pierreqi/r_code_50k · Datasets at Hugging Face

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/20160216/server.R

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weitinglin/ShinyPractice2016

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2021-01-10T01:39:52.954447

2016-02-16T15:47:17

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server.R

#20160209 shiny practice upload file server.R #By defaut, the file size limit is 5 MB, It can be changed by setting this option. library(shiny) options(shiny.maxRequestSize = 9*1024^2) #loading package library(dplyr) library(ggplot2) library(reshape2) library(XML) #define the function #==================================================================== Function pagecut <- function(raw_data) { output<-list() #seperate the data sheet tables_index<-grep("table",raw_data) for( i in 1:(1/2*length(tables_index))){ a<-tables_index[2*i-1];b<-tables_index[2*i] output[[i]]<-raw_data[a:b] } return(output) } #==================================================================== Function tablecut<-function(pages,raw_data){ output<-list() for( i in 1: length(pages)){ if(i < length(pages)){ a<-pages[i];b<-pages[(i+1)]-1 output[[i]]<-raw_data[a:b] }else{ a<-pages[i] output[[i]]<-raw_data[a:(length(raw_data)-1)] #dont include the last line "/table" in the last block } } return(output) } #==================================================================== Function filter_chr<-function(time1_raw){ for(i in 1:length(time1_raw)){ n<-regexpr("<tt>",time1_raw[i])+4 time1_raw[i]<-substring(time1_raw[i],n[1],n[1]+7) } return(time1_raw)} #==================================================================== Function readhtmltable_index<-function(page_number,tables){ index_list<-list() tables1<-grep("Date",tables[[page_number]]) page1_table<-tablecut(tables1,tables[[page_number]]) for ( no_table in 1:length(page1_table)) { lab_grep_index<-grep("<tr valign=top>",page1_table[[no_table]])[1]+tables1[no_table] lab_grep_index_last<-rev(grep("<tr valign=top>",page1_table[[no_table]]))[2]-2+tables1[no_table] index_list[[no_table]] <- c(1,2,lab_grep_index:lab_grep_index_last,length(tables[[page_number]])) } return (index_list) } #==================================================================== Function table_time <- function (page_number,tables) { time_list <- list() tables1 <- grep("Date",tables[[page_number]]) tryCatch ( page1_table <- tablecut(tables1,tables[[page_number]]), warning = function(w) { print("Something Wrong in the data, use the last page time") }, error = function(e) { print("Something Error in the data, use the last page time ") } ) error<-0 if ( length(tables1) == 0) { tables1 <- grep("Date",tables[[page_number-1]]) page1_table <- tablecut(tables1,tables[[page_number-1]]) error <- 1 } for ( no_table in 1 : length(page1_table)) { page_1_date_line <- grep("<tr><td",unlist(page1_table[[no_table]])) temporary<-unlist(page1_table[[no_table]]) temporary<-temporary[-page_1_date_line] if ( grep("<tr valign=top>",temporary)[1] >3 ) { page_date_line <- grep("<tr valign=top>",temporary)[1] time1_raw <- c( temporary[seq( 3, (page_date_line-2), by = 2)]) }else{ page_date_line <- grep("<tr valign=top>",temporary)[2] time1_raw <- c( temporary[seq( (grep("<tr valign=top>",temporary)[1]+2), (page_date_line-2), by = 2)]) } ##the lab data time time_list[[no_table]] <- filter_chr(time1_raw) } if (error == 0){ return( time_list) } else if (error ==1) { return (last(time_list)) } } #==================================================================== Main shinyServer(function(input,output){ output$contents <-renderTable({ inFile<-input$file1 # Load the data from the input raw_data<-readLines(inFile$datapath, encoding = "BIG-5") raw_data<-iconv(raw_data,from ="BIG-5",to = "UTF-8") tables<-pagecut(raw_data) #Separate the page's table into page1_table page_number<-input$pages_number index <- readhtmltable_index(page_number,tables) no_table<-input$table_number #use XML package to parse the table ***** tables_test_index <- readHTMLTable(tables[[page_number]][index[[no_table]]],encoding="UTF-8",as.data.frame = TRUE) #get the time time1<-table_time(page_number,tables) time1<-time1[[no_table]] #preprocess names(tables_test_index)<-"table" index_table<-c(2,seq(4,4*length(time1),by=4)) clean_test<-tables_test_index$table[index_table] rule_out<-sapply(clean_test[,1],function(x){ if( is.na(x) == TRUE ){ sum(!is.na(x)) }else{nchar(as.character(x))}})>0 #remove the NA column clean_test<-clean_test[rule_out,] #remove duplicate data : normale plasma mean duplicat_index <- !duplicated(clean_test[,1],fromLast = TRUE)&!duplicated(clean_test[,1]) clean_test<-clean_test[duplicat_index,] row.names(clean_test)<-clean_test[,1] colnames(clean_test)[1]<-c("Lab_data") colnames(clean_test)[2:(length(time1)+1)]<-time1 # #==================== Fetch the General Data of the Patient #Name name_raw<-raw_data[grep("姓名",raw_data)[1]+2] name<-substring(name_raw,(regexpr("tt",name_raw)+3),(regexpr("tt",name_raw)+5)) #Patient_ID id_raw<-raw_data[grep("病歷號",raw_data)[1]+2] id<-substring(id_raw,(regexpr("tt",id_raw)+3),(regexpr("tt",id_raw)+9)) #Gender gender_raw<-raw_data[grep("性別",raw_data)[1]+2] gender<-substring(gender_raw,(regexpr("tt",gender_raw)[1]+3),(regexpr("tt",gender_raw)[1]+3)) #Birthday birth_raw<-raw_data[grep("生日",raw_data)[1]+2] birth<-getlabname(birth_raw) #Final Data presentation clean_melt_test<-melt(clean_test,id.vars = "Lab_data") clean_melt_test$ID<-id clean_melt_test$BIRTH<-birth clean_melt_test$GENDER<-gender clean_melt_test }) datasetInput <- reactive({ inFile<-input$file1 # Load the data from the input raw_data<-readLines(inFile$datapath, encoding = "BIG-5") raw_data<-iconv(raw_data,from ="BIG-5",to = "UTF-8") tables<-pagecut(raw_data) #Separate the page's table into page1_table page_number<-input$pages_number index <- readhtmltable_index(page_number,tables) no_table<-input$table_number #use XML package to parse the table ***** tables_test_index <- readHTMLTable(tables[[page_number]][index[[no_table]]],encoding="UTF-8",as.data.frame = TRUE) #get the time time1<-table_time(page_number,tables) time1<-time1[[no_table]] #preprocess names(tables_test_index)<-"table" index_table<-c(2,seq(4,4*length(time1),by=4)) clean_test<-tables_test_index$table[index_table] rule_out<-sapply(clean_test[,1],function(x){ if( is.na(x) == TRUE ){ sum(!is.na(x)) }else{nchar(as.character(x))}})>0 #remove the NA column clean_test<-clean_test[rule_out,] #remove duplicate data : normale plasma mean duplicat_index <- !duplicated(clean_test[,1],fromLast = TRUE)&!duplicated(clean_test[,1]) clean_test<-clean_test[duplicat_index,] row.names(clean_test)<-clean_test[,1] colnames(clean_test)[1]<-c("Lab_data") colnames(clean_test)[2:(length(time1)+1)]<-time1 # #==================== Fetch the General Data of the Patient #Name name_raw<-raw_data[grep("姓名",raw_data)[1]+2] name<-substring(name_raw,(regexpr("tt",name_raw)+3),(regexpr("tt",name_raw)+5)) #Patient_ID id_raw<-raw_data[grep("病歷號",raw_data)[1]+2] id<-substring(id_raw,(regexpr("tt",id_raw)+3),(regexpr("tt",id_raw)+9)) #Gender gender_raw<-raw_data[grep("性別",raw_data)[1]+2] gender<-substring(gender_raw,(regexpr("tt",gender_raw)[1]+3),(regexpr("tt",gender_raw)[1]+3)) #Birthday birth_raw<-raw_data[grep("生日",raw_data)[1]+2] birth<-getlabname(birth_raw) #Final Data presentation clean_melt_test<-melt(clean_test,id.vars = "Lab_data") clean_melt_test$ID<-id clean_melt_test$BIRTH<-birth clean_melt_test$GENDER<-gender clean_melt_test }) output$downloadData <- downloadHandler( filename = function(){paste(input$FileName, '.csv', sep='')}, content = function(file){ write.csv(datasetInput(), file) }) })

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/run_analysis.R

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Commander14/tidyingdata

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refs/heads/master

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run_analysis.R

analyzeData <- function() { # Load all of the applicable files base <- "UCI HAR Dataset" trainX <- read.table(paste(base, "/train/X_train.txt", sep="")) testX <- read.table(paste(base, "/test/X_test.txt", sep="")) features <- read.table(paste(base, "/features.txt", sep="")) activities <- read.table(paste(base, "/activity_labels.txt", sep="")) testY <- read.table(paste(base, "/test/y_test.txt", sep="")) trainY <- read.table(paste(base, "/train/y_train.txt", sep="")) subjectsTest <- read.table(paste(base, "/test/subject_test.txt", sep="")) subjectsTrain <- read.table(paste(base, "/train/subject_train.txt", sep="")) # pull out the features to make the column names proper # this is the raw data for the standard deviations and the medians cnames <- features[,2] colnames(testX) <- cnames colnames(trainX) <- cnames # insert the activities into a column in the raw data for both datasets testX <- cbind(testY, testX) trainX <- cbind(trainY, trainX) colnames(testX)[1] <- colnames(trainX)[1] <- "Activity" # insert the subjects into a column in the raw data for both data sets testX <- cbind(subjectsTest, testX) trainX <- cbind(subjectsTrain, trainX) colnames(testX)[1] <- colnames(trainX)[1] <- "Subjects" # replace the numbers (1-6) with strings describing the activities (eg: WALKING) trainX[,2] <- factor(as.vector(trainX[[2]]), levels=c(1, 2, 3, 4, 5, 6), labels=as.vector(activities[[2]])) testX[,2] <- factor(as.vector(testX[[2]]), levels=c(1, 2, 3, 4, 5, 6), labels=as.vector(activities[[2]])) # reduce down to just the columns we need by grepping out anything that has mean() or std() in it # also keep the subjects and activity columns trainX <- trainX[c(grep("mean\$\$|std\$\$|Subjects|Activity", colnames(trainX)))] testX <- testX[c(grep("mean\$\$|std\$\$|Subjects|Activity", colnames(testX)))] # combine the two tables into one by appending rows onto trainX data <- rbind(trainX, testX) # make the tidy data, grouping by each subject and their activity and averaging the columns for those # eg Subject 1 WALKING happens X times, so Subject 1 WALKING will be in this once will the value in each column # being the average for all activity of that subject for that activity tidyData <- aggregate(data, by=list(data[,1], data[,2]), FUN=mean, na.rm=TRUE, simplify=TRUE) # remove the duplicate columns that resulted from the aggregate call tidyData$activity <- NULL tidyData$subjects <- NULL # rename the columns to be accurate colnames(tidyData)[1] <- "Subject" colnames(tidyData)[2] <- "Activity" # write out the final tidy output to the TTY write.table(tidyData,row.name=FALSE) }

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/shiny/server.R

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zhenhuixu/PHMO

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cfaac457050fa4f3c1c81cc8a1db35b850f80500

refs/heads/master

2022-12-10T21:14:29.009225

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server.R

# # This is the server logic of a Shiny web application. You can run the # application by clicking 'Run App' above. # # Find out more about building applications with Shiny here: # # http://shiny.rstudio.com/ # library(shiny) library(tidyverse) library(ggplot2) library(broom) library(GGally) library(shinythemes) library(kableExtra) library(plotly) # Define server logic required to draw a histogram shinyServer(function(input, output, session) { dat19 <- readRDS("Data/dat_19.rds") #remove the following PatientKeys - an extreme value (very low cost, impossible days in hospice, >20 MRIs) dat19 = dat19 %>% filter(PatientKey != 421661, PatientKey != 359067, PatientKey != 142780) dat19 = dat19 %>% filter(Sex!="Other") dat19 <- dat19 %>% mutate(PrimaryCare = replace_na(PrimaryCare, "Missing")) dat19$PrimaryCare <- factor(dat19$PrimaryCare, levels=c("Primary Care", "Specialist", "Missing")) #create percentages of primary care services dat19 <- dat19 %>% mutate(`Primary Care Services with a Primary Care Physician (%)`= `Primary Care Services with a Primary Care Physician`/`Total Primary Care Services`) %>% mutate(`Primary Care Services with a Specialist Physician (%)`= `Primary Care Services with a Specialist Physician`/`Total Primary Care Services`) %>% mutate(`Primary Care Services with APP (%)` = `Primary Care Services with a Nurse Practitioner/Physician Assistant/Clinical Nurse Specialist`/`Total Primary Care Services`) #replace missing values into 0 dat19 <- dat19 %>% mutate(`Primary Care Services with a Primary Care Physician (%)` = replace_na(`Primary Care Services with a Primary Care Physician (%)`,0)) %>% mutate(`Primary Care Services with a Specialist Physician (%)` = replace_na(`Primary Care Services with a Specialist Physician (%)`,0)) %>% mutate(`Primary Care Services with APP (%)` = replace_na(`Primary Care Services with APP (%)`,0)) #replace NA of CHF and COPD with zero dat19 <- dat19 %>% mutate(HCC_85 = replace_na(HCC_85, 0)) %>% mutate(HCC_111 = replace_na(HCC_111, 0)) #merge with correct TINs number TINs <- read.csv("Data/Practice_TIN_map.csv") TINs <- TINs %>% rename(PrimaryPractice = PracticeName) %>% rename(PrimaryPracticeTIN_mapping = PrimaryPracticeTIN) dat19 <- dat19 %>% left_join(TINs, by="PrimaryPractice") #rename null values in Primary Practice to Other dat19$TaxEntityName <- as.character(dat19$TaxEntityName) dat19 <- dat19 %>% mutate(TaxEntityName = replace_na(TaxEntityName, "Other")) dat19$TaxEntityName[dat19$TaxEntityName=="DUKE UNIVERSITY HOSPITAL"]="DUKE UNIVERSITY HEALTH SYSTEMS INC" dat19$TaxEntityName <- as.factor(dat19$TaxEntityName) com_Replace <- function(x){ x <- gsub("HCC_","",x) x <- gsub("85","CHF", x) x <- gsub("111","COPD", x) return(x) } var_Replace <- function(x){ x <- gsub("`","",x) x <- gsub("UnplannedAdmits","Unplanned Admissions",x) x <- gsub("Readmits","Readmissions",x) } output$heat <- renderPlotly({ fit_var <- c("Total Hospital Discharges", "Unplanned Admissions", "Readmissions", "ED Visits", "Computed Tomography (CT) Events", "Magnetic Resonance Imaging (MRI) Events", "Days in Hospice", "Total Primary Care Services", "Primary Care Services with a Primary Care Physician", "Skilled Nursing Facility or Unit Discharges") fit_out <- c("Log(Cost of Care)", "Log(Cost Efficiency)") CC_coef <- c(0.1588, 0.1459, 0.1630, 0.0345, 0.0380, 0.0515, 0.0019, 0.0087, -0.0034, 0.1118) CE_coef <- c(0.6021, 0.5533, 0.3249, 0.1632, 0.1834, 0.3419, 0.0073, 0.0713, -0.2087, 0.1326) mat = t(matrix(c(CC_coef, CE_coef), nrow = 10, ncol = 2)) conditions.text <- as.vector(outer(paste0("Outcome variable:",fit_out,"\n"), paste0("Hospital Utilizations:",fit_var,"\n"), paste0)) fit_num <- c(0.1588, 0.6021, 0.1459, 0.5533, 0.1630, 0.3249, 0.0345, 0.1632, 0.0380, 0.1834, 0.0515, 0.3419, 0.0019, 0.0073, 0.0087, 0.0713, -0.0034, -0.2087, 0.1118, 0.1326) conditions.text <- paste0(conditions.text,"Magnitude of Association:",fit_num) text.mat <- matrix(conditions.text, nrow= nrow(mat), ncol = ncol(mat)) colorlength <- 100 null_value <- (0 - min(fit_num)) / (max(fit_num) - min(fit_num)) border <- as.integer(null_value * colorlength) colorscale <- as.list(1:colorlength) #colorscale below zero s <- scales::seq_gradient_pal("blue", "white", "Lab")(seq(0,1,length.out=border)) for (i in 1:border) { colorscale[[i]] <- c((i - 1) / colorlength, s[i]) } #colorscale above zero s <- scales::seq_gradient_pal("white", "red", "Lab")(seq(0,1,length.out=colorlength - border)) for (i in 1:(colorlength - border)) { colorscale[[i + border]] <- c((i + border) / colorlength, s[i]) } plot_ly(source = "heat_plot") %>% add_heatmap( x=fit_var, y=fit_out, z=mat, hoverinfo = 'text', text = text.mat, colors = colorRamp(c("blue","white","red")), colorscale = colorscale, colorbar = list(len = 1, limits = c(-1,1))) }) output$models <- renderPlot({ clickData <- event_data("plotly_click", source = "heat_plot") if (is.null(clickData)) return(NULL) out <- gsub("Log\$Cost of Care\$","log_Cost_Care",clickData[["y"]]) out <- gsub("Log\$Cost Efficiency\$", "log_Cost_Eff", out) var <- gsub("Unplanned Admissions", "UnplannedAdmits", clickData[["x"]]) var <- gsub("Readmissions", "Readmits", var) if (var=="Primary Care Services with a Primary Care Physician"){ var = "Primary Care Services with a Primary Care Physician (%)" adjusted_var = "`+Sex+Age+Dual+ESRD+Disabled+`Total Primary Care Services`+Cont_Att+Death+HCC_Cancer+HCC_Diabetes+HCC_CAD+HCC_85+HCC_111+OutNetwork+HCC_CKD+PrimaryCare" } else { if (var=="Skilled Nursing Facility or Unit Discharges"){ adjusted_var = "`+Sex+Age+Dual+ESRD+Disabled+`Total Hospital Discharges`+Cont_Att+Death+HCC_Cancer+HCC_Diabetes+HCC_CAD+HCC_85+HCC_111+OutNetwork+HCC_CKD+PrimaryCare" } else { adjusted_var = "`+Sex+Age+Dual+ESRD+Disabled+Cont_Att+Death+HCC_Cancer+HCC_Diabetes+HCC_CAD+HCC_85+HCC_111+OutNetwork+HCC_CKD+PrimaryCare" } } form <- as.formula(paste0(out, "~`", var, adjusted_var)) fit <- lm(form, data = dat19) plt <- ggcoef(fit, exclude_intercept=TRUE) plt$data$term <- gsub("SexMale", "Sex(Male=1)",plt$data$term) plt$data$term <- gsub("Cont_Att", "Continuous Attribution",plt$data$term) plt$data$term <- gsub("UnplannedAdmits", "Unplanned Admissions",plt$data$term) plt$data$term <- gsub("Readmits", "Readmissions",plt$data$term) plt$data$term <- gsub("HCC_85", "HCC_CHF",plt$data$term) plt$data$term <- gsub("HCC_111", "HCC_COPD",plt$data$term) plt$data$term <- gsub("PrimaryCareSpecialist", "Primary Practice(Specialist=1)",plt$data$term) plt }) output$coef <- renderUI({ clickData <- event_data("plotly_click", source = "heat_plot") if (is.null(clickData)) return(NULL) out <- gsub("Log\$Cost of Care\$","log_Cost_Care",clickData[["y"]]) out <- gsub("Log\$Cost Efficiency\$", "log_Cost_Eff", out) var <- gsub("Unplanned Admissions", "UnplannedAdmits", clickData[["x"]]) var <- gsub("Readmissions", "Readmits", var) if (var=="Primary Care Services with a Primary Care Physician"){ var = "Primary Care Services with a Primary Care Physician (%)" adjusted_var = "`+Sex+Age+Dual+ESRD+Disabled+`Total Primary Care Services`+Cont_Att+Death+HCC_Cancer+HCC_Diabetes+HCC_CAD+HCC_85+HCC_111+OutNetwork+HCC_CKD+PrimaryCare" } else { if (var=="Skilled Nursing Facility or Unit Discharges"){ adjusted_var = "`+Sex+Age+Dual+ESRD+Disabled+`Total Hospital Discharges`+Cont_Att+Death+HCC_Cancer+HCC_Diabetes+HCC_CAD+HCC_85+HCC_111+OutNetwork+HCC_CKD+PrimaryCare" } else { adjusted_var = "`+Sex+Age+Dual+ESRD+Disabled+Cont_Att+Death+HCC_Cancer+HCC_Diabetes+HCC_CAD+HCC_85+HCC_111+OutNetwork+HCC_CKD+PrimaryCare" } } form <- as.formula(paste0(out, "~`", var, adjusted_var)) fit <- lm(form, data = dat19) fit.sum <- summary(fit) var <- var_Replace(var) HTML(paste( "Coefficients: ", "The coefficient of ", var, " is ", round(fit$coefficients[2], 3), ". The 95% confidence interval is (", round(confint(fit)[2,1], 3), ",", round(confint(fit)[2,2], 3), ").","", var, " and other variables (comorbidities, sex, age and etc.) could explain ", round(fit.sum$adj.r.squared, 3)*100, "% variability of ", out,"", "Note: ", "The dots represent the coefficients of these variables and the width of each line represents the confidence interval. ","", "The more further the dot is from the dashed line, the larger the effect of the variable is.","", "If the line around the dot crosses the vertical dashed line, this variable is not significant.", sep="" )) }) output$tab1 <- function(){ if (input$OR=="`Primary Care Services with a Primary Care Physician`"){ var = "`Primary Care Services with a Primary Care Physician (%)`" adjusted_var = "+Sex+Age+Dual+ESRD+Disabled+`Total Primary Care Services`+Cont_Att+Death+HCC_Cancer+HCC_Diabetes+HCC_CAD+HCC_85+HCC_111+OutNetwork+HCC_CKD+PrimaryCare" } else { var=input$OR if (input$OR=="`Skilled Nursing Facility or Unit Discharges`"){ adjusted_var = "+Sex+Age+Dual+ESRD+Disabled+`Total Hospital Discharges`+Cont_Att+Death+HCC_Cancer+HCC_Diabetes+HCC_CAD+HCC_85+HCC_111+OutNetwork+HCC_CKD+PrimaryCare" } else { adjusted_var = "+Sex+Age+Dual+ESRD+Disabled+Cont_Att+Death+HCC_Cancer+HCC_Diabetes+HCC_CAD+HCC_85+HCC_111+OutNetwork+HCC_CKD+PrimaryCare" } } form <- as.formula(paste0(input$out, "~", var, adjusted_var)) fit <- lm(form, data = dat19) explanatory <- gsub("`", "", var) work <- dat19 origin <- predict(fit, work) work[explanatory] = work[explanatory]*(1-input$prop) pop <- predict(fit, work) pred <- data.frame(origin = origin, pop = pop, ESRD = work$ESRD, Dual=work$Dual, NonDual = work$NonDual, Disabled = work$Disabled, PrimaryCare = work$PrimaryCare, HCC_Cancer = work$HCC_Cancer, HCC_CKD = work$HCC_CKD, HCC_85 = work$HCC_85, HCC_Diabetes = work$HCC_Diabetes, HCC_111 = work$HCC_111, HCC_CAD = work$HCC_CAD, TaxEntityName = work$TaxEntityName) ##filter by selected eligibility reason and primary practice if (input$eligibility!="all"){ pred = pred %>% filter(!!as.name(input$eligibility)==1) } if (input$primarycare!="all"){ pred = pred %>% filter(PrimaryCare==input$primarycare) pred = pred %>% filter(PrimaryCare==input$primarycare) } if (input$comorbidities!="all"){ pred = pred %>% filter(!!as.name(input$comorbidities)==1) pred = pred %>% filter(!!as.name(input$comorbidities)==1) } if (input$tins!="all"){ pred = pred %>% filter(TaxEntityName==input$tins) pred = pred %>% filter(TaxEntityName==input$tins) } if (input$out=="log_Cost_Care"){ origin <- sum(exp(pred$origin)) pop <- sum(exp(pred$pop)) popCost = paste0("$",round(origin-pop, digits = 1)) indCost = paste0("$",round((origin-pop)/dim(pred)[1], digits=1)) tab1 <- data.frame("Population Cost Reduction" = popCost, "Individual Cost Reduction" = indCost, "Number of Beneficiaries" = dim(pred)[1]) } else { origin <- sum(pred$origin) pop <- sum(pred$pop) popCost = round((origin-pop)/origin*100, digits = 1) indCost = round((origin-pop)/dim(pred)[1], digits=3) tab1 <- data.frame("Population Cost Efficiency Reduction" = paste0(popCost, "%"), "Individual Cost Efficiency Reduction" = indCost, "Number of Beneficiaries" = dim(pred)[1]) } rownames(tab1) <- "Overall Population" tab1 %>% kable() %>% kable_styling() } tab_com <- function(fit, var, elig, pc, com, prop, tins){ tab <- data.frame(matrix(NA, nrow = length(com), ncol = 3)) if (input$out2=="log_Cost_Care"){ colnames(tab) <- c("Population Cost Reduction", "Individual Cost Reduction", "Number of Beneficiaries") } else { colnames(tab) <- c("Population Cost Efficiency Reduction", "Individual Cost Efficiency Reduction", "Number of Beneficiaries") } com_name <- vector() for (i in 1:length(com)){ work <- dat19 %>% filter(!!as.name(com[i])==1) origin <- predict(fit, work) work[var] = work[var]*(1-prop) pop <- predict(fit, work) pred <- data.frame(origin = origin, pop = pop, ESRD = work$ESRD, Dual=work$Dual, NonDual = work$NonDual, Disabled = work$Disabled, PrimaryCare = work$PrimaryCare, TaxEntityName=work$TaxEntityName) if (elig!="all"){ pred = pred %>% filter(!!as.name(elig)==1) } if (pc!="all"){ pred = pred %>% filter(PrimaryCare==pc) } if (tins!="all"){ pred = pred %>% filter(TaxEntityName==tins) } if (input$out2=="log_Cost_Care"){ origin <- sum(exp(pred$origin)) pop <- sum(exp(pred$pop)) popCost = round(origin-pop, digits = 1) indCost = round((origin-pop)/dim(pred)[1], digits=1) } else { origin <- sum(pred$origin) pop <- sum(pred$pop) popCost = round((origin-pop)/origin*100, digits = 1) indCost = round((origin-pop)/dim(pred)[1], digits=3) } tab[i,] <- c(popCost, indCost, dim(pred)[1]) com_name <- append(com_name, com_Replace(com[i])) } rownames(tab) <- com_name tab } output$compare <- renderPlot({ if (input$OR2=="`Primary Care Services with a Primary Care Physician`"){ var = "`Primary Care Services with a Primary Care Physician (%)`" adjusted_var = "+Sex+Age+Dual+ESRD+Disabled+`Total Primary Care Services`+Cont_Att+Death+HCC_Cancer+HCC_Diabetes+HCC_CAD+HCC_85+HCC_111+OutNetwork+HCC_CKD+PrimaryCare" } else { var=input$OR2 if (input$OR2=="`Skilled Nursing Facility or Unit Discharges`"){ adjusted_var = "+Sex+Age+Dual+ESRD+Disabled+`Total Hospital Discharges`+Cont_Att+Death+HCC_Cancer+HCC_Diabetes+HCC_CAD+HCC_85+HCC_111+OutNetwork+HCC_CKD+PrimaryCare" } else { adjusted_var = "+Sex+Age+Dual+ESRD+Disabled+Cont_Att+Death+HCC_Cancer+HCC_Diabetes+HCC_CAD+HCC_85+HCC_111+OutNetwork+HCC_CKD+PrimaryCare" } } form <- as.formula(paste0(input$out2, "~", var, adjusted_var)) fit <- lm(form, data = dat19) explanatory <- gsub("`", "", var) tab <- tab_com(fit, explanatory, input$eligibility2, input$primarycare2, input$comorbidities2, input$prop2, input$tins2) tab$Comorbidities <- rownames(tab) if (input$out2=="log_Cost_Care"){ ggplot(tab, aes(x=Comorbidities)) + geom_bar(stat = "identity",aes(y=`Population Cost Reduction`), fill = "steelblue", width = 0.3) + geom_point(aes(y=`Individual Cost Reduction`*3000, label = `Individual Cost Reduction`), size = 2) + geom_text(aes(y=`Individual Cost Reduction`*3000, label = paste0("Individual Cost: $", `Individual Cost Reduction`)), hjust = 0, vjust=-1)+ scale_y_continuous( name = "Cost of Care", sec.axis = sec_axis(~./3000, name="Individual Cost") ) } else{ ggplot(tab, aes(x=Comorbidities)) + geom_bar(stat = "identity",aes(y=`Population Cost Efficiency Reduction`), fill = "steelblue", width = 0.3) + geom_point(aes(y=`Individual Cost Efficiency Reduction`*100, label = `Individual Cost Efficiency Reduction`), size = 2) + geom_text(aes(y=`Individual Cost Efficiency Reduction`*100, label = paste0("Individual Cost Efficiency: ", `Individual Cost Efficiency Reduction`)), hjust = 0, vjust=-1)+ scale_y_continuous( name = "Population Cost Efficiency (%)", sec.axis = sec_axis(~./100, name="Individual Cost Efficiency") ) } }) output$tab2 <- function(){ if (input$OR2=="`Primary Care Services with a Primary Care Physician`"){ var = "`Primary Care Services with a Primary Care Physician (%)`" adjusted_var = "+Sex+Age+Dual+ESRD+Disabled+`Total Primary Care Services`+Cont_Att+Death+HCC_Cancer+HCC_Diabetes+HCC_CAD+HCC_85+HCC_111+OutNetwork+HCC_CKD+PrimaryCare" } else { var=input$OR2 if (input$OR2=="`Skilled Nursing Facility or Unit Discharges`"){ adjusted_var = "+Sex+Age+Dual+ESRD+Disabled+`Total Hospital Discharges`+Cont_Att+Death+HCC_Cancer+HCC_Diabetes+HCC_CAD+HCC_85+HCC_111+OutNetwork+HCC_CKD+PrimaryCare" } else { adjusted_var = "+Sex+Age+Dual+ESRD+Disabled+Cont_Att+Death+HCC_Cancer+HCC_Diabetes+HCC_CAD+HCC_85+HCC_111+OutNetwork+HCC_CKD+PrimaryCare" } } form <- as.formula(paste0(input$out2, "~", var, adjusted_var)) fit <- lm(form, data = dat19) explanatory <- gsub("`", "", var) tab <- tab_com(fit, explanatory, input$eligibility2, input$primarycare2, input$comorbidities2, input$prop2, input$tins2) if (input$out2=="log_Cost_Care"){ tab$`Population Cost Reduction` <- paste0("$",tab$`Population Cost Reduction`) tab$`Individual Cost Reduction` <- paste0("$",tab$`Individual Cost Reduction`) } else { tab$`Population Cost Efficiency Reduction` = paste0(tab$`Population Cost Efficiency Reduction`, "%") } tab %>% kable() %>% kable_styling() } tab_elig <- function(fit, var, elig, pc, com, prop, tins){ tab <- data.frame(matrix(NA, nrow = length(elig), ncol = 3)) if (input$out3=="log_Cost_Care"){ colnames(tab) <- c("Population Cost Reduction", "Individual Cost Reduction", "Number of Beneficiaries") } else { colnames(tab) <- c("Population Cost Efficiency Reduction", "Individual Cost Efficiency Reduction", "Number of Beneficiaries") } elig_name <- vector() for (i in 1:length(elig)){ work <- dat19 %>% filter(!!as.name(elig[i])==1) origin <- predict(fit, work) work[var] = work[var]*(1-prop) pop <- predict(fit, work) pred <- data.frame(origin = origin, pop = pop, PrimaryCare = work$PrimaryCare, HCC_Cancer=work$HCC_Cancer, HCC_CAD = work$HCC_CAD, HCC_Diabetes = work$HCC_Diabetes, HCC_CKD = work$HCC_CKD, HCC_85 = work$HCC_85, HCC_111 = work$HCC_111, TaxEntityName=work$TaxEntityName) if (com!="all"){ pred = pred %>% filter(!!as.name(com)==1) } if (pc!="all"){ pred = pred %>% filter(PrimaryCare==pc) } if (tins!="all"){ pred = pred %>% filter(TaxEntityName==tins) } if (input$out3=="log_Cost_Care"){ origin <- sum(exp(pred$origin)) pop <- sum(exp(pred$pop)) popCost = round(origin-pop, digits = 1) indCost = round((origin-pop)/dim(pred)[1], digits=1) } else { origin <- sum(pred$origin) pop <- sum(pred$pop) popCost = round((origin-pop)/origin*100, digits = 1) indCost = round((origin-pop)/dim(pred)[1], digits=3) } tab[i,] <- c(popCost, indCost, dim(pred)[1]) elig_name <- append(elig_name, elig[i]) } rownames(tab) <- elig_name tab } output$bar <- renderPlot({ if (input$OR3=="`Primary Care Services with a Primary Care Physician`"){ var = "`Primary Care Services with a Primary Care Physician (%)`" adjusted_var = "+Sex+Age+Dual+ESRD+Disabled+`Total Primary Care Services`+Cont_Att+Death+HCC_Cancer+HCC_Diabetes+HCC_CAD+HCC_85+HCC_111+OutNetwork+HCC_CKD+PrimaryCare" } else { var=input$OR3 if (input$OR3=="`Skilled Nursing Facility or Unit Discharges`"){ adjusted_var = "+Sex+Age+Dual+ESRD+Disabled+`Total Hospital Discharges`+Cont_Att+Death+HCC_Cancer+HCC_Diabetes+HCC_CAD+HCC_85+HCC_111+OutNetwork+HCC_CKD+PrimaryCare" } else { adjusted_var = "+Sex+Age+Dual+ESRD+Disabled+Cont_Att+Death+HCC_Cancer+HCC_Diabetes+HCC_CAD+HCC_85+HCC_111+OutNetwork+HCC_CKD+PrimaryCare" } } form <- as.formula(paste0(input$out3, "~", var, adjusted_var)) fit <- lm(form, data = dat19) explanatory <- gsub("`", "", var) tab <- tab_elig(fit, explanatory, input$eligibility3, input$primarycare3, input$comorbidities3, input$prop3, input$tins3) tab$EligibilityReason <- rownames(tab) if (input$out3=="log_Cost_Care"){ ggplot(tab, aes(x=EligibilityReason)) + geom_bar(stat = "identity",aes(y=`Population Cost Reduction`), fill = "steelblue", width = 0.3) + geom_point(aes(y=`Individual Cost Reduction`*2000, label = `Individual Cost Reduction`), size = 2) + geom_text(aes(y=`Individual Cost Reduction`*2000, label = paste0("Individual Cost: $", `Individual Cost Reduction`)), hjust = 0, vjust=-1)+ scale_y_continuous( name = "Cost of Care", sec.axis = sec_axis(~./2000, name="Individual Cost") ) } else{ ggplot(tab, aes(x=EligibilityReason)) + geom_bar(stat = "identity",aes(y=`Population Cost Efficiency Reduction`), fill = "steelblue", width = 0.3) + geom_point(aes(y=`Individual Cost Efficiency Reduction`*100, label = `Individual Cost Efficiency Reduction`), size = 2) + geom_text(aes(y=`Individual Cost Efficiency Reduction`*100, label = paste0("Individual Cost Efficiency: ", `Individual Cost Efficiency Reduction`)), hjust = 0, vjust=-1)+ scale_y_continuous( name = "Population Cost Efficiency (%)", sec.axis = sec_axis(~./100, name="Individual Cost Efficiency") ) } }) output$tab3 <- function(){ if (input$OR3=="`Primary Care Services with a Primary Care Physician`"){ var = "`Primary Care Services with a Primary Care Physician (%)`" adjusted_var = "+Sex+Age+Dual+ESRD+Disabled+`Total Primary Care Services`+Cont_Att+Death+HCC_Cancer+HCC_Diabetes+HCC_CAD+HCC_85+HCC_111+OutNetwork+HCC_CKD+PrimaryCare" } else { var=input$OR3 if (input$OR3=="`Skilled Nursing Facility or Unit Discharges`"){ adjusted_var = "+Sex+Age+Dual+ESRD+Disabled+`Total Hospital Discharges`+Cont_Att+Death+HCC_Cancer+HCC_Diabetes+HCC_CAD+HCC_85+HCC_111+OutNetwork+HCC_CKD+PrimaryCare" } else { adjusted_var = "+Sex+Age+Dual+ESRD+Disabled+Cont_Att+Death+HCC_Cancer+HCC_Diabetes+HCC_CAD+HCC_85+HCC_111+OutNetwork+HCC_CKD+PrimaryCare" } } form <- as.formula(paste0(input$out3, "~", var, adjusted_var)) fit <- lm(form, data = dat19) explanatory <- gsub("`", "", var) tab <- tab_elig(fit, explanatory, input$eligibility3, input$primarycare3, input$comorbidities3, input$prop3, input$tins3) if (input$out3=="log_Cost_Care"){ tab$`Population Cost Reduction` <- paste0("$",tab$`Population Cost Reduction`) tab$`Individual Cost Reduction` <- paste0("$",tab$`Individual Cost Reduction`) } else { tab$`Population Cost Efficiency Reduction` <- paste0(tab$`Population Cost Efficiency Reduction`, "%") } tab %>% kable() %>% kable_styling() } tab_pc <- function(fit, var, elig, pc, com, prop, tins){ tab <- data.frame(matrix(NA, nrow = length(pc), ncol = 3)) if (input$out4=="log_Cost_Care"){ colnames(tab) <- c("Population Cost Reduction", "Individual Cost Reduction", "Number of Beneficiaries") } else { colnames(tab) <- c("Population Cost Efficiency Reduction", "Individual Cost Efficiency Reduction", "Number of Beneficiaries") } pc_name <- vector() for (i in 1:length(pc)){ work <- dat19 %>% filter(PrimaryCare==pc[i]) origin <- predict(fit, work) work[var] = work[var]*(1-prop) pop <- predict(fit, work) pred <- data.frame(origin = origin, pop = pop, ESRD = work$ESRD, Dual=work$Dual, NonDual = work$NonDual, Disabled = work$Disabled, PrimaryCare = work$PrimaryCare, HCC_Cancer=work$HCC_Cancer, HCC_CAD = work$HCC_CAD, HCC_Diabetes = work$HCC_Diabetes, HCC_CKD = work$HCC_CKD, HCC_85 = work$HCC_85, HCC_111 = work$HCC_111, TaxEntityName = work$TaxEntityName) if (elig!="all"){ pred = pred %>% filter(!!as.name(elig)==1) } if (com!="all"){ pred = pred %>% filter(!!as.name(com)==1) } if (tins!="all"){ pred = pred %>% filter(TaxEntityName==tins) } if (input$out4=="log_Cost_Care"){ origin <- sum(exp(pred$origin)) pop <- sum(exp(pred$pop)) popCost = round(origin-pop, digits = 1) indCost = round((origin-pop)/dim(pred)[1], digits=1) } else { origin <- sum(pred$origin) pop <- sum(pred$pop) popCost = round((origin-pop)/origin*100, digits = 1) indCost = round((origin-pop)/dim(pred)[1], digits=3) } tab[i,] <- c(popCost, indCost, dim(pred)[1]) pc_name <- append(pc_name, pc[i]) } rownames(tab) <- pc_name tab } output$bar2 <- renderPlot({ if (input$OR4=="`Primary Care Services with a Primary Care Physician`"){ var = "`Primary Care Services with a Primary Care Physician (%)`" adjusted_var = "+Sex+Age+Dual+ESRD+Disabled+`Total Primary Care Services`+Cont_Att+Death+HCC_Cancer+HCC_Diabetes+HCC_CAD+HCC_85+HCC_111+OutNetwork+HCC_CKD+PrimaryCare" } else { var=input$OR4 if (input$OR4=="`Skilled Nursing Facility or Unit Discharges`"){ adjusted_var = "+Sex+Age+Dual+ESRD+Disabled+`Total Hospital Discharges`+Cont_Att+Death+HCC_Cancer+HCC_Diabetes+HCC_CAD+HCC_85+HCC_111+OutNetwork+HCC_CKD+PrimaryCare" } else { adjusted_var = "+Sex+Age+Dual+ESRD+Disabled+Cont_Att+Death+HCC_Cancer+HCC_Diabetes+HCC_CAD+HCC_85+HCC_111+OutNetwork+HCC_CKD+PrimaryCare" } } form <- as.formula(paste0(input$out4, "~", var, adjusted_var)) fit <- lm(form, data = dat19) explanatory <- gsub("`", "", var) tab <- tab_pc(fit, explanatory, input$eligibility4, input$primarycare4, input$comorbidities4, input$prop4, input$tins4) tab$PrimaryPractice <- rownames(tab) if (input$out4=="log_Cost_Care"){ ggplot(tab, aes(x=PrimaryPractice)) + geom_bar(stat = "identity",aes(y=`Population Cost Reduction`), fill = "steelblue", width = 0.3) + geom_point(aes(y=`Individual Cost Reduction`*2000, label = `Individual Cost Reduction`), size = 2) + geom_text(aes(y=`Individual Cost Reduction`*2000, label = paste0("Individual Cost: $", `Individual Cost Reduction`)), hjust = 0, vjust=-1)+ scale_y_continuous( name = "Population Cost", sec.axis = sec_axis(~./2000, name="Individual Cost") ) } else { ggplot(tab, aes(x=PrimaryPractice)) + geom_bar(stat = "identity",aes(y=`Population Cost Efficiency Reduction`), fill = "steelblue", width = 0.3) + geom_point(aes(y=`Individual Cost Efficiency Reduction`*100, label = `Individual Cost Efficiency Reduction`), size = 2) + geom_text(aes(y=`Individual Cost Efficiency Reduction`*100, label = paste0("Individual Cost Efficiency: ", `Individual Cost Efficiency Reduction`)), hjust = 0, vjust=-1)+ scale_y_continuous( name = "Population Cost Efficiency (%)", sec.axis = sec_axis(~./100, name="Individual Cost Efficiency") ) } }) output$tab4 <- function(){ if (input$OR4=="`Primary Care Services with a Primary Care Physician`"){ var = "`Primary Care Services with a Primary Care Physician (%)`" adjusted_var = "+Sex+Age+Dual+ESRD+Disabled+`Total Primary Care Services`+Cont_Att+Death+HCC_Cancer+HCC_Diabetes+HCC_CAD+HCC_85+HCC_111+OutNetwork+HCC_CKD+PrimaryCare" } else { var=input$OR4 if (input$OR4=="`Skilled Nursing Facility or Unit Discharges`"){ adjusted_var = "+Sex+Age+Dual+ESRD+Disabled+`Total Hospital Discharges`+Cont_Att+Death+HCC_Cancer+HCC_Diabetes+HCC_CAD+HCC_85+HCC_111+OutNetwork+HCC_CKD+PrimaryCare" } else { adjusted_var = "+Sex+Age+Dual+ESRD+Disabled+Cont_Att+Death+HCC_Cancer+HCC_Diabetes+HCC_CAD+HCC_85+HCC_111+OutNetwork+HCC_CKD+PrimaryCare" } } form <- as.formula(paste0(input$out4, "~", var, adjusted_var)) fit <- lm(form, data = dat19) explanatory <- gsub("`", "", var) tab <- tab_pc(fit, explanatory, input$eligibility4, input$primarycare4, input$comorbidities4, input$prop4, input$tins4) if (input$out4=="log_Cost_Care"){ tab$`Population Cost Reduction` <- paste0("$",tab$`Population Cost Reduction`) tab$`Individual Cost Reduction` <- paste0("$",tab$`Individual Cost Reduction`) } else { tab$`Population Cost Efficiency Reduction` <- paste0(tab$`Population Cost Efficiency Reduction`, "%") } tab %>% kable() %>% kable_styling() } tab_tins <- function(fit, var, elig, tins, com, prop,pc){ tab <- data.frame(matrix(NA, nrow = length(tins), ncol = 3)) if (input$out5=="log_Cost_Care"){ colnames(tab) <- c("Population Cost Reduction", "Individual Cost Reduction", "Number of Beneficiaries") } else { colnames(tab) <- c("Population Cost Efficiency Reduction", "Individual Cost Efficiency Reduction", "Number of Beneficiaries") } tins_name <- vector() for (i in seq_along(tins)){ work <- dat19 %>% filter(TaxEntityName==tins[i]) origin <- predict(fit, work) work[var] = work[var]*(1-prop) pop <- predict(fit, work) pred <- data.frame(origin = origin, pop = pop, ESRD = work$ESRD, Dual=work$Dual, NonDual = work$NonDual, Disabled = work$Disabled, PrimaryCare = work$PrimaryCare, HCC_Cancer=work$HCC_Cancer, HCC_CAD = work$HCC_CAD, HCC_Diabetes = work$HCC_Diabetes, HCC_CKD = work$HCC_CKD, HCC_85 = work$HCC_85, HCC_111 = work$HCC_111) if (elig!="all"){ pred = pred %>% filter(!!as.name(elig)==1) } if (com!="all"){ pred = pred %>% filter(!!as.name(com)==1) } if (pc!="all"){ pred = pred %>% filter(PrimaryCare==pc) } if (input$out5=="log_Cost_Care"){ origin <- sum(exp(pred$origin)) pop <- sum(exp(pred$pop)) popCost = round(origin-pop, digits = 1) indCost = round((origin-pop)/dim(pred)[1], digits=1) } else { origin <- sum(pred$origin) pop <- sum(pred$pop) popCost = round((origin-pop)/origin*100, digits = 1) indCost = round((origin-pop)/dim(pred)[1], digits=3) } tab[i,] <- c(popCost, indCost, dim(pred)[1]) tins_name <- append(tins_name, tins[i]) } rownames(tab) <- tins_name tab } output$bar3 <- renderPlot({ if (input$OR5=="`Primary Care Services with a Primary Care Physician`"){ var = "`Primary Care Services with a Primary Care Physician (%)`" adjusted_var = "+Sex+Age+Dual+ESRD+Disabled+`Total Primary Care Services`+Cont_Att+Death+HCC_Cancer+HCC_Diabetes+HCC_CAD+HCC_85+HCC_111+OutNetwork+HCC_CKD+PrimaryCare" } else { var=input$OR5 if (input$OR5=="`Skilled Nursing Facility or Unit Discharges`"){ adjusted_var = "+Sex+Age+Dual+ESRD+Disabled+`Total Hospital Discharges`+Cont_Att+Death+HCC_Cancer+HCC_Diabetes+HCC_CAD+HCC_85+HCC_111+OutNetwork+HCC_CKD+PrimaryCare" } else { adjusted_var = "+Sex+Age+Dual+ESRD+Disabled+Cont_Att+Death+HCC_Cancer+HCC_Diabetes+HCC_CAD+HCC_85+HCC_111+OutNetwork+HCC_CKD+PrimaryCare" } } form <- as.formula(paste0(input$out5, "~", var, adjusted_var)) fit <- lm(form, data = dat19) explanatory <- gsub("`", "", var) tab <- tab_tins(fit, explanatory, input$eligibility5, input$tins5, input$comorbidities5, input$prop5, input$primarycare5) tab$tins <- rownames(tab) if (input$out5=="log_Cost_Care"){ ggplot(tab, aes(x=tins)) + geom_bar(stat = "identity",aes(y=`Population Cost Reduction`), fill = "steelblue", width = 0.3) + geom_point(aes(y=`Individual Cost Reduction`*2000, label = `Individual Cost Reduction`), size = 2) + geom_text(aes(y=`Individual Cost Reduction`*2000, label = paste0("Individual Cost: $", `Individual Cost Reduction`)), hjust = 0, vjust=-1)+ xlab("Primary Practice TINs")+ scale_y_continuous( name = "Population Cost", sec.axis = sec_axis(~./2000, name="Individual Cost") ) } else { ggplot(tab, aes(x=tins)) + geom_bar(stat = "identity",aes(y=`Population Cost Efficiency Reduction`), fill = "steelblue", width = 0.3) + geom_point(aes(y=`Individual Cost Efficiency Reduction`*100, label = `Individual Cost Efficiency Reduction`), size = 2) + geom_text(aes(y=`Individual Cost Efficiency Reduction`*100, label = paste0("Individual Cost Efficiency: ", `Individual Cost Efficiency Reduction`)), hjust = 0, vjust=-1)+ scale_y_continuous( name = "Population Cost Efficiency (%)", sec.axis = sec_axis(~./100, name="Individual Cost Efficiency") ) } }) output$tab5 <- function(){ if (input$OR5=="`Primary Care Services with a Primary Care Physician`"){ var = "`Primary Care Services with a Primary Care Physician (%)`" adjusted_var = "+Sex+Age+Dual+ESRD+Disabled+`Total Primary Care Services`+Cont_Att+Death+HCC_Cancer+HCC_Diabetes+HCC_CAD+HCC_85+HCC_111+OutNetwork+HCC_CKD+PrimaryCare" } else { var=input$OR5 if (input$OR5=="`Skilled Nursing Facility or Unit Discharges`"){ adjusted_var = "+Sex+Age+Dual+ESRD+Disabled+`Total Hospital Discharges`+Cont_Att+Death+HCC_Cancer+HCC_Diabetes+HCC_CAD+HCC_85+HCC_111+OutNetwork+HCC_CKD+PrimaryCare" } else { adjusted_var = "+Sex+Age+Dual+ESRD+Disabled+Cont_Att+Death+HCC_Cancer+HCC_Diabetes+HCC_CAD+HCC_85+HCC_111+OutNetwork+HCC_CKD+PrimaryCare" } } form <- as.formula(paste0(input$out5, "~", var, adjusted_var)) fit <- lm(form, data = dat19) explanatory <- gsub("`", "", var) tab <- tab_tins(fit, explanatory, input$eligibility5, input$tins5, input$comorbidities5, input$prop5, input$primarycare5) if (input$out5=="log_Cost_Care"){ tab$`Population Cost Reduction` <- paste0("$",tab$`Population Cost Reduction`) tab$`Individual Cost Reduction` <- paste0("$",tab$`Individual Cost Reduction`) } else { tab$`Population Cost Efficiency Reduction` <- paste0(tab$`Population Cost Efficiency Reduction`, "%") } tab %>% kable() %>% kable_styling() } drills <- reactiveValues( category = NULL ) categories <- unique(TINs$TaxEntityName) #current_category <- reactiveVal() tin_dat <- reactive({ #if (!length(current_category())) { if (!length(drills$category)) { return(count(dat19, TaxEntityName)) } dat19 %>% #filter(TaxEntityName == current_category()) %>% filter(TaxEntityName == drills$category) %>% count(EligibilityReason) }) output$pie <- renderPlotly({ #work <- count(dat19, TaxEntityName) work <- setNames(tin_dat(), c("x", "y")) plot_ly(work) %>% add_pie( labels = ~x, values = ~y, customdata = ~x ) }) tin_filter <- reactive({ #if (!length(current_category())) { if (!length(drills$category)) { return(dat19) } dat19 %>% filter(TaxEntityName == drills$category) }) output$heatmap2 <- renderPlotly({ d <- tin_filter() out <- c("log_Cost_Care","log_Cost_Eff") var <- c("`Total Hospital Discharges`", "UnplannedAdmits", "Readmits", "`ED Visits`", "`Computed Tomography (CT) Events`", "`Magnetic Resonance Imaging (MRI) Events`", "`Days in Hospice`", "`Total Primary Care Services`", "`Primary Care Services with a Primary Care Physician (%)`+`Total Primary Care Services`", "`Skilled Nursing Facility or Unit Discharges`+`Total Hospital Discharges`") #check the categories of primarycare if (dim(distinct(d, PrimaryCare))[1]<=1) adj = "+Sex+Age+Dual+Disabled+ESRD+Cont_Att+Death+HCC_Cancer+HCC_Diabetes+HCC_CAD+HCC_85+HCC_111+HCC_CKD+OutNetwork" else adj="+Sex+Age+Dual+Disabled+ESRD+Cont_Att+Death+HCC_Cancer+HCC_Diabetes+HCC_CAD+HCC_85+HCC_111+HCC_CKD+OutNetwork+PrimaryCare" coef_vector <- vector() conf_vector <- vector() for (j in 1:length(out)){ for (i in 1:length(var)){ form <- as.formula( paste0(out[j],"~",var[i],adj) ) fit <- lm(form, data = d) coef_vector <- append(coef_vector,fit$coefficients[2]) conf_vector <- append(conf_vector,ifelse(confint(fit)[2,1]*confint(fit)[2,2]<0,0,1)) } } coef_vector <- round(replace_na(coef_vector,0),3) ann <- list() j=0 for (i in 1:length(conf_vector)){ if (conf_vector[i]==0){ j=j+1 xaxis = ifelse(i<=10,"Log(Cost of Care)","Log(Cost Efficiency)") yaxis = gsub("Admits"," Admissions",gsub("admits","admissions",gsub("`","",names(coef_vector[i])))) ann[[j]] <- list(x=xaxis,y=yaxis, text="Uninterpretable",xref="x",yref="y",showarrow=FALSE) } } mat = t(matrix(coef_vector, nrow = 2, ncol = 10)) conditions.text <- as.vector(outer(paste0("Hospital Utilizations:",gsub("Admits"," Admissions",gsub("admits","admissions",gsub("`","",names(coef_vector[1:10])))),"\n"), paste0("Outcome variable:",c("Log(Cost Care)","Log(Cost Efficiency)"),"\n"), paste0)) conditions.text <- paste0(conditions.text,"Magnitude of Association:",coef_vector) text.mat <- matrix(conditions.text, nrow= nrow(mat), ncol = ncol(mat)) colorlength <- 100 null_value <- (0 - min(coef_vector)) / (max(coef_vector) - min(coef_vector)) border <- as.integer(null_value * colorlength) colorscale <- as.list(1:colorlength) #colorscale below zero s <- scales::seq_gradient_pal("blue", "white", "Lab")(seq(0,1,length.out=border)) for (i in 1:border) { colorscale[[i]] <- c((i - 1) / colorlength, s[i]) } #colorscale above zero s <- scales::seq_gradient_pal("white", "red", "Lab")(seq(0,1,length.out=colorlength - border)) for (i in 1:(colorlength - border)) { colorscale[[i + border]] <- c((i + border) / colorlength, s[i]) } plot_ly(source = "heat_plot") %>% add_heatmap( x=c("Log(Cost of Care)","Log(Cost Efficiency)"), y=gsub("Admits"," Admissions",gsub("admits","admissions",gsub("`","",names(coef_vector[1:10])))), z=matrix(coef_vector,nrow=10,ncol = 2), hoverinfo = 'text', text = text.mat, colors = colorRamp(c("blue","white","red")), colorscale = colorscale, colorbar = list(len = 1, limits = c(-1,1)), wdith = 500, height=1000 )%>% layout(margin=list(l=350,r=20, b=10, t=10), annotations=ann) }) observeEvent(event_data("plotly_click"), { x <- event_data("plotly_click")$customdata[[1]] if (!length(x)) return() drills$category <- x }) output$history <- renderUI({ if (!length(drills$category)) return("Click the pie chart to drilldown") categoryInput <- selectInput( "category", "TIN", choices = categories, selected = drills$category ) sd <- filter(dat19, TaxEntityName %in% drills$category) fluidRow( column(3, categoryInput) ) }) observeEvent(input$category, { drills$category <- input$category }) #observe({ # cd <- event_data("plotly_click")$customdata[[1]] # if (isTRUE(cd %in% categories)) current_category(cd) #}) output$back <- renderUI({ if (length(drills$category)) actionButton("clear", "Back", icon("chevron-left")) }) # clear the chosen category on back button press observeEvent(input$clear, {drills$category<-NULL}) session$onSessionEnded(function() { stopApp() }) })

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/species_resolution_per_db.R

