,9])),intertrial = c(mean(condensed[res0,10]),mean(condensed[res1,10]))) resstds <- data.frame(res_values=c(0,1),reach_time = c(sd(condensed[res0,9]),sd(condensed[res1,9])),intertrial = c(sd(condensed[res0,10]),sd(condensed[res1,10]))) avg_melt <- melt(resavgs,id="res_values",variable.name='type',value.name='avg') std_melt <- melt(resstds,id="res_values",variable.name='type',value.name='std') test <- merge(std_melt,avg_melt,row.names='res_values') test[is.na(test)] <- 0 plt <- ggplot(data=test,aes(x=res_values,y=avg,ymax=avg+std,ymin=avg-std,fill=type)) + geom_bar(position="dodge",stat="identity") + geom_errorbar(position=position_dodge(width=0.9),color="gray32",width=0.25) plt <- plt + scale_fill_manual(values=c("royalblue","seagreen")) + geom_text(aes(y=0.75),size=3,label=sprintf("%0.2f", round(test$avg, digits = 2)),position=position_dodge(width=0.9)) + theme_classic() + labs(title="Average Times",y="Time (s)",x="Result",fill="") + scale_x_discrete(limits=0:1,labels=c("fail","succ")) plot(plt) graphics.off() #value png(paste("time_", nhp_id,"_value.png",sep=""),width=8,height=6,units="in",res=500) vavgs <- data.frame(v_values=c(-3,-2,-1,0,1,2,3),reach_time = c(mean(condensed[v_3,9]),mean(condensed[v_2,9]),mean(condensed[v_1,9]),mean(condensed[v0,9]),mean(condensed[v1,9]),mean(condensed[v2,9]),mean(condensed[v3,9])),intertrial = c(mean(condensed[v_3,10]),mean(condensed[v_2,10]),mean(condensed[v_1,10]),mean(condensed[v0,10]),mean(condensed[v1,10]),mean(condensed[v2,10]),mean(condensed[v3,10]))) vstds <- data.frame(v_values=c(-3,-2,-1,0,1,2,3),reach_time = c(sd(condensed[v_3,9]),sd(condensed[v_2,9]),sd(condensed[v_1,9]),sd(condensed[v0,9]),sd(condensed[v1,9]),sd(condensed[v2,9]),sd(condensed[v3,9])),intertrial = c(sd(condensed[v_3,10]),sd(condensed[v_2,10]),sd(condensed[v_1,10]),sd(condensed[v0,10]),sd(condensed[v1,10]),sd(condensed[v2,10]),sd(condensed[v3,10]))) avg_melt <- melt(vavgs,id="v_values",variable.name='type',value.name='avg') std_melt <- melt(vstds,id="v_values",variable.name='type',value.name='std') test <- merge(std_melt,avg_melt,row.names='v_values') test[is.na(test)] <- 0 plt <- ggplot(data=test,aes(x=v_values,y=avg,ymax=avg+std,ymin=avg-std,fill=type)) + geom_bar(position="dodge",stat="identity") + geom_errorbar(position=position_dodge(width=0.9),color="gray32",width=0.25) plt <- plt + scale_fill_manual(values=c("royalblue","seagreen")) + geom_text(aes(y=0.75),size=3,label=sprintf("%0.2f", round(test$avg, digits = 2)),position=position_dodge(width=0.9)) + theme_classic() + labs(title="Average Times",y="Time (s)",x="Value",fill="") plot(plt) graphics.off() #motivation png(paste("time_", nhp_id,"_motivation.png",sep=""),width=8,height=6,units="in",res=500) mavgs <- data.frame(m_values=c(0,1,2,3,4,5,6),reach_time = c(mean(condensed[m0,9]),mean(condensed[m1,9]),mean(condensed[m2,9]),mean(condensed[m3,9]),mean(condensed[m4,9]),mean(condensed[m5,9]),mean(condensed[m6,9])),intertrial = c(mean(condensed[m0,10]),mean(condensed[m1,10]),mean(condensed[m2,10]),mean(condensed[m3,10]),mean(condensed[m4,10]),mean(condensed[m5,10]),mean(condensed[m6,10]))) mstds <- data.frame(m_values=c(0,1,2,3,4,5,6),reach_time = c(sd(condensed[m0,9]),sd(condensed[m1,9]),sd(condensed[m2,9]),sd(condensed[m3,9]),sd(condensed[m4,9]),sd(condensed[m5,9]),sd(condensed[m6,9])),intertrial = c(sd(condensed[m0,10]),sd(condensed[m1,10]),sd(condensed[m2,10]),sd(condensed[m3,10]),sd(condensed[m4,10]),sd(condensed[m5,10]),sd(condensed[m6,10]))) avg_melt <- melt(mavgs,id="m_values",variable.name='type',value.name='avg') std_melt <- melt(mstds,id="m_values",variable.name='type',value.name='std') test <- merge(std_melt,avg_melt,row.names='m_values') test[is.na(test)] <- 0 plt <- ggplot(data=test,aes(x=m_values,y=avg,ymax=avg+std,ymin=avg-std,fill=type)) + geom_bar(position="dodge",stat="identity") + geom_errorbar(position=position_dodge(width=0.9),color="gray32",width=0.25) plt <- plt + scale_fill_manual(values=c("royalblue","seagreen")) + geom_text(aes(y=0.75),size=3,label=sprintf("%0.2f", round(test$avg, digits = 2)),position=position_dodge(width=0.9)) + theme_classic() + labs(title="Average Times",y="Time (s)",x="Motivation",fill="") plot(plt) graphics.off() ########## ########## #reward sf png(paste("time_", nhp_id,"_reward_sf.png",sep=""),width=8,height=6,units="in",res=500) r_s_avgs <- data.frame(r_values=c(0,1,2,3),reach_time_s = c(mean(condensed[r0_succ,9]),mean(condensed[r1_succ,9]),mean(condensed[r2_succ,9]),mean(condensed[r3_succ,9])),intertrial_s = c(mean(condensed[r0_succ,10]),mean(condensed[r1_succ,10]),mean(condensed[r2_succ,10]),mean(condensed[r3_succ,10]))) r_s_stds <- data.frame(r_values=c(0,1,2,3),reach_time_s = c(sd(condensed[r0_succ,9]),sd(condensed[r1_succ,9]),sd(condensed[r2_succ,9]),sd(condensed[r3_succ,9])),intertrial_s = c(sd(condensed[r0_succ,10]),sd(condensed[r1_succ,10]),sd(condensed[r2_succ,10]),sd(condensed[r3_succ,10]))) r_f_avgs <- data.frame(r_values=c(0,1,2,3),reach_time_f = c(mean(condensed[r0_fail,9]),mean(condensed[r1_fail,9]),mean(condensed[r2_fail,9]),mean(condensed[r3_fail,9])),intertrial_f = c(mean(condensed[r0_fail,10]),mean(condensed[r1_fail,10]),mean(condensed[r2_fail,10]),mean(condensed[r3_fail,10]))) r_f_stds <- data.frame(r_values=c(0,1,2,3),reach_time_f = c(sd(condensed[r0_fail,9]),sd(condensed[r1_fail,9]),sd(condensed[r2_fail,9]),sd(condensed[r3_fail,9])),intertrial_f = c(sd(condensed[r0_fail,10]),sd(condensed[r1_fail,10]),sd(condensed[r2_fail,10]),sd(condensed[r3_fail,10]))) avg_s_melt <- melt(r_s_avgs,id="r_values",variable.name='type',value.name='avg') std_s_melt <- melt(r_s_stds,id="r_values",variable.name='type',value.name='std') avg_f_melt <- melt(r_f_avgs,id="r_values",variable.name='type',value.name='avg') std_f_melt <- melt(r_f_stds,id="r_values",variable.name='type',value.name='std') test_s <- merge(std_s_melt,avg_s_melt,row.names='r_values') test_f <- merge(std_f_melt,avg_f_melt,row.names='r_values') test <- rbind(test_s,test_f) test[is.na(test)] <- 0 plt <- ggplot(data=test,aes(x=r_values,y=avg,ymax=avg+std,ymin=avg-std,fill=type)) + geom_bar(position="dodge",stat="identity") + geom_errorbar(position=position_dodge(width=0.9),color="gray32",width=0.25) plt <- plt + scale_fill_manual(values=c("royalblue","seagreen","paleturquoise","lightgreen")) + theme_classic() + labs(title="Average Times",y="Time (s)",x="Reward",fill="") # + geom_text(aes(y=0.75),size=3,label=sprintf("%0.2f", round(test$avg, digits = 2)),position=position_dodge(width=0.9)) plot(plt) graphics.off() #punishment sf png(paste("time_", nhp_id,"_punishment_sf.png",sep=""),width=8,height=6,units="in",res=500) p_s_avgs <- data.frame(p_values=c(0,1,2,3),reach_time_s = c(mean(condensed[p0_succ,9]),mean(condensed[p1_succ,9]),mean(condensed[p2_succ,9]),mean(condensed[p3_succ,9])),intertrial_s = c(mean(condensed[p0_succ,10]),mean(condensed[p1_succ,10]),mean(condensed[p2_succ,10]),mean(condensed[p3_succ,10]))) p_s_stds <- data.frame(p_values=c(0,1,2,3),reach_time_s = c(sd(condensed[p0_succ,9]),sd(condensed[p1_succ,9]),sd(condensed[p2_succ,9]),sd(condensed[p3_succ,9])),intertrial_s = c(sd(condensed[p0_succ,10]),sd(condensed[p1_succ,10]),sd(condensed[p2_succ,10]),sd(condensed[p3_succ,10]))) p_f_avgs <- data.frame(p_values=c(0,1,2,3),reach_time_f = c(mean(condensed[p0_fail,9]),mean(condensed[p1_fail,9]),mean(condensed[p2_fail,9]),mean(condensed[p3_fail,9])),intertrial_f = c(mean(condensed[p0_fail,10]),mean(condensed[p1_fail,10]),mean(condensed[p2_fail,10]),mean(condensed[p3_fail,10]))) p_f_stds <- data.frame(p_values=c(0,1,2,3),reach_time_f = c(sd(condensed[p0_fail,9]),sd(condensed[p1_fail,9]),sd(condensed[p2_fail,9]),sd(condensed[p3_fail,9])),intertrial_f = c(sd(condensed[p0_fail,10]),sd(condensed[p1_fail,10]),sd(condensed[p2_fail,10]),sd(condensed[p3_fail,10]))) avg_s_melt <- melt(p_s_avgs,id="p_values",variable.name='type',value.name='avg') std_s_melt <- melt(p_s_stds,id="p_values",variable.name='type',value.name='std') avg_f_melt <- melt(p_f_avgs,id="p_values",variable.name='type',value.name='avg') std_f_melt <- melt(p_f_stds,id="p_values",variable.name='type',value.name='std') test_s <- merge(std_s_melt,avg_s_melt,row.names='p_values') test_f <- merge(std_f_melt,avg_f_melt,row.names='p_values') test <- rbind(test_s,test_f) test[is.na(test)] <- 0 plt <- ggplot(data=test,aes(x=p_values,y=avg,ymax=avg+std,ymin=avg-std,fill=type)) + geom_bar(position="dodge",stat="identity") + geom_errorbar(position=position_dodge(width=0.9),color="gray32",width=0.25) plt <- plt + scale_fill_manual(values=c("royalblue","seagreen","paleturquoise","lightgreen")) + theme_classic() + labs(title="Average Times",y="Time (s)",x="Punishment",fill="") # + geom_text(aes(y=0.75),size=3,label=sprintf("%0.2f", round(test$avg, digits = 2)),position=position_dodge(width=0.9)) plot(plt) graphics.off() rm(list=ls())
#' Mosaic plots. #' #' @export #' #' @description #' A mosaic plot is a convenient graphical summary of the conditional ditributions #' in a contingency table and is composed of spines in alternating directions. #' #' #' @inheritParams ggplot2::layer #' @param divider Divider function. The default divider function is mosaic() which will use spines in alternating directions. The four options for partioning: #' \itemize{ #' \item \code{vspine} Vertical spine partition: width constant, height varies. #' \item \code{hspine} Horizontal spine partition: height constant, width varies. #' \item \code{vbar} Vertical bar partition: height constant, width varies. #' \item \code{hbar} Horizontal bar partition: width constant, height varies. #' } #' @param offset Set the space between the first spine #' @param na.rm If \code{FALSE} (the default), removes missing values with a warning. If \code{TRUE} silently removes missing values. #' @param ... other arguments passed on to \code{layer}. These are often aesthetics, used to set an aesthetic to a fixed value, like \code{color = 'red'} or \code{size = 3}. They may also be parameters to the paired geom/stat. #' @examples #' #' data(Titanic) #' titanic <- as.data.frame(Titanic) #' titanic$Survived <- factor(titanic$Survived, levels=c("Yes", "No")) #' #' #' ggplot(data=titanic) + #' geom_mosaic(aes(weight=Freq, x=product(Class), fill=Survived)) #' # good practice: use the 'dependent' variable (or most important variable) #' # as fill variable #' ggplot(data=titanic) + #' geom_mosaic(aes(weight=Freq, x=product(Class, Age), fill=Survived)) #' #' # we can change where we define variables #' ggplot(data=titanic, aes(weight = Freq, fill=Survived, x=product(Class, Age))) + #' geom_mosaic() #' #' ggplot(data=titanic) + #' geom_mosaic(aes(weight=Freq, x=product(Class), conds=product(Age), fill=Survived)) #' ggplot(data=titanic) + #' geom_mosaic(aes(weight=Freq, x=product(Survived, Class), fill=Age)) #' #' \dontrun{ #' data(happy, package="productplots") #' #' ggplot(data = happy) + geom_mosaic(aes(x=product(happy))) #' ggplot(data = happy) + geom_mosaic(aes(x=product(happy))) + #' coord_flip() #' # weighting is important #' ggplot(data = happy) + #' geom_mosaic(aes(weight=wtssall, x=product(happy))) #' ggplot(data = happy) + geom_mosaic(aes(weight=wtssall, x=product(health), fill=happy)) + #' theme(axis.text.x=element_text(angle=35)) #' ggplot(data = happy) + #' geom_mosaic(aes(weight=wtssall, x=product(health), fill=happy), na.rm=TRUE) #' ggplot(data = happy) + #' geom_mosaic(aes(weight=wtssall, x=product(health, sex, degree), fill=happy), #' na.rm=TRUE) #' #' # here is where a bit more control over the spacing of the bars is helpful: #' # set labels manually: #' ggplot(data = happy) + #' geom_mosaic(aes(weight=wtssall, x=product(age), fill=happy), na.rm=TRUE, offset=0) + #' scale_x_product("Age", labels=c(17+1:72, "NA")) #' # thin out labels manually: #' labels <- c(17+1:72, NA) #' labels[labels %% 5 != 0] <- "" #' ggplot(data = happy) + #' geom_mosaic(aes(weight=wtssall, x=product(age), fill=happy), na.rm=TRUE, offset=0) + #' scale_x_product("Age", labels=labels) #' ggplot(data = happy) + #' geom_mosaic(aes(weight=wtssall, x=product(age), fill=happy, conds = sex), #' divider=mosaic("v"), na.rm=TRUE, offset=0.001) + #' scale_x_product("Age", labels=labels) #' # facetting works!!!! #' ggplot(data = happy) + #' geom_mosaic(aes(weight=wtssall, x=product(age), fill=happy), na.rm=TRUE, offset = 0) + #' facet_grid(sex~.) + #' scale_x_product("Age", labels=labels) #' #' ggplot(data = happy) + #' geom_mosaic(aes(weight = wtssall, x = product(happy, finrela, health)), #' divider=mosaic("h")) #' ggplot(data = happy) + #' geom_mosaic(aes(weight = wtssall, x = product(happy, finrela, health)), offset=.005) #' #' # Spine example #' ggplot(data = happy) + #' geom_mosaic(aes(weight = wtssall, x = product(health), fill = health)) + #' facet_grid(happy~.) #' } geom_mosaic <- function(mapping = NULL, data = NULL, stat = "mosaic", position = "identity", na.rm = FALSE, divider = mosaic(), offset = 0.01, show.legend = NA, inherit.aes = TRUE, ...) { ggplot2::layer( data = data, mapping = mapping, stat = stat, geom = GeomMosaic, position = position, show.legend = show.legend, inherit.aes = inherit.aes, params = list( na.rm = na.rm, divider = divider, offset = offset, ... ) ) } #' @importFrom grid grobTree GeomMosaic <- ggplot2::ggproto( "GeomMosaic", ggplot2::Geom, setup_data = function(data, params) { # cat("setup_data in GeomMosaic\n") # browser() data }, required_aes = c("xmin", "xmax", "ymin", "ymax"), default_aes = ggplot2::aes(width = 0.75, linetype = "solid", fontsize=5, shape = 19, colour = NA, size = .1, fill = "grey30", alpha = .8, stroke = 0.1, linewidth=.1, weight = 1, x = NULL, y = NULL, conds = NULL), draw_panel = function(data, panel_scales, coord) { # cat("draw_panel in GeomMosaic\n") # browser() if (all(is.na(data$colour))) data$colour <- alpha(data$fill, data$alpha) # regard alpha in colour determination GeomRect$draw_panel(subset(data, level==max(data$level)), panel_scales, coord) }, check_aesthetics = function(x, n) { ns <- vapply(x, length, numeric(1)) good <- ns == 1L \| ns == n if (all(good)) { return() } # browser() stop( "Aesthetics must be either length 1 or the same as the data (", n, "): ", paste(names(!good), collapse = ", "), call. = FALSE ) }, draw_key = ggplot2::draw_key_rect )	/R/geom-mosaic.r	no_license	njtierney/ggmosaic	R	false	false	5,783	r	#' Mosaic plots. #' #' @export #' #' @description #' A mosaic plot is a convenient graphical summary of the conditional ditributions #' in a contingency table and is composed of spines in alternating directions. #' #' #' @inheritParams ggplot2::layer #' @param divider Divider function. The default divider function is mosaic() which will use spines in alternating directions. The four options for partioning: #' \itemize{ #' \item \code{vspine} Vertical spine partition: width constant, height varies. #' \item \code{hspine} Horizontal spine partition: height constant, width varies. #' \item \code{vbar} Vertical bar partition: height constant, width varies. #' \item \code{hbar} Horizontal bar partition: width constant, height varies. #' } #' @param offset Set the space between the first spine #' @param na.rm If \code{FALSE} (the default), removes missing values with a warning. If \code{TRUE} silently removes missing values. #' @param ... other arguments passed on to \code{layer}. These are often aesthetics, used to set an aesthetic to a fixed value, like \code{color = 'red'} or \code{size = 3}. They may also be parameters to the paired geom/stat. #' @examples #' #' data(Titanic) #' titanic <- as.data.frame(Titanic) #' titanic$Survived <- factor(titanic$Survived, levels=c("Yes", "No")) #' #' #' ggplot(data=titanic) + #' geom_mosaic(aes(weight=Freq, x=product(Class), fill=Survived)) #' # good practice: use the 'dependent' variable (or most important variable) #' # as fill variable #' ggplot(data=titanic) + #' geom_mosaic(aes(weight=Freq, x=product(Class, Age), fill=Survived)) #' #' # we can change where we define variables #' ggplot(data=titanic, aes(weight = Freq, fill=Survived, x=product(Class, Age))) + #' geom_mosaic() #' #' ggplot(data=titanic) + #' geom_mosaic(aes(weight=Freq, x=product(Class), conds=product(Age), fill=Survived)) #' ggplot(data=titanic) + #' geom_mosaic(aes(weight=Freq, x=product(Survived, Class), fill=Age)) #' #' \dontrun{ #' data(happy, package="productplots") #' #' ggplot(data = happy) + geom_mosaic(aes(x=product(happy))) #' ggplot(data = happy) + geom_mosaic(aes(x=product(happy))) + #' coord_flip() #' # weighting is important #' ggplot(data = happy) + #' geom_mosaic(aes(weight=wtssall, x=product(happy))) #' ggplot(data = happy) + geom_mosaic(aes(weight=wtssall, x=product(health), fill=happy)) + #' theme(axis.text.x=element_text(angle=35)) #' ggplot(data = happy) + #' geom_mosaic(aes(weight=wtssall, x=product(health), fill=happy), na.rm=TRUE) #' ggplot(data = happy) + #' geom_mosaic(aes(weight=wtssall, x=product(health, sex, degree), fill=happy), #' na.rm=TRUE) #' #' # here is where a bit more control over the spacing of the bars is helpful: #' # set labels manually: #' ggplot(data = happy) + #' geom_mosaic(aes(weight=wtssall, x=product(age), fill=happy), na.rm=TRUE, offset=0) + #' scale_x_product("Age", labels=c(17+1:72, "NA")) #' # thin out labels manually: #' labels <- c(17+1:72, NA) #' labels[labels %% 5 != 0] <- "" #' ggplot(data = happy) + #' geom_mosaic(aes(weight=wtssall, x=product(age), fill=happy), na.rm=TRUE, offset=0) + #' scale_x_product("Age", labels=labels) #' ggplot(data = happy) + #' geom_mosaic(aes(weight=wtssall, x=product(age), fill=happy, conds = sex), #' divider=mosaic("v"), na.rm=TRUE, offset=0.001) + #' scale_x_product("Age", labels=labels) #' # facetting works!!!! #' ggplot(data = happy) + #' geom_mosaic(aes(weight=wtssall, x=product(age), fill=happy), na.rm=TRUE, offset = 0) + #' facet_grid(sex~.) + #' scale_x_product("Age", labels=labels) #' #' ggplot(data = happy) + #' geom_mosaic(aes(weight = wtssall, x = product(happy, finrela, health)), #' divider=mosaic("h")) #' ggplot(data = happy) + #' geom_mosaic(aes(weight = wtssall, x = product(happy, finrela, health)), offset=.005) #' #' # Spine example #' ggplot(data = happy) + #' geom_mosaic(aes(weight = wtssall, x = product(health), fill = health)) + #' facet_grid(happy~.) #' } geom_mosaic <- function(mapping = NULL, data = NULL, stat = "mosaic", position = "identity", na.rm = FALSE, divider = mosaic(), offset = 0.01, show.legend = NA, inherit.aes = TRUE, ...) { ggplot2::layer( data = data, mapping = mapping, stat = stat, geom = GeomMosaic, position = position, show.legend = show.legend, inherit.aes = inherit.aes, params = list( na.rm = na.rm, divider = divider, offset = offset, ... ) ) } #' @importFrom grid grobTree GeomMosaic <- ggplot2::ggproto( "GeomMosaic", ggplot2::Geom, setup_data = function(data, params) { # cat("setup_data in GeomMosaic\n") # browser() data }, required_aes = c("xmin", "xmax", "ymin", "ymax"), default_aes = ggplot2::aes(width = 0.75, linetype = "solid", fontsize=5, shape = 19, colour = NA, size = .1, fill = "grey30", alpha = .8, stroke = 0.1, linewidth=.1, weight = 1, x = NULL, y = NULL, conds = NULL), draw_panel = function(data, panel_scales, coord) { # cat("draw_panel in GeomMosaic\n") # browser() if (all(is.na(data$colour))) data$colour <- alpha(data$fill, data$alpha) # regard alpha in colour determination GeomRect$draw_panel(subset(data, level==max(data$level)), panel_scales, coord) }, check_aesthetics = function(x, n) { ns <- vapply(x, length, numeric(1)) good <- ns == 1L \| ns == n if (all(good)) { return() } # browser() stop( "Aesthetics must be either length 1 or the same as the data (", n, "): ", paste(names(!good), collapse = ", "), call. = FALSE ) }, draw_key = ggplot2::draw_key_rect )
% Generated by roxygen2: do not edit by hand % Please edit documentation in R/train.R \name{train.rpart} \alias{train.rpart} \title{train.rpart} \usage{ train.rpart( formula, data, weights, subset, na.action = na.rpart, method, model = TRUE, x = FALSE, y = TRUE, parms, control, cost, ... ) } \arguments{ \item{formula}{a formula, with a response but no interaction terms. If this a a data frame, that is taken as the model frame.} \item{data}{an optional data frame in which to interpret the variables named in the formula.} \item{weights}{optional case weights.} \item{subset}{optional expression saying that only a subset of the rows of the data should be used in the fit.} \item{na.action}{the default action deletes all observations for which y is missing, but keeps those in which one or more predictors are missing.} \item{method}{one of "anova", "poisson", "class" or "exp". If method is missing then the routine tries to make an intelligent guess. If y is a survival object, then method = "exp" is assumed, if y has 2 columns then method = "poisson" is assumed, if y is a factor then method = "class" is assumed, otherwise method = "anova" is assumed. It is wisest to specify the method directly, especially as more criteria may added to the function in future. Alternatively, method can be a list of functions named init, split and eval. Examples are given in the file ‘tests/usersplits.R’ in the sources, and in the vignettes ‘User Written Split Functions’.} \item{model}{if logical: keep a copy of the model frame in the result? If the input value for model is a model frame (likely from an earlier call to the rpart function), then this frame is used rather than constructing new data.} \item{x}{keep a copy of the x matrix in the result.} \item{y}{keep a copy of the dependent variable in the result. If missing and model is supplied this defaults to FALSE.} \item{parms}{optional parameters for the splitting function. Anova splitting has no parameters. Poisson splitting has a single parameter, the coefficient of variation of the prior distribution on the rates. The default value is 1. Exponential splitting has the same parameter as Poisson. For classification splitting, the list can contain any of: the vector of prior probabilities (component prior), the loss matrix (component loss) or the splitting index (component split). The priors must be positive and sum to 1. The loss matrix must have zeros on the diagonal and positive off-diagonal elements. The splitting index can be gini or information. The default priors are proportional to the data counts, the losses default to 1, and the split defaults to gini.} \item{control}{a list of options that control details of the rpart algorithm. See \code{\link[rpart]{rpart.control}}.} \item{cost}{a vector of non-negative costs, one for each variable in the model. Defaults to one for all variables. These are scalings to be applied when considering splits, so the improvement on splitting on a variable is divided by its cost in deciding which split to choose.} \item{...}{arguments to \code{\link[rpart]{rpart.control}} may also be specified in the call to rpart. They are checked against the list of valid arguments.} } \value{ A object rpart.prmdt with additional information to the model that allows to homogenize the results. } \description{ Provides a wrapping function for the \code{\link[rpart]{rpart}}. } \note{ the parameter information was taken from the original function \code{\link[rpart]{rpart}}. } \examples{ # Classification data("iris") n <- seq_len(nrow(iris)) .sample <- sample(n, length(n) * 0.75) data.train <- iris[.sample,] data.test <- iris[-.sample,] modelo.rpart <- train.rpart(Species~., data.train) modelo.rpart prob <- predict(modelo.rpart, data.test, type = "prob") prob prediccion <- predict(modelo.rpart, data.test, type = "class") prediccion # Regression len <- nrow(swiss) sampl <- sample(x = 1:len,size = len*0.20,replace = FALSE) ttesting <- swiss[sampl,] ttraining <- swiss[-sampl,] model.rpart <- train.rpart(Infant.Mortality~.,ttraining) prediction <- predict(model.rpart,ttesting) prediction } \seealso{ The internal function is from package \code{\link[rpart]{rpart}}. }	/man/train.rpart.Rd	no_license	PROMiDAT/traineR	R	false	true	4,235	rd	% Generated by roxygen2: do not edit by hand % Please edit documentation in R/train.R \name{train.rpart} \alias{train.rpart} \title{train.rpart} \usage{ train.rpart( formula, data, weights, subset, na.action = na.rpart, method, model = TRUE, x = FALSE, y = TRUE, parms, control, cost, ... ) } \arguments{ \item{formula}{a formula, with a response but no interaction terms. If this a a data frame, that is taken as the model frame.} \item{data}{an optional data frame in which to interpret the variables named in the formula.} \item{weights}{optional case weights.} \item{subset}{optional expression saying that only a subset of the rows of the data should be used in the fit.} \item{na.action}{the default action deletes all observations for which y is missing, but keeps those in which one or more predictors are missing.} \item{method}{one of "anova", "poisson", "class" or "exp". If method is missing then the routine tries to make an intelligent guess. If y is a survival object, then method = "exp" is assumed, if y has 2 columns then method = "poisson" is assumed, if y is a factor then method = "class" is assumed, otherwise method = "anova" is assumed. It is wisest to specify the method directly, especially as more criteria may added to the function in future. Alternatively, method can be a list of functions named init, split and eval. Examples are given in the file ‘tests/usersplits.R’ in the sources, and in the vignettes ‘User Written Split Functions’.} \item{model}{if logical: keep a copy of the model frame in the result? If the input value for model is a model frame (likely from an earlier call to the rpart function), then this frame is used rather than constructing new data.} \item{x}{keep a copy of the x matrix in the result.} \item{y}{keep a copy of the dependent variable in the result. If missing and model is supplied this defaults to FALSE.} \item{parms}{optional parameters for the splitting function. Anova splitting has no parameters. Poisson splitting has a single parameter, the coefficient of variation of the prior distribution on the rates. The default value is 1. Exponential splitting has the same parameter as Poisson. For classification splitting, the list can contain any of: the vector of prior probabilities (component prior), the loss matrix (component loss) or the splitting index (component split). The priors must be positive and sum to 1. The loss matrix must have zeros on the diagonal and positive off-diagonal elements. The splitting index can be gini or information. The default priors are proportional to the data counts, the losses default to 1, and the split defaults to gini.} \item{control}{a list of options that control details of the rpart algorithm. See \code{\link[rpart]{rpart.control}}.} \item{cost}{a vector of non-negative costs, one for each variable in the model. Defaults to one for all variables. These are scalings to be applied when considering splits, so the improvement on splitting on a variable is divided by its cost in deciding which split to choose.} \item{...}{arguments to \code{\link[rpart]{rpart.control}} may also be specified in the call to rpart. They are checked against the list of valid arguments.} } \value{ A object rpart.prmdt with additional information to the model that allows to homogenize the results. } \description{ Provides a wrapping function for the \code{\link[rpart]{rpart}}. } \note{ the parameter information was taken from the original function \code{\link[rpart]{rpart}}. } \examples{ # Classification data("iris") n <- seq_len(nrow(iris)) .sample <- sample(n, length(n) * 0.75) data.train <- iris[.sample,] data.test <- iris[-.sample,] modelo.rpart <- train.rpart(Species~., data.train) modelo.rpart prob <- predict(modelo.rpart, data.test, type = "prob") prob prediccion <- predict(modelo.rpart, data.test, type = "class") prediccion # Regression len <- nrow(swiss) sampl <- sample(x = 1:len,size = len*0.20,replace = FALSE) ttesting <- swiss[sampl,] ttraining <- swiss[-sampl,] model.rpart <- train.rpart(Infant.Mortality~.,ttraining) prediction <- predict(model.rpart,ttesting) prediction } \seealso{ The internal function is from package \code{\link[rpart]{rpart}}. }
library(tidyverse) source("data_cleaning.R") #plot master_covid_election %>% ggplot(aes(size = cases)) + geom_point(aes(x = state_win, y = winner, color = winner), position = position_jitter(width = .3, height = .5), alpha = 0.5) + scale_color_manual(values = c("blue", "red")) + scale_size(range = c(0.5, 10)) theme(axis.text.y = element_blank(), axis.title.y = element_text(), axis.ticks.y = element_blank())	/beeswarm_plot.R	no_license	erhs-r/group_5	R	false	false	466	r	library(tidyverse) source("data_cleaning.R") #plot master_covid_election %>% ggplot(aes(size = cases)) + geom_point(aes(x = state_win, y = winner, color = winner), position = position_jitter(width = .3, height = .5), alpha = 0.5) + scale_color_manual(values = c("blue", "red")) + scale_size(range = c(0.5, 10)) theme(axis.text.y = element_blank(), axis.title.y = element_text(), axis.ticks.y = element_blank())
% Generated by roxygen2: do not edit by hand % Please edit documentation in R/corpus_sample.R \name{corpus_sample} \alias{corpus_sample} \title{randomly sample documents from a corpus} \usage{ corpus_sample(x, size = ndoc(x), replace = FALSE, prob = NULL, by = NULL, ...) } \arguments{ \item{x}{a corpus object whose documents will be sampled} \item{size}{a positive number, the number of documents to select} \item{replace}{Should sampling be with replacement?} \item{prob}{A vector of probability weights for obtaining the elements of the vector being sampled.} \item{by}{a grouping variable for sampling. Useful for resampling sub-document units such as sentences, for instance by specifying \code{by = "document"}} \item{...}{unused} } \value{ A corpus object with number of documents equal to \code{size}, drawn from the corpus \code{x}. The returned corpus object will contain all of the meta-data of the original corpus, and the same document variables for the documents selected. } \description{ Takes a random sample or documents or features of the specified size from a corpus or document-feature matrix, with or without replacement. Works just as \code{\link{sample}} works for the documents and their associated document-level variables. } \examples{ # sampling from a corpus summary(corpus_sample(data_corpus_inaugural, 5)) summary(corpus_sample(data_corpus_inaugural, 10, replace=TRUE)) # sampling sentences within document doccorpus <- corpus(c(one = "Sentence one. Sentence two. Third sentence.", two = "First sentence, doc2. Second sentence, doc2.")) sentcorpus <- corpus_reshape(doccorpus, to = "sentences") texts(sentcorpus) texts(corpus_sample(sentcorpus, replace = TRUE, by = "document")) } \keyword{corpus}	/man/corpus_sample.Rd	no_license	leeper/quanteda	R	false	true	1,778	rd	% Generated by roxygen2: do not edit by hand % Please edit documentation in R/corpus_sample.R \name{corpus_sample} \alias{corpus_sample} \title{randomly sample documents from a corpus} \usage{ corpus_sample(x, size = ndoc(x), replace = FALSE, prob = NULL, by = NULL, ...) } \arguments{ \item{x}{a corpus object whose documents will be sampled} \item{size}{a positive number, the number of documents to select} \item{replace}{Should sampling be with replacement?} \item{prob}{A vector of probability weights for obtaining the elements of the vector being sampled.} \item{by}{a grouping variable for sampling. Useful for resampling sub-document units such as sentences, for instance by specifying \code{by = "document"}} \item{...}{unused} } \value{ A corpus object with number of documents equal to \code{size}, drawn from the corpus \code{x}. The returned corpus object will contain all of the meta-data of the original corpus, and the same document variables for the documents selected. } \description{ Takes a random sample or documents or features of the specified size from a corpus or document-feature matrix, with or without replacement. Works just as \code{\link{sample}} works for the documents and their associated document-level variables. } \examples{ # sampling from a corpus summary(corpus_sample(data_corpus_inaugural, 5)) summary(corpus_sample(data_corpus_inaugural, 10, replace=TRUE)) # sampling sentences within document doccorpus <- corpus(c(one = "Sentence one. Sentence two. Third sentence.", two = "First sentence, doc2. Second sentence, doc2.")) sentcorpus <- corpus_reshape(doccorpus, to = "sentences") texts(sentcorpus) texts(corpus_sample(sentcorpus, replace = TRUE, by = "document")) } \keyword{corpus}
#' Generates a Resolvable Row-Column Design (RowColD) #' #' #' @description It randomly generates a resolvable row-column designs (RowColD). #' Note that design optimization is only done at the level of rows and not columns; #' hence, design is suboptimal. The randomization can be done across locations. #' #' @param t Number of treatments. #' @param nrows Number of rows of a full resolvable replicate. #' @param r Number of blocks (full resolvable replicates). #' @param l Number of locations. By default \code{l = 1}. #' @param plotNumber Numeric vector with the starting plot number for each location. By default \code{plotNumber = 101}. #' @param seed (optional) Real number that specifies the starting seed to obtain reproducible designs. #' @param locationNames (optional) Names for each location. #' @param data (optional) Data frame with label list of treatments #' #' @author Didier Murillo [aut], #' Salvador Gezan [aut], #' Ana Heilman [ctb], #' Thomas Walk [ctb], #' Johan Aparicio [ctb], #' Richard Horsley [ctb] #' #' #' @importFrom stats runif na.omit #' #' @return A list with four elements. #' \itemize{ #' \item \code{infoDesign} is a list with information on the design parameters. #' \item \code{resolvableBlocks} a list with the resolvable row columns blocks. #' \item \code{concurrence} is the concurrence matrix. #' \item \code{fieldBook} is a data frame with the row-column field book. #' } #' #' #' @references #' Edmondson., R. N. (2021). blocksdesign: Nested and crossed block designs for factorial and #' unstructured treatment sets. https://CRAN.R-project.org/package=blocksdesign #' #' @examples #' #' # Example 1: Generates a row-column design with 3 full blocks and 36 treatments #' # and 6 rows. This for one location. #' rowcold1 <- row_column(t = 36, nrows = 6, r = 3, l = 1, #' plotNumber= 101, #' locationNames = "Loc1", #' seed = 21) #' rowcold1$infoDesign #' rowcold1$resolvableBlocks #' head(rowcold1$fieldBook,12) #' #' # Example 2: Generates a row-column design with 3 full blocks and 30 treatments #' # and 5 rows, for one location. #' # In this case, we show how to use the option data. #' treatments <- paste("ND-", 1:30, sep = "") #' ENTRY <- 1:30 #' treatment_list <- data.frame(list(ENTRY = ENTRY, TREATMENT = treatments)) #' head(treatment_list) #' rowcold2 <- row_column(t = 30, nrows = 5, r = 3, l = 1, #' plotNumber= c(101,1001), #' locationNames = c("A", "B"), #' seed = 15, #' data = treatment_list) #' rowcold2$infoDesign #' rowcold2$resolvableBlocks #' head(rowcold2$fieldBook,12) #' #' #' @export row_column <- function(t = NULL, nrows = NULL, r = NULL, l = 1, plotNumber= 101, locationNames = NULL, seed = NULL, data = NULL) { if (is.null(seed) \|\| !is.numeric(seed)) seed <- runif(1, min = -50000, max = 50000) set.seed(seed) k <- nrows lookup <- FALSE if(is.null(data)) { if (is.null(t) \|\| is.null(k) \|\| is.null(r) \|\| is.null(l)) { shiny::validate('Some of the basic design parameters are missing (t, k, r or l).') } arg1 <- list(k, r, l);arg2 <- c(k, r, l) if (base::any(lengths(arg1) != 1) \|\| base::any(arg2 %% 1 != 0) \|\| base::any(arg2 < 1)) { shiny::validate('row_column() requires k, r and l to be possitive integers.') } if (is.numeric(t)) { if (length(t) == 1) { if (t == 1 \|\| t < 1) { shiny::validate('row_column() requires more than one treatment.') } nt <- t }else if ((length(t) > 1)) { nt <- length(t) TRT <- t } }else if (is.character(t) \|\| is.factor(t)) { if (length(t) == 1) { shiny::validate('incomplete_blocks() requires more than one treatment.') } nt <- length(t) }else if ((length(t) > 1)) { nt <- length(t) } df <- data.frame(list(ENTRY = 1:nt, TREATMENT = paste0("G-", 1:nt))) data_RowCol <- df lookup <- TRUE }else if (!is.null(data)) { if (is.null(t) \|\| is.null(r) \|\| is.null(k) \|\| is.null(l)) { shiny::validate('Some of the basic design parameters are missing (t, r, k or l).') } if(!is.data.frame(data)) shiny::validate("Data must be a data frame.") data_up <- as.data.frame(data[,c(1,2)]) data_up <- na.omit(data_up) colnames(data_up) <- c("ENTRY", "TREATMENT") data_up$TREATMENT <- as.character(data_up$TREATMENT) new_t <- length(data_up$TREATMENT) if (t != new_t) base::stop("Number of treatments do not match with data input.") TRT <- data_up$TREATMENT nt <- length(TRT) data_RowCol <- data_up lookup <- TRUE } if (k >= nt) shiny::validate('incomplete_blocks() requires k < t.') if(is.null(locationNames) \|\| length(locationNames) != l) locationNames <- 1:l nunits <- k matdf <- incomplete_blocks(t = nt, k = nunits, r = r, l = l, plotNumber = plotNumber, seed = seed, locationNames = locationNames, data = data_RowCol) matdf <- matdf$fieldBook matdf <- as.data.frame(matdf) colnames(matdf)[5] <- "COLUMN" matdf$ROW <- matdf$UNIT OutRowCol <- matdf[,-6] OutRowCol$LOCATION <- factor(OutRowCol$LOCATION, levels = locationNames) OutRowCol <- OutRowCol[order(OutRowCol$LOCATION, OutRowCol$REP, OutRowCol$ROW),] RowCol_plots <- ibd_plot_numbers(nt = nt, plot.number = plotNumber, r = r, l = l) OutRowCol$PLOT <- as.vector(unlist(RowCol_plots)) if(lookup) { OutRowCol <- OutRowCol[,c(2,3,4,8,5,6,7)] }else OutRowCol <- OutRowCol[,c(2,3,4,7,5,6)] ID <- 1:nrow(OutRowCol) OutRowCol <- cbind(ID, OutRowCol) rownames(OutRowCol) <- 1:nrow(OutRowCol) loc <- levels(OutRowCol$LOCATION) ib <- nt/k Resolvable_rc_reps <- vector(mode = "list", length = rl) w <- 1 for (sites in 1:l) { for (j in 1:r) { z <- OutRowCol z <- subset(z, z$LOCATION == loc[sites] & z$REP == j) if (is.null(data)){ Resolvable_rc_reps[[w]] <- matrix(data = as.vector(z$ENTRY), nrow = nunits, ncol = ib, byrow = TRUE) }else { Resolvable_rc_reps[[w]] <- matrix(data = as.vector(z$TREATMENT), nrow = nunits, ncol = ib, byrow = TRUE) } w <- w + 1 } } NEW_Resolvable <- setNames(vector(mode = "list", length = l), paste0("Loc_", locationNames)) x <- seq(1, r l, r) y <- seq(r, r * l, r) z <- 1 for (loc in 1:l) { NEW_Resolvable[[loc]] <- setNames(Resolvable_rc_reps[x[z]:y[z]], paste0(rep("rep", r), 1:r)) z <- z + 1 } df <- OutRowCol trt <- "ENTRY" c1 <- concurrence_matrix(df=df, trt=trt, target='REP') c2 <- concurrence_matrix (df=df, trt=trt, target='ROW') c3 <- concurrence_matrix (df=df, trt=trt, target='COLUMN') summ <- merge(c1, c2, by="Concurrence", all=TRUE) new_summ <- merge(summ, c3, by='Concurrence', all=TRUE) infoDesign <- list( rows = nrows, columns = ib, reps = r, treatments = nt, locations = l, location_names = locationNames, seed = seed, id_design = 9 ) output <- list(infoDesign = infoDesign, resolvableBlocks = NEW_Resolvable, concurrence = new_summ, fieldBook = OutRowCol) class(output) <- "FielDHub" return(invisible(output)) }	/R/fct_row_column.R	permissive	DidierMurilloF/FielDHub	R	false	false	7,532	r	#' Generates a Resolvable Row-Column Design (RowColD) #' #' #' @description It randomly generates a resolvable row-column designs (RowColD). #' Note that design optimization is only done at the level of rows and not columns; #' hence, design is suboptimal. The randomization can be done across locations. #' #' @param t Number of treatments. #' @param nrows Number of rows of a full resolvable replicate. #' @param r Number of blocks (full resolvable replicates). #' @param l Number of locations. By default \code{l = 1}. #' @param plotNumber Numeric vector with the starting plot number for each location. By default \code{plotNumber = 101}. #' @param seed (optional) Real number that specifies the starting seed to obtain reproducible designs. #' @param locationNames (optional) Names for each location. #' @param data (optional) Data frame with label list of treatments #' #' @author Didier Murillo [aut], #' Salvador Gezan [aut], #' Ana Heilman [ctb], #' Thomas Walk [ctb], #' Johan Aparicio [ctb], #' Richard Horsley [ctb] #' #' #' @importFrom stats runif na.omit #' #' @return A list with four elements. #' \itemize{ #' \item \code{infoDesign} is a list with information on the design parameters. #' \item \code{resolvableBlocks} a list with the resolvable row columns blocks. #' \item \code{concurrence} is the concurrence matrix. #' \item \code{fieldBook} is a data frame with the row-column field book. #' } #' #' #' @references #' Edmondson., R. N. (2021). blocksdesign: Nested and crossed block designs for factorial and #' unstructured treatment sets. https://CRAN.R-project.org/package=blocksdesign #' #' @examples #' #' # Example 1: Generates a row-column design with 3 full blocks and 36 treatments #' # and 6 rows. This for one location. #' rowcold1 <- row_column(t = 36, nrows = 6, r = 3, l = 1, #' plotNumber= 101, #' locationNames = "Loc1", #' seed = 21) #' rowcold1$infoDesign #' rowcold1$resolvableBlocks #' head(rowcold1$fieldBook,12) #' #' # Example 2: Generates a row-column design with 3 full blocks and 30 treatments #' # and 5 rows, for one location. #' # In this case, we show how to use the option data. #' treatments <- paste("ND-", 1:30, sep = "") #' ENTRY <- 1:30 #' treatment_list <- data.frame(list(ENTRY = ENTRY, TREATMENT = treatments)) #' head(treatment_list) #' rowcold2 <- row_column(t = 30, nrows = 5, r = 3, l = 1, #' plotNumber= c(101,1001), #' locationNames = c("A", "B"), #' seed = 15, #' data = treatment_list) #' rowcold2$infoDesign #' rowcold2$resolvableBlocks #' head(rowcold2$fieldBook,12) #' #' #' @export row_column <- function(t = NULL, nrows = NULL, r = NULL, l = 1, plotNumber= 101, locationNames = NULL, seed = NULL, data = NULL) { if (is.null(seed) \|\| !is.numeric(seed)) seed <- runif(1, min = -50000, max = 50000) set.seed(seed) k <- nrows lookup <- FALSE if(is.null(data)) { if (is.null(t) \|\| is.null(k) \|\| is.null(r) \|\| is.null(l)) { shiny::validate('Some of the basic design parameters are missing (t, k, r or l).') } arg1 <- list(k, r, l);arg2 <- c(k, r, l) if (base::any(lengths(arg1) != 1) \|\| base::any(arg2 %% 1 != 0) \|\| base::any(arg2 < 1)) { shiny::validate('row_column() requires k, r and l to be possitive integers.') } if (is.numeric(t)) { if (length(t) == 1) { if (t == 1 \|\| t < 1) { shiny::validate('row_column() requires more than one treatment.') } nt <- t }else if ((length(t) > 1)) { nt <- length(t) TRT <- t } }else if (is.character(t) \|\| is.factor(t)) { if (length(t) == 1) { shiny::validate('incomplete_blocks() requires more than one treatment.') } nt <- length(t) }else if ((length(t) > 1)) { nt <- length(t) } df <- data.frame(list(ENTRY = 1:nt, TREATMENT = paste0("G-", 1:nt))) data_RowCol <- df lookup <- TRUE }else if (!is.null(data)) { if (is.null(t) \|\| is.null(r) \|\| is.null(k) \|\| is.null(l)) { shiny::validate('Some of the basic design parameters are missing (t, r, k or l).') } if(!is.data.frame(data)) shiny::validate("Data must be a data frame.") data_up <- as.data.frame(data[,c(1,2)]) data_up <- na.omit(data_up) colnames(data_up) <- c("ENTRY", "TREATMENT") data_up$TREATMENT <- as.character(data_up$TREATMENT) new_t <- length(data_up$TREATMENT) if (t != new_t) base::stop("Number of treatments do not match with data input.") TRT <- data_up$TREATMENT nt <- length(TRT) data_RowCol <- data_up lookup <- TRUE } if (k >= nt) shiny::validate('incomplete_blocks() requires k < t.') if(is.null(locationNames) \|\| length(locationNames) != l) locationNames <- 1:l nunits <- k matdf <- incomplete_blocks(t = nt, k = nunits, r = r, l = l, plotNumber = plotNumber, seed = seed, locationNames = locationNames, data = data_RowCol) matdf <- matdf$fieldBook matdf <- as.data.frame(matdf) colnames(matdf)[5] <- "COLUMN" matdf$ROW <- matdf$UNIT OutRowCol <- matdf[,-6] OutRowCol$LOCATION <- factor(OutRowCol$LOCATION, levels = locationNames) OutRowCol <- OutRowCol[order(OutRowCol$LOCATION, OutRowCol$REP, OutRowCol$ROW),] RowCol_plots <- ibd_plot_numbers(nt = nt, plot.number = plotNumber, r = r, l = l) OutRowCol$PLOT <- as.vector(unlist(RowCol_plots)) if(lookup) { OutRowCol <- OutRowCol[,c(2,3,4,8,5,6,7)] }else OutRowCol <- OutRowCol[,c(2,3,4,7,5,6)] ID <- 1:nrow(OutRowCol) OutRowCol <- cbind(ID, OutRowCol) rownames(OutRowCol) <- 1:nrow(OutRowCol) loc <- levels(OutRowCol$LOCATION) ib <- nt/k Resolvable_rc_reps <- vector(mode = "list", length = rl) w <- 1 for (sites in 1:l) { for (j in 1:r) { z <- OutRowCol z <- subset(z, z$LOCATION == loc[sites] & z$REP == j) if (is.null(data)){ Resolvable_rc_reps[[w]] <- matrix(data = as.vector(z$ENTRY), nrow = nunits, ncol = ib, byrow = TRUE) }else { Resolvable_rc_reps[[w]] <- matrix(data = as.vector(z$TREATMENT), nrow = nunits, ncol = ib, byrow = TRUE) } w <- w + 1 } } NEW_Resolvable <- setNames(vector(mode = "list", length = l), paste0("Loc_", locationNames)) x <- seq(1, r l, r) y <- seq(r, r * l, r) z <- 1 for (loc in 1:l) { NEW_Resolvable[[loc]] <- setNames(Resolvable_rc_reps[x[z]:y[z]], paste0(rep("rep", r), 1:r)) z <- z + 1 } df <- OutRowCol trt <- "ENTRY" c1 <- concurrence_matrix(df=df, trt=trt, target='REP') c2 <- concurrence_matrix (df=df, trt=trt, target='ROW') c3 <- concurrence_matrix (df=df, trt=trt, target='COLUMN') summ <- merge(c1, c2, by="Concurrence", all=TRUE) new_summ <- merge(summ, c3, by='Concurrence', all=TRUE) infoDesign <- list( rows = nrows, columns = ib, reps = r, treatments = nt, locations = l, location_names = locationNames, seed = seed, id_design = 9 ) output <- list(infoDesign = infoDesign, resolvableBlocks = NEW_Resolvable, concurrence = new_summ, fieldBook = OutRowCol) class(output) <- "FielDHub" return(invisible(output)) }
# Jignesh H. Parmar and Pedro Mendes, Plos Comp Biol in 2019 # Fixed Model Entities: #metabolite 'FeOutside': fixed FeOutsi=0 #compartment 'Duodenum':fixed Duodenu=3.84615e-05 #compartment 'RBC':fixed RBC=0.00079 #compartment 'Spleen':fixed Spleen=6.73077e-05 #compartment 'Liver':fixed Liver=0.0011619 #compartment 'Plasma':fixed Plasma=0.0013 #compartment 'RestOfBody':fixed RestOfB=0.0196948 #compartment 'BoneMarrow':fixed BoneMar=0.000214286 #global quantity 'kNTBI_Fe1Tf':fixed kNTBI_F=1.004e+09 #global quantity 'kInDuo':fixed kInDuo=0.0405971 #global quantity 'kInLiver':fixed kInLive=2.11666 #global quantity 'kInRBC':fixed kInRBC=5.03844e+11 #global quantity 'kInRest':fixed kInRest=4.78121 #global quantity 'KmFeFPN':fixed KmFeFPN=0.112511 #global quantity 'KiHepcidinFPN':fixed KiHepci=6.3e-09 #global quantity 'kFe1Tf_Fe2Tf':fixed kFe1Tf_=1.004e+09 #global quantity 'vDuoNTBI':fixed vDuoNTB=0.200907 #global quantity 'vLiverNTBI':fixed vLiverN=0.0444795 #global quantity 'vSpleenNTBI':fixed vSpleen=2.06738 #global quantity 'vRestNTBI':fixed vRestNT=0.0101453 #global quantity 'kDuoLoss':fixed kDuoLos=6.80738e-05 #global quantity 'kRBCSpleen':fixed kRBCSpl=0.03 #global quantity 'kInBM':fixed kInBM=4.06878e+12 #global quantity 'kBMSpleen':fixed kBMSple=0.103218 #global quantity 'vDiet_basal':fixed vDiet_b=0.00346965 #global quantity 'KaNTBI':fixed KaNTBI=0.000255016 #global quantity 'KmNTBI':fixed KmNTBI=0.000679291 #global quantity 'vNTBILiver':fixed vNTBILi=14.1511 #global quantity 'fDiet':fixed fDiet=1 #global quantity 'ksHepcidin':fixed ksHepci=0.000398766 #global quantity 'vEPO':fixed vEPO=2.62675e-09 #global quantity 'kdEPO':fixed kdEPO=4.8 #global quantity 'hEPO':fixed hEPO=6.5 #global quantity 'KiEPORBC':fixed KiEPORB=0.01 #global quantity 'KEPOHepcidin':fixed KEPOHep=5e-12 #global quantity 'hEPOHepcidin':fixed hEPOHep=4 #global quantity 'hNTBI':fixed hNTBI=1 #global quantity 'kRestLoss':fixed kRestLo=0.016862 #global quantity 'vTf':fixed vTf=1.548e-05 #global quantity 'kdTf':fixed kdTf=0.4 #global quantity 'kdHepcidin':fixed kdHepci=0.75616 (pars=dput(ls())) names(pars)=pars dput(pars) parmarPars19=c(BoneMar = BoneMar, Duodenu = Duodenu, fDiet = fDiet, FeOutsi = FeOutsi, hEPO = hEPO, hEPOHep = hEPOHep, hNTBI = hNTBI, KaNTBI = KaNTBI, kBMSple = kBMSple, kdEPO = kdEPO, kdHepci = kdHepci, kdTf = kdTf, kDuoLos = kDuoLos, KEPOHep = KEPOHep, kFe1Tf_ = kFe1Tf_, KiEPORB = KiEPORB, KiHepci = KiHepci, kInBM = kInBM, kInDuo = kInDuo, kInLive = kInLive, kInRBC = kInRBC, kInRest = kInRest, KmFeFPN = KmFeFPN, KmNTBI = KmNTBI, kNTBI_F = kNTBI_F, kRBCSpl = kRBCSpl, kRestLo = kRestLo, ksHepci = ksHepci, Liver = Liver, Plasma = Plasma, RBC = RBC, RestOfB = RestOfB, Spleen = Spleen, vDiet_b = vDiet_b, vDuoNTB = vDuoNTB, vEPO = vEPO, vLiverN = vLiverN, vNTBILi = vNTBILi, vRestNT = vRestNT, vSpleen = vSpleen, vTf = vTf) # parmarPars=c(FeOutsi=0, Duodenu=3.84615e-05, RBC=0.00079, Spleen=6.73077e-05, Liver=0.0011619, # Plasma=0.0013, RestOfB=0.0196948, BoneMar=0.000214286, # # organ volumes in Liters (Table 1) mouse= ~25gm = ~.023L # kNTBI_F=1.08432e+09, #kNTBI_Fe1Tf # kInDuo=0.0689984, kInLive=2.97791, kInRBC=1.08448, kInRest=6.16356, # Km=0.0159421, Ki=1e-09, kFe1Tf_=1.08432e+09, #kFe1Tf_Fe2Tf # VmaxDuo=0.200242, VmaxLiv=0.0261148, VmaxSpl=1.3422, VmaxRest=0.0109451, # all 4 NTBI, in Moles/Day # kDuoLos=0.0270113, #kDuoLoss # vRBCSpl=0.0235286, kRestLo=0.0235332, kInBM=15.7691, kBMSple=0.061903, # vDiet=0.00377422,vDietH=0.00415624, vDietL=0, #Hi vs. low Fe diet # quantit=6.02214e+23, # Avogadr=6.02214e+23, # #HepcidinSynthesis Hi vs. Low Fe diet (adaptation?) # vHepSyn=1.7393e-08, vHepSynH=2.30942e-08, vHepSynL=8.54927e-09, # k1=0.75616 #reaction 'HepcidinDecay': kinetic ,eter 'k1' # ) # sum(parmarPars[1:8]) #23.2 gm mouse save(parmarPars19,file="parmarPars19.rda")	/myelo/inst/doc/parmarPars19.R	no_license	radivot/myelo	R	false	false	4,050	r	# Jignesh H. Parmar and Pedro Mendes, Plos Comp Biol in 2019 # Fixed Model Entities: #metabolite 'FeOutside': fixed FeOutsi=0 #compartment 'Duodenum':fixed Duodenu=3.84615e-05 #compartment 'RBC':fixed RBC=0.00079 #compartment 'Spleen':fixed Spleen=6.73077e-05 #compartment 'Liver':fixed Liver=0.0011619 #compartment 'Plasma':fixed Plasma=0.0013 #compartment 'RestOfBody':fixed RestOfB=0.0196948 #compartment 'BoneMarrow':fixed BoneMar=0.000214286 #global quantity 'kNTBI_Fe1Tf':fixed kNTBI_F=1.004e+09 #global quantity 'kInDuo':fixed kInDuo=0.0405971 #global quantity 'kInLiver':fixed kInLive=2.11666 #global quantity 'kInRBC':fixed kInRBC=5.03844e+11 #global quantity 'kInRest':fixed kInRest=4.78121 #global quantity 'KmFeFPN':fixed KmFeFPN=0.112511 #global quantity 'KiHepcidinFPN':fixed KiHepci=6.3e-09 #global quantity 'kFe1Tf_Fe2Tf':fixed kFe1Tf_=1.004e+09 #global quantity 'vDuoNTBI':fixed vDuoNTB=0.200907 #global quantity 'vLiverNTBI':fixed vLiverN=0.0444795 #global quantity 'vSpleenNTBI':fixed vSpleen=2.06738 #global quantity 'vRestNTBI':fixed vRestNT=0.0101453 #global quantity 'kDuoLoss':fixed kDuoLos=6.80738e-05 #global quantity 'kRBCSpleen':fixed kRBCSpl=0.03 #global quantity 'kInBM':fixed kInBM=4.06878e+12 #global quantity 'kBMSpleen':fixed kBMSple=0.103218 #global quantity 'vDiet_basal':fixed vDiet_b=0.00346965 #global quantity 'KaNTBI':fixed KaNTBI=0.000255016 #global quantity 'KmNTBI':fixed KmNTBI=0.000679291 #global quantity 'vNTBILiver':fixed vNTBILi=14.1511 #global quantity 'fDiet':fixed fDiet=1 #global quantity 'ksHepcidin':fixed ksHepci=0.000398766 #global quantity 'vEPO':fixed vEPO=2.62675e-09 #global quantity 'kdEPO':fixed kdEPO=4.8 #global quantity 'hEPO':fixed hEPO=6.5 #global quantity 'KiEPORBC':fixed KiEPORB=0.01 #global quantity 'KEPOHepcidin':fixed KEPOHep=5e-12 #global quantity 'hEPOHepcidin':fixed hEPOHep=4 #global quantity 'hNTBI':fixed hNTBI=1 #global quantity 'kRestLoss':fixed kRestLo=0.016862 #global quantity 'vTf':fixed vTf=1.548e-05 #global quantity 'kdTf':fixed kdTf=0.4 #global quantity 'kdHepcidin':fixed kdHepci=0.75616 (pars=dput(ls())) names(pars)=pars dput(pars) parmarPars19=c(BoneMar = BoneMar, Duodenu = Duodenu, fDiet = fDiet, FeOutsi = FeOutsi, hEPO = hEPO, hEPOHep = hEPOHep, hNTBI = hNTBI, KaNTBI = KaNTBI, kBMSple = kBMSple, kdEPO = kdEPO, kdHepci = kdHepci, kdTf = kdTf, kDuoLos = kDuoLos, KEPOHep = KEPOHep, kFe1Tf_ = kFe1Tf_, KiEPORB = KiEPORB, KiHepci = KiHepci, kInBM = kInBM, kInDuo = kInDuo, kInLive = kInLive, kInRBC = kInRBC, kInRest = kInRest, KmFeFPN = KmFeFPN, KmNTBI = KmNTBI, kNTBI_F = kNTBI_F, kRBCSpl = kRBCSpl, kRestLo = kRestLo, ksHepci = ksHepci, Liver = Liver, Plasma = Plasma, RBC = RBC, RestOfB = RestOfB, Spleen = Spleen, vDiet_b = vDiet_b, vDuoNTB = vDuoNTB, vEPO = vEPO, vLiverN = vLiverN, vNTBILi = vNTBILi, vRestNT = vRestNT, vSpleen = vSpleen, vTf = vTf) # parmarPars=c(FeOutsi=0, Duodenu=3.84615e-05, RBC=0.00079, Spleen=6.73077e-05, Liver=0.0011619, # Plasma=0.0013, RestOfB=0.0196948, BoneMar=0.000214286, # # organ volumes in Liters (Table 1) mouse= ~25gm = ~.023L # kNTBI_F=1.08432e+09, #kNTBI_Fe1Tf # kInDuo=0.0689984, kInLive=2.97791, kInRBC=1.08448, kInRest=6.16356, # Km=0.0159421, Ki=1e-09, kFe1Tf_=1.08432e+09, #kFe1Tf_Fe2Tf # VmaxDuo=0.200242, VmaxLiv=0.0261148, VmaxSpl=1.3422, VmaxRest=0.0109451, # all 4 NTBI, in Moles/Day # kDuoLos=0.0270113, #kDuoLoss # vRBCSpl=0.0235286, kRestLo=0.0235332, kInBM=15.7691, kBMSple=0.061903, # vDiet=0.00377422,vDietH=0.00415624, vDietL=0, #Hi vs. low Fe diet # quantit=6.02214e+23, # Avogadr=6.02214e+23, # #HepcidinSynthesis Hi vs. Low Fe diet (adaptation?) # vHepSyn=1.7393e-08, vHepSynH=2.30942e-08, vHepSynL=8.54927e-09, # k1=0.75616 #reaction 'HepcidinDecay': kinetic ,eter 'k1' # ) # sum(parmarPars[1:8]) #23.2 gm mouse save(parmarPars19,file="parmarPars19.rda")
#Create a matrix object that can cache its inverse makeCacheMatrix <- function(Mtrx = matrix()) { #initialize the matrix Object mtrxInverse <- NULL #set matrix set <- function(y) { Mtrx <<- y mtrxInverse <<- NULL } #get matrix get <- function() Mtrx # set the inverse setInverse <- function(inverse) mtrxInverse <<- inverse # get function to return the cached inverse getInverse <- function() mtrxInverse #list the available function in makeCacheMatrix list(set = set, get = get, setInverse = setInverse, getInverse = getInverse) } ## Returns the inverse of matrix. #If the inverse has already been calculated then the cachesolve will retrieve and return the inverse from cache cacheSolve <- function(x, ...) { ## Return a matrix that is the inverse of 'x' # check if the inverse of the matrix exists in cache invMtrx <- x$getInverse() # if inverse of the matrix exists, then get the matrix inverse from cache and return the inverse if(!is.null(invMtrx)) { # Return the inverse from cache message("getting cached data") return(invMtrx) } #if the cache is empty , then proceed here #x$get() gets the matrix data <- x$get() # function solve returns the inverse of the matrix invMtrx <- solve(data) # set the inverse in cache x$setInverse(invMtrx) # return the inverse invMtrx }	/cachematrix.R	no_license	vinothinij/RProgrammingAssignment2	R	false	false	1,570	r	#Create a matrix object that can cache its inverse makeCacheMatrix <- function(Mtrx = matrix()) { #initialize the matrix Object mtrxInverse <- NULL #set matrix set <- function(y) { Mtrx <<- y mtrxInverse <<- NULL } #get matrix get <- function() Mtrx # set the inverse setInverse <- function(inverse) mtrxInverse <<- inverse # get function to return the cached inverse getInverse <- function() mtrxInverse #list the available function in makeCacheMatrix list(set = set, get = get, setInverse = setInverse, getInverse = getInverse) } ## Returns the inverse of matrix. #If the inverse has already been calculated then the cachesolve will retrieve and return the inverse from cache cacheSolve <- function(x, ...) { ## Return a matrix that is the inverse of 'x' # check if the inverse of the matrix exists in cache invMtrx <- x$getInverse() # if inverse of the matrix exists, then get the matrix inverse from cache and return the inverse if(!is.null(invMtrx)) { # Return the inverse from cache message("getting cached data") return(invMtrx) } #if the cache is empty , then proceed here #x$get() gets the matrix data <- x$get() # function solve returns the inverse of the matrix invMtrx <- solve(data) # set the inverse in cache x$setInverse(invMtrx) # return the inverse invMtrx }
#PCV was launched by the Union Health Minister, Shri JP Nadda on May 13, 2017 at Mandi, #Himachal Pradesh [1]. With this phased introduction, nearly 2.1 million children in #Himachal Pradesh (all 12 districts), parts of Bihar (17 out of 38 districts) #and Uttar Pradesh (6 out of 75 districts) will be vaccinated with PCV in the first year [2]. #This will be followed by introduction in Madhya Pradesh and Rajasthan next year, and eventually coverage will be expanded across the entire country in a phased manner, in the coming years. " #In uttar Pradesh it is: Lakhimpur Kheri, Sitapur, Siddharth Nagar, Bahraich, Balrampur, Shrawasti; #In Bihar: The 17 high-priority districts are Araria, Begusarai, Darbhanga, Kishanganj, Khagaria, Katihar, #Muzaffarpur, Munger, Vaishali, Madhepura, Madhubani, Purnea, Samastipur, Saran, Sitamarhi, Sheohar and Supaul #bh1<-read.csv('C:/Users/dmw63/Desktop/My documents h/GATES/india/hmis/Bihar series by district.csv') ####### #Lorine ##ACUTE RESPIRATORY INFECTION REQUIRING ADMISSION?? ##85% of DEATH OCCUR AT HOME--SHOULD BE IN HMIS; MANY ##IN PRIVATE SECTOR HOSPITALS ##CONTROLS: DIARRHEA deaths? ROTA VACCINE IS ROLLING OUT.. ##ACUTE ENCAPHALITIS IN SOUTH UP AND BIHAR. THERE ARE SOME ##VACCINE PROGRAMS BUT STARTED 5 YEARS AGO...NOT REALLY #IN WESTERN UP #ASPHYXIA DEATHS WONT BE SAME HOSPITALS -- #WOULD BE IN WOMENS HOSPITAL...MANY ARE SEPARATE #WOMENS/MENS HOSPTALS #UP HMIS--DR VISANT (Sp?) ####### library(lme4) library(lubridate) bh1<-read.csv('C:/Users/dmw63/Desktop/My documents h/GATES/india/hmis/hmis/uttarPradesh series to Mar 2019.csv') str(bh1) bh1$month<-as.numeric(substr(bh1$DATE,6,7)) bh1$year<-as.numeric(substr(bh1$DATE,2,5)) bh1$uri<-bh1$X_10_13 bh1$pneu_death<-bh1$X_16_3_1 bh1$diar_death<-bh1$X_16_3_2 bh1$measles_death<-bh1$X_16_3_4 bh1$asphyxia_death<-bh1$X_16_2_2 bh1$sepsis_death<-bh1$X_16_2_1 bh1$neonatal_death<-bh1$X_16_1 bh1$monthdate<-as.Date(paste(bh1$year,bh1$month,'01', sep='-')) up.intro.districts<-c('Lakhimpur Kheri', 'Sitapur', 'Siddharth Nagar', 'Bahraich', 'Balrampur', 'Shrawasti') bh1$pcv.status<-0 bh1$pcv.status[bh1$DISTRICT %in% up.intro.districts] <-1 unique(bh1$DISTRICT) #test123 strat1<-factor(bh1$monthdate) ds.sub<-bh1[,c('uri', 'diar_death', 'pneu_death','sepsis_death','asphyxia_death', 'measles_death', 'neonatal_death')] bh2<-aggregate(x=ds.sub, by=list( strat1) , FUN='sum', na.rm=TRUE) names(bh2)<-c('monthdate',names(ds.sub)) bh2$monthdate<-as.Date(bh2$monthdate) bh2$neonatal_death[nrow(bh2)]<-NA bh2$month<-as.factor(month(bh2$monthdate)) bh2$year<-as.factor(year(bh2$monthdate)) par(mfrow=c(3,2), mar=c(3,3,1,1)) plot(bh2$monthdate,bh2$pneu_death,main='Pneumonia deaths', type='l', bty='l') plot(bh2$monthdate,bh2$diar_death,main='Diarrhea deaths', type='l', bty='l') plot(bh2$monthdate,bh2$sepsis_death,main='Sepsis Deaths', type='l', bty='l') plot(bh2$monthdate,bh2$asphyxia_death,main='Asphyxia Deaths', type='l', bty='l') plot(bh2$monthdate,bh2$measles_death,main='Measles Deaths', type='l', bty='l') plot(bh2$monthdate, bh2$neonatal_death, main='Neonatal Deaths',type='l', bty='l') par(mfrow=c(1,1), mar=c(2,3,1,1)) plot(bh2$uri, type='l',bty='l', main='URI cases') mod.uri<- glm( uri~ month +year, family='poisson', data=bh2) summary(mod.uri) mod.pneu.death<- glm( pneu_death~ month +year, family='poisson', data=bh2[!(bh2$year %in% c('2017','2018','2019')),]) summary(mod.pneu.death) #heatmap of reporting of URI #seems to be incomplete before April 2017 library(reshape2) ds.sub<-bh1[,c('DISTRICT','monthdate','uri')] ds.m<-melt(ds.sub, id=c('DISTRICT','monthdate')) ds.c<-dcast(ds.m, monthdate~DISTRICT) par(mfrow=c(1,1), mar=c(1,1,1,1)) hm1<-heatmap(t(as.matrix(ds.c[,-1])), scale='row', Rowv=NA, Colv=NA,cexRow =0.5) hm1<-heatmap(t(as.matrix(ds.c[ds.c$monthdate>=as.Date('2017-04-01'),-1])), scale='row', Rowv=NA, Colv=NA,cexRow =0.5) hm1<-heatmap(t(as.matrix(ds.c[ds.c$monthdate>=as.Date('2017-04-01'),-1])), scale='row', Colv=NA,cexRow =0.5) #Pneumonia deaths--seems to be incomplete before April 2017 ds.sub<-bh1[,c('DISTRICT','monthdate','pneu_death')] ds.m<-melt(ds.sub, id=c('DISTRICT','monthdate')) ds.c<-dcast(ds.m, monthdate~DISTRICT) par(mfrow=c(1,1), mar=c(1,1,1,1)) hm1<-heatmap(t(as.matrix(ds.c[,-1])), scale='row', Rowv=NA, Colv=NA,cexRow =0.5) hm1<-heatmap(t(as.matrix(ds.c[ds.c$monthdate>=as.Date('2017-04-01'),-1])), scale='row', Rowv=NA, Colv=NA,cexRow =0.5) hm1<-heatmap(t(as.matrix(ds.c[ds.c$monthdate>=as.Date('2017-04-01'),-1])), scale='row', Colv=NA,cexRow =0.5) ##Simple model# #Consider Apr 2018-Sep 2018 as roll out perios and Oct 2018-Mar 2019 as eval period bh3<-bh1[bh1$monthdate>=as.Date('2017-04-01'),] bh3$post.pcv<-0 bh3$post.pcv[bh3$pcv.status==1 & bh3$monthdate>=as.Date('2018-04-01')& bh3$monthdate<=as.Date('2018-09-01')] <- 1 bh3$post.pcv[bh3$pcv.status==1 & bh3$monthdate>=as.Date('2018-10-01')& bh3$monthdate<=as.Date('2019-03-01')] <- 2 bh3$post.pcv<-as.factor(bh3$post.pcv) bh3$date.factor<-as.factor(bh3$monthdate) bh3$obs<-as.factor(1:nrow(bh3)) mod1<-glmer(pneu_death ~ (1\|date.factor) + (1\|DISTRICT) + (1\|obs)+ post.pcv, data=bh3, family='poisson' ) summary(mod1) mod1<-glmer(uri ~ (1\|DISTRICT) + (1\|obs)+ post.pcv, data=bh3, family='poisson' ) summary(mod1) #Gamm with time smooth library(mgcv) bh3$date.cont<-bh3$year+ bh3$month/12-1/12 mod2<-gamm(uri ~ s(date.cont) + post.pcv, data=bh3, family='poisson' ,random=list(DISTRICT=~1)) summary(mod2$lme) #mod2<-gamm(pneu_death ~ s(date.cont) + post.pcv, data=bh3, family='poisson' ,random=list(DISTRICT=~1), niterPQL=500) #summary(mod2$lme) #statewide ds.c.state<-dcast(ds.m, monthdate~1, fun.aggregate = sum) par(mar=c(3,2,1,1)) plot(ds.c.state[ds.c.state$monthdate>=as.Date('2017-04-01'),1],ds.c.state[ds.c.state$monthdate>=as.Date('2017-04-01'),2], type='l') #Brian: analyze by region vs district #Need a crosswalk file. Western/Central/Eastern in UP; district-> division #District hospitals--probably kids coming from district; bigger hospitals, kids coming from other places # Mortality data: is location based on residence or place of death? #--does death registry include out of hospital deaths #There is an official death registry	/Old code/hmis explore.R	no_license	DanWeinberger/hmis	R	false	false	6,182	r	#PCV was launched by the Union Health Minister, Shri JP Nadda on May 13, 2017 at Mandi, #Himachal Pradesh [1]. With this phased introduction, nearly 2.1 million children in #Himachal Pradesh (all 12 districts), parts of Bihar (17 out of 38 districts) #and Uttar Pradesh (6 out of 75 districts) will be vaccinated with PCV in the first year [2]. #This will be followed by introduction in Madhya Pradesh and Rajasthan next year, and eventually coverage will be expanded across the entire country in a phased manner, in the coming years. " #In uttar Pradesh it is: Lakhimpur Kheri, Sitapur, Siddharth Nagar, Bahraich, Balrampur, Shrawasti; #In Bihar: The 17 high-priority districts are Araria, Begusarai, Darbhanga, Kishanganj, Khagaria, Katihar, #Muzaffarpur, Munger, Vaishali, Madhepura, Madhubani, Purnea, Samastipur, Saran, Sitamarhi, Sheohar and Supaul #bh1<-read.csv('C:/Users/dmw63/Desktop/My documents h/GATES/india/hmis/Bihar series by district.csv') ####### #Lorine ##ACUTE RESPIRATORY INFECTION REQUIRING ADMISSION?? ##85% of DEATH OCCUR AT HOME--SHOULD BE IN HMIS; MANY ##IN PRIVATE SECTOR HOSPITALS ##CONTROLS: DIARRHEA deaths? ROTA VACCINE IS ROLLING OUT.. ##ACUTE ENCAPHALITIS IN SOUTH UP AND BIHAR. THERE ARE SOME ##VACCINE PROGRAMS BUT STARTED 5 YEARS AGO...NOT REALLY #IN WESTERN UP #ASPHYXIA DEATHS WONT BE SAME HOSPITALS -- #WOULD BE IN WOMENS HOSPITAL...MANY ARE SEPARATE #WOMENS/MENS HOSPTALS #UP HMIS--DR VISANT (Sp?) ####### library(lme4) library(lubridate) bh1<-read.csv('C:/Users/dmw63/Desktop/My documents h/GATES/india/hmis/hmis/uttarPradesh series to Mar 2019.csv') str(bh1) bh1$month<-as.numeric(substr(bh1$DATE,6,7)) bh1$year<-as.numeric(substr(bh1$DATE,2,5)) bh1$uri<-bh1$X_10_13 bh1$pneu_death<-bh1$X_16_3_1 bh1$diar_death<-bh1$X_16_3_2 bh1$measles_death<-bh1$X_16_3_4 bh1$asphyxia_death<-bh1$X_16_2_2 bh1$sepsis_death<-bh1$X_16_2_1 bh1$neonatal_death<-bh1$X_16_1 bh1$monthdate<-as.Date(paste(bh1$year,bh1$month,'01', sep='-')) up.intro.districts<-c('Lakhimpur Kheri', 'Sitapur', 'Siddharth Nagar', 'Bahraich', 'Balrampur', 'Shrawasti') bh1$pcv.status<-0 bh1$pcv.status[bh1$DISTRICT %in% up.intro.districts] <-1 unique(bh1$DISTRICT) #test123 strat1<-factor(bh1$monthdate) ds.sub<-bh1[,c('uri', 'diar_death', 'pneu_death','sepsis_death','asphyxia_death', 'measles_death', 'neonatal_death')] bh2<-aggregate(x=ds.sub, by=list( strat1) , FUN='sum', na.rm=TRUE) names(bh2)<-c('monthdate',names(ds.sub)) bh2$monthdate<-as.Date(bh2$monthdate) bh2$neonatal_death[nrow(bh2)]<-NA bh2$month<-as.factor(month(bh2$monthdate)) bh2$year<-as.factor(year(bh2$monthdate)) par(mfrow=c(3,2), mar=c(3,3,1,1)) plot(bh2$monthdate,bh2$pneu_death,main='Pneumonia deaths', type='l', bty='l') plot(bh2$monthdate,bh2$diar_death,main='Diarrhea deaths', type='l', bty='l') plot(bh2$monthdate,bh2$sepsis_death,main='Sepsis Deaths', type='l', bty='l') plot(bh2$monthdate,bh2$asphyxia_death,main='Asphyxia Deaths', type='l', bty='l') plot(bh2$monthdate,bh2$measles_death,main='Measles Deaths', type='l', bty='l') plot(bh2$monthdate, bh2$neonatal_death, main='Neonatal Deaths',type='l', bty='l') par(mfrow=c(1,1), mar=c(2,3,1,1)) plot(bh2$uri, type='l',bty='l', main='URI cases') mod.uri<- glm( uri~ month +year, family='poisson', data=bh2) summary(mod.uri) mod.pneu.death<- glm( pneu_death~ month +year, family='poisson', data=bh2[!(bh2$year %in% c('2017','2018','2019')),]) summary(mod.pneu.death) #heatmap of reporting of URI #seems to be incomplete before April 2017 library(reshape2) ds.sub<-bh1[,c('DISTRICT','monthdate','uri')] ds.m<-melt(ds.sub, id=c('DISTRICT','monthdate')) ds.c<-dcast(ds.m, monthdate~DISTRICT) par(mfrow=c(1,1), mar=c(1,1,1,1)) hm1<-heatmap(t(as.matrix(ds.c[,-1])), scale='row', Rowv=NA, Colv=NA,cexRow =0.5) hm1<-heatmap(t(as.matrix(ds.c[ds.c$monthdate>=as.Date('2017-04-01'),-1])), scale='row', Rowv=NA, Colv=NA,cexRow =0.5) hm1<-heatmap(t(as.matrix(ds.c[ds.c$monthdate>=as.Date('2017-04-01'),-1])), scale='row', Colv=NA,cexRow =0.5) #Pneumonia deaths--seems to be incomplete before April 2017 ds.sub<-bh1[,c('DISTRICT','monthdate','pneu_death')] ds.m<-melt(ds.sub, id=c('DISTRICT','monthdate')) ds.c<-dcast(ds.m, monthdate~DISTRICT) par(mfrow=c(1,1), mar=c(1,1,1,1)) hm1<-heatmap(t(as.matrix(ds.c[,-1])), scale='row', Rowv=NA, Colv=NA,cexRow =0.5) hm1<-heatmap(t(as.matrix(ds.c[ds.c$monthdate>=as.Date('2017-04-01'),-1])), scale='row', Rowv=NA, Colv=NA,cexRow =0.5) hm1<-heatmap(t(as.matrix(ds.c[ds.c$monthdate>=as.Date('2017-04-01'),-1])), scale='row', Colv=NA,cexRow =0.5) ##Simple model# #Consider Apr 2018-Sep 2018 as roll out perios and Oct 2018-Mar 2019 as eval period bh3<-bh1[bh1$monthdate>=as.Date('2017-04-01'),] bh3$post.pcv<-0 bh3$post.pcv[bh3$pcv.status==1 & bh3$monthdate>=as.Date('2018-04-01')& bh3$monthdate<=as.Date('2018-09-01')] <- 1 bh3$post.pcv[bh3$pcv.status==1 & bh3$monthdate>=as.Date('2018-10-01')& bh3$monthdate<=as.Date('2019-03-01')] <- 2 bh3$post.pcv<-as.factor(bh3$post.pcv) bh3$date.factor<-as.factor(bh3$monthdate) bh3$obs<-as.factor(1:nrow(bh3)) mod1<-glmer(pneu_death ~ (1\|date.factor) + (1\|DISTRICT) + (1\|obs)+ post.pcv, data=bh3, family='poisson' ) summary(mod1) mod1<-glmer(uri ~ (1\|DISTRICT) + (1\|obs)+ post.pcv, data=bh3, family='poisson' ) summary(mod1) #Gamm with time smooth library(mgcv) bh3$date.cont<-bh3$year+ bh3$month/12-1/12 mod2<-gamm(uri ~ s(date.cont) + post.pcv, data=bh3, family='poisson' ,random=list(DISTRICT=~1)) summary(mod2$lme) #mod2<-gamm(pneu_death ~ s(date.cont) + post.pcv, data=bh3, family='poisson' ,random=list(DISTRICT=~1), niterPQL=500) #summary(mod2$lme) #statewide ds.c.state<-dcast(ds.m, monthdate~1, fun.aggregate = sum) par(mar=c(3,2,1,1)) plot(ds.c.state[ds.c.state$monthdate>=as.Date('2017-04-01'),1],ds.c.state[ds.c.state$monthdate>=as.Date('2017-04-01'),2], type='l') #Brian: analyze by region vs district #Need a crosswalk file. Western/Central/Eastern in UP; district-> division #District hospitals--probably kids coming from district; bigger hospitals, kids coming from other places # Mortality data: is location based on residence or place of death? #--does death registry include out of hospital deaths #There is an official death registry
test_that("User interface is correctly rendered", { local_edition(3) cur_header <- .cytomapper_header() expect_equal(cur_header$name, "header") expect_equal(cur_header$attribs$class, "main-header") expect_null(cur_header$children[[1]]) expect_snapshot_output(cur_header$children[[2]]) expect_snapshot_output(cur_header$children[[3]]) cur_sidebar <- .cytomapper_sidebar() expect_equal(cur_sidebar$name, "aside") expect_equal(cur_sidebar$attribs$id, "sidebarCollapsed") expect_equal(cur_sidebar$attribs$class, "main-sidebar") expect_equal(cur_sidebar$attribs$`data-collapsed`, "false") expect_null(cur_sidebar$children[[1]]) expect_snapshot_output(cur_sidebar$children[[2]]) skip_on_cran() cur_body <- .cytomapper_body() expect_equal(cur_body$name, "div") expect_equal(cur_body$attribs$class, "content-wrapper") cur_tab_id <- cur_body$children[[1]][[3]][[1]][[3]][[1]][[3]][[1]][[2]]$`data-tabsetid` expect_equal(as.character(cur_body$children[[1]]), paste0('<section class=\"content\">\n <div class=\"col-sm-12\">\n <div class=\"nav-tabs-custom\">\n <ul class=\"nav nav-tabs shiny-tab-input\" id=\"tabbox1\" data-tabsetid=\"', cur_tab_id, '\">\n <li class=\"active\">\n <a href=\"#tab-', cur_tab_id, '-1\" data-toggle=\"tab\" data-value=\"tab1\">Scatter Plots</a>\n </li>\n <li>\n <a href=\"#tab-', cur_tab_id, '-2\" data-toggle=\"tab\" data-value=\"tab2\">Images</a>\n </li>\n </ul>\n <div class=\"tab-content\" data-tabsetid=\"', cur_tab_id, '\">\n <div class=\"tab-pane active\" data-value=\"tab1\" id=\"tab-', cur_tab_id, '-1\">\n <div id=\"AdditionalPlots_tab1\" class=\"shiny-html-output\"></div>\n </div>\n <div class=\"tab-pane\" data-value=\"tab2\" id=\"tab-', cur_tab_id, '-2\">\n <div id=\"AdditionalPlots_tab2\" class=\"shiny-html-output\"></div>\n </div>\n </div>\n </div>\n </div>\n</section>')) }) test_that("Sidebar is correctly rendered", { data("pancreasSCE") local_edition(3) testServer(app = cytomapperShiny(object = pancreasSCE, img_id = "ImageNb", cell_id = "CellNb"), { session$setInputs(plotCount = 1) expect_snapshot_output(.addPlots_sidebar(input)(session)$html) session$setInputs(plotCount = 2) expect_snapshot_output(.addPlots_sidebar(input)(session)$html) session$setInputs(plotCount = 3) expect_snapshot_output(.addPlots_sidebar(input)(session)$html) }) }) test_that("Plots in tab 1 are correctly rendered", { data("pancreasSCE") local_edition(3) testServer(app = cytomapperShiny(object = pancreasSCE, img_id = "ImageNb", cell_id = "CellNb"), { session$setInputs(plotCount = 1) expect_snapshot_output(.addPlots_tab1(input)(session)$html) session$setInputs(plotCount = 2) expect_snapshot_output(.addPlots_tab1(input)(session)$html) session$setInputs(plotCount = 3) expect_snapshot_output(.addPlots_tab1(input)(session)$html) }) }) test_that("Plots in tab 2 are correctly rendered", { data("pancreasSCE") data("pancreasMasks") data("pancreasImages") local_edition(3) testServer(app = cytomapperShiny(object = pancreasSCE, img_id = "ImageNb", cell_id = "CellNb"), { expect_null(.addPlots_tab2(input, object = pancreasSCE, mask = NULL, image = NULL)) }) testServer(app = cytomapperShiny(object = pancreasSCE, img_id = "ImageNb", cell_id = "CellNb", mask = pancreasMasks, image = NULL), { expect_snapshot_output(.addPlots_tab2(input, object = pancreasSCE, mask = pancreasMasks, image = NULL)(session)$html) }) testServer(app = cytomapperShiny(object = pancreasSCE, img_id = "ImageNb", cell_id = "CellNb", mask = pancreasMasks, image = pancreasImages), { expect_snapshot_output(.addPlots_tab2(input, object = pancreasSCE, mask = pancreasMasks, image = pancreasImages)(session)$html) }) })	/tests/testthat/test_shiny_ui.R	no_license	SchulzDan/cytomapper	R	false	false	4,584	r	test_that("User interface is correctly rendered", { local_edition(3) cur_header <- .cytomapper_header() expect_equal(cur_header$name, "header") expect_equal(cur_header$attribs$class, "main-header") expect_null(cur_header$children[[1]]) expect_snapshot_output(cur_header$children[[2]]) expect_snapshot_output(cur_header$children[[3]]) cur_sidebar <- .cytomapper_sidebar() expect_equal(cur_sidebar$name, "aside") expect_equal(cur_sidebar$attribs$id, "sidebarCollapsed") expect_equal(cur_sidebar$attribs$class, "main-sidebar") expect_equal(cur_sidebar$attribs$`data-collapsed`, "false") expect_null(cur_sidebar$children[[1]]) expect_snapshot_output(cur_sidebar$children[[2]]) skip_on_cran() cur_body <- .cytomapper_body() expect_equal(cur_body$name, "div") expect_equal(cur_body$attribs$class, "content-wrapper") cur_tab_id <- cur_body$children[[1]][[3]][[1]][[3]][[1]][[3]][[1]][[2]]$`data-tabsetid` expect_equal(as.character(cur_body$children[[1]]), paste0('<section class=\"content\">\n <div class=\"col-sm-12\">\n <div class=\"nav-tabs-custom\">\n <ul class=\"nav nav-tabs shiny-tab-input\" id=\"tabbox1\" data-tabsetid=\"', cur_tab_id, '\">\n <li class=\"active\">\n <a href=\"#tab-', cur_tab_id, '-1\" data-toggle=\"tab\" data-value=\"tab1\">Scatter Plots</a>\n </li>\n <li>\n <a href=\"#tab-', cur_tab_id, '-2\" data-toggle=\"tab\" data-value=\"tab2\">Images</a>\n </li>\n </ul>\n <div class=\"tab-content\" data-tabsetid=\"', cur_tab_id, '\">\n <div class=\"tab-pane active\" data-value=\"tab1\" id=\"tab-', cur_tab_id, '-1\">\n <div id=\"AdditionalPlots_tab1\" class=\"shiny-html-output\"></div>\n </div>\n <div class=\"tab-pane\" data-value=\"tab2\" id=\"tab-', cur_tab_id, '-2\">\n <div id=\"AdditionalPlots_tab2\" class=\"shiny-html-output\"></div>\n </div>\n </div>\n </div>\n </div>\n</section>')) }) test_that("Sidebar is correctly rendered", { data("pancreasSCE") local_edition(3) testServer(app = cytomapperShiny(object = pancreasSCE, img_id = "ImageNb", cell_id = "CellNb"), { session$setInputs(plotCount = 1) expect_snapshot_output(.addPlots_sidebar(input)(session)$html) session$setInputs(plotCount = 2) expect_snapshot_output(.addPlots_sidebar(input)(session)$html) session$setInputs(plotCount = 3) expect_snapshot_output(.addPlots_sidebar(input)(session)$html) }) }) test_that("Plots in tab 1 are correctly rendered", { data("pancreasSCE") local_edition(3) testServer(app = cytomapperShiny(object = pancreasSCE, img_id = "ImageNb", cell_id = "CellNb"), { session$setInputs(plotCount = 1) expect_snapshot_output(.addPlots_tab1(input)(session)$html) session$setInputs(plotCount = 2) expect_snapshot_output(.addPlots_tab1(input)(session)$html) session$setInputs(plotCount = 3) expect_snapshot_output(.addPlots_tab1(input)(session)$html) }) }) test_that("Plots in tab 2 are correctly rendered", { data("pancreasSCE") data("pancreasMasks") data("pancreasImages") local_edition(3) testServer(app = cytomapperShiny(object = pancreasSCE, img_id = "ImageNb", cell_id = "CellNb"), { expect_null(.addPlots_tab2(input, object = pancreasSCE, mask = NULL, image = NULL)) }) testServer(app = cytomapperShiny(object = pancreasSCE, img_id = "ImageNb", cell_id = "CellNb", mask = pancreasMasks, image = NULL), { expect_snapshot_output(.addPlots_tab2(input, object = pancreasSCE, mask = pancreasMasks, image = NULL)(session)$html) }) testServer(app = cytomapperShiny(object = pancreasSCE, img_id = "ImageNb", cell_id = "CellNb", mask = pancreasMasks, image = pancreasImages), { expect_snapshot_output(.addPlots_tab2(input, object = pancreasSCE, mask = pancreasMasks, image = pancreasImages)(session)$html) }) })
% Generated by roxygen2: do not edit by hand % Please edit documentation in R/shinyRange.R \docType{package} \name{shinyRange} \alias{shinyRange} \alias{shinyRange-package} \title{shinyRange: Text-box-based numeric range input widget for Shiny.} \description{ Text-box-based numeric range input widget for Shiny. }	/man/shinyRange.Rd	permissive	wkdavis/shinyRange	R	false	true	315	rd	% Generated by roxygen2: do not edit by hand % Please edit documentation in R/shinyRange.R \docType{package} \name{shinyRange} \alias{shinyRange} \alias{shinyRange-package} \title{shinyRange: Text-box-based numeric range input widget for Shiny.} \description{ Text-box-based numeric range input widget for Shiny. }
# https://elbersb.github.io/segregation/index.html?s=03 # https://elbersb.github.io/segregation/articles/segregation.html library(segregation) # example dataset with fake data provided by the package mutual_total(schools00, "race", "school", weight = "n") #> stat est #> 1: M 0.426 #> 2: H 0.419	/Segregacao/segregation.R	permissive	DATAUNIRIO/desigualdade	R	false	false	309	r	# https://elbersb.github.io/segregation/index.html?s=03 # https://elbersb.github.io/segregation/articles/segregation.html library(segregation) # example dataset with fake data provided by the package mutual_total(schools00, "race", "school", weight = "n") #> stat est #> 1: M 0.426 #> 2: H 0.419
\name{cSFM object} \alias{print.cSFM} \alias{fitted.cSFM} \alias{predict.cSFM} %- Also NEED an '\alias' for EACH other topic documented here. \title{Generic Method for 'cSFM' Objects } \description{ Print, extract fitted values and predict for cSFM object. Methods of the generic function \code{\link{print}}, \code{\link{fitted}} and \code{\link{predict}} for objects inheriting from class cSFM. %% ~~ A concise (1-5 lines) description of what the function does. ~~ } \usage{ \method{print}{cSFM}(x, ...) \method{fitted}{cSFM}(object, quantile = TRUE, quantile.level = c(0.5, 0.8, 0.9, 0.95, 0.99), ...) \method{predict}{cSFM}(object, newdata, cp.valid, tp.valid = NULL, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x}{A cSFM object; returned by the function \code{\link{cSFM.est}}} \item{object}{A cSFM object; returned by the function \code{\link{cSFM.est}}} \item{quantile}{logical; if TRUE(default), estimates of quantiles are returned } \item{quantile.level}{quantile vector; quantile levels to be estimated when \code{quantile = TRUE}} \item{newdata}{the partically observed new data set (missing data allowed) to be predicated} \item{cp.valid}{observed covariate vector for \code{newdata} with length \code{nrow{newdata}}} \item{tp.valid}{observed timepoint vector for \code{newdata} with length \code{ncol{newdata}}; See "Details".} \item{...}{other arguments passed to the generic functions} %% ~~Describe \code{x} here~~ } \details{ When use the function \code{predict}, each row of \code{newdata} corresponds to covariate information \code{cp.valid}, while the column is for the time points \code{tp.valid}. When \code{tp.valid} is \code{null}, then we assume the validation data set has the same time points as the training data set, which is used to obtain \code{object}. } %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{\code{\link{cSFM.est}}, \code{\link{print}}, \code{\link{fitted}}, \code{\link{predict}} %% ~~objects to See Also as \code{\link{help}}, ~~~ }	/man/generic.HAC.Rd	no_license	cran/cSFM	R	false	false	2,100	rd	\name{cSFM object} \alias{print.cSFM} \alias{fitted.cSFM} \alias{predict.cSFM} %- Also NEED an '\alias' for EACH other topic documented here. \title{Generic Method for 'cSFM' Objects } \description{ Print, extract fitted values and predict for cSFM object. Methods of the generic function \code{\link{print}}, \code{\link{fitted}} and \code{\link{predict}} for objects inheriting from class cSFM. %% ~~ A concise (1-5 lines) description of what the function does. ~~ } \usage{ \method{print}{cSFM}(x, ...) \method{fitted}{cSFM}(object, quantile = TRUE, quantile.level = c(0.5, 0.8, 0.9, 0.95, 0.99), ...) \method{predict}{cSFM}(object, newdata, cp.valid, tp.valid = NULL, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x}{A cSFM object; returned by the function \code{\link{cSFM.est}}} \item{object}{A cSFM object; returned by the function \code{\link{cSFM.est}}} \item{quantile}{logical; if TRUE(default), estimates of quantiles are returned } \item{quantile.level}{quantile vector; quantile levels to be estimated when \code{quantile = TRUE}} \item{newdata}{the partically observed new data set (missing data allowed) to be predicated} \item{cp.valid}{observed covariate vector for \code{newdata} with length \code{nrow{newdata}}} \item{tp.valid}{observed timepoint vector for \code{newdata} with length \code{ncol{newdata}}; See "Details".} \item{...}{other arguments passed to the generic functions} %% ~~Describe \code{x} here~~ } \details{ When use the function \code{predict}, each row of \code{newdata} corresponds to covariate information \code{cp.valid}, while the column is for the time points \code{tp.valid}. When \code{tp.valid} is \code{null}, then we assume the validation data set has the same time points as the training data set, which is used to obtain \code{object}. } %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{\code{\link{cSFM.est}}, \code{\link{print}}, \code{\link{fitted}}, \code{\link{predict}} %% ~~objects to See Also as \code{\link{help}}, ~~~ }
% Generated by roxygen2: do not edit by hand % Please edit documentation in R/FILEST_package.R \docType{package} \name{FILEST-package} \alias{FILEST} \alias{FILEST-package} \title{Fine-Level Structure Simulator} \description{ A population genetic simulator, which is able to generate synthetic datasets for single-nucleotide polymorphisms (SNP) for multiple populations. The genetic distances among populations can be set according to the Fixation Index (Fst). This tool is able to simulate outlying individuals and missing SNPs can be specified. For Genome-wide association study (GWAS), disease status can be set in desired level according risk ratio. } \details{ The R package \pkg{FILEST} requires \pkg{KRIS} and \pkg{rARPACK}. Here is the list of functions in the R package \pkg{FILEST}: \itemize{ \item \code{\link{cbind_bigmatrix}} \item \code{\link{create.template.setting}} \item \code{\link{demo.filest}} \item \code{\link{filest}} \item \code{\link{rbind_bigmatrix}} } } \seealso{ Useful links: \itemize{ \item \url{https://gitlab.com/kris.ccp/filest} \item Report bugs at \url{https://gitlab.com/kris.ccp/filest/-/issues} } } \author{ \strong{Maintainer}: Kridsadakorn Chaichoompu \email{kridsadakorn@biostatgen.org} Authors: \itemize{ \item Kristel Van Steen \item Fentaw Abegaz } } \keyword{internal}	/man/FILEST-package.Rd	no_license	cran/FILEST	R	false	true	1,327	rd	% Generated by roxygen2: do not edit by hand % Please edit documentation in R/FILEST_package.R \docType{package} \name{FILEST-package} \alias{FILEST} \alias{FILEST-package} \title{Fine-Level Structure Simulator} \description{ A population genetic simulator, which is able to generate synthetic datasets for single-nucleotide polymorphisms (SNP) for multiple populations. The genetic distances among populations can be set according to the Fixation Index (Fst). This tool is able to simulate outlying individuals and missing SNPs can be specified. For Genome-wide association study (GWAS), disease status can be set in desired level according risk ratio. } \details{ The R package \pkg{FILEST} requires \pkg{KRIS} and \pkg{rARPACK}. Here is the list of functions in the R package \pkg{FILEST}: \itemize{ \item \code{\link{cbind_bigmatrix}} \item \code{\link{create.template.setting}} \item \code{\link{demo.filest}} \item \code{\link{filest}} \item \code{\link{rbind_bigmatrix}} } } \seealso{ Useful links: \itemize{ \item \url{https://gitlab.com/kris.ccp/filest} \item Report bugs at \url{https://gitlab.com/kris.ccp/filest/-/issues} } } \author{ \strong{Maintainer}: Kridsadakorn Chaichoompu \email{kridsadakorn@biostatgen.org} Authors: \itemize{ \item Kristel Van Steen \item Fentaw Abegaz } } \keyword{internal}
source(Sys.getenv('RESULTS_BASE_PARTIAL')) source(Sys.getenv('RESULTS_DISPLAY_PARTIAL')) source(Sys.getenv('RESULTS_TABLES_PARTIAL')) library(argparse) library(tidyr, warn.conflicts = FALSE) library(wombat) parser <- ArgumentParser() parser$add_argument('--mcmc-samples-lg-online-corrected') parser$add_argument('--mcmc-samples-ln-online-corrected') parser$add_argument('--mcmc-samples-lg-offline-online-corrected') parser$add_argument('--mcmc-samples-ln-offline-online-corrected') parser$add_argument('--output') args <- parser$parse_args() read_samples <- function(filename, mode_i, raw_i) { window(readRDS(filename), start = 1001)$beta %>% as.data.frame() %>% pivot_longer(everything()) %>% mutate( mode = mode_i, raw = raw_i ) } beta_samples_df <- bind_rows( read_samples( args$mcmc_samples_lg_online_corrected, 'WOMBAT LG', 'Uncorrected retrievals' ), read_samples( args$mcmc_samples_ln_online_corrected, 'WOMBAT LN', 'Uncorrected retrievals' ), read_samples( args$mcmc_samples_lg_offline_online_corrected, 'WOMBAT LG', 'TCCON-corrected retrievals' ), read_samples( args$mcmc_samples_ln_offline_online_corrected, 'WOMBAT LN', 'TCCON-corrected retrievals' ) ) %>% mutate( parameter = factor(c( 'is_oco2:oco2_operation_modeLG' = '(Intercept)', 'is_oco2:oco2_operation_modeLG:oco2_dp' = 'dp', 'is_oco2:oco2_operation_modeLG:oco2_co2_grad_del' = 'co2_grad_del', 'is_oco2:oco2_operation_modeLG:oco2_log_dws' = 'logDWS', 'is_oco2:oco2_operation_modeLN' = '(Intercept)', 'is_oco2:oco2_operation_modeLN:oco2_dp' = 'dp', 'is_oco2:oco2_operation_modeLN:oco2_co2_grad_del' = 'co2_grad_del', 'is_oco2:oco2_operation_modeLN:oco2_log_dws' = 'logDWS' )[name], levels = c('dp', 'co2_grad_del', 'logDWS')), mode = factor( mode, levels = c( 'WOMBAT LG', 'WOMBAT LN', 'TCCON-based offline correction' ) ), value = -value ) %>% filter(parameter != '(Intercept)') %>% mutate( value = ifelse( mode == 'WOMBAT LG', value / c( 'dp' = 3.057625, 'co2_grad_del' = 21.348496, 'logDWS' = 1.135891 )[parameter], ifelse( mode == 'WOMBAT LN', value / c( 'dp' = 2.758201, 'co2_grad_del' = 19.829637, 'logDWS' = 1.144667 )[parameter], value ) ) ) output1 <- ggplot() + geom_vline( data = tibble( parameter = factor( c('dp', 'co2_grad_del', 'logDWS'), levels = c('dp', 'co2_grad_del', 'logDWS') ), value = c(0.3, 0.028, 0.6) ), mapping = aes(xintercept = value), colour = 'blue', size = 1 ) + geom_density( data = beta_samples_df %>% filter(raw == 'Uncorrected retrievals'), mapping = aes(value, colour = mode) ) + scale_colour_manual( values = c( 'WOMBAT LG' = get_colour('wombat_lg'), 'WOMBAT LN' = get_colour('wombat_ln'), 'TCCON-based offline correction' = 'blue' ), drop = FALSE, guide = guide_legend(override.aes = list(size = 1)) ) + labs(x = 'Bias correction coefficient', y = 'Density', colour = NULL) + facet_wrap(~ parameter, scales = 'free') + scale_x_continuous(expand = expansion(mult = c(0.2, 0.2))) + ggtitle('Uncorrected retrievals') + theme( axis.text.x = element_text(angle = 30, hjust = 1), legend.position = 'bottom' ) output2 <- ggplot() + geom_vline( xintercept = 0, colour = '#999999', linetype = 2, size = 1 ) + geom_density( data = beta_samples_df %>% filter(raw == 'TCCON-corrected retrievals'), mapping = aes(value, colour = mode) ) + scale_colour_manual( values = c( 'WOMBAT LG' = get_colour('wombat_lg'), 'WOMBAT LN' = get_colour('wombat_ln'), 'TCCON-based offline correction' = 'blue' ), drop = FALSE, guide = guide_legend(override.aes = list(size = 1)) ) + labs(x = 'Bias correction coefficient', y = 'Density', colour = NULL) + facet_wrap(~ parameter, scales = 'free') + scale_x_continuous(expand = expansion(mult = c(0.2, 0.2))) + ggtitle('TCCON-corrected retrievals') + theme( axis.text.x = element_text(angle = 30, hjust = 1), legend.position = 'bottom' ) output <- gridExtra::arrangeGrob( output1 + theme(legend.position = 'none'), output2 + theme(legend.position = 'none'), get_legend(output1), heights = c(5.5, 5.5, 1) ) ggsave( args$output, plot = output, width = DISPLAY_SETTINGS$full_width, height = 12, units = 'cm' )	/4_results/src/oco2-bias-correction.R	no_license	mbertolacci/wombat-paper	R	false	false	4,612	r	source(Sys.getenv('RESULTS_BASE_PARTIAL')) source(Sys.getenv('RESULTS_DISPLAY_PARTIAL')) source(Sys.getenv('RESULTS_TABLES_PARTIAL')) library(argparse) library(tidyr, warn.conflicts = FALSE) library(wombat) parser <- ArgumentParser() parser$add_argument('--mcmc-samples-lg-online-corrected') parser$add_argument('--mcmc-samples-ln-online-corrected') parser$add_argument('--mcmc-samples-lg-offline-online-corrected') parser$add_argument('--mcmc-samples-ln-offline-online-corrected') parser$add_argument('--output') args <- parser$parse_args() read_samples <- function(filename, mode_i, raw_i) { window(readRDS(filename), start = 1001)$beta %>% as.data.frame() %>% pivot_longer(everything()) %>% mutate( mode = mode_i, raw = raw_i ) } beta_samples_df <- bind_rows( read_samples( args$mcmc_samples_lg_online_corrected, 'WOMBAT LG', 'Uncorrected retrievals' ), read_samples( args$mcmc_samples_ln_online_corrected, 'WOMBAT LN', 'Uncorrected retrievals' ), read_samples( args$mcmc_samples_lg_offline_online_corrected, 'WOMBAT LG', 'TCCON-corrected retrievals' ), read_samples( args$mcmc_samples_ln_offline_online_corrected, 'WOMBAT LN', 'TCCON-corrected retrievals' ) ) %>% mutate( parameter = factor(c( 'is_oco2:oco2_operation_modeLG' = '(Intercept)', 'is_oco2:oco2_operation_modeLG:oco2_dp' = 'dp', 'is_oco2:oco2_operation_modeLG:oco2_co2_grad_del' = 'co2_grad_del', 'is_oco2:oco2_operation_modeLG:oco2_log_dws' = 'logDWS', 'is_oco2:oco2_operation_modeLN' = '(Intercept)', 'is_oco2:oco2_operation_modeLN:oco2_dp' = 'dp', 'is_oco2:oco2_operation_modeLN:oco2_co2_grad_del' = 'co2_grad_del', 'is_oco2:oco2_operation_modeLN:oco2_log_dws' = 'logDWS' )[name], levels = c('dp', 'co2_grad_del', 'logDWS')), mode = factor( mode, levels = c( 'WOMBAT LG', 'WOMBAT LN', 'TCCON-based offline correction' ) ), value = -value ) %>% filter(parameter != '(Intercept)') %>% mutate( value = ifelse( mode == 'WOMBAT LG', value / c( 'dp' = 3.057625, 'co2_grad_del' = 21.348496, 'logDWS' = 1.135891 )[parameter], ifelse( mode == 'WOMBAT LN', value / c( 'dp' = 2.758201, 'co2_grad_del' = 19.829637, 'logDWS' = 1.144667 )[parameter], value ) ) ) output1 <- ggplot() + geom_vline( data = tibble( parameter = factor( c('dp', 'co2_grad_del', 'logDWS'), levels = c('dp', 'co2_grad_del', 'logDWS') ), value = c(0.3, 0.028, 0.6) ), mapping = aes(xintercept = value), colour = 'blue', size = 1 ) + geom_density( data = beta_samples_df %>% filter(raw == 'Uncorrected retrievals'), mapping = aes(value, colour = mode) ) + scale_colour_manual( values = c( 'WOMBAT LG' = get_colour('wombat_lg'), 'WOMBAT LN' = get_colour('wombat_ln'), 'TCCON-based offline correction' = 'blue' ), drop = FALSE, guide = guide_legend(override.aes = list(size = 1)) ) + labs(x = 'Bias correction coefficient', y = 'Density', colour = NULL) + facet_wrap(~ parameter, scales = 'free') + scale_x_continuous(expand = expansion(mult = c(0.2, 0.2))) + ggtitle('Uncorrected retrievals') + theme( axis.text.x = element_text(angle = 30, hjust = 1), legend.position = 'bottom' ) output2 <- ggplot() + geom_vline( xintercept = 0, colour = '#999999', linetype = 2, size = 1 ) + geom_density( data = beta_samples_df %>% filter(raw == 'TCCON-corrected retrievals'), mapping = aes(value, colour = mode) ) + scale_colour_manual( values = c( 'WOMBAT LG' = get_colour('wombat_lg'), 'WOMBAT LN' = get_colour('wombat_ln'), 'TCCON-based offline correction' = 'blue' ), drop = FALSE, guide = guide_legend(override.aes = list(size = 1)) ) + labs(x = 'Bias correction coefficient', y = 'Density', colour = NULL) + facet_wrap(~ parameter, scales = 'free') + scale_x_continuous(expand = expansion(mult = c(0.2, 0.2))) + ggtitle('TCCON-corrected retrievals') + theme( axis.text.x = element_text(angle = 30, hjust = 1), legend.position = 'bottom' ) output <- gridExtra::arrangeGrob( output1 + theme(legend.position = 'none'), output2 + theme(legend.position = 'none'), get_legend(output1), heights = c(5.5, 5.5, 1) ) ggsave( args$output, plot = output, width = DISPLAY_SETTINGS$full_width, height = 12, units = 'cm' )
# Plot maps and NDVI, EVI, NIRv relationships for 4 CA rangeland test sites setwd("C:/Users/eordway/Desktop") # Load libraries library(raster); library(rgdal); library(sp) library(reshape2); library(dplyr); library(tidyr); library(rgeos) library(GISTools); library(sf); library(SDMTools) library(ggplot2); library(rasterVis); library(viridis) colr = inferno(100, direction=-1) #----- Load covariates & TCC ---------------------------------------------------- ## load CA shapefile CA = readOGR(dsn="CA_state_boundary", layer="CA_State_TIGER2016"); plot(CA) ## load 5 test site shapfiles bak = readOGR(dsn="Test_Sites", layer="Bakersfield"); plot(bak) las = readOGR(dsn="Test_Sites", layer="Lassen"); plot(las) mrc = readOGR(dsn="Test_Sites", layer="Merced"); plot(mrc) mh = readOGR(dsn="Test_Sites", layer="Mt_Hamilton"); plot(mh) ## load GLCF Tree Canopy Cover product (2000) ## also 2005 & 2010 tcc <- raster("D:/DEM_Precip_Soil_TCC/TCC_CA_2000.tif"); plot(tcc)# 2000 ## load & stack veg indices for all years current.list <- list.files(path="Temp_Veg_Indices/EVI/",pattern =".tif$", full.names=TRUE) #current.list <- list.files(path="Temp_Veg_Indices/NDVI/",pattern =".tif$", full.names=TRUE) #current.list <- list.files(path="Temp_Veg_Indices/NIRv/",pattern =".tif$", full.names=TRUE) veg.stack <- stack(current.list); dim(veg.stack) # should have 31 bands plot(veg.stack, zlim = c(0,1), col=colr)# zlim = c(0,1), ## add TCC to stack dat.stack <- stack(tcc,veg.stack); dim(dat.stack) # should have 32 bands plot(dat.stack, col=colr)# zlim = c(0,1), #----------------------------------------------------------------------------- ## crop stacked vegetation indices to test sites out_rastB <- crop(dat.stack, bak, snap="out"); plot(trim(out_rastB), zlim = c(0,1), col=colr) out_B <- mask(out_rastB, bak); plot(out_B, zlim = c(0,1), col=colr) out_rastL <- crop(dat.stack, las, snap="out"); plot(trim(out_rastL), zlim = c(0,1), col=colr) out_L <- mask(out_rastL, las); plot(out_L, zlim = c(0,1), col=colr) out_rastM <- crop(dat.stack, mrc, snap="out"); plot(trim(out_rastM), zlim = c(0,1), col=colr) out_M <- mask(out_rastM, mrc); plot(out_M, zlim = c(0,1), col=colr) out_rastMH <- crop(dat.stack, mh, snap="out"); plot(trim(out_rastMH), zlim = c(0,1), col=colr) out_MH <- mask(out_rastMH, mh); plot(out_MH, zlim = c(0,1), col=colr) # write rasters string_out <- "EVI_stack" # EVI_stack \| NDVI_stack \| NIRv_stack writeRaster(out_B, format="GTiff", paste('D:/Test_Site_Ouput/',string_out,'_Bakersfield.tif',sep=""), overwrite=T) writeRaster(out_L, format="GTiff", paste('D:/Test_Site_Ouput/',string_out,'_Lassen.tif',sep=""), overwrite=T) writeRaster(out_M, format="GTiff", paste('D:/Test_Site_Ouput/',string_out,'_Merced.tif',sep=""), overwrite=T) writeRaster(out_MH, format="GTiff", paste('D:/Test_Site_Ouput/',string_out,'_Mt_Hamilton.tif',sep=""), overwrite=T)	/rangeland-production/code/CA_Rangelands_create_test_site_stacks.R	no_license	eoway/soilc-ca-rangelands	R	false	false	2,932	r	# Plot maps and NDVI, EVI, NIRv relationships for 4 CA rangeland test sites setwd("C:/Users/eordway/Desktop") # Load libraries library(raster); library(rgdal); library(sp) library(reshape2); library(dplyr); library(tidyr); library(rgeos) library(GISTools); library(sf); library(SDMTools) library(ggplot2); library(rasterVis); library(viridis) colr = inferno(100, direction=-1) #----- Load covariates & TCC ---------------------------------------------------- ## load CA shapefile CA = readOGR(dsn="CA_state_boundary", layer="CA_State_TIGER2016"); plot(CA) ## load 5 test site shapfiles bak = readOGR(dsn="Test_Sites", layer="Bakersfield"); plot(bak) las = readOGR(dsn="Test_Sites", layer="Lassen"); plot(las) mrc = readOGR(dsn="Test_Sites", layer="Merced"); plot(mrc) mh = readOGR(dsn="Test_Sites", layer="Mt_Hamilton"); plot(mh) ## load GLCF Tree Canopy Cover product (2000) ## also 2005 & 2010 tcc <- raster("D:/DEM_Precip_Soil_TCC/TCC_CA_2000.tif"); plot(tcc)# 2000 ## load & stack veg indices for all years current.list <- list.files(path="Temp_Veg_Indices/EVI/",pattern =".tif$", full.names=TRUE) #current.list <- list.files(path="Temp_Veg_Indices/NDVI/",pattern =".tif$", full.names=TRUE) #current.list <- list.files(path="Temp_Veg_Indices/NIRv/",pattern =".tif$", full.names=TRUE) veg.stack <- stack(current.list); dim(veg.stack) # should have 31 bands plot(veg.stack, zlim = c(0,1), col=colr)# zlim = c(0,1), ## add TCC to stack dat.stack <- stack(tcc,veg.stack); dim(dat.stack) # should have 32 bands plot(dat.stack, col=colr)# zlim = c(0,1), #----------------------------------------------------------------------------- ## crop stacked vegetation indices to test sites out_rastB <- crop(dat.stack, bak, snap="out"); plot(trim(out_rastB), zlim = c(0,1), col=colr) out_B <- mask(out_rastB, bak); plot(out_B, zlim = c(0,1), col=colr) out_rastL <- crop(dat.stack, las, snap="out"); plot(trim(out_rastL), zlim = c(0,1), col=colr) out_L <- mask(out_rastL, las); plot(out_L, zlim = c(0,1), col=colr) out_rastM <- crop(dat.stack, mrc, snap="out"); plot(trim(out_rastM), zlim = c(0,1), col=colr) out_M <- mask(out_rastM, mrc); plot(out_M, zlim = c(0,1), col=colr) out_rastMH <- crop(dat.stack, mh, snap="out"); plot(trim(out_rastMH), zlim = c(0,1), col=colr) out_MH <- mask(out_rastMH, mh); plot(out_MH, zlim = c(0,1), col=colr) # write rasters string_out <- "EVI_stack" # EVI_stack \| NDVI_stack \| NIRv_stack writeRaster(out_B, format="GTiff", paste('D:/Test_Site_Ouput/',string_out,'_Bakersfield.tif',sep=""), overwrite=T) writeRaster(out_L, format="GTiff", paste('D:/Test_Site_Ouput/',string_out,'_Lassen.tif',sep=""), overwrite=T) writeRaster(out_M, format="GTiff", paste('D:/Test_Site_Ouput/',string_out,'_Merced.tif',sep=""), overwrite=T) writeRaster(out_MH, format="GTiff", paste('D:/Test_Site_Ouput/',string_out,'_Mt_Hamilton.tif',sep=""), overwrite=T)
#' Converts list(list(fields)) into list(fields(list)) #' #' @param dots list of lists listcomb <- function(dots) { nms <- names(dots[[1]]) ans <- as.list(nms) names(ans) <- nms for (nm in nms) { ans[[nm]] <- lapply(1:length(dots), function(i) dots[[i]][[nm]]) } ans } #' Inverse square root of a matrix #' #' @param m matrix isqrtm <- function(m) { res <- eigen(m) d <- res$values if (min(d) < -1e-5) warning("Negative eigenvalues in isqrtm") d[d < 0] <- 0 d[d > 0] <- 1/sqrt(d[d > 0]) v <- res$vectors return (v %% diag(d) %% t(v)) } #' Calls mclapply and combines the end result using listcomb #' #' @import parallel #' @examples #' lclapply(1:10, function(i) list(a = i, b = i^2), mc.cores = 1) lclapply <- function(x, f, mc.cores = 0) { if (mc.cores == 0) { return(listcomb(lapply(x, f))) } else { return(listcomb(mclapply(x, f, mc.cores = mc.cores))) } } #' Either uses lapply or mclapply #' #' @import parallel #' @examples #' lclapply(1:10, function(i) list(a = i, b = i^2), mc.cores = 1) mclapply0 <- function(x, f, mc.cores = 0) { if (mc.cores == 0) { return(lapply(x, f)) } else { return(mclapply(x, f, mc.cores = mc.cores)) } }	/R/testUtils.R	no_license	snarles/testUtils	R	false	false	1,216	r	#' Converts list(list(fields)) into list(fields(list)) #' #' @param dots list of lists listcomb <- function(dots) { nms <- names(dots[[1]]) ans <- as.list(nms) names(ans) <- nms for (nm in nms) { ans[[nm]] <- lapply(1:length(dots), function(i) dots[[i]][[nm]]) } ans } #' Inverse square root of a matrix #' #' @param m matrix isqrtm <- function(m) { res <- eigen(m) d <- res$values if (min(d) < -1e-5) warning("Negative eigenvalues in isqrtm") d[d < 0] <- 0 d[d > 0] <- 1/sqrt(d[d > 0]) v <- res$vectors return (v %% diag(d) %% t(v)) } #' Calls mclapply and combines the end result using listcomb #' #' @import parallel #' @examples #' lclapply(1:10, function(i) list(a = i, b = i^2), mc.cores = 1) lclapply <- function(x, f, mc.cores = 0) { if (mc.cores == 0) { return(listcomb(lapply(x, f))) } else { return(listcomb(mclapply(x, f, mc.cores = mc.cores))) } } #' Either uses lapply or mclapply #' #' @import parallel #' @examples #' lclapply(1:10, function(i) list(a = i, b = i^2), mc.cores = 1) mclapply0 <- function(x, f, mc.cores = 0) { if (mc.cores == 0) { return(lapply(x, f)) } else { return(mclapply(x, f, mc.cores = mc.cores)) } }
library(maps) library(sp) library(maptools) library(mapdata) library(scales) library(mapproj) library(RColorBrewer) library(rgdal) library(ggplot2) library(beyonce) library(rworldmap) library(RColorBrewer) #Percent of land spared, used, or unchanged relative to BAU scenario prop<-read.csv("Percent_map_all.csv", header=TRUE) head(prop) sPDF.prop.nt <- joinCountryData2Map(prop ,joinCode = "ISO3", nameJoinColumn = "ISO3", verbose=TRUE) sPDF.prop <- spTransform(sPDF.prop.nt, CRS="+proj=moll +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84") ##CROP LANDUSE % MAPS------ numCats <- 6 colourPalette <- colorRampPalette(brewer.pal(numCats, "RdBu")) (6) catmethod=c(-1,-0.50,-0.25,0,0.25,0.50,1) #feed #quartz() par(mfrow = c(2, 1)) par(mar = c(2, 1, 1, 1) + 0.1) # c(bottom, left, top, right) mapParams<-mapCountryData(sPDF.prop,nameColumnToPlot='MIXED_crop', catMethod=catmethod, colourPalette=colourPalette, missingCountryCol="dark gray",addLegend=F,oceanCol="white", mapTitle=" ") do.call(addMapLegend, c(mapParams, legendLabels="all", legendWidth=2, digits=1, horizontal = T)) mapParamsMAR<-mapCountryData(sPDF.prop,nameColumnToPlot='MARINE_crop', catMethod=catmethod, colourPalette=colourPalette, missingCountryCol="dark gray", addLegend=F,oceanCol="white", mapTitle=" ") ##GRAZING + CROP % MAP(S)------- mapParams<-mapCountryData(sPDF.prop,nameColumnToPlot='MIXED_c.g', catMethod=catmethod, colourPalette=colourPalette, missingCountryCol="dark gray",addLegend=F,oceanCol="white", mapTitle=" ") do.call(addMapLegend, c(mapParams, legendLabels="all", legendWidth=2, digits=1, horizontal = T)) #MARINE SCENARIO NOT DEPICTED IN STUDY BECAUSE RESULT THE SAME #mapParamsMAR<-mapCountryData(sPDF.prop,nameColumnToPlot='MARINE_c.g', catMethod=catmethod, colourPalette=colourPalette, # missingCountryCol="dark gray", addLegend=F,oceanCol="white", mapTitle=" ") #do.call(addMapLegend, c(mapParamsMAR, legendLabels="all", legendWidth=7, digits=1, horizontal = F)) ##---------- #Number of countries pos (spared) or neg (used) land num_con<-read.csv("PosNeg_plot_all.csv", header=TRUE) head(num_con) counts = num_con[,-1] head(counts) totals <- as.data.frame(colSums(counts)) names(totals) = "no_regions" totals$scenario = c("Used","Spared","Used","Spared","Used","Spared","Used","Spared") totals$id <- c("Mixed","Mixed","Marine","Marine","Mixed","Mixed","Marine","Marine") totals crop_tot = as.data.frame(totals[c(1:4),]) both_tot = totals[c(5:8),] head(crop_tot) cols <- colorRampPalette((beyonce_palette(12))) myPal <- cols(length(unique(crop_tot$id))) #Change Order of Factors crop_tot$id <-factor(crop_tot$id,levels=c("Mixed","Marine")) #change order levels(crop_tot$id) ggplot(crop_tot, aes(x=scenario, y = no_regions, fill=factor(id))) + geom_bar(stat = "identity",position=position_dodge()) + #coord_flip()+ labs(x="", y = "No. Regions" )+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.text.x = element_text(size=13),axis.text.y = element_text(size=13), axis.title.x = element_text(size=15), axis.title.y = element_text(size=15), panel.background = element_blank(), axis.line = element_line(colour = "black"), panel.border = element_rect(colour = "white", fill=NA)) + scale_y_continuous(limits = c(0, 215))+ scale_fill_manual(values = myPal) ##--------------- #Total land plots #Number of countries pos or neg total_ha<-read.csv("Total_ha_plot_all.csv", header=TRUE) head(total_ha) #Change Order of Factors levels(total_ha$Scenario) #Order of default levels total_ha$Scenario <-factor(total_ha$Scenario,levels=c("Current","BAU","Mixed", "Marine")) #change order head(total_ha$Scenario) crop_tot = total_ha[c(2,4,6,8),] crop_tot cols <- colorRampPalette((beyonce_palette(66))) myPal <- cols(length(unique(crop_tot$Scenario))) pd <- position_dodge(0.1) ggplot(crop_tot, aes(x=Scenario, y = ha_total, fill=factor(Scenario))) + geom_bar(stat = "identity",position=position_dodge()) + geom_errorbar(aes(ymin=ha_total-ha_total_sd, ymax=ha_total+ha_total_sd), width=.2, position=pd)+ labs(x="", y = "Total Cropland Use (ha)" )+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.text.x = element_text(size=13),axis.text.y = element_text(size=13), axis.title.x = element_text(size=15), axis.title.y = element_text(size=15), panel.background = element_blank(), axis.line = element_line(colour = "black"), panel.border = element_rect(colour = "white", fill=NA)) + scale_fill_manual(values = rev(myPal)) ggplot(total_ha, aes(x=Scenario, y = ha_total, fill=factor(id))) + geom_bar(stat = "identity") + labs(x="", y = "Total Land Use (ha)" )+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.text.x = element_text(size=13),axis.text.y = element_text(size=13), axis.title.x = element_text(size=15), axis.title.y = element_text(size=15), panel.background = element_blank(), axis.line = element_line(colour = "black"), panel.border = element_rect(colour = "white", fill=NA)) + scale_fill_manual(values = myPal)	/Outputs/Landuse_Maps&plots.R	no_license	brunj7/aquaculture-crop-feed	R	false	false	5,473	r	library(maps) library(sp) library(maptools) library(mapdata) library(scales) library(mapproj) library(RColorBrewer) library(rgdal) library(ggplot2) library(beyonce) library(rworldmap) library(RColorBrewer) #Percent of land spared, used, or unchanged relative to BAU scenario prop<-read.csv("Percent_map_all.csv", header=TRUE) head(prop) sPDF.prop.nt <- joinCountryData2Map(prop ,joinCode = "ISO3", nameJoinColumn = "ISO3", verbose=TRUE) sPDF.prop <- spTransform(sPDF.prop.nt, CRS="+proj=moll +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84") ##CROP LANDUSE % MAPS------ numCats <- 6 colourPalette <- colorRampPalette(brewer.pal(numCats, "RdBu")) (6) catmethod=c(-1,-0.50,-0.25,0,0.25,0.50,1) #feed #quartz() par(mfrow = c(2, 1)) par(mar = c(2, 1, 1, 1) + 0.1) # c(bottom, left, top, right) mapParams<-mapCountryData(sPDF.prop,nameColumnToPlot='MIXED_crop', catMethod=catmethod, colourPalette=colourPalette, missingCountryCol="dark gray",addLegend=F,oceanCol="white", mapTitle=" ") do.call(addMapLegend, c(mapParams, legendLabels="all", legendWidth=2, digits=1, horizontal = T)) mapParamsMAR<-mapCountryData(sPDF.prop,nameColumnToPlot='MARINE_crop', catMethod=catmethod, colourPalette=colourPalette, missingCountryCol="dark gray", addLegend=F,oceanCol="white", mapTitle=" ") ##GRAZING + CROP % MAP(S)------- mapParams<-mapCountryData(sPDF.prop,nameColumnToPlot='MIXED_c.g', catMethod=catmethod, colourPalette=colourPalette, missingCountryCol="dark gray",addLegend=F,oceanCol="white", mapTitle=" ") do.call(addMapLegend, c(mapParams, legendLabels="all", legendWidth=2, digits=1, horizontal = T)) #MARINE SCENARIO NOT DEPICTED IN STUDY BECAUSE RESULT THE SAME #mapParamsMAR<-mapCountryData(sPDF.prop,nameColumnToPlot='MARINE_c.g', catMethod=catmethod, colourPalette=colourPalette, # missingCountryCol="dark gray", addLegend=F,oceanCol="white", mapTitle=" ") #do.call(addMapLegend, c(mapParamsMAR, legendLabels="all", legendWidth=7, digits=1, horizontal = F)) ##---------- #Number of countries pos (spared) or neg (used) land num_con<-read.csv("PosNeg_plot_all.csv", header=TRUE) head(num_con) counts = num_con[,-1] head(counts) totals <- as.data.frame(colSums(counts)) names(totals) = "no_regions" totals$scenario = c("Used","Spared","Used","Spared","Used","Spared","Used","Spared") totals$id <- c("Mixed","Mixed","Marine","Marine","Mixed","Mixed","Marine","Marine") totals crop_tot = as.data.frame(totals[c(1:4),]) both_tot = totals[c(5:8),] head(crop_tot) cols <- colorRampPalette((beyonce_palette(12))) myPal <- cols(length(unique(crop_tot$id))) #Change Order of Factors crop_tot$id <-factor(crop_tot$id,levels=c("Mixed","Marine")) #change order levels(crop_tot$id) ggplot(crop_tot, aes(x=scenario, y = no_regions, fill=factor(id))) + geom_bar(stat = "identity",position=position_dodge()) + #coord_flip()+ labs(x="", y = "No. Regions" )+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.text.x = element_text(size=13),axis.text.y = element_text(size=13), axis.title.x = element_text(size=15), axis.title.y = element_text(size=15), panel.background = element_blank(), axis.line = element_line(colour = "black"), panel.border = element_rect(colour = "white", fill=NA)) + scale_y_continuous(limits = c(0, 215))+ scale_fill_manual(values = myPal) ##--------------- #Total land plots #Number of countries pos or neg total_ha<-read.csv("Total_ha_plot_all.csv", header=TRUE) head(total_ha) #Change Order of Factors levels(total_ha$Scenario) #Order of default levels total_ha$Scenario <-factor(total_ha$Scenario,levels=c("Current","BAU","Mixed", "Marine")) #change order head(total_ha$Scenario) crop_tot = total_ha[c(2,4,6,8),] crop_tot cols <- colorRampPalette((beyonce_palette(66))) myPal <- cols(length(unique(crop_tot$Scenario))) pd <- position_dodge(0.1) ggplot(crop_tot, aes(x=Scenario, y = ha_total, fill=factor(Scenario))) + geom_bar(stat = "identity",position=position_dodge()) + geom_errorbar(aes(ymin=ha_total-ha_total_sd, ymax=ha_total+ha_total_sd), width=.2, position=pd)+ labs(x="", y = "Total Cropland Use (ha)" )+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.text.x = element_text(size=13),axis.text.y = element_text(size=13), axis.title.x = element_text(size=15), axis.title.y = element_text(size=15), panel.background = element_blank(), axis.line = element_line(colour = "black"), panel.border = element_rect(colour = "white", fill=NA)) + scale_fill_manual(values = rev(myPal)) ggplot(total_ha, aes(x=Scenario, y = ha_total, fill=factor(id))) + geom_bar(stat = "identity") + labs(x="", y = "Total Land Use (ha)" )+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.text.x = element_text(size=13),axis.text.y = element_text(size=13), axis.title.x = element_text(size=15), axis.title.y = element_text(size=15), panel.background = element_blank(), axis.line = element_line(colour = "black"), panel.border = element_rect(colour = "white", fill=NA)) + scale_fill_manual(values = myPal)
# call function is similar with python # args show the definition of funtcion print(args(sd)) # the parameter can be missing even if it hasn't a default value(lazy evaluation) # Q1 cube <- function(x, n) { x^3 } # defalt value # Q2 cube(3) pow <- function(x = 4, n = 3) { x^n } pow() # ... like C # argument after ... must be named explicitly!!! # search the environment search() # global environment and user's workspace is the first element to search # package:base is always the last. print(args(lm)) print(environment(lm)) lm <- function(x) {x ^ 2} print(args(lm)) print(environment(lm)) # show free variable ls(environment(lm)) # library() import a package, dan it will be the second to serach, the rest go down library(grid) search() # documentation library(datasets) data(iris)	/ComputingForDataAnalysis/PA2/function.R	no_license	ktargows/CourseRA	R	false	false	788	r	# call function is similar with python # args show the definition of funtcion print(args(sd)) # the parameter can be missing even if it hasn't a default value(lazy evaluation) # Q1 cube <- function(x, n) { x^3 } # defalt value # Q2 cube(3) pow <- function(x = 4, n = 3) { x^n } pow() # ... like C # argument after ... must be named explicitly!!! # search the environment search() # global environment and user's workspace is the first element to search # package:base is always the last. print(args(lm)) print(environment(lm)) lm <- function(x) {x ^ 2} print(args(lm)) print(environment(lm)) # show free variable ls(environment(lm)) # library() import a package, dan it will be the second to serach, the rest go down library(grid) search() # documentation library(datasets) data(iris)
library("data.table") library("ggplot2") # Initialization SCC <- as.data.table(x = readRDS(file = "Source_Classification_Code.rds")) NEI <- as.data.table(x = readRDS(file = "summarySCC_PM25.rds")) # Gather the subset of the NEI data which corresponds to vehicles condition <- grepl("vehicle", SCC[, SCC.Level.Two], ignore.case=TRUE) vehiclesSCC <- SCC[condition, SCC] vehiclesNEI <- NEI[NEI[, SCC] %in% vehiclesSCC,] # Subset the vehicles NEI data by each city's fip and add city name. vehiclesBaltimoreNEI <- vehiclesNEI[fips == "24510",] vehiclesBaltimoreNEI[, city := c("Baltimore City")] vehiclesLANEI <- vehiclesNEI[fips == "06037",] vehiclesLANEI[, city := c("Los Angeles")] # Combine data.tables into one data.table bothNEI <- rbind(vehiclesBaltimoreNEI,vehiclesLANEI) # Output graph png("plot6.png") # Plot graph using package ggplot2 ggplot(bothNEI, aes(x=factor(year), y=Emissions, fill=city)) + geom_bar(aes(fill=year),stat="identity") + facet_grid(scales="free", space="free", .~city) + labs(x="year", y=expression("Total PM"[2.5]" Emission (Kilo-Tons)")) + labs(title=expression("PM"[2.5]" Motor Vehicle Source Emissions in Baltimore & LA, 1999-2008")) dev.off()	/plot6.R	no_license	Ra1nOWL/Exploratory_Data_Analysis_Project_2	R	false	false	1,226	r	library("data.table") library("ggplot2") # Initialization SCC <- as.data.table(x = readRDS(file = "Source_Classification_Code.rds")) NEI <- as.data.table(x = readRDS(file = "summarySCC_PM25.rds")) # Gather the subset of the NEI data which corresponds to vehicles condition <- grepl("vehicle", SCC[, SCC.Level.Two], ignore.case=TRUE) vehiclesSCC <- SCC[condition, SCC] vehiclesNEI <- NEI[NEI[, SCC] %in% vehiclesSCC,] # Subset the vehicles NEI data by each city's fip and add city name. vehiclesBaltimoreNEI <- vehiclesNEI[fips == "24510",] vehiclesBaltimoreNEI[, city := c("Baltimore City")] vehiclesLANEI <- vehiclesNEI[fips == "06037",] vehiclesLANEI[, city := c("Los Angeles")] # Combine data.tables into one data.table bothNEI <- rbind(vehiclesBaltimoreNEI,vehiclesLANEI) # Output graph png("plot6.png") # Plot graph using package ggplot2 ggplot(bothNEI, aes(x=factor(year), y=Emissions, fill=city)) + geom_bar(aes(fill=year),stat="identity") + facet_grid(scales="free", space="free", .~city) + labs(x="year", y=expression("Total PM"[2.5]" Emission (Kilo-Tons)")) + labs(title=expression("PM"[2.5]" Motor Vehicle Source Emissions in Baltimore & LA, 1999-2008")) dev.off()
install.packages("shiny") install.packages("mc2d") install.packages("ggplot2") install.packages("scales") install.packages("dplyr") install.packages("reshape2") install.packages("countrycode") install.packages("shinydashboard")	/init.R	no_license	abhatia2014/Threat-Dashboard	R	false	false	229	r	install.packages("shiny") install.packages("mc2d") install.packages("ggplot2") install.packages("scales") install.packages("dplyr") install.packages("reshape2") install.packages("countrycode") install.packages("shinydashboard")
## LIBRARIES ====================================================================================== library("tidyverse") library("lubridate") library("splines") library("caret") library("recipes") ## DATA df_tr <- read_csv("./01_data/01_raw/train.csv") %>% mutate(partition = "train") df_ts <- read_csv("./01_data/01_raw/test.csv") %>% mutate(partition = "test") df_all <- bind_rows(df_tr, df_ts) df_all %>% select(Id, SalePrice, partition, everything()) ## MANUEL ENCODING ================================================================================ order_factors = FALSE df_all <- df_all %>% dplyr::rename(FrstFlrSF = `1stFlrSF`, ScndFlrSF = `2ndFlrSF`, ThrdSsnPorch = `3SsnPorch`) %>% dplyr::mutate( LotShape = factor(LotShape, levels = c("IR3", "IR2", "IR1", "Reg"), ordered = order_factors), Utilities = factor(Utilities, levels = c("ELO", "NoSeWa", "NoSewr", "AllPub"), ordered = order_factors), LandSlope = factor(LandSlope, levels = c("Sev", "Mod", "Gtl"), ordered = order_factors), OverallQual = factor(OverallQual, levels = c(1:10), ordered = order_factors), OverallCond = factor(OverallCond, levels = c(1:10), ordered = order_factors), ExterQual = factor(ExterQual, levels = c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), ExterCond = factor(ExterCond, levels = c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), BsmtQual = factor(BsmtQual, levels = c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), BsmtCond = factor(BsmtCond, levels = c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), BsmtExposure = factor(BsmtExposure, levels = c("No", "Mn", "Av", "Gd"), ordered = order_factors), BsmtFinType1 = factor(BsmtFinType1, levels = c("Unf", "LwQ", "Rec", "BLQ", "ALQ", "GLQ"), ordered = order_factors), BsmtFinType2 = factor(BsmtFinType2, levels = c("Unf", "LwQ", "Rec", "BLQ", "ALQ", "GLQ"), ordered = order_factors), HeatingQC = factor(HeatingQC, c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), KitchenQual = factor(KitchenQual, c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), Functional = factor(Functional, c("Sal", "Sev", "Maj2", "Maj1", "Mod", "Min2", "Min1", "Typ"), ordered = order_factors), FireplaceQu = factor(FireplaceQu, levels = c("TA", "Gd", "Ex"), ordered = order_factors), GarageFinish = factor(GarageFinish, levels = c("Uf", "RFn", "Fin"), ordered = order_factors), GarageQual = factor(GarageQual, levels = c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), GarageCond = factor(GarageCond, levels = c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), PavedDrive = factor(PavedDrive, levels = c("N", "P", "Y"), ordered = order_factors), PoolQC = factor(PoolQC, levels = c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), Fence = factor(Fence, levels = c("MnWw", "GdWo", "MnPrv", "GdPrv"), ordered = order_factors), MoSold = month(MoSold, label = ), MSSubClass = factor(paste0("MS", MSSubClass), ordered = order_factors) ) %>% dplyr::mutate_if(is_character, .funs = ~ factor(.x)) ## VISUAL ANALYSIS ================================================================================ ret_col_type <- function(df) { num_vars <- colnames(df_all)[map_lgl(df_all, is.numeric)] cat_vars <- colnames(df_all)[map_lgl(df_all, is.factor)] return(list(num = num_vars, cat = cat_vars)) } col_type <- ret_col_type(df_all) num_vars <- setdiff(col_type$num, c("SalePrice", "partition")) cat_vars <- setdiff(col_type$cat, c("SalePrice", "partition")) other_vars <- setdiff(colnames(df_all), union(union(num_vars, cat_vars), c("SalePrice", "partition"))) if (length(other_vars) > 0) { print(paste0(other_cols, " are neither numeric nor factors.")) } pred_vars <- setdiff(colnames(df_all), c("SalePrice", "partition", "Id")) # for (pred_var in pred_vars) { # if (pred_var %in% num_vars) { # p <- ggplot(df_all, aes_string(x = pred_var, y = "SalePrice")) + # geom_point() + # #geom_smooth(method = 'lm') + # geom_smooth(method = 'loess') # } else if (pred_var %in% cat_vars) { # p <- ggplot(df_all, aes_string(x = pred_var, y = "SalePrice")) + # geom_boxplot() # } # print(p) # Sys.sleep(5) # } df_all %>% ggplot(aes(x = OverallQual, y = SalePrice))+ geom_point() ## MISSING VALUES ================================================================================= getmode <- function(v) { uniqv <- unique(v) uniqv[which.max(tabulate(match(v, uniqv)))] } ## REMOVE OUTLIER # df_all <- df_all %>% filter(GrLivArea < 4000) #%>% select(Id, GrLivArea, SalePrice) #df_all <- df_all %>% filter(!Id %in% c(524, 1299)) ## MISSING VALUES miss_vals <- map_dfr(df_all, ~ sum(is.na(.x))) %>% gather() %>% filter(value > 0) %>% arrange(desc(value)) miss_vals %>% print(n = 200) # df_all[is.na(df_all$GarageYrBlt), c("GarageFinish", "GarageFinish", "GarageCond", "GarageQual", "GarageType", # "GarageCars", "GarageArea", "GarageYrBlt")] ## NA means "No": na_no <- c( "Alley", "PoolQC", "MiscFeature", "Fence", "FireplaceQu", "LotFrontage", ## "GarageFinish", "GarageFinish", "GarageCond", "GarageQual", "GarageType", "GarageCars", "GarageArea", "GarageYrBlt", ## "BsmtCond", "BsmtExposure", "BsmtQual", "BsmtFinType2", "BsmtFinType1", "BsmtFullBath", "BsmtHalfBath", "BsmtFinSF2", "BsmtUnfSF", "TotalBsmtSF" ) ## NA means "missing": na_missing <- c( "MasVnrType", "MasVnrArea", "MSZoning", "Utilities", "Functional", "Exterior1st", "Exterior2nd", "BsmtFinSF1", "Electrical", "KitchenQual", "GarageYrBlt", "SaleType" ) df_tr <- df_all %>% filter(partition == "train") df_ts <- df_all %>% filter(partition == "test") df_tr <- df_tr %>% mutate_at(na_no, function(x) { if (is.factor(x)) { x <- fct_explicit_na(x, na_level = "No") } else { x <- replace_na(x, replace = 0) } }) %>% mutate_at(na_missing, function(x) { if (is.factor(x)) { x <- fct_explicit_na(x, na_level = names(which.max(table(x)))) } else if (is.numeric(x)) { # x <- replace_na(x, replace = median(x, na.rm = T)) x <- replace_na(x, replace = median(x)) } }) df_ts <- df_ts %>% mutate_at(na_no, function(x) { if (is.factor(x)) { x <- fct_explicit_na(x, na_level = "No") } else { x <- replace_na(x, replace = 0) } }) %>% mutate_at(na_missing, function(x) { if (is.factor(x)) { x <- fct_explicit_na(x, na_level = names(which.max(table(x)))) } else if (is.numeric(x)) { # x <- replace_na(x, replace = median(x, na.rm = T)) x <- replace_na(x, replace = median(x)) } }) df_all <- bind_rows(df_tr, df_ts) map_dfr(df_all, ~ sum(is.na(.x))) %>% gather() %>% filter(value > 0) %>% arrange(desc(value)) ## SIMPLE FEATURES ================================================================================ five_level_simple <- c( "ExterQual", "ExterCond", "BsmtQual", "KitchenQual", "GarageQual", "BsmtCond", "GarageCond", "HeatingQC", "PoolQC" ) five_level_recode <- function(x) { fct_recode(x, bad = "Po", avgerage = "Fa", avgerage = "TA", good = "Gd", good = "Ex") } six_level_simple <- c("BsmtFinType1", "BsmtFinType2") six_level_recode <- function(x) { fct_recode(x, `1` = "Unf", `1` = "LwQ", `2` = "Rec", `2` = "BLQ", `3` = "ALQ", `3` = "GLQ" ) } ten_level_simple <- c("OverallQual", "OverallCond") ten_level_recode <- function(x) { fct_recode(x, `1` = "1", `1` = "2", `1` = "3", `2` = "4", `2` = "5", `2` = "6", `2` = "7", `3` = "8", `3` = "9", `3` = "10" ) } df_all <- df_all %>% dplyr::mutate_at(five_level_simple, .funs = list(Simple = five_level_recode) ) %>% dplyr::mutate_at(six_level_simple, .funs = list(Simple = six_level_recode) ) %>% dplyr::mutate_at(ten_level_simple, .funs = list(Simple = ten_level_recode) ) %>% dplyr::mutate_at("Functional", .funs = list(Simple = ~ fct_recode(.x, `1` = "Sal", `1` = "Sev", `1` = "Maj2", `2` = "Maj1", `2` = "Mod", `2` = "Min2", `3` = "Min1", `3` = "Typ" )) ) colnames(df_all) <- str_replace(colnames(df_all), "_", "") ## CREATE NEW FEATURES ============================================================================ ## df_all <- df_all %>% mutate( YearsSinceBuilt = pmax(YrSold - YearBuilt, 0), YearsSinceRemodel = pmax(YrSold - YearRemodAdd, 0), ExterGrade = as.numeric(ExterCond) * as.numeric(ExterQual), ExterGradeSimple = as.numeric(ExterCondSimple) * as.numeric(ExterQualSimple), OverallGrade = as.numeric(OverallCond) * as.numeric(OverallQual), OverallGradeSimple = as.numeric(OverallCondSimple) * as.numeric(OverallQualSimple), GarageGrade = as.numeric(GarageCond) * as.numeric(GarageQual), GarageGradeSimple = as.numeric(GarageCondSimple) * as.numeric(GarageQualSimple), KitchenScore = KitchenAbvGr * as.numeric(KitchenQual), KitchenScoreSimple = KitchenAbvGr * as.numeric(KitchenQualSimple), FireplaceScore = Fireplaces * as.numeric(FireplaceQu), GarageScore = GarageArea * as.numeric(GarageQual), GarageScoreSimple = GarageArea * as.numeric(GarageQualSimple), PoolScore = PoolArea * as.numeric(PoolQC), PoolScoreSimple = PoolArea * as.numeric(PoolQCSimple), # FireplaceScoreSimple = Fireplacesas.numeric(FireplaceQuSimple), TotalBath = BsmtFullBath + .5 BsmtHalfBath + .5 * HalfBath + FullBath, AllSF = GrLivArea + TotalBsmtSF, AllFlrsSF = FrstFlrSF + ScndFlrSF, AllPorchSF = OpenPorchSF + EnclosedPorch + ThrdSsnPorch + ScreenPorch + WoodDeckSF, HasMasVnr = fct_recode(MasVnrType, Yes = "BrkCmn", Yes = "BrkFace", Yes = "CBlock", Yes = "Stone", No = "None" ), BoughtOffPlan = fct_recode(SaleCondition, Yes = "Partial", No = "Abnorml", No = "Alloca", No = "AdjLand", No = "Family", No = "Normal" ), HasPool = ifelse(PoolArea > 0, "Yes", "No"), HasScnFloor = ifelse(ScndFlrSF > 0, "Yes", "No"), HasGarage = ifelse(GarageArea > 0, "Yes", "No"), HasBsmt = ifelse(BsmtFinSF1 > 0, "Yes", "No"), HasFireplace = ifelse(Fireplaces > 0, "Yes", "No") ) %>% dplyr::mutate_if(is_character, .funs = ~ factor(.x)) # predictor types col_type <- ret_col_type(df_all) num_pred_vars <- setdiff(col_type$num, c("SalePrice", "partition", "Id")) cat_pred_vars <- setdiff(col_type$cat, c("SalePrice", "partition", "Id")) other_vars <- setdiff(colnames(df_all), union(union(num_pred_vars, cat_pred_vars), c("SalePrice", "partition", "Id"))) if (length(other_vars) > 0) { print(paste0(other_cols, " are neither numeric nor factors.")) } ## TOP FEATURES BASED ON DIFFERENT CORRELATION COEFFICIENTS cor_type <- "pearson" ## pearson, spearman, kendall cor_df <- df_all[!is.na(df_all$SalePrice), c("SalePrice", num_pred_vars)] cor_vals <- sort(cor(cor_df, method = cor_type)[1, ], decreasing = T) top_features <- setdiff( names(cor_vals)[abs(cor_vals) > .55], "SalePrice" ) ## POLYNOMIAL TERMS # df_all <- df_all %>% # dplyr::mutate_at(top_features, .funs = list( # poly2 = ~ .x2, # poly3 = ~ .x3, # poly4 = ~.x4, # sqrt = ~ .x.5, # log = ~log(.x) # )) ## SPLINES # spl <- map( # df_all[top_features], # function(x) { # res <- bs(x, degree = 5) # res # } # ) %>% reduce(cbind) # # spl <- data.frame(spl) # colnames(spl) <- flatten_chr(map( # top_features, # ~ paste0(.x, paste0("_bs", c(1, 2, 3))) # )) # df_all <- bind_cols(df_all, spl) write_csv2(df_all, "./01_data/02_processed/train_test_stacked.csv")	/02_code/01_data_prep/data_prep.R	no_license	kaijennissen/advanced_regression	R	false	false	11,532	r	## LIBRARIES ====================================================================================== library("tidyverse") library("lubridate") library("splines") library("caret") library("recipes") ## DATA df_tr <- read_csv("./01_data/01_raw/train.csv") %>% mutate(partition = "train") df_ts <- read_csv("./01_data/01_raw/test.csv") %>% mutate(partition = "test") df_all <- bind_rows(df_tr, df_ts) df_all %>% select(Id, SalePrice, partition, everything()) ## MANUEL ENCODING ================================================================================ order_factors = FALSE df_all <- df_all %>% dplyr::rename(FrstFlrSF = `1stFlrSF`, ScndFlrSF = `2ndFlrSF`, ThrdSsnPorch = `3SsnPorch`) %>% dplyr::mutate( LotShape = factor(LotShape, levels = c("IR3", "IR2", "IR1", "Reg"), ordered = order_factors), Utilities = factor(Utilities, levels = c("ELO", "NoSeWa", "NoSewr", "AllPub"), ordered = order_factors), LandSlope = factor(LandSlope, levels = c("Sev", "Mod", "Gtl"), ordered = order_factors), OverallQual = factor(OverallQual, levels = c(1:10), ordered = order_factors), OverallCond = factor(OverallCond, levels = c(1:10), ordered = order_factors), ExterQual = factor(ExterQual, levels = c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), ExterCond = factor(ExterCond, levels = c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), BsmtQual = factor(BsmtQual, levels = c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), BsmtCond = factor(BsmtCond, levels = c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), BsmtExposure = factor(BsmtExposure, levels = c("No", "Mn", "Av", "Gd"), ordered = order_factors), BsmtFinType1 = factor(BsmtFinType1, levels = c("Unf", "LwQ", "Rec", "BLQ", "ALQ", "GLQ"), ordered = order_factors), BsmtFinType2 = factor(BsmtFinType2, levels = c("Unf", "LwQ", "Rec", "BLQ", "ALQ", "GLQ"), ordered = order_factors), HeatingQC = factor(HeatingQC, c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), KitchenQual = factor(KitchenQual, c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), Functional = factor(Functional, c("Sal", "Sev", "Maj2", "Maj1", "Mod", "Min2", "Min1", "Typ"), ordered = order_factors), FireplaceQu = factor(FireplaceQu, levels = c("TA", "Gd", "Ex"), ordered = order_factors), GarageFinish = factor(GarageFinish, levels = c("Uf", "RFn", "Fin"), ordered = order_factors), GarageQual = factor(GarageQual, levels = c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), GarageCond = factor(GarageCond, levels = c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), PavedDrive = factor(PavedDrive, levels = c("N", "P", "Y"), ordered = order_factors), PoolQC = factor(PoolQC, levels = c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), Fence = factor(Fence, levels = c("MnWw", "GdWo", "MnPrv", "GdPrv"), ordered = order_factors), MoSold = month(MoSold, label = ), MSSubClass = factor(paste0("MS", MSSubClass), ordered = order_factors) ) %>% dplyr::mutate_if(is_character, .funs = ~ factor(.x)) ## VISUAL ANALYSIS ================================================================================ ret_col_type <- function(df) { num_vars <- colnames(df_all)[map_lgl(df_all, is.numeric)] cat_vars <- colnames(df_all)[map_lgl(df_all, is.factor)] return(list(num = num_vars, cat = cat_vars)) } col_type <- ret_col_type(df_all) num_vars <- setdiff(col_type$num, c("SalePrice", "partition")) cat_vars <- setdiff(col_type$cat, c("SalePrice", "partition")) other_vars <- setdiff(colnames(df_all), union(union(num_vars, cat_vars), c("SalePrice", "partition"))) if (length(other_vars) > 0) { print(paste0(other_cols, " are neither numeric nor factors.")) } pred_vars <- setdiff(colnames(df_all), c("SalePrice", "partition", "Id")) # for (pred_var in pred_vars) { # if (pred_var %in% num_vars) { # p <- ggplot(df_all, aes_string(x = pred_var, y = "SalePrice")) + # geom_point() + # #geom_smooth(method = 'lm') + # geom_smooth(method = 'loess') # } else if (pred_var %in% cat_vars) { # p <- ggplot(df_all, aes_string(x = pred_var, y = "SalePrice")) + # geom_boxplot() # } # print(p) # Sys.sleep(5) # } df_all %>% ggplot(aes(x = OverallQual, y = SalePrice))+ geom_point() ## MISSING VALUES ================================================================================= getmode <- function(v) { uniqv <- unique(v) uniqv[which.max(tabulate(match(v, uniqv)))] } ## REMOVE OUTLIER # df_all <- df_all %>% filter(GrLivArea < 4000) #%>% select(Id, GrLivArea, SalePrice) #df_all <- df_all %>% filter(!Id %in% c(524, 1299)) ## MISSING VALUES miss_vals <- map_dfr(df_all, ~ sum(is.na(.x))) %>% gather() %>% filter(value > 0) %>% arrange(desc(value)) miss_vals %>% print(n = 200) # df_all[is.na(df_all$GarageYrBlt), c("GarageFinish", "GarageFinish", "GarageCond", "GarageQual", "GarageType", # "GarageCars", "GarageArea", "GarageYrBlt")] ## NA means "No": na_no <- c( "Alley", "PoolQC", "MiscFeature", "Fence", "FireplaceQu", "LotFrontage", ## "GarageFinish", "GarageFinish", "GarageCond", "GarageQual", "GarageType", "GarageCars", "GarageArea", "GarageYrBlt", ## "BsmtCond", "BsmtExposure", "BsmtQual", "BsmtFinType2", "BsmtFinType1", "BsmtFullBath", "BsmtHalfBath", "BsmtFinSF2", "BsmtUnfSF", "TotalBsmtSF" ) ## NA means "missing": na_missing <- c( "MasVnrType", "MasVnrArea", "MSZoning", "Utilities", "Functional", "Exterior1st", "Exterior2nd", "BsmtFinSF1", "Electrical", "KitchenQual", "GarageYrBlt", "SaleType" ) df_tr <- df_all %>% filter(partition == "train") df_ts <- df_all %>% filter(partition == "test") df_tr <- df_tr %>% mutate_at(na_no, function(x) { if (is.factor(x)) { x <- fct_explicit_na(x, na_level = "No") } else { x <- replace_na(x, replace = 0) } }) %>% mutate_at(na_missing, function(x) { if (is.factor(x)) { x <- fct_explicit_na(x, na_level = names(which.max(table(x)))) } else if (is.numeric(x)) { # x <- replace_na(x, replace = median(x, na.rm = T)) x <- replace_na(x, replace = median(x)) } }) df_ts <- df_ts %>% mutate_at(na_no, function(x) { if (is.factor(x)) { x <- fct_explicit_na(x, na_level = "No") } else { x <- replace_na(x, replace = 0) } }) %>% mutate_at(na_missing, function(x) { if (is.factor(x)) { x <- fct_explicit_na(x, na_level = names(which.max(table(x)))) } else if (is.numeric(x)) { # x <- replace_na(x, replace = median(x, na.rm = T)) x <- replace_na(x, replace = median(x)) } }) df_all <- bind_rows(df_tr, df_ts) map_dfr(df_all, ~ sum(is.na(.x))) %>% gather() %>% filter(value > 0) %>% arrange(desc(value)) ## SIMPLE FEATURES ================================================================================ five_level_simple <- c( "ExterQual", "ExterCond", "BsmtQual", "KitchenQual", "GarageQual", "BsmtCond", "GarageCond", "HeatingQC", "PoolQC" ) five_level_recode <- function(x) { fct_recode(x, bad = "Po", avgerage = "Fa", avgerage = "TA", good = "Gd", good = "Ex") } six_level_simple <- c("BsmtFinType1", "BsmtFinType2") six_level_recode <- function(x) { fct_recode(x, `1` = "Unf", `1` = "LwQ", `2` = "Rec", `2` = "BLQ", `3` = "ALQ", `3` = "GLQ" ) } ten_level_simple <- c("OverallQual", "OverallCond") ten_level_recode <- function(x) { fct_recode(x, `1` = "1", `1` = "2", `1` = "3", `2` = "4", `2` = "5", `2` = "6", `2` = "7", `3` = "8", `3` = "9", `3` = "10" ) } df_all <- df_all %>% dplyr::mutate_at(five_level_simple, .funs = list(Simple = five_level_recode) ) %>% dplyr::mutate_at(six_level_simple, .funs = list(Simple = six_level_recode) ) %>% dplyr::mutate_at(ten_level_simple, .funs = list(Simple = ten_level_recode) ) %>% dplyr::mutate_at("Functional", .funs = list(Simple = ~ fct_recode(.x, `1` = "Sal", `1` = "Sev", `1` = "Maj2", `2` = "Maj1", `2` = "Mod", `2` = "Min2", `3` = "Min1", `3` = "Typ" )) ) colnames(df_all) <- str_replace(colnames(df_all), "_", "") ## CREATE NEW FEATURES ============================================================================ ## df_all <- df_all %>% mutate( YearsSinceBuilt = pmax(YrSold - YearBuilt, 0), YearsSinceRemodel = pmax(YrSold - YearRemodAdd, 0), ExterGrade = as.numeric(ExterCond) * as.numeric(ExterQual), ExterGradeSimple = as.numeric(ExterCondSimple) * as.numeric(ExterQualSimple), OverallGrade = as.numeric(OverallCond) * as.numeric(OverallQual), OverallGradeSimple = as.numeric(OverallCondSimple) * as.numeric(OverallQualSimple), GarageGrade = as.numeric(GarageCond) * as.numeric(GarageQual), GarageGradeSimple = as.numeric(GarageCondSimple) * as.numeric(GarageQualSimple), KitchenScore = KitchenAbvGr * as.numeric(KitchenQual), KitchenScoreSimple = KitchenAbvGr * as.numeric(KitchenQualSimple), FireplaceScore = Fireplaces * as.numeric(FireplaceQu), GarageScore = GarageArea * as.numeric(GarageQual), GarageScoreSimple = GarageArea * as.numeric(GarageQualSimple), PoolScore = PoolArea * as.numeric(PoolQC), PoolScoreSimple = PoolArea * as.numeric(PoolQCSimple), # FireplaceScoreSimple = Fireplacesas.numeric(FireplaceQuSimple), TotalBath = BsmtFullBath + .5 BsmtHalfBath + .5 * HalfBath + FullBath, AllSF = GrLivArea + TotalBsmtSF, AllFlrsSF = FrstFlrSF + ScndFlrSF, AllPorchSF = OpenPorchSF + EnclosedPorch + ThrdSsnPorch + ScreenPorch + WoodDeckSF, HasMasVnr = fct_recode(MasVnrType, Yes = "BrkCmn", Yes = "BrkFace", Yes = "CBlock", Yes = "Stone", No = "None" ), BoughtOffPlan = fct_recode(SaleCondition, Yes = "Partial", No = "Abnorml", No = "Alloca", No = "AdjLand", No = "Family", No = "Normal" ), HasPool = ifelse(PoolArea > 0, "Yes", "No"), HasScnFloor = ifelse(ScndFlrSF > 0, "Yes", "No"), HasGarage = ifelse(GarageArea > 0, "Yes", "No"), HasBsmt = ifelse(BsmtFinSF1 > 0, "Yes", "No"), HasFireplace = ifelse(Fireplaces > 0, "Yes", "No") ) %>% dplyr::mutate_if(is_character, .funs = ~ factor(.x)) # predictor types col_type <- ret_col_type(df_all) num_pred_vars <- setdiff(col_type$num, c("SalePrice", "partition", "Id")) cat_pred_vars <- setdiff(col_type$cat, c("SalePrice", "partition", "Id")) other_vars <- setdiff(colnames(df_all), union(union(num_pred_vars, cat_pred_vars), c("SalePrice", "partition", "Id"))) if (length(other_vars) > 0) { print(paste0(other_cols, " are neither numeric nor factors.")) } ## TOP FEATURES BASED ON DIFFERENT CORRELATION COEFFICIENTS cor_type <- "pearson" ## pearson, spearman, kendall cor_df <- df_all[!is.na(df_all$SalePrice), c("SalePrice", num_pred_vars)] cor_vals <- sort(cor(cor_df, method = cor_type)[1, ], decreasing = T) top_features <- setdiff( names(cor_vals)[abs(cor_vals) > .55], "SalePrice" ) ## POLYNOMIAL TERMS # df_all <- df_all %>% # dplyr::mutate_at(top_features, .funs = list( # poly2 = ~ .x2, # poly3 = ~ .x3, # poly4 = ~.x4, # sqrt = ~ .x.5, # log = ~log(.x) # )) ## SPLINES # spl <- map( # df_all[top_features], # function(x) { # res <- bs(x, degree = 5) # res # } # ) %>% reduce(cbind) # # spl <- data.frame(spl) # colnames(spl) <- flatten_chr(map( # top_features, # ~ paste0(.x, paste0("_bs", c(1, 2, 3))) # )) # df_all <- bind_cols(df_all, spl) write_csv2(df_all, "./01_data/02_processed/train_test_stacked.csv")
% Generated by roxygen2: do not edit by hand % Please edit documentation in R/backup_operations.R \name{backup_describe_region_settings} \alias{backup_describe_region_settings} \title{Returns the current service opt-in settings for the Region} \usage{ backup_describe_region_settings() } \description{ Returns the current service opt-in settings for the Region. If service opt-in is enabled for a service, Backup tries to protect that service's resources in this Region, when the resource is included in an on-demand backup or scheduled backup plan. Otherwise, Backup does not try to protect that service's resources in this Region. See \url{https://www.paws-r-sdk.com/docs/backup_describe_region_settings/} for full documentation. } \keyword{internal}	/cran/paws.storage/man/backup_describe_region_settings.Rd	permissive	paws-r/paws	R	false	true	754	rd	% Generated by roxygen2: do not edit by hand % Please edit documentation in R/backup_operations.R \name{backup_describe_region_settings} \alias{backup_describe_region_settings} \title{Returns the current service opt-in settings for the Region} \usage{ backup_describe_region_settings() } \description{ Returns the current service opt-in settings for the Region. If service opt-in is enabled for a service, Backup tries to protect that service's resources in this Region, when the resource is included in an on-demand backup or scheduled backup plan. Otherwise, Backup does not try to protect that service's resources in this Region. See \url{https://www.paws-r-sdk.com/docs/backup_describe_region_settings/} for full documentation. } \keyword{internal}
## The 'makeCacheMatrix' function contains four functions that allow the user to ## set a matrix and make it invertible. The 'cacheSolve' function allows the ## user to get the inverse of the previous matrix and cache it, as well as ## retrieve the cache. This is useful and saves time when attempting to retrieve ## the inverse of a matrix repeatedly. ## This function is used to create a matrix that is invertible. makeCacheMatrix <- function(x = matrix()) { m <- NULL set <- function(y) { x <<- y m <<- NULL } get <- function() x setmatrix <- function(solve) m <<- solve getmatrix <- function() m list(set = set, get = get, setmatrix = setmatrix, getmatrix = getmatrix) } ## This function inverts a matrix and caches it, or retrieves the cached matrix. cacheSolve <- function(x, ...) { m<-x$getmatrix() if(!is.null(m)){ message("getting cached data") return(m) } matrix<-x$get() m<-solve(matrix, ...) x$setmatrix(m) m }	/cachematrix.R	no_license	cervantesedd/ProgrammingAssignment2	R	false	false	1,084	r	## The 'makeCacheMatrix' function contains four functions that allow the user to ## set a matrix and make it invertible. The 'cacheSolve' function allows the ## user to get the inverse of the previous matrix and cache it, as well as ## retrieve the cache. This is useful and saves time when attempting to retrieve ## the inverse of a matrix repeatedly. ## This function is used to create a matrix that is invertible. makeCacheMatrix <- function(x = matrix()) { m <- NULL set <- function(y) { x <<- y m <<- NULL } get <- function() x setmatrix <- function(solve) m <<- solve getmatrix <- function() m list(set = set, get = get, setmatrix = setmatrix, getmatrix = getmatrix) } ## This function inverts a matrix and caches it, or retrieves the cached matrix. cacheSolve <- function(x, ...) { m<-x$getmatrix() if(!is.null(m)){ message("getting cached data") return(m) } matrix<-x$get() m<-solve(matrix, ...) x$setmatrix(m) m }
who = "World" input = commandArgs(trailingOnly = TRUE) if(length(input)) who = paste(input, collapse = " ") message("Hello ", who, "!")	/ggplot_interview/hello_world.R	no_license	michaellevy/catalyst_classes	R	false	false	141	r	who = "World" input = commandArgs(trailingOnly = TRUE) if(length(input)) who = paste(input, collapse = " ") message("Hello ", who, "!")
#Course Directory dir_local <- "exp_data_analysis_project2" if(!file.exists(dir_local)){ dir.create(dir_local) } #Set working directory setwd(dir_local) #download and extract data in working directory data_url <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip" destination_file <- "dataset.zip" if(!file.exists(destination_file)){ download.file(data_url, destfile = destination_file) unzip(destination_file) } if (!"nei_data" %in% ls()) { nei_data <- readRDS("summarySCC_PM25.rds") } if (!"scc_data" %in% ls()) { scc_data <- readRDS("Source_Classification_Code.rds") } #How have emissions from motor vehicle sources changed from 1999–2008 in Baltimore City? subset_data <- nei_data[nei_data$fips == "24510", ] par("mar"=c(5.1, 4.5, 4.1, 2.1)) png(filename = "plot5.png", width = 480, height = 480, units = "px") motor <- grep("motor", scc_data$Short.Name, ignore.case = T) motor <- scc_data[motor, ] motor <- subset_data[subset_data$SCC %in% motor$SCC, ] motorEmissions <- aggregate(motor$Emissions, list(motor$year), FUN = "sum") plot(motorEmissions, type = "l", xlab = "Year", main = "Total Emissions From Motor Vehicle Sources\n from 1999 to 2008 in Baltimore City", ylab = expression('Total PM'[2.5]*" Emission")) dev.off()	/Plot5.R	no_license	kumalok/ExData_Plotting2	R	false	false	1,266	r	#Course Directory dir_local <- "exp_data_analysis_project2" if(!file.exists(dir_local)){ dir.create(dir_local) } #Set working directory setwd(dir_local) #download and extract data in working directory data_url <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip" destination_file <- "dataset.zip" if(!file.exists(destination_file)){ download.file(data_url, destfile = destination_file) unzip(destination_file) } if (!"nei_data" %in% ls()) { nei_data <- readRDS("summarySCC_PM25.rds") } if (!"scc_data" %in% ls()) { scc_data <- readRDS("Source_Classification_Code.rds") } #How have emissions from motor vehicle sources changed from 1999–2008 in Baltimore City? subset_data <- nei_data[nei_data$fips == "24510", ] par("mar"=c(5.1, 4.5, 4.1, 2.1)) png(filename = "plot5.png", width = 480, height = 480, units = "px") motor <- grep("motor", scc_data$Short.Name, ignore.case = T) motor <- scc_data[motor, ] motor <- subset_data[subset_data$SCC %in% motor$SCC, ] motorEmissions <- aggregate(motor$Emissions, list(motor$year), FUN = "sum") plot(motorEmissions, type = "l", xlab = "Year", main = "Total Emissions From Motor Vehicle Sources\n from 1999 to 2008 in Baltimore City", ylab = expression('Total PM'[2.5]*" Emission")) dev.off()
#' Adds a Layer with Observations to a Profile Plot #' #' Function \code{\link{show_observations}} adds a layer to a plot created with #' \code{\link{plot.ceteris_paribus_explainer}} for selected observations. #' Various parameters help to decide what should be plotted, profiles, aggregated profiles, points or rugs. #' #' @param x a ceteris paribus explainer produced with function \code{ceteris_paribus()} #' @param ... other explainers that shall be plotted together #' @param color a character. Either name of a color or name of a variable that should be used for coloring #' @param size a numeric. Size of lines to be plotted #' @param alpha a numeric between 0 and 1. Opacity of lines #' @param variables if not \code{NULL} then only \code{variables} will be presented #' @param variable_type a character. If "numerical" then only numerical variables will be plotted. #' If "categorical" then only categorical variables will be plotted. #' #' @return a \code{ggplot2} layer #' #' @examples #' library("DALEX") #' library("randomForest") #' #' rf_model <- randomForest(survived == "yes" ~., #' data = titanic_imputed) #' #' explainer_rf <- explain(rf_model, data = titanic_imputed, #' y = titanic_imputed$survived == "yes", #' label = "RF", verbose = FALSE) #' #' selected_passangers <- select_sample(titanic_imputed, n = 100) #' cp_rf <- ceteris_paribus(explainer_rf, selected_passangers) #' cp_rf #' #' plot(cp_rf, variables = "age", color = "grey") + #' show_observations(cp_rf, variables = "age", color = "black") + #' show_rugs(cp_rf, variables = "age", color = "red") #' #' #' @export show_observations <- function(x, ..., size = 2, alpha = 1, color = "#371ea3", variable_type = "numerical", variables = NULL) { check_variable_type(variable_type) # if there is more explainers, they should be merged into a single data frame dfl <- c(list(x), list(...)) all_observations <- lapply(dfl, function(tmp) { attr(tmp, "observations") }) all_observations <- do.call(rbind, all_observations) all_observations$`_ids_` <- factor(rownames(all_observations)) # variables to use all_variables <- grep(colnames(all_observations), pattern = "^[^_]", value = TRUE) if (!is.null(variables)) { all_variables <- intersect(all_variables, variables) if (length(all_variables) == 0) stop(paste0("variables do not overlap with ", paste(all_variables, collapse = ", "))) } # only numerical or only factors? is_numeric <- sapply(all_observations[, all_variables, drop = FALSE], is.numeric) if (variable_type == "numerical") { vnames <- all_variables[which(is_numeric)] if (length(vnames) == 0) stop("There are no numerical variables") } else { vnames <- all_variables[which(!is_numeric)] if (length(vnames) == 0) stop("There are no non-numerical variables") } # prepare data for plotting points is_color_points_a_variable <- color %in% c(all_variables, "_label_", "_vname_", "_ids_") tmp <- lapply(vnames, function(var) { data.frame(`_x_` = all_observations[,var], `_vname_` = var, `_yhat_` = all_observations$`_yhat_`, `_y_` = if (is.null(all_observations$`_y_`)) NA else all_observations$`_y_`, `_color_` = if (!is_color_points_a_variable) NA else { if (color == "_vname_") var else all_observations[,color] }, `_ids_` = all_observations$`_ids_`, `_label_` = all_observations$`_label_`) }) all_observations_long <- do.call(rbind, tmp) colnames(all_observations_long) <- c("_x_", "_vname_", "_yhat_", "_y_", "_color_", "_ids_", "_label_") if ((is_color_points_a_variable ) & !(color %in% colnames(all_observations_long))) colnames(all_observations_long)[5] = color # show observations if (is_color_points_a_variable) { res <- geom_point(data = all_observations_long, aes_string(color = paste0("`",color,"`")), size = size, alpha = alpha) } else { res <- geom_point(data = all_observations_long, size = size, alpha = alpha, color = color) } res }	/R/show_observations.R	no_license	Alex33261/ingredients	R	false	false	4,300	r	#' Adds a Layer with Observations to a Profile Plot #' #' Function \code{\link{show_observations}} adds a layer to a plot created with #' \code{\link{plot.ceteris_paribus_explainer}} for selected observations. #' Various parameters help to decide what should be plotted, profiles, aggregated profiles, points or rugs. #' #' @param x a ceteris paribus explainer produced with function \code{ceteris_paribus()} #' @param ... other explainers that shall be plotted together #' @param color a character. Either name of a color or name of a variable that should be used for coloring #' @param size a numeric. Size of lines to be plotted #' @param alpha a numeric between 0 and 1. Opacity of lines #' @param variables if not \code{NULL} then only \code{variables} will be presented #' @param variable_type a character. If "numerical" then only numerical variables will be plotted. #' If "categorical" then only categorical variables will be plotted. #' #' @return a \code{ggplot2} layer #' #' @examples #' library("DALEX") #' library("randomForest") #' #' rf_model <- randomForest(survived == "yes" ~., #' data = titanic_imputed) #' #' explainer_rf <- explain(rf_model, data = titanic_imputed, #' y = titanic_imputed$survived == "yes", #' label = "RF", verbose = FALSE) #' #' selected_passangers <- select_sample(titanic_imputed, n = 100) #' cp_rf <- ceteris_paribus(explainer_rf, selected_passangers) #' cp_rf #' #' plot(cp_rf, variables = "age", color = "grey") + #' show_observations(cp_rf, variables = "age", color = "black") + #' show_rugs(cp_rf, variables = "age", color = "red") #' #' #' @export show_observations <- function(x, ..., size = 2, alpha = 1, color = "#371ea3", variable_type = "numerical", variables = NULL) { check_variable_type(variable_type) # if there is more explainers, they should be merged into a single data frame dfl <- c(list(x), list(...)) all_observations <- lapply(dfl, function(tmp) { attr(tmp, "observations") }) all_observations <- do.call(rbind, all_observations) all_observations$`_ids_` <- factor(rownames(all_observations)) # variables to use all_variables <- grep(colnames(all_observations), pattern = "^[^_]", value = TRUE) if (!is.null(variables)) { all_variables <- intersect(all_variables, variables) if (length(all_variables) == 0) stop(paste0("variables do not overlap with ", paste(all_variables, collapse = ", "))) } # only numerical or only factors? is_numeric <- sapply(all_observations[, all_variables, drop = FALSE], is.numeric) if (variable_type == "numerical") { vnames <- all_variables[which(is_numeric)] if (length(vnames) == 0) stop("There are no numerical variables") } else { vnames <- all_variables[which(!is_numeric)] if (length(vnames) == 0) stop("There are no non-numerical variables") } # prepare data for plotting points is_color_points_a_variable <- color %in% c(all_variables, "_label_", "_vname_", "_ids_") tmp <- lapply(vnames, function(var) { data.frame(`_x_` = all_observations[,var], `_vname_` = var, `_yhat_` = all_observations$`_yhat_`, `_y_` = if (is.null(all_observations$`_y_`)) NA else all_observations$`_y_`, `_color_` = if (!is_color_points_a_variable) NA else { if (color == "_vname_") var else all_observations[,color] }, `_ids_` = all_observations$`_ids_`, `_label_` = all_observations$`_label_`) }) all_observations_long <- do.call(rbind, tmp) colnames(all_observations_long) <- c("_x_", "_vname_", "_yhat_", "_y_", "_color_", "_ids_", "_label_") if ((is_color_points_a_variable ) & !(color %in% colnames(all_observations_long))) colnames(all_observations_long)[5] = color # show observations if (is_color_points_a_variable) { res <- geom_point(data = all_observations_long, aes_string(color = paste0("`",color,"`")), size = size, alpha = alpha) } else { res <- geom_point(data = all_observations_long, size = size, alpha = alpha, color = color) } res }
% Generated by roxygen2: do not edit by hand % Please edit documentation in R/view_image.R \name{display_set} \alias{display_set} \title{Display 2D mosaic output as a plot image} \usage{ display_set(image_list, title = NULL) } \arguments{ \item{image_list}{List output from collect_bricks() or image_to_bricks(). Contains an element \code{Img_lego}.} \item{title}{Optional title to include above plotted mosaic.} } \description{ Display 2D mosaic output as a plot image }	/man/display_set.Rd	permissive	rpodcast/brickr	R	false	true	474	rd	% Generated by roxygen2: do not edit by hand % Please edit documentation in R/view_image.R \name{display_set} \alias{display_set} \title{Display 2D mosaic output as a plot image} \usage{ display_set(image_list, title = NULL) } \arguments{ \item{image_list}{List output from collect_bricks() or image_to_bricks(). Contains an element \code{Img_lego}.} \item{title}{Optional title to include above plotted mosaic.} } \description{ Display 2D mosaic output as a plot image }
% Generated by roxygen2: do not edit by hand % Please edit documentation in R/summarise.R \name{summarise_catval} \alias{summarise_catval} \title{Summarise a value in a column} \usage{ summarise_catval(grouped_form, colname, val) } \arguments{ \item{grouped_form}{any grouped dataframe} \item{colname}{name to column to summarise} } \value{ summary dataframe } \description{ Summarise the number and percentage of that value in a column }	/man/summarise_catval.Rd	no_license	EddieZhang540/INORMUS	R	false	true	440	rd	% Generated by roxygen2: do not edit by hand % Please edit documentation in R/summarise.R \name{summarise_catval} \alias{summarise_catval} \title{Summarise a value in a column} \usage{ summarise_catval(grouped_form, colname, val) } \arguments{ \item{grouped_form}{any grouped dataframe} \item{colname}{name to column to summarise} } \value{ summary dataframe } \description{ Summarise the number and percentage of that value in a column }
############################################################################################ #' @title Produce a Summary Table of Data Product Health by Month #' @author Robert Lee \email{rlee@battelleecology.org}\cr #' @description For a specified data product ID, this function will produce a data frame of data #' product availability and validity for their period of record at a site. #' #' #' Because the full period of record for all sites are queried, #' this function can take a long time to execute. #' @inheritParams dp.survey #' #' @param dp.id Parameter of class character. The NEON data product code of the data product of #' interest. #' @param site Parameter of class character. The NEON site of interest. #' #' @param bgn.month Parameter of class character. The year-month (e.g. "2017-01") of the first month to get data for. #' @param end.month Parameter of class character. The year-month (e.g. "2017-01") of the last month to get data for. #' @param save.dir The directory for data files to be saved to. #' @return A data frame of health statisitcs by month for a given site. Raw NEON data are also saved to the specified save.dir, if supplied. #' @keywords process quality, data quality, gaps, commissioning, data product, health #' @examples #' # Summarize 2D wind perfomance at CPER: #' CPER_wind=dp.survey(dp.id = "DP1.00001.001", site="CPER") #' @export # changelog and author contributions / copyrights # Robert Lee (2017-11-21) # original creation # ############################################################################################## health.data= function(site, dp.id, bgn.month, end.month, save.dir){ if(missing(save.dir)){save.dir=tempdir()} pri.var=Noble::tis_pri_vars$data.field[which(Noble::tis_pri_vars$dp.id==dp.id)] var.name=gsub(pattern = "mean", replacement = "", x = pri.var, ignore.case = T) dp.avail = neon.avail(dp.id = dp.id) dp.avail = cbind(Month=dp.avail[,1], dp.avail[,which(colnames(dp.avail) %in% Noble::tis_site_config$site.id)]) temp.dates = zoo::as.Date(dp.avail$Month[ which( dp.avail[which(colnames(dp.avail)==site)]=="x" ) ] ) if(missing(bgn.month)&missing(end.month)){ run.dates = substr(temp.dates, start = 0, stop = 7) info.dates=run.dates }else if(missing(end.month)){ end.month=Sys.Date() info.dates=seq.Date(from=as.Date(paste0(bgn.month, "-01")), to=end.month, by="1 month") run.dates=base::substr(temp.dates[temp.dates %in% info.dates], start = 0, stop = 7) }else if(missing(bgn.month)){ info.dates=seq.Date(from=as.Date("2014-01-01"), to=end.month, by="1 month") run.dates=substr(temp.dates[temp.dates %in% info.dates], start = 0, stop = 7) }else{ info.dates=seq.Date(from=as.Date(paste0(bgn.month, "-01")), to=as.Date(paste0(end.month, "-01")), by="1 month") run.dates=substr(temp.dates[temp.dates %in% info.dates], start = 0, stop = 7) } health.data=data.frame(Month=substr(info.dates,0, 7), Availability=rep(0, times=length(info.dates)), Validity =rep(0, times=length(info.dates))) message(paste0("Working on ", site, "...")) if(length(run.dates)>0){ for(d in 1:length(run.dates)){ message(paste0("Downloading ", run.dates[d])) month.data<-try(Noble::pull.data(site = site, dp.id = dp.id, bgn.month = run.dates[d], end.month = run.dates[d], time.agr = 30, package = "basic", save.dir = save.dir)) if(!length(month.data)==0){ priData=data.frame(month.data[,grepl(pattern = pri.var, x = colnames(month.data), ignore.case = T)]) if(!length(priData)==0){ pcntData=round(sum(colSums(!is.na(priData)))/(length(priData[,1])length(priData))100, digits = 2) finalQFs=data.frame(month.data[,grepl(pattern = "finalQF", x = colnames(month.data), ignore.case = T)]) if(length(colnames(finalQFs))>length(colnames(priData))){ finalQFs=data.frame(finalQFs[,grepl(pattern = var.name, x = colnames(finalQFs), ignore.case = T)]) } pcntValid=round(100-(sum(colSums(finalQFs, na.rm = T))/(length(priData[,1])length(priData))100+ sum(colSums(is.na(finalQFs)))/(length(priData[,1])length(finalQFs))100), digits = 2) health.data$Availability[which(health.data$Month==run.dates[d])]=pcntData health.data$Validity[which(health.data$Month==run.dates[d])]=pcntValid} } }} return(health.data) }	/R/health_data.R	no_license	rhlee12/Noble	R	false	false	4,582	r	############################################################################################ #' @title Produce a Summary Table of Data Product Health by Month #' @author Robert Lee \email{rlee@battelleecology.org}\cr #' @description For a specified data product ID, this function will produce a data frame of data #' product availability and validity for their period of record at a site. #' #' #' Because the full period of record for all sites are queried, #' this function can take a long time to execute. #' @inheritParams dp.survey #' #' @param dp.id Parameter of class character. The NEON data product code of the data product of #' interest. #' @param site Parameter of class character. The NEON site of interest. #' #' @param bgn.month Parameter of class character. The year-month (e.g. "2017-01") of the first month to get data for. #' @param end.month Parameter of class character. The year-month (e.g. "2017-01") of the last month to get data for. #' @param save.dir The directory for data files to be saved to. #' @return A data frame of health statisitcs by month for a given site. Raw NEON data are also saved to the specified save.dir, if supplied. #' @keywords process quality, data quality, gaps, commissioning, data product, health #' @examples #' # Summarize 2D wind perfomance at CPER: #' CPER_wind=dp.survey(dp.id = "DP1.00001.001", site="CPER") #' @export # changelog and author contributions / copyrights # Robert Lee (2017-11-21) # original creation # ############################################################################################## health.data= function(site, dp.id, bgn.month, end.month, save.dir){ if(missing(save.dir)){save.dir=tempdir()} pri.var=Noble::tis_pri_vars$data.field[which(Noble::tis_pri_vars$dp.id==dp.id)] var.name=gsub(pattern = "mean", replacement = "", x = pri.var, ignore.case = T) dp.avail = neon.avail(dp.id = dp.id) dp.avail = cbind(Month=dp.avail[,1], dp.avail[,which(colnames(dp.avail) %in% Noble::tis_site_config$site.id)]) temp.dates = zoo::as.Date(dp.avail$Month[ which( dp.avail[which(colnames(dp.avail)==site)]=="x" ) ] ) if(missing(bgn.month)&missing(end.month)){ run.dates = substr(temp.dates, start = 0, stop = 7) info.dates=run.dates }else if(missing(end.month)){ end.month=Sys.Date() info.dates=seq.Date(from=as.Date(paste0(bgn.month, "-01")), to=end.month, by="1 month") run.dates=base::substr(temp.dates[temp.dates %in% info.dates], start = 0, stop = 7) }else if(missing(bgn.month)){ info.dates=seq.Date(from=as.Date("2014-01-01"), to=end.month, by="1 month") run.dates=substr(temp.dates[temp.dates %in% info.dates], start = 0, stop = 7) }else{ info.dates=seq.Date(from=as.Date(paste0(bgn.month, "-01")), to=as.Date(paste0(end.month, "-01")), by="1 month") run.dates=substr(temp.dates[temp.dates %in% info.dates], start = 0, stop = 7) } health.data=data.frame(Month=substr(info.dates,0, 7), Availability=rep(0, times=length(info.dates)), Validity =rep(0, times=length(info.dates))) message(paste0("Working on ", site, "...")) if(length(run.dates)>0){ for(d in 1:length(run.dates)){ message(paste0("Downloading ", run.dates[d])) month.data<-try(Noble::pull.data(site = site, dp.id = dp.id, bgn.month = run.dates[d], end.month = run.dates[d], time.agr = 30, package = "basic", save.dir = save.dir)) if(!length(month.data)==0){ priData=data.frame(month.data[,grepl(pattern = pri.var, x = colnames(month.data), ignore.case = T)]) if(!length(priData)==0){ pcntData=round(sum(colSums(!is.na(priData)))/(length(priData[,1])length(priData))100, digits = 2) finalQFs=data.frame(month.data[,grepl(pattern = "finalQF", x = colnames(month.data), ignore.case = T)]) if(length(colnames(finalQFs))>length(colnames(priData))){ finalQFs=data.frame(finalQFs[,grepl(pattern = var.name, x = colnames(finalQFs), ignore.case = T)]) } pcntValid=round(100-(sum(colSums(finalQFs, na.rm = T))/(length(priData[,1])length(priData))100+ sum(colSums(is.na(finalQFs)))/(length(priData[,1])length(finalQFs))100), digits = 2) health.data$Availability[which(health.data$Month==run.dates[d])]=pcntData health.data$Validity[which(health.data$Month==run.dates[d])]=pcntValid} } }} return(health.data) }
observe({ updateSelectizeInput(session, "plottype", choices = if (intro.numericnames()[1] == "") c("Mosaic Plot" = "mosaicplot") else if (intro.categoricnames()[1] == "") c("Histogram" = "histogram", "Normal Quantile Plot" = "quantileplot", "Scatterplot" = "scatterplot", "Line Chart" = "linechart") else c("Histogram" = "histogram", "Normal Quantile Plot" = "quantileplot", "Scatterplot" = "scatterplot", "Line Chart" = "linechart", "Boxplot" = "boxplot", "Bar Chart" = "barchart", "Pareto Chart" = "paretochart", "Mosaic Plot" = "mosaicplot")) }) observe({ input$plottype updateSelectizeInput(session, "x", choices = x_choices(), selected = x_selected()) updateSelectizeInput(session, "y", choices = y_choices(), selected = y_selected()) }) observeEvent(input$store_graphical, { cat(paste0("\n\n", paste(readLines(file.path(userdir, "code_graphical_reactive.R")), collapse = "\n")), file = file.path(userdir, "code_All.R"), append = TRUE) ## Fix this cat(paste0("\n\ninput_xdomain <- c(", input$xmin, ", ", input$xmax, ")\n"), file = file.path(userdir, "code_All.R"), append = TRUE) cat(paste0("input_ydomain <- c(", input$ymin, ", ", input$ymax, ")\n"), file = file.path(userdir, "code_All.R"), append = TRUE) cat(paste0("input_binwidth <- ", ifelse(is.na(input$binwidth), "NULL", input$binwidth), "\n"), file = file.path(userdir, "code_All.R"), append = TRUE) mystr <- paste0("X Variable: ", input$x, (if (input$plottype %in% c("histogram", "quantileplot")) "" else paste0("; Y Variable: ", input$y))) cat("\n\n", file = file.path(userdir, paste0("code_", input$plottype, ".R")), append = TRUE) cat(paste0("\n\n", paste(readLines(file.path(userdir, paste0("code_", input$plottype, ".R"))), collapse = "\n")), file = file.path(userdir, "code_All.R"), append = TRUE) if (input$plottype != "mosaicplot") cat(paste0("p.", input$plottype), file = file.path(userdir, "code_All.R"), append = TRUE) cat(paste0("\ncat('", mystr, "')"), file = file.path(userdir, "code_All.R"), append = TRUE) })	/modules/graphical/observe.R	no_license	elombardi-cleve/intRo	R	false	false	2,063	r	observe({ updateSelectizeInput(session, "plottype", choices = if (intro.numericnames()[1] == "") c("Mosaic Plot" = "mosaicplot") else if (intro.categoricnames()[1] == "") c("Histogram" = "histogram", "Normal Quantile Plot" = "quantileplot", "Scatterplot" = "scatterplot", "Line Chart" = "linechart") else c("Histogram" = "histogram", "Normal Quantile Plot" = "quantileplot", "Scatterplot" = "scatterplot", "Line Chart" = "linechart", "Boxplot" = "boxplot", "Bar Chart" = "barchart", "Pareto Chart" = "paretochart", "Mosaic Plot" = "mosaicplot")) }) observe({ input$plottype updateSelectizeInput(session, "x", choices = x_choices(), selected = x_selected()) updateSelectizeInput(session, "y", choices = y_choices(), selected = y_selected()) }) observeEvent(input$store_graphical, { cat(paste0("\n\n", paste(readLines(file.path(userdir, "code_graphical_reactive.R")), collapse = "\n")), file = file.path(userdir, "code_All.R"), append = TRUE) ## Fix this cat(paste0("\n\ninput_xdomain <- c(", input$xmin, ", ", input$xmax, ")\n"), file = file.path(userdir, "code_All.R"), append = TRUE) cat(paste0("input_ydomain <- c(", input$ymin, ", ", input$ymax, ")\n"), file = file.path(userdir, "code_All.R"), append = TRUE) cat(paste0("input_binwidth <- ", ifelse(is.na(input$binwidth), "NULL", input$binwidth), "\n"), file = file.path(userdir, "code_All.R"), append = TRUE) mystr <- paste0("X Variable: ", input$x, (if (input$plottype %in% c("histogram", "quantileplot")) "" else paste0("; Y Variable: ", input$y))) cat("\n\n", file = file.path(userdir, paste0("code_", input$plottype, ".R")), append = TRUE) cat(paste0("\n\n", paste(readLines(file.path(userdir, paste0("code_", input$plottype, ".R"))), collapse = "\n")), file = file.path(userdir, "code_All.R"), append = TRUE) if (input$plottype != "mosaicplot") cat(paste0("p.", input$plottype), file = file.path(userdir, "code_All.R"), append = TRUE) cat(paste0("\ncat('", mystr, "')"), file = file.path(userdir, "code_All.R"), append = TRUE) })
library(glmnet) mydata = read.table("../../../../TrainingSet/FullSet/Classifier/ovary.csv",head=T,sep=",") x = as.matrix(mydata[,4:ncol(mydata)]) y = as.matrix(mydata[,1]) set.seed(123) glm = cv.glmnet(x,y,nfolds=10,type.measure="mse",alpha=0.1,family="gaussian",standardize=TRUE) sink('./ovary_025.txt',append=TRUE) print(glm$glmnet.fit) sink()	/Model/EN/Classifier/ovary/ovary_025.R	no_license	esbgkannan/QSMART	R	false	false	346	r	library(glmnet) mydata = read.table("../../../../TrainingSet/FullSet/Classifier/ovary.csv",head=T,sep=",") x = as.matrix(mydata[,4:ncol(mydata)]) y = as.matrix(mydata[,1]) set.seed(123) glm = cv.glmnet(x,y,nfolds=10,type.measure="mse",alpha=0.1,family="gaussian",standardize=TRUE) sink('./ovary_025.txt',append=TRUE) print(glm$glmnet.fit) sink()
% Generated by roxygen2: do not edit by hand % Please edit documentation in R/EDK.R \name{EDK} \alias{EDK} \title{EDK to be documented} \usage{ EDK(d, h, k = 2) } \arguments{ \item{d}{to be documented} \item{h}{to be documented} \item{k}{to be documented} } \value{ to be documented } \description{ EDK to be documented } \keyword{internal}	/man/EDK.Rd	no_license	shepherdmeng/mgwrsar	R	false	true	343	rd	% Generated by roxygen2: do not edit by hand % Please edit documentation in R/EDK.R \name{EDK} \alias{EDK} \title{EDK to be documented} \usage{ EDK(d, h, k = 2) } \arguments{ \item{d}{to be documented} \item{h}{to be documented} \item{k}{to be documented} } \value{ to be documented } \description{ EDK to be documented } \keyword{internal}
#loading data load("~/simulated_app_data.R") currentdata = new.data #loading required packages require(survey) require(MCMCpack) expit = function(x){exp(x)/(1+exp(x))} #parameter specifications bootstrap=TRUE K=1000 D = dim(currentdata$treatment)[2] n = dim(currentdata$fixed.X)[1] W = 100 # initializing data structures result = list() #ipw variables save.weights = matrix(NA,nrow=n,ncol=D) save.boot.weights = matrix(1,nrow=n,ncol=W) #overlap variables save.overlap = matrix(NA,nrow=n,ncol=D) save.boot.overlap = matrix(1,nrow=n,ncol=W) #ppta variables save.membership = array(NA,dim = c(n,K,D)) ppta.boot.save.membership = array(1,dim=c(n,K,W)) cross.probs = array(NA,dim = c(n,K,D)) ppta.size = matrix(NA,nrow=D,ncol=K) ppta.treated.size = matrix(NA,nrow=D,ncol=K) ppta.control.size = matrix(NA,nrow=D,ncol=K) #stabilized variables save.stable = matrix(NA,nrow=n,ncol=D) save.boot.stable = matrix(1,nrow=n,ncol=W) #specifying input data treatment = data.frame(currentdata$treatment) fixedcovar = data.frame(currentdata$fixed.X) timecovar = currentdata$time.X #naming input data names(fixedcovar) = paste("fixed.",names(fixedcovar),sep="") names(timecovar) = paste("time.",names(timecovar),sep="") treatment.considered = data.frame(treatment[,1]) names(treatment.considered) = "treat.1" time.covars.considered = data.frame(timecovar[[1]]) #sampling bootstrap draws if(bootstrap){ boot.indicies = matrix(sample(1:n,nW,replace=TRUE),nrow=n,ncol=W) } else{ W = 1 } ################################################################### # iterate through time points for(d in 1:D){ data.matrix = cbind(treatment.considered,fixedcovar,time.covars.considered) #create datasets with bootstrapped indicies if(bootstrap){ ppta.boot.data = apply(boot.indicies,2,function(x){data.matrix[x,]}) } #specify formulas for propensity score model and model for stabilized weights if(d==1){ no.treat = names(data.matrix)[names(data.matrix)!="treat.1"] form = paste("treat.1 ~",paste(no.treat,collapse="+")) form2 = "treat.1~1" } else { current.treat = paste("treat.",d,sep="") no.treat = names(data.matrix)[names(data.matrix)!=current.treat] form = paste(current.treat,"~", paste(no.treat,collapse="+")) form2 = paste(current.treat,"~", paste(no.treat[grep("treat",no.treat)],collapse="+")) } #################################################### # ALL WEIGHTING SCHEMES PS.fit = glm(form,data=data.matrix,family="binomial") sum.ps.fit = summary(PS.fit) ipw.PS.est = predict(PS.fit,type="response") stable.fit = glm(form2,data=data.matrix,family="binomial") stable.est = predict(stable.fit,type="response") ###calculate ipw/overlap/stable weights save.weights[,d] = treatment[,d]/ipw.PS.est + (1-treatment[,d])/(1-ipw.PS.est) save.overlap[,d] = treatment[,d](1- ipw.PS.est) + (1-treatment[,d])ipw.PS.est save.stable[,d] = treatment[,d](stable.est/ipw.PS.est) + (1-treatment[,d])((1-stable.est)/(1-ipw.PS.est)) ### bootstrapped MLE if(bootstrap){ for (w in 1:W){ boot.treatment = treatment[boot.indicies[,w],] PS.fit = glm(form,data=ppta.boot.data[[w]],family="binomial") sum.ps.fit = summary(PS.fit) PS.est = predict(PS.fit,type="response") stable.est = predict(glm(form2,data=ppta.boot.data[[w]],family="binomial"),type="response") save.boot.weights[,w] = save.boot.weights[,w](boot.treatment[,d]/PS.est + (1-boot.treatment[,d])/(1-PS.est)) save.boot.stable[,w] = save.boot.stable[,w](boot.treatment[,d](stable.est/PS.est) + (1-boot.treatment[,d])((1-stable.est)/(1-PS.est))) save.boot.overlap[,w] = save.boot.overlap[,w](boot.treatment[,d](1- PS.est) + (1-boot.treatment[,d])PS.est) } } ############################################### # PPTA #draws from PS distribution posteriors = as.matrix(MCMClogit(form,data=data.matrix,burnin=K/2,mcmc=K20,thin=20)) save(posteriors,file=paste("~/final_app_",d,"_posteriors.R")) covars = as.matrix(cbind(rep(1,n),data.matrix[,-1])) ppta.PS.est = expit(covars%%t(posteriors)) #PS estimated from draws of alpha # boostrapping for PPTA if(bootstrap){ ppta.boot.posteriors = lapply(ppta.boot.data,function(x){as.matrix(MCMClogit(form,data=x,burnin=K/10,mcmc=K10,thin=10))}) ppta.boot.covars = lapply(ppta.boot.data,function(x){as.matrix(cbind(rep(1,n),x[,-1]))}) ppta.boot.PS = mapply(function(x,y){as.matrix(expit(x%%t(y)))},x = ppta.boot.covars,y=ppta.boot.posteriors,SIMPLIFY = FALSE) ppta.boot.PS = simplify2array(ppta.boot.PS) } #calculate in/out group for(k in 1:K){ cross.prob = treatment[,d](1-ppta.PS.est[,k]) + (1-treatment[,d])ppta.PS.est[,k] cross.probs[,k,d] = cross.prob if(bootstrap){ ppta.boot.treatment = apply(boot.indicies,2,function(x){treatment[x,d]}) ppta.boot.cross.prob = apply(matrix(1:W,ncol=W),2,function(x){ppta.boot.treatment[,x](1-ppta.boot.PS[,k,x])+(1-ppta.boot.treatment[,x])ppta.boot.PS[,k,x]}) ppta.boot.save.membership[,k,] =ppta.boot.save.membership[,k,]apply(ppta.boot.cross.prob,2,function(x){rbinom(n,1,x)}) } } #updating covariates if not last time point if(d<D){ treatment.considered = data.frame(treatment[,(d+1)],treatment[,d]) time.covars.considered = data.frame(timecovar[[d+1]],timecovar[[d]]) names(treatment.considered) = paste("treat.",seq(from=(d+1),to=(d)),sep="") names(time.covars.considered) = paste(rep(names(data.frame(timecovar[[d+1]])),2),rep(seq(from=(d+1),to=(d)),each=2),sep=".") } } save(save.weights,file = "~/save.weights.R") save(boot.indicies,file="~/boot.indicies.R") save(save.boot.weights,file="~/save.boot.weights.R") ############################################### # effect estimation #creating necessary data structures outcome = currentdata$outcome offset = currentdata$offset cum.treat = apply(treatment,1,sum,na.rm=TRUE) treat.out = data.frame(cum.treat,outcome,offset) treat.out$outcome.rate = outcome/offset treat.out.list = apply(boot.indicies,2,function(x,data){data[x,]},data=treat.out) # function for calculating bootstrapped SE coef.fxn = function(x,weights,data){ weights = weights[,x] dat = data[[x]] fit = glm(outcome~cum.treat+offset(log(offset)),data=dat,weights=weights,family="poisson") xx = summary(fit)$coef if(dim(xx)[1]>1){ return(xx[2,1]) }else{ return(NA) } } #####IPW print("IPW") outcome.weights = apply(save.weights,1,prod,na.rm=TRUE) ipw.weights = outcome.weights ipw.fit = glm(outcome~cum.treat+offset(log(offset)),data=treat.out,family="poisson",weights=outcome.weights) ipw.boots = apply(matrix(1:W,nrow=1),2,coef.fxn,weights=save.boot.weights,data=treat.out.list) ipw.preddelta = summary(ipw.fit)$coef[2,1] ipw.predse = summary(ipw.fit)$coef[2,2] ipw.bootse = sd(ipw.boots,na.rm=TRUE) ipw.lower = quantile(ipw.boots,prob=0.025) ipw.upper = quantile(ipw.boots,prob=0.975) ################## #overlap weighted regression print("overlap") outcome.weights = apply(save.overlap,1,prod,na.rm=TRUE) overlap.weights = outcome.weights overlap.fit = glm(outcome~cum.treat+offset(log(offset)),data=treat.out,family="poisson",weights=overlap.weights) overlap.boots = apply(matrix(1:W,nrow=1),2,coef.fxn,weights=save.boot.overlap,data=treat.out.list) overlap.preddelta = summary(overlap.fit)$coef[2,1] overlap.predse = summary(overlap.fit)$coef[2,2] overlap.bootse = sd(overlap.boots,na.rm=TRUE) overlap.lower = quantile(overlap.boots,prob=0.025) overlap.upper = quantile(overlap.boots,prob=0.975) ############################### #stablized weighting print("stabilized") outcome.weights = apply(save.stable,1,prod,na.rm=TRUE) stabilized.weights = outcome.weights stable.fit2 = glm(outcome~cum.treat+offset(log(offset)),data=treat.out,family="poisson",weights=outcome.weights) stable.boots = apply(matrix(1:W,nrow=1),2,coef.fxn,weights=save.boot.stable,data=treat.out.list) stabilized.preddelta = summary(stable.fit2)$coef[2,1] stabilized.predse = summary(stable.fit2)$coef[2,2] stabilized.bootse = sd(stable.boots,na.rm=TRUE) stabilized.lower = quantile(stable.boots,prob=0.025) stabilized.upper = quantile(stable.boots,prob=0.975) #################### #ppta print("PPTA") mean.boots = NA ppta.fail = rep(0,K) ppta.fit = rep(NA,K) ppta.boot.fit = matrix(NA,nrow = W,ncol = K) final.cross.probs = apply(cross.probs,c(1,2),prod) final.membership = apply(final.cross.probs,2,function(x){rbinom(n,1,prob=x)}) ppta.final.size = apply(final.membership,2,sum) for(k in 1:K){ is.in = final.membership[,k] if (sum(is.in)>5){ treat.out.in = treat.out[is.in==1,] ppta.fit[k] = coef(glm(outcome~cum.treat+offset(log(offset)),data = treat.out.in,family="poisson"))[2] } else { ppta.fit[k] = NA ppta.fail[k] = 1} } ppta.fxn = function(x,boot.data){ if(sum(x)>10){ boot.data.in = boot.data[x==1,] return(coef(glm(outcome ~ cum.treat + offset(log(offset)),data = boot.data.in,family="poisson"))[2]) } else{return(NA)} } for(w in 1:W){ #estimate ATE boot.data = treat.out[boot.indicies[,w],] boot.is.in = matrix(ppta.boot.save.membership[,,w],nrow=n) ppta.boot.fit[w,] = apply(boot.is.in,2,ppta.fxn,boot.data = boot.data) } ppta.fail.perc = mean(ppta.fail) ppta.marginal = apply(final.membership,1,mean,na.rm=TRUE) ppta.once = mean(apply(final.membership,1,max,na.rm=TRUE),na.rm=TRUE) ppta.preddelta = NA ppta.predse = NA ppta.boot.predse = NA mean.boots = NA if(any(!is.na(ppta.fit))){ ppta.preddelta = mean(ppta.fit,na.rm=TRUE) ppta.predse = sd(apply(ppta.boot.fit,1,mean,na.rm=TRUE),na.rm=TRUE) } if(any(!is.na(ppta.boot.fit))){ ppta.boot.predse = sd(ppta.boot.fit,na.rm=TRUE) mean.boots = apply(ppta.boot.fit,1,mean,na.rm=TRUE) ppta.lower = quantile(mean.boots,0.025) ppta.upper = quantile(mean.boots,0.975)} ########################### #save results result = list(list(ipw.preddelta,ipw.predse,ipw.bootse,ipw.weights),list(ppta.preddelta,ppta.predse, ppta.marginal,ppta.once,ppta.size, ppta.treated.size,ppta.control.size,ppta.fail.perc),list(overlap.preddelta, overlap.predse,overlap.bootse,overlap.weights),list(stabilized.preddelta,stabilized.predse,stabilized.bootse,stabilized.weights)) save(result,file="~/final_app_result.R")	/runIPW_boot_app.R	no_license	shirleyxliao/ppta-2019	R	false	false	10,509	r	#loading data load("~/simulated_app_data.R") currentdata = new.data #loading required packages require(survey) require(MCMCpack) expit = function(x){exp(x)/(1+exp(x))} #parameter specifications bootstrap=TRUE K=1000 D = dim(currentdata$treatment)[2] n = dim(currentdata$fixed.X)[1] W = 100 # initializing data structures result = list() #ipw variables save.weights = matrix(NA,nrow=n,ncol=D) save.boot.weights = matrix(1,nrow=n,ncol=W) #overlap variables save.overlap = matrix(NA,nrow=n,ncol=D) save.boot.overlap = matrix(1,nrow=n,ncol=W) #ppta variables save.membership = array(NA,dim = c(n,K,D)) ppta.boot.save.membership = array(1,dim=c(n,K,W)) cross.probs = array(NA,dim = c(n,K,D)) ppta.size = matrix(NA,nrow=D,ncol=K) ppta.treated.size = matrix(NA,nrow=D,ncol=K) ppta.control.size = matrix(NA,nrow=D,ncol=K) #stabilized variables save.stable = matrix(NA,nrow=n,ncol=D) save.boot.stable = matrix(1,nrow=n,ncol=W) #specifying input data treatment = data.frame(currentdata$treatment) fixedcovar = data.frame(currentdata$fixed.X) timecovar = currentdata$time.X #naming input data names(fixedcovar) = paste("fixed.",names(fixedcovar),sep="") names(timecovar) = paste("time.",names(timecovar),sep="") treatment.considered = data.frame(treatment[,1]) names(treatment.considered) = "treat.1" time.covars.considered = data.frame(timecovar[[1]]) #sampling bootstrap draws if(bootstrap){ boot.indicies = matrix(sample(1:n,nW,replace=TRUE),nrow=n,ncol=W) } else{ W = 1 } ################################################################### # iterate through time points for(d in 1:D){ data.matrix = cbind(treatment.considered,fixedcovar,time.covars.considered) #create datasets with bootstrapped indicies if(bootstrap){ ppta.boot.data = apply(boot.indicies,2,function(x){data.matrix[x,]}) } #specify formulas for propensity score model and model for stabilized weights if(d==1){ no.treat = names(data.matrix)[names(data.matrix)!="treat.1"] form = paste("treat.1 ~",paste(no.treat,collapse="+")) form2 = "treat.1~1" } else { current.treat = paste("treat.",d,sep="") no.treat = names(data.matrix)[names(data.matrix)!=current.treat] form = paste(current.treat,"~", paste(no.treat,collapse="+")) form2 = paste(current.treat,"~", paste(no.treat[grep("treat",no.treat)],collapse="+")) } #################################################### # ALL WEIGHTING SCHEMES PS.fit = glm(form,data=data.matrix,family="binomial") sum.ps.fit = summary(PS.fit) ipw.PS.est = predict(PS.fit,type="response") stable.fit = glm(form2,data=data.matrix,family="binomial") stable.est = predict(stable.fit,type="response") ###calculate ipw/overlap/stable weights save.weights[,d] = treatment[,d]/ipw.PS.est + (1-treatment[,d])/(1-ipw.PS.est) save.overlap[,d] = treatment[,d](1- ipw.PS.est) + (1-treatment[,d])ipw.PS.est save.stable[,d] = treatment[,d](stable.est/ipw.PS.est) + (1-treatment[,d])((1-stable.est)/(1-ipw.PS.est)) ### bootstrapped MLE if(bootstrap){ for (w in 1:W){ boot.treatment = treatment[boot.indicies[,w],] PS.fit = glm(form,data=ppta.boot.data[[w]],family="binomial") sum.ps.fit = summary(PS.fit) PS.est = predict(PS.fit,type="response") stable.est = predict(glm(form2,data=ppta.boot.data[[w]],family="binomial"),type="response") save.boot.weights[,w] = save.boot.weights[,w](boot.treatment[,d]/PS.est + (1-boot.treatment[,d])/(1-PS.est)) save.boot.stable[,w] = save.boot.stable[,w](boot.treatment[,d](stable.est/PS.est) + (1-boot.treatment[,d])((1-stable.est)/(1-PS.est))) save.boot.overlap[,w] = save.boot.overlap[,w](boot.treatment[,d](1- PS.est) + (1-boot.treatment[,d])PS.est) } } ############################################### # PPTA #draws from PS distribution posteriors = as.matrix(MCMClogit(form,data=data.matrix,burnin=K/2,mcmc=K20,thin=20)) save(posteriors,file=paste("~/final_app_",d,"_posteriors.R")) covars = as.matrix(cbind(rep(1,n),data.matrix[,-1])) ppta.PS.est = expit(covars%%t(posteriors)) #PS estimated from draws of alpha # boostrapping for PPTA if(bootstrap){ ppta.boot.posteriors = lapply(ppta.boot.data,function(x){as.matrix(MCMClogit(form,data=x,burnin=K/10,mcmc=K10,thin=10))}) ppta.boot.covars = lapply(ppta.boot.data,function(x){as.matrix(cbind(rep(1,n),x[,-1]))}) ppta.boot.PS = mapply(function(x,y){as.matrix(expit(x%%t(y)))},x = ppta.boot.covars,y=ppta.boot.posteriors,SIMPLIFY = FALSE) ppta.boot.PS = simplify2array(ppta.boot.PS) } #calculate in/out group for(k in 1:K){ cross.prob = treatment[,d](1-ppta.PS.est[,k]) + (1-treatment[,d])ppta.PS.est[,k] cross.probs[,k,d] = cross.prob if(bootstrap){ ppta.boot.treatment = apply(boot.indicies,2,function(x){treatment[x,d]}) ppta.boot.cross.prob = apply(matrix(1:W,ncol=W),2,function(x){ppta.boot.treatment[,x](1-ppta.boot.PS[,k,x])+(1-ppta.boot.treatment[,x])ppta.boot.PS[,k,x]}) ppta.boot.save.membership[,k,] =ppta.boot.save.membership[,k,]apply(ppta.boot.cross.prob,2,function(x){rbinom(n,1,x)}) } } #updating covariates if not last time point if(d<D){ treatment.considered = data.frame(treatment[,(d+1)],treatment[,d]) time.covars.considered = data.frame(timecovar[[d+1]],timecovar[[d]]) names(treatment.considered) = paste("treat.",seq(from=(d+1),to=(d)),sep="") names(time.covars.considered) = paste(rep(names(data.frame(timecovar[[d+1]])),2),rep(seq(from=(d+1),to=(d)),each=2),sep=".") } } save(save.weights,file = "~/save.weights.R") save(boot.indicies,file="~/boot.indicies.R") save(save.boot.weights,file="~/save.boot.weights.R") ############################################### # effect estimation #creating necessary data structures outcome = currentdata$outcome offset = currentdata$offset cum.treat = apply(treatment,1,sum,na.rm=TRUE) treat.out = data.frame(cum.treat,outcome,offset) treat.out$outcome.rate = outcome/offset treat.out.list = apply(boot.indicies,2,function(x,data){data[x,]},data=treat.out) # function for calculating bootstrapped SE coef.fxn = function(x,weights,data){ weights = weights[,x] dat = data[[x]] fit = glm(outcome~cum.treat+offset(log(offset)),data=dat,weights=weights,family="poisson") xx = summary(fit)$coef if(dim(xx)[1]>1){ return(xx[2,1]) }else{ return(NA) } } #####IPW print("IPW") outcome.weights = apply(save.weights,1,prod,na.rm=TRUE) ipw.weights = outcome.weights ipw.fit = glm(outcome~cum.treat+offset(log(offset)),data=treat.out,family="poisson",weights=outcome.weights) ipw.boots = apply(matrix(1:W,nrow=1),2,coef.fxn,weights=save.boot.weights,data=treat.out.list) ipw.preddelta = summary(ipw.fit)$coef[2,1] ipw.predse = summary(ipw.fit)$coef[2,2] ipw.bootse = sd(ipw.boots,na.rm=TRUE) ipw.lower = quantile(ipw.boots,prob=0.025) ipw.upper = quantile(ipw.boots,prob=0.975) ################## #overlap weighted regression print("overlap") outcome.weights = apply(save.overlap,1,prod,na.rm=TRUE) overlap.weights = outcome.weights overlap.fit = glm(outcome~cum.treat+offset(log(offset)),data=treat.out,family="poisson",weights=overlap.weights) overlap.boots = apply(matrix(1:W,nrow=1),2,coef.fxn,weights=save.boot.overlap,data=treat.out.list) overlap.preddelta = summary(overlap.fit)$coef[2,1] overlap.predse = summary(overlap.fit)$coef[2,2] overlap.bootse = sd(overlap.boots,na.rm=TRUE) overlap.lower = quantile(overlap.boots,prob=0.025) overlap.upper = quantile(overlap.boots,prob=0.975) ############################### #stablized weighting print("stabilized") outcome.weights = apply(save.stable,1,prod,na.rm=TRUE) stabilized.weights = outcome.weights stable.fit2 = glm(outcome~cum.treat+offset(log(offset)),data=treat.out,family="poisson",weights=outcome.weights) stable.boots = apply(matrix(1:W,nrow=1),2,coef.fxn,weights=save.boot.stable,data=treat.out.list) stabilized.preddelta = summary(stable.fit2)$coef[2,1] stabilized.predse = summary(stable.fit2)$coef[2,2] stabilized.bootse = sd(stable.boots,na.rm=TRUE) stabilized.lower = quantile(stable.boots,prob=0.025) stabilized.upper = quantile(stable.boots,prob=0.975) #################### #ppta print("PPTA") mean.boots = NA ppta.fail = rep(0,K) ppta.fit = rep(NA,K) ppta.boot.fit = matrix(NA,nrow = W,ncol = K) final.cross.probs = apply(cross.probs,c(1,2),prod) final.membership = apply(final.cross.probs,2,function(x){rbinom(n,1,prob=x)}) ppta.final.size = apply(final.membership,2,sum) for(k in 1:K){ is.in = final.membership[,k] if (sum(is.in)>5){ treat.out.in = treat.out[is.in==1,] ppta.fit[k] = coef(glm(outcome~cum.treat+offset(log(offset)),data = treat.out.in,family="poisson"))[2] } else { ppta.fit[k] = NA ppta.fail[k] = 1} } ppta.fxn = function(x,boot.data){ if(sum(x)>10){ boot.data.in = boot.data[x==1,] return(coef(glm(outcome ~ cum.treat + offset(log(offset)),data = boot.data.in,family="poisson"))[2]) } else{return(NA)} } for(w in 1:W){ #estimate ATE boot.data = treat.out[boot.indicies[,w],] boot.is.in = matrix(ppta.boot.save.membership[,,w],nrow=n) ppta.boot.fit[w,] = apply(boot.is.in,2,ppta.fxn,boot.data = boot.data) } ppta.fail.perc = mean(ppta.fail) ppta.marginal = apply(final.membership,1,mean,na.rm=TRUE) ppta.once = mean(apply(final.membership,1,max,na.rm=TRUE),na.rm=TRUE) ppta.preddelta = NA ppta.predse = NA ppta.boot.predse = NA mean.boots = NA if(any(!is.na(ppta.fit))){ ppta.preddelta = mean(ppta.fit,na.rm=TRUE) ppta.predse = sd(apply(ppta.boot.fit,1,mean,na.rm=TRUE),na.rm=TRUE) } if(any(!is.na(ppta.boot.fit))){ ppta.boot.predse = sd(ppta.boot.fit,na.rm=TRUE) mean.boots = apply(ppta.boot.fit,1,mean,na.rm=TRUE) ppta.lower = quantile(mean.boots,0.025) ppta.upper = quantile(mean.boots,0.975)} ########################### #save results result = list(list(ipw.preddelta,ipw.predse,ipw.bootse,ipw.weights),list(ppta.preddelta,ppta.predse, ppta.marginal,ppta.once,ppta.size, ppta.treated.size,ppta.control.size,ppta.fail.perc),list(overlap.preddelta, overlap.predse,overlap.bootse,overlap.weights),list(stabilized.preddelta,stabilized.predse,stabilized.bootse,stabilized.weights)) save(result,file="~/final_app_result.R")
############################ ### function: plotMixture() ############################ plotMixture=function(fit,dist="overall",curve="survival", xlab=NULL,ylab=NULL,main=NULL,col=NULL,lty=NULL,lwd=1,axes=T){ est=c(fit,curvesLogAFT(fit)) plotNPMLEsurv(est=est,dist=dist,curve=curve,type="l", xlab=xlab,ylab=ylab,main=main,col=col,lty=lty,lwd=lwd,axes=axes) } ############################ ### function: curvesLogAFT() ############################ curvesLogAFT=function(fit){ n.group=length(fit$groups.obs$labels) buffer=apply(as.matrix(fit$groups.obs$labels),1,strsplit,split=",") matrix.cov=matrix(1,length(buffer),1+length(buffer[[1]][[1]])) for(k in 1:length(buffer)) matrix.cov[k,]=c(1,as.numeric(buffer[[k]][[1]])) ### "p1", "xbeta", "xgamma", "xalpha", "xq" p1=as.matrix(rep(1,dim(matrix.cov)[1])) if (!is.null(fit$covariates$beta)){ xbeta=as.matrix(matrix.cov[,fit$covariates$beta])%%as.vector(fit$par$beta) p1=exp(xbeta)/(1+exp(xbeta)) if(!is.null(fit$mixturetype)){ index=which(fit$mixturetype==1) if(length(index)>0) p1[index,]=1 } } xgamma=as.matrix(matrix.cov[,fit$covariates$gamma])%%as.vector(fit$par$gamma) xalpha=as.matrix(matrix.cov[,fit$covariates$alpha])%%as.vector(fit$par$alpha) if(is.null(fit$fix.par$q)){ xq=as.matrix(matrix.cov[,fit$covariates$q])%%as.vector(fit$par$q) }else{ xq=matrix(fit$fix.par$q,n.group) } ### "surv" time=seq(fit$minmax.time[1],fit$minmax.time[2],by=0.01) y=log(time-fit$time.origin) surv=list() for (i in 1:n.group){ w=(y-xgamma[i])/exp(xalpha[i]) q=rep(xq[i],length(w)) surv1=as.data.frame(matrix(NA,length(time),7)) names(surv1)=c("time","survival","survivalD","density","densityD","hazard","hazardD") surv1[,"time"]=time surv1[,"densityD"]=dw.fun(w,q)/(timeexp(xalpha[i])) surv1[,"survivalD"]=Sw.fun(w,q) surv1[,"hazardD"]=surv1[,"densityD"]/surv1[,"survivalD"] index=which(is.na(surv1[,"hazardD"])) if(length(index)>0){ if(min(index)>1) surv1[index,"hazardD"]=surv1[min(index)-1,"hazardD"] } surv1[,"density"]=p1[i]surv1[,"densityD"] surv1[,"survival"]=p1[i]*surv1[,"survivalD"]+(1-p1[i]) surv1[,"hazard"]=surv1[,"density"]/surv1[,"survival"] surv[[i]]=surv1 } names(surv)=fit$groups.obs$labels ### "PD", "PC" PD=round(as.vector(p1),3) PC=1-PD return(list(surv=surv,PD=PD,PC=PC)) }	/R/plotMixture.R	no_license	cran/MixtureRegLTIC	R	false	false	2,487	r	############################ ### function: plotMixture() ############################ plotMixture=function(fit,dist="overall",curve="survival", xlab=NULL,ylab=NULL,main=NULL,col=NULL,lty=NULL,lwd=1,axes=T){ est=c(fit,curvesLogAFT(fit)) plotNPMLEsurv(est=est,dist=dist,curve=curve,type="l", xlab=xlab,ylab=ylab,main=main,col=col,lty=lty,lwd=lwd,axes=axes) } ############################ ### function: curvesLogAFT() ############################ curvesLogAFT=function(fit){ n.group=length(fit$groups.obs$labels) buffer=apply(as.matrix(fit$groups.obs$labels),1,strsplit,split=",") matrix.cov=matrix(1,length(buffer),1+length(buffer[[1]][[1]])) for(k in 1:length(buffer)) matrix.cov[k,]=c(1,as.numeric(buffer[[k]][[1]])) ### "p1", "xbeta", "xgamma", "xalpha", "xq" p1=as.matrix(rep(1,dim(matrix.cov)[1])) if (!is.null(fit$covariates$beta)){ xbeta=as.matrix(matrix.cov[,fit$covariates$beta])%%as.vector(fit$par$beta) p1=exp(xbeta)/(1+exp(xbeta)) if(!is.null(fit$mixturetype)){ index=which(fit$mixturetype==1) if(length(index)>0) p1[index,]=1 } } xgamma=as.matrix(matrix.cov[,fit$covariates$gamma])%%as.vector(fit$par$gamma) xalpha=as.matrix(matrix.cov[,fit$covariates$alpha])%%as.vector(fit$par$alpha) if(is.null(fit$fix.par$q)){ xq=as.matrix(matrix.cov[,fit$covariates$q])%%as.vector(fit$par$q) }else{ xq=matrix(fit$fix.par$q,n.group) } ### "surv" time=seq(fit$minmax.time[1],fit$minmax.time[2],by=0.01) y=log(time-fit$time.origin) surv=list() for (i in 1:n.group){ w=(y-xgamma[i])/exp(xalpha[i]) q=rep(xq[i],length(w)) surv1=as.data.frame(matrix(NA,length(time),7)) names(surv1)=c("time","survival","survivalD","density","densityD","hazard","hazardD") surv1[,"time"]=time surv1[,"densityD"]=dw.fun(w,q)/(timeexp(xalpha[i])) surv1[,"survivalD"]=Sw.fun(w,q) surv1[,"hazardD"]=surv1[,"densityD"]/surv1[,"survivalD"] index=which(is.na(surv1[,"hazardD"])) if(length(index)>0){ if(min(index)>1) surv1[index,"hazardD"]=surv1[min(index)-1,"hazardD"] } surv1[,"density"]=p1[i]surv1[,"densityD"] surv1[,"survival"]=p1[i]*surv1[,"survivalD"]+(1-p1[i]) surv1[,"hazard"]=surv1[,"density"]/surv1[,"survival"] surv[[i]]=surv1 } names(surv)=fit$groups.obs$labels ### "PD", "PC" PD=round(as.vector(p1),3) PC=1-PD return(list(surv=surv,PD=PD,PC=PC)) }
library(glmnet) mydata = read.table("./TrainingSet/ReliefF/NSCLC.csv",head=T,sep=",") x = as.matrix(mydata[,4:ncol(mydata)]) y = as.matrix(mydata[,1]) set.seed(123) glm = cv.glmnet(x,y,nfolds=10,type.measure="mse",alpha=0.35,family="gaussian",standardize=FALSE) sink('./Model/EN/ReliefF/NSCLC/NSCLC_046.txt',append=TRUE) print(glm$glmnet.fit) sink()	/Model/EN/ReliefF/NSCLC/NSCLC_046.R	no_license	leon1003/QSMART	R	false	false	350	r	library(glmnet) mydata = read.table("./TrainingSet/ReliefF/NSCLC.csv",head=T,sep=",") x = as.matrix(mydata[,4:ncol(mydata)]) y = as.matrix(mydata[,1]) set.seed(123) glm = cv.glmnet(x,y,nfolds=10,type.measure="mse",alpha=0.35,family="gaussian",standardize=FALSE) sink('./Model/EN/ReliefF/NSCLC/NSCLC_046.txt',append=TRUE) print(glm$glmnet.fit) sink()
setClass("RENAMECOL", representation = representation( strEqcCommand = "character", colInRename = "character", colOutRename = "character" ), prototype = prototype( strEqcCommand = "", colInRename = "", colOutRename = "" ) #contains = c("EcfReader") ) setGeneric("setRENAMECOL", function(object) standardGeneric("setRENAMECOL")) setMethod("setRENAMECOL", signature = (object = "RENAMECOL"), function(object) { aEqcSlotNamesIn = c("colInRename", "colOutRename") objEqcReader <- EqcReader(object@strEqcCommand,aEqcSlotNamesIn) if(length(objEqcReader@lsEqcSlotsOut) > 0) { for(i in 1:length(objEqcReader@lsEqcSlotsOut)) { tmpSlot <- names(objEqcReader@lsEqcSlotsOut)[i] tmpSlotVal <- objEqcReader@lsEqcSlotsOut[[i]] #if(all(!is.na(tmpSlotVal))) slot(object, tmpSlot) <- tmpSlotVal if(any(!is.na(tmpSlotVal))) slot(object, tmpSlot) <- tmpSlotVal } } return(object) }) ############################################################################################################################# validRENAMECOL <- function(objRNC) { if(objRNC@colInRename == "") stop(paste(" EASY ERROR:RENAMECOL\n No colInRename defined. Please set colInRename.", sep="")) if(objRNC@colOutRename == "") stop(paste(" EASY ERROR:RENAMECOL\n No colOutRename defined. Please set colOutRename.", sep="")) return(TRUE) } ############################################################################################################################# RENAMECOL.run <- function(objRNC, objGWA, objREPORT) { colInRename <- objRNC@colInRename colOutRename <- objRNC@colOutRename iMatchIn = match(objRNC@colInRename,objGWA@aHeader) iMatchOut = match(objRNC@colOutRename,objGWA@aHeader) isAnyRenamed = FALSE if(!is.na(iMatchIn) & is.na(iMatchOut)) { ### Column must be renamed, colOut is not in table! #names(objGWA@tblGWA)[iMatchIn] <- objGWA@aHeader[iMatchIn] <- objRNC@colOutRename objGWA <- GWADATA.renamecol(objGWA, colInRename, colOutRename) isAnyRenamed = TRUE # } else if(!is.na(iMatchIn) & !is.na(iMatchOut)) { # ### Column colInRename AND colOutRename are already in table # names(objGWA@tblGWA)[iMatchOut] <- objGWA@aHeader[iMatchOut] <- paste(objGWA@aHeader[iMatchOut],".old",sep="") # names(objGWA@tblGWA)[iMatchIn] <- objGWA@aHeader[iMatchIn] <- objRNC@colOutRename # isAnyRenamed = TRUE } else { if(!is.na(iMatchOut)) stop(paste("EASY ERROR:RENAMECOL\n New column name \n ",colOutRename,"\n does already exist!!!\n", sep="")) if(is.na(iMatchIn)) stop(paste("EASY ERROR:RENAMECOL\n Column \n ",colInRename,"\n does not exist!!!\n", sep="")) } if(!("numColRenamed" %in% names(objREPORT@tblReport))) objREPORT <- REPORT.addval(objREPORT, "numColRenamed", 0) else if(REPORT.getval(objREPORT, "numColRenamed") == "NA") objREPORT <- REPORT.addval(objREPORT, "numColRenamed", 0) if(isAnyRenamed) { ## Update Report numColRenamed = numColRenamed + 1 strNumColRenamed = REPORT.getval(objREPORT, "numColRenamed") numColRenamedNew = as.numeric(strNumColRenamed) + 1 objREPORT <- REPORT.setval(objREPORT, "numColRenamed", numColRenamedNew) } return(list(objGWA,objREPORT)) } RENAMECOL <- function(strEqcCommand){ ## Wrapper for class definition RENAMECOLout <- setRENAMECOL(new("RENAMECOL", strEqcCommand = strEqcCommand)) validRENAMECOL(RENAMECOLout) #RENAMECOLout.valid <- validRENAMECOL(RENAMECOLout) return(RENAMECOLout) #validECF(ECFout) #return(ECFout) ## Identical: # ECFin <- new("ECF5", fileECF = fileECFIn) # ECFout <- setECF5(ECFin) # return(ECFout) } # setValidity("RENAMECOL", function(object){ # print("RENAMECOL-CHECK") # print(TRUE) # return(TRUE) # })	/EasyQC/R/clsRENAMECOL3.r	no_license	barbarathorslund/warfarin	R	false	false	3,862	r	setClass("RENAMECOL", representation = representation( strEqcCommand = "character", colInRename = "character", colOutRename = "character" ), prototype = prototype( strEqcCommand = "", colInRename = "", colOutRename = "" ) #contains = c("EcfReader") ) setGeneric("setRENAMECOL", function(object) standardGeneric("setRENAMECOL")) setMethod("setRENAMECOL", signature = (object = "RENAMECOL"), function(object) { aEqcSlotNamesIn = c("colInRename", "colOutRename") objEqcReader <- EqcReader(object@strEqcCommand,aEqcSlotNamesIn) if(length(objEqcReader@lsEqcSlotsOut) > 0) { for(i in 1:length(objEqcReader@lsEqcSlotsOut)) { tmpSlot <- names(objEqcReader@lsEqcSlotsOut)[i] tmpSlotVal <- objEqcReader@lsEqcSlotsOut[[i]] #if(all(!is.na(tmpSlotVal))) slot(object, tmpSlot) <- tmpSlotVal if(any(!is.na(tmpSlotVal))) slot(object, tmpSlot) <- tmpSlotVal } } return(object) }) ############################################################################################################################# validRENAMECOL <- function(objRNC) { if(objRNC@colInRename == "") stop(paste(" EASY ERROR:RENAMECOL\n No colInRename defined. Please set colInRename.", sep="")) if(objRNC@colOutRename == "") stop(paste(" EASY ERROR:RENAMECOL\n No colOutRename defined. Please set colOutRename.", sep="")) return(TRUE) } ############################################################################################################################# RENAMECOL.run <- function(objRNC, objGWA, objREPORT) { colInRename <- objRNC@colInRename colOutRename <- objRNC@colOutRename iMatchIn = match(objRNC@colInRename,objGWA@aHeader) iMatchOut = match(objRNC@colOutRename,objGWA@aHeader) isAnyRenamed = FALSE if(!is.na(iMatchIn) & is.na(iMatchOut)) { ### Column must be renamed, colOut is not in table! #names(objGWA@tblGWA)[iMatchIn] <- objGWA@aHeader[iMatchIn] <- objRNC@colOutRename objGWA <- GWADATA.renamecol(objGWA, colInRename, colOutRename) isAnyRenamed = TRUE # } else if(!is.na(iMatchIn) & !is.na(iMatchOut)) { # ### Column colInRename AND colOutRename are already in table # names(objGWA@tblGWA)[iMatchOut] <- objGWA@aHeader[iMatchOut] <- paste(objGWA@aHeader[iMatchOut],".old",sep="") # names(objGWA@tblGWA)[iMatchIn] <- objGWA@aHeader[iMatchIn] <- objRNC@colOutRename # isAnyRenamed = TRUE } else { if(!is.na(iMatchOut)) stop(paste("EASY ERROR:RENAMECOL\n New column name \n ",colOutRename,"\n does already exist!!!\n", sep="")) if(is.na(iMatchIn)) stop(paste("EASY ERROR:RENAMECOL\n Column \n ",colInRename,"\n does not exist!!!\n", sep="")) } if(!("numColRenamed" %in% names(objREPORT@tblReport))) objREPORT <- REPORT.addval(objREPORT, "numColRenamed", 0) else if(REPORT.getval(objREPORT, "numColRenamed") == "NA") objREPORT <- REPORT.addval(objREPORT, "numColRenamed", 0) if(isAnyRenamed) { ## Update Report numColRenamed = numColRenamed + 1 strNumColRenamed = REPORT.getval(objREPORT, "numColRenamed") numColRenamedNew = as.numeric(strNumColRenamed) + 1 objREPORT <- REPORT.setval(objREPORT, "numColRenamed", numColRenamedNew) } return(list(objGWA,objREPORT)) } RENAMECOL <- function(strEqcCommand){ ## Wrapper for class definition RENAMECOLout <- setRENAMECOL(new("RENAMECOL", strEqcCommand = strEqcCommand)) validRENAMECOL(RENAMECOLout) #RENAMECOLout.valid <- validRENAMECOL(RENAMECOLout) return(RENAMECOLout) #validECF(ECFout) #return(ECFout) ## Identical: # ECFin <- new("ECF5", fileECF = fileECFIn) # ECFout <- setECF5(ECFin) # return(ECFout) } # setValidity("RENAMECOL", function(object){ # print("RENAMECOL-CHECK") # print(TRUE) # return(TRUE) # })
% Generated by roxygen2: do not edit by hand % Please edit documentation in R/aiplatform_objects.R \name{GoogleCloudAiplatformV1EvaluatedAnnotationExplanation} \alias{GoogleCloudAiplatformV1EvaluatedAnnotationExplanation} \title{GoogleCloudAiplatformV1EvaluatedAnnotationExplanation Object} \usage{ GoogleCloudAiplatformV1EvaluatedAnnotationExplanation( explanationType = NULL, explanation = NULL ) } \arguments{ \item{explanationType}{Explanation type} \item{explanation}{Explanation attribution response details} } \value{ GoogleCloudAiplatformV1EvaluatedAnnotationExplanation object } \description{ GoogleCloudAiplatformV1EvaluatedAnnotationExplanation Object } \details{ Autogenerated via \code{\link[googleAuthR]{gar_create_api_objects}} Explanation result of the prediction produced by the Model. } \concept{GoogleCloudAiplatformV1EvaluatedAnnotationExplanation functions}	/googleaiplatformv1.auto/man/GoogleCloudAiplatformV1EvaluatedAnnotationExplanation.Rd	no_license	justinjm/autoGoogleAPI	R	false	true	884	rd	% Generated by roxygen2: do not edit by hand % Please edit documentation in R/aiplatform_objects.R \name{GoogleCloudAiplatformV1EvaluatedAnnotationExplanation} \alias{GoogleCloudAiplatformV1EvaluatedAnnotationExplanation} \title{GoogleCloudAiplatformV1EvaluatedAnnotationExplanation Object} \usage{ GoogleCloudAiplatformV1EvaluatedAnnotationExplanation( explanationType = NULL, explanation = NULL ) } \arguments{ \item{explanationType}{Explanation type} \item{explanation}{Explanation attribution response details} } \value{ GoogleCloudAiplatformV1EvaluatedAnnotationExplanation object } \description{ GoogleCloudAiplatformV1EvaluatedAnnotationExplanation Object } \details{ Autogenerated via \code{\link[googleAuthR]{gar_create_api_objects}} Explanation result of the prediction produced by the Model. } \concept{GoogleCloudAiplatformV1EvaluatedAnnotationExplanation functions}
% Generated by roxygen2: do not edit by hand % Please edit documentation in R/EmptyRemoteFileResponse.r \docType{data} \name{EmptyRemoteFileResponse} \alias{EmptyRemoteFileResponse} \title{EmptyRemoteFileResponse Class} \format{An object of class \code{R6ClassGenerator} of length 24.} \usage{ EmptyRemoteFileResponse } \description{ EmptyRemoteFileResponse Class } \section{Fields}{ \describe{ \item{\code{message}}{success or failure} \item{\code{result}}{empty result} \item{\code{status}}{success or failure} }} \keyword{datasets}	/man/EmptyRemoteFileResponse.Rd	permissive	agaveplatform/r-sdk	R	false	true	539	rd	% Generated by roxygen2: do not edit by hand % Please edit documentation in R/EmptyRemoteFileResponse.r \docType{data} \name{EmptyRemoteFileResponse} \alias{EmptyRemoteFileResponse} \title{EmptyRemoteFileResponse Class} \format{An object of class \code{R6ClassGenerator} of length 24.} \usage{ EmptyRemoteFileResponse } \description{ EmptyRemoteFileResponse Class } \section{Fields}{ \describe{ \item{\code{message}}{success or failure} \item{\code{result}}{empty result} \item{\code{status}}{success or failure} }} \keyword{datasets}
% Generated by roxygen2: do not edit by hand % Please edit documentation in R/KeggPathwayView.R \docType{methods} \name{arrangePathview} \alias{arrangePathview} \title{Kegg pathway view and arrange grobs on page} \usage{ arrangePathview( genelist, pathways = c(), top = 4, ncol = 2, title = NULL, sub = NULL, organism = "hsa", output = ".", path.archive = ".", kegg.native = TRUE, verbose = TRUE ) } \arguments{ \item{genelist}{a data frame with columns of ENTREZID, Control and Treatment. The columns of Control and Treatment represent gene score in Control and Treatment sample.} \item{pathways}{character vector, the KEGG pathway ID(s), usually 5 digit, may also include the 3 letter KEGG species code.} \item{top}{integer, specifying how many top enriched pathways to be visualized.} \item{ncol}{integer, specifying how many column of figures to be arranged in each page.} \item{title}{optional string, or grob.} \item{sub}{optional string, or grob.} \item{organism}{character, either the kegg code, scientific name or the common name of the target species. This applies to both pathway and gene.data or cpd.data. When KEGG ortholog pathway is considered, species="ko". Default species="hsa", it is equivalent to use either "Homo sapiens" (scientific name) or "human" (common name).} \item{output}{Path to save plot to.} \item{path.archive}{character, the directory of KEGG pathway data file (.xml) and image file (.png). Users may supply their own data files in the same format and naming convention of KEGG's (species code + pathway id, e.g. hsa04110.xml, hsa04110.png etc) in this directory. Default kegg.dir="." (current working directory).} \item{kegg.native}{logical, whether to render pathway graph as native KEGG graph (.png) or using graphviz layout engine (.pdf). Default kegg.native=TRUE.} \item{verbose}{Boolean} } \value{ plot on the current device } \description{ Kegg pathway view and arrange grobs on page. } \examples{ file3 = file.path(system.file("extdata", package = "MAGeCKFlute"), "testdata/mle.gene_summary.txt") dd = ReadBeta(file3) colnames(dd)[2:3] = c("Control", "Treatment") # arrangePathview(dd, c("hsa00534"), title=NULL, sub=NULL, organism="hsa") } \author{ Wubing Zhang }	/man/arrangePathview.Rd	no_license	sysyangb/MAGeCKFlute	R	false	true	2,243	rd	% Generated by roxygen2: do not edit by hand % Please edit documentation in R/KeggPathwayView.R \docType{methods} \name{arrangePathview} \alias{arrangePathview} \title{Kegg pathway view and arrange grobs on page} \usage{ arrangePathview( genelist, pathways = c(), top = 4, ncol = 2, title = NULL, sub = NULL, organism = "hsa", output = ".", path.archive = ".", kegg.native = TRUE, verbose = TRUE ) } \arguments{ \item{genelist}{a data frame with columns of ENTREZID, Control and Treatment. The columns of Control and Treatment represent gene score in Control and Treatment sample.} \item{pathways}{character vector, the KEGG pathway ID(s), usually 5 digit, may also include the 3 letter KEGG species code.} \item{top}{integer, specifying how many top enriched pathways to be visualized.} \item{ncol}{integer, specifying how many column of figures to be arranged in each page.} \item{title}{optional string, or grob.} \item{sub}{optional string, or grob.} \item{organism}{character, either the kegg code, scientific name or the common name of the target species. This applies to both pathway and gene.data or cpd.data. When KEGG ortholog pathway is considered, species="ko". Default species="hsa", it is equivalent to use either "Homo sapiens" (scientific name) or "human" (common name).} \item{output}{Path to save plot to.} \item{path.archive}{character, the directory of KEGG pathway data file (.xml) and image file (.png). Users may supply their own data files in the same format and naming convention of KEGG's (species code + pathway id, e.g. hsa04110.xml, hsa04110.png etc) in this directory. Default kegg.dir="." (current working directory).} \item{kegg.native}{logical, whether to render pathway graph as native KEGG graph (.png) or using graphviz layout engine (.pdf). Default kegg.native=TRUE.} \item{verbose}{Boolean} } \value{ plot on the current device } \description{ Kegg pathway view and arrange grobs on page. } \examples{ file3 = file.path(system.file("extdata", package = "MAGeCKFlute"), "testdata/mle.gene_summary.txt") dd = ReadBeta(file3) colnames(dd)[2:3] = c("Control", "Treatment") # arrangePathview(dd, c("hsa00534"), title=NULL, sub=NULL, organism="hsa") } \author{ Wubing Zhang }
# capitalize words cw <- function(s, strict = FALSE, onlyfirst = FALSE) { cap <- function(s) paste(toupper(substring(s,1,1)), {s <- substring(s,2); if(strict) tolower(s) else s}, sep = "", collapse = " " ) if(!onlyfirst){ sapply(strsplit(s, split = " "), cap, USE.NAMES = !is.null(names(s))) } else { sapply(s, function(x) paste(toupper(substring(x,1,1)), tolower(substring(x,2)), sep="", collapse=" "), USE.NAMES=F) } } # extract pattern from a string strextract <- function(str, pattern) regmatches(str, regexpr(pattern, str)) # trim space from beginning and end of strings str_trim_ <- function(str) gsub("^\\s+\|\\s+$", "", str) # remove zero length strings nozero <- function(x) { x[nzchar(x)] } checker <- function(x, type, len) { if (!length(x) %in% len) stop(sprintf("%s input should be of length %s", type, p0c(len)), call. = FALSE) if (!is.double(x)) stop(sprintf("%s input should be of type double (a number)", type), call. = FALSE) } fmtcheck <- function(x) { if (!is.double(x) \|\| is.na(x)) stop("fmt must be an integer value", call. = FALSE) if (x < 0 \|\| x > 20) stop("fmt must be 0 and 20", call. = FALSE) } # decfmt <- function(pts, fmt) { # rapply(pts, format, nsmall = fmt, trim = TRUE, how = "list") # } centroid <- function(x, center){ if (!is.null(center)) { stopifnot(is.numeric(center)) return(center) } else { if ("geometry" %in% names(x)) { obj <- x$geometry$coordinates # tryasnum <- tryCatch(as.numeric(obj), warning = function(w) w) if (!is(obj, "list")) { obj } else { # sapply(obj, function(z) sapply(z, function(b) b[2])) lngs <- rapply(obj, function(x) x[1]) # sapply(obj, function(z) sapply(z, function(b) b[1])) lats <- rapply(obj, function(x) x[2]) c(mean(as.numeric(lngs)), mean(as.numeric(lats))) } } else { c( mean(as.numeric(sapply(x$coordinates, function(z) sapply(z, function(b) b[2])))), mean(as.numeric(sapply(x$coordinates, function(z) sapply(z, function(b) b[1])))) ) } } } check_str <- function(x) { checklog <- vapply(x, lint, logical(1)) if (!all(checklog)) { notwkt <- x[!checklog] notwkt_cs <- vapply(notwkt, class, "") notwkt[notwkt_cs != "character"] <- notwkt_cs[notwkt_cs != "character"] notwkt <- paste0(notwkt, collapse = "\n") stop("The following strings are not WKT:\n", notwkt, call. = FALSE) } stopifnot(length(x[[1]]) == 1) x[[1]] } chek_for_pkg <- function(x) { if (!requireNamespace(x, quietly = TRUE)) { stop("Please install ", x, call. = FALSE) } else { invisible(TRUE) } }	/wellknown/R/zzz.R	no_license	ingted/R-Examples	R	false	false	2,696	r	# capitalize words cw <- function(s, strict = FALSE, onlyfirst = FALSE) { cap <- function(s) paste(toupper(substring(s,1,1)), {s <- substring(s,2); if(strict) tolower(s) else s}, sep = "", collapse = " " ) if(!onlyfirst){ sapply(strsplit(s, split = " "), cap, USE.NAMES = !is.null(names(s))) } else { sapply(s, function(x) paste(toupper(substring(x,1,1)), tolower(substring(x,2)), sep="", collapse=" "), USE.NAMES=F) } } # extract pattern from a string strextract <- function(str, pattern) regmatches(str, regexpr(pattern, str)) # trim space from beginning and end of strings str_trim_ <- function(str) gsub("^\\s+\|\\s+$", "", str) # remove zero length strings nozero <- function(x) { x[nzchar(x)] } checker <- function(x, type, len) { if (!length(x) %in% len) stop(sprintf("%s input should be of length %s", type, p0c(len)), call. = FALSE) if (!is.double(x)) stop(sprintf("%s input should be of type double (a number)", type), call. = FALSE) } fmtcheck <- function(x) { if (!is.double(x) \|\| is.na(x)) stop("fmt must be an integer value", call. = FALSE) if (x < 0 \|\| x > 20) stop("fmt must be 0 and 20", call. = FALSE) } # decfmt <- function(pts, fmt) { # rapply(pts, format, nsmall = fmt, trim = TRUE, how = "list") # } centroid <- function(x, center){ if (!is.null(center)) { stopifnot(is.numeric(center)) return(center) } else { if ("geometry" %in% names(x)) { obj <- x$geometry$coordinates # tryasnum <- tryCatch(as.numeric(obj), warning = function(w) w) if (!is(obj, "list")) { obj } else { # sapply(obj, function(z) sapply(z, function(b) b[2])) lngs <- rapply(obj, function(x) x[1]) # sapply(obj, function(z) sapply(z, function(b) b[1])) lats <- rapply(obj, function(x) x[2]) c(mean(as.numeric(lngs)), mean(as.numeric(lats))) } } else { c( mean(as.numeric(sapply(x$coordinates, function(z) sapply(z, function(b) b[2])))), mean(as.numeric(sapply(x$coordinates, function(z) sapply(z, function(b) b[1])))) ) } } } check_str <- function(x) { checklog <- vapply(x, lint, logical(1)) if (!all(checklog)) { notwkt <- x[!checklog] notwkt_cs <- vapply(notwkt, class, "") notwkt[notwkt_cs != "character"] <- notwkt_cs[notwkt_cs != "character"] notwkt <- paste0(notwkt, collapse = "\n") stop("The following strings are not WKT:\n", notwkt, call. = FALSE) } stopifnot(length(x[[1]]) == 1) x[[1]] } chek_for_pkg <- function(x) { if (!requireNamespace(x, quietly = TRUE)) { stop("Please install ", x, call. = FALSE) } else { invisible(TRUE) } }
#' Simplex Projection #' #' \code{simplex} returns model statistics computed from given multiple time series #' using simplex projection. This function performs simplex projections for all #' possible combinations of \code{E}, \code{tau} and \code{tp}. #' #' @inheritParams uic #' @param lib_var #' the name or column index of a library (and target) variable. #' The specified variable is used as a response variable and its time-delay variables are #' used as explanatory variables. #' @param alpha #' the significant level to determine the embedding dimension of reference model #' (i.e., E0). If \code{alpha = NULL}, E0 is set to E - 1. If \code{0 < alpha < 1} #' E0 depends on the model results with lower embedding dimensions. #' #' @details #' Transfer entropy is computed as follows: #' \deqn{ #' \sum_{t} log p(X_{t+tp} \| X_{t}, X_{t-\tau}, \ldots, X_{t-(E -1)\tau}, Z_{t}) - #' log p(X_{t+tp} \| X_{t}, X_{t-\tau}, \ldots, X_{t-(E0-1)\tau}, Z_{t}) #' } #' where \eqn{X} and \eqn{Z} are library and condition variables, respectively. #' #' @return #' A data.frame where each row represents model statistics computed from a parameter set. #' \tabular{ll}{ #' \code{E} \tab \code{:} embedding dimension \cr #' \code{E0} \tab \code{:} embedding dimension of reference model \cr #' \code{tau} \tab \code{:} time-lag \cr #' \code{tp} \tab \code{:} time prediction horizon \cr #' \code{nn} \tab \code{:} number of nearest neighbors \cr #' \code{n_lib} \tab \code{:} number of time indices used for attractor reconstruction \cr #' \code{n_pred} \tab \code{:} number of time indices used for model predictions \cr #' \code{rmse} \tab \code{:} root mean squared error \cr #' \code{te} \tab \code{:} transfer entropy \cr #' \code{ete} \tab \code{:} effective transfer entropy \cr #' \code{pval} \tab \code{:} p-value to test alternative hypothesis, te > 0 \cr #' \code{n_surr} \tab \code{:} number of surrogate data \cr #' } #' #' @seealso \link{xmap}, \link{uic} #' #' @examples #' # simulate logistic map #' tl <- 400 # time length #' x <- y <- rep(NA, tl) #' x[1] <- 0.4 #' y[1] <- 0.2 #' for (t in 1:(tl - 1)) { # causality : x -> y #' x[t+1] = x[t] * (3.8 - 3.8 * x[t] - 0.0 * y[t]) #' y[t+1] = y[t] * (3.5 - 3.5 * y[t] - 0.1 * x[t]) #' } #' block <- data.frame(t=1:tl, x=x, y=y) #' #' # simplex projecton #' out0 <- simplex(block, lib_var="x", cond_var="y", E=0:8, tau=1, tp=1) #' out1 <- simplex(block, lib_var="y", cond_var="x", E=0:8, tau=1, tp=1) #' par(mfrow=c(2, 1)) #' with(out0, plot(E, te, type="b", pch=c(1,16)[1+(pval<0.05)])) #' with(out1, plot(E, te, type="b", pch=c(1,16)[1+(pval<0.05)])) #' simplex = function ( block, lib = c(1, NROW(block)), pred = lib, group = NULL, lib_var = 1, cond_var = NULL, norm = 2, E = 1, tau = 1, tp = 0, nn = "e+1", num_surr = 1000, alpha = NULL, exclusion_radius = NULL, epsilon = NULL, is_naive = FALSE, knn_method = c("KD","BF")) { if (norm < 1) stop("norm must be >= 1.") lib <- rbind(lib) pred <- rbind(pred) if (length(group) == 0) Group <- rep(1, nrow(block)) if (length(group) != 0) Group <- as.numeric(as.factor(block[,group[1]])) E <- sort(unique(pmax(0, E))) tau <- unique(pmax(1, tau)) tp <- unique(tp) p <- ifelse(is.finite(norm), norm, 0) num_surr <- pmax(0, num_surr) KNN <- switch(match.arg(knn_method), "KD"=0, "BF"=1) if (!is.numeric(nn) & tolower(nn) == "e+1") nn <- 0 if (nn < 0) nn <- -1 if (is.null(alpha)) alpha <- 1 if (is.null(exclusion_radius)) exclusion_radius <- 0 if (is.null(epsilon)) epsilon <- -1 X <- as.matrix(block[ lib_var]) Z <- as.matrix(block[cond_var]) if (alpha >= 1 \|\| num_surr == 0) { out <- .Call(`_rUIC_npmodel_R`, X, X, Z, Group, lib, pred, E, E-1, tau, tp, nn, p, num_surr, exclusion_radius, epsilon, is_naive, 0, KNN) } else { out <- NULL for (tpi in tp) for (taui in tau) { E0 <- 0 for (Ei in E) { outi <- .Call(`_rUIC_npmodel_R`, X, X, Z, Group, lib, pred, Ei, E0, taui, tpi, nn, p, num_surr, exclusion_radius, epsilon, is_naive, 0, KNN) if(outi$pval < alpha) E0 <- Ei out <- rbind(out, outi) } } } return(out) } # End	/R/simplex.R	no_license	yutakaos/rUIC	R	false	false	4,385	r	#' Simplex Projection #' #' \code{simplex} returns model statistics computed from given multiple time series #' using simplex projection. This function performs simplex projections for all #' possible combinations of \code{E}, \code{tau} and \code{tp}. #' #' @inheritParams uic #' @param lib_var #' the name or column index of a library (and target) variable. #' The specified variable is used as a response variable and its time-delay variables are #' used as explanatory variables. #' @param alpha #' the significant level to determine the embedding dimension of reference model #' (i.e., E0). If \code{alpha = NULL}, E0 is set to E - 1. If \code{0 < alpha < 1} #' E0 depends on the model results with lower embedding dimensions. #' #' @details #' Transfer entropy is computed as follows: #' \deqn{ #' \sum_{t} log p(X_{t+tp} \| X_{t}, X_{t-\tau}, \ldots, X_{t-(E -1)\tau}, Z_{t}) - #' log p(X_{t+tp} \| X_{t}, X_{t-\tau}, \ldots, X_{t-(E0-1)\tau}, Z_{t}) #' } #' where \eqn{X} and \eqn{Z} are library and condition variables, respectively. #' #' @return #' A data.frame where each row represents model statistics computed from a parameter set. #' \tabular{ll}{ #' \code{E} \tab \code{:} embedding dimension \cr #' \code{E0} \tab \code{:} embedding dimension of reference model \cr #' \code{tau} \tab \code{:} time-lag \cr #' \code{tp} \tab \code{:} time prediction horizon \cr #' \code{nn} \tab \code{:} number of nearest neighbors \cr #' \code{n_lib} \tab \code{:} number of time indices used for attractor reconstruction \cr #' \code{n_pred} \tab \code{:} number of time indices used for model predictions \cr #' \code{rmse} \tab \code{:} root mean squared error \cr #' \code{te} \tab \code{:} transfer entropy \cr #' \code{ete} \tab \code{:} effective transfer entropy \cr #' \code{pval} \tab \code{:} p-value to test alternative hypothesis, te > 0 \cr #' \code{n_surr} \tab \code{:} number of surrogate data \cr #' } #' #' @seealso \link{xmap}, \link{uic} #' #' @examples #' # simulate logistic map #' tl <- 400 # time length #' x <- y <- rep(NA, tl) #' x[1] <- 0.4 #' y[1] <- 0.2 #' for (t in 1:(tl - 1)) { # causality : x -> y #' x[t+1] = x[t] * (3.8 - 3.8 * x[t] - 0.0 * y[t]) #' y[t+1] = y[t] * (3.5 - 3.5 * y[t] - 0.1 * x[t]) #' } #' block <- data.frame(t=1:tl, x=x, y=y) #' #' # simplex projecton #' out0 <- simplex(block, lib_var="x", cond_var="y", E=0:8, tau=1, tp=1) #' out1 <- simplex(block, lib_var="y", cond_var="x", E=0:8, tau=1, tp=1) #' par(mfrow=c(2, 1)) #' with(out0, plot(E, te, type="b", pch=c(1,16)[1+(pval<0.05)])) #' with(out1, plot(E, te, type="b", pch=c(1,16)[1+(pval<0.05)])) #' simplex = function ( block, lib = c(1, NROW(block)), pred = lib, group = NULL, lib_var = 1, cond_var = NULL, norm = 2, E = 1, tau = 1, tp = 0, nn = "e+1", num_surr = 1000, alpha = NULL, exclusion_radius = NULL, epsilon = NULL, is_naive = FALSE, knn_method = c("KD","BF")) { if (norm < 1) stop("norm must be >= 1.") lib <- rbind(lib) pred <- rbind(pred) if (length(group) == 0) Group <- rep(1, nrow(block)) if (length(group) != 0) Group <- as.numeric(as.factor(block[,group[1]])) E <- sort(unique(pmax(0, E))) tau <- unique(pmax(1, tau)) tp <- unique(tp) p <- ifelse(is.finite(norm), norm, 0) num_surr <- pmax(0, num_surr) KNN <- switch(match.arg(knn_method), "KD"=0, "BF"=1) if (!is.numeric(nn) & tolower(nn) == "e+1") nn <- 0 if (nn < 0) nn <- -1 if (is.null(alpha)) alpha <- 1 if (is.null(exclusion_radius)) exclusion_radius <- 0 if (is.null(epsilon)) epsilon <- -1 X <- as.matrix(block[ lib_var]) Z <- as.matrix(block[cond_var]) if (alpha >= 1 \|\| num_surr == 0) { out <- .Call(`_rUIC_npmodel_R`, X, X, Z, Group, lib, pred, E, E-1, tau, tp, nn, p, num_surr, exclusion_radius, epsilon, is_naive, 0, KNN) } else { out <- NULL for (tpi in tp) for (taui in tau) { E0 <- 0 for (Ei in E) { outi <- .Call(`_rUIC_npmodel_R`, X, X, Z, Group, lib, pred, Ei, E0, taui, tpi, nn, p, num_surr, exclusion_radius, epsilon, is_naive, 0, KNN) if(outi$pval < alpha) E0 <- Ei out <- rbind(out, outi) } } } return(out) } # End
testlist <- list(AgeVector = c(-4.73074171454048e-167, 2.2262381097027e-76, -9.12990429452974e-204, 5.97087417427845e-79, 4.7390525269307e-300, 6.58361441690132e-121, 3.58611068565168e-154, -2.94504776827523e-186, 2.62380314702636e-116, -6.78950518864266e+23, 6.99695749856012e-167, 86485.676793021, 1.11271562183704e+230, 1.94114173595984e-186, 1.44833381226225e-178, -6.75217876587581e-69, 1.17166524186752e-15, -4.66902120197297e-64, -1.96807327384856e+304, 4.43806122192432e-53, 9.29588680224717e-276, -6.49633240047463e-239, -1.22140819059424e-138, 5.03155164774999e-80, -6.36956558303921e-38, 1.11830391696877e-156, -1.05546603899445e-274, -3.66720914317747e-169, -6.94681701552128e+38, 2.93126040859825e-33, 2.03804078100055e-84, 3.62794352816579e+190, 3.84224576683191e+202, 2.90661893502594e+44, -5.43046915655589e-132, -1.22315376742253e-152), ExpressionMatrix = structure(c(4.80597147865938e+96, 6.97343932706536e+155, 1.3267342810479e+281, 1.34663897260867e+171, 1.76430141680543e+158, 1.20021255064002e-241, 1.72046093489436e+274, 4.64807629890539e-66, 3.23566990107388e-38, 3.70896378162114e-42, 1.09474740380531e+92, 7.49155705745727e-308, 3.26639180474928e+224, 3.21841801500177e-79, 4.26435540037564e-295, 1.40002857639358e+82, 47573397570345336, 2.00517157311369e-187, 2.74035572944044e+70, 2.89262435086883e-308, 6.65942057982148e-198, 1.10979548758712e-208, 1.40208057226312e-220, 6.25978904299555e-111, 1.06191688875218e+167, 1.1857452172049, 7.01135380962132e-157, 4.49610615342627e-308, 8.04053421408348e+261, 6.23220855980985e+275, 1.91601752509744e+141, 2.27737212344351e-244, 1.6315101795754e+126, 3.83196182917788e+160, 1.53445011275161e-192), .Dim = c(5L, 7L)), permutations = 415362983L) result <- do.call(myTAI:::cpp_bootMatrix,testlist) str(result)	/myTAI/inst/testfiles/cpp_bootMatrix/AFL_cpp_bootMatrix/cpp_bootMatrix_valgrind_files/1615767180-test.R	no_license	akhikolla/updatedatatype-list3	R	false	false	1,803	r	testlist <- list(AgeVector = c(-4.73074171454048e-167, 2.2262381097027e-76, -9.12990429452974e-204, 5.97087417427845e-79, 4.7390525269307e-300, 6.58361441690132e-121, 3.58611068565168e-154, -2.94504776827523e-186, 2.62380314702636e-116, -6.78950518864266e+23, 6.99695749856012e-167, 86485.676793021, 1.11271562183704e+230, 1.94114173595984e-186, 1.44833381226225e-178, -6.75217876587581e-69, 1.17166524186752e-15, -4.66902120197297e-64, -1.96807327384856e+304, 4.43806122192432e-53, 9.29588680224717e-276, -6.49633240047463e-239, -1.22140819059424e-138, 5.03155164774999e-80, -6.36956558303921e-38, 1.11830391696877e-156, -1.05546603899445e-274, -3.66720914317747e-169, -6.94681701552128e+38, 2.93126040859825e-33, 2.03804078100055e-84, 3.62794352816579e+190, 3.84224576683191e+202, 2.90661893502594e+44, -5.43046915655589e-132, -1.22315376742253e-152), ExpressionMatrix = structure(c(4.80597147865938e+96, 6.97343932706536e+155, 1.3267342810479e+281, 1.34663897260867e+171, 1.76430141680543e+158, 1.20021255064002e-241, 1.72046093489436e+274, 4.64807629890539e-66, 3.23566990107388e-38, 3.70896378162114e-42, 1.09474740380531e+92, 7.49155705745727e-308, 3.26639180474928e+224, 3.21841801500177e-79, 4.26435540037564e-295, 1.40002857639358e+82, 47573397570345336, 2.00517157311369e-187, 2.74035572944044e+70, 2.89262435086883e-308, 6.65942057982148e-198, 1.10979548758712e-208, 1.40208057226312e-220, 6.25978904299555e-111, 1.06191688875218e+167, 1.1857452172049, 7.01135380962132e-157, 4.49610615342627e-308, 8.04053421408348e+261, 6.23220855980985e+275, 1.91601752509744e+141, 2.27737212344351e-244, 1.6315101795754e+126, 3.83196182917788e+160, 1.53445011275161e-192), .Dim = c(5L, 7L)), permutations = 415362983L) result <- do.call(myTAI:::cpp_bootMatrix,testlist) str(result)
#' @keywords internal deconvolve_inputs <- function(inputs, sep, header, out, control, njobs) { ## Helper function to deconvolve all inputs prior to running GIMME #require(parallel) #should be moved to import in NAMESPACE for R package #require(foreach) #require(doSNOW) #require(doParallel) if (missing(njobs)) { njobs <- parallel::detectCores(logical=TRUE) #on hyperthreading CPUs, use all threads (typically 2/core) message("In deconvolve_inputs, njobs not specified. Defaulting to number of cores: ", njobs, ".") } if (njobs > 1) { setDefaultClusterOptions(master="localhost") clusterobj <- makeSOCKcluster(njobs) registerDoSNOW(clusterobj) #clusterobj <- makeCluster(njobs) #registerDoParallel(clusterobj) on.exit(try(stopCluster(clusterobj))) } stopifnot(is.numeric(control$TR) && control$TR > 0) #user must specify TR of data for deconvolution to be well specified if (!exists('method', where=control)) { message("Defaulting to Bush and Cisler 2013 deconvolution") control$method <- "bush" } if (control$method == "bush") { if (!exists('nev_lr', where=control)) { message("nev_lr (learning rate) not specified for Bush deconvolution. Default to .01") control$nev_lr <- .01 #neural events learning rate } if (!exists('epsilon', where=control)) { message("epsilon not specified for Bush deconvolution. Default to .005") control$epsilon <- .005 #convergence criterion } if (!exists('kernel', where=control)) { message("HRF kernel not specified for Bush deconvolution. Default to spm double gamma") control$kernel <- spm_hrf(control$TR)$hrf #default to canonical SPM difference of gammas with specified TR } } if (control$method == "wu") { if (!exists('threshold', where=control)) { message("Activation threshold not specified for Wu deconvolution. Default to 1.0 SD") control$threshold <- 1.0 #SD } if (!exists('max_lag', where=control)) { message("Maximum event-to-neural onset lag not specified for Wu deconvolution. Default to 10 seconds.") control$max_lag = ceiling(10/control$TR) #10 seconds maximum lag from neural event to HRF onset } } ##now proceed on deconvolution stopifnot(file.exists(out)) deconv.dir <- file.path(out, "deconvolved_inputs") dir.create(deconv.dir, showWarnings=FALSE) #deconvolve inputs in parallel (over subjects/inputs) #for (f in inputs) { #using doSNOW, need to explicitly export functions and subfunctions for deconvolution to each worker exportFuncs <- c("deconvolve_nlreg", "sigmoid", "dsigmoid", "generate_feature", "wgr_deconv_canonhrf_par", "wgr_adjust_onset", "wgr_trigger_onset", "CanonicalBasisSet", "Fit_Canonical_HRF", "get_parameters2", "spm_hrf", "spm_get_bf", "spm_gamma_bf", "spm_orth") f = NULL files <- foreach(f=inputs, .combine=c, .export=exportFuncs, .inorder=TRUE) %dopar% { stopifnot(file.exists(f)) d <- as.matrix(read.table(f, header=header, sep=sep)) if (control$method == "wu") { d_deconv <- wgr_deconv_canonhrf_par(d, thr=control$threshold, event_lag_max=control$max_lag, TR=control$TR)$data_deconv #already supports multi-variable (column) deconvolution } else if (control$method == "bush") { #parApply? Already parallel above, so would need to rework this or do the %:% nested parallel d_deconv <- apply(d, 2, deconvolve_nlreg, kernel=control$kernel, nev_lr=control$nev_lr, epsilon=control$epsilon) } outfile <- file.path(deconv.dir, paste0(tools::file_path_sans_ext(basename(f)), "_deconvolve.txt")) write.table(d_deconv, file=outfile, sep=sep, quote=FALSE, row.names=FALSE) return(outfile) } return(files) } ## R versions of deconvolution algorithms for testing in GIMME ## Ported from MATLAB by Michael Hallquist, September 2015 ## R port of Bush and Cisler 2013, Magnetic Resonance Imaging ## Adapted from the original provided by Keith Bush ## Author: Keith Bush, PhD ## Institution: University of Arkansas at Little Rock ## Date: Aug. 9, 2013 deconvolve_nlreg <- function(BOLDobs, kernel, nev_lr=.01, epsilon=.005) { ## Description: ## This function deconvolves the BOLD signal using Bush 2011 method ## ## Inputs: ## BOLDobs - observed BOLD timeseries ## kernel - assumed kernel of the BOLD signal ## nev_lr - learning rate for the assignment of neural events ## epsilon - relative error change (termination condition) ## ## Outputs: ## encoding - reconstructed neural events ## Determine time series length N = length(BOLDobs) ##Calc simulation steps related to simulation time K = length(kernel) A = K - 1 + N ##Termination Params preverror = 1e9 #previous error currerror = 0 #current error ##Construct random activation vector (fluctuate slightly around zero between -2e-9 and 2e-9) activation = rep(2e-9, A)runif(A) - 1e-9 ##Presolve activations to fit target_adjust as encoding max_hrf_id_adjust = which.max(kernel) - 1 #element of kernel 1 before max BOLDobs_adjust = BOLDobs[max_hrf_id_adjust:N] pre_encoding = BOLDobs_adjust - min(BOLDobs_adjust) pre_encoding = pre_encoding/max(pre_encoding) #unit normalize encoding = pre_encoding activation[K:(K-1 + length(BOLDobs_adjust))] = log(pre_encoding/(1-pre_encoding)) while (abs(preverror-currerror) > epsilon) { ##Compute encoding vector encoding = sigmoid(activation) ##Construct feature space feature = generate_feature(encoding,K) ##Generate virtual bold response by multiplying feature (N x K) by kernel (K x 1) to get N x 1 estimated response ytilde = feature[K:nrow(feature),] %% kernel ##Convert to percent signal change meanCurrent = mean(ytilde) brf = ytilde - meanCurrent brf = brf/meanCurrent ##Compute dEdbrf dEdbrf = brf - BOLDobs ##Assume normalization does not impact deriv much. dEdy = dEdbrf ##Precompute derivative components dEde = diag(K) %% kernel back_error = c(rep(0, K-1), dEdy, rep(0, K-1)) ##Backpropagate Errors delta = c() for (i in 1:A) { active = activation[i] deda = dsigmoid(active); dEda = dEde deda this_error = back_error[i:(i-1+K)] delta = c(delta, sum(dEda * this_error)) } ##Update estimate activation = activation - nev_lr * delta ##Iterate Learning preverror = currerror currerror = sum(dEdbrf^2) } ## remove the initial timepoints corresponding to HRF (so that returned signal matches in time and length) encoding <- encoding[K:length(encoding)] return(encoding) } ## Support functions sigmoid <- function(x) { y <- 1/(1+exp(-x)) return(y) } dsigmoid <- function(x) { y=(1-sigmoid(x))sigmoid(x) return(y) } generate_feature <- function(encoding, K) { fmatrix = matrix(0, length(encoding), K) fmatrix[,1] = encoding for (i in 2:K) { fmatrix[,i] = c(rep(0, i-1), encoding[1:(length(encoding) - (i-1))]) } return(fmatrix) } ##### ## Wu code ## R port of Wu et al. 2013, Medical Image Analysis ## Adapted from the original provided by Daniele Marinazzo wgr_deconv_canonhrf_par <- function(data, thr=1.0, event_lag_max, TR) { ### function [data_deconv event HRF adjust_global PARA] = wgr_deconv_canonhrf_par(data,thr,event_lag_max,TR) ### this function implements the method described in ### Wu et al, ### A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data, ### Med Image Anal. 2013 Jan 29. pii: S1361-8415(13)00004-2. doi: 10.1016/j.media.2013.01.003 ### input ### data, dimensions time points x number of voxels, normalized ### threshold, assuming data are normalized ### event_lag_max: maximum time from neural event to BOLD event in bins, not time ### (e.g. if we assume 10seconds, and TR=2s, set the value to 10/2=5) ### TR is the TR parameter, in seconds. ### Some parts of the code (subfunction: Fit_Canonical_HRF, CanonicalBasisSet, get_parameters2) were modified from the hemodynamic response estimation toolbox(http://www.stat.columbia.edu/~martin/HRF_Est_Toolbox.zip). ### ### the code uses the parallel for loop parfor. In case of older matlab versions, parfor can be changed to standard for. ### ### The default is using canonical hrf and two derivatives, as described in the paper. ### The function can be modified to use instead point processes, FIR model, etc. ##force data to explicit matrix form (time x variables) for proper looping ##this will allow data to be passed in as a 1-D vector for single variable problems if (!inherits(data, "matrix")) { data <- matrix(data, ncol=1) } N = nrow(data); nvar = ncol(data) even_new = wgr_trigger_onset(data,thr) p_m=3 #define what HRF to fit ## Options: p_m=1 - only canonical HRF ## p_m=2 - canonical + temporal derivative ## p_m=3 - canonical + time and dispersion derivative T = round(30/TR) ## assume HRF effect lasts 30s. data_deconv = matrix(0, nrow=N, ncol=nvar) HRF = matrix(0, nrow=T, ncol=nvar) PARA = matrix(0, nrow=3, ncol=nvar) event = list() event_lag <- rep(0, nvar) ##warning off ##can parallelize over nvar for (i in 1:nvar) { out = wgr_adjust_onset(data[,i], even_new[[i]], event_lag_max, TR, p_m, T, N) data_deconv[,i] <- out$data_deconv HRF[,i] <- out$hrf event[[i]] <- out$events event_lag[i] <- out$event_lag PARA[,i] <- out$param } ##warning on return(list(data_deconv=data_deconv, event=event, HRF=HRF, event_lag=event_lag, PARA=PARA)) } wgr_adjust_onset <- function(dat,even_new,event_lag_max,TR,p_m,T,N) { ## global adjust. kk=1 #this is just a 1-based version of the loop iterator event_lag... hrf = matrix(NA_real_, nrow=T, ncol=event_lag_max+1) param = matrix(NA_real_, nrow=p_m, ncol=event_lag_max+1) Cov_E = rep(NA_real_, event_lag_max+1) for (event_lag in 0:event_lag_max) { RR = even_new - event_lag; RR = RR[RR >= 0] design = matrix(0, nrow=N, ncol=1) design[RR,1] = 1 #add pseudo-events to design matrix fit = Fit_Canonical_HRF(dat,TR,design,T,p_m); hrf[,kk] <- fit$hrf param[,kk] <- fit$param Cov_E[kk] <- cov(fit$e) #covariance of residual kk = kk+1; } C = min(Cov_E) ind = which.min(Cov_E) ad_global = ind - 1 #begin with 0. even_new = even_new - ad_global even_new = even_new[even_new>=0] hrf = hrf[,ind] #keep only best HRF (minimize error of pseudo-event timing) param = param[,ind] #keep only best params ## linear deconvolution. H = fft(c(hrf, rep(0, N-T))) ## H=fft([hrf; zeros(N-T,1)]); M = fft(dat) data_deconv = Re(fft(Conj(H)M/(HConj(H)+C), inverse=TRUE)/length(H)) ## only keep real part -- there is a tiny imaginary residue in R return(list(data_deconv=data_deconv, hrf=hrf, events=even_new, event_lag=ad_global, param=param)) } wgr_trigger_onset <- function(mat, thr) { ##function [oneset] = wgr_trigger_onset(mat,thr) N = nrow(mat); nvar = ncol(mat) mat = apply(mat, 2, scale) #z-score columns oneset <- list() ## Computes pseudo event. for (i in 1:nvar) { oneset_temp = c() for (t in 2:(N-1)) { if (mat[t,i] > thr && mat[t-1,i] < mat[t,i] && mat[t,i] > mat[t+1,i]) { ## detects threshold oneset_temp = c(oneset_temp, t) } } oneset[[i]] = oneset_temp } return(oneset) } ##### ## Helper functions for Wu deconvolution algorithm ## Original code from Lindquist and Wager HRF Toolbox CanonicalBasisSet <- function(TR) { len = round(30/TR) # 30 secs worth of images xBF <- list() xBF$dt = TR xBF$length= len xBF$name = 'hrf (with time and dispersion derivatives)' xBF = spm_get_bf(xBF) v1 = xBF$bf[1:len,1] v2 = xBF$bf[1:len,2] v3 = xBF$bf[1:len,3] ## orthogonalize h = v1 dh = v2 - (v2 %% v1/norm(v1, "2")^2)v1 dh2 = v3 - (v3 %% v1/norm(v1, "2")^2)v1 - (v3 %% dh/norm(dh, "2")^2)dh ## normalize amplitude h = h/max(h) dh = dh/max(dh) dh2 = dh2/max(dh2) return(list(h=h, dh=dh, dh2=dh2)) } Fit_Canonical_HRF <- function(tc, TR, Run, T, p) { ##function [hrf, e, param] = Fit_Canonical_HRF(tc,TR,Run,T,p) ## ## Fits GLM using canonical hrf (with option of using time and dispersion derivatives)'; ## ## INPUTS: ## ## tc - time course ## TR - time resolution ## Runs - expermental design ## T - length of estimated HRF ## p - Model type ## ## Options: p=1 - only canonical HRF ## p=2 - canonical + temporal derivative ## p=3 - canonical + time and dispersion derivative ## ## OUTPUTS: ## ## hrf - estimated hemodynamic response function ## fit - estimated time course ## e - residual time course ## param - estimated amplitude, height and width len = length(Run) X = matrix(0, nrow=len, ncol=p) bf = CanonicalBasisSet(TR) h = bf$h; dh = bf$dh; dh2 = bf$dh2 v = convolve(Run,rev(h), type="open") #this is the R equivalent of conv(Run, h) X[,1] = v[1:len] if (p > 1) { v = convolve(Run,rev(dh), type="open") X[,2] = v[1:len] } if (p > 2) { v = convolve(Run,rev(dh2), type="open") X[,3] = v[1:len] } X = cbind(rep(1, len), X) #add intercept b = MASS::ginv(X) %% tc e = tc - X %% b fit = X %% b b = b[2:length(b)] if (p == 2) { bc = sign(b[1])sqrt(b[1]^2 + b[2]^2) H = cbind(h, dh) } else if (p == 1) { bc = b[1] H = matrix(h, ncol=1) } else if (p>2) { bc = sign(b[1])sqrt(b[1]^2 + b[2]^2 + b[3]^2) H = cbind(h, dh, dh2) } hrf = H %% b param = get_parameters2(hrf,T) return(list(hrf=hrf, e=e, param=param)) } get_parameters2 <- function(hdrf, t) { ##function [param] = get_parameters2(hdrf,t) ## Find model parameters ## ## Height - h ## Time to peak - p (in time units of TR seconds) ## Width (at half peak) - w ## Calculate Heights and Time to peak: ## n = t(end)0.6; n = round(t0.8) p = which.max(abs(hdrf[1:n])) h = hdrf[p] ##if (p > t(end)0.6), warning('Late time to peak'), end; if (h > 0) { v = as.numeric(hdrf >= h/2) } else { v = as.numeric(hdrf <= h/2) } b = which.min(diff(v)) v[(b+1):length(v)] = 0 w = sum(v) cnt = p-1 g = hdrf[2:length(hdrf)] - hdrf[1:(length(hdrf)-1)] while(cnt > 0 && abs(g[cnt]) < 0.001) { h = hdrf[cnt] p = cnt cnt = cnt-1 } param = rep(0,3) param[1] = h param[2] = p param[3] = w return(param) }	/gimme/R/deconvolve_funcs.R	no_license	stlane/gimme	R	false	false	14,766	r	#' @keywords internal deconvolve_inputs <- function(inputs, sep, header, out, control, njobs) { ## Helper function to deconvolve all inputs prior to running GIMME #require(parallel) #should be moved to import in NAMESPACE for R package #require(foreach) #require(doSNOW) #require(doParallel) if (missing(njobs)) { njobs <- parallel::detectCores(logical=TRUE) #on hyperthreading CPUs, use all threads (typically 2/core) message("In deconvolve_inputs, njobs not specified. Defaulting to number of cores: ", njobs, ".") } if (njobs > 1) { setDefaultClusterOptions(master="localhost") clusterobj <- makeSOCKcluster(njobs) registerDoSNOW(clusterobj) #clusterobj <- makeCluster(njobs) #registerDoParallel(clusterobj) on.exit(try(stopCluster(clusterobj))) } stopifnot(is.numeric(control$TR) && control$TR > 0) #user must specify TR of data for deconvolution to be well specified if (!exists('method', where=control)) { message("Defaulting to Bush and Cisler 2013 deconvolution") control$method <- "bush" } if (control$method == "bush") { if (!exists('nev_lr', where=control)) { message("nev_lr (learning rate) not specified for Bush deconvolution. Default to .01") control$nev_lr <- .01 #neural events learning rate } if (!exists('epsilon', where=control)) { message("epsilon not specified for Bush deconvolution. Default to .005") control$epsilon <- .005 #convergence criterion } if (!exists('kernel', where=control)) { message("HRF kernel not specified for Bush deconvolution. Default to spm double gamma") control$kernel <- spm_hrf(control$TR)$hrf #default to canonical SPM difference of gammas with specified TR } } if (control$method == "wu") { if (!exists('threshold', where=control)) { message("Activation threshold not specified for Wu deconvolution. Default to 1.0 SD") control$threshold <- 1.0 #SD } if (!exists('max_lag', where=control)) { message("Maximum event-to-neural onset lag not specified for Wu deconvolution. Default to 10 seconds.") control$max_lag = ceiling(10/control$TR) #10 seconds maximum lag from neural event to HRF onset } } ##now proceed on deconvolution stopifnot(file.exists(out)) deconv.dir <- file.path(out, "deconvolved_inputs") dir.create(deconv.dir, showWarnings=FALSE) #deconvolve inputs in parallel (over subjects/inputs) #for (f in inputs) { #using doSNOW, need to explicitly export functions and subfunctions for deconvolution to each worker exportFuncs <- c("deconvolve_nlreg", "sigmoid", "dsigmoid", "generate_feature", "wgr_deconv_canonhrf_par", "wgr_adjust_onset", "wgr_trigger_onset", "CanonicalBasisSet", "Fit_Canonical_HRF", "get_parameters2", "spm_hrf", "spm_get_bf", "spm_gamma_bf", "spm_orth") f = NULL files <- foreach(f=inputs, .combine=c, .export=exportFuncs, .inorder=TRUE) %dopar% { stopifnot(file.exists(f)) d <- as.matrix(read.table(f, header=header, sep=sep)) if (control$method == "wu") { d_deconv <- wgr_deconv_canonhrf_par(d, thr=control$threshold, event_lag_max=control$max_lag, TR=control$TR)$data_deconv #already supports multi-variable (column) deconvolution } else if (control$method == "bush") { #parApply? Already parallel above, so would need to rework this or do the %:% nested parallel d_deconv <- apply(d, 2, deconvolve_nlreg, kernel=control$kernel, nev_lr=control$nev_lr, epsilon=control$epsilon) } outfile <- file.path(deconv.dir, paste0(tools::file_path_sans_ext(basename(f)), "_deconvolve.txt")) write.table(d_deconv, file=outfile, sep=sep, quote=FALSE, row.names=FALSE) return(outfile) } return(files) } ## R versions of deconvolution algorithms for testing in GIMME ## Ported from MATLAB by Michael Hallquist, September 2015 ## R port of Bush and Cisler 2013, Magnetic Resonance Imaging ## Adapted from the original provided by Keith Bush ## Author: Keith Bush, PhD ## Institution: University of Arkansas at Little Rock ## Date: Aug. 9, 2013 deconvolve_nlreg <- function(BOLDobs, kernel, nev_lr=.01, epsilon=.005) { ## Description: ## This function deconvolves the BOLD signal using Bush 2011 method ## ## Inputs: ## BOLDobs - observed BOLD timeseries ## kernel - assumed kernel of the BOLD signal ## nev_lr - learning rate for the assignment of neural events ## epsilon - relative error change (termination condition) ## ## Outputs: ## encoding - reconstructed neural events ## Determine time series length N = length(BOLDobs) ##Calc simulation steps related to simulation time K = length(kernel) A = K - 1 + N ##Termination Params preverror = 1e9 #previous error currerror = 0 #current error ##Construct random activation vector (fluctuate slightly around zero between -2e-9 and 2e-9) activation = rep(2e-9, A)runif(A) - 1e-9 ##Presolve activations to fit target_adjust as encoding max_hrf_id_adjust = which.max(kernel) - 1 #element of kernel 1 before max BOLDobs_adjust = BOLDobs[max_hrf_id_adjust:N] pre_encoding = BOLDobs_adjust - min(BOLDobs_adjust) pre_encoding = pre_encoding/max(pre_encoding) #unit normalize encoding = pre_encoding activation[K:(K-1 + length(BOLDobs_adjust))] = log(pre_encoding/(1-pre_encoding)) while (abs(preverror-currerror) > epsilon) { ##Compute encoding vector encoding = sigmoid(activation) ##Construct feature space feature = generate_feature(encoding,K) ##Generate virtual bold response by multiplying feature (N x K) by kernel (K x 1) to get N x 1 estimated response ytilde = feature[K:nrow(feature),] %% kernel ##Convert to percent signal change meanCurrent = mean(ytilde) brf = ytilde - meanCurrent brf = brf/meanCurrent ##Compute dEdbrf dEdbrf = brf - BOLDobs ##Assume normalization does not impact deriv much. dEdy = dEdbrf ##Precompute derivative components dEde = diag(K) %% kernel back_error = c(rep(0, K-1), dEdy, rep(0, K-1)) ##Backpropagate Errors delta = c() for (i in 1:A) { active = activation[i] deda = dsigmoid(active); dEda = dEde deda this_error = back_error[i:(i-1+K)] delta = c(delta, sum(dEda * this_error)) } ##Update estimate activation = activation - nev_lr * delta ##Iterate Learning preverror = currerror currerror = sum(dEdbrf^2) } ## remove the initial timepoints corresponding to HRF (so that returned signal matches in time and length) encoding <- encoding[K:length(encoding)] return(encoding) } ## Support functions sigmoid <- function(x) { y <- 1/(1+exp(-x)) return(y) } dsigmoid <- function(x) { y=(1-sigmoid(x))sigmoid(x) return(y) } generate_feature <- function(encoding, K) { fmatrix = matrix(0, length(encoding), K) fmatrix[,1] = encoding for (i in 2:K) { fmatrix[,i] = c(rep(0, i-1), encoding[1:(length(encoding) - (i-1))]) } return(fmatrix) } ##### ## Wu code ## R port of Wu et al. 2013, Medical Image Analysis ## Adapted from the original provided by Daniele Marinazzo wgr_deconv_canonhrf_par <- function(data, thr=1.0, event_lag_max, TR) { ### function [data_deconv event HRF adjust_global PARA] = wgr_deconv_canonhrf_par(data,thr,event_lag_max,TR) ### this function implements the method described in ### Wu et al, ### A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data, ### Med Image Anal. 2013 Jan 29. pii: S1361-8415(13)00004-2. doi: 10.1016/j.media.2013.01.003 ### input ### data, dimensions time points x number of voxels, normalized ### threshold, assuming data are normalized ### event_lag_max: maximum time from neural event to BOLD event in bins, not time ### (e.g. if we assume 10seconds, and TR=2s, set the value to 10/2=5) ### TR is the TR parameter, in seconds. ### Some parts of the code (subfunction: Fit_Canonical_HRF, CanonicalBasisSet, get_parameters2) were modified from the hemodynamic response estimation toolbox(http://www.stat.columbia.edu/~martin/HRF_Est_Toolbox.zip). ### ### the code uses the parallel for loop parfor. In case of older matlab versions, parfor can be changed to standard for. ### ### The default is using canonical hrf and two derivatives, as described in the paper. ### The function can be modified to use instead point processes, FIR model, etc. ##force data to explicit matrix form (time x variables) for proper looping ##this will allow data to be passed in as a 1-D vector for single variable problems if (!inherits(data, "matrix")) { data <- matrix(data, ncol=1) } N = nrow(data); nvar = ncol(data) even_new = wgr_trigger_onset(data,thr) p_m=3 #define what HRF to fit ## Options: p_m=1 - only canonical HRF ## p_m=2 - canonical + temporal derivative ## p_m=3 - canonical + time and dispersion derivative T = round(30/TR) ## assume HRF effect lasts 30s. data_deconv = matrix(0, nrow=N, ncol=nvar) HRF = matrix(0, nrow=T, ncol=nvar) PARA = matrix(0, nrow=3, ncol=nvar) event = list() event_lag <- rep(0, nvar) ##warning off ##can parallelize over nvar for (i in 1:nvar) { out = wgr_adjust_onset(data[,i], even_new[[i]], event_lag_max, TR, p_m, T, N) data_deconv[,i] <- out$data_deconv HRF[,i] <- out$hrf event[[i]] <- out$events event_lag[i] <- out$event_lag PARA[,i] <- out$param } ##warning on return(list(data_deconv=data_deconv, event=event, HRF=HRF, event_lag=event_lag, PARA=PARA)) } wgr_adjust_onset <- function(dat,even_new,event_lag_max,TR,p_m,T,N) { ## global adjust. kk=1 #this is just a 1-based version of the loop iterator event_lag... hrf = matrix(NA_real_, nrow=T, ncol=event_lag_max+1) param = matrix(NA_real_, nrow=p_m, ncol=event_lag_max+1) Cov_E = rep(NA_real_, event_lag_max+1) for (event_lag in 0:event_lag_max) { RR = even_new - event_lag; RR = RR[RR >= 0] design = matrix(0, nrow=N, ncol=1) design[RR,1] = 1 #add pseudo-events to design matrix fit = Fit_Canonical_HRF(dat,TR,design,T,p_m); hrf[,kk] <- fit$hrf param[,kk] <- fit$param Cov_E[kk] <- cov(fit$e) #covariance of residual kk = kk+1; } C = min(Cov_E) ind = which.min(Cov_E) ad_global = ind - 1 #begin with 0. even_new = even_new - ad_global even_new = even_new[even_new>=0] hrf = hrf[,ind] #keep only best HRF (minimize error of pseudo-event timing) param = param[,ind] #keep only best params ## linear deconvolution. H = fft(c(hrf, rep(0, N-T))) ## H=fft([hrf; zeros(N-T,1)]); M = fft(dat) data_deconv = Re(fft(Conj(H)M/(HConj(H)+C), inverse=TRUE)/length(H)) ## only keep real part -- there is a tiny imaginary residue in R return(list(data_deconv=data_deconv, hrf=hrf, events=even_new, event_lag=ad_global, param=param)) } wgr_trigger_onset <- function(mat, thr) { ##function [oneset] = wgr_trigger_onset(mat,thr) N = nrow(mat); nvar = ncol(mat) mat = apply(mat, 2, scale) #z-score columns oneset <- list() ## Computes pseudo event. for (i in 1:nvar) { oneset_temp = c() for (t in 2:(N-1)) { if (mat[t,i] > thr && mat[t-1,i] < mat[t,i] && mat[t,i] > mat[t+1,i]) { ## detects threshold oneset_temp = c(oneset_temp, t) } } oneset[[i]] = oneset_temp } return(oneset) } ##### ## Helper functions for Wu deconvolution algorithm ## Original code from Lindquist and Wager HRF Toolbox CanonicalBasisSet <- function(TR) { len = round(30/TR) # 30 secs worth of images xBF <- list() xBF$dt = TR xBF$length= len xBF$name = 'hrf (with time and dispersion derivatives)' xBF = spm_get_bf(xBF) v1 = xBF$bf[1:len,1] v2 = xBF$bf[1:len,2] v3 = xBF$bf[1:len,3] ## orthogonalize h = v1 dh = v2 - (v2 %% v1/norm(v1, "2")^2)v1 dh2 = v3 - (v3 %% v1/norm(v1, "2")^2)v1 - (v3 %% dh/norm(dh, "2")^2)dh ## normalize amplitude h = h/max(h) dh = dh/max(dh) dh2 = dh2/max(dh2) return(list(h=h, dh=dh, dh2=dh2)) } Fit_Canonical_HRF <- function(tc, TR, Run, T, p) { ##function [hrf, e, param] = Fit_Canonical_HRF(tc,TR,Run,T,p) ## ## Fits GLM using canonical hrf (with option of using time and dispersion derivatives)'; ## ## INPUTS: ## ## tc - time course ## TR - time resolution ## Runs - expermental design ## T - length of estimated HRF ## p - Model type ## ## Options: p=1 - only canonical HRF ## p=2 - canonical + temporal derivative ## p=3 - canonical + time and dispersion derivative ## ## OUTPUTS: ## ## hrf - estimated hemodynamic response function ## fit - estimated time course ## e - residual time course ## param - estimated amplitude, height and width len = length(Run) X = matrix(0, nrow=len, ncol=p) bf = CanonicalBasisSet(TR) h = bf$h; dh = bf$dh; dh2 = bf$dh2 v = convolve(Run,rev(h), type="open") #this is the R equivalent of conv(Run, h) X[,1] = v[1:len] if (p > 1) { v = convolve(Run,rev(dh), type="open") X[,2] = v[1:len] } if (p > 2) { v = convolve(Run,rev(dh2), type="open") X[,3] = v[1:len] } X = cbind(rep(1, len), X) #add intercept b = MASS::ginv(X) %% tc e = tc - X %% b fit = X %% b b = b[2:length(b)] if (p == 2) { bc = sign(b[1])sqrt(b[1]^2 + b[2]^2) H = cbind(h, dh) } else if (p == 1) { bc = b[1] H = matrix(h, ncol=1) } else if (p>2) { bc = sign(b[1])sqrt(b[1]^2 + b[2]^2 + b[3]^2) H = cbind(h, dh, dh2) } hrf = H %% b param = get_parameters2(hrf,T) return(list(hrf=hrf, e=e, param=param)) } get_parameters2 <- function(hdrf, t) { ##function [param] = get_parameters2(hdrf,t) ## Find model parameters ## ## Height - h ## Time to peak - p (in time units of TR seconds) ## Width (at half peak) - w ## Calculate Heights and Time to peak: ## n = t(end)0.6; n = round(t0.8) p = which.max(abs(hdrf[1:n])) h = hdrf[p] ##if (p > t(end)0.6), warning('Late time to peak'), end; if (h > 0) { v = as.numeric(hdrf >= h/2) } else { v = as.numeric(hdrf <= h/2) } b = which.min(diff(v)) v[(b+1):length(v)] = 0 w = sum(v) cnt = p-1 g = hdrf[2:length(hdrf)] - hdrf[1:(length(hdrf)-1)] while(cnt > 0 && abs(g[cnt]) < 0.001) { h = hdrf[cnt] p = cnt cnt = cnt-1 } param = rep(0,3) param[1] = h param[2] = p param[3] = w return(param) }
\name{data.examples} \alias{data.examples} \alias{example} \alias{pop01} \alias{pop02} \alias{pop03} \alias{pop03p} \alias{pop04} \alias{pop04p} \alias{pop05} \alias{pop05p} \alias{pop06p} \alias{pop07} \alias{pop07p} \alias{pop07pp} \alias{bounds} \docType{data} \title{Artificial Household Survey Data} \description{ Example data frames. Allow to run \R code contained in the \sQuote{Examples} section of the \pkg{ReGenesees} package help pages. } \usage{data(data.examples)} \format{ The main data frame, named \code{example}, contains (artificial) data from a two stage stratified cluster sampling design. The sample is made up of 3000 final units, for which the following 21 variables were observed: \describe{ \item{\code{towcod}}{Code identifying "variance PSUs": towns (PSUs) in not-self-representing (NSR) strata, families (SSUs) in self-representing (SR) strata, \code{numeric}} \item{\code{famcod}}{Code identifying families (SSUs), \code{numeric}} \item{\code{key}}{Key identifying final units (individuals), \code{numeric}} \item{\code{weight}}{Initial weights, \code{numeric}} \item{\code{stratum}}{Stratification variable, \code{factor} with levels \code{801} \code{802} \code{803} \code{901} \code{902} \code{903} \code{904} \code{905} \code{906} \code{907} \code{908} \code{1001} \code{1002} \code{1003} \code{1004} \code{1005} \code{1006} \code{1007} \code{1008} \code{1009} \code{1101} \code{1102} \code{1103} \code{1104} \code{3001} \code{3002} \code{3003} \code{3004} \code{3005} \code{3006} \code{3007} \code{3008} \code{3009} \code{3010} \code{3011} \code{3012} \code{3101} \code{3102} \code{3103} \code{3104} \code{3105} \code{3106} \code{3107} \code{3108} \code{3201} \code{3202} \code{3203} \code{3204} \code{5401} \code{5402} \code{5403} \code{5404} \code{5405} \code{5406} \code{5407} \code{5408} \code{5409} \code{5410} \code{5411} \code{5412} \code{5413} \code{5414} \code{5415} \code{5416} \code{5501} \code{5502} \code{5503} \code{5504} \code{9301} \code{9302} \code{9303} \code{9304} \code{9305} \code{9306} \code{9307} \code{9308} \code{9309} \code{9310} \code{9311} \code{9312}} \item{\code{SUPERSTRATUM}}{Collapsed strata variable (eliminates lonely PSUs), \code{factor} with levels \code{1} \code{2} \code{3} \code{4} \code{5} \code{6} \code{7} \code{8} \code{9} \code{10} \code{11} \code{12} \code{13} \code{14} \code{15} \code{16} \code{17} \code{18} \code{19} \code{20} \code{21} \code{22} \code{23} \code{24} \code{25} \code{26} \code{27} \code{28} \code{29} \code{30} \code{31} \code{32} \code{33} \code{34} \code{35} \code{36} \code{37} \code{38} \code{39} \code{40} \code{41} \code{42} \code{43} \code{44} \code{45} \code{46} \code{47} \code{48} \code{49} \code{50} \code{51} \code{52} \code{53} \code{54} \code{55}} \item{\code{sr}}{Strata type, \code{integer} with values \code{0} (NSR strata) and \code{1} (SR strata)} \item{\code{regcod}}{Code identifying regions, \code{factor} with levels \code{6} \code{7} \code{10}} \item{\code{procod}}{Code identifying provinces, \code{factor} with levels \code{8} \code{9} \code{10} \code{11} \code{30} \code{31} \code{32} \code{54} \code{55} \code{93}} \item{\code{x1}}{Indicator variable (integer), \code{numeric}} \item{\code{x2}}{Indicator variable (integer), \code{numeric}} \item{\code{x3}}{Indicator variable (integer), \code{numeric}} \item{\code{y1}}{Indicator variable (integer), \code{numeric}} \item{\code{y2}}{Indicator variable (integer), \code{numeric}} \item{\code{y3}}{Indicator variable (integer), \code{numeric}} \item{\code{age5c}}{Age variable with 5 classes, \code{factor} with levels \code{1} \code{2} \code{3} \code{4} \code{5}} \item{\code{age10c}}{Age variable with 10 classes, \code{factor} with levels \code{1} \code{2} \code{3} \code{4} \code{5} \code{6} \code{7} \code{8} \code{9} \code{10}} \item{\code{sex}}{Sex variable, \code{factor} with levels \code{f} \code{m}} \item{\code{marstat}}{Marital status variable, \code{factor} with levels \code{married} \code{unmarried} \code{widowed}} \item{\code{z}}{A continuous quantitative variable, \code{numeric}} \item{\code{income}}{Income variable, \code{numeric}} } } \details{ Objects \code{pop01}, \ldots, \code{pop07pp} contain known population totals for various calibration models. Object pairs with names differing in the '\code{p}' suffix (such as \code{pop03} and \code{pop03p}) refer to the \emph{same} calibration problem but pertain to \emph{different} solution methods (global and partitioned respectively, see \code{\link{e.calibrate}}). The two-component numeric vector \code{bounds} expresses a possible choice for the allowed range for the ratios between calibrated weights and direct weights in the aforementioned calibration problems. } \section{Warning}{ \strong{Data in the \code{example} data frame are artificial.} The \emph{structure} of \code{example} intentionally resembles the one of typical household survey data, but the \emph{values} it stores are unreliable. The only purpose of such data is that they can be fruitfully exploited to illustrate the syntax and the working mechanism of the functions provided by the \pkg{ReGenesees} package. } \examples{ data(data.examples) head(example) str(example) } \keyword{datasets}	/man/data.examples.Rd	no_license	PedroJMA/ReGenesees	R	false	false	5,305	rd	\name{data.examples} \alias{data.examples} \alias{example} \alias{pop01} \alias{pop02} \alias{pop03} \alias{pop03p} \alias{pop04} \alias{pop04p} \alias{pop05} \alias{pop05p} \alias{pop06p} \alias{pop07} \alias{pop07p} \alias{pop07pp} \alias{bounds} \docType{data} \title{Artificial Household Survey Data} \description{ Example data frames. Allow to run \R code contained in the \sQuote{Examples} section of the \pkg{ReGenesees} package help pages. } \usage{data(data.examples)} \format{ The main data frame, named \code{example}, contains (artificial) data from a two stage stratified cluster sampling design. The sample is made up of 3000 final units, for which the following 21 variables were observed: \describe{ \item{\code{towcod}}{Code identifying "variance PSUs": towns (PSUs) in not-self-representing (NSR) strata, families (SSUs) in self-representing (SR) strata, \code{numeric}} \item{\code{famcod}}{Code identifying families (SSUs), \code{numeric}} \item{\code{key}}{Key identifying final units (individuals), \code{numeric}} \item{\code{weight}}{Initial weights, \code{numeric}} \item{\code{stratum}}{Stratification variable, \code{factor} with levels \code{801} \code{802} \code{803} \code{901} \code{902} \code{903} \code{904} \code{905} \code{906} \code{907} \code{908} \code{1001} \code{1002} \code{1003} \code{1004} \code{1005} \code{1006} \code{1007} \code{1008} \code{1009} \code{1101} \code{1102} \code{1103} \code{1104} \code{3001} \code{3002} \code{3003} \code{3004} \code{3005} \code{3006} \code{3007} \code{3008} \code{3009} \code{3010} \code{3011} \code{3012} \code{3101} \code{3102} \code{3103} \code{3104} \code{3105} \code{3106} \code{3107} \code{3108} \code{3201} \code{3202} \code{3203} \code{3204} \code{5401} \code{5402} \code{5403} \code{5404} \code{5405} \code{5406} \code{5407} \code{5408} \code{5409} \code{5410} \code{5411} \code{5412} \code{5413} \code{5414} \code{5415} \code{5416} \code{5501} \code{5502} \code{5503} \code{5504} \code{9301} \code{9302} \code{9303} \code{9304} \code{9305} \code{9306} \code{9307} \code{9308} \code{9309} \code{9310} \code{9311} \code{9312}} \item{\code{SUPERSTRATUM}}{Collapsed strata variable (eliminates lonely PSUs), \code{factor} with levels \code{1} \code{2} \code{3} \code{4} \code{5} \code{6} \code{7} \code{8} \code{9} \code{10} \code{11} \code{12} \code{13} \code{14} \code{15} \code{16} \code{17} \code{18} \code{19} \code{20} \code{21} \code{22} \code{23} \code{24} \code{25} \code{26} \code{27} \code{28} \code{29} \code{30} \code{31} \code{32} \code{33} \code{34} \code{35} \code{36} \code{37} \code{38} \code{39} \code{40} \code{41} \code{42} \code{43} \code{44} \code{45} \code{46} \code{47} \code{48} \code{49} \code{50} \code{51} \code{52} \code{53} \code{54} \code{55}} \item{\code{sr}}{Strata type, \code{integer} with values \code{0} (NSR strata) and \code{1} (SR strata)} \item{\code{regcod}}{Code identifying regions, \code{factor} with levels \code{6} \code{7} \code{10}} \item{\code{procod}}{Code identifying provinces, \code{factor} with levels \code{8} \code{9} \code{10} \code{11} \code{30} \code{31} \code{32} \code{54} \code{55} \code{93}} \item{\code{x1}}{Indicator variable (integer), \code{numeric}} \item{\code{x2}}{Indicator variable (integer), \code{numeric}} \item{\code{x3}}{Indicator variable (integer), \code{numeric}} \item{\code{y1}}{Indicator variable (integer), \code{numeric}} \item{\code{y2}}{Indicator variable (integer), \code{numeric}} \item{\code{y3}}{Indicator variable (integer), \code{numeric}} \item{\code{age5c}}{Age variable with 5 classes, \code{factor} with levels \code{1} \code{2} \code{3} \code{4} \code{5}} \item{\code{age10c}}{Age variable with 10 classes, \code{factor} with levels \code{1} \code{2} \code{3} \code{4} \code{5} \code{6} \code{7} \code{8} \code{9} \code{10}} \item{\code{sex}}{Sex variable, \code{factor} with levels \code{f} \code{m}} \item{\code{marstat}}{Marital status variable, \code{factor} with levels \code{married} \code{unmarried} \code{widowed}} \item{\code{z}}{A continuous quantitative variable, \code{numeric}} \item{\code{income}}{Income variable, \code{numeric}} } } \details{ Objects \code{pop01}, \ldots, \code{pop07pp} contain known population totals for various calibration models. Object pairs with names differing in the '\code{p}' suffix (such as \code{pop03} and \code{pop03p}) refer to the \emph{same} calibration problem but pertain to \emph{different} solution methods (global and partitioned respectively, see \code{\link{e.calibrate}}). The two-component numeric vector \code{bounds} expresses a possible choice for the allowed range for the ratios between calibrated weights and direct weights in the aforementioned calibration problems. } \section{Warning}{ \strong{Data in the \code{example} data frame are artificial.} The \emph{structure} of \code{example} intentionally resembles the one of typical household survey data, but the \emph{values} it stores are unreliable. The only purpose of such data is that they can be fruitfully exploited to illustrate the syntax and the working mechanism of the functions provided by the \pkg{ReGenesees} package. } \examples{ data(data.examples) head(example) str(example) } \keyword{datasets}
#! /usr/bin/env Rscript ###################################################################################### ## Simulations of thermal death time data & prediction of derived thermal mortality ## ###################################################################################### # Loop with 100 simulations for different thermal thresholds # output: # * mort_min_tdtsim_sensors_full: predicted thermal mortality at minute resolution # * simdata_full: data of larval death time of all the simulations # * tdtsim_full: tolerance landscape (TDT curves) of all the simulations # * sensors_minute_spline_: thermal profiles form microclimatic data at minute resolution # Pacakges ---------------------------------------------------------------- library(data.table) library(tidyverse) library(readxl) library(lubridate) library(zoo) library(colorspace) library(patchwork) library(future) library(parallel) library(furrr) # Parallellization setup -------------------------------------------------- plan(multicore, workers = availableCores("multicore")-40) # Functions --------------------------------------------------------------- source("code/Thermal_landscape_functions_mod.R") source("code/function_trunk_mortality.R") # Data -------------------------------------------------------------------- tdt <- read.table("data/TDT_experiment.txt", header = T, sep = ",", dec = ",") sensor <- read.csv ("data/sensors_hourly.csv") all.sens <- sensor %>% mutate(winter_jday = if_else(winter_year == 2016, winter_jday - 1, as.double(winter_jday))) %>% #To guarantee that day correspondence of 2016 is the same as other years group_by(sensor, winter_jday, winter_hour) %>% summarise(ta.h = mean(TEMP, na.rm = T)) %>% #hourly means across years (as Rezende) split(.$sensor) %>% map(., ~ spline(.$ta.h, n = (nrow(.)-1)60)) %>% #Last hour (23 to 24h) of the last days can't be added because we don't know the last temperature (00h) map(bind_cols) %>% map(rename, hour = x, ta.min = y) juliandays <- sensor %>% mutate(winter_jday = if_else(winter_year == 2016, winter_jday - 1, as.double(winter_jday))) %>% #Pq el 2016 tingui la mateixa correspondència de dies que els altres anys group_by(sensor, winter_jday, winter_hour) %>% summarise(ta.h = mean(TEMP, na.rm = T)) %>% group_by(sensor) %>% distinct(winter_jday) %>% mutate(day = min_rank(winter_jday), duration = n_distinct(winter_jday)) # Simulation of thermal death time and calculation of thermal mort -------- ## TDT curves from original data tdt.split <- tdt %>% filter(!is.na(aprox_minute_dead), !is.na(SENSOR_mean_temp)) %>% split(.$sp) tdt_curve <- tdt.split %>% map(~ tdt.curve(ta = .$SENSOR_mean_temp, time = .$aprox_minute_dead)) ## Simulations & estimates of daily mortality thres <- c(100, 45, 42.5, 40, 37.5, 35) sims <- 100 pb <- txtProgressBar(min = 0, max = simslength(thres), initial = 0, char = "", style = 3) for (i in seq_along(thres)) { for (j in 1:sims) { ## Tolerance landscape of simulated data simdata <- tdt_curve %>% map(pluck, "model") %>% map(~ data.frame(treatment = rep(c(40, 42, 44), each = 35), inter = unname(coefficients(.)[1], force = T), slope = unname(coefficients(.)[2], force = T), sigma = sigma(.))) %>% map(mutate, error = rnorm(n = n(), sd = sigma), log10time = inter + slopetreatment + error, time = 10^log10time) tl.sim <- simdata %>% map(~ tolerance.landscape(ta = .$treatment, time = .$time)) ## Estimates of daily mortality subtibble <- trunk_mortality(tl = tl.sim, sensdata = all.sens, thres = thres[i]) %>% mutate(alive = if_else(is.na(alive), 0, alive), mort = 100-alive, day = as.numeric(day)) %>% left_join(juliandays, by = c("sensor" = "sensor", "day" = "day")) %>% group_by(sensor, winter_jday, sp_comp) %>% summarise(mort = max(mort, na.rm = T), tmax = max(ta, na.rm = T), tmin = min(ta, na.rm = T), tmean = mean(ta, na.rm = T), surv = min(alive, na.rm = T), thres = thres[i], simulation = j) if(i == 1 & j == 1){ fwrite(subtibble, file = paste0("data/sim_mortalities/mort_min_tdtsim_sensors_full", Sys.Date(), ".csv")) simdata %>% bind_rows(.id = "sp") %>% select(sp, treatment, time) %>% mutate(thres = thres[i], simulation = j) %>% fwrite(file = paste0("data/sim_mortalities/simdata_full", Sys.Date(), ".csv")) tl.sim %>% map(~ keep(., names(.) %in% c("ctmax", "z"))) %>% map(bind_rows) %>% bind_rows(.id = "sp") %>% mutate(simulation = j, thres = thres[i]) %>% fwrite(file = paste0("data/sim_mortalities/tdtsim_full", Sys.Date(), ".csv")) } else { fwrite(subtibble, file = paste0("data/sim_mortalities/mort_min_tdtsim_sensors_full", Sys.Date(), ".csv"), append = T) simdata %>% bind_rows(.id = "sp") %>% select(sp, treatment, time) %>% mutate(thres = thres[i], simulation = j) %>% fwrite(file = paste0("data/sim_mortalities/simdata_full", Sys.Date(), ".csv"), append = T) tl.sim %>% map(~ keep(., names(.) %in% c("ctmax", "z"))) %>% map(bind_rows) %>% bind_rows(.id = "sp") %>% mutate(simulation = j, thres = thres[i]) %>% fwrite(file = paste0("data/sim_mortalities/tdtsim_full", Sys.Date(), ".csv"), append = T) } setTxtProgressBar(pb, sims*(i-1)+j) } } all.sens %>% bind_rows(.id = "sensor") %>% write_rds(file = paste0("data/sim_mortalities/sensors_minute_spline_", Sys.Date(), ".RDS"))	/code/thermal_mortality/sim_thermal_mortality.R	no_license	mvives-ingla/ecotones	R	false	false	6,459	r	#! /usr/bin/env Rscript ###################################################################################### ## Simulations of thermal death time data & prediction of derived thermal mortality ## ###################################################################################### # Loop with 100 simulations for different thermal thresholds # output: # * mort_min_tdtsim_sensors_full: predicted thermal mortality at minute resolution # * simdata_full: data of larval death time of all the simulations # * tdtsim_full: tolerance landscape (TDT curves) of all the simulations # * sensors_minute_spline_: thermal profiles form microclimatic data at minute resolution # Pacakges ---------------------------------------------------------------- library(data.table) library(tidyverse) library(readxl) library(lubridate) library(zoo) library(colorspace) library(patchwork) library(future) library(parallel) library(furrr) # Parallellization setup -------------------------------------------------- plan(multicore, workers = availableCores("multicore")-40) # Functions --------------------------------------------------------------- source("code/Thermal_landscape_functions_mod.R") source("code/function_trunk_mortality.R") # Data -------------------------------------------------------------------- tdt <- read.table("data/TDT_experiment.txt", header = T, sep = ",", dec = ",") sensor <- read.csv ("data/sensors_hourly.csv") all.sens <- sensor %>% mutate(winter_jday = if_else(winter_year == 2016, winter_jday - 1, as.double(winter_jday))) %>% #To guarantee that day correspondence of 2016 is the same as other years group_by(sensor, winter_jday, winter_hour) %>% summarise(ta.h = mean(TEMP, na.rm = T)) %>% #hourly means across years (as Rezende) split(.$sensor) %>% map(., ~ spline(.$ta.h, n = (nrow(.)-1)60)) %>% #Last hour (23 to 24h) of the last days can't be added because we don't know the last temperature (00h) map(bind_cols) %>% map(rename, hour = x, ta.min = y) juliandays <- sensor %>% mutate(winter_jday = if_else(winter_year == 2016, winter_jday - 1, as.double(winter_jday))) %>% #Pq el 2016 tingui la mateixa correspondència de dies que els altres anys group_by(sensor, winter_jday, winter_hour) %>% summarise(ta.h = mean(TEMP, na.rm = T)) %>% group_by(sensor) %>% distinct(winter_jday) %>% mutate(day = min_rank(winter_jday), duration = n_distinct(winter_jday)) # Simulation of thermal death time and calculation of thermal mort -------- ## TDT curves from original data tdt.split <- tdt %>% filter(!is.na(aprox_minute_dead), !is.na(SENSOR_mean_temp)) %>% split(.$sp) tdt_curve <- tdt.split %>% map(~ tdt.curve(ta = .$SENSOR_mean_temp, time = .$aprox_minute_dead)) ## Simulations & estimates of daily mortality thres <- c(100, 45, 42.5, 40, 37.5, 35) sims <- 100 pb <- txtProgressBar(min = 0, max = simslength(thres), initial = 0, char = "", style = 3) for (i in seq_along(thres)) { for (j in 1:sims) { ## Tolerance landscape of simulated data simdata <- tdt_curve %>% map(pluck, "model") %>% map(~ data.frame(treatment = rep(c(40, 42, 44), each = 35), inter = unname(coefficients(.)[1], force = T), slope = unname(coefficients(.)[2], force = T), sigma = sigma(.))) %>% map(mutate, error = rnorm(n = n(), sd = sigma), log10time = inter + slopetreatment + error, time = 10^log10time) tl.sim <- simdata %>% map(~ tolerance.landscape(ta = .$treatment, time = .$time)) ## Estimates of daily mortality subtibble <- trunk_mortality(tl = tl.sim, sensdata = all.sens, thres = thres[i]) %>% mutate(alive = if_else(is.na(alive), 0, alive), mort = 100-alive, day = as.numeric(day)) %>% left_join(juliandays, by = c("sensor" = "sensor", "day" = "day")) %>% group_by(sensor, winter_jday, sp_comp) %>% summarise(mort = max(mort, na.rm = T), tmax = max(ta, na.rm = T), tmin = min(ta, na.rm = T), tmean = mean(ta, na.rm = T), surv = min(alive, na.rm = T), thres = thres[i], simulation = j) if(i == 1 & j == 1){ fwrite(subtibble, file = paste0("data/sim_mortalities/mort_min_tdtsim_sensors_full", Sys.Date(), ".csv")) simdata %>% bind_rows(.id = "sp") %>% select(sp, treatment, time) %>% mutate(thres = thres[i], simulation = j) %>% fwrite(file = paste0("data/sim_mortalities/simdata_full", Sys.Date(), ".csv")) tl.sim %>% map(~ keep(., names(.) %in% c("ctmax", "z"))) %>% map(bind_rows) %>% bind_rows(.id = "sp") %>% mutate(simulation = j, thres = thres[i]) %>% fwrite(file = paste0("data/sim_mortalities/tdtsim_full", Sys.Date(), ".csv")) } else { fwrite(subtibble, file = paste0("data/sim_mortalities/mort_min_tdtsim_sensors_full", Sys.Date(), ".csv"), append = T) simdata %>% bind_rows(.id = "sp") %>% select(sp, treatment, time) %>% mutate(thres = thres[i], simulation = j) %>% fwrite(file = paste0("data/sim_mortalities/simdata_full", Sys.Date(), ".csv"), append = T) tl.sim %>% map(~ keep(., names(.) %in% c("ctmax", "z"))) %>% map(bind_rows) %>% bind_rows(.id = "sp") %>% mutate(simulation = j, thres = thres[i]) %>% fwrite(file = paste0("data/sim_mortalities/tdtsim_full", Sys.Date(), ".csv"), append = T) } setTxtProgressBar(pb, sims*(i-1)+j) } } all.sens %>% bind_rows(.id = "sensor") %>% write_rds(file = paste0("data/sim_mortalities/sensors_minute_spline_", Sys.Date(), ".RDS"))
\name{E4.4} \alias{E4.4} \title{ Measures of Quality for Agencies Delivering Transportation for the Elderly and the Handicapped } \concept{Measures of Quality for Agencies Delivering Transportation for the Elderly and the Handicapped } \usage{data(E4.4)} \description{ The \code{E4.4} data frame has 40 rows and 3 columns. } \format{ This data frame contains the following columns: \describe{ \item{QUAL}{ a numeric vector, a quality measure made using psychometric methods from results of questionares. } \item{X.1}{ a numeric vector, an indicator variable for private ownership. } \item{X.2}{ a numeric vector, an indicator variable for private for profit ownership. } } } \details{ The quality data, \code{QUAL}, is constructed from questionares given to users of such services in the state of Illinois. Multiple services in the state of Illinois was scored using this method. The indicator variables was constructed to give first (\code{X.1}) a comparison between private and public services, then (\code{X.2}) a comparison between private not-for-profit and private for profit services. } \source{ Slightly modified version of data supplied by Ms. Claire McKnight of the Department of Civil Engineering, City University of New York. } \examples{ data(E4.4) summary(E4.4) } \keyword{datasets} \concept{regression}	/man/E4.4.Rd	no_license	cran/SenSrivastava	R	false	false	1,347	rd	\name{E4.4} \alias{E4.4} \title{ Measures of Quality for Agencies Delivering Transportation for the Elderly and the Handicapped } \concept{Measures of Quality for Agencies Delivering Transportation for the Elderly and the Handicapped } \usage{data(E4.4)} \description{ The \code{E4.4} data frame has 40 rows and 3 columns. } \format{ This data frame contains the following columns: \describe{ \item{QUAL}{ a numeric vector, a quality measure made using psychometric methods from results of questionares. } \item{X.1}{ a numeric vector, an indicator variable for private ownership. } \item{X.2}{ a numeric vector, an indicator variable for private for profit ownership. } } } \details{ The quality data, \code{QUAL}, is constructed from questionares given to users of such services in the state of Illinois. Multiple services in the state of Illinois was scored using this method. The indicator variables was constructed to give first (\code{X.1}) a comparison between private and public services, then (\code{X.2}) a comparison between private not-for-profit and private for profit services. } \source{ Slightly modified version of data supplied by Ms. Claire McKnight of the Department of Civil Engineering, City University of New York. } \examples{ data(E4.4) summary(E4.4) } \keyword{datasets} \concept{regression}
% Generated by roxygen2: do not edit by hand % Please edit documentation in R/caching.R \name{set.cache.dir} \alias{set.cache.dir} \title{Sets the caching directory path} \usage{ set.cache.dir(path) } \arguments{ \item{path}{the path of the caching directory} } \description{ Sets the caching directory path }	/man/set.cache.dir.Rd	permissive	pydupont/cacheR	R	false	true	310	rd	% Generated by roxygen2: do not edit by hand % Please edit documentation in R/caching.R \name{set.cache.dir} \alias{set.cache.dir} \title{Sets the caching directory path} \usage{ set.cache.dir(path) } \arguments{ \item{path}{the path of the caching directory} } \description{ Sets the caching directory path }
% Generated by roxygen2: do not edit by hand % Please edit documentation in R/rstatscn.R \name{statscnRowNamePrefix} \alias{statscnRowNamePrefix} \title{statscnRowNamePrefix} \usage{ statscnRowNamePrefix(p = "nrow") } \arguments{ \item{p}{, how to set the rowname prefix. it is 'nrow' by default , and it is the only supported value currently to unset the row name prefix, call this function with p=NULL} } \value{ no return } \description{ set the rowName prefix in the dataframe } \details{ in case you encounter the following error: Error in `row.names<-.data.frame`(`tmp`, value = value) : duplicate 'row.names' are not allowed you need to call this function }	/man/statscnRowNamePrefix.Rd	no_license	jiang-hang/rstatscn	R	false	true	673	rd	% Generated by roxygen2: do not edit by hand % Please edit documentation in R/rstatscn.R \name{statscnRowNamePrefix} \alias{statscnRowNamePrefix} \title{statscnRowNamePrefix} \usage{ statscnRowNamePrefix(p = "nrow") } \arguments{ \item{p}{, how to set the rowname prefix. it is 'nrow' by default , and it is the only supported value currently to unset the row name prefix, call this function with p=NULL} } \value{ no return } \description{ set the rowName prefix in the dataframe } \details{ in case you encounter the following error: Error in `row.names<-.data.frame`(`tmp`, value = value) : duplicate 'row.names' are not allowed you need to call this function }
library(dplyr) library(caret) set.seed(98765) if (!exists("dataTrain.raw")){dataTrain.raw <- read.csv('data/pml-training.csv')} if (!exists("dataTest.raw")){dataTest.raw <- read.csv('data/pml-testing.csv')} # Clean up the column set pml_data_column_cleanup = function(df){ # Drop purely admin related columns df <- df %>% select(-starts_with("X")) df <- df %>% select(-starts_with("raw_timestamp_")) df <- df %>% select(-starts_with("cvtd_timestamp")) df <- df %>% select(-starts_with("new_window")) df <- df %>% select(-starts_with("num_window")) # Drop columns that relate only to "New Window = Yes" df <- df %>% select(-starts_with("kurtosis_roll_")) df <- df %>% select(-starts_with("kurtosis_picth_")) df <- df %>% select(-starts_with("kurtosis_yaw_")) df <- df %>% select(-starts_with("skewness_roll_")) df <- df %>% select(-starts_with("skewness_pitch_")) df <- df %>% select(-starts_with("skewness_yaw_")) df <- df %>% select(-starts_with("max_roll_")) df <- df %>% select(-starts_with("max_picth_")) df <- df %>% select(-starts_with("max_yaw_")) df <- df %>% select(-starts_with("min_roll_")) df <- df %>% select(-starts_with("min_pitch_")) df <- df %>% select(-starts_with("min_yaw_")) df <- df %>% select(-starts_with("amplitude_roll_")) df <- df %>% select(-starts_with("amplitude_pitch_")) df <- df %>% select(-starts_with("amplitude_yaw_")) df <- df %>% select(-starts_with("var_total_accel_")) df <- df %>% select(-starts_with("avg_roll_")) df <- df %>% select(-starts_with("stddev_roll_")) df <- df %>% select(-starts_with("var_roll_")) df <- df %>% select(-starts_with("avg_pitch_")) df <- df %>% select(-starts_with("stddev_pitch_")) df <- df %>% select(-starts_with("var_pitch_")) df <- df %>% select(-starts_with("avg_yaw_")) df <- df %>% select(-starts_with("stddev_yaw_")) df <- df %>% select(-starts_with("var_yaw_")) df <- df %>% select(-starts_with("var_accel_")) df } dataTrain <- pml_data_column_cleanup(dataTrain.raw) dataTest <- pml_data_column_cleanup(dataTest.raw) if (!exists("dataTrain.model")){ # Partition our data 75% training and 25% testing dataTrain.partition <- createDataPartition(y=dataTrain$classe, p=0.75, list=FALSE) dataTrain.train <- dataTrain[dataTrain.partition,] dataTrain.test <- dataTrain[-dataTrain.partition,] # Implemented a Random Forest (limited to depth 100) training strategy dataTrain.model <- train(classe~., data=dataTrain.train, method="rf", ntree=100) } # Train Test prediction dataTrain.test.predict <- predict(dataTrain.model, newdata=dataTrain.test) # Test prediction (Validation) dataTest.predict <- predict(dataTrain.model, newdata=dataTest) # print(dataTrain.test.predict) print(dataTest.predict) # Calculate outside error rate and accuracy of the validation outsideErrorRate.accuracy <- sum(dataTrain.test.predict == dataTrain.test$classe)/length(dataTrain.test.predict) outsideErrorRate.error <- (1 - outsideErrorRate.accuracy) print( ggplot(dataTrain.model) + ggtitle("Accuracy vs. Predictor") ) print(dataTrain.model) # Wrap up with the answers for the submission pml_write_files = function(x){ dir.create(file.path(getwd(), 'submission'), showWarnings = FALSE) n = length(x) for(i in 1:n){ filename = paste0("submission/problem_id_", i, ".txt") write.table(x[i], file=filename, quote=FALSE, row.names=FALSE, col.names=FALSE) } } pml_write_files(as.character(dataTest.predict))	/Review.R	no_license	schonken/Practical_Machine_Learning_Project	R	false	false	3,502	r	library(dplyr) library(caret) set.seed(98765) if (!exists("dataTrain.raw")){dataTrain.raw <- read.csv('data/pml-training.csv')} if (!exists("dataTest.raw")){dataTest.raw <- read.csv('data/pml-testing.csv')} # Clean up the column set pml_data_column_cleanup = function(df){ # Drop purely admin related columns df <- df %>% select(-starts_with("X")) df <- df %>% select(-starts_with("raw_timestamp_")) df <- df %>% select(-starts_with("cvtd_timestamp")) df <- df %>% select(-starts_with("new_window")) df <- df %>% select(-starts_with("num_window")) # Drop columns that relate only to "New Window = Yes" df <- df %>% select(-starts_with("kurtosis_roll_")) df <- df %>% select(-starts_with("kurtosis_picth_")) df <- df %>% select(-starts_with("kurtosis_yaw_")) df <- df %>% select(-starts_with("skewness_roll_")) df <- df %>% select(-starts_with("skewness_pitch_")) df <- df %>% select(-starts_with("skewness_yaw_")) df <- df %>% select(-starts_with("max_roll_")) df <- df %>% select(-starts_with("max_picth_")) df <- df %>% select(-starts_with("max_yaw_")) df <- df %>% select(-starts_with("min_roll_")) df <- df %>% select(-starts_with("min_pitch_")) df <- df %>% select(-starts_with("min_yaw_")) df <- df %>% select(-starts_with("amplitude_roll_")) df <- df %>% select(-starts_with("amplitude_pitch_")) df <- df %>% select(-starts_with("amplitude_yaw_")) df <- df %>% select(-starts_with("var_total_accel_")) df <- df %>% select(-starts_with("avg_roll_")) df <- df %>% select(-starts_with("stddev_roll_")) df <- df %>% select(-starts_with("var_roll_")) df <- df %>% select(-starts_with("avg_pitch_")) df <- df %>% select(-starts_with("stddev_pitch_")) df <- df %>% select(-starts_with("var_pitch_")) df <- df %>% select(-starts_with("avg_yaw_")) df <- df %>% select(-starts_with("stddev_yaw_")) df <- df %>% select(-starts_with("var_yaw_")) df <- df %>% select(-starts_with("var_accel_")) df } dataTrain <- pml_data_column_cleanup(dataTrain.raw) dataTest <- pml_data_column_cleanup(dataTest.raw) if (!exists("dataTrain.model")){ # Partition our data 75% training and 25% testing dataTrain.partition <- createDataPartition(y=dataTrain$classe, p=0.75, list=FALSE) dataTrain.train <- dataTrain[dataTrain.partition,] dataTrain.test <- dataTrain[-dataTrain.partition,] # Implemented a Random Forest (limited to depth 100) training strategy dataTrain.model <- train(classe~., data=dataTrain.train, method="rf", ntree=100) } # Train Test prediction dataTrain.test.predict <- predict(dataTrain.model, newdata=dataTrain.test) # Test prediction (Validation) dataTest.predict <- predict(dataTrain.model, newdata=dataTest) # print(dataTrain.test.predict) print(dataTest.predict) # Calculate outside error rate and accuracy of the validation outsideErrorRate.accuracy <- sum(dataTrain.test.predict == dataTrain.test$classe)/length(dataTrain.test.predict) outsideErrorRate.error <- (1 - outsideErrorRate.accuracy) print( ggplot(dataTrain.model) + ggtitle("Accuracy vs. Predictor") ) print(dataTrain.model) # Wrap up with the answers for the submission pml_write_files = function(x){ dir.create(file.path(getwd(), 'submission'), showWarnings = FALSE) n = length(x) for(i in 1:n){ filename = paste0("submission/problem_id_", i, ".txt") write.table(x[i], file=filename, quote=FALSE, row.names=FALSE, col.names=FALSE) } } pml_write_files(as.character(dataTest.predict))
tabOthers <- f7Tab( tabName = "Others", icon = f7Icon("more_round"), f7Align( side = "center", h1("miniUI 2.0 brings other elements") ), # skeletons f7BlockTitle(title = "f7Skeleton") %>% f7Align(side = "center"), f7List( f7ListItem(title = "Item 1"), f7ListItem(title = "Item 2") ) %>% f7Skeleton(duration = 5000), br(), # Messages f7BlockTitle(title = "f7Messages") %>% f7Align(side = "center"), f7Messages( id = "messagelist", f7Message( "Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duo Reges: constructio interrete", src = "https://cdn.framework7.io/placeholder/people-100x100-7.jpg", author = "David", date = "2019-09-12", state = "received", type = "text" ), f7Message( "https://cdn.framework7.io/placeholder/cats-200x260-4.jpg", src = "https://cdn.framework7.io/placeholder/people-100x100-9.jpg", author = "Lia", date = NULL, state = "sent", type = "img" ), f7Message( "Hi Bro", src = "https://cdn.framework7.io/placeholder/people-100x100-9.jpg", author = NULL, date = "2019-08-15", state = "sent", type = "text" ) ), br(), # Badges f7BlockTitle(title = "f7Badge") %>% f7Align(side = "center"), f7Block( strong = TRUE, f7Badge(32, color = "purple"), f7Badge("Badge", color = "green"), f7Badge(10, color = "teal"), f7Badge("Ok", color = "orange") ), br(), # chips f7BlockTitle(title = "f7Chip") %>% f7Align(side = "center"), f7Block( strong = TRUE, f7Chip(label = "Example Chip"), f7Chip(label = "Example Chip", outline = TRUE), f7Chip(label = "Example Chip", icon = f7Icon("add_round"), icon_status = "pink"), f7Chip(label = "Example Chip", img = "https://lorempixel.com/64/64/people/9/"), f7Chip(label = "Example Chip", closable = TRUE), f7Chip(label = "Example Chip", status = "green"), f7Chip(label = "Example Chip", status = "green", outline = TRUE) ), br(), # accordion f7BlockTitle(title = "f7Accordion") %>% f7Align(side = "center"), f7Accordion( inputId = "accordion1", f7AccordionItem( title = "Item 1", f7Block("Item 1 content") ), f7AccordionItem( title = "Item 2", f7Block("Item 2 content") ) ), f7Toggle( inputId = "goAccordion", label = "Toggle accordion item 1", color = "orange" ), br(), # swiper f7BlockTitle(title = "f7Swiper") %>% f7Align(side = "center"), f7Swiper( id = "my-swiper", f7Slide( plot_ly(z = ~volcano, type = "contour") ), f7Slide( plot_ly(data = iris, x = ~Sepal.Length, y = ~Petal.Length) ) ), br(), br(), br(), # timelines f7BlockTitle(title = "f7PhotoBrowser") %>% f7Align(side = "center"), f7Block( f7PhotoBrowser( id = "photobrowser1", label = "Open", theme = "light", type = "standalone", photos = c( "https://cdn.framework7.io/placeholder/sports-1024x1024-1.jpg", "https://cdn.framework7.io/placeholder/sports-1024x1024-2.jpg", "https://cdn.framework7.io/placeholder/sports-1024x1024-3.jpg" ) ) ), br(), br(), # timelines f7BlockTitle(title = "f7Timeline") %>% f7Align(side = "center"), f7Timeline( sides = TRUE, f7TimelineItem( "Another text", date = "01 Dec", card = FALSE, time = "12:30", title = "Title", subtitle = "Subtitle", side = "left" ), f7TimelineItem( "Another text", date = "02 Dec", card = TRUE, time = "13:00", title = "Title", subtitle = "Subtitle" ), f7TimelineItem( "Another text", date = "03 Dec", card = FALSE, time = "14:45", title = "Title", subtitle = "Subtitle" ) ), br(), # progress bars f7BlockTitle(title = "f7Progress") %>% f7Align(side = "center"), f7Block( strong = TRUE, f7Progress(id = "pg1", value = 10, color = "yellow"), f7Slider( inputId = "updatepg1", label = "Update progress 1", max = 100, min = 0, value = 50, scale = TRUE ), br(), f7Progress(id = "pg2", value = 100, color = "green"), br(), f7Progress(id = "pg3", value = 50, color = "deeppurple"), br(), f7ProgressInf() ), br(), # gauges f7BlockTitle(title = "f7Gauge") %>% f7Align(side = "center"), f7Block( strong = TRUE, f7Row( f7Col( f7Gauge( id = "mygauge1", type = "semicircle", value = 50, borderColor = "#2196f3", borderWidth = 10, valueText = "50%", valueFontSize = 41, valueTextColor = "#2196f3", labelText = "amount of something" ) ), f7Col( f7Gauge( id = "mygauge2", type = "circle", value = 30, borderColor = "orange", borderWidth = 10, valueText = "30%", valueFontSize = 41, valueTextColor = "orange", labelText = "Other thing" ) ) ), f7Stepper( inputId = "updategauge1", label = "Update gauge 1", step = 10, min = 0, max = 100, value = 50 ) ), # update f7Panel br(), f7BlockTitle(title = "updateF7Panel") %>% f7Align(side = "center"), f7Block( f7Button( inputId = "goPanel", label = "Toggle left panel" ) ) )	/inst/examples/gallery/tabs/tabOthers.R	no_license	iMarcello/shinyMobile	R	false	false	5,529	r	tabOthers <- f7Tab( tabName = "Others", icon = f7Icon("more_round"), f7Align( side = "center", h1("miniUI 2.0 brings other elements") ), # skeletons f7BlockTitle(title = "f7Skeleton") %>% f7Align(side = "center"), f7List( f7ListItem(title = "Item 1"), f7ListItem(title = "Item 2") ) %>% f7Skeleton(duration = 5000), br(), # Messages f7BlockTitle(title = "f7Messages") %>% f7Align(side = "center"), f7Messages( id = "messagelist", f7Message( "Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duo Reges: constructio interrete", src = "https://cdn.framework7.io/placeholder/people-100x100-7.jpg", author = "David", date = "2019-09-12", state = "received", type = "text" ), f7Message( "https://cdn.framework7.io/placeholder/cats-200x260-4.jpg", src = "https://cdn.framework7.io/placeholder/people-100x100-9.jpg", author = "Lia", date = NULL, state = "sent", type = "img" ), f7Message( "Hi Bro", src = "https://cdn.framework7.io/placeholder/people-100x100-9.jpg", author = NULL, date = "2019-08-15", state = "sent", type = "text" ) ), br(), # Badges f7BlockTitle(title = "f7Badge") %>% f7Align(side = "center"), f7Block( strong = TRUE, f7Badge(32, color = "purple"), f7Badge("Badge", color = "green"), f7Badge(10, color = "teal"), f7Badge("Ok", color = "orange") ), br(), # chips f7BlockTitle(title = "f7Chip") %>% f7Align(side = "center"), f7Block( strong = TRUE, f7Chip(label = "Example Chip"), f7Chip(label = "Example Chip", outline = TRUE), f7Chip(label = "Example Chip", icon = f7Icon("add_round"), icon_status = "pink"), f7Chip(label = "Example Chip", img = "https://lorempixel.com/64/64/people/9/"), f7Chip(label = "Example Chip", closable = TRUE), f7Chip(label = "Example Chip", status = "green"), f7Chip(label = "Example Chip", status = "green", outline = TRUE) ), br(), # accordion f7BlockTitle(title = "f7Accordion") %>% f7Align(side = "center"), f7Accordion( inputId = "accordion1", f7AccordionItem( title = "Item 1", f7Block("Item 1 content") ), f7AccordionItem( title = "Item 2", f7Block("Item 2 content") ) ), f7Toggle( inputId = "goAccordion", label = "Toggle accordion item 1", color = "orange" ), br(), # swiper f7BlockTitle(title = "f7Swiper") %>% f7Align(side = "center"), f7Swiper( id = "my-swiper", f7Slide( plot_ly(z = ~volcano, type = "contour") ), f7Slide( plot_ly(data = iris, x = ~Sepal.Length, y = ~Petal.Length) ) ), br(), br(), br(), # timelines f7BlockTitle(title = "f7PhotoBrowser") %>% f7Align(side = "center"), f7Block( f7PhotoBrowser( id = "photobrowser1", label = "Open", theme = "light", type = "standalone", photos = c( "https://cdn.framework7.io/placeholder/sports-1024x1024-1.jpg", "https://cdn.framework7.io/placeholder/sports-1024x1024-2.jpg", "https://cdn.framework7.io/placeholder/sports-1024x1024-3.jpg" ) ) ), br(), br(), # timelines f7BlockTitle(title = "f7Timeline") %>% f7Align(side = "center"), f7Timeline( sides = TRUE, f7TimelineItem( "Another text", date = "01 Dec", card = FALSE, time = "12:30", title = "Title", subtitle = "Subtitle", side = "left" ), f7TimelineItem( "Another text", date = "02 Dec", card = TRUE, time = "13:00", title = "Title", subtitle = "Subtitle" ), f7TimelineItem( "Another text", date = "03 Dec", card = FALSE, time = "14:45", title = "Title", subtitle = "Subtitle" ) ), br(), # progress bars f7BlockTitle(title = "f7Progress") %>% f7Align(side = "center"), f7Block( strong = TRUE, f7Progress(id = "pg1", value = 10, color = "yellow"), f7Slider( inputId = "updatepg1", label = "Update progress 1", max = 100, min = 0, value = 50, scale = TRUE ), br(), f7Progress(id = "pg2", value = 100, color = "green"), br(), f7Progress(id = "pg3", value = 50, color = "deeppurple"), br(), f7ProgressInf() ), br(), # gauges f7BlockTitle(title = "f7Gauge") %>% f7Align(side = "center"), f7Block( strong = TRUE, f7Row( f7Col( f7Gauge( id = "mygauge1", type = "semicircle", value = 50, borderColor = "#2196f3", borderWidth = 10, valueText = "50%", valueFontSize = 41, valueTextColor = "#2196f3", labelText = "amount of something" ) ), f7Col( f7Gauge( id = "mygauge2", type = "circle", value = 30, borderColor = "orange", borderWidth = 10, valueText = "30%", valueFontSize = 41, valueTextColor = "orange", labelText = "Other thing" ) ) ), f7Stepper( inputId = "updategauge1", label = "Update gauge 1", step = 10, min = 0, max = 100, value = 50 ) ), # update f7Panel br(), f7BlockTitle(title = "updateF7Panel") %>% f7Align(side = "center"), f7Block( f7Button( inputId = "goPanel", label = "Toggle left panel" ) ) )
##Merges expression and DHS data ##Generates plots to check the association between DHS+ LTRs and gene expression setwd('~/Deniz_2019_AML/Expression') library(vioplot) ##expression data fpkm = read.delim('BP_RNA_FPKM.txt.gz',as.is=T) ##DHS data dhs = read.delim('../DHS_analysis/allLTR_DHS_overlaps.txt',as.is=T) ###################### #### prepare data #### ##get average expression values for cell groups hsc = rowMeans(log2(fpkm[,grep('stem.cell',colnames(fpkm))]+0.01)) mono = rowMeans(log2(fpkm[,grep('monocyte',colnames(fpkm))]+0.01)) macro = rowMeans(log2(fpkm[,grep('macrophage',colnames(fpkm))]+0.01)) aml = rowMeans(log2(fpkm[,grep('Leukemia',colnames(fpkm))]+0.01)) ##make matching dhs and expression matrices for AML samples meta = read.delim('../blueprint_files.tsv',as.is=T) dhs.meta = meta[meta$Experiment=='DNase-Seq' & meta$Format=='BED' & meta$Sub.group=='Acute Myeloid Leukemia',] for (i in 1:ncol(dhs)) { id = grep(colnames(dhs)[i],dhs.meta$URL)[1] if (!is.na(id)) colnames(dhs)[i] = dhs.meta$Donor[id] } expr.donor = gsub('Acute.Myeloid.Leukemia.','',colnames(fpkm)) aml.dhs = as.matrix(dhs[,colnames(dhs) %in% expr.donor]>=1) aml.expr = log2(as.matrix(fpkm[,match(colnames(aml.dhs),expr.donor)])+0.01) ##get average expression values from dhs and non-dhs AML groups av.dhs = numeric(nrow(aml.expr)) av.nodhs = numeric(nrow(aml.expr)) for (i in 1:nrow(aml.expr)) { if (is.na(fpkm$LTR_name[i])) { av.dhs[i] = NA av.nodhs[i] = NA } else { is.dhs = aml.dhs[fpkm$LTR_name[i]==dhs$name] av.dhs[i] = mean(aml.expr[i,is.dhs]) av.nodhs[i] = mean(aml.expr[i,!is.dhs]) } } ##make LTRs groups based on DHS data diff.dhs = dhs[,grepl('Monocytes',colnames(dhs)) \| grepl('Macrophages',colnames(dhs))]>=1 nodhs.ltrs = dhs$name[rowSums(aml.dhs)==0 & rowSums(diff.dhs)==0] aml.ltrs = dhs$name[rowSums(aml.dhs)>=2 & rowSums(diff.dhs)==0] ubi.ltrs = dhs$name[rowSums(aml.dhs)>=2 & rowSums(diff.dhs)>=2] ##set distance threshold near = fpkm$LTR_distance <= 50000 ############################################################## #### plot average AML expression for different LTR groups #### vioplot(aml[near & fpkm$LTR_name %in% nodhs.ltrs], aml[near & fpkm$LTR_name %in% aml.ltrs], aml[near & fpkm$LTR_name %in% ubi.ltrs], names=c('None','AML','Ubi'), col='wheat',yaxt='n',ylab='log2 FPKM') axis(2,seq(-5,15,5),las=1) ############################################################## #### compare expression between DHS+ and DHS- AML samples #### plus = av.dhs[near & fpkm$LTR_name %in% aml.ltrs] minus = av.nodhs[near & fpkm$LTR_name %in% aml.ltrs] plot(minus,plus,pch=19,cex=0.5,col='grey',las=1, xlab='DHS- AMLs',ylab='DHS+ AMLs') abline(0,1,lty=2,col='blue') ##pinpoint DHS-associated interest sel = (plus>0 \| minus>0) & plus-minus>2 points(minus[sel],plus[sel],pch=19,cex=0.5,col='red') aml.genes = fpkm$gene_name[near & fpkm$LTR_name %in% aml.ltrs] goi = cbind(aml.genes,minus,plus)[sel,] ######################################################### #### plot expression of AML LTRs in other cell types #### ##(not included in the paper) exp.list = list(hsc,mono,macro,av.dhs,av.nodhs) aml.list = lapply(exp.list,function(x) x[near & fpkm$LTR_name %in% aml.ltrs]) vioplot(aml.list,names=c('HSC','Mono','Macro','DHS+','DHS-'), col='wheat',yaxt='n',ylab='log2 FPKM') axis(2,seq(-5,15,5),las=1)	/Expression/LTR_nearest_expression.R	permissive	MBrancoLab/Deniz_2019_AML	R	false	false	3,364	r	##Merges expression and DHS data ##Generates plots to check the association between DHS+ LTRs and gene expression setwd('~/Deniz_2019_AML/Expression') library(vioplot) ##expression data fpkm = read.delim('BP_RNA_FPKM.txt.gz',as.is=T) ##DHS data dhs = read.delim('../DHS_analysis/allLTR_DHS_overlaps.txt',as.is=T) ###################### #### prepare data #### ##get average expression values for cell groups hsc = rowMeans(log2(fpkm[,grep('stem.cell',colnames(fpkm))]+0.01)) mono = rowMeans(log2(fpkm[,grep('monocyte',colnames(fpkm))]+0.01)) macro = rowMeans(log2(fpkm[,grep('macrophage',colnames(fpkm))]+0.01)) aml = rowMeans(log2(fpkm[,grep('Leukemia',colnames(fpkm))]+0.01)) ##make matching dhs and expression matrices for AML samples meta = read.delim('../blueprint_files.tsv',as.is=T) dhs.meta = meta[meta$Experiment=='DNase-Seq' & meta$Format=='BED' & meta$Sub.group=='Acute Myeloid Leukemia',] for (i in 1:ncol(dhs)) { id = grep(colnames(dhs)[i],dhs.meta$URL)[1] if (!is.na(id)) colnames(dhs)[i] = dhs.meta$Donor[id] } expr.donor = gsub('Acute.Myeloid.Leukemia.','',colnames(fpkm)) aml.dhs = as.matrix(dhs[,colnames(dhs) %in% expr.donor]>=1) aml.expr = log2(as.matrix(fpkm[,match(colnames(aml.dhs),expr.donor)])+0.01) ##get average expression values from dhs and non-dhs AML groups av.dhs = numeric(nrow(aml.expr)) av.nodhs = numeric(nrow(aml.expr)) for (i in 1:nrow(aml.expr)) { if (is.na(fpkm$LTR_name[i])) { av.dhs[i] = NA av.nodhs[i] = NA } else { is.dhs = aml.dhs[fpkm$LTR_name[i]==dhs$name] av.dhs[i] = mean(aml.expr[i,is.dhs]) av.nodhs[i] = mean(aml.expr[i,!is.dhs]) } } ##make LTRs groups based on DHS data diff.dhs = dhs[,grepl('Monocytes',colnames(dhs)) \| grepl('Macrophages',colnames(dhs))]>=1 nodhs.ltrs = dhs$name[rowSums(aml.dhs)==0 & rowSums(diff.dhs)==0] aml.ltrs = dhs$name[rowSums(aml.dhs)>=2 & rowSums(diff.dhs)==0] ubi.ltrs = dhs$name[rowSums(aml.dhs)>=2 & rowSums(diff.dhs)>=2] ##set distance threshold near = fpkm$LTR_distance <= 50000 ############################################################## #### plot average AML expression for different LTR groups #### vioplot(aml[near & fpkm$LTR_name %in% nodhs.ltrs], aml[near & fpkm$LTR_name %in% aml.ltrs], aml[near & fpkm$LTR_name %in% ubi.ltrs], names=c('None','AML','Ubi'), col='wheat',yaxt='n',ylab='log2 FPKM') axis(2,seq(-5,15,5),las=1) ############################################################## #### compare expression between DHS+ and DHS- AML samples #### plus = av.dhs[near & fpkm$LTR_name %in% aml.ltrs] minus = av.nodhs[near & fpkm$LTR_name %in% aml.ltrs] plot(minus,plus,pch=19,cex=0.5,col='grey',las=1, xlab='DHS- AMLs',ylab='DHS+ AMLs') abline(0,1,lty=2,col='blue') ##pinpoint DHS-associated interest sel = (plus>0 \| minus>0) & plus-minus>2 points(minus[sel],plus[sel],pch=19,cex=0.5,col='red') aml.genes = fpkm$gene_name[near & fpkm$LTR_name %in% aml.ltrs] goi = cbind(aml.genes,minus,plus)[sel,] ######################################################### #### plot expression of AML LTRs in other cell types #### ##(not included in the paper) exp.list = list(hsc,mono,macro,av.dhs,av.nodhs) aml.list = lapply(exp.list,function(x) x[near & fpkm$LTR_name %in% aml.ltrs]) vioplot(aml.list,names=c('HSC','Mono','Macro','DHS+','DHS-'), col='wheat',yaxt='n',ylab='log2 FPKM') axis(2,seq(-5,15,5),las=1)
# Plots global active power versus time to png file. plot2 <- function() { # Read required data. Uses powerUsage.R added to repository. df <- powerUsage() # Prepare to plot graph to png file. png(file = "plot2.png") # Hide x axis label. par(mar = c(3.1, 4.1, 3.1, 2.1)) # Plot global active power versus time. with(df, plot(Time, Global_active_power, # Line plot. type = "l", # y axis label. ylab = "Global Active Power (kilowatts)")) # Finish plotting. dev.off() }	/plot2.R	no_license	rochbe/ExData_Plotting1	R	false	false	546	r	# Plots global active power versus time to png file. plot2 <- function() { # Read required data. Uses powerUsage.R added to repository. df <- powerUsage() # Prepare to plot graph to png file. png(file = "plot2.png") # Hide x axis label. par(mar = c(3.1, 4.1, 3.1, 2.1)) # Plot global active power versus time. with(df, plot(Time, Global_active_power, # Line plot. type = "l", # y axis label. ylab = "Global Active Power (kilowatts)")) # Finish plotting. dev.off() }
#### Load libraries #### # Load tidyverse - note the output lets you know this contains ggplot2 library(tidyverse) # Load gapminder - this contains the data we will use library(gapminder) #### Import and view data #### # Load data gapminder <- gapminder::gapminder # Look at the structure of the data. You can use glimpse(), summary(), or head(). glimpse(gapminder) # Create a new data frame with only the data for 2007 gapminder07 <- filter(gapminder, year==2007) #### Basic plot #### # Scatterplot of population over time ggplot(gapminder) + geom_point(aes(x=year, y=pop)) # Add labels to the plot ggplot(gapminder) + geom_point(aes(x=year, y=pop)) + labs(title="Population over time", x="Year", y="Population") # Your turn: plot life expectancy as a function of GDP per capita for the year 2007, and add labels ggplot(gapminder07) + geom_point(aes(x=gdpPercap, y=lifeExp)) + labs(title="Do people in richer countries live longer?", x="GDP per capita", y="Life expectancy") #### Choosing geoms #### # Let's look at geom_smooth(). Add a geom_smooth() layer to the previous plot. ggplot(gapminder07, aes(x=gdpPercap, y=lifeExp)) + geom_point() + geom_smooth() + labs(title="Do people in richer countries live longer?", x="GDP per capita", y="Life expectancy", subtitle="Gapminder 2007 data") # How about geom_hline()? When you add it, you'll need to provide a new aesthetic option. ggplot(gapminder07) + geom_point(aes(x=gdpPercap, y=lifeExp)) + geom_hline(aes(yintercept=mean(lifeExp))) + labs(title="Do people in richer countries live longer?", x="GDP per capita", y="Life expectancy", subtitle="Gapminder 2007 data") # Your turn: Plot the life expectancy of each continent in 2007 # Look at the ggplot cheatsheet and decide what kind of geom to use: https://raw.githubusercontent.com/rstudio/cheatsheets/master/data-visualization-2.1.pdf # Option 1: Box plot ggplot(gapminder07) + geom_boxplot(aes(x=continent, y=lifeExp)) # Option 2: Column plot ggplot(gapminder07) + geom_col(aes(x=continent, y=lifeExp)) #### Grouping variables #### # Grouping by color ggplot(gapminder) + geom_point(aes(x=gdpPercap, y=lifeExp, col=year)) + labs(title="Do people in richer countries live longer?", x="GDP per capita", y="Life expectancy") # Grouping by facet ggplot(gapminder, aes(x=gdpPercap, y=lifeExp)) + geom_point() + geom_smooth() + facet_wrap(~year) + labs(title="Do people in richer countries live longer?", x="GDP per capita", y="Life expectancy") # Add scales="free" in facet_wrap() and see what happens ggplot(gapminder, aes(x=gdpPercap, y=lifeExp)) + geom_point() + geom_smooth() + facet_wrap(~year, scales="free") + labs(title="Do people in richer countries live longer?", x="GDP per capita", y="Life expectancy") # Your turn: Let's visualize life expectancy by continent in 2007 again. This time, use grouping by color or facet. # Bonus: Use two geoms. # Option 1: By colors ggplot(gapminder07, aes(x=gdpPercap, y=lifeExp, col=continent, size=pop)) + geom_point() + labs(title="Life expectancy as a function of GDP per capita, by continent", x="GDP per capita", y="Life expectancy") # Option 2: By facet ggplot(gapminder07, aes(x=gdpPercap, y=lifeExp, size=pop)) + geom_point() + geom_smooth() + facet_wrap(~continent, scales="free") + labs(title="Life expectancy as a function of GDP per capita, by continent", x="GDP per capita", y="Life expectancy") # Option 3: By facet ggplot(gapminder07, aes(x=lifeExp)) + geom_density() + facet_wrap(~continent, scales="free")+ labs(title="Life expectancy by continent", x="Life expectancy", y="") #### Choose your own adventure! #### # Create a plot that includes two geoms and facets by some variable.	/answers/exercise1_answers.R	no_license	kumarhk/2021-teaching-r-workshop	R	false	false	3,876	r	#### Load libraries #### # Load tidyverse - note the output lets you know this contains ggplot2 library(tidyverse) # Load gapminder - this contains the data we will use library(gapminder) #### Import and view data #### # Load data gapminder <- gapminder::gapminder # Look at the structure of the data. You can use glimpse(), summary(), or head(). glimpse(gapminder) # Create a new data frame with only the data for 2007 gapminder07 <- filter(gapminder, year==2007) #### Basic plot #### # Scatterplot of population over time ggplot(gapminder) + geom_point(aes(x=year, y=pop)) # Add labels to the plot ggplot(gapminder) + geom_point(aes(x=year, y=pop)) + labs(title="Population over time", x="Year", y="Population") # Your turn: plot life expectancy as a function of GDP per capita for the year 2007, and add labels ggplot(gapminder07) + geom_point(aes(x=gdpPercap, y=lifeExp)) + labs(title="Do people in richer countries live longer?", x="GDP per capita", y="Life expectancy") #### Choosing geoms #### # Let's look at geom_smooth(). Add a geom_smooth() layer to the previous plot. ggplot(gapminder07, aes(x=gdpPercap, y=lifeExp)) + geom_point() + geom_smooth() + labs(title="Do people in richer countries live longer?", x="GDP per capita", y="Life expectancy", subtitle="Gapminder 2007 data") # How about geom_hline()? When you add it, you'll need to provide a new aesthetic option. ggplot(gapminder07) + geom_point(aes(x=gdpPercap, y=lifeExp)) + geom_hline(aes(yintercept=mean(lifeExp))) + labs(title="Do people in richer countries live longer?", x="GDP per capita", y="Life expectancy", subtitle="Gapminder 2007 data") # Your turn: Plot the life expectancy of each continent in 2007 # Look at the ggplot cheatsheet and decide what kind of geom to use: https://raw.githubusercontent.com/rstudio/cheatsheets/master/data-visualization-2.1.pdf # Option 1: Box plot ggplot(gapminder07) + geom_boxplot(aes(x=continent, y=lifeExp)) # Option 2: Column plot ggplot(gapminder07) + geom_col(aes(x=continent, y=lifeExp)) #### Grouping variables #### # Grouping by color ggplot(gapminder) + geom_point(aes(x=gdpPercap, y=lifeExp, col=year)) + labs(title="Do people in richer countries live longer?", x="GDP per capita", y="Life expectancy") # Grouping by facet ggplot(gapminder, aes(x=gdpPercap, y=lifeExp)) + geom_point() + geom_smooth() + facet_wrap(~year) + labs(title="Do people in richer countries live longer?", x="GDP per capita", y="Life expectancy") # Add scales="free" in facet_wrap() and see what happens ggplot(gapminder, aes(x=gdpPercap, y=lifeExp)) + geom_point() + geom_smooth() + facet_wrap(~year, scales="free") + labs(title="Do people in richer countries live longer?", x="GDP per capita", y="Life expectancy") # Your turn: Let's visualize life expectancy by continent in 2007 again. This time, use grouping by color or facet. # Bonus: Use two geoms. # Option 1: By colors ggplot(gapminder07, aes(x=gdpPercap, y=lifeExp, col=continent, size=pop)) + geom_point() + labs(title="Life expectancy as a function of GDP per capita, by continent", x="GDP per capita", y="Life expectancy") # Option 2: By facet ggplot(gapminder07, aes(x=gdpPercap, y=lifeExp, size=pop)) + geom_point() + geom_smooth() + facet_wrap(~continent, scales="free") + labs(title="Life expectancy as a function of GDP per capita, by continent", x="GDP per capita", y="Life expectancy") # Option 3: By facet ggplot(gapminder07, aes(x=lifeExp)) + geom_density() + facet_wrap(~continent, scales="free")+ labs(title="Life expectancy by continent", x="Life expectancy", y="") #### Choose your own adventure! #### # Create a plot that includes two geoms and facets by some variable.
% Generated by roxygen2: do not edit by hand % Please edit documentation in R/config-tools.R \name{load_config} \alias{load_config} \title{Load and validate configuration file} \usage{ load_config(config_path) } \arguments{ \item{config_path}{Path to the WGSAParsr configuration file to load and validate} } \value{ a tibble that can be used for building field lists for parsing } \description{ WGSAParsr configuration files are flexible tab-separated files. They must include a header with field names, and the following fields, in any order: \itemize{ \item \strong{field} column headings matching the WGSA output file to be parsed \item \strong{SNV} logical (TRUE/FALSE) indicating whether the field should be parsed for snv annotation \item \strong{indel} logical (TRUE/FALSE) indicating whether the field should be parsed for indel annotation \item \strong{dbnsfp} logical (TRUE/FALSE) indicating whether the field should be parsed for dbnsfp annotation \item \strong{pivotGroup} numerical value to group annotations for pivoting \item \strong{pivotChar} character separating fields that should be used for pivoting \item \strong{parseGroup} numerical value to group annotations for other parsing \item \strong{transformation} a string describing the transformation to be performed. Values may include: \itemize{ \item \strong{max} select the maximum value \item \strong{min} select the minimum value \item \strong{pick_Y} select "Y" if present \item \strong{pick_N} select "N" if present \item \strong{pick_A} select A>D>P>N (MutationTaster_pred field) \item \strong{clean} remove the \{n\}. E.g.: "Enhancer\{4\}" -> "Enhancer" \item \strong{distinct} select unique values. NOTE: must have a pivotGroup and pivotChar = \| } } } \details{ Additionally, the following fields may be included, and are processed during configuration file loading, but are not required: \itemize{ \item \strong{order} numerical value for column ordering in parsed output \item \strong{sourceGroup} numerical value for column grouping/ordering in output } Other columns (such as notes) may be included in the configuration file, but will not be validated or imported with this function. The configuration file may include comments beginning with #. } \examples{ \dontrun{ local_config <- load_config("config.tsv") } freeze_5_config <- load_config(wgsaparsr_example("fr_5_config.tsv")) }	/man/load_config.Rd	no_license	bestbioinformatics/wgsaparsr	R	false	true	2,470	rd	% Generated by roxygen2: do not edit by hand % Please edit documentation in R/config-tools.R \name{load_config} \alias{load_config} \title{Load and validate configuration file} \usage{ load_config(config_path) } \arguments{ \item{config_path}{Path to the WGSAParsr configuration file to load and validate} } \value{ a tibble that can be used for building field lists for parsing } \description{ WGSAParsr configuration files are flexible tab-separated files. They must include a header with field names, and the following fields, in any order: \itemize{ \item \strong{field} column headings matching the WGSA output file to be parsed \item \strong{SNV} logical (TRUE/FALSE) indicating whether the field should be parsed for snv annotation \item \strong{indel} logical (TRUE/FALSE) indicating whether the field should be parsed for indel annotation \item \strong{dbnsfp} logical (TRUE/FALSE) indicating whether the field should be parsed for dbnsfp annotation \item \strong{pivotGroup} numerical value to group annotations for pivoting \item \strong{pivotChar} character separating fields that should be used for pivoting \item \strong{parseGroup} numerical value to group annotations for other parsing \item \strong{transformation} a string describing the transformation to be performed. Values may include: \itemize{ \item \strong{max} select the maximum value \item \strong{min} select the minimum value \item \strong{pick_Y} select "Y" if present \item \strong{pick_N} select "N" if present \item \strong{pick_A} select A>D>P>N (MutationTaster_pred field) \item \strong{clean} remove the \{n\}. E.g.: "Enhancer\{4\}" -> "Enhancer" \item \strong{distinct} select unique values. NOTE: must have a pivotGroup and pivotChar = \| } } } \details{ Additionally, the following fields may be included, and are processed during configuration file loading, but are not required: \itemize{ \item \strong{order} numerical value for column ordering in parsed output \item \strong{sourceGroup} numerical value for column grouping/ordering in output } Other columns (such as notes) may be included in the configuration file, but will not be validated or imported with this function. The configuration file may include comments beginning with #. } \examples{ \dontrun{ local_config <- load_config("config.tsv") } freeze_5_config <- load_config(wgsaparsr_example("fr_5_config.tsv")) }
% Generated by roxygen2 (4.1.1): do not edit by hand % Please edit documentation in R/exploits.r \name{shodan_exploit_search} \alias{shodan_exploit_search} \title{Search for Exploits} \usage{ shodan_exploit_search(query = NULL, facets = NULL, page = 1) } \arguments{ \item{query}{Search query used to search the database of known exploits. See \url{https://developer.shodan.io/api/exploits/rest} for all supported search filters.} \item{facets}{A comma-separated list of properties to get summary information on. The following facets are currently supported: "\code{author}", "\code{platform}", "\code{port}", "\code{source}" and "\code{type}. If \code{length(facets) > 1)} this function will concatenate the vector with commas to send to Shodan.} \item{page}{The page number to page through results \code{100} at a time (default: \code{1})} } \description{ Search across a variety of data sources for exploits and use facets to get summary information. } \references{ \url{https://developer.shodan.io/api/exploits/rest} }	/man/shodan_exploit_search.Rd	no_license	Leocodefocus/shodan	R	false	false	1,026	rd	% Generated by roxygen2 (4.1.1): do not edit by hand % Please edit documentation in R/exploits.r \name{shodan_exploit_search} \alias{shodan_exploit_search} \title{Search for Exploits} \usage{ shodan_exploit_search(query = NULL, facets = NULL, page = 1) } \arguments{ \item{query}{Search query used to search the database of known exploits. See \url{https://developer.shodan.io/api/exploits/rest} for all supported search filters.} \item{facets}{A comma-separated list of properties to get summary information on. The following facets are currently supported: "\code{author}", "\code{platform}", "\code{port}", "\code{source}" and "\code{type}. If \code{length(facets) > 1)} this function will concatenate the vector with commas to send to Shodan.} \item{page}{The page number to page through results \code{100} at a time (default: \code{1})} } \description{ Search across a variety of data sources for exploits and use facets to get summary information. } \references{ \url{https://developer.shodan.io/api/exploits/rest} }
#' Kernel RV Coefficient Test (KRV) #' #' Kernel RV coefficient test to evaluate the overall association between #' microbiome composition and high-dimensional or structured phenotype or genotype. #' #' kernels.otu should be a list of numerical n by n kernel matrices, or a single #' n by n kernel matrix, where n is sample size. #' #' When kernel.y is a method ("Gaussian" or "linear") to compute the kernel of #' phenotype, y should be a numerical phenotype matrix, and X (if not NULL) #' should be a numeric matrix of covariates. Both y and X should have n rows. #' #' When kernel.y is a kernel matrix for the phenotype, there is no need to provide #' X and y, and they will be ignored if provided. In this case, kernel.y and #' kernel.otu should both be numeric matrices with the same number of rows and columns. #' #' Missing data is not permitted. Please remove all individuals with missing #' kernel.otu, y (if not NULL), X (if not NULL), and kernel.y (if a matrix is #' entered) prior to analysis. #' #' @param y A numeric n by p matrix of p continuous phenotype variables and #' sample size n (default = NULL). If it is NULL, a #' phenotype kernel matrix must be entered for "kernel.y". Defaults to NULL. #' @param X A numeric n by q matrix, containing q additional covariates #' (default = NULL). If NULL, an intercept only model is used. If the first #' column of X is not uniformly 1, then an intercept column will be added. #' @param adjust.type Possible values are "none" (default if X is null), #' "phenotype" to adjust only the y variable (only possible if y is a numeric #' phenotype matrix rather than a pre-computed kernel), or "both" to adjust #' both the X and Y kernels. #' @param kernels.otu A numeric OTU n by n kernel matrix or a list of matrices, #' where n is the sample size. It can be constructed from microbiome data, such #' as by transforming from a distance metric. #' @param kernel.y Either a numerical n by n kernel matrix for phenotypes or a #' method to compute the kernel of phenotype. Methods are "Gaussian" or "linear". #' A Gaussian kernel (kernel.y="Gaussian") can capture the general relationship #' between microbiome and phenotypes; a linear kernel (kernel.y="linear") #' may be preferred if the underlying relationship is close to linear. #' @param omnibus A string equal to either "Cauchy" or "kernel_om" (or unambiguous #' abbreviations thereof), specifying whether to use the Cauchy combination test #' or an omnibus kernel to generate the omnibus p-value. #' @param returnKRV A logical indicating whether to return the KRV statistic. Defaults to FALSE. #' @param returnR2 A logical indicating whether to return the R-squared coefficient. Defaults to FALSE. #' #' @return #' If only one candidate kernel matrix is considered, returns a list containing the p-value for the candidate kernel matrix. #' If more than one candidate kernel matrix is considered, returns a list of two elements: #' \item{p_values}{P-value for each candidate kernel matrix} #' \item{omnibus_p}{Omnibus p-value} #' \item{KRV}{A vector of kernel RV statistics (a measure of effect size), one for each candidate kernel matrix. Only returned if returnKRV = TRUE} #' \item{R2}{A vector of R-squared statistics, one for each candidate kernel matrix. Only returned if returnR2 = TRUE} #' #'@author #'Nehemiah Wilson, Haotian Zheng, Xiang Zhan, Ni Zhao #' #'@references #' Zheng, Haotian, Zhan, X., Plantinga, A., Zhao, N., and Wu, M.C. A Fast Small-Sample Kernel Independence Test for Microbiome #' Community-Level Association Analysis. Biometrics. 2017 Mar 10. doi: 10.1111/biom.12684. #' #' Liu, Hongjiao, Ling, W., Hua, X., Moon, J.Y., Williams-Nguyen, J., Zhan, X., Plantinga, A.M., Zhao, N., #' Zhang, A., Durazo-Arzivu, R.A., Knight, R., Qi, Q., Burk, R.D., Kaplan, R.C., and Wu, M.C. #' Kernel-based genetic association analysis for microbiome phenotypes identifies host genetic #' drivers of beta-diversity. 2021+ #' #'@importFrom PearsonDS ppearsonIII #' #'@examples #'library(GUniFrac) #'library(MASS) #' #'data(throat.tree) #'data(throat.otu.tab) #'data(throat.meta) #' #' ## Simulate covariate data #' set.seed(123) #' n = nrow(throat.otu.tab) #' Sex <- throat.meta$Sex #' Smoker <- throat.meta$SmokingStatus #' anti <- throat.meta$AntibioticUsePast3Months_TimeFromAntibioticUsage #' Male = (Sex == "Male")2 #' Smoker = (Smoker == "Smoker") 2 #' Anti = (anti != "None")^2 #' cova = cbind(1, Male, Smoker, Anti) #' #' ## Simulate microbiome data #' otu.tab.rff <- Rarefy(throat.otu.tab)$otu.tab.rff #' unifracs <- GUniFrac(otu.tab.rff, throat.tree, alpha=c(0, 0.5, 1))$unifracs #' # Distance matrices #' D.weighted = unifracs[,,"d_1"] #' D.unweighted = unifracs[,,"d_UW"] #' # Kernel matrices #' K.weighted = D2K(D.weighted) #' K.unweighted = D2K(D.unweighted) #' #' if (requireNamespace("vegan")) { #' library(vegan) #' D.BC = as.matrix(vegdist(otu.tab.rff, method="bray")) #' K.BC = D2K(D.BC) #'} #' #' # Simulate phenotype data #' rho = 0.2 #' Va = matrix(rep(rho, (2n)^2), 2n, 2n)+diag(1-rho, 2n) #' Phe = mvrnorm(n, rep(0, 2n), Va) #' K.y = Phe %% t(Phe) # phenotype kernel #' #' # Simulate genotype data #' G = matrix(rbinom(n10, 2, 0.1), n, 10) #' K.g = G %% t(G) # genotype kernel #' #' #' ## Unadjusted analysis (microbiome and phenotype) #' KRV(y = Phe, kernels.otu = K.weighted, kernel.y = "Gaussian") # numeric y #' KRV(kernels.otu = K.weighted, kernel.y = K.y) # kernel y #' #' ## Adjusted analysis (phenotype only) #' KRV(kernels.otu = K.weighted, y = Phe, kernel.y = "linear", X = cova, adjust.type = "phenotype") #' #' if (requireNamespace("vegan")) { #' ## Adjusted analysis (adjust both kernels; microbiome and phenotype) #' KRV(kernels.otu = K.BC, kernel.y = K.y, X = cova, adjust.type='both') #' #' ## Adjusted analysis (adjust both kernels; microbiome and genotype) #' KRV(kernels.otu = K.BC, kernel.y = K.g, X = cova, adjust.type='both') #' } #' #' #'@export KRV <- function(y = NULL, X = NULL, adjust.type = NULL, kernels.otu, kernel.y, omnibus = "kernel_om", returnKRV = FALSE, returnR2 = FALSE){ ## Check input om <- substring(tolower(omnibus), 1, 1) if (!is.null(adjust.type)) { adjust.type <- substring(tolower(adjust.type), 1, 1) if (adjust.type == "p") { if (!kernel.y %in% c("linear", "Gaussian")) { stop("Phenotype-only adjustment may only be used with numeric y, not pre-computed phenotype kernel. Try option 'both' instead or double check that you have entered either 'linear' or 'Gaussian' for kernel.y.") } } if (!adjust.type %in% c("p", "b")) { stop("I don't know that covariate adjustment choice. Please choose 'phenotype' or 'both', or leave this option NULL.") } } if (!is.list(kernels.otu)) { kernels.otu <- list(kernels.otu) } if (!all(unlist(lapply(kernels.otu, FUN = function(k) is.matrix(k))))) { stop("Please ensure kernels.otu is either a single n x n kernel matrix or a list of n x n kernel matrices.") } if (!is.null(X) & !all(X[,1] == 1)) { X1 <- cbind(1, X) X <- X1 } if (is.null(X) & !is.null(adjust.type)) { warning("X is NULL, so no covariate adjustment will be done.") adjust.type = NULL } if (is.matrix(kernel.y)){ n = nrow(kernels.otu[[1]]) if (!is.null(y)){ warning("When a phenotype kernel is provided, argument \"y\" will be ignored.\n") } if (ncol(kernels.otu[[1]]) != n \| nrow(kernel.y) != n \| ncol(kernel.y) != n){ stop("Kernel matrices need to be n x n, where n is the sample size.\n ") } } else if (!is.matrix(kernel.y)){ if (!(kernel.y %in% c("Gaussian", "linear"))){ stop("Please choose kernel.y = \"Gaussian\" or \"linear\", or enter a kernel matrix for \"kernel.y\".\n") } if(is.null(y)){ stop("Please enter a phenotype matrix for argument \"y\" or enter a kernel matrix for argument \"kernel.y\".\n") } n = NROW(y) if (!all(unlist(lapply(kernels.otu, FUN = function(x) nrow(x) == n & ncol(x) == n)))) { stop("Kernel matrix/matrices must be n x n, where n is the sample size. \n ") } if (any(is.na(y))){ ids = which(is.na(y)) stop(paste("Missing response for subject(s)", ids, "- please remove before proceeding. \n")) } if (!is.null(X)){ if (any(is.na(X))){ stop("NAs in covariates X, please impute or remove subjects with missing covariates values.\n") } if(NROW(X)!= NROW(y)) stop("Dimensions of X and y don't match.\n") } } ## Actual test pvals <- c() if (returnKRV) { KRVs <- c() } else { KRVs = NULL } if (returnR2) { R2 <- c() } else { R2 = NULL } for(i in 1:length(kernels.otu)){ res <- inner.KRV(y = y, X = X, adjust.type = adjust.type, kernel.otu=kernels.otu[[i]], kernel.y = kernel.y, returnKRV = TRUE, returnR2 = TRUE) pvals[i] <- res$pv if (returnKRV) { KRVs[i] <- res$KRV } if (returnR2) { R2[i] <- res$R2 } } # Naming the p-values with the Kernel matrix names from Ks kernel.names <- names(kernels.otu) names(pvals) <- kernel.names if (returnKRV) { names(KRVs) <- kernel.names } if (returnR2) { names(R2) <- kernel.names } #Omnibus Test if (length(kernels.otu) > 1) { if (om == "k") { K.om <- matrix(0, nrow = nrow(kernels.otu[[1]]), ncol = ncol(kernels.otu[[1]])) for(i in 1:length(kernels.otu)){ K.om = K.om + kernels.otu[[i]]/tr(kernels.otu[[i]]) } omnibus_p <- as.numeric(inner.KRV(y = y, X = X, adjust.type = adjust.type, kernel.otu = K.om, kernel.y = kernel.y)$pv) } else if (om == "c") { cauchy.t <- sum(tan((0.5 - pvals)*pi))/length(pvals) omnibus_p <- 1 - pcauchy(cauchy.t) } else { stop("I don't know that omnibus option. Please choose 'kernel_om' or 'Cauchy'.") } ## Return for multiple kernels if (is.null(KRVs) & is.null(R2)) { return(list(p_values = pvals, omnibus_p = omnibus_p)) } else if (is.null(KRVs) & !is.null(R2)) { return(list(p_values = pvals, omnibus_p = omnibus_p, R2 = R2)) } else if (!is.null(KRVs) & is.null(R2)) { return(list(p_values = pvals, omnibus_p = omnibus_p, KRV = KRVs)) } else { return(list(p_values = pvals, omnibus_p = omnibus_p, KRV = KRVs, R2 = R2)) } } ## Return for single kernels if (is.null(KRVs) & is.null(R2)) { return(list(p_values = pvals)) } else if (is.null(KRVs) & !is.null(R2)) { return(list(p_values = pvals, R2 = R2)) } else if (!is.null(KRVs) & is.null(R2)) { return(list(p_values = pvals, KRV = KRVs)) } else { return(list(p_values = pvals, KRV = KRVs, R2 = R2)) } }	/R/KRV.R	no_license	cran/MiRKAT	R	false	false	10,847	r	#' Kernel RV Coefficient Test (KRV) #' #' Kernel RV coefficient test to evaluate the overall association between #' microbiome composition and high-dimensional or structured phenotype or genotype. #' #' kernels.otu should be a list of numerical n by n kernel matrices, or a single #' n by n kernel matrix, where n is sample size. #' #' When kernel.y is a method ("Gaussian" or "linear") to compute the kernel of #' phenotype, y should be a numerical phenotype matrix, and X (if not NULL) #' should be a numeric matrix of covariates. Both y and X should have n rows. #' #' When kernel.y is a kernel matrix for the phenotype, there is no need to provide #' X and y, and they will be ignored if provided. In this case, kernel.y and #' kernel.otu should both be numeric matrices with the same number of rows and columns. #' #' Missing data is not permitted. Please remove all individuals with missing #' kernel.otu, y (if not NULL), X (if not NULL), and kernel.y (if a matrix is #' entered) prior to analysis. #' #' @param y A numeric n by p matrix of p continuous phenotype variables and #' sample size n (default = NULL). If it is NULL, a #' phenotype kernel matrix must be entered for "kernel.y". Defaults to NULL. #' @param X A numeric n by q matrix, containing q additional covariates #' (default = NULL). If NULL, an intercept only model is used. If the first #' column of X is not uniformly 1, then an intercept column will be added. #' @param adjust.type Possible values are "none" (default if X is null), #' "phenotype" to adjust only the y variable (only possible if y is a numeric #' phenotype matrix rather than a pre-computed kernel), or "both" to adjust #' both the X and Y kernels. #' @param kernels.otu A numeric OTU n by n kernel matrix or a list of matrices, #' where n is the sample size. It can be constructed from microbiome data, such #' as by transforming from a distance metric. #' @param kernel.y Either a numerical n by n kernel matrix for phenotypes or a #' method to compute the kernel of phenotype. Methods are "Gaussian" or "linear". #' A Gaussian kernel (kernel.y="Gaussian") can capture the general relationship #' between microbiome and phenotypes; a linear kernel (kernel.y="linear") #' may be preferred if the underlying relationship is close to linear. #' @param omnibus A string equal to either "Cauchy" or "kernel_om" (or unambiguous #' abbreviations thereof), specifying whether to use the Cauchy combination test #' or an omnibus kernel to generate the omnibus p-value. #' @param returnKRV A logical indicating whether to return the KRV statistic. Defaults to FALSE. #' @param returnR2 A logical indicating whether to return the R-squared coefficient. Defaults to FALSE. #' #' @return #' If only one candidate kernel matrix is considered, returns a list containing the p-value for the candidate kernel matrix. #' If more than one candidate kernel matrix is considered, returns a list of two elements: #' \item{p_values}{P-value for each candidate kernel matrix} #' \item{omnibus_p}{Omnibus p-value} #' \item{KRV}{A vector of kernel RV statistics (a measure of effect size), one for each candidate kernel matrix. Only returned if returnKRV = TRUE} #' \item{R2}{A vector of R-squared statistics, one for each candidate kernel matrix. Only returned if returnR2 = TRUE} #' #'@author #'Nehemiah Wilson, Haotian Zheng, Xiang Zhan, Ni Zhao #' #'@references #' Zheng, Haotian, Zhan, X., Plantinga, A., Zhao, N., and Wu, M.C. A Fast Small-Sample Kernel Independence Test for Microbiome #' Community-Level Association Analysis. Biometrics. 2017 Mar 10. doi: 10.1111/biom.12684. #' #' Liu, Hongjiao, Ling, W., Hua, X., Moon, J.Y., Williams-Nguyen, J., Zhan, X., Plantinga, A.M., Zhao, N., #' Zhang, A., Durazo-Arzivu, R.A., Knight, R., Qi, Q., Burk, R.D., Kaplan, R.C., and Wu, M.C. #' Kernel-based genetic association analysis for microbiome phenotypes identifies host genetic #' drivers of beta-diversity. 2021+ #' #'@importFrom PearsonDS ppearsonIII #' #'@examples #'library(GUniFrac) #'library(MASS) #' #'data(throat.tree) #'data(throat.otu.tab) #'data(throat.meta) #' #' ## Simulate covariate data #' set.seed(123) #' n = nrow(throat.otu.tab) #' Sex <- throat.meta$Sex #' Smoker <- throat.meta$SmokingStatus #' anti <- throat.meta$AntibioticUsePast3Months_TimeFromAntibioticUsage #' Male = (Sex == "Male")2 #' Smoker = (Smoker == "Smoker") 2 #' Anti = (anti != "None")^2 #' cova = cbind(1, Male, Smoker, Anti) #' #' ## Simulate microbiome data #' otu.tab.rff <- Rarefy(throat.otu.tab)$otu.tab.rff #' unifracs <- GUniFrac(otu.tab.rff, throat.tree, alpha=c(0, 0.5, 1))$unifracs #' # Distance matrices #' D.weighted = unifracs[,,"d_1"] #' D.unweighted = unifracs[,,"d_UW"] #' # Kernel matrices #' K.weighted = D2K(D.weighted) #' K.unweighted = D2K(D.unweighted) #' #' if (requireNamespace("vegan")) { #' library(vegan) #' D.BC = as.matrix(vegdist(otu.tab.rff, method="bray")) #' K.BC = D2K(D.BC) #'} #' #' # Simulate phenotype data #' rho = 0.2 #' Va = matrix(rep(rho, (2n)^2), 2n, 2n)+diag(1-rho, 2n) #' Phe = mvrnorm(n, rep(0, 2n), Va) #' K.y = Phe %% t(Phe) # phenotype kernel #' #' # Simulate genotype data #' G = matrix(rbinom(n10, 2, 0.1), n, 10) #' K.g = G %% t(G) # genotype kernel #' #' #' ## Unadjusted analysis (microbiome and phenotype) #' KRV(y = Phe, kernels.otu = K.weighted, kernel.y = "Gaussian") # numeric y #' KRV(kernels.otu = K.weighted, kernel.y = K.y) # kernel y #' #' ## Adjusted analysis (phenotype only) #' KRV(kernels.otu = K.weighted, y = Phe, kernel.y = "linear", X = cova, adjust.type = "phenotype") #' #' if (requireNamespace("vegan")) { #' ## Adjusted analysis (adjust both kernels; microbiome and phenotype) #' KRV(kernels.otu = K.BC, kernel.y = K.y, X = cova, adjust.type='both') #' #' ## Adjusted analysis (adjust both kernels; microbiome and genotype) #' KRV(kernels.otu = K.BC, kernel.y = K.g, X = cova, adjust.type='both') #' } #' #' #'@export KRV <- function(y = NULL, X = NULL, adjust.type = NULL, kernels.otu, kernel.y, omnibus = "kernel_om", returnKRV = FALSE, returnR2 = FALSE){ ## Check input om <- substring(tolower(omnibus), 1, 1) if (!is.null(adjust.type)) { adjust.type <- substring(tolower(adjust.type), 1, 1) if (adjust.type == "p") { if (!kernel.y %in% c("linear", "Gaussian")) { stop("Phenotype-only adjustment may only be used with numeric y, not pre-computed phenotype kernel. Try option 'both' instead or double check that you have entered either 'linear' or 'Gaussian' for kernel.y.") } } if (!adjust.type %in% c("p", "b")) { stop("I don't know that covariate adjustment choice. Please choose 'phenotype' or 'both', or leave this option NULL.") } } if (!is.list(kernels.otu)) { kernels.otu <- list(kernels.otu) } if (!all(unlist(lapply(kernels.otu, FUN = function(k) is.matrix(k))))) { stop("Please ensure kernels.otu is either a single n x n kernel matrix or a list of n x n kernel matrices.") } if (!is.null(X) & !all(X[,1] == 1)) { X1 <- cbind(1, X) X <- X1 } if (is.null(X) & !is.null(adjust.type)) { warning("X is NULL, so no covariate adjustment will be done.") adjust.type = NULL } if (is.matrix(kernel.y)){ n = nrow(kernels.otu[[1]]) if (!is.null(y)){ warning("When a phenotype kernel is provided, argument \"y\" will be ignored.\n") } if (ncol(kernels.otu[[1]]) != n \| nrow(kernel.y) != n \| ncol(kernel.y) != n){ stop("Kernel matrices need to be n x n, where n is the sample size.\n ") } } else if (!is.matrix(kernel.y)){ if (!(kernel.y %in% c("Gaussian", "linear"))){ stop("Please choose kernel.y = \"Gaussian\" or \"linear\", or enter a kernel matrix for \"kernel.y\".\n") } if(is.null(y)){ stop("Please enter a phenotype matrix for argument \"y\" or enter a kernel matrix for argument \"kernel.y\".\n") } n = NROW(y) if (!all(unlist(lapply(kernels.otu, FUN = function(x) nrow(x) == n & ncol(x) == n)))) { stop("Kernel matrix/matrices must be n x n, where n is the sample size. \n ") } if (any(is.na(y))){ ids = which(is.na(y)) stop(paste("Missing response for subject(s)", ids, "- please remove before proceeding. \n")) } if (!is.null(X)){ if (any(is.na(X))){ stop("NAs in covariates X, please impute or remove subjects with missing covariates values.\n") } if(NROW(X)!= NROW(y)) stop("Dimensions of X and y don't match.\n") } } ## Actual test pvals <- c() if (returnKRV) { KRVs <- c() } else { KRVs = NULL } if (returnR2) { R2 <- c() } else { R2 = NULL } for(i in 1:length(kernels.otu)){ res <- inner.KRV(y = y, X = X, adjust.type = adjust.type, kernel.otu=kernels.otu[[i]], kernel.y = kernel.y, returnKRV = TRUE, returnR2 = TRUE) pvals[i] <- res$pv if (returnKRV) { KRVs[i] <- res$KRV } if (returnR2) { R2[i] <- res$R2 } } # Naming the p-values with the Kernel matrix names from Ks kernel.names <- names(kernels.otu) names(pvals) <- kernel.names if (returnKRV) { names(KRVs) <- kernel.names } if (returnR2) { names(R2) <- kernel.names } #Omnibus Test if (length(kernels.otu) > 1) { if (om == "k") { K.om <- matrix(0, nrow = nrow(kernels.otu[[1]]), ncol = ncol(kernels.otu[[1]])) for(i in 1:length(kernels.otu)){ K.om = K.om + kernels.otu[[i]]/tr(kernels.otu[[i]]) } omnibus_p <- as.numeric(inner.KRV(y = y, X = X, adjust.type = adjust.type, kernel.otu = K.om, kernel.y = kernel.y)$pv) } else if (om == "c") { cauchy.t <- sum(tan((0.5 - pvals)*pi))/length(pvals) omnibus_p <- 1 - pcauchy(cauchy.t) } else { stop("I don't know that omnibus option. Please choose 'kernel_om' or 'Cauchy'.") } ## Return for multiple kernels if (is.null(KRVs) & is.null(R2)) { return(list(p_values = pvals, omnibus_p = omnibus_p)) } else if (is.null(KRVs) & !is.null(R2)) { return(list(p_values = pvals, omnibus_p = omnibus_p, R2 = R2)) } else if (!is.null(KRVs) & is.null(R2)) { return(list(p_values = pvals, omnibus_p = omnibus_p, KRV = KRVs)) } else { return(list(p_values = pvals, omnibus_p = omnibus_p, KRV = KRVs, R2 = R2)) } } ## Return for single kernels if (is.null(KRVs) & is.null(R2)) { return(list(p_values = pvals)) } else if (is.null(KRVs) & !is.null(R2)) { return(list(p_values = pvals, R2 = R2)) } else if (!is.null(KRVs) & is.null(R2)) { return(list(p_values = pvals, KRV = KRVs)) } else { return(list(p_values = pvals, KRV = KRVs, R2 = R2)) } }

#service_data_new_data_eda.csv dataset <- read.csv(file.choose()) View(dataset) str(dataset) # 범주형 vs 범주형을 가지고 데이터의 분포를 확인한다면? # 1. resident2, gender2를 범주형으로 변환 dataset$resident2 <- factor(dataset$resident2) dataset$gender2 <- factor(dataset$gender2) levels(dataset$resident2) levels(dataset$gender2) # 2. 두 변수를 table()함수를 이용하여 분포를 확인해보자 resident_gender <- table(dataset$resident2, dataset$gender2) #막대 가로로 horiz이용 barplot(resident_gender,horiz = T) #한 막대로 누적되어있는거를 멀티바로 나열 barplot(resident_gender,horiz = T,beside = T) #범례 추가 barplot(resident_gender,horiz = T,beside = T, legend = row.names(resident_gender)) #색 지정 barplot(resident_gender,horiz = T,beside = T, legend = row.names(resident_gender), col = rainbow(5)) ###위 그래프랑 비슷하게 ggplot이용 resident_gender_df <- data.frame(resident_gender) ###그룹은 박스플롯할때 특정그룹으로 묶어지는데 여기서는 fill자체에서 그룹화되어짐 ggplot(data = resident_gender_df, aes(x=Freq, y = Var2, fill = Var1, group = Var1)) + geom_bar(stat = "identity", position = 'dodge') #모자이크플롯 mosaicplot(resident_gender, col = rainbow(2)) # 직업유형(job2) vs 나이(age2) dataset$job2 <- factor(dataset$job2) dataset$age2 <- factor(dataset$age2) jobage <- table(dataset$job2, dataset$age2) barplot(jobage, beside = T,legend = row.names(jobage), col = rainbow(3)) jobage_df <- data.frame(jobage) ggplot(data = jobage_df, aes(x=Freq, y=Var2, fill = Var1, group = Var1)) + geom_bar(stat = "identity", position = "dodge") + coord_flip() # 숫자형 vs 범주형 # 직업유형에 따른 나이 비율 # 카테고리 유형별 시각화 install.packages("lattice") library(lattice) ?densityplot #density는 x축값 먼저정의하고 데이터정의, 근데 팩터형이어야함 densityplot(dataset$age2, dataset) str(dataset) #auto.key = T 로 범례 설정 densityplot(dataset$age, dataset, group = dataset$job2, auto.key = T) ggplot(data = dataset, aes(x= age, fill = job2)) + geom_bar(width = .5, position = "dodge") ##실습3!!! # 데이터 프레임의 복사본 생성하기 library(ggplot2) midwest midwest_raw <- data.frame(midwest) midwest_new <- midwest_raw str(midwest_new) head(midwest_new) # [문제] # poptotal(전체인구) 변수를 total로, # popasian(아시안 인구) 변수를 asian으로 수정하세요. midwest_new <- rename(midwest_new, "total" = "poptotal") midwest_new <- rename(midwest_new, "asian" = "popasian") library(dplyr) # [문제] # total, asian 변수를 이용해 '전체 인구 대비 아시아 인구 백분율' percasian 파생변수를 만들고, # 히스토그램을 만들어 도시들이 어떻게 분포하는지 살펴보세요. midwest_new$"percasian2" <- (midwest_new$asian / midwest_new$total)*100 hist(midwest_new$percasian2) # [문제] # 아시아 인구 백분율 전체 평균을 구하고, # 평균을 초과하면 "large", # 그 외에는 "small"을 부여하는 mean 파생변수를 만들어 보세요. mean(midwest_new$percasian2) midwest_new$mean_percasian2 <- ifelse(midwest_new$percasian2 > mean(midwest_new$percasian2),"large","small") # [문제] # "large"와 "small"에 해당하는 지역이 얼마나 되는지 빈도표와 빈도 막대 그래프를 만들어 확인해 보세요. densityplot(midwest_new$percasian2, midwest_new, group = midwest_new$country, auto.key = T) ggplot(data = midwest_new, aes(x=percasian2,y=county)) + geom_bar(stat = "identity",width = .1, position = "dodge") + geom_point() teacher <- as.data.frame(table(midwest_new$mean_percasian2)) ggplot(teacher, aes(x=Var1, y=Freq)) + geom_bar(stat = "identity", width = .2) # ggplot2의 midwest 데이터를 사용하여 데이터 분석을 실습하는 문제 입니다. # popadults는 해당 지역의 성인 인구, poptotal은 전체 인구를 나타냅니다. # 1번 문제 # midwest 데이터에 '전체 인구 대비 미성년 인구 백분율' 변수를 추가하세요. midwest_new$"전체 인구 대비 미성년 인구 백분율" <- ((midwest_new$total - midwest_new$popadults) / midwest_new$total)*100 ##강사님 답 midwest_new <- midwest_new %>% mutate(percyouth = (poptotal - popadults)/poptotal * 100) # 2번 문제 # 미성년 인구 백분율이 가장 높은 상위 5개 county(지역)의 미성년 인구 백분율을 출력하시오. tail(sort(midwest_new$"전체 인구 대비 미성년 인구 백분율"), n=5) ##강사님 답 midwest_new %>% arrange(desc(percyouth)) %>% dplyr::select(county, percyouth) %>% head(5) # 3번 문제 # 다음과 같은 분류표의 기준에 따라 미성년 비율 등급 변수를 추가하고, 각 등급에 몇 개의 지역이 있는지 알아보세요. # 분류 기준 # large 40%이상 # middle 30 ~ 40미만 # small 30미만 midwest_new$"미성년 비율 등급" <- ifelse("전체 인구 대비 미성년 인구 백분율" >= 40,"large",ifelse("전체 인구 대비 미성년 인구 백분율" >= 30,"middle","small")) ##강사님 답 midwest_new <- midwest_new %>% mutate(gradeyouth = ifelse(percyouth >= 40 ,"large", ifelse(percyouth >= 30,"middle","small"))) table(midwest_new$gradeyouth) # 4번 문제 # popasian은 해당 지역의 아시아인 인구를 나타냅니다. # '전체 인구 대비 아시아인 인구 백분율' 변수를 추가하고 하위 10개 지역의 state(주), county(지역), 아시아인 인구 백분율을 출력하세요. midwest_new$"전체 인구 대비 아시아인 인구 백분율" <- (midwest_new$asian / midwest_new$total)*100 head(sort(midwest_new$state), n=10) head(sort(midwest_new$county), n=10) head(sort(midwest_new$"전체 인구 대비 아시아인 인구 백분율"), n=10) ##강사님 답 midwest_new <- midwest_new %>% mutate(ratio_asian = popasian/poptotal*100) midwest_new %>% arrange(ratio_asian) %>% dplyr::select(state, county, ratio_asian) %>% head(10) # 시계열(time series) # 변수간의 상관성 # iris 시계열 데이터 만들기 iris seq <- as.integer( rownames(iris) ) ?cbind irisDF <- cbind(seq = as.integer(rownames(iris)),iris) # x축은 seq # y축은 -Species #랜덤하게 4색 추출 colsColor <- topo.colors(4,alpha = .4) #2열부터 5열까지 이름부여 names(colsColor) <- names(irisDF)[2:5] #melt함수 이용해서 기준seq, species #나머지 컬럼을 variable해서 wide -> long irisDF library(reshape2) iris_melt <- melt(irisDF, id = c("seq","Species")) ##버젼떄문에 오류가 나면 id대신 id.vars로 표현 library(ggplot2) g <- ggplot(data = iris_melt, aes(x=seq, y=value, col = variable)) + geom_line(cex = 0.8, show.legend = T) #추가적으로 선의 색상과 범례 라벨링 g <- g + scale_color_manual( name = "", values = colsColor[iris_melt$variable], labels = c("꽃받침 길이", "꽃받침 너비", "꽃잎 길이", "꽃잎 너비") ) # 날짜 # 문자변수를 날짜변수 변환 # R의 날짜 데이터 타입 "POSIXct" # as.POSIXct() str_date <- "200730 13:40" as.POSIXct(str_date, format = "%y%m%d %H:%M") #2020이라Y 20이면y str_date <- "2020-07-30 13:40:01 PM" as.POSIXct(str_date, format = "%Y-%m-%d %H:%M:%S") str_date <- "07/30/20 13:40:01" as.POSIXct(str_date, format = "%m/%d/%y %H:%M:%S") cospi <- read.csv(file.choose()) #날짜별 주가 만들기(시간이 있어서 따로 시계열 만들필요 없음) cospi_test <- cospi cospi_melt <- melt(cospi_test, id = c("Date","Volume")) ggplot(data = cospi_melt, aes(x= Date,y=value,col = variable, group = variable)) + geom_line(cex = 0.5, show.legend = T) ##실습4!!! spanish_train <- read.csv(file.choose()) str(spanish_train) # 1. # 데이터 내에 결측치 여부를 확인한다. # NA값이 310681개 있는 것을 확인할 수 있다. #결측치 확인방법 spanish_train[!complete.cases(spanish_train),] str(spanish_train[!complete.cases(spanish_train),]) #결측치 제거 test_renfe2 <- spanish_train[complete.cases(spanish_train),] str(test_renfe2) # 2. # filter와 !is.na함수를 통해 결측치를 모두 제거했다. # 3. # 마드리드 출발 # 마드리드에서 출발하는 열차 데이터만을 떼어내 madrid_origin이라는 변수로 저장하고 # 우선, 마드리드에서 출발하는 열차 데이터만을 이용해 비교해보기로 한다. madrid_origin <- filter(test_renfe2, test_renfe2$origin == "MADRID") str(madrid_origin) # 4. # summary함수를 통해 일반적 데이터 정보를 다시 확인한다. summary(test_renfe2) # 5. # 마드리드 출발 열차의 빈도 수 # 마드리드를 출발하는 기차의 도착 도시별 운행빈도 수를 바형태로 나타내보자 ggplot(data = madrid_origin, aes(x=destination,fill=origin)) + geom_bar(width = .5, position = "dodge") # 6. # 마드리드발 도착지별 가격 박스플롯으로 티켓가격의 높은 순을 확인해보자 boxplot(price ~ destination, data=madrid_origin) str(madrid_origin) # 7. # AVE의좌석 등급별 가격박스플롯이 시각화 # 똑같은 열차와 똑같은 좌석등급, 똑같은 도착지라 하더라도 가격이 차이가 나는 것을 확인할 수 있다. AVE_MADRID <- filter(madrid_origin, madrid_origin$train_type == "AVE") boxplot(price ~ destination,data= AVE_MADRID) # 8. # 이 차이를 이해하고 싶어 시계열로 데이터를 만들어보았다. # 9. #날짜 데이터 변환. as.POSIXct는 factor형식의 날짜 사용가능 # 10. # 컬럼이름지정 # colnames(a_b) <- c("preferente","Turista") # 11. # 도착지별, 트레인 클래스별로 가격을 박스플롯형태로 나타낼 수도 있다.