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species_resolution_per_db.R

#!/usr/bin/env Rscript rm(list=ls()); # Load libraries ---- .cran_packages <- c("stringr", "reshape2", "tidyverse") # .cran_packages <- c("stringr", "tidyverse", "reshape2", "ggplot2") # 1. cran_package .inst <- .cran_packages %in% installed.packages() if(any(!.inst)) { install.packages(.cran_packages[!.inst], dep=TRUE, repos='http://cran.us.r-project.org') } # Load packages into session, and print package version sapply(c(.cran_packages), require, character.only = TRUE) args = commandArgs(trailingOnly=TRUE) # wangtax = args[1] # tag <- strsplit(wangtax, "[.]")[[1]][1] # wang tax from: path = '/Users/cigom/metagenomics/COI/species_resolution_per_db' bold.file = "run014_t2_ASVs.BOLD_public_species_v02.wang.taxonomy" midori.file = "run014_t2_ASVs.midori_unique_DB_0.wang.taxonomy" coarbitrator.file = "run014_t2_ASVs.Coarbitrator_COI_nuc_curated.wang.taxonomy" # To load file: wang.taxonomy source(file = "~/Documents/GitHub/metagenomics/converted2worms/cigom_load_dada2_tables.R") setwd(path) ## Load assignation result ---- bold <- read.wangtax(bold.file) midori <- read.wangtax(midori.file) coarbitrator <- read.wangtax(coarbitrator.file) TL <- names(midori)[-c(1,ncol(midori))] if (identical(names(bold), names(midori) )) { TL <- names(bold)[-c(1,ncol(bold))] } # Contamos el numero de indeterminados por base de datos a nivel filo ---- n_undetermined <- function(x) ( nrow(x[x[,3] == 'Undetermined',]) ) print(n_undetermined_bold <- n_undetermined(bold)) print(n_undetermined_midori <- n_undetermined(midori)) print(n_undetermined_coarbitrator <- n_undetermined(coarbitrator)) # Back the SL position per database ---- coarbitrator_ <- NULL for (i in 1:nrow(coarbitrator)) { rl <- coarbitrator$SL[i] + 1 coarbitrator_[[i]] <- list(rank=names(coarbitrator)[rl], linage=coarbitrator[i,rl]) } coarbitrator_ <- do.call(rbind, coarbitrator_) midori_ <- NULL for (i in 1:nrow(midori)) { rl <- midori$SL[i] + 1 midori_[[i]] <- list(rank=names(midori)[rl], linage=midori[i,rl]) } midori_ <- do.call(rbind, midori_) # bold_ <-NULL for (i in 1:nrow(bold)) { rl <- bold$SL[i] + 1 bold_[[i]] <- list(rank=names(bold)[rl], linage=bold[i,rl]) } bold_ <- do.call(rbind, bold_) # compare the last parent between db ---- names(midori)[-1] <- paste("midori", names(midori)[-1], sep="_") names(bold)[-1] <- paste("bold", names(bold)[-1], sep="_") names(coarbitrator)[-1] <- paste("coarbitrator", names(coarbitrator)[-1], sep="_") # bold vs midori ---- LCR <- data.frame(midori, bold[,-1], coarbitrator[,-1], diff_x_y = midori[,9] - bold[,9], diff_x_z = coarbitrator[,9] - bold[,9], stringsAsFactors = FALSE) # aniadir una columna, rango 1 LCR$bold_vs_midori <- 1 # recorrer hacia el 7 en loop for(t in 1:7){ # Generar nombre de la columna con taxonomia por morfologia midori_rank <- paste("midori", TL[t], sep="_") # Generar nombre de la columna con taxonomica por molecular bold_rank <- paste("bold", TL[t], sep="_") # Aquellos renglones donde coincidan los nombres tienen LCR = t LCR$bold_vs_midori[ which(LCR[, midori_rank] == LCR[, bold_rank]) ] <- t } # bold vs coarbitrator ---- LCR$bold_vs_coarbitrator <- 1 # recorrer hacia el 7 en loop for(t in 1:7){ # Generar nombre de la columna con taxonomia por morfologia coarbitrator_rank <- paste("coarbitrator", TL[t], sep="_") # Generar nombre de la columna con taxonomica por molecular bold_rank <- paste("bold", TL[t], sep="_") # Aquellos renglones donde coincidan los nombres tienen LCR = t LCR$bold_vs_coarbitrator[ which(LCR[, midori_rank] == LCR[, bold_rank]) ] <- t } # getback the Last Common Rank bold_LCR <-NULL for (i in 1:nrow(bold)) { rl <- LCR[,'bold_SL'][i] + 1 bold_LCR[[i]] <- list(rank=names(bold)[rl], linage=bold[i,rl]) } bold_LCR <- do.call(rbind, bold_LCR) bold_LCR <- data.frame(rank = do.call(rbind, bold_LCR[,1]), lineage = do.call(rbind, bold_LCR[,2])) bold_LCR$rank <- sapply(strsplit(as.vector(bold_LCR$rank), "_"), "[[", 2) # ES NECESARIO VERIFICAR ESTA PARTE, midori_LCR <-NULL for (i in 1:nrow(midori)) { rl <- LCR[,'midori_SL'][i] + 1 midori_LCR[[i]] <- list(rank=names(midori)[rl], linage=midori[i,rl]) } midori_LCR <- do.call(rbind, midori_LCR) midori_LCR <- data.frame(rank = do.call(rbind, midori_LCR[,1]), lineage = do.call(rbind, midori_LCR[,2])) midori_LCR$rank <- sapply(strsplit(as.vector(midori_LCR$rank), "_"), "[[", 2) coarbitrator_LCR <-NULL for (i in 1:nrow(coarbitrator)) { rl <- LCR[,'bold_vs_coarbitrator'][i] + 1 coarbitrator_LCR[[i]] <- list(rank=names(coarbitrator)[rl], linage=coarbitrator[i,rl]) } coarbitrator_LCR <- do.call(rbind, coarbitrator_LCR) coarbitrator_LCR <- data.frame(rank = do.call(rbind, coarbitrator_LCR[,1]), lineage = do.call(rbind, coarbitrator_LCR[,2])) coarbitrator_LCR$rank <- sapply(strsplit(as.vector(coarbitrator_LCR$rank), "_"), "[[", 2) # and save data imput data <- as_tibble(data.frame( ASV = midori$ASV, #midori = do.call(rbind, midori_[,1]), #bold = do.call(rbind, bold_[,1]), lineage_x = do.call(rbind, bold_[,2]), lineage_y = do.call(rbind, midori_[,2]), lineage_z = do.call(rbind, coarbitrator_[,2]), # Anadimos el valor de cambio en las asignaciones: rank_x = bold[,9], rank_y = midori[,9], rank_z = coarbitrator[,9], diff_x_y = bold[,9] - midori[,9], diff_x_z = bold[,9] - coarbitrator[,9], LCR_x_y = LCR$bold_vs_midori, LCR_x_z = LCR$bold_vs_coarbitrator, LCR_x = bold_LCR$lineage, LCR_y = midori_LCR$lineage, LCR_z = coarbitrator_LCR$lineage, stringsAsFactors = FALSE) ) # NOT ABS VALUE IN DIFF data$diff_x_y_z <- data$diff_x_y - data$diff_x_z # Calculate distance: # Computes pairwise string distances between elements library('stringdist') # We use is the Levenshtein Distance which is simply the # single character edit distance between two strings by # a combination of insertions, deletions, or substitutions dist_x_y <- stringdist(data$lineage_x, data$lineage_y, method = 'lv') # inf values reported dist_x_z <- stringdist(data$lineage_x, data$lineage_z, method = 'lv') # es necesario arreglar la nomenclatura de coarbitrator # esto cambia filter(dist_x_y, dist > 0) %>% select(midori, bold, diff) %>% melt(id.vars = "diff", variable.name = "DB", value.name = "Rank") %>% as_tibble() %>% # mutate(diff = abs(diff)) mutate(Rank = factor(Rank, levels = TL[-1]) ) -> y # ahora comprobamos que los niveles de cambio zero sean consistentes en su asignacion: disvis <- dist[dist$diff == 0 & dist$dist > 0,] disvis <- dist[ dist$dist > 0,] filter(dist, dist > 0) %>% select(LCR, LCR_x, LCR_y, dist) %>% melt(id.vars = c("LCR", "dist"), variable.name = "LCR_xy", value.name = "lineage") %>% as_tibble() -> dist_xy p <- ggplot(dist_xy, aes(x=dist, fill=lineage)) + #geom_histogram(position="dodge2", bins = 40) + geom_density(alpha = 0.6) + facet_wrap (~ lineage , scales = 'free_y') + theme(legend.position="top") p p <- ggplot(dist_xy, aes(x=dist)) + geom_density(alpha = 0.6) + facet_wrap (~ LCR_xy, scales = 'free_y') plot(density(dist$dist), main = 'Comparing DB assignations by String Distance [LCR]') lines(stats::lowess(dist$dist)) d <- stringdistmatrix(disvis$LCR_x, disvis$LCR_y, useNames="none", method = 'lv') library(superheat) superheat(d, row.dendrogram = TRUE, col.dendrogram = TRUE) quit(save = 'no') data$weight <- 'None' data[data$diff > 0 , 'weight'] <- 'midori' data[data$diff < 0 , 'weight'] <- 'bold' filter(data, weight != 'None') %>% select(midori, bold, diff) %>% melt(id.vars = "diff", variable.name = "DB", value.name = "Rank") %>% as_tibble() %>% # mutate(diff = abs(diff)) mutate(Rank = factor(Rank, levels = TL[-1]) ) -> y # peces entre las bases de datos (bold) ntaxa <- function(r) { r <- data.frame(table(r)) r <- r[order(r$Freq, decreasing = TRUE), ] # r <- r[order(r$r, decreasing = FALSE), ] return(r) } ntaxa(bold[,4]) # Actinopterygii, 691 ntaxa(midori[,4]) # Actinopteri, 582 ntaxa(coarbitrator[,4]) # Actinopteri, 388 # arreglar la taxonomia primero acti_bold <- midori[bold[,4] == 'Actinopterygii', ] # 13056 acti_midori <- midori[midori[,4] == 'Actinopteri', ] # 13014 acti_coarbitrator <- coarbitrator[coarbitrator[,4] == 'Actinopteri', ] # 13023 # Barplot # use SL_diff or SL_tidy # Midori and bold taxonomy resolution # Won (+) and lost (−) levels (shoul) SL <- select(data, ASV, diff_x_y, diff_x_z) SL$weight_x_y <- 'shared_x_y' SL[SL$diff_x_y > 0 , 'weight_x_y'] <- 'midori' SL[SL$diff_x_y < 0 , 'weight_x_y'] <- 'bold' SL$weight_x_z <- 'shared_x_z' SL[SL$diff_x_z > 0 , 'weight_x_z'] <- 'coarbitrator' SL[SL$diff_x_z < 0 , 'weight_x_z'] <- 'bold' SL[,'diff_x_y'] <- abs(SL[,'diff_x_y']) SL[,'diff_x_z'] <- abs(SL[,'diff_x_z']) table(SL$weight_x_z) SL_melt <- melt(SL, id.vars= c("ASV","diff_x_y", "diff_x_z"), value.name = "DB", variable.name = 'weight') SL_melt <- melt(SL_melt) SL_diff <- aggregate(SL_melt[,'value'], by=list(SL_melt[,'DB']), FUN = table) SL_diff <- data.frame(level = SL_diff[,1], SL_diff[,2]) SL_diff <- melt(SL_diff) # barplot ggplot(SL_melt, aes(value, variable, fill = diff)) + geom_col(width = 0.4, position = position_dodge(), alpha = 0.7) + geom_text(aes(label=ASVs), size=3, vjust=-0.2) + scale_fill_brewer(palette = "Set1") #+ #labs(x="Levels changed", y="ASVs (n)", title="Midori - Bold taxonomy Difference", subtitle=paste0("Midori and Bold levels\nTotal ASVs = ", nrow(midori), "\n", "ASVs without levels change across data bases: ",no_change), caption=paste0("Number of ASVs with levels change across databases: ","\n", "Midori: ", midori_lc, " and Bold: ", bold_lc)) + theme() # ALLUVIAL #filter(data, weight != 'shared') %>% data %>% select(rank_x, rank_y, rank_z) %>% mutate(diff = abs(diff), weight = as.factor(weight), midori = factor(midori, levels = TL[-1]), bold = factor(bold, levels = TL[-1])) -> alluvia # library('ggalluvial') ggplot(data = alluvia, aes(axis1 = midori, axis2 = bold)) + geom_alluvium(aes(fill = weight)) + geom_stratum() + scale_fill_brewer(type = "qual", palette = "Set2") + geom_text(stat = "stratum", label.strata = TRUE) + # theme(legend.position = "bottom") + theme_minimal() library(alluvial) alluvial(select(data, lineage_x, lineage_y, lineage_z), freq=data$diff_x_y_z, col = ifelse(data$diff_x_y_z > 0, "orange", "grey"), border = ifelse(data$diff_x_y_z > 0, "orange", "grey"), hide = data$diff_x_y_z == 0, cex = 0.7) head(as.data.frame(Titanic, stringsAsFactors = FALSE)) # # Parallel Coordinates library(GGally) paralle <- select(data, rank_x, rank_y, rank_z, diff_x_y, diff_x_z) # sampleTree = hclust(dist(paralle[,1:3]), method = "average"); # plot(sampleTree, main = "", sub="", xlab="", cex.lab = 1.5, cex.axis = 1.5, cex.main = 2) cbind(data.frame(table(paralle$diff_x_y)), data.frame(table(paralle$diff_x_z))) paralle$weight_x_y <- 'shared_x_y' paralle[paralle$diff_x_y > 0 , 'weight_x_y'] <- 'midori' paralle[paralle$diff_x_y < 0 , 'weight_x_y'] <- 'bold' paralle$weight_x_z <- 'shared_x_z' paralle[paralle$diff_x_z > 0 , 'weight_x_z'] <- 'coarbitrator' paralle[paralle$diff_x_z < 0 , 'weight_x_z'] <- 'bold' ggparcoord(data, columns = 4:8, groupColumn = "sex") ggparcoord(paralle, columns = 1:3, groupColumn = "weight_x_y") # Lineplot. Distribucion de las asignaciones entre bases de datos L_tables <- data.frame(Level=-5:5, x_y=data.frame(table(data$diff_x_y))[,'Freq'], x_z=data.frame(table(data$diff_x_z))[, 'Freq']) L_tidy <- melt(L_tables, id.vars = 'Level', variable.name = 'diff', value.name = 'n') # wrap by standar (BOLD) and reference (midori and GBank) L_tidy$Ref <- 'Standard(BOLD)' L_tidy[L_tidy$Level > 0, 'Ref'] <- 'Reference' # L_tidy$database <- '' L_tidy[L_tidy$diff == 'x_y', 'database'] <- 'Midori' L_tidy[L_tidy$diff == 'x_z', 'database'] <- 'GBank' L_tidy$Level <- abs(L_tidy$Level) L_tidy$Level <- factor(L_tidy$Level, levels = 0:5, ordered = T) ggplot(subset(L_tidy, Level > 0), aes(x=Level, y=n, group=database, fill=database, color=database)) + geom_point(size=2, alpha=0.6) + geom_line(size=1, alpha=0.6, linetype="dashed") + geom_text(data = subset(L_tidy, Level > 0), aes(label=n), size=4, vjust=1, color = 'black') + # geom_text(aes(label=diff), size=3, vjust=-0.2, color = 'black') + scale_color_brewer(palette = "Set1") + #theme(panel.grid = element_blank(), # panel.background = element_blank()) + labs(y="ASVs", title="Taxonomy resolution Distribution", subtitle=paste0("Total ASVs in (run14) = ", nrow(midori), "\n", "Undetermined Bold = ", n_undetermined_bold, "\n", "Undetermined Midori = ", n_undetermined_midori, "\n", "Undetermined GBank = ", n_undetermined_coarbitrator), caption = 'Reference panel show how best the database was against the standard \nwhereas the Standard(BOLD) panel shows how best the BOLD db was against either, Midori or GBank db') + facet_grid(~Ref)

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sim_data.Rd.R

library(NRejections) ### Name: sim_data ### Title: Simulate MVN data ### Aliases: sim_data ### ** Examples cor = make_corr_mat( nX = 5, nY = 2, rho.XX = -0.06, rho.YY = 0.1, rho.XY = -0.1, prop.corr = 8/40 ) d = sim_data( n = 50, cor = cor )

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/Grenades.r

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dannyweiland/CSGO-Analysis

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Grenades.r

#Mapping Grenade Spots in CSGO #My Goal here is to see where the most common spots to throw and be hit by grenades are by visualizing them on the game map. #Import Libraries library(readxl) library(dplyr) library(ggplot2) library(ggpubr) library(png) #Import Data grenades1 <- read.csv("mm_grenades_demos.csv") de_dust2_map <- png::readPNG("de_dust2.png") de_nuke_map <- png::readPNG("de_new_nuke.png") de_cache_map <- png::readPNG("de_cache.png") de_cache_map_test <- png::readPNG("de_cache_test.png") #Plotting on map ----- #Dust2 Attacker Plot dust2 <- grenades1 %>% filter(map == "de_dust2") %>% ggplot(., aes(att_pos_x, att_pos_y)) + background_image(de_dust2_map)+ geom_jitter(alpha = 0.1, color = "red") dust2 #Nuke Attacker Plot nuke <- grenades1 %>% filter(map == "de_nuke") %>% ggplot(., aes(att_pos_x, att_pos_y)) + background_image(de_nuke_map)+ geom_jitter(alpha = 0.1, color = "red") #This one needs to be more properly scaled. Lets figure that out #We can solve this by finding the min and max values for each player position. mapScale <- grenades1 %>% select(map, att_pos_x, att_pos_y) %>% group_by(map) %>% summarise( max_att_pos_x = max(att_pos_x), max_att_pos_y = max(att_pos_y), min_att_pos_x = min(att_pos_x), min_att_pos_y = min(att_pos_y) ) %>% arrange(map) #Increase the value to essentially "shrink" the size of the plot relative to the image #Cache Attacker Plot cache <- grenades1 %>% filter(map == "de_cache") %>% ggplot(., aes(att_pos_x, att_pos_y)) + background_image(de_cache_map_test)+ geom_jitter(color = "red")+ cache #When completed, re-add alpha = .1 next to color to show density

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test-function-hsSubstSpecChars.R

test_that("hsSubstSpecChars() works", { f <- kwb.utils:::hsSubstSpecChars expect_error(expect_warning(f(), "deprecated")) })

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sen_nominal_votes.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/sen_nominal_votes.R \docType{data} \name{sen_nominal_votes} \alias{sen_nominal_votes} \title{Nominal votes in the Brazilian Federal Senate} \format{ A data frame with 60691 rows and 8 variables } \usage{ sen_nominal_votes } \value{ \itemize{ \item{\code{vote_date: }}{\code{POSIXct}, date the vote took place.} \item{\code{bill_id: }}{id of the bill in the Senate API database.} \item{\code{bill: }}{bill type, year and number.} \item{\code{legislature: }}{legislature number.} \item{\code{senator_id: }}{unique id of the senator.} \item{\code{senator_name: }}{the senator's name.} \item{\code{senator_vote: }}\code{numeric}{vote cast. 1 = "yes"; 0 = "no", NA = other.} \item{\code{senator_party: }}{political party the senator was in when the vote took place.} \item{\code{senator_state: }}{state the senator represented when the vote took place.} } } \description{ This is a dataset of the nominal votes in the Brazilian Federal Senate, from all those available on the API from 1991 onwards. } \note{ These data can easily be grouped by legislature if so desired, using the \code{legislature} variable. } \keyword{datasets}

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clustering.R

# Set current directory to file location setwd(dirname(rstudioapi::getSourceEditorContext()$path)) getwd() # Load data load(paste0(getwd(), '/dd_complete.RData')) # Data dimensionality; hence p is number of predictors (without response value) N <- nrow(dd.complete); p <- ncol(dd.complete) -1 str(dd.complete) library(cluster) library(dplyr) library(ggplot2) library(readr) library(Rtsne) # data = dd.complete # Clustering on a reduced portion of the dataset example <- createDataPartition(dd.complete$y, p = .2, list = FALSE) results <- dd.complete[example,20] example <- dd.complete[example,-20] # Compute Gower distance gower_dist <- daisy(example, metric = 'gower') gower_mat <- as.matrix(gower_dist) sil_width <- c(NA) for (i in 2:10) { pam_fit <- pam(gower_dist, diss = TRUE, k = i) sil_width[i] <- pam_fit$silinfo$avg.width } plot(1:10, sil_width, xlab = 'Number of clusters', ylab = 'Silhouette Width', col = 'red') lines(1:10, sil_width, col = 'red') k <- 4 pam_fit <- pam(gower_dist, diss = TRUE, k) pam_results <- example %>% mutate(cluster = pam_fit$clustering) %>% group_by(cluster) %>% do(the_summary = summary(.)) pam_results$the_summary # Visualization in a lower dimensional space tsne_obj <- Rtsne(gower_dist, is_distance = TRUE) tsne_data <- tsne_obj$Y %>% data.frame() %>% setNames(c('X','Y')) %>% mutate(cluster = factor(pam_fit$clustering)) ggplot(aes(x = X, y = Y), data = tsne_data) + geom_point(aes(color = cluster)) # Try to interpret our results with 2, 4 and 8 clusters results2 <- pam_results$the_summary table2 <- table(pam_fit$clustering, results) results4 <- pam_results$the_summary table4 <- table(pam_fit$clustering, results) results8 <- pam_results$the_summary table8 <- table(pam_fit$clustering, results) # Visualize the results results2 table2 results4 table4 results8 table8 # Proportion of class 'yes' in every cluster table2[,2] / rowSums(table2) * 100 table4[,2] / rowSums(table4) * 100 table8[,2] / rowSums(table8) * 100 # It looks like clusters 3,4 (for k = 4) and 7,8 (for k = 8) have a much higher proportion of 'yes' ratio than the other clusters. # So we conclude that there might be some characteristics of those clusters that specifically make costumers fall to category 'yes'. # Try to visualize difference between cluster variables in order to classify between 'yes' or 'no # We are going to take 4 as the number of clusters aux <- cbind(example, pam_fit$clustering) colnames(aux)[20] <- 'Cluster' # Check the 'euribor' variance between clusters boxplot(aux$euribor3m ~ aux$Cluster, main = 'Cluster comparision', xlab = 'Cluster', ylab = 'Euribor') # Check the 'nr.employed' variance between clusters boxplot(aux$nr.employed ~ aux$Cluster, main = 'Cluster comparision', xlab = 'Cluster', ylab = 'nr.employed') # age boxplot(aux$age ~ aux$Cluster)

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plot4.R

#This function loads the data from the working directory and adds a time/date column and filters to the correct dates getData <- function () { # Create columns types vector colTypes <- c("character", "character", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric") #Read in data from file in project folder elecData <- read.table ("./household_power_consumption.txt", header = TRUE, sep=";", na.strings="?", colClasses=colTypes) # Combine the data and time fields into one date/time field elecData$dt <- as.POSIXlt(paste(elecData$Date, elecData$Time, sep=" "), format = "%d/%m/%Y %H:%M:%S") # Set the start and end dates to be used to filter only dates to be used in plots startDate <- as.POSIXlt("1/2/2007", format="%d/%m/%Y") endDate <- as.POSIXlt("3/2/2007", format="%d/%m/%Y") # filter data based on start and end dates above and return data set d1 <- elecData[elecData$dt>=startDate, ] d1 <<- d1[d1$dt<endDate, ] d1 } #This function creates a png of the plot and places it in the working directory plot4png <- function () { png(file = "./plot4.png") # Plot 4 par(mfrow = c(2,2), mar=c(4,4,2,1), oma=c(0,0,2,0)) with(d1, { plot(dt, Global_active_power, type="l", ylab="Global Active Power (kilowatts)", xlab="") plot(dt, Voltage, type="l", ylab="Voltage", xlab="") plot(dt, Sub_metering_1, type="l", ylab="Energy sub metering", xlab="") with(subset(d1), points(dt, Sub_metering_2, col = "red", type="l")) with(subset(d1), points(dt, Sub_metering_3, col = "blue", type="l")) legend("topright", lwd=1, col=c("black", "red", "blue"), legend=c("Sub_metering_1", "Sub_metering_2","Sub_metering_3")) plot(dt, Global_reactive_power, type="l", ylab="Global Reactive Power (kilowatts)", xlab="") }) dev.off() } #This funtion will get the data and create the plot in one funciton plot4 <- function() { getData() plot4png()}

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weiqqci.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/qq.R \name{weiqqci} \alias{weiqqci} \title{plot a qqplot relative to a weibull distribution} \usage{ weiqqci(x, conf.int = 0.95, ...) } \arguments{ \item{x}{vector of numbers} \item{conf.int}{numeric confidence coefficient for EDF CIs} \item{\dots}{not used} } \description{ plot a qqplot relative to a weibull distribution } \details{ The weibull parameters are estimated using survival::survreg }

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## Conditional and Block statements a = 10 b = 110 #if(a >b) { print(a) } else { print(b) } if(a>b) { print(a) } else if(a==b) { print("equal") } else { print(b) } #Block Statements ################### #Switch ################### #a = switch(expression, case1,case2,case3,.....casen) a = switch(3, FALSE,"case2","case3") print(a) a = 9 b = 10 a>b c = switch(a>b, FALSE,TRUE) print(c) selector = "logical" c = switch(selector,"color"="red", "number"=1, "logical"=TRUE) print(c) Invite = "Welcome" y = switch(Invite, Hello = { print("Hello to besant1") print("Hello to R Lang1") }, Welcome = { print("Welcome to besant2") print("Welcome to R Lang2") "I get to returned to the caller" }) y ################## #For statement ################## for(a in c(1,3,4)) { print(sqrt(a)) } V <- LETTERS month_abb <- month.abb months_name <- month.name letters vowels = c('a','e','i','o','u') for(x in V) { print(x) } ################### #Repeat ################### x = 1 repeat{ print(x*2) x = x+1 if(x >1000){ break } } ################### #while ################### a = 100 b =1000 while(a < b) { a = a + 100 print(paste("a is lesser", a)) }

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test-input-validate.R

context("input validation") test_that("validate mime", { expect_error( pkgapi_input_validate_mime(NULL, "application/json"), "Content-Type was not set (expected 'application/json')", fixed = TRUE, class = "pkgapi_error") expect_error( pkgapi_input_validate_mime("application/octet-stream", "application/json"), paste("Expected content type 'application/json'", "but was sent 'application/octet-stream'"), fixed = TRUE, class = "pkgapi_error") })

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docaids.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/docaids-package.r \docType{package} \name{docaids} \alias{docaids} \alias{docaids-package} \title{docaids.} \description{ docaids. }

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get_relevant_date.R

get_relevant_date <-function() { relevenant_dates<-tibble::tibble( yesterdays_date = Sys.Date()-1, ytd_date=lubridate::floor_date(Sys.Date(),unit="year"), yoy_date = lubridate::ceiling_date(Sys.Date()-365, unit="week",week_start = 1), past_30_days = Sys.Date()-31, past_7_days = Sys.Date()-8, month_to_date = lubridate::floor_date(Sys.Date()-1, unit="month") ) relevenant_dates }

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07_script_ensemble.R

### disciplina - modelagem de nicho ecológico: teoria e pratica ### ### ppg ecologia e biodiversidade - unesp 2017 ### # Thadeu Sobral de Souza - thadeusobral@gmail.com # Maurício Humberto Vancine - mauricio.vancine@gmail.com ###-----------------------------------------------------------------------------------------### ### 3. script ensemble ### ###-----------------------------------------------------------------------------------------### # 1. limpara a memoria e carregar os pacotes # limpar o workspace e aumentar a memoria para o r rm(list = ls()) memory.limit(size = 10000000000000) # instalar e carregar pacotes # install.packages(c("raster", "rgdal", "vegan"), dep = T) # carregar pacotes library(raster) # manejo de arquivos sig library(rgdal) # manejo de arquivos sig library(vegan) # diversas analises multivariadas # verificar pacotes carregados search() ###------------------------------------------------------------------------------------------------------### # diretorio setwd("D:/90_aulas_montadas/_disciplina_enm_R_unesp_2017/scripts_r/03_saidas_enm") # importando as tabelas com as avaliacoes eval.bioclim <- read.table ("zEval_CCSM_Bioclim_B.balansae.txt") eval.Gower <- read.table ("zEval_CCSM_Gower_B.balansae.txt") eval.Maha <- read.table ("zEval_CCSM_Maha_B.balansae.txt") eval.Maxent <- read.table ("zEval_CCSM_Maxent_B.balansae.txt") eval.SVM <- read.table ("zEval_CCSM_SVM_B.balansae.txt") ###------------------------------------------------------------------------------------------------------### ### ensemble por frequencia - parte 1 ### # bioclim # importar os .asc bioc1 <- raster ("CCSM_Bioclim_0k_B.balansae1.asc") bioc2 <- raster ("CCSM_Bioclim_0k_B.balansae2.asc") bioc3 <- raster ("CCSM_Bioclim_0k_B.balansae3.asc") bioc4 <- raster ("CCSM_Bioclim_0k_B.balansae4.asc") bioc5 <- raster ("CCSM_Bioclim_0k_B.balansae5.asc") bioc211 <- raster ("CCSM_Bioclim_21k_B.balansae1.asc") bioc212 <- raster ("CCSM_Bioclim_21k_B.balansae2.asc") bioc213 <- raster ("CCSM_Bioclim_21k_B.balansae3.asc") bioc214 <- raster ("CCSM_Bioclim_21k_B.balansae4.asc") bioc215 <- raster ("CCSM_Bioclim_21k_B.balansae5.asc") bioc61 <- raster ("CCSM_Bioclim_6k_B.balansae1.asc") bioc62 <- raster ("CCSM_Bioclim_6k_B.balansae2.asc") bioc63 <- raster ("CCSM_Bioclim_6k_B.balansae3.asc") bioc64 <- raster ("CCSM_Bioclim_6k_B.balansae4.asc") bioc65 <- raster ("CCSM_Bioclim_6k_B.balansae5.asc") # soma dos mapas e corte do threshold 0 bioclim0k <- sum(bioc1 >= eval.bioclim[1, 1], bioc2 >= eval.bioclim[2, 1], bioc3 >= eval.bioclim[3, 1], bioc4 >= eval.bioclim[4, 1], bioc5 >= eval.bioclim[5, 1]) # plot plot(bioclim0k) # exportando o .asc da soma por frequencia writeRaster (bioclim0k, "Bioclim_0k.asc", format="ascii") # bioclim 21k bioclim21k <- sum(bioc211 >= eval.bioclim[1, 1], bioc212 >= eval.bioclim[2, 1], bioc213 >= eval.bioclim[3, 1], bioc214 >= eval.bioclim[4, 1], bioc215 >= eval.bioclim[5, 1]) plot(bioclim21k) writeRaster (bioclim21k, "Bioclim_21k.asc", format="ascii") # bioclim 6k bioclim6k <- sum(bioc61 >= eval.bioclim[1, 1], bioc62 >= eval.bioclim[2, 1], bioc63 >= eval.bioclim[3, 1], bioc64 >= eval.bioclim[4, 1], bioc65 >= eval.bioclim[5, 1]) plot(bioclim6k) writeRaster (bioclim6k, "Bioclim_6k.asc", format = "ascii") ###------------------------------------------------------------------------------------------------------### # gower # importar bioc1 <- raster ("CCSM_Gower_0k_B.balansae1.asc") bioc2 <- raster ("CCSM_Gower_0k_B.balansae2.asc") bioc3 <- raster ("CCSM_Gower_0k_B.balansae3.asc") bioc4 <- raster ("CCSM_Gower_0k_B.balansae4.asc") bioc5 <- raster ("CCSM_Gower_0k_B.balansae5.asc") bioc211 <- raster ("CCSM_Gower_21K_B.balansae1.asc") bioc212 <- raster ("CCSM_Gower_21K_B.balansae2.asc") bioc213 <- raster ("CCSM_Gower_21K_B.balansae3.asc") bioc214 <- raster ("CCSM_Gower_21K_B.balansae4.asc") bioc215 <- raster ("CCSM_Gower_21K_B.balansae5.asc") bioc61 <- raster ("CCSM_Gower_6K_B.balansae1.asc") bioc62 <- raster ("CCSM_Gower_6K_B.balansae2.asc") bioc63 <- raster ("CCSM_Gower_6K_B.balansae3.asc") bioc64 <- raster ("CCSM_Gower_6K_B.balansae4.asc") bioc65 <- raster ("CCSM_Gower_6K_B.balansae5.asc") # soma Gower0k <- sum(bioc1 >= eval.Gower[1, 1], bioc2 >= eval.Gower[2, 1], bioc3 >= eval.Gower[3, 1], bioc4 >= eval.Gower[4, 1], bioc5 >= eval.Gower[5, 1]) plot(Gower0k) writeRaster (Gower0k, "Gower_0k.asc", format="ascii") # gower 21k Gower21k <- sum(bioc211 >= eval.Gower[1, 1], bioc212 >= eval.Gower[2, 1], bioc213 >= eval.Gower[3, 1], bioc214 >= eval.Gower[4, 1], bioc215 >= eval.Gower[5, 1]) plot(Gower21k) writeRaster (Gower21k, "Gower_21k.asc", format="ascii") # gower 6k Gower6k <- sum(bioc61 >= eval.Gower[1, 1], bioc62 >= eval.Gower[2, 1], bioc63 >= eval.Gower[3, 1], bioc64 >= eval.Gower[4, 1], bioc65 >= eval.Gower[5, 1]) plot(Gower6k) writeRaster (Gower6k, "Gower_6k.asc", format="ascii") ###------------------------------------------------------------------------------------------------------### # mahalanobis # importar bioc1 <- raster ("CCSM_Maha_0k_B.balansae1.asc") bioc2 <- raster ("CCSM_Maha_0k_B.balansae2.asc") bioc3 <- raster ("CCSM_Maha_0k_B.balansae3.asc") bioc4 <- raster ("CCSM_Maha_0k_B.balansae4.asc") bioc5 <- raster ("CCSM_Maha_0k_B.balansae5.asc") bioc211 <- raster ("CCSM_Maha_21K_B.balansae1.asc") bioc212 <- raster ("CCSM_Maha_21K_B.balansae2.asc") bioc213 <- raster ("CCSM_Maha_21K_B.balansae3.asc") bioc214 <- raster ("CCSM_Maha_21K_B.balansae4.asc") bioc215 <- raster ("CCSM_Maha_21K_B.balansae5.asc") bioc61 <- raster ("CCSM_Maha_6K_B.balansae1.asc") bioc62 <- raster ("CCSM_Maha_6K_B.balansae2.asc") bioc63 <- raster ("CCSM_Maha_6K_B.balansae3.asc") bioc64 <- raster ("CCSM_Maha_6K_B.balansae4.asc") bioc65 <- raster ("CCSM_Maha_6K_B.balansae5.asc") # soma Maha0k <- sum(bioc1 >= eval.Maha[1, 1], bioc2 >= eval.Maha[2, 1], bioc3 >= eval.Maha[3, 1], bioc4 >= eval.Maha[4, 1], bioc5 >= eval.Maha[5, 1]) plot(Maha0k) writeRaster (Maha0k, "Maha_0k.asc", format="ascii") # maha 21k Maha21k <- sum(bioc211 >= eval.Maha[1, 1], bioc212 >= eval.Maha[2, 1], bioc213 >= eval.Maha[3, 1], bioc214 >= eval.Maha[4, 1], bioc215 >= eval.Maha[5, 1]) plot(Maha21k) writeRaster (Maha21k, "Maha_21k.asc", format="ascii") # maha 6k Maha6k <- sum(bioc61 >= eval.Maha[1, 1], bioc62 >= eval.Maha[2, 1], bioc63 >= eval.Maha[3, 1], bioc64 >= eval.Maha[4, 1], bioc65 >= eval.Maha[5, 1]) plot(Maha6k) writeRaster (Maha6k, "Maha_6k.asc", format="ascii") ###------------------------------------------------------------------------------------------------------### # maxent # importar bioc1 <- raster ("CCSM_Maxent_0k_B.balansae1.asc") bioc2 <- raster ("CCSM_Maxent_0k_B.balansae2.asc") bioc3 <- raster ("CCSM_Maxent_0k_B.balansae3.asc") bioc4 <- raster ("CCSM_Maxent_0k_B.balansae4.asc") bioc5 <- raster ("CCSM_Maxent_0k_B.balansae5.asc") bioc211 <- raster ("CCSM_Maxent_21K_B.balansae1.asc") bioc212 <- raster ("CCSM_Maxent_21K_B.balansae2.asc") bioc213 <- raster ("CCSM_Maxent_21K_B.balansae3.asc") bioc214 <- raster ("CCSM_Maxent_21K_B.balansae4.asc") bioc215 <- raster ("CCSM_Maxent_21K_B.balansae5.asc") bioc61 <- raster ("CCSM_Maxent_6K_B.balansae1.asc") bioc62 <- raster ("CCSM_Maxent_6K_B.balansae2.asc") bioc63 <- raster ("CCSM_Maxent_6K_B.balansae3.asc") bioc64 <- raster ("CCSM_Maxent_6K_B.balansae4.asc") bioc65 <- raster ("CCSM_Maxent_6K_B.balansae5.asc") # soma Maxent0k <- sum(bioc1 >= eval.Maxent[1, 1], bioc2 >= eval.Maxent[2, 1], bioc3 >= eval.Maxent[3, 1], bioc4 >= eval.Maxent[4, 1], bioc5 >= eval.Maxent[5, 1]) plot(Maxent0k) writeRaster (Maxent0k, "Maxent_0k.asc", format="ascii") # maxent 21k Maxent21k <- sum(bioc211 >= eval.Maxent[1, 1], bioc212 >= eval.Maxent[2, 1], bioc213 >= eval.Maxent[3, 1], bioc214 >= eval.Maxent[4, 1], bioc215 >= eval.Maxent[5, 1]) plot(Maxent21k) writeRaster (Maxent21k, "Maxent_21k.asc", format="ascii") # maxent 6k Maxent6k <- sum(bioc61 >= eval.Maxent[1, 1], bioc62 >= eval.Maxent[2, 1], bioc63 >= eval.Maxent[3, 1], bioc64 >= eval.Maxent[4, 1], bioc65 >= eval.Maxent[5, 1]) plot(Maxent6k) writeRaster (Maxent6k, "Maxent_6k.asc", format="ascii") ###------------------------------------------------------------------------------------------------------### # svm bioc1 <- raster ("CCSM_SVM_0k_B.balansae1.asc") bioc2 <- raster ("CCSM_SVM_0k_B.balansae2.asc") bioc3 <- raster ("CCSM_SVM_0k_B.balansae3.asc") bioc4 <- raster ("CCSM_SVM_0k_B.balansae4.asc") bioc5 <- raster ("CCSM_SVM_0k_B.balansae5.asc") bioc211 <- raster ("CCSM_SVM_21K_B.balansae1.asc") bioc212 <- raster ("CCSM_SVM_21K_B.balansae2.asc") bioc213 <- raster ("CCSM_SVM_21K_B.balansae3.asc") bioc214 <- raster ("CCSM_SVM_21K_B.balansae4.asc") bioc215 <- raster ("CCSM_SVM_21K_B.balansae5.asc") bioc61 <- raster ("CCSM_SVM_6K_B.balansae1.asc") bioc62 <- raster ("CCSM_SVM_6K_B.balansae2.asc") bioc63 <- raster ("CCSM_SVM_6K_B.balansae3.asc") bioc64 <- raster ("CCSM_SVM_6K_B.balansae4.asc") bioc65 <- raster ("CCSM_SVM_6K_B.balansae5.asc") SVM0k <- sum(bioc1 >= eval.SVM[1, 1], bioc2 >= eval.SVM[2, 1], bioc3 >= eval.SVM[3, 1], bioc4 >= eval.SVM[4, 1], bioc5 >= eval.SVM[5, 1]) plot(SVM0k) writeRaster (SVM0k, "SVM_0k.asc", format="ascii") # svm 21k SVM21k <- sum(bioc211 >= eval.SVM[1, 1], bioc212 >= eval.SVM[2, 1], bioc213 >= eval.SVM[3, 1], bioc214 >= eval.SVM[4, 1], bioc215 >= eval.SVM[5, 1]) plot(SVM21k) writeRaster (SVM21k, "SVM_21k.asc", format="ascii") # svm 6k SVM6k <- sum(bioc61 >= eval.SVM[1, 1], bioc62 >= eval.SVM[2, 1], bioc63 >= eval.SVM[3, 1], bioc64 >= eval.SVM[4, 1], bioc65 >= eval.SVM[5, 1]) plot(SVM6k) writeRaster (SVM6k, "SVM_6k.asc", format="ascii") ###------------------------------------------------------------------------------------------------------### ### ensamble por frequencia - parte 2 ### # somando todos os algoritmos # 0k m1 <- raster ("Bioclim_0k.asc") m2 <- raster ("Gower_0k.asc") m3 <- raster ("Maha_0k.asc") m4 <- raster ("Maxent_0k.asc") m5 <- raster ("SVM_0k.asc") ensemble <- m1 + m2 + m3 + m4 + m5 plot(ensemble) writeRaster (ensemble, "ensemble_0k.asc", format="ascii") # 21k m1 <- raster ("Bioclim_21k.asc") m2 <- raster ("Gower_21k.asc") m3 <- raster ("Maha_21k.asc") m4 <- raster ("Maxent_21k.asc") m5 <- raster ("SVM_21k.asc") ensemble21k <- m1 + m2 + m3 + m4 + m5 plot(ensemble21k) writeRaster (ensemble21k, "ensemble_21k.asc", format="ascii") # 6k m1 <- raster ("Bioclim_6k.asc") m2 <- raster ("Gower_6k.asc") m3 <- raster ("Maha_6k.asc") m4 <- raster ("Maxent_6k.asc") m5 <- raster ("SVM_6k.asc") ensemble6k <- m1 + m2 + m3 + m4 + m5 plot(ensemble6k) writeRaster (ensemble6k, "ensemble_6k.asc", format="ascii") # dividindo por 25 para ter um mapa de 0 a 1 ensemble0kcut <- ensemble/25 writeRaster (ensemble0kcut, "ensemble0k_0_1.asc", format="ascii") ensemble6kcut <-ensemble6k/25 writeRaster (ensemble6kcut, "ensemble6k_0_1.asc", format="ascii") ensemble21kcut <- ensemble21k/25 writeRaster (ensemble21kcut, "ensemble21k_0_1.asc", format="ascii") par(mfrow = c(1, 3)) plot(ensemble0kcut, main = "Atual") plot(ensemble6kcut, main = "Holoceno") plot(ensemble21kcut, main = "LGM") ###------------------------------------------------------------------------------------------------------### ### ensemble por media - parte 1 ### # colocando todos os loops em um arquivo único # bioclim Bioclim0k1 <- raster("CCSM_Bioclim_0k_B.balansae1.asc") Bioclim0k2 <- raster ("CCSM_Bioclim_0k_B.balansae2.asc") Bioclim0k3 <- raster ("CCSM_Bioclim_0k_B.balansae3.asc") Bioclim0k4 <- raster("CCSM_Bioclim_0k_B.balansae4.asc") Bioclim0k5 <- raster ("CCSM_Bioclim_0k_B.balansae5.asc") Bioclim6k1 <- raster("CCSM_Bioclim_6k_B.balansae1.asc") Bioclim6k2 <- raster ("CCSM_Bioclim_6k_B.balansae2.asc") Bioclim6k3 <- raster ("CCSM_Bioclim_6k_B.balansae3.asc") Bioclim6k4 <- raster("CCSM_Bioclim_6k_B.balansae4.asc") Bioclim6k5 <- raster ("CCSM_Bioclim_6k_B.balansae5.asc") Bioclim21k1 <- raster("CCSM_Bioclim_21k_B.balansae1.asc") Bioclim21k2 <- raster ("CCSM_Bioclim_21k_B.balansae2.asc") Bioclim21k3 <- raster ("CCSM_Bioclim_21k_B.balansae3.asc") Bioclim21k4 <- raster("CCSM_Bioclim_21k_B.balansae4.asc") Bioclim21k5 <- raster ("CCSM_Bioclim_21k_B.balansae5.asc") Bioclim0k1 <- values (Bioclim0k1) Bioclim0k2 <- values (Bioclim0k2) Bioclim0k3 <- values (Bioclim0k3) Bioclim0k4 <- values (Bioclim0k4) Bioclim0k5 <- values (Bioclim0k5) Bioclim6k1 <- values (Bioclim6k1) Bioclim6k2 <- values (Bioclim6k2) Bioclim6k3 <- values (Bioclim6k3) Bioclim6k4 <- values (Bioclim6k4) Bioclim6k5 <- values (Bioclim6k5) Bioclim21k1 <- values (Bioclim21k1) Bioclim21k2 <- values (Bioclim21k2) Bioclim21k3 <- values (Bioclim21k3) Bioclim21k4 <- values (Bioclim21k4) Bioclim21k5 <- values (Bioclim21k5) Bioclim0k <-cbind (Bioclim0k1, Bioclim0k2, Bioclim0k3, Bioclim0k4, Bioclim0k5) dim(Bioclim0k) Bioclim6k <-cbind (Bioclim6k1, Bioclim6k2, Bioclim6k3, Bioclim6k4, Bioclim6k5) dim(Bioclim6k) Bioclim21k <-cbind (Bioclim21k1, Bioclim21k2, Bioclim21k3, Bioclim21k4, Bioclim21k5) dim(Bioclim21k) # gower Gower0k1 <- raster ("CCSM_Gower_0k_B.balansae1.asc") Gower0k2 <- raster ("CCSM_Gower_0k_B.balansae2.asc") Gower0k3 <- raster ("CCSM_Gower_0k_B.balansae3.asc") Gower0k4 <- raster ("CCSM_Gower_0k_B.balansae4.asc") Gower0k5 <- raster ("CCSM_Gower_0k_B.balansae5.asc") Gower6k1 <- raster ("CCSM_Gower_6k_B.balansae1.asc") Gower6k2 <- raster ("CCSM_Gower_6k_B.balansae2.asc") Gower6k3 <- raster ("CCSM_Gower_6k_B.balansae3.asc") Gower6k4 <- raster ("CCSM_Gower_6k_B.balansae4.asc") Gower6k5 <- raster ("CCSM_Gower_6k_B.balansae5.asc") Gower21k1 <- raster ("CCSM_Gower_21k_B.balansae1.asc") Gower21k2 <- raster ("CCSM_Gower_21k_B.balansae2.asc") Gower21k3 <- raster ("CCSM_Gower_21k_B.balansae3.asc") Gower21k4 <- raster ("CCSM_Gower_21k_B.balansae4.asc") Gower21k5 <- raster ("CCSM_Gower_21k_B.balansae5.asc") Gower0k1 <- values (Gower0k1) Gower0k2 <- values (Gower0k2) Gower0k3 <- values (Gower0k3) Gower0k4 <- values (Gower0k4) Gower0k5 <- values (Gower0k5) Gower6k1 <- values (Gower6k1) Gower6k2 <- values (Gower6k2) Gower6k3 <- values (Gower6k3) Gower6k4 <- values (Gower6k4) Gower6k5 <- values (Gower6k5) Gower21k1 <- values (Gower21k1) Gower21k2 <- values (Gower21k2) Gower21k3 <- values (Gower21k3) Gower21k4 <- values (Gower21k4) Gower21k5 <- values (Gower21k5) Gower0k <- cbind(Gower0k1, Gower0k2, Gower0k3, Gower0k4, Gower0k5) dim(Gower0k) Gower6k <- cbind(Gower6k1, Gower6k2, Gower6k3, Gower6k4, Gower6k5) dim(Gower6k) Gower21k <- cbind(Gower21k1, Gower21k2, Gower21k3, Gower21k4, Gower21k5) dim(Gower21k) # mahanalobis Maha0k1 <- raster ("CCSM_Maha_0k_B.balansae1.asc") Maha0k2 <- raster ("CCSM_Maha_0k_B.balansae2.asc") Maha0k3 <- raster ("CCSM_Maha_0k_B.balansae3.asc") Maha0k4 <- raster ("CCSM_Maha_0k_B.balansae4.asc") Maha0k5 <- raster ("CCSM_Maha_0k_B.balansae5.asc") Maha6k1 <- raster ("CCSM_Maha_6k_B.balansae1.asc") Maha6k2 <- raster ("CCSM_Maha_6k_B.balansae2.asc") Maha6k3 <- raster ("CCSM_Maha_6k_B.balansae3.asc") Maha6k4 <- raster ("CCSM_Maha_6k_B.balansae4.asc") Maha6k5 <- raster ("CCSM_Maha_6k_B.balansae5.asc") Maha21k1 <- raster ("CCSM_Maha_21k_B.balansae1.asc") Maha21k2 <- raster ("CCSM_Maha_21k_B.balansae2.asc") Maha21k3 <- raster ("CCSM_Maha_21k_B.balansae3.asc") Maha21k4 <- raster ("CCSM_Maha_21k_B.balansae4.asc") Maha21k5 <- raster ("CCSM_Maha_21k_B.balansae5.asc") Maha0k1 <- values (Maha0k1) Maha0k2 <- values (Maha0k2) Maha0k3 <- values (Maha0k3) Maha0k4 <- values (Maha0k4) Maha0k5 <- values (Maha0k5) Maha6k1 <- values (Maha6k1) Maha6k2 <- values (Maha6k2) Maha6k3 <- values (Maha6k3) Maha6k4 <- values (Maha6k4) Maha6k5 <- values (Maha6k5) Maha21k1 <- values (Maha21k1) Maha21k2 <- values (Maha21k2) Maha21k3 <- values (Maha21k3) Maha21k4 <- values (Maha21k4) Maha21k5 <- values (Maha21k5) Maha0k <- cbind(Maha0k1, Maha0k2, Maha0k3, Maha0k4, Maha0k5 ) dim(Maha0k) Maha6k <- cbind(Maha6k1, Maha6k2, Maha6k3, Maha6k4, Maha6k5) dim(Maha6k) Maha21k <- cbind(Maha21k1, Maha21k2, Maha21k3, Maha21k4, Maha21k5) dim(Maha21k) # maxent Maxent0k1 <- raster ("CCSM_Maxent_0k_B.balansae1.asc") Maxent0k2 <- raster ("CCSM_Maxent_0k_B.balansae2.asc") Maxent0k3 <- raster ("CCSM_Maxent_0k_B.balansae3.asc") Maxent0k4 <- raster ("CCSM_Maxent_0k_B.balansae4.asc") Maxent0k5 <- raster ("CCSM_Maxent_0k_B.balansae5.asc") Maxent6k1 <- raster ("CCSM_Maxent_6k_B.balansae1.asc") Maxent6k2 <- raster ("CCSM_Maxent_6k_B.balansae2.asc") Maxent6k3 <- raster ("CCSM_Maxent_6k_B.balansae3.asc") Maxent6k4 <- raster ("CCSM_Maxent_6k_B.balansae4.asc") Maxent6k5 <- raster ("CCSM_Maxent_6k_B.balansae5.asc") Maxent21k1 <- raster ("CCSM_Maxent_21k_B.balansae1.asc") Maxent21k2 <- raster ("CCSM_Maxent_21k_B.balansae2.asc") Maxent21k3 <- raster ("CCSM_Maxent_21k_B.balansae3.asc") Maxent21k4 <- raster ("CCSM_Maxent_21k_B.balansae4.asc") Maxent21k5 <- raster ("CCSM_Maxent_21k_B.balansae5.asc") Maxent0k1 <- values (Maxent0k1) Maxent0k2 <- values (Maxent0k2) Maxent0k3 <- values (Maxent0k3) Maxent0k4 <- values (Maxent0k4) Maxent0k5 <- values (Maxent0k5) Maxent6k1 <- values (Maxent6k1) Maxent6k2 <- values (Maxent6k2) Maxent6k3 <- values (Maxent6k3) Maxent6k4 <- values (Maxent6k4) Maxent6k5 <- values (Maxent6k5) Maxent21k1 <- values (Maxent21k1) Maxent21k2 <- values (Maxent21k2) Maxent21k3 <- values (Maxent21k3) Maxent21k4 <- values (Maxent21k4) Maxent21k5 <- values (Maxent21k5) Maxent0k <- cbind(Maxent0k1, Maxent0k2, Maxent0k3, Maxent0k4, Maxent0k5 ) dim(Maxent0k) Maxent6k <- cbind(Maxent6k1, Maxent6k2, Maxent6k3, Maxent6k4, Maxent6k5 ) dim(Maxent0k) Maxent21k <- cbind(Maxent21k1, Maxent21k2, Maxent21k3, Maxent21k4, Maxent21k5 ) dim(Maxent0k) # svm SVM0k1 <- raster ("CCSM_SVM_0k_B.balansae1.asc") SVM0k2 <- raster ("CCSM_SVM_0k_B.balansae2.asc") SVM0k3 <- raster ("CCSM_SVM_0k_B.balansae3.asc") SVM0k4 <- raster ("CCSM_SVM_0k_B.balansae4.asc") SVM0k5 <- raster ("CCSM_SVM_0k_B.balansae5.asc") SVM6k1 <- raster ("CCSM_SVM_6k_B.balansae1.asc") SVM6k2 <- raster ("CCSM_SVM_6k_B.balansae2.asc") SVM6k3 <- raster ("CCSM_SVM_6k_B.balansae3.asc") SVM6k4 <- raster ("CCSM_SVM_6k_B.balansae4.asc") SVM6k5 <- raster ("CCSM_SVM_6k_B.balansae5.asc") SVM21k1 <- raster ("CCSM_SVM_21k_B.balansae1.asc") SVM21k2 <- raster ("CCSM_SVM_21k_B.balansae2.asc") SVM21k3 <- raster ("CCSM_SVM_21k_B.balansae3.asc") SVM21k4 <- raster ("CCSM_SVM_21k_B.balansae4.asc") SVM21k5 <- raster ("CCSM_SVM_21k_B.balansae5.asc") SVM0k1 <- values (SVM0k1) SVM0k2 <- values (SVM0k2) SVM0k3 <- values (SVM0k3) SVM0k4 <- values (SVM0k4) SVM0k5 <- values (SVM0k5) SVM6k1 <- values (SVM6k1) SVM6k2 <- values (SVM6k2) SVM6k3 <- values (SVM6k3) SVM6k4 <- values (SVM6k4) SVM6k5 <- values (SVM6k5) SVM21k1 <- values (SVM21k1) SVM21k2 <- values (SVM21k2) SVM21k3 <- values (SVM21k3) SVM21k4 <- values (SVM21k4) SVM21k5 <- values (SVM21k5) SVM0k <- cbind(SVM0k1, SVM0k2, SVM0k3, SVM0k4, SVM0k5 ) dim(SVM0k) SVM6k <- cbind(SVM6k1, SVM6k2, SVM6k3, SVM6k4, SVM6k5 ) dim(SVM6k) SVM21k <- cbind(SVM21k1, SVM21k2, SVM21k3, SVM21k4, SVM21k5 ) dim(SVM21k) ###------------------------------------------------------------------------------------------------------### ### ensemble por media - parte 2 ### # media head(Bioclim0k) Bioclim.0k.mean <- apply(Bioclim0k, 1, mean) Gower.0k.mean <- apply(Gower0k, 1, mean) Maha.0k.mean <- apply(Maha0k, 1, mean) Maxent.0k.mean <- apply(Maxent0k, 1, mean) SVM.0k.mean <- apply(SVM0k, 1, mean) Bioclim.6k.mean <- apply(Bioclim6k, 1, mean) Gower.6k.mean <- apply(Gower6k, 1, mean) Maha.6k.mean <- apply(Maha6k, 1, mean) Maxent.6k.mean <- apply(Maxent6k, 1, mean) SVM.6k.mean <- apply(SVM6k, 1, mean) Bioclim.21k.mean <- apply(Bioclim21k, 1, mean) Gower.21k.mean <- apply(Gower21k, 1, mean) Maha.21k.mean <- apply(Maha21k, 1, mean) Maxent.21k.mean <- apply(Maxent21k, 1, mean) SVM.21k.mean <- apply(SVM21k, 1, mean) # organizar os valores das medias dos algoritmos em um arquivo Output0k <- cbind(Bioclim = Bioclim.0k.mean, Gower = Gower.0k.mean, Maha = Maha.0k.mean, SVM= SVM.0k.mean, Maxent = Maxent.0k.mean) head(Output0k) Output6k <- cbind(Bioclim = Bioclim.6k.mean, Gower = Gower.6k.mean, Maha = Maha.6k.mean, SVM = SVM.6k.mean, Maxent = Maxent.6k.mean) head(Output6k) Output21k <- cbind(Bioclim = Bioclim.21k.mean, Gower = Gower.21k.mean, Maha = Maha.21k.mean, SVM = SVM.21k.mean, Maxent = Maxent.21k.mean) head(Output21k) # conferindo a dimensao dim(Output0k) dim(Output6k) dim(Output21k) # inserindo coordenadas geograficas aos outputs setwd("D:/90_aulas_montadas/_disciplina_enm_R_unesp_2017/scripts_r/01_dados") env <- raster ("CCSM_0k_am_bio02.asc") coords <- xyFromCell(env, 1:ncell(env)) Output0k <- cbind(coords, Output0k) Output6k <- cbind(coords, Output6k) Output21k <- cbind(coords, Output21k) head(Output0k) # excluindo NAs Output0k <- na.omit(Output0k) Output21k <- na.omit(Output21k) Output6k <- na.omit(Output6k) nrow(Output0k) nrow(Output6k) nrow(Output21k) # coercao em dataframe Output0k <- as.data.frame(Output0k) head(Output0k) Output6k <- as.data.frame(Output6k) Output21k <- as.data.frame(Output21k) # transformando em arquivos espacializados - grid gridded(Output0k) <- ~x + y gridded(Output6k) <- ~x + y gridded(Output21k) <- ~x + y # transformando em arquivos stack raster output.0k <- stack(Output0k) output.6k <- stack(Output6k) output.21k <- stack(Output21k) # plot par(mfrow = c(2, 3)) plot(output.0k) plot(output.6k) plot(output.21k) # salvando os arquivos setwd("C:/Users/leec/Dropbox/disciplina_enm_R_unicamp_2016/scripts_r/saidas_enm") write.table (Output0k, "Output0k.txt") write.table (Output6k, "Output6k.txt") write.table (Output21k, "Output21k.txt") ###------------------------------------------------------------------------------------------------------###