/eighth_class.R

no_license

Lucidhomme/R

R

false

10,122

r

#service_data_new_data_eda.csv dataset <- read.csv(file.choose()) View(dataset) str(dataset) # 범주형 vs 범주형을 가지고 데이터의 분포를 확인한다면? # 1. resident2, gender2를 범주형으로 변환 dataset$resident2 <- factor(dataset$resident2) dataset$gender2 <- factor(dataset$gender2) levels(dataset$resident2) levels(dataset$gender2) # 2. 두 변수를 table()함수를 이용하여 분포를 확인해보자 resident_gender <- table(dataset$resident2, dataset$gender2) #막대 가로로 horiz이용 barplot(resident_gender,horiz = T) #한 막대로 누적되어있는거를 멀티바로 나열 barplot(resident_gender,horiz = T,beside = T) #범례 추가 barplot(resident_gender,horiz = T,beside = T, legend = row.names(resident_gender)) #색 지정 barplot(resident_gender,horiz = T,beside = T, legend = row.names(resident_gender), col = rainbow(5)) ###위 그래프랑 비슷하게 ggplot이용 resident_gender_df <- data.frame(resident_gender) ###그룹은 박스플롯할때 특정그룹으로 묶어지는데 여기서는 fill자체에서 그룹화되어짐 ggplot(data = resident_gender_df, aes(x=Freq, y = Var2, fill = Var1, group = Var1)) + geom_bar(stat = "identity", position = 'dodge') #모자이크플롯 mosaicplot(resident_gender, col = rainbow(2)) # 직업유형(job2) vs 나이(age2) dataset$job2 <- factor(dataset$job2) dataset$age2 <- factor(dataset$age2) jobage <- table(dataset$job2, dataset$age2) barplot(jobage, beside = T,legend = row.names(jobage), col = rainbow(3)) jobage_df <- data.frame(jobage) ggplot(data = jobage_df, aes(x=Freq, y=Var2, fill = Var1, group = Var1)) + geom_bar(stat = "identity", position = "dodge") + coord_flip() # 숫자형 vs 범주형 # 직업유형에 따른 나이 비율 # 카테고리 유형별 시각화 install.packages("lattice") library(lattice) ?densityplot #density는 x축값 먼저정의하고 데이터정의, 근데 팩터형이어야함 densityplot(dataset$age2, dataset) str(dataset) #auto.key = T 로 범례 설정 densityplot(dataset$age, dataset, group = dataset$job2, auto.key = T) ggplot(data = dataset, aes(x= age, fill = job2)) + geom_bar(width = .5, position = "dodge") ##실습3!!! # 데이터 프레임의 복사본 생성하기 library(ggplot2) midwest midwest_raw <- data.frame(midwest) midwest_new <- midwest_raw str(midwest_new) head(midwest_new) # [문제] # poptotal(전체인구) 변수를 total로, # popasian(아시안 인구) 변수를 asian으로 수정하세요. midwest_new <- rename(midwest_new, "total" = "poptotal") midwest_new <- rename(midwest_new, "asian" = "popasian") library(dplyr) # [문제] # total, asian 변수를 이용해 '전체 인구 대비 아시아 인구 백분율' percasian 파생변수를 만들고, # 히스토그램을 만들어 도시들이 어떻게 분포하는지 살펴보세요. midwest_new$"percasian2" <- (midwest_new$asian / midwest_new$total)*100 hist(midwest_new$percasian2) # [문제] # 아시아 인구 백분율 전체 평균을 구하고, # 평균을 초과하면 "large", # 그 외에는 "small"을 부여하는 mean 파생변수를 만들어 보세요. mean(midwest_new$percasian2) midwest_new$mean_percasian2 <- ifelse(midwest_new$percasian2 > mean(midwest_new$percasian2),"large","small") # [문제] # "large"와 "small"에 해당하는 지역이 얼마나 되는지 빈도표와 빈도 막대 그래프를 만들어 확인해 보세요. densityplot(midwest_new$percasian2, midwest_new, group = midwest_new$country, auto.key = T) ggplot(data = midwest_new, aes(x=percasian2,y=county)) + geom_bar(stat = "identity",width = .1, position = "dodge") + geom_point() teacher <- as.data.frame(table(midwest_new$mean_percasian2)) ggplot(teacher, aes(x=Var1, y=Freq)) + geom_bar(stat = "identity", width = .2) # ggplot2의 midwest 데이터를 사용하여 데이터 분석을 실습하는 문제 입니다. # popadults는 해당 지역의 성인 인구, poptotal은 전체 인구를 나타냅니다. # 1번 문제 # midwest 데이터에 '전체 인구 대비 미성년 인구 백분율' 변수를 추가하세요. midwest_new$"전체 인구 대비 미성년 인구 백분율" <- ((midwest_new$total - midwest_new$popadults) / midwest_new$total)*100 ##강사님 답 midwest_new <- midwest_new %>% mutate(percyouth = (poptotal - popadults)/poptotal * 100) # 2번 문제 # 미성년 인구 백분율이 가장 높은 상위 5개 county(지역)의 미성년 인구 백분율을 출력하시오. tail(sort(midwest_new$"전체 인구 대비 미성년 인구 백분율"), n=5) ##강사님 답 midwest_new %>% arrange(desc(percyouth)) %>% dplyr::select(county, percyouth) %>% head(5) # 3번 문제 # 다음과 같은 분류표의 기준에 따라 미성년 비율 등급 변수를 추가하고, 각 등급에 몇 개의 지역이 있는지 알아보세요. # 분류 기준 # large 40%이상 # middle 30 ~ 40미만 # small 30미만 midwest_new$"미성년 비율 등급" <- ifelse("전체 인구 대비 미성년 인구 백분율" >= 40,"large",ifelse("전체 인구 대비 미성년 인구 백분율" >= 30,"middle","small")) ##강사님 답 midwest_new <- midwest_new %>% mutate(gradeyouth = ifelse(percyouth >= 40 ,"large", ifelse(percyouth >= 30,"middle","small"))) table(midwest_new$gradeyouth) # 4번 문제 # popasian은 해당 지역의 아시아인 인구를 나타냅니다. # '전체 인구 대비 아시아인 인구 백분율' 변수를 추가하고 하위 10개 지역의 state(주), county(지역), 아시아인 인구 백분율을 출력하세요. midwest_new$"전체 인구 대비 아시아인 인구 백분율" <- (midwest_new$asian / midwest_new$total)*100 head(sort(midwest_new$state), n=10) head(sort(midwest_new$county), n=10) head(sort(midwest_new$"전체 인구 대비 아시아인 인구 백분율"), n=10) ##강사님 답 midwest_new <- midwest_new %>% mutate(ratio_asian = popasian/poptotal*100) midwest_new %>% arrange(ratio_asian) %>% dplyr::select(state, county, ratio_asian) %>% head(10) # 시계열(time series) # 변수간의 상관성 # iris 시계열 데이터 만들기 iris seq <- as.integer( rownames(iris) ) ?cbind irisDF <- cbind(seq = as.integer(rownames(iris)),iris) # x축은 seq # y축은 -Species #랜덤하게 4색 추출 colsColor <- topo.colors(4,alpha = .4) #2열부터 5열까지 이름부여 names(colsColor) <- names(irisDF)[2:5] #melt함수 이용해서 기준seq, species #나머지 컬럼을 variable해서 wide -> long irisDF library(reshape2) iris_melt <- melt(irisDF, id = c("seq","Species")) ##버젼떄문에 오류가 나면 id대신 id.vars로 표현 library(ggplot2) g <- ggplot(data = iris_melt, aes(x=seq, y=value, col = variable)) + geom_line(cex = 0.8, show.legend = T) #추가적으로 선의 색상과 범례 라벨링 g <- g + scale_color_manual( name = "", values = colsColor[iris_melt$variable], labels = c("꽃받침 길이", "꽃받침 너비", "꽃잎 길이", "꽃잎 너비") ) # 날짜 # 문자변수를 날짜변수 변환 # R의 날짜 데이터 타입 "POSIXct" # as.POSIXct() str_date <- "200730 13:40" as.POSIXct(str_date, format = "%y%m%d %H:%M") #2020이라Y 20이면y str_date <- "2020-07-30 13:40:01 PM" as.POSIXct(str_date, format = "%Y-%m-%d %H:%M:%S") str_date <- "07/30/20 13:40:01" as.POSIXct(str_date, format = "%m/%d/%y %H:%M:%S") cospi <- read.csv(file.choose()) #날짜별 주가 만들기(시간이 있어서 따로 시계열 만들필요 없음) cospi_test <- cospi cospi_melt <- melt(cospi_test, id = c("Date","Volume")) ggplot(data = cospi_melt, aes(x= Date,y=value,col = variable, group = variable)) + geom_line(cex = 0.5, show.legend = T) ##실습4!!! spanish_train <- read.csv(file.choose()) str(spanish_train) # 1. # 데이터 내에 결측치 여부를 확인한다. # NA값이 310681개 있는 것을 확인할 수 있다. #결측치 확인방법 spanish_train[!complete.cases(spanish_train),] str(spanish_train[!complete.cases(spanish_train),]) #결측치 제거 test_renfe2 <- spanish_train[complete.cases(spanish_train),] str(test_renfe2) # 2. # filter와 !is.na함수를 통해 결측치를 모두 제거했다. # 3. # 마드리드 출발 # 마드리드에서 출발하는 열차 데이터만을 떼어내 madrid_origin이라는 변수로 저장하고 # 우선, 마드리드에서 출발하는 열차 데이터만을 이용해 비교해보기로 한다. madrid_origin <- filter(test_renfe2, test_renfe2$origin == "MADRID") str(madrid_origin) # 4. # summary함수를 통해 일반적 데이터 정보를 다시 확인한다. summary(test_renfe2) # 5. # 마드리드 출발 열차의 빈도 수 # 마드리드를 출발하는 기차의 도착 도시별 운행빈도 수를 바형태로 나타내보자 ggplot(data = madrid_origin, aes(x=destination,fill=origin)) + geom_bar(width = .5, position = "dodge") # 6. # 마드리드발 도착지별 가격 박스플롯으로 티켓가격의 높은 순을 확인해보자 boxplot(price ~ destination, data=madrid_origin) str(madrid_origin) # 7. # AVE의좌석 등급별 가격박스플롯이 시각화 # 똑같은 열차와 똑같은 좌석등급, 똑같은 도착지라 하더라도 가격이 차이가 나는 것을 확인할 수 있다. AVE_MADRID <- filter(madrid_origin, madrid_origin$train_type == "AVE") boxplot(price ~ destination,data= AVE_MADRID) # 8. # 이 차이를 이해하고 싶어 시계열로 데이터를 만들어보았다. # 9. #날짜 데이터 변환. as.POSIXct는 factor형식의 날짜 사용가능 # 10. # 컬럼이름지정 # colnames(a_b) <- c("preferente","Turista") # 11. # 도착지별, 트레인 클래스별로 가격을 박스플롯형태로 나타낼 수도 있다.

#Access data from the openFEC api using R wrapper from Stephen Holzman #https://stephenholzman.github.io/tidyusafec/articles/intro.html library(tidyverse) library(tidyusafec) library(tigris) library(leaflet) #signup for api key at https://api.open.fec.gov/developers/. Save a one line file called "data.gov.key" in the root project folder, that one line assigning the key to a variable like the next line: save_datagov_apikey(key = "n3BB27dCbHpsI0BAIyYi5i4nMa3xJk9AXF7cG2Hc", install = TRUE) #I want to do the opposite of in, later as part of my filter, so I'm definining the %notin% function here `%notin%` <- Negate(`%in%`) #select all candidates running for Senate, unnest the data, deliver it in a df, and make sure they raised money #For North Carolina races #senate <- search_candidates(state = "NC", election_year = "2020", office = "S", candidate_status = "C" , has_raised_funds = TRUE, unnest_committees = TRUE ) %>% #for alaska races (this is useful because the file is somewhat small..... See my sampling script at the bottom for a way to take a radom sample to make testing much speedier) #senate <- search_candidates(state = "AK", election_year = "2020", office = "S", candidate_status = "C" , has_raised_funds = TRUE, unnest_committees = TRUE ) %>% #for colorado races #senate <- search_candidates(state = "CO", election_year = "2020", office = "S", candidate_status = "C" , has_raised_funds = TRUE, unnest_committees = TRUE ) %>% #for arizona races #### Here's the queries I've been using for counties #senate <- search_candidates(name = c("MCSALLY, MARTHA"), election_year = "2020", office = "S", candidate_status = "C" , has_raised_funds = TRUE, unnest_committees = TRUE ) %>% #I've split these into two queries, because my key currently only allows for 1,000 queries in an hour. This is the one for Kelly. When we make the maps, we can address this. senate <- search_candidates(name = c("KELLY, MARK"), election_year = "2020", office = "S", candidate_status = "C" , has_raised_funds = TRUE, unnest_committees = TRUE ) %>% #get all their itemized contributions get_itemized_contributions(data_structure = "tidy") %>% #unnests the pesky committee column and creates unique columns for each of the nested list items. #here's a great tutorial on how to get rid of nested lists https://cfss.uchicago.edu/notes/simplify-nested-lists/ Thanks Andrew Tran for pointing me to this #something else he suggested. I didn't need it, but am putting it here for safe keeping https://jennybc.github.io/purrr-tutorial/ls00_inspect-explore.html unnest_wider(committee, names_repair = "unique") %>% #select only the fields I want. select(schedule_type_full, report_type, line_number, line_number_label, contributor_state, is_individual, report_year, contributor_city, contribution_receipt_date, contributor_aggregate_ytd, two_year_transaction_period, contribution_receipt_amount, contributor_zip, contributor_name, state, party, committee_type_full, state_full, name, fec_election_type_desc, memo_code) %>% # itemized individual contributions are recorded on line 11ai & we want to get the contributions given in the last 2-year cycle. We also want to exclude a double-count that happens with win red and act blue contributions. By excluding the x memo_code, we can eliminate those double-counts filter(report_year > 2018 & line_number %in% "11AI" & memo_code %notin% "X" ) #preserve the zeroes senate$contributor_zip <- as.character(senate$contributor_zip) #Rural/urban codes were downloaded from https://www.ers.usda.gov/data-products/rural-urban-continuum-codes.aspx #fec data accessed from https://classic.fec.gov/disclosurep/PDownload.do #subsets the first 5 numbers of the zipcodes row https://www.rdocumentation.org/packages/base/versions/3.6.0/topics/substr senate$contributor_zip <- substr(senate$contributor_zip, 1, 5) #FYI, if you want the last 5, use https://www.rdocumentation.org/packages/FedData/versions/1.1.0/topics/substrRight ########## #let's see what and where these senators are getting their funding. This query is just for Tillis donations... and I need to go back and make sure there's no pacs in it. Right now it's set up just to make the map work. totals <- senate %>% #this filter changes based on the race group_by(contributor_zip) %>% summarise( total_raised = sum(contribution_receipt_amount)) #returns tigris query file as a shapefile (if not, it's a weird list that doesn't join) options(tigris_class = "sf") #Download a Zip Code Tabulation Area (ZCTA) shapefile into R #read in our shapefile options(tigris_use_cache = TRUE) #code_shapefile <- zctas(cb = FALSE, year = 2010, state = "NC") #code_shapefile <- zctas(cb = FALSE, year = 2010, state = "AK") #code_shapefile <- zctas(cb = FALSE, year = 2010, state = "CO") code_shapefile <- zctas(cb = FALSE, year = 2010, state = "AZ") #join democratic fundraising numbers with shapefile #make sure the zip column is numeric and then join the two, so we can make the shapefile and contributions data work together like happy friends in leaflet cong_tot <- left_join(code_shapefile, totals , by = c("ZCTA5CE10"= "contributor_zip" )) #and there's two counties that have a total of 0 (their populations are a total of 27k residents), so we're going to replace those values with zeroes.We'll create a new column just in case cong_tot <- cong_tot %>% mutate(total_raised_no_na = replace_na(total_raised, 0)) ########## #########works, but is so slow because of all the shapefiles to plot. #ggplot(cong_tot) + # geom_sf(data = cong_tot) + # aes(fill= cut(total_raised,breaks = c(-1, 10000, 30000, 60000, 120000), labels= c("0-9k", "10k-29k", "30k-59k", "60k-120k") )) + # geom_sf(color="black") + # scale_fill_manual(values = c("#FFFFFF", "#C9C5DB", "#938CB8", "#3E386D")) + # theme_void() + # labs(title="Funds raised by Thom Tillis in NC zip codes", caption="Source: Federal Elections Commission", color='legend', fill='legend title') #leaflet works much faster and it gives us a more interactive graphic, which i'm partial to. bins <- c(0, 100, 1000, 10000, 100000, 200000, Inf) pal1 <- colorBin(palette = c("#FFFFFF", "#C9C5DB","#05B69C", "#F9A51A", "#C73D49"), domain = cong_tot$total_raised_no_na, bins = bins) map <- leaflet(cong_tot) %>% addTiles() state_popup1 <- paste0("<strong> District: </strong>", cong_tot$ZCTA5CE10, "<br><strong>Total Raised: </strong>", cong_tot$total_raised_no_na) leaflet(data = cong_tot) %>% addProviderTiles("CartoDB.Positron") %>% addPolygons(fillColor = ~pal1(total_raised_no_na), fillOpacity = 0.8, color = "#BDBDC3", weight = 1, popup = state_popup1) %>% addLegend("bottomright", pal = pal1, values = ~total_raised_no_na, title = "Total raised", labFormat = labelFormat(prefix = " ")) #helpful for running code in a smaller subset of data for testing purposes dt <- #df %>% #slices off number of rows from 1:xx in df #slice(1:4) #slices off random number of rows from data sample_n(senate, 10) #from zip code and noncensus packages. Was used in original code. And Supposedly cleans up zip codes, gets me FIPS codes. Cran has been rudely pulled down the orphan packages which I found rude. It's useful. But if it's gone, it's gone. #library(zipcode) #library(noncensus) #senate$contributor_zip <- clean.zipcodes(senate$contributor_zip) #conjures up the zip_codes table from the noncensus library. Which is a great resource for this project. Because, unlike the zip codes data(zipcodes) table. It has fips codes. #data(zip_codes) #however, there was a lack of zeroes in the fips code fields. And since the clean.zipcodes puts zeroes in front of some four-digit I'm using it here to match up the Census database. #zip_codes$fips <- clean.zipcodes(zip_codes$fips) #############This will be useful if we need to group by county. Not just by zip. Until then, it shall be noted and sit idle. #counties <- read_csv("ZIP_COUNTY.csv") #props to Dhmontgomery in newsnerdery. to helping me eliminate the excess dupes by using a combination of rank and filter...basically what top_n does. But with the added ability of adding the ties.method element. #I was able to select the highest ratios and then, in the case of ties, R randomly chose one. #nodupes <- counties %>% # group_by(zip) %>% # mutate(rank = rank(tot_ratio, ties.method = "random")) %>% # filter(rank < 2) ###################### #USEFUL DOCUMENTATION # #https://s3.amazonaws.com/ire16/campaign-finance/MiningFECData.pdf

/old or unused scripts/vulnerable_senators_zips.R

no_license

ChrisCioffi/districts

R

false

8,643

r

#Access data from the openFEC api using R wrapper from Stephen Holzman #https://stephenholzman.github.io/tidyusafec/articles/intro.html library(tidyverse) library(tidyusafec) library(tigris) library(leaflet) #signup for api key at https://api.open.fec.gov/developers/. Save a one line file called "data.gov.key" in the root project folder, that one line assigning the key to a variable like the next line: save_datagov_apikey(key = "n3BB27dCbHpsI0BAIyYi5i4nMa3xJk9AXF7cG2Hc", install = TRUE) #I want to do the opposite of in, later as part of my filter, so I'm definining the %notin% function here `%notin%` <- Negate(`%in%`) #select all candidates running for Senate, unnest the data, deliver it in a df, and make sure they raised money #For North Carolina races #senate <- search_candidates(state = "NC", election_year = "2020", office = "S", candidate_status = "C" , has_raised_funds = TRUE, unnest_committees = TRUE ) %>% #for alaska races (this is useful because the file is somewhat small..... See my sampling script at the bottom for a way to take a radom sample to make testing much speedier) #senate <- search_candidates(state = "AK", election_year = "2020", office = "S", candidate_status = "C" , has_raised_funds = TRUE, unnest_committees = TRUE ) %>% #for colorado races #senate <- search_candidates(state = "CO", election_year = "2020", office = "S", candidate_status = "C" , has_raised_funds = TRUE, unnest_committees = TRUE ) %>% #for arizona races #### Here's the queries I've been using for counties #senate <- search_candidates(name = c("MCSALLY, MARTHA"), election_year = "2020", office = "S", candidate_status = "C" , has_raised_funds = TRUE, unnest_committees = TRUE ) %>% #I've split these into two queries, because my key currently only allows for 1,000 queries in an hour. This is the one for Kelly. When we make the maps, we can address this. senate <- search_candidates(name = c("KELLY, MARK"), election_year = "2020", office = "S", candidate_status = "C" , has_raised_funds = TRUE, unnest_committees = TRUE ) %>% #get all their itemized contributions get_itemized_contributions(data_structure = "tidy") %>% #unnests the pesky committee column and creates unique columns for each of the nested list items. #here's a great tutorial on how to get rid of nested lists https://cfss.uchicago.edu/notes/simplify-nested-lists/ Thanks Andrew Tran for pointing me to this #something else he suggested. I didn't need it, but am putting it here for safe keeping https://jennybc.github.io/purrr-tutorial/ls00_inspect-explore.html unnest_wider(committee, names_repair = "unique") %>% #select only the fields I want. select(schedule_type_full, report_type, line_number, line_number_label, contributor_state, is_individual, report_year, contributor_city, contribution_receipt_date, contributor_aggregate_ytd, two_year_transaction_period, contribution_receipt_amount, contributor_zip, contributor_name, state, party, committee_type_full, state_full, name, fec_election_type_desc, memo_code) %>% # itemized individual contributions are recorded on line 11ai & we want to get the contributions given in the last 2-year cycle. We also want to exclude a double-count that happens with win red and act blue contributions. By excluding the x memo_code, we can eliminate those double-counts filter(report_year > 2018 & line_number %in% "11AI" & memo_code %notin% "X" ) #preserve the zeroes senate$contributor_zip <- as.character(senate$contributor_zip) #Rural/urban codes were downloaded from https://www.ers.usda.gov/data-products/rural-urban-continuum-codes.aspx #fec data accessed from https://classic.fec.gov/disclosurep/PDownload.do #subsets the first 5 numbers of the zipcodes row https://www.rdocumentation.org/packages/base/versions/3.6.0/topics/substr senate$contributor_zip <- substr(senate$contributor_zip, 1, 5) #FYI, if you want the last 5, use https://www.rdocumentation.org/packages/FedData/versions/1.1.0/topics/substrRight ########## #let's see what and where these senators are getting their funding. This query is just for Tillis donations... and I need to go back and make sure there's no pacs in it. Right now it's set up just to make the map work. totals <- senate %>% #this filter changes based on the race group_by(contributor_zip) %>% summarise( total_raised = sum(contribution_receipt_amount)) #returns tigris query file as a shapefile (if not, it's a weird list that doesn't join) options(tigris_class = "sf") #Download a Zip Code Tabulation Area (ZCTA) shapefile into R #read in our shapefile options(tigris_use_cache = TRUE) #code_shapefile <- zctas(cb = FALSE, year = 2010, state = "NC") #code_shapefile <- zctas(cb = FALSE, year = 2010, state = "AK") #code_shapefile <- zctas(cb = FALSE, year = 2010, state = "CO") code_shapefile <- zctas(cb = FALSE, year = 2010, state = "AZ") #join democratic fundraising numbers with shapefile #make sure the zip column is numeric and then join the two, so we can make the shapefile and contributions data work together like happy friends in leaflet cong_tot <- left_join(code_shapefile, totals , by = c("ZCTA5CE10"= "contributor_zip" )) #and there's two counties that have a total of 0 (their populations are a total of 27k residents), so we're going to replace those values with zeroes.We'll create a new column just in case cong_tot <- cong_tot %>% mutate(total_raised_no_na = replace_na(total_raised, 0)) ########## #########works, but is so slow because of all the shapefiles to plot. #ggplot(cong_tot) + # geom_sf(data = cong_tot) + # aes(fill= cut(total_raised,breaks = c(-1, 10000, 30000, 60000, 120000), labels= c("0-9k", "10k-29k", "30k-59k", "60k-120k") )) + # geom_sf(color="black") + # scale_fill_manual(values = c("#FFFFFF", "#C9C5DB", "#938CB8", "#3E386D")) + # theme_void() + # labs(title="Funds raised by Thom Tillis in NC zip codes", caption="Source: Federal Elections Commission", color='legend', fill='legend title') #leaflet works much faster and it gives us a more interactive graphic, which i'm partial to. bins <- c(0, 100, 1000, 10000, 100000, 200000, Inf) pal1 <- colorBin(palette = c("#FFFFFF", "#C9C5DB","#05B69C", "#F9A51A", "#C73D49"), domain = cong_tot$total_raised_no_na, bins = bins) map <- leaflet(cong_tot) %>% addTiles() state_popup1 <- paste0("<strong> District: </strong>", cong_tot$ZCTA5CE10, "<br><strong>Total Raised: </strong>", cong_tot$total_raised_no_na) leaflet(data = cong_tot) %>% addProviderTiles("CartoDB.Positron") %>% addPolygons(fillColor = ~pal1(total_raised_no_na), fillOpacity = 0.8, color = "#BDBDC3", weight = 1, popup = state_popup1) %>% addLegend("bottomright", pal = pal1, values = ~total_raised_no_na, title = "Total raised", labFormat = labelFormat(prefix = " ")) #helpful for running code in a smaller subset of data for testing purposes dt <- #df %>% #slices off number of rows from 1:xx in df #slice(1:4) #slices off random number of rows from data sample_n(senate, 10) #from zip code and noncensus packages. Was used in original code. And Supposedly cleans up zip codes, gets me FIPS codes. Cran has been rudely pulled down the orphan packages which I found rude. It's useful. But if it's gone, it's gone. #library(zipcode) #library(noncensus) #senate$contributor_zip <- clean.zipcodes(senate$contributor_zip) #conjures up the zip_codes table from the noncensus library. Which is a great resource for this project. Because, unlike the zip codes data(zipcodes) table. It has fips codes. #data(zip_codes) #however, there was a lack of zeroes in the fips code fields. And since the clean.zipcodes puts zeroes in front of some four-digit I'm using it here to match up the Census database. #zip_codes$fips <- clean.zipcodes(zip_codes$fips) #############This will be useful if we need to group by county. Not just by zip. Until then, it shall be noted and sit idle. #counties <- read_csv("ZIP_COUNTY.csv") #props to Dhmontgomery in newsnerdery. to helping me eliminate the excess dupes by using a combination of rank and filter...basically what top_n does. But with the added ability of adding the ties.method element. #I was able to select the highest ratios and then, in the case of ties, R randomly chose one. #nodupes <- counties %>% # group_by(zip) %>% # mutate(rank = rank(tot_ratio, ties.method = "random")) %>% # filter(rank < 2) ###################### #USEFUL DOCUMENTATION # #https://s3.amazonaws.com/ire16/campaign-finance/MiningFECData.pdf

# load necessary libraries library("shiny") # intro page intro <- mainPanel( h1("Reproductive Health and Resources"), strong("Purpose and Importance of the Project"), p("As a group, we believe that women should have reproductive rights. We think this would serve as an opportunity to delve into learning which reproductive resources are provided in the United States and the differences between in each geographic region. We also find it interesting how there are many different sub-categories that fall under reproductive health such as teen health, resources, infant health, abortion, etc."), strong("Origin of Sources"), p( "The", a("Guttmacher Institute", href = "https://data.guttmacher.org/states/table?dataset=data&state=AL+AK+AZ+AR+CA+CO+CT+DE+DC+FL+GA+HI+ID+IL+IN+IA+KS+KY+LA+ME+MD+MA+MI+MN+MS+MO+MT+NE+NV+NH+NJ+NM+NY+NC+ND+OH+OK+OR+PA+RI+SC+SD+TN+TX+UT+VT+VA+WA+WV+WI+WY&topics=57+283+65"), " dataframe has information on number of abortions, number of abortion clinics, percent of women taking contraceptive, etc. The", a("Kaggle", href = "https://www.kaggle.com/omer2040/usa-states-to-region"), " dataframe labels each of the United States into the four different geographic regions. We then joined these dataframes to create our new dataset that's grouped by regions." ), img("We support women's reproductive health!", src = "https://www.plannedparenthood.org/uploads/filer_public_thumbnails/filer_public/cb/c4/cbc45653-b657-4b15-97fd-490aeefcd262/damore_ppla_da_7867.jpg__1200x675_q75_crop_subsampling-2.jpg") ) # Interactive page one interactive_one <- sidebarLayout( sidebarPanel( h4("Choose which region you want to highlight"), selectInput( inputId = "chart_regions", label = "Pick a geographic region below:", choices = region_grouped$Region ) ), mainPanel( h1("Number of Abortion Clinics Per Region"), plotlyOutput("bar_graph"), tags$p( id = "graph_two_paragraph", "This bar graph shows the number of abortion clinics in each region. It attempts to show the general differences while attempting to highlight specific regions upon user selection on the left. A bar graph is ideal because the numerical data points aren’t closely related. As shown, the West region has the most abortion clinics - more than 250, with the midwest region having the least - less than 100. When hovering with the cursor over each bar, the specific number of abortion clinics is displayed." ) ) ) # Interactive page two interactive_two <- sidebarLayout(# creates a side bar and provides a wdiget to adjust Y axis sidebarPanel( h4("Zoom in or out on the Abortion Rates Axis"), sliderInput( inputId = "Max_y", "Pick a Max y Value", min = 0, max = 300, value = 300 ), sliderInput( inputId = "Min_y", "Pick a Min y Value", min = 0, max = 300, value = 80 ) ), mainPanel( h1("Abortion Clinics x Abortion Rates"), # page title plotlyOutput("clin_rates"), # ID tags$p( id = "graph_two_paragraph", # page paragraph definition "This point chart attempts to show the relationship between the total number of abortion clinics and the total abortion rate in each region. I used a scatterplot to show this correlation, however from the results it demostrates there is a small correlation between the two. However, the results disprove our hypothesis that the more amount of abortion clinics the more about of abortion rates. The graph displays (with one outlier) that the more abortion clinics the less total abortion rates in that region. This implies that less people need abortions in regions that have more abortion clinics available. This could be due to the fact that abortion clinics also provide other health care resources such as contraceptive, family planning, Plan B, health education and more, which can decrease the need for an unwanted pregnancy and therefore lessens the need for an abortion. " ) ) ) # Interactive page three interactive_three <- sidebarLayout( sidebarPanel( h4("Zoom in or out on the Percent of Contraceptives Axis"), sliderInput( inputId = "Max", "Pick a Max y Value", min = 75, max = 115, value = 80 ), sliderInput( inputId = "Min", "Pick a Min y Value", min = 20, max = 60, value = 60 ) ), mainPanel( h1("Abortion Clinics x Contraceptives"), plotlyOutput("clin_con"), tags$p( id = "graph_three_paragraph", "This bubble chart attempts to show the relationship, if any, between the number of abortion clinics in a region and the percentage of women aged 18 to 49 that use contraceptives. If there is a relationship then one could argue that abortion clincs being around might sway the number of women on contraceptives. As you can see this chart shows little to no correlation between the number of abortion clincs and the percentage of women on contraceptives. This implies that it doesn't matter if you have an easy option or not to abort, because either way most women are going to be on contraceptives." ) ) ) # Conclusion Page summary <- mainPanel( h1("Summary Information and Data"), h3("Takeaway #1"), h4("Table"), tableOutput("table_1"), h4("Explanation"), tags$p( id = "Expanation_1", "When looking at the total number of abortion clinics throughout the four different regions of the United States we found that the West had the most recorded number of clinics - 277 clinics - and the least being the Midwest - 91 clinics. We can see that the West had over three times the number of abortion clinics compared to Midwest. This could imply that there's a lower need for demand in the Midwest than in the other regions, hence the lower number of clinics. Higher population regions such as the West and Northeast may have more clinics to serve more people. Beliefs and feeling towards abortion in specific regions may also be a large contributor to the significant difference in how many clinics that are opened." ), h3("Takeaway #2"), h4("Table"), tableOutput("table_clin_rates"), h4("Explanation"), tags$p( id = "Explanation_2", "As we took a look at the total number of abortion clinics to the total abortion rate in each of the four regions we found that there was no clear correlations in the data. While the West had the most number of abortion clinics, it had the 2nd lowest abortion rate. However the South, which had the 2nd least number of clinics, surprisingly had the highest recorded abortion rates. Meanwhile the Midwest had the lowest number of abortion clinics and rates. These insights inform us that the abortion rate can vary region to region regardless of the number of clinics provided." ), h3("Takeaway #3"), h4("Table"), tableOutput("table_clin_con"), h4("Explanation"), tags$p( id = "Explanation_3", "As we pulled and analyzed the information on the total abortion clinics and the percentage of contraceptive usage in each region, we found that data for contraceptive was fairly the same falling within the bracket of 71.86% - 68.68 %. This is very interesting to observe as that tells us the rate of contraceptive usage isn't directly impacted by the total number of clinics provided in the region." ) ) # UI ui <- fluidPage( includeCSS("style.css"), navbarPage( "Womens Reproductive Resources", tabPanel("Overview", intro), tabPanel("Abortion Clinics/Region", interactive_one), tabPanel("Abortion Clinics x Abortion Rates", interactive_two), tabPanel("Abortion Clinics x Contraceptives", interactive_three), tabPanel("Summary Information", summary) ) )

/Shiny Website/app_ui.R

permissive

info-201a-sp20/final-project-achenn98

R

false

8,461

r

# load necessary libraries library("shiny") # intro page intro <- mainPanel( h1("Reproductive Health and Resources"), strong("Purpose and Importance of the Project"), p("As a group, we believe that women should have reproductive rights. We think this would serve as an opportunity to delve into learning which reproductive resources are provided in the United States and the differences between in each geographic region. We also find it interesting how there are many different sub-categories that fall under reproductive health such as teen health, resources, infant health, abortion, etc."), strong("Origin of Sources"), p( "The", a("Guttmacher Institute", href = "https://data.guttmacher.org/states/table?dataset=data&state=AL+AK+AZ+AR+CA+CO+CT+DE+DC+FL+GA+HI+ID+IL+IN+IA+KS+KY+LA+ME+MD+MA+MI+MN+MS+MO+MT+NE+NV+NH+NJ+NM+NY+NC+ND+OH+OK+OR+PA+RI+SC+SD+TN+TX+UT+VT+VA+WA+WV+WI+WY&topics=57+283+65"), " dataframe has information on number of abortions, number of abortion clinics, percent of women taking contraceptive, etc. The", a("Kaggle", href = "https://www.kaggle.com/omer2040/usa-states-to-region"), " dataframe labels each of the United States into the four different geographic regions. We then joined these dataframes to create our new dataset that's grouped by regions." ), img("We support women's reproductive health!", src = "https://www.plannedparenthood.org/uploads/filer_public_thumbnails/filer_public/cb/c4/cbc45653-b657-4b15-97fd-490aeefcd262/damore_ppla_da_7867.jpg__1200x675_q75_crop_subsampling-2.jpg") ) # Interactive page one interactive_one <- sidebarLayout( sidebarPanel( h4("Choose which region you want to highlight"), selectInput( inputId = "chart_regions", label = "Pick a geographic region below:", choices = region_grouped$Region ) ), mainPanel( h1("Number of Abortion Clinics Per Region"), plotlyOutput("bar_graph"), tags$p( id = "graph_two_paragraph", "This bar graph shows the number of abortion clinics in each region. It attempts to show the general differences while attempting to highlight specific regions upon user selection on the left. A bar graph is ideal because the numerical data points aren’t closely related. As shown, the West region has the most abortion clinics - more than 250, with the midwest region having the least - less than 100. When hovering with the cursor over each bar, the specific number of abortion clinics is displayed." ) ) ) # Interactive page two interactive_two <- sidebarLayout(# creates a side bar and provides a wdiget to adjust Y axis sidebarPanel( h4("Zoom in or out on the Abortion Rates Axis"), sliderInput( inputId = "Max_y", "Pick a Max y Value", min = 0, max = 300, value = 300 ), sliderInput( inputId = "Min_y", "Pick a Min y Value", min = 0, max = 300, value = 80 ) ), mainPanel( h1("Abortion Clinics x Abortion Rates"), # page title plotlyOutput("clin_rates"), # ID tags$p( id = "graph_two_paragraph", # page paragraph definition "This point chart attempts to show the relationship between the total number of abortion clinics and the total abortion rate in each region. I used a scatterplot to show this correlation, however from the results it demostrates there is a small correlation between the two. However, the results disprove our hypothesis that the more amount of abortion clinics the more about of abortion rates. The graph displays (with one outlier) that the more abortion clinics the less total abortion rates in that region. This implies that less people need abortions in regions that have more abortion clinics available. This could be due to the fact that abortion clinics also provide other health care resources such as contraceptive, family planning, Plan B, health education and more, which can decrease the need for an unwanted pregnancy and therefore lessens the need for an abortion. " ) ) ) # Interactive page three interactive_three <- sidebarLayout( sidebarPanel( h4("Zoom in or out on the Percent of Contraceptives Axis"), sliderInput( inputId = "Max", "Pick a Max y Value", min = 75, max = 115, value = 80 ), sliderInput( inputId = "Min", "Pick a Min y Value", min = 20, max = 60, value = 60 ) ), mainPanel( h1("Abortion Clinics x Contraceptives"), plotlyOutput("clin_con"), tags$p( id = "graph_three_paragraph", "This bubble chart attempts to show the relationship, if any, between the number of abortion clinics in a region and the percentage of women aged 18 to 49 that use contraceptives. If there is a relationship then one could argue that abortion clincs being around might sway the number of women on contraceptives. As you can see this chart shows little to no correlation between the number of abortion clincs and the percentage of women on contraceptives. This implies that it doesn't matter if you have an easy option or not to abort, because either way most women are going to be on contraceptives." ) ) ) # Conclusion Page summary <- mainPanel( h1("Summary Information and Data"), h3("Takeaway #1"), h4("Table"), tableOutput("table_1"), h4("Explanation"), tags$p( id = "Expanation_1", "When looking at the total number of abortion clinics throughout the four different regions of the United States we found that the West had the most recorded number of clinics - 277 clinics - and the least being the Midwest - 91 clinics. We can see that the West had over three times the number of abortion clinics compared to Midwest. This could imply that there's a lower need for demand in the Midwest than in the other regions, hence the lower number of clinics. Higher population regions such as the West and Northeast may have more clinics to serve more people. Beliefs and feeling towards abortion in specific regions may also be a large contributor to the significant difference in how many clinics that are opened." ), h3("Takeaway #2"), h4("Table"), tableOutput("table_clin_rates"), h4("Explanation"), tags$p( id = "Explanation_2", "As we took a look at the total number of abortion clinics to the total abortion rate in each of the four regions we found that there was no clear correlations in the data. While the West had the most number of abortion clinics, it had the 2nd lowest abortion rate. However the South, which had the 2nd least number of clinics, surprisingly had the highest recorded abortion rates. Meanwhile the Midwest had the lowest number of abortion clinics and rates. These insights inform us that the abortion rate can vary region to region regardless of the number of clinics provided." ), h3("Takeaway #3"), h4("Table"), tableOutput("table_clin_con"), h4("Explanation"), tags$p( id = "Explanation_3", "As we pulled and analyzed the information on the total abortion clinics and the percentage of contraceptive usage in each region, we found that data for contraceptive was fairly the same falling within the bracket of 71.86% - 68.68 %. This is very interesting to observe as that tells us the rate of contraceptive usage isn't directly impacted by the total number of clinics provided in the region." ) ) # UI ui <- fluidPage( includeCSS("style.css"), navbarPage( "Womens Reproductive Resources", tabPanel("Overview", intro), tabPanel("Abortion Clinics/Region", interactive_one), tabPanel("Abortion Clinics x Abortion Rates", interactive_two), tabPanel("Abortion Clinics x Contraceptives", interactive_three), tabPanel("Summary Information", summary) ) )

#' Base class #' #' @export SimpleQuestion <- R6::R6Class( "SimpleQuestion", public = list( type = "abstract", quiz = NULL, initialize = function(text, data = quote({}), hidden_data = quote({}), seed = NULL, hidden_seed = NULL, feedback = NULL, answer = NULL, tags = NULL, header = NULL) { private$.text <- text private$.data <- data private$.hidden_data <- hidden_data private$.seed <- seed private$.hidden_seed <- hidden_seed private$.answer <- answer private$.feedback <- feedback private$.tags <- tags private$.header <- header # Default placeholders private$placeholders <- list( TITLE = "get_title", REC_DATA_CHUNK = "get_rec_data_chunk", DATA_CHUNK = "get_data_chunk", ANSWER_INFO = "get_answer_info", INST_TEXT = "get_inst_text", SEED_CHUNK = "get_seed_chunk", EVALUATED_ANSWER = "get_evaluated_answer", FEEDBACK = "get_feedback", FEEDBACK_ANSWER = "get_feedback_answer", ANSWER_STRING = "get_answer_string") # Specific placeholders for XML export private$xml_placeholders <- list( TYPE = "get_type", TITLE = "get_title", XML_QUESTION_TEXT = "get_XML_question_text", XML_GENERALFEEDBACK = "get_XML_generalfeedback", XML_ANSWER = "get_XML_answer" ) }, instantiate_placeholders = function(template, placeholders, opts, info) { placeholders_regex <- paste0("@", names(placeholders), "@", collapse = "|") repeat { match <- stringi::stri_locate_first_regex(template, placeholders_regex) if(any(is.na(match))) # breaking if no match break if(nrow(match) == 0) break # Computing replacement string id <- substring(template, match[1] + 1, match[2] - 1) funcname <- placeholders[[id]] if (is.null(funcname)) stop("Unable to find corresponding function for ", sQuote(id)) replacement <- self[[funcname]](opts, info) # If replacement is NULL, try to replace whole line if (is.null(replacement)) { template <- stringi::stri_replace_first_regex(template, paste0("^ *@", id, "@ *\n"), "", opts_regex = list(multiline = TRUE)) template <- stringi::stri_replace_first_regex(template, paste0("@", id, "@"), "") } else { # Escape backslash and dollar in replacement string replacement <- gsub("\\\\", "\\\\\\\\", replacement) replacement <- gsub("\\$", "\\\\$", replacement) template <- stringi::stri_replace_first_regex(template, paste0("@", id, "@"), replacement) } } if(nchar(template) == 0) NULL else template }, update_quiz = function(quiz) { self$quiz <- quiz self$invalidate_hidden_data_list() }, get_data_and_environment = function() { if(is.null(self$quiz)) data <- self$recursive_instantiated_data() else data <- self$quiz$recursive_instantiated_data() env <- empty_env() eval(data, env) list(data = data, env = env) }, validate_data = function(parent_data = NULL) {}, get_markdown_from_template = function(template, opts = list(), info = list()) { self$instantiate_placeholders(template, private$placeholders, opts, info) }, to_markdown = function(opts = list(), info = list()) { opts <- update_list(private$default_options, opts) opts$export <- "markdown" private$validate_options(opts, info) template <- "@INST_TEXT@\n\n@FEEDBACK@\n" self$instantiate_placeholders(template, private$placeholders, opts, info) }, #' Feedback options if none is provided get_default_feedback = function() { list(text = self$answer) }, #' Sanitize provided feedback get_feedback_from_field = function(feedback) { feedback <- if(is.null(feedback)) { self$get_default_feedback() } else if(is.character(feedback)) { list(text = feedback) } else if(is.numeric(feedback)) { list(text = feedback) } else if(is.language(feedback)) { list(text = feedback) } else if(is.list(feedback)) { feedback } update_list(private$default_feedback_options, feedback) }, #' Template for full feedback get_feedback = function(opts, info) { # No feedback if (!is.null(opts$feedback) && !opts$feedback) return(NULL) "@ANSWER_INFO@\n\n@ANSWER_STRING@@EVALUATED_ANSWER@\n\n@FEEDBACK_ANSWER@\n" }, get_answer_string = function(opts, info) { if(is.null(info$answer_string)) "**R\u00E9ponse :** " else info$answer_string }, #' Feedback itself giving the right answer get_feedback_answer = function(opts, info) { # Get feedback as a proper named list with default arguments feedback_opts <- self$instantiated_feedback # Check for spurious arguments in feedback_options unknown_opts <- setdiff(names(feedback_opts), names(private$default_feedback_options)) if (length(unknown_opts) > 0) { stop("Unknown options: ", paste0(unknown_opts)) } # Override feedback_options with upstream opts feedback_opts <- update_list(feedback_opts, opts) # Check that options are coherent private$validate_feedback_options(feedback_opts, info) feedback <- if (feedback_opts$eval) { feedback_opts$text } else { if (is.null(feedback_opts$noeval_text)) feedback_opts$text else feedback_opts$noeval_text } feedback <- if (is.character(feedback)) { feedback } else if (is.language(feedback)) sprintf("```{r}\n%s\n```\n", answerstr(feedback)) else if (is.numeric(feedback)) sprintf("```{r}\n%s\n```\n", answerstr(feedback)) else stop("Unsupported feedback type: ", sQuote(feedback)) # Maybe indent the feedback if(is.null(opts$indent)) feedback else add_spaces_left(feedback, 4) }, get_XML_question_text = function(opts, info) { # md_question <- self$get_XML_question_markdown(opts, info) md_question <- self$get_inst_text(opts, info) HTML_question <- render_HTML(md_question, opts, info) trimws(HTML_question) # pandoc seems to add some leading newlines }, # Return XML "generalfeedback" node with feedback in HTML as CDATA get_XML_generalfeedback = function(opts, info) { if(!opts$feedback) return(NULL) # Generate HTML for feedback placeholders <- update_list(private$placeholders, private$xml_placeholders) tmpl <- self$get_feedback(opts, info) md_feedback <- self$instantiate_placeholders(tmpl, placeholders, opts, info) HTML_feedback <- render_HTML(md_feedback, opts, info) HTML_feedback0 <- trimws(HTML_feedback) # pandoc seems to add some leading newlines # Return XML with inner HTML tmpl <-"<generalfeedback format=\"html\"> <text><![CDATA[%s]]></text> </generalfeedback>" tmpl0 <- add_spaces_left(tmpl, opts$indent) sprintf(tmpl0, HTML_feedback0) }, get_XML_answer = function(opts, info) { as.character(self$get_evaluated_answer2(opts, info)) }, #' Export Question as XML to_XML = function(opts = NULL, info = NULL) { # Set up environment for evaluating data if not already if (is.null(info$env)) { info0 <- self$get_data_and_environment() info <- update_list(info, info0) } # Setting options default_opts <- update_list(private$default_options, private$xml_default_options) opts <- update_list(default_opts, opts) opts$export <- "xml" private$validate_options(opts, info) template <- add_spaces_left(private$xml_question_template, opts$indent) placeholders <- update_list(private$placeholders, private$xml_placeholders) # Answers might modify datasets stored in info$env self$instantiate_placeholders(template, placeholders, opts, info) }, invalidate_all = function() { self$invalidate_text() self$invalidate_answer() self$invalidate_feedback() self$invalidate_hidden_data() self$invalidate_data() self$invalidate_hidden_data_list() }, invalidate_ancestor = function() { if (is.null(self$ancestor)) self$invalidate_hidden_data() else self$ancestor$invalidate_hidden_data() }, invalidate_data = function() { private$is_data_available <- FALSE self$invalidate_inst_data() }, invalidate_hidden_data = function() { private$is_hidden_data_available <- FALSE self$invalidate_inst_text() self$invalidate_inst_answer() self$invalidate_inst_feedback() self$invalidate_inst_data() self$invalidate_hidden_data_list() }, invalidate_hidden_data_list = function() { private$is_hidden_data_list_available <- FALSE self$invalidate_inst_text() self$invalidate_inst_answer() self$invalidate_inst_feedback() self$invalidate_inst_data() }, invalidate_inst_data = function() { private$is_data_instantiated <- FALSE }, invalidate_inst_text = function() { private$is_text_instantiated <- FALSE }, invalidate_inst_answer = function() { private$is_answer_instantiated <- FALSE }, invalidate_inst_feedback = function() { private$is_feedback_instantiated <- FALSE }, invalidate_header = function() { self$invalidate_text() }, invalidate_text = function() { private$is_text_available <- FALSE self$invalidate_inst_text() }, invalidate_answer = function() { private$is_answer_available <- FALSE self$invalidate_inst_answer() }, invalidate_feedback = function() { private$is_feedback_available <- FALSE self$invalidate_inst_feedback() }, recursive_instantiated_data = function(seed_init = FALSE) { if (!seed_init & !is_empty_language(self$data) & is.null(self$seed)) stop("Some data but no seed to initialize") if(is.null(self$seed)) self$instantiated_data else merge_languages( instantiate_data_list( bquote(set.seed(.(self$seed))), self$hidden_data_list), self$instantiated_data) }, get_type = function(opts, info) { self$type }, get_title = function(opts, info) { self$title }, get_data_chunk = function(opts, info) { l <- sanitize_language(self$local_instantiated_data) if (is.null(l)) NULL else sprintf("```{r include = FALSE}\n%s\n```", answerstr(l)) }, get_rec_data_chunk = function(opts, info) { if (is.null(self$quiz)) stop("No defined Quiz in question ", sQuote(self$title)) l <- sanitize_language(self$quiz$recursive_instantiated_data()) if (is.null(l)) NULL else sprintf("```{r include = FALSE}\n%s\n```", answerstr(l)) }, # Data chunk for formatting answer get_answer_info = function(opts, info) { sprintf("```{r, include = FALSE}\n%s\nanswer <- {\n%s}\n```", answerstr(quote(cquote <- function(s) { if (is.character(s)) "`" else if (is.numeric(s)) "$" else stop("Argument is not character or numeric ", sQuote(s)) })), answerstr(self$instantiated_answer)) }, get_evaluated_answer = function(opts, info) { "`r cquote(answer)``r answer``r cquote(answer)`" }, get_evaluated_answer2 = function(opts, info) { # Set up environment for evaluating data if not already if (is.null(info$env)) { info0 <- self$get_data_and_environment() info <- update_list(info, info0) } eval(self$instantiated_answer, info$env) }, get_seed_chunk = function(opts, info) { if (is.null(self$seed)) NULL else sprintf("```{r}\nset.seed(%d)\n```", self$seed) }, get_text = function(opts, info) { self$text }, get_inst_text = function(opts, info) { if (!is.null(opts$numbered) && opts$numbered) { paste0("**Question ", info$num, " :** ", self$instantiated_text) } else self$instantiated_text }, get_inst_cookie = function(opts, info) { stop("Abstract method") }, get_inst_text_and_cookie = function(opts, info) { sprintf("%s\n\n%s", self$get_inst_text(opts, info), self$get_inst_cookie(opts, info)) }, get_inst_text_and_number = function(opts, info) { stopifnot(is.numeric(info$index)) sprintf("%s (%d)", self$get_inst_text(opts, info), info$index) }, get_guess = function() { }, get_is_correct_icon = function() { }, instantiate_hidden_data_list = function(var_list = NULL, seed_init = FALSE) { if (!seed_init & !is_empty_language(self$hidden_data) & is.null(self$hidden_seed)) stop("Some hidden data but no seed to instantiate them") # VAR_LIST must be a named list for list2env to work if (is.null(var_list)) var_list <- list() env <- list2env(var_list, envir = empty_env()) # Set seed if any and eval hidden data if (!is.null(self$hidden_seed)) eval(bquote(set.seed(.(self$hidden_seed))), envir = env) eval(self$hidden_data, envir = env) # Update variables in VAR_LIST with ENV new_var_list <- update_list(var_list, as.list(env, all.names = TRUE)) self$hidden_data_list <- new_var_list ## private$.hidden_data_list <- new_var_list ## private$is_hidden_data_list_available <- TRUE }, instantiate_feedback_list = function(feedback, var_list) { text <- instantiate_object(feedback$text, self$hidden_data_list) noeval_text <- instantiate_object(feedback$noeval_text, self$hidden_data_list) feedback$text <- text feedback$noeval_text <- noeval_text feedback }, hidden_data_names = function() { env <- empty_env() if (!is.null(self$hidden_seed)) eval(bquote(set.seed(.(self$hidden_seed))), envir = env) eval(self$hidden_data, envir = env) ls(envir = env, all.names = TRUE) }, rename = function(prefix, names = self$hidden_data_names()) { self$rename_text(prefix, names) self$rename_header(prefix, names) self$rename_answer(prefix, names) self$rename_feedback(prefix, names) self$rename_data(prefix, names) self$rename_hidden_data(prefix, names) self$invalidate_text() self$invalidate_answer() self$invalidate_hidden_data() self$invalidate_hidden_data_list() self$invalidate_feedback() self$invalidate_data() self }, rename_header = function(prefix, names = self$hidden_data_names()) { self$header <- prefix_object(prefix, names, self$header) self }, rename_text = function(prefix, names = self$hidden_data_names()) { self$text <- prefix_object(prefix, names, private$.text) self$invalidate_text() self }, rename_answer = function(prefix, names = self$hidden_data_names()) { self$answer <- prefix_object(prefix, names, self$answer) self$invalidate_answer() self }, rename_feedback = function(prefix, names = self$hidden_data_names()) { feedback <- self$feedback text <- prefix_object(prefix, names, feedback$text) noeval_text <- prefix_object(prefix, names, feedback$noeval_text) if (!is.null(text)) feedback$text <- text if (!is.null(noeval_text)) feedback$noeval_text <- noeval_text self$feedback <- feedback self$invalidate_feedback() self }, rename_data = function(prefix, names = self$hidden_data_names()) { self$data <- prefix_object(prefix, names, self$data) self$invalidate_data() self }, rename_hidden_data = function(prefix, names = self$hidden_data_names()) { self$hidden_data <- prefix_object(prefix, names, self$hidden_data) self$invalidate_hidden_data() self }, copy = function() { Question(self$text, type = self$type, seed = self$seed, hidden_seed = self$hidden_seed, hidden_data = self$hidden_data, data = self$data, answer = self$answer, feedback = self$feedback) } ), active = list( title = function(title) { if (missing(title)) { if (is.null(private$.title)) { if (nchar(self$instantiated_text) > 60) paste0(substr(self$instantiated_text, 0, 57), "...") else self$instantiated_text } else private$.title } else { private$.title <- title } }, header = function(header) { if (missing(header)) { private$.header } else { private$.header <- header self$invalidate_header() private$.header } }, ancestor = function(ancestor) { if (missing(ancestor)) { private$.ancestor } else { private$.ancestor <- ancestor self$invalidate_ancestor() private$.ancestor } }, text = function(text) { if (missing(text)) { paste(c(trimws(self$header), trimws(private$.text)), collapse = "\n\n") } else { private$.text <- text self$invalidate_inst_text() } }, instantiated_text = function() { if (private$is_text_instantiated) private$.instantiated_text else { private$.instantiated_text <- instantiate_text_list( self$text, self$hidden_data_list) private$is_text_instantiated <- TRUE private$.instantiated_text } }, answer = function(answer) { if (missing(answer)) { private$.answer } else { private$.answer <- answer self$invalidate_inst_answer() } }, instantiated_answer = function() { if (private$is_answer_instantiated) private$.instantiated_answer else { private$.instantiated_answer <- instantiate_object( self$answer, self$hidden_data_list) private$is_answer_instantiated <- TRUE private$.instantiated_answer } }, feedback = function(feedback) { if (missing(feedback)) { if (private$is_feedback_available) private$.feedback else { private$.feedback <- self$get_feedback_from_field(private$.feedback) private$is_feedback_available <- TRUE private$.feedback } } else { private$.feedback <- self$get_feedback_from_field(feedback) self$invalidate_inst_feedback() private$is_feedback_available <- TRUE } }, instantiated_feedback = function() { if (private$is_feedback_instantiated) private$.instantiated_feedback else { inst_feedback <- self$instantiate_feedback_list(self$feedback, self$hidden_data_list) private$is_feedback_instantiated <- TRUE private$.instantiated_feedback <- inst_feedback } }, hidden_seed = function(seed) { if (missing(seed)) private$.hidden_seed else { private$.hidden_seed <- seed self$invalidate_ancestor() } }, hidden_data = function(hidden_data) { if (missing(hidden_data)) { private$.hidden_data } else { private$.hidden_data <- hidden_data self$invalidate_ancestor() } }, hidden_data_list = function(hidden_data_list) { if (missing(hidden_data_list)) { if (private$is_hidden_data_list_available) private$.hidden_data_list else { root <- self while (!is.null(root$ancestor)) root <- root$ancestor root$instantiate_hidden_data_list() private$is_hidden_data_list_available <- TRUE private$.hidden_data_list } } else { private$is_hidden_data_list_available <- TRUE private$.hidden_data_list <- hidden_data_list ## Need to be recomputed self$invalidate_inst_text() self$invalidate_inst_answer() self$invalidate_inst_feedback() self$invalidate_inst_data() } }, seed = function(seed) { if (missing(seed)) private$.seed else { private$.seed <- seed } }, data = function(data) { if (missing(data)) { private$.data } else { private$.data <- data self$invalidate_inst_data() } }, instantiated_data = function() { if (private$is_data_instantiated) private$.instantiated_data else { private$.instantiated_data <- instantiate_data_list( private$.data, self$hidden_data_list) private$is_data_instantiated <- TRUE private$.instantiated_data } }, local_instantiated_data = function() { if(is.null(self$seed)) self$instantiated_data else merge_languages( instantiate_data_list( bquote(set.seed(.(self$seed))), self$hidden_data_list), self$instantiated_data) }, tags = function(tags) { if (missing(tags)) { private$.tags } else { private$.tags <- tags } } ), private = list( .title = NULL, .header = NULL, .ancestor = NULL, .text = NULL, is_text_available = FALSE, .answer = NULL, is_answer_available = FALSE, .feedback = NULL, is_feedback_available = FALSE, .data = NULL, is_data_available = FALSE, .seed = NULL, .hidden_seed = NULL, .hidden_data = NULL, is_hidden_data_available = FALSE, .hidden_data_list = NULL, is_hidden_data_list_available = FALSE, .instantiated_text = NULL, is_text_instantiated = FALSE, .instantiated_answer = NULL, is_answer_instantiated = FALSE, .instantiated_feedback = NULL, is_feedback_instantiated = FALSE, .instantiated_data = NULL, is_data_instantiated = FALSE, .tags = NULL, default_feedback_options = list(text = "", noeval_text = NULL, eval = TRUE, indent = 2), default_options = list(numbered = TRUE, export = "markdown", feedback = TRUE, quiet = FALSE), xml_default_options = list( numbered = FALSE, indent = 0 ), feedback_options = NULL, placeholders = NULL, placeholders_regex = NULL, validate_options = function(opts, info) { if (!is.null(opts$numbered) && opts$numbered && is.null(info$num)) stop("`numbered` option is enabled but no `num` available") }, validate_feedback_options = function(opts, info) { ## super$validate_feedback_options(opts, info) }, xml_question_template = trimws(" <question type=\"@TYPE@\"> <name> <text><![CDATA[@TITLE@]]></text> </name> <questiontext format=\"html\"> <text><![CDATA[@XML_QUESTION_TEXT@]]></text> </questiontext> <answer fraction=\"100\" format=\"plain_text\"> <text>@XML_ANSWER@</text> </answer> @XML_GENERALFEEDBACK@ <hidden>0</hidden> </question> "), xml_placeholders = NULL))

/R/simple_question.R

no_license

thisirs/quizR

R

false

27,951

r

#' Base class #' #' @export SimpleQuestion <- R6::R6Class( "SimpleQuestion", public = list( type = "abstract", quiz = NULL, initialize = function(text, data = quote({}), hidden_data = quote({}), seed = NULL, hidden_seed = NULL, feedback = NULL, answer = NULL, tags = NULL, header = NULL) { private$.text <- text private$.data <- data private$.hidden_data <- hidden_data private$.seed <- seed private$.hidden_seed <- hidden_seed private$.answer <- answer private$.feedback <- feedback private$.tags <- tags private$.header <- header # Default placeholders private$placeholders <- list( TITLE = "get_title", REC_DATA_CHUNK = "get_rec_data_chunk", DATA_CHUNK = "get_data_chunk", ANSWER_INFO = "get_answer_info", INST_TEXT = "get_inst_text", SEED_CHUNK = "get_seed_chunk", EVALUATED_ANSWER = "get_evaluated_answer", FEEDBACK = "get_feedback", FEEDBACK_ANSWER = "get_feedback_answer", ANSWER_STRING = "get_answer_string") # Specific placeholders for XML export private$xml_placeholders <- list( TYPE = "get_type", TITLE = "get_title", XML_QUESTION_TEXT = "get_XML_question_text", XML_GENERALFEEDBACK = "get_XML_generalfeedback", XML_ANSWER = "get_XML_answer" ) }, instantiate_placeholders = function(template, placeholders, opts, info) { placeholders_regex <- paste0("@", names(placeholders), "@", collapse = "|") repeat { match <- stringi::stri_locate_first_regex(template, placeholders_regex) if(any(is.na(match))) # breaking if no match break if(nrow(match) == 0) break # Computing replacement string id <- substring(template, match[1] + 1, match[2] - 1) funcname <- placeholders[[id]] if (is.null(funcname)) stop("Unable to find corresponding function for ", sQuote(id)) replacement <- self[[funcname]](opts, info) # If replacement is NULL, try to replace whole line if (is.null(replacement)) { template <- stringi::stri_replace_first_regex(template, paste0("^ *@", id, "@ *\n"), "", opts_regex = list(multiline = TRUE)) template <- stringi::stri_replace_first_regex(template, paste0("@", id, "@"), "") } else { # Escape backslash and dollar in replacement string replacement <- gsub("\\\\", "\\\\\\\\", replacement) replacement <- gsub("\\$", "\\\\$", replacement) template <- stringi::stri_replace_first_regex(template, paste0("@", id, "@"), replacement) } } if(nchar(template) == 0) NULL else template }, update_quiz = function(quiz) { self$quiz <- quiz self$invalidate_hidden_data_list() }, get_data_and_environment = function() { if(is.null(self$quiz)) data <- self$recursive_instantiated_data() else data <- self$quiz$recursive_instantiated_data() env <- empty_env() eval(data, env) list(data = data, env = env) }, validate_data = function(parent_data = NULL) {}, get_markdown_from_template = function(template, opts = list(), info = list()) { self$instantiate_placeholders(template, private$placeholders, opts, info) }, to_markdown = function(opts = list(), info = list()) { opts <- update_list(private$default_options, opts) opts$export <- "markdown" private$validate_options(opts, info) template <- "@INST_TEXT@\n\n@FEEDBACK@\n" self$instantiate_placeholders(template, private$placeholders, opts, info) }, #' Feedback options if none is provided get_default_feedback = function() { list(text = self$answer) }, #' Sanitize provided feedback get_feedback_from_field = function(feedback) { feedback <- if(is.null(feedback)) { self$get_default_feedback() } else if(is.character(feedback)) { list(text = feedback) } else if(is.numeric(feedback)) { list(text = feedback) } else if(is.language(feedback)) { list(text = feedback) } else if(is.list(feedback)) { feedback } update_list(private$default_feedback_options, feedback) }, #' Template for full feedback get_feedback = function(opts, info) { # No feedback if (!is.null(opts$feedback) && !opts$feedback) return(NULL) "@ANSWER_INFO@\n\n@ANSWER_STRING@@EVALUATED_ANSWER@\n\n@FEEDBACK_ANSWER@\n" }, get_answer_string = function(opts, info) { if(is.null(info$answer_string)) "**R\u00E9ponse :** " else info$answer_string }, #' Feedback itself giving the right answer get_feedback_answer = function(opts, info) { # Get feedback as a proper named list with default arguments feedback_opts <- self$instantiated_feedback # Check for spurious arguments in feedback_options unknown_opts <- setdiff(names(feedback_opts), names(private$default_feedback_options)) if (length(unknown_opts) > 0) { stop("Unknown options: ", paste0(unknown_opts)) } # Override feedback_options with upstream opts feedback_opts <- update_list(feedback_opts, opts) # Check that options are coherent private$validate_feedback_options(feedback_opts, info) feedback <- if (feedback_opts$eval) { feedback_opts$text } else { if (is.null(feedback_opts$noeval_text)) feedback_opts$text else feedback_opts$noeval_text } feedback <- if (is.character(feedback)) { feedback } else if (is.language(feedback)) sprintf("```{r}\n%s\n```\n", answerstr(feedback)) else if (is.numeric(feedback)) sprintf("```{r}\n%s\n```\n", answerstr(feedback)) else stop("Unsupported feedback type: ", sQuote(feedback)) # Maybe indent the feedback if(is.null(opts$indent)) feedback else add_spaces_left(feedback, 4) }, get_XML_question_text = function(opts, info) { # md_question <- self$get_XML_question_markdown(opts, info) md_question <- self$get_inst_text(opts, info) HTML_question <- render_HTML(md_question, opts, info) trimws(HTML_question) # pandoc seems to add some leading newlines }, # Return XML "generalfeedback" node with feedback in HTML as CDATA get_XML_generalfeedback = function(opts, info) { if(!opts$feedback) return(NULL) # Generate HTML for feedback placeholders <- update_list(private$placeholders, private$xml_placeholders) tmpl <- self$get_feedback(opts, info) md_feedback <- self$instantiate_placeholders(tmpl, placeholders, opts, info) HTML_feedback <- render_HTML(md_feedback, opts, info) HTML_feedback0 <- trimws(HTML_feedback) # pandoc seems to add some leading newlines # Return XML with inner HTML tmpl <-"<generalfeedback format=\"html\"> <text><![CDATA[%s]]></text> </generalfeedback>" tmpl0 <- add_spaces_left(tmpl, opts$indent) sprintf(tmpl0, HTML_feedback0) }, get_XML_answer = function(opts, info) { as.character(self$get_evaluated_answer2(opts, info)) }, #' Export Question as XML to_XML = function(opts = NULL, info = NULL) { # Set up environment for evaluating data if not already if (is.null(info$env)) { info0 <- self$get_data_and_environment() info <- update_list(info, info0) } # Setting options default_opts <- update_list(private$default_options, private$xml_default_options) opts <- update_list(default_opts, opts) opts$export <- "xml" private$validate_options(opts, info) template <- add_spaces_left(private$xml_question_template, opts$indent) placeholders <- update_list(private$placeholders, private$xml_placeholders) # Answers might modify datasets stored in info$env self$instantiate_placeholders(template, placeholders, opts, info) }, invalidate_all = function() { self$invalidate_text() self$invalidate_answer() self$invalidate_feedback() self$invalidate_hidden_data() self$invalidate_data() self$invalidate_hidden_data_list() }, invalidate_ancestor = function() { if (is.null(self$ancestor)) self$invalidate_hidden_data() else self$ancestor$invalidate_hidden_data() }, invalidate_data = function() { private$is_data_available <- FALSE self$invalidate_inst_data() }, invalidate_hidden_data = function() { private$is_hidden_data_available <- FALSE self$invalidate_inst_text() self$invalidate_inst_answer() self$invalidate_inst_feedback() self$invalidate_inst_data() self$invalidate_hidden_data_list() }, invalidate_hidden_data_list = function() { private$is_hidden_data_list_available <- FALSE self$invalidate_inst_text() self$invalidate_inst_answer() self$invalidate_inst_feedback() self$invalidate_inst_data() }, invalidate_inst_data = function() { private$is_data_instantiated <- FALSE }, invalidate_inst_text = function() { private$is_text_instantiated <- FALSE }, invalidate_inst_answer = function() { private$is_answer_instantiated <- FALSE }, invalidate_inst_feedback = function() { private$is_feedback_instantiated <- FALSE }, invalidate_header = function() { self$invalidate_text() }, invalidate_text = function() { private$is_text_available <- FALSE self$invalidate_inst_text() }, invalidate_answer = function() { private$is_answer_available <- FALSE self$invalidate_inst_answer() }, invalidate_feedback = function() { private$is_feedback_available <- FALSE self$invalidate_inst_feedback() }, recursive_instantiated_data = function(seed_init = FALSE) { if (!seed_init & !is_empty_language(self$data) & is.null(self$seed)) stop("Some data but no seed to initialize") if(is.null(self$seed)) self$instantiated_data else merge_languages( instantiate_data_list( bquote(set.seed(.(self$seed))), self$hidden_data_list), self$instantiated_data) }, get_type = function(opts, info) { self$type }, get_title = function(opts, info) { self$title }, get_data_chunk = function(opts, info) { l <- sanitize_language(self$local_instantiated_data) if (is.null(l)) NULL else sprintf("```{r include = FALSE}\n%s\n```", answerstr(l)) }, get_rec_data_chunk = function(opts, info) { if (is.null(self$quiz)) stop("No defined Quiz in question ", sQuote(self$title)) l <- sanitize_language(self$quiz$recursive_instantiated_data()) if (is.null(l)) NULL else sprintf("```{r include = FALSE}\n%s\n```", answerstr(l)) }, # Data chunk for formatting answer get_answer_info = function(opts, info) { sprintf("```{r, include = FALSE}\n%s\nanswer <- {\n%s}\n```", answerstr(quote(cquote <- function(s) { if (is.character(s)) "`" else if (is.numeric(s)) "$" else stop("Argument is not character or numeric ", sQuote(s)) })), answerstr(self$instantiated_answer)) }, get_evaluated_answer = function(opts, info) { "`r cquote(answer)``r answer``r cquote(answer)`" }, get_evaluated_answer2 = function(opts, info) { # Set up environment for evaluating data if not already if (is.null(info$env)) { info0 <- self$get_data_and_environment() info <- update_list(info, info0) } eval(self$instantiated_answer, info$env) }, get_seed_chunk = function(opts, info) { if (is.null(self$seed)) NULL else sprintf("```{r}\nset.seed(%d)\n```", self$seed) }, get_text = function(opts, info) { self$text }, get_inst_text = function(opts, info) { if (!is.null(opts$numbered) && opts$numbered) { paste0("**Question ", info$num, " :** ", self$instantiated_text) } else self$instantiated_text }, get_inst_cookie = function(opts, info) { stop("Abstract method") }, get_inst_text_and_cookie = function(opts, info) { sprintf("%s\n\n%s", self$get_inst_text(opts, info), self$get_inst_cookie(opts, info)) }, get_inst_text_and_number = function(opts, info) { stopifnot(is.numeric(info$index)) sprintf("%s (%d)", self$get_inst_text(opts, info), info$index) }, get_guess = function() { }, get_is_correct_icon = function() { }, instantiate_hidden_data_list = function(var_list = NULL, seed_init = FALSE) { if (!seed_init & !is_empty_language(self$hidden_data) & is.null(self$hidden_seed)) stop("Some hidden data but no seed to instantiate them") # VAR_LIST must be a named list for list2env to work if (is.null(var_list)) var_list <- list() env <- list2env(var_list, envir = empty_env()) # Set seed if any and eval hidden data if (!is.null(self$hidden_seed)) eval(bquote(set.seed(.(self$hidden_seed))), envir = env) eval(self$hidden_data, envir = env) # Update variables in VAR_LIST with ENV new_var_list <- update_list(var_list, as.list(env, all.names = TRUE)) self$hidden_data_list <- new_var_list ## private$.hidden_data_list <- new_var_list ## private$is_hidden_data_list_available <- TRUE }, instantiate_feedback_list = function(feedback, var_list) { text <- instantiate_object(feedback$text, self$hidden_data_list) noeval_text <- instantiate_object(feedback$noeval_text, self$hidden_data_list) feedback$text <- text feedback$noeval_text <- noeval_text feedback }, hidden_data_names = function() { env <- empty_env() if (!is.null(self$hidden_seed)) eval(bquote(set.seed(.(self$hidden_seed))), envir = env) eval(self$hidden_data, envir = env) ls(envir = env, all.names = TRUE) }, rename = function(prefix, names = self$hidden_data_names()) { self$rename_text(prefix, names) self$rename_header(prefix, names) self$rename_answer(prefix, names) self$rename_feedback(prefix, names) self$rename_data(prefix, names) self$rename_hidden_data(prefix, names) self$invalidate_text() self$invalidate_answer() self$invalidate_hidden_data() self$invalidate_hidden_data_list() self$invalidate_feedback() self$invalidate_data() self }, rename_header = function(prefix, names = self$hidden_data_names()) { self$header <- prefix_object(prefix, names, self$header) self }, rename_text = function(prefix, names = self$hidden_data_names()) { self$text <- prefix_object(prefix, names, private$.text) self$invalidate_text() self }, rename_answer = function(prefix, names = self$hidden_data_names()) { self$answer <- prefix_object(prefix, names, self$answer) self$invalidate_answer() self }, rename_feedback = function(prefix, names = self$hidden_data_names()) { feedback <- self$feedback text <- prefix_object(prefix, names, feedback$text) noeval_text <- prefix_object(prefix, names, feedback$noeval_text) if (!is.null(text)) feedback$text <- text if (!is.null(noeval_text)) feedback$noeval_text <- noeval_text self$feedback <- feedback self$invalidate_feedback() self }, rename_data = function(prefix, names = self$hidden_data_names()) { self$data <- prefix_object(prefix, names, self$data) self$invalidate_data() self }, rename_hidden_data = function(prefix, names = self$hidden_data_names()) { self$hidden_data <- prefix_object(prefix, names, self$hidden_data) self$invalidate_hidden_data() self }, copy = function() { Question(self$text, type = self$type, seed = self$seed, hidden_seed = self$hidden_seed, hidden_data = self$hidden_data, data = self$data, answer = self$answer, feedback = self$feedback) } ), active = list( title = function(title) { if (missing(title)) { if (is.null(private$.title)) { if (nchar(self$instantiated_text) > 60) paste0(substr(self$instantiated_text, 0, 57), "...") else self$instantiated_text } else private$.title } else { private$.title <- title } }, header = function(header) { if (missing(header)) { private$.header } else { private$.header <- header self$invalidate_header() private$.header } }, ancestor = function(ancestor) { if (missing(ancestor)) { private$.ancestor } else { private$.ancestor <- ancestor self$invalidate_ancestor() private$.ancestor } }, text = function(text) { if (missing(text)) { paste(c(trimws(self$header), trimws(private$.text)), collapse = "\n\n") } else { private$.text <- text self$invalidate_inst_text() } }, instantiated_text = function() { if (private$is_text_instantiated) private$.instantiated_text else { private$.instantiated_text <- instantiate_text_list( self$text, self$hidden_data_list) private$is_text_instantiated <- TRUE private$.instantiated_text } }, answer = function(answer) { if (missing(answer)) { private$.answer } else { private$.answer <- answer self$invalidate_inst_answer() } }, instantiated_answer = function() { if (private$is_answer_instantiated) private$.instantiated_answer else { private$.instantiated_answer <- instantiate_object( self$answer, self$hidden_data_list) private$is_answer_instantiated <- TRUE private$.instantiated_answer } }, feedback = function(feedback) { if (missing(feedback)) { if (private$is_feedback_available) private$.feedback else { private$.feedback <- self$get_feedback_from_field(private$.feedback) private$is_feedback_available <- TRUE private$.feedback } } else { private$.feedback <- self$get_feedback_from_field(feedback) self$invalidate_inst_feedback() private$is_feedback_available <- TRUE } }, instantiated_feedback = function() { if (private$is_feedback_instantiated) private$.instantiated_feedback else { inst_feedback <- self$instantiate_feedback_list(self$feedback, self$hidden_data_list) private$is_feedback_instantiated <- TRUE private$.instantiated_feedback <- inst_feedback } }, hidden_seed = function(seed) { if (missing(seed)) private$.hidden_seed else { private$.hidden_seed <- seed self$invalidate_ancestor() } }, hidden_data = function(hidden_data) { if (missing(hidden_data)) { private$.hidden_data } else { private$.hidden_data <- hidden_data self$invalidate_ancestor() } }, hidden_data_list = function(hidden_data_list) { if (missing(hidden_data_list)) { if (private$is_hidden_data_list_available) private$.hidden_data_list else { root <- self while (!is.null(root$ancestor)) root <- root$ancestor root$instantiate_hidden_data_list() private$is_hidden_data_list_available <- TRUE private$.hidden_data_list } } else { private$is_hidden_data_list_available <- TRUE private$.hidden_data_list <- hidden_data_list ## Need to be recomputed self$invalidate_inst_text() self$invalidate_inst_answer() self$invalidate_inst_feedback() self$invalidate_inst_data() } }, seed = function(seed) { if (missing(seed)) private$.seed else { private$.seed <- seed } }, data = function(data) { if (missing(data)) { private$.data } else { private$.data <- data self$invalidate_inst_data() } }, instantiated_data = function() { if (private$is_data_instantiated) private$.instantiated_data else { private$.instantiated_data <- instantiate_data_list( private$.data, self$hidden_data_list) private$is_data_instantiated <- TRUE private$.instantiated_data } }, local_instantiated_data = function() { if(is.null(self$seed)) self$instantiated_data else merge_languages( instantiate_data_list( bquote(set.seed(.(self$seed))), self$hidden_data_list), self$instantiated_data) }, tags = function(tags) { if (missing(tags)) { private$.tags } else { private$.tags <- tags } } ), private = list( .title = NULL, .header = NULL, .ancestor = NULL, .text = NULL, is_text_available = FALSE, .answer = NULL, is_answer_available = FALSE, .feedback = NULL, is_feedback_available = FALSE, .data = NULL, is_data_available = FALSE, .seed = NULL, .hidden_seed = NULL, .hidden_data = NULL, is_hidden_data_available = FALSE, .hidden_data_list = NULL, is_hidden_data_list_available = FALSE, .instantiated_text = NULL, is_text_instantiated = FALSE, .instantiated_answer = NULL, is_answer_instantiated = FALSE, .instantiated_feedback = NULL, is_feedback_instantiated = FALSE, .instantiated_data = NULL, is_data_instantiated = FALSE, .tags = NULL, default_feedback_options = list(text = "", noeval_text = NULL, eval = TRUE, indent = 2), default_options = list(numbered = TRUE, export = "markdown", feedback = TRUE, quiet = FALSE), xml_default_options = list( numbered = FALSE, indent = 0 ), feedback_options = NULL, placeholders = NULL, placeholders_regex = NULL, validate_options = function(opts, info) { if (!is.null(opts$numbered) && opts$numbered && is.null(info$num)) stop("`numbered` option is enabled but no `num` available") }, validate_feedback_options = function(opts, info) { ## super$validate_feedback_options(opts, info) }, xml_question_template = trimws(" <question type=\"@TYPE@\"> <name> <text><![CDATA[@TITLE@]]></text> </name> <questiontext format=\"html\"> <text><![CDATA[@XML_QUESTION_TEXT@]]></text> </questiontext> <answer fraction=\"100\" format=\"plain_text\"> <text>@XML_ANSWER@</text> </answer> @XML_GENERALFEEDBACK@ <hidden>0</hidden> </question> "), xml_placeholders = NULL))

# 30DayChartChallenge Day 27 - Educational # load packages library(tidyverse) library(gganimate) # load data datasaurus <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-10-13/datasaurus.csv') # compute summary statistics dino_stats <- datasaurus %>% #datasauRus::datasaurus_dozen %>% group_by(dataset) %>% summarize( mean_x = mean(x), mean_y = mean(y), std_dev_x = sd(x), std_dev_y = sd(y), corr_x_y = cor(x, y) ) # merge raw data & summary stats d <- datasaurus %>% #datasauRus::datasaurus_dozen %>% left_join(dino_stats, by = "dataset") # plot data d %>% filter(dataset %in% c("dino", "star", "circle", "x_shape")) %>% ggplot(aes(x = x, y = y, colour = dataset)) + geom_point(size = 2) + xlim(0,100) + ylim(0,100) + coord_fixed(clip = "off") + scale_colour_manual(values = c("#298fca", "#4C5F28", "#ffe12b", "#E3242B")) + labs(title = "Same stats, different graph", subtitle = "The importance of visually inspecting your data", caption = "Source: datasauRus package / TidyTuesday", x = "", y = "") + geom_text(x = 10, y = 98, label = paste("r = ", signif(d$corr_x_y[1], 3), sep = ""), colour = "black", hjust = 0.5, size = 6) + theme_bw() + theme(legend.position = "none", plot.title = element_text(size = 22, hjust = 0.5, face = "bold"), plot.subtitle = element_text(size = 16, hjust = 0.5), plot.caption = element_text(size = 12, face = "italic"), plot.background = element_rect(fill = "#ffffff"), panel.background = element_rect(fill = "#ffffff"), axis.text = element_text(size = 16)) + transition_states(dataset, state_length = 1) # save gif anim_save("Day27_Educational/Day27.gif")

/Day27_Educational/Day27_Educational.R

no_license

timschoof/30DayChartChallenge

R

false

1,793

r

# 30DayChartChallenge Day 27 - Educational # load packages library(tidyverse) library(gganimate) # load data datasaurus <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-10-13/datasaurus.csv') # compute summary statistics dino_stats <- datasaurus %>% #datasauRus::datasaurus_dozen %>% group_by(dataset) %>% summarize( mean_x = mean(x), mean_y = mean(y), std_dev_x = sd(x), std_dev_y = sd(y), corr_x_y = cor(x, y) ) # merge raw data & summary stats d <- datasaurus %>% #datasauRus::datasaurus_dozen %>% left_join(dino_stats, by = "dataset") # plot data d %>% filter(dataset %in% c("dino", "star", "circle", "x_shape")) %>% ggplot(aes(x = x, y = y, colour = dataset)) + geom_point(size = 2) + xlim(0,100) + ylim(0,100) + coord_fixed(clip = "off") + scale_colour_manual(values = c("#298fca", "#4C5F28", "#ffe12b", "#E3242B")) + labs(title = "Same stats, different graph", subtitle = "The importance of visually inspecting your data", caption = "Source: datasauRus package / TidyTuesday", x = "", y = "") + geom_text(x = 10, y = 98, label = paste("r = ", signif(d$corr_x_y[1], 3), sep = ""), colour = "black", hjust = 0.5, size = 6) + theme_bw() + theme(legend.position = "none", plot.title = element_text(size = 22, hjust = 0.5, face = "bold"), plot.subtitle = element_text(size = 16, hjust = 0.5), plot.caption = element_text(size = 12, face = "italic"), plot.background = element_rect(fill = "#ffffff"), panel.background = element_rect(fill = "#ffffff"), axis.text = element_text(size = 16)) + transition_states(dataset, state_length = 1) # save gif anim_save("Day27_Educational/Day27.gif")

library(sfsmisc) sapply(list.files(pattern="[.]R$", path="R/", full.names=TRUE), source); filenames <- list.files("test/steve", pattern="autism*", full.names=TRUE) matrices = list() #for (i in 1:3) { for (i in 1:length(filenames)) { matrices[[i]] <- as.matrix(read.table(filenames[[i]], skip=1)) } images = new("IMaGES", matrices = matrices, penalty=1.5)

/call_gies.R

no_license

noahfl/pcalg

R

false

362

r

library(sfsmisc) sapply(list.files(pattern="[.]R$", path="R/", full.names=TRUE), source); filenames <- list.files("test/steve", pattern="autism*", full.names=TRUE) matrices = list() #for (i in 1:3) { for (i in 1:length(filenames)) { matrices[[i]] <- as.matrix(read.table(filenames[[i]], skip=1)) } images = new("IMaGES", matrices = matrices, penalty=1.5)

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tally.R \name{prop} \alias{count} \alias{perc} \alias{prop} \alias{prop1} \title{Compute proportions, percents, or counts for a single level} \usage{ prop(x, data = parent.frame(), useNA = "no", ..., level = NULL, long.names = TRUE, sep = ".", format = "proportion", quiet = TRUE, pval.adjust = FALSE) prop1(..., pval.adjust = TRUE) count(x, data = parent.frame(), ..., format = "count") perc(x, data = parent.frame(), ..., format = "percent") } \arguments{ \item{x}{an R object, usually a formula} \item{data}{a data frame in which \code{x} is to be evaluated} \item{useNA}{an indication of how NA's should be handled. By default, they are ignored.} \item{level}{the level for which counts, proportions or percents are calculated} \item{long.names}{a logical indicating whether long names should be when there is a conditioning variable} \item{sep}{a character used to separate portions of long names} \item{format}{one of \code{proportion}, \code{percent}, or \code{count}, possibly abbrevaited} \item{quiet}{a logical indicating whether messages regarding the target level should be supressed.} \item{pval.adjust}{a logical indicating whether the "p-value" adjustment should be applied. This adjustment adds 1 to the numerator and denominator counts.} \item{\dots}{arguments passed through to \code{\link{tally}}} } \description{ Compute proportions, percents, or counts for a single level } \details{ \code{prop1} is intended for the computation of p-values from randomization distributions and differs from \code{prop} only in its default value of \code{pval.adjust}. } \note{ For 0-1 data, level is set to 1 by default since that a standard coding scheme for success and failure. } \examples{ if (require(mosaicData)) { prop( ~sex, data=HELPrct) prop( ~sex, data=HELPrct, level='male') count( ~sex | substance, data=HELPrct) prop( ~sex | substance, data=HELPrct) perc( ~sex | substance, data=HELPrct) } }

/man/prop.Rd

no_license

aaronbaggett/mosaic

R

false

true

2,014

rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tally.R \name{prop} \alias{count} \alias{perc} \alias{prop} \alias{prop1} \title{Compute proportions, percents, or counts for a single level} \usage{ prop(x, data = parent.frame(), useNA = "no", ..., level = NULL, long.names = TRUE, sep = ".", format = "proportion", quiet = TRUE, pval.adjust = FALSE) prop1(..., pval.adjust = TRUE) count(x, data = parent.frame(), ..., format = "count") perc(x, data = parent.frame(), ..., format = "percent") } \arguments{ \item{x}{an R object, usually a formula} \item{data}{a data frame in which \code{x} is to be evaluated} \item{useNA}{an indication of how NA's should be handled. By default, they are ignored.} \item{level}{the level for which counts, proportions or percents are calculated} \item{long.names}{a logical indicating whether long names should be when there is a conditioning variable} \item{sep}{a character used to separate portions of long names} \item{format}{one of \code{proportion}, \code{percent}, or \code{count}, possibly abbrevaited} \item{quiet}{a logical indicating whether messages regarding the target level should be supressed.} \item{pval.adjust}{a logical indicating whether the "p-value" adjustment should be applied. This adjustment adds 1 to the numerator and denominator counts.} \item{\dots}{arguments passed through to \code{\link{tally}}} } \description{ Compute proportions, percents, or counts for a single level } \details{ \code{prop1} is intended for the computation of p-values from randomization distributions and differs from \code{prop} only in its default value of \code{pval.adjust}. } \note{ For 0-1 data, level is set to 1 by default since that a standard coding scheme for success and failure. } \examples{ if (require(mosaicData)) { prop( ~sex, data=HELPrct) prop( ~sex, data=HELPrct, level='male') count( ~sex | substance, data=HELPrct) prop( ~sex | substance, data=HELPrct) perc( ~sex | substance, data=HELPrct) } }

# A242310: Ilya Lopatin and Juri-Stepan Gerasimov, May 10 2014 # A000056: _N. J. A. Sloane_ # A051419: Paul L. Chessin (pchess(AT)ix.netcom.com) # A000045: _N. J. A. Sloane, 1964_ # A169890: _David Applegate_, _Marc LeBrun_ and _N. J. A. Sloane_, Jul 06 2010 # A169888: _N. J. A. Sloane_, Jul 07 2010, based on a letter from _Jean-Claude # Babois_. id <- "A169890" test_that("OEIS sequence A169890 has three authors", { testthat::expect_equal(id %>% OEIS_author %>% length, 3) })

/tests/testthat/test_OEIS_author.R

permissive

EnriquePH/OEIS.R

R

false

539

r

# A242310: Ilya Lopatin and Juri-Stepan Gerasimov, May 10 2014 # A000056: _N. J. A. Sloane_ # A051419: Paul L. Chessin (pchess(AT)ix.netcom.com) # A000045: _N. J. A. Sloane, 1964_ # A169890: _David Applegate_, _Marc LeBrun_ and _N. J. A. Sloane_, Jul 06 2010 # A169888: _N. J. A. Sloane_, Jul 07 2010, based on a letter from _Jean-Claude # Babois_. id <- "A169890" test_that("OEIS sequence A169890 has three authors", { testthat::expect_equal(id %>% OEIS_author %>% length, 3) })

## This following function takes a square invertible matrix ## and return a list of functions to: ## 1.- Set the matrix. ## 2.- Get the matrix. ## 3.- Set the inverse of matrix. ## 4.- Get the inverse of matrix. ## Finally, this is used as input in cacheSolve function. makeCacheMatrix <- function(x = matrix()) { m <- NULL ## Set the matrix set <- function(y){ x <<- y m <<- NULL } ## Get the matrix get <- function() x ## Set the inverse of matrix. setinverse <- function(solve) m <<- solve ## Get the inverse of matrix. getinverse <- function() m ## Return the list of functions. list(set = set, get = get, setinverse = setinverse, getinverse = getinverse) } ## In cacheSolve function, we take the matrix and ## calculate the inverse. ## Finally we return it. cacheSolve <- function(x, ...) { ## Get m <- x$getinverse() if(!is.null(m)) { ## If the value already exists, returns m. message("getting cached data") return(m) } ## Else data <- x$get() m <- solve(data, ...) x$setinverse(m) return(m) } ## Running Example # > matriz <- matrix(runif(9,1,100),3,3) # Show "matriz" # > matriz # [,1] [,2] [,3] # [1,] 90.15773 26.488523 72.41762 # [2,] 60.19124 9.766337 91.25337 # [3,] 96.55614 47.140896 67.54101 # Generating the cache matrix # > matrixExample <- makeCacheMatrix(matriz) # Finally calculate or retrieve the value of # Inverted matrix using cacheSolve: # > cacheSolve(matrixExample) # [,1] [,2] [,3] # [1,] -0.162284059 -0.12840965 0.22513611 # [2,] 0.194560788 -0.05779459 -0.08093354 # [3,] 0.004915707 0.20579781 -0.04886617

/LexicalScoping.R

no_license

fsaavedraolmos/RProgramming

R

false

1,706

r

## This following function takes a square invertible matrix ## and return a list of functions to: ## 1.- Set the matrix. ## 2.- Get the matrix. ## 3.- Set the inverse of matrix. ## 4.- Get the inverse of matrix. ## Finally, this is used as input in cacheSolve function. makeCacheMatrix <- function(x = matrix()) { m <- NULL ## Set the matrix set <- function(y){ x <<- y m <<- NULL } ## Get the matrix get <- function() x ## Set the inverse of matrix. setinverse <- function(solve) m <<- solve ## Get the inverse of matrix. getinverse <- function() m ## Return the list of functions. list(set = set, get = get, setinverse = setinverse, getinverse = getinverse) } ## In cacheSolve function, we take the matrix and ## calculate the inverse. ## Finally we return it. cacheSolve <- function(x, ...) { ## Get m <- x$getinverse() if(!is.null(m)) { ## If the value already exists, returns m. message("getting cached data") return(m) } ## Else data <- x$get() m <- solve(data, ...) x$setinverse(m) return(m) } ## Running Example # > matriz <- matrix(runif(9,1,100),3,3) # Show "matriz" # > matriz # [,1] [,2] [,3] # [1,] 90.15773 26.488523 72.41762 # [2,] 60.19124 9.766337 91.25337 # [3,] 96.55614 47.140896 67.54101 # Generating the cache matrix # > matrixExample <- makeCacheMatrix(matriz) # Finally calculate or retrieve the value of # Inverted matrix using cacheSolve: # > cacheSolve(matrixExample) # [,1] [,2] [,3] # [1,] -0.162284059 -0.12840965 0.22513611 # [2,] 0.194560788 -0.05779459 -0.08093354 # [3,] 0.004915707 0.20579781 -0.04886617

# # IKI Bangladesh (MIOASI): S6 Exceedence Probabilities # # Make netcdf of estimates of mean, and low and upper credible intervals of mean gust speed. # This process fits a GAM to the ensemble data, estimates a posterior distribution, # and samples them to find the mean and credible interval based on a set of quantiles. # # # Author: HS # Created: Jan 2020 # QA: TE 17/3/20 library(RNetCDF) library(abind) library(doParallel) registerDoParallel(cores = 10) # Define Bangladesh tropical cyclon categories in knots converted to m/s # WINDTHRESHOLDS <- c(17, 22, 28, 34, 48, 64, 120) * 0.514444 WINDTHRESHOLDS <- seq(0, 100, 1) INDIR <- "" VAR <- 'fg.T1Hmax' RES <- '4p4' # Load base netcdf to get lat/lon variables filename <- paste('fp.', VAR, '.AILA.', RES, 'km.nc', sep = '') stormsnc <- open.nc(paste(INDIR, filename, sep = '/')) # Extract netcdf variables lat <- var.get.nc(stormsnc, 'latitude') lon <- var.get.nc(stormsnc, 'longitude') nLon <- length(lon) nLat <- length(lat) nCells <- nLon*nLat # Make lon-lat-ens grid lonlat <- expand.grid(lon = lon, lat = lat) # Load predictions data from RData object predsfile <- "grand_fp_preds.Rdata" load(predsfile) # Calculate posterior stats from preds # Use the simulated values at each gridpoint to calculate exceedend probabilities # Do in parallel with %dopar% and write out to list qlist plist <- foreach(i = seq_along(WINDTHRESHOLDS)) %dopar% { # Make bool array of T/F values based on wind thresholds bool <- preds >= WINDTHRESHOLDS[i] # Find threshold probabilities based on total True values / total number of preds -> mean # N.B. Here the mean is the Monte Carlo approximation to the integral -- so unrelated to the mean of any distribution p <- apply(bool, 2, mean) matrix(p, nrow = nLon, ncol = nLat) } # Convert list to 3D matrix exceedm <- abind(plist, along=0) # Write out netCDF file outfile <- paste('pgrand.exceedance_tmp.', VAR, '.', RES, 'km.nc', sep = '') outnc <- create.nc(outfile, large=T) # Dimensions dim.def.nc(outnc, 'latitude', nLat) dim.def.nc(outnc, 'longitude', nLon) dim.def.nc(outnc, 'gust_threshold', length(WINDTHRESHOLDS)) # Variables var.def.nc(outnc, 'latitude', 'NC_FLOAT', 'latitude') var.def.nc(outnc, 'longitude', 'NC_FLOAT', 'longitude') var.def.nc(outnc, 'gust_threshold', 'NC_INT', 'gust_threshold') var.def.nc(outnc, 'latitude_longitude', 'NC_CHAR', NA) var.def.nc(outnc, 'probability_of_exceedance', 'NC_FLOAT', c('gust_threshold', 'longitude', 'latitude')) # Define some attributes att.put.nc(outnc, "latitude", "axis", "NC_CHAR", "Y") att.put.nc(outnc, "latitude", "units", "NC_CHAR", "degrees_north") att.put.nc(outnc, "latitude", "standard_name", "NC_CHAR", "latitude") att.put.nc(outnc, "longitude", "axis", "NC_CHAR", "X") att.put.nc(outnc, "longitude", "units", "NC_CHAR", "degrees_east") att.put.nc(outnc, "longitude", "standard_name", "NC_CHAR", "longitude") att.put.nc(outnc, "gust_threshold", "long_name", "NC_CHAR", "Gust speed classification threshold") att.put.nc(outnc, "gust_threshold", "units", "NC_CHAR", "m s-1") att.put.nc(outnc, "probability_of_exceedance", "long_name", "NC_CHAR", "posterior wind_speed_of_gust probability_of_exceedance") att.put.nc(outnc, "probability_of_exceedance", "units", "NC_INT", 1) att.put.nc(outnc, "probability_of_exceedance", "grid_mapping", "NC_CHAR", "latitude_longitude") att.put.nc(outnc, "latitude_longitude", "grid_mapping_name", "NC_CHAR", "latitude_longitude") att.put.nc(outnc, "latitude_longitude", "longitude_of_prime_meridian", "NC_INT", 0) att.put.nc(outnc, "latitude_longitude", "earth_radius", "NC_INT", 6371229.) att.put.nc(outnc, "latitude_longitude", "proj4", "NC_CHAR", "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs") att.put.nc(outnc, "NC_GLOBAL", "comment", "NC_CHAR", "Supported by the International Climate Initiative (IKI) and the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety, based on a decision of the Germany Bundestag") att.put.nc(outnc, "NC_GLOBAL", "contact", "NC_CHAR", "enquiries@metoffice.gov.uk") att.put.nc(outnc, "NC_GLOBAL", "data_type", "NC_CHAR", "grid") att.put.nc(outnc, "NC_GLOBAL", "date_created", "NC_CHAR", format(Sys.time(), "%Y%m%dT%H:%M:%S")) att.put.nc(outnc, "NC_GLOBAL", "geospatial_lat_max", "NC_FLOAT", max(lat)) att.put.nc(outnc, "NC_GLOBAL", "geospatial_lat_min", "NC_FLOAT", min(lat)) att.put.nc(outnc, "NC_GLOBAL", "geospatial_lat_resolution", "NC_FLOAT", 0.04) att.put.nc(outnc, "NC_GLOBAL", "geospatial_lat_units", "NC_CHAR", "degrees_north") att.put.nc(outnc, "NC_GLOBAL", "geospatial_lon_max", "NC_FLOAT", max(lon)) att.put.nc(outnc, "NC_GLOBAL", "geospatial_lon_min", "NC_FLOAT", min(lon)) att.put.nc(outnc, "NC_GLOBAL", "geospatial_lon_resolution", "NC_FLOAT", 0.04) att.put.nc(outnc, "NC_GLOBAL", "geospatial_lon_units", "NC_CHAR", "degrees_east") att.put.nc(outnc, "NC_GLOBAL", "history", "NC_CHAR", "(1.0) Initial release") att.put.nc(outnc, "NC_GLOBAL", "id", "NC_CHAR", paste("fpgrandw.", VAR, ".", RES, "km.nc", sep="")) att.put.nc(outnc, "NC_GLOBAL", "institution", "NC_CHAR", "Met Office, UK") att.put.nc(outnc, "NC_GLOBAL", "licence", "NC_CHAR", "Creative Commons Attribution 4.0 International (CC BY 4.0)") att.put.nc(outnc, "NC_GLOBAL", "product_version", "NC_CHAR", "v1.0") att.put.nc(outnc, "NC_GLOBAL", "project", "NC_CHAR", "Oasis Platform for Climate and Catastrophe Risk Assessment – Asia") att.put.nc(outnc, "NC_GLOBAL", "institution", "NC_CHAR", "Met Office, UK") att.put.nc(outnc, "NC_GLOBAL", "keywords", "NC_CHAR", "Bangladesh, footprint, quantiles, Met Office") att.put.nc(outnc, "NC_GLOBAL", "source", "NC_CHAR", "Met Office UM RA2T CON") att.put.nc(outnc, "NC_GLOBAL", "spatial_resolution", "NC_CHAR", "4.4km") att.put.nc(outnc, "NC_GLOBAL", "summary", "NC_CHAR", "Tropical cyclone gust speed threshold exceedence probabilities over Bangladesh") att.put.nc(outnc, "NC_GLOBAL", "keywords", "NC_CHAR", "Bangladesh, footprint, posterior, exceedence probabilities, Met Office") # Write in data var.put.nc(outnc, 'latitude', lat) var.put.nc(outnc, 'longitude', lon) var.put.nc(outnc, 'gust_threshold', WINDTHRESHOLDS) var.put.nc(outnc, 'probability_of_exceedance', exceedm) close.nc(outnc)

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# # IKI Bangladesh (MIOASI): S6 Exceedence Probabilities # # Make netcdf of estimates of mean, and low and upper credible intervals of mean gust speed. # This process fits a GAM to the ensemble data, estimates a posterior distribution, # and samples them to find the mean and credible interval based on a set of quantiles. # # # Author: HS # Created: Jan 2020 # QA: TE 17/3/20 library(RNetCDF) library(abind) library(doParallel) registerDoParallel(cores = 10) # Define Bangladesh tropical cyclon categories in knots converted to m/s # WINDTHRESHOLDS <- c(17, 22, 28, 34, 48, 64, 120) * 0.514444 WINDTHRESHOLDS <- seq(0, 100, 1) INDIR <- "" VAR <- 'fg.T1Hmax' RES <- '4p4' # Load base netcdf to get lat/lon variables filename <- paste('fp.', VAR, '.AILA.', RES, 'km.nc', sep = '') stormsnc <- open.nc(paste(INDIR, filename, sep = '/')) # Extract netcdf variables lat <- var.get.nc(stormsnc, 'latitude') lon <- var.get.nc(stormsnc, 'longitude') nLon <- length(lon) nLat <- length(lat) nCells <- nLon*nLat # Make lon-lat-ens grid lonlat <- expand.grid(lon = lon, lat = lat) # Load predictions data from RData object predsfile <- "grand_fp_preds.Rdata" load(predsfile) # Calculate posterior stats from preds # Use the simulated values at each gridpoint to calculate exceedend probabilities # Do in parallel with %dopar% and write out to list qlist plist <- foreach(i = seq_along(WINDTHRESHOLDS)) %dopar% { # Make bool array of T/F values based on wind thresholds bool <- preds >= WINDTHRESHOLDS[i] # Find threshold probabilities based on total True values / total number of preds -> mean # N.B. Here the mean is the Monte Carlo approximation to the integral -- so unrelated to the mean of any distribution p <- apply(bool, 2, mean) matrix(p, nrow = nLon, ncol = nLat) } # Convert list to 3D matrix exceedm <- abind(plist, along=0) # Write out netCDF file outfile <- paste('pgrand.exceedance_tmp.', VAR, '.', RES, 'km.nc', sep = '') outnc <- create.nc(outfile, large=T) # Dimensions dim.def.nc(outnc, 'latitude', nLat) dim.def.nc(outnc, 'longitude', nLon) dim.def.nc(outnc, 'gust_threshold', length(WINDTHRESHOLDS)) # Variables var.def.nc(outnc, 'latitude', 'NC_FLOAT', 'latitude') var.def.nc(outnc, 'longitude', 'NC_FLOAT', 'longitude') var.def.nc(outnc, 'gust_threshold', 'NC_INT', 'gust_threshold') var.def.nc(outnc, 'latitude_longitude', 'NC_CHAR', NA) var.def.nc(outnc, 'probability_of_exceedance', 'NC_FLOAT', c('gust_threshold', 'longitude', 'latitude')) # Define some attributes att.put.nc(outnc, "latitude", "axis", "NC_CHAR", "Y") att.put.nc(outnc, "latitude", "units", "NC_CHAR", "degrees_north") att.put.nc(outnc, "latitude", "standard_name", "NC_CHAR", "latitude") att.put.nc(outnc, "longitude", "axis", "NC_CHAR", "X") att.put.nc(outnc, "longitude", "units", "NC_CHAR", "degrees_east") att.put.nc(outnc, "longitude", "standard_name", "NC_CHAR", "longitude") att.put.nc(outnc, "gust_threshold", "long_name", "NC_CHAR", "Gust speed classification threshold") att.put.nc(outnc, "gust_threshold", "units", "NC_CHAR", "m s-1") att.put.nc(outnc, "probability_of_exceedance", "long_name", "NC_CHAR", "posterior wind_speed_of_gust probability_of_exceedance") att.put.nc(outnc, "probability_of_exceedance", "units", "NC_INT", 1) att.put.nc(outnc, "probability_of_exceedance", "grid_mapping", "NC_CHAR", "latitude_longitude") att.put.nc(outnc, "latitude_longitude", "grid_mapping_name", "NC_CHAR", "latitude_longitude") att.put.nc(outnc, "latitude_longitude", "longitude_of_prime_meridian", "NC_INT", 0) att.put.nc(outnc, "latitude_longitude", "earth_radius", "NC_INT", 6371229.) att.put.nc(outnc, "latitude_longitude", "proj4", "NC_CHAR", "+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs") att.put.nc(outnc, "NC_GLOBAL", "comment", "NC_CHAR", "Supported by the International Climate Initiative (IKI) and the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety, based on a decision of the Germany Bundestag") att.put.nc(outnc, "NC_GLOBAL", "contact", "NC_CHAR", "enquiries@metoffice.gov.uk") att.put.nc(outnc, "NC_GLOBAL", "data_type", "NC_CHAR", "grid") att.put.nc(outnc, "NC_GLOBAL", "date_created", "NC_CHAR", format(Sys.time(), "%Y%m%dT%H:%M:%S")) att.put.nc(outnc, "NC_GLOBAL", "geospatial_lat_max", "NC_FLOAT", max(lat)) att.put.nc(outnc, "NC_GLOBAL", "geospatial_lat_min", "NC_FLOAT", min(lat)) att.put.nc(outnc, "NC_GLOBAL", "geospatial_lat_resolution", "NC_FLOAT", 0.04) att.put.nc(outnc, "NC_GLOBAL", "geospatial_lat_units", "NC_CHAR", "degrees_north") att.put.nc(outnc, "NC_GLOBAL", "geospatial_lon_max", "NC_FLOAT", max(lon)) att.put.nc(outnc, "NC_GLOBAL", "geospatial_lon_min", "NC_FLOAT", min(lon)) att.put.nc(outnc, "NC_GLOBAL", "geospatial_lon_resolution", "NC_FLOAT", 0.04) att.put.nc(outnc, "NC_GLOBAL", "geospatial_lon_units", "NC_CHAR", "degrees_east") att.put.nc(outnc, "NC_GLOBAL", "history", "NC_CHAR", "(1.0) Initial release") att.put.nc(outnc, "NC_GLOBAL", "id", "NC_CHAR", paste("fpgrandw.", VAR, ".", RES, "km.nc", sep="")) att.put.nc(outnc, "NC_GLOBAL", "institution", "NC_CHAR", "Met Office, UK") att.put.nc(outnc, "NC_GLOBAL", "licence", "NC_CHAR", "Creative Commons Attribution 4.0 International (CC BY 4.0)") att.put.nc(outnc, "NC_GLOBAL", "product_version", "NC_CHAR", "v1.0") att.put.nc(outnc, "NC_GLOBAL", "project", "NC_CHAR", "Oasis Platform for Climate and Catastrophe Risk Assessment – Asia") att.put.nc(outnc, "NC_GLOBAL", "institution", "NC_CHAR", "Met Office, UK") att.put.nc(outnc, "NC_GLOBAL", "keywords", "NC_CHAR", "Bangladesh, footprint, quantiles, Met Office") att.put.nc(outnc, "NC_GLOBAL", "source", "NC_CHAR", "Met Office UM RA2T CON") att.put.nc(outnc, "NC_GLOBAL", "spatial_resolution", "NC_CHAR", "4.4km") att.put.nc(outnc, "NC_GLOBAL", "summary", "NC_CHAR", "Tropical cyclone gust speed threshold exceedence probabilities over Bangladesh") att.put.nc(outnc, "NC_GLOBAL", "keywords", "NC_CHAR", "Bangladesh, footprint, posterior, exceedence probabilities, Met Office") # Write in data var.put.nc(outnc, 'latitude', lat) var.put.nc(outnc, 'longitude', lon) var.put.nc(outnc, 'gust_threshold', WINDTHRESHOLDS) var.put.nc(outnc, 'probability_of_exceedance', exceedm) close.nc(outnc)

#' Returns information on how to partition y_data #' #' This function returns information on how to partition (randomly or paired) y_data #' importFrom magrittr "%>%" #' @param data_object argument is the output produced by as.MLinput, which contains a single x data frame or a list of x data frames, a y data frames and attributes #' @param partition_style one of 'random' or 'paired' (character string) indicating type of partition #' @param folds integer of k for k-fold cross validation #' @param repeats integer of number of iterations to repeat cross validation #' @param holdout_perc numeric between 0 and 1 indicating the percentage of data to withold for the holdout validation set #' @export dataPartitioning = function(data_object, partition_style = "random", folds = 4, repeats = 100, holdout_perc = 0.25) { # extract y_data from data_object and cnames y_data = data_object$Y group_identifier = attr(data_object, "cnames")$outcome_cname pair_identifier = attr(data_object, "cnames")$pair_cname # initial checks if (!(partition_style %in% c("random", "paired"))) stop("'partition_style' must be one of, 'random' or 'paired'") if (!inherits(y_data, "data.frame")) stop("'y_data' must be of class 'data.frame'") if (partition_style == "paired" & is.null(pair_identifier)) stop("'pair_identifier' is required for a 'paired' partition_style") repeats_new = repeats * 1.5 repeats_extra = repeats_new - repeats if (partition_style == "random") { partition_result = pureRandomTestingTraining(data = y_data, group_identifier = group_identifier, folds = folds, repeats = repeats, holdout_perc = holdout_perc) # rearrrange partition_result data into test data and training data train_list = lapply(partition_result, function(item) { item$train }) test_list = lapply(partition_result, function(item) { item$test }) # form fold and repeat lists fld = rep(1:folds, repeats) fld = as.list(fld) reps = as.list(1:repeats) reps = lapply(reps, rep, folds) reps = unlist(reps) reps = as.list(reps) # form folds repeats data frames train_df = mapply(fold_rep_train, train_list, fld, reps) train_df = as.data.frame(train_df) train_df = lapply(train_df, as.data.frame, stringsAsFactors = F) train_df = do.call(rbind, train_df) test_df = mapply(fold_rep_test, test_list, fld, reps) test_df = as.data.frame(test_df) test_df = lapply(test_df, as.data.frame, stringsAsFactors = F) test_df = do.call(rbind, test_df) final_result = list(train = train_df, test = test_df) final_result = lapply(final_result, function(x) { split(data.table::as.data.table(x), by = c("Fold", "Rep")) }) ########## partition data for repeats_extra########## ########## partition_result_extra = pureRandomTestingTraining(data = y_data, group_identifier = group_identifier, folds = folds, repeats = repeats_extra, holdout_perc = holdout_perc) # rearrrange partition_result data into test data and training data train_list = lapply(partition_result_extra, function(item) { item$train }) test_list = lapply(partition_result_extra, function(item) { item$test }) # form fold and repeat lists fld = rep(1:folds, repeats_extra) fld = as.list(fld) reps = as.list(1:repeats_extra) reps = lapply(reps, rep, folds) reps = unlist(reps) reps = as.list(reps) # form folds repeats data frames train_df = mapply(fold_rep_train, train_list, fld, reps) train_df = as.data.frame(train_df) train_df = lapply(train_df, as.data.frame, stringsAsFactors = F) train_df = do.call(rbind, train_df) test_df = mapply(fold_rep_test, test_list, fld, reps) test_df = as.data.frame(test_df) test_df = lapply(test_df, as.data.frame, stringsAsFactors = F) test_df = do.call(rbind, test_df) final_result_extra = list(train = train_df, test = test_df) final_result_extra = lapply(final_result_extra, function(x) { split(data.table::as.data.table(x), by = c("Fold", "Rep")) }) } else if (partition_style == "paired") { partition_result = pairedCaseControlTestingTraining(data = y_data, group_identifier = group_identifier, pair_identifier = pair_identifier, folds = folds, repeats = repeats) # rearrrange partition_result data into two tibble data frames, one for test data one for train data train_list = lapply(partition_result, function(item) { item$train }) test_list = lapply(partition_result, function(item) { item$test }) # form fold and repeat lists fld = rep(1:folds, repeats) fld = as.list(fld) reps = as.list(1:repeats) reps = lapply(reps, rep, folds) reps = unlist(reps) reps = as.list(reps) # form folds repeats data frames train_df = mapply(fold_rep_train, train_list, fld, reps) train_df = as.data.frame(train_df) train_df = lapply(train_df, as.data.frame, stringsAsFactors = F) train_df = do.call(rbind, train_df) test_df = mapply(fold_rep_test, test_list, fld, reps) test_df = as.data.frame(test_df) test_df = lapply(test_df, as.data.frame, stringsAsFactors = F) test_df = do.call(rbind, test_df) final_result = list(train = train_df, test = test_df) final_result = lapply(final_result, function(x) { split(data.table::as.data.table(x), by = c("Fold", "Rep")) }) ########## partition data for repeats_extra########## ########## partition_result_extra = pairedCaseControlTestingTraining(data = y_data, group_identifier = group_identifier, pair_identifier = pair_identifier, folds = folds, repeats = repeats) # rearrrange partition_result data into two tibble data frames, one for test data one for train data train_list = lapply(partition_result_extra, function(item) { item$train }) test_list = lapply(partition_result_extra, function(item) { item$test }) # form fold and repeat lists fld = rep(1:folds, repeats_extra) fld = as.list(fld) reps = as.list(1:repeats_extra) reps = lapply(reps, rep, folds) reps = unlist(reps) reps = as.list(reps) # form folds repeats data frames train_df = mapply(fold_rep_train, train_list, fld, reps) train_df = as.data.frame(train_df) train_df = lapply(train_df, as.data.frame, stringsAsFactors = F) train_df = do.call(rbind, train_df) test_df = mapply(fold_rep_test, test_list, fld, reps) test_df = as.data.frame(test_df) test_df = lapply(test_df, as.data.frame, stringsAsFactors = F) test_df = do.call(rbind, test_df) final_result_extra = list(train = train_df, test = test_df) final_result_extra = lapply(final_result_extra, function(x) { split(data.table::as.data.table(x), by = c("Fold", "Rep")) }) } attr(data_object, "partition_info") = final_result attr(data_object, "extra_partitions") = final_result_extra attr(data_object, "foldrep") = list(folds = folds, repeats = repeats) return(data_object) } pureRandomTestingTraining <- function(data, group_identifier, folds = 4, repeats = 100, holdout_perc = 0.25) { groups <- data[, group_identifier] seed <- 42 #------ determine training indeces ----------# # returns a k x repeats size list of training indeces set seed for reproducability purposes set.seed(42) training_ind_list <- caret::createMultiFolds(y = groups, k = folds, times = repeats) #------ diff data and train to return testing indeces -----# # returns a k x repeats size list of grouped testing and training indeces ind_list <- lapply(training_ind_list, function(training_inds) { partition_list <- list() partition_list$train <- training_inds partition_list$test <- setdiff(1:nrow(data), training_inds) return(partition_list) }) return(ind_list) } pairedCaseControlTestingTraining <- function(data, group_identifier, pair_identifier, folds = 4, repeats = 100, holdout_set = NULL) { tuning_set <- FALSE seed <- 42 #------- create a map to keep orignal indexing in tact -----# data$old_index <- 1:nrow(data) #------- remove the holdout set if there is one ---------# if (length(holdout_set) > 0) { data <- data[-which(data[, pair_identifier] %in% holdout_set), ] } #------- use map to find unique pairs -----# singled_pair_subset <- data %>% dplyr::arrange_(group_identifier) %>% dplyr::distinct_(pair_identifier, .keep_all = TRUE) #keep only unique identifiers for training subset singled_pair_subset[1:nrow(singled_pair_subset)/2, group_identifier] <- 1 # convert half the labes to zeros for caret's balancing act index_map <- data.frame(old = singled_pair_subset$old_index, new = 1:nrow(singled_pair_subset), pair = singled_pair_subset[, pair_identifier]) rownames(index_map) <- index_map$new #------ vector of group labels to split ------# group <- singled_pair_subset[, group_identifier] #------ determine training indeces ----------# # returns a k x repeats size list of training indeces training_ind_list <- caret::createMultiFolds(y = group, k = folds, times = repeats) #------- match case-control pairs --------# ind_list <- lapply(training_ind_list, function(x) { pairs <- index_map[x, "pair"] partition_list <- list() partition_list$train <- data[which(data[, pair_identifier] %in% pairs), "old_index"] partition_list$test <- data[which(!data[, pair_identifier] %in% pairs), "old_index"] return(partition_list) }) #------ diff data and train to return testing indeces -----# # returns a k x repeats size list of grouped testing and training indeces ind_list <- lapply(training_ind_list_mapped, # function(training_inds){ pairs <- index_map[x, 'pair'] partition_list <- list() partition_list$train <- training_inds # partition_list$test <- data[which(!data[,pair_identifier] %in% pairs), 'old_index'] return(partition_list) }) } # folds repeats helper functions for training data and test data fold_rep_train = function(train_vec, fold, repp) { result = data.frame(Fold = rep(fold, length(train_vec)), Rep = rep(repp, length(train_vec)), Train = train_vec) return(result) } fold_rep_test = function(train_vec, fold, repp) { result = data.frame(Fold = rep(fold, length(train_vec)), Rep = rep(repp, length(train_vec)), Test = train_vec) return(result) }

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r

#' Returns information on how to partition y_data #' #' This function returns information on how to partition (randomly or paired) y_data #' importFrom magrittr "%>%" #' @param data_object argument is the output produced by as.MLinput, which contains a single x data frame or a list of x data frames, a y data frames and attributes #' @param partition_style one of 'random' or 'paired' (character string) indicating type of partition #' @param folds integer of k for k-fold cross validation #' @param repeats integer of number of iterations to repeat cross validation #' @param holdout_perc numeric between 0 and 1 indicating the percentage of data to withold for the holdout validation set #' @export dataPartitioning = function(data_object, partition_style = "random", folds = 4, repeats = 100, holdout_perc = 0.25) { # extract y_data from data_object and cnames y_data = data_object$Y group_identifier = attr(data_object, "cnames")$outcome_cname pair_identifier = attr(data_object, "cnames")$pair_cname # initial checks if (!(partition_style %in% c("random", "paired"))) stop("'partition_style' must be one of, 'random' or 'paired'") if (!inherits(y_data, "data.frame")) stop("'y_data' must be of class 'data.frame'") if (partition_style == "paired" & is.null(pair_identifier)) stop("'pair_identifier' is required for a 'paired' partition_style") repeats_new = repeats * 1.5 repeats_extra = repeats_new - repeats if (partition_style == "random") { partition_result = pureRandomTestingTraining(data = y_data, group_identifier = group_identifier, folds = folds, repeats = repeats, holdout_perc = holdout_perc) # rearrrange partition_result data into test data and training data train_list = lapply(partition_result, function(item) { item$train }) test_list = lapply(partition_result, function(item) { item$test }) # form fold and repeat lists fld = rep(1:folds, repeats) fld = as.list(fld) reps = as.list(1:repeats) reps = lapply(reps, rep, folds) reps = unlist(reps) reps = as.list(reps) # form folds repeats data frames train_df = mapply(fold_rep_train, train_list, fld, reps) train_df = as.data.frame(train_df) train_df = lapply(train_df, as.data.frame, stringsAsFactors = F) train_df = do.call(rbind, train_df) test_df = mapply(fold_rep_test, test_list, fld, reps) test_df = as.data.frame(test_df) test_df = lapply(test_df, as.data.frame, stringsAsFactors = F) test_df = do.call(rbind, test_df) final_result = list(train = train_df, test = test_df) final_result = lapply(final_result, function(x) { split(data.table::as.data.table(x), by = c("Fold", "Rep")) }) ########## partition data for repeats_extra########## ########## partition_result_extra = pureRandomTestingTraining(data = y_data, group_identifier = group_identifier, folds = folds, repeats = repeats_extra, holdout_perc = holdout_perc) # rearrrange partition_result data into test data and training data train_list = lapply(partition_result_extra, function(item) { item$train }) test_list = lapply(partition_result_extra, function(item) { item$test }) # form fold and repeat lists fld = rep(1:folds, repeats_extra) fld = as.list(fld) reps = as.list(1:repeats_extra) reps = lapply(reps, rep, folds) reps = unlist(reps) reps = as.list(reps) # form folds repeats data frames train_df = mapply(fold_rep_train, train_list, fld, reps) train_df = as.data.frame(train_df) train_df = lapply(train_df, as.data.frame, stringsAsFactors = F) train_df = do.call(rbind, train_df) test_df = mapply(fold_rep_test, test_list, fld, reps) test_df = as.data.frame(test_df) test_df = lapply(test_df, as.data.frame, stringsAsFactors = F) test_df = do.call(rbind, test_df) final_result_extra = list(train = train_df, test = test_df) final_result_extra = lapply(final_result_extra, function(x) { split(data.table::as.data.table(x), by = c("Fold", "Rep")) }) } else if (partition_style == "paired") { partition_result = pairedCaseControlTestingTraining(data = y_data, group_identifier = group_identifier, pair_identifier = pair_identifier, folds = folds, repeats = repeats) # rearrrange partition_result data into two tibble data frames, one for test data one for train data train_list = lapply(partition_result, function(item) { item$train }) test_list = lapply(partition_result, function(item) { item$test }) # form fold and repeat lists fld = rep(1:folds, repeats) fld = as.list(fld) reps = as.list(1:repeats) reps = lapply(reps, rep, folds) reps = unlist(reps) reps = as.list(reps) # form folds repeats data frames train_df = mapply(fold_rep_train, train_list, fld, reps) train_df = as.data.frame(train_df) train_df = lapply(train_df, as.data.frame, stringsAsFactors = F) train_df = do.call(rbind, train_df) test_df = mapply(fold_rep_test, test_list, fld, reps) test_df = as.data.frame(test_df) test_df = lapply(test_df, as.data.frame, stringsAsFactors = F) test_df = do.call(rbind, test_df) final_result = list(train = train_df, test = test_df) final_result = lapply(final_result, function(x) { split(data.table::as.data.table(x), by = c("Fold", "Rep")) }) ########## partition data for repeats_extra########## ########## partition_result_extra = pairedCaseControlTestingTraining(data = y_data, group_identifier = group_identifier, pair_identifier = pair_identifier, folds = folds, repeats = repeats) # rearrrange partition_result data into two tibble data frames, one for test data one for train data train_list = lapply(partition_result_extra, function(item) { item$train }) test_list = lapply(partition_result_extra, function(item) { item$test }) # form fold and repeat lists fld = rep(1:folds, repeats_extra) fld = as.list(fld) reps = as.list(1:repeats_extra) reps = lapply(reps, rep, folds) reps = unlist(reps) reps = as.list(reps) # form folds repeats data frames train_df = mapply(fold_rep_train, train_list, fld, reps) train_df = as.data.frame(train_df) train_df = lapply(train_df, as.data.frame, stringsAsFactors = F) train_df = do.call(rbind, train_df) test_df = mapply(fold_rep_test, test_list, fld, reps) test_df = as.data.frame(test_df) test_df = lapply(test_df, as.data.frame, stringsAsFactors = F) test_df = do.call(rbind, test_df) final_result_extra = list(train = train_df, test = test_df) final_result_extra = lapply(final_result_extra, function(x) { split(data.table::as.data.table(x), by = c("Fold", "Rep")) }) } attr(data_object, "partition_info") = final_result attr(data_object, "extra_partitions") = final_result_extra attr(data_object, "foldrep") = list(folds = folds, repeats = repeats) return(data_object) } pureRandomTestingTraining <- function(data, group_identifier, folds = 4, repeats = 100, holdout_perc = 0.25) { groups <- data[, group_identifier] seed <- 42 #------ determine training indeces ----------# # returns a k x repeats size list of training indeces set seed for reproducability purposes set.seed(42) training_ind_list <- caret::createMultiFolds(y = groups, k = folds, times = repeats) #------ diff data and train to return testing indeces -----# # returns a k x repeats size list of grouped testing and training indeces ind_list <- lapply(training_ind_list, function(training_inds) { partition_list <- list() partition_list$train <- training_inds partition_list$test <- setdiff(1:nrow(data), training_inds) return(partition_list) }) return(ind_list) } pairedCaseControlTestingTraining <- function(data, group_identifier, pair_identifier, folds = 4, repeats = 100, holdout_set = NULL) { tuning_set <- FALSE seed <- 42 #------- create a map to keep orignal indexing in tact -----# data$old_index <- 1:nrow(data) #------- remove the holdout set if there is one ---------# if (length(holdout_set) > 0) { data <- data[-which(data[, pair_identifier] %in% holdout_set), ] } #------- use map to find unique pairs -----# singled_pair_subset <- data %>% dplyr::arrange_(group_identifier) %>% dplyr::distinct_(pair_identifier, .keep_all = TRUE) #keep only unique identifiers for training subset singled_pair_subset[1:nrow(singled_pair_subset)/2, group_identifier] <- 1 # convert half the labes to zeros for caret's balancing act index_map <- data.frame(old = singled_pair_subset$old_index, new = 1:nrow(singled_pair_subset), pair = singled_pair_subset[, pair_identifier]) rownames(index_map) <- index_map$new #------ vector of group labels to split ------# group <- singled_pair_subset[, group_identifier] #------ determine training indeces ----------# # returns a k x repeats size list of training indeces training_ind_list <- caret::createMultiFolds(y = group, k = folds, times = repeats) #------- match case-control pairs --------# ind_list <- lapply(training_ind_list, function(x) { pairs <- index_map[x, "pair"] partition_list <- list() partition_list$train <- data[which(data[, pair_identifier] %in% pairs), "old_index"] partition_list$test <- data[which(!data[, pair_identifier] %in% pairs), "old_index"] return(partition_list) }) #------ diff data and train to return testing indeces -----# # returns a k x repeats size list of grouped testing and training indeces ind_list <- lapply(training_ind_list_mapped, # function(training_inds){ pairs <- index_map[x, 'pair'] partition_list <- list() partition_list$train <- training_inds # partition_list$test <- data[which(!data[,pair_identifier] %in% pairs), 'old_index'] return(partition_list) }) } # folds repeats helper functions for training data and test data fold_rep_train = function(train_vec, fold, repp) { result = data.frame(Fold = rep(fold, length(train_vec)), Rep = rep(repp, length(train_vec)), Train = train_vec) return(result) } fold_rep_test = function(train_vec, fold, repp) { result = data.frame(Fold = rep(fold, length(train_vec)), Rep = rep(repp, length(train_vec)), Test = train_vec) return(result) }

library(Hmisc) library(caret) library(dplyr) library(ggplot2) library(data.table) # 파일 불러오기 test <- read.csv("test.csv") train <- read.csv("train.csv") train <- train %>% filter(!is.na(Upc)) # 데이터 확인 head(train) describe(train) str(train) table(train$TripType) table(train$Upc) table(train$DepartmentDescription) summary(train) boxplot(train$ScanCount) table(train$ScanCount) hist(train$VisitNumber) table(train$VisitNumber) train %>% filter(TripType == 39) %>% select(VisitNumber) train %>% filter(ScanCount < -4) train %>% filter(is.na(Upc)) train %>% filter(VisitNumber == 8) train %>% filter(DepartmentDescription == 'PHARMACY RX') train %>% filter(ScanCount > 19) %>% arrange(TripType, VisitNumber, ScanCount) train %>% filter(FinelineNumber == 9998) train %>% filter(TripType == 999) train %>% filter(FinelineNumber == 1000) train %>% filter(floor(log10(train$Upc))+1 == 12) # DepartmentDescription과 Weekday와의 관계 확인 week_depart <- list() wd_data <- tapply(train$DepartmentDescription, train$Weekday, table) for(i in 1 : NROW(unique(train$Weekday))){ week_depart <- c(week_depart, list(sort(wd_data[[i]], decreasing = T))) } names(week_depart) <- names(wd_data) sort_week_depart <- c(week_depart['Monday'], week_depart['Tuesday'], week_depart['Wednesday'], week_depart['Thursday'], week_depart['Friday'], week_depart['Saturday'], week_depart['Sunday']) # DepartmentDescription과 TripType과의 관계 triptype_depart <- list() trde_data <- tapply(train$DepartmentDescription, train$TripType, table) for(i in 1 : NROW(unique(train$TripType))){ triptype_depart <- c(triptype_depart, list(sort(trde_data[[i]], decreasing = T))) } names(triptype_depart) <- names(trde_data) trde_top10 <- list() for(i in 1 : NROW(unique(train$TripType))){ trde_top10 <- c(trde_top10, list(triptype_depart[[i]][1 : 10])) } names(trde_top10) <- names(trde_data) trde_top10_plot_data <- train %>% group_by(TripType, DepartmentDescription) %>% summarise(n = n()) %>% arrange(TripType, desc(n)) trde_top5_plot_result <- c() for(i in unique(trde_top10_plot_data$TripType)){ trde_top5_plot_result <- rbind(trde_top5_plot_result, trde_top10_plot_data %>% filter(TripType == i) %>% head(5)) } mosaicplot(~ DepartmentDescription + TripType, data = trde_top5_plot_result, las = 1) # Read Data File test <- read.csv("test.csv") train <- read.csv("train.csv") train <- train %>% filter(!is.na(Upc)) options(scipen = 100) # UPC 12자리 채우기 # First : 11자리까지 채우기(0으로) Upc12 <- matrix(0, nrow = nrow(train), ncol = 1) ZeroFillFunc <- function(x, y){ zeros <- c() zeroc <- 11 - (floor(log10(x)) + 1) for(j in 1 : zeroc){ zeros <- paste0(zeros, '0') } return (paste0(zeros, x)) } for(i in 1 : nrow(train)){ Upc12[i] <- ifelse(floor(log10(train$Upc[i])) > 9, train$Upc[i], ZeroFillFunc(train$Upc[i])) } Upc12 <- as.vector(Upc12) # Second : CheckSumDigit 만들기 MakeCheckSumDigitFunc <- function(x){ odds_sum <- 0 evens_sum <- 0 digit11 <- "" split_upc <- strsplit(x, "")[[1]] for(i in 1 : 11){ digit11 <- paste0(digit11, split_upc[i]) if (i %% 2 == 1) { odds_sum <- odds_sum + as.numeric(split_upc[i]) }else{ evens_sum <- evens_sum + as.numeric(split_upc[i]) } } total_sum <- odds_sum * 3 + evens_sum sum_result <- ifelse(total_sum %% 10 == 0, 0, (10 - (total_sum %% 10))) return (paste0(digit11, sum_result)) } Upc12 <- sapply(Upc12, MakeCheckSumDigitFunc) names(Upc12) <- NULL # Final : Train데이터와 합치기 train <- data.frame(train, Upc12) head(train, 20) colnames(train)[8] <- "UPC" train$Upc <- NULL # train Copy / Divide Company Code / Divide Product Code cptrain <- train MakeCompanyCodeFunc <- function(x){ ccode <- "" upc <- strsplit(as.character(x), "")[[1]] for(i in 1 : 6){ ccode <- paste0(ccode, upc[i]) } return (ccode) } Upc6 <- sapply(cptrain$UPC, MakeCompanyCodeFunc) cptrain <- data.frame(cptrain, Upc6) colnames(cptrain)[8] <- "CompanyCode" cptrain$UPC <- NULL cptrain$CompanyCode <- as.character(cptrain$CompanyCode) write.csv(cptrain, "htrain.csv", row.names = F) pUpc <- matrix(0, nrow = nrow(cptrain), ncol = 5) for(i in 1 : 5){ pUpc[, i] <- sapply(cptrain$UPC, function(x){ pcode <- "" upc <- strsplit(as.character(x), "")[[1]] for(j in (7 + (i - 1)) : 11){ pcode <- paste0(pcode, upc[j]) } return (pcode) }) } cptrain <- data.frame(cptrain, pUpc) colnames(cptrain)[8 : 12] <- c("ProductCode5", "ProductCode4", "ProductCode3", "ProductCode2", "ProductCode1") for(i in 8 : 12){ cptrain[, i] <- as.character(cptrain[, i]) } write.csv(cptrain, "ptrain.csv", row.names = F) write.table(cptrain, "ptrain.txt", sep = ",", col.names = T) tt <- fread("ptrain.csv") str(cptrain) head(tt) # Read Refined Data & Set Company Code len 6 library(data.table) library(dplyr) library(caret) rtrain <- fread("htrain.csv", stringsAsFactors = T) rtrain <- as.data.frame(rtrain) rtrain <- rtrain %>% filter(TripType %in% c(3 : 20)) rtrain$TripType <- as.factor(rtrain$TripType) rtrain <- rtrain %>% arrange(DepartmentDescription, FinelineNumber) t <- rtrain %>% select(DepartmentDescription, FinelineNumber) %>% arrange(DepartmentDescription, FinelineNumber) %>% distinct() t$defi <- 1 : nrow(t) rtrain <- merge(rtrain, t, by = c("DepartmentDescription", "FinelineNumber"), all.x = T) # Divide Train / Validation sample_idx <- rtrain %>% sample_frac(0.7) %>% rownames() %>% as.numeric() rtrain_train <- rtrain[sample_idx, ] rtrain_val <- rtrain[-sample_idx, ] # Multinomial Logistic Regression library(nnet) mlr_model <- multinom(TripType ~ ., data = rtrain_train) str(rtrain) summary(mlr_model) table(fitted(mlr_model)) # Decision Tree library(party) tree_control <- ctree_control(mincriterion = 0.9, minsplit = 10, maxdepth = 6) tree_model <- ctree(TripType ~ ., data = rtrain_train) plot(tree_model) tree_pred <- predict(tree_model, newdata = rtrain_val) confusionMatrix(tree_pred, rtrain_val$TripType_3) # RandomForest library(randomForest) rf_model <- randomForest(TripType ~ ScanCount, data = rtrain_train) rf_pred <- predict(rf_model, newdata = rtrain_val) confusionMatrix(rf_pred, rtrain_val$TripType) rf_model <- randomForest(TripType ~ VisitNumber, data = rtrain_train, ntree = 200, importance = TRUE, do.trace = TRUE) rf_pred <- predict(rf_model, newdata = rtrain_val) rf_cfm <- confusionMatrix(rf_pred, rtrain_val$TripType) rf_imp <- importance(rf_model) plot(rf_model) rf_model1 <- randomForest(TripType ~ VisitNumber + FinelineNumber, data = rtrain_train, ntree = 200, importance = T, do.trace = T) rf_pred1 <- predict(rf_model1, newdata = rtrain_val) rf_cfm1 <- confusionMatrix(rf_pred1, rtrain_val$TripType) rf_imp <- importance(rf_model1) varImpPlot(rf_model1) rf_model2 <- randomForest(TripType ~ VisitNumber + Weekday, data = rtrain_train, ntree = 100, importance = T, do.trace = T) rf_pred2 <- predict(rf_model2, newdata = rtrain_val) rf_cfm <- confusionMatrix(rf_pred2, rtrain_val$TripType) levels(rtrain_train$DepartmentDescription) <- c(1 : 68) table(rtrain_train$DepartmentDescription) rtrain_train$DepartmentDescription <- as.numeric(rtrain_train$DepartmentDescription) levels(rtrain_val$DepartmentDescription) <- c(1 : 68) table(rtrain_val$DepartmentDescription) rtrain_val$DepartmentDescription <- as.numeric(rtrain_val$DepartmentDescription) rf_model3 <- randomForest(TripType ~ VisitNumber + DepartmentDescription, data = rtrain_train, ntree = 100, importance = T, do.trace = T) rf_pred3 <- predict(rf_model3, newdata = rtrain_val) rf_cfm3 <- confusionMatrix(rf_pred3, rtrain_val$TripType) rf_model4 <- randomForest(TripType ~ DepartmentDescription, data = rtrain_train, ntree = 100, importance = T, do.trace = T) rf_pred4 <- predict(rf_model4, rtrain_val) rf_cfm <- confusionMatrix(rf_pred4, rtrain_val$TripType) rtrain_train <- rtrain_train %>% arrange(DepartmentDescription, FinelineNumber) t <- rtrain_train %>% select(DepartmentDescription, FinelineNumber) %>% group_by(DepartmentDescription, FinelineNumber) %>% arrange(DepartmentDescription, FinelineNumber) %>% unique() rtrain_train <- merge(rtrain_train, t, by = c("DepartmentDescription", "FinelineNumber"), all.x = T) rtrain_val <- merge(rtrain_val, t, by = c("DepartmentDescription", "FinelineNumber"), all.x = T) rf_model5 <- randomForest(TripType ~ VisitNumber + defi, data = rtrain_train, ntree = 200, importance = T, do.trace = T) rf_pred5 <- predict(rf_model5, newdata = rtrain_val) rf_cfm5 <- confusionMatrix(rf_pred5, rtrain_val$TripType) rf_imp5 <- importance(rf_model5) varImpPlot(rf_model5) t1 <- rtrain_train %>% select(defi, CompanyCode) %>% group_by(defi, CompanyCode) %>% arrange(defi, CompanyCode) %>% unique() rtrain_train %>% filter(TripType %in% c(7, 8)) %>% head(20) t1$CompanyCode <- as.character(t1$CompanyCode) t1 <- t1 %>% arrange(CompanyCode, defi) t1$defico <- 1 : nrow(t1) rtrain_train <- merge(rtrain_train, t1, by = c("defi", "CompanyCode"), all.x = T) rtrain_val <- merge(rtrain_val, t1, by = c("defi", "CompanyCode"), all.x = T) rf_model6 <- randomForest(TripType ~ VisitNumber + defico, data = rtrain_train, ntree = 200, importance = T, do.trace = T) rf_pred6 <- predict(rf_model6, newdata = rtrain_val) rf_cfm6 <- confusionMatrix(rf_pred6, rtrain_val$TripType) # Make New DataSet head(train) sumscancount <- train %>% filter(ScanCount > 0) %>% group_by(VisitNumber) %>% summarise(sumscancount = sum(ScanCount)) minuscancount <- train %>% filter(ScanCount < 0) %>% group_by(VisitNumber) %>% summarise(n = n()) colnames(minuscancount) <- c("id", "RefundCount") maxperdesc <- train %>% group_by(VisitNumber, DepartmentDescription) %>% summarise(n = n()) maxperdesc1 <- train %>% group_by(VisitNumber) %>% summarise(n = n()) maxperdescs <- merge(maxperdesc, maxperdesc1, by = "VisitNumber", all.x = T) maxperdesc <- maxperdescs %>% group_by(VisitNumber) %>% summarise(maxdesc = max(n.x), totaldesc = mean(n.y), per = round(maxdesc / totaldesc, 3)) newdata <- read.csv("newdata.csv") newdata <- merge(newdata, minuscancount, by = "id", all.x = T) newdata$RefundCount <- ifelse(is.na(newdata$RefundCount), 0, newdata$RefundCount) colnames(maxperdesc)[1] <- "id" newdata <- merge(newdata, maxperdesc, by = "id", all.x = T) head(newdata) colnames(newdata)[5 : 7] <- c("MaxDescCount", "TotalDescCount", "max_prodrate") write.csv(newdata, "newdata.csv", row.names = F) library(dummies) tt <- dummy(train$DepartmentDescription) train <- data.frame(train, tt) train$DepartmentDescription <- NULL colnames(train) head(tt) library(dplyr) # 데이터 읽어오기 & NA 지우기 train <- read.csv("train.csv") train <- train %>% filter(!is.na(Upc)) # VisitNumber와 DepartmentDescription 그룹으로 묶어서 # 각 그룹에 대한 총 갯수 데이터 셋 만들기 -- ㉠ descdummy <- train %>% group_by(VisitNumber, DepartmentDescription) %>% summarise(sumcategory = n()) # DepartmentDescription 더미변수를 담을 데이터프레임 공간 만들기 # column 이름 변경해주기 departdummy <- data.frame(unique(train$VisitNumber), matrix(0, nrow = length(unique(train$VisitNumber)), ncol = length(levels(train$DepartmentDescription)))) colnames(departdummy)[1] <- "id" colnames(departdummy)[2 : ncol(departdummy)] <- levels(train$DepartmentDescription) # 이 전처리 방식의 핵심은 DepartmentDescription의 레벨을 1부터 69까지로 변경 후에 # 그 숫자 + 1에 해당하는 인덱스에 위의 ㉠부분에서 구한 총 갯수를 넣어주는 것 # 또한, factor형식은 사칙연산에 대해서 의미가 없기 때문에 # 이를 numeric으로 변경 후에 연산을 진행 levels(descdummy$DepartmentDescription) <- 1 : length(levels(descdummy$DepartmentDescription)) descdummy$DepartmentDescription <- as.numeric(descdummy$DepartmentDescription) departindex <- 1 # departdummy(데이터를 저장해야할 변수)의 행의 인덱스 descindex <- 1 # descdummy(㉠의 그룹에 대한 총 갯수가 담긴 변수)의 행의 인덱스 repeat{ # 서로의 VisitNumber를 비교, # departdummy의 id가 VisitNumber(열이름만 위에서 바꾼것) # why? 밑에서 데이터 셋과 조인하기위해 이름을 id로 변경 해준것. if(descdummy$VisitNumber[descindex] == departdummy$id[departindex]){ # 위에서 간단히 설명한대로 VisitNumber가 같다면 총 갯수 데이터를 # departmentdescription의 값 + 1의 인덱스에 삽입 # 그리고 나서 총 갯수 데이터가 담긴 descindex의 값을 1증가하여 아래 행으로 이동 departdummy[departindex, descdummy$DepartmentDescription[descindex] + 1] <- descdummy$sumcategory[descindex] descindex <- descindex + 1 }else{ # 만약 VisitNumber가 다르다면 우리가 담고자 했던 departdummy의 인덱스를 1 증가 departindex <- departindex + 1 } print(paste(departindex, descindex)) if(descindex == (nrow(descdummy) + 1)){ break } } # 데이터 셋 불러온 후 더미변수와 조인시킨후에 저장 newdata <- read.csv("newdata.csv") newdata <- merge(newdata, departdummy, by = "id", all.x = T) write.csv(newdata, "newdata.csv", row.names = F) colnames(newdata) # TripType - a categorical id representing the type of shopping trip the customer made. # This is the ground truth that you are predicting. # TripType_999 is an "other" category. # VisitNumber - an id corresponding to a single trip by a single customer # Weekday - the weekday of the trip # Upc - the UPC number of the product purchased # ScanCount - the number of the given item that was purchased. # A negative value indicates a product return. # DepartmentDescription - a high-level description of the item's department # FinelineNumber - a more refined category for each of the products, created by Walmart # upc 설명 : https://www.kaggle.com/c/walmart-recruiting-trip-type-classification/discussion/18158 # https://www.kaggle.com/c/walmart-recruiting-trip-type-classification/discussion/18163 # https://www.kaggle.com/c/walmart-recruiting-trip-type-classification/discussion/30345 # https://en.wikipedia.org/wiki/Check_digit

/Walmart.R

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SubAkBa/Kaggle_Walmart

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14,666

r

library(Hmisc) library(caret) library(dplyr) library(ggplot2) library(data.table) # 파일 불러오기 test <- read.csv("test.csv") train <- read.csv("train.csv") train <- train %>% filter(!is.na(Upc)) # 데이터 확인 head(train) describe(train) str(train) table(train$TripType) table(train$Upc) table(train$DepartmentDescription) summary(train) boxplot(train$ScanCount) table(train$ScanCount) hist(train$VisitNumber) table(train$VisitNumber) train %>% filter(TripType == 39) %>% select(VisitNumber) train %>% filter(ScanCount < -4) train %>% filter(is.na(Upc)) train %>% filter(VisitNumber == 8) train %>% filter(DepartmentDescription == 'PHARMACY RX') train %>% filter(ScanCount > 19) %>% arrange(TripType, VisitNumber, ScanCount) train %>% filter(FinelineNumber == 9998) train %>% filter(TripType == 999) train %>% filter(FinelineNumber == 1000) train %>% filter(floor(log10(train$Upc))+1 == 12) # DepartmentDescription과 Weekday와의 관계 확인 week_depart <- list() wd_data <- tapply(train$DepartmentDescription, train$Weekday, table) for(i in 1 : NROW(unique(train$Weekday))){ week_depart <- c(week_depart, list(sort(wd_data[[i]], decreasing = T))) } names(week_depart) <- names(wd_data) sort_week_depart <- c(week_depart['Monday'], week_depart['Tuesday'], week_depart['Wednesday'], week_depart['Thursday'], week_depart['Friday'], week_depart['Saturday'], week_depart['Sunday']) # DepartmentDescription과 TripType과의 관계 triptype_depart <- list() trde_data <- tapply(train$DepartmentDescription, train$TripType, table) for(i in 1 : NROW(unique(train$TripType))){ triptype_depart <- c(triptype_depart, list(sort(trde_data[[i]], decreasing = T))) } names(triptype_depart) <- names(trde_data) trde_top10 <- list() for(i in 1 : NROW(unique(train$TripType))){ trde_top10 <- c(trde_top10, list(triptype_depart[[i]][1 : 10])) } names(trde_top10) <- names(trde_data) trde_top10_plot_data <- train %>% group_by(TripType, DepartmentDescription) %>% summarise(n = n()) %>% arrange(TripType, desc(n)) trde_top5_plot_result <- c() for(i in unique(trde_top10_plot_data$TripType)){ trde_top5_plot_result <- rbind(trde_top5_plot_result, trde_top10_plot_data %>% filter(TripType == i) %>% head(5)) } mosaicplot(~ DepartmentDescription + TripType, data = trde_top5_plot_result, las = 1) # Read Data File test <- read.csv("test.csv") train <- read.csv("train.csv") train <- train %>% filter(!is.na(Upc)) options(scipen = 100) # UPC 12자리 채우기 # First : 11자리까지 채우기(0으로) Upc12 <- matrix(0, nrow = nrow(train), ncol = 1) ZeroFillFunc <- function(x, y){ zeros <- c() zeroc <- 11 - (floor(log10(x)) + 1) for(j in 1 : zeroc){ zeros <- paste0(zeros, '0') } return (paste0(zeros, x)) } for(i in 1 : nrow(train)){ Upc12[i] <- ifelse(floor(log10(train$Upc[i])) > 9, train$Upc[i], ZeroFillFunc(train$Upc[i])) } Upc12 <- as.vector(Upc12) # Second : CheckSumDigit 만들기 MakeCheckSumDigitFunc <- function(x){ odds_sum <- 0 evens_sum <- 0 digit11 <- "" split_upc <- strsplit(x, "")[[1]] for(i in 1 : 11){ digit11 <- paste0(digit11, split_upc[i]) if (i %% 2 == 1) { odds_sum <- odds_sum + as.numeric(split_upc[i]) }else{ evens_sum <- evens_sum + as.numeric(split_upc[i]) } } total_sum <- odds_sum * 3 + evens_sum sum_result <- ifelse(total_sum %% 10 == 0, 0, (10 - (total_sum %% 10))) return (paste0(digit11, sum_result)) } Upc12 <- sapply(Upc12, MakeCheckSumDigitFunc) names(Upc12) <- NULL # Final : Train데이터와 합치기 train <- data.frame(train, Upc12) head(train, 20) colnames(train)[8] <- "UPC" train$Upc <- NULL # train Copy / Divide Company Code / Divide Product Code cptrain <- train MakeCompanyCodeFunc <- function(x){ ccode <- "" upc <- strsplit(as.character(x), "")[[1]] for(i in 1 : 6){ ccode <- paste0(ccode, upc[i]) } return (ccode) } Upc6 <- sapply(cptrain$UPC, MakeCompanyCodeFunc) cptrain <- data.frame(cptrain, Upc6) colnames(cptrain)[8] <- "CompanyCode" cptrain$UPC <- NULL cptrain$CompanyCode <- as.character(cptrain$CompanyCode) write.csv(cptrain, "htrain.csv", row.names = F) pUpc <- matrix(0, nrow = nrow(cptrain), ncol = 5) for(i in 1 : 5){ pUpc[, i] <- sapply(cptrain$UPC, function(x){ pcode <- "" upc <- strsplit(as.character(x), "")[[1]] for(j in (7 + (i - 1)) : 11){ pcode <- paste0(pcode, upc[j]) } return (pcode) }) } cptrain <- data.frame(cptrain, pUpc) colnames(cptrain)[8 : 12] <- c("ProductCode5", "ProductCode4", "ProductCode3", "ProductCode2", "ProductCode1") for(i in 8 : 12){ cptrain[, i] <- as.character(cptrain[, i]) } write.csv(cptrain, "ptrain.csv", row.names = F) write.table(cptrain, "ptrain.txt", sep = ",", col.names = T) tt <- fread("ptrain.csv") str(cptrain) head(tt) # Read Refined Data & Set Company Code len 6 library(data.table) library(dplyr) library(caret) rtrain <- fread("htrain.csv", stringsAsFactors = T) rtrain <- as.data.frame(rtrain) rtrain <- rtrain %>% filter(TripType %in% c(3 : 20)) rtrain$TripType <- as.factor(rtrain$TripType) rtrain <- rtrain %>% arrange(DepartmentDescription, FinelineNumber) t <- rtrain %>% select(DepartmentDescription, FinelineNumber) %>% arrange(DepartmentDescription, FinelineNumber) %>% distinct() t$defi <- 1 : nrow(t) rtrain <- merge(rtrain, t, by = c("DepartmentDescription", "FinelineNumber"), all.x = T) # Divide Train / Validation sample_idx <- rtrain %>% sample_frac(0.7) %>% rownames() %>% as.numeric() rtrain_train <- rtrain[sample_idx, ] rtrain_val <- rtrain[-sample_idx, ] # Multinomial Logistic Regression library(nnet) mlr_model <- multinom(TripType ~ ., data = rtrain_train) str(rtrain) summary(mlr_model) table(fitted(mlr_model)) # Decision Tree library(party) tree_control <- ctree_control(mincriterion = 0.9, minsplit = 10, maxdepth = 6) tree_model <- ctree(TripType ~ ., data = rtrain_train) plot(tree_model) tree_pred <- predict(tree_model, newdata = rtrain_val) confusionMatrix(tree_pred, rtrain_val$TripType_3) # RandomForest library(randomForest) rf_model <- randomForest(TripType ~ ScanCount, data = rtrain_train) rf_pred <- predict(rf_model, newdata = rtrain_val) confusionMatrix(rf_pred, rtrain_val$TripType) rf_model <- randomForest(TripType ~ VisitNumber, data = rtrain_train, ntree = 200, importance = TRUE, do.trace = TRUE) rf_pred <- predict(rf_model, newdata = rtrain_val) rf_cfm <- confusionMatrix(rf_pred, rtrain_val$TripType) rf_imp <- importance(rf_model) plot(rf_model) rf_model1 <- randomForest(TripType ~ VisitNumber + FinelineNumber, data = rtrain_train, ntree = 200, importance = T, do.trace = T) rf_pred1 <- predict(rf_model1, newdata = rtrain_val) rf_cfm1 <- confusionMatrix(rf_pred1, rtrain_val$TripType) rf_imp <- importance(rf_model1) varImpPlot(rf_model1) rf_model2 <- randomForest(TripType ~ VisitNumber + Weekday, data = rtrain_train, ntree = 100, importance = T, do.trace = T) rf_pred2 <- predict(rf_model2, newdata = rtrain_val) rf_cfm <- confusionMatrix(rf_pred2, rtrain_val$TripType) levels(rtrain_train$DepartmentDescription) <- c(1 : 68) table(rtrain_train$DepartmentDescription) rtrain_train$DepartmentDescription <- as.numeric(rtrain_train$DepartmentDescription) levels(rtrain_val$DepartmentDescription) <- c(1 : 68) table(rtrain_val$DepartmentDescription) rtrain_val$DepartmentDescription <- as.numeric(rtrain_val$DepartmentDescription) rf_model3 <- randomForest(TripType ~ VisitNumber + DepartmentDescription, data = rtrain_train, ntree = 100, importance = T, do.trace = T) rf_pred3 <- predict(rf_model3, newdata = rtrain_val) rf_cfm3 <- confusionMatrix(rf_pred3, rtrain_val$TripType) rf_model4 <- randomForest(TripType ~ DepartmentDescription, data = rtrain_train, ntree = 100, importance = T, do.trace = T) rf_pred4 <- predict(rf_model4, rtrain_val) rf_cfm <- confusionMatrix(rf_pred4, rtrain_val$TripType) rtrain_train <- rtrain_train %>% arrange(DepartmentDescription, FinelineNumber) t <- rtrain_train %>% select(DepartmentDescription, FinelineNumber) %>% group_by(DepartmentDescription, FinelineNumber) %>% arrange(DepartmentDescription, FinelineNumber) %>% unique() rtrain_train <- merge(rtrain_train, t, by = c("DepartmentDescription", "FinelineNumber"), all.x = T) rtrain_val <- merge(rtrain_val, t, by = c("DepartmentDescription", "FinelineNumber"), all.x = T) rf_model5 <- randomForest(TripType ~ VisitNumber + defi, data = rtrain_train, ntree = 200, importance = T, do.trace = T) rf_pred5 <- predict(rf_model5, newdata = rtrain_val) rf_cfm5 <- confusionMatrix(rf_pred5, rtrain_val$TripType) rf_imp5 <- importance(rf_model5) varImpPlot(rf_model5) t1 <- rtrain_train %>% select(defi, CompanyCode) %>% group_by(defi, CompanyCode) %>% arrange(defi, CompanyCode) %>% unique() rtrain_train %>% filter(TripType %in% c(7, 8)) %>% head(20) t1$CompanyCode <- as.character(t1$CompanyCode) t1 <- t1 %>% arrange(CompanyCode, defi) t1$defico <- 1 : nrow(t1) rtrain_train <- merge(rtrain_train, t1, by = c("defi", "CompanyCode"), all.x = T) rtrain_val <- merge(rtrain_val, t1, by = c("defi", "CompanyCode"), all.x = T) rf_model6 <- randomForest(TripType ~ VisitNumber + defico, data = rtrain_train, ntree = 200, importance = T, do.trace = T) rf_pred6 <- predict(rf_model6, newdata = rtrain_val) rf_cfm6 <- confusionMatrix(rf_pred6, rtrain_val$TripType) # Make New DataSet head(train) sumscancount <- train %>% filter(ScanCount > 0) %>% group_by(VisitNumber) %>% summarise(sumscancount = sum(ScanCount)) minuscancount <- train %>% filter(ScanCount < 0) %>% group_by(VisitNumber) %>% summarise(n = n()) colnames(minuscancount) <- c("id", "RefundCount") maxperdesc <- train %>% group_by(VisitNumber, DepartmentDescription) %>% summarise(n = n()) maxperdesc1 <- train %>% group_by(VisitNumber) %>% summarise(n = n()) maxperdescs <- merge(maxperdesc, maxperdesc1, by = "VisitNumber", all.x = T) maxperdesc <- maxperdescs %>% group_by(VisitNumber) %>% summarise(maxdesc = max(n.x), totaldesc = mean(n.y), per = round(maxdesc / totaldesc, 3)) newdata <- read.csv("newdata.csv") newdata <- merge(newdata, minuscancount, by = "id", all.x = T) newdata$RefundCount <- ifelse(is.na(newdata$RefundCount), 0, newdata$RefundCount) colnames(maxperdesc)[1] <- "id" newdata <- merge(newdata, maxperdesc, by = "id", all.x = T) head(newdata) colnames(newdata)[5 : 7] <- c("MaxDescCount", "TotalDescCount", "max_prodrate") write.csv(newdata, "newdata.csv", row.names = F) library(dummies) tt <- dummy(train$DepartmentDescription) train <- data.frame(train, tt) train$DepartmentDescription <- NULL colnames(train) head(tt) library(dplyr) # 데이터 읽어오기 & NA 지우기 train <- read.csv("train.csv") train <- train %>% filter(!is.na(Upc)) # VisitNumber와 DepartmentDescription 그룹으로 묶어서 # 각 그룹에 대한 총 갯수 데이터 셋 만들기 -- ㉠ descdummy <- train %>% group_by(VisitNumber, DepartmentDescription) %>% summarise(sumcategory = n()) # DepartmentDescription 더미변수를 담을 데이터프레임 공간 만들기 # column 이름 변경해주기 departdummy <- data.frame(unique(train$VisitNumber), matrix(0, nrow = length(unique(train$VisitNumber)), ncol = length(levels(train$DepartmentDescription)))) colnames(departdummy)[1] <- "id" colnames(departdummy)[2 : ncol(departdummy)] <- levels(train$DepartmentDescription) # 이 전처리 방식의 핵심은 DepartmentDescription의 레벨을 1부터 69까지로 변경 후에 # 그 숫자 + 1에 해당하는 인덱스에 위의 ㉠부분에서 구한 총 갯수를 넣어주는 것 # 또한, factor형식은 사칙연산에 대해서 의미가 없기 때문에 # 이를 numeric으로 변경 후에 연산을 진행 levels(descdummy$DepartmentDescription) <- 1 : length(levels(descdummy$DepartmentDescription)) descdummy$DepartmentDescription <- as.numeric(descdummy$DepartmentDescription) departindex <- 1 # departdummy(데이터를 저장해야할 변수)의 행의 인덱스 descindex <- 1 # descdummy(㉠의 그룹에 대한 총 갯수가 담긴 변수)의 행의 인덱스 repeat{ # 서로의 VisitNumber를 비교, # departdummy의 id가 VisitNumber(열이름만 위에서 바꾼것) # why? 밑에서 데이터 셋과 조인하기위해 이름을 id로 변경 해준것. if(descdummy$VisitNumber[descindex] == departdummy$id[departindex]){ # 위에서 간단히 설명한대로 VisitNumber가 같다면 총 갯수 데이터를 # departmentdescription의 값 + 1의 인덱스에 삽입 # 그리고 나서 총 갯수 데이터가 담긴 descindex의 값을 1증가하여 아래 행으로 이동 departdummy[departindex, descdummy$DepartmentDescription[descindex] + 1] <- descdummy$sumcategory[descindex] descindex <- descindex + 1 }else{ # 만약 VisitNumber가 다르다면 우리가 담고자 했던 departdummy의 인덱스를 1 증가 departindex <- departindex + 1 } print(paste(departindex, descindex)) if(descindex == (nrow(descdummy) + 1)){ break } } # 데이터 셋 불러온 후 더미변수와 조인시킨후에 저장 newdata <- read.csv("newdata.csv") newdata <- merge(newdata, departdummy, by = "id", all.x = T) write.csv(newdata, "newdata.csv", row.names = F) colnames(newdata) # TripType - a categorical id representing the type of shopping trip the customer made. # This is the ground truth that you are predicting. # TripType_999 is an "other" category. # VisitNumber - an id corresponding to a single trip by a single customer # Weekday - the weekday of the trip # Upc - the UPC number of the product purchased # ScanCount - the number of the given item that was purchased. # A negative value indicates a product return. # DepartmentDescription - a high-level description of the item's department # FinelineNumber - a more refined category for each of the products, created by Walmart # upc 설명 : https://www.kaggle.com/c/walmart-recruiting-trip-type-classification/discussion/18158 # https://www.kaggle.com/c/walmart-recruiting-trip-type-classification/discussion/18163 # https://www.kaggle.com/c/walmart-recruiting-trip-type-classification/discussion/30345 # https://en.wikipedia.org/wiki/Check_digit

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/1_score.R \name{log_loss} \alias{log_loss} \title{Logarithmic Loss} \usage{ log_loss(act, pred, allow_inf = FALSE) } \arguments{ \item{act}{actual results} \item{pred}{predicted probabilties} \item{allow_inf}{pass through infinite loss? \code{FALSE} replaces Inf with large but finite penalties at the extremes. Default \code{FALSE}.} } \value{ numeric. } \description{ \code{log_loss()} returns the logarithmic loss obtained from a given prediction and known result } \details{ The allow_inf parameter controls whether infinite loss is allowed (default is FALSE) Setting allow_inf to FALSE will cause large but finite penalties at the extremes }

/man/log_loss.Rd

no_license

deepfriar/corsicaUtils

R

false

true

727

rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/1_score.R \name{log_loss} \alias{log_loss} \title{Logarithmic Loss} \usage{ log_loss(act, pred, allow_inf = FALSE) } \arguments{ \item{act}{actual results} \item{pred}{predicted probabilties} \item{allow_inf}{pass through infinite loss? \code{FALSE} replaces Inf with large but finite penalties at the extremes. Default \code{FALSE}.} } \value{ numeric. } \description{ \code{log_loss()} returns the logarithmic loss obtained from a given prediction and known result } \details{ The allow_inf parameter controls whether infinite loss is allowed (default is FALSE) Setting allow_inf to FALSE will cause large but finite penalties at the extremes }

■ R 을 활용한 머신러닝 11장 - 모델성능개선 5. random forest - 자동튜닝 library(caret) library(C50) library(irr) data(iris) head(iris) #0.shuffle 을 먼저 합니다. set.seed(123) iris_shuffle <- sample(1:150, 150) iris_shuffle iris2 <- iris[iris_shuffle,] iris2 set.seed(123) in_train <- createDataPartition(iris2$Species, p = 0.75, list = FALSE) iris_train <- iris2[in_train, ] # 훈련 데이터 구성 iris_test <- iris2[-in_train, ] # 테스트 데이터 구성 m <- train( Species~ . , data=iris_train, method="rf" ) # 랜덤포레스트: 의사결정트리 + 앙상블 기법 m # 튜닝한 결과를 확인할 수 있다. p <- predict( m , iris_test ) table(p, iris_test$Species) library(gmodels) y <- CrossTable(iris_test$Species ,p) sum(y$prop.tbl * diag(3))

/[R] R 을 활용한 머신러닝 11장 - 모델성능개선 5. random forest - 자동튜닝.R

no_license

data-githu/R-MACHINE-LEARNING

R

false

830

r

■ R 을 활용한 머신러닝 11장 - 모델성능개선 5. random forest - 자동튜닝 library(caret) library(C50) library(irr) data(iris) head(iris) #0.shuffle 을 먼저 합니다. set.seed(123) iris_shuffle <- sample(1:150, 150) iris_shuffle iris2 <- iris[iris_shuffle,] iris2 set.seed(123) in_train <- createDataPartition(iris2$Species, p = 0.75, list = FALSE) iris_train <- iris2[in_train, ] # 훈련 데이터 구성 iris_test <- iris2[-in_train, ] # 테스트 데이터 구성 m <- train( Species~ . , data=iris_train, method="rf" ) # 랜덤포레스트: 의사결정트리 + 앙상블 기법 m # 튜닝한 결과를 확인할 수 있다. p <- predict( m , iris_test ) table(p, iris_test$Species) library(gmodels) y <- CrossTable(iris_test$Species ,p) sum(y$prop.tbl * diag(3))

\name{get.ISFrag.results} \alias{get.ISFrag.results} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Summarizes ISFrag Results } \description{ Creates a list containing ISFrag parent-fragment trees and feature table containing parent-fragment relationship information. } \usage{ get.ISFrag.results(ISF_List, featureTable) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{ISF_List}{ List of tables generated by find.level1() function. } \item{featureTable}{ featureTable generated by feature.annotaion() function } } \details{ %% ~~ If necessary, more details than the description above ~~ } \value{ Returns a list containing [1]: a list of tree objects where each tree contains ISF relationship of each parent feature, [2]: a dataframe of the original feature table containing additional columns denoting each feature's ISF annotation/relationship. } \references{ %% ~put references to the literature/web site here ~ } \author{ Sam Shen, Jian Guo, Tao Huan } \note{ %% ~~further notes~~ } %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ %% ~~objects to See Also as \code{\link{help}}, ~~~ } \examples{ library(ISFrag) MS1directory <- "X:/Users/Sam_Shen/ISFtest20210127/HILIC(+)/HILIC(+)3/fullscan" MS2directory <- "X:/Users/Sam_Shen/ISFtest20210127/HILIC(+)/HILIC(+)3/DDA" type <- "single" lib_directory <- "E:/SAM" lib_name <- "convertedLibraryPos.msp" featureTable <- generate.featuretable(MS1directory = MS1directory, type = type, ppm=10, peakwidth=c(10,120), mzdiff = 0.01, snthresh = 6, integrate = 1, prefilter = c(3,100), noise = 100, bw = 5, mzwid = 0.015, max = 100, CAMERA = F) featureTable <- ms2.tofeaturetable(MS2directory = MS2directory, featureTable = featureTable) featureTable <- feature.annotation(featureTable = featureTable, lib_directory = lib_directory, lib_name = lib_name, dp = 0.7) level3 <- find.level3(MS1directory = MS1directory, MS1.files = MS1.files, featureTable = featureTable, type = type) level2 <- find.level2(ISFtable = level3) level1 <- find.level1(ISF_putative = level2) results <- get.ISFrag.results(ISF_List = level1, featureTable = featureTable) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory (show via RShowDoc("KEYWORDS")): % \keyword{ ~kwd1 } % \keyword{ ~kwd2 } % Use only one keyword per line. % For non-standard keywords, use \concept instead of \keyword: % \concept{ ~cpt1 } % \concept{ ~cpt2 } % Use only one concept per line.

/man/get.ISFrag.results.Rd

no_license

shen420/ISFrag

R

false

2,593

rd

\name{get.ISFrag.results} \alias{get.ISFrag.results} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Summarizes ISFrag Results } \description{ Creates a list containing ISFrag parent-fragment trees and feature table containing parent-fragment relationship information. } \usage{ get.ISFrag.results(ISF_List, featureTable) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{ISF_List}{ List of tables generated by find.level1() function. } \item{featureTable}{ featureTable generated by feature.annotaion() function } } \details{ %% ~~ If necessary, more details than the description above ~~ } \value{ Returns a list containing [1]: a list of tree objects where each tree contains ISF relationship of each parent feature, [2]: a dataframe of the original feature table containing additional columns denoting each feature's ISF annotation/relationship. } \references{ %% ~put references to the literature/web site here ~ } \author{ Sam Shen, Jian Guo, Tao Huan } \note{ %% ~~further notes~~ } %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{ %% ~~objects to See Also as \code{\link{help}}, ~~~ } \examples{ library(ISFrag) MS1directory <- "X:/Users/Sam_Shen/ISFtest20210127/HILIC(+)/HILIC(+)3/fullscan" MS2directory <- "X:/Users/Sam_Shen/ISFtest20210127/HILIC(+)/HILIC(+)3/DDA" type <- "single" lib_directory <- "E:/SAM" lib_name <- "convertedLibraryPos.msp" featureTable <- generate.featuretable(MS1directory = MS1directory, type = type, ppm=10, peakwidth=c(10,120), mzdiff = 0.01, snthresh = 6, integrate = 1, prefilter = c(3,100), noise = 100, bw = 5, mzwid = 0.015, max = 100, CAMERA = F) featureTable <- ms2.tofeaturetable(MS2directory = MS2directory, featureTable = featureTable) featureTable <- feature.annotation(featureTable = featureTable, lib_directory = lib_directory, lib_name = lib_name, dp = 0.7) level3 <- find.level3(MS1directory = MS1directory, MS1.files = MS1.files, featureTable = featureTable, type = type) level2 <- find.level2(ISFtable = level3) level1 <- find.level1(ISF_putative = level2) results <- get.ISFrag.results(ISF_List = level1, featureTable = featureTable) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory (show via RShowDoc("KEYWORDS")): % \keyword{ ~kwd1 } % \keyword{ ~kwd2 } % Use only one keyword per line. % For non-standard keywords, use \concept instead of \keyword: % \concept{ ~cpt1 } % \concept{ ~cpt2 } % Use only one concept per line.