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/implementations/set_ops.R

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abhi8893/Intensive-R

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refs/heads/master

2020-09-22T00:57:36.118504

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set_ops.R

# Implement set operations find.unique <- function(v){ elems <- c() for (e in v){ if (! e %in% elems){ elems <- c(elems, e) } } return(elems) } has.elem <- function(elem, v){ status <- F for (e in v){ if (e == elem){ status <- T break } } return(status) } set.union <- function(A, B){ A.uniq <- unique(A) B.uniq <- unique(B) return(unique(c(A.uniq, B.uniq))) } set.intersect <- function(A, B){ int.set <- rep(0, length(A)) i <- 0 for (a in A){ for (b in B){ if ((a == b) & (!a %in% int.set)){ i <- i + 1 int.set[i] <- b } } } int.set <- int.set[1:i] return(int.set) } set.diff <- function(A, B){ diff.set <- rep(0, length(A)) n.B <- length(B) i <- 0 for (a in A){ i.B <- 1 for (b in B){ if (a == b){ break } else if(i.B == n.B){ i <- i + 1 diff.set[i] <- a } i.B <- i.B + 1 } } diff.set <- diff.set[1:i] return(diff.set) } set.diff(c(1, 2, 3, 4, 99), c(5, 3, 2, 1, 12, 1, 87)) set.equal <- function(A, B){ n.A <- length(A) n.B <- length(B) if (n.A >= n.B){ longer <- A shorter <- B } else { longer <- B shorter <- A } n.s <- length(shorter) for (l in longer){ i.s <- 1 for (s in shorter){ if (l == s){ break }else if (i.s == n.s){ return(FALSE) } i.s <- i.s + 1 } } return(TRUE) } set.equal(c(1, 2), c(1, 2, 1, 1))

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plot_signal <- function(signal_map) { ch_1 <- signal_map$nucl ch_2 <- signal_map$cyto ch_ori <- signal_map$input my_x <- ncol(ch_1) my_y <- nrow(ch_1) ch_0 <- matrix(0, ncol = my_x, nrow = my_y) ch_1[ch_1 == 0] <- NA ch_2[ch_2 == 0] <- NA # #faux palettes greys <- colorpanel(n = 15, low = "black", high = "grey99") cyans <- colorpanel(n = 15, low = "black", high = "cyan") greens <- colorpanel(n = 15, low = "black", high = "limegreen") # curr_par <- par(no.readonly = T) par(mfrow = c(2,2)) image(ch_ori, col = greys, add = F, axes = F, main = "input image", useRaster = T) # image(ch_0, col = "black", add = F, axes = F, main = "faux colors", useRaster = T) image(ch_1, col = greens, add = T, axes = F, useRaster = T) image(ch_2, col = cyans, add = T, axes = F, useRaster = T) # image(ch_0, col = "black", add = F, axes = F, main = "nucl. signal", useRaster = T) image(ch_1, col = greys, add = T, axes = F, useRaster = T) # image(ch_0, col = "black", add = F, axes = F, main = "cyto. signal", useRaster = T) image(ch_2, col = greys, add = T, axes = F, useRaster = T) # par(curr_par) }

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library(shiny.semantic) library(shiny) library(dplyr) library(ggplot2)

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot_methods.R \name{plot_temporal_network} \alias{plot_temporal_network} \title{Temporal graph for a station} \usage{ plot_temporal_network(net, meta, dead, ...) } \arguments{ \item{net}{data.frame contaning year variable and network list} \item{meta}{an object created by build_metaweb} \item{dead}{character vector indicating which node are basal} \item{...}{Other arguments to pass to set_layout_graph} } \value{ a tibble containing a list of graph in a net_graph column } \description{ Temporal graph for a station } \seealso{ set_layout_graph }

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IRR.R

IRR <- function (cf) { uniroot(netPresentValue, cashFlows = cf, lower = 1e-04, upper = 100)$root }

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library(fields) ## set parameters art <- "NMF" #"AMr","AMp","OLr","OLp", "PCA" q <- 100 #300, 600 it <- 50 # 150 ## load dictionary load(file=paste("dict_",art,q,"_it",it,".rda",sep = "")) # load(file=paste("dict_",art,q,".rda",sep = "")) # for art="PCA" dict <- spdict$H Z <- t(dict) score <- spdict$W ## Look at range of values in H and A range(dict) range(score) ########################################### ## Print first 20 atoms of dictionary ############### ## Prepare color palette for blue(neg)-white(0)-red(pos) depiction of dictonary elements ## (adjusted code from 'https://stackoverflow.com/questions/29262824/r-center-color-palette-on-0' for our purpose) nHalf <- round(nrow(dict)/2) Min <- -0.01 #!# choose according to dictionary range Max <- 0.5 #!#choose according to dictionary range Thresh <-0 ## Make vector of colors for values below threshold rc1 <- colorRampPalette(colors = c("blue", "white"), space="Lab")(nHalf) ## Make vector of colors for values above threshold rc2 <- colorRampPalette(colors = c("white", "red"), space="Lab")(nHalf) rampcols <- c(rc1, rc2) rb1 <- seq(Min, Thresh, length.out=nHalf+1) rb2 <- seq(Thresh, Max, length.out=nHalf+1)[-1] rampbreaks <- c(rb1, rb2) ## dictionary elements (red and blue) (print first 20 atoms) png(filename = paste("Dictionary_elements_",art,q,"it",it,".png",sep = ""), width = 770, height = 530) par(mfrow=c(4,5),mar=rep(0,4),oma=c(0.5,rep(0,2),4.7)) for (qc in 1:20){ image(matrix(dict[qc,], nrow=92),col = rampcols, breaks = rampbreaks, axes=FALSE) } ## add color scale image.plot(matrix(dict[20,], nrow=92),col = rampcols, breaks = rampbreaks, axes=FALSE, axis.args = list(cex.axis = 2), legend.width = 3.5, legend.only = TRUE, smallplot= c(0.95,1,0.04,1)) dev.off() ## some info about dictionaries spH1 <- spH2 <- spH3 <- numeric(nrow(score)) for(i in 1:nrow(dict)){ spH1[i] <- sum(dict[i,]==0) spH2[i] <- sum(abs(dict[i,])<=0.005) spH3[i] <- sum(abs(dict[i,]) <=1) } sum(spH1);sum(spH1)/length(dict);mean(spH1);range(spH1) sum(spH2);sum(spH2)/length(dict);mean(spH2);range(spH2) sum(spH3);sum(spH3)/length(dict);mean(spH3);range(spH3) ########################################### ## Print image of score matrix ############### png(filename = paste("Scores",art,q,"it",it,".png",sep = ""), width = 520, height = 310) par(mfrow=c(1,1),mar=c(2,2,1,0),oma=c(0,0,0,1.5)) image.plot(t(score)[,nrow(score):1],axis.args = list(cex.axis=1.5)) dev.off() ## some info about dictionaries spA1 <- spA2 <- spA3 <- numeric(nrow(score)) for(i in 1:nrow(score)){ spA1[i] <- sum(score[i,]==0) spA2[i] <- sum(abs(score[i,])<=0.005) spA3[i] <- sum(abs(score[i,]) <=1) } sum(spA1);sum(spA1)/length(score);mean(spA1);range(spA1) sum(spA2);sum(spA2)/length(score);mean(spA2);range(spA2) sum(spA3);sum(spA3)/length(score);mean(spA3);range(spA3)

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# generic functions if (!isGeneric("unbiased_estimator")) { setGeneric("unbiased_estimator", function(object) standardGeneric("unbiased_estimator")) } if (!isGeneric("expected_value")) { setGeneric("expected_value", function(object) standardGeneric("expected_value")) } if (!isGeneric("variance")) { setGeneric("variance", function(object) standardGeneric("variance")) } if (!isGeneric("psi_lowerbound")) { setGeneric("psi_lowerbound", function(object, obs) standardGeneric("psi_lowerbound")) } if (!isGeneric("plot_psi")) { setGeneric("plot_psi", function(object, ...) standardGeneric("plot_psi")) } if (!isGeneric("unbiased")) { setGeneric("unbiased", function(object, obs) standardGeneric("unbiased")) }

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iotdataplane_operations.R

# This file is generated by make.paws. Please do not edit here. #' @importFrom paws.common get_config new_operation new_request send_request #' @include iotdataplane_service.R NULL #' Deletes the shadow for the specified thing #' #' @description #' Deletes the shadow for the specified thing. #' #' For more information, see #' [`delete_thing_shadow`][iotdataplane_delete_thing_shadow] in the AWS IoT #' Developer Guide. #' #' @usage #' iotdataplane_delete_thing_shadow(thingName, shadowName) #' #' @param thingName [required] The name of the thing. #' @param shadowName The name of the shadow. #' #' @return #' A list with the following syntax: #' ``` #' list( #' payload = raw #' ) #' ``` #' #' @section Request syntax: #' ``` #' svc$delete_thing_shadow( #' thingName = "string", #' shadowName = "string" #' ) #' ``` #' #' @keywords internal #' #' @rdname iotdataplane_delete_thing_shadow iotdataplane_delete_thing_shadow <- function(thingName, shadowName = NULL) { op <- new_operation( name = "DeleteThingShadow", http_method = "DELETE", http_path = "/things/{thingName}/shadow", paginator = list() ) input <- .iotdataplane$delete_thing_shadow_input(thingName = thingName, shadowName = shadowName) output <- .iotdataplane$delete_thing_shadow_output() config <- get_config() svc <- .iotdataplane$service(config) request <- new_request(svc, op, input, output) response <- send_request(request) return(response) } .iotdataplane$operations$delete_thing_shadow <- iotdataplane_delete_thing_shadow #' Gets the shadow for the specified thing #' #' @description #' Gets the shadow for the specified thing. #' #' For more information, see #' [`get_thing_shadow`][iotdataplane_get_thing_shadow] in the AWS IoT #' Developer Guide. #' #' @usage #' iotdataplane_get_thing_shadow(thingName, shadowName) #' #' @param thingName [required] The name of the thing. #' @param shadowName The name of the shadow. #' #' @return #' A list with the following syntax: #' ``` #' list( #' payload = raw #' ) #' ``` #' #' @section Request syntax: #' ``` #' svc$get_thing_shadow( #' thingName = "string", #' shadowName = "string" #' ) #' ``` #' #' @keywords internal #' #' @rdname iotdataplane_get_thing_shadow iotdataplane_get_thing_shadow <- function(thingName, shadowName = NULL) { op <- new_operation( name = "GetThingShadow", http_method = "GET", http_path = "/things/{thingName}/shadow", paginator = list() ) input <- .iotdataplane$get_thing_shadow_input(thingName = thingName, shadowName = shadowName) output <- .iotdataplane$get_thing_shadow_output() config <- get_config() svc <- .iotdataplane$service(config) request <- new_request(svc, op, input, output) response <- send_request(request) return(response) } .iotdataplane$operations$get_thing_shadow <- iotdataplane_get_thing_shadow #' Lists the shadows for the specified thing #' #' @description #' Lists the shadows for the specified thing. #' #' @usage #' iotdataplane_list_named_shadows_for_thing(thingName, nextToken, #' pageSize) #' #' @param thingName [required] The name of the thing. #' @param nextToken The token to retrieve the next set of results. #' @param pageSize The result page size. #' #' @return #' A list with the following syntax: #' ``` #' list( #' results = list( #' "string" #' ), #' nextToken = "string", #' timestamp = 123 #' ) #' ``` #' #' @section Request syntax: #' ``` #' svc$list_named_shadows_for_thing( #' thingName = "string", #' nextToken = "string", #' pageSize = 123 #' ) #' ``` #' #' @keywords internal #' #' @rdname iotdataplane_list_named_shadows_for_thing iotdataplane_list_named_shadows_for_thing <- function(thingName, nextToken = NULL, pageSize = NULL) { op <- new_operation( name = "ListNamedShadowsForThing", http_method = "GET", http_path = "/api/things/shadow/ListNamedShadowsForThing/{thingName}", paginator = list() ) input <- .iotdataplane$list_named_shadows_for_thing_input(thingName = thingName, nextToken = nextToken, pageSize = pageSize) output <- .iotdataplane$list_named_shadows_for_thing_output() config <- get_config() svc <- .iotdataplane$service(config) request <- new_request(svc, op, input, output) response <- send_request(request) return(response) } .iotdataplane$operations$list_named_shadows_for_thing <- iotdataplane_list_named_shadows_for_thing #' Publishes state information #' #' @description #' Publishes state information. #' #' For more information, see [HTTP #' Protocol](https://docs.aws.amazon.com/iot/latest/developerguide/protocols.html#http) #' in the AWS IoT Developer Guide. #' #' @usage #' iotdataplane_publish(topic, qos, payload) #' #' @param topic [required] The name of the MQTT topic. #' @param qos The Quality of Service (QoS) level. #' @param payload The state information, in JSON format. #' #' @return #' An empty list. #' #' @section Request syntax: #' ``` #' svc$publish( #' topic = "string", #' qos = 123, #' payload = raw #' ) #' ``` #' #' @keywords internal #' #' @rdname iotdataplane_publish iotdataplane_publish <- function(topic, qos = NULL, payload = NULL) { op <- new_operation( name = "Publish", http_method = "POST", http_path = "/topics/{topic}", paginator = list() ) input <- .iotdataplane$publish_input(topic = topic, qos = qos, payload = payload) output <- .iotdataplane$publish_output() config <- get_config() svc <- .iotdataplane$service(config) request <- new_request(svc, op, input, output) response <- send_request(request) return(response) } .iotdataplane$operations$publish <- iotdataplane_publish #' Updates the shadow for the specified thing #' #' @description #' Updates the shadow for the specified thing. #' #' For more information, see #' [`update_thing_shadow`][iotdataplane_update_thing_shadow] in the AWS IoT #' Developer Guide. #' #' @usage #' iotdataplane_update_thing_shadow(thingName, shadowName, payload) #' #' @param thingName [required] The name of the thing. #' @param shadowName The name of the shadow. #' @param payload [required] The state information, in JSON format. #' #' @return #' A list with the following syntax: #' ``` #' list( #' payload = raw #' ) #' ``` #' #' @section Request syntax: #' ``` #' svc$update_thing_shadow( #' thingName = "string", #' shadowName = "string", #' payload = raw #' ) #' ``` #' #' @keywords internal #' #' @rdname iotdataplane_update_thing_shadow iotdataplane_update_thing_shadow <- function(thingName, shadowName = NULL, payload) { op <- new_operation( name = "UpdateThingShadow", http_method = "POST", http_path = "/things/{thingName}/shadow", paginator = list() ) input <- .iotdataplane$update_thing_shadow_input(thingName = thingName, shadowName = shadowName, payload = payload) output <- .iotdataplane$update_thing_shadow_output() config <- get_config() svc <- .iotdataplane$service(config) request <- new_request(svc, op, input, output) response <- send_request(request) return(response) } .iotdataplane$operations$update_thing_shadow <- iotdataplane_update_thing_shadow

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library(R.matlab) library(ggplot2) setwd('/media/marcelo/OS/Cps/') df_average <- read.csv('df_cp_average.csv') tpu_data <- dir(pattern = '.mat') first = TRUE for (file in tpu_data){ print(file) opened_file = readMat(file) df_location = opened_file$Location.of.measured.points df_cp = opened_file$Wind.pressure.coefficients mean_cp = apply(df_cp, 2, mean) bldg_type = ifelse(substr(file,1,2)=="Cp","lowrise","highrise") height = opened_file$Building.height if(first){ df = data.frame( "file"=rep(file,length(mean_cp)), "height"=rep(height,length(mean_cp)), "bldg_type"=rep(bldg_type,length(mean_cp)), "ratio"=rep(opened_file$Building.breadth/opened_file$Building.depth,length(mean_cp)), "Point"=seq(length(mean_cp)), "facade"=df_location[4,], "angle"=opened_file$Wind.direction.angle, "Cp"=mean_cp) first = FALSE }else{ df_i = data.frame( "file"=rep(file,length(mean_cp)), "height"=rep(height,length(mean_cp)), "bldg_type"=rep(bldg_type,length(mean_cp)), "ratio"=rep(opened_file$Building.breadth/opened_file$Building.depth,length(mean_cp)), "Point"=seq(length(mean_cp)), "facade"=df_location[4,], "angle"=opened_file$Wind.direction.angle, "Cp"=mean_cp) df = rbind(df,df_i) } } df = subset(df, df$facade != 5 & (df$angle == 0 | df$angle == 30 | df$angle == 60 | df$angle == 90 | df$angle == 120 | df$angle == 150 | df$angle == 180 | df$angle == 210 | df$angle == 240 | df$angle == 270 | df$angle == 300 | df$angle == 330)) df$ratio = as.numeric(as.character(df$ratio)) df_average$ratio = as.numeric(as.character(df_average$ratio)) df$cp_average = NA df$bldg_type = as.character(df$bldg_type) for(row in 1:nrow(df)){ average_cp = subset( df_average, df_average$bldg_type == df$bldg_type[row] & df_average$ratio == df$ratio[row] & df_average$facade == df$facade[row] & df_average$angle == df$angle[row] ) df$cp_average[row] = average_cp$Cp } df$cp_diff = df$Cp - df$cp_average hist(df$Cp) hist(df$cp_average) hist(df$cp_diff) ratio = unique(df$ratio) facade = unique(df$facade) files = unique(df$file) bldg_type = unique(df$bldg_type) maxdiff = 0 ratio_worst = '' bldg_worst = '' height_worst = '' for(bldg in bldg_type){ ratio = unique(df$ratio[df$bldg_type == bldg]) for(r in ratio){ height = unique(df$height[df$bldg_type == bldg & df$ratio == r]) for(h in height){ dfplot = subset(df, df$bldg_type == bldg & df$ratio == r & df$height == h) diferenca = mean(dfplot$cp_diff**2)**(1/2) # print(diferenca) if(diferenca > maxdiff){ ratio_worst = r bldg_worst = bldg height_worst = h maxdiff = diferenca print(maxdiff) } } } } dfplot = subset(df, df$height == .2 & df$ratio == 2) print(ggplot(dfplot,aes(dfplot$cp_diff)) + geom_histogram(binwidth = .01))

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test_assortativity.R

context("assortativity") test_that("assortativity works", { library(igraph) g <- read_graph(f <- gzfile("celegansneural.gml.gz"), format="gml") assR <- function(graph) { indeg <- degree(graph, mode="in") outdeg <- degree(graph, mode="out") el <- as_edgelist(graph, names=FALSE) J <- outdeg[el[,1]]-1 K <- indeg[el[,2]]-1 num <- sum(J*K) - sum(J)*sum(K)/ecount(graph) den1 <- sum(J*J) - sum(J)^2/ecount(graph) den2 <- sum(K*K) - sum(K)^2/ecount(graph) num / sqrt(den1) / sqrt(den2) } asd <- assortativity_degree(g) as <- assortativity(g, degree(g, mode="out"), degree(g, mode="in")) as2 <- assR(g) expect_that(asd, equals(as)) expect_that(asd, equals(as2)) asu <- assortativity_degree(simplify(as.undirected(g, mode="collapse"))) expect_that(asu, equals(-0.16319921031570466807)) p <- read_graph(f <- gzfile("power.gml.gz"), format="gml") p.asd <- assortativity_degree(p) p.as <- assortativity(p, degree(p)) p.as2 <- assR(as.directed(p, mode="mutual")) expect_that(p.asd, equals(p.as)) expect_that(p.asd, equals(p.as2)) }) test_that("nominal assortativity works", { library(igraph) o <- read_graph(f <- gzfile("football.gml.gz"), format="gml") o <- simplify(o) an <- assortativity_nominal(o, V(o)$value+1) el <- as_edgelist(o, names=FALSE) etm <- matrix(0, nr=max(V(o)$value)+1, nc=max(V(o)$value)+1) for (e in 1:nrow(el)) { t1 <- V(o)$value[ el[e,1] ]+1 t2 <- V(o)$value[ el[e,2] ]+1 etm[t1, t2] <- etm[t1, t2] + 1 etm[t2, t1] <- etm[t2, t1] + 1 } etm <- etm/sum(etm) an2 <- ( sum(diag(etm))-sum(etm %*% etm) ) / ( 1-sum(etm %*% etm) ) expect_that(an, equals(an2)) })

0e8e6d2bfd4169a5d46860959e8cac584d4664ab

91945b9420a2447e53265865b7f96b87f2a9c515

/server.R

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[]

no_license

ajaypillarisetti/squid

d8de50e6164f578004e438169a4fdbc0c497faad

dd1bbe55d670f895636df45e43ead459667e2180

refs/heads/master

2021-05-04T10:50:13.393879

2017-02-28T01:31:13

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server.R

### Ajay Pillarisetti, University of California, Berkeley, 2015 ### V1.0N shinyServer(function(input, output) { Sys.setenv(TZ="Asia/Kathmandu") # read in data - for uploads datasetInput <- reactive({ inFile <- input$files if (is.null(inFile)){return(NULL)} dta<- read.squid(inFile$datapath[1]) }) datasetName <- reactive({ inFile <- input$files inFile$name }) data_cleaned <- reactive({ if (is.null(datasetInput())) return(NULL) data_d <- datasetInput()[,with=F] }) #################### ##### datasets ##### #################### dataXTS.plainplot <- reactive({ dta<-data_cleaned() }) #################### ##### dygraphs ##### interactivity - to subset data to user-selected range #################### #timezone support is broken for these selections, so manually input the offset. from_z1 <- reactive({ if (!is.null(input$zeroPlot1_date_window)) ymd_hms(strftime(gsub(".000Z","",gsub("T"," ",input$zeroPlot1_date_window[[1]])), "%Y-%m-%d %H:%M:%S"), tz='Asia/Kathmandu')+(5.75*60*60) }) to_z1 <- reactive({ if (!is.null(input$zeroPlot1_date_window)) ymd_hms(strftime(gsub(".000Z","",gsub("T"," ",input$zeroPlot1_date_window[[2]])), "%Y-%m-%d %H:%M:%S"), tz='Asia/Kathmandu')+(5.75*60*60) }) output$zero1Title <- renderText({paste("Zero 1:", paste(from_z1(), to_z1(), sep=" to ")," ")}) from_z2 <- reactive({ if (!is.null(input$zeroPlot2_date_window)) ymd_hms(strftime(gsub(".000Z","",gsub("T"," ",input$zeroPlot2_date_window[[1]])), "%Y-%m-%d %H:%M:%S"), tz='Asia/Kathmandu')+(5.75*60*60) }) to_z2 <- reactive({ if (!is.null(input$zeroPlot2_date_window)) ymd_hms(strftime(gsub(".000Z","",gsub("T"," ",input$zeroPlot2_date_window[[2]])), "%Y-%m-%d %H:%M:%S"), tz='Asia/Kathmandu')+(5.75*60*60) }) output$zero2Title <- renderText({paste("Zero 2:", paste(from_z2(), to_z2(), sep=" to ")," ")}) from_sample <- reactive({ if (!is.null(input$samplePlot_date_window)) ymd_hms(strftime(gsub(".000Z","",gsub("T"," ",input$samplePlot_date_window[[1]])), "%Y-%m-%d %H:%M:%S"), tz='Asia/Kathmandu')+(5.75*60*60) }) to_sample <- reactive({ if (!is.null(input$samplePlot_date_window)) ymd_hms(strftime(gsub(".000Z","",gsub("T"," ",input$samplePlot_date_window[[2]])), "%Y-%m-%d %H:%M:%S"), tz='Asia/Kathmandu')+(5.75*60*60) }) output$sampleTitle <- renderText({paste("Tracer Decay:", paste(from_sample(), to_sample(), sep=" to ")," ")}) #################### ####### Boxes ###### #################### output$background1 <- renderValueBox({ datum <- as.data.table(data_cleaned()) datum[,index:=ymd_hms(index,tz='Asia/Kathmandu')] mean_ppm1 <- round(datum[index>=from_z1() & index<=to_z1(), mean(ppm1, na.rm=T)],1) mean_ppm2 <- round(datum[index>=from_z1() & index<=to_z1(), mean(ppm2, na.rm=T)],1) mean_ppm3 <- round(datum[index>=from_z1() & index<=to_z1(), mean(ppm3, na.rm=T)],1) mean_ppm4 <- round(datum[index>=from_z1() & index<=to_z1(), mean(ppm4, na.rm=T)],1) infoBox( value = paste(mean_ppm1, mean_ppm2, mean_ppm3, mean_ppm4, sep="\n"), title = "Background (Zero 1)", icon = icon("time", lib='glyphicon'), color = "aqua" ) }) bg1 <- reactive({ datum <- as.data.table(data_cleaned()) datum[,index:=ymd_hms(index,tz='Asia/Kathmandu')] data.table( mean_ppm1_1 <- round(datum[index>=from_z1() & index<=to_z1(), mean(ppm1, na.rm=T)],1), mean_ppm2_1 <- round(datum[index>=from_z1() & index<=to_z1(), mean(ppm2, na.rm=T)],1), mean_ppm3_1 <- round(datum[index>=from_z1() & index<=to_z1(), mean(ppm3, na.rm=T)],1), mean_ppm4_1 <- round(datum[index>=from_z1() & index<=to_z1(), mean(ppm4, na.rm=T)],1) ) }) output$background2 <- renderValueBox({ datum <- as.data.table(data_cleaned()) datum[,index:=ymd_hms(index,tz='Asia/Kathmandu')] mean_ppm1 <- round(datum[index>=from_z2() & index<=to_z2(), mean(ppm1, na.rm=T)],1) mean_ppm2 <- round(datum[index>=from_z2() & index<=to_z2(), mean(ppm2, na.rm=T)],1) mean_ppm3 <- round(datum[index>=from_z2() & index<=to_z2(), mean(ppm3, na.rm=T)],1) mean_ppm4 <- round(datum[index>=from_z2() & index<=to_z2(), mean(ppm4, na.rm=T)],1) infoBox( value = paste(mean_ppm1, mean_ppm2, mean_ppm3, mean_ppm4, sep="\n"), title = "Background (Zero 2)", icon = icon("time", lib='glyphicon'), color = "aqua" ) }) bg2 <- reactive({ datum <- as.data.table(data_cleaned()) datum[,index:=ymd_hms(index,tz='Asia/Kathmandu')] data.table( mean_ppm1_2 <- round(datum[index>=from_z2() & index<=to_z2(), mean(ppm1, na.rm=T)],1), mean_ppm2_2 <- round(datum[index>=from_z2() & index<=to_z2(), mean(ppm2, na.rm=T)],1), mean_ppm3_2 <- round(datum[index>=from_z2() & index<=to_z2(), mean(ppm3, na.rm=T)],1), mean_ppm4_2 <- round(datum[index>=from_z2() & index<=to_z2(), mean(ppm4, na.rm=T)],1) ) }) bg_mean <- reactive({ datum <- rbind(bg1(), bg2()) setnames(datum, c("ppm1", 'ppm2', 'ppm3', 'ppm4')) datum <- datum[,list( ppm1=mean(ppm1, na.rm=T), ppm2=mean(ppm2, na.rm=T), ppm3=mean(ppm3, na.rm=T), ppm4=mean(ppm4, na.rm=T) )] }) output$background_mean <- renderValueBox({ infoBox( value = bg_mean(), title = "Background (Mean)", icon = icon("time", lib='glyphicon'), color = "aqua" ) }) #################### ##### Analysis ##### #################### correctedData <- reactive({ datum <- as.data.table(data_cleaned()) datum[,index:=ymd_hms(index,tz='Asia/Kathmandu')] datum[,ppm1:=ppm1-bg_mean()[,ppm1]] datum[,ppm2:=ppm2-bg_mean()[,ppm2]] datum[,ppm3:=ppm3-bg_mean()[,ppm3]] datum[,ppm4:=ppm4-bg_mean()[,ppm4]] as.xts.data.table(datum) }) correctedData_lm <- reactive({ datum <- as.data.table(correctedData()) datum[,index:=ymd_hms(index,tz='Asia/Kathmandu')] constrained <- melt(datum, id.var='index') constrained <- constrained[index>=from_sample() & index<=to_sample()] constrained[,timeproxy:=1:length(value),by='variable'] fits <- lmList(log(value) ~ timeproxy | variable, data=as.data.frame(constrained)) ach <- as.data.table(t(abs(coef(fits)[2]*3600))) ach <- ach[,`:=`( hhid=strsplit(datasetName(), "-")[[1]][1], location=strsplit(datasetName(), "-")[[1]][3], sqd=strsplit(datasetName(), "-")[[1]][2], date=as.character(constrained[,unique(ymd(substring(index,1,10)),tz='Asia/Kathmandu')]), file=datasetName(), zero1_start= as.character(from_z1()), zero1_stop= as.character(to_z1()), sample_start= as.character(from_sample()), sample_stop= as.character(to_sample()), zero2_start= as.character(from_z2()), zero2_stop= as.character(to_z2()) )] # not all the same width, so we have an is.unsorted ach <- melt(ach, id.var=c("hhid","location","date", 'zero1_start', 'zero1_stop', 'sample_start', 'sample_stop', 'zero2_start', 'zero2_stop', 'file'), measure.var=grep("ppm",names(ach))) ach.all <- ach[,with=F] ach.all[, value:=round(value, 2)] ach.all }) #################### ###### Tables ###### #################### output$achTable <- renderTable({ correctedData_lm() }, include.rownames=FALSE) #################### ####### PLOTS ###### #################### output$plainPlot<- renderDygraph({ dygraph(dataXTS.plainplot()) %>% dyOptions(axisLineWidth = 1.5, fillGraph = F, drawGrid = FALSE, useDataTimezone=T, colors = RColorBrewer::brewer.pal(4, "Set2")) }) output$zeroPlot1<- renderDygraph({ dygraph(dataXTS.plainplot()) %>% dyOptions(axisLineWidth = 1.5, fillGraph = F, drawGrid = FALSE, useDataTimezone=T, colors = RColorBrewer::brewer.pal(4, "Set2")) }) output$zeroPlot2<- renderDygraph({ dygraph(dataXTS.plainplot()) %>% dyOptions(axisLineWidth = 1.5, fillGraph = F, drawGrid = FALSE, useDataTimezone=T, colors = RColorBrewer::brewer.pal(4, "Set2")) }) output$samplePlot<- renderDygraph({ dygraph(log(correctedData()), group = 'SamplePlots') %>% dyOptions(axisLineWidth = 1.5, fillGraph = F, drawGrid = FALSE, useDataTimezone=T, colors = RColorBrewer::brewer.pal(4, "Set2")) }) output$samplePlotnolog<- renderDygraph({ dygraph(correctedData(), group = 'SamplePlots') %>% dyOptions(axisLineWidth = 1.5, fillGraph = F, drawGrid = FALSE, useDataTimezone=T, colors = RColorBrewer::brewer.pal(4, "Set2")) }) ########################## ####### DL HANDLERS ###### ########################## output$downloadCSV <- downloadHandler( filename = function() {paste(datasetName(), '.cleaned.csv', sep='') }, content = function(file) { write.csv(correctedData_lm(), file, row.names=F) } ) })

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/R/FL-Airline_Data/week1.R

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bithu30/myRepo

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refs/heads/master

2022-10-21T16:26:45.997395

2018-05-31T02:51:31

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week1.R

#import all of the data from 2008 dataset airline=read.csv('C://myRepo//R//FL-Airline_Data//2008.csv') head(airline$Origin == 'IND') sum(airline$Origin == 'IND') sum(airline$Origin == 'ORD') sum(airline$Dest == 'ORD') sum( (airline$Origin == 'IND') & (airline$Dest == 'ORD') ) myIndieFlights <- subset(airline,airline$Origin=='IND') myIndieDest <- subset(airline,airline$Dest=='IND') plot(table(myIndieFlights$Month)) myTupFlights <- subset(airline,airline$Origin=='TUP') mean(myTupFlights$DepDelay) head(myIndieFlights) sum(myIndieFlights$DepTime<600,na.rm = TRUE) sum(airline$Dest=='LAX') temp_flight<-subset(airline,(airline$Origin == 'ATL') & (airline$Dest == 'LAX') ) sum(temp_flight$DepTime < 1200,na.rm = TRUE)

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/cachematrix.R

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milosgajdos/ProgrammingAssignment2

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refs/heads/master

2021-05-31T00:05:57.077423

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cachematrix.R

# makeCacheMatrix accepts a matrix as an argument and generates a list object # which provides various functions to retrieve and modify the underlying matrix # and its inverse matrix makeCacheMatrix <- function(x = matrix()) { # inverse matrix inv <- NULL # Initialize the underlying matrix object, NULLs its inverse cache # If the matrix hasn't changed we won't modify the underlying matrix set <- function(y = matrix()) { # only change the underlying matrix IF new matrix is not the same if (!(dim(x) == dim(y) && all(x == y))) { x <<- y inv <<- NULL } } # returns underlying matrix get <- function() x # sets matrix's inverse cache setinv <- function(invMatrix = matrix()) inv <<- invMatrix # returns inverse cache value getinv <- function() inv # returns matrix object list(set = set, get = get, setinv = setinv, getinv = getinv) } # cacheSolve accepts a special list object that provides get, set, setinv and getinv # function objects which allow to calculat an inverse matrix of passed in matrix # object or returns a cached data if there is any. cacheSolve <- function(x = list(), ...) { # retrieve cached inverse matrix inv <- x$getinv() # check if the cache exists and if the matrix hasn't changed if(!is.null(inv)){ message("getting cached data") return(inv) } # read in matrix data <- x$get() # calculate inverse matrix inv <- solve(data) # set inverse cache value x$setinv(inv) # return inverse matrix inv }

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/SL_CCLE_del.R

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CBigOxf/synthetic_lethality

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refs/heads/main

2023-03-25T03:56:43.665837

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SL_CCLE_del.R

SL_CCLE_del <- function() { ########################################################################### # This function implements the SL genome scan for CCLE_20Q2 dataset # It scans through the genome looking for which genes are # co-mutated (only deleterious) or WT based on tumour samples that # contain information for AGO2 and MYC CNA ########################################################################### ########################################################################### # source the data-set ; the .zip folder contains all data in //inputs subfolder # change as appropriate ... input_dir <- paste0(getwd(),"//inputs") print("Loading CCLE data-sets...") load(file=paste0(input_dir,"/CCLE_20Q2.RData")) print("Done...") disease_name <- readline(prompt="Enter type of disease (e.g. breast, lung etc) - 'all' for full CCLE run: ") disease_name_bkp <- disease_name gene <- readline(prompt="Enter the gene to analyze (e.g. AGO2): ") if (disease_name == "all") { disease_name = "CCLE.csv"} else{disease_name = paste0(disease_name,".csv")} # Sink is a command to transfer all console output to a .txt file here # This prevents any output while running - can deactivate with command : CloseAllConnections() sink(paste0("tp_del_",disease_name_bkp,".txt")) lib_dir <- paste0(getwd(),"/libs") .libPaths( c( lib_dir , .libPaths() ) ) print("Loading libraries required...") list.of.packages <- c("dplyr","ggplot2","ggrepel","ggpubr","tibble","stringr", "tidyverse", "data.table", "stringi","openxlsx", "data.table" ) invisible(lapply(list.of.packages, library, character.only = TRUE)) colnames(expr_matrix_csv)[1] <- "CELLLINE" if (disease_name == "CCLE.csv") { print("Running through all cancer cell lines in CCLE...") } else{ print(paste0("Running for ",disease_name)) cell_line_dir = paste0(input_dir,"/CELL_LINES") command <- paste0(cell_line_dir,"//",disease_name) command <- shQuote(command) command <- paste0("read.csv(",command) command <- paste0(command,",header = TRUE)") command <-paste0("as.data.frame(",command) command <- paste0(command,")") disease_csv <- eval(parse(text = command)) disease_cell_lines = disease_csv[, 1] expr_matrix_csv <- expr_matrix_csv %>% dplyr::filter(CELLLINE %in% disease_cell_lines) mut_matrix_csv <- mut_matrix_csv %>% dplyr::filter(Tumor_Sample_Barcode %in% disease_cell_lines) } # initialize lists to be populated later on master_pvalues <- c() ratio1 <- c() ratio2 <- c() mt <- c() # mutational frequency for each gene tp_flag <- c() # twenty percent mutated flag, true or false all_genes <- colnames(expr_matrix_csv) c_matrix <- c() ### contingency table freqs gene_list <- sapply(strsplit(all_genes[2:length(all_genes)], split='..', fixed=TRUE),function(x) (x[1])) gene_list_IDs <- sapply(strsplit(all_genes[2:length(all_genes)], split='..', fixed=TRUE),function(x) (x[2])) gene_list_IDs <- sapply(str_sub(gene_list_IDs,1,-2),function(x) (x[1])) user <- length(gene_list) # can change to scan first x genes (1:x) limit <- 100 # how many pvalues and genes to plot (top in ascending order) title_cancer = paste0("% done - Analyzing genes in ",disease_name_bkp," ...") ##################################################################################################### cell_line_names <- as.data.frame(read.csv(paste0(input_dir,"/cell_line_names.csv"), header = TRUE)) names_mat <- cell_line_names %>% dplyr::select(c("BROAD_ID","CCLE_ID")) colnames(names_mat) <- c("Tumor_Sample_Barcode","NAME") calls <- read.table(file = paste0(input_dir,"/calls.txt"), sep = '\t', header = TRUE) colnames(calls)[3] <- "TARGET" colnames(calls)[4] <- "MYC" calls <- calls[,c(1,2,3,4)] colnames(expr_matrix_csv)[1] <- "Tumor_Sample_Barcode" colnames(calls)[2] <- "NAME" pb <- winProgressBar(title = "progress bar", min = 0,max = user,width = 300) df_t <- expr_matrix_csv %>% dplyr::select(c("Tumor_Sample_Barcode")) df_t <- merge(df_t, names_mat, by="Tumor_Sample_Barcode") df_t <- merge(df_t,calls, by = "NAME") # filter by MYC diploidy: df_t <- df_t %>% dplyr::filter(MYC == 0) colnames(df_t)[2] <- "Tumor_Sample_Barcode" for (i in 1:user) { setWinProgressBar(pb,i,title = paste(round(i / user * 100, 0), title_cancer)) c_gene <- gene_list[i] target_mutations <- mut_matrix_csv %>% dplyr::filter(Hugo_Symbol %in% c_gene & !(Variant_Classification %in% "Silent") ) check_1 <- nrow(target_mutations) df <- merge(df_t,target_mutations, by = "Tumor_Sample_Barcode", all = TRUE) check_2 <- nrow(df) check <- check_1/check_2 # mutational frequency for the gene check_all <- paste0(check_1,"/",check_2," (",check,")") df <- df[order(df[,'Variant_Classification']), ] df[,'Variant_Classification'][is.na(df[,'Variant_Classification'])] <- "WT" df[,'isDeleterious'][is.na(df[,'isDeleterious'])] <- "False" df <- tibble::add_column(df, status = "MT") df$status <- ifelse(df$Variant_Classification == "WT", "WT", "MT") df_small <- df %>% dplyr::select(c("Tumor_Sample_Barcode", "Variant_Classification", "TARGET","status","isDeleterious")) # -2 or Deep Deletion indicates a deep loss, possibly a homozygous deletion # -1 or Shallow Deletion indicates a shallow loss, possibley a heterozygous deletion # 0 is diploid # 1 or Gain indicates a low-level gain (a few additional copies, often broad) # 2 or Amplification indicate a high-level amplification (more copies, often focal) df_small <- df_small %>% dplyr::mutate (TARGET=replace(TARGET, TARGET==-2, "deep_deletion")) df_small <- df_small %>% dplyr::mutate (TARGET=replace(TARGET, TARGET==-1, "shallow_deletion")) df_small <- df_small %>% dplyr::mutate (TARGET=replace(TARGET, TARGET==0, "diploid")) df_small <- df_small %>% dplyr::mutate (TARGET=replace(TARGET, TARGET==1, "gain")) df_small <- df_small %>% dplyr::mutate (TARGET=replace(TARGET, TARGET==2, "amplification")) df_small <- df_small[order(df_small[,"TARGET"]), ] ################################## df_small <- tibble::add_column(df_small, AMPL_MUT = 0) df_small <- tibble::add_column(df_small, NON_AMPL_MUT = 0) df_small <- tibble::add_column(df_small, AMPL_WT = 0) df_small <- tibble::add_column(df_small, NON_AMPL_WT = 0) df_small$AMPL_MUT <- ifelse(df_small$isDeleterious == "True" & (df_small$TARGET == "amplification" | df_small$TARGET == "gain"), 1, 0) df_small$NON_AMPL_MUT <- ifelse(df_small$isDeleterious == "True" & (df_small$TARGET != "amplification" & df_small$TARGET != "gain"), 1, 0) df_small$AMPL_WT <- ifelse(df_small$status == "WT" & (df_small$TARGET == "amplification" | df_small$TARGET == "gain"), 1, 0) df_small$NON_AMPL_WT <- ifelse(df_small$status == "WT" & (df_small$TARGET != "amplification" & df_small$TARGET != "gain"), 1, 0) df_small <- na.omit(df_small) df_small <- df_small[order(df_small[,'status']), ] # prepare matrix for Fisher's exact test: c1 <- table(df_small$AMPL_MUT) c2 <- table(df_small$AMPL_WT) c3 <- table(df_small$NON_AMPL_MUT) c4 <- table(df_small$NON_AMPL_WT) c <- matrix(c(c1[2],c2[2],c3[2],c4[2]),2,2) c_string <- paste0("(",c1[2],",",c3[2],",",c2[2],",",c4[2],")") colnames(c) <- c(paste0(gene," amplified/gain"),paste0(gene," not amplified/gain (deep_deletion/diploid)")) rownames(c) <- c(paste0(c_gene," deleterious"),paste0(c_gene," WT")) c[is.na(c)] <- 0 print("'######################################################################") print(c) res <- fisher.test(c) # now filter out anything that is mutant in less than 20% of samples: ratio1 <- c(ratio1, c[1,1]/(c[1,1]+c[2,1])) ratio2 <- c(ratio2,c[1,2]/(c[1,2]+c[2,2])) if ( check >= 0.1 ) { tp_flag <- c(tp_flag,"TRUE") } else{ tp_flag <- c(tp_flag,"FALSE") } master_pvalues <- c(master_pvalues, res$p.value) mt <- c(mt, check_all) c_matrix <- c(c_matrix,c_string) ### print(res) } close(pb) names(master_pvalues) <- gene_list[1:user] df <- matrix(data = , nrow = user, ncol = 1) df[, 1] = master_pvalues row.names(df) <- names(master_pvalues) colnames(df) <- "pvalues" df <- data.frame(df) # add ratio columns df <- add_column(df, ratios1 = 0) df <- add_column(df, ratios2 = 0) df <- add_column(df, ctn = 0) ### df <- add_column(df,tp_flags = "FALSE") df <- add_column(df,Mutational_Freq = 0) df$ratios1 <- ratio1 df$ratios2 <- ratio2 df$ctn <- c_matrix ### df$tp_flags <- tp_flag df$Mutational_Freq <- mt df <- df %>% arrange(desc(-pvalues)) # adjust p values colnames(df)[2] <- "AMPLIFIED_DELETERIOUS/(AMPLIFIED_DELETERIOUS+AMPLIFIED_WT)" colnames(df)[3] <- "NON_AMPLIFIED_DELETERIOUS/(NON_AMPLIFIED_DELETERIOUS+NON_AMPLIFIED_WT)" colnames(df)[4] <- "contingency table frequencies (A,B,C,D)" ### dfshort <- df %>% dplyr::filter(tp_flags=="TRUE") # & pvalues < 0.05) dfshort <- add_column(dfshort, adjusted_pvalue = 0) dfshort$adjusted_pvalue <- p.adjust(dfshort$pvalues, method = "BH") colnames(df)[5] <- "Mutational_Freq > 10%" write.csv(dfshort,paste0("CCLE_DEL_VS_WT_adjpvalues_",disease_name)) write.csv(as.data.frame(df), paste0("genome_results_del_",disease_name)) if (user>limit) {df2 <- head(df,limit)} png(paste0("sp_del_",disease_name_bkp,".png"), width=1920, height=1080, res=100) sp <- ggplot(data = df2,aes(x=factor(row.names(df2), levels=row.names(df2)), y = pvalues ))+ geom_point(size = 4,color = dplyr::case_when(df2$pvalues > 0.05 ~ "#FF0000", df2$pvalues < 0.05 ~ "#00CC00", TRUE ~ "#00CC00"), alpha = 0.8) + geom_hline(yintercept = 0.05, color = "#00CC00") + geom_label_repel(aes(label=row.names(df2)), box.padding = 0.5, point.padding = 0.005, size = 2) + ylab( c("P-value of Fisher exact test") ) + xlab( c("Hugo Symbol") ) + font("xylab",size=10)+ font("xy",size=10)+ font("xy.text", size = 10) + font("legend.text",size = 10) + theme(axis.text.x=element_text(size=10, angle=90,hjust=0.95,vjust=0.02)) + ggtitle(paste0("Genome-wide comparison of ", gene, " amplification VS Deleterious/WT")) print(sp) dev.off() return(df) }

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project1_plot3.R

# Cousera Course 4, week1, Project1 29.06.2018 rm(list=ls()) # rm all variable = clear # read input file dat_inp <- read.table("data/household_power_consumption.txt", header=TRUE, sep=";", stringsAsFactors = FALSE, na.strings=c("NA","?")) # select all features between two dates (as character) tmp <- dat_inp[dat_inp$Date >= "1/2/2007" & dat_inp$Date <= "2/2/2007", ] # generate a date-time as POSIXct called my_time tmp$Date <- as.Date(tmp$Date, format = "%d/%m/%Y") tmp$Time <- format(tmp$Time, format = "%H:%M:%S") tmp2 <- paste(tmp$Date, tmp$Time) # add my_time to dat tmp$my_time <- strptime(tmp2, "%Y-%m-%d %H:%M:%S") pip <- unique(tmp$Date) dat <- tmp[tmp$Date >= "2007-2-1" & tmp$Date <= "2007-2-2", ] plot3 dev.new() png(file = "Plot3.png",width = 480, height = 480, units = "px") plot(dat$my_time , dat$Sub_metering_1, type= "l", lty=1, col="black", ylab="Energy sub metering", xlab="") points(dat$my_time , dat$Sub_metering_2, type= "l", lty=1, col="red", ylab="", xlab="") points(dat$my_time , dat$Sub_metering_3, type= "l", lty=1, col="blue", ylab="", xlab="") # add a legend legend("topright",legend =c("Sub_metering_1","Sub_metering_2","Sub_metering_3"), col =c("black","red","blue")) dev.off()

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/example/mergeStringtieTPM.R

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##==========================Input========================## # path_stringtie='/home/huangwb8/Project/RNA-Seq_A2B1-KO/output/stringtie/hisat2' ##==============assistant base function=================## source("/home/huangwb8/bin/ShellBase.R",encoding = "UTF-8") ##====================environment=======================## nd.pac=c("dplyr","plyr","readr") Plus.library(nd.pac) # grobal options: options(scipen = 100) ##===========================Programe====================## f <- list.files(path_stringtie,pattern = 'gene_abund.tab',full.names = T,recursive = T) df_tpm <- NULL; df_fpkm <- NULL for(i in 1:length(f)){ # i=1 f.i <- f[i] n.i <- rev(Fastextra(f.i,'[/]'))[2] df.i <- read.table(f.i,header = T,check.names = F,sep = '\t') gene.i <- Fastextra(df.i$`Gene ID`, '[.]',1) # table(duplicated(gene.i)) # gene.i[duplicated(gene.i)] tpm.i <- tapply(df.i[,'TPM'], gene.i, sum) fpkm.i <- tapply(df.i[,'FPKM'], gene.i, sum) # TPM df.i2 <- data.frame(Gene_ID = names(tpm.i), TPM = tpm.i, stringsAsFactors = F,row.names = 1:length(tpm.i)) colnames(df.i2)[2] <- n.i if(is.null(df_tpm)){ df_tpm <- df.i2 } else { df_tpm <- full_join(df_tpm,df.i2,by = "Gene_ID") } # FPKM df.i3 <- data.frame(Gene_ID = names(fpkm.i), FPKM = fpkm.i, stringsAsFactors = F,row.names = 1:length(fpkm.i)) colnames(df.i3)[2] <- n.i if(is.null(df_fpkm)){ df_fpkm <- df.i3 } else { df_fpkm <- full_join(df_fpkm,df.i3,by = "Gene_ID") } # TPM for SUPPA # df.i2.m <- df.i2[-1]; rownames(df.i2.m) <- df.i2[,1] # write.table(df.i2.m, paste0(Fastextra(f.i,n.i,1),n.i,'.tpm.tab'),sep = "\t",col.names = T,row.names = T,quote = F) if(T){ x <- readr::read_delim(paste0(path_stringtie,'/',n.i,'/merged.gtf'), delim = '\t', comment = "#",col_names = F) %>% as.data.frame() x2 <- x[x[,3] == 'transcript',] a <- apply(as.matrix(x2[,9]), 1, function(i){ # i = 'gene_id \"ENSG00000228794.9\"; transcript_id \"ENST00000671208.1\"; ref_gene_name \"LINC01128\"; cov \"0.0\"; FPKM \"0.000000\"; TPM \"0.000000\";' i2 <- Fastextra(i,'\"') i3 <- i2[seq(2,12,2)] # names(i3) <- gsub(';| ','',i2[seq(1,11,2)]) return(i3) }) a2 <- t(a); colnames(a2) <- c("gene_id","transcript_id" ,"ref_gene_name","cov","FPKM","TPM") # View(a2[1:10,]) # rownames(a2) <- Fastextra(a2[,"transcript_id"], "[.]",1) rownames(a2) <- a2[,"transcript_id"] a2 <- as.data.frame(a2) a3 <- a2["TPM"]; colnames(a3) <- n.i a3[,1] <- as.numeric(as.character(a3[,1])) a3[,1][is.na(a3[,1])] <- 0 # a3 <- na.omit(a3) # table(is.na(a3$D1)); a3[is.na(a3$D1),] # Output write.table(a3, paste0(Fastextra(f.i,n.i,1),n.i,'.transcript.tpm.tab'),sep = "\t",col.names = T,row.names = T,quote = F) } } # table(duplicated(df_tpm$Gene_ID)) # table(duplicated(df_fpkm$Gene_ID)) # Output df_tpm2 <- as.matrix(df_tpm[,-1]); rownames(df_tpm2) <- df_tpm[,1] for(j in 1:ncol(df_tpm2)) df_tpm2[,j][is.na(df_tpm2[,j])] <- 0 saveRDS(df_tpm2, paste0(path_stringtie,'/stringtie.tpm.rds')) write.table(df_tpm2, paste0(path_stringtie,"/stringtie.tpm.txt"),sep = "\t",col.names = T,row.names = T,quote = F) df_fpkm2 <- as.matrix(df_fpkm[,-1]); rownames(df_fpkm2) <- df_fpkm[,1] for(j in 1:ncol(df_fpkm2)) df_fpkm2[,j][is.na(df_fpkm2[,j])] <- 0 saveRDS(df_fpkm2, paste0(path_stringtie,'/stringtie.fpkm.rds')) write.table(df_fpkm2, paste0(path_stringtie,"/stringtie.fpkm.txt"),sep = "\t",col.names = T,row.names = T,quote = F) # grobal options: options(scipen = 1)

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test-public_api-2.R

test_that("styler can style R, Rmd and Rmarkdown files via style_dir()", { msg <- capture_output( style_dir(testthat_file("public-api", "xyz-r-and-rmd-dir"), filetype = c("R", "Rmd", "Rmarkdown") ) ) expect_true(any(grepl("random-script-in-sub-dir.R", msg, fixed = TRUE))) expect_true(any(grepl("random-rmd-script.Rmd", msg, fixed = TRUE))) expect_true(any(grepl("random-rmd-script.Rmarkdown", msg, fixed = TRUE))) }) test_that("styler can style Rmd files only via style_dir()", { msg <- capture_output( style_dir(testthat_file("public-api", "xyz-r-and-rmd-dir"), filetype = "Rmd" ) ) expect_true(any(grepl("random-rmd-script.Rmd", msg, fixed = TRUE))) expect_false(any(grepl("random-script-in-sub-dir.R", msg, fixed = TRUE))) expect_false(any(grepl("random-rmd-script.Rmarkdown", msg, fixed = TRUE))) }) test_that("styler can style .r and .rmd files only via style_dir()", { msg <- capture_output( style_dir(testthat_file("public-api", "xyz-r-and-rmd-dir"), filetype = c(".r", ".rmd") ) ) expect_true(any(grepl("random-script-in-sub-dir.R", msg, fixed = TRUE))) expect_true(any(grepl("random-rmd-script.Rmd", msg, fixed = TRUE))) expect_false(any(grepl("random-rmd-script.Rmarkdown", msg, fixed = TRUE))) }) test_that("styler can style R and Rmd files via style_pkg()", { msg <- capture_output( style_pkg(testthat_file("public-api", "xyzpackage-rmd"), filetype = c("R", "Rmd", "Rmarkdown") ) ) expect_true(any(grepl("hello-world.R", msg, fixed = TRUE))) expect_true(any(grepl("test-package-xyz.R", msg, fixed = TRUE))) expect_true(any(grepl("random.Rmd", msg, fixed = TRUE))) expect_true(any(grepl("random.Rmarkdown", msg, fixed = TRUE))) expect_true(any(grepl("README.Rmd", msg, fixed = TRUE))) expect_false(any(grepl("RcppExports.R", msg, fixed = TRUE))) }) test_that("style_pkg() styles qmd files by default", { msg <- capture_output( style_pkg(testthat_file("public-api", "xyzpackage-qmd")) ) expect_true(any(grepl("hello-world.R", msg, fixed = TRUE))) expect_true(any(grepl("test-package-xyz.R", msg, fixed = TRUE))) expect_true(any(grepl("random.Rmd", msg, fixed = TRUE))) expect_true(any(grepl("random.Rmarkdown", msg, fixed = TRUE))) expect_true(any(grepl("README.Rmd", msg, fixed = TRUE))) expect_false(any(grepl("RcppExports.R", msg, fixed = TRUE))) expect_true(any(grepl("new.qmd", msg, fixed = TRUE))) }) test_that("style_pkg() can find qmd anywhere", { msg <- capture_output( style_pkg(testthat_file("public-api", "xyzpackage-qmd"), filetype = ".Qmd" ) ) expect_no_match(msg, "hello-world.R", fixed = TRUE) expect_no_match(msg, "test-package-xyz.R", fixed = TRUE) expect_no_match(msg, "random.Rmd", fixed = TRUE) expect_no_match(msg, "random.Rmarkdown", fixed = TRUE) expect_no_match(msg, "README.Rmd", fixed = TRUE) expect_no_match(msg, "RcppExports.R", fixed = TRUE) expect_match(msg, "new.qmd", fixed = TRUE) }) test_that("styler can style Rmd files only via style_pkg()", { msg <- capture_output( style_pkg(testthat_file("public-api", "xyzpackage-rmd"), filetype = "Rmd" ) ) expect_false(any(grepl("hello-world.R", msg, fixed = TRUE))) expect_false(any(grepl("test-package-xyz.R", msg, fixed = TRUE))) expect_true(any(grepl("random.Rmd", msg, fixed = TRUE))) expect_false(any(grepl("random.Rmarkdown", msg, fixed = TRUE))) expect_true(any(grepl("README.Rmd", msg, fixed = TRUE))) expect_false(any(grepl("RcppExports.R", msg, fixed = TRUE))) }) test_that("styler can style Rmarkdown files only via style_pkg()", { msg <- capture_output( style_pkg(testthat_file("public-api", "xyzpackage-rmd"), filetype = "Rmarkdown" ) ) expect_false(any(grepl("hello-world.R", msg, fixed = TRUE))) expect_false(any(grepl("test-package-xyz.R", msg, fixed = TRUE))) expect_false(any(grepl("random.Rmd", msg, fixed = TRUE))) expect_true(any(grepl("random.Rmarkdown", msg, fixed = TRUE))) expect_false(any(grepl("README.Rmd", msg, fixed = TRUE))) expect_false(any(grepl("RcppExports.R", msg, fixed = TRUE))) }) test_that("insufficient R version returns error", { expect_error(stop_insufficient_r_version()) })