# samples that did not sequence well source("scripts/lab_helpers.R") # library(ggplot2) lab <- read_db("Laboratory") # # get the list of missing samples from amphiprion that have been edited to just Ligation number in text editor using the find: (APCL_\d+)(L\d+) and the replace: \2 and the find: (APCL_)(L\d+) and the replace: \2 # fails <- read.csv("data/lowDP.indv.csv", header = F) # names(fails) <- "ligation_id" # # # find sample_ids and extract ids # lab <- read_db("Laboratory") # ligs <- lab %>% # tbl("ligation") %>% # collect() %>% # filter(ligation_id %in% fails$ligation_id) %>% # select(ligation_id, digest_id, total_reads, retained) # # digs <- lab %>% # tbl("digest") %>% # collect() %>% # filter(digest_id %in% ligs$digest_id) %>% # select(digest_id, extraction_id, quant) %>% # rename(dig_quant = quant) # # digs <- left_join(ligs, digs, by = "digest_id") # rm(ligs) # # sample <- lab %>% # tbl("extraction") %>% # collect() %>% # filter(extraction_id %in% digs$extraction_id) %>% # select(extraction_id, sample_id, quant, gel) %>% # rename(extr_quant = quant) # # sample <- left_join(digs, sample, by = "extraction_id") # # sample <- sample %>% # select(sample_id, extraction_id, extr_quant, gel, digest_id, dig_quant, ligation_id, total_reads, retained) # # # write.csv(sample, "data/problem_samples.csv", row.names = F) # pull in successful ligations from Rdata ligs <- readRDS("data/passed_ligs.Rdata") temp <- lab %>% tbl("ligation") %>% filter(ligation_id %in% ligs$ligation_id) %>% select(ligation_id, digest_id, DNA, retained) %>% collect() ligs <- left_join(ligs, temp, by = "ligation_id") failed_ligs <- lab %>% tbl("ligation") %>% filter(!ligation_id %in% ligs$ligation_id) %>% select(ligation_id, digest_id, DNA, retained) %>% collect() temp <- lab %>% tbl("digest") %>% filter(digest_id %in% ligs$digest_id) %>% select(digest_id, extraction_id, quant) %>% collect() ligs <- left_join(ligs, temp, by = "digest_id") %>% rename(dig_quant = quant) temp <- lab %>% tbl("digest") %>% filter(digest_id %in% failed_ligs$digest_id) %>% select(digest_id, extraction_id, quant) %>% collect() failed_ligs <- left_join(failed_ligs, temp, by = "digest_id") %>% rename(dig_quant = quant) temp <- lab %>% tbl("extraction") %>% filter(extraction_id %in% ligs$extraction_id) %>% select(extraction_id, quant, sample_id) %>% collect() ligs <- left_join(ligs, temp, by = "extraction_id") %>% rename(extr_quant = quant) temp <- lab %>% tbl("extraction") %>% filter(extraction_id %in% failed_ligs$extraction_id) %>% select(extraction_id, quant, sample_id) %>% collect() failed_ligs <- left_join(failed_ligs, temp, by = "extraction_id") %>% rename(extr_quant = quant) #1343 # remove any sample_ids from the failed_ligs that are also on the ligs failed_ligs <- anti_join(failed_ligs, ligs, by = "sample_id") %>% #1052 select(sample_id, extraction_id, extr_quant, digest_id, dig_quant, ligation_id,DNA, retained) %>% arrange(sample_id) # remove samples that are not clarkii removed <- failed_ligs %>% filter(!grepl("APCL", sample_id)) failed_ligs <- failed_ligs %>% filter(grepl("APCL", sample_id)) # separate out into low conc digest and regular digest # brks<-seq(0,800,50) # plot concentration of failed seqs hist(x = ligs$quant,col="blue",breaks=brks,ylim=c(0,150)) hist(x = sample$extr_quant,col="red",breaks=brks,ylim=c(0,150), add = T) # how many digests with a quant < 10 led to successful sequences? low_dig_succ <- ligs %>% filter(ligs$dig_quant < 10) low_dig_fail <- sample %>% filter(sample$dig_quant < 10) # rm(digs) # # # make a list of samples that need to be re-processed # sample <- sample %>% # select(sample_id, extraction_id, extr_quant, digest_id, dig_quant, ligation_id) %>% # arrange(sample_id) # # # inform the leyte database that these samples failed sequencing # nam <- sample %>% # distinct(sample_id) # # leyte <- write_db("Leyte") # clownfish <- dbReadTable(leyte, "clownfish") # change <- clownfish %>% # filter(sample_id %in% nam$sample_id) # # test <- anti_join(nam, change, by = "sample_id") # # # find out if these extractions were ligated successfully # digs <- lab %>% # tbl("digest") %>% # filter(extraction_id %in% sample$extraction_id) %>% # select(digest_id, extraction_id) %>% # collect() # # ligs <- lab %>% # tbl("ligation") %>% # filter(digest_id %in% digs$digest_id, # !ligation_id %in% sample$ligation_id, # notes != "PCR failed", # ligation_id != "L0548" # ) %>% # select(ligation_id, digest_id, notes) %>% # collect() # # ligs <- left_join(ligs, digs, by = "digest_id") # rm(digs) # # # remove successful ligations from the samples # sample <- anti_join(sample, ligs, by = "extraction_id") %>% # arrange(extr_quant, sample_id) # # # write.csv(sample, paste("data/samples_to_retry", Sys.Date(), ".csv", sep = ""), row.names = F) # # # plot a histogram of extr_quants # ggplot(data = sample) + # geom_histogram(mapping = aes(x=extr_quant), binwidth = 10) # # # based on this histogram, maybe do a low concentration enzyme digest with any samples with a quant lower than 10? # # low_conc <- sample %>% # filter(extr_quant < 10) # # # ggplot(data = low_conc) + # geom_histogram(mapping = aes(x = extr_quant), binwidth = 0.5) # # # in order to make a plate of low concentration samples to digest, need to import locations from db # low_conc <- low_conc %>% # distinct(extraction_id) # # low_conc_plate <- lab %>% # tbl("extraction") %>% # filter(extraction_id %in% low_conc$extraction_id) %>% # select(extraction_id, sample_id, quant, well, plate, notes) %>% # collect() %>% # filter(!grepl("empty", notes)) # remove empty wells # # # # get a list of plate names and the count of samples from each plate # counts <- low_conc_plate %>% # group_by(plate) %>% # summarise(samples = n()) %>% # count the number of samples to be digested on each plate # arrange(plate) # # # add a digest plate name # low_conc_plate <- low_conc_plate %>% # mutate(dig_plate = "low_conc_plate") %>% # arrange(extraction_id) # sort by extraction_id # # plate_master <- data.frame( row = rep(LETTERS[1:8], 12), col = unlist(lapply(1:12, rep, 8))) # plate_master <- plate_master %>% # mutate(dig_well = paste(row, col, sep = "")) # plate_master <- plate_master[1:nrow(low_conc_plate), ] # low_conc_plate <- cbind(low_conc_plate, plate_master) # low_conc_plate <- low_conc_plate %>% # mutate(col = formatC(as.numeric(col), width = 2, format = "d", flag = "0"))%>% # arrange(col, row) # low_conc_plate <- low_conc_plate %>% # mutate(digest_id = 1:nrow(low_conc_plate)) # # # # # #

/scripts/problem_samples.R

no_license

mstuart1/laboratory_old

R

false

6,824

r

# samples that did not sequence well source("scripts/lab_helpers.R") # library(ggplot2) lab <- read_db("Laboratory") # # get the list of missing samples from amphiprion that have been edited to just Ligation number in text editor using the find: (APCL_\d+)(L\d+) and the replace: \2 and the find: (APCL_)(L\d+) and the replace: \2 # fails <- read.csv("data/lowDP.indv.csv", header = F) # names(fails) <- "ligation_id" # # # find sample_ids and extract ids # lab <- read_db("Laboratory") # ligs <- lab %>% # tbl("ligation") %>% # collect() %>% # filter(ligation_id %in% fails$ligation_id) %>% # select(ligation_id, digest_id, total_reads, retained) # # digs <- lab %>% # tbl("digest") %>% # collect() %>% # filter(digest_id %in% ligs$digest_id) %>% # select(digest_id, extraction_id, quant) %>% # rename(dig_quant = quant) # # digs <- left_join(ligs, digs, by = "digest_id") # rm(ligs) # # sample <- lab %>% # tbl("extraction") %>% # collect() %>% # filter(extraction_id %in% digs$extraction_id) %>% # select(extraction_id, sample_id, quant, gel) %>% # rename(extr_quant = quant) # # sample <- left_join(digs, sample, by = "extraction_id") # # sample <- sample %>% # select(sample_id, extraction_id, extr_quant, gel, digest_id, dig_quant, ligation_id, total_reads, retained) # # # write.csv(sample, "data/problem_samples.csv", row.names = F) # pull in successful ligations from Rdata ligs <- readRDS("data/passed_ligs.Rdata") temp <- lab %>% tbl("ligation") %>% filter(ligation_id %in% ligs$ligation_id) %>% select(ligation_id, digest_id, DNA, retained) %>% collect() ligs <- left_join(ligs, temp, by = "ligation_id") failed_ligs <- lab %>% tbl("ligation") %>% filter(!ligation_id %in% ligs$ligation_id) %>% select(ligation_id, digest_id, DNA, retained) %>% collect() temp <- lab %>% tbl("digest") %>% filter(digest_id %in% ligs$digest_id) %>% select(digest_id, extraction_id, quant) %>% collect() ligs <- left_join(ligs, temp, by = "digest_id") %>% rename(dig_quant = quant) temp <- lab %>% tbl("digest") %>% filter(digest_id %in% failed_ligs$digest_id) %>% select(digest_id, extraction_id, quant) %>% collect() failed_ligs <- left_join(failed_ligs, temp, by = "digest_id") %>% rename(dig_quant = quant) temp <- lab %>% tbl("extraction") %>% filter(extraction_id %in% ligs$extraction_id) %>% select(extraction_id, quant, sample_id) %>% collect() ligs <- left_join(ligs, temp, by = "extraction_id") %>% rename(extr_quant = quant) temp <- lab %>% tbl("extraction") %>% filter(extraction_id %in% failed_ligs$extraction_id) %>% select(extraction_id, quant, sample_id) %>% collect() failed_ligs <- left_join(failed_ligs, temp, by = "extraction_id") %>% rename(extr_quant = quant) #1343 # remove any sample_ids from the failed_ligs that are also on the ligs failed_ligs <- anti_join(failed_ligs, ligs, by = "sample_id") %>% #1052 select(sample_id, extraction_id, extr_quant, digest_id, dig_quant, ligation_id,DNA, retained) %>% arrange(sample_id) # remove samples that are not clarkii removed <- failed_ligs %>% filter(!grepl("APCL", sample_id)) failed_ligs <- failed_ligs %>% filter(grepl("APCL", sample_id)) # separate out into low conc digest and regular digest # brks<-seq(0,800,50) # plot concentration of failed seqs hist(x = ligs$quant,col="blue",breaks=brks,ylim=c(0,150)) hist(x = sample$extr_quant,col="red",breaks=brks,ylim=c(0,150), add = T) # how many digests with a quant < 10 led to successful sequences? low_dig_succ <- ligs %>% filter(ligs$dig_quant < 10) low_dig_fail <- sample %>% filter(sample$dig_quant < 10) # rm(digs) # # # make a list of samples that need to be re-processed # sample <- sample %>% # select(sample_id, extraction_id, extr_quant, digest_id, dig_quant, ligation_id) %>% # arrange(sample_id) # # # inform the leyte database that these samples failed sequencing # nam <- sample %>% # distinct(sample_id) # # leyte <- write_db("Leyte") # clownfish <- dbReadTable(leyte, "clownfish") # change <- clownfish %>% # filter(sample_id %in% nam$sample_id) # # test <- anti_join(nam, change, by = "sample_id") # # # find out if these extractions were ligated successfully # digs <- lab %>% # tbl("digest") %>% # filter(extraction_id %in% sample$extraction_id) %>% # select(digest_id, extraction_id) %>% # collect() # # ligs <- lab %>% # tbl("ligation") %>% # filter(digest_id %in% digs$digest_id, # !ligation_id %in% sample$ligation_id, # notes != "PCR failed", # ligation_id != "L0548" # ) %>% # select(ligation_id, digest_id, notes) %>% # collect() # # ligs <- left_join(ligs, digs, by = "digest_id") # rm(digs) # # # remove successful ligations from the samples # sample <- anti_join(sample, ligs, by = "extraction_id") %>% # arrange(extr_quant, sample_id) # # # write.csv(sample, paste("data/samples_to_retry", Sys.Date(), ".csv", sep = ""), row.names = F) # # # plot a histogram of extr_quants # ggplot(data = sample) + # geom_histogram(mapping = aes(x=extr_quant), binwidth = 10) # # # based on this histogram, maybe do a low concentration enzyme digest with any samples with a quant lower than 10? # # low_conc <- sample %>% # filter(extr_quant < 10) # # # ggplot(data = low_conc) + # geom_histogram(mapping = aes(x = extr_quant), binwidth = 0.5) # # # in order to make a plate of low concentration samples to digest, need to import locations from db # low_conc <- low_conc %>% # distinct(extraction_id) # # low_conc_plate <- lab %>% # tbl("extraction") %>% # filter(extraction_id %in% low_conc$extraction_id) %>% # select(extraction_id, sample_id, quant, well, plate, notes) %>% # collect() %>% # filter(!grepl("empty", notes)) # remove empty wells # # # # get a list of plate names and the count of samples from each plate # counts <- low_conc_plate %>% # group_by(plate) %>% # summarise(samples = n()) %>% # count the number of samples to be digested on each plate # arrange(plate) # # # add a digest plate name # low_conc_plate <- low_conc_plate %>% # mutate(dig_plate = "low_conc_plate") %>% # arrange(extraction_id) # sort by extraction_id # # plate_master <- data.frame( row = rep(LETTERS[1:8], 12), col = unlist(lapply(1:12, rep, 8))) # plate_master <- plate_master %>% # mutate(dig_well = paste(row, col, sep = "")) # plate_master <- plate_master[1:nrow(low_conc_plate), ] # low_conc_plate <- cbind(low_conc_plate, plate_master) # low_conc_plate <- low_conc_plate %>% # mutate(col = formatC(as.numeric(col), width = 2, format = "d", flag = "0"))%>% # arrange(col, row) # low_conc_plate <- low_conc_plate %>% # mutate(digest_id = 1:nrow(low_conc_plate)) # # # # # #

# put your ASV table and taxonomy table in Database/Tax4Fun2 folder... library(Tax4Fun2) library(seqinr) setwd("~/R/Database/Tax4Fun2") ASV.table <- read.table(file="ASV_table.txt",header=T,row.names=1) ASV <- ASV.table [,1:(ncol(ASV.table)-6)] # I've changed 7--> 6 ASV <- cbind (rownames(ASV),ASV) ASV <- rbind (colnames(ASV),ASV) ASV[1,1] <- "ID" rownames(ASV) <- NULL colnames(ASV) <- NULL write.table(ASV, "ASV.txt",sep="\t",col.names = F, row.names = F,quote=F) taxonomy <- read.table(file="taxonomy.txt",header=T, row.names=1) tax <- subset(taxonomy, Family != "Mitochondria" & Class != "Chloroplast" & Kingdom != "NA") write.fasta (sequences = as.list(rownames(tax)),names = rownames(ASV.table),file.out="seqs.fasta") dir.create("Tax4Fun2") #Step 2: Generate your own reference datasets #1. Extracting SSU seqeunces (16S rRNA and 18S rRNA) extractSSU(genome_file = "OneProkaryoticGenome.fasta", file_extension = "fasta", path_to_reference_data ="Tax4Fun2_ReferenceData_v2") #2. Assigning functions to prokayotic genomes assignFunction(genome_file = "OneProkaryoticGenome.fasta", file_extension = "fasta", path_to_reference_data = "Tax4Fun2_ReferenceData_v2", num_of_threads = 1, fast = TRUE) #3. Generate the reference data generateUserData(path_to_reference_data ="Tax4Fun2_ReferenceData_v2", path_to_user_data = ".", name_of_user_data = "User_Ref0", SSU_file_extension = "_16SrRNA.ffn", KEGG_file_extension = "_funPro.txt") #Step 3: Making functional predictions #1. Making functional predictions using the default reference data only #1. Run the reference blast runRefBlast(path_to_otus = "seqs.fasta" , path_to_reference_data ="Tax4Fun2_ReferenceData_v2", path_to_temp_folder = "Tax4Fun2", database_mode = "Ref99NR", use_force = T, num_threads = 6) # 2) Predicting functional profiles # Remove the first row's # and the second row's # of "17_otu_97_table_taxonomy.txt" --> "17_otu_97_table_taxonomy_tax4fun.txt" makeFunctionalPrediction(path_to_otu_table = "ASV.txt", path_to_reference_data = "Tax4Fun2_ReferenceData_v2", path_to_temp_folder = "Tax4Fun2", database_mode = "Ref99NR", normalize_by_copy_number = TRUE, min_identity_to_reference = 0.97, normalize_pathways = FALSE) # note. normalize_pathways = FALSE will affiliate the rel. abundance of each KO to each pathway it belongs to. By setting it to true, the rel. abundance is equally distributed to all pathways it was assigned to.) #Step 4: Calculating (multi-)functional redundancy indices (experimental) calculateFunctionalRedundancy(path_to_otu_table = "ASV.txt", path_to_reference_data = "Tax4Fun2_ReferenceData_v2", path_to_temp_folder = "Tax4Fun2", database_mode = "Ref99NR", min_identity_to_reference = 0.97) # Don't forget to move the output file (Tax4Fun2) for your project file

/tax4fun2_from_dada2.R

no_license

HaihuaWang-hub/NGS-Data-Processing

R

false

2,862

r

# put your ASV table and taxonomy table in Database/Tax4Fun2 folder... library(Tax4Fun2) library(seqinr) setwd("~/R/Database/Tax4Fun2") ASV.table <- read.table(file="ASV_table.txt",header=T,row.names=1) ASV <- ASV.table [,1:(ncol(ASV.table)-6)] # I've changed 7--> 6 ASV <- cbind (rownames(ASV),ASV) ASV <- rbind (colnames(ASV),ASV) ASV[1,1] <- "ID" rownames(ASV) <- NULL colnames(ASV) <- NULL write.table(ASV, "ASV.txt",sep="\t",col.names = F, row.names = F,quote=F) taxonomy <- read.table(file="taxonomy.txt",header=T, row.names=1) tax <- subset(taxonomy, Family != "Mitochondria" & Class != "Chloroplast" & Kingdom != "NA") write.fasta (sequences = as.list(rownames(tax)),names = rownames(ASV.table),file.out="seqs.fasta") dir.create("Tax4Fun2") #Step 2: Generate your own reference datasets #1. Extracting SSU seqeunces (16S rRNA and 18S rRNA) extractSSU(genome_file = "OneProkaryoticGenome.fasta", file_extension = "fasta", path_to_reference_data ="Tax4Fun2_ReferenceData_v2") #2. Assigning functions to prokayotic genomes assignFunction(genome_file = "OneProkaryoticGenome.fasta", file_extension = "fasta", path_to_reference_data = "Tax4Fun2_ReferenceData_v2", num_of_threads = 1, fast = TRUE) #3. Generate the reference data generateUserData(path_to_reference_data ="Tax4Fun2_ReferenceData_v2", path_to_user_data = ".", name_of_user_data = "User_Ref0", SSU_file_extension = "_16SrRNA.ffn", KEGG_file_extension = "_funPro.txt") #Step 3: Making functional predictions #1. Making functional predictions using the default reference data only #1. Run the reference blast runRefBlast(path_to_otus = "seqs.fasta" , path_to_reference_data ="Tax4Fun2_ReferenceData_v2", path_to_temp_folder = "Tax4Fun2", database_mode = "Ref99NR", use_force = T, num_threads = 6) # 2) Predicting functional profiles # Remove the first row's # and the second row's # of "17_otu_97_table_taxonomy.txt" --> "17_otu_97_table_taxonomy_tax4fun.txt" makeFunctionalPrediction(path_to_otu_table = "ASV.txt", path_to_reference_data = "Tax4Fun2_ReferenceData_v2", path_to_temp_folder = "Tax4Fun2", database_mode = "Ref99NR", normalize_by_copy_number = TRUE, min_identity_to_reference = 0.97, normalize_pathways = FALSE) # note. normalize_pathways = FALSE will affiliate the rel. abundance of each KO to each pathway it belongs to. By setting it to true, the rel. abundance is equally distributed to all pathways it was assigned to.) #Step 4: Calculating (multi-)functional redundancy indices (experimental) calculateFunctionalRedundancy(path_to_otu_table = "ASV.txt", path_to_reference_data = "Tax4Fun2_ReferenceData_v2", path_to_temp_folder = "Tax4Fun2", database_mode = "Ref99NR", min_identity_to_reference = 0.97) # Don't forget to move the output file (Tax4Fun2) for your project file

setwd("~/Dropbox/umich_2017_winter/stats503/project") library(stringr) library(rvest) ######################################################## # players drafted start_year = 2010 end_year = 2015 seasons <- seq(start_year, end_year, 1) # scrape player names and ranking from nbadraft.net draftplayer_read <- function(){ urls <- sapply(seasons, function(e) str_c("http://www.nbadraft.net/nba_final_draft/",e)) page <- lapply(urls, read_html) draftees <- lapply(1:length(seasons), function(e) html_table(html_nodes(page[[e]], "table"))[[1]]) return(draftees) } player_list <- do.call(rbind, draftplayer_read()) names(player_list) # convert wide format to long format player_list <- rbind(player_list[,1:8], player_list[,9:16]) names(player_list) # select only useful columns player_list <- player_list[,c("#", "Player", "H", "P")] sapply(draftplayer_read(), nrow) # include draft_class lable draft_class = as.numeric(rep(str_c("201", 0:5), each = 60)) # complete 360 rows player_list_df <- cbind(player_list, draft_class) # incomplete player_name <- player_list_df$Player player_name <- str_replace_all(player_name, " ", "-") # to lower player_name <- tolower(player_name) # create list container player_html <- vector("list", length(player_name)) # load each player's college stat into each element in the list for (i in 1:length(player_name)){ player_html[[i]] <- tryCatch(read_html(str_c("http://www.sports-reference.com/cbb/players/", player_name[i], "-1.html")), error = function(w){print(str_c("error!", i, player_name[i]))}, warning = function(e){print(str_c("warning!",i, player_name[i]))} ) } player_html_orginal <- player_html # save(player_html_orginal, file = "player_html_orginal.rda") # leave only draf players whose college stats available ind_original <- unlist(lapply(player_html_orginal, function(e){length(e) == 2})) player_list_df <- player_list_df[ind_original, ] head(player_list_df) # save(player_list_df, file = "player_list_df") load("player_list_df.rda") # find college statistics # convert html table to dataframes player_ind <- player_html[ind_original] playercollege_stats <- lapply(1:length(player_ind), function(e) html_table(html_nodes(player_ind[[e]], "table"))[[1]]) # save(playercollege_stats, file = "playercollege_stats.rda") load("playercollege_stats.rda") # player names playername <- player_list_df$Player # check each player's stat's dimension a <- sapply(1:length(playername), function(e) ncol(playercollege_stats[[e]])) min(a) # 24 max(a) # 26 player_stats <- matrix(rep(NA, n = min(a)*149), ncol = min(a), nrow = length(playername)) player_stats <- as.data.frame(player_stats) for(i in 1:length(playercollege_stats)){ player_stats[i,1:24]<- tail(playercollege_stats[[i]][,1:24], n = 1) } names(player_stats) <- names(playercollege_stats[[1]][,1:24]) # remove conference variable player_stats <- player_stats[,-3] rownames(player_stats) <- playername head(player_stats) # combine draft list and college stats player_stats_original <- cbind(player_stats[, -c(1,2)], player_list_df[, c("#", "H", "P", "draft_class")]) x = player_stats_original$H a <- str_replace_all(x, "-[0-9]+", "") a <- cm(as.numeric(a)*12) b <- str_replace_all(x, "[0-9]+-", "") b <- cm(as.numeric(b)) player_stats_original$H <- a + b # save(player_stats_original, file = "player_stats_original.rda") load("player_stats_original.rda") head(player_stats_original) nrow(player_stats_original) ############################################################################## ############################################################################## ############################################################################## ############################################################################## ############################################################################## ############################################################################## ############################################################################## ############################################################################## ######################################################## # players drafted start_year = 2009 end_year = 2015 seasons <- seq(start_year, end_year, 1) # scrape player names and ranking from nbadraft.net draftplayer_read_1 <- function(){ urls <- sapply(seasons, function(e) str_c("http://www.nbadraft.net/nba_final_draft/",e)) page <- lapply(urls, read_html) draftees <- lapply(1:length(seasons), function(e) html_table(html_nodes(page[[e]], "table"))[[1]]) return(draftees) } player_list_1 <- do.call(rbind, draftplayer_read_1()) player_list_1 <- rbind(player_list_1[,1:8], player_list_1[,9:16]) names(player_list_1) draft_class = as.numeric(rep(c("2009",str_c("201", 0:5)), each = 60)) player_list_1 <- cbind(player_list_1, draft_class) player_list_1 <- player_list_1[,c("#", "Player", "H", "P", "draft_class")] names(player_list_1) # players drafted start_year = 2000 end_year = 2008 seasons <- seq(start_year, end_year, 1) # scrape player names and ranking from nbadraft.net draftplayer_read_2 <- function(){ urls <- sapply(seasons, function(e) str_c("http://www.nbadraft.net/nba_draft_history/",e, ".html")) page <- lapply(urls, read_html) draftees <- lapply(1:length(seasons), function(e) html_table(html_nodes(page[[e]], "table"))[[1]]) draftees return(draftees) } ll <- draftplayer_read_2() for(i in 1:9){ ll[[i]] <- cbind(ll[[i]], draft_class = rep(str_c("200", i-1), each = nrow(ll[[i]]))) x <- ll[[i]] x <- x[,-c(2, 6)] names(x) <- c("#", "Team", "info", "#", "Team", "info", "draft_class") player_list_21 <- cbind(x[,1:3], draft_class = x[,7]) player_list_22 <- cbind(x[,4:6], draft_class = x[,7]) ll[[i]] <- rbind(player_list_21, player_list_22) } player_list_2 <- do.call(rbind, ll) # extract player names x4 <- player_list_2$info # x4 <- x4[-c(47, 82, 145)] x4 <- str_replace_all(x4, "\\n", " ") x4 <- str_replace_all(x4, "[0-9]-[0-9]*[[:print:]]*$", "") x4 <- str_replace_all(x4, "[[:blank:]]*$", "") x4 <- str_replace_all(x4, "\\*", "") # extract player height hei <- player_list_2$info hei <- str_replace_all(hei, "\\n", " ") hei <- as.vector(str_extract_all(hei, "[0-9]-[0-9]", "")) # extract player position pos <- player_list_2$info pos <- str_replace_all(pos, "\\n", " ") pos <- str_replace_all(pos, "^[[:print:]]*[0-9]-[0-9]*", "") pos <- str_replace_all(pos, "^[[:blank:]]+", "") pos <- str_replace_all(pos, "[[:digit:]]*", "") pos <- str_replace_all(pos, "^[[:blank:]]+", "") pos <- str_replace_all(pos, "[[:punct:]]+", "") pos <- str_replace_all(pos, "^[[:blank:]]+", "") pos <- strsplit(pos, " ") pos <- unlist(lapply(pos, function(e) e[1])) remov <- str_which(x4, "[[:punct:]]") x4 <- x4[-remov] # remove everything irregular hei <- hei[-remov] pos <- pos[-remov] draft_class <- player_list_2$draft_class draft_class <- draft_class[-remov] player_list_2 <- player_list_2[-remov,] player_list_2 <- data.frame(player_list_2$`#`, x4, hei, pos, draft_class) names(player_list_2) <- c("#", "Player", "H", "P", "draft_class") # combine 2000-2015 player_list_tot <- rbind(player_list_1, player_list_2) # save(player_list_tot, file = "player_list_tot.rda") ######################################################## load("player_list_tot.rda") # webscraping format: # add hyphen between first and last name dt <- player_list_tot player_name <- dt$Player player_name <- str_replace_all(player_name, " ", "-") # to lower player_name <- tolower(player_name) # create list container player_html <- vector("list", length(player_name)) # load each player's college stat into each element in the list for (i in 1:684){ player_html[[i]] <- tryCatch(read_html(str_c("http://www.sports-reference.com/cbb/players/", player_name[i], "-1.html")), error = function(w){print(str_c("error!", i, player_name[i]))}, warning = function(e){print(str_c("warning!",i, player_name[i]))} ) } for (i in 685:length(player_name)){ player_html[[i]] <- tryCatch(read_html(str_c("http://www.sports-reference.com/cbb/players/", player_name[i], "-1.html")), error = function(w){print(str_c("error!", i, player_name[i]))}, warning = function(e){print(str_c("warning!",i, player_name[i]))} ) } # leave only players whose college stats available ind_tot <- unlist(lapply(player_html, function(e){length(e) == 2})) # save(ind_tot, file = "ind_tot.rda") load("ind_tot.rda") player_list_tot <- player_list_tot[ind_tot, ] head(player_list_tot) # save(player_list_tot, file = "player_list_tot.rda") player_ind <- player_html[ind_tot] ################################################################################ # convert html table to dataframes playercollege_stats_tot <- lapply(1:length(player_ind), function(e) html_table(html_nodes(player_ind[[e]], "table"))[[1]]) # save(playercollege_stats_tot, file = "playercollege_stats_tot.rda") load("playercollege_stats_tot.rda") load("player_list_tot.rda") playername <- player_list_tot$Player length(playername) a <- sapply(1:length(playername), function(e) ncol(playercollege_stats_tot[[e]])) min(a) # 24 max(a) # 26 player_stats <- matrix(rep(NA, n = min(a)*length(playername)), ncol = min(a), nrow = length(playername)) dim(player_stats) player_stats <- as.data.frame(player_stats) for(i in 1:length(playercollege_stats_tot)){ player_stats[i,1:24]<- tail(playercollege_stats_tot[[i]][,1:24], n = 1) } names(player_stats) <- names(playercollege_stats_tot[[1]][,1:24]) # remove conference variable player_stats <- player_stats[,-3] # rownames dupl <- which(duplicated(playername) == TRUE) dup <- which(playername == playername[dupl]) rownames(player_stats)[1:dup[1]] <- playername[1:dup[1]] rownames(player_stats)[dup[1]] <- "Marcus Williams.1" rownames(player_stats)[(dup[1]+1):dup[2]] <- playername[(dup[1]+1):dup[2]] rownames(player_stats)[dup[2]] <- "Marcus Williams.2" rownames(player_stats)[(dup[2]+1):length(playername)] <- playername[(dup[2]+1):length(playername)] head(player_stats) # combine draft list and college stats player_stats <- cbind(player_stats[, -c(1,2)], player_list_tot[, c("#", "H", "P", "draft_class")]) player_stats_tot <- player_stats x = player_stats_tot$H a <- str_replace_all(x, "-[0-9]+", "") a <- cm(as.numeric(a)*12) b <- str_replace_all(x, "[0-9]+-", "") b <- cm(as.numeric(b)) player_stats_tot$H <- a + b head(player_stats_tot) # save(player_stats_tot, file = "player_stats_tot.rda") load("player_stats_tot.rda") ############################################################################## # original 2010 - 2015 load("player_stats_original.rda") head(player_stats_original) nrow(player_stats_original) ############################################################################## # expanded 2000 - 2015 load("player_stats_tot.rda") head(player_stats_tot) nrow(player_stats_tot)

/data_scraping.R

no_license

lizeyuyuz/NBA_Clustering

R

false

11,406

r

setwd("~/Dropbox/umich_2017_winter/stats503/project") library(stringr) library(rvest) ######################################################## # players drafted start_year = 2010 end_year = 2015 seasons <- seq(start_year, end_year, 1) # scrape player names and ranking from nbadraft.net draftplayer_read <- function(){ urls <- sapply(seasons, function(e) str_c("http://www.nbadraft.net/nba_final_draft/",e)) page <- lapply(urls, read_html) draftees <- lapply(1:length(seasons), function(e) html_table(html_nodes(page[[e]], "table"))[[1]]) return(draftees) } player_list <- do.call(rbind, draftplayer_read()) names(player_list) # convert wide format to long format player_list <- rbind(player_list[,1:8], player_list[,9:16]) names(player_list) # select only useful columns player_list <- player_list[,c("#", "Player", "H", "P")] sapply(draftplayer_read(), nrow) # include draft_class lable draft_class = as.numeric(rep(str_c("201", 0:5), each = 60)) # complete 360 rows player_list_df <- cbind(player_list, draft_class) # incomplete player_name <- player_list_df$Player player_name <- str_replace_all(player_name, " ", "-") # to lower player_name <- tolower(player_name) # create list container player_html <- vector("list", length(player_name)) # load each player's college stat into each element in the list for (i in 1:length(player_name)){ player_html[[i]] <- tryCatch(read_html(str_c("http://www.sports-reference.com/cbb/players/", player_name[i], "-1.html")), error = function(w){print(str_c("error!", i, player_name[i]))}, warning = function(e){print(str_c("warning!",i, player_name[i]))} ) } player_html_orginal <- player_html # save(player_html_orginal, file = "player_html_orginal.rda") # leave only draf players whose college stats available ind_original <- unlist(lapply(player_html_orginal, function(e){length(e) == 2})) player_list_df <- player_list_df[ind_original, ] head(player_list_df) # save(player_list_df, file = "player_list_df") load("player_list_df.rda") # find college statistics # convert html table to dataframes player_ind <- player_html[ind_original] playercollege_stats <- lapply(1:length(player_ind), function(e) html_table(html_nodes(player_ind[[e]], "table"))[[1]]) # save(playercollege_stats, file = "playercollege_stats.rda") load("playercollege_stats.rda") # player names playername <- player_list_df$Player # check each player's stat's dimension a <- sapply(1:length(playername), function(e) ncol(playercollege_stats[[e]])) min(a) # 24 max(a) # 26 player_stats <- matrix(rep(NA, n = min(a)*149), ncol = min(a), nrow = length(playername)) player_stats <- as.data.frame(player_stats) for(i in 1:length(playercollege_stats)){ player_stats[i,1:24]<- tail(playercollege_stats[[i]][,1:24], n = 1) } names(player_stats) <- names(playercollege_stats[[1]][,1:24]) # remove conference variable player_stats <- player_stats[,-3] rownames(player_stats) <- playername head(player_stats) # combine draft list and college stats player_stats_original <- cbind(player_stats[, -c(1,2)], player_list_df[, c("#", "H", "P", "draft_class")]) x = player_stats_original$H a <- str_replace_all(x, "-[0-9]+", "") a <- cm(as.numeric(a)*12) b <- str_replace_all(x, "[0-9]+-", "") b <- cm(as.numeric(b)) player_stats_original$H <- a + b # save(player_stats_original, file = "player_stats_original.rda") load("player_stats_original.rda") head(player_stats_original) nrow(player_stats_original) ############################################################################## ############################################################################## ############################################################################## ############################################################################## ############################################################################## ############################################################################## ############################################################################## ############################################################################## ######################################################## # players drafted start_year = 2009 end_year = 2015 seasons <- seq(start_year, end_year, 1) # scrape player names and ranking from nbadraft.net draftplayer_read_1 <- function(){ urls <- sapply(seasons, function(e) str_c("http://www.nbadraft.net/nba_final_draft/",e)) page <- lapply(urls, read_html) draftees <- lapply(1:length(seasons), function(e) html_table(html_nodes(page[[e]], "table"))[[1]]) return(draftees) } player_list_1 <- do.call(rbind, draftplayer_read_1()) player_list_1 <- rbind(player_list_1[,1:8], player_list_1[,9:16]) names(player_list_1) draft_class = as.numeric(rep(c("2009",str_c("201", 0:5)), each = 60)) player_list_1 <- cbind(player_list_1, draft_class) player_list_1 <- player_list_1[,c("#", "Player", "H", "P", "draft_class")] names(player_list_1) # players drafted start_year = 2000 end_year = 2008 seasons <- seq(start_year, end_year, 1) # scrape player names and ranking from nbadraft.net draftplayer_read_2 <- function(){ urls <- sapply(seasons, function(e) str_c("http://www.nbadraft.net/nba_draft_history/",e, ".html")) page <- lapply(urls, read_html) draftees <- lapply(1:length(seasons), function(e) html_table(html_nodes(page[[e]], "table"))[[1]]) draftees return(draftees) } ll <- draftplayer_read_2() for(i in 1:9){ ll[[i]] <- cbind(ll[[i]], draft_class = rep(str_c("200", i-1), each = nrow(ll[[i]]))) x <- ll[[i]] x <- x[,-c(2, 6)] names(x) <- c("#", "Team", "info", "#", "Team", "info", "draft_class") player_list_21 <- cbind(x[,1:3], draft_class = x[,7]) player_list_22 <- cbind(x[,4:6], draft_class = x[,7]) ll[[i]] <- rbind(player_list_21, player_list_22) } player_list_2 <- do.call(rbind, ll) # extract player names x4 <- player_list_2$info # x4 <- x4[-c(47, 82, 145)] x4 <- str_replace_all(x4, "\\n", " ") x4 <- str_replace_all(x4, "[0-9]-[0-9]*[[:print:]]*$", "") x4 <- str_replace_all(x4, "[[:blank:]]*$", "") x4 <- str_replace_all(x4, "\\*", "") # extract player height hei <- player_list_2$info hei <- str_replace_all(hei, "\\n", " ") hei <- as.vector(str_extract_all(hei, "[0-9]-[0-9]", "")) # extract player position pos <- player_list_2$info pos <- str_replace_all(pos, "\\n", " ") pos <- str_replace_all(pos, "^[[:print:]]*[0-9]-[0-9]*", "") pos <- str_replace_all(pos, "^[[:blank:]]+", "") pos <- str_replace_all(pos, "[[:digit:]]*", "") pos <- str_replace_all(pos, "^[[:blank:]]+", "") pos <- str_replace_all(pos, "[[:punct:]]+", "") pos <- str_replace_all(pos, "^[[:blank:]]+", "") pos <- strsplit(pos, " ") pos <- unlist(lapply(pos, function(e) e[1])) remov <- str_which(x4, "[[:punct:]]") x4 <- x4[-remov] # remove everything irregular hei <- hei[-remov] pos <- pos[-remov] draft_class <- player_list_2$draft_class draft_class <- draft_class[-remov] player_list_2 <- player_list_2[-remov,] player_list_2 <- data.frame(player_list_2$`#`, x4, hei, pos, draft_class) names(player_list_2) <- c("#", "Player", "H", "P", "draft_class") # combine 2000-2015 player_list_tot <- rbind(player_list_1, player_list_2) # save(player_list_tot, file = "player_list_tot.rda") ######################################################## load("player_list_tot.rda") # webscraping format: # add hyphen between first and last name dt <- player_list_tot player_name <- dt$Player player_name <- str_replace_all(player_name, " ", "-") # to lower player_name <- tolower(player_name) # create list container player_html <- vector("list", length(player_name)) # load each player's college stat into each element in the list for (i in 1:684){ player_html[[i]] <- tryCatch(read_html(str_c("http://www.sports-reference.com/cbb/players/", player_name[i], "-1.html")), error = function(w){print(str_c("error!", i, player_name[i]))}, warning = function(e){print(str_c("warning!",i, player_name[i]))} ) } for (i in 685:length(player_name)){ player_html[[i]] <- tryCatch(read_html(str_c("http://www.sports-reference.com/cbb/players/", player_name[i], "-1.html")), error = function(w){print(str_c("error!", i, player_name[i]))}, warning = function(e){print(str_c("warning!",i, player_name[i]))} ) } # leave only players whose college stats available ind_tot <- unlist(lapply(player_html, function(e){length(e) == 2})) # save(ind_tot, file = "ind_tot.rda") load("ind_tot.rda") player_list_tot <- player_list_tot[ind_tot, ] head(player_list_tot) # save(player_list_tot, file = "player_list_tot.rda") player_ind <- player_html[ind_tot] ################################################################################ # convert html table to dataframes playercollege_stats_tot <- lapply(1:length(player_ind), function(e) html_table(html_nodes(player_ind[[e]], "table"))[[1]]) # save(playercollege_stats_tot, file = "playercollege_stats_tot.rda") load("playercollege_stats_tot.rda") load("player_list_tot.rda") playername <- player_list_tot$Player length(playername) a <- sapply(1:length(playername), function(e) ncol(playercollege_stats_tot[[e]])) min(a) # 24 max(a) # 26 player_stats <- matrix(rep(NA, n = min(a)*length(playername)), ncol = min(a), nrow = length(playername)) dim(player_stats) player_stats <- as.data.frame(player_stats) for(i in 1:length(playercollege_stats_tot)){ player_stats[i,1:24]<- tail(playercollege_stats_tot[[i]][,1:24], n = 1) } names(player_stats) <- names(playercollege_stats_tot[[1]][,1:24]) # remove conference variable player_stats <- player_stats[,-3] # rownames dupl <- which(duplicated(playername) == TRUE) dup <- which(playername == playername[dupl]) rownames(player_stats)[1:dup[1]] <- playername[1:dup[1]] rownames(player_stats)[dup[1]] <- "Marcus Williams.1" rownames(player_stats)[(dup[1]+1):dup[2]] <- playername[(dup[1]+1):dup[2]] rownames(player_stats)[dup[2]] <- "Marcus Williams.2" rownames(player_stats)[(dup[2]+1):length(playername)] <- playername[(dup[2]+1):length(playername)] head(player_stats) # combine draft list and college stats player_stats <- cbind(player_stats[, -c(1,2)], player_list_tot[, c("#", "H", "P", "draft_class")]) player_stats_tot <- player_stats x = player_stats_tot$H a <- str_replace_all(x, "-[0-9]+", "") a <- cm(as.numeric(a)*12) b <- str_replace_all(x, "[0-9]+-", "") b <- cm(as.numeric(b)) player_stats_tot$H <- a + b head(player_stats_tot) # save(player_stats_tot, file = "player_stats_tot.rda") load("player_stats_tot.rda") ############################################################################## # original 2010 - 2015 load("player_stats_original.rda") head(player_stats_original) nrow(player_stats_original) ############################################################################## # expanded 2000 - 2015 load("player_stats_tot.rda") head(player_stats_tot) nrow(player_stats_tot)

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/vis.R \name{print.vis} \alias{print.vis} \title{Print method for a vis object} \usage{ \method{print}{vis}(x, min.prob = 0.3, print.full.model = FALSE, ...) } \arguments{ \item{x}{a \code{vis} object, the result of \code{\link{vis}}} \item{min.prob}{a lower bound on the probability of selection before the result is printed} \item{print.full.model}{logical, determines if the full model gets printed or not. Default=\code{FALSE}.} \item{...}{further arguments (currently unused)} } \description{ Prints basic output of the bootstrap results of an vis object. }

/man/print.vis.Rd

no_license

garthtarr/mplot

R

false

true

644

rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/vis.R \name{print.vis} \alias{print.vis} \title{Print method for a vis object} \usage{ \method{print}{vis}(x, min.prob = 0.3, print.full.model = FALSE, ...) } \arguments{ \item{x}{a \code{vis} object, the result of \code{\link{vis}}} \item{min.prob}{a lower bound on the probability of selection before the result is printed} \item{print.full.model}{logical, determines if the full model gets printed or not. Default=\code{FALSE}.} \item{...}{further arguments (currently unused)} } \description{ Prints basic output of the bootstrap results of an vis object. }

df<-data.frame( sleep=c(7,8,9,6), college=c("Saga","Cendana","Cendana","Saga") ) aggregate(sleep~college, data=df, mean) #we want to permute the data #we will use SAMPLE set.seed(123456789) sample(c("a","b","c")) #create a new data frame with only college as the permuted variable df_p<-df df_p$college<-sample(df$college) aggregate(sleep~college, data=df_p, mean) #simulating permutations in R data(faithful) cor(faithful$eruptions,faithful$waiting) #0.90 trials<-10000 responses<-numeric(trials) cor(sample(faithful$eruptions), faithful$waiting) #0.04 for(i in 1:trials) responses[i]<-cor(sample(faithful$eruptions), faithful$waiting) hist(responses, xlab="Correlation") #IN-CLASS PREP titanic<-read.csv("titanic.csv", stringsAsFactors = F) trials<-20 d<-5 res<-matrix(NA, trials, d) for(i in 1:trials) res[i,]<-sample(1:10, 5, replace=F) res[2,] res[,5] #IN-CLASS WORK #Coin flipping simulation: Is Tim right? p_val<-seq(0.1, 0.9, by=0.1) p_dim<-length(p_val) #number of coin tosses n<-10 #number of trials trials<-10000 outcomes<-c("H","T") results<-matrix(NA, trials, p_dim) for(i in 1:p_dim){ for(j in 1:trials){ sim_coin<-sample(outcomes, size=n, replace=T, prob=c(p_val[i], 1-p_val[i])) results[j,i]<-sum(sim_coin=="H")/n } } par(mfrow=c(3,3)) for(i in 1:p_dim){ p<-hist(results[,i], probability = T, breaks=seq(0,1,by=0.1), xlim=c(0,1), main=paste("p=",p_val[i]), xaxt="n") } #TITANIC DATA titanic<-read.csv("titanic.csv", stringsAsFactors = F) freq_tables<-function(cat_data){ tab<-table(cat_data) #create a table from the data that's been read in print(tab) print(tab/length(cat_data)) } freq_tables(titanic$gender) freq_tables(titanic$class) freq_tables(titanic$survived) freq_tables(titanic$age>=18) table(titanic$survived, titanic$gender) table(titanic$survived, titanic$gender) / nrow(titanic)*100 freq_tables(titanic$survived[titanic$gender=="Male"])*100 freq_tables(titanic$survived[titanic$gender=="Female"])*100 #simulation solution response<-function(dying_people){ death_rate_males<- 1- mean(dying_people$survived[dying_people$gender=="Male"]) death_rate_females<- 1- mean(dying_people$survived[dying_people$gender=="Female"]) death_rate_males/death_rate_females #finding the ratio of death rate between males and females } response(titanic) permute<-function(){ tmp<-titanic #permute gender tmp$gender<-sample(tmp$gender) response(tmp) } permute() #is our observed difference of 3 actually statistacally significant?? trials<-10000 # results<-numeric(trials) for(i in 1:trials){ results[i]<-permute() } par(mfrow=c(1,1)) hist(results) titanic$total_price<-titanic$ticket_pound + titanic$ticket_shilling/20 +titanic$ticket_penny/240 tit<-titanic[titanic$total_price!=0 & titanic$gender=="Male" & titanic$class=="2nd",] model<-lm(log(tit$total_price)~tit$age, data=tit) plot(log(tit$total_price)~tit$age, col="blue") abline(model, lwd=3) coef(model) slope<-model$coefficients[2] #is the slope significant trials<-10000 results<-numeric(trials) for(i in 1:trials){ results[i]<-lm(log(total_price)~sample(age), data=tit)$coefficients[2] } hist(results) mean(results<=slope | results>=-slope) #p=0.06

/PERMUTATIONS/Permutations.R

no_license

stefanroata/quantitative-reasoning

R

false

3,248

r

df<-data.frame( sleep=c(7,8,9,6), college=c("Saga","Cendana","Cendana","Saga") ) aggregate(sleep~college, data=df, mean) #we want to permute the data #we will use SAMPLE set.seed(123456789) sample(c("a","b","c")) #create a new data frame with only college as the permuted variable df_p<-df df_p$college<-sample(df$college) aggregate(sleep~college, data=df_p, mean) #simulating permutations in R data(faithful) cor(faithful$eruptions,faithful$waiting) #0.90 trials<-10000 responses<-numeric(trials) cor(sample(faithful$eruptions), faithful$waiting) #0.04 for(i in 1:trials) responses[i]<-cor(sample(faithful$eruptions), faithful$waiting) hist(responses, xlab="Correlation") #IN-CLASS PREP titanic<-read.csv("titanic.csv", stringsAsFactors = F) trials<-20 d<-5 res<-matrix(NA, trials, d) for(i in 1:trials) res[i,]<-sample(1:10, 5, replace=F) res[2,] res[,5] #IN-CLASS WORK #Coin flipping simulation: Is Tim right? p_val<-seq(0.1, 0.9, by=0.1) p_dim<-length(p_val) #number of coin tosses n<-10 #number of trials trials<-10000 outcomes<-c("H","T") results<-matrix(NA, trials, p_dim) for(i in 1:p_dim){ for(j in 1:trials){ sim_coin<-sample(outcomes, size=n, replace=T, prob=c(p_val[i], 1-p_val[i])) results[j,i]<-sum(sim_coin=="H")/n } } par(mfrow=c(3,3)) for(i in 1:p_dim){ p<-hist(results[,i], probability = T, breaks=seq(0,1,by=0.1), xlim=c(0,1), main=paste("p=",p_val[i]), xaxt="n") } #TITANIC DATA titanic<-read.csv("titanic.csv", stringsAsFactors = F) freq_tables<-function(cat_data){ tab<-table(cat_data) #create a table from the data that's been read in print(tab) print(tab/length(cat_data)) } freq_tables(titanic$gender) freq_tables(titanic$class) freq_tables(titanic$survived) freq_tables(titanic$age>=18) table(titanic$survived, titanic$gender) table(titanic$survived, titanic$gender) / nrow(titanic)*100 freq_tables(titanic$survived[titanic$gender=="Male"])*100 freq_tables(titanic$survived[titanic$gender=="Female"])*100 #simulation solution response<-function(dying_people){ death_rate_males<- 1- mean(dying_people$survived[dying_people$gender=="Male"]) death_rate_females<- 1- mean(dying_people$survived[dying_people$gender=="Female"]) death_rate_males/death_rate_females #finding the ratio of death rate between males and females } response(titanic) permute<-function(){ tmp<-titanic #permute gender tmp$gender<-sample(tmp$gender) response(tmp) } permute() #is our observed difference of 3 actually statistacally significant?? trials<-10000 # results<-numeric(trials) for(i in 1:trials){ results[i]<-permute() } par(mfrow=c(1,1)) hist(results) titanic$total_price<-titanic$ticket_pound + titanic$ticket_shilling/20 +titanic$ticket_penny/240 tit<-titanic[titanic$total_price!=0 & titanic$gender=="Male" & titanic$class=="2nd",] model<-lm(log(tit$total_price)~tit$age, data=tit) plot(log(tit$total_price)~tit$age, col="blue") abline(model, lwd=3) coef(model) slope<-model$coefficients[2] #is the slope significant trials<-10000 results<-numeric(trials) for(i in 1:trials){ results[i]<-lm(log(total_price)~sample(age), data=tit)$coefficients[2] } hist(results) mean(results<=slope | results>=-slope) #p=0.06

I am done

/third_party/virtualbox/src/VBox/ExtPacks/VBoxDTrace/onnv/cmd/dtrace/test/tst/common/dtraceUtil/tst.ZeroProviderProbes.d.ksh.out

permissive

thalium/icebox

R

false

10

out

I am done

library(OpenMx) # Simulate some data sampleSize <- 250 x=rnorm(sampleSize, mean=0, sd=1) y= 0.5*x + rnorm(sampleSize, mean=0, sd=1) tmpFrame <- data.frame(x, y) tmpNames <- names(tmpFrame) # Create a model that includes an expected covariance matrix, # an expectation function, a fit function, and an observed covariance matrix data <- mxData(cov(tmpFrame), type="cov", numObs = sampleSize) expCov <- mxMatrix(type="Symm", nrow=2, ncol=2, values=c(.2,.1,.2), free=TRUE, name="expCov") expFunction <- mxExpectationNormal(covariance="expCov", dimnames=tmpNames) fitFunction <- mxFitFunctionML() testModel <- mxModel(model="testModel", expCov, data, expFunction, fitFunction) #Use mxRun to optimize the free parameters in the expected covariance matrix modelOut <- mxRun(testModel, checkpoint = TRUE) modelOut$expCov # Save the ending state of modelOut in a checkpoint file mxSave(modelOut) modelRestored <- mxRestore(testModel) omxCheckCloseEnough(modelRestored$expCov$values, modelOut$expCov$values, 1e-5)

/inst/models/passing/mxSave.R

permissive

falkcarl/OpenMx

R

false

1,012

r

library(OpenMx) # Simulate some data sampleSize <- 250 x=rnorm(sampleSize, mean=0, sd=1) y= 0.5*x + rnorm(sampleSize, mean=0, sd=1) tmpFrame <- data.frame(x, y) tmpNames <- names(tmpFrame) # Create a model that includes an expected covariance matrix, # an expectation function, a fit function, and an observed covariance matrix data <- mxData(cov(tmpFrame), type="cov", numObs = sampleSize) expCov <- mxMatrix(type="Symm", nrow=2, ncol=2, values=c(.2,.1,.2), free=TRUE, name="expCov") expFunction <- mxExpectationNormal(covariance="expCov", dimnames=tmpNames) fitFunction <- mxFitFunctionML() testModel <- mxModel(model="testModel", expCov, data, expFunction, fitFunction) #Use mxRun to optimize the free parameters in the expected covariance matrix modelOut <- mxRun(testModel, checkpoint = TRUE) modelOut$expCov # Save the ending state of modelOut in a checkpoint file mxSave(modelOut) modelRestored <- mxRestore(testModel) omxCheckCloseEnough(modelRestored$expCov$values, modelOut$expCov$values, 1e-5)

context("comparing get_kgram_freqs() and get_kgram_freqs_fast()") test_that("coincidence on long char vector", { freqs <- get_kgram_freqs(twitter_train[1:10000], 3, twitter_dict) freqs_fast <- get_kgram_freqs_fast(twitter_train[1:10000], 3, twitter_dict) transform <- . %>% arrange(across(starts_with("w"))) freqs_attr_bckp <- attributes(freqs) freqs %<>% lapply(transform) attributes(freqs) <- freqs_attr_bckp freqs_fast_attr_bckp <- attributes(freqs_fast) freqs_fast %<>% lapply(transform) attributes(freqs_fast) <- freqs_fast_attr_bckp attr(freqs_fast, ".preprocess") <- attr(freqs, ".preprocess") expect_identical(freqs, freqs_fast) })

/tests/testthat/test-get_kgram_freqs_comparisons.R

no_license

minghao2016/sbo

R

false

758

r

context("comparing get_kgram_freqs() and get_kgram_freqs_fast()") test_that("coincidence on long char vector", { freqs <- get_kgram_freqs(twitter_train[1:10000], 3, twitter_dict) freqs_fast <- get_kgram_freqs_fast(twitter_train[1:10000], 3, twitter_dict) transform <- . %>% arrange(across(starts_with("w"))) freqs_attr_bckp <- attributes(freqs) freqs %<>% lapply(transform) attributes(freqs) <- freqs_attr_bckp freqs_fast_attr_bckp <- attributes(freqs_fast) freqs_fast %<>% lapply(transform) attributes(freqs_fast) <- freqs_fast_attr_bckp attr(freqs_fast, ".preprocess") <- attr(freqs, ".preprocess") expect_identical(freqs, freqs_fast) })

#' @title #' Create a New Game. #' #' @description #' `create_game()` randomly assigns a specified number of both goats and cars to the #' number #' #' @details #' This funtion creates a new game for the monty hall problem. There are three doors: #' two goats and one car. this simulation gives you the opportunity to test the #' probabilities! #' #' #' @param x Numeric vector #' #' @param values must be whole integers greater than zero #' #' @return #' Returns a length 3 charater vector showing the positions of the goats and the car #' #' @examples #' create_game() #' #' #' @export create_game <- function() { a.game <- sample( x=c("goat","goat","car"), size=3, replace=F ) return( a.game ) } #' @title #' Select Door. #' #' @description #' `select_door()` A door is randomly selected as the contestant choice. #' #' @details #' This function assignes a random door as the contestant's first choice #' of door before anything is revealed. The contestant can only choose between door 1, 2, or 3. #' #' @param x Numeric vector #' #' @return #' an integer that represents the number of the door that the participant selects. #' #' @examples #' select_door() #' #' @export select_door <- function( ) { doors <- c(1,2,3) a.pick <- sample( doors, size=1 ) return( a.pick ) # number between 1 and 3 } #' @title #' Open the Goat Door. #' #' @description #' `open_goat_door()` opens a door with a goat behind #' it and that is also not the same door the contestant #' chose. #' #' @details #' The opened door must have a goat behind it, #' meaning that the door must not have a car behind it. #' #' @param ... the arguments are `game` and `a.pick` #' #' @return #' This returns a number representing the number of #' the goat door that the host opened. #' #' @example #' open_goat_door(game = newGame, a.pick = firstDoor #' #' @export open_goat_door <- function( game, a.pick ) { doors <- c(1,2,3) # if contestant selected car, # randomly select one of two goats if( game[ a.pick ] == "car" ) { goat.doors <- doors[ game != "car" ] opened.door <- sample( goat.doors, size=1 ) } if( game[ a.pick ] == "goat" ) { opened.door <- doors[ game != "car" & doors != a.pick ] } return( opened.door ) # number between 1 and 3 } #' @title #' Changing Door Function. #' #' @description #' `change_door()` the contestant is asked if they would like to change their #' selection to a different door or if they would like to keep their first choice. #' #' @details #' a door is generated that represents the door that the #' contestant chose, whether they switched or stayed. #' #' @param ... If the argument is `TRUE` it means that the contestant stayed #' and `FALSE` means that the contestant switched doors. #' #' @return #' This function returns a number representing the number of the final door choice for the contestant. #' #' @examples #' change_door(stay = F, opened.door = 2, a.pick = 1) #' #' @export change_door <- function( stay=T, opened.door, a.pick ) { doors <- c(1,2,3) if( stay ) { final.pick <- a.pick } if( ! stay ) { final.pick <- doors[ doors != opened.door & doors != a.pick ] } return( final.pick ) # number between 1 and 3 } #' @title #' Determine Winner. #' #' @description #' `determine_winner()` returns the item behind the final door chosen, and assigns #' choosing the car as winning, and choosing the goat as losing. #' #' @details #' Provides final door and determines whether they won or lost. #' #' @param ... `final.pick` and `game` are the arguments #' #' @return #' the function returns a "WIN" or "LOSE" based on whether or not the game was won. #' #' @examples #' determine_winner(final.pick = finalDoor, game = newGame) #' #' @export determine_winner <- function( final.pick, game ) { if( game[ final.pick ] == "car" ) { return( "WIN" ) } if( game[ final.pick ] == "goat" ) { return( "LOSE" ) } } #' @title #' Play the Game #' #' @description #' `play_game()` combines the five functions above into this one function. #' #' @details #' This function lists the corresponding outcomes for each strategy, which will vary each time. #' #' @param ... No parameters #' #' @return #' This returns a dataframe with columns `strategy` and `outcome` #' strategy can be either stay or switch #' outcome can be either win or lose #' #' @examples #' play_game() #' #' @export play_game <- function( ) { new.game <- create_game() first.pick <- select_door() opened.door <- open_goat_door( new.game, first.pick ) final.pick.stay <- change_door( stay=T, opened.door, first.pick ) final.pick.switch <- change_door( stay=F, opened.door, first.pick ) outcome.stay <- determine_winner( final.pick.stay, new.game ) outcome.switch <- determine_winner( final.pick.switch, new.game ) strategy <- c("stay","switch") outcome <- c(outcome.stay,outcome.switch) game.results <- data.frame( strategy, outcome, stringsAsFactors=F ) return( game.results )} #' @title #' Play the Game n times (by default, n=1000). #' #' @description #' creates the game structure on a loop to examine the outcomes #' of running the game a multitude of times. Gives probability by #' playing the game as many times as needed using `play_n_games()`. #' #' @details #' The default for the game is 1000 times, but it can be played n time #' Corresponding outcomes are listed for each game. #' #' @param ... "n", or the number of times to run the loop #' #' @return #' This function creates a dataframe that combines the outcomes of total games and lists then out of 1 #' #' @examples #' play_n_games(n=1000) #' #' @export play_n_games <- function( n=1000 ) { library( dplyr ) results.list <- list() # collector loop.count <- 1 for( i in 1:n ) # iterator { game.outcome <- play_game() results.list[[ loop.count ]] <- game.outcome loop.count <- loop.count + 1 } results.df <- dplyr::bind_rows( results.list ) table( results.df ) %>% prop.table( margin=1 ) %>% # row proportions round( 2 ) %>% print() return( results.df ) }

/R/monty-hall-problem.R

no_license

brittanyharb/montyhall

R

false

6,207

r

#' @title #' Create a New Game. #' #' @description #' `create_game()` randomly assigns a specified number of both goats and cars to the #' number #' #' @details #' This funtion creates a new game for the monty hall problem. There are three doors: #' two goats and one car. this simulation gives you the opportunity to test the #' probabilities! #' #' #' @param x Numeric vector #' #' @param values must be whole integers greater than zero #' #' @return #' Returns a length 3 charater vector showing the positions of the goats and the car #' #' @examples #' create_game() #' #' #' @export create_game <- function() { a.game <- sample( x=c("goat","goat","car"), size=3, replace=F ) return( a.game ) } #' @title #' Select Door. #' #' @description #' `select_door()` A door is randomly selected as the contestant choice. #' #' @details #' This function assignes a random door as the contestant's first choice #' of door before anything is revealed. The contestant can only choose between door 1, 2, or 3. #' #' @param x Numeric vector #' #' @return #' an integer that represents the number of the door that the participant selects. #' #' @examples #' select_door() #' #' @export select_door <- function( ) { doors <- c(1,2,3) a.pick <- sample( doors, size=1 ) return( a.pick ) # number between 1 and 3 } #' @title #' Open the Goat Door. #' #' @description #' `open_goat_door()` opens a door with a goat behind #' it and that is also not the same door the contestant #' chose. #' #' @details #' The opened door must have a goat behind it, #' meaning that the door must not have a car behind it. #' #' @param ... the arguments are `game` and `a.pick` #' #' @return #' This returns a number representing the number of #' the goat door that the host opened. #' #' @example #' open_goat_door(game = newGame, a.pick = firstDoor #' #' @export open_goat_door <- function( game, a.pick ) { doors <- c(1,2,3) # if contestant selected car, # randomly select one of two goats if( game[ a.pick ] == "car" ) { goat.doors <- doors[ game != "car" ] opened.door <- sample( goat.doors, size=1 ) } if( game[ a.pick ] == "goat" ) { opened.door <- doors[ game != "car" & doors != a.pick ] } return( opened.door ) # number between 1 and 3 } #' @title #' Changing Door Function. #' #' @description #' `change_door()` the contestant is asked if they would like to change their #' selection to a different door or if they would like to keep their first choice. #' #' @details #' a door is generated that represents the door that the #' contestant chose, whether they switched or stayed. #' #' @param ... If the argument is `TRUE` it means that the contestant stayed #' and `FALSE` means that the contestant switched doors. #' #' @return #' This function returns a number representing the number of the final door choice for the contestant. #' #' @examples #' change_door(stay = F, opened.door = 2, a.pick = 1) #' #' @export change_door <- function( stay=T, opened.door, a.pick ) { doors <- c(1,2,3) if( stay ) { final.pick <- a.pick } if( ! stay ) { final.pick <- doors[ doors != opened.door & doors != a.pick ] } return( final.pick ) # number between 1 and 3 } #' @title #' Determine Winner. #' #' @description #' `determine_winner()` returns the item behind the final door chosen, and assigns #' choosing the car as winning, and choosing the goat as losing. #' #' @details #' Provides final door and determines whether they won or lost. #' #' @param ... `final.pick` and `game` are the arguments #' #' @return #' the function returns a "WIN" or "LOSE" based on whether or not the game was won. #' #' @examples #' determine_winner(final.pick = finalDoor, game = newGame) #' #' @export determine_winner <- function( final.pick, game ) { if( game[ final.pick ] == "car" ) { return( "WIN" ) } if( game[ final.pick ] == "goat" ) { return( "LOSE" ) } } #' @title #' Play the Game #' #' @description #' `play_game()` combines the five functions above into this one function. #' #' @details #' This function lists the corresponding outcomes for each strategy, which will vary each time. #' #' @param ... No parameters #' #' @return #' This returns a dataframe with columns `strategy` and `outcome` #' strategy can be either stay or switch #' outcome can be either win or lose #' #' @examples #' play_game() #' #' @export play_game <- function( ) { new.game <- create_game() first.pick <- select_door() opened.door <- open_goat_door( new.game, first.pick ) final.pick.stay <- change_door( stay=T, opened.door, first.pick ) final.pick.switch <- change_door( stay=F, opened.door, first.pick ) outcome.stay <- determine_winner( final.pick.stay, new.game ) outcome.switch <- determine_winner( final.pick.switch, new.game ) strategy <- c("stay","switch") outcome <- c(outcome.stay,outcome.switch) game.results <- data.frame( strategy, outcome, stringsAsFactors=F ) return( game.results )} #' @title #' Play the Game n times (by default, n=1000). #' #' @description #' creates the game structure on a loop to examine the outcomes #' of running the game a multitude of times. Gives probability by #' playing the game as many times as needed using `play_n_games()`. #' #' @details #' The default for the game is 1000 times, but it can be played n time #' Corresponding outcomes are listed for each game. #' #' @param ... "n", or the number of times to run the loop #' #' @return #' This function creates a dataframe that combines the outcomes of total games and lists then out of 1 #' #' @examples #' play_n_games(n=1000) #' #' @export play_n_games <- function( n=1000 ) { library( dplyr ) results.list <- list() # collector loop.count <- 1 for( i in 1:n ) # iterator { game.outcome <- play_game() results.list[[ loop.count ]] <- game.outcome loop.count <- loop.count + 1 } results.df <- dplyr::bind_rows( results.list ) table( results.df ) %>% prop.table( margin=1 ) %>% # row proportions round( 2 ) %>% print() return( results.df ) }

\name{recreate.olddata} \alias{recreate.olddata} \title{Convert data stored in msm object to old format} \description{ Converts the \code{data} element of msm objects to the old format. } \usage{ recreate.olddata(x) } \arguments{ \item{x}{Object returned by the \code{\link{msm}} function, representing a fitted multi-state model.} } \value{ A list of vectors and matrices in the undocumented ad-hoc format used for the \code{data} component of \code{msm} objects in \pkg{msm} versions 1.3.1 and earlier. } \details{ This is just provided for convenience and to illustrate the changes. It is not guaranteed to be complete, and is liable to be withdrawn. Users who were relying on the previous undocumented format are advised to upgrade their code to use the new format, which uses model frames and model design matrices in the standard format used in version 1.4, based on \code{\link{model.frame}} and \code{\link{model.matrix}}. }

/man/recreate.olddata.Rd

no_license

cran/msm

R

false

963

rd

\name{recreate.olddata} \alias{recreate.olddata} \title{Convert data stored in msm object to old format} \description{ Converts the \code{data} element of msm objects to the old format. } \usage{ recreate.olddata(x) } \arguments{ \item{x}{Object returned by the \code{\link{msm}} function, representing a fitted multi-state model.} } \value{ A list of vectors and matrices in the undocumented ad-hoc format used for the \code{data} component of \code{msm} objects in \pkg{msm} versions 1.3.1 and earlier. } \details{ This is just provided for convenience and to illustrate the changes. It is not guaranteed to be complete, and is liable to be withdrawn. Users who were relying on the previous undocumented format are advised to upgrade their code to use the new format, which uses model frames and model design matrices in the standard format used in version 1.4, based on \code{\link{model.frame}} and \code{\link{model.matrix}}. }

#' Correction Coefficient of Travel Speed in 2+1 Lane Road #' #' It correct travel speed in 2+1 lane road. #' This function follows <Table 7-15> in KHCM(2013), p.188. #' @param v Traffic volume in 2+1 lane road(pcphpl). #' @keywords 2+1 lane road correction coefficient TDR total delay ratio #' @export fs_pl_2lp1 Correction coefficient of travel speed in 2+1 lane road Section(f_pl) #' @examples #' fs_pl_2lp1(v = 1391) #' fs_pl_2lp1(999) fs_pl_2lp1 <- function(v = NULL){ if (v > 0 & v <= 100){f <- 1.025} else if (v > 100 & v <= 200){f <- 1.034} else if (v > 200 & v <= 300){f <- 1.042} else if (v > 300 & v <= 400){f <- 1.050} else if (v > 400 & v <= 500){f <- 1.057} else if (v > 500 & v <= 600){f <- 1.063} else if (v > 600 & v <= 700){f <- 1.069} else if (v > 700 & v <= 800){f <- 1.074} else if (v > 800 & v <= 900){f <- 1.078} else if (v > 900 & v <= 1000){f <- 1.081} else if (v > 1000 & v <= 1100){f <- 1.084} else if (v > 1100 & v <= 1200){f <- 1.086} else if (v > 1200 & v <= 1300){f <- 1.088} else if (v > 1300 & v <= 1400){f <- 1.089} else if (v > 1400 & v <= 1500){f <- 1.089} else if (v > 1500 & v <= 1600){f <- 1.088} else if (v > 1600 & v <= 1700){f <- 1.087} else {f <- 'Error : [v] must be >= 0 and <= 1700(pcphpl). Please check that.'} f }

/R/fs_pl_2lp1.R

no_license

regenesis90/KHCMinR

R

false

1,304

r

#' Correction Coefficient of Travel Speed in 2+1 Lane Road #' #' It correct travel speed in 2+1 lane road. #' This function follows <Table 7-15> in KHCM(2013), p.188. #' @param v Traffic volume in 2+1 lane road(pcphpl). #' @keywords 2+1 lane road correction coefficient TDR total delay ratio #' @export fs_pl_2lp1 Correction coefficient of travel speed in 2+1 lane road Section(f_pl) #' @examples #' fs_pl_2lp1(v = 1391) #' fs_pl_2lp1(999) fs_pl_2lp1 <- function(v = NULL){ if (v > 0 & v <= 100){f <- 1.025} else if (v > 100 & v <= 200){f <- 1.034} else if (v > 200 & v <= 300){f <- 1.042} else if (v > 300 & v <= 400){f <- 1.050} else if (v > 400 & v <= 500){f <- 1.057} else if (v > 500 & v <= 600){f <- 1.063} else if (v > 600 & v <= 700){f <- 1.069} else if (v > 700 & v <= 800){f <- 1.074} else if (v > 800 & v <= 900){f <- 1.078} else if (v > 900 & v <= 1000){f <- 1.081} else if (v > 1000 & v <= 1100){f <- 1.084} else if (v > 1100 & v <= 1200){f <- 1.086} else if (v > 1200 & v <= 1300){f <- 1.088} else if (v > 1300 & v <= 1400){f <- 1.089} else if (v > 1400 & v <= 1500){f <- 1.089} else if (v > 1500 & v <= 1600){f <- 1.088} else if (v > 1600 & v <= 1700){f <- 1.087} else {f <- 'Error : [v] must be >= 0 and <= 1700(pcphpl). Please check that.'} f }

# discretises a list of vectors # for each column we get a list of (discretised and breaks) discretise <- function( inp=NULL, # an input list with cols 'A', 'B' etc. levels=5, # number of discrete levels method='mltools' ){ whoami=paste0(match.call()[[1]],'()',collapse='') cnames = names(inp) ncnames = length(cnames) ret=vector("list", ncnames) names(ret) <- cnames for(acolname in cnames){ ret[[acolname]] = discretise_vector( x=inp[[acolname]], levels=levels, method=method ) } return(ret) } # discretises a single vector # returned is a list of (discretised and breaks) discretise_vector <- function( x=NULL, # an input vector levels=5, # number of discrete levels method='mltools' ){ whoami=paste0(match.call()[[1]],'()',collapse='') if( method == 'arules' ){ library(arules) thecuts=arules::discretize( x=x, method="frequency", categories=levels, onlycuts = T ) if( is.null(thecuts) ){ cat(whoami, " : call to arules::discretize() has failed.\n") return(NULL) } } else if( method == 'mltools' ){ library(mltools) library(stringr) dummy <- mltools::bin_data( x=x, bins=levels, binType='quantile', returnDT=TRUE ) if( is.null(dummy) ){ cat(whoami, " : call to mltools::bin_data() has failed.\n") return(NULL) } badR = levels(dummy$Bin) # these are text levels!!!! thecuts=c() for(alevel in badR){ regres <- str_match(alevel,"[\\[\$](.*?),\\s*(.*?)[\\]\$]$") thecuts=append(thecuts, as.numeric(regres[2])) } thecuts=append(thecuts, as.numeric(regres[3])) } else { cat(whoami, " : method '", method, "' is not recognised.\n") return(NULL) } # extract unique breaks! unique_cuts = c() for(aval in thecuts){ if( ! (aval %in% unique_cuts) ){ unique_cuts=append(unique_cuts, aval) } } # NA's are introduced at the boundaries so extends a bit the breaks unique_cuts[1] = unique_cuts[1]-0.1 unique_cuts[length(unique_cuts)] = unique_cuts[length(unique_cuts)]+0.1 discre_data <- as.numeric(cut(x, breaks=unique_cuts)) ret=list() ret[['discretised']] = discre_data ret[['breaks']] = unique_cuts ret[['frequencies']] = table(cut(x,unique_cuts, include.lowest=TRUE)) return(ret) } histo <- function( inp=NULL, # input list unique_vals=NULL # optional unique_vals list ){ whoami=paste0(match.call()[[1]],'()',collapse='') cnames = names(inp) ncnames = length(cnames) ret=vector("list", ncnames) names(ret) <- cnames for(acolname in cnames){ ret[[acolname]] = histo_vector( x=inp[[acolname]], breaks=unique_vals[['histograms']][[acolname]]$breaks ) } return(ret) } histo_vector <- function( x=NULL, # input vector breaks=NULL # optional breaks ){ whoami=paste0(match.call()[[1]],'()',collapse='') ahist = NULL if( is.null(breaks) ){ ahist <- hist(x=x, plot=F) } else { ahist <- hist(x=x,breaks=breaks, plot=F) } ncounts = length(ahist$counts) ret=matrix(ncol=2,nrow=ncounts) colnames(ret) <- c('value', 'count') for(i in 1:ncounts){ ret[i, 'value'] = ahist$breaks[i]+(ahist$breaks[i+1]-ahist$breaks[i])/2 ret[i, 'count'] = ahist$counts[i] } return(ret) } unique_values <- function( inp=NULL ){ whoami=paste0(match.call()[[1]],'()',collapse='') cnames = names(inp) ncnames = length(cnames) unix=vector("list", ncnames) names(unix) <- cnames num_unix=matrix(nrow=1,ncol=ncnames) histos=vector("list", ncnames) colnames(num_unix) <- cnames for(acolname in cnames){ unix[[acolname]] = unique(inp[[acolname]]) num_unix[,acolname] = length(unix[[acolname]]) histos[[acolname]] = hist(inp[[acolname]], breaks=unix[[acolname]], plot=F) } ret=list() ret[['unique_values']] = unix ret[['num_unique_values']] = num_unix ret[['histograms']] = histos return(ret) } unique_values_vector <- function( x=NULL ){ whoami=paste0(match.call()[[1]],'()',collapse='') ret=list() ret[['unique_values']] = unique(x) ret[['num_unique_values']] = length(ret[['unique_values']]) ret[['histograms']] = hist(x=x, breaks=ret[['unique_values']], plot=F) return(ret) }

/lib/DATA.R

no_license

hadjiprocopis/DUTC

R

false

4,032

r

# discretises a list of vectors # for each column we get a list of (discretised and breaks) discretise <- function( inp=NULL, # an input list with cols 'A', 'B' etc. levels=5, # number of discrete levels method='mltools' ){ whoami=paste0(match.call()[[1]],'()',collapse='') cnames = names(inp) ncnames = length(cnames) ret=vector("list", ncnames) names(ret) <- cnames for(acolname in cnames){ ret[[acolname]] = discretise_vector( x=inp[[acolname]], levels=levels, method=method ) } return(ret) } # discretises a single vector # returned is a list of (discretised and breaks) discretise_vector <- function( x=NULL, # an input vector levels=5, # number of discrete levels method='mltools' ){ whoami=paste0(match.call()[[1]],'()',collapse='') if( method == 'arules' ){ library(arules) thecuts=arules::discretize( x=x, method="frequency", categories=levels, onlycuts = T ) if( is.null(thecuts) ){ cat(whoami, " : call to arules::discretize() has failed.\n") return(NULL) } } else if( method == 'mltools' ){ library(mltools) library(stringr) dummy <- mltools::bin_data( x=x, bins=levels, binType='quantile', returnDT=TRUE ) if( is.null(dummy) ){ cat(whoami, " : call to mltools::bin_data() has failed.\n") return(NULL) } badR = levels(dummy$Bin) # these are text levels!!!! thecuts=c() for(alevel in badR){ regres <- str_match(alevel,"[\\[\$](.*?),\\s*(.*?)[\\]\$]$") thecuts=append(thecuts, as.numeric(regres[2])) } thecuts=append(thecuts, as.numeric(regres[3])) } else { cat(whoami, " : method '", method, "' is not recognised.\n") return(NULL) } # extract unique breaks! unique_cuts = c() for(aval in thecuts){ if( ! (aval %in% unique_cuts) ){ unique_cuts=append(unique_cuts, aval) } } # NA's are introduced at the boundaries so extends a bit the breaks unique_cuts[1] = unique_cuts[1]-0.1 unique_cuts[length(unique_cuts)] = unique_cuts[length(unique_cuts)]+0.1 discre_data <- as.numeric(cut(x, breaks=unique_cuts)) ret=list() ret[['discretised']] = discre_data ret[['breaks']] = unique_cuts ret[['frequencies']] = table(cut(x,unique_cuts, include.lowest=TRUE)) return(ret) } histo <- function( inp=NULL, # input list unique_vals=NULL # optional unique_vals list ){ whoami=paste0(match.call()[[1]],'()',collapse='') cnames = names(inp) ncnames = length(cnames) ret=vector("list", ncnames) names(ret) <- cnames for(acolname in cnames){ ret[[acolname]] = histo_vector( x=inp[[acolname]], breaks=unique_vals[['histograms']][[acolname]]$breaks ) } return(ret) } histo_vector <- function( x=NULL, # input vector breaks=NULL # optional breaks ){ whoami=paste0(match.call()[[1]],'()',collapse='') ahist = NULL if( is.null(breaks) ){ ahist <- hist(x=x, plot=F) } else { ahist <- hist(x=x,breaks=breaks, plot=F) } ncounts = length(ahist$counts) ret=matrix(ncol=2,nrow=ncounts) colnames(ret) <- c('value', 'count') for(i in 1:ncounts){ ret[i, 'value'] = ahist$breaks[i]+(ahist$breaks[i+1]-ahist$breaks[i])/2 ret[i, 'count'] = ahist$counts[i] } return(ret) } unique_values <- function( inp=NULL ){ whoami=paste0(match.call()[[1]],'()',collapse='') cnames = names(inp) ncnames = length(cnames) unix=vector("list", ncnames) names(unix) <- cnames num_unix=matrix(nrow=1,ncol=ncnames) histos=vector("list", ncnames) colnames(num_unix) <- cnames for(acolname in cnames){ unix[[acolname]] = unique(inp[[acolname]]) num_unix[,acolname] = length(unix[[acolname]]) histos[[acolname]] = hist(inp[[acolname]], breaks=unix[[acolname]], plot=F) } ret=list() ret[['unique_values']] = unix ret[['num_unique_values']] = num_unix ret[['histograms']] = histos return(ret) } unique_values_vector <- function( x=NULL ){ whoami=paste0(match.call()[[1]],'()',collapse='') ret=list() ret[['unique_values']] = unique(x) ret[['num_unique_values']] = length(ret[['unique_values']]) ret[['histograms']] = hist(x=x, breaks=ret[['unique_values']], plot=F) return(ret) }

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/Collection of Functions.R \name{B_T_with_Qua_u_fhome} \alias{B_T_with_Qua_u_fhome} \title{Bradely-Terry Model Weighted Likelihood function with fixed home parameter, Lagrangian and Quadratic Penalty} \usage{ B_T_with_Qua_u_fhome(df, ability, i0, theta, n, v, uij, u, home) } \description{ Bradely-Terry Model Weighted Likelihood function with fixed home parameter, Lagrangian and Quadratic Penalty }

/man/B_T_with_Qua_u_fhome.Rd

no_license

heilokchow/MWLE-Lasso

R

false

true

478

rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/Collection of Functions.R \name{B_T_with_Qua_u_fhome} \alias{B_T_with_Qua_u_fhome} \title{Bradely-Terry Model Weighted Likelihood function with fixed home parameter, Lagrangian and Quadratic Penalty} \usage{ B_T_with_Qua_u_fhome(df, ability, i0, theta, n, v, uij, u, home) } \description{ Bradely-Terry Model Weighted Likelihood function with fixed home parameter, Lagrangian and Quadratic Penalty }

############################################################## ### Simulating Moris'chance of beating the record of Babe Ruth ##############################################################

/codeSnippets/record-breaker-Ruth.R

no_license

Amitabh-G/rUtility

R

false

188

r

############################################################## ### Simulating Moris'chance of beating the record of Babe Ruth ##############################################################

# Computes the standard normal quantile function of the vector x, # 0<x<1. Phiinv <- function(x) { val <- sqrt(2)*erfinv(2*x-1) return(val) }

/Témata/09 Tobit model/R/Support/Phiinv.R

no_license

JanMelicharik/baan_python

R

false

158

r

# Computes the standard normal quantile function of the vector x, # 0<x<1. Phiinv <- function(x) { val <- sqrt(2)*erfinv(2*x-1) return(val) }

\name{apprPower} \alias{apprPower} \title{ Approximate power (any rejection!) for many-to-one comparison of binomial proportions } \description{ Approximative power to reject the hypothesis that all of the k differences of proportions of treatment groups vs. control group are zero, i.e.: probability to reject any H0[i]: p[i]-p[0] = 0, For a given setting of n[i], and p[i] assumed under the alternative. } \usage{ apprPower(n, pH1, alpha = 0.05, alternative = "greater", method = "Add4") } \arguments{ \item{n}{ vector of integers specifying the number of observations in each group, where the first value is taken as sample size of control group} \item{pH1}{ numeric vector with values between 0 and 1, specifying the proportions of success under the alternative hypothesis, should have the same length as n } \item{alpha}{ pre-specified type-I-error } \item{alternative}{ character string defining the alternative hypothesis, take care, that it fits to the parameters settings specified in pH1 } \item{method}{ character sring defining the confidence interval method to be used, one of "Add4", "Add2", "Wald" } } \details{ This function uses approximative calculation of any-pair-power of a maximum test as described in Bretz and Hothorn (2002) for a Wald test of multiple contrasts of binary data. Differing from Bretz and Hothorn (2002), unpooled variance estimators are used in the present function. In case of "Add4" and "Add2"-method, the Wald expectation and variance are replaced by that of add-4 and add-2. Since the approximate calculation assumes normality, this function can give misleading results, if sample size is small and/or proportions of success are extreme. The present function only calcualtes power for the test adjusting via the multivariate-normal-distribution. For Bonferroni-adjusted or unadjusted tests, one can make use of well-known formulas for power and sample size for binary data. The use of the function simPower in this package will result in power estimation closer to the true performance of the methods but is less convenient. } \value{ a single numeric value: the approximate any-pair power } \references{Bretz,F and Hothorn, LA (2002): Detecting dose-response using contrasts: asymptotic power and sample size determination for binomial data. Statistics in Medicine 21, 3325-3335. } \author{ Frank Schaarschmidt } \note{ The results of this functions are roughly checked by comparison with results of power simualtion, which indicate that the approximations are reasonable for at least moderate n and not too extreme proportions. The performance of a corresponding test using the add-4 or add-2 adjustment is not described. } \seealso{ simPower } \examples{ # Recalculate the power of the Dunnett-contrast # for the first setting in Bretz and Hothorn (2002, Table III), # using a balanced design and the allocation rule n0/ni=sqrt(k) # of Dunnett(1955), desiring a power of 80 percent. # Note that differing from Bretz and Hothorn (2002) # in the present function unpooled variance estimators # are used, what might lead to different results. apprPower(n=c(196, 196, 196, 196, 196), pH1=c(0.45, 0.45, 0.5, 0.5, 0.6), alpha=0.05, alternative="greater", method="Wald") apprPower(n=c(294, 147, 147, 147, 147 ), pH1=c(0.45, 0.45, 0.5, 0.5, 0.6), alpha=0.05, alternative="greater", method="Wald") } \keyword{ htest }

/man/apprPower.rd

no_license

cran/binMto

R

false

3,462

rd

\name{apprPower} \alias{apprPower} \title{ Approximate power (any rejection!) for many-to-one comparison of binomial proportions } \description{ Approximative power to reject the hypothesis that all of the k differences of proportions of treatment groups vs. control group are zero, i.e.: probability to reject any H0[i]: p[i]-p[0] = 0, For a given setting of n[i], and p[i] assumed under the alternative. } \usage{ apprPower(n, pH1, alpha = 0.05, alternative = "greater", method = "Add4") } \arguments{ \item{n}{ vector of integers specifying the number of observations in each group, where the first value is taken as sample size of control group} \item{pH1}{ numeric vector with values between 0 and 1, specifying the proportions of success under the alternative hypothesis, should have the same length as n } \item{alpha}{ pre-specified type-I-error } \item{alternative}{ character string defining the alternative hypothesis, take care, that it fits to the parameters settings specified in pH1 } \item{method}{ character sring defining the confidence interval method to be used, one of "Add4", "Add2", "Wald" } } \details{ This function uses approximative calculation of any-pair-power of a maximum test as described in Bretz and Hothorn (2002) for a Wald test of multiple contrasts of binary data. Differing from Bretz and Hothorn (2002), unpooled variance estimators are used in the present function. In case of "Add4" and "Add2"-method, the Wald expectation and variance are replaced by that of add-4 and add-2. Since the approximate calculation assumes normality, this function can give misleading results, if sample size is small and/or proportions of success are extreme. The present function only calcualtes power for the test adjusting via the multivariate-normal-distribution. For Bonferroni-adjusted or unadjusted tests, one can make use of well-known formulas for power and sample size for binary data. The use of the function simPower in this package will result in power estimation closer to the true performance of the methods but is less convenient. } \value{ a single numeric value: the approximate any-pair power } \references{Bretz,F and Hothorn, LA (2002): Detecting dose-response using contrasts: asymptotic power and sample size determination for binomial data. Statistics in Medicine 21, 3325-3335. } \author{ Frank Schaarschmidt } \note{ The results of this functions are roughly checked by comparison with results of power simualtion, which indicate that the approximations are reasonable for at least moderate n and not too extreme proportions. The performance of a corresponding test using the add-4 or add-2 adjustment is not described. } \seealso{ simPower } \examples{ # Recalculate the power of the Dunnett-contrast # for the first setting in Bretz and Hothorn (2002, Table III), # using a balanced design and the allocation rule n0/ni=sqrt(k) # of Dunnett(1955), desiring a power of 80 percent. # Note that differing from Bretz and Hothorn (2002) # in the present function unpooled variance estimators # are used, what might lead to different results. apprPower(n=c(196, 196, 196, 196, 196), pH1=c(0.45, 0.45, 0.5, 0.5, 0.6), alpha=0.05, alternative="greater", method="Wald") apprPower(n=c(294, 147, 147, 147, 147 ), pH1=c(0.45, 0.45, 0.5, 0.5, 0.6), alpha=0.05, alternative="greater", method="Wald") } \keyword{ htest }

testlist <- list(A = structure(c(2.31584307392677e+77, 9.53818252170339e+295, 4.94661240579261e+173, 4.12396251261199e-221, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(5L, 7L)), B = structure(0, .Dim = c(1L, 1L))) result <- do.call(multivariance:::match_rows,testlist) str(result)

/multivariance/inst/testfiles/match_rows/AFL_match_rows/match_rows_valgrind_files/1613102680-test.R

no_license

akhikolla/updatedatatype-list3

R

false

343

r

testlist <- list(A = structure(c(2.31584307392677e+77, 9.53818252170339e+295, 4.94661240579261e+173, 4.12396251261199e-221, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(5L, 7L)), B = structure(0, .Dim = c(1L, 1L))) result <- do.call(multivariance:::match_rows,testlist) str(result)

ResizeEtcDialog <- function() { initializeDialog(title=gettextRcmdr("Resize Panels")) resizeFrame <- tkframe(top) c.listVar <- tclVar("") condlevelsNameVar <- tclVar("") x.sameVar <- tclVar("") y.sameVar <- tclVar("") layoutVar <- tclVar("") strip.valuesVar <- tclVar("") strip.left.valuesVar <- tclVar("") strip.parVar <- tclVar("") strip.left.parVar <- tclVar("") resize.heightVar <- tclVar("") resize.widthVar <- tclVar("") mainVar <- tclVar("") mainMiddleVar <- tclVar("") c.listEntry <- tkentry(resizeFrame, width="48", textvariable=c.listVar) condlevelsNameEntry <- tkentry(resizeFrame, width="48", textvariable=condlevelsNameVar) x.sameEntry <- tkentry(resizeFrame, width="48", textvariable=x.sameVar) y.sameEntry <- tkentry(resizeFrame, width="48", textvariable=y.sameVar) layoutEntry <- tkentry(resizeFrame, width="48", textvariable=layoutVar) strip.valuesEntry <- tkentry(resizeFrame, width="48", textvariable=strip.valuesVar) strip.left.valuesEntry <- tkentry(resizeFrame, width="48", textvariable=strip.left.valuesVar) strip.parEntry <- tkentry(resizeFrame, width="48", textvariable=strip.parVar) strip.left.parEntry <- tkentry(resizeFrame, width="48", textvariable=strip.left.parVar) resize.heightEntry <- tkentry(resizeFrame, width="48", textvariable=resize.heightVar) resize.widthEntry <- tkentry(resizeFrame, width="48", textvariable=resize.widthVar) mainEntry <- tkentry(resizeFrame, width="48", textvariable=mainVar) mainMiddleEntry <- tkentry(resizeFrame, width="48", textvariable=mainMiddleVar) onOK <- function() { #on.exit(recover()) c.listValue <- tclvalue(c.listVar) condlevelsNameValue <- tclvalue(condlevelsNameVar) x.sameValue <- tclvalue(x.sameVar) y.sameValue <- tclvalue(y.sameVar) layoutValue <- tclvalue(layoutVar) strip.valuesValue <- tclvalue(strip.valuesVar) strip.left.valuesValue <- tclvalue(strip.left.valuesVar) strip.parValue <- tclvalue(strip.parVar) strip.left.parValue <- tclvalue(strip.left.parVar) resize.heightValue <- tclvalue(resize.heightVar) resize.widthValue <- tclvalue(resize.widthVar) mainValue <- tclvalue(mainVar) mainMiddleValue <- tclvalue(mainMiddleVar) closeDialog() if (nchar(c.listValue) == 0) { errorCondition(recall=ResizeEtcDialog, message=gettextRcmdr("c.list must be specified.")) return() } ##command.xmiddle <- "x.middle <- diff(current.panel.limits()$xlim)/2" ##doItAndPrint(command.xmiddle) if (nchar(mainMiddleValue)==0) mainMiddleValue <- ".5" command <- paste("ResizeEtc(", paste( "c.list=", c.listValue, sep=""), if (nchar(condlevelsNameValue ) != 0) paste(", condlevelsName='", condlevelsNameValue , "'", sep=""), if (nchar(x.sameValue ) != 0) paste(", x.same=", x.sameValue , sep=""), if (nchar(y.sameValue ) != 0) paste(", y.same=", y.sameValue , sep=""), if (nchar(layoutValue ) != 0) paste(", layout=", layoutValue , sep=""), if (nchar(strip.valuesValue ) != 0) paste(", strip.values=", strip.valuesValue , sep=""), if (nchar(strip.left.valuesValue) != 0) paste(", strip.left.values=", strip.left.valuesValue, sep=""), if (nchar(strip.parValue ) != 0) paste(", strip.par=", strip.parValue , sep=""), if (nchar(strip.left.parValue ) != 0) paste(", strip.left.par=", strip.left.parValue , sep=""), if (nchar(resize.heightValue ) != 0) paste(", resize.height=", resize.heightValue , sep=""), if (nchar(resize.widthValue ) != 0) paste(", resize.width=", resize.widthValue , sep=""), if (nchar(mainValue ) != 0) paste(", main='", mainValue , "'", sep=""), if (nchar(mainMiddleValue ) != 0) paste(", main.middle=", mainMiddleValue , sep=""), ")", sep="") doItAndPrint(command) activateMenus() tkfocus(CommanderWindow()) if (version$os == "mingw32") justDoIt("bringToTop()") } OKCancelHelp(helpSubject="ResizeEtcDialog") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("c.list:")), c.listEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("condlevelsName:")), condlevelsNameEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("x.same:")), x.sameEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("y.same:")), y.sameEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("layout:")), layoutEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("strip.values:")), strip.valuesEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("strip.left.values:")), strip.left.valuesEntry, sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("strip.par:")), strip.parEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("strip.left.par:")), strip.left.parEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("resize.height:")), resize.heightEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("resize.width:")), resize.widthEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("main:")), mainEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("mainMiddle:")), mainMiddleEntry , sticky="w") tkgrid(resizeFrame, sticky="w") tkgrid(buttonsFrame, columnspan=2, sticky="w") dialogSuffix(rows=13, columns=2) } listAllTrellisObjects <- function (envir = .GlobalEnv, ...) { objects <- ls(envir = envir, ...) if (length(objects) == 0) return(NULL) objects[sapply(objects, function(.x) { "trellis" %in% class(get(.x, envir = envir)) })] } ## source("c:/HOME/rmh/HH-R.package/RcmdrPlugin.HH2/R/ResizeEtcDialog.R")

/R/ResizeEtcDialog.R

no_license

cran/RcmdrPlugin.HH

R

false

6,893

r

ResizeEtcDialog <- function() { initializeDialog(title=gettextRcmdr("Resize Panels")) resizeFrame <- tkframe(top) c.listVar <- tclVar("") condlevelsNameVar <- tclVar("") x.sameVar <- tclVar("") y.sameVar <- tclVar("") layoutVar <- tclVar("") strip.valuesVar <- tclVar("") strip.left.valuesVar <- tclVar("") strip.parVar <- tclVar("") strip.left.parVar <- tclVar("") resize.heightVar <- tclVar("") resize.widthVar <- tclVar("") mainVar <- tclVar("") mainMiddleVar <- tclVar("") c.listEntry <- tkentry(resizeFrame, width="48", textvariable=c.listVar) condlevelsNameEntry <- tkentry(resizeFrame, width="48", textvariable=condlevelsNameVar) x.sameEntry <- tkentry(resizeFrame, width="48", textvariable=x.sameVar) y.sameEntry <- tkentry(resizeFrame, width="48", textvariable=y.sameVar) layoutEntry <- tkentry(resizeFrame, width="48", textvariable=layoutVar) strip.valuesEntry <- tkentry(resizeFrame, width="48", textvariable=strip.valuesVar) strip.left.valuesEntry <- tkentry(resizeFrame, width="48", textvariable=strip.left.valuesVar) strip.parEntry <- tkentry(resizeFrame, width="48", textvariable=strip.parVar) strip.left.parEntry <- tkentry(resizeFrame, width="48", textvariable=strip.left.parVar) resize.heightEntry <- tkentry(resizeFrame, width="48", textvariable=resize.heightVar) resize.widthEntry <- tkentry(resizeFrame, width="48", textvariable=resize.widthVar) mainEntry <- tkentry(resizeFrame, width="48", textvariable=mainVar) mainMiddleEntry <- tkentry(resizeFrame, width="48", textvariable=mainMiddleVar) onOK <- function() { #on.exit(recover()) c.listValue <- tclvalue(c.listVar) condlevelsNameValue <- tclvalue(condlevelsNameVar) x.sameValue <- tclvalue(x.sameVar) y.sameValue <- tclvalue(y.sameVar) layoutValue <- tclvalue(layoutVar) strip.valuesValue <- tclvalue(strip.valuesVar) strip.left.valuesValue <- tclvalue(strip.left.valuesVar) strip.parValue <- tclvalue(strip.parVar) strip.left.parValue <- tclvalue(strip.left.parVar) resize.heightValue <- tclvalue(resize.heightVar) resize.widthValue <- tclvalue(resize.widthVar) mainValue <- tclvalue(mainVar) mainMiddleValue <- tclvalue(mainMiddleVar) closeDialog() if (nchar(c.listValue) == 0) { errorCondition(recall=ResizeEtcDialog, message=gettextRcmdr("c.list must be specified.")) return() } ##command.xmiddle <- "x.middle <- diff(current.panel.limits()$xlim)/2" ##doItAndPrint(command.xmiddle) if (nchar(mainMiddleValue)==0) mainMiddleValue <- ".5" command <- paste("ResizeEtc(", paste( "c.list=", c.listValue, sep=""), if (nchar(condlevelsNameValue ) != 0) paste(", condlevelsName='", condlevelsNameValue , "'", sep=""), if (nchar(x.sameValue ) != 0) paste(", x.same=", x.sameValue , sep=""), if (nchar(y.sameValue ) != 0) paste(", y.same=", y.sameValue , sep=""), if (nchar(layoutValue ) != 0) paste(", layout=", layoutValue , sep=""), if (nchar(strip.valuesValue ) != 0) paste(", strip.values=", strip.valuesValue , sep=""), if (nchar(strip.left.valuesValue) != 0) paste(", strip.left.values=", strip.left.valuesValue, sep=""), if (nchar(strip.parValue ) != 0) paste(", strip.par=", strip.parValue , sep=""), if (nchar(strip.left.parValue ) != 0) paste(", strip.left.par=", strip.left.parValue , sep=""), if (nchar(resize.heightValue ) != 0) paste(", resize.height=", resize.heightValue , sep=""), if (nchar(resize.widthValue ) != 0) paste(", resize.width=", resize.widthValue , sep=""), if (nchar(mainValue ) != 0) paste(", main='", mainValue , "'", sep=""), if (nchar(mainMiddleValue ) != 0) paste(", main.middle=", mainMiddleValue , sep=""), ")", sep="") doItAndPrint(command) activateMenus() tkfocus(CommanderWindow()) if (version$os == "mingw32") justDoIt("bringToTop()") } OKCancelHelp(helpSubject="ResizeEtcDialog") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("c.list:")), c.listEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("condlevelsName:")), condlevelsNameEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("x.same:")), x.sameEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("y.same:")), y.sameEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("layout:")), layoutEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("strip.values:")), strip.valuesEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("strip.left.values:")), strip.left.valuesEntry, sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("strip.par:")), strip.parEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("strip.left.par:")), strip.left.parEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("resize.height:")), resize.heightEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("resize.width:")), resize.widthEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("main:")), mainEntry , sticky="w") tkgrid(tklabel(resizeFrame, text=gettextRcmdr("mainMiddle:")), mainMiddleEntry , sticky="w") tkgrid(resizeFrame, sticky="w") tkgrid(buttonsFrame, columnspan=2, sticky="w") dialogSuffix(rows=13, columns=2) } listAllTrellisObjects <- function (envir = .GlobalEnv, ...) { objects <- ls(envir = envir, ...) if (length(objects) == 0) return(NULL) objects[sapply(objects, function(.x) { "trellis" %in% class(get(.x, envir = envir)) })] } ## source("c:/HOME/rmh/HH-R.package/RcmdrPlugin.HH2/R/ResizeEtcDialog.R")

## These two functions cache the inverse of a matrix. ## Function 1 - makeCacheMatrix creates a special "matrix" object that can cache its inverse. makeCacheMatrix <- function(x = matrix()) { inv <- NULL set <- function(y) { x <<- y inv <<- NULL } get <- function() x setInverse <- function(solveMatrix) inv <<- solveMatrix getInverse <- function() inv list(set = set, get = get, setInverse = setInverse, getInverse = getInverse) } ## Function 2 - cacheSolve computes the inverse of the special "matrix" returned by makeCacheMatrix. If the inverse has already been calculated (and the matrix has not changed), then the cachesolve should retrieve the inverse from the cache. cacheSolve <- function(x, ...) { ## Return a matrix that is the inverse of 'x' inv <- x$getInverse() if(!is.null(inv)) { message("getting cached data") return(inv) } data <- x$get() inv <- solve(data, ...) x$setInverse(inv) inv }

/cachematrix.R

no_license

terryg40/ProgrammingAssignment2

R

false

985

r

## These two functions cache the inverse of a matrix. ## Function 1 - makeCacheMatrix creates a special "matrix" object that can cache its inverse. makeCacheMatrix <- function(x = matrix()) { inv <- NULL set <- function(y) { x <<- y inv <<- NULL } get <- function() x setInverse <- function(solveMatrix) inv <<- solveMatrix getInverse <- function() inv list(set = set, get = get, setInverse = setInverse, getInverse = getInverse) } ## Function 2 - cacheSolve computes the inverse of the special "matrix" returned by makeCacheMatrix. If the inverse has already been calculated (and the matrix has not changed), then the cachesolve should retrieve the inverse from the cache. cacheSolve <- function(x, ...) { ## Return a matrix that is the inverse of 'x' inv <- x$getInverse() if(!is.null(inv)) { message("getting cached data") return(inv) } data <- x$get() inv <- solve(data, ...) x$setInverse(inv) inv }

# Generic makeAinv <- function(pedigree, f = NULL, ggroups = NULL, fuzz = NULL, gOnTop = FALSE, det = FALSE, ...){ if(is(fuzz, "matrix") | is(fuzz, "Matrix")) class(fuzz) <- "fuzzy" UseMethod("makeAinv", fuzz) } ############################################################################### # Methods: makeAinv.default <- function(pedigree, f = NULL, ggroups = NULL, fuzz = NULL, gOnTop = FALSE, det = FALSE, ...){ if(is.null(ggroups)){ ptype <- "O" renPed <- order(genAssign(pedigree), pedigree[, 2], pedigree[, 3], na.last = FALSE) nPed <- numPed(pedigree[renPed, ]) groupRows <- NULL nggroups <- 0 } else { if(length(ggroups) == dim(pedigree)[1]){ stop("length(ggroups) should either be:\n == 1 and a numeric indicating the number of genetic groups (type 'A')\n == length(unique(ggroups)) and a character vector indicating the names of each unique genetic goup (type 'D')") } if(!is.null(fuzz)) stop("fuzzy genetic groups not yet implemented: fuzz must be NULL") zerNA <- union(which(pedigree[, 2] == "0"), which(pedigree[, 3] == "0")) astNA <- union(which(pedigree[, 2] == "*"), which(pedigree[, 3] == "*")) naNA <- union(which(is.na(pedigree[, 2])), which(is.na(pedigree[, 3]))) naPed <- union(union(zerNA, astNA), naNA) #### pedigree type "D" #### if(!is.numeric(ggroups) && length(ggroups) == length(unique(ggroups))){ ptype <- "D" if(is.null(fuzz) && length(naPed) > 0){ stop("When supplying a vector of unique genetic group names in the 'ggroups' argument, all individuals in the pedigree must have a genetic group when a parent is unknown (<NA>, '0' and '*' are considered unknown parents)")# or be identified as a phantom parent in 'fuzz'") } if(any(is.na(ggroups))) ggroups <- na.omit(ggroups) pedalt <- data.frame(id = c(ggroups, as.character(pedigree[, 1])), dam = c(rep(NA, length(ggroups)), as.character(pedigree[, 2])), sire = c(rep(NA, length(ggroups)), as.character(pedigree[, 3]))) #TODO: write method for numPed to handle genetic group pedigree ## same genetic group for dam and sire won't throw warning about selfing nPed <- suppressWarnings(numPed(pedalt)) # FIXME: remove suppressWarnings() throughout file ## return command below as attribute groupRows <- nPed[which(nPed[, 2] == -998), 1] } #### pedigree type "A" #### if(length(ggroups) == 1){ ptype <- "A" if(length(naPed) == ggroups){ nPed <- suppressWarnings(numPed(pedigree)) groupRows <- naPed } else { stop("Only rows identifying genetic groups should have missing parents.\n All individuals in the pedigree must have a genetic group when a parent is unknown")# or be identified as a phantom parent in 'fuzz'") } } nggroups <- length(groupRows) renPed <- order(suppressWarnings(genAssign(nPed)), nPed[, 2], nPed[, 3]) nPed <- numPed(ronPed(nPed, renPed)) } #### N <- nrow(nPed) eN <- N - nggroups dnmiss <- which(nPed[, 2] != -998) snmiss <- which(nPed[, 3] != -998) Tinv.row <- c(nPed[, 1][dnmiss], nPed[, 1][snmiss], 1:N) Tinv.col <- c(nPed[, 2][dnmiss], nPed[, 3][snmiss], 1:N) el.order <- order(Tinv.col + Tinv.row/(N + 1), decreasing = FALSE) if(is.null(ggroups)){ sTinv <- sparseMatrix(i = as.integer(Tinv.row[el.order] - 1), p = as.integer(c(match(1:N, Tinv.col[el.order]), length(el.order) + 1) - 1), index1 = FALSE, dims = c(N, N), symmetric = FALSE, dimnames = list(as.character(nPed[, 1]), NULL)) } else { sTinv <- sparseMatrix(i = as.integer(Tinv.row[el.order] - 1), p = as.integer(c(match(1:N, Tinv.col[el.order]), length(el.order) + 1) - 1), index1 = FALSE, dims = c(N, N), symmetric = FALSE, dimnames = list(as.character(nPed[, 1]), NULL))[-groupRows, ] } Ainv <- t(crossprod(sTinv)) # transpose gives lower triangle # 1: Adds Ainv elements in same for loop as calculation of f # 2: First checks to see if individual k has same dam and sire as k-1, if so then just assigns k-1's f # 3: simplifies the calculation of the addition to the Ainv element (instead of alphai * 0.25 - defines alphai=alphai*0.25). nPed[nPed == -998] <- N + 1 f <- c(rep(-1, nggroups), rep(0, eN), -1) Cout <- .C("ainvml", as.integer(nPed[, 2] - 1), #dam as.integer(nPed[, 3] - 1), #sire as.double(f), #f as.double(rep(0, N)), #dii as.integer(N), #n as.integer(nggroups), #g as.double(rep(0, length(Ainv@i))), #xA as.integer(Ainv@i), #iA as.integer(Ainv@p), #pA as.integer(length(Ainv@i))) #nzmaxA Ainv <- as(Ainv, "dsCMatrix") Ainv@x <- Cout[[7]] fsOrd <- as(as.integer(renPed), "pMatrix") Ainv <- as(t(fsOrd) %*% Ainv %*% fsOrd, "dgCMatrix") if(ptype == "D"){ Ainv@Dimnames <- list(as.character(pedalt[, 1]), NULL) f <- Cout[[3]][t(fsOrd)@perm][-seq(nggroups)] } else { Ainv@Dimnames <- list(as.character(pedigree[, 1]), NULL) f <- c(rep(0, nggroups), Cout[[3]][t(fsOrd)@perm][(nggroups+1):(nggroups + eN)]) } if(!is.null(ggroups) && !gOnTop){ permute <- as(as.integer(c(seq(eN+1, N, 1), seq(eN))), "pMatrix") Ainv <- t(permute) %*% Ainv %*% permute } if(det) logDet <- -1*determinant(Ainv, logarithm = TRUE)$modulus[1] else logDet <- NULL return(list(Ainv = Ainv, listAinv = sm2list(Ainv, rownames = rownames(Ainv), colnames = c("row", "column", "Ainv")), f = f, logDet = logDet)) } ############################################################################### ############################################################################### makeAinv.fuzzy <- function(pedigree, f = NULL, ggroups = NULL, fuzz, gOnTop = FALSE, det = FALSE, ...){ if(!is.null(ggroups)){ stop("when 'fuzz' is non-NULL, 'ggroups' should not have any arguments (i.e., 'ggroups==NULL") } naPed2 <- which(pedigree[, 2] == "0") naPed3 <- which(pedigree[, 3] == "0") naPed2 <- union(naPed2, which(pedigree[, 2] == "*")) naPed3 <- union(naPed3, which(pedigree[, 3] == "*")) naPed2 <- union(naPed2, which(is.na(pedigree[, 2]))) naPed3 <- union(naPed3, which(is.na(pedigree[, 3]))) # checks on fuzzy classification matrix and pedigree consistency: ## 'fuzz' is a type of matrix if(!is(fuzz, "matrix") && !is(fuzz, "Matrix")){ cat("'fuzz' of class", class(fuzz), "\n") stop("class of 'fuzz' must be either 'matrix' or 'Matrix'") } ## rows of 'fuzz' add up to 1 if(any(rowSums(fuzz) != 1)){ cat("rows:", which(rowSums(fuzz) != 1), "\ndo not equal 1\n") stop("all rowSums(fuzz) must equal 1\n(check for possible rounding errors, e.g., 3*0.33333 != 1)") } ## fuzz has dimnames if(is.null(dimnames(fuzz)[[1]]) | is.null(dimnames(fuzz)[[2]])){ stop("'fuzz' must have row and column names") } ## pedigree does not have genetic groups if(any(colnames(fuzz) %in% pedigree[, 1])){ cat("colnames:", which(colnames(fuzz) %in% pedigree[, 1]), "\nare in 'pedigree'\n") stop("colnames of 'fuzz' (genetic groups) must NOT be identities in the first column of 'pedigree'") } ## pedigree has phantom parents in 'fuzz' if(!all(rownames(fuzz) %in% pedigree[, 1])){ cat("rownames:", which(!rownames(fuzz) %in% pedigree[, 1]), "\nnot in 'pedigree'\n") stop("rownames of 'fuzz' (phantom parents) must all be identities in the first column of 'pedigree'. See the `prepPed()` function to help prepare the pedigree") } ## individuals can only have both parents missing in 'pedigree' or none if(length(naPed2) != length(naPed3) | any(!naPed2 %in% naPed3)){ stop("Individuals must have either two or zero missing parents in 'pedigree'") } ## IDs with missing parents (if passed above check, naPed2==naPed3) in 'pedigree' are phantom parents in 'fuzz' if(!all(pedigree[naPed2, 1] %in% rownames(fuzz))){ cat("IDs for 'pedigree' rows:", naPed2[which(!pedigree[naPed2, 1] %in% rownames(fuzz))], "\nare not rownames in 'fuzz'\n") stop("Individuals with missing parents (phantom individuals) must have a rowname in 'fuzz'") } # order of genetic groups in pedalt/A^-1 is same as column order of 'fuzz' ggroups <- colnames(fuzz) nggroups <- length(ggroups) # No. genetic groups p <- nrow(fuzz) # No. phantom parents eN <- nrow(pedigree) - p # No. observed IDs N <- nggroups + eN # No. GGs + observed IDs # order of phantom parents in pedalt is same as row order in 'fuzz' phantomPars <- seq(p) + nggroups groupRows <- seq(nggroups) # order pedigree: generations, order phantom parents in fuzz, dam, & sire ## genetic groups first renPed <- c(groupRows, nggroups + order(genAssign(pedigree), match(pedigree[, 1], rownames(fuzz), nomatch = p+1), pedigree[, 2], pedigree[, 3])) pedalt <- data.frame(id = c(ggroups, as.character(pedigree[, 1])), dam = c(rep(NA, nggroups), as.character(pedigree[, 2])), sire = c(rep(NA, nggroups), as.character(pedigree[, 3])))[renPed, ] nPed <- numPed(pedalt) phantomless <- cbind(numPed(nPed[-phantomPars, ], check = FALSE), nPed[-phantomPars, -1]) if(!all(which(phantomless[, 4] == -998) == groupRows)){ stop("Something wicked happened with the dams in phantomless numeric pedigree:\n Contact package maintainer: <matthewwolak@gmail.com>\n or raise an issue on: <https://github.com/matthewwolak/nadiv/issues>") } if(!all(which(phantomless[, 5] == -998) == groupRows)){ stop("Something wicked happened with the sires in phantomless numeric pedigree:\n Contact package maintainer: <matthewwolak@gmail.com>\n or raise an issue on: <https://github.com/matthewwolak/nadiv/issues>") } groupFuzz <- Diagonal(x = 1, n = nggroups) groupFuzz@Dimnames <- list(as.character(ggroups), as.character(ggroups)) fuzzmat <- rBind(groupFuzz, as(fuzz, "sparseMatrix")) # predict non-zero elements of Astar ## make H from Quaas 1988: ## H = [-Pb Qb : Tinv] ## Astar = H' %*% D^-1 %*% H ### sTinv: n x n observed IDs (i.e., no GGs or phantom parent IDs) dnmiss <- which(phantomless[, 2] != -998) snmiss <- which(phantomless[, 3] != -998) Tinv.row <- c(c(phantomless[, 1][dnmiss], phantomless[, 1][snmiss]) - nggroups, 1:eN) Tinv.col <- c(c(phantomless[, 2][dnmiss], phantomless[, 3][snmiss]) - nggroups, 1:eN) el.order <- order(Tinv.col + Tinv.row/(eN + 1), decreasing = FALSE) sTinv <- sparseMatrix(i = as.integer(Tinv.row[el.order] - 1), p = as.integer(c(match(1:eN, Tinv.col[el.order]), length(el.order) + 1) - 1), index1 = FALSE, dims = c(eN, eN), symmetric = FALSE, dimnames = list(as.character(phantomless[-groupRows, 1]), NULL)) ### Pb: n x p version of sTinv pdnmiss <- which(phantomless[, 4] %in% phantomPars) psnmiss <- which(phantomless[, 5] %in% phantomPars) Pb.row <- c(phantomless[, 1][pdnmiss], phantomless[, 1][psnmiss]) - nggroups Pb.col <- c(phantomless[, 4][pdnmiss], phantomless[, 5][psnmiss]) - nggroups el.order <- order(Pb.col + Pb.row/(p + 1), decreasing = FALSE) sPb <- sparseMatrix(i = as.integer(Pb.row[el.order] - 1), p = as.integer(c(match(1:p, Pb.col[el.order]), length(el.order) + 1) - 1), index1 = FALSE, dims = c(eN, p), symmetric = FALSE, dimnames = list(NULL, as.character(pedalt[phantomPars, 1]))) ### Qb is the fuzzy classification matrix ('fuzz') Qb <- as(fuzzmat[-groupRows, ][match(rownames(fuzzmat)[-groupRows], colnames(sPb)), ], "sparseMatrix") sQb <- sparseMatrix(i = Qb@i, p = Qb@p, index1 = FALSE, dims = Qb@Dim, symmetric = FALSE, dimnames = Qb@Dimnames) ## sH = [-(sPb %*% sQb) : sTinv] sH <- cBind((sPb %*% sQb), sTinv) Ainv <- t(crossprod(sH)) # transpose stores lower triangle phantomless[phantomless == -998] <- N + 1 # for now, phantom parents cannot be inbred (just like genetic groups) f <- c(rep(-1, nggroups), rep(0, eN), -1) Cout <- .C("ainvfuzz", as.integer(phantomless[, 2] - 1), #dam as.integer(phantomless[, 3] - 1), #sire as.integer(phantomless[, 4] - 1), #phantom dam as.integer(phantomless[, 5] - 1), #phantom sire as.double(f), #f as.double(rep(0, N)), #dii as.integer(N), #n as.integer(nggroups), #g as.double(fuzzmat@x), #xF as.integer(fuzzmat@i), #iF as.integer(fuzzmat@p), #pF as.double(rep(0, length(Ainv@i))), #xA as.integer(Ainv@i), #iA as.integer(Ainv@p)) #pA Ainv <- as(Ainv, "dsCMatrix") Ainv@x <- Cout[[12]] fsOrd1 <- as(as(as.integer(renPed), "pMatrix")[, -c(naPed2 + nggroups)], "CsparseMatrix") fsOrd <- as(as(fsOrd1 %*% matrix(seq(N), nrow = N), "sparseMatrix")@x, "pMatrix") Ainv <- as(t(fsOrd) %*% Ainv %*% fsOrd, "dgCMatrix") Ainv@Dimnames <- list(as.character(pedalt[(t(fsOrd1) %*% matrix(seq(N+p), ncol = 1))@x, 1]), NULL) f <- (fsOrd1 %*% Cout[[5]][-c(N+1)])@x[-groupRows] if(!gOnTop){ permute <- as(as.integer(c(seq(eN+1, N, 1), seq(eN))), "pMatrix") Ainv <- t(permute) %*% Ainv %*% permute } if(det) logDet <- -1*determinant(Ainv, logarithm = TRUE)$modulus[1] else logDet <- NULL return(list(Ainv = Ainv, listAinv = sm2list(Ainv, rownames = rownames(Ainv), colnames = c("row", "column", "Ainv")), f = f, logDet = logDet)) }

/nadiv/R/makeAinv.R

no_license

ingted/R-Examples

R

false

13,381

r

# Generic makeAinv <- function(pedigree, f = NULL, ggroups = NULL, fuzz = NULL, gOnTop = FALSE, det = FALSE, ...){ if(is(fuzz, "matrix") | is(fuzz, "Matrix")) class(fuzz) <- "fuzzy" UseMethod("makeAinv", fuzz) } ############################################################################### # Methods: makeAinv.default <- function(pedigree, f = NULL, ggroups = NULL, fuzz = NULL, gOnTop = FALSE, det = FALSE, ...){ if(is.null(ggroups)){ ptype <- "O" renPed <- order(genAssign(pedigree), pedigree[, 2], pedigree[, 3], na.last = FALSE) nPed <- numPed(pedigree[renPed, ]) groupRows <- NULL nggroups <- 0 } else { if(length(ggroups) == dim(pedigree)[1]){ stop("length(ggroups) should either be:\n == 1 and a numeric indicating the number of genetic groups (type 'A')\n == length(unique(ggroups)) and a character vector indicating the names of each unique genetic goup (type 'D')") } if(!is.null(fuzz)) stop("fuzzy genetic groups not yet implemented: fuzz must be NULL") zerNA <- union(which(pedigree[, 2] == "0"), which(pedigree[, 3] == "0")) astNA <- union(which(pedigree[, 2] == "*"), which(pedigree[, 3] == "*")) naNA <- union(which(is.na(pedigree[, 2])), which(is.na(pedigree[, 3]))) naPed <- union(union(zerNA, astNA), naNA) #### pedigree type "D" #### if(!is.numeric(ggroups) && length(ggroups) == length(unique(ggroups))){ ptype <- "D" if(is.null(fuzz) && length(naPed) > 0){ stop("When supplying a vector of unique genetic group names in the 'ggroups' argument, all individuals in the pedigree must have a genetic group when a parent is unknown (<NA>, '0' and '*' are considered unknown parents)")# or be identified as a phantom parent in 'fuzz'") } if(any(is.na(ggroups))) ggroups <- na.omit(ggroups) pedalt <- data.frame(id = c(ggroups, as.character(pedigree[, 1])), dam = c(rep(NA, length(ggroups)), as.character(pedigree[, 2])), sire = c(rep(NA, length(ggroups)), as.character(pedigree[, 3]))) #TODO: write method for numPed to handle genetic group pedigree ## same genetic group for dam and sire won't throw warning about selfing nPed <- suppressWarnings(numPed(pedalt)) # FIXME: remove suppressWarnings() throughout file ## return command below as attribute groupRows <- nPed[which(nPed[, 2] == -998), 1] } #### pedigree type "A" #### if(length(ggroups) == 1){ ptype <- "A" if(length(naPed) == ggroups){ nPed <- suppressWarnings(numPed(pedigree)) groupRows <- naPed } else { stop("Only rows identifying genetic groups should have missing parents.\n All individuals in the pedigree must have a genetic group when a parent is unknown")# or be identified as a phantom parent in 'fuzz'") } } nggroups <- length(groupRows) renPed <- order(suppressWarnings(genAssign(nPed)), nPed[, 2], nPed[, 3]) nPed <- numPed(ronPed(nPed, renPed)) } #### N <- nrow(nPed) eN <- N - nggroups dnmiss <- which(nPed[, 2] != -998) snmiss <- which(nPed[, 3] != -998) Tinv.row <- c(nPed[, 1][dnmiss], nPed[, 1][snmiss], 1:N) Tinv.col <- c(nPed[, 2][dnmiss], nPed[, 3][snmiss], 1:N) el.order <- order(Tinv.col + Tinv.row/(N + 1), decreasing = FALSE) if(is.null(ggroups)){ sTinv <- sparseMatrix(i = as.integer(Tinv.row[el.order] - 1), p = as.integer(c(match(1:N, Tinv.col[el.order]), length(el.order) + 1) - 1), index1 = FALSE, dims = c(N, N), symmetric = FALSE, dimnames = list(as.character(nPed[, 1]), NULL)) } else { sTinv <- sparseMatrix(i = as.integer(Tinv.row[el.order] - 1), p = as.integer(c(match(1:N, Tinv.col[el.order]), length(el.order) + 1) - 1), index1 = FALSE, dims = c(N, N), symmetric = FALSE, dimnames = list(as.character(nPed[, 1]), NULL))[-groupRows, ] } Ainv <- t(crossprod(sTinv)) # transpose gives lower triangle # 1: Adds Ainv elements in same for loop as calculation of f # 2: First checks to see if individual k has same dam and sire as k-1, if so then just assigns k-1's f # 3: simplifies the calculation of the addition to the Ainv element (instead of alphai * 0.25 - defines alphai=alphai*0.25). nPed[nPed == -998] <- N + 1 f <- c(rep(-1, nggroups), rep(0, eN), -1) Cout <- .C("ainvml", as.integer(nPed[, 2] - 1), #dam as.integer(nPed[, 3] - 1), #sire as.double(f), #f as.double(rep(0, N)), #dii as.integer(N), #n as.integer(nggroups), #g as.double(rep(0, length(Ainv@i))), #xA as.integer(Ainv@i), #iA as.integer(Ainv@p), #pA as.integer(length(Ainv@i))) #nzmaxA Ainv <- as(Ainv, "dsCMatrix") Ainv@x <- Cout[[7]] fsOrd <- as(as.integer(renPed), "pMatrix") Ainv <- as(t(fsOrd) %*% Ainv %*% fsOrd, "dgCMatrix") if(ptype == "D"){ Ainv@Dimnames <- list(as.character(pedalt[, 1]), NULL) f <- Cout[[3]][t(fsOrd)@perm][-seq(nggroups)] } else { Ainv@Dimnames <- list(as.character(pedigree[, 1]), NULL) f <- c(rep(0, nggroups), Cout[[3]][t(fsOrd)@perm][(nggroups+1):(nggroups + eN)]) } if(!is.null(ggroups) && !gOnTop){ permute <- as(as.integer(c(seq(eN+1, N, 1), seq(eN))), "pMatrix") Ainv <- t(permute) %*% Ainv %*% permute } if(det) logDet <- -1*determinant(Ainv, logarithm = TRUE)$modulus[1] else logDet <- NULL return(list(Ainv = Ainv, listAinv = sm2list(Ainv, rownames = rownames(Ainv), colnames = c("row", "column", "Ainv")), f = f, logDet = logDet)) } ############################################################################### ############################################################################### makeAinv.fuzzy <- function(pedigree, f = NULL, ggroups = NULL, fuzz, gOnTop = FALSE, det = FALSE, ...){ if(!is.null(ggroups)){ stop("when 'fuzz' is non-NULL, 'ggroups' should not have any arguments (i.e., 'ggroups==NULL") } naPed2 <- which(pedigree[, 2] == "0") naPed3 <- which(pedigree[, 3] == "0") naPed2 <- union(naPed2, which(pedigree[, 2] == "*")) naPed3 <- union(naPed3, which(pedigree[, 3] == "*")) naPed2 <- union(naPed2, which(is.na(pedigree[, 2]))) naPed3 <- union(naPed3, which(is.na(pedigree[, 3]))) # checks on fuzzy classification matrix and pedigree consistency: ## 'fuzz' is a type of matrix if(!is(fuzz, "matrix") && !is(fuzz, "Matrix")){ cat("'fuzz' of class", class(fuzz), "\n") stop("class of 'fuzz' must be either 'matrix' or 'Matrix'") } ## rows of 'fuzz' add up to 1 if(any(rowSums(fuzz) != 1)){ cat("rows:", which(rowSums(fuzz) != 1), "\ndo not equal 1\n") stop("all rowSums(fuzz) must equal 1\n(check for possible rounding errors, e.g., 3*0.33333 != 1)") } ## fuzz has dimnames if(is.null(dimnames(fuzz)[[1]]) | is.null(dimnames(fuzz)[[2]])){ stop("'fuzz' must have row and column names") } ## pedigree does not have genetic groups if(any(colnames(fuzz) %in% pedigree[, 1])){ cat("colnames:", which(colnames(fuzz) %in% pedigree[, 1]), "\nare in 'pedigree'\n") stop("colnames of 'fuzz' (genetic groups) must NOT be identities in the first column of 'pedigree'") } ## pedigree has phantom parents in 'fuzz' if(!all(rownames(fuzz) %in% pedigree[, 1])){ cat("rownames:", which(!rownames(fuzz) %in% pedigree[, 1]), "\nnot in 'pedigree'\n") stop("rownames of 'fuzz' (phantom parents) must all be identities in the first column of 'pedigree'. See the `prepPed()` function to help prepare the pedigree") } ## individuals can only have both parents missing in 'pedigree' or none if(length(naPed2) != length(naPed3) | any(!naPed2 %in% naPed3)){ stop("Individuals must have either two or zero missing parents in 'pedigree'") } ## IDs with missing parents (if passed above check, naPed2==naPed3) in 'pedigree' are phantom parents in 'fuzz' if(!all(pedigree[naPed2, 1] %in% rownames(fuzz))){ cat("IDs for 'pedigree' rows:", naPed2[which(!pedigree[naPed2, 1] %in% rownames(fuzz))], "\nare not rownames in 'fuzz'\n") stop("Individuals with missing parents (phantom individuals) must have a rowname in 'fuzz'") } # order of genetic groups in pedalt/A^-1 is same as column order of 'fuzz' ggroups <- colnames(fuzz) nggroups <- length(ggroups) # No. genetic groups p <- nrow(fuzz) # No. phantom parents eN <- nrow(pedigree) - p # No. observed IDs N <- nggroups + eN # No. GGs + observed IDs # order of phantom parents in pedalt is same as row order in 'fuzz' phantomPars <- seq(p) + nggroups groupRows <- seq(nggroups) # order pedigree: generations, order phantom parents in fuzz, dam, & sire ## genetic groups first renPed <- c(groupRows, nggroups + order(genAssign(pedigree), match(pedigree[, 1], rownames(fuzz), nomatch = p+1), pedigree[, 2], pedigree[, 3])) pedalt <- data.frame(id = c(ggroups, as.character(pedigree[, 1])), dam = c(rep(NA, nggroups), as.character(pedigree[, 2])), sire = c(rep(NA, nggroups), as.character(pedigree[, 3])))[renPed, ] nPed <- numPed(pedalt) phantomless <- cbind(numPed(nPed[-phantomPars, ], check = FALSE), nPed[-phantomPars, -1]) if(!all(which(phantomless[, 4] == -998) == groupRows)){ stop("Something wicked happened with the dams in phantomless numeric pedigree:\n Contact package maintainer: <matthewwolak@gmail.com>\n or raise an issue on: <https://github.com/matthewwolak/nadiv/issues>") } if(!all(which(phantomless[, 5] == -998) == groupRows)){ stop("Something wicked happened with the sires in phantomless numeric pedigree:\n Contact package maintainer: <matthewwolak@gmail.com>\n or raise an issue on: <https://github.com/matthewwolak/nadiv/issues>") } groupFuzz <- Diagonal(x = 1, n = nggroups) groupFuzz@Dimnames <- list(as.character(ggroups), as.character(ggroups)) fuzzmat <- rBind(groupFuzz, as(fuzz, "sparseMatrix")) # predict non-zero elements of Astar ## make H from Quaas 1988: ## H = [-Pb Qb : Tinv] ## Astar = H' %*% D^-1 %*% H ### sTinv: n x n observed IDs (i.e., no GGs or phantom parent IDs) dnmiss <- which(phantomless[, 2] != -998) snmiss <- which(phantomless[, 3] != -998) Tinv.row <- c(c(phantomless[, 1][dnmiss], phantomless[, 1][snmiss]) - nggroups, 1:eN) Tinv.col <- c(c(phantomless[, 2][dnmiss], phantomless[, 3][snmiss]) - nggroups, 1:eN) el.order <- order(Tinv.col + Tinv.row/(eN + 1), decreasing = FALSE) sTinv <- sparseMatrix(i = as.integer(Tinv.row[el.order] - 1), p = as.integer(c(match(1:eN, Tinv.col[el.order]), length(el.order) + 1) - 1), index1 = FALSE, dims = c(eN, eN), symmetric = FALSE, dimnames = list(as.character(phantomless[-groupRows, 1]), NULL)) ### Pb: n x p version of sTinv pdnmiss <- which(phantomless[, 4] %in% phantomPars) psnmiss <- which(phantomless[, 5] %in% phantomPars) Pb.row <- c(phantomless[, 1][pdnmiss], phantomless[, 1][psnmiss]) - nggroups Pb.col <- c(phantomless[, 4][pdnmiss], phantomless[, 5][psnmiss]) - nggroups el.order <- order(Pb.col + Pb.row/(p + 1), decreasing = FALSE) sPb <- sparseMatrix(i = as.integer(Pb.row[el.order] - 1), p = as.integer(c(match(1:p, Pb.col[el.order]), length(el.order) + 1) - 1), index1 = FALSE, dims = c(eN, p), symmetric = FALSE, dimnames = list(NULL, as.character(pedalt[phantomPars, 1]))) ### Qb is the fuzzy classification matrix ('fuzz') Qb <- as(fuzzmat[-groupRows, ][match(rownames(fuzzmat)[-groupRows], colnames(sPb)), ], "sparseMatrix") sQb <- sparseMatrix(i = Qb@i, p = Qb@p, index1 = FALSE, dims = Qb@Dim, symmetric = FALSE, dimnames = Qb@Dimnames) ## sH = [-(sPb %*% sQb) : sTinv] sH <- cBind((sPb %*% sQb), sTinv) Ainv <- t(crossprod(sH)) # transpose stores lower triangle phantomless[phantomless == -998] <- N + 1 # for now, phantom parents cannot be inbred (just like genetic groups) f <- c(rep(-1, nggroups), rep(0, eN), -1) Cout <- .C("ainvfuzz", as.integer(phantomless[, 2] - 1), #dam as.integer(phantomless[, 3] - 1), #sire as.integer(phantomless[, 4] - 1), #phantom dam as.integer(phantomless[, 5] - 1), #phantom sire as.double(f), #f as.double(rep(0, N)), #dii as.integer(N), #n as.integer(nggroups), #g as.double(fuzzmat@x), #xF as.integer(fuzzmat@i), #iF as.integer(fuzzmat@p), #pF as.double(rep(0, length(Ainv@i))), #xA as.integer(Ainv@i), #iA as.integer(Ainv@p)) #pA Ainv <- as(Ainv, "dsCMatrix") Ainv@x <- Cout[[12]] fsOrd1 <- as(as(as.integer(renPed), "pMatrix")[, -c(naPed2 + nggroups)], "CsparseMatrix") fsOrd <- as(as(fsOrd1 %*% matrix(seq(N), nrow = N), "sparseMatrix")@x, "pMatrix") Ainv <- as(t(fsOrd) %*% Ainv %*% fsOrd, "dgCMatrix") Ainv@Dimnames <- list(as.character(pedalt[(t(fsOrd1) %*% matrix(seq(N+p), ncol = 1))@x, 1]), NULL) f <- (fsOrd1 %*% Cout[[5]][-c(N+1)])@x[-groupRows] if(!gOnTop){ permute <- as(as.integer(c(seq(eN+1, N, 1), seq(eN))), "pMatrix") Ainv <- t(permute) %*% Ainv %*% permute } if(det) logDet <- -1*determinant(Ainv, logarithm = TRUE)$modulus[1] else logDet <- NULL return(list(Ainv = Ainv, listAinv = sm2list(Ainv, rownames = rownames(Ainv), colnames = c("row", "column", "Ainv")), f = f, logDet = logDet)) }

############################# DNN Model ################################################## ## Function DNN.test.training ## Parameters: ## list: data input ## epochs: number of epochs ## unitsi (i from 1 to 6): Number of neurons in each hidden layer library(keras) library(yardstick) library(dplyr) DNN.test.training<-function(list,epochs,units1,units2,units3,units4,units5,units6){ ## Empty dataframes to save the results. According to the number of epochs results.epochs.loss<-matrix(ncol = 10,nrow = epochs) #ncol number of folds, nrow = number of epochs results.epochs.accuracy<-matrix(ncol = 10,nrow = epochs) #ncol number of folds, nrow = number of epochs results.epochs.val_loss<-matrix(ncol = 10,nrow = epochs) results.epochs.val_accuracy<-matrix(ncol = 10,nrow = epochs) results.predictions<-list() results.training<-list() results.metrics<-setNames(data.frame(matrix(ncol = 7,nrow = 10)),c("Acurracy Test","Acurracy Training","AUC","PR AUC","Precision","Recall","F1")) results.times<-setNames(data.frame(matrix(ncol = 3,nrow = 10)),c("start","end","end_time")) for(i in 1:length(list)){ results.times[i,1]<-Sys.time() ### Prepare data ###### df<-list[[i]] df$DEFAULT<-as.numeric(as.character(df$DEFAULT)) #Training df.training<-df%>%filter(SET.IS == "TRAINING")%>%select(c(LIMIT_BAL:DEFAULT)) df.training.targer<-as.matrix(df.training%>%select(DEFAULT)) df.training<-df.training%>%select(LIMIT_BAL:PAY_AMT6) df.training<-as.matrix(df.training) dimnames(df.training)<-NULL #Validation df.validation<-df%>%filter(SET.IS == "TEST")%>%select(c(LIMIT_BAL:DEFAULT)) df.validation.targer<-as.matrix(df.validation%>%select(DEFAULT)) df.validation<-df.validation%>%select(LIMIT_BAL:PAY_AMT6) df.validation<-as.matrix(df.validation) dimnames(df.validation)<-NULL ### DNN Model ######## use_session_with_seed(1) # same seed options(keras.view_metrics = FALSE) # show the loss and gain chart model<-keras_model_sequential()%>% layer_flatten(input_shape = dim(df.training)[2])%>% #input layer layer_dense(units = units1, activation = "relu", use_bias = TRUE)%>% #hidden laye layer_dense(units = units2, activation = "relu", use_bias = TRUE)%>% #hidden laye layer_dense(units = units3, activation = "relu", use_bias = TRUE)%>% #hidden laye layer_dense(units = units4, activation = "relu", use_bias = TRUE)%>% #hidden laye layer_dense(units = units5, activation = "relu", use_bias = TRUE)%>% #hidden laye layer_dense(units = units6, activation = "relu", use_bias = TRUE)%>% #hidden laye layer_dense(1,activation = "sigmoid", use_bias = TRUE) #output layer model %>% compile(loss = "binary_crossentropy",optimizer = "adam",metrics = "accuracy") history<-model %>% fit( df.training, df.training.targer, epochs = epochs, batch_size = 32, verbose = 0, # Print the epochs validation_split = 0, validation_data = list(df.validation, df.validation.targer)) results.epochs.loss[,i]<-history$metrics$loss # loss per epoch training results.epochs.accuracy[,i]<-history$metrics$acc # accuracy per epoch training results.epochs.val_loss[,i]<-history$metrics$val_loss # loss per epoch validation results.epochs.val_accuracy[,i]<-history$metrics$val_acc #accuracy per epoch validation predictions<-model%>%predict_classes(df.validation) # Predictions predictions.prob<-model%>%predict_proba(df.validation) %>% as.vector() # Prob Predictions predictions.training<-model%>%predict_classes(df.training) # Predictions Training results.predictions[[i]]<-tibble::tibble( Real = as.factor(df.validation.targer), Estimate = as.factor(predictions), Prob = predictions.prob) results.training[[i]]<-tibble::tibble( Real.training = as.factor(df.training.targer), Estimate.training = as.factor(predictions.training)) options(yardstick.event_first = FALSE) results.metrics[i,1]<-data.frame(results.predictions[[i]] %>% yardstick::metrics(Real, Estimate))[1,3] #accuracy test results.metrics[i,2]<-data.frame(results.training[[i]] %>% yardstick::metrics(Real.training, Estimate.training))[1,3] #accuracy training results.metrics[i,3]<-data.frame(results.predictions[[i]] %>% yardstick::roc_auc(Real, Prob))[,3] # ROC AUC results.metrics[i,4]<-data.frame(results.predictions[[i]] %>% mutate(Estimate=as.numeric(as.character(Estimate)))%>%yardstick::pr_auc(Real,Estimate))[3] # PR AUC results.metrics[i,5]<-data.frame(results.predictions[[i]] %>% yardstick::precision(Real, Estimate))[,3] #Precision results.metrics[i,6]<-data.frame(results.predictions[[i]] %>% yardstick::recall(Real, Estimate))[,3] # Recall results.metrics[i,7]<-data.frame(results.predictions[[i]] %>% yardstick::f_meas(Real, Estimate, beta = 1))[,3] # F1 results.times[i,2]<-Sys.time() #Time results.times[i,3]<-(results.times[i,2]-results.times[i,1])/60 #Time } return(list(results.epochs.loss,results.epochs.accuracy,results.epochs.val_loss,results.epochs.val_accuracy,results.predictions,results.metrics,results.times)) } ############### 2 hidden layers ###################################### results.dnn.targetmean.2h.1000<-DNN.test.training(kfold.targetMean.scale,1000,23,23) results.dnn.frequency.2h.1000<-DNN.test.training(kfold.frequencyEncoding.scale,1000,23,23) results.dnn.onehot.2h.1000<-DNN.test.training(kfold.onehotEncoding.sale,1000,27,27) ############### 4 Hidden layers ####################################### results.dnn.targetmean.4h.1000<-DNN.test.training(kfold.targetMean.scale,1000,23,23,23,23) results.dnn.frequency.4h.1000<-DNN.test.training(kfold.frequencyEncoding.scale,1000,23,23,23,23) results.dnn.onehot.4h.1000<-DNN.test.training(kfold.onehotEncoding.sale,1000,27,27,27,27) ############### 6 Hidden layers ####################################### results.dnn.targetmean.6h.1800<-DNN.test.training(kfold.targetMean.scale,1800,23,23,23,23,23,23) results.dnn.frequency.6h.1800<-DNN.test.training(kfold.frequencyEncoding.scale,1800,23,23,23,23,23,23) results.dnn.onehot.6h.1800<-DNN.test.training(kfold.onehotEncoding.sale,1800,27,27,27,27,27,27)

/DNN Model.R

no_license

oordenesg/deep_learning_research_project

R

false

6,383

r

############################# DNN Model ################################################## ## Function DNN.test.training ## Parameters: ## list: data input ## epochs: number of epochs ## unitsi (i from 1 to 6): Number of neurons in each hidden layer library(keras) library(yardstick) library(dplyr) DNN.test.training<-function(list,epochs,units1,units2,units3,units4,units5,units6){ ## Empty dataframes to save the results. According to the number of epochs results.epochs.loss<-matrix(ncol = 10,nrow = epochs) #ncol number of folds, nrow = number of epochs results.epochs.accuracy<-matrix(ncol = 10,nrow = epochs) #ncol number of folds, nrow = number of epochs results.epochs.val_loss<-matrix(ncol = 10,nrow = epochs) results.epochs.val_accuracy<-matrix(ncol = 10,nrow = epochs) results.predictions<-list() results.training<-list() results.metrics<-setNames(data.frame(matrix(ncol = 7,nrow = 10)),c("Acurracy Test","Acurracy Training","AUC","PR AUC","Precision","Recall","F1")) results.times<-setNames(data.frame(matrix(ncol = 3,nrow = 10)),c("start","end","end_time")) for(i in 1:length(list)){ results.times[i,1]<-Sys.time() ### Prepare data ###### df<-list[[i]] df$DEFAULT<-as.numeric(as.character(df$DEFAULT)) #Training df.training<-df%>%filter(SET.IS == "TRAINING")%>%select(c(LIMIT_BAL:DEFAULT)) df.training.targer<-as.matrix(df.training%>%select(DEFAULT)) df.training<-df.training%>%select(LIMIT_BAL:PAY_AMT6) df.training<-as.matrix(df.training) dimnames(df.training)<-NULL #Validation df.validation<-df%>%filter(SET.IS == "TEST")%>%select(c(LIMIT_BAL:DEFAULT)) df.validation.targer<-as.matrix(df.validation%>%select(DEFAULT)) df.validation<-df.validation%>%select(LIMIT_BAL:PAY_AMT6) df.validation<-as.matrix(df.validation) dimnames(df.validation)<-NULL ### DNN Model ######## use_session_with_seed(1) # same seed options(keras.view_metrics = FALSE) # show the loss and gain chart model<-keras_model_sequential()%>% layer_flatten(input_shape = dim(df.training)[2])%>% #input layer layer_dense(units = units1, activation = "relu", use_bias = TRUE)%>% #hidden laye layer_dense(units = units2, activation = "relu", use_bias = TRUE)%>% #hidden laye layer_dense(units = units3, activation = "relu", use_bias = TRUE)%>% #hidden laye layer_dense(units = units4, activation = "relu", use_bias = TRUE)%>% #hidden laye layer_dense(units = units5, activation = "relu", use_bias = TRUE)%>% #hidden laye layer_dense(units = units6, activation = "relu", use_bias = TRUE)%>% #hidden laye layer_dense(1,activation = "sigmoid", use_bias = TRUE) #output layer model %>% compile(loss = "binary_crossentropy",optimizer = "adam",metrics = "accuracy") history<-model %>% fit( df.training, df.training.targer, epochs = epochs, batch_size = 32, verbose = 0, # Print the epochs validation_split = 0, validation_data = list(df.validation, df.validation.targer)) results.epochs.loss[,i]<-history$metrics$loss # loss per epoch training results.epochs.accuracy[,i]<-history$metrics$acc # accuracy per epoch training results.epochs.val_loss[,i]<-history$metrics$val_loss # loss per epoch validation results.epochs.val_accuracy[,i]<-history$metrics$val_acc #accuracy per epoch validation predictions<-model%>%predict_classes(df.validation) # Predictions predictions.prob<-model%>%predict_proba(df.validation) %>% as.vector() # Prob Predictions predictions.training<-model%>%predict_classes(df.training) # Predictions Training results.predictions[[i]]<-tibble::tibble( Real = as.factor(df.validation.targer), Estimate = as.factor(predictions), Prob = predictions.prob) results.training[[i]]<-tibble::tibble( Real.training = as.factor(df.training.targer), Estimate.training = as.factor(predictions.training)) options(yardstick.event_first = FALSE) results.metrics[i,1]<-data.frame(results.predictions[[i]] %>% yardstick::metrics(Real, Estimate))[1,3] #accuracy test results.metrics[i,2]<-data.frame(results.training[[i]] %>% yardstick::metrics(Real.training, Estimate.training))[1,3] #accuracy training results.metrics[i,3]<-data.frame(results.predictions[[i]] %>% yardstick::roc_auc(Real, Prob))[,3] # ROC AUC results.metrics[i,4]<-data.frame(results.predictions[[i]] %>% mutate(Estimate=as.numeric(as.character(Estimate)))%>%yardstick::pr_auc(Real,Estimate))[3] # PR AUC results.metrics[i,5]<-data.frame(results.predictions[[i]] %>% yardstick::precision(Real, Estimate))[,3] #Precision results.metrics[i,6]<-data.frame(results.predictions[[i]] %>% yardstick::recall(Real, Estimate))[,3] # Recall results.metrics[i,7]<-data.frame(results.predictions[[i]] %>% yardstick::f_meas(Real, Estimate, beta = 1))[,3] # F1 results.times[i,2]<-Sys.time() #Time results.times[i,3]<-(results.times[i,2]-results.times[i,1])/60 #Time } return(list(results.epochs.loss,results.epochs.accuracy,results.epochs.val_loss,results.epochs.val_accuracy,results.predictions,results.metrics,results.times)) } ############### 2 hidden layers ###################################### results.dnn.targetmean.2h.1000<-DNN.test.training(kfold.targetMean.scale,1000,23,23) results.dnn.frequency.2h.1000<-DNN.test.training(kfold.frequencyEncoding.scale,1000,23,23) results.dnn.onehot.2h.1000<-DNN.test.training(kfold.onehotEncoding.sale,1000,27,27) ############### 4 Hidden layers ####################################### results.dnn.targetmean.4h.1000<-DNN.test.training(kfold.targetMean.scale,1000,23,23,23,23) results.dnn.frequency.4h.1000<-DNN.test.training(kfold.frequencyEncoding.scale,1000,23,23,23,23) results.dnn.onehot.4h.1000<-DNN.test.training(kfold.onehotEncoding.sale,1000,27,27,27,27) ############### 6 Hidden layers ####################################### results.dnn.targetmean.6h.1800<-DNN.test.training(kfold.targetMean.scale,1800,23,23,23,23,23,23) results.dnn.frequency.6h.1800<-DNN.test.training(kfold.frequencyEncoding.scale,1800,23,23,23,23,23,23) results.dnn.onehot.6h.1800<-DNN.test.training(kfold.onehotEncoding.sale,1800,27,27,27,27,27,27)

## upload data onto wfu library(RPostgreSQL) # set up the connection drv <- dbDriver("PostgreSQL") con <- dbConnect(drv, user='postgres', password='postgres', dbname='U01') ## PRACTICE FOR CODE SESSION 06/10/2020 upload_practice <- Olivier_Cocaine_df_sql %>% distinct(rfid, exp, .keep_all = T) ## temporary dbWriteTable(con, c("u01_olivier_george_cocaine","olivier_rewards"), value = upload_practice, row.names = FALSE) dbExecute(con,"ALTER TABLE u01_olivier_george_cocaine.olivier_rewards ADD PRIMARY KEY(rfid,exp)") # troubleshoot dbExecute(con,"drop table if exists u01_olivier_george_cocaine.olivier_rewards") # write data frame to database ## to add dataframes (by cohort) ## write to a temporary table in the database with dbWriteTable # then issue an SQL statement to insert into your table as you would within the db environment drv <- dbDriver("PostgreSQL") con <- dbConnect(drv, user='postgres', password='postgres', dbname='U01') # update_sql_db <- function(df, df2){ # df_to_add <- anti_join(df2, df) # # } test1 <- Olivier_Cocaine_df_sql %>% distinct() %>% subset(cohort != "C11") test2 <- Olivier_Cocaine_df_sql %>% distinct() %>% subset(cohort == "C11") dbWriteTable(con, c("u01_olivier_george_cocaine","olivier_rewards"), value = test1, row.names = FALSE) dbWriteTable(con, c("u01_olivier_george_cocaine","olivier_rewards_temp"), value = test2, row.names = FALSE) dbExecute(con,"ALTER TABLE u01_olivier_george_cocaine.olivier_rewards ADD PRIMARY KEY(rfid,exp)") dbExecute(con,"insert into u01_olivier_george_cocaine.olivier_rewards select * from u01_olivier_george_cocaine.olivier_rewards_temp") dbExecute(con,"drop table if exists u01_olivier_george_cocaine.olivier_rewards_temp") # for troubleshooting dbExecute(con,"drop table if exists u01_olivier_george_cocaine.olivier_rewards") # disconnect dbDisconnect(con) ## in terminal cd /tmp sudo su postgres pg_dump -d U01 -t u01_olivier_george_cocaine.olivier_rewards > olivier_rewards.sql exit cp olivier_rewards.sql /home/bonnie/Dropbox\ $Palmer\ Lab$/PalmerLab_Datasets/u01_george_oliviercocaine/database ## operation not permitted # To move a file or directory, you need to have write permissions on both SOURCE and DESTINATION. Otherwise, you will receive a permission denied error. ## mkdir {C01,C02,C03,C04,C05,C06,C07,C08,C09,C10,C11}

/CREATE/R_sql_pgdump.R

no_license

BeverlyPeng/Olivier_U01Cocaine

R

false

2,337

r

## upload data onto wfu library(RPostgreSQL) # set up the connection drv <- dbDriver("PostgreSQL") con <- dbConnect(drv, user='postgres', password='postgres', dbname='U01') ## PRACTICE FOR CODE SESSION 06/10/2020 upload_practice <- Olivier_Cocaine_df_sql %>% distinct(rfid, exp, .keep_all = T) ## temporary dbWriteTable(con, c("u01_olivier_george_cocaine","olivier_rewards"), value = upload_practice, row.names = FALSE) dbExecute(con,"ALTER TABLE u01_olivier_george_cocaine.olivier_rewards ADD PRIMARY KEY(rfid,exp)") # troubleshoot dbExecute(con,"drop table if exists u01_olivier_george_cocaine.olivier_rewards") # write data frame to database ## to add dataframes (by cohort) ## write to a temporary table in the database with dbWriteTable # then issue an SQL statement to insert into your table as you would within the db environment drv <- dbDriver("PostgreSQL") con <- dbConnect(drv, user='postgres', password='postgres', dbname='U01') # update_sql_db <- function(df, df2){ # df_to_add <- anti_join(df2, df) # # } test1 <- Olivier_Cocaine_df_sql %>% distinct() %>% subset(cohort != "C11") test2 <- Olivier_Cocaine_df_sql %>% distinct() %>% subset(cohort == "C11") dbWriteTable(con, c("u01_olivier_george_cocaine","olivier_rewards"), value = test1, row.names = FALSE) dbWriteTable(con, c("u01_olivier_george_cocaine","olivier_rewards_temp"), value = test2, row.names = FALSE) dbExecute(con,"ALTER TABLE u01_olivier_george_cocaine.olivier_rewards ADD PRIMARY KEY(rfid,exp)") dbExecute(con,"insert into u01_olivier_george_cocaine.olivier_rewards select * from u01_olivier_george_cocaine.olivier_rewards_temp") dbExecute(con,"drop table if exists u01_olivier_george_cocaine.olivier_rewards_temp") # for troubleshooting dbExecute(con,"drop table if exists u01_olivier_george_cocaine.olivier_rewards") # disconnect dbDisconnect(con) ## in terminal cd /tmp sudo su postgres pg_dump -d U01 -t u01_olivier_george_cocaine.olivier_rewards > olivier_rewards.sql exit cp olivier_rewards.sql /home/bonnie/Dropbox\ $Palmer\ Lab$/PalmerLab_Datasets/u01_george_oliviercocaine/database ## operation not permitted # To move a file or directory, you need to have write permissions on both SOURCE and DESTINATION. Otherwise, you will receive a permission denied error. ## mkdir {C01,C02,C03,C04,C05,C06,C07,C08,C09,C10,C11}

load("family.rda") # After loading the dataset above, you will have the following # objects: # # > ls() # [1] "fage" "fgender" "fheight" "fnames" "fweight" # (1 point) Create a numeric vector fbmi # A person's body mass index (BMI) should be calculated as their # weight divided by their squared heights multiplied by 703 fbmi <- fweight / fheight^2 * 703 # (1 point) Create a logical vector foverWt # A person should be considered overweight if their BMI is greater than 25 foverWt <- fbmi > 25 # (1 point) Create a dataframe family # The dataframe should contain fnames, fgender, fage, fheight, fweight, fbmi, # and foverWt. The names should be as follows: # # > names(family) # [1] "name" "gender" "age" "height" "weight" "bmi" "overWt" family <- data.frame(fnames, fgender, fage, fheight, fweight, fbmi, foverWt) names(family) <- c("name", "gender", "age", "height", "weight", "bmi", "overWt") # (5 points) Plot age vs bmi # For each individual in the family dataframe, plot their age (x-axis) # against their bmi (y-axis). Males should be represented with 'red' # circles and females with 'blue' circles. You may need to set pch to # 'o'. The x-axis should range from 23 to 80 and your y-axis from 16 to # 31. Finally put a legend in the 'topright' corner of the plot with a # 'red' circle in front the label 'male' and a 'blue' circle in front the # label 'female'. You may want to look at the documentation for R's 'plot' # function family.male <- family[family$gender == 'm', ] family.female <- family[family$gender == 'f', ] plot(x = family.female$age, y=family.female$bmi, xlim=c(23,80), ylim=c(16,31), col='blue', pch='o', xlab="bmi", ylab="age") points(x = family.male$age, y=family.male$bmi, xlim=c(23,80), ylim=c(16,31), col='red', pch='o', xlab="bmi", ylab="age") legend("topright", c("male", "female"), pch = c('o', 'o'), col=c('red', 'blue'))

/midterm/ex1.r

no_license

jhkim81691/stat133

R

false

1,881

r

load("family.rda") # After loading the dataset above, you will have the following # objects: # # > ls() # [1] "fage" "fgender" "fheight" "fnames" "fweight" # (1 point) Create a numeric vector fbmi # A person's body mass index (BMI) should be calculated as their # weight divided by their squared heights multiplied by 703 fbmi <- fweight / fheight^2 * 703 # (1 point) Create a logical vector foverWt # A person should be considered overweight if their BMI is greater than 25 foverWt <- fbmi > 25 # (1 point) Create a dataframe family # The dataframe should contain fnames, fgender, fage, fheight, fweight, fbmi, # and foverWt. The names should be as follows: # # > names(family) # [1] "name" "gender" "age" "height" "weight" "bmi" "overWt" family <- data.frame(fnames, fgender, fage, fheight, fweight, fbmi, foverWt) names(family) <- c("name", "gender", "age", "height", "weight", "bmi", "overWt") # (5 points) Plot age vs bmi # For each individual in the family dataframe, plot their age (x-axis) # against their bmi (y-axis). Males should be represented with 'red' # circles and females with 'blue' circles. You may need to set pch to # 'o'. The x-axis should range from 23 to 80 and your y-axis from 16 to # 31. Finally put a legend in the 'topright' corner of the plot with a # 'red' circle in front the label 'male' and a 'blue' circle in front the # label 'female'. You may want to look at the documentation for R's 'plot' # function family.male <- family[family$gender == 'm', ] family.female <- family[family$gender == 'f', ] plot(x = family.female$age, y=family.female$bmi, xlim=c(23,80), ylim=c(16,31), col='blue', pch='o', xlab="bmi", ylab="age") points(x = family.male$age, y=family.male$bmi, xlim=c(23,80), ylim=c(16,31), col='red', pch='o', xlab="bmi", ylab="age") legend("topright", c("male", "female"), pch = c('o', 'o'), col=c('red', 'blue'))

## 24th May 2017 ## BISCUIT postprocessing ## ## Code author SP ## ### ### if(num_gene_batches ==1){ source("BISCUIT_parallel_impute_onegenebatch.R") source("BISCUIT_extras_onegenebatch_1.R") source("Per_marker_histogram.R") }else{ source("BISCUIT_parallel_impute.R") source("BISCUIT_extras.R") }

/superpixel_MIBI_python/BISCUIT_post_process.R

no_license

sandhya212/CSI_MIBI_Model

R

false

333

r

## 24th May 2017 ## BISCUIT postprocessing ## ## Code author SP ## ### ### if(num_gene_batches ==1){ source("BISCUIT_parallel_impute_onegenebatch.R") source("BISCUIT_extras_onegenebatch_1.R") source("Per_marker_histogram.R") }else{ source("BISCUIT_parallel_impute.R") source("BISCUIT_extras.R") }

# Copyright 2020 Observational Health Data Sciences and Informatics # # This file is part of Epi786FirstRound # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. .createCohorts <- function(connection, cdmDatabaseSchema, vocabularyDatabaseSchema = cdmDatabaseSchema, cohortDatabaseSchema, cohortTable, oracleTempSchema, outputFolder) { # Create study cohort table structure: sql <- SqlRender::loadRenderTranslateSql(sqlFilename = "CreateCohortTable.sql", packageName = "Epi786FirstRound", dbms = attr(connection, "dbms"), oracleTempSchema = oracleTempSchema, cohort_database_schema = cohortDatabaseSchema, cohort_table = cohortTable) DatabaseConnector::executeSql(connection, sql, progressBar = FALSE, reportOverallTime = FALSE) # Instantiate cohorts: pathToCsv <- system.file("settings", "CohortsToCreate.csv", package = "Epi786FirstRound") cohortsToCreate <- read.csv(pathToCsv) for (i in 1:nrow(cohortsToCreate)) { writeLines(paste("Creating cohort:", cohortsToCreate$name[i])) sql <- SqlRender::loadRenderTranslateSql(sqlFilename = paste0(cohortsToCreate$name[i], ".sql"), packageName = "Epi786FirstRound", dbms = attr(connection, "dbms"), oracleTempSchema = oracleTempSchema, cdm_database_schema = cdmDatabaseSchema, vocabulary_database_schema = vocabularyDatabaseSchema, target_database_schema = cohortDatabaseSchema, target_cohort_table = cohortTable, target_cohort_id = cohortsToCreate$cohortId[i]) DatabaseConnector::executeSql(connection, sql) } # Fetch cohort counts: sql <- "SELECT cohort_definition_id, COUNT(*) AS count FROM @cohort_database_schema.@cohort_table GROUP BY cohort_definition_id" sql <- SqlRender::render(sql, cohort_database_schema = cohortDatabaseSchema, cohort_table = cohortTable) sql <- SqlRender::translate(sql, targetDialect = attr(connection, "dbms")) counts <- DatabaseConnector::querySql(connection, sql) names(counts) <- SqlRender::snakeCaseToCamelCase(names(counts)) counts <- merge(counts, data.frame(cohortDefinitionId = cohortsToCreate$cohortId, cohortName = cohortsToCreate$name)) write.csv(counts, file.path(outputFolder, "CohortCounts.csv")) }

/Code/R/CreateCohorts.R

permissive

ChenyuL/Covid19EstimationFamotidine

R

false

3,534

r

# Copyright 2020 Observational Health Data Sciences and Informatics # # This file is part of Epi786FirstRound # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. .createCohorts <- function(connection, cdmDatabaseSchema, vocabularyDatabaseSchema = cdmDatabaseSchema, cohortDatabaseSchema, cohortTable, oracleTempSchema, outputFolder) { # Create study cohort table structure: sql <- SqlRender::loadRenderTranslateSql(sqlFilename = "CreateCohortTable.sql", packageName = "Epi786FirstRound", dbms = attr(connection, "dbms"), oracleTempSchema = oracleTempSchema, cohort_database_schema = cohortDatabaseSchema, cohort_table = cohortTable) DatabaseConnector::executeSql(connection, sql, progressBar = FALSE, reportOverallTime = FALSE) # Instantiate cohorts: pathToCsv <- system.file("settings", "CohortsToCreate.csv", package = "Epi786FirstRound") cohortsToCreate <- read.csv(pathToCsv) for (i in 1:nrow(cohortsToCreate)) { writeLines(paste("Creating cohort:", cohortsToCreate$name[i])) sql <- SqlRender::loadRenderTranslateSql(sqlFilename = paste0(cohortsToCreate$name[i], ".sql"), packageName = "Epi786FirstRound", dbms = attr(connection, "dbms"), oracleTempSchema = oracleTempSchema, cdm_database_schema = cdmDatabaseSchema, vocabulary_database_schema = vocabularyDatabaseSchema, target_database_schema = cohortDatabaseSchema, target_cohort_table = cohortTable, target_cohort_id = cohortsToCreate$cohortId[i]) DatabaseConnector::executeSql(connection, sql) } # Fetch cohort counts: sql <- "SELECT cohort_definition_id, COUNT(*) AS count FROM @cohort_database_schema.@cohort_table GROUP BY cohort_definition_id" sql <- SqlRender::render(sql, cohort_database_schema = cohortDatabaseSchema, cohort_table = cohortTable) sql <- SqlRender::translate(sql, targetDialect = attr(connection, "dbms")) counts <- DatabaseConnector::querySql(connection, sql) names(counts) <- SqlRender::snakeCaseToCamelCase(names(counts)) counts <- merge(counts, data.frame(cohortDefinitionId = cohortsToCreate$cohortId, cohortName = cohortsToCreate$name)) write.csv(counts, file.path(outputFolder, "CohortCounts.csv")) }

//////////////////////////////////////////////////////////// #Create Map Graph library (ggmap) library (osmar) library(prettymapr) #Import data on Greater London src <- osmsource_api () bb <- center_bbox(-0.129390, 51.532401, 300, 300) #bb <- osmar::corner_bbox(-0.1430397, 51.5265924, -0.1214227, 51.5400746) ua <- osmar::get_osm(bb, source = src) #The osmdata contains an osmar object, which has all the 'nodes' and 'ways' associated with the data. #Two ways of visualizing osmar::plot_nodes(ua) osmar::plot_ways(ua) #Subset the data such that we only have data associated with the roads hways_data <- subset(ua, way_ids = find(ua, way(tags(k == "highway")))) hways <- find(hways_data, way(tags(k == "name"))) hways <- find_down(ua, way(hways)) hways_data <- subset(ua, ids = hways) plot(hways_data) #visualization par(bg = "black") osmar::plot_ways(hways_data, col="blue", bg ='green') osmar::plot_nodes(hways_data, pch=19, cex=0.1, add=T, col="red") #convert to igraph hways_graph = as_igraph(hways_data) hways_graph = as.undirected(hways_graph) plot(hways_graph, vertex.label=NA, vertex.size=4, edge.color="blue") //////////////////////////////////////////////////////////// #Let's get the trace data library(plotKML) library(urltools) library(rgdal) url = "http://api.openstreetmap.org/api/0.6/trackpoints?bbox=12&page=pagenumber" mat = matrix(bb)[,1] val = capture.output(mat[]) url <- param_set(url, key = "bbox", value = "-0.1430397,51.5265924,-0.1214227,51.5400746") for (i in 0:20){ url <- param_set(url, key = "page", value = i) filename <- paste(i,"trace_data.gpx") download.file(url, destfile=filename) } #Read in GPX files that are located in the working directory files <- dir(pattern = "\\.gpx") #route <- readOGR(files[2],"tracks") #coordinates = coordinates(route)[[1]][[1]] #Set up the three vectors to hold your coordinate and path data index <- c() latitude <- c() longitude <- c() for (i in 1:length(files)) { route <- readOGR(files[i], "tracks") coordinates = coordinates(route)[[1]][[1]] index_el <- 0 : nrow(coordinates) index <- c(index, index_el) latitude <- c(latitude, coordinates[,2]) longitude <- c(longitude, coordinates[,1]) } routes <- data.frame(cbind(index, latitude, longitude)) #Get the map of Amsterdam from Google london_map <- qmap('kings cross station', zoom =18, color = 'bw') #Plot the map and the routes on top of that london_map + geom_path(aes(x = longitude, y = latitude, group = factor(index)), colour="#5F35D8", data = routes, alpha=0.3)

/OSM_ver/CW_GPS.R

no_license

soma11soma11/GPS_trace

R

false

2,559

r

//////////////////////////////////////////////////////////// #Create Map Graph library (ggmap) library (osmar) library(prettymapr) #Import data on Greater London src <- osmsource_api () bb <- center_bbox(-0.129390, 51.532401, 300, 300) #bb <- osmar::corner_bbox(-0.1430397, 51.5265924, -0.1214227, 51.5400746) ua <- osmar::get_osm(bb, source = src) #The osmdata contains an osmar object, which has all the 'nodes' and 'ways' associated with the data. #Two ways of visualizing osmar::plot_nodes(ua) osmar::plot_ways(ua) #Subset the data such that we only have data associated with the roads hways_data <- subset(ua, way_ids = find(ua, way(tags(k == "highway")))) hways <- find(hways_data, way(tags(k == "name"))) hways <- find_down(ua, way(hways)) hways_data <- subset(ua, ids = hways) plot(hways_data) #visualization par(bg = "black") osmar::plot_ways(hways_data, col="blue", bg ='green') osmar::plot_nodes(hways_data, pch=19, cex=0.1, add=T, col="red") #convert to igraph hways_graph = as_igraph(hways_data) hways_graph = as.undirected(hways_graph) plot(hways_graph, vertex.label=NA, vertex.size=4, edge.color="blue") //////////////////////////////////////////////////////////// #Let's get the trace data library(plotKML) library(urltools) library(rgdal) url = "http://api.openstreetmap.org/api/0.6/trackpoints?bbox=12&page=pagenumber" mat = matrix(bb)[,1] val = capture.output(mat[]) url <- param_set(url, key = "bbox", value = "-0.1430397,51.5265924,-0.1214227,51.5400746") for (i in 0:20){ url <- param_set(url, key = "page", value = i) filename <- paste(i,"trace_data.gpx") download.file(url, destfile=filename) } #Read in GPX files that are located in the working directory files <- dir(pattern = "\\.gpx") #route <- readOGR(files[2],"tracks") #coordinates = coordinates(route)[[1]][[1]] #Set up the three vectors to hold your coordinate and path data index <- c() latitude <- c() longitude <- c() for (i in 1:length(files)) { route <- readOGR(files[i], "tracks") coordinates = coordinates(route)[[1]][[1]] index_el <- 0 : nrow(coordinates) index <- c(index, index_el) latitude <- c(latitude, coordinates[,2]) longitude <- c(longitude, coordinates[,1]) } routes <- data.frame(cbind(index, latitude, longitude)) #Get the map of Amsterdam from Google london_map <- qmap('kings cross station', zoom =18, color = 'bw') #Plot the map and the routes on top of that london_map + geom_path(aes(x = longitude, y = latitude, group = factor(index)), colour="#5F35D8", data = routes, alpha=0.3)

################################################################################## ##### 1 - INITIALIZE SCRIPT PARAMETERS ################################################################################## ## Load the needed libraries and functions library(gdata) library(tm) library(SnowballC) ## Set script parameters set.seed(12345) # for repeatability of random numbers #datafileName<-"c:\\idm\\Step4ReducedNatGeoFileAP1.xlsx" # source file datafileName<-"c:\\idm\\Step4ReducedNatGeoFileAPminusModelA.xlsx" # source file outputFile<-"c:\\idm\\term.csv" ################################################################################## ##### 2 - READ DATA FILE INTO R AND PERFORM DATA PREPROCESSING ################################################################################## ## Read file into R df<-read.xls(datafileName,sheet=1,header=TRUE) numSuppliers<-length(unique(df$Supplier.ID)) ## Create blank dataframe supplier<-sort(unique(df$Supplier.ID),decreasing=FALSE) # supplierIDs in asc order description<-c(rep("",numSuppliers)) procdf<-data.frame(supplier,description) procdf$description<-as.character(procdf$description) ## populate dataframe with text from each supplier from original file ## Loop below operates once for each unique supplier referenced in both ## supplier and procdf variables for (ctr in 1:numSuppliers){ # Loop is running for supplier referenced by supplier[ctr] # Retrieve rows from original dataframe that have # suppliers matching with supplier[ctr] idx<-which(df$Supplier.ID==supplier[ctr]) # how many rows have this supplier? l<-length(idx) # initialize working variable workingText<-c("") # Loop through the matching records and concatenate # text of interest into workingText variable for (j in 1:l){ # get row reference rowidx<-idx[j] # retrieve and concatenate text workingText<-paste0(workingText,df[rowidx,3]) } # perform stemming on the text workingText<-wordStem(workingText,language='porter') # remove duplicate words from the text post stemming and assign # back into procdf procdf[ctr,2]<- vapply(lapply(strsplit(workingText, " "), unique), paste, character(1L), collapse = " ") } ################################################################################## ##### 3 - CREATE DTM AND TFXIDF MATRICES ################################################################################## ## Create corpus object and remove english stop words corpus<-Corpus(VectorSource(procdf$description)) cleancorpus<-tm_map(corpus,removeWords,stopwords("english")) cleancorpus<-tm_map(cleancorpus,removeNumbers) cleancorpus<-tm_map(cleancorpus,removePunctuation) ## Create document term matrix dtm<-DocumentTermMatrix(cleancorpus) ## Create term frequency - Inverse Document frequency matrix dtm_tfxidf<-weightTfIdf(dtm) m<-as.matrix(dtm_tfxidf) # convert into matrix object rownames(m) <- 1:nrow(m) # give appropriate row names (?) test<-rowSums(m) erridx<-which(test==0) zerov<-rep(0,ncol(m)) ################################################################################## ##### 4 - RUN K-MEANS CLUSTERING ON DATA ################################################################################## norm_eucl <- function(m) m/apply(m, MARGIN=1, FUN=function(x) sum(x^2)^.5) m_norm <- norm_eucl(m) # we have four rows that are full of zeroes cl <- kmeans(m_norm, 4) ################################################################################## ##### 5 - PREPARE DATAFRAME TO INSPECT TERMS MANUALLY ################################################################################## tdm<-TermDocumentMatrix(cleancorpus) workingm<-as.matrix(tdm) v <- sort(rowSums(workingm),decreasing=TRUE) termsFreqdf <- data.frame(word = names(v),freq=v) terms<-as.character(tdm[[6]][1]) termofInterest<-c("royalties") container<-subset(workingm,rownames(workingm)==termofInterest) docsWithTerm<-which(container>0) qualifyingSuppliers<-supplier[docsWithTerm] write.csv(qualifyingSuppliers,outputFile)

/R1.3/TextClusteringonNatGeoforClassification.R

no_license

nandatascientist/XeevaIDM

R

false

4,305

r

################################################################################## ##### 1 - INITIALIZE SCRIPT PARAMETERS ################################################################################## ## Load the needed libraries and functions library(gdata) library(tm) library(SnowballC) ## Set script parameters set.seed(12345) # for repeatability of random numbers #datafileName<-"c:\\idm\\Step4ReducedNatGeoFileAP1.xlsx" # source file datafileName<-"c:\\idm\\Step4ReducedNatGeoFileAPminusModelA.xlsx" # source file outputFile<-"c:\\idm\\term.csv" ################################################################################## ##### 2 - READ DATA FILE INTO R AND PERFORM DATA PREPROCESSING ################################################################################## ## Read file into R df<-read.xls(datafileName,sheet=1,header=TRUE) numSuppliers<-length(unique(df$Supplier.ID)) ## Create blank dataframe supplier<-sort(unique(df$Supplier.ID),decreasing=FALSE) # supplierIDs in asc order description<-c(rep("",numSuppliers)) procdf<-data.frame(supplier,description) procdf$description<-as.character(procdf$description) ## populate dataframe with text from each supplier from original file ## Loop below operates once for each unique supplier referenced in both ## supplier and procdf variables for (ctr in 1:numSuppliers){ # Loop is running for supplier referenced by supplier[ctr] # Retrieve rows from original dataframe that have # suppliers matching with supplier[ctr] idx<-which(df$Supplier.ID==supplier[ctr]) # how many rows have this supplier? l<-length(idx) # initialize working variable workingText<-c("") # Loop through the matching records and concatenate # text of interest into workingText variable for (j in 1:l){ # get row reference rowidx<-idx[j] # retrieve and concatenate text workingText<-paste0(workingText,df[rowidx,3]) } # perform stemming on the text workingText<-wordStem(workingText,language='porter') # remove duplicate words from the text post stemming and assign # back into procdf procdf[ctr,2]<- vapply(lapply(strsplit(workingText, " "), unique), paste, character(1L), collapse = " ") } ################################################################################## ##### 3 - CREATE DTM AND TFXIDF MATRICES ################################################################################## ## Create corpus object and remove english stop words corpus<-Corpus(VectorSource(procdf$description)) cleancorpus<-tm_map(corpus,removeWords,stopwords("english")) cleancorpus<-tm_map(cleancorpus,removeNumbers) cleancorpus<-tm_map(cleancorpus,removePunctuation) ## Create document term matrix dtm<-DocumentTermMatrix(cleancorpus) ## Create term frequency - Inverse Document frequency matrix dtm_tfxidf<-weightTfIdf(dtm) m<-as.matrix(dtm_tfxidf) # convert into matrix object rownames(m) <- 1:nrow(m) # give appropriate row names (?) test<-rowSums(m) erridx<-which(test==0) zerov<-rep(0,ncol(m)) ################################################################################## ##### 4 - RUN K-MEANS CLUSTERING ON DATA ################################################################################## norm_eucl <- function(m) m/apply(m, MARGIN=1, FUN=function(x) sum(x^2)^.5) m_norm <- norm_eucl(m) # we have four rows that are full of zeroes cl <- kmeans(m_norm, 4) ################################################################################## ##### 5 - PREPARE DATAFRAME TO INSPECT TERMS MANUALLY ################################################################################## tdm<-TermDocumentMatrix(cleancorpus) workingm<-as.matrix(tdm) v <- sort(rowSums(workingm),decreasing=TRUE) termsFreqdf <- data.frame(word = names(v),freq=v) terms<-as.character(tdm[[6]][1]) termofInterest<-c("royalties") container<-subset(workingm,rownames(workingm)==termofInterest) docsWithTerm<-which(container>0) qualifyingSuppliers<-supplier[docsWithTerm] write.csv(qualifyingSuppliers,outputFile)

setwd("/media/vivekkulkarni/362480E21B181E95/Fodder/src") d = read.csv('../real_data/ANMO_Zchannel.csv') d = as.numeric(unlist(d)) plot(as.ts(d)) datas = as.ts(d[1:50000]) plot(datas) a = acf(datas,100) p = pacf(datas,100) d = diff(d) plot(d)

/src/fit_anmodata.R

permissive

vivekkulkarni1/signal_detection

R

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245

r

setwd("/media/vivekkulkarni/362480E21B181E95/Fodder/src") d = read.csv('../real_data/ANMO_Zchannel.csv') d = as.numeric(unlist(d)) plot(as.ts(d)) datas = as.ts(d[1:50000]) plot(datas) a = acf(datas,100) p = pacf(datas,100) d = diff(d) plot(d)

library(openxlsx) library(ggplot2) library(reshape2) #source("~/documents/lab/workspace/Classification_scripts/multiplot.R") source("~/workspace/classification_scripts/multiplot.R") library(zoo) library(gplots) library(RColorBrewer) library(abind) library(gridGraphics) library(grid) library(gridExtra) library(R.matlab) saveAsPng <- T ######### nhp_id <- '504' if (nhp_id == '0059'){condensed <- readRDS('0059_total_condensed.rds') }else if (nhp_id == '504'){condensed <- readRDS('504_total_condensed.rds')} r0 <- which(condensed[,4] == 0) r1 <- which(condensed[,4] == 1) r2 <- which(condensed[,4] == 2) r3 <- which(condensed[,4] == 3) p0 <- which(condensed[,5] == 0) p1 <- which(condensed[,5] == 1) p2 <- which(condensed[,5] == 2) p3 <- which(condensed[,5] == 3) v_3 <- which(condensed[,7] == -3) v_2 <- which(condensed[,7] == -2) v_1 <- which(condensed[,7] == -1) v0 <- which(condensed[,7] == 0) v1 <- which(condensed[,7] == 1) v2 <- which(condensed[,7] == 2) v3 <- which(condensed[,7] == 3) m0 <- which(condensed[,8] == 0) m1 <- which(condensed[,8] == 1) m2 <- which(condensed[,8] == 2) m3 <- which(condensed[,8] == 3) m4 <- which(condensed[,8] == 4) m5 <- which(condensed[,8] == 5) m6 <- which(condensed[,8] == 6) res0 <- which(condensed[,6] == 0) res1 <- which(condensed[,6] == 1) r0_fail <- res0[which(res0 %in% r0)] r1_fail <- res0[which(res0 %in% r1)] r2_fail <- res0[which(res0 %in% r2)] r3_fail <- res0[which(res0 %in% r3)] r0_succ <- res1[which(res1 %in% r0)] r1_succ <- res1[which(res1 %in% r1)] r2_succ <- res1[which(res1 %in% r2)] r3_succ <- res1[which(res1 %in% r3)] p0_fail <- res0[which(res0 %in% p0)] p1_fail <- res0[which(res0 %in% p1)] p2_fail <- res0[which(res0 %in% p2)] p3_fail <- res0[which(res0 %in% p3)] p0_succ <- res1[which(res1 %in% p0)] p1_succ <- res1[which(res1 %in% p1)] p2_succ <- res1[which(res1 %in% p2)] p3_succ <- res1[which(res1 %in% p3)] #reward png(paste("time_", nhp_id,"_reward.png",sep=""),width=8,height=6,units="in",res=500) ravgs <- data.frame(r_values=c(0,1,2,3),reach_time = c(mean(condensed[r0,9]),mean(condensed[r1,9]),mean(condensed[r2,9]),mean(condensed[r3,9])),intertrial = c(mean(condensed[r0,10]),mean(condensed[r1,10]),mean(condensed[r2,10]),mean(condensed[r3,10]))) rstds <- data.frame(r_values=c(0,1,2,3),reach_time = c(sd(condensed[r0,9]),sd(condensed[r1,9]),sd(condensed[r2,9]),sd(condensed[r3,9])),intertrial = c(sd(condensed[r0,10]),sd(condensed[r1,10]),sd(condensed[r2,10]),sd(condensed[r3,10]))) avg_melt <- melt(ravgs,id="r_values",variable.name='type',value.name='avg') std_melt <- melt(rstds,id="r_values",variable.name='type',value.name='std') test <- merge(std_melt,avg_melt,row.names='r_values') test[is.na(test)] <- 0 plt <- ggplot(data=test,aes(x=r_values,y=avg,ymax=avg+std,ymin=avg-std,fill=type)) + geom_bar(position="dodge",stat="identity") + geom_errorbar(position=position_dodge(width=0.9),color="gray32",width=0.25) plt <- plt + scale_fill_manual(values=c("royalblue","seagreen")) + geom_text(aes(y=0.75),size=3,label=sprintf("%0.2f", round(test$avg, digits = 2)),position=position_dodge(width=0.9)) + theme_classic() + labs(title="Average Times",y="Time (s)",x="Reward",fill="") plot(plt) graphics.off() #punishment png(paste("time_", nhp_id,"_punishment.png",sep=""),width=8,height=6,units="in",res=500) pavgs <- data.frame(p_values=c(0,1,2,3),reach_time = c(mean(condensed[p0,9]),mean(condensed[p1,9]),mean(condensed[p2,9]),mean(condensed[p3,9])),intertrial = c(mean(condensed[p0,10]),mean(condensed[p1,10]),mean(condensed[p2,10]),mean(condensed[p3,10]))) pstds <- data.frame(p_values=c(0,1,2,3),reach_time = c(sd(condensed[p0,9]),sd(condensed[p1,9]),sd(condensed[p2,9]),sd(condensed[p3,9])),intertrial = c(sd(condensed[p0,10]),sd(condensed[p1,10]),sd(condensed[p2,10]),sd(condensed[p3,10]))) avg_melt <- melt(pavgs,id="p_values",variable.name='type',value.name='avg') std_melt <- melt(pstds,id="p_values",variable.name='type',value.name='std') test <- merge(std_melt,avg_melt,row.names='p_values') test[is.na(test)] <- 0 plt <- ggplot(data=test,aes(x=p_values,y=avg,ymax=avg+std,ymin=avg-std,fill=type)) + geom_bar(position="dodge",stat="identity") + geom_errorbar(position=position_dodge(width=0.9),color="gray32",width=0.25) plt <- plt + scale_fill_manual(values=c("royalblue","seagreen")) + geom_text(aes(y=0.75),size=3,label=sprintf("%0.2f", round(test$avg, digits = 2)),position=position_dodge(width=0.9)) + theme_classic() + labs(title="Average Times",y="Time (s)",x="Punishment",fill="") plot(plt) graphics.off() #result png(paste("time_", nhp_id,"_result.png",sep=""),width=8,height=6,units="in",res=500) resavgs <- data.frame(res_values=c(0,1),reach_time = c(mean(condensed[res0,9]),mean(condensed[res1,9])),intertrial = c(mean(condensed[res0,10]),mean(condensed[res1,10]))) resstds <- data.frame(res_values=c(0,1),reach_time = c(sd(condensed[res0,9]),sd(condensed[res1,9])),intertrial = c(sd(condensed[res0,10]),sd(condensed[res1,10]))) avg_melt <- melt(resavgs,id="res_values",variable.name='type',value.name='avg') std_melt <- melt(resstds,id="res_values",variable.name='type',value.name='std') test <- merge(std_melt,avg_melt,row.names='res_values') test[is.na(test)] <- 0 plt <- ggplot(data=test,aes(x=res_values,y=avg,ymax=avg+std,ymin=avg-std,fill=type)) + geom_bar(position="dodge",stat="identity") + geom_errorbar(position=position_dodge(width=0.9),color="gray32",width=0.25) plt <- plt + scale_fill_manual(values=c("royalblue","seagreen")) + geom_text(aes(y=0.75),size=3,label=sprintf("%0.2f", round(test$avg, digits = 2)),position=position_dodge(width=0.9)) + theme_classic() + labs(title="Average Times",y="Time (s)",x="Result",fill="") + scale_x_discrete(limits=0:1,labels=c("fail","succ")) plot(plt) graphics.off() #value png(paste("time_", nhp_id,"_value.png",sep=""),width=8,height=6,units="in",res=500) vavgs <- data.frame(v_values=c(-3,-2,-1,0,1,2,3),reach_time = c(mean(condensed[v_3,9]),mean(condensed[v_2,9]),mean(condensed[v_1,9]),mean(condensed[v0,9]),mean(condensed[v1,9]),mean(condensed[v2,9]),mean(condensed[v3,9])),intertrial = c(mean(condensed[v_3,10]),mean(condensed[v_2,10]),mean(condensed[v_1,10]),mean(condensed[v0,10]),mean(condensed[v1,10]),mean(condensed[v2,10]),mean(condensed[v3,10]))) vstds <- data.frame(v_values=c(-3,-2,-1,0,1,2,3),reach_time = c(sd(condensed[v_3,9]),sd(condensed[v_2,9]),sd(condensed[v_1,9]),sd(condensed[v0,9]),sd(condensed[v1,9]),sd(condensed[v2,9]),sd(condensed[v3,9])),intertrial = c(sd(condensed[v_3,10]),sd(condensed[v_2,10]),sd(condensed[v_1,10]),sd(condensed[v0,10]),sd(condensed[v1,10]),sd(condensed[v2,10]),sd(condensed[v3,10]))) avg_melt <- melt(vavgs,id="v_values",variable.name='type',value.name='avg') std_melt <- melt(vstds,id="v_values",variable.name='type',value.name='std') test <- merge(std_melt,avg_melt,row.names='v_values') test[is.na(test)] <- 0 plt <- ggplot(data=test,aes(x=v_values,y=avg,ymax=avg+std,ymin=avg-std,fill=type)) + geom_bar(position="dodge",stat="identity") + geom_errorbar(position=position_dodge(width=0.9),color="gray32",width=0.25) plt <- plt + scale_fill_manual(values=c("royalblue","seagreen")) + geom_text(aes(y=0.75),size=3,label=sprintf("%0.2f", round(test$avg, digits = 2)),position=position_dodge(width=0.9)) + theme_classic() + labs(title="Average Times",y="Time (s)",x="Value",fill="") plot(plt) graphics.off() #motivation png(paste("time_", nhp_id,"_motivation.png",sep=""),width=8,height=6,units="in",res=500) mavgs <- data.frame(m_values=c(0,1,2,3,4,5,6),reach_time = c(mean(condensed[m0,9]),mean(condensed[m1,9]),mean(condensed[m2,9]),mean(condensed[m3,9]),mean(condensed[m4,9]),mean(condensed[m5,9]),mean(condensed[m6,9])),intertrial = c(mean(condensed[m0,10]),mean(condensed[m1,10]),mean(condensed[m2,10]),mean(condensed[m3,10]),mean(condensed[m4,10]),mean(condensed[m5,10]),mean(condensed[m6,10]))) mstds <- data.frame(m_values=c(0,1,2,3,4,5,6),reach_time = c(sd(condensed[m0,9]),sd(condensed[m1,9]),sd(condensed[m2,9]),sd(condensed[m3,9]),sd(condensed[m4,9]),sd(condensed[m5,9]),sd(condensed[m6,9])),intertrial = c(sd(condensed[m0,10]),sd(condensed[m1,10]),sd(condensed[m2,10]),sd(condensed[m3,10]),sd(condensed[m4,10]),sd(condensed[m5,10]),sd(condensed[m6,10]))) avg_melt <- melt(mavgs,id="m_values",variable.name='type',value.name='avg') std_melt <- melt(mstds,id="m_values",variable.name='type',value.name='std') test <- merge(std_melt,avg_melt,row.names='m_values') test[is.na(test)] <- 0 plt <- ggplot(data=test,aes(x=m_values,y=avg,ymax=avg+std,ymin=avg-std,fill=type)) + geom_bar(position="dodge",stat="identity") + geom_errorbar(position=position_dodge(width=0.9),color="gray32",width=0.25) plt <- plt + scale_fill_manual(values=c("royalblue","seagreen")) + geom_text(aes(y=0.75),size=3,label=sprintf("%0.2f", round(test$avg, digits = 2)),position=position_dodge(width=0.9)) + theme_classic() + labs(title="Average Times",y="Time (s)",x="Motivation",fill="") plot(plt) graphics.off() ########## ########## #reward sf png(paste("time_", nhp_id,"_reward_sf.png",sep=""),width=8,height=6,units="in",res=500) r_s_avgs <- data.frame(r_values=c(0,1,2,3),reach_time_s = c(mean(condensed[r0_succ,9]),mean(condensed[r1_succ,9]),mean(condensed[r2_succ,9]),mean(condensed[r3_succ,9])),intertrial_s = c(mean(condensed[r0_succ,10]),mean(condensed[r1_succ,10]),mean(condensed[r2_succ,10]),mean(condensed[r3_succ,10]))) r_s_stds <- data.frame(r_values=c(0,1,2,3),reach_time_s = c(sd(condensed[r0_succ,9]),sd(condensed[r1_succ,9]),sd(condensed[r2_succ,9]),sd(condensed[r3_succ,9])),intertrial_s = c(sd(condensed[r0_succ,10]),sd(condensed[r1_succ,10]),sd(condensed[r2_succ,10]),sd(condensed[r3_succ,10]))) r_f_avgs <- data.frame(r_values=c(0,1,2,3),reach_time_f = c(mean(condensed[r0_fail,9]),mean(condensed[r1_fail,9]),mean(condensed[r2_fail,9]),mean(condensed[r3_fail,9])),intertrial_f = c(mean(condensed[r0_fail,10]),mean(condensed[r1_fail,10]),mean(condensed[r2_fail,10]),mean(condensed[r3_fail,10]))) r_f_stds <- data.frame(r_values=c(0,1,2,3),reach_time_f = c(sd(condensed[r0_fail,9]),sd(condensed[r1_fail,9]),sd(condensed[r2_fail,9]),sd(condensed[r3_fail,9])),intertrial_f = c(sd(condensed[r0_fail,10]),sd(condensed[r1_fail,10]),sd(condensed[r2_fail,10]),sd(condensed[r3_fail,10]))) avg_s_melt <- melt(r_s_avgs,id="r_values",variable.name='type',value.name='avg') std_s_melt <- melt(r_s_stds,id="r_values",variable.name='type',value.name='std') avg_f_melt <- melt(r_f_avgs,id="r_values",variable.name='type',value.name='avg') std_f_melt <- melt(r_f_stds,id="r_values",variable.name='type',value.name='std') test_s <- merge(std_s_melt,avg_s_melt,row.names='r_values') test_f <- merge(std_f_melt,avg_f_melt,row.names='r_values') test <- rbind(test_s,test_f) test[is.na(test)] <- 0 plt <- ggplot(data=test,aes(x=r_values,y=avg,ymax=avg+std,ymin=avg-std,fill=type)) + geom_bar(position="dodge",stat="identity") + geom_errorbar(position=position_dodge(width=0.9),color="gray32",width=0.25) plt <- plt + scale_fill_manual(values=c("royalblue","seagreen","paleturquoise","lightgreen")) + theme_classic() + labs(title="Average Times",y="Time (s)",x="Reward",fill="") # + geom_text(aes(y=0.75),size=3,label=sprintf("%0.2f", round(test$avg, digits = 2)),position=position_dodge(width=0.9)) plot(plt) graphics.off() #punishment sf png(paste("time_", nhp_id,"_punishment_sf.png",sep=""),width=8,height=6,units="in",res=500) p_s_avgs <- data.frame(p_values=c(0,1,2,3),reach_time_s = c(mean(condensed[p0_succ,9]),mean(condensed[p1_succ,9]),mean(condensed[p2_succ,9]),mean(condensed[p3_succ,9])),intertrial_s = c(mean(condensed[p0_succ,10]),mean(condensed[p1_succ,10]),mean(condensed[p2_succ,10]),mean(condensed[p3_succ,10]))) p_s_stds <- data.frame(p_values=c(0,1,2,3),reach_time_s = c(sd(condensed[p0_succ,9]),sd(condensed[p1_succ,9]),sd(condensed[p2_succ,9]),sd(condensed[p3_succ,9])),intertrial_s = c(sd(condensed[p0_succ,10]),sd(condensed[p1_succ,10]),sd(condensed[p2_succ,10]),sd(condensed[p3_succ,10]))) p_f_avgs <- data.frame(p_values=c(0,1,2,3),reach_time_f = c(mean(condensed[p0_fail,9]),mean(condensed[p1_fail,9]),mean(condensed[p2_fail,9]),mean(condensed[p3_fail,9])),intertrial_f = c(mean(condensed[p0_fail,10]),mean(condensed[p1_fail,10]),mean(condensed[p2_fail,10]),mean(condensed[p3_fail,10]))) p_f_stds <- data.frame(p_values=c(0,1,2,3),reach_time_f = c(sd(condensed[p0_fail,9]),sd(condensed[p1_fail,9]),sd(condensed[p2_fail,9]),sd(condensed[p3_fail,9])),intertrial_f = c(sd(condensed[p0_fail,10]),sd(condensed[p1_fail,10]),sd(condensed[p2_fail,10]),sd(condensed[p3_fail,10]))) avg_s_melt <- melt(p_s_avgs,id="p_values",variable.name='type',value.name='avg') std_s_melt <- melt(p_s_stds,id="p_values",variable.name='type',value.name='std') avg_f_melt <- melt(p_f_avgs,id="p_values",variable.name='type',value.name='avg') std_f_melt <- melt(p_f_stds,id="p_values",variable.name='type',value.name='std') test_s <- merge(std_s_melt,avg_s_melt,row.names='p_values') test_f <- merge(std_f_melt,avg_f_melt,row.names='p_values') test <- rbind(test_s,test_f) test[is.na(test)] <- 0 plt <- ggplot(data=test,aes(x=p_values,y=avg,ymax=avg+std,ymin=avg-std,fill=type)) + geom_bar(position="dodge",stat="identity") + geom_errorbar(position=position_dodge(width=0.9),color="gray32",width=0.25) plt <- plt + scale_fill_manual(values=c("royalblue","seagreen","paleturquoise","lightgreen")) + theme_classic() + labs(title="Average Times",y="Time (s)",x="Punishment",fill="") # + geom_text(aes(y=0.75),size=3,label=sprintf("%0.2f", round(test$avg, digits = 2)),position=position_dodge(width=0.9)) plot(plt) graphics.off() rm(list=ls())

/timing_plots_all.R

no_license

jhess90/classification_scripts

R

false

13,598

r

library(openxlsx) library(ggplot2) library(reshape2) #source("~/documents/lab/workspace/Classification_scripts/multiplot.R") source("~/workspace/classification_scripts/multiplot.R") library(zoo) library(gplots) library(RColorBrewer) library(abind) library(gridGraphics) library(grid) library(gridExtra) library(R.matlab) saveAsPng <- T ######### nhp_id <- '504' if (nhp_id == '0059'){condensed <- readRDS('0059_total_condensed.rds') }else if (nhp_id == '504'){condensed <- readRDS('504_total_condensed.rds')} r0 <- which(condensed[,4] == 0) r1 <- which(condensed[,4] == 1) r2 <- which(condensed[,4] == 2) r3 <- which(condensed[,4] == 3) p0 <- which(condensed[,5] == 0) p1 <- which(condensed[,5] == 1) p2 <- which(condensed[,5] == 2) p3 <- which(condensed[,5] == 3) v_3 <- which(condensed[,7] == -3) v_2 <- which(condensed[,7] == -2) v_1 <- which(condensed[,7] == -1) v0 <- which(condensed[,7] == 0) v1 <- which(condensed[,7] == 1) v2 <- which(condensed[,7] == 2) v3 <- which(condensed[,7] == 3) m0 <- which(condensed[,8] == 0) m1 <- which(condensed[,8] == 1) m2 <- which(condensed[,8] == 2) m3 <- which(condensed[,8] == 3) m4 <- which(condensed[,8] == 4) m5 <- which(condensed[,8] == 5) m6 <- which(condensed[,8] == 6) res0 <- which(condensed[,6] == 0) res1 <- which(condensed[,6] == 1) r0_fail <- res0[which(res0 %in% r0)] r1_fail <- res0[which(res0 %in% r1)] r2_fail <- res0[which(res0 %in% r2)] r3_fail <- res0[which(res0 %in% r3)] r0_succ <- res1[which(res1 %in% r0)] r1_succ <- res1[which(res1 %in% r1)] r2_succ <- res1[which(res1 %in% r2)] r3_succ <- res1[which(res1 %in% r3)] p0_fail <- res0[which(res0 %in% p0)] p1_fail <- res0[which(res0 %in% p1)] p2_fail <- res0[which(res0 %in% p2)] p3_fail <- res0[which(res0 %in% p3)] p0_succ <- res1[which(res1 %in% p0)] p1_succ <- res1[which(res1 %in% p1)] p2_succ <- res1[which(res1 %in% p2)] p3_succ <- res1[which(res1 %in% p3)] #reward png(paste("time_", nhp_id,"_reward.png",sep=""),width=8,height=6,units="in",res=500) ravgs <- data.frame(r_values=c(0,1,2,3),reach_time = c(mean(condensed[r0,9]),mean(condensed[r1,9]),mean(condensed[r2,9]),mean(condensed[r3,9])),intertrial = c(mean(condensed[r0,10]),mean(condensed[r1,10]),mean(condensed[r2,10]),mean(condensed[r3,10]))) rstds <- data.frame(r_values=c(0,1,2,3),reach_time = c(sd(condensed[r0,9]),sd(condensed[r1,9]),sd(condensed[r2,9]),sd(condensed[r3,9])),intertrial = c(sd(condensed[r0,10]),sd(condensed[r1,10]),sd(condensed[r2,10]),sd(condensed[r3,10]))) avg_melt <- melt(ravgs,id="r_values",variable.name='type',value.name='avg') std_melt <- melt(rstds,id="r_values",variable.name='type',value.name='std') test <- merge(std_melt,avg_melt,row.names='r_values') test[is.na(test)] <- 0 plt <- ggplot(data=test,aes(x=r_values,y=avg,ymax=avg+std,ymin=avg-std,fill=type)) + geom_bar(position="dodge",stat="identity") + geom_errorbar(position=position_dodge(width=0.9),color="gray32",width=0.25) plt <- plt + scale_fill_manual(values=c("royalblue","seagreen")) + geom_text(aes(y=0.75),size=3,label=sprintf("%0.2f", round(test$avg, digits = 2)),position=position_dodge(width=0.9)) + theme_classic() + labs(title="Average Times",y="Time (s)",x="Reward",fill="") plot(plt) graphics.off() #punishment png(paste("time_", nhp_id,"_punishment.png",sep=""),width=8,height=6,units="in",res=500) pavgs <- data.frame(p_values=c(0,1,2,3),reach_time = c(mean(condensed[p0,9]),mean(condensed[p1,9]),mean(condensed[p2,9]),mean(condensed[p3,9])),intertrial = c(mean(condensed[p0,10]),mean(condensed[p1,10]),mean(condensed[p2,10]),mean(condensed[p3,10]))) pstds <- data.frame(p_values=c(0,1,2,3),reach_time = c(sd(condensed[p0,9]),sd(condensed[p1,9]),sd(condensed[p2,9]),sd(condensed[p3,9])),intertrial = c(sd(condensed[p0,10]),sd(condensed[p1,10]),sd(condensed[p2,10]),sd(condensed[p3,10]))) avg_melt <- melt(pavgs,id="p_values",variable.name='type',value.name='avg') std_melt <- melt(pstds,id="p_values",variable.name='type',value.name='std') test <- merge(std_melt,avg_melt,row.names='p_values') test[is.na(test)] <- 0 plt <- ggplot(data=test,aes(x=p_values,y=avg,ymax=avg+std,ymin=avg-std,fill=type)) + geom_bar(position="dodge",stat="identity") + geom_errorbar(position=position_dodge(width=0.9),color="gray32",width=0.25) plt <- plt + scale_fill_manual(values=c("royalblue","seagreen")) + geom_text(aes(y=0.75),size=3,label=sprintf("%0.2f", round(test$avg, digits = 2)),position=position_dodge(width=0.9)) + theme_classic() + labs(title="Average Times",y="Time (s)",x="Punishment",fill="") plot(plt) graphics.off() #result png(paste("time_", nhp_id,"_result.png",sep=""),width=8,height=6,units="in",res=500) resavgs <- data.frame(res_values=c(0,1),reach_time = c(mean(condensed[res0,9]),mean(condensed[res1,9])),intertrial = c(mean(condensed[res0,10]),mean(condensed[res1,10]))) resstds <- data.frame(res_values=c(0,1),reach_time = c(sd(condensed[res0,9]),sd(condensed[res1,9])),intertrial = c(sd(condensed[res0,10]),sd(condensed[res1,10]))) avg_melt <- melt(resavgs,id="res_values",variable.name='type',value.name='avg') std_melt <- melt(resstds,id="res_values",variable.name='type',value.name='std') test <- merge(std_melt,avg_melt,row.names='res_values') test[is.na(test)] <- 0 plt <- ggplot(data=test,aes(x=res_values,y=avg,ymax=avg+std,ymin=avg-std,fill=type)) + geom_bar(position="dodge",stat="identity") + geom_errorbar(position=position_dodge(width=0.9),color="gray32",width=0.25) plt <- plt + scale_fill_manual(values=c("royalblue","seagreen")) + geom_text(aes(y=0.75),size=3,label=sprintf("%0.2f", round(test$avg, digits = 2)),position=position_dodge(width=0.9)) + theme_classic() + labs(title="Average Times",y="Time (s)",x="Result",fill="") + scale_x_discrete(limits=0:1,labels=c("fail","succ")) plot(plt) graphics.off() #value png(paste("time_", nhp_id,"_value.png",sep=""),width=8,height=6,units="in",res=500) vavgs <- data.frame(v_values=c(-3,-2,-1,0,1,2,3),reach_time = c(mean(condensed[v_3,9]),mean(condensed[v_2,9]),mean(condensed[v_1,9]),mean(condensed[v0,9]),mean(condensed[v1,9]),mean(condensed[v2,9]),mean(condensed[v3,9])),intertrial = c(mean(condensed[v_3,10]),mean(condensed[v_2,10]),mean(condensed[v_1,10]),mean(condensed[v0,10]),mean(condensed[v1,10]),mean(condensed[v2,10]),mean(condensed[v3,10]))) vstds <- data.frame(v_values=c(-3,-2,-1,0,1,2,3),reach_time = c(sd(condensed[v_3,9]),sd(condensed[v_2,9]),sd(condensed[v_1,9]),sd(condensed[v0,9]),sd(condensed[v1,9]),sd(condensed[v2,9]),sd(condensed[v3,9])),intertrial = c(sd(condensed[v_3,10]),sd(condensed[v_2,10]),sd(condensed[v_1,10]),sd(condensed[v0,10]),sd(condensed[v1,10]),sd(condensed[v2,10]),sd(condensed[v3,10]))) avg_melt <- melt(vavgs,id="v_values",variable.name='type',value.name='avg') std_melt <- melt(vstds,id="v_values",variable.name='type',value.name='std') test <- merge(std_melt,avg_melt,row.names='v_values') test[is.na(test)] <- 0 plt <- ggplot(data=test,aes(x=v_values,y=avg,ymax=avg+std,ymin=avg-std,fill=type)) + geom_bar(position="dodge",stat="identity") + geom_errorbar(position=position_dodge(width=0.9),color="gray32",width=0.25) plt <- plt + scale_fill_manual(values=c("royalblue","seagreen")) + geom_text(aes(y=0.75),size=3,label=sprintf("%0.2f", round(test$avg, digits = 2)),position=position_dodge(width=0.9)) + theme_classic() + labs(title="Average Times",y="Time (s)",x="Value",fill="") plot(plt) graphics.off() #motivation png(paste("time_", nhp_id,"_motivation.png",sep=""),width=8,height=6,units="in",res=500) mavgs <- data.frame(m_values=c(0,1,2,3,4,5,6),reach_time = c(mean(condensed[m0,9]),mean(condensed[m1,9]),mean(condensed[m2,9]),mean(condensed[m3,9]),mean(condensed[m4,9]),mean(condensed[m5,9]),mean(condensed[m6,9])),intertrial = c(mean(condensed[m0,10]),mean(condensed[m1,10]),mean(condensed[m2,10]),mean(condensed[m3,10]),mean(condensed[m4,10]),mean(condensed[m5,10]),mean(condensed[m6,10]))) mstds <- data.frame(m_values=c(0,1,2,3,4,5,6),reach_time = c(sd(condensed[m0,9]),sd(condensed[m1,9]),sd(condensed[m2,9]),sd(condensed[m3,9]),sd(condensed[m4,9]),sd(condensed[m5,9]),sd(condensed[m6,9])),intertrial = c(sd(condensed[m0,10]),sd(condensed[m1,10]),sd(condensed[m2,10]),sd(condensed[m3,10]),sd(condensed[m4,10]),sd(condensed[m5,10]),sd(condensed[m6,10]))) avg_melt <- melt(mavgs,id="m_values",variable.name='type',value.name='avg') std_melt <- melt(mstds,id="m_values",variable.name='type',value.name='std') test <- merge(std_melt,avg_melt,row.names='m_values') test[is.na(test)] <- 0 plt <- ggplot(data=test,aes(x=m_values,y=avg,ymax=avg+std,ymin=avg-std,fill=type)) + geom_bar(position="dodge",stat="identity") + geom_errorbar(position=position_dodge(width=0.9),color="gray32",width=0.25) plt <- plt + scale_fill_manual(values=c("royalblue","seagreen")) + geom_text(aes(y=0.75),size=3,label=sprintf("%0.2f", round(test$avg, digits = 2)),position=position_dodge(width=0.9)) + theme_classic() + labs(title="Average Times",y="Time (s)",x="Motivation",fill="") plot(plt) graphics.off() ########## ########## #reward sf png(paste("time_", nhp_id,"_reward_sf.png",sep=""),width=8,height=6,units="in",res=500) r_s_avgs <- data.frame(r_values=c(0,1,2,3),reach_time_s = c(mean(condensed[r0_succ,9]),mean(condensed[r1_succ,9]),mean(condensed[r2_succ,9]),mean(condensed[r3_succ,9])),intertrial_s = c(mean(condensed[r0_succ,10]),mean(condensed[r1_succ,10]),mean(condensed[r2_succ,10]),mean(condensed[r3_succ,10]))) r_s_stds <- data.frame(r_values=c(0,1,2,3),reach_time_s = c(sd(condensed[r0_succ,9]),sd(condensed[r1_succ,9]),sd(condensed[r2_succ,9]),sd(condensed[r3_succ,9])),intertrial_s = c(sd(condensed[r0_succ,10]),sd(condensed[r1_succ,10]),sd(condensed[r2_succ,10]),sd(condensed[r3_succ,10]))) r_f_avgs <- data.frame(r_values=c(0,1,2,3),reach_time_f = c(mean(condensed[r0_fail,9]),mean(condensed[r1_fail,9]),mean(condensed[r2_fail,9]),mean(condensed[r3_fail,9])),intertrial_f = c(mean(condensed[r0_fail,10]),mean(condensed[r1_fail,10]),mean(condensed[r2_fail,10]),mean(condensed[r3_fail,10]))) r_f_stds <- data.frame(r_values=c(0,1,2,3),reach_time_f = c(sd(condensed[r0_fail,9]),sd(condensed[r1_fail,9]),sd(condensed[r2_fail,9]),sd(condensed[r3_fail,9])),intertrial_f = c(sd(condensed[r0_fail,10]),sd(condensed[r1_fail,10]),sd(condensed[r2_fail,10]),sd(condensed[r3_fail,10]))) avg_s_melt <- melt(r_s_avgs,id="r_values",variable.name='type',value.name='avg') std_s_melt <- melt(r_s_stds,id="r_values",variable.name='type',value.name='std') avg_f_melt <- melt(r_f_avgs,id="r_values",variable.name='type',value.name='avg') std_f_melt <- melt(r_f_stds,id="r_values",variable.name='type',value.name='std') test_s <- merge(std_s_melt,avg_s_melt,row.names='r_values') test_f <- merge(std_f_melt,avg_f_melt,row.names='r_values') test <- rbind(test_s,test_f) test[is.na(test)] <- 0 plt <- ggplot(data=test,aes(x=r_values,y=avg,ymax=avg+std,ymin=avg-std,fill=type)) + geom_bar(position="dodge",stat="identity") + geom_errorbar(position=position_dodge(width=0.9),color="gray32",width=0.25) plt <- plt + scale_fill_manual(values=c("royalblue","seagreen","paleturquoise","lightgreen")) + theme_classic() + labs(title="Average Times",y="Time (s)",x="Reward",fill="") # + geom_text(aes(y=0.75),size=3,label=sprintf("%0.2f", round(test$avg, digits = 2)),position=position_dodge(width=0.9)) plot(plt) graphics.off() #punishment sf png(paste("time_", nhp_id,"_punishment_sf.png",sep=""),width=8,height=6,units="in",res=500) p_s_avgs <- data.frame(p_values=c(0,1,2,3),reach_time_s = c(mean(condensed[p0_succ,9]),mean(condensed[p1_succ,9]),mean(condensed[p2_succ,9]),mean(condensed[p3_succ,9])),intertrial_s = c(mean(condensed[p0_succ,10]),mean(condensed[p1_succ,10]),mean(condensed[p2_succ,10]),mean(condensed[p3_succ,10]))) p_s_stds <- data.frame(p_values=c(0,1,2,3),reach_time_s = c(sd(condensed[p0_succ,9]),sd(condensed[p1_succ,9]),sd(condensed[p2_succ,9]),sd(condensed[p3_succ,9])),intertrial_s = c(sd(condensed[p0_succ,10]),sd(condensed[p1_succ,10]),sd(condensed[p2_succ,10]),sd(condensed[p3_succ,10]))) p_f_avgs <- data.frame(p_values=c(0,1,2,3),reach_time_f = c(mean(condensed[p0_fail,9]),mean(condensed[p1_fail,9]),mean(condensed[p2_fail,9]),mean(condensed[p3_fail,9])),intertrial_f = c(mean(condensed[p0_fail,10]),mean(condensed[p1_fail,10]),mean(condensed[p2_fail,10]),mean(condensed[p3_fail,10]))) p_f_stds <- data.frame(p_values=c(0,1,2,3),reach_time_f = c(sd(condensed[p0_fail,9]),sd(condensed[p1_fail,9]),sd(condensed[p2_fail,9]),sd(condensed[p3_fail,9])),intertrial_f = c(sd(condensed[p0_fail,10]),sd(condensed[p1_fail,10]),sd(condensed[p2_fail,10]),sd(condensed[p3_fail,10]))) avg_s_melt <- melt(p_s_avgs,id="p_values",variable.name='type',value.name='avg') std_s_melt <- melt(p_s_stds,id="p_values",variable.name='type',value.name='std') avg_f_melt <- melt(p_f_avgs,id="p_values",variable.name='type',value.name='avg') std_f_melt <- melt(p_f_stds,id="p_values",variable.name='type',value.name='std') test_s <- merge(std_s_melt,avg_s_melt,row.names='p_values') test_f <- merge(std_f_melt,avg_f_melt,row.names='p_values') test <- rbind(test_s,test_f) test[is.na(test)] <- 0 plt <- ggplot(data=test,aes(x=p_values,y=avg,ymax=avg+std,ymin=avg-std,fill=type)) + geom_bar(position="dodge",stat="identity") + geom_errorbar(position=position_dodge(width=0.9),color="gray32",width=0.25) plt <- plt + scale_fill_manual(values=c("royalblue","seagreen","paleturquoise","lightgreen")) + theme_classic() + labs(title="Average Times",y="Time (s)",x="Punishment",fill="") # + geom_text(aes(y=0.75),size=3,label=sprintf("%0.2f", round(test$avg, digits = 2)),position=position_dodge(width=0.9)) plot(plt) graphics.off() rm(list=ls())

#' Mosaic plots. #' #' @export #' #' @description #' A mosaic plot is a convenient graphical summary of the conditional ditributions #' in a contingency table and is composed of spines in alternating directions. #' #' #' @inheritParams ggplot2::layer #' @param divider Divider function. The default divider function is mosaic() which will use spines in alternating directions. The four options for partioning: #' \itemize{ #' \item \code{vspine} Vertical spine partition: width constant, height varies. #' \item \code{hspine} Horizontal spine partition: height constant, width varies. #' \item \code{vbar} Vertical bar partition: height constant, width varies. #' \item \code{hbar} Horizontal bar partition: width constant, height varies. #' } #' @param offset Set the space between the first spine #' @param na.rm If \code{FALSE} (the default), removes missing values with a warning. If \code{TRUE} silently removes missing values. #' @param ... other arguments passed on to \code{layer}. These are often aesthetics, used to set an aesthetic to a fixed value, like \code{color = 'red'} or \code{size = 3}. They may also be parameters to the paired geom/stat. #' @examples #' #' data(Titanic) #' titanic <- as.data.frame(Titanic) #' titanic$Survived <- factor(titanic$Survived, levels=c("Yes", "No")) #' #' #' ggplot(data=titanic) + #' geom_mosaic(aes(weight=Freq, x=product(Class), fill=Survived)) #' # good practice: use the 'dependent' variable (or most important variable) #' # as fill variable #' ggplot(data=titanic) + #' geom_mosaic(aes(weight=Freq, x=product(Class, Age), fill=Survived)) #' #' # we can change where we define variables #' ggplot(data=titanic, aes(weight = Freq, fill=Survived, x=product(Class, Age))) + #' geom_mosaic() #' #' ggplot(data=titanic) + #' geom_mosaic(aes(weight=Freq, x=product(Class), conds=product(Age), fill=Survived)) #' ggplot(data=titanic) + #' geom_mosaic(aes(weight=Freq, x=product(Survived, Class), fill=Age)) #' #' \dontrun{ #' data(happy, package="productplots") #' #' ggplot(data = happy) + geom_mosaic(aes(x=product(happy))) #' ggplot(data = happy) + geom_mosaic(aes(x=product(happy))) + #' coord_flip() #' # weighting is important #' ggplot(data = happy) + #' geom_mosaic(aes(weight=wtssall, x=product(happy))) #' ggplot(data = happy) + geom_mosaic(aes(weight=wtssall, x=product(health), fill=happy)) + #' theme(axis.text.x=element_text(angle=35)) #' ggplot(data = happy) + #' geom_mosaic(aes(weight=wtssall, x=product(health), fill=happy), na.rm=TRUE) #' ggplot(data = happy) + #' geom_mosaic(aes(weight=wtssall, x=product(health, sex, degree), fill=happy), #' na.rm=TRUE) #' #' # here is where a bit more control over the spacing of the bars is helpful: #' # set labels manually: #' ggplot(data = happy) + #' geom_mosaic(aes(weight=wtssall, x=product(age), fill=happy), na.rm=TRUE, offset=0) + #' scale_x_product("Age", labels=c(17+1:72, "NA")) #' # thin out labels manually: #' labels <- c(17+1:72, NA) #' labels[labels %% 5 != 0] <- "" #' ggplot(data = happy) + #' geom_mosaic(aes(weight=wtssall, x=product(age), fill=happy), na.rm=TRUE, offset=0) + #' scale_x_product("Age", labels=labels) #' ggplot(data = happy) + #' geom_mosaic(aes(weight=wtssall, x=product(age), fill=happy, conds = sex), #' divider=mosaic("v"), na.rm=TRUE, offset=0.001) + #' scale_x_product("Age", labels=labels) #' # facetting works!!!! #' ggplot(data = happy) + #' geom_mosaic(aes(weight=wtssall, x=product(age), fill=happy), na.rm=TRUE, offset = 0) + #' facet_grid(sex~.) + #' scale_x_product("Age", labels=labels) #' #' ggplot(data = happy) + #' geom_mosaic(aes(weight = wtssall, x = product(happy, finrela, health)), #' divider=mosaic("h")) #' ggplot(data = happy) + #' geom_mosaic(aes(weight = wtssall, x = product(happy, finrela, health)), offset=.005) #' #' # Spine example #' ggplot(data = happy) + #' geom_mosaic(aes(weight = wtssall, x = product(health), fill = health)) + #' facet_grid(happy~.) #' } geom_mosaic <- function(mapping = NULL, data = NULL, stat = "mosaic", position = "identity", na.rm = FALSE, divider = mosaic(), offset = 0.01, show.legend = NA, inherit.aes = TRUE, ...) { ggplot2::layer( data = data, mapping = mapping, stat = stat, geom = GeomMosaic, position = position, show.legend = show.legend, inherit.aes = inherit.aes, params = list( na.rm = na.rm, divider = divider, offset = offset, ... ) ) } #' @importFrom grid grobTree GeomMosaic <- ggplot2::ggproto( "GeomMosaic", ggplot2::Geom, setup_data = function(data, params) { # cat("setup_data in GeomMosaic\n") # browser() data }, required_aes = c("xmin", "xmax", "ymin", "ymax"), default_aes = ggplot2::aes(width = 0.75, linetype = "solid", fontsize=5, shape = 19, colour = NA, size = .1, fill = "grey30", alpha = .8, stroke = 0.1, linewidth=.1, weight = 1, x = NULL, y = NULL, conds = NULL), draw_panel = function(data, panel_scales, coord) { # cat("draw_panel in GeomMosaic\n") # browser() if (all(is.na(data$colour))) data$colour <- alpha(data$fill, data$alpha) # regard alpha in colour determination GeomRect$draw_panel(subset(data, level==max(data$level)), panel_scales, coord) }, check_aesthetics = function(x, n) { ns <- vapply(x, length, numeric(1)) good <- ns == 1L | ns == n if (all(good)) { return() } # browser() stop( "Aesthetics must be either length 1 or the same as the data (", n, "): ", paste(names(!good), collapse = ", "), call. = FALSE ) }, draw_key = ggplot2::draw_key_rect )

/R/geom-mosaic.r

no_license

njtierney/ggmosaic

R

false

5,783

r

#' Mosaic plots. #' #' @export #' #' @description #' A mosaic plot is a convenient graphical summary of the conditional ditributions #' in a contingency table and is composed of spines in alternating directions. #' #' #' @inheritParams ggplot2::layer #' @param divider Divider function. The default divider function is mosaic() which will use spines in alternating directions. The four options for partioning: #' \itemize{ #' \item \code{vspine} Vertical spine partition: width constant, height varies. #' \item \code{hspine} Horizontal spine partition: height constant, width varies. #' \item \code{vbar} Vertical bar partition: height constant, width varies. #' \item \code{hbar} Horizontal bar partition: width constant, height varies. #' } #' @param offset Set the space between the first spine #' @param na.rm If \code{FALSE} (the default), removes missing values with a warning. If \code{TRUE} silently removes missing values. #' @param ... other arguments passed on to \code{layer}. These are often aesthetics, used to set an aesthetic to a fixed value, like \code{color = 'red'} or \code{size = 3}. They may also be parameters to the paired geom/stat. #' @examples #' #' data(Titanic) #' titanic <- as.data.frame(Titanic) #' titanic$Survived <- factor(titanic$Survived, levels=c("Yes", "No")) #' #' #' ggplot(data=titanic) + #' geom_mosaic(aes(weight=Freq, x=product(Class), fill=Survived)) #' # good practice: use the 'dependent' variable (or most important variable) #' # as fill variable #' ggplot(data=titanic) + #' geom_mosaic(aes(weight=Freq, x=product(Class, Age), fill=Survived)) #' #' # we can change where we define variables #' ggplot(data=titanic, aes(weight = Freq, fill=Survived, x=product(Class, Age))) + #' geom_mosaic() #' #' ggplot(data=titanic) + #' geom_mosaic(aes(weight=Freq, x=product(Class), conds=product(Age), fill=Survived)) #' ggplot(data=titanic) + #' geom_mosaic(aes(weight=Freq, x=product(Survived, Class), fill=Age)) #' #' \dontrun{ #' data(happy, package="productplots") #' #' ggplot(data = happy) + geom_mosaic(aes(x=product(happy))) #' ggplot(data = happy) + geom_mosaic(aes(x=product(happy))) + #' coord_flip() #' # weighting is important #' ggplot(data = happy) + #' geom_mosaic(aes(weight=wtssall, x=product(happy))) #' ggplot(data = happy) + geom_mosaic(aes(weight=wtssall, x=product(health), fill=happy)) + #' theme(axis.text.x=element_text(angle=35)) #' ggplot(data = happy) + #' geom_mosaic(aes(weight=wtssall, x=product(health), fill=happy), na.rm=TRUE) #' ggplot(data = happy) + #' geom_mosaic(aes(weight=wtssall, x=product(health, sex, degree), fill=happy), #' na.rm=TRUE) #' #' # here is where a bit more control over the spacing of the bars is helpful: #' # set labels manually: #' ggplot(data = happy) + #' geom_mosaic(aes(weight=wtssall, x=product(age), fill=happy), na.rm=TRUE, offset=0) + #' scale_x_product("Age", labels=c(17+1:72, "NA")) #' # thin out labels manually: #' labels <- c(17+1:72, NA) #' labels[labels %% 5 != 0] <- "" #' ggplot(data = happy) + #' geom_mosaic(aes(weight=wtssall, x=product(age), fill=happy), na.rm=TRUE, offset=0) + #' scale_x_product("Age", labels=labels) #' ggplot(data = happy) + #' geom_mosaic(aes(weight=wtssall, x=product(age), fill=happy, conds = sex), #' divider=mosaic("v"), na.rm=TRUE, offset=0.001) + #' scale_x_product("Age", labels=labels) #' # facetting works!!!! #' ggplot(data = happy) + #' geom_mosaic(aes(weight=wtssall, x=product(age), fill=happy), na.rm=TRUE, offset = 0) + #' facet_grid(sex~.) + #' scale_x_product("Age", labels=labels) #' #' ggplot(data = happy) + #' geom_mosaic(aes(weight = wtssall, x = product(happy, finrela, health)), #' divider=mosaic("h")) #' ggplot(data = happy) + #' geom_mosaic(aes(weight = wtssall, x = product(happy, finrela, health)), offset=.005) #' #' # Spine example #' ggplot(data = happy) + #' geom_mosaic(aes(weight = wtssall, x = product(health), fill = health)) + #' facet_grid(happy~.) #' } geom_mosaic <- function(mapping = NULL, data = NULL, stat = "mosaic", position = "identity", na.rm = FALSE, divider = mosaic(), offset = 0.01, show.legend = NA, inherit.aes = TRUE, ...) { ggplot2::layer( data = data, mapping = mapping, stat = stat, geom = GeomMosaic, position = position, show.legend = show.legend, inherit.aes = inherit.aes, params = list( na.rm = na.rm, divider = divider, offset = offset, ... ) ) } #' @importFrom grid grobTree GeomMosaic <- ggplot2::ggproto( "GeomMosaic", ggplot2::Geom, setup_data = function(data, params) { # cat("setup_data in GeomMosaic\n") # browser() data }, required_aes = c("xmin", "xmax", "ymin", "ymax"), default_aes = ggplot2::aes(width = 0.75, linetype = "solid", fontsize=5, shape = 19, colour = NA, size = .1, fill = "grey30", alpha = .8, stroke = 0.1, linewidth=.1, weight = 1, x = NULL, y = NULL, conds = NULL), draw_panel = function(data, panel_scales, coord) { # cat("draw_panel in GeomMosaic\n") # browser() if (all(is.na(data$colour))) data$colour <- alpha(data$fill, data$alpha) # regard alpha in colour determination GeomRect$draw_panel(subset(data, level==max(data$level)), panel_scales, coord) }, check_aesthetics = function(x, n) { ns <- vapply(x, length, numeric(1)) good <- ns == 1L | ns == n if (all(good)) { return() } # browser() stop( "Aesthetics must be either length 1 or the same as the data (", n, "): ", paste(names(!good), collapse = ", "), call. = FALSE ) }, draw_key = ggplot2::draw_key_rect )

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/train.R \name{train.rpart} \alias{train.rpart} \title{train.rpart} \usage{ train.rpart( formula, data, weights, subset, na.action = na.rpart, method, model = TRUE, x = FALSE, y = TRUE, parms, control, cost, ... ) } \arguments{ \item{formula}{a formula, with a response but no interaction terms. If this a a data frame, that is taken as the model frame.} \item{data}{an optional data frame in which to interpret the variables named in the formula.} \item{weights}{optional case weights.} \item{subset}{optional expression saying that only a subset of the rows of the data should be used in the fit.} \item{na.action}{the default action deletes all observations for which y is missing, but keeps those in which one or more predictors are missing.} \item{method}{one of "anova", "poisson", "class" or "exp". If method is missing then the routine tries to make an intelligent guess. If y is a survival object, then method = "exp" is assumed, if y has 2 columns then method = "poisson" is assumed, if y is a factor then method = "class" is assumed, otherwise method = "anova" is assumed. It is wisest to specify the method directly, especially as more criteria may added to the function in future. Alternatively, method can be a list of functions named init, split and eval. Examples are given in the file ‘tests/usersplits.R’ in the sources, and in the vignettes ‘User Written Split Functions’.} \item{model}{if logical: keep a copy of the model frame in the result? If the input value for model is a model frame (likely from an earlier call to the rpart function), then this frame is used rather than constructing new data.} \item{x}{keep a copy of the x matrix in the result.} \item{y}{keep a copy of the dependent variable in the result. If missing and model is supplied this defaults to FALSE.} \item{parms}{optional parameters for the splitting function. Anova splitting has no parameters. Poisson splitting has a single parameter, the coefficient of variation of the prior distribution on the rates. The default value is 1. Exponential splitting has the same parameter as Poisson. For classification splitting, the list can contain any of: the vector of prior probabilities (component prior), the loss matrix (component loss) or the splitting index (component split). The priors must be positive and sum to 1. The loss matrix must have zeros on the diagonal and positive off-diagonal elements. The splitting index can be gini or information. The default priors are proportional to the data counts, the losses default to 1, and the split defaults to gini.} \item{control}{a list of options that control details of the rpart algorithm. See \code{\link[rpart]{rpart.control}}.} \item{cost}{a vector of non-negative costs, one for each variable in the model. Defaults to one for all variables. These are scalings to be applied when considering splits, so the improvement on splitting on a variable is divided by its cost in deciding which split to choose.} \item{...}{arguments to \code{\link[rpart]{rpart.control}} may also be specified in the call to rpart. They are checked against the list of valid arguments.} } \value{ A object rpart.prmdt with additional information to the model that allows to homogenize the results. } \description{ Provides a wrapping function for the \code{\link[rpart]{rpart}}. } \note{ the parameter information was taken from the original function \code{\link[rpart]{rpart}}. } \examples{ # Classification data("iris") n <- seq_len(nrow(iris)) .sample <- sample(n, length(n) * 0.75) data.train <- iris[.sample,] data.test <- iris[-.sample,] modelo.rpart <- train.rpart(Species~., data.train) modelo.rpart prob <- predict(modelo.rpart, data.test, type = "prob") prob prediccion <- predict(modelo.rpart, data.test, type = "class") prediccion # Regression len <- nrow(swiss) sampl <- sample(x = 1:len,size = len*0.20,replace = FALSE) ttesting <- swiss[sampl,] ttraining <- swiss[-sampl,] model.rpart <- train.rpart(Infant.Mortality~.,ttraining) prediction <- predict(model.rpart,ttesting) prediction } \seealso{ The internal function is from package \code{\link[rpart]{rpart}}. }

/man/train.rpart.Rd

no_license

PROMiDAT/traineR

R

false

true

4,235

rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/train.R \name{train.rpart} \alias{train.rpart} \title{train.rpart} \usage{ train.rpart( formula, data, weights, subset, na.action = na.rpart, method, model = TRUE, x = FALSE, y = TRUE, parms, control, cost, ... ) } \arguments{ \item{formula}{a formula, with a response but no interaction terms. If this a a data frame, that is taken as the model frame.} \item{data}{an optional data frame in which to interpret the variables named in the formula.} \item{weights}{optional case weights.} \item{subset}{optional expression saying that only a subset of the rows of the data should be used in the fit.} \item{na.action}{the default action deletes all observations for which y is missing, but keeps those in which one or more predictors are missing.} \item{method}{one of "anova", "poisson", "class" or "exp". If method is missing then the routine tries to make an intelligent guess. If y is a survival object, then method = "exp" is assumed, if y has 2 columns then method = "poisson" is assumed, if y is a factor then method = "class" is assumed, otherwise method = "anova" is assumed. It is wisest to specify the method directly, especially as more criteria may added to the function in future. Alternatively, method can be a list of functions named init, split and eval. Examples are given in the file ‘tests/usersplits.R’ in the sources, and in the vignettes ‘User Written Split Functions’.} \item{model}{if logical: keep a copy of the model frame in the result? If the input value for model is a model frame (likely from an earlier call to the rpart function), then this frame is used rather than constructing new data.} \item{x}{keep a copy of the x matrix in the result.} \item{y}{keep a copy of the dependent variable in the result. If missing and model is supplied this defaults to FALSE.} \item{parms}{optional parameters for the splitting function. Anova splitting has no parameters. Poisson splitting has a single parameter, the coefficient of variation of the prior distribution on the rates. The default value is 1. Exponential splitting has the same parameter as Poisson. For classification splitting, the list can contain any of: the vector of prior probabilities (component prior), the loss matrix (component loss) or the splitting index (component split). The priors must be positive and sum to 1. The loss matrix must have zeros on the diagonal and positive off-diagonal elements. The splitting index can be gini or information. The default priors are proportional to the data counts, the losses default to 1, and the split defaults to gini.} \item{control}{a list of options that control details of the rpart algorithm. See \code{\link[rpart]{rpart.control}}.} \item{cost}{a vector of non-negative costs, one for each variable in the model. Defaults to one for all variables. These are scalings to be applied when considering splits, so the improvement on splitting on a variable is divided by its cost in deciding which split to choose.} \item{...}{arguments to \code{\link[rpart]{rpart.control}} may also be specified in the call to rpart. They are checked against the list of valid arguments.} } \value{ A object rpart.prmdt with additional information to the model that allows to homogenize the results. } \description{ Provides a wrapping function for the \code{\link[rpart]{rpart}}. } \note{ the parameter information was taken from the original function \code{\link[rpart]{rpart}}. } \examples{ # Classification data("iris") n <- seq_len(nrow(iris)) .sample <- sample(n, length(n) * 0.75) data.train <- iris[.sample,] data.test <- iris[-.sample,] modelo.rpart <- train.rpart(Species~., data.train) modelo.rpart prob <- predict(modelo.rpart, data.test, type = "prob") prob prediccion <- predict(modelo.rpart, data.test, type = "class") prediccion # Regression len <- nrow(swiss) sampl <- sample(x = 1:len,size = len*0.20,replace = FALSE) ttesting <- swiss[sampl,] ttraining <- swiss[-sampl,] model.rpart <- train.rpart(Infant.Mortality~.,ttraining) prediction <- predict(model.rpart,ttesting) prediction } \seealso{ The internal function is from package \code{\link[rpart]{rpart}}. }

library(tidyverse) source("data_cleaning.R") #plot master_covid_election %>% ggplot(aes(size = cases)) + geom_point(aes(x = state_win, y = winner, color = winner), position = position_jitter(width = .3, height = .5), alpha = 0.5) + scale_color_manual(values = c("blue", "red")) + scale_size(range = c(0.5, 10)) theme(axis.text.y = element_blank(), axis.title.y = element_text(), axis.ticks.y = element_blank())

/beeswarm_plot.R

no_license

erhs-r/group_5

R

false

466

r

library(tidyverse) source("data_cleaning.R") #plot master_covid_election %>% ggplot(aes(size = cases)) + geom_point(aes(x = state_win, y = winner, color = winner), position = position_jitter(width = .3, height = .5), alpha = 0.5) + scale_color_manual(values = c("blue", "red")) + scale_size(range = c(0.5, 10)) theme(axis.text.y = element_blank(), axis.title.y = element_text(), axis.ticks.y = element_blank())

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/corpus_sample.R \name{corpus_sample} \alias{corpus_sample} \title{randomly sample documents from a corpus} \usage{ corpus_sample(x, size = ndoc(x), replace = FALSE, prob = NULL, by = NULL, ...) } \arguments{ \item{x}{a corpus object whose documents will be sampled} \item{size}{a positive number, the number of documents to select} \item{replace}{Should sampling be with replacement?} \item{prob}{A vector of probability weights for obtaining the elements of the vector being sampled.} \item{by}{a grouping variable for sampling. Useful for resampling sub-document units such as sentences, for instance by specifying \code{by = "document"}} \item{...}{unused} } \value{ A corpus object with number of documents equal to \code{size}, drawn from the corpus \code{x}. The returned corpus object will contain all of the meta-data of the original corpus, and the same document variables for the documents selected. } \description{ Takes a random sample or documents or features of the specified size from a corpus or document-feature matrix, with or without replacement. Works just as \code{\link{sample}} works for the documents and their associated document-level variables. } \examples{ # sampling from a corpus summary(corpus_sample(data_corpus_inaugural, 5)) summary(corpus_sample(data_corpus_inaugural, 10, replace=TRUE)) # sampling sentences within document doccorpus <- corpus(c(one = "Sentence one. Sentence two. Third sentence.", two = "First sentence, doc2. Second sentence, doc2.")) sentcorpus <- corpus_reshape(doccorpus, to = "sentences") texts(sentcorpus) texts(corpus_sample(sentcorpus, replace = TRUE, by = "document")) } \keyword{corpus}

/man/corpus_sample.Rd

no_license

leeper/quanteda

R

false

true

1,778

rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/corpus_sample.R \name{corpus_sample} \alias{corpus_sample} \title{randomly sample documents from a corpus} \usage{ corpus_sample(x, size = ndoc(x), replace = FALSE, prob = NULL, by = NULL, ...) } \arguments{ \item{x}{a corpus object whose documents will be sampled} \item{size}{a positive number, the number of documents to select} \item{replace}{Should sampling be with replacement?} \item{prob}{A vector of probability weights for obtaining the elements of the vector being sampled.} \item{by}{a grouping variable for sampling. Useful for resampling sub-document units such as sentences, for instance by specifying \code{by = "document"}} \item{...}{unused} } \value{ A corpus object with number of documents equal to \code{size}, drawn from the corpus \code{x}. The returned corpus object will contain all of the meta-data of the original corpus, and the same document variables for the documents selected. } \description{ Takes a random sample or documents or features of the specified size from a corpus or document-feature matrix, with or without replacement. Works just as \code{\link{sample}} works for the documents and their associated document-level variables. } \examples{ # sampling from a corpus summary(corpus_sample(data_corpus_inaugural, 5)) summary(corpus_sample(data_corpus_inaugural, 10, replace=TRUE)) # sampling sentences within document doccorpus <- corpus(c(one = "Sentence one. Sentence two. Third sentence.", two = "First sentence, doc2. Second sentence, doc2.")) sentcorpus <- corpus_reshape(doccorpus, to = "sentences") texts(sentcorpus) texts(corpus_sample(sentcorpus, replace = TRUE, by = "document")) } \keyword{corpus}

#' Generates a Resolvable Row-Column Design (RowColD) #' #' #' @description It randomly generates a resolvable row-column designs (RowColD). #' Note that design optimization is only done at the level of rows and not columns; #' hence, design is suboptimal. The randomization can be done across locations. #' #' @param t Number of treatments. #' @param nrows Number of rows of a full resolvable replicate. #' @param r Number of blocks (full resolvable replicates). #' @param l Number of locations. By default \code{l = 1}. #' @param plotNumber Numeric vector with the starting plot number for each location. By default \code{plotNumber = 101}. #' @param seed (optional) Real number that specifies the starting seed to obtain reproducible designs. #' @param locationNames (optional) Names for each location. #' @param data (optional) Data frame with label list of treatments #' #' @author Didier Murillo [aut], #' Salvador Gezan [aut], #' Ana Heilman [ctb], #' Thomas Walk [ctb], #' Johan Aparicio [ctb], #' Richard Horsley [ctb] #' #' #' @importFrom stats runif na.omit #' #' @return A list with four elements. #' \itemize{ #' \item \code{infoDesign} is a list with information on the design parameters. #' \item \code{resolvableBlocks} a list with the resolvable row columns blocks. #' \item \code{concurrence} is the concurrence matrix. #' \item \code{fieldBook} is a data frame with the row-column field book. #' } #' #' #' @references #' Edmondson., R. N. (2021). blocksdesign: Nested and crossed block designs for factorial and #' unstructured treatment sets. https://CRAN.R-project.org/package=blocksdesign #' #' @examples #' #' # Example 1: Generates a row-column design with 3 full blocks and 36 treatments #' # and 6 rows. This for one location. #' rowcold1 <- row_column(t = 36, nrows = 6, r = 3, l = 1, #' plotNumber= 101, #' locationNames = "Loc1", #' seed = 21) #' rowcold1$infoDesign #' rowcold1$resolvableBlocks #' head(rowcold1$fieldBook,12) #' #' # Example 2: Generates a row-column design with 3 full blocks and 30 treatments #' # and 5 rows, for one location. #' # In this case, we show how to use the option data. #' treatments <- paste("ND-", 1:30, sep = "") #' ENTRY <- 1:30 #' treatment_list <- data.frame(list(ENTRY = ENTRY, TREATMENT = treatments)) #' head(treatment_list) #' rowcold2 <- row_column(t = 30, nrows = 5, r = 3, l = 1, #' plotNumber= c(101,1001), #' locationNames = c("A", "B"), #' seed = 15, #' data = treatment_list) #' rowcold2$infoDesign #' rowcold2$resolvableBlocks #' head(rowcold2$fieldBook,12) #' #' #' @export row_column <- function(t = NULL, nrows = NULL, r = NULL, l = 1, plotNumber= 101, locationNames = NULL, seed = NULL, data = NULL) { if (is.null(seed) || !is.numeric(seed)) seed <- runif(1, min = -50000, max = 50000) set.seed(seed) k <- nrows lookup <- FALSE if(is.null(data)) { if (is.null(t) || is.null(k) || is.null(r) || is.null(l)) { shiny::validate('Some of the basic design parameters are missing (t, k, r or l).') } arg1 <- list(k, r, l);arg2 <- c(k, r, l) if (base::any(lengths(arg1) != 1) || base::any(arg2 %% 1 != 0) || base::any(arg2 < 1)) { shiny::validate('row_column() requires k, r and l to be possitive integers.') } if (is.numeric(t)) { if (length(t) == 1) { if (t == 1 || t < 1) { shiny::validate('row_column() requires more than one treatment.') } nt <- t }else if ((length(t) > 1)) { nt <- length(t) TRT <- t } }else if (is.character(t) || is.factor(t)) { if (length(t) == 1) { shiny::validate('incomplete_blocks() requires more than one treatment.') } nt <- length(t) }else if ((length(t) > 1)) { nt <- length(t) } df <- data.frame(list(ENTRY = 1:nt, TREATMENT = paste0("G-", 1:nt))) data_RowCol <- df lookup <- TRUE }else if (!is.null(data)) { if (is.null(t) || is.null(r) || is.null(k) || is.null(l)) { shiny::validate('Some of the basic design parameters are missing (t, r, k or l).') } if(!is.data.frame(data)) shiny::validate("Data must be a data frame.") data_up <- as.data.frame(data[,c(1,2)]) data_up <- na.omit(data_up) colnames(data_up) <- c("ENTRY", "TREATMENT") data_up$TREATMENT <- as.character(data_up$TREATMENT) new_t <- length(data_up$TREATMENT) if (t != new_t) base::stop("Number of treatments do not match with data input.") TRT <- data_up$TREATMENT nt <- length(TRT) data_RowCol <- data_up lookup <- TRUE } if (k >= nt) shiny::validate('incomplete_blocks() requires k < t.') if(is.null(locationNames) || length(locationNames) != l) locationNames <- 1:l nunits <- k matdf <- incomplete_blocks(t = nt, k = nunits, r = r, l = l, plotNumber = plotNumber, seed = seed, locationNames = locationNames, data = data_RowCol) matdf <- matdf$fieldBook matdf <- as.data.frame(matdf) colnames(matdf)[5] <- "COLUMN" matdf$ROW <- matdf$UNIT OutRowCol <- matdf[,-6] OutRowCol$LOCATION <- factor(OutRowCol$LOCATION, levels = locationNames) OutRowCol <- OutRowCol[order(OutRowCol$LOCATION, OutRowCol$REP, OutRowCol$ROW),] RowCol_plots <- ibd_plot_numbers(nt = nt, plot.number = plotNumber, r = r, l = l) OutRowCol$PLOT <- as.vector(unlist(RowCol_plots)) if(lookup) { OutRowCol <- OutRowCol[,c(2,3,4,8,5,6,7)] }else OutRowCol <- OutRowCol[,c(2,3,4,7,5,6)] ID <- 1:nrow(OutRowCol) OutRowCol <- cbind(ID, OutRowCol) rownames(OutRowCol) <- 1:nrow(OutRowCol) loc <- levels(OutRowCol$LOCATION) ib <- nt/k Resolvable_rc_reps <- vector(mode = "list", length = r*l) w <- 1 for (sites in 1:l) { for (j in 1:r) { z <- OutRowCol z <- subset(z, z$LOCATION == loc[sites] & z$REP == j) if (is.null(data)){ Resolvable_rc_reps[[w]] <- matrix(data = as.vector(z$ENTRY), nrow = nunits, ncol = ib, byrow = TRUE) }else { Resolvable_rc_reps[[w]] <- matrix(data = as.vector(z$TREATMENT), nrow = nunits, ncol = ib, byrow = TRUE) } w <- w + 1 } } NEW_Resolvable <- setNames(vector(mode = "list", length = l), paste0("Loc_", locationNames)) x <- seq(1, r * l, r) y <- seq(r, r * l, r) z <- 1 for (loc in 1:l) { NEW_Resolvable[[loc]] <- setNames(Resolvable_rc_reps[x[z]:y[z]], paste0(rep("rep", r), 1:r)) z <- z + 1 } df <- OutRowCol trt <- "ENTRY" c1 <- concurrence_matrix(df=df, trt=trt, target='REP') c2 <- concurrence_matrix (df=df, trt=trt, target='ROW') c3 <- concurrence_matrix (df=df, trt=trt, target='COLUMN') summ <- merge(c1, c2, by="Concurrence", all=TRUE) new_summ <- merge(summ, c3, by='Concurrence', all=TRUE) infoDesign <- list( rows = nrows, columns = ib, reps = r, treatments = nt, locations = l, location_names = locationNames, seed = seed, id_design = 9 ) output <- list(infoDesign = infoDesign, resolvableBlocks = NEW_Resolvable, concurrence = new_summ, fieldBook = OutRowCol) class(output) <- "FielDHub" return(invisible(output)) }

/R/fct_row_column.R

permissive

DidierMurilloF/FielDHub

R

false

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r

#' Generates a Resolvable Row-Column Design (RowColD) #' #' #' @description It randomly generates a resolvable row-column designs (RowColD). #' Note that design optimization is only done at the level of rows and not columns; #' hence, design is suboptimal. The randomization can be done across locations. #' #' @param t Number of treatments. #' @param nrows Number of rows of a full resolvable replicate. #' @param r Number of blocks (full resolvable replicates). #' @param l Number of locations. By default \code{l = 1}. #' @param plotNumber Numeric vector with the starting plot number for each location. By default \code{plotNumber = 101}. #' @param seed (optional) Real number that specifies the starting seed to obtain reproducible designs. #' @param locationNames (optional) Names for each location. #' @param data (optional) Data frame with label list of treatments #' #' @author Didier Murillo [aut], #' Salvador Gezan [aut], #' Ana Heilman [ctb], #' Thomas Walk [ctb], #' Johan Aparicio [ctb], #' Richard Horsley [ctb] #' #' #' @importFrom stats runif na.omit #' #' @return A list with four elements. #' \itemize{ #' \item \code{infoDesign} is a list with information on the design parameters. #' \item \code{resolvableBlocks} a list with the resolvable row columns blocks. #' \item \code{concurrence} is the concurrence matrix. #' \item \code{fieldBook} is a data frame with the row-column field book. #' } #' #' #' @references #' Edmondson., R. N. (2021). blocksdesign: Nested and crossed block designs for factorial and #' unstructured treatment sets. https://CRAN.R-project.org/package=blocksdesign #' #' @examples #' #' # Example 1: Generates a row-column design with 3 full blocks and 36 treatments #' # and 6 rows. This for one location. #' rowcold1 <- row_column(t = 36, nrows = 6, r = 3, l = 1, #' plotNumber= 101, #' locationNames = "Loc1", #' seed = 21) #' rowcold1$infoDesign #' rowcold1$resolvableBlocks #' head(rowcold1$fieldBook,12) #' #' # Example 2: Generates a row-column design with 3 full blocks and 30 treatments #' # and 5 rows, for one location. #' # In this case, we show how to use the option data. #' treatments <- paste("ND-", 1:30, sep = "") #' ENTRY <- 1:30 #' treatment_list <- data.frame(list(ENTRY = ENTRY, TREATMENT = treatments)) #' head(treatment_list) #' rowcold2 <- row_column(t = 30, nrows = 5, r = 3, l = 1, #' plotNumber= c(101,1001), #' locationNames = c("A", "B"), #' seed = 15, #' data = treatment_list) #' rowcold2$infoDesign #' rowcold2$resolvableBlocks #' head(rowcold2$fieldBook,12) #' #' #' @export row_column <- function(t = NULL, nrows = NULL, r = NULL, l = 1, plotNumber= 101, locationNames = NULL, seed = NULL, data = NULL) { if (is.null(seed) || !is.numeric(seed)) seed <- runif(1, min = -50000, max = 50000) set.seed(seed) k <- nrows lookup <- FALSE if(is.null(data)) { if (is.null(t) || is.null(k) || is.null(r) || is.null(l)) { shiny::validate('Some of the basic design parameters are missing (t, k, r or l).') } arg1 <- list(k, r, l);arg2 <- c(k, r, l) if (base::any(lengths(arg1) != 1) || base::any(arg2 %% 1 != 0) || base::any(arg2 < 1)) { shiny::validate('row_column() requires k, r and l to be possitive integers.') } if (is.numeric(t)) { if (length(t) == 1) { if (t == 1 || t < 1) { shiny::validate('row_column() requires more than one treatment.') } nt <- t }else if ((length(t) > 1)) { nt <- length(t) TRT <- t } }else if (is.character(t) || is.factor(t)) { if (length(t) == 1) { shiny::validate('incomplete_blocks() requires more than one treatment.') } nt <- length(t) }else if ((length(t) > 1)) { nt <- length(t) } df <- data.frame(list(ENTRY = 1:nt, TREATMENT = paste0("G-", 1:nt))) data_RowCol <- df lookup <- TRUE }else if (!is.null(data)) { if (is.null(t) || is.null(r) || is.null(k) || is.null(l)) { shiny::validate('Some of the basic design parameters are missing (t, r, k or l).') } if(!is.data.frame(data)) shiny::validate("Data must be a data frame.") data_up <- as.data.frame(data[,c(1,2)]) data_up <- na.omit(data_up) colnames(data_up) <- c("ENTRY", "TREATMENT") data_up$TREATMENT <- as.character(data_up$TREATMENT) new_t <- length(data_up$TREATMENT) if (t != new_t) base::stop("Number of treatments do not match with data input.") TRT <- data_up$TREATMENT nt <- length(TRT) data_RowCol <- data_up lookup <- TRUE } if (k >= nt) shiny::validate('incomplete_blocks() requires k < t.') if(is.null(locationNames) || length(locationNames) != l) locationNames <- 1:l nunits <- k matdf <- incomplete_blocks(t = nt, k = nunits, r = r, l = l, plotNumber = plotNumber, seed = seed, locationNames = locationNames, data = data_RowCol) matdf <- matdf$fieldBook matdf <- as.data.frame(matdf) colnames(matdf)[5] <- "COLUMN" matdf$ROW <- matdf$UNIT OutRowCol <- matdf[,-6] OutRowCol$LOCATION <- factor(OutRowCol$LOCATION, levels = locationNames) OutRowCol <- OutRowCol[order(OutRowCol$LOCATION, OutRowCol$REP, OutRowCol$ROW),] RowCol_plots <- ibd_plot_numbers(nt = nt, plot.number = plotNumber, r = r, l = l) OutRowCol$PLOT <- as.vector(unlist(RowCol_plots)) if(lookup) { OutRowCol <- OutRowCol[,c(2,3,4,8,5,6,7)] }else OutRowCol <- OutRowCol[,c(2,3,4,7,5,6)] ID <- 1:nrow(OutRowCol) OutRowCol <- cbind(ID, OutRowCol) rownames(OutRowCol) <- 1:nrow(OutRowCol) loc <- levels(OutRowCol$LOCATION) ib <- nt/k Resolvable_rc_reps <- vector(mode = "list", length = r*l) w <- 1 for (sites in 1:l) { for (j in 1:r) { z <- OutRowCol z <- subset(z, z$LOCATION == loc[sites] & z$REP == j) if (is.null(data)){ Resolvable_rc_reps[[w]] <- matrix(data = as.vector(z$ENTRY), nrow = nunits, ncol = ib, byrow = TRUE) }else { Resolvable_rc_reps[[w]] <- matrix(data = as.vector(z$TREATMENT), nrow = nunits, ncol = ib, byrow = TRUE) } w <- w + 1 } } NEW_Resolvable <- setNames(vector(mode = "list", length = l), paste0("Loc_", locationNames)) x <- seq(1, r * l, r) y <- seq(r, r * l, r) z <- 1 for (loc in 1:l) { NEW_Resolvable[[loc]] <- setNames(Resolvable_rc_reps[x[z]:y[z]], paste0(rep("rep", r), 1:r)) z <- z + 1 } df <- OutRowCol trt <- "ENTRY" c1 <- concurrence_matrix(df=df, trt=trt, target='REP') c2 <- concurrence_matrix (df=df, trt=trt, target='ROW') c3 <- concurrence_matrix (df=df, trt=trt, target='COLUMN') summ <- merge(c1, c2, by="Concurrence", all=TRUE) new_summ <- merge(summ, c3, by='Concurrence', all=TRUE) infoDesign <- list( rows = nrows, columns = ib, reps = r, treatments = nt, locations = l, location_names = locationNames, seed = seed, id_design = 9 ) output <- list(infoDesign = infoDesign, resolvableBlocks = NEW_Resolvable, concurrence = new_summ, fieldBook = OutRowCol) class(output) <- "FielDHub" return(invisible(output)) }

# Jignesh H. Parmar and Pedro Mendes, Plos Comp Biol in 2019 # Fixed Model Entities: #metabolite 'FeOutside': fixed FeOutsi=0 #compartment 'Duodenum':fixed Duodenu=3.84615e-05 #compartment 'RBC':fixed RBC=0.00079 #compartment 'Spleen':fixed Spleen=6.73077e-05 #compartment 'Liver':fixed Liver=0.0011619 #compartment 'Plasma':fixed Plasma=0.0013 #compartment 'RestOfBody':fixed RestOfB=0.0196948 #compartment 'BoneMarrow':fixed BoneMar=0.000214286 #global quantity 'kNTBI_Fe1Tf':fixed kNTBI_F=1.004e+09 #global quantity 'kInDuo':fixed kInDuo=0.0405971 #global quantity 'kInLiver':fixed kInLive=2.11666 #global quantity 'kInRBC':fixed kInRBC=5.03844e+11 #global quantity 'kInRest':fixed kInRest=4.78121 #global quantity 'KmFeFPN':fixed KmFeFPN=0.112511 #global quantity 'KiHepcidinFPN':fixed KiHepci=6.3e-09 #global quantity 'kFe1Tf_Fe2Tf':fixed kFe1Tf_=1.004e+09 #global quantity 'vDuoNTBI':fixed vDuoNTB=0.200907 #global quantity 'vLiverNTBI':fixed vLiverN=0.0444795 #global quantity 'vSpleenNTBI':fixed vSpleen=2.06738 #global quantity 'vRestNTBI':fixed vRestNT=0.0101453 #global quantity 'kDuoLoss':fixed kDuoLos=6.80738e-05 #global quantity 'kRBCSpleen':fixed kRBCSpl=0.03 #global quantity 'kInBM':fixed kInBM=4.06878e+12 #global quantity 'kBMSpleen':fixed kBMSple=0.103218 #global quantity 'vDiet_basal':fixed vDiet_b=0.00346965 #global quantity 'KaNTBI':fixed KaNTBI=0.000255016 #global quantity 'KmNTBI':fixed KmNTBI=0.000679291 #global quantity 'vNTBILiver':fixed vNTBILi=14.1511 #global quantity 'fDiet':fixed fDiet=1 #global quantity 'ksHepcidin':fixed ksHepci=0.000398766 #global quantity 'vEPO':fixed vEPO=2.62675e-09 #global quantity 'kdEPO':fixed kdEPO=4.8 #global quantity 'hEPO':fixed hEPO=6.5 #global quantity 'KiEPORBC':fixed KiEPORB=0.01 #global quantity 'KEPOHepcidin':fixed KEPOHep=5e-12 #global quantity 'hEPOHepcidin':fixed hEPOHep=4 #global quantity 'hNTBI':fixed hNTBI=1 #global quantity 'kRestLoss':fixed kRestLo=0.016862 #global quantity 'vTf':fixed vTf=1.548e-05 #global quantity 'kdTf':fixed kdTf=0.4 #global quantity 'kdHepcidin':fixed kdHepci=0.75616 (pars=dput(ls())) names(pars)=pars dput(pars) parmarPars19=c(BoneMar = BoneMar, Duodenu = Duodenu, fDiet = fDiet, FeOutsi = FeOutsi, hEPO = hEPO, hEPOHep = hEPOHep, hNTBI = hNTBI, KaNTBI = KaNTBI, kBMSple = kBMSple, kdEPO = kdEPO, kdHepci = kdHepci, kdTf = kdTf, kDuoLos = kDuoLos, KEPOHep = KEPOHep, kFe1Tf_ = kFe1Tf_, KiEPORB = KiEPORB, KiHepci = KiHepci, kInBM = kInBM, kInDuo = kInDuo, kInLive = kInLive, kInRBC = kInRBC, kInRest = kInRest, KmFeFPN = KmFeFPN, KmNTBI = KmNTBI, kNTBI_F = kNTBI_F, kRBCSpl = kRBCSpl, kRestLo = kRestLo, ksHepci = ksHepci, Liver = Liver, Plasma = Plasma, RBC = RBC, RestOfB = RestOfB, Spleen = Spleen, vDiet_b = vDiet_b, vDuoNTB = vDuoNTB, vEPO = vEPO, vLiverN = vLiverN, vNTBILi = vNTBILi, vRestNT = vRestNT, vSpleen = vSpleen, vTf = vTf) # parmarPars=c(FeOutsi=0, Duodenu=3.84615e-05, RBC=0.00079, Spleen=6.73077e-05, Liver=0.0011619, # Plasma=0.0013, RestOfB=0.0196948, BoneMar=0.000214286, # # organ volumes in Liters (Table 1) mouse= ~25gm = ~.023L # kNTBI_F=1.08432e+09, #kNTBI_Fe1Tf # kInDuo=0.0689984, kInLive=2.97791, kInRBC=1.08448, kInRest=6.16356, # Km=0.0159421, Ki=1e-09, kFe1Tf_=1.08432e+09, #kFe1Tf_Fe2Tf # VmaxDuo=0.200242, VmaxLiv=0.0261148, VmaxSpl=1.3422, VmaxRest=0.0109451, # all 4 NTBI, in Moles/Day # kDuoLos=0.0270113, #kDuoLoss # vRBCSpl=0.0235286, kRestLo=0.0235332, kInBM=15.7691, kBMSple=0.061903, # vDiet=0.00377422,vDietH=0.00415624, vDietL=0, #Hi vs. low Fe diet # quantit=6.02214e+23, # Avogadr=6.02214e+23, # #HepcidinSynthesis Hi vs. Low Fe diet (adaptation?) # vHepSyn=1.7393e-08, vHepSynH=2.30942e-08, vHepSynL=8.54927e-09, # k1=0.75616 #reaction 'HepcidinDecay': kinetic ,eter 'k1' # ) # sum(parmarPars[1:8]) #23.2 gm mouse save(parmarPars19,file="parmarPars19.rda")

/myelo/inst/doc/parmarPars19.R

no_license

radivot/myelo

R

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4,050

r

# Jignesh H. Parmar and Pedro Mendes, Plos Comp Biol in 2019 # Fixed Model Entities: #metabolite 'FeOutside': fixed FeOutsi=0 #compartment 'Duodenum':fixed Duodenu=3.84615e-05 #compartment 'RBC':fixed RBC=0.00079 #compartment 'Spleen':fixed Spleen=6.73077e-05 #compartment 'Liver':fixed Liver=0.0011619 #compartment 'Plasma':fixed Plasma=0.0013 #compartment 'RestOfBody':fixed RestOfB=0.0196948 #compartment 'BoneMarrow':fixed BoneMar=0.000214286 #global quantity 'kNTBI_Fe1Tf':fixed kNTBI_F=1.004e+09 #global quantity 'kInDuo':fixed kInDuo=0.0405971 #global quantity 'kInLiver':fixed kInLive=2.11666 #global quantity 'kInRBC':fixed kInRBC=5.03844e+11 #global quantity 'kInRest':fixed kInRest=4.78121 #global quantity 'KmFeFPN':fixed KmFeFPN=0.112511 #global quantity 'KiHepcidinFPN':fixed KiHepci=6.3e-09 #global quantity 'kFe1Tf_Fe2Tf':fixed kFe1Tf_=1.004e+09 #global quantity 'vDuoNTBI':fixed vDuoNTB=0.200907 #global quantity 'vLiverNTBI':fixed vLiverN=0.0444795 #global quantity 'vSpleenNTBI':fixed vSpleen=2.06738 #global quantity 'vRestNTBI':fixed vRestNT=0.0101453 #global quantity 'kDuoLoss':fixed kDuoLos=6.80738e-05 #global quantity 'kRBCSpleen':fixed kRBCSpl=0.03 #global quantity 'kInBM':fixed kInBM=4.06878e+12 #global quantity 'kBMSpleen':fixed kBMSple=0.103218 #global quantity 'vDiet_basal':fixed vDiet_b=0.00346965 #global quantity 'KaNTBI':fixed KaNTBI=0.000255016 #global quantity 'KmNTBI':fixed KmNTBI=0.000679291 #global quantity 'vNTBILiver':fixed vNTBILi=14.1511 #global quantity 'fDiet':fixed fDiet=1 #global quantity 'ksHepcidin':fixed ksHepci=0.000398766 #global quantity 'vEPO':fixed vEPO=2.62675e-09 #global quantity 'kdEPO':fixed kdEPO=4.8 #global quantity 'hEPO':fixed hEPO=6.5 #global quantity 'KiEPORBC':fixed KiEPORB=0.01 #global quantity 'KEPOHepcidin':fixed KEPOHep=5e-12 #global quantity 'hEPOHepcidin':fixed hEPOHep=4 #global quantity 'hNTBI':fixed hNTBI=1 #global quantity 'kRestLoss':fixed kRestLo=0.016862 #global quantity 'vTf':fixed vTf=1.548e-05 #global quantity 'kdTf':fixed kdTf=0.4 #global quantity 'kdHepcidin':fixed kdHepci=0.75616 (pars=dput(ls())) names(pars)=pars dput(pars) parmarPars19=c(BoneMar = BoneMar, Duodenu = Duodenu, fDiet = fDiet, FeOutsi = FeOutsi, hEPO = hEPO, hEPOHep = hEPOHep, hNTBI = hNTBI, KaNTBI = KaNTBI, kBMSple = kBMSple, kdEPO = kdEPO, kdHepci = kdHepci, kdTf = kdTf, kDuoLos = kDuoLos, KEPOHep = KEPOHep, kFe1Tf_ = kFe1Tf_, KiEPORB = KiEPORB, KiHepci = KiHepci, kInBM = kInBM, kInDuo = kInDuo, kInLive = kInLive, kInRBC = kInRBC, kInRest = kInRest, KmFeFPN = KmFeFPN, KmNTBI = KmNTBI, kNTBI_F = kNTBI_F, kRBCSpl = kRBCSpl, kRestLo = kRestLo, ksHepci = ksHepci, Liver = Liver, Plasma = Plasma, RBC = RBC, RestOfB = RestOfB, Spleen = Spleen, vDiet_b = vDiet_b, vDuoNTB = vDuoNTB, vEPO = vEPO, vLiverN = vLiverN, vNTBILi = vNTBILi, vRestNT = vRestNT, vSpleen = vSpleen, vTf = vTf) # parmarPars=c(FeOutsi=0, Duodenu=3.84615e-05, RBC=0.00079, Spleen=6.73077e-05, Liver=0.0011619, # Plasma=0.0013, RestOfB=0.0196948, BoneMar=0.000214286, # # organ volumes in Liters (Table 1) mouse= ~25gm = ~.023L # kNTBI_F=1.08432e+09, #kNTBI_Fe1Tf # kInDuo=0.0689984, kInLive=2.97791, kInRBC=1.08448, kInRest=6.16356, # Km=0.0159421, Ki=1e-09, kFe1Tf_=1.08432e+09, #kFe1Tf_Fe2Tf # VmaxDuo=0.200242, VmaxLiv=0.0261148, VmaxSpl=1.3422, VmaxRest=0.0109451, # all 4 NTBI, in Moles/Day # kDuoLos=0.0270113, #kDuoLoss # vRBCSpl=0.0235286, kRestLo=0.0235332, kInBM=15.7691, kBMSple=0.061903, # vDiet=0.00377422,vDietH=0.00415624, vDietL=0, #Hi vs. low Fe diet # quantit=6.02214e+23, # Avogadr=6.02214e+23, # #HepcidinSynthesis Hi vs. Low Fe diet (adaptation?) # vHepSyn=1.7393e-08, vHepSynH=2.30942e-08, vHepSynL=8.54927e-09, # k1=0.75616 #reaction 'HepcidinDecay': kinetic ,eter 'k1' # ) # sum(parmarPars[1:8]) #23.2 gm mouse save(parmarPars19,file="parmarPars19.rda")

#Create a matrix object that can cache its inverse makeCacheMatrix <- function(Mtrx = matrix()) { #initialize the matrix Object mtrxInverse <- NULL #set matrix set <- function(y) { Mtrx <<- y mtrxInverse <<- NULL } #get matrix get <- function() Mtrx # set the inverse setInverse <- function(inverse) mtrxInverse <<- inverse # get function to return the cached inverse getInverse <- function() mtrxInverse #list the available function in makeCacheMatrix list(set = set, get = get, setInverse = setInverse, getInverse = getInverse) } ## Returns the inverse of matrix. #If the inverse has already been calculated then the cachesolve will retrieve and return the inverse from cache cacheSolve <- function(x, ...) { ## Return a matrix that is the inverse of 'x' # check if the inverse of the matrix exists in cache invMtrx <- x$getInverse() # if inverse of the matrix exists, then get the matrix inverse from cache and return the inverse if(!is.null(invMtrx)) { # Return the inverse from cache message("getting cached data") return(invMtrx) } #if the cache is empty , then proceed here #x$get() gets the matrix data <- x$get() # function solve returns the inverse of the matrix invMtrx <- solve(data) # set the inverse in cache x$setInverse(invMtrx) # return the inverse invMtrx }

/cachematrix.R

no_license

vinothinij/RProgrammingAssignment2

R

false

1,570

r

#Create a matrix object that can cache its inverse makeCacheMatrix <- function(Mtrx = matrix()) { #initialize the matrix Object mtrxInverse <- NULL #set matrix set <- function(y) { Mtrx <<- y mtrxInverse <<- NULL } #get matrix get <- function() Mtrx # set the inverse setInverse <- function(inverse) mtrxInverse <<- inverse # get function to return the cached inverse getInverse <- function() mtrxInverse #list the available function in makeCacheMatrix list(set = set, get = get, setInverse = setInverse, getInverse = getInverse) } ## Returns the inverse of matrix. #If the inverse has already been calculated then the cachesolve will retrieve and return the inverse from cache cacheSolve <- function(x, ...) { ## Return a matrix that is the inverse of 'x' # check if the inverse of the matrix exists in cache invMtrx <- x$getInverse() # if inverse of the matrix exists, then get the matrix inverse from cache and return the inverse if(!is.null(invMtrx)) { # Return the inverse from cache message("getting cached data") return(invMtrx) } #if the cache is empty , then proceed here #x$get() gets the matrix data <- x$get() # function solve returns the inverse of the matrix invMtrx <- solve(data) # set the inverse in cache x$setInverse(invMtrx) # return the inverse invMtrx }

#PCV was launched by the Union Health Minister, Shri JP Nadda on May 13, 2017 at Mandi, #Himachal Pradesh [1]. With this phased introduction, nearly 2.1 million children in #Himachal Pradesh (all 12 districts), parts of Bihar (17 out of 38 districts) #and Uttar Pradesh (6 out of 75 districts) will be vaccinated with PCV in the first year [2]. #This will be followed by introduction in Madhya Pradesh and Rajasthan next year, and eventually coverage will be expanded across the entire country in a phased manner, in the coming years. " #In uttar Pradesh it is: Lakhimpur Kheri, Sitapur, Siddharth Nagar, Bahraich, Balrampur, Shrawasti; #In Bihar: The 17 high-priority districts are Araria, Begusarai, Darbhanga, Kishanganj, Khagaria, Katihar, #Muzaffarpur, Munger, Vaishali, Madhepura, Madhubani, Purnea, Samastipur, Saran, Sitamarhi, Sheohar and Supaul #bh1<-read.csv('C:/Users/dmw63/Desktop/My documents h/GATES/india/hmis/Bihar series by district.csv') ####### #Lorine ##ACUTE RESPIRATORY INFECTION REQUIRING ADMISSION?? ##85% of DEATH OCCUR AT HOME--SHOULD BE IN HMIS; MANY ##IN PRIVATE SECTOR HOSPITALS ##CONTROLS: DIARRHEA deaths? ROTA VACCINE IS ROLLING OUT.. ##ACUTE ENCAPHALITIS IN SOUTH UP AND BIHAR. THERE ARE SOME ##VACCINE PROGRAMS BUT STARTED 5 YEARS AGO...NOT REALLY #IN WESTERN UP #ASPHYXIA DEATHS WONT BE SAME HOSPITALS -- #WOULD BE IN WOMENS HOSPITAL...MANY ARE SEPARATE #WOMENS/MENS HOSPTALS #UP HMIS--DR VISANT (Sp?) ####### library(lme4) library(lubridate) bh1<-read.csv('C:/Users/dmw63/Desktop/My documents h/GATES/india/hmis/hmis/uttarPradesh series to Mar 2019.csv') str(bh1) bh1$month<-as.numeric(substr(bh1$DATE,6,7)) bh1$year<-as.numeric(substr(bh1$DATE,2,5)) bh1$uri<-bh1$X_10_13 bh1$pneu_death<-bh1$X_16_3_1 bh1$diar_death<-bh1$X_16_3_2 bh1$measles_death<-bh1$X_16_3_4 bh1$asphyxia_death<-bh1$X_16_2_2 bh1$sepsis_death<-bh1$X_16_2_1 bh1$neonatal_death<-bh1$X_16_1 bh1$monthdate<-as.Date(paste(bh1$year,bh1$month,'01', sep='-')) up.intro.districts<-c('Lakhimpur Kheri', 'Sitapur', 'Siddharth Nagar', 'Bahraich', 'Balrampur', 'Shrawasti') bh1$pcv.status<-0 bh1$pcv.status[bh1$DISTRICT %in% up.intro.districts] <-1 unique(bh1$DISTRICT) #test123 strat1<-factor(bh1$monthdate) ds.sub<-bh1[,c('uri', 'diar_death', 'pneu_death','sepsis_death','asphyxia_death', 'measles_death', 'neonatal_death')] bh2<-aggregate(x=ds.sub, by=list( strat1) , FUN='sum', na.rm=TRUE) names(bh2)<-c('monthdate',names(ds.sub)) bh2$monthdate<-as.Date(bh2$monthdate) bh2$neonatal_death[nrow(bh2)]<-NA bh2$month<-as.factor(month(bh2$monthdate)) bh2$year<-as.factor(year(bh2$monthdate)) par(mfrow=c(3,2), mar=c(3,3,1,1)) plot(bh2$monthdate,bh2$pneu_death,main='Pneumonia deaths', type='l', bty='l') plot(bh2$monthdate,bh2$diar_death,main='Diarrhea deaths', type='l', bty='l') plot(bh2$monthdate,bh2$sepsis_death,main='Sepsis Deaths', type='l', bty='l') plot(bh2$monthdate,bh2$asphyxia_death,main='Asphyxia Deaths', type='l', bty='l') plot(bh2$monthdate,bh2$measles_death,main='Measles Deaths', type='l', bty='l') plot(bh2$monthdate, bh2$neonatal_death, main='Neonatal Deaths',type='l', bty='l') par(mfrow=c(1,1), mar=c(2,3,1,1)) plot(bh2$uri, type='l',bty='l', main='URI cases') mod.uri<- glm( uri~ month +year, family='poisson', data=bh2) summary(mod.uri) mod.pneu.death<- glm( pneu_death~ month +year, family='poisson', data=bh2[!(bh2$year %in% c('2017','2018','2019')),]) summary(mod.pneu.death) #heatmap of reporting of URI #seems to be incomplete before April 2017 library(reshape2) ds.sub<-bh1[,c('DISTRICT','monthdate','uri')] ds.m<-melt(ds.sub, id=c('DISTRICT','monthdate')) ds.c<-dcast(ds.m, monthdate~DISTRICT) par(mfrow=c(1,1), mar=c(1,1,1,1)) hm1<-heatmap(t(as.matrix(ds.c[,-1])), scale='row', Rowv=NA, Colv=NA,cexRow =0.5) hm1<-heatmap(t(as.matrix(ds.c[ds.c$monthdate>=as.Date('2017-04-01'),-1])), scale='row', Rowv=NA, Colv=NA,cexRow =0.5) hm1<-heatmap(t(as.matrix(ds.c[ds.c$monthdate>=as.Date('2017-04-01'),-1])), scale='row', Colv=NA,cexRow =0.5) #Pneumonia deaths--seems to be incomplete before April 2017 ds.sub<-bh1[,c('DISTRICT','monthdate','pneu_death')] ds.m<-melt(ds.sub, id=c('DISTRICT','monthdate')) ds.c<-dcast(ds.m, monthdate~DISTRICT) par(mfrow=c(1,1), mar=c(1,1,1,1)) hm1<-heatmap(t(as.matrix(ds.c[,-1])), scale='row', Rowv=NA, Colv=NA,cexRow =0.5) hm1<-heatmap(t(as.matrix(ds.c[ds.c$monthdate>=as.Date('2017-04-01'),-1])), scale='row', Rowv=NA, Colv=NA,cexRow =0.5) hm1<-heatmap(t(as.matrix(ds.c[ds.c$monthdate>=as.Date('2017-04-01'),-1])), scale='row', Colv=NA,cexRow =0.5) ##Simple model# #Consider Apr 2018-Sep 2018 as roll out perios and Oct 2018-Mar 2019 as eval period bh3<-bh1[bh1$monthdate>=as.Date('2017-04-01'),] bh3$post.pcv<-0 bh3$post.pcv[bh3$pcv.status==1 & bh3$monthdate>=as.Date('2018-04-01')& bh3$monthdate<=as.Date('2018-09-01')] <- 1 bh3$post.pcv[bh3$pcv.status==1 & bh3$monthdate>=as.Date('2018-10-01')& bh3$monthdate<=as.Date('2019-03-01')] <- 2 bh3$post.pcv<-as.factor(bh3$post.pcv) bh3$date.factor<-as.factor(bh3$monthdate) bh3$obs<-as.factor(1:nrow(bh3)) mod1<-glmer(pneu_death ~ (1|date.factor) + (1|DISTRICT) + (1|obs)+ post.pcv, data=bh3, family='poisson' ) summary(mod1) mod1<-glmer(uri ~ (1|DISTRICT) + (1|obs)+ post.pcv, data=bh3, family='poisson' ) summary(mod1) #Gamm with time smooth library(mgcv) bh3$date.cont<-bh3$year+ bh3$month/12-1/12 mod2<-gamm(uri ~ s(date.cont) + post.pcv, data=bh3, family='poisson' ,random=list(DISTRICT=~1)) summary(mod2$lme) #mod2<-gamm(pneu_death ~ s(date.cont) + post.pcv, data=bh3, family='poisson' ,random=list(DISTRICT=~1), niterPQL=500) #summary(mod2$lme) #statewide ds.c.state<-dcast(ds.m, monthdate~1, fun.aggregate = sum) par(mar=c(3,2,1,1)) plot(ds.c.state[ds.c.state$monthdate>=as.Date('2017-04-01'),1],ds.c.state[ds.c.state$monthdate>=as.Date('2017-04-01'),2], type='l') #Brian: analyze by region vs district #Need a crosswalk file. Western/Central/Eastern in UP; district-> division #District hospitals--probably kids coming from district; bigger hospitals, kids coming from other places # Mortality data: is location based on residence or place of death? #--does death registry include out of hospital deaths #There is an official death registry

/Old code/hmis explore.R

no_license

DanWeinberger/hmis

R

false

6,182

r

#PCV was launched by the Union Health Minister, Shri JP Nadda on May 13, 2017 at Mandi, #Himachal Pradesh [1]. With this phased introduction, nearly 2.1 million children in #Himachal Pradesh (all 12 districts), parts of Bihar (17 out of 38 districts) #and Uttar Pradesh (6 out of 75 districts) will be vaccinated with PCV in the first year [2]. #This will be followed by introduction in Madhya Pradesh and Rajasthan next year, and eventually coverage will be expanded across the entire country in a phased manner, in the coming years. " #In uttar Pradesh it is: Lakhimpur Kheri, Sitapur, Siddharth Nagar, Bahraich, Balrampur, Shrawasti; #In Bihar: The 17 high-priority districts are Araria, Begusarai, Darbhanga, Kishanganj, Khagaria, Katihar, #Muzaffarpur, Munger, Vaishali, Madhepura, Madhubani, Purnea, Samastipur, Saran, Sitamarhi, Sheohar and Supaul #bh1<-read.csv('C:/Users/dmw63/Desktop/My documents h/GATES/india/hmis/Bihar series by district.csv') ####### #Lorine ##ACUTE RESPIRATORY INFECTION REQUIRING ADMISSION?? ##85% of DEATH OCCUR AT HOME--SHOULD BE IN HMIS; MANY ##IN PRIVATE SECTOR HOSPITALS ##CONTROLS: DIARRHEA deaths? ROTA VACCINE IS ROLLING OUT.. ##ACUTE ENCAPHALITIS IN SOUTH UP AND BIHAR. THERE ARE SOME ##VACCINE PROGRAMS BUT STARTED 5 YEARS AGO...NOT REALLY #IN WESTERN UP #ASPHYXIA DEATHS WONT BE SAME HOSPITALS -- #WOULD BE IN WOMENS HOSPITAL...MANY ARE SEPARATE #WOMENS/MENS HOSPTALS #UP HMIS--DR VISANT (Sp?) ####### library(lme4) library(lubridate) bh1<-read.csv('C:/Users/dmw63/Desktop/My documents h/GATES/india/hmis/hmis/uttarPradesh series to Mar 2019.csv') str(bh1) bh1$month<-as.numeric(substr(bh1$DATE,6,7)) bh1$year<-as.numeric(substr(bh1$DATE,2,5)) bh1$uri<-bh1$X_10_13 bh1$pneu_death<-bh1$X_16_3_1 bh1$diar_death<-bh1$X_16_3_2 bh1$measles_death<-bh1$X_16_3_4 bh1$asphyxia_death<-bh1$X_16_2_2 bh1$sepsis_death<-bh1$X_16_2_1 bh1$neonatal_death<-bh1$X_16_1 bh1$monthdate<-as.Date(paste(bh1$year,bh1$month,'01', sep='-')) up.intro.districts<-c('Lakhimpur Kheri', 'Sitapur', 'Siddharth Nagar', 'Bahraich', 'Balrampur', 'Shrawasti') bh1$pcv.status<-0 bh1$pcv.status[bh1$DISTRICT %in% up.intro.districts] <-1 unique(bh1$DISTRICT) #test123 strat1<-factor(bh1$monthdate) ds.sub<-bh1[,c('uri', 'diar_death', 'pneu_death','sepsis_death','asphyxia_death', 'measles_death', 'neonatal_death')] bh2<-aggregate(x=ds.sub, by=list( strat1) , FUN='sum', na.rm=TRUE) names(bh2)<-c('monthdate',names(ds.sub)) bh2$monthdate<-as.Date(bh2$monthdate) bh2$neonatal_death[nrow(bh2)]<-NA bh2$month<-as.factor(month(bh2$monthdate)) bh2$year<-as.factor(year(bh2$monthdate)) par(mfrow=c(3,2), mar=c(3,3,1,1)) plot(bh2$monthdate,bh2$pneu_death,main='Pneumonia deaths', type='l', bty='l') plot(bh2$monthdate,bh2$diar_death,main='Diarrhea deaths', type='l', bty='l') plot(bh2$monthdate,bh2$sepsis_death,main='Sepsis Deaths', type='l', bty='l') plot(bh2$monthdate,bh2$asphyxia_death,main='Asphyxia Deaths', type='l', bty='l') plot(bh2$monthdate,bh2$measles_death,main='Measles Deaths', type='l', bty='l') plot(bh2$monthdate, bh2$neonatal_death, main='Neonatal Deaths',type='l', bty='l') par(mfrow=c(1,1), mar=c(2,3,1,1)) plot(bh2$uri, type='l',bty='l', main='URI cases') mod.uri<- glm( uri~ month +year, family='poisson', data=bh2) summary(mod.uri) mod.pneu.death<- glm( pneu_death~ month +year, family='poisson', data=bh2[!(bh2$year %in% c('2017','2018','2019')),]) summary(mod.pneu.death) #heatmap of reporting of URI #seems to be incomplete before April 2017 library(reshape2) ds.sub<-bh1[,c('DISTRICT','monthdate','uri')] ds.m<-melt(ds.sub, id=c('DISTRICT','monthdate')) ds.c<-dcast(ds.m, monthdate~DISTRICT) par(mfrow=c(1,1), mar=c(1,1,1,1)) hm1<-heatmap(t(as.matrix(ds.c[,-1])), scale='row', Rowv=NA, Colv=NA,cexRow =0.5) hm1<-heatmap(t(as.matrix(ds.c[ds.c$monthdate>=as.Date('2017-04-01'),-1])), scale='row', Rowv=NA, Colv=NA,cexRow =0.5) hm1<-heatmap(t(as.matrix(ds.c[ds.c$monthdate>=as.Date('2017-04-01'),-1])), scale='row', Colv=NA,cexRow =0.5) #Pneumonia deaths--seems to be incomplete before April 2017 ds.sub<-bh1[,c('DISTRICT','monthdate','pneu_death')] ds.m<-melt(ds.sub, id=c('DISTRICT','monthdate')) ds.c<-dcast(ds.m, monthdate~DISTRICT) par(mfrow=c(1,1), mar=c(1,1,1,1)) hm1<-heatmap(t(as.matrix(ds.c[,-1])), scale='row', Rowv=NA, Colv=NA,cexRow =0.5) hm1<-heatmap(t(as.matrix(ds.c[ds.c$monthdate>=as.Date('2017-04-01'),-1])), scale='row', Rowv=NA, Colv=NA,cexRow =0.5) hm1<-heatmap(t(as.matrix(ds.c[ds.c$monthdate>=as.Date('2017-04-01'),-1])), scale='row', Colv=NA,cexRow =0.5) ##Simple model# #Consider Apr 2018-Sep 2018 as roll out perios and Oct 2018-Mar 2019 as eval period bh3<-bh1[bh1$monthdate>=as.Date('2017-04-01'),] bh3$post.pcv<-0 bh3$post.pcv[bh3$pcv.status==1 & bh3$monthdate>=as.Date('2018-04-01')& bh3$monthdate<=as.Date('2018-09-01')] <- 1 bh3$post.pcv[bh3$pcv.status==1 & bh3$monthdate>=as.Date('2018-10-01')& bh3$monthdate<=as.Date('2019-03-01')] <- 2 bh3$post.pcv<-as.factor(bh3$post.pcv) bh3$date.factor<-as.factor(bh3$monthdate) bh3$obs<-as.factor(1:nrow(bh3)) mod1<-glmer(pneu_death ~ (1|date.factor) + (1|DISTRICT) + (1|obs)+ post.pcv, data=bh3, family='poisson' ) summary(mod1) mod1<-glmer(uri ~ (1|DISTRICT) + (1|obs)+ post.pcv, data=bh3, family='poisson' ) summary(mod1) #Gamm with time smooth library(mgcv) bh3$date.cont<-bh3$year+ bh3$month/12-1/12 mod2<-gamm(uri ~ s(date.cont) + post.pcv, data=bh3, family='poisson' ,random=list(DISTRICT=~1)) summary(mod2$lme) #mod2<-gamm(pneu_death ~ s(date.cont) + post.pcv, data=bh3, family='poisson' ,random=list(DISTRICT=~1), niterPQL=500) #summary(mod2$lme) #statewide ds.c.state<-dcast(ds.m, monthdate~1, fun.aggregate = sum) par(mar=c(3,2,1,1)) plot(ds.c.state[ds.c.state$monthdate>=as.Date('2017-04-01'),1],ds.c.state[ds.c.state$monthdate>=as.Date('2017-04-01'),2], type='l') #Brian: analyze by region vs district #Need a crosswalk file. Western/Central/Eastern in UP; district-> division #District hospitals--probably kids coming from district; bigger hospitals, kids coming from other places # Mortality data: is location based on residence or place of death? #--does death registry include out of hospital deaths #There is an official death registry

test_that("User interface is correctly rendered", { local_edition(3) cur_header <- .cytomapper_header() expect_equal(cur_header$name, "header") expect_equal(cur_header$attribs$class, "main-header") expect_null(cur_header$children[[1]]) expect_snapshot_output(cur_header$children[[2]]) expect_snapshot_output(cur_header$children[[3]]) cur_sidebar <- .cytomapper_sidebar() expect_equal(cur_sidebar$name, "aside") expect_equal(cur_sidebar$attribs$id, "sidebarCollapsed") expect_equal(cur_sidebar$attribs$class, "main-sidebar") expect_equal(cur_sidebar$attribs$`data-collapsed`, "false") expect_null(cur_sidebar$children[[1]]) expect_snapshot_output(cur_sidebar$children[[2]]) skip_on_cran() cur_body <- .cytomapper_body() expect_equal(cur_body$name, "div") expect_equal(cur_body$attribs$class, "content-wrapper") cur_tab_id <- cur_body$children[[1]][[3]][[1]][[3]][[1]][[3]][[1]][[2]]$`data-tabsetid` expect_equal(as.character(cur_body$children[[1]]), paste0('<section class=\"content\">\n <div class=\"col-sm-12\">\n <div class=\"nav-tabs-custom\">\n <ul class=\"nav nav-tabs shiny-tab-input\" id=\"tabbox1\" data-tabsetid=\"', cur_tab_id, '\">\n <li class=\"active\">\n <a href=\"#tab-', cur_tab_id, '-1\" data-toggle=\"tab\" data-value=\"tab1\">Scatter Plots</a>\n </li>\n <li>\n <a href=\"#tab-', cur_tab_id, '-2\" data-toggle=\"tab\" data-value=\"tab2\">Images</a>\n </li>\n </ul>\n <div class=\"tab-content\" data-tabsetid=\"', cur_tab_id, '\">\n <div class=\"tab-pane active\" data-value=\"tab1\" id=\"tab-', cur_tab_id, '-1\">\n <div id=\"AdditionalPlots_tab1\" class=\"shiny-html-output\"></div>\n </div>\n <div class=\"tab-pane\" data-value=\"tab2\" id=\"tab-', cur_tab_id, '-2\">\n <div id=\"AdditionalPlots_tab2\" class=\"shiny-html-output\"></div>\n </div>\n </div>\n </div>\n </div>\n</section>')) }) test_that("Sidebar is correctly rendered", { data("pancreasSCE") local_edition(3) testServer(app = cytomapperShiny(object = pancreasSCE, img_id = "ImageNb", cell_id = "CellNb"), { session$setInputs(plotCount = 1) expect_snapshot_output(.addPlots_sidebar(input)(session)$html) session$setInputs(plotCount = 2) expect_snapshot_output(.addPlots_sidebar(input)(session)$html) session$setInputs(plotCount = 3) expect_snapshot_output(.addPlots_sidebar(input)(session)$html) }) }) test_that("Plots in tab 1 are correctly rendered", { data("pancreasSCE") local_edition(3) testServer(app = cytomapperShiny(object = pancreasSCE, img_id = "ImageNb", cell_id = "CellNb"), { session$setInputs(plotCount = 1) expect_snapshot_output(.addPlots_tab1(input)(session)$html) session$setInputs(plotCount = 2) expect_snapshot_output(.addPlots_tab1(input)(session)$html) session$setInputs(plotCount = 3) expect_snapshot_output(.addPlots_tab1(input)(session)$html) }) }) test_that("Plots in tab 2 are correctly rendered", { data("pancreasSCE") data("pancreasMasks") data("pancreasImages") local_edition(3) testServer(app = cytomapperShiny(object = pancreasSCE, img_id = "ImageNb", cell_id = "CellNb"), { expect_null(.addPlots_tab2(input, object = pancreasSCE, mask = NULL, image = NULL)) }) testServer(app = cytomapperShiny(object = pancreasSCE, img_id = "ImageNb", cell_id = "CellNb", mask = pancreasMasks, image = NULL), { expect_snapshot_output(.addPlots_tab2(input, object = pancreasSCE, mask = pancreasMasks, image = NULL)(session)$html) }) testServer(app = cytomapperShiny(object = pancreasSCE, img_id = "ImageNb", cell_id = "CellNb", mask = pancreasMasks, image = pancreasImages), { expect_snapshot_output(.addPlots_tab2(input, object = pancreasSCE, mask = pancreasMasks, image = pancreasImages)(session)$html) }) })

/tests/testthat/test_shiny_ui.R

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test_that("User interface is correctly rendered", { local_edition(3) cur_header <- .cytomapper_header() expect_equal(cur_header$name, "header") expect_equal(cur_header$attribs$class, "main-header") expect_null(cur_header$children[[1]]) expect_snapshot_output(cur_header$children[[2]]) expect_snapshot_output(cur_header$children[[3]]) cur_sidebar <- .cytomapper_sidebar() expect_equal(cur_sidebar$name, "aside") expect_equal(cur_sidebar$attribs$id, "sidebarCollapsed") expect_equal(cur_sidebar$attribs$class, "main-sidebar") expect_equal(cur_sidebar$attribs$`data-collapsed`, "false") expect_null(cur_sidebar$children[[1]]) expect_snapshot_output(cur_sidebar$children[[2]]) skip_on_cran() cur_body <- .cytomapper_body() expect_equal(cur_body$name, "div") expect_equal(cur_body$attribs$class, "content-wrapper") cur_tab_id <- cur_body$children[[1]][[3]][[1]][[3]][[1]][[3]][[1]][[2]]$`data-tabsetid` expect_equal(as.character(cur_body$children[[1]]), paste0('<section class=\"content\">\n <div class=\"col-sm-12\">\n <div class=\"nav-tabs-custom\">\n <ul class=\"nav nav-tabs shiny-tab-input\" id=\"tabbox1\" data-tabsetid=\"', cur_tab_id, '\">\n <li class=\"active\">\n <a href=\"#tab-', cur_tab_id, '-1\" data-toggle=\"tab\" data-value=\"tab1\">Scatter Plots</a>\n </li>\n <li>\n <a href=\"#tab-', cur_tab_id, '-2\" data-toggle=\"tab\" data-value=\"tab2\">Images</a>\n </li>\n </ul>\n <div class=\"tab-content\" data-tabsetid=\"', cur_tab_id, '\">\n <div class=\"tab-pane active\" data-value=\"tab1\" id=\"tab-', cur_tab_id, '-1\">\n <div id=\"AdditionalPlots_tab1\" class=\"shiny-html-output\"></div>\n </div>\n <div class=\"tab-pane\" data-value=\"tab2\" id=\"tab-', cur_tab_id, '-2\">\n <div id=\"AdditionalPlots_tab2\" class=\"shiny-html-output\"></div>\n </div>\n </div>\n </div>\n </div>\n</section>')) }) test_that("Sidebar is correctly rendered", { data("pancreasSCE") local_edition(3) testServer(app = cytomapperShiny(object = pancreasSCE, img_id = "ImageNb", cell_id = "CellNb"), { session$setInputs(plotCount = 1) expect_snapshot_output(.addPlots_sidebar(input)(session)$html) session$setInputs(plotCount = 2) expect_snapshot_output(.addPlots_sidebar(input)(session)$html) session$setInputs(plotCount = 3) expect_snapshot_output(.addPlots_sidebar(input)(session)$html) }) }) test_that("Plots in tab 1 are correctly rendered", { data("pancreasSCE") local_edition(3) testServer(app = cytomapperShiny(object = pancreasSCE, img_id = "ImageNb", cell_id = "CellNb"), { session$setInputs(plotCount = 1) expect_snapshot_output(.addPlots_tab1(input)(session)$html) session$setInputs(plotCount = 2) expect_snapshot_output(.addPlots_tab1(input)(session)$html) session$setInputs(plotCount = 3) expect_snapshot_output(.addPlots_tab1(input)(session)$html) }) }) test_that("Plots in tab 2 are correctly rendered", { data("pancreasSCE") data("pancreasMasks") data("pancreasImages") local_edition(3) testServer(app = cytomapperShiny(object = pancreasSCE, img_id = "ImageNb", cell_id = "CellNb"), { expect_null(.addPlots_tab2(input, object = pancreasSCE, mask = NULL, image = NULL)) }) testServer(app = cytomapperShiny(object = pancreasSCE, img_id = "ImageNb", cell_id = "CellNb", mask = pancreasMasks, image = NULL), { expect_snapshot_output(.addPlots_tab2(input, object = pancreasSCE, mask = pancreasMasks, image = NULL)(session)$html) }) testServer(app = cytomapperShiny(object = pancreasSCE, img_id = "ImageNb", cell_id = "CellNb", mask = pancreasMasks, image = pancreasImages), { expect_snapshot_output(.addPlots_tab2(input, object = pancreasSCE, mask = pancreasMasks, image = pancreasImages)(session)$html) }) })

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/shinyRange.R \docType{package} \name{shinyRange} \alias{shinyRange} \alias{shinyRange-package} \title{shinyRange: Text-box-based numeric range input widget for Shiny.} \description{ Text-box-based numeric range input widget for Shiny. }

/man/shinyRange.Rd

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/shinyRange.R \docType{package} \name{shinyRange} \alias{shinyRange} \alias{shinyRange-package} \title{shinyRange: Text-box-based numeric range input widget for Shiny.} \description{ Text-box-based numeric range input widget for Shiny. }

# https://elbersb.github.io/segregation/index.html?s=03 # https://elbersb.github.io/segregation/articles/segregation.html library(segregation) # example dataset with fake data provided by the package mutual_total(schools00, "race", "school", weight = "n") #> stat est #> 1: M 0.426 #> 2: H 0.419

/Segregacao/segregation.R

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DATAUNIRIO/desigualdade

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# https://elbersb.github.io/segregation/index.html?s=03 # https://elbersb.github.io/segregation/articles/segregation.html library(segregation) # example dataset with fake data provided by the package mutual_total(schools00, "race", "school", weight = "n") #> stat est #> 1: M 0.426 #> 2: H 0.419

\name{cSFM object} \alias{print.cSFM} \alias{fitted.cSFM} \alias{predict.cSFM} %- Also NEED an '\alias' for EACH other topic documented here. \title{Generic Method for 'cSFM' Objects } \description{ Print, extract fitted values and predict for cSFM object. Methods of the generic function \code{\link{print}}, \code{\link{fitted}} and \code{\link{predict}} for objects inheriting from class cSFM. %% ~~ A concise (1-5 lines) description of what the function does. ~~ } \usage{ \method{print}{cSFM}(x, ...) \method{fitted}{cSFM}(object, quantile = TRUE, quantile.level = c(0.5, 0.8, 0.9, 0.95, 0.99), ...) \method{predict}{cSFM}(object, newdata, cp.valid, tp.valid = NULL, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x}{A cSFM object; returned by the function \code{\link{cSFM.est}}} \item{object}{A cSFM object; returned by the function \code{\link{cSFM.est}}} \item{quantile}{logical; if TRUE(default), estimates of quantiles are returned } \item{quantile.level}{quantile vector; quantile levels to be estimated when \code{quantile = TRUE}} \item{newdata}{the partically observed new data set (missing data allowed) to be predicated} \item{cp.valid}{observed covariate vector for \code{newdata} with length \code{nrow{newdata}}} \item{tp.valid}{observed timepoint vector for \code{newdata} with length \code{ncol{newdata}}; See "Details".} \item{...}{other arguments passed to the generic functions} %% ~~Describe \code{x} here~~ } \details{ When use the function \code{predict}, each row of \code{newdata} corresponds to covariate information \code{cp.valid}, while the column is for the time points \code{tp.valid}. When \code{tp.valid} is \code{null}, then we assume the validation data set has the same time points as the training data set, which is used to obtain \code{object}. } %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{\code{\link{cSFM.est}}, \code{\link{print}}, \code{\link{fitted}}, \code{\link{predict}} %% ~~objects to See Also as \code{\link{help}}, ~~~ }

/man/generic.HAC.Rd

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\name{cSFM object} \alias{print.cSFM} \alias{fitted.cSFM} \alias{predict.cSFM} %- Also NEED an '\alias' for EACH other topic documented here. \title{Generic Method for 'cSFM' Objects } \description{ Print, extract fitted values and predict for cSFM object. Methods of the generic function \code{\link{print}}, \code{\link{fitted}} and \code{\link{predict}} for objects inheriting from class cSFM. %% ~~ A concise (1-5 lines) description of what the function does. ~~ } \usage{ \method{print}{cSFM}(x, ...) \method{fitted}{cSFM}(object, quantile = TRUE, quantile.level = c(0.5, 0.8, 0.9, 0.95, 0.99), ...) \method{predict}{cSFM}(object, newdata, cp.valid, tp.valid = NULL, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x}{A cSFM object; returned by the function \code{\link{cSFM.est}}} \item{object}{A cSFM object; returned by the function \code{\link{cSFM.est}}} \item{quantile}{logical; if TRUE(default), estimates of quantiles are returned } \item{quantile.level}{quantile vector; quantile levels to be estimated when \code{quantile = TRUE}} \item{newdata}{the partically observed new data set (missing data allowed) to be predicated} \item{cp.valid}{observed covariate vector for \code{newdata} with length \code{nrow{newdata}}} \item{tp.valid}{observed timepoint vector for \code{newdata} with length \code{ncol{newdata}}; See "Details".} \item{...}{other arguments passed to the generic functions} %% ~~Describe \code{x} here~~ } \details{ When use the function \code{predict}, each row of \code{newdata} corresponds to covariate information \code{cp.valid}, while the column is for the time points \code{tp.valid}. When \code{tp.valid} is \code{null}, then we assume the validation data set has the same time points as the training data set, which is used to obtain \code{object}. } %% ~Make other sections like Warning with \section{Warning }{....} ~ \seealso{\code{\link{cSFM.est}}, \code{\link{print}}, \code{\link{fitted}}, \code{\link{predict}} %% ~~objects to See Also as \code{\link{help}}, ~~~ }

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/FILEST_package.R \docType{package} \name{FILEST-package} \alias{FILEST} \alias{FILEST-package} \title{Fine-Level Structure Simulator} \description{ A population genetic simulator, which is able to generate synthetic datasets for single-nucleotide polymorphisms (SNP) for multiple populations. The genetic distances among populations can be set according to the Fixation Index (Fst). This tool is able to simulate outlying individuals and missing SNPs can be specified. For Genome-wide association study (GWAS), disease status can be set in desired level according risk ratio. } \details{ The R package \pkg{FILEST} requires \pkg{KRIS} and \pkg{rARPACK}. Here is the list of functions in the R package \pkg{FILEST}: \itemize{ \item \code{\link{cbind_bigmatrix}} \item \code{\link{create.template.setting}} \item \code{\link{demo.filest}} \item \code{\link{filest}} \item \code{\link{rbind_bigmatrix}} } } \seealso{ Useful links: \itemize{ \item \url{https://gitlab.com/kris.ccp/filest} \item Report bugs at \url{https://gitlab.com/kris.ccp/filest/-/issues} } } \author{ \strong{Maintainer}: Kridsadakorn Chaichoompu \email{kridsadakorn@biostatgen.org} Authors: \itemize{ \item Kristel Van Steen \item Fentaw Abegaz } } \keyword{internal}