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transformation_functions.R

# this file has feature transformations # # Example: # getCategTransform returns list (column, function), where column is the name # of the output column for the transformation and the function transforms # a data.table to feature vector. library(data.table) #' get function that encodes away less common values in a feature #' saves you from those errors on missing factor levels in test set #' #' @param col: transformation function will transform this column #' @param fit_dt: use data in fit_dt to calculate less common feature values #' @param threshold: retain only those values that occur more than this many times in fit_dt #' #' @return list(col, fun) where fun is the transformation function #' @export getCategTransform <- function(col, fit_dt, threshold=0, default_value="other") { # count how often each value in x occurs retain_values <- ( fit_dt [, .(val=fit_dt[[col]])] [, .(n=.N), by=.(val)] [n > threshold,] $val ) # return input_dt[[col]] but less common values set to default_value # # @param input_dt: data.table with column <col> # # @return vector that has transformed input feature categTransform <- function(input_dt) { x <- input_dt[[col]] x[!(x %in% retain_values)] <- default_value # TODO: handle unseen column values return(x) } rm(fit_dt) return(list(col=col, fun=categTransform)) } #' get function that discretizes numerical feature to factor #' #' @param col: transformation function will transform this column #' @param fit_dt: use data in fit_dt to calculate discretization boundaries #' @param n: calculate this many discretization boundary points #' #' @return list(col, fun) where fun is the transformation function #' @export getDiscretizeTransform <- function(col, fit_dt, n=10) { countUniq <- function(x) {length(unique(x))} # get discretization boundaries from fit_dt suppressWarnings( col_disc <- discretize( fit_dt[[col]], method = "frequency", #frequency implementation is broken breaks = min(n, countUniq(fit_dt[[col]])-1), right = FALSE, infinity=TRUE ) ) breaks <- attr(col_disc, "discretized:breaks") labels <- attr(col_disc, "levels") rm(col_disc) rm(fit_dt) discretizeTransform <- function(input_dt) { x <- input_dt[[col]] y <- cut(x, breaks=breaks, labels=labels, right=FALSE) # add additional "missing" that is not NA because models fail on NAs y <- factor(y, levels=c("Unknown", levels(y))) y[is.na(y)] <- "Unknown" y } return( list(col=col, fun=discretizeTransform) ) } #' set NA values in column col to 0 and add new column that is 0/1 NA indicator #' #' @param input_dt: data.table with column <col> #' @param col: column name in input_dt, is of numeric type #' #' @return TRUE, modifies input_dt by reference #' @export processNAColumn <- function(input_dt, col) { col_na <- paste0(col, "_na") # create new column input_dt[, (col_na) := 0 ] input_dt[is.na(input_dt[[col]]), (col_na) := 1 ] input_dt[is.na(input_dt[[col]]), (col) := 0 ] return(TRUE) } #' get function that calculates new feature: average of <avg_col> by group #' #' @param group_cols: vector of column names to group by the average calculation #' @param avg_col: column to average #' @param output_col: column name to output the new feature #' @param fit_dt: data.table with columns in group_cols and avg_col #' @param n_threshold: calculate average only when group size exceed this #' #' @return list(col, fun) where fun is the transformation function #' @export getAveragingTransform <- function( group_cols, avg_col, out_col, fit_dt, n_threshold=500) { # calculate average for each group averaging_dt <- ( fit_dt[, .( n=.N, avg = mean(.SD[[avg_col]]) ), keyby=group_cols ] [n > n_threshold, ] [, n := NULL ] ) rm(fit_dt) # merge each row of input_dt to correct average value averagingTransform <- function(input_dt) { averaging_dt[input_dt, on=group_cols]$avg } return(list(col=out_col, fun=averagingTransform)) }

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\name{summary.jmdem} \alias{summary.jmdem} \alias{print.summary.jmdem} \title{ Summarising Joint Mean and Dispersion Effects Model Fits } \description{ These functions are all \code{\link{methods}} for class \code{jmdem} or \code{summary.jmdem} objects. } \usage{ \method{summary}{jmdem}(object, correlation = FALSE, symbolic.cor = FALSE, ...) \method{print}{summary.jmdem}(x, digits = max(3L, getOption("digits") - 3L), scientific = FALSE, symbolic.cor = x$symbolic.cor, signif.stars = getOption("show.signif.stars"), ...) } \arguments{ \item{object}{an object of class "\code{jmdem}", usually, a result of a call to \code{\link{jmdem}}.} \item{x}{an object of class "\code{summary.jmdem}", usually, a result of a call to \code{summary.jmdem}.} \item{correlation}{logical; if \code{TRUE}, the correlation matrix of the estimated parameters is returned and printed.} \item{digits}{the number of significant digits to use when printing.} \item{scientific}{logical; if \code{TRUE}, scientific notation is used when printing.} \item{symbolic.cor}{logical. If \code{TRUE}, print the correlations in a symbolic form (see \code{\link{symnum}}) rather than as numbers.} \item{signif.stars}{logical. If \code{TRUE}, 'significance stars' are printed for each coefficient.} \item{...}{further arguments passed to or from other methods.} } \details{ \code{print.summary.jmdem} tries to be smart about formatting the coefficients, standard errors, etc. and additionally gives 'significance stars' if \code{signif.stars} is \code{TRUE}. The \code{coefficients}, \code{mean.coefficients} and \code{dispersion.coefficients} components of the result give the estimated coefficients and their estimated standard errors, together with their ratio. This third column is labelled \code{t-ratio} and a fourth column gives the two-tailed p-value corresponding to the \code{t-ratio} based on a Student t distribution. Aliased coefficients are omitted in the returned object but restored by the \code{print} method. Correlations are printed to the same decimal places specified in \code{digits} (or symbolically): to see the actual correlations print \code{summary(object)$correlation} directly. For more details, see \code{\link{summary.glm}}. } \value{ \item{call}{the component from \code{object}.} \item{mean.family}{the component from \code{object}.} \item{dispersion.family}{the component from \code{object}.} \item{deviance}{the component from \code{object}.} \item{mean.terms}{the component from \code{object}.} \item{dispersion.terms}{the component from \code{object}.} \item{aic}{the component from \code{object}.} \item{mean.contrasts}{the component from \code{object}.} \item{dispersion.contrasts}{the component from \code{object}.} \item{df.residual}{the component from \code{object}.} \item{null.deviance}{the component from \code{object}.} \item{df.null}{the component from \code{object}.} \item{information.type}{the component from \code{object}.} \item{iter}{the component from \code{object}.} \item{mean.na.action}{the component from \code{object}.} \item{dispersion.na.action}{the component from \code{object}.} \item{deviance.resid}{the deviance residuals.} \item{pearson.resid}{the pearson residuals.} \item{resid}{the working residuals depends on the setting of \code{deviance.type}.} \item{coefficients}{the matrix of coefficients, standard errors, z-values and p-values. Aliased coefficients are omitted.} \item{mean.coefficients}{the matrix of coefficients, standard errors, z-values and p-values of the mean submodel.} \item{dispersion.coefficients}{the matrix of coefficients, standard errors, z-values and p-values of the dispersion submodel.} \item{deviance.type}{the type of redidual deviance specified, it is either "\code{deviance}" or "\code{pearson}".} \item{aliased}{named logical vector showing if the original coefficients are aliased.} \item{df}{a 3-vector of the rank of the model and the number of residual degrees of freedom, plus number of coefficients (including aliased ones).} \item{covariance}{the estimated covariance matrix of the estimated coefficients.} \item{digits}{the number of significant digits to use when printing.} \item{scientific}{logical value of using scientific notation when printing.} \item{covmat.method}{named method used to invert the covariance matrix.} \item{correlation}{(only if correlation is true.) The estimated correlations of the estimated coefficients.} \item{symbolic.cor}{(only if correlation is true.) The value of the argument symbolic.cor.} } \author{ Karl Wu Ka Yui (karlwuky@suss.edu.sg) } \seealso{ \code{\link{jmdem}}, \code{\link{summary}} } \examples{ ## Example in jmdem(...) MyData <- simdata.jmdem.sim(mformula = y ~ x, dformula = ~ z, mfamily = poisson(), dfamily = Gamma(link = "log"), beta.true = c(0.5, 4), lambda.true = c(2.5, 3), n = 100) fit <- jmdem(mformula = y ~ x, dformula = ~ z, data = MyData, mfamily = poisson, dfamily = Gamma(link = "log"), dev.type = "deviance", method = "CG") ## Summarise fit with correlation matrix summary(fit, correlation = TRUE, digits = 4) }

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Part8_10mer threshold.R

# get the threshold for 10-mers motifs= c("BCL11A", "CTCF", "EGR1", "GABPA", "JUN", "JUND", "MAX", "NANOG", "POU5F1", "RAD21", "REST", "RFX5", "SIX5", "SRF", "STAT1", "TCF12", "USF1", "USF2", "YY1") motif = motifs[2] # the size of sizes <- 100000 # ten-mer threshold load(paste0("H:/10-mers/07062020_Motifs_over_SNP_deltaSVM/d_matrix/",motif,"/Part1/d.rda")) # de = as.matrix(d) # test = d d[,1] = as.character(d[,1]) # # generate the random collection of 10-mers d_rand <- d[sample(seq(nrow(d)), size = sizes, replace = F),] # random positions within 10-mers pos <- sample(1:10,size = sizes,replace = T) # the reference 10-mer sequences seq.ref <- as.character(d_rand[,1]) # the SNP in reference 10-mer d_rand_ref <- substr(seq.ref,pos,pos) # function to change targeted SNP within the 10-mer sequence # x = d_rand_ref[1] mut_allele <- function(x){ temp <- c("A","C","G","T") temp2<- temp[-which(temp==x)] xx <- temp2[sample(1:3,1)] return(xx) } # get the SNP in the corresponding alternative 10-mers d_rand_mut <- mapply(mut_allele,d_rand_ref) # the alternative 10-mer sequences seq.mut <- paste0(substr(seq.ref,start = 1,stop = pos-1),d_rand_mut,substr(seq.ref,start = pos+1, stop = 10)) # match corresponding weights for 10-mer seq10_weight = function(x){ xlistr= chartr("ATGC","TACG",x) m1 = d[match(x,d[,1]),2] m2 = d[match(xlistr,d[,1]),2] m1[which(is.na(m1))]=m2[which(is.na(m1))] return(m1) } # seq10_weight(seq.ref[1]) # get the weights for reference 10-mer weight.ref <- d[match(seq.ref, d[,1]), 2] seq.mut2 <- chartr("ATGC","TACG",seq.mut) m1 <- d[match(seq.mut, d[,1]), 2] m2 <- d[match(seq.mut2, d[,1]), 2] m1[which(is.na(m1))]=m2[which(is.na(m1))] # get the weights for alternative 10-mer weight.mut <- m1 rm(m1,m2,seq.mut2) # the weight difference of 10000 10-mers should be: weight_diff <- weight.mut - weight.ref # the empirical quantiles quantile(weight_diff,probs = c(0.025,0.975)) # you can use sink() here to get the results saved in a txt file # here is some example code, please revise according to your settings sink(file = "Your path to save the results", append = T) # if you set append=T and use the same file name, it will append in the same file cat(c(motif,quantile(weight_diff,probs = c(0.025,0.975)))) sink()

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/scripts/parkrun_efforts.R

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#devtools::install_github('fawda123/rStrava') #devtools::install_github("timelyportfolio/listviewer") mise::mise(vars = TRUE, console = TRUE) library(rStrava) library(tidyr) library(dplyr) library(ggplot2) library(broom) library(purrr) library(ggjoy) help.search('token', package = 'rStrava') app_name <- 'stRava' app_client_id <- '12962' app_secret <- 'e58f0335795fb08674d2a412c9d35efb0d740761' # create token my_token <- httr::config(token = strava_oauth(app_name, app_client_id, app_secret)) # functions compile_effort <- function(x){ temp <- data.frame(unlist(x)) %>% mutate(ColNames = rownames(.)) %>% spread(., ColNames, unlist.x.) desired_cols <- c('athlete.id', 'distance', 'elapsed_time', 'moving_time', 'name', 'start_date', 'start_date_local') # check which columns arent present cols_not_present <- desired_cols[! desired_cols %in% colnames(temp)] %>% data.frame(cols = .) %>% mutate(., value = NA) if(nrow(cols_not_present) >= 1){cols_not_present <- spread(cols_not_present, cols, value)} if(nrow(cols_not_present) == 1){temp <- cbind(temp, cols_not_present)} temp <- select(temp, athlete.id, distance, elapsed_time, moving_time, name, start_date, start_date_local) return(temp) } compile_segment_efforts <- function(x, stoken){ temp1 <- get_efforts_list(stoken, id = x) temp2 <- suppressWarnings(purrr::map_df(temp1, compile_effort)) return(temp2) } # bind segment info together segments <- read.csv("~/Desktop/park_runs/raw/park_run_segmentIDs.csv", stringsAsFactors = FALSE) %>% filter(., !is.na(segment_id) & !is.na(Parkrun)) # get leaderboard for parkruns # test run through #test <- segments[1:5,] d_parkrun <- segments$segment_id %>% purrr::map_df(., .f = compile_segment_efforts, stoken = my_token, .id = 'id') # make some columns numeric d_parkrun <- mutate_at(d_parkrun, c('distance', 'moving_time', 'elapsed_time'), as.numeric) segments <- mutate(segments, id = 1:n()) %>% rename(., parkrun = Parkrun) # filter and merge with ids d <- filter(d_parkrun, distance > 4750 & distance < 5200 & elapsed_time < 60 * 60) %>% merge(., select(segments, id, parkrun), by = 'id') saveRDS(d, '~/Desktop/park_runs/parkrun_efforts.rds') # plot as a joyplot ggplot(filter(d, elapsed_time < 3000)) + geom_joy(aes(elapsed_time, parkrun)) + theme_bw() + xlab('Time (s)') + ylab('') # create date columnn MaenMurderData$date <- gsub("T.*$", '', MaenMurderData$start_date) %>% as.POSIXct(., format = '%Y-%m-%d') MaenMurderData <- mutate(MaenMurderData, month = format(date, "%m"), day = format(date, "%d"), year = format(date, "%Y")) MaenMurderData[c('month', 'day', 'year')] <- lapply(MaenMurderData[c('month', 'day', 'year')], as.numeric)

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/man/opal.execute.Rd

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opal.execute.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/opal.execute.R \name{opal.execute} \alias{opal.execute} \title{Execute a R script} \usage{ opal.execute(opal, script, async = FALSE) } \arguments{ \item{opal}{Opal object or list of opal objects.} \item{script}{R script to execute.} \item{async}{R script is executed asynchronously within the session (default is FALSE). If TRUE, the value returned is the ID of the command to look for (from Opal 2.1).} } \description{ Execute a R script in the remote R session. } \examples{ \dontrun{ o <- opal.login('administrator','password', url='https://opal-demo.obiba.org') opal.execute(o, "x <- 'foo'") opal.execute(o, "ls()") opal.logout(o) } } \seealso{ Other execution functions: \code{\link{opal.execute.source}()}, \code{\link{opal.load_package}()}, \code{\link{opal.unload_package}()} } \concept{execution functions}

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parcoords.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/parcoords.R \name{parcoords} \alias{parcoords} \title{htmlwidget for d3.js parallel-coordinates} \usage{ parcoords(data, rownames = T, color = NULL, brushMode = NULL, brushPredicate = "and", reorderable = F, axisDots = NULL, margin = NULL, composite = NULL, alpha = NULL, queue = F, mode = F, rate = NULL, tasks = NULL, width = NULL, height = NULL) } \arguments{ \item{data}{data.frame with data to use in the chart} \item{rownames}{logical use rownames from the data.frame in the chart. Regardless of this parameter, we will append rownames to the data that we send to JavaScript. If \code{rownames} equals \code{FALSE}, then we will use parallel coordinates to hide it.} \item{color}{see \href{https://github.com/syntagmatic/parallel-coordinates\#parcoords_color}{parcoords.color( color )}. Color can be a single color as rgb or hex. For a color function, provide a list( colorScale = , colorBy = ) where colorScale is a function such as \code{d3.scale.category10()} and colorBy is the column name from the data to determine color.} \item{brushMode}{string, either \code{"1D-axes"}, \code{"1D-axes-multi"}, or \code{"2D-strums"} giving the type of desired brush behavior for the chart.} \item{brushPredicate}{string, either \code{"and"} or \code{"or"} giving the logic forthe join with multiple brushes.} \item{reorderable}{logical enable reordering of axes} \item{axisDots}{logical mark the points where polylines meet an axis with dots} \item{margin}{list of sizes of margins in pixels. Currently \code{brushMode = "2D-strums"} requires left margin = 0, so this will change automatically and might result in unexpected behavior.} \item{alpha}{opacity from 0 to 1 of the polylines} \item{queue}{logical (default FALSE) to change rendering mode to queue for progressive rendering. Usually \code{ queue = T } for very large datasets.} \item{mode}{string see\code{queue} above; \code{ queue = T } will set \code{ mode = "queue" }} \item{tasks}{a character string or \code{\link[htmlwidgets]{JS}} or list of strings or \code{JS} representing a JavaScript function(s) to run after the \code{parcoords} has rendered. These provide an opportunity for advanced customization. Note, the \code{function} will use the JavaScript \code{call} mechanism, so within the function, \code{this} will be an object with {this.el} representing the containing element of the \code{parcoords} and {this.parcoords} representing the \code{parcoords} instance.} \item{width}{integer in pixels defining the width of the widget. Autosizing to 100% of the widget container will occur if \code{ width = NULL }.} \item{height}{integer in pixels defining the height of the widget. Autosizing to 400px of the widget container will occur if \code{ height = NULL }.} } \value{ An object of class \code{htmlwidget} that will intelligently print itself into HTML in a variety of contexts including the R console, within R Markdown documents, and within Shiny output bindings. } \description{ Create interactive parallel coordinates charts with this htmlwidget wrapper for d3.js \href{http://syntagmatic.github.io/parallel-coordinates/}{parallel-coordinates}. } \examples{ \dontrun{ # simple example using the mtcars dataset data( mtcars ) parcoords( mtcars ) # various ways to change color # in these all lines are the specified color parcoords( mtcars, color = "green" ) parcoords( mtcars, color=RColorBrewer::brewer.pal(3,"BuPu")[3] ) parcoords( mtcars, color = "#f0c" ) # in these we supply a function for our color parcoords( mtcars , color = list( colorBy="cyl" ,colorScale=htmlwidgets::JS('d3.scale.category10()') ) ) ### be careful; this might strain your system ####### ### ####### data( diamonds, package = "ggplot2" ) parcoords( diamonds ,rownames=F ,brushMode = "1d-axes" ,reorderable=T ,queue = T ,color= list( colorBy="cut" ,colorScale = htmlwidgets::JS("d3.scale.category10()") ) ) # or if we want to add in a dplyr chain library(dplyr) data( diamonds, package = "ggplot2" ) diamonds \%>\% mutate( carat = cut(carat,breaks = c(0,1,2,3,4,5), right =F) ) \%>\% group_by( carat ) \%>\% summarise_each(funs(mean),-carat) \%>\% parcoords( rownames= F ,color = list( colorScale = htmlwidgets::JS('d3.scale.category10()' ) , colorBy = "carat" ) ,brushMode = "1D" ) } }

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/craving/scripts/createEV_scannervas.R

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pederisager/Opioid_Reward_fMRI

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## Loop through all Observe and Regulate EVs and construct Food Wanting and Regulate Success EVs attach(scannervas_long) # For all included subjects for (subj in unique(subject)) { # For all sessions for (sess in unique(session)) { # For all the conditions (Observe/Regulate) for (cond in unique(condition)) { # Load the EV file assign( x = "EV", value = read.table(file = paste("EVs/session",sess,"/",subj,"/",cond,"_",subj,".txt", sep = "")) ) # Create either a FoodWanting or a RegSuccess EV and put it in the appropriate directory if (cond=="Observer"){ EV$V3 <- demvas[subject==subj & session==sess & condition==cond] # Substitute 1s in V3 with demeaned vas variance assign(paste("FoodWanting","_",subj,"_",sess, sep = ""), EV) # Create a variable for visual inspection # Write the new EV content to a txt file, for use in first level FEAT write.table( x = EV, file = paste("EVs/session",sess,"/",subj,"/","FoodWanting","_",subj,".txt", sep = ""), sep = "\t", dec = ".", row.names = FALSE, col.names = FALSE ) } if (cond=="Reguler"){ EV <- EV[,c(1,2)] EV$V3 <- -demvas[subject==subj & session==sess & condition==cond] assign(paste("RegSuccess","_",subj,"_",sess, sep = ""), EV) write.table( x = EV, file = paste("EVs/session",sess,"/",subj,"/","RegSuccess","_",subj,".txt", sep = ""), sep = "\t", dec = ".", row.names = FALSE, col.names = FALSE ) } } } } ## Load and combine the condition-separated VAS and instruct EVs. Then save the combined EVs in the appropriate session/subject directory #attach(scannervas_long) # For all included subjects for (subj in unique(subject)) { # For all sessions for (sess in unique(session)) { # Load the relevant EVs assign( x = "EVinstrreg", value = read.table(file = paste("EVs/session",sess,"/",subj,"/","Reguler","_instruct_",subj,".txt", sep = "")) ) assign( x = "EVinstrobs", value = read.table(file = paste("EVs/session",sess,"/",subj,"/","Observer","_instruct_",subj,".txt", sep = "")) ) assign( x = "EVvasreg", value = read.table(file = paste("EVs/session",sess,"/",subj,"/","Reguler","_VAS_",subj,".txt", sep = "")) ) assign( x = "EVvasobs", value = read.table(file = paste("EVs/session",sess,"/",subj,"/","Observer","_VAS_",subj,".txt", sep = "")) ) # Combine EVs EVinst <- rbind(EVinstrobs, EVinstrreg) EVvas <- rbind(EVvasobs, EVvasreg) # Save combined vas and instruct EVs as tab delimited .txt file # Save the instruct EV write.table( x = EVinst, file = paste("EVs/session",sess,"/",subj,"/","Instruct","_",subj,".txt", sep = ""), sep = "\t", dec = ".", row.names = FALSE, col.names = FALSE ) # Save the VAS EV as tab delimited .txt file write.table( <<<<<<< HEAD x = EVvas, ======= x = EVinst, >>>>>>> e9f456e8f38254ca983353581547507885683647 file = paste("EVs/session",sess,"/",subj,"/","VAS","_",subj,".txt", sep = ""), sep = "\t", dec = ".", row.names = FALSE, col.names = FALSE ) } }

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#' @title #' Default model predictions #' #' @description #' Default method of the \code{\link[stats]{predict}} generic #' function, which can be used when the model object is empty. #' #' @param object #' A model object, possibly empty. #' #' @param newdata #' An optional data frame in which to look for variables #' with which to predict. #' If omitted, the fitted values are used. #' #' @param ... #' Additional arguments. #' #' @return #' A vector of predictions. #' #' @seealso #' \code{\link[stats]{predict}} from package \pkg{stats}. #' #' @export #' #' @examples #' stats::predict(NULL) #' stats::predict(NULL, newdata = data.frame(x = 1:2, y = 2:3)) #' predict.default <- function(object, newdata, ...) { if (length(object) == 0L) { n <- if (missing(newdata)) 0L else nrow(newdata) return(rep(NA, n)) } NextMethod("predict") } #' @importFrom stats predict #' @export #' stats::predict

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01-Solution-Exercicios-Cap13-Parte1.R

# Lista de Exercícios - Capítulo 13 # Para este script, vamos usar o mlbench (Machine Learning Benchmark Problems) # https://cran.r-project.org/web/packages/mlbench/mlbench.pdf # Este pacote contém diversos datasets e usaremos um com os dados # de votação do congresso americano # Seu trabalho é prever os votos em republicanos e democratas (variável Class) # Import install.packages("mlbench") library(mlbench) # Carregando o dataset ?HouseVotes84 data("HouseVotes84") View(HouseVotes84) # Analise exploratória de dados plot(as.factor(HouseVotes84[,2])) title(main = "Votes cast for issue", xlab = "vote", ylab = "# reps") plot(as.factor(HouseVotes84[HouseVotes84$Class == 'republican', 2])) title(main = "Republican votes cast for issue 1", xlab = "vote", ylab = "# reps") plot(as.factor(HouseVotes84[HouseVotes84$Class == 'democrat',2])) title(main = "Democrat votes cast for issue 1", xlab = "vote", ylab = "# reps") # Funções usadas para imputation # Função que retorna o numeros de NA's por voto e classe (democrat or republican) na_by_col_class <- function (col,cls){return(sum(is.na(HouseVotes84[,col]) & HouseVotes84$Class==cls))} p_y_col_class <- function(col,cls){ sum_y <- sum(HouseVotes84[,col] == 'y' & HouseVotes84$Class == cls, na.rm = TRUE) sum_n <- sum(HouseVotes84[,col] == 'n' & HouseVotes84$Class == cls, na.rm = TRUE) return(sum_y/(sum_y+sum_n))} # Testando a função p_y_col_class(2,'democrat') p_y_col_class(2,'republican') na_by_col_class(2,'democrat') na_by_col_class(2,'republican') # Impute missing values for (i in 2:ncol(HouseVotes84)) { if(sum(is.na(HouseVotes84[,i])>0)) { c1 <- which(is.na(HouseVotes84[,i]) & HouseVotes84$Class == 'democrat',arr.ind = TRUE) c2 <- which(is.na(HouseVotes84[,i]) & HouseVotes84$Class == 'republican',arr.ind = TRUE) HouseVotes84[c1,i] <- ifelse(runif(na_by_col_class(i,'democrat'))<p_y_col_class(i,'democrat'),'y','n') HouseVotes84[c2,i] <- ifelse(runif(na_by_col_class(i,'republican'))<p_y_col_class(i,'republican'),'y','n')} } # Gerando dados de treino e dados de teste HouseVotes84[,"train"] <- ifelse(runif(nrow(HouseVotes84)) < 0.80,1,0) trainColNum <- grep("train",names(HouseVotes84)) # Gerando os dados de treino e de teste a partir da coluna de treino trainHouseVotes84 <- HouseVotes84[HouseVotes84$train == 1, -trainColNum] testHouseVotes84 <- HouseVotes84[HouseVotes84$train == 0, -trainColNum] # Invocando o método NaiveBayes install.packages("e1071") library(e1071) # Exercício 1 - Crie o modelo NaiveBayes # Treine o modelo ?naiveBayes nb_model <- naiveBayes(Class ~ ., data = trainHouseVotes84) # Visualizando o resultado nb_model summary(nb_model) str(nb_model) # Faça as Previsões nb_test_predict <- predict(nb_model, testHouseVotes84[,-1]) # Crie a Confusion matrix table(pred = nb_test_predict, true = testHouseVotes84$Class) # Média mean(nb_test_predict == testHouseVotes84$Class) # Função para executar o registrar todos os resultados do modelo nb_multiple_runs <- function(train_fraction, n) { fraction_correct <- rep(NA,n) for (i in 1:n) { HouseVotes84[,"train"] <- ifelse(runif(nrow(HouseVotes84))<train_fraction,1,0) trainColNum <- grep("train", names(HouseVotes84)) trainHouseVotes84 <- HouseVotes84[HouseVotes84$train == 1,-trainColNum] testHouseVotes84 <- HouseVotes84[HouseVotes84$train == 0,-trainColNum] nb_model <- naiveBayes(Class ~ ., data = trainHouseVotes84) nb_test_predict <- predict(nb_model, testHouseVotes84[,-1]) fraction_correct[i] <- mean(nb_test_predict == testHouseVotes84$Class) } return(fraction_correct) } # Executando o modelo 20 vezes fraction_correct_predictions <- nb_multiple_runs(0.8, 20) fraction_correct_predictions # Resumo dos resultados summary(fraction_correct_predictions) # Desvio padrão sd(fraction_correct_predictions) # Os resultados das execuções estão bem próximos, entre 0.87 e 0.95, # com um desvio padrão de 0.02. # O Naive Bayes fez um bom trabalho com este conjunto de dados

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#' helper function for findDupes #' #' @param fastaFile the name of the FASTA file #' #' return the index, invisibly #' #' @export indexIfNoneFound <- function(fastaFile) { if (!file.exists(paste0(fastaFile, ".fai"))) { message("Indexing ", fastaFile, " to extract sequence names...") invisible(indexFa(fastaFile)) } }

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download.Rd

\name{download} \alias{download} \title{Download a file.} \usage{ download(url) } \description{ Download a file. }

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classifierdemo.R

require(dplyr) require(shiny) require(keras) require(rjson) install_keras(tensorflow = "gpu") setwd("~/datalandz_biosecurity") # instantiate the model model <- application_resnet50(weights = 'imagenet') # load the image img_path <- "./data/)" img <- image_load(img_path, target_size = c(224,224)) x <- image_to_array(img) # ensure we have a 4d tensor with single element in the batch dimension, # the preprocess the input for prediction using resnet50 x <- array_reshape(x, c(1, dim(x))) x <- imagenet_preprocess_input(x) # make predictions then decode and print them preds <- model %>% predict(x) imagenet_decode_predictions(preds, top = 3)[[1]] # } labels <- labelImage("./data/anastrepha_striata.jpg") print(labels) json_data <- fromJSON(file="./UOR.json") json_data <- lapply(json_data, function(x) { x[sapply(x, is.null)] <- NA unlist(x) }) dataset<- as.data.frame(do.call("rbind", json_data)) dataset %>% filter(sp_type_name == "Amphibian") %>% head() %>% select(name_sci)

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plot2.R

source("dataHelper.R") source("plotHelper.R", local = TRUE) pwrc <- GetData() # Start plotting device using default options startDev("plot2") # Calculate date start/end for graph plot(pwrc$DateTime, pwrc$Global_active_power, type = "l", xlab = "Day", ylab = "Global Active Power (kW)", cex.lab=0.7, cex.axis=0.8 ) # Close plotting device closeDev()

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dataUploadUI.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/dataUpload.R \name{dataUploadUI} \alias{dataUploadUI} \title{Shiny module UI for user-uploaded data} \usage{ dataUploadUI(id, ...) } \arguments{ \item{id}{Character ID for specifying namespace, see \code{shiny::\link[shiny]{NS}}.} \item{...}{Named list of params passed to \code{shiny::\link[shiny]{fileInput}}. Note that \code{multiple = FALSE} (the default) must not be overridden.} } \value{ UI components. } \description{ Create UI that contains a main \code{shiny::\link[shiny]{fileInput}} and two optional features: a file-remove button and link to display more info (for communicating file upload requirements and/or instructions). } \seealso{ Other dataUpload functions: \code{\link{dataUploadServer}()} } \concept{dataUpload functions}

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2.cfa.R

library(lavaan) library(lavaan.survey) library(semPlot) library(sjlabelled) memory.limit(15000) options(survey.lonely.psu="adjust") InverseCod <- c("BS4G6","BS4G9","BS4G13","IS2P24C","IS2P24D","IS2P24F","IS3G24C","IS3G24D","IS3G24F") #Cycles 2-3 vars reverse whole scale C1vars <- VarsToUse %>% filter(Domain == "Scales" & Dataset %in% c("ISG","ISE")) %>% select(VariableC1) %>% na.omit() %>% pull() C1vars <- C1vars[grepl(paste0(InverseCod, collapse = "|"), C1vars)] C2vars <- VarsToUse %>% filter(Domain == "Scales" & Dataset %in% c("ISG","ISE")) %>% select(VariableC2) %>% na.omit() %>% pull() C2vars <- C2vars[!grepl(paste0(InverseCod, collapse = "|"), C2vars)] C3vars <- VarsToUse %>% filter(Domain == "Scales" & Dataset %in% c("ISG","ISE")) %>% select(VariableC3) %>% na.omit() %>% pull() C3vars <- C3vars[!grepl(paste0(InverseCod, collapse = "|"), C3vars)] torecod <- c(C1vars, C2vars, C3vars) if(!any(colnames(ISC_cfa) %in% paste0(c(C1vars, C2vars, C3vars),"."))){ dat <- data.frame(psych::reverse.code(keys = rep(-1,length(torecod)), items = ISC_cfa[torecod], mini = rep(1,length(torecod)), maxi = rep(4,length(torecod)))) colnames(dat) <- colnames(ISC_cfa[, torecod]) } #Copy labels of old variables label <- NULL for (i in 1:length(torecod)) { label[i] <- eval(parse(text=paste0("get_label(ISC_cfa$",torecod[i],")"))) } #Add (r) to label for inverse coded variables labelr <- NULL for (j in 1:length(InverseCod)) { labelr[j] <- paste0(eval(parse(text=paste0("get_label(ISC_cfa$",InverseCod[j],")"))),"(r)") } ISC_cfa <- cbind(ISC_cfa[,!colnames(ISC_cfa) %in% torecod], dat) ISC_cfa[torecod] <- set_label(ISC_cfa[torecod], label) ISC_cfa[InverseCod] <- set_label(ISC_cfa[InverseCod], labelr) ISC_cfa[Scales] <- set_labels(ISC_cfa[Scales], labels = c("strongly disagree" = 1, "disagree" = 2, "agree" = 3, "strongly agree" = 4)) if (any(grepl("99", years$year))){ model99E <-' Gend_Equal =~ BS4G1 + BS4G4 + BS4G6 + BS4G9 + BS4G11 + BS4G13 Immi_Equal =~ BS4H1 + BS4H2 + BS4H3 + BS4H4 + BS4H5 Ethn_Equal =~ BS4G2 + BS4G5 + BS4G8 + BS4G12 BS4H1 ~~ BS4H4 BS4G9 ~~ BS4G13 BS4G6 ~~ BS4G13 BS4G6 ~~ BS4G9 ' model99nE <-' Gend_Equal =~ BS4G1 + BS4G4 + BS4G6 + BS4G9 + BS4G11 + BS4G13 Immi_Equal =~ BS4H1 + BS4H2 + BS4H3 + BS4H4 + BS4H5 Ethn_Equal =~ BS4G2 + BS4G5 + BS4G8 + BS4G12 BS4H1 ~~ BS4H4 BS4G9 ~~ BS4G13 BS4G6 ~~ BS4G13 BS4G6 ~~ BS4G9 ' cat('## CIVED 1999 \n') #############1999######### index99 <- Itemdesc %>% filter(item != "index") %>% dplyr::select(CIVED_1999) %>% na.omit() %>% pull() ds991 <- ISC_cfa %>% filter(!is.na(TOTWGT_Gc1)) %>% dplyr::select(all_of(index99), all_of(Id), IDJK, IDCL, SENWGT_Gc1, GENDER) %>% mutate(GENDER = as.character(GENDER)) ds99 <- ds991 %>% mutate_at(.funs = as.numeric, .vars = index99) #European countries ds99E <- ds99 %>% filter(!COUNTRY %in% c(CNTne,CNT2cne)) survey.design99E <- svydesign(ids= ~ IDCL, weights = ~ SENWGT_Gc1, strata = ~ IDJK, nest = TRUE, data = ds99E) cfa99E <- cfa(model99E, data = ds99E, cluster = c("COUNTRY", "IDSCHOOL"), missing = "fiml") survey.fit99E <- lavaan.survey(lavaan.fit = cfa99E, survey.design = survey.design99E, estimator= "MLMVS") #cfa <- cfa(model99E, data = ds99E, cluster = c("COUNTRY", "IDSCHOOL")) #summary(cfa, fit.measures=TRUE) #print(modindices(cfa, sort=T)[1:10,]) #Non European countries ds99nE <- ds99 %>% filter(COUNTRY %in% c(CNTne,CNT2cne)) survey.design99nE <- svydesign(ids= ~ IDCL, weights = ~ SENWGT_Gc1, strata = ~ IDJK, nest = TRUE, data = ds99nE) cfa99nE <- cfa(model99nE, data = ds99nE, cluster = c("COUNTRY", "IDSCHOOL"), missing = "fiml") survey.fit99nE <- lavaan.survey(lavaan.fit = cfa99nE, survey.design = survey.design99nE, estimator= "MLMVS") #cfa <- cfa(model99nE, data = ds99nE, cluster = c("COUNTRY", "IDSCHOOL")) #summary(cfa, fit.measures=TRUE) #print(modindices(cfa,sort=T)[1:10,]) #Factor scores for latent variables p99E <- cbind(ds99E, predict(cfa99E)) #Prediction of factor scores should be based on survey design but not possible to obtain in R using FIML p99nE <- cbind(ds99nE, predict(cfa99nE)) p99 <- p99E %>% bind_rows(p99nE) %>% mutate(cycle = "C1") %>% dplyr::select(all_of(Id), all_of(Newscales)) rm(cfa99E, cfa99nE) #Remove fit to save space in disk #Fit for each country separately cnt99 <- unique(ds99$COUNTRY) meast99 <- NULL stdl99 <- NULL for (c99 in cnt99) { dscNT <- ds99[ds99$COUNTRY == c99,] survey.cnt99 <- svydesign(ids = ~ IDCL, weights = ~ SENWGT_Gc1, strata = ~ IDJK, nest = TRUE, data = dscNT) if(!c99 %in% c(CNTne,CNT2cne)) model <- model99E else model <- model99nE CNTcfa <- cfa(model, cluster = c("IDSCHOOL"), data = dscNT) survey.CNTfit <- lavaan.survey(lavaan.fit = CNTcfa, survey.design = survey.cnt99, estimator= "MLMVS") meas <- fitMeasures(survey.CNTfit, c("chisq","df", "cfi", "tli","rmsea", "srmr"), output = "matrix") meas <- rbind(n = nobs(survey.CNTfit), meas) meast99 <- cbind(meast99, meas) stdl <- standardizedSolution(survey.CNTfit) %>% filter(op == "=~") %>% mutate(cntry = c99) stdl99 <- rbind(stdl99, stdl) } meast99 <- as.data.frame(t(meast99)) rownames(meast99) <- cnt99 rm(CNTcfa) cat('### CFA - ICCS 1999') cat(' \n') cat(' \n') tmeasE <- t(fitMeasures(survey.fit99E, c("chisq","df","cfi", "tli","rmsea", "srmr"), output = "matrix")) tmeasnE <- t(fitMeasures(survey.fit99nE, c("chisq","df","cfi", "tli","rmsea", "srmr"), output = "matrix")) meas99 <- rbind(data.frame(Quest = "European", n = nobs(survey.fit99E), round(tmeasE, 3)), data.frame(Quest = "Non-european", n = nobs(survey.fit99nE), round(tmeasnE, 3))) knitr::kable(meas99) %>% print() cat(' \n') cat('### CFA - ICCS 1999, by countries') cat(' \n') knitr::kable(meast99,digits = 3) %>% print() cat(' \n') cat(' \n') invisible(semPaths(survey.fit99E,"model", "std", "lisrel", edge.label.cex = 0.6, intercepts = FALSE, groups = "latent", pastel = TRUE, title = FALSE, nCharNodes = 10, nDigits = 1)) title("CFA measurement European model", line = 2) invisible(semPaths(survey.fit99nE,"model", "std", "lisrel", edge.label.cex = 0.6, intercepts = FALSE, groups = "latent", pastel = TRUE, title = FALSE, nCharNodes = 10, nDigits = 1)) title("CFA measurement Non European model", line = 2) cat(' \n') cat(' \n') labels <- data.frame(label = tolower(sjlabelled::get_label(ds991))) labels <- labels %>% filter(!str_detect(rownames(labels), c("IDSTUD|IDSCHOOL|COUNTRY|TOTWGT|GENDER"))) %>% mutate(variable = rownames(.)) stdl99 <- stdl99 %>% mutate(rhs = factor(rhs, levels = labels$variable, labels = labels$label)) %>% mutate(Model = ifelse(cntry %in% c(CNTne,CNT2cne), "Non-European", "European")) l1 <- stdl99 %>% data.frame() %>% ggplot(aes(x = est.std, y = rhs, color = reorder(cntry, desc(cntry)))) + geom_linerange(aes(xmin = ci.lower, xmax = ci.upper), position = position_dodge(0.4)) + geom_jitter(position = position_dodge(0.4)) + facet_grid(lhs~Model, scales = "free") + geom_text(aes(label=cntry),hjust=0, vjust=1, position = position_dodge(0.4), size = 2) + theme(legend.position = "none", axis.line.y = element_blank()) + ggtitle("Loading distribution of scales - ICCS 1999") + ylab("") + xlab("Loadings with Confidence Interval") + scale_y_discrete(labels = function(x) str_wrap(x, 25)) + scale_x_continuous(breaks = c(0, 0.25, 0.5, 0.75, 1), limits = c(0,1), labels = function(x) sprintf("%.1f", x)) print(l1) cat(' \n') cat(' \n') cat('### Invariance between COUNTRY') cat(' \n') cat(' \n') inv.conf99E <- cfa(model99E, data = ds99E, cluster = "IDSCHOOL", group = "COUNTRY") inv.conf99E <- lavaan.survey(lavaan.fit = inv.conf99E, survey.design = survey.design99E, estimator= "MLMVS") inv.metr99E <- cfa(model99E, data = ds99E, cluster = "IDSCHOOL", group = "COUNTRY", group.equal = c("loadings")) inv.metr99E <- lavaan.survey(lavaan.fit = inv.metr99E, survey.design = survey.design99E, estimator= "MLMVS") inv.scal99E <- cfa(model99E, data = ds99E, cluster = "IDSCHOOL", group = "COUNTRY", group.equal = c("loadings","intercepts")) inv.scal99E <- lavaan.survey(lavaan.fit = inv.scal99E, survey.design = survey.design99E, estimator= "MLMVS") inv.stri99E <- cfa(model99E, data = ds99E, cluster = "IDSCHOOL", group = "COUNTRY", group.equal = c("loadings","intercepts","lv.variances")) inv.stri99E <- lavaan.survey(lavaan.fit = inv.stri99E, survey.design = survey.design99E, estimator= "MLMVS") invarCNT1 <- data.frame(Quest = "European", round(rbind(Configural = fitMeasures(inv.conf99E, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Metric = fitMeasures(inv.metr99E, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Scalar = fitMeasures(inv.scal99E, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Strict = fitMeasures(inv.stri99E, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea"))),3)) inv.conf99nE <- cfa(model99nE, data = ds99nE, cluster = "IDSCHOOL", group = "COUNTRY") inv.conf99nE <- lavaan.survey(lavaan.fit = inv.conf99nE, survey.design = survey.design99nE, estimator= "MLMVS") inv.metr99nE <- cfa(model99nE, data = ds99nE, cluster = "IDSCHOOL", group = "COUNTRY", group.equal = c("loadings")) inv.metr99nE <- lavaan.survey(lavaan.fit = inv.metr99nE, survey.design = survey.design99nE, estimator= "MLMVS") inv.scal99nE <- cfa(model99nE, data = ds99nE, cluster = "IDSCHOOL", group = "COUNTRY", group.equal = c("loadings","intercepts")) inv.scal99nE <- lavaan.survey(lavaan.fit = inv.scal99nE, survey.design = survey.design99nE, estimator= "MLMVS") inv.stri99nE <- cfa(model99nE, data = ds99nE, cluster = "IDSCHOOL", group = "COUNTRY", group.equal = c("loadings","intercepts","lv.variances")) inv.stri99nE <- lavaan.survey(lavaan.fit = inv.stri99nE, survey.design = survey.design99nE, estimator= "MLMVS") invarCNT2 <- data.frame(Quest = "Non-European", round(rbind(Configural = fitMeasures(inv.conf99nE, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Metric = fitMeasures(inv.metr99nE, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Scalar = fitMeasures(inv.scal99nE, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Strict = fitMeasures(inv.stri99nE, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea"))),3)) invarCNT <- invarCNT1 %>% mutate(Invariance = rownames(invarCNT1)) %>% relocate(Invariance, .before = npar) %>% mutate(D_tli = tli-lag(tli), D_cfi = cfi-lag(cfi), D_rmsea = rmsea-lag(rmsea)) %>% bind_rows(invarCNT2 %>% mutate(Invariance = rownames(invarCNT2)) %>% relocate(Invariance, .before = npar) %>% mutate(D_tli = tli-lag(tli), D_cfi = cfi-lag(cfi), D_rmsea = rmsea-lag(rmsea))) %>% knitr::kable() %>% print() cat(' \n') cat(' \n') rm(inv.conf99E, inv.metr99E, inv.scal99E, inv.stri99E, inv.conf99nE, inv.metr99nE, inv.scal99nE, inv.stri99nE) #Remove to save space in disk cat('### Invariance between GENDER') cat(' \n') cat(' \n') inv.conf99E <- cfa(model99E, data = ds99E, cluster = "IDSCHOOL", group = "GENDER") inv.conf99E <- lavaan.survey(lavaan.fit = inv.conf99E, survey.design = survey.design99E, estimator= "MLMVS") inv.metr99E <- cfa(model99E, data = ds99E, cluster = "IDSCHOOL", group = "GENDER", group.equal = c("loadings")) inv.metr99E <- lavaan.survey(lavaan.fit = inv.metr99E, survey.design = survey.design99E, estimator= "MLMVS") inv.scal99E <- cfa(model99E, data = ds99E, cluster = "IDSCHOOL", group = "GENDER", group.equal = c("loadings","intercepts")) inv.scal99E <- lavaan.survey(lavaan.fit = inv.scal99E, survey.design = survey.design99E, estimator= "MLMVS") inv.stri99E <- cfa(model99E, data = ds99E, cluster = "IDSCHOOL", group = "GENDER", group.equal = c("loadings","intercepts","lv.variances")) inv.stri99E <- lavaan.survey(lavaan.fit = inv.stri99E, survey.design = survey.design99E, estimator= "MLMVS") invarGNDR1 <- data.frame(Quest = "European", round(rbind(Configural = fitMeasures(inv.conf99E, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Metric = fitMeasures(inv.metr99E, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Scalar = fitMeasures(inv.scal99E, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Strict = fitMeasures(inv.stri99E, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea"))),3)) inv.conf99nE <- cfa(model99nE, data = ds99nE, cluster = "IDSCHOOL", group = "GENDER") inv.conf99nE <- lavaan.survey(lavaan.fit = inv.conf99nE, survey.design = survey.design99nE, estimator= "MLMVS") inv.metr99nE <- cfa(model99nE, data = ds99nE, cluster = "IDSCHOOL", group = "GENDER", group.equal = c("loadings")) inv.metr99nE <- lavaan.survey(lavaan.fit = inv.metr99nE, survey.design = survey.design99nE, estimator= "MLMVS") inv.scal99nE <- cfa(model99nE, data = ds99nE, cluster = "IDSCHOOL", group = "GENDER", group.equal = c("loadings","intercepts")) inv.scal99nE <- lavaan.survey(lavaan.fit = inv.scal99nE, survey.design = survey.design99nE, estimator= "MLMVS") inv.stri99nE <- cfa(model99nE, data = ds99nE, cluster = "IDSCHOOL", group = "GENDER", group.equal = c("loadings","intercepts","lv.variances")) inv.stri99nE <- lavaan.survey(lavaan.fit = inv.stri99nE, survey.design = survey.design99nE, estimator= "MLMVS") invarGNDR2 <- data.frame(Quest = "Non-European", round(rbind(Configural = fitMeasures(inv.conf99nE, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Metric = fitMeasures(inv.metr99nE, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Scalar = fitMeasures(inv.scal99nE, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Strict = fitMeasures(inv.stri99nE, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea"))),3)) invarGNDR <- invarGNDR1 %>% mutate(Invariance = rownames(invarGNDR1)) %>% relocate(Invariance, .before = npar) %>% mutate(D_tli = tli-lag(tli), D_cfi = cfi-lag(cfi), D_rmsea = rmsea-lag(rmsea)) %>% bind_rows(invarGNDR2 %>% mutate(Invariance = rownames(invarGNDR2)) %>% relocate(Invariance, .before = npar) %>% mutate(D_tli = tli-lag(tli), D_cfi = cfi-lag(cfi), D_rmsea = rmsea-lag(rmsea))) %>% knitr::kable() %>% print() cat(' \n') cat(' \n') rm(inv.conf99E, inv.metr99E, inv.scal99E, inv.stri99E, inv.conf99nE, inv.metr99nE, inv.scal99nE, inv.stri99nE) #Remove to save space in disk } if (any(grepl("09", years$year))){ model09E<-' Gend_Equal =~ IS2P24A + IS2P24B + IS2P24C + IS2P24D + IS2P24E Immi_Equal =~ IS2P26A + IS2P26B + IS2P26C + IS2P26D + IS2P26E Ethn_Equal =~ IS2P25A + IS2P25B + IS2P25C + IS2P25D + IS2P25E IS2P24C ~~ IS2P24D IS2P25A ~~ IS2P25B IS2P26A ~~ IS2P26D IS2P24A ~~ IS2P24B ' model09nE<-' Gend_Equal =~ IS2P24A + IS2P24B + IS2P24C + IS2P24D + IS2P24E Immi_Equal =~ IS2P26A + IS2P26B + IS2P26C + IS2P26D + IS2P26E Ethn_Equal =~ IS2P25A + IS2P25B + IS2P25C + IS2P25D + IS2P25E IS2P24C ~~ IS2P24D IS2P25A ~~ IS2P25B IS2P26A ~~ IS2P26D ' #############2009######### cat('## ICCS 2009 \n') index09 <- Itemdesc %>% filter(item != "index") %>% dplyr::select(ICCS_2009) %>% na.omit() %>% pull() ds091 <- ISC_cfa %>% filter(!is.na(TOTWGT_Gc2)) %>% dplyr::select(all_of(index09), all_of(Id), IDJK, IDCL, SENWGT_Gc2, SGENDER) %>% mutate(GENDER = as.character(SGENDER)) ds09 <- ds091 %>% mutate_at(.funs = as.numeric, .vars = index09) #European countries ds09E <- ds09 %>% filter(!COUNTRY %in% c(CNTne,CNT2cne)) survey.design09E <- svydesign(ids= ~ IDCL, weights = ~ SENWGT_Gc2, strata = ~ IDJK, nest = TRUE, data = ds09E) cfa09E <- cfa(model09E, data = ds09E, cluster = c("COUNTRY", "IDSCHOOL"), missing = "fiml") survey.fit09E <- lavaan.survey(lavaan.fit = cfa09E, survey.design = survey.design09E, estimator= "MLMVS") # cfa <- cfa(model09E, data = ds09E, cluster = c("COUNTRY", "IDSCHOOL")) # summary(cfa, fit.measures=TRUE) # print(modindices(cfa, sort=T)[1:10,]) #Non European countries ds09nE <- ds09 %>% filter(COUNTRY %in% c(CNTne,CNT2cne)) survey.design09nE <- svydesign(ids= ~ IDCL, weights = ~ SENWGT_Gc2, strata = ~ IDJK, nest = TRUE, data = ds09nE) cfa09nE <- cfa(model09nE, data = ds09nE, cluster = c("COUNTRY", "IDSCHOOL"), missing = "fiml") survey.fit09nE <- lavaan.survey(lavaan.fit = cfa09nE, survey.design = survey.design09nE, estimator= "MLMVS") # cfa <- cfa(model09nE, data = ds09nE, cluster = c("COUNTRY", "IDSCHOOL")) # summary(cfa, fit.measures=TRUE) # print(modindices(cfa,sort=T)[1:10,]) #Factor scores for latent variables p09E <- cbind(ds09E, predict(cfa09E)) #Prediction of factor scores should be based on survey design but not possible to obtain in R using FIML p09nE <- cbind(ds09nE, predict(cfa09nE)) p09 <- p09E %>% bind_rows(p09nE) %>% mutate(cycle = "C2") %>% dplyr::select(all_of(Id), all_of(Newscales)) rm(cfa09E, cfa09nE) #Remove fit to save space in disk #Fit for each country separately cnt09 <- unique(ds09$COUNTRY) meast09 <- NULL stdl09 <- NULL for (c09 in cnt09) { dscNT <- ds09[ds09$COUNTRY == c09,] survey.cnt09 <- svydesign(ids = ~ IDCL, weights = ~ SENWGT_Gc2, strata = ~ IDJK, nest = TRUE, data = dscNT) if(!c09 %in% c(CNTne,CNT2cne)) model <- model09E else model <- model09nE CNTcfa <- cfa(model, cluster = c("IDSCHOOL"), data = dscNT) survey.CNTfit <- lavaan.survey(lavaan.fit = CNTcfa, survey.design = survey.cnt09, estimator= "MLMVS") meas <- fitMeasures(survey.CNTfit, c("chisq","df", "cfi", "tli","rmsea", "srmr"), output = "matrix") meas <- rbind(n = nobs(survey.CNTfit), meas) meast09 <- cbind(meast09, meas) stdl <- standardizedSolution(survey.CNTfit) %>% filter(op == "=~") %>% mutate(cntry = c09) stdl09 <- rbind(stdl09, stdl) } meast09 <- as.data.frame(t(meast09)) rownames(meast09) <- cnt09 rm(CNTcfa) cat('### CFA - ICCS 2009') cat(' \n') cat(' \n') tmeasE <- t(fitMeasures(survey.fit09E, c("chisq","df","cfi", "tli","rmsea", "srmr"), output = "matrix")) tmeasnE <- t(fitMeasures(survey.fit09nE, c("chisq","df","cfi", "tli","rmsea", "srmr"), output = "matrix")) meas09 <- rbind(data.frame(Quest = "European", n = nobs(survey.fit09E), round(tmeasE, 3)), data.frame(Quest = "Non-european", n = nobs(survey.fit09nE), round(tmeasnE, 3))) knitr::kable(meas09) %>% print() cat(' \n') cat('### CFA - ICCS 2009, by countries') cat(' \n') knitr::kable(meast09,digits = 3) %>% print() cat(' \n') cat(' \n') invisible(semPaths(survey.fit09E,"model", "std", "lisrel", edge.label.cex = 0.6, intercepts = FALSE, groups = "latent", pastel = TRUE, title = FALSE, nCharNodes = 10, nDigits = 1)) title("CFA measurement European model", line = 2) invisible(semPaths(survey.fit09nE,"model", "std", "lisrel", edge.label.cex = 0.6, intercepts = FALSE, groups = "latent", pastel = TRUE, title = FALSE, nCharNodes = 10, nDigits = 1)) title("CFA measurement Non European model", line = 2) cat(' \n') cat(' \n') labels <- data.frame(label = tolower(sjlabelled::get_label(ds091))) labels <- labels %>% filter(!str_detect(rownames(labels), c("IDSTUD|IDSCHOOL|COUNTRY|TOTWGT|GENDER"))) %>% mutate(variable = rownames(.)) stdl09 <- stdl09 %>% mutate(rhs = factor(rhs, levels = labels$variable, labels = labels$label)) %>% mutate(Model = ifelse(cntry %in% c(CNTne,CNT2cne), "Non-European", "European")) l1 <- stdl09 %>% data.frame() %>% ggplot(aes(x = est.std, y = rhs, color = reorder(cntry, desc(cntry)))) + geom_linerange(aes(xmin = ci.lower, xmax = ci.upper), position = position_dodge(0.4)) + geom_jitter(position = position_dodge(0.4)) + facet_grid(lhs~Model, scales = "free") + geom_text(aes(label=cntry),hjust=0, vjust=1, position = position_dodge(0.4), size = 2) + theme(legend.position = "none", axis.line.y = element_blank()) + ggtitle("Loading distribution of scales - ICCS 2009") + ylab("") + xlab("Loadings with Confidence Interval") + scale_y_discrete(labels = function(x) str_wrap(x, 25)) + scale_x_continuous(breaks = c(0, 0.25, 0.5, 0.75, 1), limits = c(0,1), labels = function(x) sprintf("%.1f", x)) print(l1) cat(' \n') cat(' \n') cat('### Invariance between COUNTRY') cat(' \n') cat(' \n') inv.conf09E <- cfa(model09E, data = ds09E, cluster = "IDSCHOOL", group = "COUNTRY") inv.conf09E <- lavaan.survey(lavaan.fit = inv.conf09E, survey.design = survey.design09E, estimator= "MLMVS") inv.metr09E <- cfa(model09E, data = ds09E, cluster = "IDSCHOOL", group = "COUNTRY", group.equal = c("loadings")) inv.metr09E <- lavaan.survey(lavaan.fit = inv.metr09E, survey.design = survey.design09E, estimator= "MLMVS") inv.scal09E <- cfa(model09E, data = ds09E, cluster = "IDSCHOOL", group = "COUNTRY", group.equal = c("loadings","intercepts")) inv.scal09E <- lavaan.survey(lavaan.fit = inv.scal09E, survey.design = survey.design09E, estimator= "MLMVS") inv.stri09E <- cfa(model09E, data = ds09E, cluster = "IDSCHOOL", group = "COUNTRY", group.equal = c("loadings","intercepts","lv.variances")) inv.stri09E <- lavaan.survey(lavaan.fit = inv.stri09E, survey.design = survey.design09E, estimator= "MLMVS") invarCNT1 <- data.frame(Quest = "European", round(rbind(Configural = fitMeasures(inv.conf09E, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Metric = fitMeasures(inv.metr09E, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Scalar = fitMeasures(inv.scal09E, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Strict = fitMeasures(inv.stri09E, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea"))),3)) inv.conf09nE <- cfa(model09nE, data = ds09nE, cluster = "IDSCHOOL", group = "COUNTRY") inv.conf09nE <- lavaan.survey(lavaan.fit = inv.conf09nE, survey.design = survey.design09nE, estimator= "MLMVS") inv.metr09nE <- cfa(model09nE, data = ds09nE, cluster = "IDSCHOOL", group = "COUNTRY", group.equal = c("loadings")) inv.metr09nE <- lavaan.survey(lavaan.fit = inv.metr09nE, survey.design = survey.design09nE, estimator= "MLMVS") inv.scal09nE <- cfa(model09nE, data = ds09nE, cluster = "IDSCHOOL", group = "COUNTRY", group.equal = c("loadings","intercepts")) inv.scal09nE <- lavaan.survey(lavaan.fit = inv.scal09nE, survey.design = survey.design09nE, estimator= "MLMVS") inv.stri09nE <- cfa(model09nE, data = ds09nE, cluster = "IDSCHOOL", group = "COUNTRY", group.equal = c("loadings","intercepts","lv.variances")) inv.stri09nE <- lavaan.survey(lavaan.fit = inv.stri09nE, survey.design = survey.design09nE, estimator= "MLMVS") invarCNT2 <- data.frame(Quest = "Non-European", round(rbind(Configural = fitMeasures(inv.conf09nE, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Metric = fitMeasures(inv.metr09nE, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Scalar = fitMeasures(inv.scal09nE, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Strict = fitMeasures(inv.stri09nE, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea"))),3)) invarCNT <- invarCNT1 %>% mutate(Invariance = rownames(invarCNT1)) %>% relocate(Invariance, .before = npar) %>% mutate(D_tli = tli-lag(tli), D_cfi = cfi-lag(cfi), D_rmsea = rmsea-lag(rmsea)) %>% bind_rows(invarCNT2 %>% mutate(Invariance = rownames(invarCNT2)) %>% relocate(Invariance, .before = npar) %>% mutate(D_tli = tli-lag(tli), D_cfi = cfi-lag(cfi), D_rmsea = rmsea-lag(rmsea))) %>% knitr::kable() %>% print() cat(' \n') cat(' \n') rm(inv.conf09E, inv.metr09E, inv.scal09E, inv.stri09E, inv.conf09nE, inv.metr09nE, inv.scal09nE, inv.stri09nE) #Remove to save space in disk cat('### Invariance between GENDER') cat(' \n') cat(' \n') inv.conf09E <- cfa(model09E, data = ds09E, cluster = "IDSCHOOL", group = "GENDER") inv.conf09E <- lavaan.survey(lavaan.fit = inv.conf09E, survey.design = survey.design09E, estimator= "MLMVS") inv.metr09E <- cfa(model09E, data = ds09E, cluster = "IDSCHOOL", group = "GENDER", group.equal = c("loadings")) inv.metr09E <- lavaan.survey(lavaan.fit = inv.metr09E, survey.design = survey.design09E, estimator= "MLMVS") inv.scal09E <- cfa(model09E, data = ds09E, cluster = "IDSCHOOL", group = "GENDER", group.equal = c("loadings","intercepts")) inv.scal09E <- lavaan.survey(lavaan.fit = inv.scal09E, survey.design = survey.design09E, estimator= "MLMVS") inv.stri09E <- cfa(model09E, data = ds09E, cluster = "IDSCHOOL", group = "GENDER", group.equal = c("loadings","intercepts","lv.variances")) inv.stri09E <- lavaan.survey(lavaan.fit = inv.stri09E, survey.design = survey.design09E, estimator= "MLMVS") invarGNDR1 <- data.frame(Quest = "European", round(rbind(Configural = fitMeasures(inv.conf09E, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Metric = fitMeasures(inv.metr09E, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Scalar = fitMeasures(inv.scal09E, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Strict = fitMeasures(inv.stri09E, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea"))),3)) inv.conf09nE <- cfa(model09nE, data = ds09nE, cluster = "IDSCHOOL", group = "GENDER") inv.conf09nE <- lavaan.survey(lavaan.fit = inv.conf09nE, survey.design = survey.design09nE, estimator= "MLMVS") inv.metr09nE <- cfa(model09nE, data = ds09nE, cluster = "IDSCHOOL", group = "GENDER", group.equal = c("loadings")) inv.metr09nE <- lavaan.survey(lavaan.fit = inv.metr09nE, survey.design = survey.design09nE, estimator= "MLMVS") inv.scal09nE <- cfa(model09nE, data = ds09nE, cluster = "IDSCHOOL", group = "GENDER", group.equal = c("loadings","intercepts")) inv.scal09nE <- lavaan.survey(lavaan.fit = inv.scal09nE, survey.design = survey.design09nE, estimator= "MLMVS") inv.stri09nE <- cfa(model09nE, data = ds09nE, cluster = "IDSCHOOL", group = "GENDER", group.equal = c("loadings","intercepts","lv.variances")) inv.stri09nE <- lavaan.survey(lavaan.fit = inv.stri09nE, survey.design = survey.design09nE, estimator= "MLMVS") invarGNDR2 <- data.frame(Quest = "Non-European", round(rbind(Configural = fitMeasures(inv.conf09nE, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Metric = fitMeasures(inv.metr09nE, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Scalar = fitMeasures(inv.scal09nE, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Strict = fitMeasures(inv.stri09nE, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea"))),3)) invarGNDR <- invarGNDR1 %>% mutate(Invariance = rownames(invarGNDR1)) %>% relocate(Invariance, .before = npar) %>% mutate(D_tli = tli-lag(tli), D_cfi = cfi-lag(cfi), D_rmsea = rmsea-lag(rmsea)) %>% bind_rows(invarGNDR2 %>% mutate(Invariance = rownames(invarGNDR2)) %>% relocate(Invariance, .before = npar) %>% mutate(D_tli = tli-lag(tli), D_cfi = cfi-lag(cfi), D_rmsea = rmsea-lag(rmsea))) %>% knitr::kable() %>% print() cat(' \n') cat(' \n') rm(inv.conf09E, inv.metr09E, inv.scal09E, inv.stri09E, inv.conf09nE, inv.metr09nE, inv.scal09nE, inv.stri09nE) #Remove to save space in disk } if (any(grepl("16", years$year))){ model16E<-' Gend_Equal =~ IS3G24A + IS3G24B + IS3G24C + IS3G24D + IS3G24E Immi_Equal =~ ES3G04A + ES3G04B + ES3G04C + ES3G04D + ES3G04E Ethn_Equal =~ IS3G25A + IS3G25B + IS3G25C + IS3G25D + IS3G25E IS3G24A ~~ IS3G24B IS3G25A ~~ IS3G25B ES3G04A ~~ ES3G04D ' model16nE<-' Gend_Equal =~ IS3G24A + IS3G24B + IS3G24C + IS3G24D + IS3G24E Ethn_Equal =~ IS3G25A + IS3G25B + IS3G25C + IS3G25D + IS3G25E IS3G24C ~~ IS3G24D IS3G25A ~~ IS3G25B IS3G24A ~~ IS3G24B ' #############2016######### cat('## ICCS 2016 \n') index16 <- Itemdesc %>% filter(item != "index") %>% dplyr::select(ICCS_2016) %>% na.omit() %>% pull() ds161 <- ISC_cfa %>% filter(!is.na(TOTWGT_Gc3)) %>% dplyr::select(all_of(index16), all_of(Id), IDJK, IDCL, SENWGT_Gc3, S_GENDER) %>% mutate(GENDER = as.character(S_GENDER)) ds16 <- ds161 %>% mutate_at(.funs = as.numeric, .vars = index16) #European countries ds16E <- ds16 %>% filter(!COUNTRY %in% c(CNTne,CNT2cne)) survey.design16E <- svydesign(ids= ~ IDCL, weights = ~ SENWGT_Gc3, strata = ~ IDJK, nest = TRUE, data = ds16E) cfa16E <- cfa(model16E, data = ds16E, cluster = c("COUNTRY", "IDSCHOOL"), missing = "fiml") survey.fit16E <- lavaan.survey(lavaan.fit = cfa16E, survey.design = survey.design16E, estimator= "MLMVS") #cfa <- cfa(model16E, data = ds16E, cluster = c("COUNTRY", "IDSCHOOL")) #summary(cfa, fit.measures=TRUE) #print(modindices(cfa, sort=T)[1:10,]) #Non European countries ds16nE <- ds16 %>% filter(COUNTRY %in% c(CNTne,CNT2cne)) survey.design16nE <- svydesign(ids= ~ IDCL, weights = ~ SENWGT_Gc3, strata = ~ IDJK, nest = TRUE, data = ds16nE) cfa16nE <- cfa(model16nE, data = ds16nE, cluster = c("COUNTRY", "IDSCHOOL"), missing = "fiml") survey.fit16nE <- lavaan.survey(lavaan.fit = cfa16nE, survey.design = survey.design16nE, estimator= "MLMVS") # cfa <- cfa(model16nE, data = ds16nE, cluster = c("COUNTRY", "IDSCHOOL")) # summary(cfa, fit.measures=TRUE) # print(modindices(cfa,sort=T)[1:10,]) #Factor scores for latent variables p16E <- cbind(ds16E, predict(cfa16E)) #Prediction of factor scores should be based on survey design but not possible to obtain in R using FIML p16nE <- cbind(ds16nE, predict(cfa16nE)) p16 <- p16E %>% bind_rows(p16nE) %>% mutate(cycle = "C3") %>% dplyr::select(all_of(Id), all_of(Newscales)) rm(cfa16E, cfa16nE) #Remove fit to save space in disk #Fit for each country separately cnt16 <- unique(ds16$COUNTRY) meast16 <- NULL stdl16 <- NULL for (c16 in cnt16) { dscNT <- ds16[ds16$COUNTRY == c16,] survey.cnt16 <- svydesign(ids = ~ IDCL, weights = ~ SENWGT_Gc3, strata = ~ IDJK, nest = TRUE, data = dscNT) if(!c16 %in% c(CNTne,CNT2cne)) model <- model16E else model <- model16nE CNTcfa <- cfa(model, cluster = c("IDSCHOOL"), data = dscNT) survey.CNTfit <- lavaan.survey(lavaan.fit = CNTcfa, survey.design = survey.cnt16, estimator= "MLMVS") meas <- fitMeasures(survey.CNTfit, c("chisq","df", "cfi", "tli","rmsea", "srmr"), output = "matrix") meas <- rbind(n = nobs(survey.CNTfit), meas) meast16 <- cbind(meast16, meas) stdl <- standardizedSolution(survey.CNTfit) %>% filter(op == "=~") %>% mutate(cntry = c16) stdl16 <- rbind(stdl16, stdl) } meast16 <- as.data.frame(t(meast16)) rownames(meast16) <- cnt16 rm(CNTcfa) cat('### CFA - ICCS 2016') cat(' \n') cat(' \n') tmeasE <- t(fitMeasures(survey.fit16E, c("chisq","df","cfi", "tli","rmsea", "srmr"), output = "matrix")) tmeasnE <- t(fitMeasures(survey.fit16nE, c("chisq","df","cfi", "tli","rmsea", "srmr"), output = "matrix")) meas16 <- rbind(data.frame(Quest = "European", n = nobs(survey.fit16E), round(tmeasE, 3)), data.frame(Quest = "Non-european", n = nobs(survey.fit16nE), round(tmeasnE, 3))) knitr::kable(meas16) %>% print() cat(' \n') cat('### CFA - ICCS 2016, by countries') cat(' \n') knitr::kable(meast16,digits = 3) %>% print() cat(' \n') cat(' \n') invisible(semPaths(survey.fit16E,"model", "std", "lisrel", edge.label.cex = 0.6, intercepts = FALSE, groups = "latent", pastel = TRUE, title = FALSE, nCharNodes = 10, nDigits = 1)) title("CFA measurement European model", line = 2) invisible(semPaths(survey.fit16nE,"model", "std", "lisrel", edge.label.cex = 0.6, intercepts = FALSE, groups = "latent", pastel = TRUE, title = FALSE, nCharNodes = 10, nDigits = 1)) title("CFA measurement Non European model", line = 2) cat(' \n') cat(' \n') labels <- data.frame(label = tolower(sjlabelled::get_label(ds161))) labels <- labels %>% filter(!str_detect(rownames(labels), c("IDSTUD|IDSCHOOL|COUNTRY|TOTWGT|GENDER"))) %>% mutate(variable = rownames(.), label = str_remove(label, "moving/<immigrants> |moving/<immigrant> |rights and responsibilities/rights and responsibilities/|rights and responsibilities/roles women and men/")) stdl162 <- stdl16 %>% mutate(rhs = factor(rhs, levels = labels$variable, labels = labels$label)) %>% mutate(Model = ifelse(cntry %in% c(CNTne,CNT2cne), "Non-European", "European")) l1 <- stdl162 %>% data.frame() %>% ggplot(aes(x = est.std, y = rhs, color = reorder(cntry, desc(cntry)))) + geom_linerange(aes(xmin = ci.lower, xmax = ci.upper), position = position_dodge(0.4)) + geom_jitter(position = position_dodge(0.4)) + facet_grid(lhs~Model, scales = "free") + geom_text(aes(label=cntry),hjust=0, vjust=1, position = position_dodge(0.4), size = 2) + theme(legend.position = "none", axis.line.y = element_blank()) + ggtitle("Loading distribution of scales - ICCS 2016") + ylab("") + xlab("Loadings with Confidence Interval") + scale_y_discrete(labels = function(x) str_wrap(x, 25)) + scale_x_continuous(breaks = c(0, 0.25, 0.5, 0.75, 1), limits = c(0,1), labels = function(x) sprintf("%.1f", x)) print(l1) cat(' \n') cat(' \n') cat('### Invariance between COUNTRY') cat(' \n') cat(' \n') inv.conf16E <- cfa(model16E, data = ds16E, cluster = "IDSCHOOL", group = "COUNTRY") inv.conf16E <- lavaan.survey(lavaan.fit = inv.conf16E, survey.design = survey.design16E, estimator= "MLMVS") inv.metr16E <- cfa(model16E, data = ds16E, cluster = "IDSCHOOL", group = "COUNTRY", group.equal = c("loadings")) inv.metr16E <- lavaan.survey(lavaan.fit = inv.metr16E, survey.design = survey.design16E, estimator= "MLMVS") inv.scal16E <- cfa(model16E, data = ds16E, cluster = "IDSCHOOL", group = "COUNTRY", group.equal = c("loadings","intercepts")) inv.scal16E <- lavaan.survey(lavaan.fit = inv.scal16E, survey.design = survey.design16E, estimator= "MLMVS") inv.stri16E <- cfa(model16E, data = ds16E, cluster = "IDSCHOOL", group = "COUNTRY", group.equal = c("loadings","intercepts","lv.variances")) inv.stri16E <- lavaan.survey(lavaan.fit = inv.stri16E, survey.design = survey.design16E, estimator= "MLMVS") invarCNT1 <- data.frame(Quest = "European", round(rbind(Configural = fitMeasures(inv.conf16E, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Metric = fitMeasures(inv.metr16E, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Scalar = fitMeasures(inv.scal16E, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Strict = fitMeasures(inv.stri16E, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea"))),3)) inv.conf16nE <- cfa(model16nE, data = ds16nE, cluster = "IDSCHOOL", group = "COUNTRY") inv.conf16nE <- lavaan.survey(lavaan.fit = inv.conf16nE, survey.design = survey.design16nE, estimator= "MLMVS") inv.metr16nE <- cfa(model16nE, data = ds16nE, cluster = "IDSCHOOL", group = "COUNTRY", group.equal = c("loadings")) inv.metr16nE <- lavaan.survey(lavaan.fit = inv.metr16nE, survey.design = survey.design16nE, estimator= "MLMVS") inv.scal16nE <- cfa(model16nE, data = ds16nE, cluster = "IDSCHOOL", group = "COUNTRY", group.equal = c("loadings","intercepts")) inv.scal16nE <- lavaan.survey(lavaan.fit = inv.scal16nE, survey.design = survey.design16nE, estimator= "MLMVS") inv.stri16nE <- cfa(model16nE, data = ds16nE, cluster = "IDSCHOOL", group = "COUNTRY", group.equal = c("loadings","intercepts","lv.variances")) inv.stri16nE <- lavaan.survey(lavaan.fit = inv.stri16nE, survey.design = survey.design16nE, estimator= "MLMVS") invarCNT2 <- data.frame(Quest = "Non-European", round(rbind(Configural = fitMeasures(inv.conf16nE, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Metric = fitMeasures(inv.metr16nE, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Scalar = fitMeasures(inv.scal16nE, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Strict = fitMeasures(inv.stri16nE, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea"))),3)) invarCNT <- invarCNT1 %>% mutate(Invariance = rownames(invarCNT1)) %>% relocate(Invariance, .before = npar) %>% mutate(D_tli = tli-lag(tli), D_cfi = cfi-lag(cfi), D_rmsea = rmsea-lag(rmsea)) %>% bind_rows(invarCNT2 %>% mutate(Invariance = rownames(invarCNT2)) %>% relocate(Invariance, .before = npar) %>% mutate(D_tli = tli-lag(tli), D_cfi = cfi-lag(cfi), D_rmsea = rmsea-lag(rmsea))) %>% knitr::kable() %>% print() cat(' \n') cat(' \n') rm(inv.conf16E, inv.metr16E, inv.scal16E, inv.stri16E, inv.conf16nE, inv.metr16nE, inv.scal16nE, inv.stri16nE) #Remove to save space in disk cat('### Invariance between GENDER') cat(' \n') cat(' \n') inv.conf16E <- cfa(model16E, data = ds16E, cluster = "IDSCHOOL", group = "GENDER") inv.conf16E <- lavaan.survey(lavaan.fit = inv.conf16E, survey.design = survey.design16E, estimator= "MLMVS") inv.metr16E <- cfa(model16E, data = ds16E, cluster = "IDSCHOOL", group = "GENDER", group.equal = c("loadings")) inv.metr16E <- lavaan.survey(lavaan.fit = inv.metr16E, survey.design = survey.design16E, estimator= "MLMVS") inv.scal16E <- cfa(model16E, data = ds16E, cluster = "IDSCHOOL", group = "GENDER", group.equal = c("loadings","intercepts")) inv.scal16E <- lavaan.survey(lavaan.fit = inv.scal16E, survey.design = survey.design16E, estimator= "MLMVS") inv.stri16E <- cfa(model16E, data = ds16E, cluster = "IDSCHOOL", group = "GENDER", group.equal = c("loadings","intercepts","lv.variances")) inv.stri16E <- lavaan.survey(lavaan.fit = inv.stri16E, survey.design = survey.design16E, estimator= "MLMVS") invarGNDR1 <- data.frame(Quest = "European", round(rbind(Configural = fitMeasures(inv.conf16E, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Metric = fitMeasures(inv.metr16E, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Scalar = fitMeasures(inv.scal16E, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Strict = fitMeasures(inv.stri16E, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea"))),3)) inv.conf16nE <- cfa(model16nE, data = ds16nE, cluster = "IDSCHOOL", group = "GENDER") inv.conf16nE <- lavaan.survey(lavaan.fit = inv.conf16nE, survey.design = survey.design16nE, estimator= "MLMVS") inv.metr16nE <- cfa(model16nE, data = ds16nE, cluster = "IDSCHOOL", group = "GENDER", group.equal = c("loadings")) inv.metr16nE <- lavaan.survey(lavaan.fit = inv.metr16nE, survey.design = survey.design16nE, estimator= "MLMVS") inv.scal16nE <- cfa(model16nE, data = ds16nE, cluster = "IDSCHOOL", group = "GENDER", group.equal = c("loadings","intercepts")) inv.scal16nE <- lavaan.survey(lavaan.fit = inv.scal16nE, survey.design = survey.design16nE, estimator= "MLMVS") inv.stri16nE <- cfa(model16nE, data = ds16nE, cluster = "IDSCHOOL", group = "GENDER", group.equal = c("loadings","intercepts","lv.variances")) inv.stri16nE <- lavaan.survey(lavaan.fit = inv.stri16nE, survey.design = survey.design16nE, estimator= "MLMVS") invarGNDR2 <- data.frame(Quest = "Non-European", round(rbind(Configural = fitMeasures(inv.conf16nE, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Metric = fitMeasures(inv.metr16nE, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Scalar = fitMeasures(inv.scal16nE, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea")), Strict = fitMeasures(inv.stri16nE, c("npar", "logl","chisq", "df", "tli", "cfi", "rmsea"))),3)) invarGNDR <- invarGNDR1 %>% mutate(Invariance = rownames(invarGNDR1)) %>% relocate(Invariance, .before = npar) %>% mutate(D_tli = tli-lag(tli), D_cfi = cfi-lag(cfi), D_rmsea = rmsea-lag(rmsea)) %>% bind_rows(invarGNDR2 %>% mutate(Invariance = rownames(invarGNDR2)) %>% relocate(Invariance, .before = npar) %>% mutate(D_tli = tli-lag(tli), D_cfi = cfi-lag(cfi), D_rmsea = rmsea-lag(rmsea))) %>% knitr::kable() %>% print() cat(' \n') cat(' \n') rm(inv.conf16E, inv.metr16E, inv.scal16E, inv.stri16E, inv.conf16nE, inv.metr16nE, inv.scal16nE, inv.stri16nE) #Remove to save space in disk } cat(' \n') cat(' \n') cat('## Summary indexes, all countries, all cycles') cat(' \n') cat(' \n') pall <- plyr::rbind.fill(p99, p09, p16) ISC_cfa <- left_join(ISC_cfa, pall, by = all_of(Id)) set_label(ISC_cfa$Gend_Equal) <- "Positive attitudes toward gender equality" set_label(ISC_cfa$Immi_Equal) <- "Positive attitudes toward equal rights for immigrants" set_label(ISC_cfa$Ethn_Equal) <- "Positive attitudes toward equal rights for ethnic/racial groups" mg <- ISC_cfa %>% dplyr::select(cycle, all_of(Newscales)) %>% group_by(cycle) %>% summarise_at(Newscales, list(~ mean(., na.rm = TRUE))) %>% mutate(cycle = as.factor(cycle)) %>% data.frame() for(i in 1:length(Newscales)){ vargra <- Newscales[i] s2 <- ISC_cfa %>% dplyr::select("cycle", "COUNTRY", vargra) %>% na.omit() %>% ggplot(aes_string(x = vargra, y = paste0("reorder(COUNTRY, desc(COUNTRY))"), group = paste0("interaction(cycle, COUNTRY)"), fill = "COUNTRY")) + geom_boxplot() + facet_grid(.~ cycle)+ geom_vline(aes_string(xintercept = vargra), mg, linetype="dotted", size = 0.8) + ggtitle(eval(parse(text=paste0("attributes(ISC_cfa$",vargra,")$label")))) + ylab("Distribution of Scores CFA") + xlab(paste0(vargra)) + scale_color_brewer(palette="Accent") + theme(legend.position = "none") print(s2) } ISC_cfa <- ISC_cfa %>% select(all_of(Id), all_of(Scales), all_of(Newscales))

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2df6b8f6f367e980b7f5cbd438a2b06b24b302d2

/datai1/R script (1).R

233e0b14824835c8921a444c7e12ee1f8a2f4a3d

[]

no_license

dcamposliz/dtsc1

af4f6c6c0b0f340b5baa4fb1fc1d5cdee7db0cf4

09cfa6c5ff32046b0c91bd6bd1206eb280be47ce

refs/heads/master

2021-01-10T11:53:00.278925

2015-12-28T08:40:39

48,018,237

0

1

null

2015-12-28T08:40:39

2015-12-15T03:50:16

R

UTF-8

R

false

26,557

r

R script (1).R

## Note separate functions should be used in this file # However for this project perhaps it can all be on one document ## Set Working Directory to the datai1 folder setwd("C:/Users/Peter/myProjects/dtsc1/datai1") ## These must be installed the first time install.packages("xlsx") install.packages("dplyr") install.packages("lubridate") install.packages("reshape2") install.packages("zoo") ## Linear interpolation NA values install.packages("sqldf") ## Calls the packages library(xlsx) library(dplyr) library(lubridate) library(reshape2) library(zoo) library(sqldf) ## DOWNLOAD FILE #if(!file.exists("data")) {dir.create("data")} #fileUrl <- "http://www.worldeconomics.com/Files/Maddison_NorthAmerica_Full.xlsx" #download.file(fileUrl, destfile = "./data/America.xlsx") ## method = "curl" removed for windows #dateDownloaded <- date() ## Load data from excel NorthAmericaPopulation <- read.xlsx2("./Maddison_NorthAmerica_Full.xlsx", sheetIndex=2, startRow = 64, endRow = 202, colIndex = 1:3, header=FALSE) NorthAmericaGDP <- read.xlsx2("./Maddison_NorthAmerica_Full.xlsx", sheetIndex=3, startRow = 64, endRow = 202, colIndex = 1:3, header=FALSE) NorthAmericaPerCapitaGDP <- read.xlsx2("./Maddison_NorthAmerica_Full.xlsx", sheetIndex=4, startRow = 64, endRow = 202, colIndex = 1:3, header=FALSE) EuropePopulation <- read.xlsx2("./Maddison_Europe_Full.xlsx", sheetIndex=2, startRow = 64, endRow = 202, colIndex = c(1:13,16,17), header=FALSE) EuropeGDP <- read.xlsx2("./Maddison_Europe_Full.xlsx", sheetIndex=3, startRow = 64, endRow = 202, colIndex = c(1:13,16,17), header=FALSE) EuropePerCapitaGDP <- read.xlsx2("./Maddison_Europe_Full.xlsx", sheetIndex=4, startRow = 64, endRow = 202, colIndex = c(1:13,16,17), header=FALSE) CentralAndSouthAmericaPopulation <- read.xlsx2("./Maddison_CentralAndSouthAmerica_Full.xlsx", sheetIndex=2, startRow = 64, endRow = 202, colIndex = 1:24, header=FALSE) CentralAndSouthAmericaGDP <- read.xlsx2("./Maddison_CentralAndSouthAmerica_Full.xlsx", sheetIndex=3, startRow = 64, endRow = 202, colIndex = 1:24, header=FALSE) CentralAndSouthAmericaPerCapitaGDP <- read.xlsx2("./Maddison_CentralAndSouthAmerica_Full.xlsx", sheetIndex=4, startRow = 64, endRow = 202, colIndex = 1:24, header=FALSE) AsiaPopulation <- read.xlsx2("./Maddison_Asia_Full.xlsx", sheetIndex=2, startRow = 64, endRow = 202, colIndex = 1:41, header=FALSE) AsiaGDP <- read.xlsx2("./Maddison_Asia_Full.xlsx", sheetIndex=3, startRow = 64, endRow = 202, colIndex = 1:41, header=FALSE) AsiaPerCapitaGDP <- read.xlsx2("./Maddison_Asia_Full.xlsx", sheetIndex=4, startRow = 64, endRow = 202, colIndex = 1:41, header=FALSE) AfricaPopulation <- read.xlsx2("./Maddison_Africa_Full.xlsx", sheetIndex=2, startRow = 64, endRow = 202, colIndex = 1:54, header=FALSE) AfricaGDP <- read.xlsx2("./Maddison_Africa_Full.xlsx", sheetIndex=3, startRow = 64, endRow = 202, colIndex = 1:54, header=FALSE) AfricaPerCapitaGDP <- read.xlsx2("./Maddison_Africa_Full.xlsx", sheetIndex=4, startRow = 64, endRow = 202, colIndex = 1:54, header=FALSE) ## GIVE NAMES BEFORE MELT names(NorthAmericaPopulation) <- c("year", "canada","usa") names(NorthAmericaGDP) <- c("year", "canada", "usa") names(NorthAmericaPerCapitaGDP) <- c("year", "canada", "usa") names(EuropePopulation) <- c("year", "Austria","Belgium","Denmark","Finland","France","Germany","Italy","Netherlands","Norway","Sweden","Switzerland","UK","Portugal","Spain") names(EuropeGDP) <- c("year", "Austria","Belgium","Denmark","Finland","France","Germany","Italy","Netherlands","Norway","Sweden","Switzerland","UK","Portugal","Spain") names(EuropePerCapitaGDP) <- c("year", "Austria","Belgium","Denmark","Finland","France","Germany","Italy","Netherlands","Norway","Sweden","Switzerland","UK","Portugal","Spain") names(CentralAndSouthAmericaPopulation) <- c("year", "Argentina","Brazil","Chile","Colombia","Mexico","Peru","Uruguay","Venezuela","Bolivia","Costa.Rica","Cuba","Dominican.Rep.", "Ecuador","El.Salvador","Guatemala","Haiti","Honduras","Jamaica","Nicaragua","Panama","Paraguay","Puerto.Rico","T.and.Tobago") names(CentralAndSouthAmericaGDP) <- c("year", "Argentina","Brazil","Chile","Colombia","Mexico","Peru","Uruguay","Venezuela","Bolivia","Costa.Rica","Cuba","Dominican.Rep.", "Ecuador","El.Salvador","Guatemala","Haiti","Honduras","Jamaica","Nicaragua","Panama","Paraguay","Puerto.Rico","T.and.Tobago") names(CentralAndSouthAmericaPerCapitaGDP) <- c("year", "Argentina","Brazil","Chile","Colombia","Mexico","Peru","Uruguay","Venezuela","Bolivia","Costa.Rica","Cuba","Dominican.Rep.", "Ecuador","El.Salvador","Guatemala","Haiti","Honduras","Jamaica","Nicaragua","Panama","Paraguay","Puerto.Rico","T.and.Tobago") names(AsiaPopulation) <- c("year", "China","India","Indonesia","Japan","Philippines","South.Korea","Thailand","Taiwan","Bangladesh","Burma","Hong.Kong","Malaysia", "Nepal","Pakistan","Singapore","Sri.Lanka","Afghanistan","Cambodia","Laos","Mongolia","North.Korea","Vietnam","24.Sm.E.Asia","Bahrain","Iran","Iraq","Israel","Jordan","Kuwait", "Lebanon","Oman","Qatar","Saudi.Arabia","Syria","Turkey","UAE","Yemen","W.Bank.and.Gaza","Australia","New.Zealand") names(AsiaGDP) <- c("year", "China","India","Indonesia","Japan","Philippines","South.Korea","Thailand","Taiwan","Bangladesh","Burma","Hong.Kong","Malaysia", "Nepal","Pakistan","Singapore","Sri.Lanka","Afghanistan","Cambodia","Laos","Mongolia","North.Korea","Vietnam","24.Sm.E.Asia","Bahrain","Iran","Iraq","Israel","Jordan","Kuwait", "Lebanon","Oman","Qatar","Saudi.Arabia","Syria","Turkey","UAE","Yemen","W.Bank.and.Gaza","Australia","New.Zealand") names(AsiaPerCapitaGDP) <- c("year", "China","India","Indonesia","Japan","Philippines","South.Korea","Thailand","Taiwan","Bangladesh","Burma","Hong.Kong","Malaysia", "Nepal","Pakistan","Singapore","Sri.Lanka","Afghanistan","Cambodia","Laos","Mongolia","North.Korea","Vietnam","24.Sm.E.Asia","Bahrain","Iran","Iraq","Israel","Jordan","Kuwait", "Lebanon","Oman","Qatar","Saudi.Arabia","Syria","Turkey","UAE","Yemen","W.Bank.and.Gaza","Australia","New.Zealand") names(AfricaPopulation) <- c("year", "Algeria","Angola","Benin","Botswana","Burkina.Faso","Burundi","Cameroon","Cape.Verde","Central.Africa.Republic","Chad","Comoro.Islands","Congo", "Cote.d.Ivoire","Djibouti","Egypt","Equatorial.Guinea","Eritrea.and.Ethiopia","Gabon","Gambia","Ghana","Guinea","Guinea.Bissau","Kenya","Lesotho","Liberia","Libya","Madagascar", "Malawi","Mali","Mauritania","Mauritius","Morocco","Mozambique","Namibia","Niger","Nigeria","Rwanda","Sao.Tome.and.Principe","Senegal","Seychelles","Sierra.Leone","Somalia", "South.Africa","Sudan","Swaziland","Tanzania","Togo","Tunisia","Uganda","Zaire","Zambia","Zimbabwe","Three.Small.Afr.") names(AfricaGDP) <- c("year", "Algeria","Angola","Benin","Botswana","Burkina.Faso","Burundi","Cameroon","Cape.Verde","Central.Africa.Republic","Chad","Comoro.Islands","Congo", "Cote.d.Ivoire","Djibouti","Egypt","Equatorial.Guinea","Eritrea.and.Ethiopia","Gabon","Gambia","Ghana","Guinea","Guinea.Bissau","Kenya","Lesotho","Liberia","Libya","Madagascar", "Malawi","Mali","Mauritania","Mauritius","Morocco","Mozambique","Namibia","Niger","Nigeria","Rwanda","Sao.Tome.and.Principe","Senegal","Seychelles","Sierra.Leone","Somalia", "South.Africa","Sudan","Swaziland","Tanzania","Togo","Tunisia","Uganda","Zaire","Zambia","Zimbabwe","Three.Small.Afr.") names(AfricaPerCapitaGDP) <- c("year", "Algeria","Angola","Benin","Botswana","Burkina.Faso","Burundi","Cameroon","Cape.Verde","Central.Africa.Republic","Chad","Comoro.Islands","Congo", "Cote.d.Ivoire","Djibouti","Egypt","Equatorial.Guinea","Eritrea.and.Ethiopia","Gabon","Gambia","Ghana","Guinea","Guinea.Bissau","Kenya","Lesotho","Liberia","Libya","Madagascar", "Malawi","Mali","Mauritania","Mauritius","Morocco","Mozambique","Namibia","Niger","Nigeria","Rwanda","Sao.Tome.and.Principe","Senegal","Seychelles","Sierra.Leone","Somalia", "South.Africa","Sudan","Swaziland","Tanzania","Togo","Tunisia","Uganda","Zaire","Zambia","Zimbabwe","Three.Small.Afr.") ## Set data types to numeric for (i in 2:3) { NorthAmericaPopulation[ , i] <- as.numeric(as.character(NorthAmericaPopulation[ , i])) NorthAmericaGDP[ , i] <- as.numeric(as.character(NorthAmericaGDP[ , i])) NorthAmericaPerCapitaGDP[ , i] <- as.numeric(as.character(NorthAmericaPerCapitaGDP[ , i])) } for (i in 2:15) { EuropePopulation[ , i] <- as.numeric(as.character(EuropePopulation[ , i])) EuropeGDP[ , i] <- as.numeric(as.character(EuropeGDP[ , i])) EuropePerCapitaGDP[ , i] <- as.numeric(as.character(EuropePerCapitaGDP[ , i])) } for (i in 2:24) { CentralAndSouthAmericaPopulation[ , i] <- as.numeric(as.character(CentralAndSouthAmericaPopulation[ , i])) CentralAndSouthAmericaGDP[ , i] <- as.numeric(as.character(CentralAndSouthAmericaGDP[ , i])) CentralAndSouthAmericaPerCapitaGDP[ , i] <- as.numeric(as.character(CentralAndSouthAmericaPerCapitaGDP[ , i])) } for (i in 2:41) { AsiaPopulation[ , i] <- as.numeric(as.character(AsiaPopulation[ , i])) AsiaGDP[ , i] <- as.numeric(as.character(AsiaGDP[ , i])) AsiaPerCapitaGDP[ , i] <- as.numeric(as.character(AsiaPerCapitaGDP[ , i])) } for (i in 2:54) { AfricaPopulation[ , i] <- as.numeric(as.character(AfricaPopulation[ , i])) AfricaGDP[ , i] <- as.numeric(as.character(AfricaGDP[ , i])) AfricaPerCapitaGDP[ , i] <- as.numeric(as.character(AfricaPerCapitaGDP[ , i])) } ## Fill in the 1870 values where they are null CentralAndSouthAmericaGDP1870 = c(); CentralAndSouthAmericaGDP1950 = c(); CentralAndSouthAmericaPerCapitaGDP1870 = c(); CentralAndSouthAmericaPerCapitaGDP1950 = c(); AsiaPopulation1870 = c(); AsiaPopulation1950 = c(); AsiaGDP1870 = c(); AsiaGDP1950 = c(); AsiaPerCapitaGDP1870 = c(); AsiaPerCapitaGDP1950 = c(); AfricaPopulation1870 = c(); AfricaPopulation1950 = c(); AfricaGDP1870 = c(); AfricaGDP1950 = c(); AfricaPerCapitaGDP1870 = c(); AfricaPerCapitaGDP1950 = c(); # Pull the row with nulls (1870) and the row with no nulls (1950). for (i in 2:24) { CentralAndSouthAmericaGDP1870[i-1] <- CentralAndSouthAmericaGDP[[i]][[1]] CentralAndSouthAmericaGDP1950[i-1] <- CentralAndSouthAmericaGDP[[i]][[81]] CentralAndSouthAmericaGDPgood <- complete.cases(CentralAndSouthAmericaGDP1870,CentralAndSouthAmericaGDP1950) CentralAndSouthAmericaPerCapitaGDP1870[i-1] <- CentralAndSouthAmericaPerCapitaGDP[[i]][[1]] CentralAndSouthAmericaPerCapitaGDP1950[i-1] <- CentralAndSouthAmericaPerCapitaGDP[[i]][[81]] CentralAndSouthAmericaPerCapitaGDPgood <- complete.cases(CentralAndSouthAmericaPerCapitaGDP1870,CentralAndSouthAmericaPerCapitaGDP1950) } for (i in 2:41) { AsiaPopulation1870[i-1] <- AsiaPopulation[[i]][[1]] AsiaPopulation1950[i-1] <- AsiaPopulation[[i]][[81]] AsiaPopulationgood <- complete.cases(AsiaPopulation1870,AsiaPopulation1950) AsiaGDP1870[i-1] <- AsiaGDP[[i]][[1]] AsiaGDP1950[i-1] <- AsiaGDP[[i]][[81]] AsiaGDPgood <- complete.cases(AsiaGDP1870,AsiaGDP1950) AsiaPerCapitaGDP1870[i-1] <- AsiaPerCapitaGDP[[i]][[1]] AsiaPerCapitaGDP1950[i-1] <- AsiaPerCapitaGDP[[i]][[81]] AsiaPerCapitaGDPgood <- complete.cases(AsiaPerCapitaGDP1870,AsiaPerCapitaGDP1950) } for (i in 2:54) { AfricaPopulation1870[i-1] <- AfricaPopulation[[i]][[1]] AfricaPopulation1950[i-1] <- AfricaPopulation[[i]][[81]] AfricaPopulationgood <- complete.cases(AfricaPopulation1870,AfricaPopulation1950) AfricaGDP1870[i-1] <- AfricaGDP[[i]][[1]] AfricaGDP1950[i-1] <- AfricaGDP[[i]][[81]] AfricaGDPgood <- complete.cases(AfricaGDP1870,AfricaGDP1950) AfricaPerCapitaGDP1870[i-1] <- AfricaPerCapitaGDP[[i]][[1]] AfricaPerCapitaGDP1950[i-1] <- AfricaPerCapitaGDP[[i]][[81]] AfricaPerCapitaGDPgood <- complete.cases(AfricaPerCapitaGDP1870,AfricaPerCapitaGDP1950) } # Find the average change from 1870 to 1950 CentralAndSouthAmericaGDPArr <- array(dim=c(23,1)); CentralAndSouthAmericaPerCapitaGDPArr <- array(dim=c(23,1)) for (i in 1:23) { if (CentralAndSouthAmericaGDPgood[i]) { CentralAndSouthAmericaGDPArr[i] <- CentralAndSouthAmericaGDP1950[i] / CentralAndSouthAmericaGDP1870[i] } if (CentralAndSouthAmericaPerCapitaGDPgood[i]) { CentralAndSouthAmericaPerCapitaGDPArr[i] <- CentralAndSouthAmericaPerCapitaGDP1950[i] / CentralAndSouthAmericaPerCapitaGDP1870[i] } } CentralAndSouthAmericaGDPAverageChange <- mean(CentralAndSouthAmericaGDPArr, na.rm=TRUE) CentralAndSouthAmericaPerCapitaGDPAverageChange <- mean(CentralAndSouthAmericaPerCapitaGDPArr, na.rm=TRUE) AsiaPopulationArr <- array(dim=c(40,1)); AsiaGDPArr <- array(dim=c(40,1)); AsiaPerCapitaGDPArr <- array(dim=c(40,1)) for (i in 1:40) { if (AsiaPopulationgood[i]) { AsiaPopulationArr[i] <- AsiaPopulation1950[i] / AsiaPopulation1870[i] } if (AsiaGDPgood[i]) { AsiaGDPArr[i] <- AsiaGDP1950[i] / AsiaGDP1870[i] } if (AsiaPerCapitaGDPgood[i]) { AsiaPerCapitaGDPArr[i] <- AsiaPerCapitaGDP1950[i] / AsiaPerCapitaGDP1870[i] } } AsiaPopulationAverageChange <- mean(AsiaPopulationArr, na.rm=TRUE) AsiaGDPAverageChange <- mean(AsiaGDPArr, na.rm=TRUE) AsiaPerCapitaGDPAverageChange <- mean(AsiaPerCapitaGDPArr, na.rm=TRUE) AfricaPopulationArr <- array(dim=c(53,1)); AfricaGDPArr <- array(dim=c(53,1)); AfricaPerCapitaGDPArr <- array(dim=c(40,1)) for (i in 1:53) { if (AfricaPopulationgood[i]) { AfricaPopulationArr[i] <- AfricaPopulation1950[i] / AfricaPopulation1870[i] } if (AfricaGDPgood[i]) { AfricaGDPArr[i] <- AfricaGDP1950[i] / AfricaGDP1870[i] } if (AfricaPerCapitaGDPgood[i]) { AfricaPerCapitaGDPArr[i] <- AfricaPerCapitaGDP1950[i] / AfricaPerCapitaGDP1870[i] } } AfricaPopulationAverageChange <- mean(AfricaPopulationArr, na.rm=TRUE) AfricaGDPAverageChange <- mean(AfricaGDPArr, na.rm=TRUE) AfricaPerCapitaGDPAverageChange <- mean(AfricaPerCapitaGDPArr, na.rm=TRUE) # Apply this average change to 1850 data for (i in 2:24) { if(is.na(CentralAndSouthAmericaGDP[[i]][[1]])) { CentralAndSouthAmericaGDP[[i]][[1]] <- CentralAndSouthAmericaGDP[[i]][[81]] / CentralAndSouthAmericaGDPAverageChange } if(is.na(CentralAndSouthAmericaPerCapitaGDP[[i]][[1]])) { CentralAndSouthAmericaPerCapitaGDP[[i]][[1]] <- CentralAndSouthAmericaPerCapitaGDP[[i]][[81]] / CentralAndSouthAmericaPerCapitaGDPAverageChange } } for (i in 2:41) { if(is.na(AsiaPopulation[[i]][[1]])) { AsiaPopulation[[i]][[1]] <- AsiaPopulation[[i]][[81]] / AsiaPopulationAverageChange } if(is.na(AsiaGDP[[i]][[1]])) { AsiaGDP[[i]][[1]] <- AsiaGDP[[i]][[81]] / AsiaGDPAverageChange } if(is.na(AsiaPerCapitaGDP[[i]][[1]])) { AsiaPerCapitaGDP[[i]][[1]] <- AsiaPerCapitaGDP[[i]][[81]] / AsiaPerCapitaGDPAverageChange } } for (i in 2:54) { if(is.na(AfricaPopulation[[i]][[1]])) { AfricaPopulation[[i]][[1]] <- AfricaPopulation[[i]][[81]] / AfricaPopulationAverageChange } if(is.na(AfricaGDP[[i]][[1]])) { AfricaGDP[[i]][[1]] <- AfricaGDP[[i]][[81]] / AfricaGDPAverageChange } if(is.na(AfricaPerCapitaGDP[[i]][[1]])) { AfricaPerCapitaGDP[[i]][[1]] <- AfricaPerCapitaGDP[[i]][[81]] / AfricaPerCapitaGDPAverageChange } } ## Linear interpolation NA values in between 1870 and 1950 #Population CentralAndSouthAmericaPopulationZoo <- zoo(CentralAndSouthAmericaPopulation) index(CentralAndSouthAmericaPopulationZoo) <- CentralAndSouthAmericaPopulationZoo[,1] CentralAndSouthAmericaPopulation <- na.approx(CentralAndSouthAmericaPopulationZoo) CentralAndSouthAmericaGDPZoo <- zoo(CentralAndSouthAmericaGDP) index(CentralAndSouthAmericaGDPZoo) <- CentralAndSouthAmericaGDPZoo[,1] CentralAndSouthAmericaGDP <- na.approx(CentralAndSouthAmericaGDPZoo) CentralAndSouthAmericaPerCapitaGDPZoo <- zoo(CentralAndSouthAmericaPerCapitaGDP) index(CentralAndSouthAmericaPerCapitaGDPZoo) <- CentralAndSouthAmericaPerCapitaGDPZoo[,1] CentralAndSouthAmericaPerCapitaGDP <- na.approx(CentralAndSouthAmericaPerCapitaGDPZoo) AsiaPopulationZoo <- zoo(AsiaPopulation) index(AsiaPopulationZoo) <- AsiaPopulationZoo[,1] AsiaPopulation <- na.approx(AsiaPopulationZoo) AsiaGDPZoo <- zoo(AsiaGDP) index(AsiaGDPZoo) <- AsiaGDPZoo[,1] AsiaGDP <- na.approx(AsiaGDPZoo) AsiaPerCapitaGDPZoo <- zoo(AsiaPerCapitaGDP) index(AsiaPerCapitaGDPZoo) <- AsiaPerCapitaGDPZoo[,1] AsiaPerCapitaGDP <- na.approx(AsiaPerCapitaGDPZoo) AfricaPopulationZoo <- zoo(AfricaPopulation) index(AfricaPopulationZoo) <- AfricaPopulationZoo[,1] AfricaPopulation <- na.approx(AfricaPopulationZoo) AfricaGDPZoo <- zoo(AfricaGDP) index(AfricaGDPZoo) <- AfricaGDPZoo[,1] AfricaGDP <- na.approx(AfricaGDPZoo) AfricaPerCapitaGDPZoo <- zoo(AfricaPerCapitaGDP) index(AfricaPerCapitaGDPZoo) <- AfricaPerCapitaGDPZoo[,1] AfricaPerCapitaGDP <- na.approx(AfricaPerCapitaGDPZoo) ## Assign data types again after zoo CentralAndSouthAmericaPopulation <- data.frame(CentralAndSouthAmericaPopulation) CentralAndSouthAmericaGDP <- data.frame(CentralAndSouthAmericaGDP) CentralAndSouthAmericaPerCapitaGDP <- data.frame(CentralAndSouthAmericaPerCapitaGDP) for (i in 2:24) { CentralAndSouthAmericaPopulation[ , i] <- as.numeric(as.character(CentralAndSouthAmericaPopulation[ , i])) CentralAndSouthAmericaGDP[ , i] <- as.numeric(as.character(CentralAndSouthAmericaGDP[ , i])) CentralAndSouthAmericaPerCapitaGDP[ , i] <- as.numeric(as.character(CentralAndSouthAmericaPerCapitaGDP[ , i])) } AsiaPopulation <- data.frame(AsiaPopulation) AsiaGDP <- data.frame(AsiaGDP) AsiaPerCapitaGDP <- data.frame(AsiaPerCapitaGDP) for (i in 2:41) { AsiaPopulation[ , i] <- as.numeric(as.character(AsiaPopulation[ , i])) AsiaGDP[ , i] <- as.numeric(as.character(AsiaGDP[ , i])) AsiaPerCapitaGDP[ , i] <- as.numeric(as.character(AsiaPerCapitaGDP[ , i])) } AfricaPopulation <- data.frame(AfricaPopulation) AfricaGDP <- data.frame(AfricaGDP) AfricaPerCapitaGDP <- data.frame(AfricaPerCapitaGDP) for (i in 2:54) { AfricaPopulation[ , i] <- as.numeric(as.character(AfricaPopulation[ , i])) AfricaGDP[ , i] <- as.numeric(as.character(AfricaGDP[ , i])) AfricaPerCapitaGDP[ , i] <- as.numeric(as.character(AfricaPerCapitaGDP[ , i])) } ## Melt down to 3 columns each NorthAmericaPopulationMelt <- melt(NorthAmericaPopulation,id="year") NorthAmericaGDPMelt <- melt(NorthAmericaGDP,id="year") NorthAmericaPerCapitaGDPMelt <- melt(NorthAmericaPerCapitaGDP,id="year") EuropePopulationMelt <- melt(EuropePopulation,id="year") EuropeGDPMelt <- melt(EuropeGDP,id="year") EuropePerCapitaGDPMelt <- melt(EuropePerCapitaGDP,id="year") CentralAndSouthAmericaPopulationMelt <- melt(CentralAndSouthAmericaPopulation,id="year") CentralAndSouthAmericaGDPMelt <- melt(CentralAndSouthAmericaGDP,id="year") CentralAndSouthAmericaPerCapitaGDPMelt <- melt(CentralAndSouthAmericaPerCapitaGDP,id="year") AsiaPopulationMelt <- melt(AsiaPopulation,id="year") AsiaGDPMelt <- melt(AsiaGDP,id="year") AsiaPerCapitaGDPMelt <- melt(AsiaPerCapitaGDP,id="year") AfricaPopulationMelt <- melt(AfricaPopulation,id="year") AfricaGDPMelt <- melt(AfricaGDP,id="year") AfricaPerCapitaGDPMelt <- melt(AfricaPerCapitaGDP,id="year") ## Rename columns after melting names(NorthAmericaPopulationMelt) <- c("year", "country", "population") names(NorthAmericaGDPMelt) <- c("year", "country", "gdp") names(NorthAmericaPerCapitaGDPMelt) <- c("year", "country", "per_capita_gdp") names(EuropePopulationMelt) <- c("year", "country", "population") names(EuropeGDPMelt) <- c("year", "country", "gdp") names(EuropePerCapitaGDPMelt) <- c("year", "country", "per_capita_gdp") names(CentralAndSouthAmericaPopulationMelt) <- c("year", "country", "population") names(CentralAndSouthAmericaGDPMelt) <- c("year", "country", "gdp") names(CentralAndSouthAmericaPerCapitaGDPMelt) <- c("year", "country", "per_capita_gdp") names(AsiaPopulationMelt) <- c("year", "country", "population") names(AsiaGDPMelt) <- c("year", "country", "gdp") names(AsiaPerCapitaGDPMelt) <- c("year", "country", "per_capita_gdp") names(AfricaPopulationMelt) <- c("year", "country", "population") names(AfricaGDPMelt) <- c("year", "country", "gdp") names(AfricaPerCapitaGDPMelt) <- c("year", "country", "per_capita_gdp") ## Convert to correct units; We may want to convert; I don't know the right conversion we want to use; #NorthAmericaPopulationMelt <- mutate(NorthAmericaPopulationMelt, population = population * 1000) #NorthAmericaGDPMelt <- mutate(NorthAmericaGDPMelt, gdp = gdp * 1000000) #EuropePopulationMelt <- mutate(EuropePopulationMelt, population = population * 1000) #EuropeGDPMelt <- mutate(EuropeGDPMelt, gdp = gdp * 1000000) #CentralAndSouthAmericaPopulationMelt <- mutate(CentralAndSouthAmericaPopulationMelt, population = population * 1000) #CentralAndSouthAmericaGDPMelt <- mutate(CentralAndSouthAmericaGDPMelt, gdp = gdp * 1000000) ### CONVERT YEAR TO DATE ## Add 0101 to dates paste function #NorthAmericaPopulation <- mutate(NorthAmericaPopulation, year = paste(NorthAmericaPopulation$date,"0101", sep = "")) #NorthAmericaGDP <- mutate(NorthAmericaGDP, year = paste(NorthAmericaGDP$year,"0101", sep = "")) ## ymd read in with lubridate #NorthAmericaPopulation <- mutate(NorthAmericaPopulation, year = ymd(NorthAmericaPopulation$date)) #NorthAmericaGDP <- mutate(NorthAmericaGDP, year = ymd(NorthAmericaGDP$year)) ## Merge data into one set yearly_stats1 <- merge(NorthAmericaPopulationMelt,NorthAmericaGDPMelt) yearly_stats1 <- merge(yearly_stats1,NorthAmericaPerCapitaGDPMelt) yearly_stats2 <- merge(EuropePopulationMelt,EuropeGDPMelt) yearly_stats2 <- merge(yearly_stats2,EuropePerCapitaGDPMelt) yearly_stats3 <- merge(CentralAndSouthAmericaPopulationMelt,CentralAndSouthAmericaGDPMelt) yearly_stats3 <- merge(yearly_stats3,CentralAndSouthAmericaPerCapitaGDPMelt) yearly_stats4 <- merge(AsiaPopulationMelt,AsiaGDPMelt) yearly_stats4 <- merge(yearly_stats4,AsiaPerCapitaGDPMelt) yearly_stats5 <- merge(AfricaPopulationMelt,AfricaGDPMelt) yearly_stats5 <- merge(yearly_stats5,AfricaPerCapitaGDPMelt) ## Write to file (Test Purposes) #write.table(year_fact1, "./year_fact1.csv", sep = ",",row.names=FALSE) #write.table(year_fact2, "./year_fact2.csv", sep = ",",row.names=FALSE) #write.table(year_fact3, "./year_fact3.csv", sep = ",",row.names=FALSE) #write.table(year_fact4, "./year_fact4.csv", sep = ",",row.names=FALSE) #write.table(year_fact5, "./year_fact5.csv", sep = ",",row.names=FALSE) ## Create combined year_fact with sqldf yearly_stats <- sqldf(" SELECT year, country, population, gdp, per_capita_gdp FROM (SELECT year, country, population, gdp, per_capita_gdp FROM yearly_stats1 UNION ALL SELECT year, country, population, gdp, per_capita_gdp FROM yearly_stats2 UNION ALL SELECT year, country, population, gdp, per_capita_gdp FROM yearly_stats3 UNION ALL SELECT year, country, population, gdp, per_capita_gdp FROM yearly_stats4 UNION ALL SELECT year, country, population, gdp, per_capita_gdp FROM yearly_stats5 ) x ORDER BY 1 ASC, 2 ASC ") yearly_stats[ , 1] <- as.numeric(as.character(yearly_stats[ , 1])) write.table(yearly_stats, "./yearly_stats.csv", sep = ",",row.names=FALSE) ## Create stats_across_five_years using sqldf # concat isn't allowed so wasn't able to add 'display_year' # CONCAT(CAST(five_years_ago.year AS char), '-', CAST(main_year.year AS char)) AS display_year, stats_across_five_years <- sqldf(" SELECT main_year.country AS Country, five_years_ago.year AS start_year, main_year.year AS end_year, main_year.population - five_years_ago.population AS Delta_Population, main_year.gdp - five_years_ago.gdp AS Delta_GDP, main_year.per_capita_gdp - five_years_ago.per_capita_gdp AS Delta_Per_Capita_GDP, (main_year.population - five_years_ago.population) / five_years_ago.population AS pct_change_Population, (main_year.gdp - five_years_ago.gdp) / five_years_ago.gdp AS pct_change_GDP, (main_year.per_capita_gdp - five_years_ago.per_capita_gdp) / five_years_ago.per_capita_gdp AS pct_change_Per_Capita_GDP FROM (SELECT year, country, population, gdp, per_capita_gdp FROM yearly_stats ) AS main_year INNER JOIN (SELECT year, country, population, gdp, per_capita_gdp FROM yearly_stats ) AS five_years_ago ON five_years_ago.country = main_year.country and five_years_ago.year = main_year.year - 5 WHERE main_year.year % 5 == 0 ORDER BY 1 ASC, 2 ASC ") write.table(stats_across_five_years, "./stats_across_five_years.csv", sep = ",",row.names=FALSE) top_5_year_period_per_country <- sqldf(" SELECT fact.country, fact.start_year, fact.end_year, fact.Delta_Population, fact.Delta_GDP, fact.Delta_Per_Capita_GDP, fact.pct_change_Population, fact.pct_change_GDP, fact.pct_change_Per_Capita_GDP, a.greatest_change_in AS Greatest_Change1, b.greatest_change_in AS Greatest_Change2, c.greatest_change_in AS Greatest_Change3, d.greatest_change_in AS Greatest_Change4, e.greatest_change_in AS Greatest_Change5, f.greatest_change_in AS Greatest_Change6 FROM stats_across_five_years fact LEFT JOIN (SELECT country, MAX(Delta_Population) AS Delta_Population, max('Delta Population') AS greatest_change_in FROM stats_across_five_years group by 1 ) a on a.country = fact.country and a.Delta_Population = fact.Delta_Population LEFT JOIN (SELECT country, MAX(Delta_GDP) AS Delta_GDP, max('Delta_GDP') AS greatest_change_in FROM stats_across_five_years group by 1 ) b on b.country = fact.country and b.Delta_GDP = fact.Delta_GDP LEFT JOIN (SELECT country, MAX(Delta_Per_Capita_GDP) AS Delta_Per_Capita_GDP, max('Delta_Per_Capita_GDP') AS greatest_change_in FROM stats_across_five_years group by 1 ) c on c.country = fact.country and c.Delta_Per_Capita_GDP = fact.Delta_Per_Capita_GDP LEFT JOIN (SELECT country, MAX(pct_change_Population) AS pct_change_Population, max('%_change_Population') AS greatest_change_in FROM stats_across_five_years group by 1 ) d on d.country = fact.country and d.pct_change_Population = fact.pct_change_Population LEFT JOIN (SELECT country, MAX(pct_change_GDP) AS pct_change_GDP, max('% change_GDP') AS greatest_change_in FROM stats_across_five_years group by 1 ) e on e.country = fact.country and e.pct_change_GDP = fact.pct_change_GDP LEFT JOIN (SELECT country, MAX(pct_change_Per_Capita_GDP) AS pct_change_Per_Capita_GDP, max('% change_Per_Capita_GDP') AS greatest_change_in FROM stats_across_five_years group by 1 ) f on f.country = fact.country and f.pct_change_Per_Capita_GDP = fact.pct_change_Per_Capita_GDP WHERE a.country is not null OR b.country is not null OR c.country is not null OR d.country is not null OR e.country is not null OR f.country is not null ") write.table(top_5_year_period_per_country, "./top_5_year_period_per_country.csv", sep = ",",row.names=FALSE)

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/plot4.R

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[]

no_license

zackgouttel/ExData_Plotting1

03bfe07fbbc901ab80891e283cf7d164a4e9b9f9

49ad10daaa8b2e12904994051485d713f4a72492

refs/heads/master

2022-09-22T09:39:56.077110

2020-06-07T00:37:18

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null

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plot4.R

sub_data<-function(df=household_power_consumption) { d1<- dmy("1-2-2007") d2<- dmy("2-2-2007") p1_sub<-subset(x = df, Date==d1 | Date==d2) } draw_plot4<-function(df=household_power_consumption) { plotdata<-sub_data(df=household_power_consumption) plotdata$datetime<-as.POSIXct(paste(plotdata$Date,plotdata$Time)) png(filename = "plot4.png",width = 480,height = 480) par(mfrow= c(2,2)) plot(plotdata$datetime,plotdata$Global_active_power,type = "l", ylab = "Global Active Power") plot(plotdata$datetime,plotdata$Voltage, type = "l", xlab="datetime",ylab = "Voltage") plot(plotdata$datetime,plotdata$Sub_metering_1, type = "l", ylab = "Energy sub metering") lines(plotdata$datetime,plotdata$Sub_metering_2, type = "l", col="red") lines(plotdata$datetime,plotdata$Sub_metering_3,type = "l",col="blue") legend("topright", legend=c("sub_metering_1","sub_metering_2","sub_metering_3"),pch=c(19,19,19),col = c("black","red","blue") ) plot(plotdata$datetime,plotdata$Global_reactive_power,type="l",xlab = "datetime",ylab = "Global Reactive Power") dev.off() }