/man/FILEST-package.Rd

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rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/FILEST_package.R \docType{package} \name{FILEST-package} \alias{FILEST} \alias{FILEST-package} \title{Fine-Level Structure Simulator} \description{ A population genetic simulator, which is able to generate synthetic datasets for single-nucleotide polymorphisms (SNP) for multiple populations. The genetic distances among populations can be set according to the Fixation Index (Fst). This tool is able to simulate outlying individuals and missing SNPs can be specified. For Genome-wide association study (GWAS), disease status can be set in desired level according risk ratio. } \details{ The R package \pkg{FILEST} requires \pkg{KRIS} and \pkg{rARPACK}. Here is the list of functions in the R package \pkg{FILEST}: \itemize{ \item \code{\link{cbind_bigmatrix}} \item \code{\link{create.template.setting}} \item \code{\link{demo.filest}} \item \code{\link{filest}} \item \code{\link{rbind_bigmatrix}} } } \seealso{ Useful links: \itemize{ \item \url{https://gitlab.com/kris.ccp/filest} \item Report bugs at \url{https://gitlab.com/kris.ccp/filest/-/issues} } } \author{ \strong{Maintainer}: Kridsadakorn Chaichoompu \email{kridsadakorn@biostatgen.org} Authors: \itemize{ \item Kristel Van Steen \item Fentaw Abegaz } } \keyword{internal}

source(Sys.getenv('RESULTS_BASE_PARTIAL')) source(Sys.getenv('RESULTS_DISPLAY_PARTIAL')) source(Sys.getenv('RESULTS_TABLES_PARTIAL')) library(argparse) library(tidyr, warn.conflicts = FALSE) library(wombat) parser <- ArgumentParser() parser$add_argument('--mcmc-samples-lg-online-corrected') parser$add_argument('--mcmc-samples-ln-online-corrected') parser$add_argument('--mcmc-samples-lg-offline-online-corrected') parser$add_argument('--mcmc-samples-ln-offline-online-corrected') parser$add_argument('--output') args <- parser$parse_args() read_samples <- function(filename, mode_i, raw_i) { window(readRDS(filename), start = 1001)$beta %>% as.data.frame() %>% pivot_longer(everything()) %>% mutate( mode = mode_i, raw = raw_i ) } beta_samples_df <- bind_rows( read_samples( args$mcmc_samples_lg_online_corrected, 'WOMBAT LG', 'Uncorrected retrievals' ), read_samples( args$mcmc_samples_ln_online_corrected, 'WOMBAT LN', 'Uncorrected retrievals' ), read_samples( args$mcmc_samples_lg_offline_online_corrected, 'WOMBAT LG', 'TCCON-corrected retrievals' ), read_samples( args$mcmc_samples_ln_offline_online_corrected, 'WOMBAT LN', 'TCCON-corrected retrievals' ) ) %>% mutate( parameter = factor(c( 'is_oco2:oco2_operation_modeLG' = '(Intercept)', 'is_oco2:oco2_operation_modeLG:oco2_dp' = 'dp', 'is_oco2:oco2_operation_modeLG:oco2_co2_grad_del' = 'co2_grad_del', 'is_oco2:oco2_operation_modeLG:oco2_log_dws' = 'logDWS', 'is_oco2:oco2_operation_modeLN' = '(Intercept)', 'is_oco2:oco2_operation_modeLN:oco2_dp' = 'dp', 'is_oco2:oco2_operation_modeLN:oco2_co2_grad_del' = 'co2_grad_del', 'is_oco2:oco2_operation_modeLN:oco2_log_dws' = 'logDWS' )[name], levels = c('dp', 'co2_grad_del', 'logDWS')), mode = factor( mode, levels = c( 'WOMBAT LG', 'WOMBAT LN', 'TCCON-based offline correction' ) ), value = -value ) %>% filter(parameter != '(Intercept)') %>% mutate( value = ifelse( mode == 'WOMBAT LG', value / c( 'dp' = 3.057625, 'co2_grad_del' = 21.348496, 'logDWS' = 1.135891 )[parameter], ifelse( mode == 'WOMBAT LN', value / c( 'dp' = 2.758201, 'co2_grad_del' = 19.829637, 'logDWS' = 1.144667 )[parameter], value ) ) ) output1 <- ggplot() + geom_vline( data = tibble( parameter = factor( c('dp', 'co2_grad_del', 'logDWS'), levels = c('dp', 'co2_grad_del', 'logDWS') ), value = c(0.3, 0.028, 0.6) ), mapping = aes(xintercept = value), colour = 'blue', size = 1 ) + geom_density( data = beta_samples_df %>% filter(raw == 'Uncorrected retrievals'), mapping = aes(value, colour = mode) ) + scale_colour_manual( values = c( 'WOMBAT LG' = get_colour('wombat_lg'), 'WOMBAT LN' = get_colour('wombat_ln'), 'TCCON-based offline correction' = 'blue' ), drop = FALSE, guide = guide_legend(override.aes = list(size = 1)) ) + labs(x = 'Bias correction coefficient', y = 'Density', colour = NULL) + facet_wrap(~ parameter, scales = 'free') + scale_x_continuous(expand = expansion(mult = c(0.2, 0.2))) + ggtitle('Uncorrected retrievals') + theme( axis.text.x = element_text(angle = 30, hjust = 1), legend.position = 'bottom' ) output2 <- ggplot() + geom_vline( xintercept = 0, colour = '#999999', linetype = 2, size = 1 ) + geom_density( data = beta_samples_df %>% filter(raw == 'TCCON-corrected retrievals'), mapping = aes(value, colour = mode) ) + scale_colour_manual( values = c( 'WOMBAT LG' = get_colour('wombat_lg'), 'WOMBAT LN' = get_colour('wombat_ln'), 'TCCON-based offline correction' = 'blue' ), drop = FALSE, guide = guide_legend(override.aes = list(size = 1)) ) + labs(x = 'Bias correction coefficient', y = 'Density', colour = NULL) + facet_wrap(~ parameter, scales = 'free') + scale_x_continuous(expand = expansion(mult = c(0.2, 0.2))) + ggtitle('TCCON-corrected retrievals') + theme( axis.text.x = element_text(angle = 30, hjust = 1), legend.position = 'bottom' ) output <- gridExtra::arrangeGrob( output1 + theme(legend.position = 'none'), output2 + theme(legend.position = 'none'), get_legend(output1), heights = c(5.5, 5.5, 1) ) ggsave( args$output, plot = output, width = DISPLAY_SETTINGS$full_width, height = 12, units = 'cm' )

/4_results/src/oco2-bias-correction.R

no_license

mbertolacci/wombat-paper

R

false

4,612

r

source(Sys.getenv('RESULTS_BASE_PARTIAL')) source(Sys.getenv('RESULTS_DISPLAY_PARTIAL')) source(Sys.getenv('RESULTS_TABLES_PARTIAL')) library(argparse) library(tidyr, warn.conflicts = FALSE) library(wombat) parser <- ArgumentParser() parser$add_argument('--mcmc-samples-lg-online-corrected') parser$add_argument('--mcmc-samples-ln-online-corrected') parser$add_argument('--mcmc-samples-lg-offline-online-corrected') parser$add_argument('--mcmc-samples-ln-offline-online-corrected') parser$add_argument('--output') args <- parser$parse_args() read_samples <- function(filename, mode_i, raw_i) { window(readRDS(filename), start = 1001)$beta %>% as.data.frame() %>% pivot_longer(everything()) %>% mutate( mode = mode_i, raw = raw_i ) } beta_samples_df <- bind_rows( read_samples( args$mcmc_samples_lg_online_corrected, 'WOMBAT LG', 'Uncorrected retrievals' ), read_samples( args$mcmc_samples_ln_online_corrected, 'WOMBAT LN', 'Uncorrected retrievals' ), read_samples( args$mcmc_samples_lg_offline_online_corrected, 'WOMBAT LG', 'TCCON-corrected retrievals' ), read_samples( args$mcmc_samples_ln_offline_online_corrected, 'WOMBAT LN', 'TCCON-corrected retrievals' ) ) %>% mutate( parameter = factor(c( 'is_oco2:oco2_operation_modeLG' = '(Intercept)', 'is_oco2:oco2_operation_modeLG:oco2_dp' = 'dp', 'is_oco2:oco2_operation_modeLG:oco2_co2_grad_del' = 'co2_grad_del', 'is_oco2:oco2_operation_modeLG:oco2_log_dws' = 'logDWS', 'is_oco2:oco2_operation_modeLN' = '(Intercept)', 'is_oco2:oco2_operation_modeLN:oco2_dp' = 'dp', 'is_oco2:oco2_operation_modeLN:oco2_co2_grad_del' = 'co2_grad_del', 'is_oco2:oco2_operation_modeLN:oco2_log_dws' = 'logDWS' )[name], levels = c('dp', 'co2_grad_del', 'logDWS')), mode = factor( mode, levels = c( 'WOMBAT LG', 'WOMBAT LN', 'TCCON-based offline correction' ) ), value = -value ) %>% filter(parameter != '(Intercept)') %>% mutate( value = ifelse( mode == 'WOMBAT LG', value / c( 'dp' = 3.057625, 'co2_grad_del' = 21.348496, 'logDWS' = 1.135891 )[parameter], ifelse( mode == 'WOMBAT LN', value / c( 'dp' = 2.758201, 'co2_grad_del' = 19.829637, 'logDWS' = 1.144667 )[parameter], value ) ) ) output1 <- ggplot() + geom_vline( data = tibble( parameter = factor( c('dp', 'co2_grad_del', 'logDWS'), levels = c('dp', 'co2_grad_del', 'logDWS') ), value = c(0.3, 0.028, 0.6) ), mapping = aes(xintercept = value), colour = 'blue', size = 1 ) + geom_density( data = beta_samples_df %>% filter(raw == 'Uncorrected retrievals'), mapping = aes(value, colour = mode) ) + scale_colour_manual( values = c( 'WOMBAT LG' = get_colour('wombat_lg'), 'WOMBAT LN' = get_colour('wombat_ln'), 'TCCON-based offline correction' = 'blue' ), drop = FALSE, guide = guide_legend(override.aes = list(size = 1)) ) + labs(x = 'Bias correction coefficient', y = 'Density', colour = NULL) + facet_wrap(~ parameter, scales = 'free') + scale_x_continuous(expand = expansion(mult = c(0.2, 0.2))) + ggtitle('Uncorrected retrievals') + theme( axis.text.x = element_text(angle = 30, hjust = 1), legend.position = 'bottom' ) output2 <- ggplot() + geom_vline( xintercept = 0, colour = '#999999', linetype = 2, size = 1 ) + geom_density( data = beta_samples_df %>% filter(raw == 'TCCON-corrected retrievals'), mapping = aes(value, colour = mode) ) + scale_colour_manual( values = c( 'WOMBAT LG' = get_colour('wombat_lg'), 'WOMBAT LN' = get_colour('wombat_ln'), 'TCCON-based offline correction' = 'blue' ), drop = FALSE, guide = guide_legend(override.aes = list(size = 1)) ) + labs(x = 'Bias correction coefficient', y = 'Density', colour = NULL) + facet_wrap(~ parameter, scales = 'free') + scale_x_continuous(expand = expansion(mult = c(0.2, 0.2))) + ggtitle('TCCON-corrected retrievals') + theme( axis.text.x = element_text(angle = 30, hjust = 1), legend.position = 'bottom' ) output <- gridExtra::arrangeGrob( output1 + theme(legend.position = 'none'), output2 + theme(legend.position = 'none'), get_legend(output1), heights = c(5.5, 5.5, 1) ) ggsave( args$output, plot = output, width = DISPLAY_SETTINGS$full_width, height = 12, units = 'cm' )

# Plot maps and NDVI, EVI, NIRv relationships for 4 CA rangeland test sites setwd("C:/Users/eordway/Desktop") # Load libraries library(raster); library(rgdal); library(sp) library(reshape2); library(dplyr); library(tidyr); library(rgeos) library(GISTools); library(sf); library(SDMTools) library(ggplot2); library(rasterVis); library(viridis) colr = inferno(100, direction=-1) #----- Load covariates & TCC ---------------------------------------------------- ## load CA shapefile CA = readOGR(dsn="CA_state_boundary", layer="CA_State_TIGER2016"); plot(CA) ## load 5 test site shapfiles bak = readOGR(dsn="Test_Sites", layer="Bakersfield"); plot(bak) las = readOGR(dsn="Test_Sites", layer="Lassen"); plot(las) mrc = readOGR(dsn="Test_Sites", layer="Merced"); plot(mrc) mh = readOGR(dsn="Test_Sites", layer="Mt_Hamilton"); plot(mh) ## load GLCF Tree Canopy Cover product (2000) ## also 2005 & 2010 tcc <- raster("D:/DEM_Precip_Soil_TCC/TCC_CA_2000.tif"); plot(tcc)# 2000 ## load & stack veg indices for all years current.list <- list.files(path="Temp_Veg_Indices/EVI/",pattern =".tif$", full.names=TRUE) #current.list <- list.files(path="Temp_Veg_Indices/NDVI/",pattern =".tif$", full.names=TRUE) #current.list <- list.files(path="Temp_Veg_Indices/NIRv/",pattern =".tif$", full.names=TRUE) veg.stack <- stack(current.list); dim(veg.stack) # should have 31 bands plot(veg.stack, zlim = c(0,1), col=colr)# zlim = c(0,1), ## add TCC to stack dat.stack <- stack(tcc,veg.stack); dim(dat.stack) # should have 32 bands plot(dat.stack, col=colr)# zlim = c(0,1), #----------------------------------------------------------------------------- ## crop stacked vegetation indices to test sites out_rastB <- crop(dat.stack, bak, snap="out"); plot(trim(out_rastB), zlim = c(0,1), col=colr) out_B <- mask(out_rastB, bak); plot(out_B, zlim = c(0,1), col=colr) out_rastL <- crop(dat.stack, las, snap="out"); plot(trim(out_rastL), zlim = c(0,1), col=colr) out_L <- mask(out_rastL, las); plot(out_L, zlim = c(0,1), col=colr) out_rastM <- crop(dat.stack, mrc, snap="out"); plot(trim(out_rastM), zlim = c(0,1), col=colr) out_M <- mask(out_rastM, mrc); plot(out_M, zlim = c(0,1), col=colr) out_rastMH <- crop(dat.stack, mh, snap="out"); plot(trim(out_rastMH), zlim = c(0,1), col=colr) out_MH <- mask(out_rastMH, mh); plot(out_MH, zlim = c(0,1), col=colr) # write rasters string_out <- "EVI_stack" # EVI_stack | NDVI_stack | NIRv_stack writeRaster(out_B, format="GTiff", paste('D:/Test_Site_Ouput/',string_out,'_Bakersfield.tif',sep=""), overwrite=T) writeRaster(out_L, format="GTiff", paste('D:/Test_Site_Ouput/',string_out,'_Lassen.tif',sep=""), overwrite=T) writeRaster(out_M, format="GTiff", paste('D:/Test_Site_Ouput/',string_out,'_Merced.tif',sep=""), overwrite=T) writeRaster(out_MH, format="GTiff", paste('D:/Test_Site_Ouput/',string_out,'_Mt_Hamilton.tif',sep=""), overwrite=T)

/rangeland-production/code/CA_Rangelands_create_test_site_stacks.R

no_license

eoway/soilc-ca-rangelands

R

false

2,932

r

# Plot maps and NDVI, EVI, NIRv relationships for 4 CA rangeland test sites setwd("C:/Users/eordway/Desktop") # Load libraries library(raster); library(rgdal); library(sp) library(reshape2); library(dplyr); library(tidyr); library(rgeos) library(GISTools); library(sf); library(SDMTools) library(ggplot2); library(rasterVis); library(viridis) colr = inferno(100, direction=-1) #----- Load covariates & TCC ---------------------------------------------------- ## load CA shapefile CA = readOGR(dsn="CA_state_boundary", layer="CA_State_TIGER2016"); plot(CA) ## load 5 test site shapfiles bak = readOGR(dsn="Test_Sites", layer="Bakersfield"); plot(bak) las = readOGR(dsn="Test_Sites", layer="Lassen"); plot(las) mrc = readOGR(dsn="Test_Sites", layer="Merced"); plot(mrc) mh = readOGR(dsn="Test_Sites", layer="Mt_Hamilton"); plot(mh) ## load GLCF Tree Canopy Cover product (2000) ## also 2005 & 2010 tcc <- raster("D:/DEM_Precip_Soil_TCC/TCC_CA_2000.tif"); plot(tcc)# 2000 ## load & stack veg indices for all years current.list <- list.files(path="Temp_Veg_Indices/EVI/",pattern =".tif$", full.names=TRUE) #current.list <- list.files(path="Temp_Veg_Indices/NDVI/",pattern =".tif$", full.names=TRUE) #current.list <- list.files(path="Temp_Veg_Indices/NIRv/",pattern =".tif$", full.names=TRUE) veg.stack <- stack(current.list); dim(veg.stack) # should have 31 bands plot(veg.stack, zlim = c(0,1), col=colr)# zlim = c(0,1), ## add TCC to stack dat.stack <- stack(tcc,veg.stack); dim(dat.stack) # should have 32 bands plot(dat.stack, col=colr)# zlim = c(0,1), #----------------------------------------------------------------------------- ## crop stacked vegetation indices to test sites out_rastB <- crop(dat.stack, bak, snap="out"); plot(trim(out_rastB), zlim = c(0,1), col=colr) out_B <- mask(out_rastB, bak); plot(out_B, zlim = c(0,1), col=colr) out_rastL <- crop(dat.stack, las, snap="out"); plot(trim(out_rastL), zlim = c(0,1), col=colr) out_L <- mask(out_rastL, las); plot(out_L, zlim = c(0,1), col=colr) out_rastM <- crop(dat.stack, mrc, snap="out"); plot(trim(out_rastM), zlim = c(0,1), col=colr) out_M <- mask(out_rastM, mrc); plot(out_M, zlim = c(0,1), col=colr) out_rastMH <- crop(dat.stack, mh, snap="out"); plot(trim(out_rastMH), zlim = c(0,1), col=colr) out_MH <- mask(out_rastMH, mh); plot(out_MH, zlim = c(0,1), col=colr) # write rasters string_out <- "EVI_stack" # EVI_stack | NDVI_stack | NIRv_stack writeRaster(out_B, format="GTiff", paste('D:/Test_Site_Ouput/',string_out,'_Bakersfield.tif',sep=""), overwrite=T) writeRaster(out_L, format="GTiff", paste('D:/Test_Site_Ouput/',string_out,'_Lassen.tif',sep=""), overwrite=T) writeRaster(out_M, format="GTiff", paste('D:/Test_Site_Ouput/',string_out,'_Merced.tif',sep=""), overwrite=T) writeRaster(out_MH, format="GTiff", paste('D:/Test_Site_Ouput/',string_out,'_Mt_Hamilton.tif',sep=""), overwrite=T)

#' Converts list(list(fields)) into list(fields(list)) #' #' @param dots list of lists listcomb <- function(dots) { nms <- names(dots[[1]]) ans <- as.list(nms) names(ans) <- nms for (nm in nms) { ans[[nm]] <- lapply(1:length(dots), function(i) dots[[i]][[nm]]) } ans } #' Inverse square root of a matrix #' #' @param m matrix isqrtm <- function(m) { res <- eigen(m) d <- res$values if (min(d) < -1e-5) warning("Negative eigenvalues in isqrtm") d[d < 0] <- 0 d[d > 0] <- 1/sqrt(d[d > 0]) v <- res$vectors return (v %*% diag(d) %*% t(v)) } #' Calls mclapply and combines the end result using listcomb #' #' @import parallel #' @examples #' lclapply(1:10, function(i) list(a = i, b = i^2), mc.cores = 1) lclapply <- function(x, f, mc.cores = 0) { if (mc.cores == 0) { return(listcomb(lapply(x, f))) } else { return(listcomb(mclapply(x, f, mc.cores = mc.cores))) } } #' Either uses lapply or mclapply #' #' @import parallel #' @examples #' lclapply(1:10, function(i) list(a = i, b = i^2), mc.cores = 1) mclapply0 <- function(x, f, mc.cores = 0) { if (mc.cores == 0) { return(lapply(x, f)) } else { return(mclapply(x, f, mc.cores = mc.cores)) } }

/R/testUtils.R

no_license

snarles/testUtils

R

false

1,216

r

#' Converts list(list(fields)) into list(fields(list)) #' #' @param dots list of lists listcomb <- function(dots) { nms <- names(dots[[1]]) ans <- as.list(nms) names(ans) <- nms for (nm in nms) { ans[[nm]] <- lapply(1:length(dots), function(i) dots[[i]][[nm]]) } ans } #' Inverse square root of a matrix #' #' @param m matrix isqrtm <- function(m) { res <- eigen(m) d <- res$values if (min(d) < -1e-5) warning("Negative eigenvalues in isqrtm") d[d < 0] <- 0 d[d > 0] <- 1/sqrt(d[d > 0]) v <- res$vectors return (v %*% diag(d) %*% t(v)) } #' Calls mclapply and combines the end result using listcomb #' #' @import parallel #' @examples #' lclapply(1:10, function(i) list(a = i, b = i^2), mc.cores = 1) lclapply <- function(x, f, mc.cores = 0) { if (mc.cores == 0) { return(listcomb(lapply(x, f))) } else { return(listcomb(mclapply(x, f, mc.cores = mc.cores))) } } #' Either uses lapply or mclapply #' #' @import parallel #' @examples #' lclapply(1:10, function(i) list(a = i, b = i^2), mc.cores = 1) mclapply0 <- function(x, f, mc.cores = 0) { if (mc.cores == 0) { return(lapply(x, f)) } else { return(mclapply(x, f, mc.cores = mc.cores)) } }

library(maps) library(sp) library(maptools) library(mapdata) library(scales) library(mapproj) library(RColorBrewer) library(rgdal) library(ggplot2) library(beyonce) library(rworldmap) library(RColorBrewer) #Percent of land spared, used, or unchanged relative to BAU scenario prop<-read.csv("Percent_map_all.csv", header=TRUE) head(prop) sPDF.prop.nt <- joinCountryData2Map(prop ,joinCode = "ISO3", nameJoinColumn = "ISO3", verbose=TRUE) sPDF.prop <- spTransform(sPDF.prop.nt, CRS="+proj=moll +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84") ##CROP LANDUSE % MAPS------ numCats <- 6 colourPalette <- colorRampPalette(brewer.pal(numCats, "RdBu")) (6) catmethod=c(-1,-0.50,-0.25,0,0.25,0.50,1) #feed #quartz() par(mfrow = c(2, 1)) par(mar = c(2, 1, 1, 1) + 0.1) # c(bottom, left, top, right) mapParams<-mapCountryData(sPDF.prop,nameColumnToPlot='MIXED_crop', catMethod=catmethod, colourPalette=colourPalette, missingCountryCol="dark gray",addLegend=F,oceanCol="white", mapTitle=" ") do.call(addMapLegend, c(mapParams, legendLabels="all", legendWidth=2, digits=1, horizontal = T)) mapParamsMAR<-mapCountryData(sPDF.prop,nameColumnToPlot='MARINE_crop', catMethod=catmethod, colourPalette=colourPalette, missingCountryCol="dark gray", addLegend=F,oceanCol="white", mapTitle=" ") ##GRAZING + CROP % MAP(S)------- mapParams<-mapCountryData(sPDF.prop,nameColumnToPlot='MIXED_c.g', catMethod=catmethod, colourPalette=colourPalette, missingCountryCol="dark gray",addLegend=F,oceanCol="white", mapTitle=" ") do.call(addMapLegend, c(mapParams, legendLabels="all", legendWidth=2, digits=1, horizontal = T)) #MARINE SCENARIO NOT DEPICTED IN STUDY BECAUSE RESULT THE SAME #mapParamsMAR<-mapCountryData(sPDF.prop,nameColumnToPlot='MARINE_c.g', catMethod=catmethod, colourPalette=colourPalette, # missingCountryCol="dark gray", addLegend=F,oceanCol="white", mapTitle=" ") #do.call(addMapLegend, c(mapParamsMAR, legendLabels="all", legendWidth=7, digits=1, horizontal = F)) ##---------- #Number of countries pos (spared) or neg (used) land num_con<-read.csv("PosNeg_plot_all.csv", header=TRUE) head(num_con) counts = num_con[,-1] head(counts) totals <- as.data.frame(colSums(counts)) names(totals) = "no_regions" totals$scenario = c("Used","Spared","Used","Spared","Used","Spared","Used","Spared") totals$id <- c("Mixed","Mixed","Marine","Marine","Mixed","Mixed","Marine","Marine") totals crop_tot = as.data.frame(totals[c(1:4),]) both_tot = totals[c(5:8),] head(crop_tot) cols <- colorRampPalette((beyonce_palette(12))) myPal <- cols(length(unique(crop_tot$id))) #Change Order of Factors crop_tot$id <-factor(crop_tot$id,levels=c("Mixed","Marine")) #change order levels(crop_tot$id) ggplot(crop_tot, aes(x=scenario, y = no_regions, fill=factor(id))) + geom_bar(stat = "identity",position=position_dodge()) + #coord_flip()+ labs(x="", y = "No. Regions" )+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.text.x = element_text(size=13),axis.text.y = element_text(size=13), axis.title.x = element_text(size=15), axis.title.y = element_text(size=15), panel.background = element_blank(), axis.line = element_line(colour = "black"), panel.border = element_rect(colour = "white", fill=NA)) + scale_y_continuous(limits = c(0, 215))+ scale_fill_manual(values = myPal) ##--------------- #Total land plots #Number of countries pos or neg total_ha<-read.csv("Total_ha_plot_all.csv", header=TRUE) head(total_ha) #Change Order of Factors levels(total_ha$Scenario) #Order of default levels total_ha$Scenario <-factor(total_ha$Scenario,levels=c("Current","BAU","Mixed", "Marine")) #change order head(total_ha$Scenario) crop_tot = total_ha[c(2,4,6,8),] crop_tot cols <- colorRampPalette((beyonce_palette(66))) myPal <- cols(length(unique(crop_tot$Scenario))) pd <- position_dodge(0.1) ggplot(crop_tot, aes(x=Scenario, y = ha_total, fill=factor(Scenario))) + geom_bar(stat = "identity",position=position_dodge()) + geom_errorbar(aes(ymin=ha_total-ha_total_sd, ymax=ha_total+ha_total_sd), width=.2, position=pd)+ labs(x="", y = "Total Cropland Use (ha)" )+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.text.x = element_text(size=13),axis.text.y = element_text(size=13), axis.title.x = element_text(size=15), axis.title.y = element_text(size=15), panel.background = element_blank(), axis.line = element_line(colour = "black"), panel.border = element_rect(colour = "white", fill=NA)) + scale_fill_manual(values = rev(myPal)) ggplot(total_ha, aes(x=Scenario, y = ha_total, fill=factor(id))) + geom_bar(stat = "identity") + labs(x="", y = "Total Land Use (ha)" )+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.text.x = element_text(size=13),axis.text.y = element_text(size=13), axis.title.x = element_text(size=15), axis.title.y = element_text(size=15), panel.background = element_blank(), axis.line = element_line(colour = "black"), panel.border = element_rect(colour = "white", fill=NA)) + scale_fill_manual(values = myPal)

/Outputs/Landuse_Maps&plots.R

no_license

brunj7/aquaculture-crop-feed

R

false

5,473

r

library(maps) library(sp) library(maptools) library(mapdata) library(scales) library(mapproj) library(RColorBrewer) library(rgdal) library(ggplot2) library(beyonce) library(rworldmap) library(RColorBrewer) #Percent of land spared, used, or unchanged relative to BAU scenario prop<-read.csv("Percent_map_all.csv", header=TRUE) head(prop) sPDF.prop.nt <- joinCountryData2Map(prop ,joinCode = "ISO3", nameJoinColumn = "ISO3", verbose=TRUE) sPDF.prop <- spTransform(sPDF.prop.nt, CRS="+proj=moll +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84") ##CROP LANDUSE % MAPS------ numCats <- 6 colourPalette <- colorRampPalette(brewer.pal(numCats, "RdBu")) (6) catmethod=c(-1,-0.50,-0.25,0,0.25,0.50,1) #feed #quartz() par(mfrow = c(2, 1)) par(mar = c(2, 1, 1, 1) + 0.1) # c(bottom, left, top, right) mapParams<-mapCountryData(sPDF.prop,nameColumnToPlot='MIXED_crop', catMethod=catmethod, colourPalette=colourPalette, missingCountryCol="dark gray",addLegend=F,oceanCol="white", mapTitle=" ") do.call(addMapLegend, c(mapParams, legendLabels="all", legendWidth=2, digits=1, horizontal = T)) mapParamsMAR<-mapCountryData(sPDF.prop,nameColumnToPlot='MARINE_crop', catMethod=catmethod, colourPalette=colourPalette, missingCountryCol="dark gray", addLegend=F,oceanCol="white", mapTitle=" ") ##GRAZING + CROP % MAP(S)------- mapParams<-mapCountryData(sPDF.prop,nameColumnToPlot='MIXED_c.g', catMethod=catmethod, colourPalette=colourPalette, missingCountryCol="dark gray",addLegend=F,oceanCol="white", mapTitle=" ") do.call(addMapLegend, c(mapParams, legendLabels="all", legendWidth=2, digits=1, horizontal = T)) #MARINE SCENARIO NOT DEPICTED IN STUDY BECAUSE RESULT THE SAME #mapParamsMAR<-mapCountryData(sPDF.prop,nameColumnToPlot='MARINE_c.g', catMethod=catmethod, colourPalette=colourPalette, # missingCountryCol="dark gray", addLegend=F,oceanCol="white", mapTitle=" ") #do.call(addMapLegend, c(mapParamsMAR, legendLabels="all", legendWidth=7, digits=1, horizontal = F)) ##---------- #Number of countries pos (spared) or neg (used) land num_con<-read.csv("PosNeg_plot_all.csv", header=TRUE) head(num_con) counts = num_con[,-1] head(counts) totals <- as.data.frame(colSums(counts)) names(totals) = "no_regions" totals$scenario = c("Used","Spared","Used","Spared","Used","Spared","Used","Spared") totals$id <- c("Mixed","Mixed","Marine","Marine","Mixed","Mixed","Marine","Marine") totals crop_tot = as.data.frame(totals[c(1:4),]) both_tot = totals[c(5:8),] head(crop_tot) cols <- colorRampPalette((beyonce_palette(12))) myPal <- cols(length(unique(crop_tot$id))) #Change Order of Factors crop_tot$id <-factor(crop_tot$id,levels=c("Mixed","Marine")) #change order levels(crop_tot$id) ggplot(crop_tot, aes(x=scenario, y = no_regions, fill=factor(id))) + geom_bar(stat = "identity",position=position_dodge()) + #coord_flip()+ labs(x="", y = "No. Regions" )+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.text.x = element_text(size=13),axis.text.y = element_text(size=13), axis.title.x = element_text(size=15), axis.title.y = element_text(size=15), panel.background = element_blank(), axis.line = element_line(colour = "black"), panel.border = element_rect(colour = "white", fill=NA)) + scale_y_continuous(limits = c(0, 215))+ scale_fill_manual(values = myPal) ##--------------- #Total land plots #Number of countries pos or neg total_ha<-read.csv("Total_ha_plot_all.csv", header=TRUE) head(total_ha) #Change Order of Factors levels(total_ha$Scenario) #Order of default levels total_ha$Scenario <-factor(total_ha$Scenario,levels=c("Current","BAU","Mixed", "Marine")) #change order head(total_ha$Scenario) crop_tot = total_ha[c(2,4,6,8),] crop_tot cols <- colorRampPalette((beyonce_palette(66))) myPal <- cols(length(unique(crop_tot$Scenario))) pd <- position_dodge(0.1) ggplot(crop_tot, aes(x=Scenario, y = ha_total, fill=factor(Scenario))) + geom_bar(stat = "identity",position=position_dodge()) + geom_errorbar(aes(ymin=ha_total-ha_total_sd, ymax=ha_total+ha_total_sd), width=.2, position=pd)+ labs(x="", y = "Total Cropland Use (ha)" )+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.text.x = element_text(size=13),axis.text.y = element_text(size=13), axis.title.x = element_text(size=15), axis.title.y = element_text(size=15), panel.background = element_blank(), axis.line = element_line(colour = "black"), panel.border = element_rect(colour = "white", fill=NA)) + scale_fill_manual(values = rev(myPal)) ggplot(total_ha, aes(x=Scenario, y = ha_total, fill=factor(id))) + geom_bar(stat = "identity") + labs(x="", y = "Total Land Use (ha)" )+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.text.x = element_text(size=13),axis.text.y = element_text(size=13), axis.title.x = element_text(size=15), axis.title.y = element_text(size=15), panel.background = element_blank(), axis.line = element_line(colour = "black"), panel.border = element_rect(colour = "white", fill=NA)) + scale_fill_manual(values = myPal)

# call function is similar with python # args show the definition of funtcion print(args(sd)) # the parameter can be missing even if it hasn't a default value(lazy evaluation) # Q1 cube <- function(x, n) { x^3 } # defalt value # Q2 cube(3) pow <- function(x = 4, n = 3) { x^n } pow() # ... like C # argument after ... must be named explicitly!!! # search the environment search() # global environment and user's workspace is the first element to search # package:base is always the last. print(args(lm)) print(environment(lm)) lm <- function(x) {x ^ 2} print(args(lm)) print(environment(lm)) # show free variable ls(environment(lm)) # library() import a package, dan it will be the second to serach, the rest go down library(grid) search() # documentation library(datasets) data(iris)

/ComputingForDataAnalysis/PA2/function.R

no_license

ktargows/CourseRA

R

false

788

r

# call function is similar with python # args show the definition of funtcion print(args(sd)) # the parameter can be missing even if it hasn't a default value(lazy evaluation) # Q1 cube <- function(x, n) { x^3 } # defalt value # Q2 cube(3) pow <- function(x = 4, n = 3) { x^n } pow() # ... like C # argument after ... must be named explicitly!!! # search the environment search() # global environment and user's workspace is the first element to search # package:base is always the last. print(args(lm)) print(environment(lm)) lm <- function(x) {x ^ 2} print(args(lm)) print(environment(lm)) # show free variable ls(environment(lm)) # library() import a package, dan it will be the second to serach, the rest go down library(grid) search() # documentation library(datasets) data(iris)

library("data.table") library("ggplot2") # Initialization SCC <- as.data.table(x = readRDS(file = "Source_Classification_Code.rds")) NEI <- as.data.table(x = readRDS(file = "summarySCC_PM25.rds")) # Gather the subset of the NEI data which corresponds to vehicles condition <- grepl("vehicle", SCC[, SCC.Level.Two], ignore.case=TRUE) vehiclesSCC <- SCC[condition, SCC] vehiclesNEI <- NEI[NEI[, SCC] %in% vehiclesSCC,] # Subset the vehicles NEI data by each city's fip and add city name. vehiclesBaltimoreNEI <- vehiclesNEI[fips == "24510",] vehiclesBaltimoreNEI[, city := c("Baltimore City")] vehiclesLANEI <- vehiclesNEI[fips == "06037",] vehiclesLANEI[, city := c("Los Angeles")] # Combine data.tables into one data.table bothNEI <- rbind(vehiclesBaltimoreNEI,vehiclesLANEI) # Output graph png("plot6.png") # Plot graph using package ggplot2 ggplot(bothNEI, aes(x=factor(year), y=Emissions, fill=city)) + geom_bar(aes(fill=year),stat="identity") + facet_grid(scales="free", space="free", .~city) + labs(x="year", y=expression("Total PM"[2.5]*" Emission (Kilo-Tons)")) + labs(title=expression("PM"[2.5]*" Motor Vehicle Source Emissions in Baltimore & LA, 1999-2008")) dev.off()

/plot6.R

no_license

Ra1nOWL/Exploratory_Data_Analysis_Project_2

R

false

1,226

r

library("data.table") library("ggplot2") # Initialization SCC <- as.data.table(x = readRDS(file = "Source_Classification_Code.rds")) NEI <- as.data.table(x = readRDS(file = "summarySCC_PM25.rds")) # Gather the subset of the NEI data which corresponds to vehicles condition <- grepl("vehicle", SCC[, SCC.Level.Two], ignore.case=TRUE) vehiclesSCC <- SCC[condition, SCC] vehiclesNEI <- NEI[NEI[, SCC] %in% vehiclesSCC,] # Subset the vehicles NEI data by each city's fip and add city name. vehiclesBaltimoreNEI <- vehiclesNEI[fips == "24510",] vehiclesBaltimoreNEI[, city := c("Baltimore City")] vehiclesLANEI <- vehiclesNEI[fips == "06037",] vehiclesLANEI[, city := c("Los Angeles")] # Combine data.tables into one data.table bothNEI <- rbind(vehiclesBaltimoreNEI,vehiclesLANEI) # Output graph png("plot6.png") # Plot graph using package ggplot2 ggplot(bothNEI, aes(x=factor(year), y=Emissions, fill=city)) + geom_bar(aes(fill=year),stat="identity") + facet_grid(scales="free", space="free", .~city) + labs(x="year", y=expression("Total PM"[2.5]*" Emission (Kilo-Tons)")) + labs(title=expression("PM"[2.5]*" Motor Vehicle Source Emissions in Baltimore & LA, 1999-2008")) dev.off()

install.packages("shiny") install.packages("mc2d") install.packages("ggplot2") install.packages("scales") install.packages("dplyr") install.packages("reshape2") install.packages("countrycode") install.packages("shinydashboard")

/init.R

no_license

abhatia2014/Threat-Dashboard

R

false

229

r

install.packages("shiny") install.packages("mc2d") install.packages("ggplot2") install.packages("scales") install.packages("dplyr") install.packages("reshape2") install.packages("countrycode") install.packages("shinydashboard")

## LIBRARIES ====================================================================================== library("tidyverse") library("lubridate") library("splines") library("caret") library("recipes") ## DATA df_tr <- read_csv("./01_data/01_raw/train.csv") %>% mutate(partition = "train") df_ts <- read_csv("./01_data/01_raw/test.csv") %>% mutate(partition = "test") df_all <- bind_rows(df_tr, df_ts) df_all %>% select(Id, SalePrice, partition, everything()) ## MANUEL ENCODING ================================================================================ order_factors = FALSE df_all <- df_all %>% dplyr::rename(FrstFlrSF = `1stFlrSF`, ScndFlrSF = `2ndFlrSF`, ThrdSsnPorch = `3SsnPorch`) %>% dplyr::mutate( LotShape = factor(LotShape, levels = c("IR3", "IR2", "IR1", "Reg"), ordered = order_factors), Utilities = factor(Utilities, levels = c("ELO", "NoSeWa", "NoSewr", "AllPub"), ordered = order_factors), LandSlope = factor(LandSlope, levels = c("Sev", "Mod", "Gtl"), ordered = order_factors), OverallQual = factor(OverallQual, levels = c(1:10), ordered = order_factors), OverallCond = factor(OverallCond, levels = c(1:10), ordered = order_factors), ExterQual = factor(ExterQual, levels = c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), ExterCond = factor(ExterCond, levels = c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), BsmtQual = factor(BsmtQual, levels = c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), BsmtCond = factor(BsmtCond, levels = c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), BsmtExposure = factor(BsmtExposure, levels = c("No", "Mn", "Av", "Gd"), ordered = order_factors), BsmtFinType1 = factor(BsmtFinType1, levels = c("Unf", "LwQ", "Rec", "BLQ", "ALQ", "GLQ"), ordered = order_factors), BsmtFinType2 = factor(BsmtFinType2, levels = c("Unf", "LwQ", "Rec", "BLQ", "ALQ", "GLQ"), ordered = order_factors), HeatingQC = factor(HeatingQC, c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), KitchenQual = factor(KitchenQual, c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), Functional = factor(Functional, c("Sal", "Sev", "Maj2", "Maj1", "Mod", "Min2", "Min1", "Typ"), ordered = order_factors), FireplaceQu = factor(FireplaceQu, levels = c("TA", "Gd", "Ex"), ordered = order_factors), GarageFinish = factor(GarageFinish, levels = c("Uf", "RFn", "Fin"), ordered = order_factors), GarageQual = factor(GarageQual, levels = c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), GarageCond = factor(GarageCond, levels = c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), PavedDrive = factor(PavedDrive, levels = c("N", "P", "Y"), ordered = order_factors), PoolQC = factor(PoolQC, levels = c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), Fence = factor(Fence, levels = c("MnWw", "GdWo", "MnPrv", "GdPrv"), ordered = order_factors), MoSold = month(MoSold, label = ), MSSubClass = factor(paste0("MS", MSSubClass), ordered = order_factors) ) %>% dplyr::mutate_if(is_character, .funs = ~ factor(.x)) ## VISUAL ANALYSIS ================================================================================ ret_col_type <- function(df) { num_vars <- colnames(df_all)[map_lgl(df_all, is.numeric)] cat_vars <- colnames(df_all)[map_lgl(df_all, is.factor)] return(list(num = num_vars, cat = cat_vars)) } col_type <- ret_col_type(df_all) num_vars <- setdiff(col_type$num, c("SalePrice", "partition")) cat_vars <- setdiff(col_type$cat, c("SalePrice", "partition")) other_vars <- setdiff(colnames(df_all), union(union(num_vars, cat_vars), c("SalePrice", "partition"))) if (length(other_vars) > 0) { print(paste0(other_cols, " are neither numeric nor factors.")) } pred_vars <- setdiff(colnames(df_all), c("SalePrice", "partition", "Id")) # for (pred_var in pred_vars) { # if (pred_var %in% num_vars) { # p <- ggplot(df_all, aes_string(x = pred_var, y = "SalePrice")) + # geom_point() + # #geom_smooth(method = 'lm') + # geom_smooth(method = 'loess') # } else if (pred_var %in% cat_vars) { # p <- ggplot(df_all, aes_string(x = pred_var, y = "SalePrice")) + # geom_boxplot() # } # print(p) # Sys.sleep(5) # } df_all %>% ggplot(aes(x = OverallQual, y = SalePrice))+ geom_point() ## MISSING VALUES ================================================================================= getmode <- function(v) { uniqv <- unique(v) uniqv[which.max(tabulate(match(v, uniqv)))] } ## REMOVE OUTLIER # df_all <- df_all %>% filter(GrLivArea < 4000) #%>% select(Id, GrLivArea, SalePrice) #df_all <- df_all %>% filter(!Id %in% c(524, 1299)) ## MISSING VALUES miss_vals <- map_dfr(df_all, ~ sum(is.na(.x))) %>% gather() %>% filter(value > 0) %>% arrange(desc(value)) miss_vals %>% print(n = 200) # df_all[is.na(df_all$GarageYrBlt), c("GarageFinish", "GarageFinish", "GarageCond", "GarageQual", "GarageType", # "GarageCars", "GarageArea", "GarageYrBlt")] ## NA means "No": na_no <- c( "Alley", "PoolQC", "MiscFeature", "Fence", "FireplaceQu", "LotFrontage", ## "GarageFinish", "GarageFinish", "GarageCond", "GarageQual", "GarageType", "GarageCars", "GarageArea", "GarageYrBlt", ## "BsmtCond", "BsmtExposure", "BsmtQual", "BsmtFinType2", "BsmtFinType1", "BsmtFullBath", "BsmtHalfBath", "BsmtFinSF2", "BsmtUnfSF", "TotalBsmtSF" ) ## NA means "missing": na_missing <- c( "MasVnrType", "MasVnrArea", "MSZoning", "Utilities", "Functional", "Exterior1st", "Exterior2nd", "BsmtFinSF1", "Electrical", "KitchenQual", "GarageYrBlt", "SaleType" ) df_tr <- df_all %>% filter(partition == "train") df_ts <- df_all %>% filter(partition == "test") df_tr <- df_tr %>% mutate_at(na_no, function(x) { if (is.factor(x)) { x <- fct_explicit_na(x, na_level = "No") } else { x <- replace_na(x, replace = 0) } }) %>% mutate_at(na_missing, function(x) { if (is.factor(x)) { x <- fct_explicit_na(x, na_level = names(which.max(table(x)))) } else if (is.numeric(x)) { # x <- replace_na(x, replace = median(x, na.rm = T)) x <- replace_na(x, replace = median(x)) } }) df_ts <- df_ts %>% mutate_at(na_no, function(x) { if (is.factor(x)) { x <- fct_explicit_na(x, na_level = "No") } else { x <- replace_na(x, replace = 0) } }) %>% mutate_at(na_missing, function(x) { if (is.factor(x)) { x <- fct_explicit_na(x, na_level = names(which.max(table(x)))) } else if (is.numeric(x)) { # x <- replace_na(x, replace = median(x, na.rm = T)) x <- replace_na(x, replace = median(x)) } }) df_all <- bind_rows(df_tr, df_ts) map_dfr(df_all, ~ sum(is.na(.x))) %>% gather() %>% filter(value > 0) %>% arrange(desc(value)) ## SIMPLE FEATURES ================================================================================ five_level_simple <- c( "ExterQual", "ExterCond", "BsmtQual", "KitchenQual", "GarageQual", "BsmtCond", "GarageCond", "HeatingQC", "PoolQC" ) five_level_recode <- function(x) { fct_recode(x, bad = "Po", avgerage = "Fa", avgerage = "TA", good = "Gd", good = "Ex") } six_level_simple <- c("BsmtFinType1", "BsmtFinType2") six_level_recode <- function(x) { fct_recode(x, `1` = "Unf", `1` = "LwQ", `2` = "Rec", `2` = "BLQ", `3` = "ALQ", `3` = "GLQ" ) } ten_level_simple <- c("OverallQual", "OverallCond") ten_level_recode <- function(x) { fct_recode(x, `1` = "1", `1` = "2", `1` = "3", `2` = "4", `2` = "5", `2` = "6", `2` = "7", `3` = "8", `3` = "9", `3` = "10" ) } df_all <- df_all %>% dplyr::mutate_at(five_level_simple, .funs = list(Simple = five_level_recode) ) %>% dplyr::mutate_at(six_level_simple, .funs = list(Simple = six_level_recode) ) %>% dplyr::mutate_at(ten_level_simple, .funs = list(Simple = ten_level_recode) ) %>% dplyr::mutate_at("Functional", .funs = list(Simple = ~ fct_recode(.x, `1` = "Sal", `1` = "Sev", `1` = "Maj2", `2` = "Maj1", `2` = "Mod", `2` = "Min2", `3` = "Min1", `3` = "Typ" )) ) colnames(df_all) <- str_replace(colnames(df_all), "_", "") ## CREATE NEW FEATURES ============================================================================ ## df_all <- df_all %>% mutate( YearsSinceBuilt = pmax(YrSold - YearBuilt, 0), YearsSinceRemodel = pmax(YrSold - YearRemodAdd, 0), ExterGrade = as.numeric(ExterCond) * as.numeric(ExterQual), ExterGradeSimple = as.numeric(ExterCondSimple) * as.numeric(ExterQualSimple), OverallGrade = as.numeric(OverallCond) * as.numeric(OverallQual), OverallGradeSimple = as.numeric(OverallCondSimple) * as.numeric(OverallQualSimple), GarageGrade = as.numeric(GarageCond) * as.numeric(GarageQual), GarageGradeSimple = as.numeric(GarageCondSimple) * as.numeric(GarageQualSimple), KitchenScore = KitchenAbvGr * as.numeric(KitchenQual), KitchenScoreSimple = KitchenAbvGr * as.numeric(KitchenQualSimple), FireplaceScore = Fireplaces * as.numeric(FireplaceQu), GarageScore = GarageArea * as.numeric(GarageQual), GarageScoreSimple = GarageArea * as.numeric(GarageQualSimple), PoolScore = PoolArea * as.numeric(PoolQC), PoolScoreSimple = PoolArea * as.numeric(PoolQCSimple), # FireplaceScoreSimple = Fireplaces*as.numeric(FireplaceQuSimple), TotalBath = BsmtFullBath + .5 * BsmtHalfBath + .5 * HalfBath + FullBath, AllSF = GrLivArea + TotalBsmtSF, AllFlrsSF = FrstFlrSF + ScndFlrSF, AllPorchSF = OpenPorchSF + EnclosedPorch + ThrdSsnPorch + ScreenPorch + WoodDeckSF, HasMasVnr = fct_recode(MasVnrType, Yes = "BrkCmn", Yes = "BrkFace", Yes = "CBlock", Yes = "Stone", No = "None" ), BoughtOffPlan = fct_recode(SaleCondition, Yes = "Partial", No = "Abnorml", No = "Alloca", No = "AdjLand", No = "Family", No = "Normal" ), HasPool = ifelse(PoolArea > 0, "Yes", "No"), HasScnFloor = ifelse(ScndFlrSF > 0, "Yes", "No"), HasGarage = ifelse(GarageArea > 0, "Yes", "No"), HasBsmt = ifelse(BsmtFinSF1 > 0, "Yes", "No"), HasFireplace = ifelse(Fireplaces > 0, "Yes", "No") ) %>% dplyr::mutate_if(is_character, .funs = ~ factor(.x)) # predictor types col_type <- ret_col_type(df_all) num_pred_vars <- setdiff(col_type$num, c("SalePrice", "partition", "Id")) cat_pred_vars <- setdiff(col_type$cat, c("SalePrice", "partition", "Id")) other_vars <- setdiff(colnames(df_all), union(union(num_pred_vars, cat_pred_vars), c("SalePrice", "partition", "Id"))) if (length(other_vars) > 0) { print(paste0(other_cols, " are neither numeric nor factors.")) } ## TOP FEATURES BASED ON DIFFERENT CORRELATION COEFFICIENTS cor_type <- "pearson" ## pearson, spearman, kendall cor_df <- df_all[!is.na(df_all$SalePrice), c("SalePrice", num_pred_vars)] cor_vals <- sort(cor(cor_df, method = cor_type)[1, ], decreasing = T) top_features <- setdiff( names(cor_vals)[abs(cor_vals) > .55], "SalePrice" ) ## POLYNOMIAL TERMS # df_all <- df_all %>% # dplyr::mutate_at(top_features, .funs = list( # poly2 = ~ .x**2, # poly3 = ~ .x**3, # poly4 = ~.x**4, # sqrt = ~ .x**.5, # log = ~log(.x) # )) ## SPLINES # spl <- map( # df_all[top_features], # function(x) { # res <- bs(x, degree = 5) # res # } # ) %>% reduce(cbind) # # spl <- data.frame(spl) # colnames(spl) <- flatten_chr(map( # top_features, # ~ paste0(.x, paste0("_bs", c(1, 2, 3))) # )) # df_all <- bind_cols(df_all, spl) write_csv2(df_all, "./01_data/02_processed/train_test_stacked.csv")

/02_code/01_data_prep/data_prep.R

no_license

kaijennissen/advanced_regression

R

false

11,532

r

## LIBRARIES ====================================================================================== library("tidyverse") library("lubridate") library("splines") library("caret") library("recipes") ## DATA df_tr <- read_csv("./01_data/01_raw/train.csv") %>% mutate(partition = "train") df_ts <- read_csv("./01_data/01_raw/test.csv") %>% mutate(partition = "test") df_all <- bind_rows(df_tr, df_ts) df_all %>% select(Id, SalePrice, partition, everything()) ## MANUEL ENCODING ================================================================================ order_factors = FALSE df_all <- df_all %>% dplyr::rename(FrstFlrSF = `1stFlrSF`, ScndFlrSF = `2ndFlrSF`, ThrdSsnPorch = `3SsnPorch`) %>% dplyr::mutate( LotShape = factor(LotShape, levels = c("IR3", "IR2", "IR1", "Reg"), ordered = order_factors), Utilities = factor(Utilities, levels = c("ELO", "NoSeWa", "NoSewr", "AllPub"), ordered = order_factors), LandSlope = factor(LandSlope, levels = c("Sev", "Mod", "Gtl"), ordered = order_factors), OverallQual = factor(OverallQual, levels = c(1:10), ordered = order_factors), OverallCond = factor(OverallCond, levels = c(1:10), ordered = order_factors), ExterQual = factor(ExterQual, levels = c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), ExterCond = factor(ExterCond, levels = c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), BsmtQual = factor(BsmtQual, levels = c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), BsmtCond = factor(BsmtCond, levels = c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), BsmtExposure = factor(BsmtExposure, levels = c("No", "Mn", "Av", "Gd"), ordered = order_factors), BsmtFinType1 = factor(BsmtFinType1, levels = c("Unf", "LwQ", "Rec", "BLQ", "ALQ", "GLQ"), ordered = order_factors), BsmtFinType2 = factor(BsmtFinType2, levels = c("Unf", "LwQ", "Rec", "BLQ", "ALQ", "GLQ"), ordered = order_factors), HeatingQC = factor(HeatingQC, c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), KitchenQual = factor(KitchenQual, c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), Functional = factor(Functional, c("Sal", "Sev", "Maj2", "Maj1", "Mod", "Min2", "Min1", "Typ"), ordered = order_factors), FireplaceQu = factor(FireplaceQu, levels = c("TA", "Gd", "Ex"), ordered = order_factors), GarageFinish = factor(GarageFinish, levels = c("Uf", "RFn", "Fin"), ordered = order_factors), GarageQual = factor(GarageQual, levels = c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), GarageCond = factor(GarageCond, levels = c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), PavedDrive = factor(PavedDrive, levels = c("N", "P", "Y"), ordered = order_factors), PoolQC = factor(PoolQC, levels = c("Po", "Fa", "TA", "Gd", "Ex"), ordered = order_factors), Fence = factor(Fence, levels = c("MnWw", "GdWo", "MnPrv", "GdPrv"), ordered = order_factors), MoSold = month(MoSold, label = ), MSSubClass = factor(paste0("MS", MSSubClass), ordered = order_factors) ) %>% dplyr::mutate_if(is_character, .funs = ~ factor(.x)) ## VISUAL ANALYSIS ================================================================================ ret_col_type <- function(df) { num_vars <- colnames(df_all)[map_lgl(df_all, is.numeric)] cat_vars <- colnames(df_all)[map_lgl(df_all, is.factor)] return(list(num = num_vars, cat = cat_vars)) } col_type <- ret_col_type(df_all) num_vars <- setdiff(col_type$num, c("SalePrice", "partition")) cat_vars <- setdiff(col_type$cat, c("SalePrice", "partition")) other_vars <- setdiff(colnames(df_all), union(union(num_vars, cat_vars), c("SalePrice", "partition"))) if (length(other_vars) > 0) { print(paste0(other_cols, " are neither numeric nor factors.")) } pred_vars <- setdiff(colnames(df_all), c("SalePrice", "partition", "Id")) # for (pred_var in pred_vars) { # if (pred_var %in% num_vars) { # p <- ggplot(df_all, aes_string(x = pred_var, y = "SalePrice")) + # geom_point() + # #geom_smooth(method = 'lm') + # geom_smooth(method = 'loess') # } else if (pred_var %in% cat_vars) { # p <- ggplot(df_all, aes_string(x = pred_var, y = "SalePrice")) + # geom_boxplot() # } # print(p) # Sys.sleep(5) # } df_all %>% ggplot(aes(x = OverallQual, y = SalePrice))+ geom_point() ## MISSING VALUES ================================================================================= getmode <- function(v) { uniqv <- unique(v) uniqv[which.max(tabulate(match(v, uniqv)))] } ## REMOVE OUTLIER # df_all <- df_all %>% filter(GrLivArea < 4000) #%>% select(Id, GrLivArea, SalePrice) #df_all <- df_all %>% filter(!Id %in% c(524, 1299)) ## MISSING VALUES miss_vals <- map_dfr(df_all, ~ sum(is.na(.x))) %>% gather() %>% filter(value > 0) %>% arrange(desc(value)) miss_vals %>% print(n = 200) # df_all[is.na(df_all$GarageYrBlt), c("GarageFinish", "GarageFinish", "GarageCond", "GarageQual", "GarageType", # "GarageCars", "GarageArea", "GarageYrBlt")] ## NA means "No": na_no <- c( "Alley", "PoolQC", "MiscFeature", "Fence", "FireplaceQu", "LotFrontage", ## "GarageFinish", "GarageFinish", "GarageCond", "GarageQual", "GarageType", "GarageCars", "GarageArea", "GarageYrBlt", ## "BsmtCond", "BsmtExposure", "BsmtQual", "BsmtFinType2", "BsmtFinType1", "BsmtFullBath", "BsmtHalfBath", "BsmtFinSF2", "BsmtUnfSF", "TotalBsmtSF" ) ## NA means "missing": na_missing <- c( "MasVnrType", "MasVnrArea", "MSZoning", "Utilities", "Functional", "Exterior1st", "Exterior2nd", "BsmtFinSF1", "Electrical", "KitchenQual", "GarageYrBlt", "SaleType" ) df_tr <- df_all %>% filter(partition == "train") df_ts <- df_all %>% filter(partition == "test") df_tr <- df_tr %>% mutate_at(na_no, function(x) { if (is.factor(x)) { x <- fct_explicit_na(x, na_level = "No") } else { x <- replace_na(x, replace = 0) } }) %>% mutate_at(na_missing, function(x) { if (is.factor(x)) { x <- fct_explicit_na(x, na_level = names(which.max(table(x)))) } else if (is.numeric(x)) { # x <- replace_na(x, replace = median(x, na.rm = T)) x <- replace_na(x, replace = median(x)) } }) df_ts <- df_ts %>% mutate_at(na_no, function(x) { if (is.factor(x)) { x <- fct_explicit_na(x, na_level = "No") } else { x <- replace_na(x, replace = 0) } }) %>% mutate_at(na_missing, function(x) { if (is.factor(x)) { x <- fct_explicit_na(x, na_level = names(which.max(table(x)))) } else if (is.numeric(x)) { # x <- replace_na(x, replace = median(x, na.rm = T)) x <- replace_na(x, replace = median(x)) } }) df_all <- bind_rows(df_tr, df_ts) map_dfr(df_all, ~ sum(is.na(.x))) %>% gather() %>% filter(value > 0) %>% arrange(desc(value)) ## SIMPLE FEATURES ================================================================================ five_level_simple <- c( "ExterQual", "ExterCond", "BsmtQual", "KitchenQual", "GarageQual", "BsmtCond", "GarageCond", "HeatingQC", "PoolQC" ) five_level_recode <- function(x) { fct_recode(x, bad = "Po", avgerage = "Fa", avgerage = "TA", good = "Gd", good = "Ex") } six_level_simple <- c("BsmtFinType1", "BsmtFinType2") six_level_recode <- function(x) { fct_recode(x, `1` = "Unf", `1` = "LwQ", `2` = "Rec", `2` = "BLQ", `3` = "ALQ", `3` = "GLQ" ) } ten_level_simple <- c("OverallQual", "OverallCond") ten_level_recode <- function(x) { fct_recode(x, `1` = "1", `1` = "2", `1` = "3", `2` = "4", `2` = "5", `2` = "6", `2` = "7", `3` = "8", `3` = "9", `3` = "10" ) } df_all <- df_all %>% dplyr::mutate_at(five_level_simple, .funs = list(Simple = five_level_recode) ) %>% dplyr::mutate_at(six_level_simple, .funs = list(Simple = six_level_recode) ) %>% dplyr::mutate_at(ten_level_simple, .funs = list(Simple = ten_level_recode) ) %>% dplyr::mutate_at("Functional", .funs = list(Simple = ~ fct_recode(.x, `1` = "Sal", `1` = "Sev", `1` = "Maj2", `2` = "Maj1", `2` = "Mod", `2` = "Min2", `3` = "Min1", `3` = "Typ" )) ) colnames(df_all) <- str_replace(colnames(df_all), "_", "") ## CREATE NEW FEATURES ============================================================================ ## df_all <- df_all %>% mutate( YearsSinceBuilt = pmax(YrSold - YearBuilt, 0), YearsSinceRemodel = pmax(YrSold - YearRemodAdd, 0), ExterGrade = as.numeric(ExterCond) * as.numeric(ExterQual), ExterGradeSimple = as.numeric(ExterCondSimple) * as.numeric(ExterQualSimple), OverallGrade = as.numeric(OverallCond) * as.numeric(OverallQual), OverallGradeSimple = as.numeric(OverallCondSimple) * as.numeric(OverallQualSimple), GarageGrade = as.numeric(GarageCond) * as.numeric(GarageQual), GarageGradeSimple = as.numeric(GarageCondSimple) * as.numeric(GarageQualSimple), KitchenScore = KitchenAbvGr * as.numeric(KitchenQual), KitchenScoreSimple = KitchenAbvGr * as.numeric(KitchenQualSimple), FireplaceScore = Fireplaces * as.numeric(FireplaceQu), GarageScore = GarageArea * as.numeric(GarageQual), GarageScoreSimple = GarageArea * as.numeric(GarageQualSimple), PoolScore = PoolArea * as.numeric(PoolQC), PoolScoreSimple = PoolArea * as.numeric(PoolQCSimple), # FireplaceScoreSimple = Fireplaces*as.numeric(FireplaceQuSimple), TotalBath = BsmtFullBath + .5 * BsmtHalfBath + .5 * HalfBath + FullBath, AllSF = GrLivArea + TotalBsmtSF, AllFlrsSF = FrstFlrSF + ScndFlrSF, AllPorchSF = OpenPorchSF + EnclosedPorch + ThrdSsnPorch + ScreenPorch + WoodDeckSF, HasMasVnr = fct_recode(MasVnrType, Yes = "BrkCmn", Yes = "BrkFace", Yes = "CBlock", Yes = "Stone", No = "None" ), BoughtOffPlan = fct_recode(SaleCondition, Yes = "Partial", No = "Abnorml", No = "Alloca", No = "AdjLand", No = "Family", No = "Normal" ), HasPool = ifelse(PoolArea > 0, "Yes", "No"), HasScnFloor = ifelse(ScndFlrSF > 0, "Yes", "No"), HasGarage = ifelse(GarageArea > 0, "Yes", "No"), HasBsmt = ifelse(BsmtFinSF1 > 0, "Yes", "No"), HasFireplace = ifelse(Fireplaces > 0, "Yes", "No") ) %>% dplyr::mutate_if(is_character, .funs = ~ factor(.x)) # predictor types col_type <- ret_col_type(df_all) num_pred_vars <- setdiff(col_type$num, c("SalePrice", "partition", "Id")) cat_pred_vars <- setdiff(col_type$cat, c("SalePrice", "partition", "Id")) other_vars <- setdiff(colnames(df_all), union(union(num_pred_vars, cat_pred_vars), c("SalePrice", "partition", "Id"))) if (length(other_vars) > 0) { print(paste0(other_cols, " are neither numeric nor factors.")) } ## TOP FEATURES BASED ON DIFFERENT CORRELATION COEFFICIENTS cor_type <- "pearson" ## pearson, spearman, kendall cor_df <- df_all[!is.na(df_all$SalePrice), c("SalePrice", num_pred_vars)] cor_vals <- sort(cor(cor_df, method = cor_type)[1, ], decreasing = T) top_features <- setdiff( names(cor_vals)[abs(cor_vals) > .55], "SalePrice" ) ## POLYNOMIAL TERMS # df_all <- df_all %>% # dplyr::mutate_at(top_features, .funs = list( # poly2 = ~ .x**2, # poly3 = ~ .x**3, # poly4 = ~.x**4, # sqrt = ~ .x**.5, # log = ~log(.x) # )) ## SPLINES # spl <- map( # df_all[top_features], # function(x) { # res <- bs(x, degree = 5) # res # } # ) %>% reduce(cbind) # # spl <- data.frame(spl) # colnames(spl) <- flatten_chr(map( # top_features, # ~ paste0(.x, paste0("_bs", c(1, 2, 3))) # )) # df_all <- bind_cols(df_all, spl) write_csv2(df_all, "./01_data/02_processed/train_test_stacked.csv")

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/backup_operations.R \name{backup_describe_region_settings} \alias{backup_describe_region_settings} \title{Returns the current service opt-in settings for the Region} \usage{ backup_describe_region_settings() } \description{ Returns the current service opt-in settings for the Region. If service opt-in is enabled for a service, Backup tries to protect that service's resources in this Region, when the resource is included in an on-demand backup or scheduled backup plan. Otherwise, Backup does not try to protect that service's resources in this Region. See \url{https://www.paws-r-sdk.com/docs/backup_describe_region_settings/} for full documentation. } \keyword{internal}

/cran/paws.storage/man/backup_describe_region_settings.Rd

permissive

paws-r/paws

R

false

true

754

rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/backup_operations.R \name{backup_describe_region_settings} \alias{backup_describe_region_settings} \title{Returns the current service opt-in settings for the Region} \usage{ backup_describe_region_settings() } \description{ Returns the current service opt-in settings for the Region. If service opt-in is enabled for a service, Backup tries to protect that service's resources in this Region, when the resource is included in an on-demand backup or scheduled backup plan. Otherwise, Backup does not try to protect that service's resources in this Region. See \url{https://www.paws-r-sdk.com/docs/backup_describe_region_settings/} for full documentation. } \keyword{internal}

## The 'makeCacheMatrix' function contains four functions that allow the user to ## set a matrix and make it invertible. The 'cacheSolve' function allows the ## user to get the inverse of the previous matrix and cache it, as well as ## retrieve the cache. This is useful and saves time when attempting to retrieve ## the inverse of a matrix repeatedly. ## This function is used to create a matrix that is invertible. makeCacheMatrix <- function(x = matrix()) { m <- NULL set <- function(y) { x <<- y m <<- NULL } get <- function() x setmatrix <- function(solve) m <<- solve getmatrix <- function() m list(set = set, get = get, setmatrix = setmatrix, getmatrix = getmatrix) } ## This function inverts a matrix and caches it, or retrieves the cached matrix. cacheSolve <- function(x, ...) { m<-x$getmatrix() if(!is.null(m)){ message("getting cached data") return(m) } matrix<-x$get() m<-solve(matrix, ...) x$setmatrix(m) m }

/cachematrix.R

no_license

cervantesedd/ProgrammingAssignment2

R

false

1,084

r

## The 'makeCacheMatrix' function contains four functions that allow the user to ## set a matrix and make it invertible. The 'cacheSolve' function allows the ## user to get the inverse of the previous matrix and cache it, as well as ## retrieve the cache. This is useful and saves time when attempting to retrieve ## the inverse of a matrix repeatedly. ## This function is used to create a matrix that is invertible. makeCacheMatrix <- function(x = matrix()) { m <- NULL set <- function(y) { x <<- y m <<- NULL } get <- function() x setmatrix <- function(solve) m <<- solve getmatrix <- function() m list(set = set, get = get, setmatrix = setmatrix, getmatrix = getmatrix) } ## This function inverts a matrix and caches it, or retrieves the cached matrix. cacheSolve <- function(x, ...) { m<-x$getmatrix() if(!is.null(m)){ message("getting cached data") return(m) } matrix<-x$get() m<-solve(matrix, ...) x$setmatrix(m) m }

who = "World" input = commandArgs(trailingOnly = TRUE) if(length(input)) who = paste(input, collapse = " ") message("Hello ", who, "!")

/ggplot_interview/hello_world.R

no_license

michaellevy/catalyst_classes

R

false

141

r

who = "World" input = commandArgs(trailingOnly = TRUE) if(length(input)) who = paste(input, collapse = " ") message("Hello ", who, "!")

#Course Directory dir_local <- "exp_data_analysis_project2" if(!file.exists(dir_local)){ dir.create(dir_local) } #Set working directory setwd(dir_local) #download and extract data in working directory data_url <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip" destination_file <- "dataset.zip" if(!file.exists(destination_file)){ download.file(data_url, destfile = destination_file) unzip(destination_file) } if (!"nei_data" %in% ls()) { nei_data <- readRDS("summarySCC_PM25.rds") } if (!"scc_data" %in% ls()) { scc_data <- readRDS("Source_Classification_Code.rds") } #How have emissions from motor vehicle sources changed from 1999–2008 in Baltimore City? subset_data <- nei_data[nei_data$fips == "24510", ] par("mar"=c(5.1, 4.5, 4.1, 2.1)) png(filename = "plot5.png", width = 480, height = 480, units = "px") motor <- grep("motor", scc_data$Short.Name, ignore.case = T) motor <- scc_data[motor, ] motor <- subset_data[subset_data$SCC %in% motor$SCC, ] motorEmissions <- aggregate(motor$Emissions, list(motor$year), FUN = "sum") plot(motorEmissions, type = "l", xlab = "Year", main = "Total Emissions From Motor Vehicle Sources\n from 1999 to 2008 in Baltimore City", ylab = expression('Total PM'[2.5]*" Emission")) dev.off()

/Plot5.R

no_license

kumalok/ExData_Plotting2

R

false

1,266

r

#Course Directory dir_local <- "exp_data_analysis_project2" if(!file.exists(dir_local)){ dir.create(dir_local) } #Set working directory setwd(dir_local) #download and extract data in working directory data_url <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip" destination_file <- "dataset.zip" if(!file.exists(destination_file)){ download.file(data_url, destfile = destination_file) unzip(destination_file) } if (!"nei_data" %in% ls()) { nei_data <- readRDS("summarySCC_PM25.rds") } if (!"scc_data" %in% ls()) { scc_data <- readRDS("Source_Classification_Code.rds") } #How have emissions from motor vehicle sources changed from 1999–2008 in Baltimore City? subset_data <- nei_data[nei_data$fips == "24510", ] par("mar"=c(5.1, 4.5, 4.1, 2.1)) png(filename = "plot5.png", width = 480, height = 480, units = "px") motor <- grep("motor", scc_data$Short.Name, ignore.case = T) motor <- scc_data[motor, ] motor <- subset_data[subset_data$SCC %in% motor$SCC, ] motorEmissions <- aggregate(motor$Emissions, list(motor$year), FUN = "sum") plot(motorEmissions, type = "l", xlab = "Year", main = "Total Emissions From Motor Vehicle Sources\n from 1999 to 2008 in Baltimore City", ylab = expression('Total PM'[2.5]*" Emission")) dev.off()

#' Adds a Layer with Observations to a Profile Plot #' #' Function \code{\link{show_observations}} adds a layer to a plot created with #' \code{\link{plot.ceteris_paribus_explainer}} for selected observations. #' Various parameters help to decide what should be plotted, profiles, aggregated profiles, points or rugs. #' #' @param x a ceteris paribus explainer produced with function \code{ceteris_paribus()} #' @param ... other explainers that shall be plotted together #' @param color a character. Either name of a color or name of a variable that should be used for coloring #' @param size a numeric. Size of lines to be plotted #' @param alpha a numeric between 0 and 1. Opacity of lines #' @param variables if not \code{NULL} then only \code{variables} will be presented #' @param variable_type a character. If "numerical" then only numerical variables will be plotted. #' If "categorical" then only categorical variables will be plotted. #' #' @return a \code{ggplot2} layer #' #' @examples #' library("DALEX") #' library("randomForest") #' #' rf_model <- randomForest(survived == "yes" ~., #' data = titanic_imputed) #' #' explainer_rf <- explain(rf_model, data = titanic_imputed, #' y = titanic_imputed$survived == "yes", #' label = "RF", verbose = FALSE) #' #' selected_passangers <- select_sample(titanic_imputed, n = 100) #' cp_rf <- ceteris_paribus(explainer_rf, selected_passangers) #' cp_rf #' #' plot(cp_rf, variables = "age", color = "grey") + #' show_observations(cp_rf, variables = "age", color = "black") + #' show_rugs(cp_rf, variables = "age", color = "red") #' #' #' @export show_observations <- function(x, ..., size = 2, alpha = 1, color = "#371ea3", variable_type = "numerical", variables = NULL) { check_variable_type(variable_type) # if there is more explainers, they should be merged into a single data frame dfl <- c(list(x), list(...)) all_observations <- lapply(dfl, function(tmp) { attr(tmp, "observations") }) all_observations <- do.call(rbind, all_observations) all_observations$`_ids_` <- factor(rownames(all_observations)) # variables to use all_variables <- grep(colnames(all_observations), pattern = "^[^_]", value = TRUE) if (!is.null(variables)) { all_variables <- intersect(all_variables, variables) if (length(all_variables) == 0) stop(paste0("variables do not overlap with ", paste(all_variables, collapse = ", "))) } # only numerical or only factors? is_numeric <- sapply(all_observations[, all_variables, drop = FALSE], is.numeric) if (variable_type == "numerical") { vnames <- all_variables[which(is_numeric)] if (length(vnames) == 0) stop("There are no numerical variables") } else { vnames <- all_variables[which(!is_numeric)] if (length(vnames) == 0) stop("There are no non-numerical variables") } # prepare data for plotting points is_color_points_a_variable <- color %in% c(all_variables, "_label_", "_vname_", "_ids_") tmp <- lapply(vnames, function(var) { data.frame(`_x_` = all_observations[,var], `_vname_` = var, `_yhat_` = all_observations$`_yhat_`, `_y_` = if (is.null(all_observations$`_y_`)) NA else all_observations$`_y_`, `_color_` = if (!is_color_points_a_variable) NA else { if (color == "_vname_") var else all_observations[,color] }, `_ids_` = all_observations$`_ids_`, `_label_` = all_observations$`_label_`) }) all_observations_long <- do.call(rbind, tmp) colnames(all_observations_long) <- c("_x_", "_vname_", "_yhat_", "_y_", "_color_", "_ids_", "_label_") if ((is_color_points_a_variable ) & !(color %in% colnames(all_observations_long))) colnames(all_observations_long)[5] = color # show observations if (is_color_points_a_variable) { res <- geom_point(data = all_observations_long, aes_string(color = paste0("`",color,"`")), size = size, alpha = alpha) } else { res <- geom_point(data = all_observations_long, size = size, alpha = alpha, color = color) } res }

/R/show_observations.R

no_license

Alex33261/ingredients

R

false

4,300

r

#' Adds a Layer with Observations to a Profile Plot #' #' Function \code{\link{show_observations}} adds a layer to a plot created with #' \code{\link{plot.ceteris_paribus_explainer}} for selected observations. #' Various parameters help to decide what should be plotted, profiles, aggregated profiles, points or rugs. #' #' @param x a ceteris paribus explainer produced with function \code{ceteris_paribus()} #' @param ... other explainers that shall be plotted together #' @param color a character. Either name of a color or name of a variable that should be used for coloring #' @param size a numeric. Size of lines to be plotted #' @param alpha a numeric between 0 and 1. Opacity of lines #' @param variables if not \code{NULL} then only \code{variables} will be presented #' @param variable_type a character. If "numerical" then only numerical variables will be plotted. #' If "categorical" then only categorical variables will be plotted. #' #' @return a \code{ggplot2} layer #' #' @examples #' library("DALEX") #' library("randomForest") #' #' rf_model <- randomForest(survived == "yes" ~., #' data = titanic_imputed) #' #' explainer_rf <- explain(rf_model, data = titanic_imputed, #' y = titanic_imputed$survived == "yes", #' label = "RF", verbose = FALSE) #' #' selected_passangers <- select_sample(titanic_imputed, n = 100) #' cp_rf <- ceteris_paribus(explainer_rf, selected_passangers) #' cp_rf #' #' plot(cp_rf, variables = "age", color = "grey") + #' show_observations(cp_rf, variables = "age", color = "black") + #' show_rugs(cp_rf, variables = "age", color = "red") #' #' #' @export show_observations <- function(x, ..., size = 2, alpha = 1, color = "#371ea3", variable_type = "numerical", variables = NULL) { check_variable_type(variable_type) # if there is more explainers, they should be merged into a single data frame dfl <- c(list(x), list(...)) all_observations <- lapply(dfl, function(tmp) { attr(tmp, "observations") }) all_observations <- do.call(rbind, all_observations) all_observations$`_ids_` <- factor(rownames(all_observations)) # variables to use all_variables <- grep(colnames(all_observations), pattern = "^[^_]", value = TRUE) if (!is.null(variables)) { all_variables <- intersect(all_variables, variables) if (length(all_variables) == 0) stop(paste0("variables do not overlap with ", paste(all_variables, collapse = ", "))) } # only numerical or only factors? is_numeric <- sapply(all_observations[, all_variables, drop = FALSE], is.numeric) if (variable_type == "numerical") { vnames <- all_variables[which(is_numeric)] if (length(vnames) == 0) stop("There are no numerical variables") } else { vnames <- all_variables[which(!is_numeric)] if (length(vnames) == 0) stop("There are no non-numerical variables") } # prepare data for plotting points is_color_points_a_variable <- color %in% c(all_variables, "_label_", "_vname_", "_ids_") tmp <- lapply(vnames, function(var) { data.frame(`_x_` = all_observations[,var], `_vname_` = var, `_yhat_` = all_observations$`_yhat_`, `_y_` = if (is.null(all_observations$`_y_`)) NA else all_observations$`_y_`, `_color_` = if (!is_color_points_a_variable) NA else { if (color == "_vname_") var else all_observations[,color] }, `_ids_` = all_observations$`_ids_`, `_label_` = all_observations$`_label_`) }) all_observations_long <- do.call(rbind, tmp) colnames(all_observations_long) <- c("_x_", "_vname_", "_yhat_", "_y_", "_color_", "_ids_", "_label_") if ((is_color_points_a_variable ) & !(color %in% colnames(all_observations_long))) colnames(all_observations_long)[5] = color # show observations if (is_color_points_a_variable) { res <- geom_point(data = all_observations_long, aes_string(color = paste0("`",color,"`")), size = size, alpha = alpha) } else { res <- geom_point(data = all_observations_long, size = size, alpha = alpha, color = color) } res }

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/view_image.R \name{display_set} \alias{display_set} \title{Display 2D mosaic output as a plot image} \usage{ display_set(image_list, title = NULL) } \arguments{ \item{image_list}{List output from collect_bricks() or image_to_bricks(). Contains an element \code{Img_lego}.} \item{title}{Optional title to include above plotted mosaic.} } \description{ Display 2D mosaic output as a plot image }

/man/display_set.Rd

permissive

rpodcast/brickr

R

false

true

474

rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/view_image.R \name{display_set} \alias{display_set} \title{Display 2D mosaic output as a plot image} \usage{ display_set(image_list, title = NULL) } \arguments{ \item{image_list}{List output from collect_bricks() or image_to_bricks(). Contains an element \code{Img_lego}.} \item{title}{Optional title to include above plotted mosaic.} } \description{ Display 2D mosaic output as a plot image }

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/summarise.R \name{summarise_catval} \alias{summarise_catval} \title{Summarise a value in a column} \usage{ summarise_catval(grouped_form, colname, val) } \arguments{ \item{grouped_form}{any grouped dataframe} \item{colname}{name to column to summarise} } \value{ summary dataframe } \description{ Summarise the number and percentage of that value in a column }

/man/summarise_catval.Rd

no_license

EddieZhang540/INORMUS

R

false

true

440

rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/summarise.R \name{summarise_catval} \alias{summarise_catval} \title{Summarise a value in a column} \usage{ summarise_catval(grouped_form, colname, val) } \arguments{ \item{grouped_form}{any grouped dataframe} \item{colname}{name to column to summarise} } \value{ summary dataframe } \description{ Summarise the number and percentage of that value in a column }

############################################################################################ #' @title Produce a Summary Table of Data Product Health by Month #' @author Robert Lee \email{rlee@battelleecology.org}\cr #' @description For a specified data product ID, this function will produce a data frame of data #' product availability and validity for their period of record at a site. #' #' #' Because the full period of record for all sites are queried, #' this function can take a long time to execute. #' @inheritParams dp.survey #' #' @param dp.id Parameter of class character. The NEON data product code of the data product of #' interest. #' @param site Parameter of class character. The NEON site of interest. #' #' @param bgn.month Parameter of class character. The year-month (e.g. "2017-01") of the first month to get data for. #' @param end.month Parameter of class character. The year-month (e.g. "2017-01") of the last month to get data for. #' @param save.dir The directory for data files to be saved to. #' @return A data frame of health statisitcs by month for a given site. Raw NEON data are also saved to the specified save.dir, if supplied. #' @keywords process quality, data quality, gaps, commissioning, data product, health #' @examples #' # Summarize 2D wind perfomance at CPER: #' CPER_wind=dp.survey(dp.id = "DP1.00001.001", site="CPER") #' @export # changelog and author contributions / copyrights # Robert Lee (2017-11-21) # original creation # ############################################################################################## health.data= function(site, dp.id, bgn.month, end.month, save.dir){ if(missing(save.dir)){save.dir=tempdir()} pri.var=Noble::tis_pri_vars$data.field[which(Noble::tis_pri_vars$dp.id==dp.id)] var.name=gsub(pattern = "mean", replacement = "", x = pri.var, ignore.case = T) dp.avail = neon.avail(dp.id = dp.id) dp.avail = cbind(Month=dp.avail[,1], dp.avail[,which(colnames(dp.avail) %in% Noble::tis_site_config$site.id)]) temp.dates = zoo::as.Date(dp.avail$Month[ which( dp.avail[which(colnames(dp.avail)==site)]=="x" ) ] ) if(missing(bgn.month)&missing(end.month)){ run.dates = substr(temp.dates, start = 0, stop = 7) info.dates=run.dates }else if(missing(end.month)){ end.month=Sys.Date() info.dates=seq.Date(from=as.Date(paste0(bgn.month, "-01")), to=end.month, by="1 month") run.dates=base::substr(temp.dates[temp.dates %in% info.dates], start = 0, stop = 7) }else if(missing(bgn.month)){ info.dates=seq.Date(from=as.Date("2014-01-01"), to=end.month, by="1 month") run.dates=substr(temp.dates[temp.dates %in% info.dates], start = 0, stop = 7) }else{ info.dates=seq.Date(from=as.Date(paste0(bgn.month, "-01")), to=as.Date(paste0(end.month, "-01")), by="1 month") run.dates=substr(temp.dates[temp.dates %in% info.dates], start = 0, stop = 7) } health.data=data.frame(Month=substr(info.dates,0, 7), Availability=rep(0, times=length(info.dates)), Validity =rep(0, times=length(info.dates))) message(paste0("Working on ", site, "...")) if(length(run.dates)>0){ for(d in 1:length(run.dates)){ message(paste0("Downloading ", run.dates[d])) month.data<-try(Noble::pull.data(site = site, dp.id = dp.id, bgn.month = run.dates[d], end.month = run.dates[d], time.agr = 30, package = "basic", save.dir = save.dir)) if(!length(month.data)==0){ priData=data.frame(month.data[,grepl(pattern = pri.var, x = colnames(month.data), ignore.case = T)]) if(!length(priData)==0){ pcntData=round(sum(colSums(!is.na(priData)))/(length(priData[,1])*length(priData))*100, digits = 2) finalQFs=data.frame(month.data[,grepl(pattern = "*finalQF*", x = colnames(month.data), ignore.case = T)]) if(length(colnames(finalQFs))>length(colnames(priData))){ finalQFs=data.frame(finalQFs[,grepl(pattern = var.name, x = colnames(finalQFs), ignore.case = T)]) } pcntValid=round(100-(sum(colSums(finalQFs, na.rm = T))/(length(priData[,1])*length(priData))*100+ sum(colSums(is.na(finalQFs)))/(length(priData[,1])*length(finalQFs))*100), digits = 2) health.data$Availability[which(health.data$Month==run.dates[d])]=pcntData health.data$Validity[which(health.data$Month==run.dates[d])]=pcntValid} } }} return(health.data) }

/R/health_data.R

no_license

rhlee12/Noble

R

false

4,582

r

############################################################################################ #' @title Produce a Summary Table of Data Product Health by Month #' @author Robert Lee \email{rlee@battelleecology.org}\cr #' @description For a specified data product ID, this function will produce a data frame of data #' product availability and validity for their period of record at a site. #' #' #' Because the full period of record for all sites are queried, #' this function can take a long time to execute. #' @inheritParams dp.survey #' #' @param dp.id Parameter of class character. The NEON data product code of the data product of #' interest. #' @param site Parameter of class character. The NEON site of interest. #' #' @param bgn.month Parameter of class character. The year-month (e.g. "2017-01") of the first month to get data for. #' @param end.month Parameter of class character. The year-month (e.g. "2017-01") of the last month to get data for. #' @param save.dir The directory for data files to be saved to. #' @return A data frame of health statisitcs by month for a given site. Raw NEON data are also saved to the specified save.dir, if supplied. #' @keywords process quality, data quality, gaps, commissioning, data product, health #' @examples #' # Summarize 2D wind perfomance at CPER: #' CPER_wind=dp.survey(dp.id = "DP1.00001.001", site="CPER") #' @export # changelog and author contributions / copyrights # Robert Lee (2017-11-21) # original creation # ############################################################################################## health.data= function(site, dp.id, bgn.month, end.month, save.dir){ if(missing(save.dir)){save.dir=tempdir()} pri.var=Noble::tis_pri_vars$data.field[which(Noble::tis_pri_vars$dp.id==dp.id)] var.name=gsub(pattern = "mean", replacement = "", x = pri.var, ignore.case = T) dp.avail = neon.avail(dp.id = dp.id) dp.avail = cbind(Month=dp.avail[,1], dp.avail[,which(colnames(dp.avail) %in% Noble::tis_site_config$site.id)]) temp.dates = zoo::as.Date(dp.avail$Month[ which( dp.avail[which(colnames(dp.avail)==site)]=="x" ) ] ) if(missing(bgn.month)&missing(end.month)){ run.dates = substr(temp.dates, start = 0, stop = 7) info.dates=run.dates }else if(missing(end.month)){ end.month=Sys.Date() info.dates=seq.Date(from=as.Date(paste0(bgn.month, "-01")), to=end.month, by="1 month") run.dates=base::substr(temp.dates[temp.dates %in% info.dates], start = 0, stop = 7) }else if(missing(bgn.month)){ info.dates=seq.Date(from=as.Date("2014-01-01"), to=end.month, by="1 month") run.dates=substr(temp.dates[temp.dates %in% info.dates], start = 0, stop = 7) }else{ info.dates=seq.Date(from=as.Date(paste0(bgn.month, "-01")), to=as.Date(paste0(end.month, "-01")), by="1 month") run.dates=substr(temp.dates[temp.dates %in% info.dates], start = 0, stop = 7) } health.data=data.frame(Month=substr(info.dates,0, 7), Availability=rep(0, times=length(info.dates)), Validity =rep(0, times=length(info.dates))) message(paste0("Working on ", site, "...")) if(length(run.dates)>0){ for(d in 1:length(run.dates)){ message(paste0("Downloading ", run.dates[d])) month.data<-try(Noble::pull.data(site = site, dp.id = dp.id, bgn.month = run.dates[d], end.month = run.dates[d], time.agr = 30, package = "basic", save.dir = save.dir)) if(!length(month.data)==0){ priData=data.frame(month.data[,grepl(pattern = pri.var, x = colnames(month.data), ignore.case = T)]) if(!length(priData)==0){ pcntData=round(sum(colSums(!is.na(priData)))/(length(priData[,1])*length(priData))*100, digits = 2) finalQFs=data.frame(month.data[,grepl(pattern = "*finalQF*", x = colnames(month.data), ignore.case = T)]) if(length(colnames(finalQFs))>length(colnames(priData))){ finalQFs=data.frame(finalQFs[,grepl(pattern = var.name, x = colnames(finalQFs), ignore.case = T)]) } pcntValid=round(100-(sum(colSums(finalQFs, na.rm = T))/(length(priData[,1])*length(priData))*100+ sum(colSums(is.na(finalQFs)))/(length(priData[,1])*length(finalQFs))*100), digits = 2) health.data$Availability[which(health.data$Month==run.dates[d])]=pcntData health.data$Validity[which(health.data$Month==run.dates[d])]=pcntValid} } }} return(health.data) }

observe({ updateSelectizeInput(session, "plottype", choices = if (intro.numericnames()[1] == "") c("Mosaic Plot" = "mosaicplot") else if (intro.categoricnames()[1] == "") c("Histogram" = "histogram", "Normal Quantile Plot" = "quantileplot", "Scatterplot" = "scatterplot", "Line Chart" = "linechart") else c("Histogram" = "histogram", "Normal Quantile Plot" = "quantileplot", "Scatterplot" = "scatterplot", "Line Chart" = "linechart", "Boxplot" = "boxplot", "Bar Chart" = "barchart", "Pareto Chart" = "paretochart", "Mosaic Plot" = "mosaicplot")) }) observe({ input$plottype updateSelectizeInput(session, "x", choices = x_choices(), selected = x_selected()) updateSelectizeInput(session, "y", choices = y_choices(), selected = y_selected()) }) observeEvent(input$store_graphical, { cat(paste0("\n\n", paste(readLines(file.path(userdir, "code_graphical_reactive.R")), collapse = "\n")), file = file.path(userdir, "code_All.R"), append = TRUE) ## Fix this cat(paste0("\n\ninput_xdomain <- c(", input$xmin, ", ", input$xmax, ")\n"), file = file.path(userdir, "code_All.R"), append = TRUE) cat(paste0("input_ydomain <- c(", input$ymin, ", ", input$ymax, ")\n"), file = file.path(userdir, "code_All.R"), append = TRUE) cat(paste0("input_binwidth <- ", ifelse(is.na(input$binwidth), "NULL", input$binwidth), "\n"), file = file.path(userdir, "code_All.R"), append = TRUE) mystr <- paste0("X Variable: ", input$x, (if (input$plottype %in% c("histogram", "quantileplot")) "" else paste0("; Y Variable: ", input$y))) cat("\n\n", file = file.path(userdir, paste0("code_", input$plottype, ".R")), append = TRUE) cat(paste0("\n\n", paste(readLines(file.path(userdir, paste0("code_", input$plottype, ".R"))), collapse = "\n")), file = file.path(userdir, "code_All.R"), append = TRUE) if (input$plottype != "mosaicplot") cat(paste0("p.", input$plottype), file = file.path(userdir, "code_All.R"), append = TRUE) cat(paste0("\ncat('", mystr, "')"), file = file.path(userdir, "code_All.R"), append = TRUE) })

/modules/graphical/observe.R

no_license

elombardi-cleve/intRo

R

false

2,063

r

observe({ updateSelectizeInput(session, "plottype", choices = if (intro.numericnames()[1] == "") c("Mosaic Plot" = "mosaicplot") else if (intro.categoricnames()[1] == "") c("Histogram" = "histogram", "Normal Quantile Plot" = "quantileplot", "Scatterplot" = "scatterplot", "Line Chart" = "linechart") else c("Histogram" = "histogram", "Normal Quantile Plot" = "quantileplot", "Scatterplot" = "scatterplot", "Line Chart" = "linechart", "Boxplot" = "boxplot", "Bar Chart" = "barchart", "Pareto Chart" = "paretochart", "Mosaic Plot" = "mosaicplot")) }) observe({ input$plottype updateSelectizeInput(session, "x", choices = x_choices(), selected = x_selected()) updateSelectizeInput(session, "y", choices = y_choices(), selected = y_selected()) }) observeEvent(input$store_graphical, { cat(paste0("\n\n", paste(readLines(file.path(userdir, "code_graphical_reactive.R")), collapse = "\n")), file = file.path(userdir, "code_All.R"), append = TRUE) ## Fix this cat(paste0("\n\ninput_xdomain <- c(", input$xmin, ", ", input$xmax, ")\n"), file = file.path(userdir, "code_All.R"), append = TRUE) cat(paste0("input_ydomain <- c(", input$ymin, ", ", input$ymax, ")\n"), file = file.path(userdir, "code_All.R"), append = TRUE) cat(paste0("input_binwidth <- ", ifelse(is.na(input$binwidth), "NULL", input$binwidth), "\n"), file = file.path(userdir, "code_All.R"), append = TRUE) mystr <- paste0("X Variable: ", input$x, (if (input$plottype %in% c("histogram", "quantileplot")) "" else paste0("; Y Variable: ", input$y))) cat("\n\n", file = file.path(userdir, paste0("code_", input$plottype, ".R")), append = TRUE) cat(paste0("\n\n", paste(readLines(file.path(userdir, paste0("code_", input$plottype, ".R"))), collapse = "\n")), file = file.path(userdir, "code_All.R"), append = TRUE) if (input$plottype != "mosaicplot") cat(paste0("p.", input$plottype), file = file.path(userdir, "code_All.R"), append = TRUE) cat(paste0("\ncat('", mystr, "')"), file = file.path(userdir, "code_All.R"), append = TRUE) })

library(glmnet) mydata = read.table("../../../../TrainingSet/FullSet/Classifier/ovary.csv",head=T,sep=",") x = as.matrix(mydata[,4:ncol(mydata)]) y = as.matrix(mydata[,1]) set.seed(123) glm = cv.glmnet(x,y,nfolds=10,type.measure="mse",alpha=0.1,family="gaussian",standardize=TRUE) sink('./ovary_025.txt',append=TRUE) print(glm$glmnet.fit) sink()

/Model/EN/Classifier/ovary/ovary_025.R

no_license

esbgkannan/QSMART

R

false

346

r

library(glmnet) mydata = read.table("../../../../TrainingSet/FullSet/Classifier/ovary.csv",head=T,sep=",") x = as.matrix(mydata[,4:ncol(mydata)]) y = as.matrix(mydata[,1]) set.seed(123) glm = cv.glmnet(x,y,nfolds=10,type.measure="mse",alpha=0.1,family="gaussian",standardize=TRUE) sink('./ovary_025.txt',append=TRUE) print(glm$glmnet.fit) sink()

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/EDK.R \name{EDK} \alias{EDK} \title{EDK to be documented} \usage{ EDK(d, h, k = 2) } \arguments{ \item{d}{to be documented} \item{h}{to be documented} \item{k}{to be documented} } \value{ to be documented } \description{ EDK to be documented } \keyword{internal}

/man/EDK.Rd

no_license

shepherdmeng/mgwrsar

R

false

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/EDK.R \name{EDK} \alias{EDK} \title{EDK to be documented} \usage{ EDK(d, h, k = 2) } \arguments{ \item{d}{to be documented} \item{h}{to be documented} \item{k}{to be documented} } \value{ to be documented } \description{ EDK to be documented } \keyword{internal}

#loading data load("~/simulated_app_data.R") currentdata = new.data #loading required packages require(survey) require(MCMCpack) expit = function(x){exp(x)/(1+exp(x))} #parameter specifications bootstrap=TRUE K=1000 D = dim(currentdata$treatment)[2] n = dim(currentdata$fixed.X)[1] W = 100 # initializing data structures result = list() #ipw variables save.weights = matrix(NA,nrow=n,ncol=D) save.boot.weights = matrix(1,nrow=n,ncol=W) #overlap variables save.overlap = matrix(NA,nrow=n,ncol=D) save.boot.overlap = matrix(1,nrow=n,ncol=W) #ppta variables save.membership = array(NA,dim = c(n,K,D)) ppta.boot.save.membership = array(1,dim=c(n,K,W)) cross.probs = array(NA,dim = c(n,K,D)) ppta.size = matrix(NA,nrow=D,ncol=K) ppta.treated.size = matrix(NA,nrow=D,ncol=K) ppta.control.size = matrix(NA,nrow=D,ncol=K) #stabilized variables save.stable = matrix(NA,nrow=n,ncol=D) save.boot.stable = matrix(1,nrow=n,ncol=W) #specifying input data treatment = data.frame(currentdata$treatment) fixedcovar = data.frame(currentdata$fixed.X) timecovar = currentdata$time.X #naming input data names(fixedcovar) = paste("fixed.",names(fixedcovar),sep="") names(timecovar) = paste("time.",names(timecovar),sep="") treatment.considered = data.frame(treatment[,1]) names(treatment.considered) = "treat.1" time.covars.considered = data.frame(timecovar[[1]]) #sampling bootstrap draws if(bootstrap){ boot.indicies = matrix(sample(1:n,n*W,replace=TRUE),nrow=n,ncol=W) } else{ W = 1 } ################################################################### # iterate through time points for(d in 1:D){ data.matrix = cbind(treatment.considered,fixedcovar,time.covars.considered) #create datasets with bootstrapped indicies if(bootstrap){ ppta.boot.data = apply(boot.indicies,2,function(x){data.matrix[x,]}) } #specify formulas for propensity score model and model for stabilized weights if(d==1){ no.treat = names(data.matrix)[names(data.matrix)!="treat.1"] form = paste("treat.1 ~",paste(no.treat,collapse="+")) form2 = "treat.1~1" } else { current.treat = paste("treat.",d,sep="") no.treat = names(data.matrix)[names(data.matrix)!=current.treat] form = paste(current.treat,"~", paste(no.treat,collapse="+")) form2 = paste(current.treat,"~", paste(no.treat[grep("treat*",no.treat)],collapse="+")) } #################################################### # ALL WEIGHTING SCHEMES PS.fit = glm(form,data=data.matrix,family="binomial") sum.ps.fit = summary(PS.fit) ipw.PS.est = predict(PS.fit,type="response") stable.fit = glm(form2,data=data.matrix,family="binomial") stable.est = predict(stable.fit,type="response") ###calculate ipw/overlap/stable weights save.weights[,d] = treatment[,d]/ipw.PS.est + (1-treatment[,d])/(1-ipw.PS.est) save.overlap[,d] = treatment[,d]*(1- ipw.PS.est) + (1-treatment[,d])*ipw.PS.est save.stable[,d] = treatment[,d]*(stable.est/ipw.PS.est) + (1-treatment[,d])*((1-stable.est)/(1-ipw.PS.est)) ### bootstrapped MLE if(bootstrap){ for (w in 1:W){ boot.treatment = treatment[boot.indicies[,w],] PS.fit = glm(form,data=ppta.boot.data[[w]],family="binomial") sum.ps.fit = summary(PS.fit) PS.est = predict(PS.fit,type="response") stable.est = predict(glm(form2,data=ppta.boot.data[[w]],family="binomial"),type="response") save.boot.weights[,w] = save.boot.weights[,w]*(boot.treatment[,d]/PS.est + (1-boot.treatment[,d])/(1-PS.est)) save.boot.stable[,w] = save.boot.stable[,w]*(boot.treatment[,d]*(stable.est/PS.est) + (1-boot.treatment[,d])*((1-stable.est)/(1-PS.est))) save.boot.overlap[,w] = save.boot.overlap[,w]*(boot.treatment[,d]*(1- PS.est) + (1-boot.treatment[,d])*PS.est) } } ############################################### # PPTA #draws from PS distribution posteriors = as.matrix(MCMClogit(form,data=data.matrix,burnin=K/2,mcmc=K*20,thin=20)) save(posteriors,file=paste("~/final_app_",d,"_posteriors.R")) covars = as.matrix(cbind(rep(1,n),data.matrix[,-1])) ppta.PS.est = expit(covars%*%t(posteriors)) #PS estimated from draws of alpha # boostrapping for PPTA if(bootstrap){ ppta.boot.posteriors = lapply(ppta.boot.data,function(x){as.matrix(MCMClogit(form,data=x,burnin=K/10,mcmc=K*10,thin=10))}) ppta.boot.covars = lapply(ppta.boot.data,function(x){as.matrix(cbind(rep(1,n),x[,-1]))}) ppta.boot.PS = mapply(function(x,y){as.matrix(expit(x%*%t(y)))},x = ppta.boot.covars,y=ppta.boot.posteriors,SIMPLIFY = FALSE) ppta.boot.PS = simplify2array(ppta.boot.PS) } #calculate in/out group for(k in 1:K){ cross.prob = treatment[,d]*(1-ppta.PS.est[,k]) + (1-treatment[,d])*ppta.PS.est[,k] cross.probs[,k,d] = cross.prob if(bootstrap){ ppta.boot.treatment = apply(boot.indicies,2,function(x){treatment[x,d]}) ppta.boot.cross.prob = apply(matrix(1:W,ncol=W),2,function(x){ppta.boot.treatment[,x]*(1-ppta.boot.PS[,k,x])+(1-ppta.boot.treatment[,x])*ppta.boot.PS[,k,x]}) ppta.boot.save.membership[,k,] =ppta.boot.save.membership[,k,]*apply(ppta.boot.cross.prob,2,function(x){rbinom(n,1,x)}) } } #updating covariates if not last time point if(d<D){ treatment.considered = data.frame(treatment[,(d+1)],treatment[,d]) time.covars.considered = data.frame(timecovar[[d+1]],timecovar[[d]]) names(treatment.considered) = paste("treat.",seq(from=(d+1),to=(d)),sep="") names(time.covars.considered) = paste(rep(names(data.frame(timecovar[[d+1]])),2),rep(seq(from=(d+1),to=(d)),each=2),sep=".") } } save(save.weights,file = "~/save.weights.R") save(boot.indicies,file="~/boot.indicies.R") save(save.boot.weights,file="~/save.boot.weights.R") ############################################### # effect estimation #creating necessary data structures outcome = currentdata$outcome offset = currentdata$offset cum.treat = apply(treatment,1,sum,na.rm=TRUE) treat.out = data.frame(cum.treat,outcome,offset) treat.out$outcome.rate = outcome/offset treat.out.list = apply(boot.indicies,2,function(x,data){data[x,]},data=treat.out) # function for calculating bootstrapped SE coef.fxn = function(x,weights,data){ weights = weights[,x] dat = data[[x]] fit = glm(outcome~cum.treat+offset(log(offset)),data=dat,weights=weights,family="poisson") xx = summary(fit)$coef if(dim(xx)[1]>1){ return(xx[2,1]) }else{ return(NA) } } #####IPW print("IPW") outcome.weights = apply(save.weights,1,prod,na.rm=TRUE) ipw.weights = outcome.weights ipw.fit = glm(outcome~cum.treat+offset(log(offset)),data=treat.out,family="poisson",weights=outcome.weights) ipw.boots = apply(matrix(1:W,nrow=1),2,coef.fxn,weights=save.boot.weights,data=treat.out.list) ipw.preddelta = summary(ipw.fit)$coef[2,1] ipw.predse = summary(ipw.fit)$coef[2,2] ipw.bootse = sd(ipw.boots,na.rm=TRUE) ipw.lower = quantile(ipw.boots,prob=0.025) ipw.upper = quantile(ipw.boots,prob=0.975) ################## #overlap weighted regression print("overlap") outcome.weights = apply(save.overlap,1,prod,na.rm=TRUE) overlap.weights = outcome.weights overlap.fit = glm(outcome~cum.treat+offset(log(offset)),data=treat.out,family="poisson",weights=overlap.weights) overlap.boots = apply(matrix(1:W,nrow=1),2,coef.fxn,weights=save.boot.overlap,data=treat.out.list) overlap.preddelta = summary(overlap.fit)$coef[2,1] overlap.predse = summary(overlap.fit)$coef[2,2] overlap.bootse = sd(overlap.boots,na.rm=TRUE) overlap.lower = quantile(overlap.boots,prob=0.025) overlap.upper = quantile(overlap.boots,prob=0.975) ############################### #stablized weighting print("stabilized") outcome.weights = apply(save.stable,1,prod,na.rm=TRUE) stabilized.weights = outcome.weights stable.fit2 = glm(outcome~cum.treat+offset(log(offset)),data=treat.out,family="poisson",weights=outcome.weights) stable.boots = apply(matrix(1:W,nrow=1),2,coef.fxn,weights=save.boot.stable,data=treat.out.list) stabilized.preddelta = summary(stable.fit2)$coef[2,1] stabilized.predse = summary(stable.fit2)$coef[2,2] stabilized.bootse = sd(stable.boots,na.rm=TRUE) stabilized.lower = quantile(stable.boots,prob=0.025) stabilized.upper = quantile(stable.boots,prob=0.975) #################### #ppta print("PPTA") mean.boots = NA ppta.fail = rep(0,K) ppta.fit = rep(NA,K) ppta.boot.fit = matrix(NA,nrow = W,ncol = K) final.cross.probs = apply(cross.probs,c(1,2),prod) final.membership = apply(final.cross.probs,2,function(x){rbinom(n,1,prob=x)}) ppta.final.size = apply(final.membership,2,sum) for(k in 1:K){ is.in = final.membership[,k] if (sum(is.in)>5){ treat.out.in = treat.out[is.in==1,] ppta.fit[k] = coef(glm(outcome~cum.treat+offset(log(offset)),data = treat.out.in,family="poisson"))[2] } else { ppta.fit[k] = NA ppta.fail[k] = 1} } ppta.fxn = function(x,boot.data){ if(sum(x)>10){ boot.data.in = boot.data[x==1,] return(coef(glm(outcome ~ cum.treat + offset(log(offset)),data = boot.data.in,family="poisson"))[2]) } else{return(NA)} } for(w in 1:W){ #estimate ATE boot.data = treat.out[boot.indicies[,w],] boot.is.in = matrix(ppta.boot.save.membership[,,w],nrow=n) ppta.boot.fit[w,] = apply(boot.is.in,2,ppta.fxn,boot.data = boot.data) } ppta.fail.perc = mean(ppta.fail) ppta.marginal = apply(final.membership,1,mean,na.rm=TRUE) ppta.once = mean(apply(final.membership,1,max,na.rm=TRUE),na.rm=TRUE) ppta.preddelta = NA ppta.predse = NA ppta.boot.predse = NA mean.boots = NA if(any(!is.na(ppta.fit))){ ppta.preddelta = mean(ppta.fit,na.rm=TRUE) ppta.predse = sd(apply(ppta.boot.fit,1,mean,na.rm=TRUE),na.rm=TRUE) } if(any(!is.na(ppta.boot.fit))){ ppta.boot.predse = sd(ppta.boot.fit,na.rm=TRUE) mean.boots = apply(ppta.boot.fit,1,mean,na.rm=TRUE) ppta.lower = quantile(mean.boots,0.025) ppta.upper = quantile(mean.boots,0.975)} ########################### #save results result = list(list(ipw.preddelta,ipw.predse,ipw.bootse,ipw.weights),list(ppta.preddelta,ppta.predse, ppta.marginal,ppta.once,ppta.size, ppta.treated.size,ppta.control.size,ppta.fail.perc),list(overlap.preddelta, overlap.predse,overlap.bootse,overlap.weights),list(stabilized.preddelta,stabilized.predse,stabilized.bootse,stabilized.weights)) save(result,file="~/final_app_result.R")

/runIPW_boot_app.R

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shirleyxliao/ppta-2019

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10,509

r

#loading data load("~/simulated_app_data.R") currentdata = new.data #loading required packages require(survey) require(MCMCpack) expit = function(x){exp(x)/(1+exp(x))} #parameter specifications bootstrap=TRUE K=1000 D = dim(currentdata$treatment)[2] n = dim(currentdata$fixed.X)[1] W = 100 # initializing data structures result = list() #ipw variables save.weights = matrix(NA,nrow=n,ncol=D) save.boot.weights = matrix(1,nrow=n,ncol=W) #overlap variables save.overlap = matrix(NA,nrow=n,ncol=D) save.boot.overlap = matrix(1,nrow=n,ncol=W) #ppta variables save.membership = array(NA,dim = c(n,K,D)) ppta.boot.save.membership = array(1,dim=c(n,K,W)) cross.probs = array(NA,dim = c(n,K,D)) ppta.size = matrix(NA,nrow=D,ncol=K) ppta.treated.size = matrix(NA,nrow=D,ncol=K) ppta.control.size = matrix(NA,nrow=D,ncol=K) #stabilized variables save.stable = matrix(NA,nrow=n,ncol=D) save.boot.stable = matrix(1,nrow=n,ncol=W) #specifying input data treatment = data.frame(currentdata$treatment) fixedcovar = data.frame(currentdata$fixed.X) timecovar = currentdata$time.X #naming input data names(fixedcovar) = paste("fixed.",names(fixedcovar),sep="") names(timecovar) = paste("time.",names(timecovar),sep="") treatment.considered = data.frame(treatment[,1]) names(treatment.considered) = "treat.1" time.covars.considered = data.frame(timecovar[[1]]) #sampling bootstrap draws if(bootstrap){ boot.indicies = matrix(sample(1:n,n*W,replace=TRUE),nrow=n,ncol=W) } else{ W = 1 } ################################################################### # iterate through time points for(d in 1:D){ data.matrix = cbind(treatment.considered,fixedcovar,time.covars.considered) #create datasets with bootstrapped indicies if(bootstrap){ ppta.boot.data = apply(boot.indicies,2,function(x){data.matrix[x,]}) } #specify formulas for propensity score model and model for stabilized weights if(d==1){ no.treat = names(data.matrix)[names(data.matrix)!="treat.1"] form = paste("treat.1 ~",paste(no.treat,collapse="+")) form2 = "treat.1~1" } else { current.treat = paste("treat.",d,sep="") no.treat = names(data.matrix)[names(data.matrix)!=current.treat] form = paste(current.treat,"~", paste(no.treat,collapse="+")) form2 = paste(current.treat,"~", paste(no.treat[grep("treat*",no.treat)],collapse="+")) } #################################################### # ALL WEIGHTING SCHEMES PS.fit = glm(form,data=data.matrix,family="binomial") sum.ps.fit = summary(PS.fit) ipw.PS.est = predict(PS.fit,type="response") stable.fit = glm(form2,data=data.matrix,family="binomial") stable.est = predict(stable.fit,type="response") ###calculate ipw/overlap/stable weights save.weights[,d] = treatment[,d]/ipw.PS.est + (1-treatment[,d])/(1-ipw.PS.est) save.overlap[,d] = treatment[,d]*(1- ipw.PS.est) + (1-treatment[,d])*ipw.PS.est save.stable[,d] = treatment[,d]*(stable.est/ipw.PS.est) + (1-treatment[,d])*((1-stable.est)/(1-ipw.PS.est)) ### bootstrapped MLE if(bootstrap){ for (w in 1:W){ boot.treatment = treatment[boot.indicies[,w],] PS.fit = glm(form,data=ppta.boot.data[[w]],family="binomial") sum.ps.fit = summary(PS.fit) PS.est = predict(PS.fit,type="response") stable.est = predict(glm(form2,data=ppta.boot.data[[w]],family="binomial"),type="response") save.boot.weights[,w] = save.boot.weights[,w]*(boot.treatment[,d]/PS.est + (1-boot.treatment[,d])/(1-PS.est)) save.boot.stable[,w] = save.boot.stable[,w]*(boot.treatment[,d]*(stable.est/PS.est) + (1-boot.treatment[,d])*((1-stable.est)/(1-PS.est))) save.boot.overlap[,w] = save.boot.overlap[,w]*(boot.treatment[,d]*(1- PS.est) + (1-boot.treatment[,d])*PS.est) } } ############################################### # PPTA #draws from PS distribution posteriors = as.matrix(MCMClogit(form,data=data.matrix,burnin=K/2,mcmc=K*20,thin=20)) save(posteriors,file=paste("~/final_app_",d,"_posteriors.R")) covars = as.matrix(cbind(rep(1,n),data.matrix[,-1])) ppta.PS.est = expit(covars%*%t(posteriors)) #PS estimated from draws of alpha # boostrapping for PPTA if(bootstrap){ ppta.boot.posteriors = lapply(ppta.boot.data,function(x){as.matrix(MCMClogit(form,data=x,burnin=K/10,mcmc=K*10,thin=10))}) ppta.boot.covars = lapply(ppta.boot.data,function(x){as.matrix(cbind(rep(1,n),x[,-1]))}) ppta.boot.PS = mapply(function(x,y){as.matrix(expit(x%*%t(y)))},x = ppta.boot.covars,y=ppta.boot.posteriors,SIMPLIFY = FALSE) ppta.boot.PS = simplify2array(ppta.boot.PS) } #calculate in/out group for(k in 1:K){ cross.prob = treatment[,d]*(1-ppta.PS.est[,k]) + (1-treatment[,d])*ppta.PS.est[,k] cross.probs[,k,d] = cross.prob if(bootstrap){ ppta.boot.treatment = apply(boot.indicies,2,function(x){treatment[x,d]}) ppta.boot.cross.prob = apply(matrix(1:W,ncol=W),2,function(x){ppta.boot.treatment[,x]*(1-ppta.boot.PS[,k,x])+(1-ppta.boot.treatment[,x])*ppta.boot.PS[,k,x]}) ppta.boot.save.membership[,k,] =ppta.boot.save.membership[,k,]*apply(ppta.boot.cross.prob,2,function(x){rbinom(n,1,x)}) } } #updating covariates if not last time point if(d<D){ treatment.considered = data.frame(treatment[,(d+1)],treatment[,d]) time.covars.considered = data.frame(timecovar[[d+1]],timecovar[[d]]) names(treatment.considered) = paste("treat.",seq(from=(d+1),to=(d)),sep="") names(time.covars.considered) = paste(rep(names(data.frame(timecovar[[d+1]])),2),rep(seq(from=(d+1),to=(d)),each=2),sep=".") } } save(save.weights,file = "~/save.weights.R") save(boot.indicies,file="~/boot.indicies.R") save(save.boot.weights,file="~/save.boot.weights.R") ############################################### # effect estimation #creating necessary data structures outcome = currentdata$outcome offset = currentdata$offset cum.treat = apply(treatment,1,sum,na.rm=TRUE) treat.out = data.frame(cum.treat,outcome,offset) treat.out$outcome.rate = outcome/offset treat.out.list = apply(boot.indicies,2,function(x,data){data[x,]},data=treat.out) # function for calculating bootstrapped SE coef.fxn = function(x,weights,data){ weights = weights[,x] dat = data[[x]] fit = glm(outcome~cum.treat+offset(log(offset)),data=dat,weights=weights,family="poisson") xx = summary(fit)$coef if(dim(xx)[1]>1){ return(xx[2,1]) }else{ return(NA) } } #####IPW print("IPW") outcome.weights = apply(save.weights,1,prod,na.rm=TRUE) ipw.weights = outcome.weights ipw.fit = glm(outcome~cum.treat+offset(log(offset)),data=treat.out,family="poisson",weights=outcome.weights) ipw.boots = apply(matrix(1:W,nrow=1),2,coef.fxn,weights=save.boot.weights,data=treat.out.list) ipw.preddelta = summary(ipw.fit)$coef[2,1] ipw.predse = summary(ipw.fit)$coef[2,2] ipw.bootse = sd(ipw.boots,na.rm=TRUE) ipw.lower = quantile(ipw.boots,prob=0.025) ipw.upper = quantile(ipw.boots,prob=0.975) ################## #overlap weighted regression print("overlap") outcome.weights = apply(save.overlap,1,prod,na.rm=TRUE) overlap.weights = outcome.weights overlap.fit = glm(outcome~cum.treat+offset(log(offset)),data=treat.out,family="poisson",weights=overlap.weights) overlap.boots = apply(matrix(1:W,nrow=1),2,coef.fxn,weights=save.boot.overlap,data=treat.out.list) overlap.preddelta = summary(overlap.fit)$coef[2,1] overlap.predse = summary(overlap.fit)$coef[2,2] overlap.bootse = sd(overlap.boots,na.rm=TRUE) overlap.lower = quantile(overlap.boots,prob=0.025) overlap.upper = quantile(overlap.boots,prob=0.975) ############################### #stablized weighting print("stabilized") outcome.weights = apply(save.stable,1,prod,na.rm=TRUE) stabilized.weights = outcome.weights stable.fit2 = glm(outcome~cum.treat+offset(log(offset)),data=treat.out,family="poisson",weights=outcome.weights) stable.boots = apply(matrix(1:W,nrow=1),2,coef.fxn,weights=save.boot.stable,data=treat.out.list) stabilized.preddelta = summary(stable.fit2)$coef[2,1] stabilized.predse = summary(stable.fit2)$coef[2,2] stabilized.bootse = sd(stable.boots,na.rm=TRUE) stabilized.lower = quantile(stable.boots,prob=0.025) stabilized.upper = quantile(stable.boots,prob=0.975) #################### #ppta print("PPTA") mean.boots = NA ppta.fail = rep(0,K) ppta.fit = rep(NA,K) ppta.boot.fit = matrix(NA,nrow = W,ncol = K) final.cross.probs = apply(cross.probs,c(1,2),prod) final.membership = apply(final.cross.probs,2,function(x){rbinom(n,1,prob=x)}) ppta.final.size = apply(final.membership,2,sum) for(k in 1:K){ is.in = final.membership[,k] if (sum(is.in)>5){ treat.out.in = treat.out[is.in==1,] ppta.fit[k] = coef(glm(outcome~cum.treat+offset(log(offset)),data = treat.out.in,family="poisson"))[2] } else { ppta.fit[k] = NA ppta.fail[k] = 1} } ppta.fxn = function(x,boot.data){ if(sum(x)>10){ boot.data.in = boot.data[x==1,] return(coef(glm(outcome ~ cum.treat + offset(log(offset)),data = boot.data.in,family="poisson"))[2]) } else{return(NA)} } for(w in 1:W){ #estimate ATE boot.data = treat.out[boot.indicies[,w],] boot.is.in = matrix(ppta.boot.save.membership[,,w],nrow=n) ppta.boot.fit[w,] = apply(boot.is.in,2,ppta.fxn,boot.data = boot.data) } ppta.fail.perc = mean(ppta.fail) ppta.marginal = apply(final.membership,1,mean,na.rm=TRUE) ppta.once = mean(apply(final.membership,1,max,na.rm=TRUE),na.rm=TRUE) ppta.preddelta = NA ppta.predse = NA ppta.boot.predse = NA mean.boots = NA if(any(!is.na(ppta.fit))){ ppta.preddelta = mean(ppta.fit,na.rm=TRUE) ppta.predse = sd(apply(ppta.boot.fit,1,mean,na.rm=TRUE),na.rm=TRUE) } if(any(!is.na(ppta.boot.fit))){ ppta.boot.predse = sd(ppta.boot.fit,na.rm=TRUE) mean.boots = apply(ppta.boot.fit,1,mean,na.rm=TRUE) ppta.lower = quantile(mean.boots,0.025) ppta.upper = quantile(mean.boots,0.975)} ########################### #save results result = list(list(ipw.preddelta,ipw.predse,ipw.bootse,ipw.weights),list(ppta.preddelta,ppta.predse, ppta.marginal,ppta.once,ppta.size, ppta.treated.size,ppta.control.size,ppta.fail.perc),list(overlap.preddelta, overlap.predse,overlap.bootse,overlap.weights),list(stabilized.preddelta,stabilized.predse,stabilized.bootse,stabilized.weights)) save(result,file="~/final_app_result.R")

############################ ### function: plotMixture() ############################ plotMixture=function(fit,dist="overall",curve="survival", xlab=NULL,ylab=NULL,main=NULL,col=NULL,lty=NULL,lwd=1,axes=T){ est=c(fit,curvesLogAFT(fit)) plotNPMLEsurv(est=est,dist=dist,curve=curve,type="l", xlab=xlab,ylab=ylab,main=main,col=col,lty=lty,lwd=lwd,axes=axes) } ############################ ### function: curvesLogAFT() ############################ curvesLogAFT=function(fit){ n.group=length(fit$groups.obs$labels) buffer=apply(as.matrix(fit$groups.obs$labels),1,strsplit,split=",") matrix.cov=matrix(1,length(buffer),1+length(buffer[[1]][[1]])) for(k in 1:length(buffer)) matrix.cov[k,]=c(1,as.numeric(buffer[[k]][[1]])) ### "p1", "xbeta", "xgamma", "xalpha", "xq" p1=as.matrix(rep(1,dim(matrix.cov)[1])) if (!is.null(fit$covariates$beta)){ xbeta=as.matrix(matrix.cov[,fit$covariates$beta])%*%as.vector(fit$par$beta) p1=exp(xbeta)/(1+exp(xbeta)) if(!is.null(fit$mixturetype)){ index=which(fit$mixturetype==1) if(length(index)>0) p1[index,]=1 } } xgamma=as.matrix(matrix.cov[,fit$covariates$gamma])%*%as.vector(fit$par$gamma) xalpha=as.matrix(matrix.cov[,fit$covariates$alpha])%*%as.vector(fit$par$alpha) if(is.null(fit$fix.par$q)){ xq=as.matrix(matrix.cov[,fit$covariates$q])%*%as.vector(fit$par$q) }else{ xq=matrix(fit$fix.par$q,n.group) } ### "surv" time=seq(fit$minmax.time[1],fit$minmax.time[2],by=0.01) y=log(time-fit$time.origin) surv=list() for (i in 1:n.group){ w=(y-xgamma[i])/exp(xalpha[i]) q=rep(xq[i],length(w)) surv1=as.data.frame(matrix(NA,length(time),7)) names(surv1)=c("time","survival","survivalD","density","densityD","hazard","hazardD") surv1[,"time"]=time surv1[,"densityD"]=dw.fun(w,q)/(time*exp(xalpha[i])) surv1[,"survivalD"]=Sw.fun(w,q) surv1[,"hazardD"]=surv1[,"densityD"]/surv1[,"survivalD"] index=which(is.na(surv1[,"hazardD"])) if(length(index)>0){ if(min(index)>1) surv1[index,"hazardD"]=surv1[min(index)-1,"hazardD"] } surv1[,"density"]=p1[i]*surv1[,"densityD"] surv1[,"survival"]=p1[i]*surv1[,"survivalD"]+(1-p1[i]) surv1[,"hazard"]=surv1[,"density"]/surv1[,"survival"] surv[[i]]=surv1 } names(surv)=fit$groups.obs$labels ### "PD", "PC" PD=round(as.vector(p1),3) PC=1-PD return(list(surv=surv,PD=PD,PC=PC)) }

/R/plotMixture.R

no_license

cran/MixtureRegLTIC

R

false

2,487

r

############################ ### function: plotMixture() ############################ plotMixture=function(fit,dist="overall",curve="survival", xlab=NULL,ylab=NULL,main=NULL,col=NULL,lty=NULL,lwd=1,axes=T){ est=c(fit,curvesLogAFT(fit)) plotNPMLEsurv(est=est,dist=dist,curve=curve,type="l", xlab=xlab,ylab=ylab,main=main,col=col,lty=lty,lwd=lwd,axes=axes) } ############################ ### function: curvesLogAFT() ############################ curvesLogAFT=function(fit){ n.group=length(fit$groups.obs$labels) buffer=apply(as.matrix(fit$groups.obs$labels),1,strsplit,split=",") matrix.cov=matrix(1,length(buffer),1+length(buffer[[1]][[1]])) for(k in 1:length(buffer)) matrix.cov[k,]=c(1,as.numeric(buffer[[k]][[1]])) ### "p1", "xbeta", "xgamma", "xalpha", "xq" p1=as.matrix(rep(1,dim(matrix.cov)[1])) if (!is.null(fit$covariates$beta)){ xbeta=as.matrix(matrix.cov[,fit$covariates$beta])%*%as.vector(fit$par$beta) p1=exp(xbeta)/(1+exp(xbeta)) if(!is.null(fit$mixturetype)){ index=which(fit$mixturetype==1) if(length(index)>0) p1[index,]=1 } } xgamma=as.matrix(matrix.cov[,fit$covariates$gamma])%*%as.vector(fit$par$gamma) xalpha=as.matrix(matrix.cov[,fit$covariates$alpha])%*%as.vector(fit$par$alpha) if(is.null(fit$fix.par$q)){ xq=as.matrix(matrix.cov[,fit$covariates$q])%*%as.vector(fit$par$q) }else{ xq=matrix(fit$fix.par$q,n.group) } ### "surv" time=seq(fit$minmax.time[1],fit$minmax.time[2],by=0.01) y=log(time-fit$time.origin) surv=list() for (i in 1:n.group){ w=(y-xgamma[i])/exp(xalpha[i]) q=rep(xq[i],length(w)) surv1=as.data.frame(matrix(NA,length(time),7)) names(surv1)=c("time","survival","survivalD","density","densityD","hazard","hazardD") surv1[,"time"]=time surv1[,"densityD"]=dw.fun(w,q)/(time*exp(xalpha[i])) surv1[,"survivalD"]=Sw.fun(w,q) surv1[,"hazardD"]=surv1[,"densityD"]/surv1[,"survivalD"] index=which(is.na(surv1[,"hazardD"])) if(length(index)>0){ if(min(index)>1) surv1[index,"hazardD"]=surv1[min(index)-1,"hazardD"] } surv1[,"density"]=p1[i]*surv1[,"densityD"] surv1[,"survival"]=p1[i]*surv1[,"survivalD"]+(1-p1[i]) surv1[,"hazard"]=surv1[,"density"]/surv1[,"survival"] surv[[i]]=surv1 } names(surv)=fit$groups.obs$labels ### "PD", "PC" PD=round(as.vector(p1),3) PC=1-PD return(list(surv=surv,PD=PD,PC=PC)) }

library(glmnet) mydata = read.table("./TrainingSet/ReliefF/NSCLC.csv",head=T,sep=",") x = as.matrix(mydata[,4:ncol(mydata)]) y = as.matrix(mydata[,1]) set.seed(123) glm = cv.glmnet(x,y,nfolds=10,type.measure="mse",alpha=0.35,family="gaussian",standardize=FALSE) sink('./Model/EN/ReliefF/NSCLC/NSCLC_046.txt',append=TRUE) print(glm$glmnet.fit) sink()

/Model/EN/ReliefF/NSCLC/NSCLC_046.R

no_license

leon1003/QSMART

R

false

350

r

library(glmnet) mydata = read.table("./TrainingSet/ReliefF/NSCLC.csv",head=T,sep=",") x = as.matrix(mydata[,4:ncol(mydata)]) y = as.matrix(mydata[,1]) set.seed(123) glm = cv.glmnet(x,y,nfolds=10,type.measure="mse",alpha=0.35,family="gaussian",standardize=FALSE) sink('./Model/EN/ReliefF/NSCLC/NSCLC_046.txt',append=TRUE) print(glm$glmnet.fit) sink()

setClass("RENAMECOL", representation = representation( strEqcCommand = "character", colInRename = "character", colOutRename = "character" ), prototype = prototype( strEqcCommand = "", colInRename = "", colOutRename = "" ) #contains = c("EcfReader") ) setGeneric("setRENAMECOL", function(object) standardGeneric("setRENAMECOL")) setMethod("setRENAMECOL", signature = (object = "RENAMECOL"), function(object) { aEqcSlotNamesIn = c("colInRename", "colOutRename") objEqcReader <- EqcReader(object@strEqcCommand,aEqcSlotNamesIn) if(length(objEqcReader@lsEqcSlotsOut) > 0) { for(i in 1:length(objEqcReader@lsEqcSlotsOut)) { tmpSlot <- names(objEqcReader@lsEqcSlotsOut)[i] tmpSlotVal <- objEqcReader@lsEqcSlotsOut[[i]] #if(all(!is.na(tmpSlotVal))) slot(object, tmpSlot) <- tmpSlotVal if(any(!is.na(tmpSlotVal))) slot(object, tmpSlot) <- tmpSlotVal } } return(object) }) ############################################################################################################################# validRENAMECOL <- function(objRNC) { if(objRNC@colInRename == "") stop(paste(" EASY ERROR:RENAMECOL\n No colInRename defined. Please set colInRename.", sep="")) if(objRNC@colOutRename == "") stop(paste(" EASY ERROR:RENAMECOL\n No colOutRename defined. Please set colOutRename.", sep="")) return(TRUE) } ############################################################################################################################# RENAMECOL.run <- function(objRNC, objGWA, objREPORT) { colInRename <- objRNC@colInRename colOutRename <- objRNC@colOutRename iMatchIn = match(objRNC@colInRename,objGWA@aHeader) iMatchOut = match(objRNC@colOutRename,objGWA@aHeader) isAnyRenamed = FALSE if(!is.na(iMatchIn) & is.na(iMatchOut)) { ### Column must be renamed, colOut is not in table! #names(objGWA@tblGWA)[iMatchIn] <- objGWA@aHeader[iMatchIn] <- objRNC@colOutRename objGWA <- GWADATA.renamecol(objGWA, colInRename, colOutRename) isAnyRenamed = TRUE # } else if(!is.na(iMatchIn) & !is.na(iMatchOut)) { # ### Column colInRename AND colOutRename are already in table # names(objGWA@tblGWA)[iMatchOut] <- objGWA@aHeader[iMatchOut] <- paste(objGWA@aHeader[iMatchOut],".old",sep="") # names(objGWA@tblGWA)[iMatchIn] <- objGWA@aHeader[iMatchIn] <- objRNC@colOutRename # isAnyRenamed = TRUE } else { if(!is.na(iMatchOut)) stop(paste("EASY ERROR:RENAMECOL\n New column name \n ",colOutRename,"\n does already exist!!!\n", sep="")) if(is.na(iMatchIn)) stop(paste("EASY ERROR:RENAMECOL\n Column \n ",colInRename,"\n does not exist!!!\n", sep="")) } if(!("numColRenamed" %in% names(objREPORT@tblReport))) objREPORT <- REPORT.addval(objREPORT, "numColRenamed", 0) else if(REPORT.getval(objREPORT, "numColRenamed") == "NA") objREPORT <- REPORT.addval(objREPORT, "numColRenamed", 0) if(isAnyRenamed) { ## Update Report numColRenamed = numColRenamed + 1 strNumColRenamed = REPORT.getval(objREPORT, "numColRenamed") numColRenamedNew = as.numeric(strNumColRenamed) + 1 objREPORT <- REPORT.setval(objREPORT, "numColRenamed", numColRenamedNew) } return(list(objGWA,objREPORT)) } RENAMECOL <- function(strEqcCommand){ ## Wrapper for class definition RENAMECOLout <- setRENAMECOL(new("RENAMECOL", strEqcCommand = strEqcCommand)) validRENAMECOL(RENAMECOLout) #RENAMECOLout.valid <- validRENAMECOL(RENAMECOLout) return(RENAMECOLout) #validECF(ECFout) #return(ECFout) ## Identical: # ECFin <- new("ECF5", fileECF = fileECFIn) # ECFout <- setECF5(ECFin) # return(ECFout) } # setValidity("RENAMECOL", function(object){ # print("RENAMECOL-CHECK") # print(TRUE) # return(TRUE) # })

/EasyQC/R/clsRENAMECOL3.r

no_license

barbarathorslund/warfarin

R

false

3,862

r

setClass("RENAMECOL", representation = representation( strEqcCommand = "character", colInRename = "character", colOutRename = "character" ), prototype = prototype( strEqcCommand = "", colInRename = "", colOutRename = "" ) #contains = c("EcfReader") ) setGeneric("setRENAMECOL", function(object) standardGeneric("setRENAMECOL")) setMethod("setRENAMECOL", signature = (object = "RENAMECOL"), function(object) { aEqcSlotNamesIn = c("colInRename", "colOutRename") objEqcReader <- EqcReader(object@strEqcCommand,aEqcSlotNamesIn) if(length(objEqcReader@lsEqcSlotsOut) > 0) { for(i in 1:length(objEqcReader@lsEqcSlotsOut)) { tmpSlot <- names(objEqcReader@lsEqcSlotsOut)[i] tmpSlotVal <- objEqcReader@lsEqcSlotsOut[[i]] #if(all(!is.na(tmpSlotVal))) slot(object, tmpSlot) <- tmpSlotVal if(any(!is.na(tmpSlotVal))) slot(object, tmpSlot) <- tmpSlotVal } } return(object) }) ############################################################################################################################# validRENAMECOL <- function(objRNC) { if(objRNC@colInRename == "") stop(paste(" EASY ERROR:RENAMECOL\n No colInRename defined. Please set colInRename.", sep="")) if(objRNC@colOutRename == "") stop(paste(" EASY ERROR:RENAMECOL\n No colOutRename defined. Please set colOutRename.", sep="")) return(TRUE) } ############################################################################################################################# RENAMECOL.run <- function(objRNC, objGWA, objREPORT) { colInRename <- objRNC@colInRename colOutRename <- objRNC@colOutRename iMatchIn = match(objRNC@colInRename,objGWA@aHeader) iMatchOut = match(objRNC@colOutRename,objGWA@aHeader) isAnyRenamed = FALSE if(!is.na(iMatchIn) & is.na(iMatchOut)) { ### Column must be renamed, colOut is not in table! #names(objGWA@tblGWA)[iMatchIn] <- objGWA@aHeader[iMatchIn] <- objRNC@colOutRename objGWA <- GWADATA.renamecol(objGWA, colInRename, colOutRename) isAnyRenamed = TRUE # } else if(!is.na(iMatchIn) & !is.na(iMatchOut)) { # ### Column colInRename AND colOutRename are already in table # names(objGWA@tblGWA)[iMatchOut] <- objGWA@aHeader[iMatchOut] <- paste(objGWA@aHeader[iMatchOut],".old",sep="") # names(objGWA@tblGWA)[iMatchIn] <- objGWA@aHeader[iMatchIn] <- objRNC@colOutRename # isAnyRenamed = TRUE } else { if(!is.na(iMatchOut)) stop(paste("EASY ERROR:RENAMECOL\n New column name \n ",colOutRename,"\n does already exist!!!\n", sep="")) if(is.na(iMatchIn)) stop(paste("EASY ERROR:RENAMECOL\n Column \n ",colInRename,"\n does not exist!!!\n", sep="")) } if(!("numColRenamed" %in% names(objREPORT@tblReport))) objREPORT <- REPORT.addval(objREPORT, "numColRenamed", 0) else if(REPORT.getval(objREPORT, "numColRenamed") == "NA") objREPORT <- REPORT.addval(objREPORT, "numColRenamed", 0) if(isAnyRenamed) { ## Update Report numColRenamed = numColRenamed + 1 strNumColRenamed = REPORT.getval(objREPORT, "numColRenamed") numColRenamedNew = as.numeric(strNumColRenamed) + 1 objREPORT <- REPORT.setval(objREPORT, "numColRenamed", numColRenamedNew) } return(list(objGWA,objREPORT)) } RENAMECOL <- function(strEqcCommand){ ## Wrapper for class definition RENAMECOLout <- setRENAMECOL(new("RENAMECOL", strEqcCommand = strEqcCommand)) validRENAMECOL(RENAMECOLout) #RENAMECOLout.valid <- validRENAMECOL(RENAMECOLout) return(RENAMECOLout) #validECF(ECFout) #return(ECFout) ## Identical: # ECFin <- new("ECF5", fileECF = fileECFIn) # ECFout <- setECF5(ECFin) # return(ECFout) } # setValidity("RENAMECOL", function(object){ # print("RENAMECOL-CHECK") # print(TRUE) # return(TRUE) # })

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/aiplatform_objects.R \name{GoogleCloudAiplatformV1EvaluatedAnnotationExplanation} \alias{GoogleCloudAiplatformV1EvaluatedAnnotationExplanation} \title{GoogleCloudAiplatformV1EvaluatedAnnotationExplanation Object} \usage{ GoogleCloudAiplatformV1EvaluatedAnnotationExplanation( explanationType = NULL, explanation = NULL ) } \arguments{ \item{explanationType}{Explanation type} \item{explanation}{Explanation attribution response details} } \value{ GoogleCloudAiplatformV1EvaluatedAnnotationExplanation object } \description{ GoogleCloudAiplatformV1EvaluatedAnnotationExplanation Object } \details{ Autogenerated via \code{\link[googleAuthR]{gar_create_api_objects}} Explanation result of the prediction produced by the Model. } \concept{GoogleCloudAiplatformV1EvaluatedAnnotationExplanation functions}

/googleaiplatformv1.auto/man/GoogleCloudAiplatformV1EvaluatedAnnotationExplanation.Rd

no_license

justinjm/autoGoogleAPI

R

false

true

884

rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/aiplatform_objects.R \name{GoogleCloudAiplatformV1EvaluatedAnnotationExplanation} \alias{GoogleCloudAiplatformV1EvaluatedAnnotationExplanation} \title{GoogleCloudAiplatformV1EvaluatedAnnotationExplanation Object} \usage{ GoogleCloudAiplatformV1EvaluatedAnnotationExplanation( explanationType = NULL, explanation = NULL ) } \arguments{ \item{explanationType}{Explanation type} \item{explanation}{Explanation attribution response details} } \value{ GoogleCloudAiplatformV1EvaluatedAnnotationExplanation object } \description{ GoogleCloudAiplatformV1EvaluatedAnnotationExplanation Object } \details{ Autogenerated via \code{\link[googleAuthR]{gar_create_api_objects}} Explanation result of the prediction produced by the Model. } \concept{GoogleCloudAiplatformV1EvaluatedAnnotationExplanation functions}

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/EmptyRemoteFileResponse.r \docType{data} \name{EmptyRemoteFileResponse} \alias{EmptyRemoteFileResponse} \title{EmptyRemoteFileResponse Class} \format{An object of class \code{R6ClassGenerator} of length 24.} \usage{ EmptyRemoteFileResponse } \description{ EmptyRemoteFileResponse Class } \section{Fields}{ \describe{ \item{\code{message}}{success or failure} \item{\code{result}}{empty result} \item{\code{status}}{success or failure} }} \keyword{datasets}

/man/EmptyRemoteFileResponse.Rd

permissive

agaveplatform/r-sdk

R

false

true

539

rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/EmptyRemoteFileResponse.r \docType{data} \name{EmptyRemoteFileResponse} \alias{EmptyRemoteFileResponse} \title{EmptyRemoteFileResponse Class} \format{An object of class \code{R6ClassGenerator} of length 24.} \usage{ EmptyRemoteFileResponse } \description{ EmptyRemoteFileResponse Class } \section{Fields}{ \describe{ \item{\code{message}}{success or failure} \item{\code{result}}{empty result} \item{\code{status}}{success or failure} }} \keyword{datasets}

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/KeggPathwayView.R \docType{methods} \name{arrangePathview} \alias{arrangePathview} \title{Kegg pathway view and arrange grobs on page} \usage{ arrangePathview( genelist, pathways = c(), top = 4, ncol = 2, title = NULL, sub = NULL, organism = "hsa", output = ".", path.archive = ".", kegg.native = TRUE, verbose = TRUE ) } \arguments{ \item{genelist}{a data frame with columns of ENTREZID, Control and Treatment. The columns of Control and Treatment represent gene score in Control and Treatment sample.} \item{pathways}{character vector, the KEGG pathway ID(s), usually 5 digit, may also include the 3 letter KEGG species code.} \item{top}{integer, specifying how many top enriched pathways to be visualized.} \item{ncol}{integer, specifying how many column of figures to be arranged in each page.} \item{title}{optional string, or grob.} \item{sub}{optional string, or grob.} \item{organism}{character, either the kegg code, scientific name or the common name of the target species. This applies to both pathway and gene.data or cpd.data. When KEGG ortholog pathway is considered, species="ko". Default species="hsa", it is equivalent to use either "Homo sapiens" (scientific name) or "human" (common name).} \item{output}{Path to save plot to.} \item{path.archive}{character, the directory of KEGG pathway data file (.xml) and image file (.png). Users may supply their own data files in the same format and naming convention of KEGG's (species code + pathway id, e.g. hsa04110.xml, hsa04110.png etc) in this directory. Default kegg.dir="." (current working directory).} \item{kegg.native}{logical, whether to render pathway graph as native KEGG graph (.png) or using graphviz layout engine (.pdf). Default kegg.native=TRUE.} \item{verbose}{Boolean} } \value{ plot on the current device } \description{ Kegg pathway view and arrange grobs on page. } \examples{ file3 = file.path(system.file("extdata", package = "MAGeCKFlute"), "testdata/mle.gene_summary.txt") dd = ReadBeta(file3) colnames(dd)[2:3] = c("Control", "Treatment") # arrangePathview(dd, c("hsa00534"), title=NULL, sub=NULL, organism="hsa") } \author{ Wubing Zhang }

/man/arrangePathview.Rd

no_license

sysyangb/MAGeCKFlute

R

false

true

2,243

rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/KeggPathwayView.R \docType{methods} \name{arrangePathview} \alias{arrangePathview} \title{Kegg pathway view and arrange grobs on page} \usage{ arrangePathview( genelist, pathways = c(), top = 4, ncol = 2, title = NULL, sub = NULL, organism = "hsa", output = ".", path.archive = ".", kegg.native = TRUE, verbose = TRUE ) } \arguments{ \item{genelist}{a data frame with columns of ENTREZID, Control and Treatment. The columns of Control and Treatment represent gene score in Control and Treatment sample.} \item{pathways}{character vector, the KEGG pathway ID(s), usually 5 digit, may also include the 3 letter KEGG species code.} \item{top}{integer, specifying how many top enriched pathways to be visualized.} \item{ncol}{integer, specifying how many column of figures to be arranged in each page.} \item{title}{optional string, or grob.} \item{sub}{optional string, or grob.} \item{organism}{character, either the kegg code, scientific name or the common name of the target species. This applies to both pathway and gene.data or cpd.data. When KEGG ortholog pathway is considered, species="ko". Default species="hsa", it is equivalent to use either "Homo sapiens" (scientific name) or "human" (common name).} \item{output}{Path to save plot to.} \item{path.archive}{character, the directory of KEGG pathway data file (.xml) and image file (.png). Users may supply their own data files in the same format and naming convention of KEGG's (species code + pathway id, e.g. hsa04110.xml, hsa04110.png etc) in this directory. Default kegg.dir="." (current working directory).} \item{kegg.native}{logical, whether to render pathway graph as native KEGG graph (.png) or using graphviz layout engine (.pdf). Default kegg.native=TRUE.} \item{verbose}{Boolean} } \value{ plot on the current device } \description{ Kegg pathway view and arrange grobs on page. } \examples{ file3 = file.path(system.file("extdata", package = "MAGeCKFlute"), "testdata/mle.gene_summary.txt") dd = ReadBeta(file3) colnames(dd)[2:3] = c("Control", "Treatment") # arrangePathview(dd, c("hsa00534"), title=NULL, sub=NULL, organism="hsa") } \author{ Wubing Zhang }

# capitalize words cw <- function(s, strict = FALSE, onlyfirst = FALSE) { cap <- function(s) paste(toupper(substring(s,1,1)), {s <- substring(s,2); if(strict) tolower(s) else s}, sep = "", collapse = " " ) if(!onlyfirst){ sapply(strsplit(s, split = " "), cap, USE.NAMES = !is.null(names(s))) } else { sapply(s, function(x) paste(toupper(substring(x,1,1)), tolower(substring(x,2)), sep="", collapse=" "), USE.NAMES=F) } } # extract pattern from a string strextract <- function(str, pattern) regmatches(str, regexpr(pattern, str)) # trim space from beginning and end of strings str_trim_ <- function(str) gsub("^\\s+|\\s+$", "", str) # remove zero length strings nozero <- function(x) { x[nzchar(x)] } checker <- function(x, type, len) { if (!length(x) %in% len) stop(sprintf("%s input should be of length %s", type, p0c(len)), call. = FALSE) if (!is.double(x)) stop(sprintf("%s input should be of type double (a number)", type), call. = FALSE) } fmtcheck <- function(x) { if (!is.double(x) || is.na(x)) stop("fmt must be an integer value", call. = FALSE) if (x < 0 || x > 20) stop("fmt must be 0 and 20", call. = FALSE) } # decfmt <- function(pts, fmt) { # rapply(pts, format, nsmall = fmt, trim = TRUE, how = "list") # } centroid <- function(x, center){ if (!is.null(center)) { stopifnot(is.numeric(center)) return(center) } else { if ("geometry" %in% names(x)) { obj <- x$geometry$coordinates # tryasnum <- tryCatch(as.numeric(obj), warning = function(w) w) if (!is(obj, "list")) { obj } else { # sapply(obj, function(z) sapply(z, function(b) b[2])) lngs <- rapply(obj, function(x) x[1]) # sapply(obj, function(z) sapply(z, function(b) b[1])) lats <- rapply(obj, function(x) x[2]) c(mean(as.numeric(lngs)), mean(as.numeric(lats))) } } else { c( mean(as.numeric(sapply(x$coordinates, function(z) sapply(z, function(b) b[2])))), mean(as.numeric(sapply(x$coordinates, function(z) sapply(z, function(b) b[1])))) ) } } } check_str <- function(x) { checklog <- vapply(x, lint, logical(1)) if (!all(checklog)) { notwkt <- x[!checklog] notwkt_cs <- vapply(notwkt, class, "") notwkt[notwkt_cs != "character"] <- notwkt_cs[notwkt_cs != "character"] notwkt <- paste0(notwkt, collapse = "\n") stop("The following strings are not WKT:\n", notwkt, call. = FALSE) } stopifnot(length(x[[1]]) == 1) x[[1]] } chek_for_pkg <- function(x) { if (!requireNamespace(x, quietly = TRUE)) { stop("Please install ", x, call. = FALSE) } else { invisible(TRUE) } }

/wellknown/R/zzz.R

no_license

ingted/R-Examples

R

false

2,696

r

# capitalize words cw <- function(s, strict = FALSE, onlyfirst = FALSE) { cap <- function(s) paste(toupper(substring(s,1,1)), {s <- substring(s,2); if(strict) tolower(s) else s}, sep = "", collapse = " " ) if(!onlyfirst){ sapply(strsplit(s, split = " "), cap, USE.NAMES = !is.null(names(s))) } else { sapply(s, function(x) paste(toupper(substring(x,1,1)), tolower(substring(x,2)), sep="", collapse=" "), USE.NAMES=F) } } # extract pattern from a string strextract <- function(str, pattern) regmatches(str, regexpr(pattern, str)) # trim space from beginning and end of strings str_trim_ <- function(str) gsub("^\\s+|\\s+$", "", str) # remove zero length strings nozero <- function(x) { x[nzchar(x)] } checker <- function(x, type, len) { if (!length(x) %in% len) stop(sprintf("%s input should be of length %s", type, p0c(len)), call. = FALSE) if (!is.double(x)) stop(sprintf("%s input should be of type double (a number)", type), call. = FALSE) } fmtcheck <- function(x) { if (!is.double(x) || is.na(x)) stop("fmt must be an integer value", call. = FALSE) if (x < 0 || x > 20) stop("fmt must be 0 and 20", call. = FALSE) } # decfmt <- function(pts, fmt) { # rapply(pts, format, nsmall = fmt, trim = TRUE, how = "list") # } centroid <- function(x, center){ if (!is.null(center)) { stopifnot(is.numeric(center)) return(center) } else { if ("geometry" %in% names(x)) { obj <- x$geometry$coordinates # tryasnum <- tryCatch(as.numeric(obj), warning = function(w) w) if (!is(obj, "list")) { obj } else { # sapply(obj, function(z) sapply(z, function(b) b[2])) lngs <- rapply(obj, function(x) x[1]) # sapply(obj, function(z) sapply(z, function(b) b[1])) lats <- rapply(obj, function(x) x[2]) c(mean(as.numeric(lngs)), mean(as.numeric(lats))) } } else { c( mean(as.numeric(sapply(x$coordinates, function(z) sapply(z, function(b) b[2])))), mean(as.numeric(sapply(x$coordinates, function(z) sapply(z, function(b) b[1])))) ) } } } check_str <- function(x) { checklog <- vapply(x, lint, logical(1)) if (!all(checklog)) { notwkt <- x[!checklog] notwkt_cs <- vapply(notwkt, class, "") notwkt[notwkt_cs != "character"] <- notwkt_cs[notwkt_cs != "character"] notwkt <- paste0(notwkt, collapse = "\n") stop("The following strings are not WKT:\n", notwkt, call. = FALSE) } stopifnot(length(x[[1]]) == 1) x[[1]] } chek_for_pkg <- function(x) { if (!requireNamespace(x, quietly = TRUE)) { stop("Please install ", x, call. = FALSE) } else { invisible(TRUE) } }

#' Simplex Projection #' #' \code{simplex} returns model statistics computed from given multiple time series #' using simplex projection. This function performs simplex projections for all #' possible combinations of \code{E}, \code{tau} and \code{tp}. #' #' @inheritParams uic #' @param lib_var #' the name or column index of a library (and target) variable. #' The specified variable is used as a response variable and its time-delay variables are #' used as explanatory variables. #' @param alpha #' the significant level to determine the embedding dimension of reference model #' (i.e., E0). If \code{alpha = NULL}, E0 is set to E - 1. If \code{0 < alpha < 1} #' E0 depends on the model results with lower embedding dimensions. #' #' @details #' Transfer entropy is computed as follows: #' \deqn{ #' \sum_{t} log p(X_{t+tp} | X_{t}, X_{t-\tau}, \ldots, X_{t-(E -1)\tau}, Z_{t}) - #' log p(X_{t+tp} | X_{t}, X_{t-\tau}, \ldots, X_{t-(E0-1)\tau}, Z_{t}) #' } #' where \eqn{X} and \eqn{Z} are library and condition variables, respectively. #' #' @return #' A data.frame where each row represents model statistics computed from a parameter set. #' \tabular{ll}{ #' \code{E} \tab \code{:} embedding dimension \cr #' \code{E0} \tab \code{:} embedding dimension of reference model \cr #' \code{tau} \tab \code{:} time-lag \cr #' \code{tp} \tab \code{:} time prediction horizon \cr #' \code{nn} \tab \code{:} number of nearest neighbors \cr #' \code{n_lib} \tab \code{:} number of time indices used for attractor reconstruction \cr #' \code{n_pred} \tab \code{:} number of time indices used for model predictions \cr #' \code{rmse} \tab \code{:} root mean squared error \cr #' \code{te} \tab \code{:} transfer entropy \cr #' \code{ete} \tab \code{:} effective transfer entropy \cr #' \code{pval} \tab \code{:} p-value to test alternative hypothesis, te > 0 \cr #' \code{n_surr} \tab \code{:} number of surrogate data \cr #' } #' #' @seealso \link{xmap}, \link{uic} #' #' @examples #' # simulate logistic map #' tl <- 400 # time length #' x <- y <- rep(NA, tl) #' x[1] <- 0.4 #' y[1] <- 0.2 #' for (t in 1:(tl - 1)) { # causality : x -> y #' x[t+1] = x[t] * (3.8 - 3.8 * x[t] - 0.0 * y[t]) #' y[t+1] = y[t] * (3.5 - 3.5 * y[t] - 0.1 * x[t]) #' } #' block <- data.frame(t=1:tl, x=x, y=y) #' #' # simplex projecton #' out0 <- simplex(block, lib_var="x", cond_var="y", E=0:8, tau=1, tp=1) #' out1 <- simplex(block, lib_var="y", cond_var="x", E=0:8, tau=1, tp=1) #' par(mfrow=c(2, 1)) #' with(out0, plot(E, te, type="b", pch=c(1,16)[1+(pval<0.05)])) #' with(out1, plot(E, te, type="b", pch=c(1,16)[1+(pval<0.05)])) #' simplex = function ( block, lib = c(1, NROW(block)), pred = lib, group = NULL, lib_var = 1, cond_var = NULL, norm = 2, E = 1, tau = 1, tp = 0, nn = "e+1", num_surr = 1000, alpha = NULL, exclusion_radius = NULL, epsilon = NULL, is_naive = FALSE, knn_method = c("KD","BF")) { if (norm < 1) stop("norm must be >= 1.") lib <- rbind(lib) pred <- rbind(pred) if (length(group) == 0) Group <- rep(1, nrow(block)) if (length(group) != 0) Group <- as.numeric(as.factor(block[,group[1]])) E <- sort(unique(pmax(0, E))) tau <- unique(pmax(1, tau)) tp <- unique(tp) p <- ifelse(is.finite(norm), norm, 0) num_surr <- pmax(0, num_surr) KNN <- switch(match.arg(knn_method), "KD"=0, "BF"=1) if (!is.numeric(nn) & tolower(nn) == "e+1") nn <- 0 if (nn < 0) nn <- -1 if (is.null(alpha)) alpha <- 1 if (is.null(exclusion_radius)) exclusion_radius <- 0 if (is.null(epsilon)) epsilon <- -1 X <- as.matrix(block[ lib_var]) Z <- as.matrix(block[cond_var]) if (alpha >= 1 || num_surr == 0) { out <- .Call(`_rUIC_npmodel_R`, X, X, Z, Group, lib, pred, E, E-1, tau, tp, nn, p, num_surr, exclusion_radius, epsilon, is_naive, 0, KNN) } else { out <- NULL for (tpi in tp) for (taui in tau) { E0 <- 0 for (Ei in E) { outi <- .Call(`_rUIC_npmodel_R`, X, X, Z, Group, lib, pred, Ei, E0, taui, tpi, nn, p, num_surr, exclusion_radius, epsilon, is_naive, 0, KNN) if(outi$pval < alpha) E0 <- Ei out <- rbind(out, outi) } } } return(out) } # End

/R/simplex.R

no_license

yutakaos/rUIC

R

false

4,385

r

#' Simplex Projection #' #' \code{simplex} returns model statistics computed from given multiple time series #' using simplex projection. This function performs simplex projections for all #' possible combinations of \code{E}, \code{tau} and \code{tp}. #' #' @inheritParams uic #' @param lib_var #' the name or column index of a library (and target) variable. #' The specified variable is used as a response variable and its time-delay variables are #' used as explanatory variables. #' @param alpha #' the significant level to determine the embedding dimension of reference model #' (i.e., E0). If \code{alpha = NULL}, E0 is set to E - 1. If \code{0 < alpha < 1} #' E0 depends on the model results with lower embedding dimensions. #' #' @details #' Transfer entropy is computed as follows: #' \deqn{ #' \sum_{t} log p(X_{t+tp} | X_{t}, X_{t-\tau}, \ldots, X_{t-(E -1)\tau}, Z_{t}) - #' log p(X_{t+tp} | X_{t}, X_{t-\tau}, \ldots, X_{t-(E0-1)\tau}, Z_{t}) #' } #' where \eqn{X} and \eqn{Z} are library and condition variables, respectively. #' #' @return #' A data.frame where each row represents model statistics computed from a parameter set. #' \tabular{ll}{ #' \code{E} \tab \code{:} embedding dimension \cr #' \code{E0} \tab \code{:} embedding dimension of reference model \cr #' \code{tau} \tab \code{:} time-lag \cr #' \code{tp} \tab \code{:} time prediction horizon \cr #' \code{nn} \tab \code{:} number of nearest neighbors \cr #' \code{n_lib} \tab \code{:} number of time indices used for attractor reconstruction \cr #' \code{n_pred} \tab \code{:} number of time indices used for model predictions \cr #' \code{rmse} \tab \code{:} root mean squared error \cr #' \code{te} \tab \code{:} transfer entropy \cr #' \code{ete} \tab \code{:} effective transfer entropy \cr #' \code{pval} \tab \code{:} p-value to test alternative hypothesis, te > 0 \cr #' \code{n_surr} \tab \code{:} number of surrogate data \cr #' } #' #' @seealso \link{xmap}, \link{uic} #' #' @examples #' # simulate logistic map #' tl <- 400 # time length #' x <- y <- rep(NA, tl) #' x[1] <- 0.4 #' y[1] <- 0.2 #' for (t in 1:(tl - 1)) { # causality : x -> y #' x[t+1] = x[t] * (3.8 - 3.8 * x[t] - 0.0 * y[t]) #' y[t+1] = y[t] * (3.5 - 3.5 * y[t] - 0.1 * x[t]) #' } #' block <- data.frame(t=1:tl, x=x, y=y) #' #' # simplex projecton #' out0 <- simplex(block, lib_var="x", cond_var="y", E=0:8, tau=1, tp=1) #' out1 <- simplex(block, lib_var="y", cond_var="x", E=0:8, tau=1, tp=1) #' par(mfrow=c(2, 1)) #' with(out0, plot(E, te, type="b", pch=c(1,16)[1+(pval<0.05)])) #' with(out1, plot(E, te, type="b", pch=c(1,16)[1+(pval<0.05)])) #' simplex = function ( block, lib = c(1, NROW(block)), pred = lib, group = NULL, lib_var = 1, cond_var = NULL, norm = 2, E = 1, tau = 1, tp = 0, nn = "e+1", num_surr = 1000, alpha = NULL, exclusion_radius = NULL, epsilon = NULL, is_naive = FALSE, knn_method = c("KD","BF")) { if (norm < 1) stop("norm must be >= 1.") lib <- rbind(lib) pred <- rbind(pred) if (length(group) == 0) Group <- rep(1, nrow(block)) if (length(group) != 0) Group <- as.numeric(as.factor(block[,group[1]])) E <- sort(unique(pmax(0, E))) tau <- unique(pmax(1, tau)) tp <- unique(tp) p <- ifelse(is.finite(norm), norm, 0) num_surr <- pmax(0, num_surr) KNN <- switch(match.arg(knn_method), "KD"=0, "BF"=1) if (!is.numeric(nn) & tolower(nn) == "e+1") nn <- 0 if (nn < 0) nn <- -1 if (is.null(alpha)) alpha <- 1 if (is.null(exclusion_radius)) exclusion_radius <- 0 if (is.null(epsilon)) epsilon <- -1 X <- as.matrix(block[ lib_var]) Z <- as.matrix(block[cond_var]) if (alpha >= 1 || num_surr == 0) { out <- .Call(`_rUIC_npmodel_R`, X, X, Z, Group, lib, pred, E, E-1, tau, tp, nn, p, num_surr, exclusion_radius, epsilon, is_naive, 0, KNN) } else { out <- NULL for (tpi in tp) for (taui in tau) { E0 <- 0 for (Ei in E) { outi <- .Call(`_rUIC_npmodel_R`, X, X, Z, Group, lib, pred, Ei, E0, taui, tpi, nn, p, num_surr, exclusion_radius, epsilon, is_naive, 0, KNN) if(outi$pval < alpha) E0 <- Ei out <- rbind(out, outi) } } } return(out) } # End

testlist <- list(AgeVector = c(-4.73074171454048e-167, 2.2262381097027e-76, -9.12990429452974e-204, 5.97087417427845e-79, 4.7390525269307e-300, 6.58361441690132e-121, 3.58611068565168e-154, -2.94504776827523e-186, 2.62380314702636e-116, -6.78950518864266e+23, 6.99695749856012e-167, 86485.676793021, 1.11271562183704e+230, 1.94114173595984e-186, 1.44833381226225e-178, -6.75217876587581e-69, 1.17166524186752e-15, -4.66902120197297e-64, -1.96807327384856e+304, 4.43806122192432e-53, 9.29588680224717e-276, -6.49633240047463e-239, -1.22140819059424e-138, 5.03155164774999e-80, -6.36956558303921e-38, 1.11830391696877e-156, -1.05546603899445e-274, -3.66720914317747e-169, -6.94681701552128e+38, 2.93126040859825e-33, 2.03804078100055e-84, 3.62794352816579e+190, 3.84224576683191e+202, 2.90661893502594e+44, -5.43046915655589e-132, -1.22315376742253e-152), ExpressionMatrix = structure(c(4.80597147865938e+96, 6.97343932706536e+155, 1.3267342810479e+281, 1.34663897260867e+171, 1.76430141680543e+158, 1.20021255064002e-241, 1.72046093489436e+274, 4.64807629890539e-66, 3.23566990107388e-38, 3.70896378162114e-42, 1.09474740380531e+92, 7.49155705745727e-308, 3.26639180474928e+224, 3.21841801500177e-79, 4.26435540037564e-295, 1.40002857639358e+82, 47573397570345336, 2.00517157311369e-187, 2.74035572944044e+70, 2.89262435086883e-308, 6.65942057982148e-198, 1.10979548758712e-208, 1.40208057226312e-220, 6.25978904299555e-111, 1.06191688875218e+167, 1.1857452172049, 7.01135380962132e-157, 4.49610615342627e-308, 8.04053421408348e+261, 6.23220855980985e+275, 1.91601752509744e+141, 2.27737212344351e-244, 1.6315101795754e+126, 3.83196182917788e+160, 1.53445011275161e-192), .Dim = c(5L, 7L)), permutations = 415362983L) result <- do.call(myTAI:::cpp_bootMatrix,testlist) str(result)

/myTAI/inst/testfiles/cpp_bootMatrix/AFL_cpp_bootMatrix/cpp_bootMatrix_valgrind_files/1615767180-test.R

no_license

akhikolla/updatedatatype-list3

R

false

1,803

r

testlist <- list(AgeVector = c(-4.73074171454048e-167, 2.2262381097027e-76, -9.12990429452974e-204, 5.97087417427845e-79, 4.7390525269307e-300, 6.58361441690132e-121, 3.58611068565168e-154, -2.94504776827523e-186, 2.62380314702636e-116, -6.78950518864266e+23, 6.99695749856012e-167, 86485.676793021, 1.11271562183704e+230, 1.94114173595984e-186, 1.44833381226225e-178, -6.75217876587581e-69, 1.17166524186752e-15, -4.66902120197297e-64, -1.96807327384856e+304, 4.43806122192432e-53, 9.29588680224717e-276, -6.49633240047463e-239, -1.22140819059424e-138, 5.03155164774999e-80, -6.36956558303921e-38, 1.11830391696877e-156, -1.05546603899445e-274, -3.66720914317747e-169, -6.94681701552128e+38, 2.93126040859825e-33, 2.03804078100055e-84, 3.62794352816579e+190, 3.84224576683191e+202, 2.90661893502594e+44, -5.43046915655589e-132, -1.22315376742253e-152), ExpressionMatrix = structure(c(4.80597147865938e+96, 6.97343932706536e+155, 1.3267342810479e+281, 1.34663897260867e+171, 1.76430141680543e+158, 1.20021255064002e-241, 1.72046093489436e+274, 4.64807629890539e-66, 3.23566990107388e-38, 3.70896378162114e-42, 1.09474740380531e+92, 7.49155705745727e-308, 3.26639180474928e+224, 3.21841801500177e-79, 4.26435540037564e-295, 1.40002857639358e+82, 47573397570345336, 2.00517157311369e-187, 2.74035572944044e+70, 2.89262435086883e-308, 6.65942057982148e-198, 1.10979548758712e-208, 1.40208057226312e-220, 6.25978904299555e-111, 1.06191688875218e+167, 1.1857452172049, 7.01135380962132e-157, 4.49610615342627e-308, 8.04053421408348e+261, 6.23220855980985e+275, 1.91601752509744e+141, 2.27737212344351e-244, 1.6315101795754e+126, 3.83196182917788e+160, 1.53445011275161e-192), .Dim = c(5L, 7L)), permutations = 415362983L) result <- do.call(myTAI:::cpp_bootMatrix,testlist) str(result)

#' @keywords internal deconvolve_inputs <- function(inputs, sep, header, out, control, njobs) { ## Helper function to deconvolve all inputs prior to running GIMME #require(parallel) #should be moved to import in NAMESPACE for R package #require(foreach) #require(doSNOW) #require(doParallel) if (missing(njobs)) { njobs <- parallel::detectCores(logical=TRUE) #on hyperthreading CPUs, use all threads (typically 2/core) message("In deconvolve_inputs, njobs not specified. Defaulting to number of cores: ", njobs, ".") } if (njobs > 1) { setDefaultClusterOptions(master="localhost") clusterobj <- makeSOCKcluster(njobs) registerDoSNOW(clusterobj) #clusterobj <- makeCluster(njobs) #registerDoParallel(clusterobj) on.exit(try(stopCluster(clusterobj))) } stopifnot(is.numeric(control$TR) && control$TR > 0) #user must specify TR of data for deconvolution to be well specified if (!exists('method', where=control)) { message("Defaulting to Bush and Cisler 2013 deconvolution") control$method <- "bush" } if (control$method == "bush") { if (!exists('nev_lr', where=control)) { message("nev_lr (learning rate) not specified for Bush deconvolution. Default to .01") control$nev_lr <- .01 #neural events learning rate } if (!exists('epsilon', where=control)) { message("epsilon not specified for Bush deconvolution. Default to .005") control$epsilon <- .005 #convergence criterion } if (!exists('kernel', where=control)) { message("HRF kernel not specified for Bush deconvolution. Default to spm double gamma") control$kernel <- spm_hrf(control$TR)$hrf #default to canonical SPM difference of gammas with specified TR } } if (control$method == "wu") { if (!exists('threshold', where=control)) { message("Activation threshold not specified for Wu deconvolution. Default to 1.0 SD") control$threshold <- 1.0 #SD } if (!exists('max_lag', where=control)) { message("Maximum event-to-neural onset lag not specified for Wu deconvolution. Default to 10 seconds.") control$max_lag = ceiling(10/control$TR) #10 seconds maximum lag from neural event to HRF onset } } ##now proceed on deconvolution stopifnot(file.exists(out)) deconv.dir <- file.path(out, "deconvolved_inputs") dir.create(deconv.dir, showWarnings=FALSE) #deconvolve inputs in parallel (over subjects/inputs) #for (f in inputs) { #using doSNOW, need to explicitly export functions and subfunctions for deconvolution to each worker exportFuncs <- c("deconvolve_nlreg", "sigmoid", "dsigmoid", "generate_feature", "wgr_deconv_canonhrf_par", "wgr_adjust_onset", "wgr_trigger_onset", "CanonicalBasisSet", "Fit_Canonical_HRF", "get_parameters2", "spm_hrf", "spm_get_bf", "spm_gamma_bf", "spm_orth") f = NULL files <- foreach(f=inputs, .combine=c, .export=exportFuncs, .inorder=TRUE) %dopar% { stopifnot(file.exists(f)) d <- as.matrix(read.table(f, header=header, sep=sep)) if (control$method == "wu") { d_deconv <- wgr_deconv_canonhrf_par(d, thr=control$threshold, event_lag_max=control$max_lag, TR=control$TR)$data_deconv #already supports multi-variable (column) deconvolution } else if (control$method == "bush") { #parApply? Already parallel above, so would need to rework this or do the %:% nested parallel d_deconv <- apply(d, 2, deconvolve_nlreg, kernel=control$kernel, nev_lr=control$nev_lr, epsilon=control$epsilon) } outfile <- file.path(deconv.dir, paste0(tools::file_path_sans_ext(basename(f)), "_deconvolve.txt")) write.table(d_deconv, file=outfile, sep=sep, quote=FALSE, row.names=FALSE) return(outfile) } return(files) } ## R versions of deconvolution algorithms for testing in GIMME ## Ported from MATLAB by Michael Hallquist, September 2015 ## R port of Bush and Cisler 2013, Magnetic Resonance Imaging ## Adapted from the original provided by Keith Bush ## Author: Keith Bush, PhD ## Institution: University of Arkansas at Little Rock ## Date: Aug. 9, 2013 deconvolve_nlreg <- function(BOLDobs, kernel, nev_lr=.01, epsilon=.005) { ## Description: ## This function deconvolves the BOLD signal using Bush 2011 method ## ## Inputs: ## BOLDobs - observed BOLD timeseries ## kernel - assumed kernel of the BOLD signal ## nev_lr - learning rate for the assignment of neural events ## epsilon - relative error change (termination condition) ## ## Outputs: ## encoding - reconstructed neural events ## Determine time series length N = length(BOLDobs) ##Calc simulation steps related to simulation time K = length(kernel) A = K - 1 + N ##Termination Params preverror = 1e9 #previous error currerror = 0 #current error ##Construct random activation vector (fluctuate slightly around zero between -2e-9 and 2e-9) activation = rep(2e-9, A)*runif(A) - 1e-9 ##Presolve activations to fit target_adjust as encoding max_hrf_id_adjust = which.max(kernel) - 1 #element of kernel 1 before max BOLDobs_adjust = BOLDobs[max_hrf_id_adjust:N] pre_encoding = BOLDobs_adjust - min(BOLDobs_adjust) pre_encoding = pre_encoding/max(pre_encoding) #unit normalize encoding = pre_encoding activation[K:(K-1 + length(BOLDobs_adjust))] = log(pre_encoding/(1-pre_encoding)) while (abs(preverror-currerror) > epsilon) { ##Compute encoding vector encoding = sigmoid(activation) ##Construct feature space feature = generate_feature(encoding,K) ##Generate virtual bold response by multiplying feature (N x K) by kernel (K x 1) to get N x 1 estimated response ytilde = feature[K:nrow(feature),] %*% kernel ##Convert to percent signal change meanCurrent = mean(ytilde) brf = ytilde - meanCurrent brf = brf/meanCurrent ##Compute dEdbrf dEdbrf = brf - BOLDobs ##Assume normalization does not impact deriv much. dEdy = dEdbrf ##Precompute derivative components dEde = diag(K) %*% kernel back_error = c(rep(0, K-1), dEdy, rep(0, K-1)) ##Backpropagate Errors delta = c() for (i in 1:A) { active = activation[i] deda = dsigmoid(active); dEda = dEde * deda this_error = back_error[i:(i-1+K)] delta = c(delta, sum(dEda * this_error)) } ##Update estimate activation = activation - nev_lr * delta ##Iterate Learning preverror = currerror currerror = sum(dEdbrf^2) } ## remove the initial timepoints corresponding to HRF (so that returned signal matches in time and length) encoding <- encoding[K:length(encoding)] return(encoding) } ## Support functions sigmoid <- function(x) { y <- 1/(1+exp(-x)) return(y) } dsigmoid <- function(x) { y=(1-sigmoid(x))*sigmoid(x) return(y) } generate_feature <- function(encoding, K) { fmatrix = matrix(0, length(encoding), K) fmatrix[,1] = encoding for (i in 2:K) { fmatrix[,i] = c(rep(0, i-1), encoding[1:(length(encoding) - (i-1))]) } return(fmatrix) } ##### ## Wu code ## R port of Wu et al. 2013, Medical Image Analysis ## Adapted from the original provided by Daniele Marinazzo wgr_deconv_canonhrf_par <- function(data, thr=1.0, event_lag_max, TR) { ### function [data_deconv event HRF adjust_global PARA] = wgr_deconv_canonhrf_par(data,thr,event_lag_max,TR) ### this function implements the method described in ### Wu et al, ### A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data, ### Med Image Anal. 2013 Jan 29. pii: S1361-8415(13)00004-2. doi: 10.1016/j.media.2013.01.003 ### input ### data, dimensions time points x number of voxels, normalized ### threshold, assuming data are normalized ### event_lag_max: maximum time from neural event to BOLD event in bins, not time ### (e.g. if we assume 10seconds, and TR=2s, set the value to 10/2=5) ### TR is the TR parameter, in seconds. ### Some parts of the code (subfunction: Fit_Canonical_HRF, CanonicalBasisSet, get_parameters2) were modified from the hemodynamic response estimation toolbox(http://www.stat.columbia.edu/~martin/HRF_Est_Toolbox.zip). ### ### the code uses the parallel for loop parfor. In case of older matlab versions, parfor can be changed to standard for. ### ### The default is using canonical hrf and two derivatives, as described in the paper. ### The function can be modified to use instead point processes, FIR model, etc. ##force data to explicit matrix form (time x variables) for proper looping ##this will allow data to be passed in as a 1-D vector for single variable problems if (!inherits(data, "matrix")) { data <- matrix(data, ncol=1) } N = nrow(data); nvar = ncol(data) even_new = wgr_trigger_onset(data,thr) p_m=3 #define what HRF to fit ## Options: p_m=1 - only canonical HRF ## p_m=2 - canonical + temporal derivative ## p_m=3 - canonical + time and dispersion derivative T = round(30/TR) ## assume HRF effect lasts 30s. data_deconv = matrix(0, nrow=N, ncol=nvar) HRF = matrix(0, nrow=T, ncol=nvar) PARA = matrix(0, nrow=3, ncol=nvar) event = list() event_lag <- rep(0, nvar) ##warning off ##can parallelize over nvar for (i in 1:nvar) { out = wgr_adjust_onset(data[,i], even_new[[i]], event_lag_max, TR, p_m, T, N) data_deconv[,i] <- out$data_deconv HRF[,i] <- out$hrf event[[i]] <- out$events event_lag[i] <- out$event_lag PARA[,i] <- out$param } ##warning on return(list(data_deconv=data_deconv, event=event, HRF=HRF, event_lag=event_lag, PARA=PARA)) } wgr_adjust_onset <- function(dat,even_new,event_lag_max,TR,p_m,T,N) { ## global adjust. kk=1 #this is just a 1-based version of the loop iterator event_lag... hrf = matrix(NA_real_, nrow=T, ncol=event_lag_max+1) param = matrix(NA_real_, nrow=p_m, ncol=event_lag_max+1) Cov_E = rep(NA_real_, event_lag_max+1) for (event_lag in 0:event_lag_max) { RR = even_new - event_lag; RR = RR[RR >= 0] design = matrix(0, nrow=N, ncol=1) design[RR,1] = 1 #add pseudo-events to design matrix fit = Fit_Canonical_HRF(dat,TR,design,T,p_m); hrf[,kk] <- fit$hrf param[,kk] <- fit$param Cov_E[kk] <- cov(fit$e) #covariance of residual kk = kk+1; } C = min(Cov_E) ind = which.min(Cov_E) ad_global = ind - 1 #begin with 0. even_new = even_new - ad_global even_new = even_new[even_new>=0] hrf = hrf[,ind] #keep only best HRF (minimize error of pseudo-event timing) param = param[,ind] #keep only best params ## linear deconvolution. H = fft(c(hrf, rep(0, N-T))) ## H=fft([hrf; zeros(N-T,1)]); M = fft(dat) data_deconv = Re(fft(Conj(H)*M/(H*Conj(H)+C), inverse=TRUE)/length(H)) ## only keep real part -- there is a tiny imaginary residue in R return(list(data_deconv=data_deconv, hrf=hrf, events=even_new, event_lag=ad_global, param=param)) } wgr_trigger_onset <- function(mat, thr) { ##function [oneset] = wgr_trigger_onset(mat,thr) N = nrow(mat); nvar = ncol(mat) mat = apply(mat, 2, scale) #z-score columns oneset <- list() ## Computes pseudo event. for (i in 1:nvar) { oneset_temp = c() for (t in 2:(N-1)) { if (mat[t,i] > thr && mat[t-1,i] < mat[t,i] && mat[t,i] > mat[t+1,i]) { ## detects threshold oneset_temp = c(oneset_temp, t) } } oneset[[i]] = oneset_temp } return(oneset) } ##### ## Helper functions for Wu deconvolution algorithm ## Original code from Lindquist and Wager HRF Toolbox CanonicalBasisSet <- function(TR) { len = round(30/TR) # 30 secs worth of images xBF <- list() xBF$dt = TR xBF$length= len xBF$name = 'hrf (with time and dispersion derivatives)' xBF = spm_get_bf(xBF) v1 = xBF$bf[1:len,1] v2 = xBF$bf[1:len,2] v3 = xBF$bf[1:len,3] ## orthogonalize h = v1 dh = v2 - (v2 %*% v1/norm(v1, "2")^2)*v1 dh2 = v3 - (v3 %*% v1/norm(v1, "2")^2)*v1 - (v3 %*% dh/norm(dh, "2")^2)*dh ## normalize amplitude h = h/max(h) dh = dh/max(dh) dh2 = dh2/max(dh2) return(list(h=h, dh=dh, dh2=dh2)) } Fit_Canonical_HRF <- function(tc, TR, Run, T, p) { ##function [hrf, e, param] = Fit_Canonical_HRF(tc,TR,Run,T,p) ## ## Fits GLM using canonical hrf (with option of using time and dispersion derivatives)'; ## ## INPUTS: ## ## tc - time course ## TR - time resolution ## Runs - expermental design ## T - length of estimated HRF ## p - Model type ## ## Options: p=1 - only canonical HRF ## p=2 - canonical + temporal derivative ## p=3 - canonical + time and dispersion derivative ## ## OUTPUTS: ## ## hrf - estimated hemodynamic response function ## fit - estimated time course ## e - residual time course ## param - estimated amplitude, height and width len = length(Run) X = matrix(0, nrow=len, ncol=p) bf = CanonicalBasisSet(TR) h = bf$h; dh = bf$dh; dh2 = bf$dh2 v = convolve(Run,rev(h), type="open") #this is the R equivalent of conv(Run, h) X[,1] = v[1:len] if (p > 1) { v = convolve(Run,rev(dh), type="open") X[,2] = v[1:len] } if (p > 2) { v = convolve(Run,rev(dh2), type="open") X[,3] = v[1:len] } X = cbind(rep(1, len), X) #add intercept b = MASS::ginv(X) %*% tc e = tc - X %*% b fit = X %*% b b = b[2:length(b)] if (p == 2) { bc = sign(b[1])*sqrt(b[1]^2 + b[2]^2) H = cbind(h, dh) } else if (p == 1) { bc = b[1] H = matrix(h, ncol=1) } else if (p>2) { bc = sign(b[1])*sqrt(b[1]^2 + b[2]^2 + b[3]^2) H = cbind(h, dh, dh2) } hrf = H %*% b param = get_parameters2(hrf,T) return(list(hrf=hrf, e=e, param=param)) } get_parameters2 <- function(hdrf, t) { ##function [param] = get_parameters2(hdrf,t) ## Find model parameters ## ## Height - h ## Time to peak - p (in time units of TR seconds) ## Width (at half peak) - w ## Calculate Heights and Time to peak: ## n = t(end)*0.6; n = round(t*0.8) p = which.max(abs(hdrf[1:n])) h = hdrf[p] ##if (p > t(end)*0.6), warning('Late time to peak'), end; if (h > 0) { v = as.numeric(hdrf >= h/2) } else { v = as.numeric(hdrf <= h/2) } b = which.min(diff(v)) v[(b+1):length(v)] = 0 w = sum(v) cnt = p-1 g = hdrf[2:length(hdrf)] - hdrf[1:(length(hdrf)-1)] while(cnt > 0 && abs(g[cnt]) < 0.001) { h = hdrf[cnt] p = cnt cnt = cnt-1 } param = rep(0,3) param[1] = h param[2] = p param[3] = w return(param) }

/gimme/R/deconvolve_funcs.R

no_license

stlane/gimme

R

false

14,766

r

#' @keywords internal deconvolve_inputs <- function(inputs, sep, header, out, control, njobs) { ## Helper function to deconvolve all inputs prior to running GIMME #require(parallel) #should be moved to import in NAMESPACE for R package #require(foreach) #require(doSNOW) #require(doParallel) if (missing(njobs)) { njobs <- parallel::detectCores(logical=TRUE) #on hyperthreading CPUs, use all threads (typically 2/core) message("In deconvolve_inputs, njobs not specified. Defaulting to number of cores: ", njobs, ".") } if (njobs > 1) { setDefaultClusterOptions(master="localhost") clusterobj <- makeSOCKcluster(njobs) registerDoSNOW(clusterobj) #clusterobj <- makeCluster(njobs) #registerDoParallel(clusterobj) on.exit(try(stopCluster(clusterobj))) } stopifnot(is.numeric(control$TR) && control$TR > 0) #user must specify TR of data for deconvolution to be well specified if (!exists('method', where=control)) { message("Defaulting to Bush and Cisler 2013 deconvolution") control$method <- "bush" } if (control$method == "bush") { if (!exists('nev_lr', where=control)) { message("nev_lr (learning rate) not specified for Bush deconvolution. Default to .01") control$nev_lr <- .01 #neural events learning rate } if (!exists('epsilon', where=control)) { message("epsilon not specified for Bush deconvolution. Default to .005") control$epsilon <- .005 #convergence criterion } if (!exists('kernel', where=control)) { message("HRF kernel not specified for Bush deconvolution. Default to spm double gamma") control$kernel <- spm_hrf(control$TR)$hrf #default to canonical SPM difference of gammas with specified TR } } if (control$method == "wu") { if (!exists('threshold', where=control)) { message("Activation threshold not specified for Wu deconvolution. Default to 1.0 SD") control$threshold <- 1.0 #SD } if (!exists('max_lag', where=control)) { message("Maximum event-to-neural onset lag not specified for Wu deconvolution. Default to 10 seconds.") control$max_lag = ceiling(10/control$TR) #10 seconds maximum lag from neural event to HRF onset } } ##now proceed on deconvolution stopifnot(file.exists(out)) deconv.dir <- file.path(out, "deconvolved_inputs") dir.create(deconv.dir, showWarnings=FALSE) #deconvolve inputs in parallel (over subjects/inputs) #for (f in inputs) { #using doSNOW, need to explicitly export functions and subfunctions for deconvolution to each worker exportFuncs <- c("deconvolve_nlreg", "sigmoid", "dsigmoid", "generate_feature", "wgr_deconv_canonhrf_par", "wgr_adjust_onset", "wgr_trigger_onset", "CanonicalBasisSet", "Fit_Canonical_HRF", "get_parameters2", "spm_hrf", "spm_get_bf", "spm_gamma_bf", "spm_orth") f = NULL files <- foreach(f=inputs, .combine=c, .export=exportFuncs, .inorder=TRUE) %dopar% { stopifnot(file.exists(f)) d <- as.matrix(read.table(f, header=header, sep=sep)) if (control$method == "wu") { d_deconv <- wgr_deconv_canonhrf_par(d, thr=control$threshold, event_lag_max=control$max_lag, TR=control$TR)$data_deconv #already supports multi-variable (column) deconvolution } else if (control$method == "bush") { #parApply? Already parallel above, so would need to rework this or do the %:% nested parallel d_deconv <- apply(d, 2, deconvolve_nlreg, kernel=control$kernel, nev_lr=control$nev_lr, epsilon=control$epsilon) } outfile <- file.path(deconv.dir, paste0(tools::file_path_sans_ext(basename(f)), "_deconvolve.txt")) write.table(d_deconv, file=outfile, sep=sep, quote=FALSE, row.names=FALSE) return(outfile) } return(files) } ## R versions of deconvolution algorithms for testing in GIMME ## Ported from MATLAB by Michael Hallquist, September 2015 ## R port of Bush and Cisler 2013, Magnetic Resonance Imaging ## Adapted from the original provided by Keith Bush ## Author: Keith Bush, PhD ## Institution: University of Arkansas at Little Rock ## Date: Aug. 9, 2013 deconvolve_nlreg <- function(BOLDobs, kernel, nev_lr=.01, epsilon=.005) { ## Description: ## This function deconvolves the BOLD signal using Bush 2011 method ## ## Inputs: ## BOLDobs - observed BOLD timeseries ## kernel - assumed kernel of the BOLD signal ## nev_lr - learning rate for the assignment of neural events ## epsilon - relative error change (termination condition) ## ## Outputs: ## encoding - reconstructed neural events ## Determine time series length N = length(BOLDobs) ##Calc simulation steps related to simulation time K = length(kernel) A = K - 1 + N ##Termination Params preverror = 1e9 #previous error currerror = 0 #current error ##Construct random activation vector (fluctuate slightly around zero between -2e-9 and 2e-9) activation = rep(2e-9, A)*runif(A) - 1e-9 ##Presolve activations to fit target_adjust as encoding max_hrf_id_adjust = which.max(kernel) - 1 #element of kernel 1 before max BOLDobs_adjust = BOLDobs[max_hrf_id_adjust:N] pre_encoding = BOLDobs_adjust - min(BOLDobs_adjust) pre_encoding = pre_encoding/max(pre_encoding) #unit normalize encoding = pre_encoding activation[K:(K-1 + length(BOLDobs_adjust))] = log(pre_encoding/(1-pre_encoding)) while (abs(preverror-currerror) > epsilon) { ##Compute encoding vector encoding = sigmoid(activation) ##Construct feature space feature = generate_feature(encoding,K) ##Generate virtual bold response by multiplying feature (N x K) by kernel (K x 1) to get N x 1 estimated response ytilde = feature[K:nrow(feature),] %*% kernel ##Convert to percent signal change meanCurrent = mean(ytilde) brf = ytilde - meanCurrent brf = brf/meanCurrent ##Compute dEdbrf dEdbrf = brf - BOLDobs ##Assume normalization does not impact deriv much. dEdy = dEdbrf ##Precompute derivative components dEde = diag(K) %*% kernel back_error = c(rep(0, K-1), dEdy, rep(0, K-1)) ##Backpropagate Errors delta = c() for (i in 1:A) { active = activation[i] deda = dsigmoid(active); dEda = dEde * deda this_error = back_error[i:(i-1+K)] delta = c(delta, sum(dEda * this_error)) } ##Update estimate activation = activation - nev_lr * delta ##Iterate Learning preverror = currerror currerror = sum(dEdbrf^2) } ## remove the initial timepoints corresponding to HRF (so that returned signal matches in time and length) encoding <- encoding[K:length(encoding)] return(encoding) } ## Support functions sigmoid <- function(x) { y <- 1/(1+exp(-x)) return(y) } dsigmoid <- function(x) { y=(1-sigmoid(x))*sigmoid(x) return(y) } generate_feature <- function(encoding, K) { fmatrix = matrix(0, length(encoding), K) fmatrix[,1] = encoding for (i in 2:K) { fmatrix[,i] = c(rep(0, i-1), encoding[1:(length(encoding) - (i-1))]) } return(fmatrix) } ##### ## Wu code ## R port of Wu et al. 2013, Medical Image Analysis ## Adapted from the original provided by Daniele Marinazzo wgr_deconv_canonhrf_par <- function(data, thr=1.0, event_lag_max, TR) { ### function [data_deconv event HRF adjust_global PARA] = wgr_deconv_canonhrf_par(data,thr,event_lag_max,TR) ### this function implements the method described in ### Wu et al, ### A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data, ### Med Image Anal. 2013 Jan 29. pii: S1361-8415(13)00004-2. doi: 10.1016/j.media.2013.01.003 ### input ### data, dimensions time points x number of voxels, normalized ### threshold, assuming data are normalized ### event_lag_max: maximum time from neural event to BOLD event in bins, not time ### (e.g. if we assume 10seconds, and TR=2s, set the value to 10/2=5) ### TR is the TR parameter, in seconds. ### Some parts of the code (subfunction: Fit_Canonical_HRF, CanonicalBasisSet, get_parameters2) were modified from the hemodynamic response estimation toolbox(http://www.stat.columbia.edu/~martin/HRF_Est_Toolbox.zip). ### ### the code uses the parallel for loop parfor. In case of older matlab versions, parfor can be changed to standard for. ### ### The default is using canonical hrf and two derivatives, as described in the paper. ### The function can be modified to use instead point processes, FIR model, etc. ##force data to explicit matrix form (time x variables) for proper looping ##this will allow data to be passed in as a 1-D vector for single variable problems if (!inherits(data, "matrix")) { data <- matrix(data, ncol=1) } N = nrow(data); nvar = ncol(data) even_new = wgr_trigger_onset(data,thr) p_m=3 #define what HRF to fit ## Options: p_m=1 - only canonical HRF ## p_m=2 - canonical + temporal derivative ## p_m=3 - canonical + time and dispersion derivative T = round(30/TR) ## assume HRF effect lasts 30s. data_deconv = matrix(0, nrow=N, ncol=nvar) HRF = matrix(0, nrow=T, ncol=nvar) PARA = matrix(0, nrow=3, ncol=nvar) event = list() event_lag <- rep(0, nvar) ##warning off ##can parallelize over nvar for (i in 1:nvar) { out = wgr_adjust_onset(data[,i], even_new[[i]], event_lag_max, TR, p_m, T, N) data_deconv[,i] <- out$data_deconv HRF[,i] <- out$hrf event[[i]] <- out$events event_lag[i] <- out$event_lag PARA[,i] <- out$param } ##warning on return(list(data_deconv=data_deconv, event=event, HRF=HRF, event_lag=event_lag, PARA=PARA)) } wgr_adjust_onset <- function(dat,even_new,event_lag_max,TR,p_m,T,N) { ## global adjust. kk=1 #this is just a 1-based version of the loop iterator event_lag... hrf = matrix(NA_real_, nrow=T, ncol=event_lag_max+1) param = matrix(NA_real_, nrow=p_m, ncol=event_lag_max+1) Cov_E = rep(NA_real_, event_lag_max+1) for (event_lag in 0:event_lag_max) { RR = even_new - event_lag; RR = RR[RR >= 0] design = matrix(0, nrow=N, ncol=1) design[RR,1] = 1 #add pseudo-events to design matrix fit = Fit_Canonical_HRF(dat,TR,design,T,p_m); hrf[,kk] <- fit$hrf param[,kk] <- fit$param Cov_E[kk] <- cov(fit$e) #covariance of residual kk = kk+1; } C = min(Cov_E) ind = which.min(Cov_E) ad_global = ind - 1 #begin with 0. even_new = even_new - ad_global even_new = even_new[even_new>=0] hrf = hrf[,ind] #keep only best HRF (minimize error of pseudo-event timing) param = param[,ind] #keep only best params ## linear deconvolution. H = fft(c(hrf, rep(0, N-T))) ## H=fft([hrf; zeros(N-T,1)]); M = fft(dat) data_deconv = Re(fft(Conj(H)*M/(H*Conj(H)+C), inverse=TRUE)/length(H)) ## only keep real part -- there is a tiny imaginary residue in R return(list(data_deconv=data_deconv, hrf=hrf, events=even_new, event_lag=ad_global, param=param)) } wgr_trigger_onset <- function(mat, thr) { ##function [oneset] = wgr_trigger_onset(mat,thr) N = nrow(mat); nvar = ncol(mat) mat = apply(mat, 2, scale) #z-score columns oneset <- list() ## Computes pseudo event. for (i in 1:nvar) { oneset_temp = c() for (t in 2:(N-1)) { if (mat[t,i] > thr && mat[t-1,i] < mat[t,i] && mat[t,i] > mat[t+1,i]) { ## detects threshold oneset_temp = c(oneset_temp, t) } } oneset[[i]] = oneset_temp } return(oneset) } ##### ## Helper functions for Wu deconvolution algorithm ## Original code from Lindquist and Wager HRF Toolbox CanonicalBasisSet <- function(TR) { len = round(30/TR) # 30 secs worth of images xBF <- list() xBF$dt = TR xBF$length= len xBF$name = 'hrf (with time and dispersion derivatives)' xBF = spm_get_bf(xBF) v1 = xBF$bf[1:len,1] v2 = xBF$bf[1:len,2] v3 = xBF$bf[1:len,3] ## orthogonalize h = v1 dh = v2 - (v2 %*% v1/norm(v1, "2")^2)*v1 dh2 = v3 - (v3 %*% v1/norm(v1, "2")^2)*v1 - (v3 %*% dh/norm(dh, "2")^2)*dh ## normalize amplitude h = h/max(h) dh = dh/max(dh) dh2 = dh2/max(dh2) return(list(h=h, dh=dh, dh2=dh2)) } Fit_Canonical_HRF <- function(tc, TR, Run, T, p) { ##function [hrf, e, param] = Fit_Canonical_HRF(tc,TR,Run,T,p) ## ## Fits GLM using canonical hrf (with option of using time and dispersion derivatives)'; ## ## INPUTS: ## ## tc - time course ## TR - time resolution ## Runs - expermental design ## T - length of estimated HRF ## p - Model type ## ## Options: p=1 - only canonical HRF ## p=2 - canonical + temporal derivative ## p=3 - canonical + time and dispersion derivative ## ## OUTPUTS: ## ## hrf - estimated hemodynamic response function ## fit - estimated time course ## e - residual time course ## param - estimated amplitude, height and width len = length(Run) X = matrix(0, nrow=len, ncol=p) bf = CanonicalBasisSet(TR) h = bf$h; dh = bf$dh; dh2 = bf$dh2 v = convolve(Run,rev(h), type="open") #this is the R equivalent of conv(Run, h) X[,1] = v[1:len] if (p > 1) { v = convolve(Run,rev(dh), type="open") X[,2] = v[1:len] } if (p > 2) { v = convolve(Run,rev(dh2), type="open") X[,3] = v[1:len] } X = cbind(rep(1, len), X) #add intercept b = MASS::ginv(X) %*% tc e = tc - X %*% b fit = X %*% b b = b[2:length(b)] if (p == 2) { bc = sign(b[1])*sqrt(b[1]^2 + b[2]^2) H = cbind(h, dh) } else if (p == 1) { bc = b[1] H = matrix(h, ncol=1) } else if (p>2) { bc = sign(b[1])*sqrt(b[1]^2 + b[2]^2 + b[3]^2) H = cbind(h, dh, dh2) } hrf = H %*% b param = get_parameters2(hrf,T) return(list(hrf=hrf, e=e, param=param)) } get_parameters2 <- function(hdrf, t) { ##function [param] = get_parameters2(hdrf,t) ## Find model parameters ## ## Height - h ## Time to peak - p (in time units of TR seconds) ## Width (at half peak) - w ## Calculate Heights and Time to peak: ## n = t(end)*0.6; n = round(t*0.8) p = which.max(abs(hdrf[1:n])) h = hdrf[p] ##if (p > t(end)*0.6), warning('Late time to peak'), end; if (h > 0) { v = as.numeric(hdrf >= h/2) } else { v = as.numeric(hdrf <= h/2) } b = which.min(diff(v)) v[(b+1):length(v)] = 0 w = sum(v) cnt = p-1 g = hdrf[2:length(hdrf)] - hdrf[1:(length(hdrf)-1)] while(cnt > 0 && abs(g[cnt]) < 0.001) { h = hdrf[cnt] p = cnt cnt = cnt-1 } param = rep(0,3) param[1] = h param[2] = p param[3] = w return(param) }

\name{data.examples} \alias{data.examples} \alias{example} \alias{pop01} \alias{pop02} \alias{pop03} \alias{pop03p} \alias{pop04} \alias{pop04p} \alias{pop05} \alias{pop05p} \alias{pop06p} \alias{pop07} \alias{pop07p} \alias{pop07pp} \alias{bounds} \docType{data} \title{Artificial Household Survey Data} \description{ Example data frames. Allow to run \R code contained in the \sQuote{Examples} section of the \pkg{ReGenesees} package help pages. } \usage{data(data.examples)} \format{ The main data frame, named \code{example}, contains (artificial) data from a two stage stratified cluster sampling design. The sample is made up of 3000 final units, for which the following 21 variables were observed: \describe{ \item{\code{towcod}}{Code identifying "variance PSUs": towns (PSUs) in not-self-representing (NSR) strata, families (SSUs) in self-representing (SR) strata, \code{numeric}} \item{\code{famcod}}{Code identifying families (SSUs), \code{numeric}} \item{\code{key}}{Key identifying final units (individuals), \code{numeric}} \item{\code{weight}}{Initial weights, \code{numeric}} \item{\code{stratum}}{Stratification variable, \code{factor} with levels \code{801} \code{802} \code{803} \code{901} \code{902} \code{903} \code{904} \code{905} \code{906} \code{907} \code{908} \code{1001} \code{1002} \code{1003} \code{1004} \code{1005} \code{1006} \code{1007} \code{1008} \code{1009} \code{1101} \code{1102} \code{1103} \code{1104} \code{3001} \code{3002} \code{3003} \code{3004} \code{3005} \code{3006} \code{3007} \code{3008} \code{3009} \code{3010} \code{3011} \code{3012} \code{3101} \code{3102} \code{3103} \code{3104} \code{3105} \code{3106} \code{3107} \code{3108} \code{3201} \code{3202} \code{3203} \code{3204} \code{5401} \code{5402} \code{5403} \code{5404} \code{5405} \code{5406} \code{5407} \code{5408} \code{5409} \code{5410} \code{5411} \code{5412} \code{5413} \code{5414} \code{5415} \code{5416} \code{5501} \code{5502} \code{5503} \code{5504} \code{9301} \code{9302} \code{9303} \code{9304} \code{9305} \code{9306} \code{9307} \code{9308} \code{9309} \code{9310} \code{9311} \code{9312}} \item{\code{SUPERSTRATUM}}{Collapsed strata variable (eliminates lonely PSUs), \code{factor} with levels \code{1} \code{2} \code{3} \code{4} \code{5} \code{6} \code{7} \code{8} \code{9} \code{10} \code{11} \code{12} \code{13} \code{14} \code{15} \code{16} \code{17} \code{18} \code{19} \code{20} \code{21} \code{22} \code{23} \code{24} \code{25} \code{26} \code{27} \code{28} \code{29} \code{30} \code{31} \code{32} \code{33} \code{34} \code{35} \code{36} \code{37} \code{38} \code{39} \code{40} \code{41} \code{42} \code{43} \code{44} \code{45} \code{46} \code{47} \code{48} \code{49} \code{50} \code{51} \code{52} \code{53} \code{54} \code{55}} \item{\code{sr}}{Strata type, \code{integer} with values \code{0} (NSR strata) and \code{1} (SR strata)} \item{\code{regcod}}{Code identifying regions, \code{factor} with levels \code{6} \code{7} \code{10}} \item{\code{procod}}{Code identifying provinces, \code{factor} with levels \code{8} \code{9} \code{10} \code{11} \code{30} \code{31} \code{32} \code{54} \code{55} \code{93}} \item{\code{x1}}{Indicator variable (integer), \code{numeric}} \item{\code{x2}}{Indicator variable (integer), \code{numeric}} \item{\code{x3}}{Indicator variable (integer), \code{numeric}} \item{\code{y1}}{Indicator variable (integer), \code{numeric}} \item{\code{y2}}{Indicator variable (integer), \code{numeric}} \item{\code{y3}}{Indicator variable (integer), \code{numeric}} \item{\code{age5c}}{Age variable with 5 classes, \code{factor} with levels \code{1} \code{2} \code{3} \code{4} \code{5}} \item{\code{age10c}}{Age variable with 10 classes, \code{factor} with levels \code{1} \code{2} \code{3} \code{4} \code{5} \code{6} \code{7} \code{8} \code{9} \code{10}} \item{\code{sex}}{Sex variable, \code{factor} with levels \code{f} \code{m}} \item{\code{marstat}}{Marital status variable, \code{factor} with levels \code{married} \code{unmarried} \code{widowed}} \item{\code{z}}{A continuous quantitative variable, \code{numeric}} \item{\code{income}}{Income variable, \code{numeric}} } } \details{ Objects \code{pop01}, \ldots, \code{pop07pp} contain known population totals for various calibration models. Object pairs with names differing in the '\code{p}' suffix (such as \code{pop03} and \code{pop03p}) refer to the \emph{same} calibration problem but pertain to \emph{different} solution methods (global and partitioned respectively, see \code{\link{e.calibrate}}). The two-component numeric vector \code{bounds} expresses a possible choice for the allowed range for the ratios between calibrated weights and direct weights in the aforementioned calibration problems. } \section{Warning}{ \strong{Data in the \code{example} data frame are artificial.} The \emph{structure} of \code{example} intentionally resembles the one of typical household survey data, but the \emph{values} it stores are unreliable. The only purpose of such data is that they can be fruitfully exploited to illustrate the syntax and the working mechanism of the functions provided by the \pkg{ReGenesees} package. } \examples{ data(data.examples) head(example) str(example) } \keyword{datasets}

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\name{data.examples} \alias{data.examples} \alias{example} \alias{pop01} \alias{pop02} \alias{pop03} \alias{pop03p} \alias{pop04} \alias{pop04p} \alias{pop05} \alias{pop05p} \alias{pop06p} \alias{pop07} \alias{pop07p} \alias{pop07pp} \alias{bounds} \docType{data} \title{Artificial Household Survey Data} \description{ Example data frames. Allow to run \R code contained in the \sQuote{Examples} section of the \pkg{ReGenesees} package help pages. } \usage{data(data.examples)} \format{ The main data frame, named \code{example}, contains (artificial) data from a two stage stratified cluster sampling design. The sample is made up of 3000 final units, for which the following 21 variables were observed: \describe{ \item{\code{towcod}}{Code identifying "variance PSUs": towns (PSUs) in not-self-representing (NSR) strata, families (SSUs) in self-representing (SR) strata, \code{numeric}} \item{\code{famcod}}{Code identifying families (SSUs), \code{numeric}} \item{\code{key}}{Key identifying final units (individuals), \code{numeric}} \item{\code{weight}}{Initial weights, \code{numeric}} \item{\code{stratum}}{Stratification variable, \code{factor} with levels \code{801} \code{802} \code{803} \code{901} \code{902} \code{903} \code{904} \code{905} \code{906} \code{907} \code{908} \code{1001} \code{1002} \code{1003} \code{1004} \code{1005} \code{1006} \code{1007} \code{1008} \code{1009} \code{1101} \code{1102} \code{1103} \code{1104} \code{3001} \code{3002} \code{3003} \code{3004} \code{3005} \code{3006} \code{3007} \code{3008} \code{3009} \code{3010} \code{3011} \code{3012} \code{3101} \code{3102} \code{3103} \code{3104} \code{3105} \code{3106} \code{3107} \code{3108} \code{3201} \code{3202} \code{3203} \code{3204} \code{5401} \code{5402} \code{5403} \code{5404} \code{5405} \code{5406} \code{5407} \code{5408} \code{5409} \code{5410} \code{5411} \code{5412} \code{5413} \code{5414} \code{5415} \code{5416} \code{5501} \code{5502} \code{5503} \code{5504} \code{9301} \code{9302} \code{9303} \code{9304} \code{9305} \code{9306} \code{9307} \code{9308} \code{9309} \code{9310} \code{9311} \code{9312}} \item{\code{SUPERSTRATUM}}{Collapsed strata variable (eliminates lonely PSUs), \code{factor} with levels \code{1} \code{2} \code{3} \code{4} \code{5} \code{6} \code{7} \code{8} \code{9} \code{10} \code{11} \code{12} \code{13} \code{14} \code{15} \code{16} \code{17} \code{18} \code{19} \code{20} \code{21} \code{22} \code{23} \code{24} \code{25} \code{26} \code{27} \code{28} \code{29} \code{30} \code{31} \code{32} \code{33} \code{34} \code{35} \code{36} \code{37} \code{38} \code{39} \code{40} \code{41} \code{42} \code{43} \code{44} \code{45} \code{46} \code{47} \code{48} \code{49} \code{50} \code{51} \code{52} \code{53} \code{54} \code{55}} \item{\code{sr}}{Strata type, \code{integer} with values \code{0} (NSR strata) and \code{1} (SR strata)} \item{\code{regcod}}{Code identifying regions, \code{factor} with levels \code{6} \code{7} \code{10}} \item{\code{procod}}{Code identifying provinces, \code{factor} with levels \code{8} \code{9} \code{10} \code{11} \code{30} \code{31} \code{32} \code{54} \code{55} \code{93}} \item{\code{x1}}{Indicator variable (integer), \code{numeric}} \item{\code{x2}}{Indicator variable (integer), \code{numeric}} \item{\code{x3}}{Indicator variable (integer), \code{numeric}} \item{\code{y1}}{Indicator variable (integer), \code{numeric}} \item{\code{y2}}{Indicator variable (integer), \code{numeric}} \item{\code{y3}}{Indicator variable (integer), \code{numeric}} \item{\code{age5c}}{Age variable with 5 classes, \code{factor} with levels \code{1} \code{2} \code{3} \code{4} \code{5}} \item{\code{age10c}}{Age variable with 10 classes, \code{factor} with levels \code{1} \code{2} \code{3} \code{4} \code{5} \code{6} \code{7} \code{8} \code{9} \code{10}} \item{\code{sex}}{Sex variable, \code{factor} with levels \code{f} \code{m}} \item{\code{marstat}}{Marital status variable, \code{factor} with levels \code{married} \code{unmarried} \code{widowed}} \item{\code{z}}{A continuous quantitative variable, \code{numeric}} \item{\code{income}}{Income variable, \code{numeric}} } } \details{ Objects \code{pop01}, \ldots, \code{pop07pp} contain known population totals for various calibration models. Object pairs with names differing in the '\code{p}' suffix (such as \code{pop03} and \code{pop03p}) refer to the \emph{same} calibration problem but pertain to \emph{different} solution methods (global and partitioned respectively, see \code{\link{e.calibrate}}). The two-component numeric vector \code{bounds} expresses a possible choice for the allowed range for the ratios between calibrated weights and direct weights in the aforementioned calibration problems. } \section{Warning}{ \strong{Data in the \code{example} data frame are artificial.} The \emph{structure} of \code{example} intentionally resembles the one of typical household survey data, but the \emph{values} it stores are unreliable. The only purpose of such data is that they can be fruitfully exploited to illustrate the syntax and the working mechanism of the functions provided by the \pkg{ReGenesees} package. } \examples{ data(data.examples) head(example) str(example) } \keyword{datasets}

#! /usr/bin/env Rscript ###################################################################################### ## Simulations of thermal death time data & prediction of derived thermal mortality ## ###################################################################################### # Loop with 100 simulations for different thermal thresholds # output: # * mort_min_tdtsim_sensors_full: predicted thermal mortality at minute resolution # * simdata_full: data of larval death time of all the simulations # * tdtsim_full: tolerance landscape (TDT curves) of all the simulations # * sensors_minute_spline_: thermal profiles form microclimatic data at minute resolution # Pacakges ---------------------------------------------------------------- library(data.table) library(tidyverse) library(readxl) library(lubridate) library(zoo) library(colorspace) library(patchwork) library(future) library(parallel) library(furrr) # Parallellization setup -------------------------------------------------- plan(multicore, workers = availableCores("multicore")-40) # Functions --------------------------------------------------------------- source("code/Thermal_landscape_functions_mod.R") source("code/function_trunk_mortality.R") # Data -------------------------------------------------------------------- tdt <- read.table("data/TDT_experiment.txt", header = T, sep = ",", dec = ",") sensor <- read.csv ("data/sensors_hourly.csv") all.sens <- sensor %>% mutate(winter_jday = if_else(winter_year == 2016, winter_jday - 1, as.double(winter_jday))) %>% #To guarantee that day correspondence of 2016 is the same as other years group_by(sensor, winter_jday, winter_hour) %>% summarise(ta.h = mean(TEMP, na.rm = T)) %>% #hourly means across years (as Rezende) split(.$sensor) %>% map(., ~ spline(.$ta.h, n = (nrow(.)-1)*60)) %>% #Last hour (23 to 24h) of the last days can't be added because we don't know the last temperature (00h) map(bind_cols) %>% map(rename, hour = x, ta.min = y) juliandays <- sensor %>% mutate(winter_jday = if_else(winter_year == 2016, winter_jday - 1, as.double(winter_jday))) %>% #Pq el 2016 tingui la mateixa correspondència de dies que els altres anys group_by(sensor, winter_jday, winter_hour) %>% summarise(ta.h = mean(TEMP, na.rm = T)) %>% group_by(sensor) %>% distinct(winter_jday) %>% mutate(day = min_rank(winter_jday), duration = n_distinct(winter_jday)) # Simulation of thermal death time and calculation of thermal mort -------- ## TDT curves from original data tdt.split <- tdt %>% filter(!is.na(aprox_minute_dead), !is.na(SENSOR_mean_temp)) %>% split(.$sp) tdt_curve <- tdt.split %>% map(~ tdt.curve(ta = .$SENSOR_mean_temp, time = .$aprox_minute_dead)) ## Simulations & estimates of daily mortality thres <- c(100, 45, 42.5, 40, 37.5, 35) sims <- 100 pb <- txtProgressBar(min = 0, max = sims*length(thres), initial = 0, char = "*", style = 3) for (i in seq_along(thres)) { for (j in 1:sims) { ## Tolerance landscape of simulated data simdata <- tdt_curve %>% map(pluck, "model") %>% map(~ data.frame(treatment = rep(c(40, 42, 44), each = 35), inter = unname(coefficients(.)[1], force = T), slope = unname(coefficients(.)[2], force = T), sigma = sigma(.))) %>% map(mutate, error = rnorm(n = n(), sd = sigma), log10time = inter + slope*treatment + error, time = 10^log10time) tl.sim <- simdata %>% map(~ tolerance.landscape(ta = .$treatment, time = .$time)) ## Estimates of daily mortality subtibble <- trunk_mortality(tl = tl.sim, sensdata = all.sens, thres = thres[i]) %>% mutate(alive = if_else(is.na(alive), 0, alive), mort = 100-alive, day = as.numeric(day)) %>% left_join(juliandays, by = c("sensor" = "sensor", "day" = "day")) %>% group_by(sensor, winter_jday, sp_comp) %>% summarise(mort = max(mort, na.rm = T), tmax = max(ta, na.rm = T), tmin = min(ta, na.rm = T), tmean = mean(ta, na.rm = T), surv = min(alive, na.rm = T), thres = thres[i], simulation = j) if(i == 1 & j == 1){ fwrite(subtibble, file = paste0("data/sim_mortalities/mort_min_tdtsim_sensors_full", Sys.Date(), ".csv")) simdata %>% bind_rows(.id = "sp") %>% select(sp, treatment, time) %>% mutate(thres = thres[i], simulation = j) %>% fwrite(file = paste0("data/sim_mortalities/simdata_full", Sys.Date(), ".csv")) tl.sim %>% map(~ keep(., names(.) %in% c("ctmax", "z"))) %>% map(bind_rows) %>% bind_rows(.id = "sp") %>% mutate(simulation = j, thres = thres[i]) %>% fwrite(file = paste0("data/sim_mortalities/tdtsim_full", Sys.Date(), ".csv")) } else { fwrite(subtibble, file = paste0("data/sim_mortalities/mort_min_tdtsim_sensors_full", Sys.Date(), ".csv"), append = T) simdata %>% bind_rows(.id = "sp") %>% select(sp, treatment, time) %>% mutate(thres = thres[i], simulation = j) %>% fwrite(file = paste0("data/sim_mortalities/simdata_full", Sys.Date(), ".csv"), append = T) tl.sim %>% map(~ keep(., names(.) %in% c("ctmax", "z"))) %>% map(bind_rows) %>% bind_rows(.id = "sp") %>% mutate(simulation = j, thres = thres[i]) %>% fwrite(file = paste0("data/sim_mortalities/tdtsim_full", Sys.Date(), ".csv"), append = T) } setTxtProgressBar(pb, sims*(i-1)+j) } } all.sens %>% bind_rows(.id = "sensor") %>% write_rds(file = paste0("data/sim_mortalities/sensors_minute_spline_", Sys.Date(), ".RDS"))

/code/thermal_mortality/sim_thermal_mortality.R

no_license

mvives-ingla/ecotones

R

false

6,459

r

#! /usr/bin/env Rscript ###################################################################################### ## Simulations of thermal death time data & prediction of derived thermal mortality ## ###################################################################################### # Loop with 100 simulations for different thermal thresholds # output: # * mort_min_tdtsim_sensors_full: predicted thermal mortality at minute resolution # * simdata_full: data of larval death time of all the simulations # * tdtsim_full: tolerance landscape (TDT curves) of all the simulations # * sensors_minute_spline_: thermal profiles form microclimatic data at minute resolution # Pacakges ---------------------------------------------------------------- library(data.table) library(tidyverse) library(readxl) library(lubridate) library(zoo) library(colorspace) library(patchwork) library(future) library(parallel) library(furrr) # Parallellization setup -------------------------------------------------- plan(multicore, workers = availableCores("multicore")-40) # Functions --------------------------------------------------------------- source("code/Thermal_landscape_functions_mod.R") source("code/function_trunk_mortality.R") # Data -------------------------------------------------------------------- tdt <- read.table("data/TDT_experiment.txt", header = T, sep = ",", dec = ",") sensor <- read.csv ("data/sensors_hourly.csv") all.sens <- sensor %>% mutate(winter_jday = if_else(winter_year == 2016, winter_jday - 1, as.double(winter_jday))) %>% #To guarantee that day correspondence of 2016 is the same as other years group_by(sensor, winter_jday, winter_hour) %>% summarise(ta.h = mean(TEMP, na.rm = T)) %>% #hourly means across years (as Rezende) split(.$sensor) %>% map(., ~ spline(.$ta.h, n = (nrow(.)-1)*60)) %>% #Last hour (23 to 24h) of the last days can't be added because we don't know the last temperature (00h) map(bind_cols) %>% map(rename, hour = x, ta.min = y) juliandays <- sensor %>% mutate(winter_jday = if_else(winter_year == 2016, winter_jday - 1, as.double(winter_jday))) %>% #Pq el 2016 tingui la mateixa correspondència de dies que els altres anys group_by(sensor, winter_jday, winter_hour) %>% summarise(ta.h = mean(TEMP, na.rm = T)) %>% group_by(sensor) %>% distinct(winter_jday) %>% mutate(day = min_rank(winter_jday), duration = n_distinct(winter_jday)) # Simulation of thermal death time and calculation of thermal mort -------- ## TDT curves from original data tdt.split <- tdt %>% filter(!is.na(aprox_minute_dead), !is.na(SENSOR_mean_temp)) %>% split(.$sp) tdt_curve <- tdt.split %>% map(~ tdt.curve(ta = .$SENSOR_mean_temp, time = .$aprox_minute_dead)) ## Simulations & estimates of daily mortality thres <- c(100, 45, 42.5, 40, 37.5, 35) sims <- 100 pb <- txtProgressBar(min = 0, max = sims*length(thres), initial = 0, char = "*", style = 3) for (i in seq_along(thres)) { for (j in 1:sims) { ## Tolerance landscape of simulated data simdata <- tdt_curve %>% map(pluck, "model") %>% map(~ data.frame(treatment = rep(c(40, 42, 44), each = 35), inter = unname(coefficients(.)[1], force = T), slope = unname(coefficients(.)[2], force = T), sigma = sigma(.))) %>% map(mutate, error = rnorm(n = n(), sd = sigma), log10time = inter + slope*treatment + error, time = 10^log10time) tl.sim <- simdata %>% map(~ tolerance.landscape(ta = .$treatment, time = .$time)) ## Estimates of daily mortality subtibble <- trunk_mortality(tl = tl.sim, sensdata = all.sens, thres = thres[i]) %>% mutate(alive = if_else(is.na(alive), 0, alive), mort = 100-alive, day = as.numeric(day)) %>% left_join(juliandays, by = c("sensor" = "sensor", "day" = "day")) %>% group_by(sensor, winter_jday, sp_comp) %>% summarise(mort = max(mort, na.rm = T), tmax = max(ta, na.rm = T), tmin = min(ta, na.rm = T), tmean = mean(ta, na.rm = T), surv = min(alive, na.rm = T), thres = thres[i], simulation = j) if(i == 1 & j == 1){ fwrite(subtibble, file = paste0("data/sim_mortalities/mort_min_tdtsim_sensors_full", Sys.Date(), ".csv")) simdata %>% bind_rows(.id = "sp") %>% select(sp, treatment, time) %>% mutate(thres = thres[i], simulation = j) %>% fwrite(file = paste0("data/sim_mortalities/simdata_full", Sys.Date(), ".csv")) tl.sim %>% map(~ keep(., names(.) %in% c("ctmax", "z"))) %>% map(bind_rows) %>% bind_rows(.id = "sp") %>% mutate(simulation = j, thres = thres[i]) %>% fwrite(file = paste0("data/sim_mortalities/tdtsim_full", Sys.Date(), ".csv")) } else { fwrite(subtibble, file = paste0("data/sim_mortalities/mort_min_tdtsim_sensors_full", Sys.Date(), ".csv"), append = T) simdata %>% bind_rows(.id = "sp") %>% select(sp, treatment, time) %>% mutate(thres = thres[i], simulation = j) %>% fwrite(file = paste0("data/sim_mortalities/simdata_full", Sys.Date(), ".csv"), append = T) tl.sim %>% map(~ keep(., names(.) %in% c("ctmax", "z"))) %>% map(bind_rows) %>% bind_rows(.id = "sp") %>% mutate(simulation = j, thres = thres[i]) %>% fwrite(file = paste0("data/sim_mortalities/tdtsim_full", Sys.Date(), ".csv"), append = T) } setTxtProgressBar(pb, sims*(i-1)+j) } } all.sens %>% bind_rows(.id = "sensor") %>% write_rds(file = paste0("data/sim_mortalities/sensors_minute_spline_", Sys.Date(), ".RDS"))

\name{E4.4} \alias{E4.4} \title{ Measures of Quality for Agencies Delivering Transportation for the Elderly and the Handicapped } \concept{Measures of Quality for Agencies Delivering Transportation for the Elderly and the Handicapped } \usage{data(E4.4)} \description{ The \code{E4.4} data frame has 40 rows and 3 columns. } \format{ This data frame contains the following columns: \describe{ \item{QUAL}{ a numeric vector, a quality measure made using psychometric methods from results of questionares. } \item{X.1}{ a numeric vector, an indicator variable for private ownership. } \item{X.2}{ a numeric vector, an indicator variable for private for profit ownership. } } } \details{ The quality data, \code{QUAL}, is constructed from questionares given to users of such services in the state of Illinois. Multiple services in the state of Illinois was scored using this method. The indicator variables was constructed to give first (\code{X.1}) a comparison between private and public services, then (\code{X.2}) a comparison between private not-for-profit and private for profit services. } \source{ Slightly modified version of data supplied by Ms. Claire McKnight of the Department of Civil Engineering, City University of New York. } \examples{ data(E4.4) summary(E4.4) } \keyword{datasets} \concept{regression}

/man/E4.4.Rd

no_license

cran/SenSrivastava

R

false

1,347

rd

\name{E4.4} \alias{E4.4} \title{ Measures of Quality for Agencies Delivering Transportation for the Elderly and the Handicapped } \concept{Measures of Quality for Agencies Delivering Transportation for the Elderly and the Handicapped } \usage{data(E4.4)} \description{ The \code{E4.4} data frame has 40 rows and 3 columns. } \format{ This data frame contains the following columns: \describe{ \item{QUAL}{ a numeric vector, a quality measure made using psychometric methods from results of questionares. } \item{X.1}{ a numeric vector, an indicator variable for private ownership. } \item{X.2}{ a numeric vector, an indicator variable for private for profit ownership. } } } \details{ The quality data, \code{QUAL}, is constructed from questionares given to users of such services in the state of Illinois. Multiple services in the state of Illinois was scored using this method. The indicator variables was constructed to give first (\code{X.1}) a comparison between private and public services, then (\code{X.2}) a comparison between private not-for-profit and private for profit services. } \source{ Slightly modified version of data supplied by Ms. Claire McKnight of the Department of Civil Engineering, City University of New York. } \examples{ data(E4.4) summary(E4.4) } \keyword{datasets} \concept{regression}

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/caching.R \name{set.cache.dir} \alias{set.cache.dir} \title{Sets the caching directory path} \usage{ set.cache.dir(path) } \arguments{ \item{path}{the path of the caching directory} } \description{ Sets the caching directory path }

/man/set.cache.dir.Rd

permissive

pydupont/cacheR

R

false

true

310

rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/caching.R \name{set.cache.dir} \alias{set.cache.dir} \title{Sets the caching directory path} \usage{ set.cache.dir(path) } \arguments{ \item{path}{the path of the caching directory} } \description{ Sets the caching directory path }

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/rstatscn.R \name{statscnRowNamePrefix} \alias{statscnRowNamePrefix} \title{statscnRowNamePrefix} \usage{ statscnRowNamePrefix(p = "nrow") } \arguments{ \item{p}{, how to set the rowname prefix. it is 'nrow' by default , and it is the only supported value currently to unset the row name prefix, call this function with p=NULL} } \value{ no return } \description{ set the rowName prefix in the dataframe } \details{ in case you encounter the following error: Error in `row.names<-.data.frame`(`*tmp*`, value = value) : duplicate 'row.names' are not allowed you need to call this function }

/man/statscnRowNamePrefix.Rd

no_license

jiang-hang/rstatscn

R

false

true

673

rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/rstatscn.R \name{statscnRowNamePrefix} \alias{statscnRowNamePrefix} \title{statscnRowNamePrefix} \usage{ statscnRowNamePrefix(p = "nrow") } \arguments{ \item{p}{, how to set the rowname prefix. it is 'nrow' by default , and it is the only supported value currently to unset the row name prefix, call this function with p=NULL} } \value{ no return } \description{ set the rowName prefix in the dataframe } \details{ in case you encounter the following error: Error in `row.names<-.data.frame`(`*tmp*`, value = value) : duplicate 'row.names' are not allowed you need to call this function }

library(dplyr) library(caret) set.seed(98765) if (!exists("dataTrain.raw")){dataTrain.raw <- read.csv('data/pml-training.csv')} if (!exists("dataTest.raw")){dataTest.raw <- read.csv('data/pml-testing.csv')} # Clean up the column set pml_data_column_cleanup = function(df){ # Drop purely admin related columns df <- df %>% select(-starts_with("X")) df <- df %>% select(-starts_with("raw_timestamp_")) df <- df %>% select(-starts_with("cvtd_timestamp")) df <- df %>% select(-starts_with("new_window")) df <- df %>% select(-starts_with("num_window")) # Drop columns that relate only to "New Window = Yes" df <- df %>% select(-starts_with("kurtosis_roll_")) df <- df %>% select(-starts_with("kurtosis_picth_")) df <- df %>% select(-starts_with("kurtosis_yaw_")) df <- df %>% select(-starts_with("skewness_roll_")) df <- df %>% select(-starts_with("skewness_pitch_")) df <- df %>% select(-starts_with("skewness_yaw_")) df <- df %>% select(-starts_with("max_roll_")) df <- df %>% select(-starts_with("max_picth_")) df <- df %>% select(-starts_with("max_yaw_")) df <- df %>% select(-starts_with("min_roll_")) df <- df %>% select(-starts_with("min_pitch_")) df <- df %>% select(-starts_with("min_yaw_")) df <- df %>% select(-starts_with("amplitude_roll_")) df <- df %>% select(-starts_with("amplitude_pitch_")) df <- df %>% select(-starts_with("amplitude_yaw_")) df <- df %>% select(-starts_with("var_total_accel_")) df <- df %>% select(-starts_with("avg_roll_")) df <- df %>% select(-starts_with("stddev_roll_")) df <- df %>% select(-starts_with("var_roll_")) df <- df %>% select(-starts_with("avg_pitch_")) df <- df %>% select(-starts_with("stddev_pitch_")) df <- df %>% select(-starts_with("var_pitch_")) df <- df %>% select(-starts_with("avg_yaw_")) df <- df %>% select(-starts_with("stddev_yaw_")) df <- df %>% select(-starts_with("var_yaw_")) df <- df %>% select(-starts_with("var_accel_")) df } dataTrain <- pml_data_column_cleanup(dataTrain.raw) dataTest <- pml_data_column_cleanup(dataTest.raw) if (!exists("dataTrain.model")){ # Partition our data 75% training and 25% testing dataTrain.partition <- createDataPartition(y=dataTrain$classe, p=0.75, list=FALSE) dataTrain.train <- dataTrain[dataTrain.partition,] dataTrain.test <- dataTrain[-dataTrain.partition,] # Implemented a Random Forest (limited to depth 100) training strategy dataTrain.model <- train(classe~., data=dataTrain.train, method="rf", ntree=100) } # Train Test prediction dataTrain.test.predict <- predict(dataTrain.model, newdata=dataTrain.test) # Test prediction (Validation) dataTest.predict <- predict(dataTrain.model, newdata=dataTest) # print(dataTrain.test.predict) print(dataTest.predict) # Calculate outside error rate and accuracy of the validation outsideErrorRate.accuracy <- sum(dataTrain.test.predict == dataTrain.test$classe)/length(dataTrain.test.predict) outsideErrorRate.error <- (1 - outsideErrorRate.accuracy) print( ggplot(dataTrain.model) + ggtitle("Accuracy vs. Predictor") ) print(dataTrain.model) # Wrap up with the answers for the submission pml_write_files = function(x){ dir.create(file.path(getwd(), 'submission'), showWarnings = FALSE) n = length(x) for(i in 1:n){ filename = paste0("submission/problem_id_", i, ".txt") write.table(x[i], file=filename, quote=FALSE, row.names=FALSE, col.names=FALSE) } } pml_write_files(as.character(dataTest.predict))

/Review.R

no_license

schonken/Practical_Machine_Learning_Project

R

false

3,502

r

library(dplyr) library(caret) set.seed(98765) if (!exists("dataTrain.raw")){dataTrain.raw <- read.csv('data/pml-training.csv')} if (!exists("dataTest.raw")){dataTest.raw <- read.csv('data/pml-testing.csv')} # Clean up the column set pml_data_column_cleanup = function(df){ # Drop purely admin related columns df <- df %>% select(-starts_with("X")) df <- df %>% select(-starts_with("raw_timestamp_")) df <- df %>% select(-starts_with("cvtd_timestamp")) df <- df %>% select(-starts_with("new_window")) df <- df %>% select(-starts_with("num_window")) # Drop columns that relate only to "New Window = Yes" df <- df %>% select(-starts_with("kurtosis_roll_")) df <- df %>% select(-starts_with("kurtosis_picth_")) df <- df %>% select(-starts_with("kurtosis_yaw_")) df <- df %>% select(-starts_with("skewness_roll_")) df <- df %>% select(-starts_with("skewness_pitch_")) df <- df %>% select(-starts_with("skewness_yaw_")) df <- df %>% select(-starts_with("max_roll_")) df <- df %>% select(-starts_with("max_picth_")) df <- df %>% select(-starts_with("max_yaw_")) df <- df %>% select(-starts_with("min_roll_")) df <- df %>% select(-starts_with("min_pitch_")) df <- df %>% select(-starts_with("min_yaw_")) df <- df %>% select(-starts_with("amplitude_roll_")) df <- df %>% select(-starts_with("amplitude_pitch_")) df <- df %>% select(-starts_with("amplitude_yaw_")) df <- df %>% select(-starts_with("var_total_accel_")) df <- df %>% select(-starts_with("avg_roll_")) df <- df %>% select(-starts_with("stddev_roll_")) df <- df %>% select(-starts_with("var_roll_")) df <- df %>% select(-starts_with("avg_pitch_")) df <- df %>% select(-starts_with("stddev_pitch_")) df <- df %>% select(-starts_with("var_pitch_")) df <- df %>% select(-starts_with("avg_yaw_")) df <- df %>% select(-starts_with("stddev_yaw_")) df <- df %>% select(-starts_with("var_yaw_")) df <- df %>% select(-starts_with("var_accel_")) df } dataTrain <- pml_data_column_cleanup(dataTrain.raw) dataTest <- pml_data_column_cleanup(dataTest.raw) if (!exists("dataTrain.model")){ # Partition our data 75% training and 25% testing dataTrain.partition <- createDataPartition(y=dataTrain$classe, p=0.75, list=FALSE) dataTrain.train <- dataTrain[dataTrain.partition,] dataTrain.test <- dataTrain[-dataTrain.partition,] # Implemented a Random Forest (limited to depth 100) training strategy dataTrain.model <- train(classe~., data=dataTrain.train, method="rf", ntree=100) } # Train Test prediction dataTrain.test.predict <- predict(dataTrain.model, newdata=dataTrain.test) # Test prediction (Validation) dataTest.predict <- predict(dataTrain.model, newdata=dataTest) # print(dataTrain.test.predict) print(dataTest.predict) # Calculate outside error rate and accuracy of the validation outsideErrorRate.accuracy <- sum(dataTrain.test.predict == dataTrain.test$classe)/length(dataTrain.test.predict) outsideErrorRate.error <- (1 - outsideErrorRate.accuracy) print( ggplot(dataTrain.model) + ggtitle("Accuracy vs. Predictor") ) print(dataTrain.model) # Wrap up with the answers for the submission pml_write_files = function(x){ dir.create(file.path(getwd(), 'submission'), showWarnings = FALSE) n = length(x) for(i in 1:n){ filename = paste0("submission/problem_id_", i, ".txt") write.table(x[i], file=filename, quote=FALSE, row.names=FALSE, col.names=FALSE) } } pml_write_files(as.character(dataTest.predict))

tabOthers <- f7Tab( tabName = "Others", icon = f7Icon("more_round"), f7Align( side = "center", h1("miniUI 2.0 brings other elements") ), # skeletons f7BlockTitle(title = "f7Skeleton") %>% f7Align(side = "center"), f7List( f7ListItem(title = "Item 1"), f7ListItem(title = "Item 2") ) %>% f7Skeleton(duration = 5000), br(), # Messages f7BlockTitle(title = "f7Messages") %>% f7Align(side = "center"), f7Messages( id = "messagelist", f7Message( "Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duo Reges: constructio interrete", src = "https://cdn.framework7.io/placeholder/people-100x100-7.jpg", author = "David", date = "2019-09-12", state = "received", type = "text" ), f7Message( "https://cdn.framework7.io/placeholder/cats-200x260-4.jpg", src = "https://cdn.framework7.io/placeholder/people-100x100-9.jpg", author = "Lia", date = NULL, state = "sent", type = "img" ), f7Message( "Hi Bro", src = "https://cdn.framework7.io/placeholder/people-100x100-9.jpg", author = NULL, date = "2019-08-15", state = "sent", type = "text" ) ), br(), # Badges f7BlockTitle(title = "f7Badge") %>% f7Align(side = "center"), f7Block( strong = TRUE, f7Badge(32, color = "purple"), f7Badge("Badge", color = "green"), f7Badge(10, color = "teal"), f7Badge("Ok", color = "orange") ), br(), # chips f7BlockTitle(title = "f7Chip") %>% f7Align(side = "center"), f7Block( strong = TRUE, f7Chip(label = "Example Chip"), f7Chip(label = "Example Chip", outline = TRUE), f7Chip(label = "Example Chip", icon = f7Icon("add_round"), icon_status = "pink"), f7Chip(label = "Example Chip", img = "https://lorempixel.com/64/64/people/9/"), f7Chip(label = "Example Chip", closable = TRUE), f7Chip(label = "Example Chip", status = "green"), f7Chip(label = "Example Chip", status = "green", outline = TRUE) ), br(), # accordion f7BlockTitle(title = "f7Accordion") %>% f7Align(side = "center"), f7Accordion( inputId = "accordion1", f7AccordionItem( title = "Item 1", f7Block("Item 1 content") ), f7AccordionItem( title = "Item 2", f7Block("Item 2 content") ) ), f7Toggle( inputId = "goAccordion", label = "Toggle accordion item 1", color = "orange" ), br(), # swiper f7BlockTitle(title = "f7Swiper") %>% f7Align(side = "center"), f7Swiper( id = "my-swiper", f7Slide( plot_ly(z = ~volcano, type = "contour") ), f7Slide( plot_ly(data = iris, x = ~Sepal.Length, y = ~Petal.Length) ) ), br(), br(), br(), # timelines f7BlockTitle(title = "f7PhotoBrowser") %>% f7Align(side = "center"), f7Block( f7PhotoBrowser( id = "photobrowser1", label = "Open", theme = "light", type = "standalone", photos = c( "https://cdn.framework7.io/placeholder/sports-1024x1024-1.jpg", "https://cdn.framework7.io/placeholder/sports-1024x1024-2.jpg", "https://cdn.framework7.io/placeholder/sports-1024x1024-3.jpg" ) ) ), br(), br(), # timelines f7BlockTitle(title = "f7Timeline") %>% f7Align(side = "center"), f7Timeline( sides = TRUE, f7TimelineItem( "Another text", date = "01 Dec", card = FALSE, time = "12:30", title = "Title", subtitle = "Subtitle", side = "left" ), f7TimelineItem( "Another text", date = "02 Dec", card = TRUE, time = "13:00", title = "Title", subtitle = "Subtitle" ), f7TimelineItem( "Another text", date = "03 Dec", card = FALSE, time = "14:45", title = "Title", subtitle = "Subtitle" ) ), br(), # progress bars f7BlockTitle(title = "f7Progress") %>% f7Align(side = "center"), f7Block( strong = TRUE, f7Progress(id = "pg1", value = 10, color = "yellow"), f7Slider( inputId = "updatepg1", label = "Update progress 1", max = 100, min = 0, value = 50, scale = TRUE ), br(), f7Progress(id = "pg2", value = 100, color = "green"), br(), f7Progress(id = "pg3", value = 50, color = "deeppurple"), br(), f7ProgressInf() ), br(), # gauges f7BlockTitle(title = "f7Gauge") %>% f7Align(side = "center"), f7Block( strong = TRUE, f7Row( f7Col( f7Gauge( id = "mygauge1", type = "semicircle", value = 50, borderColor = "#2196f3", borderWidth = 10, valueText = "50%", valueFontSize = 41, valueTextColor = "#2196f3", labelText = "amount of something" ) ), f7Col( f7Gauge( id = "mygauge2", type = "circle", value = 30, borderColor = "orange", borderWidth = 10, valueText = "30%", valueFontSize = 41, valueTextColor = "orange", labelText = "Other thing" ) ) ), f7Stepper( inputId = "updategauge1", label = "Update gauge 1", step = 10, min = 0, max = 100, value = 50 ) ), # update f7Panel br(), f7BlockTitle(title = "updateF7Panel") %>% f7Align(side = "center"), f7Block( f7Button( inputId = "goPanel", label = "Toggle left panel" ) ) )

/inst/examples/gallery/tabs/tabOthers.R

no_license

iMarcello/shinyMobile

R

false

5,529

r

tabOthers <- f7Tab( tabName = "Others", icon = f7Icon("more_round"), f7Align( side = "center", h1("miniUI 2.0 brings other elements") ), # skeletons f7BlockTitle(title = "f7Skeleton") %>% f7Align(side = "center"), f7List( f7ListItem(title = "Item 1"), f7ListItem(title = "Item 2") ) %>% f7Skeleton(duration = 5000), br(), # Messages f7BlockTitle(title = "f7Messages") %>% f7Align(side = "center"), f7Messages( id = "messagelist", f7Message( "Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duo Reges: constructio interrete", src = "https://cdn.framework7.io/placeholder/people-100x100-7.jpg", author = "David", date = "2019-09-12", state = "received", type = "text" ), f7Message( "https://cdn.framework7.io/placeholder/cats-200x260-4.jpg", src = "https://cdn.framework7.io/placeholder/people-100x100-9.jpg", author = "Lia", date = NULL, state = "sent", type = "img" ), f7Message( "Hi Bro", src = "https://cdn.framework7.io/placeholder/people-100x100-9.jpg", author = NULL, date = "2019-08-15", state = "sent", type = "text" ) ), br(), # Badges f7BlockTitle(title = "f7Badge") %>% f7Align(side = "center"), f7Block( strong = TRUE, f7Badge(32, color = "purple"), f7Badge("Badge", color = "green"), f7Badge(10, color = "teal"), f7Badge("Ok", color = "orange") ), br(), # chips f7BlockTitle(title = "f7Chip") %>% f7Align(side = "center"), f7Block( strong = TRUE, f7Chip(label = "Example Chip"), f7Chip(label = "Example Chip", outline = TRUE), f7Chip(label = "Example Chip", icon = f7Icon("add_round"), icon_status = "pink"), f7Chip(label = "Example Chip", img = "https://lorempixel.com/64/64/people/9/"), f7Chip(label = "Example Chip", closable = TRUE), f7Chip(label = "Example Chip", status = "green"), f7Chip(label = "Example Chip", status = "green", outline = TRUE) ), br(), # accordion f7BlockTitle(title = "f7Accordion") %>% f7Align(side = "center"), f7Accordion( inputId = "accordion1", f7AccordionItem( title = "Item 1", f7Block("Item 1 content") ), f7AccordionItem( title = "Item 2", f7Block("Item 2 content") ) ), f7Toggle( inputId = "goAccordion", label = "Toggle accordion item 1", color = "orange" ), br(), # swiper f7BlockTitle(title = "f7Swiper") %>% f7Align(side = "center"), f7Swiper( id = "my-swiper", f7Slide( plot_ly(z = ~volcano, type = "contour") ), f7Slide( plot_ly(data = iris, x = ~Sepal.Length, y = ~Petal.Length) ) ), br(), br(), br(), # timelines f7BlockTitle(title = "f7PhotoBrowser") %>% f7Align(side = "center"), f7Block( f7PhotoBrowser( id = "photobrowser1", label = "Open", theme = "light", type = "standalone", photos = c( "https://cdn.framework7.io/placeholder/sports-1024x1024-1.jpg", "https://cdn.framework7.io/placeholder/sports-1024x1024-2.jpg", "https://cdn.framework7.io/placeholder/sports-1024x1024-3.jpg" ) ) ), br(), br(), # timelines f7BlockTitle(title = "f7Timeline") %>% f7Align(side = "center"), f7Timeline( sides = TRUE, f7TimelineItem( "Another text", date = "01 Dec", card = FALSE, time = "12:30", title = "Title", subtitle = "Subtitle", side = "left" ), f7TimelineItem( "Another text", date = "02 Dec", card = TRUE, time = "13:00", title = "Title", subtitle = "Subtitle" ), f7TimelineItem( "Another text", date = "03 Dec", card = FALSE, time = "14:45", title = "Title", subtitle = "Subtitle" ) ), br(), # progress bars f7BlockTitle(title = "f7Progress") %>% f7Align(side = "center"), f7Block( strong = TRUE, f7Progress(id = "pg1", value = 10, color = "yellow"), f7Slider( inputId = "updatepg1", label = "Update progress 1", max = 100, min = 0, value = 50, scale = TRUE ), br(), f7Progress(id = "pg2", value = 100, color = "green"), br(), f7Progress(id = "pg3", value = 50, color = "deeppurple"), br(), f7ProgressInf() ), br(), # gauges f7BlockTitle(title = "f7Gauge") %>% f7Align(side = "center"), f7Block( strong = TRUE, f7Row( f7Col( f7Gauge( id = "mygauge1", type = "semicircle", value = 50, borderColor = "#2196f3", borderWidth = 10, valueText = "50%", valueFontSize = 41, valueTextColor = "#2196f3", labelText = "amount of something" ) ), f7Col( f7Gauge( id = "mygauge2", type = "circle", value = 30, borderColor = "orange", borderWidth = 10, valueText = "30%", valueFontSize = 41, valueTextColor = "orange", labelText = "Other thing" ) ) ), f7Stepper( inputId = "updategauge1", label = "Update gauge 1", step = 10, min = 0, max = 100, value = 50 ) ), # update f7Panel br(), f7BlockTitle(title = "updateF7Panel") %>% f7Align(side = "center"), f7Block( f7Button( inputId = "goPanel", label = "Toggle left panel" ) ) )

##Merges expression and DHS data ##Generates plots to check the association between DHS+ LTRs and gene expression setwd('~/Deniz_2019_AML/Expression') library(vioplot) ##expression data fpkm = read.delim('BP_RNA_FPKM.txt.gz',as.is=T) ##DHS data dhs = read.delim('../DHS_analysis/allLTR_DHS_overlaps.txt',as.is=T) ###################### #### prepare data #### ##get average expression values for cell groups hsc = rowMeans(log2(fpkm[,grep('stem.cell',colnames(fpkm))]+0.01)) mono = rowMeans(log2(fpkm[,grep('monocyte',colnames(fpkm))]+0.01)) macro = rowMeans(log2(fpkm[,grep('macrophage',colnames(fpkm))]+0.01)) aml = rowMeans(log2(fpkm[,grep('Leukemia',colnames(fpkm))]+0.01)) ##make matching dhs and expression matrices for AML samples meta = read.delim('../blueprint_files.tsv',as.is=T) dhs.meta = meta[meta$Experiment=='DNase-Seq' & meta$Format=='BED' & meta$Sub.group=='Acute Myeloid Leukemia',] for (i in 1:ncol(dhs)) { id = grep(colnames(dhs)[i],dhs.meta$URL)[1] if (!is.na(id)) colnames(dhs)[i] = dhs.meta$Donor[id] } expr.donor = gsub('Acute.Myeloid.Leukemia.','',colnames(fpkm)) aml.dhs = as.matrix(dhs[,colnames(dhs) %in% expr.donor]>=1) aml.expr = log2(as.matrix(fpkm[,match(colnames(aml.dhs),expr.donor)])+0.01) ##get average expression values from dhs and non-dhs AML groups av.dhs = numeric(nrow(aml.expr)) av.nodhs = numeric(nrow(aml.expr)) for (i in 1:nrow(aml.expr)) { if (is.na(fpkm$LTR_name[i])) { av.dhs[i] = NA av.nodhs[i] = NA } else { is.dhs = aml.dhs[fpkm$LTR_name[i]==dhs$name] av.dhs[i] = mean(aml.expr[i,is.dhs]) av.nodhs[i] = mean(aml.expr[i,!is.dhs]) } } ##make LTRs groups based on DHS data diff.dhs = dhs[,grepl('Monocytes',colnames(dhs)) | grepl('Macrophages',colnames(dhs))]>=1 nodhs.ltrs = dhs$name[rowSums(aml.dhs)==0 & rowSums(diff.dhs)==0] aml.ltrs = dhs$name[rowSums(aml.dhs)>=2 & rowSums(diff.dhs)==0] ubi.ltrs = dhs$name[rowSums(aml.dhs)>=2 & rowSums(diff.dhs)>=2] ##set distance threshold near = fpkm$LTR_distance <= 50000 ############################################################## #### plot average AML expression for different LTR groups #### vioplot(aml[near & fpkm$LTR_name %in% nodhs.ltrs], aml[near & fpkm$LTR_name %in% aml.ltrs], aml[near & fpkm$LTR_name %in% ubi.ltrs], names=c('None','AML','Ubi'), col='wheat',yaxt='n',ylab='log2 FPKM') axis(2,seq(-5,15,5),las=1) ############################################################## #### compare expression between DHS+ and DHS- AML samples #### plus = av.dhs[near & fpkm$LTR_name %in% aml.ltrs] minus = av.nodhs[near & fpkm$LTR_name %in% aml.ltrs] plot(minus,plus,pch=19,cex=0.5,col='grey',las=1, xlab='DHS- AMLs',ylab='DHS+ AMLs') abline(0,1,lty=2,col='blue') ##pinpoint DHS-associated interest sel = (plus>0 | minus>0) & plus-minus>2 points(minus[sel],plus[sel],pch=19,cex=0.5,col='red') aml.genes = fpkm$gene_name[near & fpkm$LTR_name %in% aml.ltrs] goi = cbind(aml.genes,minus,plus)[sel,] ######################################################### #### plot expression of AML LTRs in other cell types #### ##(not included in the paper) exp.list = list(hsc,mono,macro,av.dhs,av.nodhs) aml.list = lapply(exp.list,function(x) x[near & fpkm$LTR_name %in% aml.ltrs]) vioplot(aml.list,names=c('HSC','Mono','Macro','DHS+','DHS-'), col='wheat',yaxt='n',ylab='log2 FPKM') axis(2,seq(-5,15,5),las=1)

/Expression/LTR_nearest_expression.R

permissive

MBrancoLab/Deniz_2019_AML

R

false

3,364

r

##Merges expression and DHS data ##Generates plots to check the association between DHS+ LTRs and gene expression setwd('~/Deniz_2019_AML/Expression') library(vioplot) ##expression data fpkm = read.delim('BP_RNA_FPKM.txt.gz',as.is=T) ##DHS data dhs = read.delim('../DHS_analysis/allLTR_DHS_overlaps.txt',as.is=T) ###################### #### prepare data #### ##get average expression values for cell groups hsc = rowMeans(log2(fpkm[,grep('stem.cell',colnames(fpkm))]+0.01)) mono = rowMeans(log2(fpkm[,grep('monocyte',colnames(fpkm))]+0.01)) macro = rowMeans(log2(fpkm[,grep('macrophage',colnames(fpkm))]+0.01)) aml = rowMeans(log2(fpkm[,grep('Leukemia',colnames(fpkm))]+0.01)) ##make matching dhs and expression matrices for AML samples meta = read.delim('../blueprint_files.tsv',as.is=T) dhs.meta = meta[meta$Experiment=='DNase-Seq' & meta$Format=='BED' & meta$Sub.group=='Acute Myeloid Leukemia',] for (i in 1:ncol(dhs)) { id = grep(colnames(dhs)[i],dhs.meta$URL)[1] if (!is.na(id)) colnames(dhs)[i] = dhs.meta$Donor[id] } expr.donor = gsub('Acute.Myeloid.Leukemia.','',colnames(fpkm)) aml.dhs = as.matrix(dhs[,colnames(dhs) %in% expr.donor]>=1) aml.expr = log2(as.matrix(fpkm[,match(colnames(aml.dhs),expr.donor)])+0.01) ##get average expression values from dhs and non-dhs AML groups av.dhs = numeric(nrow(aml.expr)) av.nodhs = numeric(nrow(aml.expr)) for (i in 1:nrow(aml.expr)) { if (is.na(fpkm$LTR_name[i])) { av.dhs[i] = NA av.nodhs[i] = NA } else { is.dhs = aml.dhs[fpkm$LTR_name[i]==dhs$name] av.dhs[i] = mean(aml.expr[i,is.dhs]) av.nodhs[i] = mean(aml.expr[i,!is.dhs]) } } ##make LTRs groups based on DHS data diff.dhs = dhs[,grepl('Monocytes',colnames(dhs)) | grepl('Macrophages',colnames(dhs))]>=1 nodhs.ltrs = dhs$name[rowSums(aml.dhs)==0 & rowSums(diff.dhs)==0] aml.ltrs = dhs$name[rowSums(aml.dhs)>=2 & rowSums(diff.dhs)==0] ubi.ltrs = dhs$name[rowSums(aml.dhs)>=2 & rowSums(diff.dhs)>=2] ##set distance threshold near = fpkm$LTR_distance <= 50000 ############################################################## #### plot average AML expression for different LTR groups #### vioplot(aml[near & fpkm$LTR_name %in% nodhs.ltrs], aml[near & fpkm$LTR_name %in% aml.ltrs], aml[near & fpkm$LTR_name %in% ubi.ltrs], names=c('None','AML','Ubi'), col='wheat',yaxt='n',ylab='log2 FPKM') axis(2,seq(-5,15,5),las=1) ############################################################## #### compare expression between DHS+ and DHS- AML samples #### plus = av.dhs[near & fpkm$LTR_name %in% aml.ltrs] minus = av.nodhs[near & fpkm$LTR_name %in% aml.ltrs] plot(minus,plus,pch=19,cex=0.5,col='grey',las=1, xlab='DHS- AMLs',ylab='DHS+ AMLs') abline(0,1,lty=2,col='blue') ##pinpoint DHS-associated interest sel = (plus>0 | minus>0) & plus-minus>2 points(minus[sel],plus[sel],pch=19,cex=0.5,col='red') aml.genes = fpkm$gene_name[near & fpkm$LTR_name %in% aml.ltrs] goi = cbind(aml.genes,minus,plus)[sel,] ######################################################### #### plot expression of AML LTRs in other cell types #### ##(not included in the paper) exp.list = list(hsc,mono,macro,av.dhs,av.nodhs) aml.list = lapply(exp.list,function(x) x[near & fpkm$LTR_name %in% aml.ltrs]) vioplot(aml.list,names=c('HSC','Mono','Macro','DHS+','DHS-'), col='wheat',yaxt='n',ylab='log2 FPKM') axis(2,seq(-5,15,5),las=1)

# Plots global active power versus time to png file. plot2 <- function() { # Read required data. Uses powerUsage.R added to repository. df <- powerUsage() # Prepare to plot graph to png file. png(file = "plot2.png") # Hide x axis label. par(mar = c(3.1, 4.1, 3.1, 2.1)) # Plot global active power versus time. with(df, plot(Time, Global_active_power, # Line plot. type = "l", # y axis label. ylab = "Global Active Power (kilowatts)")) # Finish plotting. dev.off() }

/plot2.R

no_license

rochbe/ExData_Plotting1

R

false

546

r

# Plots global active power versus time to png file. plot2 <- function() { # Read required data. Uses powerUsage.R added to repository. df <- powerUsage() # Prepare to plot graph to png file. png(file = "plot2.png") # Hide x axis label. par(mar = c(3.1, 4.1, 3.1, 2.1)) # Plot global active power versus time. with(df, plot(Time, Global_active_power, # Line plot. type = "l", # y axis label. ylab = "Global Active Power (kilowatts)")) # Finish plotting. dev.off() }

#### Load libraries #### # Load tidyverse - note the output lets you know this contains ggplot2 library(tidyverse) # Load gapminder - this contains the data we will use library(gapminder) #### Import and view data #### # Load data gapminder <- gapminder::gapminder # Look at the structure of the data. You can use glimpse(), summary(), or head(). glimpse(gapminder) # Create a new data frame with only the data for 2007 gapminder07 <- filter(gapminder, year==2007) #### Basic plot #### # Scatterplot of population over time ggplot(gapminder) + geom_point(aes(x=year, y=pop)) # Add labels to the plot ggplot(gapminder) + geom_point(aes(x=year, y=pop)) + labs(title="Population over time", x="Year", y="Population") # Your turn: plot life expectancy as a function of GDP per capita for the year 2007, and add labels ggplot(gapminder07) + geom_point(aes(x=gdpPercap, y=lifeExp)) + labs(title="Do people in richer countries live longer?", x="GDP per capita", y="Life expectancy") #### Choosing geoms #### # Let's look at geom_smooth(). Add a geom_smooth() layer to the previous plot. ggplot(gapminder07, aes(x=gdpPercap, y=lifeExp)) + geom_point() + geom_smooth() + labs(title="Do people in richer countries live longer?", x="GDP per capita", y="Life expectancy", subtitle="Gapminder 2007 data") # How about geom_hline()? When you add it, you'll need to provide a new aesthetic option. ggplot(gapminder07) + geom_point(aes(x=gdpPercap, y=lifeExp)) + geom_hline(aes(yintercept=mean(lifeExp))) + labs(title="Do people in richer countries live longer?", x="GDP per capita", y="Life expectancy", subtitle="Gapminder 2007 data") # Your turn: Plot the life expectancy of each continent in 2007 # Look at the ggplot cheatsheet and decide what kind of geom to use: https://raw.githubusercontent.com/rstudio/cheatsheets/master/data-visualization-2.1.pdf # Option 1: Box plot ggplot(gapminder07) + geom_boxplot(aes(x=continent, y=lifeExp)) # Option 2: Column plot ggplot(gapminder07) + geom_col(aes(x=continent, y=lifeExp)) #### Grouping variables #### # Grouping by color ggplot(gapminder) + geom_point(aes(x=gdpPercap, y=lifeExp, col=year)) + labs(title="Do people in richer countries live longer?", x="GDP per capita", y="Life expectancy") # Grouping by facet ggplot(gapminder, aes(x=gdpPercap, y=lifeExp)) + geom_point() + geom_smooth() + facet_wrap(~year) + labs(title="Do people in richer countries live longer?", x="GDP per capita", y="Life expectancy") # Add scales="free" in facet_wrap() and see what happens ggplot(gapminder, aes(x=gdpPercap, y=lifeExp)) + geom_point() + geom_smooth() + facet_wrap(~year, scales="free") + labs(title="Do people in richer countries live longer?", x="GDP per capita", y="Life expectancy") # Your turn: Let's visualize life expectancy by continent in 2007 again. This time, use grouping by color or facet. # Bonus: Use two geoms. # Option 1: By colors ggplot(gapminder07, aes(x=gdpPercap, y=lifeExp, col=continent, size=pop)) + geom_point() + labs(title="Life expectancy as a function of GDP per capita, by continent", x="GDP per capita", y="Life expectancy") # Option 2: By facet ggplot(gapminder07, aes(x=gdpPercap, y=lifeExp, size=pop)) + geom_point() + geom_smooth() + facet_wrap(~continent, scales="free") + labs(title="Life expectancy as a function of GDP per capita, by continent", x="GDP per capita", y="Life expectancy") # Option 3: By facet ggplot(gapminder07, aes(x=lifeExp)) + geom_density() + facet_wrap(~continent, scales="free")+ labs(title="Life expectancy by continent", x="Life expectancy", y="") #### Choose your own adventure! #### # Create a plot that includes two geoms and facets by some variable.

/answers/exercise1_answers.R

no_license

kumarhk/2021-teaching-r-workshop

R

false

3,876

r

#### Load libraries #### # Load tidyverse - note the output lets you know this contains ggplot2 library(tidyverse) # Load gapminder - this contains the data we will use library(gapminder) #### Import and view data #### # Load data gapminder <- gapminder::gapminder # Look at the structure of the data. You can use glimpse(), summary(), or head(). glimpse(gapminder) # Create a new data frame with only the data for 2007 gapminder07 <- filter(gapminder, year==2007) #### Basic plot #### # Scatterplot of population over time ggplot(gapminder) + geom_point(aes(x=year, y=pop)) # Add labels to the plot ggplot(gapminder) + geom_point(aes(x=year, y=pop)) + labs(title="Population over time", x="Year", y="Population") # Your turn: plot life expectancy as a function of GDP per capita for the year 2007, and add labels ggplot(gapminder07) + geom_point(aes(x=gdpPercap, y=lifeExp)) + labs(title="Do people in richer countries live longer?", x="GDP per capita", y="Life expectancy") #### Choosing geoms #### # Let's look at geom_smooth(). Add a geom_smooth() layer to the previous plot. ggplot(gapminder07, aes(x=gdpPercap, y=lifeExp)) + geom_point() + geom_smooth() + labs(title="Do people in richer countries live longer?", x="GDP per capita", y="Life expectancy", subtitle="Gapminder 2007 data") # How about geom_hline()? When you add it, you'll need to provide a new aesthetic option. ggplot(gapminder07) + geom_point(aes(x=gdpPercap, y=lifeExp)) + geom_hline(aes(yintercept=mean(lifeExp))) + labs(title="Do people in richer countries live longer?", x="GDP per capita", y="Life expectancy", subtitle="Gapminder 2007 data") # Your turn: Plot the life expectancy of each continent in 2007 # Look at the ggplot cheatsheet and decide what kind of geom to use: https://raw.githubusercontent.com/rstudio/cheatsheets/master/data-visualization-2.1.pdf # Option 1: Box plot ggplot(gapminder07) + geom_boxplot(aes(x=continent, y=lifeExp)) # Option 2: Column plot ggplot(gapminder07) + geom_col(aes(x=continent, y=lifeExp)) #### Grouping variables #### # Grouping by color ggplot(gapminder) + geom_point(aes(x=gdpPercap, y=lifeExp, col=year)) + labs(title="Do people in richer countries live longer?", x="GDP per capita", y="Life expectancy") # Grouping by facet ggplot(gapminder, aes(x=gdpPercap, y=lifeExp)) + geom_point() + geom_smooth() + facet_wrap(~year) + labs(title="Do people in richer countries live longer?", x="GDP per capita", y="Life expectancy") # Add scales="free" in facet_wrap() and see what happens ggplot(gapminder, aes(x=gdpPercap, y=lifeExp)) + geom_point() + geom_smooth() + facet_wrap(~year, scales="free") + labs(title="Do people in richer countries live longer?", x="GDP per capita", y="Life expectancy") # Your turn: Let's visualize life expectancy by continent in 2007 again. This time, use grouping by color or facet. # Bonus: Use two geoms. # Option 1: By colors ggplot(gapminder07, aes(x=gdpPercap, y=lifeExp, col=continent, size=pop)) + geom_point() + labs(title="Life expectancy as a function of GDP per capita, by continent", x="GDP per capita", y="Life expectancy") # Option 2: By facet ggplot(gapminder07, aes(x=gdpPercap, y=lifeExp, size=pop)) + geom_point() + geom_smooth() + facet_wrap(~continent, scales="free") + labs(title="Life expectancy as a function of GDP per capita, by continent", x="GDP per capita", y="Life expectancy") # Option 3: By facet ggplot(gapminder07, aes(x=lifeExp)) + geom_density() + facet_wrap(~continent, scales="free")+ labs(title="Life expectancy by continent", x="Life expectancy", y="") #### Choose your own adventure! #### # Create a plot that includes two geoms and facets by some variable.

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/config-tools.R \name{load_config} \alias{load_config} \title{Load and validate configuration file} \usage{ load_config(config_path) } \arguments{ \item{config_path}{Path to the WGSAParsr configuration file to load and validate} } \value{ a tibble that can be used for building field lists for parsing } \description{ WGSAParsr configuration files are flexible tab-separated files. They must include a header with field names, and the following fields, in any order: \itemize{ \item \strong{field} column headings matching the WGSA output file to be parsed \item \strong{SNV} logical (TRUE/FALSE) indicating whether the field should be parsed for snv annotation \item \strong{indel} logical (TRUE/FALSE) indicating whether the field should be parsed for indel annotation \item \strong{dbnsfp} logical (TRUE/FALSE) indicating whether the field should be parsed for dbnsfp annotation \item \strong{pivotGroup} numerical value to group annotations for pivoting \item \strong{pivotChar} character separating fields that should be used for pivoting \item \strong{parseGroup} numerical value to group annotations for other parsing \item \strong{transformation} a string describing the transformation to be performed. Values may include: \itemize{ \item \strong{max} select the maximum value \item \strong{min} select the minimum value \item \strong{pick_Y} select "Y" if present \item \strong{pick_N} select "N" if present \item \strong{pick_A} select A>D>P>N (MutationTaster_pred field) \item \strong{clean} remove the \{n\}. E.g.: "Enhancer\{4\}" -> "Enhancer" \item \strong{distinct} select unique values. NOTE: must have a pivotGroup and pivotChar = | } } } \details{ Additionally, the following fields may be included, and are processed during configuration file loading, but are not required: \itemize{ \item \strong{order} numerical value for column ordering in parsed output \item \strong{sourceGroup} numerical value for column grouping/ordering in output } Other columns (such as notes) may be included in the configuration file, but will not be validated or imported with this function. The configuration file may include comments beginning with #. } \examples{ \dontrun{ local_config <- load_config("config.tsv") } freeze_5_config <- load_config(wgsaparsr_example("fr_5_config.tsv")) }

/man/load_config.Rd

no_license

bestbioinformatics/wgsaparsr

R

false

true

2,470

rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/config-tools.R \name{load_config} \alias{load_config} \title{Load and validate configuration file} \usage{ load_config(config_path) } \arguments{ \item{config_path}{Path to the WGSAParsr configuration file to load and validate} } \value{ a tibble that can be used for building field lists for parsing } \description{ WGSAParsr configuration files are flexible tab-separated files. They must include a header with field names, and the following fields, in any order: \itemize{ \item \strong{field} column headings matching the WGSA output file to be parsed \item \strong{SNV} logical (TRUE/FALSE) indicating whether the field should be parsed for snv annotation \item \strong{indel} logical (TRUE/FALSE) indicating whether the field should be parsed for indel annotation \item \strong{dbnsfp} logical (TRUE/FALSE) indicating whether the field should be parsed for dbnsfp annotation \item \strong{pivotGroup} numerical value to group annotations for pivoting \item \strong{pivotChar} character separating fields that should be used for pivoting \item \strong{parseGroup} numerical value to group annotations for other parsing \item \strong{transformation} a string describing the transformation to be performed. Values may include: \itemize{ \item \strong{max} select the maximum value \item \strong{min} select the minimum value \item \strong{pick_Y} select "Y" if present \item \strong{pick_N} select "N" if present \item \strong{pick_A} select A>D>P>N (MutationTaster_pred field) \item \strong{clean} remove the \{n\}. E.g.: "Enhancer\{4\}" -> "Enhancer" \item \strong{distinct} select unique values. NOTE: must have a pivotGroup and pivotChar = | } } } \details{ Additionally, the following fields may be included, and are processed during configuration file loading, but are not required: \itemize{ \item \strong{order} numerical value for column ordering in parsed output \item \strong{sourceGroup} numerical value for column grouping/ordering in output } Other columns (such as notes) may be included in the configuration file, but will not be validated or imported with this function. The configuration file may include comments beginning with #. } \examples{ \dontrun{ local_config <- load_config("config.tsv") } freeze_5_config <- load_config(wgsaparsr_example("fr_5_config.tsv")) }

% Generated by roxygen2 (4.1.1): do not edit by hand % Please edit documentation in R/exploits.r \name{shodan_exploit_search} \alias{shodan_exploit_search} \title{Search for Exploits} \usage{ shodan_exploit_search(query = NULL, facets = NULL, page = 1) } \arguments{ \item{query}{Search query used to search the database of known exploits. See \url{https://developer.shodan.io/api/exploits/rest} for all supported search filters.} \item{facets}{A comma-separated list of properties to get summary information on. The following facets are currently supported: "\code{author}", "\code{platform}", "\code{port}", "\code{source}" and "\code{type}. If \code{length(facets) > 1)} this function will concatenate the vector with commas to send to Shodan.} \item{page}{The page number to page through results \code{100} at a time (default: \code{1})} } \description{ Search across a variety of data sources for exploits and use facets to get summary information. } \references{ \url{https://developer.shodan.io/api/exploits/rest} }

/man/shodan_exploit_search.Rd

no_license

Leocodefocus/shodan

R

false

1,026

rd

% Generated by roxygen2 (4.1.1): do not edit by hand % Please edit documentation in R/exploits.r \name{shodan_exploit_search} \alias{shodan_exploit_search} \title{Search for Exploits} \usage{ shodan_exploit_search(query = NULL, facets = NULL, page = 1) } \arguments{ \item{query}{Search query used to search the database of known exploits. See \url{https://developer.shodan.io/api/exploits/rest} for all supported search filters.} \item{facets}{A comma-separated list of properties to get summary information on. The following facets are currently supported: "\code{author}", "\code{platform}", "\code{port}", "\code{source}" and "\code{type}. If \code{length(facets) > 1)} this function will concatenate the vector with commas to send to Shodan.} \item{page}{The page number to page through results \code{100} at a time (default: \code{1})} } \description{ Search across a variety of data sources for exploits and use facets to get summary information. } \references{ \url{https://developer.shodan.io/api/exploits/rest} }

#' Kernel RV Coefficient Test (KRV) #' #' Kernel RV coefficient test to evaluate the overall association between #' microbiome composition and high-dimensional or structured phenotype or genotype. #' #' kernels.otu should be a list of numerical n by n kernel matrices, or a single #' n by n kernel matrix, where n is sample size. #' #' When kernel.y is a method ("Gaussian" or "linear") to compute the kernel of #' phenotype, y should be a numerical phenotype matrix, and X (if not NULL) #' should be a numeric matrix of covariates. Both y and X should have n rows. #' #' When kernel.y is a kernel matrix for the phenotype, there is no need to provide #' X and y, and they will be ignored if provided. In this case, kernel.y and #' kernel.otu should both be numeric matrices with the same number of rows and columns. #' #' Missing data is not permitted. Please remove all individuals with missing #' kernel.otu, y (if not NULL), X (if not NULL), and kernel.y (if a matrix is #' entered) prior to analysis. #' #' @param y A numeric n by p matrix of p continuous phenotype variables and #' sample size n (default = NULL). If it is NULL, a #' phenotype kernel matrix must be entered for "kernel.y". Defaults to NULL. #' @param X A numeric n by q matrix, containing q additional covariates #' (default = NULL). If NULL, an intercept only model is used. If the first #' column of X is not uniformly 1, then an intercept column will be added. #' @param adjust.type Possible values are "none" (default if X is null), #' "phenotype" to adjust only the y variable (only possible if y is a numeric #' phenotype matrix rather than a pre-computed kernel), or "both" to adjust #' both the X and Y kernels. #' @param kernels.otu A numeric OTU n by n kernel matrix or a list of matrices, #' where n is the sample size. It can be constructed from microbiome data, such #' as by transforming from a distance metric. #' @param kernel.y Either a numerical n by n kernel matrix for phenotypes or a #' method to compute the kernel of phenotype. Methods are "Gaussian" or "linear". #' A Gaussian kernel (kernel.y="Gaussian") can capture the general relationship #' between microbiome and phenotypes; a linear kernel (kernel.y="linear") #' may be preferred if the underlying relationship is close to linear. #' @param omnibus A string equal to either "Cauchy" or "kernel_om" (or unambiguous #' abbreviations thereof), specifying whether to use the Cauchy combination test #' or an omnibus kernel to generate the omnibus p-value. #' @param returnKRV A logical indicating whether to return the KRV statistic. Defaults to FALSE. #' @param returnR2 A logical indicating whether to return the R-squared coefficient. Defaults to FALSE. #' #' @return #' If only one candidate kernel matrix is considered, returns a list containing the p-value for the candidate kernel matrix. #' If more than one candidate kernel matrix is considered, returns a list of two elements: #' \item{p_values}{P-value for each candidate kernel matrix} #' \item{omnibus_p}{Omnibus p-value} #' \item{KRV}{A vector of kernel RV statistics (a measure of effect size), one for each candidate kernel matrix. Only returned if returnKRV = TRUE} #' \item{R2}{A vector of R-squared statistics, one for each candidate kernel matrix. Only returned if returnR2 = TRUE} #' #'@author #'Nehemiah Wilson, Haotian Zheng, Xiang Zhan, Ni Zhao #' #'@references #' Zheng, Haotian, Zhan, X., Plantinga, A., Zhao, N., and Wu, M.C. A Fast Small-Sample Kernel Independence Test for Microbiome #' Community-Level Association Analysis. Biometrics. 2017 Mar 10. doi: 10.1111/biom.12684. #' #' Liu, Hongjiao, Ling, W., Hua, X., Moon, J.Y., Williams-Nguyen, J., Zhan, X., Plantinga, A.M., Zhao, N., #' Zhang, A., Durazo-Arzivu, R.A., Knight, R., Qi, Q., Burk, R.D., Kaplan, R.C., and Wu, M.C. #' Kernel-based genetic association analysis for microbiome phenotypes identifies host genetic #' drivers of beta-diversity. 2021+ #' #'@importFrom PearsonDS ppearsonIII #' #'@examples #'library(GUniFrac) #'library(MASS) #' #'data(throat.tree) #'data(throat.otu.tab) #'data(throat.meta) #' #' ## Simulate covariate data #' set.seed(123) #' n = nrow(throat.otu.tab) #' Sex <- throat.meta$Sex #' Smoker <- throat.meta$SmokingStatus #' anti <- throat.meta$AntibioticUsePast3Months_TimeFromAntibioticUsage #' Male = (Sex == "Male")**2 #' Smoker = (Smoker == "Smoker") **2 #' Anti = (anti != "None")^2 #' cova = cbind(1, Male, Smoker, Anti) #' #' ## Simulate microbiome data #' otu.tab.rff <- Rarefy(throat.otu.tab)$otu.tab.rff #' unifracs <- GUniFrac(otu.tab.rff, throat.tree, alpha=c(0, 0.5, 1))$unifracs #' # Distance matrices #' D.weighted = unifracs[,,"d_1"] #' D.unweighted = unifracs[,,"d_UW"] #' # Kernel matrices #' K.weighted = D2K(D.weighted) #' K.unweighted = D2K(D.unweighted) #' #' if (requireNamespace("vegan")) { #' library(vegan) #' D.BC = as.matrix(vegdist(otu.tab.rff, method="bray")) #' K.BC = D2K(D.BC) #'} #' #' # Simulate phenotype data #' rho = 0.2 #' Va = matrix(rep(rho, (2*n)^2), 2*n, 2*n)+diag(1-rho, 2*n) #' Phe = mvrnorm(n, rep(0, 2*n), Va) #' K.y = Phe %*% t(Phe) # phenotype kernel #' #' # Simulate genotype data #' G = matrix(rbinom(n*10, 2, 0.1), n, 10) #' K.g = G %*% t(G) # genotype kernel #' #' #' ## Unadjusted analysis (microbiome and phenotype) #' KRV(y = Phe, kernels.otu = K.weighted, kernel.y = "Gaussian") # numeric y #' KRV(kernels.otu = K.weighted, kernel.y = K.y) # kernel y #' #' ## Adjusted analysis (phenotype only) #' KRV(kernels.otu = K.weighted, y = Phe, kernel.y = "linear", X = cova, adjust.type = "phenotype") #' #' if (requireNamespace("vegan")) { #' ## Adjusted analysis (adjust both kernels; microbiome and phenotype) #' KRV(kernels.otu = K.BC, kernel.y = K.y, X = cova, adjust.type='both') #' #' ## Adjusted analysis (adjust both kernels; microbiome and genotype) #' KRV(kernels.otu = K.BC, kernel.y = K.g, X = cova, adjust.type='both') #' } #' #' #'@export KRV <- function(y = NULL, X = NULL, adjust.type = NULL, kernels.otu, kernel.y, omnibus = "kernel_om", returnKRV = FALSE, returnR2 = FALSE){ ## Check input om <- substring(tolower(omnibus), 1, 1) if (!is.null(adjust.type)) { adjust.type <- substring(tolower(adjust.type), 1, 1) if (adjust.type == "p") { if (!kernel.y %in% c("linear", "Gaussian")) { stop("Phenotype-only adjustment may only be used with numeric y, not pre-computed phenotype kernel. Try option 'both' instead or double check that you have entered either 'linear' or 'Gaussian' for kernel.y.") } } if (!adjust.type %in% c("p", "b")) { stop("I don't know that covariate adjustment choice. Please choose 'phenotype' or 'both', or leave this option NULL.") } } if (!is.list(kernels.otu)) { kernels.otu <- list(kernels.otu) } if (!all(unlist(lapply(kernels.otu, FUN = function(k) is.matrix(k))))) { stop("Please ensure kernels.otu is either a single n x n kernel matrix or a list of n x n kernel matrices.") } if (!is.null(X) & !all(X[,1] == 1)) { X1 <- cbind(1, X) X <- X1 } if (is.null(X) & !is.null(adjust.type)) { warning("X is NULL, so no covariate adjustment will be done.") adjust.type = NULL } if (is.matrix(kernel.y)){ n = nrow(kernels.otu[[1]]) if (!is.null(y)){ warning("When a phenotype kernel is provided, argument \"y\" will be ignored.\n") } if (ncol(kernels.otu[[1]]) != n | nrow(kernel.y) != n | ncol(kernel.y) != n){ stop("Kernel matrices need to be n x n, where n is the sample size.\n ") } } else if (!is.matrix(kernel.y)){ if (!(kernel.y %in% c("Gaussian", "linear"))){ stop("Please choose kernel.y = \"Gaussian\" or \"linear\", or enter a kernel matrix for \"kernel.y\".\n") } if(is.null(y)){ stop("Please enter a phenotype matrix for argument \"y\" or enter a kernel matrix for argument \"kernel.y\".\n") } n = NROW(y) if (!all(unlist(lapply(kernels.otu, FUN = function(x) nrow(x) == n & ncol(x) == n)))) { stop("Kernel matrix/matrices must be n x n, where n is the sample size. \n ") } if (any(is.na(y))){ ids = which(is.na(y)) stop(paste("Missing response for subject(s)", ids, "- please remove before proceeding. \n")) } if (!is.null(X)){ if (any(is.na(X))){ stop("NAs in covariates X, please impute or remove subjects with missing covariates values.\n") } if(NROW(X)!= NROW(y)) stop("Dimensions of X and y don't match.\n") } } ## Actual test pvals <- c() if (returnKRV) { KRVs <- c() } else { KRVs = NULL } if (returnR2) { R2 <- c() } else { R2 = NULL } for(i in 1:length(kernels.otu)){ res <- inner.KRV(y = y, X = X, adjust.type = adjust.type, kernel.otu=kernels.otu[[i]], kernel.y = kernel.y, returnKRV = TRUE, returnR2 = TRUE) pvals[i] <- res$pv if (returnKRV) { KRVs[i] <- res$KRV } if (returnR2) { R2[i] <- res$R2 } } # Naming the p-values with the Kernel matrix names from Ks kernel.names <- names(kernels.otu) names(pvals) <- kernel.names if (returnKRV) { names(KRVs) <- kernel.names } if (returnR2) { names(R2) <- kernel.names } #Omnibus Test if (length(kernels.otu) > 1) { if (om == "k") { K.om <- matrix(0, nrow = nrow(kernels.otu[[1]]), ncol = ncol(kernels.otu[[1]])) for(i in 1:length(kernels.otu)){ K.om = K.om + kernels.otu[[i]]/tr(kernels.otu[[i]]) } omnibus_p <- as.numeric(inner.KRV(y = y, X = X, adjust.type = adjust.type, kernel.otu = K.om, kernel.y = kernel.y)$pv) } else if (om == "c") { cauchy.t <- sum(tan((0.5 - pvals)*pi))/length(pvals) omnibus_p <- 1 - pcauchy(cauchy.t) } else { stop("I don't know that omnibus option. Please choose 'kernel_om' or 'Cauchy'.") } ## Return for multiple kernels if (is.null(KRVs) & is.null(R2)) { return(list(p_values = pvals, omnibus_p = omnibus_p)) } else if (is.null(KRVs) & !is.null(R2)) { return(list(p_values = pvals, omnibus_p = omnibus_p, R2 = R2)) } else if (!is.null(KRVs) & is.null(R2)) { return(list(p_values = pvals, omnibus_p = omnibus_p, KRV = KRVs)) } else { return(list(p_values = pvals, omnibus_p = omnibus_p, KRV = KRVs, R2 = R2)) } } ## Return for single kernels if (is.null(KRVs) & is.null(R2)) { return(list(p_values = pvals)) } else if (is.null(KRVs) & !is.null(R2)) { return(list(p_values = pvals, R2 = R2)) } else if (!is.null(KRVs) & is.null(R2)) { return(list(p_values = pvals, KRV = KRVs)) } else { return(list(p_values = pvals, KRV = KRVs, R2 = R2)) } }

/R/KRV.R

no_license

cran/MiRKAT

R

false

10,847

r

#' Kernel RV Coefficient Test (KRV) #' #' Kernel RV coefficient test to evaluate the overall association between #' microbiome composition and high-dimensional or structured phenotype or genotype. #' #' kernels.otu should be a list of numerical n by n kernel matrices, or a single #' n by n kernel matrix, where n is sample size. #' #' When kernel.y is a method ("Gaussian" or "linear") to compute the kernel of #' phenotype, y should be a numerical phenotype matrix, and X (if not NULL) #' should be a numeric matrix of covariates. Both y and X should have n rows. #' #' When kernel.y is a kernel matrix for the phenotype, there is no need to provide #' X and y, and they will be ignored if provided. In this case, kernel.y and #' kernel.otu should both be numeric matrices with the same number of rows and columns. #' #' Missing data is not permitted. Please remove all individuals with missing #' kernel.otu, y (if not NULL), X (if not NULL), and kernel.y (if a matrix is #' entered) prior to analysis. #' #' @param y A numeric n by p matrix of p continuous phenotype variables and #' sample size n (default = NULL). If it is NULL, a #' phenotype kernel matrix must be entered for "kernel.y". Defaults to NULL. #' @param X A numeric n by q matrix, containing q additional covariates #' (default = NULL). If NULL, an intercept only model is used. If the first #' column of X is not uniformly 1, then an intercept column will be added. #' @param adjust.type Possible values are "none" (default if X is null), #' "phenotype" to adjust only the y variable (only possible if y is a numeric #' phenotype matrix rather than a pre-computed kernel), or "both" to adjust #' both the X and Y kernels. #' @param kernels.otu A numeric OTU n by n kernel matrix or a list of matrices, #' where n is the sample size. It can be constructed from microbiome data, such #' as by transforming from a distance metric. #' @param kernel.y Either a numerical n by n kernel matrix for phenotypes or a #' method to compute the kernel of phenotype. Methods are "Gaussian" or "linear". #' A Gaussian kernel (kernel.y="Gaussian") can capture the general relationship #' between microbiome and phenotypes; a linear kernel (kernel.y="linear") #' may be preferred if the underlying relationship is close to linear. #' @param omnibus A string equal to either "Cauchy" or "kernel_om" (or unambiguous #' abbreviations thereof), specifying whether to use the Cauchy combination test #' or an omnibus kernel to generate the omnibus p-value. #' @param returnKRV A logical indicating whether to return the KRV statistic. Defaults to FALSE. #' @param returnR2 A logical indicating whether to return the R-squared coefficient. Defaults to FALSE. #' #' @return #' If only one candidate kernel matrix is considered, returns a list containing the p-value for the candidate kernel matrix. #' If more than one candidate kernel matrix is considered, returns a list of two elements: #' \item{p_values}{P-value for each candidate kernel matrix} #' \item{omnibus_p}{Omnibus p-value} #' \item{KRV}{A vector of kernel RV statistics (a measure of effect size), one for each candidate kernel matrix. Only returned if returnKRV = TRUE} #' \item{R2}{A vector of R-squared statistics, one for each candidate kernel matrix. Only returned if returnR2 = TRUE} #' #'@author #'Nehemiah Wilson, Haotian Zheng, Xiang Zhan, Ni Zhao #' #'@references #' Zheng, Haotian, Zhan, X., Plantinga, A., Zhao, N., and Wu, M.C. A Fast Small-Sample Kernel Independence Test for Microbiome #' Community-Level Association Analysis. Biometrics. 2017 Mar 10. doi: 10.1111/biom.12684. #' #' Liu, Hongjiao, Ling, W., Hua, X., Moon, J.Y., Williams-Nguyen, J., Zhan, X., Plantinga, A.M., Zhao, N., #' Zhang, A., Durazo-Arzivu, R.A., Knight, R., Qi, Q., Burk, R.D., Kaplan, R.C., and Wu, M.C. #' Kernel-based genetic association analysis for microbiome phenotypes identifies host genetic #' drivers of beta-diversity. 2021+ #' #'@importFrom PearsonDS ppearsonIII #' #'@examples #'library(GUniFrac) #'library(MASS) #' #'data(throat.tree) #'data(throat.otu.tab) #'data(throat.meta) #' #' ## Simulate covariate data #' set.seed(123) #' n = nrow(throat.otu.tab) #' Sex <- throat.meta$Sex #' Smoker <- throat.meta$SmokingStatus #' anti <- throat.meta$AntibioticUsePast3Months_TimeFromAntibioticUsage #' Male = (Sex == "Male")**2 #' Smoker = (Smoker == "Smoker") **2 #' Anti = (anti != "None")^2 #' cova = cbind(1, Male, Smoker, Anti) #' #' ## Simulate microbiome data #' otu.tab.rff <- Rarefy(throat.otu.tab)$otu.tab.rff #' unifracs <- GUniFrac(otu.tab.rff, throat.tree, alpha=c(0, 0.5, 1))$unifracs #' # Distance matrices #' D.weighted = unifracs[,,"d_1"] #' D.unweighted = unifracs[,,"d_UW"] #' # Kernel matrices #' K.weighted = D2K(D.weighted) #' K.unweighted = D2K(D.unweighted) #' #' if (requireNamespace("vegan")) { #' library(vegan) #' D.BC = as.matrix(vegdist(otu.tab.rff, method="bray")) #' K.BC = D2K(D.BC) #'} #' #' # Simulate phenotype data #' rho = 0.2 #' Va = matrix(rep(rho, (2*n)^2), 2*n, 2*n)+diag(1-rho, 2*n) #' Phe = mvrnorm(n, rep(0, 2*n), Va) #' K.y = Phe %*% t(Phe) # phenotype kernel #' #' # Simulate genotype data #' G = matrix(rbinom(n*10, 2, 0.1), n, 10) #' K.g = G %*% t(G) # genotype kernel #' #' #' ## Unadjusted analysis (microbiome and phenotype) #' KRV(y = Phe, kernels.otu = K.weighted, kernel.y = "Gaussian") # numeric y #' KRV(kernels.otu = K.weighted, kernel.y = K.y) # kernel y #' #' ## Adjusted analysis (phenotype only) #' KRV(kernels.otu = K.weighted, y = Phe, kernel.y = "linear", X = cova, adjust.type = "phenotype") #' #' if (requireNamespace("vegan")) { #' ## Adjusted analysis (adjust both kernels; microbiome and phenotype) #' KRV(kernels.otu = K.BC, kernel.y = K.y, X = cova, adjust.type='both') #' #' ## Adjusted analysis (adjust both kernels; microbiome and genotype) #' KRV(kernels.otu = K.BC, kernel.y = K.g, X = cova, adjust.type='both') #' } #' #' #'@export KRV <- function(y = NULL, X = NULL, adjust.type = NULL, kernels.otu, kernel.y, omnibus = "kernel_om", returnKRV = FALSE, returnR2 = FALSE){ ## Check input om <- substring(tolower(omnibus), 1, 1) if (!is.null(adjust.type)) { adjust.type <- substring(tolower(adjust.type), 1, 1) if (adjust.type == "p") { if (!kernel.y %in% c("linear", "Gaussian")) { stop("Phenotype-only adjustment may only be used with numeric y, not pre-computed phenotype kernel. Try option 'both' instead or double check that you have entered either 'linear' or 'Gaussian' for kernel.y.") } } if (!adjust.type %in% c("p", "b")) { stop("I don't know that covariate adjustment choice. Please choose 'phenotype' or 'both', or leave this option NULL.") } } if (!is.list(kernels.otu)) { kernels.otu <- list(kernels.otu) } if (!all(unlist(lapply(kernels.otu, FUN = function(k) is.matrix(k))))) { stop("Please ensure kernels.otu is either a single n x n kernel matrix or a list of n x n kernel matrices.") } if (!is.null(X) & !all(X[,1] == 1)) { X1 <- cbind(1, X) X <- X1 } if (is.null(X) & !is.null(adjust.type)) { warning("X is NULL, so no covariate adjustment will be done.") adjust.type = NULL } if (is.matrix(kernel.y)){ n = nrow(kernels.otu[[1]]) if (!is.null(y)){ warning("When a phenotype kernel is provided, argument \"y\" will be ignored.\n") } if (ncol(kernels.otu[[1]]) != n | nrow(kernel.y) != n | ncol(kernel.y) != n){ stop("Kernel matrices need to be n x n, where n is the sample size.\n ") } } else if (!is.matrix(kernel.y)){ if (!(kernel.y %in% c("Gaussian", "linear"))){ stop("Please choose kernel.y = \"Gaussian\" or \"linear\", or enter a kernel matrix for \"kernel.y\".\n") } if(is.null(y)){ stop("Please enter a phenotype matrix for argument \"y\" or enter a kernel matrix for argument \"kernel.y\".\n") } n = NROW(y) if (!all(unlist(lapply(kernels.otu, FUN = function(x) nrow(x) == n & ncol(x) == n)))) { stop("Kernel matrix/matrices must be n x n, where n is the sample size. \n ") } if (any(is.na(y))){ ids = which(is.na(y)) stop(paste("Missing response for subject(s)", ids, "- please remove before proceeding. \n")) } if (!is.null(X)){ if (any(is.na(X))){ stop("NAs in covariates X, please impute or remove subjects with missing covariates values.\n") } if(NROW(X)!= NROW(y)) stop("Dimensions of X and y don't match.\n") } } ## Actual test pvals <- c() if (returnKRV) { KRVs <- c() } else { KRVs = NULL } if (returnR2) { R2 <- c() } else { R2 = NULL } for(i in 1:length(kernels.otu)){ res <- inner.KRV(y = y, X = X, adjust.type = adjust.type, kernel.otu=kernels.otu[[i]], kernel.y = kernel.y, returnKRV = TRUE, returnR2 = TRUE) pvals[i] <- res$pv if (returnKRV) { KRVs[i] <- res$KRV } if (returnR2) { R2[i] <- res$R2 } } # Naming the p-values with the Kernel matrix names from Ks kernel.names <- names(kernels.otu) names(pvals) <- kernel.names if (returnKRV) { names(KRVs) <- kernel.names } if (returnR2) { names(R2) <- kernel.names } #Omnibus Test if (length(kernels.otu) > 1) { if (om == "k") { K.om <- matrix(0, nrow = nrow(kernels.otu[[1]]), ncol = ncol(kernels.otu[[1]])) for(i in 1:length(kernels.otu)){ K.om = K.om + kernels.otu[[i]]/tr(kernels.otu[[i]]) } omnibus_p <- as.numeric(inner.KRV(y = y, X = X, adjust.type = adjust.type, kernel.otu = K.om, kernel.y = kernel.y)$pv) } else if (om == "c") { cauchy.t <- sum(tan((0.5 - pvals)*pi))/length(pvals) omnibus_p <- 1 - pcauchy(cauchy.t) } else { stop("I don't know that omnibus option. Please choose 'kernel_om' or 'Cauchy'.") } ## Return for multiple kernels if (is.null(KRVs) & is.null(R2)) { return(list(p_values = pvals, omnibus_p = omnibus_p)) } else if (is.null(KRVs) & !is.null(R2)) { return(list(p_values = pvals, omnibus_p = omnibus_p, R2 = R2)) } else if (!is.null(KRVs) & is.null(R2)) { return(list(p_values = pvals, omnibus_p = omnibus_p, KRV = KRVs)) } else { return(list(p_values = pvals, omnibus_p = omnibus_p, KRV = KRVs, R2 = R2)) } } ## Return for single kernels if (is.null(KRVs) & is.null(R2)) { return(list(p_values = pvals)) } else if (is.null(KRVs) & !is.null(R2)) { return(list(p_values = pvals, R2 = R2)) } else if (!is.null(KRVs) & is.null(R2)) { return(list(p_values = pvals, KRV = KRVs)) } else { return(list(p_values = pvals, KRV = KRVs, R2 = R2)) } }