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c7c9fbc40a719c1d2198697ebcbd9f48b2496572

/global.R

650fc94d5865d847a55885ef02e38b123241d8d9

[]

no_license

sarikayamehmet/ShinyLdap

cd6066282b35a85cfdc313c664b46ac85bde7010

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refs/heads/master

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global.R

# GLOBAL.R # instala os pacotes faltando. list.of.packages <- c("shiny", "readr") new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])] if(length(new.packages)) install.packages(new.packages) library('shiny') library('readr') ldap_url <- 'ldap://jacarta.intranet....:389' ldap_dc <- 'dc=intranet,dc=mpgo' ldap_filtro <- 'sAMAccountName' ldap_dominio <- 'intranet' ldap_dc <- 'dc=intranet,dc=mpgo' ldap_campos <- c('dn:', 'cn:', 'sn:', 'title:','displayName:', 'name:', 'employeeID:', 'sAMAccountName:', 'mail:','G_MPTV_MEMBROS', 'G_MPTV_Users','title:' ) comando <- 'ldapsearch -H ldap_url -x -D "ldap_dominio\\ldap_user" -w ldap_pass -b "ldap_dc" "(&(ldap_filtro=ldap_user))"' debug_mode = 1; # 0 sem debug. #tem comando temComando <- function(comando) { tmp <- Sys.which(comando) return (!(paste0(tmp)=='')) } consultaLdap <- function(usuario, senha) { newC <- sub('ldap_url', ldap_url, comando) newC <- sub('ldap_dominio', ldap_dominio,newC) newC <- gsub('ldap_user',usuario,newC) newC <- sub('ldap_pass',senha,newC) newC <- sub('ldap_filtro',ldap_filtro,newC) newC <- sub('ldap_dc',ldap_dc,newC) cat(file=stderr(),"Comando: ", newC, "\n") tmp <- system(paste0(newC),intern = TRUE) cat(file=stderr(),"tmp: ", tmp, "\n") atributos <- attr(tmp,which = "status") cat(file=stderr(),"atributos: ", atributos, "\n") if (is.null(atributos)) atributos = 0 if (atributos == 49) { # erro de usuário / senha res <- 49 } else { res <- tmp; } return (res) } separaTxt <- function(info) { return (c(strsplit(info,': '))[[1]]) } userLdap <- function(usuario, senha) { resLdap <- consultaLdap(usuario, senha) cat(file=stderr(),"resLdap: ", resLdap, "\n") if (!is.numeric(resLdap)) { dt_usuario <- unique (grep(paste(ldap_campos,collapse="|"), resLdap, value=TRUE)) dados <- data.frame('id' = separaTxt(dt_usuario)[1], 'dados' = separaTxt(dt_usuario)[2]) return (dados[2]) } else { return (resLdap) } }

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/plot2.R

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svsubbarao/ExData_Plotting1

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plot2.R

## read the data from the file dataR <- read.csv("household_power_consumption.txt" ,header=T,sep=";",stringsAsFactors=F, na.strings="?",colClasses=c("character", "character", "numeric", "numeric", "numeric","numeric","numeric", "numeric", "numeric")) ## subset the data from large data set dataR <- dataR[dataR$Date %in% c("1/2/2007","2/2/2007") ,] datetime <- strptime(paste(dataR$Date, dataR$Time, sep=" "), "%d/%m/%Y %H:%M:%S") ## create the png file png(filename="plot2.png", width=480, height=480) ## draw the graph on to the file plot(datetime, dataR$Global_active_power, type="l", xlab="", ylab="Global Active Power (kilowatts)") dev.off()

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arey0pushpa/dcnf-autarky

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2021-06-09T00:56:32.937250

2021-02-19T15:15:23

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query57_query49_1344.R

c DCNF-Autarky [version 0.0.1]. c Copyright (c) 2018-2019 Swansea University. c c Input Clause Count: 10617 c Performing E1-Autarky iteration. c Remaining clauses count after E-Reduction: 10617 c c Input Parameter (command line, file): c input filename QBFLIB/Jordan-Kaiser/reduction-finding-full-set-params-k1c3n4/query57_query49_1344.qdimacs c output filename /tmp/dcnfAutarky.dimacs c autarky level 1 c conformity level 0 c encoding type 2 c no.of var 3029 c no.of clauses 10617 c no.of taut cls 0 c c Output Parameters: c remaining no.of clauses 10617 c c QBFLIB/Jordan-Kaiser/reduction-finding-full-set-params-k1c3n4/query57_query49_1344.qdimacs 3029 10617 E1 [] 0 16 3013 10617 NONE

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/man/Class_Spells.Rd

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vinny-paris/DnD

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Class_Spells.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/Class_Spells.R \docType{data} \name{Class_Spells} \alias{Class_Spells} \title{Spells Known by Class fully labeld} \format{An object of class \code{list} of length 8.} \source{ {Dungeon and Dragons Fifth Edition Player's Hand Book} } \usage{ Class_Spells } \description{ This is a list made up of the 8 spell casting classes. We do not preserve spell level but we do preserve the full name. } \keyword{datasets}

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/man/read_recoveries.Rd

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jnshsrs/corona

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2020-04-06T19:16:26

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read_recoveries.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/read-data.R \name{read_recoveries} \alias{read_recoveries} \title{Read Corona Recovered Numbers} \usage{ read_recoveries(file = NULL) } \arguments{ \item{file}{The file path to load from. Do not specify it, to load from JHU github repository (which is recommended)} } \value{ A tibble containing country, state, date, number of recoveries } \description{ Read Corona Recovered Numbers } \examples{ \dontrun{ read_recoveries() } }

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/A4-w1.R

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layaSharifi/ExData_Plotting1

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2020-12-31T05:57:34.953417

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A4-w1.R

##Data Loading and Processing install.packages("data.table") library(dplyr) setwd("C:/Users/lsharifi/Desktop/Rot2/coursera/A4-w1") power<- read.table ('household_power_consumption.txt', sep=";" , header=TRUE, na.strings="?", stringsAsFactors=FALSE ) View(power) #convert date and time variables to Date/Time class power$Time <- strptime(paste(power$Date, power$Time), "%d/%m/%Y %H:%M:%S") power$Date <- as.Date(power$Date, "%d/%m/%Y") # We will only be using data from the dates 2007-02-01 and 2007-02-02 dates <- as.Date(c("2007-02-01", "2007-02-02"), "%Y-%m-%d") power <- subset(power, Date %in% dates) power --------------------------------------------------------------Plot1------------------------------------------------ plot1 <- hist(power$Global_active_power, main = paste("Global Active Power"), col="red", ylab="Frequency" , xlab="Global Active Power (kilowatts)") dev.copy(png, file="plot1.png", width=480, height=480) dev.off() --------------------------------------------------------------Plot2------------------------------------------------ plot2<- plot(power$Time,power$Global_active_power, type="l", xlab="", ylab="Global Active Power (kilowatts)") dev.copy(png, file="plot2.png", width=480, height=480) dev.off() --------------------------------------------------------------Plot3------------------------------------------------ plot3 <- plot(power$Time,power$Sub_metering_1, type="l", xlab="", ylab="Energy sub metering") lines(power$Time,power$Sub_metering_2,col="red") lines(power$Time,power$Sub_metering_3,col="blue") legend("topright", col=c("black","red","blue"), c("Sub_metering_1","Sub_metering_2", "Sub_metering_3"),lty=c(1,1), lwd=c(1,1)) dev.copy(png, file="plot3.png", width=480, height=480) dev.off() -------------------------------------------------------------Plot4---------------------------------------------------- plot4 <- par(mfrow=c(2,2)) ##PLOT 1 plot(power$Time,power$Global_active_power, type="l", xlab="", ylab="Global Active Power") ##PLOT 2 plot(power$Time,power$Voltage, type="l", xlab="datetime", ylab="Voltage") ##PLOT 3 plot(power$Time,power$Sub_metering_1, type="l", xlab="", ylab="Energy sub metering") lines(power$Time,power$Sub_metering_2,col="red") lines(power$Time,power$Sub_metering_3,col="blue") legend("topright", col=c("black","red","blue"), c("Sub_metering_1 ","Sub_metering_2 ", "Sub_metering_3 "),lty=c(1,1), bty="n", cex=.5) #bty removes the box, cex shrinks the text, spacing added after labels so it renders correctly #PLOT 4 plot(power$Time,power$Global_reactive_power, type="l", xlab="datetime", ylab="Global_reactive_power") #OUTPUT dev.copy(png, file="plot4.png", width=480, height=480) dev.off()

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/week2.R

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aarjan/strategic-business-modeling

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2020-03-18T23:56:01.883265

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week2.R

rm(list=ls(all=TRUE)) # Let's load our dataset and call it data data=read.table('data/DATA_3.01_CREDIT.csv',sep=',',header=TRUE) # The function read.table enables us to read flat files such as .csv files # Now let's have a look at our variables and see some summary statistics str(data) # The str() function shows the structure of your dataset and details the type of variables that it contains summary(data) # The summary() function provides for each variable in your dataset the minimum, mean, maximum and quartiles hist(data$Rating) # Produce a histogram of the credit scores cor(data[,c(1:5,10)]) # Compute the correlation between all the numerical variables of the sample linreg=lm(Rating~.,data=data) # Estimate a linear regression model of Rating as a function of everything else. cor(linreg$fitted.values,data$Rating) # Computes the correlation between the fitted values and the actual ones plot(data$Rating,linreg$fitted.values) # Plot the fitted values vs. the actual ones summary(linreg) # Reports the results of the regression plot(data$Balance,data$Rating) # Allows to visualize the relationship between Balance and Rating plot(data$Income,data$Rating) # Allows to visualize the relationship between Income and Rating

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/data/genthat_extracted_code/rem/examples/reciprocityStat.Rd.R

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surayaaramli/typeRrh

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2023-05-05T04:05:31.617869

2019-04-25T22:10:06

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reciprocityStat.Rd.R

library(rem) ### Name: reciprocityStat ### Title: Calculate reciprocity statistics ### Aliases: reciprocityStat reciprocity ### ** Examples # create some data with 'sender', 'target' and a 'time'-variable # (Note: Data used here are random events from the Correlates of War Project) sender <- c('TUN', 'NIR', 'NIR', 'TUR', 'TUR', 'USA', 'URU', 'IRQ', 'MOR', 'BEL', 'EEC', 'USA', 'IRN', 'IRN', 'USA', 'AFG', 'ETH', 'USA', 'SAU', 'IRN', 'IRN', 'ROM', 'USA', 'USA', 'PAN', 'USA', 'USA', 'YEM', 'SYR', 'AFG', 'NAT', 'NAT', 'USA') target <- c('BNG', 'ZAM', 'JAM', 'SAU', 'MOM', 'CHN', 'IRQ', 'AFG', 'AFG', 'EEC', 'BEL', 'ITA', 'RUS', 'UNK', 'IRN', 'RUS', 'AFG', 'ISR', 'ARB', 'USA', 'USA', 'USA', 'AFG', 'IRN', 'IRN', 'IRN', 'AFG', 'PAL', 'ARB', 'USA', 'EEC', 'BEL', 'PAK') time <- c('800107', '800107', '800107', '800109', '800109', '800109', '800111', '800111', '800111', '800113', '800113', '800113', '800114', '800114', '800114', '800116', '800116', '800116', '800119', '800119', '800119', '800122', '800122', '800122', '800124', '800125', '800125', '800127', '800127', '800127', '800204', '800204', '800204') type <- sample(c('cooperation', 'conflict'), 33, replace = TRUE) important <- sample(c('important', 'not important'), 33, replace = TRUE) # combine them into a data.frame dt <- data.frame(sender, target, time, type, important) # create event sequence and order the data dt <- eventSequence(datevar = dt$time, dateformat = "%y%m%d", data = dt, type = "continuous", byTime = "daily", returnData = TRUE, sortData = TRUE) # create counting process data set (with null-events) - conditional logit setting dts <- createRemDataset(dt, dt$sender, dt$target, dt$event.seq.cont, eventAttribute = dt$type, atEventTimesOnly = TRUE, untilEventOccurrs = TRUE, returnInputData = TRUE) ## divide up the results: counting process data = 1, original data = 2 dtrem <- dts[[1]] dt <- dts[[2]] ## merge all necessary event attribute variables back in dtrem$type <- dt$type[match(dtrem$eventID, dt$eventID)] dtrem$important <- dt$important[match(dtrem$eventID, dt$eventID)] # manually sort the data set dtrem <- dtrem[order(dtrem$eventTime), ] # calculate reciprocity statistic dtrem$recip <- reciprocityStat(data = dtrem, time = dtrem$eventTime, sender = dtrem$sender, target = dtrem$target, eventvar = dtrem$eventDummy, halflife = 2) # plot sender-outdegree over time library("ggplot2") ggplot(dtrem, aes(eventTime, recip, group = factor(eventDummy), color = factor(eventDummy)) ) + geom_point()+ geom_smooth() # calculate reciprocity statistic with typematch # if a cooperated with b in the past, does # b cooperate with a now? dtrem$recip.typematch <- reciprocityStat(data = dtrem, time = dtrem$eventTime, sender = dtrem$sender, target = dtrem$target, eventvar = dtrem$eventDummy, eventtypevar = dtrem$type, eventtypevalue = 'valuematch', halflife = 2) # calculate reciprocity with valuemix on type dtrem <- reciprocityStat(data = dtrem, time = dtrem$eventTime, sender = dtrem$sender, target = dtrem$target, eventvar = dtrem$eventDummy, eventtypevar = dtrem$type, eventtypevalue = 'valuemix', halflife = 2, returnData = TRUE) # calculate reciprocity and count important events only dtrem$recip.filtered <- reciprocityStat(data = dtrem, time = dtrem$eventTime, sender = dtrem$sender, target = dtrem$target, eventvar = dtrem$eventDummy, eventfiltervar = dtrem$important, eventfiltervalue = 'important', halflife = 2)

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/R/tam.jml.R

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yaozeyang90/TAM

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tam.jml.R

## File Name: tam.jml.R ## File Version: 9.27 tam.jml <- function( resp , group = NULL , adj=.3 , disattenuate = FALSE , bias = TRUE, xsi.fixed=NULL , xsi.inits = NULL , theta.fixed = NULL , A=NULL , B=NULL , Q=NULL , ndim=1 , pweights = NULL , verbose = TRUE , control = list() , version = 2 ) { CALL <- match.call() #**** handle verbose argument args_CALL <- as.list( sys.call() ) control$progress <- tam_args_CALL_search( args_CALL=args_CALL , variable="verbose" , default_value = TRUE ) #******* if ( ! is.null(theta.fixed) ){ version <- 1 } #**** version = 1 if (version == 1){ res <- tam_jml_version1( resp=resp , group = group , adj=adj , disattenuate = disattenuate , bias = bias, xsi.fixed=xsi.fixed , xsi.inits = xsi.inits , A=A , B=B , Q=Q , ndim=ndim , theta.fixed = theta.fixed , pweights = pweights , control = control ) } #**** version = 2 if (version == 2){ res <- tam_jml_version2( resp=resp , group = group , adj=adj , disattenuate = disattenuate , bias = bias, xsi.fixed=xsi.fixed , xsi.inits = xsi.inits , A=A , B=B , Q=Q , ndim=ndim , pweights = pweights , control = control ) } res$CALL <- CALL return(res) }

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/man/gd_df_by.Rd

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acobos/gocDescriptives

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gd_df_by.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/gd_df_by.R \name{gd_df_by} \alias{gd_df_by} \title{Describes a dataframe by groups} \usage{ gd_df_by(df, by, ...) } \arguments{ \item{df}{\code{dataframe} to be described.} \item{by}{Name of the variable defining groups. Should be \code{character} or \code{factor}.} \item{...}{Further arguments passed to function \code{gd_df}.} } \value{ A \code{dataframe} with columns \code{Variable}, \code{Key}, and one additional column for each level/value of the \code{by} variable. } \description{ Describes a dataframe by groups, defined by the \code{by} variable. } \examples{ # Example data set.seed(123) Sex <- sample(c("Male", "Female"), 100, replace=TRUE) Age <- floor(sample(50 + 10 * rnorm(100))) Group <- sample(LETTERS[1:2], 100, replace=TRUE) dat <- data.frame(Sex, Age, Group) # Describe dataframe dat by Group gd_df_by(dat, "Group") # Same, using pipe library(dplyr) dat \%>\% gd_df_by("Group") # Introduce some missings and pass further arguments dat$Sex[1:5] <- NA gd_df_by(dat, "Group") # Change the descriptor for missings gd_df_by(dat, "Group", NA_label = "Unknown") }

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/term-project/sample/code/03_02_OneSamplet.R

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tmussa1/r-programming

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2023-01-23T21:47:29.837096

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03_02_OneSamplet.R

# Title: Comparing one mean to a population: # One-sample t-test # File: 03_02_OneSamplet.R # Project: R_EssT_2; R Essential Training, Part 2: # Modeling Data # INSTALL AND LOAD PACKAGES ################################ # Install pacman ("package manager") if needed if (!require("pacman")) install.packages("pacman") # pacman must already be installed; then load contributed # packages (including pacman) with pacman pacman::p_load(datasets, magrittr, pacman, tidyverse) # datasets: for demonstration purposes # magrittr: for pipes # pacman: for loading/unloading packages # tidyverse: for so many reasons # LOAD AND PREPARE DATA #################################### # Get info on "quakes" dataset ?quakes # Get variable names quakes %>% names() # Save "mag" to "df" and print first few data points df <- quakes %>% pull(mag) %>% # Save mag as vector glimpse() # Show beginning values # EXPLORE DATA ############################################# # Histogram (with base graphics) df %>% hist() # Boxplot of values df %>% boxplot(horizontal = T) # Summary of magnitude df %>% summary() # T-TESTS ################################################## # Use t-test for one-sample. By default, the sample mean is # compared to a population mean of 0. df %>% t.test() # One-sided t-test with population mean = 4.6 df %>% t.test( alternative = "greater", # Use directional hypothesis mu = 4.6 # Set population mean ) # CLEAN UP ################################################# # Clear data rm(list = ls()) # Removes all objects from environment # Clear packages detach("package:datasets", unload = T) # For base packages p_unload(all) # Remove all contributed packages # Clear plots graphics.off() # Clears plots, closes all graphics devices # Clear console cat("\014") # Mimics ctrl+L # Clear mind :)

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/htree/man/hrf.Rd

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joxton/htree

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hrf.Rd

\name{hrf} \alias{hrf} \title{Random forest for longitudinal data} \description{Fits a random forest of historical regression trees to longitudinal data.} \usage{ hrf(x, time=NULL, id=NULL, yindx, ntrees = 100, method="freq", mtry=NULL, se=FALSE, B=100, R=10, nsamp=5, historical=TRUE, vh=NULL, vc=NULL, delta=NULL, classify=FALSE, control=list()) } \arguments{ \item{x}{A data frame containing response and predictors} \item{time}{A vector of observation times associated with rows of \code{x}.} \item{id}{Subject identifier, if \code{NULL} observations are assumed independent} \item{yindx}{Name of response variable, alt. its column number in \code{x}} \item{ntrees}{Number of trees in ensemble} \item{method}{Historical summary method, can be \code{freq}, \code{frac}, \code{mean0}, \code{freqw}, \code{fracw} and \code{mean0w} (see below).} \item{mtry}{Number of predictors sampled at each split} \item{se}{If \code{TRUE} then bootstrap standard errors are computed. Total number of trees fit for bootstrapping is \code{B*R}. } \item{B}{Only used if \code{se=TRUE}, number of bootstrap samples, defaults to \code{100}.} \item{R}{Only used if \code{se=TRUE}, forest size for each bootstrap sample, defaults to \code{R=10}.} \item{nsamp}{Number of sampled \code{delta} values, see below} \item{historical}{If \code{TRUE} then historical splitting is done, else only standard (ie concurrent predictor) splitting.} \item{vh}{Optional vector of variable names to be used as historical predictors, can be a numeric vector giving column numbers of \code{x}.} \item{vc}{Optional vector of variable names to be used as concurrent predictors, can be a numeric vector giving column numbers of \code{x}.} \item{delta}{An optional vector of time-lags to be used (see below).} \item{classify}{If \code{TRUE} then a classification tree is built (using gini-impurity node splitting).} \item{control}{A list of control parameters (see below). All arguments, except those describing the data, can be set in \code{control}. Arguments in \code{control} are used if both are given.} } \details{ The \code{hrf} function fits a random forest model to longitudinal data. Data is assumed to be of form: \eqn{z_{ij}=(y_{ij},t_{ij},x_{ij})} for \eqn{i=1,..,n} and \eqn{j=1,..,n_i}, with \eqn{y_{ij}} being the response for the \eqn{i}-th subject at the \eqn{j}-th observation time \eqn{t_{ij}}. The vector of predictors at time \eqn{t_{ij}} are \eqn{x_{ij}}. The number of observations can vary across subjects, and the sampling in time can be irregular. \code{hrf} estimates a model for the response \eqn{y_{ij}} using both \eqn{(t_{ij},x_{ij})} (the observations concurrent with \eqn{y_{ij}}) and all preceeding observations of the \eqn{i}-th subject up to (but not including) time \eqn{t_{ij}}. The model is fit using \code{historical} regression (alt. classification) trees. Here a predictor is one of two types, either \code{concurrent} or \code{historical}. The concurrent predictors for \eqn{y_{ij}} are the elements of the vector (\eqn{(t_{ij},x_{ij})}), while a historic predictor is the set of all preceeding values (i.e. prior to time \eqn{t_{ij}}) of a given element of \eqn{(y_{ij},t_{ij},x_{ij})}, for subject \eqn{i}. In a historic regression tree, node splitting on a \code{concurrent} predictor follows the approach in standard regression (classification) trees. For \code{historical} predictors the splitting is modified since, associated with each observed response \eqn{y_{ij}}, the number (and observation times) of observations of a \code{historical} predictor will vary according to \eqn{i} and \eqn{j}. For these, the splitting is done by first transforming the preceeding values of a predictor using a \code{summary function}. This summary is invertible, in the sense that knowledge of it is equivalent to knowledge of the covariate history. Letting \eqn{\bar{z}_{ijk}} denote the set of historical values of the \eqn{k}-th element of \eqn{z_{ij}}, the summary function is denoted \eqn{s(\eta;\bar{z}_{ijk})} where \eqn{\eta} is the argument vector of the summary function. Node splitting based on a historical predictor is done by solving \deqn{\mbox{argmin}\sum_{(ij)\in Node} (y_{ij}-\mu_L I(s(\eta;\bar{z}_{ijk})<c)-\mu_R I(s(\eta;\bar{z}_{ijk})\geq c))^2,} where the minimization is over the vector \eqn{(k,\mu,c,\eta)}. Each node of \code{historical} regression tree is split using the best split among all splits of concurrent and historical predictors. Different \code{summary functions} are available in \code{hrf}, specified by the argument \code{method}. If \code{method="freq"} the summary function summarizing a covariate history is: \deqn{s(\eta;\bar{z}_{ijk})=\sum_{h: t_{ij}-\eta_1\leq t_{ih}<t_{ij}} I(z_{ihk}<\eta_2);} \code{method="frac"}: \deqn{s(\eta;\bar{z}_{ijk})=\sum_{h: t_{ij}-\eta_1\leq t_{ih}<t_{ij}} I(z_{ihk}<\eta_2)/n_{ij}(\eta);} \code{method="mean0"}: \deqn{s(\eta;\bar{z}_{ijk})=\sum_{h: t_{ij}-\eta_1\leq t_{ih}<t_{ij}} z_{ihk}/n_{ij}(\eta);} \code{method="freqw"}: \deqn{s(\eta;\bar{z}_{ijk})=\sum_{h: t_{ij}-\eta_1\leq t_{ih}<t_{ij}-\eta_2} I(z_{ihk}<\eta_3);} \code{method="fracw"}: \deqn{s(\eta;\bar{z}_{ijk})=\sum_{h: t_{ij}-\eta_1\leq t_{ih}<t_{ij}-\eta_2} I(z_{ihk}<\eta_3)/n_{ij}(\eta);} \code{method="meanw0"}: \deqn{s(\eta;\bar{z}_{ijk})=\sum_{h: t_{ij}-\eta_1\leq t_{ih}<t_{ij}-\eta_2} z_{ihk}/n_{ij}(\eta).} Here \eqn{n_{ij}(\eta)} denotes the number of observations of subject \eqn{i} in the time window \eqn{[t_{ij}-\eta_1,t_{ij}-\eta_2)}. In the case \eqn{n_{ij}(\eta)=0}, the summary function is set to zero, i.e \eqn{s(\eta;\bar{z}_{ijk})=0}. The default is \code{method="freq"}. The possible values of \eqn{\eta_1} in the summary function can be set by the argument \code{delta}. If not supplied, the set of possible values of \eqn{\eta_1} is determined by the difference in time between within-subject successive observations in the data. When a split is attempted on a historical predictor, a sample of this set is taken from which the best split is selected. The size of this set equals that of the \code{nsamp} argument. See below on \code{control} for futher arguments governing the historical splitting. See below on \code{control} for futher arguments governing the historical splitting. Setting \code{se=TRUE} performs standard error estimation. The number of bootstrap samples (sampling subjects with replacement) is determined by \code{B}. For each bootstrap sample a random forest with \code{R} trees is built, which defaults to \code{R=10}. The bias induced by using smaller bootstrap ensemble sizes is corrected for in the estimate. Using \code{se=TRUE} will influence summaries from the fitted model, such as providing approximate confidence intervals for partial dependence plots (when running \code{partdep_hrf}), and give standard errors for predictions when \code{predict_hrf} is used. All arguments (except those decribing the data \code{x}, \code{yindx},\code{time} and \code{id}) can be set in the \code{control} list. The arguments supplied in \code{control} are used if both are supplied. So if \code{ntrees=300} and \code{control=list(ntrees=500)} then \code{500} trees are fit. Besides the arguments described above, a number of other parameters can be set in control. These are: \code{nodesize} giving the minimum number of training observations in a terminal node; \code{sample_fraction} giving the fraction of data sample to train each tree; \code{dtry} the number of sampled \code{delta} values used when splitting based on a historical variable (note this is alternatively controlled by the argument \code{nsamp} above ; \code{ndelta} the number of \code{delta} values to use if \code{delta} is not supplied, these are taken as the quantiles from the distribution of observed \code{delta} values in the data; \code{qtry} the number of sampled values of \eqn{\eta_2} for \code{method=freq/frac}, \eqn{\eta_3} for \code{method=freqw/fracw}; \code{quantiles} is a vector of probabilities, and is used when \code{method=frac} or \code{method=fracw}, ie when covariate histories are split on their windowed empirical distributions. Splitting is restricted to these quantiles. } \value{ Returns a \code{list} with elements, the most important being: \code{h} is a list returned by \code{parLapply} for fitting trees in parallel, \code{error} gives the OOB error (mse for regression, misclassification rate for classification), \code{control} a list containing control arguments, \code{boot} gives bootstrapped model estimates (if \code{se=TRUE}), \code{x,id,time} give the training data. } \references{ L. Breiman (2001). \dQuote{Random Forests,} \emph{Machine Learning} 45(1):5-32. Zhang and Singer (2010) \dQuote{Recursive Partioning and Applications} \emph{Springer}. Sexton and Laake (2009) \dQuote{Standard errors for bagged and random forest estimators,} \emph{Computational Statistics and Data Analysis}. } \author{Joe Sexton \email{joesexton0@gmail.com}} \seealso{ \code{\link{predict_hrf}}, \code{\link{partdep_hrf}}, \code{\link{varimp_hrf}}. } \examples{ \dontrun{ # ----------------------------------------------------------------------------------------- ## # Mother's stress on child illness: # Investigate whether mother's stress is (Granger) causal for child illness # 'hrf' model is fit using previous observations of mother's stress to predict # child's illness at given time point, but not mother's stress at that time point # # Predictor variables are classified into "historical" and "concurrent" # # A predictor is "historical" if its prior realizations can be used to predict # the outcome. # # A predictor is "concurrent" if its realization at the same timepoint as the outcome # can be used to predict the outcome at that timepoint # # A predictor can be both "concurrent" and "historical", the status of the predictors # can be set by the 'vh' and 'vc' arguments of 'hrf'. # (if not set these are automatically determined) # # ------------------------------------------------------------------------------------------- ## data(mscm) mscm=as.data.frame(na.omit(mscm)) # -- set concurrent and historical predictors historical_predictors=match(c("stress","illness"),names(mscm)) concurrent_predictors=which(names(mscm)!="stress") control=list(vh=historical_predictors,vc=concurrent_predictors) # -- fit historical random forest # (NOTE: response is 0/1 so a regression tree is # the same as a classification tree with Gini-index splitting) ff=hrf(x=mscm,id=mscm$id,time=mscm$day,yindx="illness",control=control) # out-of-bag error plot(1:length(ff$error),ff$error,type="l",main="OOB error",xlab="forest size",ylab="mse") # .. larger nodesize works slightly better control$nodesize=20 ff=hrf(x=mscm,id=mscm$id,time=mscm$day,yindx="illness",control=control) points(1:length(ff$error),ff$error,type="l",col="blue") # -- variable importance table vi=varimp_hrf(ff) vi # -- fit historical random forest with 'se=TRUE' control$se=TRUE ff=hrf(x=mscm,id=mscm$id,time=mscm$day,yindx="illness",control=control) # -- partial dependence for top 4 predictors (with +/-2 SE estimates) par(mfrow=c(2,2)) for(k in 1:4) pd=partdep_hrf(ff,xindx=as.character(vi$Predictor[k])) par(mfrow=c(1,1)) ## -- Classification trees ## setting classify=TRUE builds classification tree (gini-impurity node splitting) control$classify=TRUE ## ... standard error estimation not implemented .. turn off bootstrapping control$se=FALSE ff=hrf(x=mscm,id=mscm$id,time=mscm$day,yindx="illness",control=control) # -- plot oob classification error plot(1:length(ff$error),ff$error,type="l",xlab="forest size",ylab="oob classification error") abline(mean(mscm$illness),0,lty=2) ## error of constant model p=predict_hrf(ff) ## variable importance table (model error measured by gini-impurity) vi=varimp_hrf(ff) vi # -------------------------------- # # Data w/irregular observation times # ------------------------------- # data(cd4) control=list(se=TRUE) ff=hrf(x=cd4,id=cd4$id,time=cd4$time,yindx="count",control=control) vi=varimp_hrf(ff) # -- partial dependence for top 4 predictors (with +/-2 SE estimates) par(mfrow=c(2,2)) for(k in 1:4) pd=partdep_hrf(ff,xindx=as.character(vi$Predictor[k])) par(mfrow=c(1,1)) plot(1:length(ff$error),ff$error,xlab="forest size",ylab="oob mse",type="l") ## by default, the number of delta values (parameter 'eta_1' above) is 20 ## can set this using 'ndelta' control$ndelta=50 control$se=FALSE # -- turning off bootstrapping .. ff=hrf(x=cd4,id=cd4$id,time=cd4$time,yindx="count",control=control) points(1:length(ff$error),ff$error,type="l",lty=2) # the grid of delta values ff$control$delta # --------------------------------------- ## # Boston Housing data (not longitudinal) # --------------------------------------- ## # library(htree) library(mlbench) library(randomForest) data(BostonHousing) dat=as.data.frame(na.omit(BostonHousing)) ## omitting arguments time/id assumes rows are iid control=list(ntrees=500,sample_fraction=.5,nodesize=1) h=hrf(x=dat,yindx="medv",control=control) ## randomForest comparison ## (by default, randomForest samples with replacement, while hrf samples without) r=randomForest(medv~.,data=dat,replace=F,sampsize=ceiling(.5*nrow(dat)),nodesize=1) ## plot oob-error for both plot(1:length(r$mse),r$mse,type="l",ylim=c(min(r$mse,h$error),max(r$mse,h$error)), main="BostonHousing",xlab="forest size",ylab="out-of-bag mse") points(1:length(h$error),h$error,type="l",col="blue") ## -- variable importance table vi=varimp_hrf(h) vi ## -- partial dependence plots with approximate 95% C.I control$se=TRUE h=hrf(x=dat,yindx="medv",control=control) par(mfrow=c(2,2)) for(k in 1:4) pd=partdep_hrf(h,xindx=as.character(vi$Predictor[k])) par(mfrow=c(1,1)) } } \keyword{nonparametric} \keyword{tree} \keyword{longitudinal}

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/Make_R0_Incursions.R

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Make_R0_Incursions.R

# Make incursions to simulate multiple runs to compute R0, Rebecca Mancy, started 2021-08-10 # Note that the files required to run this script are large and are not provided. rm(list=ls()) library(ggplot2) library(dplyr) library(tidyr) # library(sp) library(sp) ff <- "java_output/" end.in.days <- 5112 f_LOWER_VAX <- "Round3_LOWER_VAX_1000" f_R0_MULTI <- "Round3_R0_MULTI_1000" f_R0_by_location <- "Round3_R0_by_location_1000" n_runs <- 1000 load("data/grd_1000.rda") grd_pops <- grd.1000@data[, 3:ncol(grd.1000)] sum_pops <- colSums(grd_pops); plot(1:168, sum_pops) # totals by time month_bounds <- as.numeric(names(grd_pops)) sum_all <- sum(grd_pops) tw_weight = sum_pops/sum_all # time-window weight gc_weight = grd_pops[,1]/sum(grd_pops[,1]) # gridcell weight xy_weight = rep(1,nrow(grd_pops))/nrow(grd_pops) popID_levels <- grd.1000$popID system.time({ for (i in 1:n_runs) { # Read in output from the LOWER_VAX scenario sim_output_LOWER_VAX <- read.csv(file = paste0(ff, f_LOWER_VAX, "/output_files/", f_LOWER_VAX, "_sim_cases_",sprintf("%03d",i),".csv")) n_incursions <- nrow(sim_output_LOWER_VAX) print(paste0("Run ", i, " with ", n_incursions, " cases")) # Draw a gridcell and time window from grd.1000 by drawing proportional to grd_pops time_window_start <- as.numeric(sample(names(sum_pops), size = n_incursions, replace = T, prob = tw_weight)) populations <- sample(grd.1000@data$popID, size = n_incursions, replace = T, prob = gc_weight) # head(time_window_start); head(populations) incursion_cases_x <- numeric(n_incursions) incursion_cases_y <- numeric(n_incursions) # Draw random date within each population from above sample_days <- time_window_start + runif(n = n_incursions, min = 0, max = 28) # Draw random location within each population from above for (j in 1:n_incursions) { sample_coords <- spsample(x = subset(grd.1000, grd.1000@data$popID == populations[j]), n = 1, type = "random", iter = 5) incursion_cases_x[j] <- sample_coords@coords[1] incursion_cases_y[j] <- sample_coords@coords[2] } # Replace new dates and coordinates into sim_output_LOWER_VAX incursion_list <- sim_output_LOWER_VAX incursion_list[, 3:ncol(incursion_list)] <- -1 incursion_list$popID <- populations # incursion_cases$popID incursion_list$x_coord <- incursion_cases_x # incursion_cases$x_coord incursion_list$y_coord <- incursion_cases_y # incursion_cases$y_coord incursion_list$dayInfectious <- sample_days # incursion_cases$dayInfectious # Transform all cases to being recorded as INCURSION type incursion_list$typeOfCase <- "INCURSION" # Write out to file print(paste0(ff, f_R0_MULTI, "/incursions/incursions_",sprintf("%03d",i),".csv")) write.table(incursion_list, file = paste0(ff, f_R0_MULTI, "/incursions/incursions_",sprintf("%03d",i),".csv"), append = FALSE, quote = FALSE, sep = ",", eol = "\n", na = "NA", dec = ".", row.names = FALSE, col.names = TRUE, fileEncoding = "") } }) ############################### # PLOT OLD INCURSIONS (with N/S gradient) # PLOT NEW INCURSIONS (by pop density) end.in.days <- 5112 pdf(paste0(ff, f_R0_MULTI, "/incursions/incursionsTEST.pdf")) par(mfrow = c(2,1), mar = c(2,2,1,1)) # set up plotting area for (i in 1:n_runs) { print(i) # sim_incursions <- read.csv(file = paste0(ff, f_R0_MULTI, "/incursions/incursions_",sprintf("%03d",i),".csv")) sim_incursions <- read.csv(file = paste0(ff, f_R0_by_location, "/incursions/incursions_",sprintf("%03d",i),".csv")) sim_incursions <- filter(sim_incursions, dayInfectious < end.in.days) sim_incursions$popID_Factor <- factor(sim_incursions$popID, levels = popID_levels) inc_summary <- sim_incursions %>% group_by(popID_Factor, .drop = F) %>% summarise(nCases = length(caseID)) cases = sum(inc_summary$nCases) # plots for the first 100 runs to sanity check some outputs! if(i < 100) { hist(sim_incursions$dayInfectious, breaks = seq(0, end.in.days + 30.5, 30.5), main = paste0("Cases = ", cases)) plot(sim_incursions$x_coord, sim_incursions$y_coord, cex = 0.1, col = alpha("black", 0.4)) } } dev.off() ############################### # now try to generate index cases by grid cell system.time({ for (i in 1:n_runs) { #for (i in 297:302) { # Read in output from the LOWER_VAX scenario sim_output_LOWER_VAX <- read.csv(file = paste0(ff, f_LOWER_VAX, "/output_files/", f_LOWER_VAX, "_sim_cases_",sprintf("%03d",i),".csv")) n_incursions <- nrow(sim_output_LOWER_VAX) print(paste0("Run ", i, " with ", n_incursions, " cases")) # Draw a gridcell and time window from grd.1000 by drawing proportional to grd_pops time_window_start <- as.numeric(sample(names(sum_pops), size = n_incursions, replace = T, prob = tw_weight)) populations <- sample(grd.1000@data$popID, size = n_incursions, replace = T, prob = xy_weight) # head(time_window_start); head(populations) incursion_cases_x <- numeric(n_incursions) incursion_cases_y <- numeric(n_incursions) # Draw random date within each population from above sample_days <- time_window_start + runif(n = n_incursions, min = 0, max = 28) # Draw random location within each population from above for (j in 1:n_incursions) { sample_coords <- spsample(x = subset(grd.1000, grd.1000@data$popID == populations[j]), n = 1, type = "random", iter = 8) incursion_cases_x[j] <- sample_coords@coords[1] incursion_cases_y[j] <- sample_coords@coords[2] } # Replace new dates and coordinates into sim_output_LOWER_VAX incursion_list <- sim_output_LOWER_VAX incursion_list[, 3:ncol(incursion_list)] <- -1 incursion_list$popID <- populations # incursion_cases$popID incursion_list$x_coord <- incursion_cases_x # incursion_cases$x_coord incursion_list$y_coord <- incursion_cases_y # incursion_cases$y_coord incursion_list$dayInfectious <- sample_days # incursion_cases$dayInfectious # Transform all cases to being recorded as INCURSION type incursion_list$typeOfCase <- "INCURSION" # Write out to file print(paste0(ff, f_R0_by_location, "/incursions/incursions_",sprintf("%03d",i),".csv")) write.table(incursion_list, file = paste0(ff, f_R0_by_location, "/incursions/incursions_",sprintf("%03d",i),".csv"), append = FALSE, quote = FALSE, sep = ",", eol = "\n", na = "NA", dec = ".", row.names = FALSE, col.names = TRUE, fileEncoding = "") } }) ############################### # PLOT OLD INCURSIONS (with N/S gradient) # PLOT NEW INCURSIONS (by pop density) end.in.days <- 5112 pdf(paste0(ff, f_R0_by_location, "/incursions/incursionsTEST.pdf")) par(mfrow = c(2,1), mar = c(2,2,1,1)) # set up plotting area for (i in 1:28) { # for (i in 1:n_runs) { print(i) # sim_incursions <- read.csv(file = paste0(ff, f_R0_MULTI, "/incursions/incursions_",sprintf("%03d",i),".csv")) sim_incursions <- read.csv(file = paste0(ff, f_R0_by_location, "/incursions/incursions_",sprintf("%03d",i),".csv")) sim_incursions <- filter(sim_incursions, dayInfectious < end.in.days) sim_incursions$popID_Factor <- factor(sim_incursions$popID, levels = popID_levels) inc_summary <- sim_incursions %>% group_by(popID_Factor, .drop = F) %>% summarise(nCases = length(caseID)) cases = sum(inc_summary$nCases) # plots for the first 100 runs to sanity check some outputs! if(i < 100) { hist(sim_incursions$dayInfectious, breaks = seq(0, end.in.days + 30.5, 30.5), main = paste0("Cases = ", cases)) plot(sim_incursions$x_coord, sim_incursions$y_coord, cex = 0.1, col = alpha("black", 0.4)) } } dev.off() for (i in 1:n_runs) { # Read in output from the LOWER_VAX scenario sim_output_LOWER_VAX <- read.csv(file = paste0(ff, f_LOWER_VAX, "/output_files/", f_LOWER_VAX, "_sim_cases_",sprintf("%03d",i),".csv")) #### CHECK WITH A SPEEDIER VERSION #### sim_output_LOWER_VAX <- sim_output_LOWER_VAX[1:floor(nrow(sim_output_LOWER_VAX)/10),] n_incursions <- nrow(sim_output_LOWER_VAX) print(paste0("Run ", i, " with ", n_incursions, " cases")) # Draw a gridcell and time window from grd.1000 by drawing proportional to grd_pops time_window_start <- as.numeric(sample(names(sum_pops), size = n_incursions, replace = T, prob = tw_weight)) populations <- sample(grd.1000@data$popID, size = n_incursions, replace = T, prob = xy_weight) # THIS GENERATED SAMPLING BY ID NOT DENSITY ### populations <- sample(grd.1000@data$popID, size = n_incursions, replace = T, prob = grd.1000@data$popID/sum(grd.1000@data$popID, na.rm=T)) # head(time_window_start); head(populations) incursion_cases_x <- numeric(n_incursions) incursion_cases_y <- numeric(n_incursions) sample_days <- time_window_start + runif(n = n_incursions, min = 0, max = 28) # Draw random date and location within each population from above for (j in 1:n_incursions) { # max_days <- month_bounds[match(time_window_start[j], month_bounds)+1] #max_days <- month_bounds[which(month_bounds == as.numeric(time_window_start[j]))+1] # if (is.na(max_days)) { max_days <- end.in.days } # print(paste0("min = ", as.numeric(time_window_start[j]), " and max = ", max_days)) # sample_days <- runif(n = 1, min = as.numeric(time_window_start[j]), max = max_days) sample_coords <- spsample(x = subset(grd.1000, grd.1000@data$popID == populations[j]), n = 1, type = "random", iter = 5) incursion_cases_x[j] <- sample_coords@coords[1] # sample_x = incursion_cases_y[j] <- sample_coords@coords[2] # sample_y = # if(nrow(incursion_cases) == 0) { incursion_cases <- data.frame(popID = populations[j], x_coord = sample_coords@coords[1], y_coord = sample_coords@coords[2], dayInfectious = sample_days[j]) } else { # incursion_cases <- rbind(incursion_cases, data.frame(popID = populations[j], x_coord = sample_coords@coords[1], y_coord = sample_coords@coords[2], dayInfectious = sample_days[j]))} } # Replace new dates and coordinates into sim_output_LOWER_VAX incursion_list <- sim_output_LOWER_VAX incursion_list[, 3:ncol(incursion_list)] <- -1 incursion_list$popID <- populations # incursion_cases$popID incursion_list$x_coord <- # incursion_cases$x_coord incursion_list$y_coord <- # incursion_cases$y_coord incursion_list$dayInfectious <- sample_days # incursion_cases$dayInfectious # Transform all cases to being recorded as INCURSION type incursion_list$typeOfCase <- "INCURSION" # Write out to file print(paste0(ff, f_R0_by_location, "/incursions/incursions_",sprintf("%03d",i),".csv")) write.table(incursion_list, file = paste0(ff, f_R0_by_location, "/incursions/incursions_",sprintf("%03d",i),".csv"), append = FALSE, quote = FALSE, sep = ",", eol = "\n", na = "NA", dec = ".", row.names = FALSE, col.names = TRUE, fileEncoding = "") } # TEST 238 lines - is this working?

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/materials/labs/5_Friday/proteomics/proteomics.R

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BioXiao/CSAMA2015

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refs/heads/master

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proteomics.R

## ----env0, message=FALSE, echo=FALSE, warning=FALSE---------------------- library("knitr") opts_knit$set(error = FALSE) library("BiocInstaller") library("RforProteomics") library("nloptr") ## ---- env, message=FALSE, echo=TRUE, warning=FALSE----------------------- library("mzR") library("mzID") library("MSnID") library("MSnbase") library("rpx") library("MLInterfaces") library("pRoloc") library("pRolocdata") library("MSGFplus") library("rols") library("hpar") ## ----r4pinstall, eval=FALSE---------------------------------------------- ## library("BiocInstaller") ## biocLite("RforProteomics", dependencies = TRUE) ## ---- pk, echo=FALSE, warning=FALSE, cache=TRUE-------------------------- biocv <- as.character(biocVersion()) pp <- proteomicsPackages(biocv) msp <- massSpectrometryPackages(biocv) msdp <- massSpectrometryDataPackages(biocv) ## ---- pp, eval=FALSE----------------------------------------------------- ## library("RforProteomics") ## pp <- proteomicsPackages() ## display(pp) ## ---- datatab, results='asis', echo=FALSE-------------------------------- datatab <- data.frame(Type = c("raw", "identification", "quantitation", "peak lists", "other"), Format = c("mzML, mzXML, netCDF, mzData", "mzIdentML", "mzQuantML", "mgf", "mzTab"), Package = c( "[`mzR`](http://bioconductor.org/packages/release/bioc/html/mzR.html) (read)", paste("[`mzR`](http://bioconductor.org/packages/release/bioc/html/mzR.html) (read) and", "[`mzID`](http://bioconductor.org/packages/release/bioc/html/mzID.html) (read)"), "", "[`MSnbase`](http://bioconductor.org/packages/release/bioc/html/MSnbase.html) (read/write)", "[`MSnbase`](http://bioconductor.org/packages/release/bioc/html/MSnbase.html) (read/write)")) library("knitr") kable(datatab) ## ---- rpx---------------------------------------------------------------- library("rpx") pxannounced() ## ---- pxd, cache=TRUE---------------------------------------------------- px <- PXDataset("PXD000001") px pxfiles(px) ## ---- pxvar-------------------------------------------------------------- pxtax(px) pxurl(px) pxref(px) ## ---- pxget, eval=FALSE-------------------------------------------------- ## mzf <- pxget(px, pxfiles(px)[6]) ## ----pxd1---------------------------------------------------------------- library("PXD000001") px1files() mzf <- px1files()[5] px1get(mzf) ## ---- rawms-------------------------------------------------------------- library("mzR") ms <- openMSfile(mzf) ms ## ---- hd----------------------------------------------------------------- hd <- header(ms) dim(hd) names(hd) ## ------------------------------------------------------------------------ hd[1000, ] head(peaks(ms, 1000)) plot(peaks(ms, 1000), type = "h") ## ---- msmap-------------------------------------------------------------- ## a set of spectra of interest: MS1 spectra eluted ## between 30 and 35 minutes retention time ms1 <- which(hd$msLevel == 1) rtsel <- hd$retentionTime[ms1] / 60 > 30 & hd$retentionTime[ms1] / 60 < 35 ## the map M <- MSmap(ms, ms1[rtsel], 521, 523, .005, hd) plot(M, aspect = 1, allTicks = FALSE) plot3D(M) ## With some MS2 spectra i <- ms1[which(rtsel)][1] j <- ms1[which(rtsel)][2] M2 <- MSmap(ms, i:j, 100, 1000, 1, hd) plot3D(M2) ## ---- id, cache=TRUE----------------------------------------------------- library("mzID") f <- dir(system.file("extdata", package = "RforProteomics"), pattern = "mzid", full.names=TRUE) basename(f) id <- mzID(f) id ## ----mzrvsid, eval = TRUE------------------------------------------------ library("mzR") f <- dir(system.file("extdata", package = "RforProteomics"), pattern = "mzid", full.names=TRUE) id1 <- openIDfile(f) fid1 <- mzR::psms(id1) head(fid1) ## ---- rtandem, eval=FALSE------------------------------------------------ ## library("rTANDEM") ## ?rtandem ## library("shinyTANDEM") ## ?shinyTANDEM ## ----ex_getfas, eval=FALSE----------------------------------------------- ## fas <- pxget(px, pxfiles(px)[10]) ## basename(fas) ## ----ex_getfaslocal------------------------------------------------------ fas <- px1files()[1] px1get(fas) ## ----ex_msgfplus, message=FALSE, cache=TRUE------------------------------ library("MSGFplus") msgfpar <- msgfPar(database = fas, instrument = 'HighRes', tda = TRUE, enzyme = 'Trypsin', protocol = 'iTRAQ') idres <- runMSGF(msgfpar, mzf, memory=1000) idres ## identification file (needed below) basename(mzID::files(idres)$id) ## ----msgfgui, eval=FALSE------------------------------------------------- ## library("MSGFgui") ## MSGFgui() ## ---- msnid-------------------------------------------------------------- library("MSnID") msnid <- MSnID(".") msnid <- read_mzIDs(msnid, basename(mzID::files(idres)$id)) show(msnid) ## ----msnvars------------------------------------------------------------- msnid <- correct_peak_selection(msnid) msnid$msmsScore <- -log10(msnid$`MS-GF:SpecEValue`) msnid$absParentMassErrorPPM <- abs(mass_measurement_error(msnid)) ## ----idplot, echo=FALSE-------------------------------------------------- x <- psms(msnid)$msmsScore gr <- psms(msnid)$isDecoy library("lattice") densityplot(x, group = gr, auto.key=TRUE) ## ----msnidfilt----------------------------------------------------------- filtObj <- MSnIDFilter(msnid) filtObj$absParentMassErrorPPM <- list(comparison="<", threshold=5.0) filtObj$msmsScore <- list(comparison=">", threshold=8.0) filtObj evaluate_filter(msnid, filtObj) ## ----filtopt------------------------------------------------------------- filtObj.grid <- optimize_filter(filtObj, msnid, fdr.max=0.01, method="Grid", level="PSM", n.iter=50000) filtObj.grid evaluate_filter(msnid, filtObj.grid) ## ----applyfilt----------------------------------------------------------- msnid <- apply_filter(msnid, filtObj.grid) msnid ## ---- msnbase------------------------------------------------------------ library("MSnbase") rawFile <- dir(system.file(package = "MSnbase", dir = "extdata"), full.name = TRUE, pattern = "mzXML$") basename(rawFile) msexp <- readMSData(rawFile, verbose = FALSE) msexp ## ------------------------------------------------------------------------ length(msexp) msexp[1:2] msexp[[2]] ## ---- addid-------------------------------------------------------------- fData(msexp) ## find path to a mzIdentML file identFile <- dir(system.file(package = "MSnbase", dir = "extdata"), full.name = TRUE, pattern = "dummyiTRAQ.mzid") basename(identFile) msexp <- addIdentificationData(msexp, identFile) fData(msexp) ## ---- specplot----------------------------------------------------------- msexp[[1]] plot(msexp[[1]], full=TRUE) ## ---- specplot2---------------------------------------------------------- msexp[1:3] plot(msexp[1:3], full=TRUE) ## ---- quanttab, echo=FALSE, results='asis'------------------------------- qtb <- matrix(c("XIC", "Counting", "SILAC, 15N", "iTRAQ, TMT"), nrow = 2, ncol = 2) dimnames(qtb) <- list( 'MS level' = c("MS1", "MS2"), 'Quantitation' = c("Label-free", "Labelled")) kable(qtb) ## ---- itraq4plot--------------------------------------------------------- plot(msexp[[1]], full=TRUE, reporters = iTRAQ4) ## ---- quantitraq--------------------------------------------------------- msset <- quantify(msexp, method = "trap", reporters = iTRAQ4, verbose=FALSE) exprs(msset) processingData(msset) ## ---- lfms2-------------------------------------------------------------- exprs(si <- quantify(msexp, method = "SIn")) exprs(saf <- quantify(msexp, method = "NSAF")) ## ----mztab, cache=TRUE, eval = FALSE------------------------------------- ## mztf <- pxget(px, pxfiles(px)[2]) ## (mzt <- readMzTabData(mztf, what = "PEP")) ## ----mztablocal---------------------------------------------------------- mztf <- px1files()[3] px1get(mztf) (mzt <- readMzTabData(mztf, what = "PEP")) ## ---- readmsnset2-------------------------------------------------------- csv <- dir(system.file ("extdata" , package = "pRolocdata"), full.names = TRUE, pattern = "pr800866n_si_004-rep1.csv") getEcols(csv, split = ",") ecols <- 7:10 res <- readMSnSet2(csv, ecols) head(exprs(res)) head(fData(res)) ## ---- pure--------------------------------------------------------------- data(itraqdata) qnt <- quantify(itraqdata, method = "trap", reporters = iTRAQ4, verbose = FALSE) impurities <- matrix(c(0.929,0.059,0.002,0.000, 0.020,0.923,0.056,0.001, 0.000,0.030,0.924,0.045, 0.000,0.001,0.040,0.923), nrow=4, byrow = TRUE) ## or, using makeImpuritiesMatrix() ## impurities <- makeImpuritiesMatrix(4) qnt.crct <- purityCorrect(qnt, impurities) processingData(qnt.crct) ## ---- pureplot, echo=FALSE----------------------------------------------- plot0 <- function(x, y, main = "") { old.par <- par(no.readonly = TRUE) on.exit(par(old.par)) par(mar = c(4, 4, 1, 1)) par(mfrow = c(2, 2)) sx <- sampleNames(x) sy <- sampleNames(y) for (i in seq_len(ncol(x))) { plot(exprs(x)[, i], exprs(y)[, i], log = "xy", xlab = sx[i], ylab = sy[i]) abline(0, 1) grid() } } ## plot0(qnt, qnt.crct, main = "Before/after correction") ## ---- norm--------------------------------------------------------------- qnt.crct.nrm <- normalise(qnt.crct, "quantiles") ## ---- plotnorm, echo=FALSE----------------------------------------------- ## plot0(qnt, qnt.crct.nrm) ## ---- comb--------------------------------------------------------------- ## arbitraty grouping g <- factor(c(rep(1, 25), rep(2, 15), rep(3, 15))) g prt <- combineFeatures(qnt.crct.nrm, groupBy = g, fun = "sum") processingData(prt) ## ----impute0------------------------------------------------------------- set.seed(1) qnt0 <- qnt exprs(qnt0)[sample(prod(dim(qnt0)), 10)] <- NA table(is.na(qnt0)) image(qnt0) ## ----filterNA------------------------------------------------------------ ## remove features with missing values qnt00 <- filterNA(qnt0) dim(qnt00) any(is.na(qnt00)) ## ----impute-------------------------------------------------------------- ## impute missing values using knn imputation qnt.imp <- impute(qnt0, method = "knn") dim(qnt.imp) any(is.na(qnt.imp)) ## ---- ml----------------------------------------------------------------- library("MLInterfaces") library("pRoloc") library("pRolocdata") data(dunkley2006) traininds <- which(fData(dunkley2006)$markers != "unknown") ans <- MLearn(markers ~ ., data = t(dunkley2006), knnI(k = 5), traininds) ans ## ----clust--------------------------------------------------------------- kcl <- MLearn( ~ ., data = dunkley2006, kmeansI, centers = 12) kcl plot(kcl, exprs(dunkley2006)) ## ----nont, echo=FALSE, cache=TRUE---------------------------------------- library("rols") nont <- nrow(ontologies()) ## ----rols---------------------------------------------------------------- library("rols") olsQuery("ESI", "MS") ## ---- si, echo=FALSE----------------------------------------------------- print(sessionInfo(), local = FALSE)

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/man/read_taxa.Rd

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cran/benthos

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refs/heads/master

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read_taxa.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/io.R \name{read_taxa} \alias{read_taxa} \alias{get_taxa} \alias{validate_taxa} \title{Read and Validate Taxa Data} \usage{ read_taxa(filename) get_taxa() validate_taxa(.data) } \arguments{ \item{filename}{name of taxa file} \item{.data}{table in taxa-format} } \description{ This function reads files in the taxa format. } \details{ Taxa files have the following format: \itemize{ \item{group} {taxonomic group} \item{provided} {provided taxon name} \item{accepted} {accepted taxon name} \item{level} {taxonomic level} } Other columns are allowed, but silently ingored. } \section{Functions}{ \itemize{ \item \code{get_taxa()}: get default taxa list (TWN list extended with species Southern North Sea) \item \code{validate_taxa()}: validator for taxa-format }}

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/TodosAlgoritmosSemestre/euler.R

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SantiagoRomeroPineda/AnalisisNumerico

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2023-01-20T15:47:32.796976

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euler.R

library(PolynomF) require(deSolve) rm(list=ls()) euler1 = function(f, t0, y0, h, n) { #Datos igualmente espaciados iniciando en x0 = a, paso h. "n" datos #f es la derivada es decie dy/dt =f(t,y) t = seq(t0, t0 + (n-1)*h, by = h) # n datos y = rep(NA, times=n) # n datos y[1]=y0 for(i in 2:n ) y[i]= y[i-1]+h*f(t[i-1], y[i-1]) print(cbind(t,y)) # print plot(t,y, pch=19, col="red", xlab = "ti", ylab = "solución", main = "Euler vs RK4") } options(digits = 15) f = function(t,y) (0.7*y)-(t^2)+1 euler1(f, 1, 1, 0.1, 10) ### #install.packages("deSolve")###################################### fp = function(t,y, parms){ s = (0.7*y)-(t^2)+1 return(list(s)) # #ode requiere salida sea una lista } tis= seq(1,2,0.1)# Usamos la funcion ode() sol = ode(c(1,1), tis, fp, parms=NULL, method = "rk4")# metodo Runge Kutta orden 4 # Salida tabla = cbind(tis, sol[,2] ) colnames(tabla) = c("ti", " Ti ") tabla # Representacion par(new=T) plot(tis, sol[,2], xlab = "ti", ylab = "solucion", main = "Euler vs RK4") a=poly_calc(tis,sol[,2]) curve(a,add=T) abline(h=0,v=0,col="red") print(a)

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/analysis/4-binary-effectmod/4g-PR-prev-adj-scoop.R

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LaKwong/WBB-STH-Kato-Katz

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refs/heads/master

2020-09-02T05:37:17.925364

2019-04-24T19:43:26

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4g-PR-prev-adj-scoop.R

############################################## # WASH Benefits Bangladesh STH Kato-Katz Study # Primary outcome analysis # STH adjusted analysis # Binary STH outcomes # Effect modification by whether household # has a dedicated scoop for removing feces # by Jade Benjamin-Chung # jadebc@berkeley.edu ############################################## rm(list=ls()) source(here::here("0-config.R")) #---------------------------------------------- # load and pre-process analysis dataset #---------------------------------------------- data = read.csv(sth_data_path,stringsAsFactors=TRUE) d=preprocess.sth(data) d=preprocess.adj.sth(d) #---------------------------------------------- # create separate datasets for households with # vs. without a dedicated scoop for removing feces #---------------------------------------------- d1=d[d$scoop==1,] d0=d[d$scoop==0,] # roof and landphone excluded due to low prevalence W=c("counter","birthord","month","hfiacat","aged","sex","momage","momheight","momedu", "Nlt18","Ncomp","watmin","walls","floor", "elec","asset_wardrobe","asset_table","asset_chair","asset_khat","asset_chouki", "asset_tv","asset_refrig","asset_bike","asset_moto","asset_sewmach","asset_mobile") dW1=d1[,c("block","tr","clusterid","sth","al","hw","tt",W)] dW0=d0[,c("block","tr","clusterid","sth","al","hw","tt",W)] #---------------------------------------------- # H1: Unadjusted prevalence ratios; each arm vs. # control. PR, CI, P-value #---------------------------------------------- #---------------------------------------------- # Has dedicated feces scoop #---------------------------------------------- trlist=c("Water","Sanitation","Handwashing", "WSH","Nutrition","Nutrition + WSH") SL.library=c("SL.mean","SL.glm","SL.bayesglm","SL.gam","SL.glmnet") est.al.h1.fec1=apply(matrix(trlist), 1,function(x) washb_tmle(Y=dW1$al,tr=dW1$tr, pair=dW1$block, id=dW1$block,W=dW1[,W], family="binomial",contrast=c("Control",x),Q.SL.library=SL.library, g.SL.library=SL.library, pval=0.2, seed=12345, print=TRUE)) est.hw.h1.fec1=apply(matrix(trlist), 1,function(x) washb_tmle(Y=dW1$hw,tr=dW1$tr, pair=dW1$block, id=dW1$block,W=dW1[,W], family="binomial",contrast=c("Control",x),Q.SL.library=SL.library, g.SL.library=SL.library, pval=0.2, seed=12345, print=TRUE)) est.tt.h1.fec1=apply(matrix(trlist), 1,function(x) washb_tmle(Y=dW1$tt,tr=dW1$tr, pair=dW1$block, id=dW1$block,W=dW1[,W], family="binomial",contrast=c("Control",x),Q.SL.library=SL.library, g.SL.library=SL.library, pval=0.2, seed=12345, print=TRUE)) est.sth.h1.fec1=apply(matrix(trlist), 1,function(x) washb_tmle(Y=dW1$sth,tr=dW1$tr, pair=dW1$block, id=dW1$block,W=dW1[,W], family="binomial",contrast=c("Control",x),Q.SL.library=SL.library, g.SL.library=SL.library, pval=0.2, seed=12345, print=TRUE)) al_rr_h1_fec1_j=format.tmle(est.al.h1.fec1,family="binomial")$rr al_rd_h1_fec1_j=format.tmle(est.al.h1.fec1,family="binomial")$rd hw_rr_h1_fec1_j=format.tmle(est.hw.h1.fec1,family="binomial")$rr hw_rd_h1_fec1_j=format.tmle(est.hw.h1.fec1,family="binomial")$rd tt_rr_h1_fec1_j=format.tmle(est.tt.h1.fec1,family="binomial")$rr tt_rd_h1_fec1_j=format.tmle(est.tt.h1.fec1,family="binomial")$rd sth_rr_h1_fec1_j=format.tmle(est.sth.h1.fec1,family="binomial")$rr sth_rd_h1_fec1_j=format.tmle(est.sth.h1.fec1,family="binomial")$rd rownames(al_rr_h1_fec1_j)=c("Water vs C","Sanitation vs C","Handwashing vs C", "WSH vs C","Nutrition vs C","Nutrition + WSH vs C") rownames(hw_rr_h1_fec1_j)=c("Water vs C","Sanitation vs C","Handwashing vs C", "WSH vs C","Nutrition vs C","Nutrition + WSH vs C") rownames(tt_rr_h1_fec1_j)=c("Water vs C","Sanitation vs C","Handwashing vs C", "WSH vs C","Nutrition vs C","Nutrition + WSH vs C") rownames(sth_rr_h1_fec1_j)=c("Water vs C","Sanitation vs C","Handwashing vs C", "WSH vs C","Nutrition vs C","Nutrition + WSH vs C") rownames(al_rd_h1_fec1_j)=c("Water vs C","Sanitation vs C","Handwashing vs C", "WSH vs C","Nutrition vs C","Nutrition + WSH vs C") rownames(hw_rd_h1_fec1_j)=c("Water vs C","Sanitation vs C","Handwashing vs C", "WSH vs C","Nutrition vs C","Nutrition + WSH vs C") rownames(tt_rd_h1_fec1_j)=c("Water vs C","Sanitation vs C","Handwashing vs C", "WSH vs C","Nutrition vs C","Nutrition + WSH vs C") rownames(sth_rd_h1_fec1_j)=c("Water vs C","Sanitation vs C","Handwashing vs C", "WSH vs C","Nutrition vs C","Nutrition + WSH vs C") #---------------------------------------------- # Doesn't have dedicated feces scoop #---------------------------------------------- est.al.h1.fec0=apply(matrix(trlist), 1,function(x) washb_tmle(Y=dW0$al,tr=dW0$tr, pair=dW0$block, id=dW0$block,W=dW0[,W], family="binomial",contrast=c("Control",x),Q.SL.library=SL.library, g.SL.library=SL.library, pval=0.2, seed=12345, print=TRUE)) est.hw.h1.fec0=apply(matrix(trlist), 1,function(x) washb_tmle(Y=dW0$hw,tr=dW0$tr, pair=dW0$block, id=dW0$block,W=dW0[,W], family="binomial",contrast=c("Control",x),Q.SL.library=SL.library, g.SL.library=SL.library, pval=0.2, seed=12345, print=TRUE)) est.tt.h1.fec0=apply(matrix(trlist), 1,function(x) washb_tmle(Y=dW0$tt,tr=dW0$tr, pair=dW0$block, id=dW0$block,W=dW0[,W], family="binomial",contrast=c("Control",x),Q.SL.library=SL.library, g.SL.library=SL.library, pval=0.2, seed=12345, print=TRUE)) est.sth.h1.fec0=apply(matrix(trlist), 1,function(x) washb_tmle(Y=dW0$sth,tr=dW0$tr, pair=dW0$block, id=dW0$block,W=dW0[,W], family="binomial",contrast=c("Control",x),Q.SL.library=SL.library, g.SL.library=SL.library, pval=0.2, seed=12345, print=TRUE)) al_rr_h1_fec0_j=format.tmle(est.al.h1.fec0,family="binomial")$rr al_rd_h1_fec0_j=format.tmle(est.al.h1.fec0,family="binomial")$rd hw_rr_h1_fec0_j=format.tmle(est.hw.h1.fec0,family="binomial")$rr hw_rd_h1_fec0_j=format.tmle(est.hw.h1.fec0,family="binomial")$rd tt_rr_h1_fec0_j=format.tmle(est.tt.h1.fec0,family="binomial")$rr tt_rd_h1_fec0_j=format.tmle(est.tt.h1.fec0,family="binomial")$rd sth_rr_h1_fec0_j=format.tmle(est.sth.h1.fec0,family="binomial")$rr sth_rd_h1_fec0_j=format.tmle(est.sth.h1.fec0,family="binomial")$rd rownames(al_rr_h1_fec0_j)=c("Water vs C","Sanitation vs C","Handwashing vs C", "WSH vs C","Nutrition vs C","Nutrition + WSH vs C") rownames(hw_rr_h1_fec0_j)=c("Water vs C","Sanitation vs C","Handwashing vs C", "WSH vs C","Nutrition vs C","Nutrition + WSH vs C") rownames(tt_rr_h1_fec0_j)=c("Water vs C","Sanitation vs C","Handwashing vs C", "WSH vs C","Nutrition vs C","Nutrition + WSH vs C") rownames(sth_rr_h1_fec0_j)=c("Water vs C","Sanitation vs C","Handwashing vs C", "WSH vs C","Nutrition vs C","Nutrition + WSH vs C") rownames(al_rd_h1_fec0_j)=c("Water vs C","Sanitation vs C","Handwashing vs C", "WSH vs C","Nutrition vs C","Nutrition + WSH vs C") rownames(hw_rd_h1_fec0_j)=c("Water vs C","Sanitation vs C","Handwashing vs C", "WSH vs C","Nutrition vs C","Nutrition + WSH vs C") rownames(tt_rd_h1_fec0_j)=c("Water vs C","Sanitation vs C","Handwashing vs C", "WSH vs C","Nutrition vs C","Nutrition + WSH vs C") rownames(sth_rd_h1_fec0_j)=c("Water vs C","Sanitation vs C","Handwashing vs C", "WSH vs C","Nutrition vs C","Nutrition + WSH vs C") #---------------------------------------------- # save objects #---------------------------------------------- save(al_rr_h1_fec1_j,hw_rr_h1_fec1_j,tt_rr_h1_fec1_j,sth_rr_h1_fec1_j, al_rd_h1_fec1_j,hw_rd_h1_fec1_j,tt_rd_h1_fec1_j,sth_rd_h1_fec1_j, al_rr_h1_fec0_j,hw_rr_h1_fec0_j,tt_rr_h1_fec0_j,sth_rr_h1_fec0_j, al_rd_h1_fec0_j,hw_rd_h1_fec0_j,tt_rd_h1_fec0_j,sth_rd_h1_fec0_j, file=paste0(save_data_path, "sth_pr_adj_scoop.RData"))

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/Siganus_sutor.R

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zanbi/https-github.com-merrillrudd-LIME_application

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refs/heads/master

2022-04-16T16:43:25.864524

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R

false

50,435

r

Siganus_sutor.R

rm(list=ls()) ## load LIME package devtools::install_github("kaskr/TMB_contrib_R/TMBhelper") devtools::install_github("merrillrudd/LIME", build.vignettes=TRUE, dependencies=TRUE) library(LIME) ## load LBSPR package devtools::install_github("adrianhordyk/LBSPR", build.vignettes=TRUE, dependencies=TRUE) library(LBSPR) ## load Rfishbase library(rfishbase) library(gplots) ################################### ## Directories ################################### main_dir <- "C:\\merrill\\LIME_application" R_dir <- file.path(main_dir, "R_functions") funs <- list.files(R_dir) ignore <- sapply(1:length(funs), function(x) source(file.path(R_dir, funs[x]))) region_dir <- file.path(main_dir, "kenyan_reef_fish") stock_dir <- file.path(region_dir, "Siganus_sutor") dir.create(stock_dir, showWarnings=FALSE) data_dir <- file.path(region_dir, "data") dir.create(data_dir, showWarnings=FALSE) figs_dir <- file.path(stock_dir, "figures") dir.create(figs_dir, showWarnings=FALSE) ###################################### ## Species life history ###################################### plist <- readRDS(file.path(data_dir, "Siganus_sutor_life_history_annual.rds")) plist_m <- readRDS(file.path(data_dir, "Siganus_sutor_life_history_monthly.rds")) ###################################### ## Load data ###################################### LCraw <- readRDS(file.path(data_dir, "Siganus_sutor_LCraw.rds")) LCm_raw <- readRDS(file.path(data_dir, "Siganus_sutor_LCraw_monthly.rds")) LCweight <- readRDS(file.path(data_dir, "Siganus_sutor_LC_weighted.rds")) LCm_weight <- readRDS(file.path(data_dir, "Siganus_sutor_LCmonthly_weighted.rds")) LCm_bt <- readRDS(file.path(data_dir, "Siganus_sutor_LCmonthly_baskettrap.rds")) LCm_highN <- readRDS(file.path(data_dir, "Siganus_sutor_LCmonthly_weighted_Nup.rds")) LCy_bt <- readRDS(file.path(data_dir, "Siganus_sutor_LC_baskettrap.rds")) LCy_up <- readRDS(file.path(data_dir, "Siganus_sutor_LC_weighted_Nup.rds")) #################################### ## run assessments #################################### res_dir <- file.path(stock_dir, "results") dir.create(res_dir, showWarnings=FALSE) years_o <- as.numeric(rownames(LCraw)) years_t <- min(years_o):max(years_o) months_o <- as.numeric(rownames(LCm_raw)) months_t <- min(months_o):max(months_o) input_data_y <- list("years"=years_t, "LF"=LCweight) input_data_m <- list("years"=months_t, "LF"=LCm_weight) input_data_bt <- list("years"=months_t, "LF"=LCm_bt) input_data_up <- list("years"=months_t, "LF"=LCm_highN) input_data_y_bt <- list("years"=years_t, "LF"=LCy_bt) input_data_y_up <- list("years"=years_t, "LF"=LCy_up) ############### weighted length comps ########################## ## annual LBPSR out <- file.path(res_dir, "LBSPR_LCy") dir.create(out, showWarnings=FALSE) run <- run_LBSPR(modpath=out, lh=plist, species="x", input_data=input_data_y, rewrite=TRUE, simulation=FALSE) lbspr_res <- readRDS(file.path(out, "LBSPR_results.rds")) lbspr_res$SPR[length(lbspr_res$SPR)] - 1.96*sqrt(lbspr_res$SPR_Var[length(lbspr_res$SPR_Var)]) lbspr_res$SPR[length(lbspr_res$SPR)] + 1.96*sqrt(lbspr_res$SPR_Var[length(lbspr_res$SPR_Var)]) png(file.path(out, "LCfits.png"), width=16, height=10, res=200, units="in") plot_LCfits(Inputs=input_data_y, Report=NULL, true_lc_years=rownames(input_data_y$LF), LBSPR=lbspr_res, ylim=c(0,0.2), ML50=plist$ML50, SL50=lbspr_res$SL50) dev.off() ## annual LBPSR out <- file.path(res_dir, "LBSPR_LCy_baskettrap") dir.create(out, showWarnings=FALSE) run <- run_LBSPR(modpath=out, lh=plist, species="x", input_data=input_data_y_bt, rewrite=TRUE, simulation=FALSE) lbspr_res_bt <- readRDS(file.path(out, "LBSPR_results.rds")) ## annual LBPSR out <- file.path(res_dir, "LBSPR_LCy_highN") dir.create(out, showWarnings=FALSE) run <- run_LBSPR(modpath=out, lh=plist, species="x", input_data=input_data_y_up, rewrite=TRUE, simulation=FALSE) lbspr_res_highN <- readRDS(file.path(out, "LBSPR_results.rds")) ## annual LBPSR out <- file.path(res_dir, "LBSPR_LCy_altM") dir.create(out, showWarnings=FALSE) plist_new <- plist plist_new$M <- 1.49 run <- run_LBSPR(modpath=out, lh=plist_new, species="x", input_data=input_data_y, rewrite=TRUE, simulation=FALSE) lbspr_res_altM <- readRDS(file.path(out, "LBSPR_results.rds")) ## annual LIME out <- file.path(res_dir, "LCy") dir.create(out, showWarnings=FALSE) res <- run_LIME(modpath=out, lh=plist, input_data=input_data_y, est_sigma="log_sigma_R", data_avail="LC", itervec=NULL, rewrite=FALSE, simulation=FALSE, f_true=FALSE, C_opt=0, LFdist=1, param_adjust=c("SigmaF","SigmaR","SigmaC","SigmaI"), val_adjust=c(0.2,0.737,0.2,0.2), F_up=10, S_l_input=-1, fix_param=FALSE, theta_type=1, randomR=TRUE) Report <- readRDS(file.path(out, "Report.rds")) Sdreport <- readRDS(file.path(out, "Sdreport.rds")) Inputs <- readRDS(file.path(out, "Inputs.rds")) png(file.path(out, "output.png"), width=16, height=10, res=200, units="in") plot_output(Inputs=Inputs, Report=Report, Sdreport=Sdreport, all_years=input_data_y$years, lc_years=rownames(input_data_y$LF), LBSPR=lbspr_res, true_years=years_t) dev.off() png(file.path(out, "LCfits.png"), width=16, height=10, res=200, units="in") plot_LCfits(Inputs=Inputs$Data, Report=Report, true_lc_years=rownames(input_data_y$LF), LBSPR=lbspr_res, ylim=c(0,0.3)) dev.off() ### weighted length comp - counts only - all months out <- file.path(res_dir, "LCm") dir.create(out, showWarnings=FALSE) res <- run_LIME(modpath=out, lh=plist_m, input_data=input_data_m, est_sigma=c("log_sigma_R"), data_avail="LC", itervec=NULL, rewrite=TRUE, simulation=FALSE, f_true=FALSE, C_opt=0, LFdist=1, param_adjust=c("SigmaF","SigmaR","SigmaC","SigmaI"), val_adjust=c(0.2,0.737,0.2,0.2), F_up=10, S_l_input=-1, fix_param=FALSE, theta_type=1, randomR=TRUE) df <- readRDS(file.path(out, "check_convergence.rds")) Report <- readRDS(file.path(out, "Report.rds")) Sdreport <- readRDS(file.path(out, "Sdreport.rds")) Inputs <- readRDS(file.path(out, "Inputs.rds")) base_rep <- Report F40 <- tryCatch(uniroot(calc_ref, lower=0, upper=200, ages=plist_m$ages, Mat_a=Report$Mat_a, W_a=Report$W_a, M=Report$M, S_a=Report$S_a, ref=0.4)$root, error=function(e) NA) Report$F_t/F40 sd <- summary(Sdreport)[which(rownames(summary(Sdreport))=="lF_y"),] sd[,2][which(is.na(sd[,2]))] <- 0 read_sdreport(sd, log=TRUE)[c(length(Report$F_y), length(Report$F_y)+1)] sd <- summary(Sdreport)[which(rownames(summary(Sdreport))=="SPR_t"),] sd[,2][which(is.na(sd[,2]))] <- 0 read_sdreport(sd, log=FALSE)[c(length(Report$SPR_t), length(Report$SPR_t)+1)] pthresh <- pnorm(0.3, Report$SPR_t[length(Report$SPR_t)], sd[,2][length(Report$SPR_t)]) ptarget <- pnorm(0.45, Report$SPR_t[length(Report$SPR_t)], sd[,2][length(Report$SPR_t)]) - pnorm(0.35, Report$SPR_t[length(Report$SPR_t)], sd[,2][length(Report$SPR_t)]) pup <- 1-pnorm(0.40, Report$SPR_t[length(Report$SPR_t)], sd[,2][length(Report$SPR_t)]) png(file.path(out, "output.png"), width=16, height=10, res=200, units="in") plot_output(Inputs=Inputs, Report=Report, Sdreport=Sdreport, all_years=input_data_m$years, lc_years=rownames(input_data_m$LF), LBSPR=lbspr_res, lh=plist_m, true_years=years_t) dev.off() png(file.path(out, "LCfits.png"), width=16, height=10, res=200, units="in") plot_LCfits(Inputs=Inputs$Data, Report=Report, true_lc_years=rownames(input_data_m$LF), LBSPR=NULL, ylim=c(0,0.3)) dev.off() ### weighted length comp - counts only - all months - basket trap only out <- file.path(res_dir, "LCm_baskettrap") dir.create(out, showWarnings=FALSE) res <- run_LIME(modpath=out, lh=plist_m, input_data=input_data_bt, est_sigma="log_sigma_R", data_avail="LC", itervec=NULL, rewrite=FALSE, simulation=FALSE, f_true=FALSE, C_opt=0, LFdist=1, param_adjust=c("SigmaF","SigmaR","SigmaC","SigmaI"), val_adjust=c(0.2,0.737,0.2,0.2), F_up=10, S_l_input=-1, fix_param=FALSE, theta_type=1, randomR=TRUE) df <- readRDS(file.path(out, "check_convergence.rds")) Report <- readRDS(file.path(out, "Report.rds")) Sdreport <- readRDS(file.path(out, "Sdreport.rds")) Inputs <- readRDS(file.path(out, "Inputs.rds")) png(file.path(out, "output.png"), width=16, height=10, res=200, units="in") plot_output(Inputs=Inputs, Report=Report, Sdreport=Sdreport, all_years=input_data_bt$years, lc_years=rownames(input_data_bt$LF), LBSPR=lbspr_res_bt, lh=plist_m, true_years=years_t) dev.off() png(file.path(out, "LCfits.png"), width=16, height=10, res=200, units="in") plot_LCfits(Inputs=Inputs$Data, Report=Report, true_lc_years=rownames(input_data_bt$LF), LBSPR=NULL, ylim=c(0,0.3)) dev.off() ### weighted length comp - counts only - months with high sample size out <- file.path(res_dir, "LCm_highN") dir.create(out, showWarnings=FALSE) res <- run_LIME(modpath=out, lh=plist_m, input_data=input_data_up, est_sigma="log_sigma_R", data_avail="LC", itervec=NULL, rewrite=FALSE, simulation=FALSE, f_true=FALSE, C_opt=0, LFdist=1, param_adjust=c("SigmaF","SigmaR","SigmaC","SigmaI"), val_adjust=c(0.2,0.737,0.2,0.2), F_up=10, S_l_input=-1, fix_param=FALSE, theta_type=1, randomR=TRUE) df <- readRDS(file.path(out, "check_convergence.rds")) Report <- readRDS(file.path(out, "Report.rds")) Sdreport <- readRDS(file.path(out, "Sdreport.rds")) Inputs <- readRDS(file.path(out, "Inputs.rds")) png(file.path(out, "output.png"), width=16, height=10, res=200, units="in") plot_output(Inputs=Inputs, Report=Report, Sdreport=Sdreport, all_years=input_data_up$years, lc_years=rownames(input_data_up$LF), LBSPR=lbspr_res_highN, lh=plist_m, true_years=years_t) dev.off() png(file.path(out, "LCfits.png"), width=16, height=10, res=200, units="in") plot_LCfits(Inputs=Inputs$Data, Report=Report, true_lc_years=rownames(input_data_up$LF), LBSPR=NULL, ylim=c(0,0.3)) dev.off() ### weighted length comp - counts only - all months out <- file.path(res_dir, "LCm_altM") dir.create(out, showWarnings=FALSE) res <- run_LIME(modpath=out, lh=plist_m, input_data=input_data_m, est_sigma="log_sigma_R", data_avail="LC", itervec=NULL, rewrite=FALSE, simulation=FALSE, f_true=FALSE, C_opt=0, LFdist=1, param_adjust=c("SigmaF","SigmaR","SigmaC","SigmaI", "M"), val_adjust=c(0.2,0.737,0.2,0.2, 1.49), F_up=10, S_l_input=-1, fix_param=FALSE, theta_type=1, randomR=TRUE) df <- readRDS(file.path(out, "check_convergence.rds")) Report <- readRDS(file.path(out, "Report.rds")) Sdreport <- readRDS(file.path(out, "Sdreport.rds")) Inputs <- readRDS(file.path(out, "Inputs.rds")) F40 <- tryCatch(uniroot(calc_ref, lower=0, upper=200, ages=plist_m$ages, Mat_a=Report$Mat_a, W_a=Report$W_a, M=Report$M, S_a=Report$S_a, ref=0.4)$root, error=function(e) NA) Report$F_t/F40 sd <- summary(Sdreport)[which(rownames(summary(Sdreport))=="lF_y"),] sd[,2][which(is.na(sd[,2]))] <- 0 read_sdreport(sd, log=TRUE)[c(length(Report$F_y), length(Report$F_y)+1)] sd <- summary(Sdreport)[which(rownames(summary(Sdreport))=="SPR_t"),] sd[,2][which(is.na(sd[,2]))] <- 0 read_sdreport(sd, log=FALSE)[c(length(Report$SPR_t), length(Report$SPR_t)+1)] pthresh <- pnorm(0.3, Report$SPR_t[length(Report$SPR_t)], sd[,2][length(Report$SPR_t)]) ptarget <- pnorm(0.45, Report$SPR_t[length(Report$SPR_t)], sd[,2][length(Report$SPR_t)]) - pnorm(0.35, Report$SPR_t[length(Report$SPR_t)], sd[,2][length(Report$SPR_t)]) png(file.path(out, "output.png"), width=16, height=10, res=200, units="in") plot_output(Inputs=Inputs, Report=Report, Sdreport=Sdreport, all_years=input_data_m$years, lc_years=rownames(input_data_m$LF), LBSPR=lbspr_res_altM, lh=plist_m, true_years=years_t) dev.off() png(file.path(out, "LCfits.png"), width=16, height=10, res=200, units="in") plot_LCfits(Inputs=Inputs$Data, Report=Report, true_lc_years=rownames(input_data_m$LF), LBSPR=NULL, ylim=c(0,0.3)) dev.off() ### weighted length comp - counts only - all months out <- file.path(res_dir, "LCm_sigRstartlow") dir.create(out, showWarnings=FALSE) res <- run_LIME(modpath=out, lh=plist_m, input_data=input_data_m, est_sigma=c("log_sigma_R"), data_avail="LC", itervec=NULL, rewrite=TRUE, simulation=FALSE, f_true=FALSE, C_opt=0, LFdist=1, param_adjust=c("SigmaF","SigmaR","SigmaC","SigmaI"), val_adjust=c(0.2,0.3,0.2,0.2), F_up=10, S_l_input=-1, fix_param=FALSE, theta_type=1, randomR=TRUE) df <- readRDS(file.path(out, "check_convergence.rds")) Report <- readRDS(file.path(out, "Report.rds")) Sdreport <- readRDS(file.path(out, "Sdreport.rds")) Inputs <- readRDS(file.path(out, "Inputs.rds")) png(file.path(out, "output.png"), width=16, height=10, res=200, units="in") plot_output(Inputs=Inputs, Report=Report, Sdreport=Sdreport, all_years=input_data_m$years, lc_years=rownames(input_data_m$LF), LBSPR=NULL, lh=plist_m, true_years=years_t) dev.off() png(file.path(out, "LCfits.png"), width=16, height=10, res=200, units="in") plot_LCfits(Inputs=Inputs$Data, Report=Report, true_lc_years=rownames(input_data_m$LF), LBSPR=NULL, ylim=c(0,0.3)) dev.off() ### weighted length comp - counts only - all months out <- file.path(res_dir, "LCm_selexstartalt") dir.create(out, showWarnings=FALSE) plist_m_alt <- create_lh_list(vbk=plist$vbk, linf=plist$linf, lwa=plist$lwa, lwb=plist$lwb, S50=20, S95=25, selex_input="length", M50=plist$ML50, maturity_input="length", SigmaR=0.7, SigmaF=0.2, M=plist$M, F1=NULL, CVlen=0.1, nseasons=12, binwidth=1) res <- run_LIME(modpath=out, lh=plist_m_alt, input_data=input_data_m, est_sigma=c("log_sigma_R"), data_avail="LC", itervec=NULL, rewrite=TRUE, simulation=FALSE, f_true=FALSE, C_opt=0, LFdist=1, param_adjust=c("SigmaF","SigmaR","SigmaC","SigmaI"), val_adjust=c(0.2,0.737,0.2,0.2), F_up=10, S_l_input=-1, fix_param=FALSE, theta_type=1, randomR=TRUE) df <- readRDS(file.path(out, "check_convergence.rds")) Report <- readRDS(file.path(out, "Report.rds")) Sdreport <- readRDS(file.path(out, "Sdreport.rds")) Inputs <- readRDS(file.path(out, "Inputs.rds")) png(file.path(out, "output.png"), width=16, height=10, res=200, units="in") plot_output(Inputs=Inputs, Report=Report, Sdreport=Sdreport, all_years=input_data_m$years, lc_years=rownames(input_data_m$LF), LBSPR=NULL, lh=plist_m, true_years=years_t) dev.off() png(file.path(out, "LCfits.png"), width=16, height=10, res=200, units="in") plot_LCfits(Inputs=Inputs$Data, Report=Report, true_lc_years=rownames(input_data_m$LF), LBSPR=NULL, ylim=c(0,0.3)) dev.off() ## dome-shaped selex plist_m_lowdome <- create_lh_list(vbk=plist$vbk, linf=plist$linf, t0=plist$t0, M=plist$M, lwa=plist$lwa, lwb=plist$lwb, S50=plist$SL50, S95=plist$SL95, M50=plist$ML50, M95=plist$ML95, selex_input="length", maturity_input="length", nseasons=12, binwidth=1, SigmaR=0.777, SigmaF=0.2, selex_type="dome", dome_sd=41) plist_m_highdome <- create_lh_list(vbk=plist$vbk, linf=plist$linf, t0=plist$t0, M=plist$M, lwa=plist$lwa, lwb=plist$lwb, S50=plist$SL50, S95=plist$SL95, M50=plist$ML50, M95=plist$ML95, selex_input="length", maturity_input="length", nseasons=12, binwidth=1, SigmaR=0.777, SigmaF=0.2, selex_type="dome", dome_sd=22) par(mfrow=c(1,1), mar=c(5,5,4,4)) plot(plist_m$S_l, type="o", lwd=2, pch=19, cex.axis=2, xlab="Length (cm)", ylab="Proportion", cex.lab=2) lines(plist_m_lowdome$S_l, type="o", lwd=2, lty=2, col="steelblue", pch=19) lines(plist_m_highdome$S_l, type="o", lwd=2, lty=3, col="tomato", pch=19) ## low-dome out <- file.path(res_dir, "LCm_lowdome") dir.create(out, showWarnings=FALSE) res <- run_LIME(modpath=out, lh=plist_m, input_data=input_data_m, est_sigma=FALSE, data_avail="LC", itervec=NULL, rewrite=FALSE, simulation=FALSE, f_true=FALSE, C_opt=0, LFdist=1, param_adjust=c("SigmaF","SigmaR","SigmaC","SigmaI"), val_adjust=c(0.2,0.737,0.2,0.2), F_up=10, S_l_input=plist_m_lowdome$S_l, fix_param=FALSE, theta_type=1, randomR=TRUE) df <- readRDS(file.path(out, "check_convergence.rds")) Report <- readRDS(file.path(out, "Report.rds")) Sdreport <- readRDS(file.path(out, "Sdreport.rds")) Inputs <- readRDS(file.path(out, "Inputs.rds")) png(file.path(out, "output.png"), width=16, height=10, res=200, units="in") plot_output(Inputs=Inputs, Report=Report, Sdreport=Sdreport, all_years=input_data_m$years, lc_years=rownames(input_data_m$LF), LBSPR=lbspr_res, lh=plist_m, true_years=years_t) dev.off() png(file.path(out, "LCfits.png"), width=16, height=10, res=200, units="in") plot_LCfits(Inputs=Inputs$Data, Report=Report, true_lc_years=rownames(input_data_m$LF), LBSPR=NULL, ylim=c(0,0.3)) dev.off() ## high-dome out <- file.path(res_dir, "LCm_highdome") dir.create(out, showWarnings=FALSE) res <- run_LIME(modpath=out, lh=plist_m, input_data=input_data_m, est_sigma=FALSE, data_avail="LC", itervec=NULL, rewrite=FALSE, simulation=FALSE, f_true=FALSE, C_opt=0, LFdist=1, param_adjust=c("SigmaF","SigmaR","SigmaC","SigmaI"), val_adjust=c(0.2,0.737,0.2,0.2), F_up=10, S_l_input=plist_m_highdome$S_l, fix_param=FALSE, theta_type=1, randomR=TRUE) df <- readRDS(file.path(out, "check_convergence.rds")) Report <- readRDS(file.path(out, "Report.rds")) Sdreport <- readRDS(file.path(out, "Sdreport.rds")) Inputs <- readRDS(file.path(out, "Inputs.rds")) png(file.path(out, "output.png"), width=16, height=10, res=200, units="in") plot_output(Inputs=Inputs, Report=Report, Sdreport=Sdreport, all_years=input_data_up$years, lc_years=rownames(input_data_up$LF), LBSPR=NULL, lh=plist_m, true_years=years_t) dev.off() png(file.path(out, "LCfits.png"), width=16, height=10, res=200, units="in") plot_LCfits(Inputs=Inputs$Data, Report=Report, true_lc_years=rownames(input_data_up$LF), LBSPR=NULL, ylim=c(0,0.3)) dev.off() ########################### ## likelihood profiles ########################### ## sigma R out <- file.path(res_dir, "sens_SigmaR") dir.create(out, showWarnings=FALSE) sigR_vec <- seq(0.05, 0.95, length=10) for(i in 1:length(sigR_vec)){ out2 <- file.path(out, i) dir.create(out2, showWarnings=FALSE) res <- run_LIME(modpath=out2, lh=plist_m, input_data=input_data_m, est_sigma=FALSE, data_avail="LC", itervec=NULL, rewrite=FALSE, simulation=FALSE, f_true=FALSE, C_opt=0, LFdist=1, param_adjust=c("SigmaF","SigmaR"), val_adjust=c(0.2,sigR_vec[i]), F_up=10, S_l_input=-1, fix_param=FALSE, theta_type=1, randomR=TRUE) } rep1 <- readRDS(file.path(res_dir, "LCm", "Report.rds")) sigR_like <- rep(NA, length(sigR_vec)) col_conv <- rep("black", length(sigR_vec)) for(i in 1:length(sigR_vec)){ rep <- readRDS(file.path(out, i, "Report.rds")) sdrep <- readRDS(file.path(out, i, "Sdreport.rds")) sigR_like[i] <- rep$jnll if(file.exists(file.path(out, i, "high_final_gradient.txt")) | all(is.na(sdrep))) col_conv[i] <- "gray" } png(file.path(figs_dir, "sigR_likeprof.png"), height=10, width=14, res=200, units="in") plot(sigR_vec, sigR_like, pch=19, cex=2, xlab="Fixed SigmaR", ylab="NLL", col=col_conv) points(rep1$sigma_R, rep1$jnll, pch=19, cex=2, col="blue") dev.off() ## check fishbase and natural mortality toolfor life history distributions proper_name <- "Siganus sutor" genus <- strsplit(proper_name, " ")[[1]][1] # growth2 <- popgrowth(species_list(Genus=genus)) # saveRDS(growth2, file.path(data_dir, "genus_info.rds")) ## linf out <- file.path(res_dir, "sens_linf") dir.create(out, showWarnings=FALSE) growth2 <- readRDS(file.path(data_dir, "genus_info.rds")) linf_genus <- growth2$Loo linf_vec <- seq(min(linf_genus), max(linf_genus), length=10) for(i in 1:length(linf_vec)){ out2 <- file.path(out, i) dir.create(out2, showWarnings=FALSE) res <- run_LIME(modpath=out2, lh=plist_m, input_data=input_data_m, est_sigma=FALSE, data_avail="LC", itervec=NULL, rewrite=TRUE, simulation=FALSE, f_true=FALSE, C_opt=0, LFdist=1, param_adjust=c("SigmaF","SigmaR","SigmaC","SigmaI", "linf"), val_adjust=c(0.2,0.737,0.2,0.2, linf_vec[i]), F_up=10, S_l_input=-1, fix_param=FALSE, theta_type=1, randomR=TRUE) } rep1 <- readRDS(file.path(res_dir, "LCm", "Report.rds")) linf_like <- rep(NA, length(linf_vec)) col_conv <- rep("black", length(linf_vec)) for(i in 1:length(linf_vec)){ rep <- readRDS(file.path(out, i, "Report.rds")) sdrep <- readRDS(file.path(out, i, "Sdreport.rds")) linf_like[i] <- rep$jnll if(file.exists(file.path(out, i, "high_final_gradient.txt")) | all(is.na(sdrep))) col_conv[i] <- "gray" } png(file.path(figs_dir, "linf_likeprof.png"), height=10, width=14, res=200, units="in") plot(linf_vec, linf_like, pch=19, cex=2, xlab="Assumed Linf", ylab="NLL", col=col_conv) points(plist_m$linf, rep1$jnll, pch=19, cex=2, col="blue") dev.off() ## vbk out <- file.path(res_dir, "sens_vbk") dir.create(out, showWarnings=FALSE) growth2 <- readRDS(file.path(data_dir, "genus_info.rds")) vbk_genus <- growth2$K vbk_vec <- seq(min(vbk_genus), max(vbk_genus), length=10) for(i in 1:length(vbk_vec)){ out2 <- file.path(out, i) dir.create(out2, showWarnings=FALSE) res <- run_LIME(modpath=out2, lh=plist_m, input_data=input_data_m, est_sigma=FALSE, data_avail="LC", itervec=NULL, rewrite=TRUE, simulation=FALSE, f_true=FALSE, C_opt=0, LFdist=1, param_adjust=c("SigmaF","SigmaR","vbk"), val_adjust=c(0.2,0.737,vbk_vec[i]), F_up=10, S_l_input=-1, fix_param=FALSE, theta_type=1, randomR=TRUE) } rep1 <- readRDS(file.path(res_dir, "LCm", "Report.rds")) vbk_like <- rep(NA, length(vbk_vec)) col_conv <- rep("black", length(vbk_vec)) for(i in 1:length(vbk_vec)){ rep <- readRDS(file.path(out, i, "Report.rds")) sdrep <- readRDS(file.path(out, i, "Sdreport.rds")) vbk_like[i] <- rep$jnll if(file.exists(file.path(out, i, "high_final_gradient.txt")) | all(is.na(sdrep))) col_conv[i] <- "gray" } png(file.path(figs_dir, "vbk_likeprof.png"), height=10, width=14, res=200, units="in") plot(vbk_vec, vbk_like, pch=19, cex=2, xlab="Assumed vbk", ylab="NLL", col=col_conv) points(plist_m$vbk, rep1$jnll, pch=19, cex=2, col="blue") dev.off() ## M out <- file.path(res_dir, "sens_M") dir.create(out, showWarnings=FALSE) M_tool <- c(1.37, 1.28, 1.37, 1.35, 0.95, 0.53, 1.30, 1.39, 1.88, 1.65) M_vec <- seq(min(M_tool), max(M_tool), length=10) for(i in 1:length(M_vec)){ out2 <- file.path(out, i) dir.create(out2, showWarnings=FALSE) res <- run_LIME(modpath=out2, lh=plist_m, input_data=input_data_m, est_sigma=FALSE, data_avail="LC", itervec=NULL, rewrite=TRUE, simulation=FALSE, f_true=FALSE, C_opt=0, LFdist=1, param_adjust=c("SigmaF","SigmaR","SigmaC","SigmaI", "M"), val_adjust=c(0.2,0.737,0.2,0.2, M_vec[i]), F_up=10, S_l_input=-1, fix_param=FALSE, theta_type=1, randomR=TRUE) } rep1 <- readRDS(file.path(res_dir, "LCm", "Report.rds")) M_like <- rep(NA, length(M_vec)) col_conv <- rep("black", length(M_vec)) for(i in 1:length(M_vec)){ inp <- readRDS(file.path(out, i, "Inputs.rds")) rep <- readRDS(file.path(out, i, "Report.rds")) sdrep <- readRDS(file.path(out, i, "Sdreport.rds")) M_like[i] <- rep$jnll if(file.exists(file.path(out, i, "high_final_gradient.txt")) | all(is.na(sdrep))) col_conv[i] <- "gray" } png(file.path(figs_dir, "M_likeprof.png"), height=10, width=14, res=200, units="in") plot(M_vec, M_like, pch=19, cex=2, xlab="Assumed M", ylab="NLL", col=col_conv) points(plist_m$M*12, rep1$jnll, pch=19, cex=2, col="blue") dev.off() png(file.path(figs_dir, "like_prof_compare.png"), height=10, width=15, units="in", res=200) mat <- matrix(c(1,2,3,4, 1,2,3,4, 5,6,7,8, 5,6,7,8, 9,9,9,9, 10,11,12,13, 10,11,12,13), nrow=7, ncol=4, byrow=TRUE) nf <- layout(mat) layout.show(nf) par(mar=c(0,4,0,0), omi=c(0.5,0.5,0.5,0.5)) rep1 <- readRDS(file.path(res_dir, "LCm", "Report.rds")) growth2 <- readRDS(file.path(data_dir, "genus_info.rds")) ## using input AgeMax=4, Linf=36.2, vbk=0.87, Age at maturity(years)=1 M_tool <- c(1.37, 1.28, 1.37, 1.35, 0.95, 0.53, 1.30, 1.39, 1.88, 1.65) M_toUse <- median(M_tool) M_h <- hist(M_tool, plot=FALSE) M_x <- seq(min(M_tool), max(M_tool), length=60) M_y <- dnorm(M_x, mean=mean(M_tool), sd=sd(M_tool)) M_y2 <- (M_y*diff(M_h$mids[1:2])*length(M_tool))/sum((M_y*diff(M_h$mids[1:2])*length(M_tool))) plot(M_x, M_y2, col="black", lwd=2, type="l", xaxt="n", xlab="", ylab="", xaxs="i", yaxs="i", cex.axis=2) rug(M_tool, ticksize=0.15, lwd=2) # M_d <- rtnorm(1, mean=mean(M_tool), sd=sd(M_tool), lower=min(M_tool), upper=max(M_tool)) # abline(v=M_d, lty=2, col="red", lwd=2) abline(v=plist$M, lwd=2, col="blue") mtext(side=3, "M", cex=2.5, line=1) mtext("Probability", side=2, line=3, cex=2) print.letter("(a)", xy=c(0.9,0.95), cex=2) linf_genus <- growth2$Loo linf_h <- hist(linf_genus, plot=FALSE) linf_x <- seq(min(linf_genus), max(linf_genus), length=60) linf_y <- dnorm(linf_x, mean=mean(linf_genus), sd=sd(linf_genus)) linf_y2 <- linf_y*diff(linf_h$mids[1:2])*length(linf_genus)/sum(linf_y*diff(linf_h$mids[1:2])*length(linf_genus)) plot(linf_x, linf_y2, col="black", lwd=2, type="l", xaxt="n",xlab="", ylab="", xaxs="i", yaxs="i", cex.axis=2) rug(linf_genus, ticksize=0.15, lwd=2) # linf_d <- rtnorm(1, mean=mean(linf_genus), sd=sd(linf_genus), lower=min(linf_genus), upper=max(linf_genus)) # abline(v=linf_d, lty=2, col="red", lwd=2) abline(v=plist$linf, lwd=2, col="blue") mtext(side=3, "Linf", cex=2.5, line=1) print.letter("(b)", xy=c(0.9,0.95), cex=2) vbk_genus <- growth2$K vbk_h <- hist(vbk_genus, plot=FALSE) vbk_x <- seq(min(vbk_genus), max(vbk_genus), length=60) vbk_y <- dnorm(vbk_x, mean=mean(vbk_genus), sd=sd(vbk_genus)) vbk_y2 <- vbk_y*diff(vbk_h$mids[1:2])*length(vbk_genus)/sum(vbk_y*diff(vbk_h$mids[1:2])*length(vbk_genus)) plot(vbk_x, vbk_y2, col="black", lwd=2, type="l", xaxt="n",xlab="", ylab="", xaxs="i", yaxs="i", cex.axis=2) rug(vbk_genus, ticksize=0.15, lwd=2) # vbk_d <- rtnorm(1, mean=mean(vbk_genus), sd=sd(vbk_genus), lower=min(vbk_genus), upper=max(vbk_genus)) # abline(v=vbk_d, lty=2, col="red", lwd=2) abline(v=plist$vbk, lwd=2, col="blue") mtext(side=3, "k", cex=2.5, line=1) print.letter("(c)", xy=c(0.9,0.95), cex=2) sigR_x <- seq(0, 1.5, length=60) sigR_y <- dnorm(sigR_x, mean=mean(0.737), sd=0.353)/sum(dnorm(sigR_x, mean=mean(0.737), sd=0.353)) plot(sigR_x, sigR_y, col="black", lwd=2, type="l", xaxt="n",xlab="", ylab="", xaxs="i", yaxs="i", cex.axis=2) # sigR_d <- rtnorm(1, mean=mean(sigR_genus), sd=sd(sigR_genus), lower=min(sigR_genus), upper=max(sigR_genus)) # abline(v=sigR_d, lty=2, col="red", lwd=2) abline(v=0.737, lwd=2, col="blue") mtext(side=3, "SigmaR", cex=2.5, line=1) print.letter("(d)", xy=c(0.9,0.95), cex=2) out <- file.path(res_dir, "sens_M") dir.create(out, showWarnings=FALSE) M_vec <- seq(min(M_tool), max(M_tool), length=10) M_like <- rep(NA, length(M_vec)) col_conv <- rep("black", length(M_vec)) for(i in 1:length(M_vec)){ inp <- readRDS(file.path(out, i, "Inputs.rds")) rep <- readRDS(file.path(out, i, "Report.rds")) sdrep <- readRDS(file.path(out, i, "Sdreport.rds")) M_like[i] <- rep$jnll if(file.exists(file.path(out, i, "high_final_gradient.txt")) | all(is.na(sdrep))) col_conv[i] <- "gray" } i1 <- 1 i2 <- 8 col_conv[i1] <- "orange" col_conv[i2] <- "orangered" plot(x=M_vec, y=M_like, pch=19, cex=3, col=col_conv, xpd=NA, cex.axis=2, xlab="", ylab="", yaxt="n") axis(2, cex.axis=2, at=pretty(c(min(M_like),max(M_like)))[-length(pretty(c(min(M_like),max(M_like))))]) points(plist_m$M*12, rep1$jnll, pch=19, cex=3, col="blue") mtext(side=2, "NLL", cex=2, line=3) print.letter("(e)", xy=c(0.9,0.95), cex=2) ### linf out <- file.path(res_dir, "sens_linf") dir.create(out, showWarnings=FALSE) linf_genus <- growth2$Loo linf_vec <- seq(min(linf_genus), max(linf_genus), length=10) linf_like <- rep(NA, length(linf_vec)) col_conv_linf <- rep("black", length(linf_vec)) for(i in 1:length(linf_vec)){ rep <- readRDS(file.path(out, i, "Report.rds")) sdrep <- readRDS(file.path(out, i, "Sdreport.rds")) linf_like[i] <- rep$jnll if(file.exists(file.path(out, i, "high_final_gradient.txt")) | all(is.na(sdrep))) col_conv_linf[i] <- "gray" } i1 <- which(col_conv_linf=="black")[1] i2 <- 8 col_conv_linf[i1] <- "orange" col_conv_linf[i2] <- "orangered" plot(x=linf_vec, y=linf_like, pch=19, cex=3, col=col_conv_linf, xpd=NA, cex.axis=2, xlab="", ylab="", yaxt="n") axis(2, cex.axis=2, at=pretty(c(min(linf_like),max(linf_like)))[-length(pretty(c(min(linf_like),max(linf_like))))]) points(plist_m$linf, rep1$jnll, pch=19, cex=3, col="blue") print.letter("(f)", xy=c(0.9,0.95), cex=2) ## vbk out <- file.path(res_dir, "sens_vbk") dir.create(out, showWarnings=FALSE) vbk_genus <- growth2$K vbk_vec <- seq(min(vbk_genus), max(vbk_genus), length=10) vbk_like <- rep(NA, length(vbk_vec)) col_conv_vbk <- rep("black", length(vbk_vec)) for(i in 1:length(vbk_vec)){ rep <- readRDS(file.path(out, i, "Report.rds")) sdrep <- readRDS(file.path(out, i, "Sdreport.rds")) vbk_like[i] <- rep$jnll if(file.exists(file.path(out, i, "high_final_gradient.txt")) | all(is.na(sdrep))) col_conv_vbk[i] <- "gray" } i1 <- 2 i2 <- 5 col_conv_vbk[i1] <- "orange" col_conv_vbk[i2] <- "orangered" plot(x=vbk_vec, y=vbk_like, pch=19, cex=3, col=col_conv_vbk, xpd=NA, cex.axis=2, xlab="", ylab="", yaxt="n") axis(2, cex.axis=2, at=pretty(c(min(vbk_like),max(vbk_like)))[-length(pretty(c(min(vbk_like),max(vbk_like))))]) points(plist_m$vbk, rep1$jnll, pch=19, cex=3, col="blue") print.letter("(g)", xy=c(0.9,0.95), cex=2) ## sigma R out <- file.path(res_dir, "sens_SigmaR") dir.create(out, showWarnings=FALSE) sigR_vec <- seq(0.05, 0.95, length=10) sigR_like <- rep(NA, length(sigR_vec)) col_conv_sigR <- rep("black", length(sigR_vec)) for(i in 1:length(sigR_vec)){ rep <- readRDS(file.path(out, i, "Report.rds")) sdrep <- readRDS(file.path(out, i, "Sdreport.rds")) sigR_like[i] <- rep$jnll if(file.exists(file.path(out, i, "high_final_gradient.txt")) | all(is.na(sdrep))) col_conv_sigR[i] <- "gray" } i1 <- 4 i2 <- 7 col_conv_sigR[i1] <- "orange" col_conv_sigR[i2] <- "orangered" plot(x=sigR_vec, y=sigR_like, pch=19, cex=3, col=col_conv_sigR, xpd=NA, cex.axis=2, xlab="", ylab="", xlim=c(min(sigR_x), max(sigR_x)), yaxt="n") axis(2, cex.axis=2, at=pretty(c(min(sigR_like),max(sigR_like)))[-length(pretty(c(min(sigR_like),max(sigR_like))))]) points(rep1$sigma_R, rep1$jnll, pch=19, cex=3, col="blue") print.letter("(h)", xy=c(0.9,0.95), cex=2) plot(x=1,y=1,type="n", axes=F, ann=F) ### SPR out <- file.path(res_dir, "sens_M") i1 <- 1 i2 <- 8 replow <- readRDS(file.path(out, i1, "Report.rds")) rephigh <- readRDS(file.path(out, i2, "Report.rds")) plot(x=years_t, y=unique(rep1$SPR_t), col="blue", lwd=2, type="l", ylim=c(0,1.1), xlab="", ylab="", cex.axis=2) lines(x=years_t, y=unique(replow$SPR_t), col="orange", lwd=2) lines(x=years_t, y=unique(rephigh$SPR_t), col="orangered", lwd=2) abline(h=0.4, lty=2) mtext(side=2, "SPR", line=3, cex=2) mtext(side=1, "Year", line=3, cex=2) print.letter("(i)", xy=c(0.9,0.95), cex=2) out <- file.path(res_dir, "sens_linf") i1 <- which(col_conv_linf=="black")[1] i2 <- 8 replow <- readRDS(file.path(out, i1, "Report.rds")) rephigh <- readRDS(file.path(out, i2, "Report.rds")) plot(x=years_t, y=unique(rep1$SPR_t), col="blue", lwd=2, type="l", ylim=c(0,1.1), xlab="", ylab="", cex.axis=2) lines(x=years_t, y=unique(replow$SPR_t), col="orange", lwd=2) lines(x=years_t, y=unique(rephigh$SPR_t), col="orangered", lwd=2) abline(h=0.4, lty=2) mtext(side=1, "Year", line=3, cex=2) print.letter("(j)", xy=c(0.9,0.95), cex=2) out <- file.path(res_dir, "sens_vbk") i1 <- 2 i2 <- 5 replow <- readRDS(file.path(out, i1, "Report.rds")) rephigh <- readRDS(file.path(out, i2, "Report.rds")) plot(x=years_t, y=unique(rep1$SPR_t), col="blue", lwd=2, type="l", ylim=c(0,1.1), xlab="", ylab="", cex.axis=2) lines(x=years_t, y=unique(replow$SPR_t), col="orange", lwd=2) lines(x=years_t, y=unique(rephigh$SPR_t), col="orangered", lwd=2) abline(h=0.4, lty=2) mtext(side=1, "Year", line=3, cex=2) print.letter("(k)", xy=c(0.9,0.95), cex=2) out <- file.path(res_dir, "sens_SigmaR") i1 <- 4 i2 <- 7 replow <- readRDS(file.path(out, i1, "Report.rds")) rephigh <- readRDS(file.path(out, i2, "Report.rds")) plot(x=years_t, y=unique(rep1$SPR_t), col="blue", lwd=2, type="l", ylim=c(0,1.1), xlab="", ylab="", cex.axis=2) lines(x=years_t, y=unique(replow$SPR_t), col="orange", lwd=2) lines(x=years_t, y=unique(rephigh$SPR_t), col="orangered", lwd=2) abline(h=0.4, lty=2) mtext(side=1, "Year", line=3, cex=2) print.letter("(l)", xy=c(0.9,0.95), cex=2) dev.off() ### model fits figure out <- file.path(res_dir, "LCm") dir.create(out, showWarnings=FALSE) df <- readRDS(file.path(out, "check_convergence.rds")) Report <- readRDS(file.path(out, "Report.rds")) Sdreport <- readRDS(file.path(out, "Sdreport.rds")) Inputs <- readRDS(file.path(out, "Inputs.rds")) base_rep <- Report out <- file.path(res_dir, "LBSPR_LCy") dir.create(out, showWarnings=FALSE) lbspr_res <- readRDS(file.path(out, "LBSPR_results.rds")) obs1 <- input_data_m$LF all_mos <- 1:(length(years_t)*12) mo_yrs <- as.vector(sapply(1:length(years_t), function(x) rep(years_t[x], 12))) choose_mo <- rep(NA, length(years_o)) for(i in 1:length(years_o)){ sub_mo <- months_o[which(months_o %in% all_mos[which(mo_yrs==years_o[i])])] choose_mo[i] <- sub_mo[1] } obs2 <- input_data_y$LF png(file.path(figs_dir, "CompareModelFits.png"), height=15, width=8, res=200, units="in") par(mfcol=c(length(years_o),2), mar=c(0,0,0,0), omi=c(1,1,1,1)) for(i in 1:length(years_o)){ barplot(obs1[which(rownames(obs1)==choose_mo[i]),]/sum(obs1[which(rownames(obs1)==choose_mo[i]),]), xaxs="i", yaxs="i", xaxt="n", yaxt="n", ylim=c(0,0.2), col=gray(0.6), border=NA, space=0) lines(base_rep$plb[choose_mo[i],], lwd=4, col="blue") if(i==length(years_o)) axis(1, cex.axis=2) axis(2, cex.axis=2, at=c(0,0.15), las=2) abline(v=plist$ML50, lty=2) box() } for(i in 1:length(years_o)){ barplot(obs2[which(rownames(obs2)==years_o[i]),]/sum(obs2[which(rownames(obs2)==years_o[i]),]), xaxs="i", yaxs="i", xaxt="n", yaxt="n", ylim=c(0,0.2), col=gray(0.6), border=NA, space=0) lines(lbspr_res$pLF[,i], lwd=4, col="red") if(i==length(years_o)) axis(1, cex.axis=2) print.letter(years_o[i], xy=c(0.8,0.8), cex=3) abline(v=plist$ML50, lty=2) box() } mtext(side=1, "Length bin (cm)", cex=2, line=4, outer=TRUE) mtext(side=2, "Proportions", cex=2, line=5, outer=TRUE) dev.off() # dev.new() all_years <- months_t lc_years <- months_o true_years <- years_t png(file.path(fig_dir, "BaseModelOutput.png"), height=10, width=18, units="in", res=200) plot_output(all_years=months_t, lc_years=months_o, true_years=years_t, Report=Report, Inputs=Inputs, Sdreport=Sdreport, LBSPR=LBSPR, lh=plist_m) dev.off() png(file.path(fig_dir, "BaseModelLCfits.png"), width=16, height=14, res=200, units="in") plot_LCfits(Inputs=Inputs$Data, Report=Report, true_lc_years=NULL, LBSPR=NULL, ylim=c(0,0.3)) dev.off() ## compare models png(file.path(fig_dir, "CompareModels.png"), height=8, width=15, res=200, units="in") dir_list <- list() dir_list[[1]] <- file.path(res_dir, "LCm_highN") dir_list[[2]] <- file.path(res_dir, "LCm_baskettrap") dir_list[[3]] <- file.path(res_dir, "LCm") rep_list <- sdrep_list <- inp_list <- list() for(i in 1:length(dir_list)){ rep_list[[i]] <- readRDS(file.path(dir_list[[i]], "Report.rds")) sdrep_list[[i]] <- readRDS(file.path(dir_list[[i]], "Sdreport.rds")) inp_list[[i]] <- readRDS(file.path(dir_list[[i]], "Inputs.rds")) } col_list <- list() col_list[[1]] <- col2hex("orange") col_list[[2]] <- col2hex("orangered") col_list[[3]] <- "#0000AA" by <- round(length(true_years)/5) lab <- rev(seq(from=true_years[length(true_years)], to=min(true_years), by=-by)) ilab <- which(true_years %in% lab) ilab2 <- sapply(1:length(ilab), function(x){ sub <- which(Inputs$Data$S_yrs %in% ilab[x]) return(sub[length(sub)]) }) par(mfrow=c(1,3), mar=c(4,5,1,1), omi=c(0.2,0.2,0.2,0.2)) plot(x=1,y=1,type="n", xlim=c(1,length(true_years)), ylim=c(0,3.5), xaxs="i", yaxs="i", cex.axis=2, xlab="Year", ylab="Fishing mortality", cex.lab=2, xaxt="n") for(i in 1:length(dir_list)){ Inputs <- inp_list[[i]] Report <- rep_list[[i]] Sdreport <- sdrep_list[[i]] if(Inputs$Data$n_s==1){ xY <- seq_along(all_years) xLC <- which(all_years %in% lc_years) } if(Inputs$Data$n_s>1){ xY <- 1:Inputs$Data$n_y xLC <- unique(Inputs$Data$S_yrs[which(all_years %in% lc_years)]) } F40 <- tryCatch(uniroot(calc_ref, lower=0, upper=200, ages=plist_m$ages, Mat_a=Report$Mat_a, W_a=Report$W_a, M=Report$M, S_a=Report$S_a, ref=0.4)$root, error=function(e) NA) F30 <- tryCatch(uniroot(calc_ref, lower=0, upper=200, ages=plist_m$ages, Mat_a=Report$Mat_a, W_a=Report$W_a, M=Report$M, S_a=Report$S_a, ref=0.3)$root, error=function(e) NA) lines(x=xY, y=Report$F_y, lwd=2, col=col_list[[i]]) points(x=xLC, y=Report$F_y[xLC], col=col_list[[i]], pch=19, xpd=NA) if(all(is.na(Sdreport))==FALSE){ sd <- summary(Sdreport)[which(rownames(summary(Sdreport))=="lF_y"),] sd[,2][which(is.na(sd[,2]))] <- 0 polygon( y=read_sdreport(sd, log=TRUE), x=c(which(is.na(sd[,2])==FALSE), rev(which(is.na(sd[,2])==FALSE))), col=paste0(col_list[[i]],"35"), border=NA) } if(i==1) axis(1, cex.axis=2, at=ilab, labels=lab) if(i==length(dir_list)){ abline(h=F40*Inputs$Data$n_s, lwd=2, lty=2) abline(h=F30*Inputs$Data$n_s, lwd=2, lty=3) } if(i==1) legend("bottomleft", legend=c("All data", "Basket trap only", "High sampling months"), col=rev(unlist(col_list)), lty=1, lwd=4, cex=2, bty="n") } plot(x=1,y=1,type="n", xlim=c(1,length(all_years)), ylim=c(0,2.5), xaxs="i", yaxs="i", cex.axis=2, xlab="Year", ylab="Recruitment", cex.lab=2, xaxt="n") for(i in 1:length(dir_list)){ Inputs <- inp_list[[i]] Report <- rep_list[[i]] Sdreport <- sdrep_list[[i]] lines(x=seq_along(all_years), y=Report$R_t, lwd=2, col=col_list[[i]]) points(x=which(all_years %in% lc_years), y=Report$R_t[which(all_years %in% lc_years)], col=col_list[[i]], pch=19, xpd=NA) if(all(is.na(Sdreport))==FALSE){ sd <- summary(Sdreport)[which(rownames(summary(Sdreport))=="lR_t"),] sd[,2][which(is.na(sd[,2]))] <- 0 polygon( y=read_sdreport(sd, log=TRUE), x=c(which(is.na(sd[,2])==FALSE), rev(which(is.na(sd[,2])==FALSE))), col=paste0(col_list[[i]],"35"), border=NA) } if(i==1) axis(1, cex.axis=2, at=ilab2, labels=lab) } plot(x=1,y=1,type="n", xlim=c(1,length(all_years)), ylim=c(0,0.6), xaxs="i", yaxs="i", cex.axis=2, xlab="Year", ylab="SPR", cex.lab=2, xaxt="n") for(i in 1:length(dir_list)){ Inputs <- inp_list[[i]] Report <- rep_list[[i]] Sdreport <- sdrep_list[[i]] lines(x=seq_along(all_years), y=Report$SPR_t, lwd=2, col=col_list[[i]]) points(x=which(all_years %in% lc_years), y=Report$SPR_t[which(all_years%in%lc_years)], col=col_list[[i]], pch=19, xpd=NA) if(all(is.na(Sdreport))==FALSE){ sd <- summary(Sdreport)[which(rownames(summary(Sdreport))=="SPR_t"),] sd[,2][which(is.na(sd[,2]))] <- 0 polygon( y=read_sdreport(sd, log=FALSE), x=c(which(is.na(sd[,2])==FALSE), rev(which(is.na(sd[,2])==FALSE))), col=paste0(col_list[[i]],"35"), border=NA) } if(i==length(dir_list)){ abline(h=0.4, lwd=2, lty=2) abline(h=0.3, lwd=2, lty=3) } if(i==1) axis(1, cex.axis=2, at=ilab2, labels=lab) } dev.off() ## compare selectivity models png(file.path(fig_dir, "CompareSelex.png"), height=8, width=15, res=200, units="in") dir_list <- list() dir_list[[1]] <- file.path(res_dir, "LCm_highdome") dir_list[[2]] <- file.path(res_dir, "LCm_lowdome") dir_list[[3]] <- file.path(res_dir, "LCm") rep_list <- sdrep_list <- inp_list <- list() for(i in 1:length(dir_list)){ rep_list[[i]] <- readRDS(file.path(dir_list[[i]], "Report.rds")) sdrep_list[[i]] <- readRDS(file.path(dir_list[[i]], "Sdreport.rds")) inp_list[[i]] <- readRDS(file.path(dir_list[[i]], "Inputs.rds")) } col_list <- list() col_list[[1]] <- col2hex("orange") col_list[[2]] <- col2hex("orangered") col_list[[3]] <- "#0000AA" par(mfrow=c(2,2), mar=c(4,5,1,1), omi=c(0.2,0.2,0.2,0.2)) plot(x=1,y=1,type="n", xlim=c(1, length(plist_m$S_l)), ylim=c(0,1.1), xaxs="i", yaxs="i", cex.axis=2, xlab="Length bin (cm)", ylab="Selectivity", cex.lab=2, xaxt="n", yaxt="n") axis(2, cex.axis=2, at=seq(0,1,by=0.25)) for(i in 1:length(dir_list)){ Inputs <- inp_list[[i]] Report <- rep_list[[i]] Sdreport <- sdrep_list[[i]] lines(x=1:length(Report$S_l), y=Report$S_l, lwd=4, col=col_list[[i]]) if(all(is.na(Sdreport))==FALSE){ sd <- summary(Sdreport)[which(rownames(summary(Sdreport))=="S_l"),] sd[,2][which(is.na(sd[,2]))] <- 0 polygon( y=read_sdreport(sd, log=FALSE), x=c(which(is.na(sd[,2])==FALSE), rev(which(is.na(sd[,2])==FALSE))), col=paste0(col_list[[i]],"35"), border=NA) } if(i==1) axis(1, cex.axis=2, at=pretty(c(min(plist_m$highs),max(plist_m$highs)))) } plot(x=1,y=1,type="n", xlim=c(1,length(true_years)), ylim=c(0,3.5), xaxs="i", yaxs="i", cex.axis=2, xlab="Year", ylab="Fishing mortality", cex.lab=2, xaxt="n") for(i in 1:length(dir_list)){ Inputs <- inp_list[[i]] Report <- rep_list[[i]] Sdreport <- sdrep_list[[i]] if(Inputs$Data$n_s==1){ xY <- seq_along(all_years) xLC <- which(all_years %in% lc_years) } if(Inputs$Data$n_s>1){ xY <- 1:Inputs$Data$n_y xLC <- unique(Inputs$Data$S_yrs[which(all_years %in% lc_years)]) } F40 <- tryCatch(uniroot(calc_ref, lower=0, upper=200, ages=plist_m$ages, Mat_a=Report$Mat_a, W_a=Report$W_a, M=Report$M, S_a=Report$S_a, ref=0.4)$root, error=function(e) NA) F30 <- tryCatch(uniroot(calc_ref, lower=0, upper=200, ages=plist_m$ages, Mat_a=Report$Mat_a, W_a=Report$W_a, M=Report$M, S_a=Report$S_a, ref=0.3)$root, error=function(e) NA) lines(x=xY, y=Report$F_y, lwd=2, col=col_list[[i]]) points(x=xLC, y=Report$F_y[xLC], col=col_list[[i]], pch=19, xpd=NA) if(all(is.na(Sdreport))==FALSE){ sd <- summary(Sdreport)[which(rownames(summary(Sdreport))=="lF_y"),] sd[,2][which(is.na(sd[,2]))] <- 0 polygon( y=read_sdreport(sd, log=TRUE), x=c(which(is.na(sd[,2])==FALSE), rev(which(is.na(sd[,2])==FALSE))), col=paste0(col_list[[i]],"35"), border=NA) } if(i==1) axis(1, cex.axis=2, at=ilab, labels=lab) if(i==length(dir_list)){ abline(h=F40*Inputs$Data$n_s, lwd=2, lty=2) abline(h=F30*Inputs$Data$n_s, lwd=2, lty=3) } if(i==1) legend("topright", legend=c("Logistic", "Low dome", "High dome"), col=rev(unlist(col_list)), lty=1, lwd=4, cex=2, bty="n") } plot(x=1,y=1,type="n", xlim=c(1,length(all_years)), ylim=c(0,2.5), xaxs="i", yaxs="i", cex.axis=2, xlab="Year", ylab="Recruitment", cex.lab=2, xaxt="n") for(i in 1:length(dir_list)){ Inputs <- inp_list[[i]] Report <- rep_list[[i]] Sdreport <- sdrep_list[[i]] lines(x=seq_along(all_years), y=Report$R_t, lwd=2, col=col_list[[i]]) points(x=which(all_years %in% lc_years), y=Report$R_t[which(all_years %in% lc_years)], col=col_list[[i]], pch=19, xpd=NA) if(all(is.na(Sdreport))==FALSE){ sd <- summary(Sdreport)[which(rownames(summary(Sdreport))=="lR_t"),] sd[,2][which(is.na(sd[,2]))] <- 0 polygon( y=read_sdreport(sd, log=TRUE), x=c(which(is.na(sd[,2])==FALSE), rev(which(is.na(sd[,2])==FALSE))), col=paste0(col_list[[i]],"35"), border=NA) } if(i==1) axis(1, cex.axis=2, at=ilab2, labels=lab) } plot(x=1,y=1,type="n", xlim=c(1,length(all_years)), ylim=c(0,0.6), xaxs="i", yaxs="i", cex.axis=2, xlab="Year", ylab="SPR", cex.lab=2, xaxt="n") for(i in 1:length(dir_list)){ Inputs <- inp_list[[i]] Report <- rep_list[[i]] Sdreport <- sdrep_list[[i]] lines(x=seq_along(all_years), y=Report$SPR_t, lwd=2, col=col_list[[i]]) points(x=which(all_years %in% lc_years), y=Report$SPR_t[which(all_years%in%lc_years)], col=col_list[[i]], pch=19, xpd=NA) if(all(is.na(Sdreport))==FALSE){ sd <- summary(Sdreport)[which(rownames(summary(Sdreport))=="SPR_t"),] sd[,2][which(is.na(sd[,2]))] <- 0 polygon( y=read_sdreport(sd, log=FALSE), x=c(which(is.na(sd[,2])==FALSE), rev(which(is.na(sd[,2])==FALSE))), col=paste0(col_list[[i]],"35"), border=NA) } if(i==length(dir_list)){ abline(h=0.4, lwd=2, lty=2) abline(h=0.3, lwd=2, lty=3) } if(i==1) axis(1, cex.axis=2, at=ilab2, labels=lab) } dev.off() ## sensitivity to biological inputs png(file.path(fig_dir, "SensitivityBioInputs.png"), height=8, width=15, res=200, units="in") dir_list <- list() dir_list[[1]] <- file.path(res_dir, "sens_linf",1) dir_list[[2]] <- file.path(res_dir, "LCm") dir_list[[3]] <- file.path(res_dir, "sens_linf",10) rep_list <- sdrep_list <- inp_list <- list() for(i in 1:length(dir_list)){ rep_list[[i]] <- readRDS(file.path(dir_list[[i]], "Report.rds")) sdrep_list[[i]] <- readRDS(file.path(dir_list[[i]], "Sdreport.rds")) inp_list[[i]] <- readRDS(file.path(dir_list[[i]], "Inputs.rds")) } col_list <- list() col_list[[1]] <- col2hex("orange") col_list[[2]] <- "#0000AA" col_list[[3]] <- col2hex("orangered") by <- round(length(true_years)/5) lab <- rev(seq(from=true_years[length(true_years)], to=min(true_years), by=-by)) ilab <- which(true_years %in% lab) ilab2 <- sapply(1:length(ilab), function(x){ sub <- which(Inputs$Data$S_yrs %in% ilab[x]) return(sub[length(sub)]) }) par(mfrow=c(2,2), mar=c(0,0,0,0), omi=c(1,1,1,1)) plot(x=1,y=1,type="n", xlim=c(1,length(all_years)), ylim=c(0,1.2), xaxs="i", yaxs="i", cex.axis=2, xlab="", ylab="", cex.lab=2, xaxt="n", yaxt="n") for(i in 1:length(dir_list)){ Inputs <- inp_list[[i]] Report <- rep_list[[i]] Sdreport <- sdrep_list[[i]] lines(x=seq_along(all_years), y=Report$SPR_t, lwd=2, col=col_list[[i]]) points(x=which(all_years %in% lc_years), y=Report$SPR_t[which(all_years%in%lc_years)], col=col_list[[i]], pch=19, xpd=NA) if(all(is.na(Sdreport))==FALSE){ sd <- summary(Sdreport)[which(rownames(summary(Sdreport))=="SPR_t"),] sd[,2][which(is.na(sd[,2]))] <- 0 polygon( y=read_sdreport(sd, log=FALSE), x=c(which(is.na(sd[,2])==FALSE), rev(which(is.na(sd[,2])==FALSE))), col=paste0(col_list[[i]],"35"), border=NA) } if(i==length(dir_list)){ abline(h=0.4, lwd=2, lty=2) abline(h=0.3, lwd=2, lty=3) } # if(i==1) axis(1, cex.axis=2, at=ilab2, labels=lab) } print.letter("Asymptotic length", xy=c(0.25,0.9), cex=2, font=2) axis(2, seq(0,1,by=0.5), cex.axis=2) dir_list <- list() dir_list[[1]] <- file.path(res_dir, "sens_vbk",1) dir_list[[2]] <- file.path(res_dir, "LCm") dir_list[[3]] <- file.path(res_dir, "sens_vbk",10) rep_list <- sdrep_list <- inp_list <- list() for(i in 1:length(dir_list)){ rep_list[[i]] <- readRDS(file.path(dir_list[[i]], "Report.rds")) sdrep_list[[i]] <- readRDS(file.path(dir_list[[i]], "Sdreport.rds")) inp_list[[i]] <- readRDS(file.path(dir_list[[i]], "Inputs.rds")) } plot(x=1,y=1,type="n", xlim=c(1,length(all_years)), ylim=c(0,1.2), xaxs="i", yaxs="i", cex.axis=2, xlab="", ylab="", cex.lab=2, xaxt="n", yaxt="n") for(i in 1:length(dir_list)){ Inputs <- inp_list[[i]] Report <- rep_list[[i]] Sdreport <- sdrep_list[[i]] lines(x=seq_along(all_years), y=Report$SPR_t, lwd=2, col=col_list[[i]]) points(x=which(all_years %in% lc_years), y=Report$SPR_t[which(all_years%in%lc_years)], col=col_list[[i]], pch=19, xpd=NA) if(all(is.na(Sdreport))==FALSE){ sd <- summary(Sdreport)[which(rownames(summary(Sdreport))=="SPR_t"),] sd[,2][which(is.na(sd[,2]))] <- 0 polygon( y=read_sdreport(sd, log=FALSE), x=c(which(is.na(sd[,2])==FALSE), rev(which(is.na(sd[,2])==FALSE))), col=paste0(col_list[[i]],"35"), border=NA) } if(i==length(dir_list)){ abline(h=0.4, lwd=2, lty=2) abline(h=0.3, lwd=2, lty=3) } # if(i==1) axis(1, cex.axis=2, at=ilab2, labels=lab) } print.letter("von Bertalanffy k", xy=c(0.25,0.9), cex=2, font=2) dir_list <- list() dir_list[[1]] <- file.path(res_dir, "sens_M",1) dir_list[[2]] <- file.path(res_dir, "LCm") dir_list[[3]] <- file.path(res_dir, "sens_M",10) rep_list <- sdrep_list <- inp_list <- list() for(i in 1:length(dir_list)){ rep_list[[i]] <- readRDS(file.path(dir_list[[i]], "Report.rds")) sdrep_list[[i]] <- readRDS(file.path(dir_list[[i]], "Sdreport.rds")) inp_list[[i]] <- readRDS(file.path(dir_list[[i]], "Inputs.rds")) } plot(x=1,y=1,type="n", xlim=c(1,length(all_years)), ylim=c(0,1.2), xaxs="i", yaxs="i", cex.axis=2, xlab="", ylab="", cex.lab=2, xaxt="n", yaxt="n") for(i in 1:length(dir_list)){ Inputs <- inp_list[[i]] Report <- rep_list[[i]] Sdreport <- sdrep_list[[i]] lines(x=seq_along(all_years), y=Report$SPR_t, lwd=2, col=col_list[[i]]) points(x=which(all_years %in% lc_years), y=Report$SPR_t[which(all_years%in%lc_years)], col=col_list[[i]], pch=19, xpd=NA) if(all(is.na(Sdreport))==FALSE){ sd <- summary(Sdreport)[which(rownames(summary(Sdreport))=="SPR_t"),] sd[,2][which(is.na(sd[,2]))] <- 0 polygon( y=read_sdreport(sd, log=FALSE), x=c(which(is.na(sd[,2])==FALSE), rev(which(is.na(sd[,2])==FALSE))), col=paste0(col_list[[i]],"35"), border=NA) } if(i==length(dir_list)){ abline(h=0.4, lwd=2, lty=2) abline(h=0.3, lwd=2, lty=3) } if(i==1) axis(1, cex.axis=2, at=ilab2, labels=lab) } print.letter("Natural mortality", xy=c(0.25,0.9), cex=2, font=2) axis(2, seq(0,1,by=0.5), cex.axis=2) dir_list <- list() dir_list[[1]] <- file.path(res_dir, "sens_ML50",1) dir_list[[2]] <- file.path(res_dir, "LCm") dir_list[[3]] <- file.path(res_dir, "sens_ML50",10) rep_list <- sdrep_list <- inp_list <- list() for(i in 1:length(dir_list)){ rep_list[[i]] <- readRDS(file.path(dir_list[[i]], "Report.rds")) sdrep_list[[i]] <- readRDS(file.path(dir_list[[i]], "Sdreport.rds")) inp_list[[i]] <- readRDS(file.path(dir_list[[i]], "Inputs.rds")) } plot(x=1,y=1,type="n", xlim=c(1,length(all_years)), ylim=c(0,1.2), xaxs="i", yaxs="i", cex.axis=2, xlab="", ylab="", cex.lab=2, xaxt="n", yaxt="n") for(i in 1:length(dir_list)){ Inputs <- inp_list[[i]] Report <- rep_list[[i]] Sdreport <- sdrep_list[[i]] lines(x=seq_along(all_years), y=Report$SPR_t, lwd=2, col=col_list[[i]]) points(x=which(all_years %in% lc_years), y=Report$SPR_t[which(all_years%in%lc_years)], col=col_list[[i]], pch=19, xpd=NA) if(all(is.na(Sdreport))==FALSE){ sd <- summary(Sdreport)[which(rownames(summary(Sdreport))=="SPR_t"),] sd[,2][which(is.na(sd[,2]))] <- 0 polygon( y=read_sdreport(sd, log=FALSE), x=c(which(is.na(sd[,2])==FALSE), rev(which(is.na(sd[,2])==FALSE))), col=paste0(col_list[[i]],"35"), border=NA) } if(i==length(dir_list)){ abline(h=0.4, lwd=2, lty=2) abline(h=0.3, lwd=2, lty=3) } if(i==1) axis(1, cex.axis=2, at=ilab2, labels=lab) } legend("topright", legend=c("-25%", "base", "+25%"), col=unlist(col_list), lwd=4, bty="n", cex=2, title="Input value") print.letter("Length at 50% maturity", xy=c(0.25,0.9), cex=2, font=2) mtext(side=1, "Year", cex=2, line=4, outer=TRUE) mtext(side=2, "SPR", cex=2, line=4, outer=TRUE) dev.off()

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FlowWest/Explore-SIT-Model

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refs/heads/master

2021-06-20T21:59:30.842006

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about.R

aboutUI <- function(id) { ns <- NS(id) tagList( fluidRow( column(width = 12, style = 'padding-left: 30px;', tags$h1('Fall Chinook Science Integration Team Model'), tags$br(), tags$img(src = 'model.png', width = '80%'), tags$br(), tags$br(), tags$h4('Primary Author', tags$a(href = 'mailto:jt.peterson@oregonstate.edu', 'James T. Peterson')), tags$h5('U.S. Geological Survey, Oregon Cooperative Cooperative'), tags$h5('Fish and Wildlife Research Unit, Oregon State University'), tags$h5('Corvallis, Oregon 97331-3803'), tags$br()) ), fluidRow( column(width = 12, tags$a(tags$img(src = 'TransLogoTreb.png', width = 200), href = 'http://www.flowwest.com/', target = '_blank', style = 'display: inline-block;'), tags$h5('App created and maintained by', tags$a(href = 'mailto:sgill@flowwest.com', 'Sadie Gill', target = '_blank'), style = 'display: inline-block; font-weight: 300;')) ) ) } about <- function(input, output, session) { }

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/R/app/server/helpers.R

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no_license

shi-xin/density_estimation

8e7868cb0bdca1a544f68efabe21178c8ab48ab5

86c4adee22df9b918e7b2514a3b84716185a72f6

refs/heads/master

2022-09-17T05:55:15.007284

2020-05-27T02:24:26

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R

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r

helpers.R

source("server/helpers_mixture_modal.R") # Initialize python ------------------------------------------------------------ use_python_custom = function(path) { tryCatch({ reticulate::use_python( python = path ) suppressWarnings(invisible(reticulate::py_config())) path_found = dirname(reticulate:::.globals$py_config$python) if (path != path_found) { showNotification( paste( stringi::stri_wrap( paste( "Python was found in", path_found, "which differs from your input." ), width = 30), collapse = "\n" ), type = "warning", duration = 7 ) } showNotification( paste("Python version:", reticulate:::.globals$py_config$version), type = "message", duration = 7 ) return(path_found) }, error = function(c) { showNotification( paste("The following error was thrown when trying to load Python<\br>", c), type = "error", duration = 7 ) }) } init_python_custom <- function(input) { withProgress( message = "Getting Python ready", expr = { incProgress(0, detail = "Configuring Python...") PYTHON_PATH <- use_python_custom(input$python_path) incProgress(0.33, detail = "Checking packages...") tryCatch({ msg <- reticulate::py_capture_output( reticulate::source_python("python/check_pkgs.py")) showNotification( HTML(gsub("\n"," ", msg)), type = "message", duration = 7) }, error = function(c) { showNotification( HTML(paste("Not all required packages have been found ", c)), type = "error", duration = 7 ) return(invisible(NULL)) }) incProgress(0.33, detail = "Loading functions...") reticulate::source_python("python/density_utils.py", envir = globalenv()) incProgress(0.34, detail = "Done!") } ) return(invisible(PYTHON_PATH)) } init_python_shiny <- function(input) { tryCatch({ withProgress( message = "Getting Python ready", expr = { incProgress(0, detail = "Creating virtual environment...") reticulate::virtualenv_create( envname = "python35_env", python = "/usr/bin/python3") incProgress(0.2, detail = "Installing packages...") reticulate::virtualenv_install( envname = "python35_env", packages = c("numpy", "scipy")) incProgress(0.2, detail = "Loading virtual environment...") reticulate::use_virtualenv( virtualenv = "python35_env", required = TRUE) incProgress(0.2, detail = "Loading functions...") reticulate::source_python( file = "python/density_utils.py", envir = globalenv()) incProgress(0.2, detail = "Done!") } ) return(TRUE) }, error = function(c){ showNotification( HTML(paste( stringi::stri_wrap(paste("Python could not be loaded. Error:", c), width = 30), collapse = " ")), type = "error", duration = 7 ) return(FALSE) }) } # Restart session -------------------------------------------------------------- restart_r <- function() if (tolower(.Platform$GUI) == "rstudio") { # rm(list = ls()) # .rs.restartR() } # Data wrangling functions ----------------------------------------------------- df_filter <- function(df, .pdf, .estimator, .bw, .size) { df %>% filter( pdf %in% .pdf, estimator %in% .estimator, bw %in% .bw, size %in% .size ) } df_facet_order <- function(df, input) { cols <- input[["boxplots_facet_vars"]] if (!any(cols %in% c("bw", "estimator", "pdf"))) return(df) cols_match <- c( "bw" = "boxplots_bw", "estimator" = "boxplots_estimator", "pdf" = "boxplots_pdf") for (col in cols) { lvls <- input[[cols_match[[col]]]] if (col == "pdf") { lbls <- unname(pdf_facet_lbls[lvls]) } else { lbls <- lvls } df[[col]] <- factor( x = df[[col]], levels = lvls, labels = lbls) } return(df) } df_trim <- function(df, .group_vars, .trim_var, .quantile) { .group_vars <- purrr::map(.group_vars, as.name) .trim_var <- as.name(.trim_var) df %>% group_by(!!!.group_vars) %>% mutate( upper_bound = quantile(!!.trim_var, .quantile, na.rm = TRUE) ) %>% mutate( !!.trim_var := ifelse(!!.trim_var >= upper_bound, NA, !!.trim_var) ) %>% select(-upper_bound) } # Plotting functions ----------------------------------------------------------- check_boxplot_args <- function(args) { # First sapply checks NULL as well any(sapply(args, function (x) length(x) == 0)) | anyNA(args) } initialize_plot <- function(df, .metric) { .metric <- as.name(.metric) ggplot( df, aes( x = factor(size), y = !!.metric, fill = factor(size) ) ) } add_boxplot <- function(outlier.shape = 19) { geom_boxplot( outlier.fill = alpha(DARK_GRAY, 0.3), outlier.color = alpha(DARK_GRAY, 0.3), outlier.shape = outlier.shape ) } custom_fill <- function(alpha = 0.6) { scale_fill_viridis_d(alpha = alpha) } custom_theme <- function() { theme_gray() + theme( panel.grid.minor.x = element_blank(), panel.grid.major.x = element_blank(), panel.border = element_blank(), legend.position = "none", strip.background = element_blank() ) } custom_scale <- function(limits = NULL, prec = 0.1, log10 = FALSE, time_flag = FALSE) { scale_func <- if (log10) scale_y_log10 else scale_y_continuous if (time_flag) { scale_func( limits = limits, breaks = waiver(), labels = get_labels(prec) ) } else { scale_func( limits = limits, breaks = waiver() ) } } precision <- function (x) { rng <- range(x, na.rm = TRUE, finite = TRUE) span <- diff(rng) if (span == 0) { return(1) } 10 ^ (floor(log10(span))) } get_suffix <- function(prec) { if (prec >= 0.1) { " sec" } else if (prec %in% c(0.01, 0.001)) { " ms" } else if (prec <= 0.0001) { " \u03BCs" } } get_scale <- function(prec) { if (prec >= 0.1) { 1 } else if (prec %in% c(0.01, 0.001)) { 1 / 1e-03 } else if (prec <= 0.0001) { 1 / 1e-06 } else { 1 / prec } } get_acc <- function(prec) { if (prec >= 0.1) { digits = 2 - log10(prec) } else if (prec %in% c(0.01, 0.001)) { digits = 2 - log10(prec / 1e-03) } else if (prec <= 0.0001) { digits = 2 - log10(prec / 1e-06) } else { digits = 2 } 1 / 10 ^ digits } get_labels <- function(prec) { scales::number_format( accuracy = get_acc(prec), scale = get_scale(prec), suffix = get_suffix(prec) ) } # x <- c(rnorm(10, mean = 0.000200 , sd = 0.0001), # rnorm(10, mean = 0.0000500 , sd = 0.00001), # rnorm(10, mean = 0.000003, sd = 0.000001)) # prec <- precision(x) # get_labels(prec)(x) # # x # # x <- c(rnorm(10, mean = 0.05 , sd = 0.01), # rnorm(10, mean = 0.004, sd = 0.001)) # prec <- precision(x) # get_labels(prec)(x) # x # # x <- c(rnorm(10, mean = 0.7, sd = 0.1), # rnorm(10, mean = 0.05 , sd = 0.01), # rnorm(10, mean = 0.004, sd = 0.001)) # prec <- precision(x) # get_labels(prec)(x) # x # # x <- rnorm(50, mean = 1 , sd = 0.01) # prec <- precision(x) # get_labels(prec)(x) # x # # x <- rnorm(50, mean = 5 , sd = 1) # prec <- precision(x) # get_labels(prec)(x) # x custom_facet <- function(..., free_y, nrow = 1) { args <- as.list(...) if (!any(args %in% c("bw", "estimator", "pdf"))) return(NULL) scales <- if (free_y) "free_y" else "fixed" if (length(args) == 1) { col_labs <- lbls_list[[args[[1]]]] form <- as.formula(paste(".", args[[1]], sep = "~")) facet_grid(form, scales = scales, labeller = labeller(.cols = col_labs)) } else if (length(args) == 2) { row_labs <- lbls_list[[args[[1]]]] col_labs <- lbls_list[[args[[2]]]] form <- as.formula(paste(args[[1]], args[[2]], sep = "~")) facet_grid(form, scales = scales, labeller = labeller(.cols = col_labs, .rows = row_labs)) } } get_bw_choices <- function(x) { f <- function (x) switch(x, "mixture" = choices_bw_mixture, choices_bw_classic) vec <- unlist(lapply(x, f)) vec[!duplicated(vec)] } get_size_choices <- function(x) { f <- function (x) switch(x, "sj" = choices_size_sj, choices_size_default) l <- lapply(x, f) Reduce(union, l) } # Continuous variables arguments ----------------------------------------------- cont_dist_labels <- list( norm = c("Mean", "Standard Deviation"), t = c("Degrees of freedom", "NCP"), gamma = c("Shape", "Scale"), exp = c("Rate"), beta = c("Shape 1", "Shape 2"), lnorm = c("Log mean", "Log SD"), weibull = c("Shape", "Scale"), unif = c("Lower bound", "Upper bound") ) cont_dist_values <- list( norm = 0:1, t = 1:0, gamma = c(1, 1), exp = 1, beta = c(1.5, 1.5), lnorm = 0:1, weibull = c(0.5, 1), unif = c(-1, 1) ) cont_dist_mins <- list( norm = c(-100, 0), t = 1:0, gamma = c(0, 0), exp = 0, beta = c(0, 0), lnorm = c(-5, 0), weibull = c(0, 0), unif = c(-100, -100) ) cont_dist_mins_check <- list( norm = c(-100, 0.001), t = 1:0, gamma = c(0, 0.001), exp = 0.001, beta = c(0.01, 0.01), lnorm = c(-5, 0.01), weibull = c(0.01, 0.01), unif = c(-100, -100) ) cont_dist_maxs <- list( norm = c(100, 100), t = c(200, 50), gamma = c(100, 100), exp = 100, beta = c(100, 100), lnorm = c(100, 100), weibull = c(100, 100), unif = c(100, 100) ) cont_dist_bounds <- list( "norm" = function(.params) { width <- 3 * .params[[2]] c(.params[[1]] - width, .params[[1]] + width) }, "t" = function(.params) { qt(c(0.005, 0.995), .params[[1]], .params[[2]]) }, "gamma" = function(.params) { c(0, qgamma(0.995, .params[[1]], .params[[2]])) }, "exp" = function(.params) { c(0, qexp(0.995, .params[[1]])) }, "beta" = function(.params) { c(0, 1) }, "lnorm" = function(.params) { c(0, qlnorm(0.995, .params[[1]], .params[[2]])) }, "weibull" = function(.params) { c(0, qweibull(0.995, .params[[1]], .params[[2]])) }, "unif" = function(.params) { c(.params[[1]], .params[[2]]) } ) all_equal_length <- function(...) { length(unique(vapply(list(...), length, 1L))) == 1 } numeric_params <- function(label, value, min = NA, max = NA, step = 0.5, prefix) { if (all(is.na(min))) min <- rep(NA, length(label)) if (all(is.na(max))) max <- rep(NA, length(label)) step <- rep(step, length(label)) stopifnot(all_equal_length(label, value, min, max, step)) ids <- paste0(prefix, "_input", seq_along(label)) out <- purrr::pmap(list(ids, label, value, min, max, step), numericInput) purrr::iwalk(out, `[[`) } distribution_parameters_cont <- function(distribution, prefix = "fixed_params") { numeric_params( label = cont_dist_labels[[distribution]], value = cont_dist_values[[distribution]], min = cont_dist_mins[[distribution]], max = cont_dist_maxs[[distribution]], prefix = prefix ) } custom_column <- function(id, label, value, min, max, step) { column( width = 4, numericInput( inputId = id, label = label, value = value, min = min, max = max, step = step, width = "100%" ) ) } generate_mixture_ui <- function(label, value, min = NA, max = NA, step = NA, prefix) { if (all(is.na(min))) min <- rep(NA, length(label)) if (all(is.na(max))) max <- rep(NA, length(label)) if (all(is.na(step))) step <- rep(NA, length(label)) stopifnot(all_equal_length(label, value, min, max, step)) ids <- paste0(prefix, "_input", seq_along(label)) out <- purrr::pmap(list(ids, label, value, min, max, step), custom_column) purrr::iwalk(out, `[[`) } render_mixture_ui <- function() { distribution = "rnorm" prefix = "mixture1" renderUI({ fluidRow( generate_mixture_ui( label = cont_dist_labels[[distribution]], value = cont_dist_values[[distribution]], min = cont_dist_mins[[distribution]], max = cont_dist_maxs[[distribution]], prefix = prefix ) ) }) } # Plot functions --------------------------------------------------------------- component_rvs <- function(distribution, .params, size) { .f <- paste0("r", distribution) .args <- c(list(size), .params) do.call(.f, .args) } mixture_rvs <- function(distributions, .params, size, wts = NULL) { if (is.null(wts)) wts <- rep(1 / length(distributions), length(distributions)) stopifnot(length(distributions) == length(.params), length(.params) == length(wts)) stopifnot(all.equal(sum(wts), 1, tolerance = 0.011)) .l <- list(distributions, .params, round(wts * size)) unlist(purrr::pmap(.l, component_rvs)) } component_pdf <- function(distribution, .params, x_grid) { .f <- paste0("d", distribution) .args <- c(list(x_grid), .params) do.call(.f, .args) } pdf_bounds <- function(distribution, .params) { .f <- cont_dist_bounds[[distribution]] .f(.params) } mixture_grid <- function(distributions, .params) { .l <- list(distributions, .params) out <- unlist(purrr::pmap(.l, pdf_bounds)) seq(min(out), max(out), length.out = 250) } mixture_pdf <- function(distributions, .params, wts = NULL) { if (is.null(wts)) wts <- rep(1 / length(distributions), length(distributions)) stopifnot(length(distributions) == length(.params), length(.params) == length(wts)) stopifnot(all.equal(sum(wts), 1, tolerance = 0.011)) x_grid <- mixture_grid(distributions, .params) .l <- list(distributions, .params) pdf <- unlist(purrr::pmap(.l, component_pdf, x_grid = x_grid)) pdf <- as.vector(matrix(pdf, ncol = length(wts)) %*% wts) pdf[is.infinite(pdf)] <- NA # In some edge cases pdf is `Inf` return(list("x" = x_grid, "pdf" = pdf)) } get_mixture_distributions <- function(input, mixture_n) { vapply( X = paste0("mixture_component", seq_len(mixture_n)), FUN = function(x) input[[x]], FUN.VALUE = character(1), USE.NAMES = FALSE ) } get_mixture_params <- function(input, mixture_n) { # Starts with mixture_component1_input and ends with digit pattern <- paste0("(mixture_component", seq_len(mixture_n), "_input)(\\d$)") lapply( X = pattern, FUN = function(x) { .nms <- names(input)[grepl(x, names(input))] # Names are not sorted in `input`. Not very solid, but worked so far .nms <- stringr::str_sort(.nms) # Reactive values allow subsetting only one element out <- vector("numeric", length(.nms)) for (i in seq_along(.nms)) { out[[i]] <- input[[.nms[[i]]]] } out } ) } get_mixture_wts <- function(input, mixture_n) { vapply( X = paste0("mixture_component", seq_len(mixture_n), "_input_wt"), FUN = function(x) input[[x]], FUN.VALUE = numeric(1), USE.NAMES = FALSE ) } check_mixture_wts_gen <- function() { wts_previous <- 1 f <- function(wts, mixture_n) { if (any(is.na(wts))) { wts <- round(rep(1 / mixture_n, mixture_n), 3) } else if (!((sum(wts) > 1 - 0.0011) && (sum(wts) < 1 + 0.0011))) { changed <- length(wts) != length(wts_previous) | !all(wts == wts_previous) if (changed) { showNotification( ui = HTML(paste(c("Sum of weights is not equal to 1.", "Weighting all components equally."), collapse = " ")), type = "warning" ) wts_previous <<- wts } wts <- round(rep(1 / mixture_n, mixture_n), 3) } wts } return(f) } check_mixture_wts <- check_mixture_wts_gen() stop_custom <- function(.subclass, message, call = NULL, ...) { .error <- structure( list( message = message, call = call, ... ), class = c(.subclass, "error", "condition") ) stop(.error) } check_density_args_inner <- function(.params, mins, maxs) { vapply( X = seq_along(.params), FUN = function(i) .params[[i]] >= mins[[i]] & .params[[i]] <= maxs[[i]], FUN.VALUE = logical(1), USE.NAMES = FALSE ) } check_density_args <- function(distributions, .params) { mins <- purrr::map(distributions, .f = function(x) cont_dist_mins_check[[x]]) maxs <- purrr::map(distributions, .f = function(x) cont_dist_maxs[[x]]) lgls <- unlist(purrr::pmap(list(.params, mins, maxs), check_density_args_inner)) if (any(!lgls)) { stop_custom( .subclass = "bad-parameters", message = paste( "At least one parameter value is not supported.", "Possible causes:", "* Very low/high values.", "* Value not in domain of parameter (i.e. negative SD).", sep = "\n") ) } return(invisible(NULL)) } get_density_params <- function(input) { if (input$density_dist_type == "mixture") { if (is.null(input[["mixture_component1_input2"]])) { stop_custom( .subclass = "missing-component", message = "Specify at least one component for the mixture" ) } mixture_n <- input$density_mixture_n distributions <- get_mixture_distributions(input, mixture_n) .params <- get_mixture_params(input, mixture_n) check_density_args(distributions, .params) wts <- get_mixture_wts(input, mixture_n) wts <- check_mixture_wts(wts, mixture_n) rvs <- mixture_rvs(distributions, .params, input$density_sample_size, wts) pdf <- mixture_pdf(distributions, .params, wts) } else { distribution <- input$density_fixed_distribution dist_params <- if (distribution == "exp") { list(input$fixed_params_input1) } else { list(input$fixed_params_input1, input$fixed_params_input2) } check_density_args(distribution, list(dist_params)) rvs <- mixture_rvs(distribution, list(dist_params), input$density_sample_size, wts = 1) pdf <- mixture_pdf(distribution, list(dist_params), wts = 1) } x_true <- pdf$x y_true <- pdf$pdf x_range <- range(x_true) y_range <- c(0, 1.16 * max(y_true, na.rm = TRUE)) if (input$density_estimator == "gaussian_kde") { estimation <- estimate_density( rvs, bw = input$density_bw_method, extend = input$density_extend_limits, bound_correction = input$density_bound_correction ) } else if (input$density_estimator == "adaptive_kde") { estimation <- estimate_density( rvs, bw = input$density_bw_method, extend = input$density_extend_limits, bound_correction = input$density_bound_correction, adaptive = TRUE ) } else { estimation <- estimate_density_em( rvs, extend = input$density_extend_limits ) } params = list("rvs" = rvs, "x_range" = x_range, "y_range" = y_range, "x_estimation" = estimation[[1]], "y_estimation" = estimation[[2]], "x_true" = x_true, "y_true" = y_true) return(params) } add_hist = function(x, x_range, y_range, breaks_n = 40) { hist(x, breaks = breaks_n, prob = TRUE, main = NULL, xlab = "X", ylab = "Probabilty density function", xlim = x_range, ylim = y_range, col = LIGHT_BLUE ) } add_lines = function(x_estimation, y_estimation, x_true, y_true) { lines(x_true, y_true, lwd = 4, col = "black", lty = "dashed") lines(x_estimation, y_estimation, lwd = 5, col = DARK_RED) } density_plot_generator <- function(params) { rvs = params[["rvs"]] x_range = params[["x_range"]] y_range = params[["y_range"]] x_estimation = params[["x_estimation"]] y_estimation = params[["y_estimation"]] x_true = params[["x_true"]] y_true = params[["y_true"]] f <- function() { add_hist(rvs, x_range, y_range) add_lines(x_estimation, y_estimation, x_true, y_true) legend("topright", legend = c("True density", "Estimation"), col = c("black", DARK_RED), lwd = 5, inset = 0.015) } return(f) } get_density_plot <- function(input) { show_spinner() density_plot_params <- get_density_params(input) density_plot <- density_plot_generator(density_plot_params) hide_spinner() return(density_plot) }

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/man/dynamic_structure.Rd

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cran/Bios2cor

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refs/heads/master

2022-07-31T18:21:28.244917

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dynamic_structure.Rd

\name{dynamic_structure} \Rdversion{1.1} \alias{dynamic_structure} \title{ Creates the data structure for the analysis of side chain dihedral angles } \description{ Given a structure pdb file, a trajectory dcd file and frame indices, gathers information on side chain dihedral angles in a unique structure. This structure will be used in correlation/covariation methods aimed at analyzing side chain rotational motions during molecular dynamics simulations. } \usage{ dynamic_structure(pdb, trj, frames=NULL) } \arguments{ \item{pdb}{ Filepath of the pdb file } \item{trj}{ Filepath of trajectory file (dcd file) } \item{frames}{ Indicates the selected frames for the analysis, created with the \code{seq} function (Usage: frames <-seq(from ,to , by= ). Default is NULL (all the frames of the trajectory are taken into account). } } \value{ Returns a list of class 'structure' with the following elements containing information on the sequence, structure, trajectory and side chain dihedral angles (values and names) of the protein during the simulation: \item{pdb}{ an object of class 'pdb' created by the \code{read.pdb}function from the \code{bio3d} package } \item{dcd}{ A numeric matrix of xyz coordinates with a frame per row and a Cartesian coordinate per column. Created by the \code{read.dcd}function from the \code{bio3d} package } \item{xyz}{ A numeric matrix of xyz coordinates with a frame per row and a Cartesian coordinate per column. For each frame, the protein coordinates have been fitted on the pdb structure using the \code{fit.xyz} from the \code{bio3d} package } \item{tor}{ A numeric matrix of side chain dihedral angles with a frame per row and a dihedral angle per column. Contains only side chain dihedral angles. Created by the \code{xyz2tor} function from the \code{bio3d} package } \item{tor.names}{ a character vector with the names of all side chain chi dihedral angles. They are written as "M.chiN" where M is the residue number and N the dihedral angle chi (chi1, chi2,...). Alanine and Glycine residues which do not have side chain dihedral angles are omitted. } \item{tor.resno}{ a character vector with the residue number M of all side chain chi dihedral angles. } \item{tor.angle}{ a character vector with the dihedral angle (chiN) of all side chain chi dihedral angles. } \item{nb_torsions}{ a numeric value indicating the total number of dihedral angles } \item{prot.seq}{ a character vector with the sequence of the protein } \item{nb_residues}{ a numeric value indicating the number of residues in the protein } \item{nb_frames}{ a numeric value indicating the total number of selected frames } \item{frames}{ a numeric vector indicating the selected frames } } \author{ Bruck TADDESE, Antoine GARNIER and Marie CHABBERT } \examples{ #Reading pdb and dcd files pdb <- system.file("rotamer/toy_coordinates.pdb", package= "Bios2cor") trj <- system.file("rotamer/toy_dynamics.dcd", package= "Bios2cor") #Creating dynamic_structure object for selected frames wanted_frames <- seq(from = 1, to = 40, by = 2) dynamic_structure <- dynamic_structure(pdb, trj, wanted_frames) #Creating dynamic_structure object for all the frames #dynamic_structure <- dynamic_structure(pdb, trj) }

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/man/video-images.Rd

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jsta/glatos

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refs/heads/master

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video-images.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/extdata.r \name{video images} \alias{video images} \title{Video frames of walleye movements in Lake Huron} \format{Folder contains 30 sequentially labeled .png image files} \source{ \url{http://glatos.glos.us/home/project/HECWL} } \usage{ system.file("extdata", "frames", package="glatos") } \description{ Sequential images of walleye movements in Lake Huron for testing functionality of ffmpeg function. } \section{Filename}{ frames } \author{ Todd Hayden }

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/plot6.R

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andhdo/coursera_ds_exdata_project2

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plot6.R

# see complete source code at: https://github.com/andhdo/coursera_ds_exdata_project2 # plot6 <- function() { # be sure to load the files and precomputations source("load_data.R") load_prerequisites() # load libraries library(ggplot2) # get the specific data to plot NEI <- load_data_nei() SCC <- load_data_scc() # names(NEI) # head(NEI) #precomputations # Similar to exercise 5, we are going to select the cities with the fips code # and aggregate the data of the two populations subsetNEI <- NEI[(NEI$fips %in% c("24510","06037")) & (NEI$type=="ON-ROAD"), ] # then we can aggregate and plot the data processed_ds <- aggregate(Emissions ~ year+fips , data=subsetNEI, sum) # + type # calculate target filename & open DC target_file <- paste0 (output_figures_folder,"/","plot6.png") png(filename=target_file, width=480,height=480,units="px") # process information to produce graphic g <- ggplot(processed_ds, aes(x=year, y=Emissions, group=fips, colour=fips)) #, group=type, colour=type g <- g + geom_point() + geom_line() g <- g + xlab("Year") + ylab("PM") + ggtitle("Motor Emissions [1999..2008] / Baltimore & LosAngeles") g <- g + scale_colour_discrete(label = c("LosAngeles","Baltimore")) # change legend labels print(g) # Turn off deviceContext dev.off() }

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arun_ex4.R

# ----------------------------------------------------------------- set.seed(20170703L) DF1 = data.frame(id = sample(1:2, 9L, TRUE), code = sample(letters[1:3], 9, TRUE), valA = 1:9, valB = 10:18, stringsAsFactors = FALSE) DF2 = data.frame(id = c(3L, 1L, 1L, 2L, 3L), code = c("b", "a", "c", "c", "d"), mul = 5:1, stringsAsFactors = FALSE) #create data tables library(data.table) DT1 <- as.data.table(DF1) DT2 <- as.data.table(DF2) #---------------------------------------------------------------------- # 1. In DT1, on those rows where id != 2, replace valA and valB with valA+1 and valB+1 respectively. ans1 <- DT1[id != 2, `:=`(valA = valA+1L, valB = valB+1L)] ans1 # 2. On those rows where id == 2, replace valA with valB if valA is <= 7, else with valB^2. ans2 <- DT1[id == 2, val := ifelse(valA <= 7L, valB, as.integer(valB^2))] ans2 # 3. Create a new column `tmp` and assign `NA` to it by reference. ans3 <- DT1[, temp := NA] ans3 # 4. What’s the type (or class) of `tmp` column that we just created? class(DT1[, temp]) class(DT1$temp) # 5. Do DT1[, tmp := NULL] and observe the output.. What’s the difference compared to (3)? ans3[, temp := NULL][] # 6. Create a new column named “rank” which takes value 1 where code == “a”, 2 where code == “b” and 3 where code == “c”. # Do it in as many different ways you could think of :-). ans6 <- DT1[code == "a", rank := 1L][ code == "b", rank := 2L][ code == "c", rank := 3L] ans6 <- DT1[, rank := as.integer(factor(code))] tmp_dt <- data.table(keys=c("a", "b", "c"), vals =1:3) ans6 <- DT1[tmp_dt, on=.(code=keys), rank := i.vals] # 7. Let DT3 = DT2[!duplicated(code)]. Update both valA and valB columns with ‘valA*mul’ and ‘valB*mul’ # wherever DT3$code matches DT1$code.. What happens to those rows where there are no matches in DT1? Why? DT3 = DT2[!duplicated(code)] ans7 <- DT1[DT3, on=.(code), c("valA", "valB") := lapply(.SD, `*`, i.mul), .SDcols= valA:valB] ans7 # 8. Add the column ‘mul’ from DT2 to DT1 by reference where DT2$id matches DT1$id. # What happens to those values where DT2$id has the same value occurring more than once? ans8 <- DT1[DT2, on=.(id), mul := i.mul] ans8 # 9. Replace DT2$mul with NA where DT1$id, DT1$code matches DT2$id, DT2$code. ans9 <- DT2[DT1, on=.(id, code), mul := NA] ans9

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liuzh811/ForestDegradationWestAfrica

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Plot_prop_change.r

clasnames <- c("Water", "RainForest", "Woody Savannas", "Savannas", "Grasslands", "Cropland/Natural Mosaic", "Others") #after run calculating all indices and plot trend lc_rc1 = lc_rc*100 + TCW.trd2.grd lc_rc1.df = data.frame(freq(lc_rc1))[-29,] lc_rc1.df$lc = floor(lc_rc1.df$value/100) lc_rc1.df$trend = lc_rc1.df$value - lc_rc1.df$lc*100 lc_sum = aggregate(count~lc, data = lc_rc1.df, FUN = "sum") lc_rc1.df$prop = 100*lc_rc1.df$count/rep(lc_sum$count, each = 4) lc_rc2 = lc_rc*100 + EVI.trd2.grd lc_rc2.df = data.frame(freq(lc_rc2))[-29,] lc_rc2.df$lc = floor(lc_rc2.df$value/100) lc_rc2.df$trend = lc_rc2.df$value - lc_rc2.df$lc*100 lc_sum = aggregate(count~lc, data = lc_rc2.df, FUN = "sum") lc_rc2.df$prop = 100*lc_rc2.df$count/rep(lc_sum$count, each = 4) lc_rc3 = lc_rc*100 + NDWI.trd2.grd lc_rc3.df = data.frame(freq(lc_rc3))[-29,] lc_rc3.df$lc = floor(lc_rc3.df$value/100) lc_rc3.df$trend = lc_rc3.df$value - lc_rc3.df$lc*100 lc_sum = aggregate(count~lc, data = lc_rc3.df, FUN = "sum") lc_rc3.df$prop = 100*lc_rc3.df$count/rep(lc_sum$count, each = 4) lc_rc4 = lc_rc*100 + TCG.trd2.grd lc_rc4.df = data.frame(freq(lc_rc4))[-29,] lc_rc4.df$lc = floor(lc_rc4.df$value/100) lc_rc4.df$trend = lc_rc4.df$value - lc_rc4.df$lc*100 lc_sum = aggregate(count~lc, data = lc_rc4.df, FUN = "sum") lc_rc4.df$prop = 100*lc_rc4.df$count/rep(lc_sum$count, each = 4) lc_rc5 = lc_rc*100 + TCA.trd2.grd lc_rc5.df = data.frame(freq(lc_rc5))[-29,] lc_rc5.df$lc = floor(lc_rc5.df$value/100) lc_rc5.df$trend = lc_rc5.df$value - lc_rc5.df$lc*100 lc_sum = aggregate(count~lc, data = lc_rc5.df, FUN = "sum") lc_rc5.df$prop = 100*lc_rc5.df$count/rep(lc_sum$count, each = 4) lc_rc.df = rbind(data.frame(lc_rc1.df, VI = rep("TCW", nrow(lc_rc1.df))), data.frame(lc_rc2.df, VI = rep("EVI", nrow(lc_rc2.df))), data.frame(lc_rc3.df, VI = rep("NDWI", nrow(lc_rc3.df))), data.frame(lc_rc4.df, VI = rep("TCG", nrow(lc_rc4.df))), data.frame(lc_rc5.df, VI = rep("TCA", nrow(lc_rc5.df)))) lc_rc.df = lc_rc.df[-which(lc_rc.df$lc == 1 | lc_rc.df$lc == 7| lc_rc.df$lc == 5), ] #Remove class 1: water; and 7: other class lc_rc.df = lc_rc.df[-which(lc_rc.df$trend == 4), ] #remove Not Calculated classes library(reshape2) library(ggplot2) lc_rc1.df.long = melt(lc_rc.df[,c("lc","trend","prop","VI")], id.vars=c("lc", "trend","VI")) lc_rc1.df.long$trend = factor(lc_rc1.df.long$trend) levels(lc_rc1.df.long$trend) <- c("Negative","Positive", "No Trend") lc_rc1.df.long$lc = factor(lc_rc1.df.long$lc) levels(lc_rc1.df.long$lc) <- clasnames[as.numeric(levels(lc_rc1.df.long$lc))] ggplot(data=lc_rc1.df.long, aes(x=trend, y=value, fill=trend)) + facet_grid(VI ~ lc) + geom_bar(stat="identity", position=position_dodge(), colour="black") + xlab("") + ylab("Percentage of Land Cover") + theme(legend.position="none") ggsave(".\\NBAR_results3\\trend_biomes.png", width = 10, height = 7.5, units = "in")

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Gig77/ccri-iamp21

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clusterProfiler.R

# install source("http://bioconductor.org/biocLite.R") biocLite("clusterProfiler") biocLite("org.Hs.eg.db") library(clusterProfiler) # example data library(DOSE) data(geneList) # get genes IDs and their log2 fold changes deseq <- read.delim("/mnt/projects/iamp/results/deseq/iAMP-vs-DS.tsv") # merge Entrez gene ID library(biomaRt) mart <- useMart(biomart="ENSEMBL_MART_ENSEMBL", host="grch37.ensembl.org", path="/biomart/martservice" ,dataset="hsapiens_gene_ensembl") # GRCh37, v75 bm <- getBM(attributes=c("ensembl_gene_id", "entrezgene"), mart=mart) deseq <- unique(merge(deseq, bm, by.x="id", by.y="ensembl_gene_id", all.x = TRUE)) # GO enrichment library(GO.db) genes.diff <-unique(deseq$entrezgene[!is.na(deseq$entrezgene) & abs(deseq$log2FoldChange) >= 2]) # differentially expressed genes genes.bg <- unique(deseq$entrezgene[!is.na(deseq$entrezgene) & deseq$baseMean>0]) # all expresed genes ego <- enrichGO( gene = genes.diff, universe = genes.bg, organism = "human", ont = "BP", pAdjustMethod = "BH", pvalueCutoff = 0.01, qvalueCutoff = 0.05, readable = TRUE ) head(summary(ego)) # GO gene set enrichment valid <- !is.na(deseq$pvalue) & !is.na(deseq$log2FoldChange) & !is.na(deseq$entrezgene) & !duplicated(deseq$entrezgene) genes <- deseq$log2FoldChange[valid] names(genes) <- deseq$entrezgene[valid] genes <- sort(genes, decreasing=TRUE) # NOTE: clusterProfiler produces non-sensical results, because all p-values are the same (0.009999)? # Also, if I inject 200 genes with identical BP GO term GO:0007507 with the two lines below, this gene set does not come out as significant #gogenes <- getBM(attributes=c('entrezgene', 'go_id'), filters = 'go_id', values = 'GO:0007507', mart = mart) #names(genes[1:200]) <- gogenes ego2 <- gseGO( geneList = genes, organism = "human", ont = "BP", nPerm = 100, minGSSize = 10, pvalueCutoff = 0.1, verbose = TRUE) result <- summary(ego2) head(result[order(result$pvalue),], n=20)

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rm.R

rm.coef<-function(mat, indices) { # Computes the matrix correlation between data matrices and their # regression on a subset of their variables. Expected input is a # variance-covariance (or correlation) matrix. # error checking if (sum(!(as.integer(indices) == indices)) > 0) stop("\n The variable indices must be integers") if (!is.matrix(mat)) { stop("Data is missing or is not given in matrix form")} if (dim(mat)[1] != dim(mat)[2]) { mat<-cor(mat) warning("Data must be given as a covariance or correlation matrix. \n It has been assumed that you wanted the correlation matrix of the \n data matrix which was supplied.") } tr<-function(mat){sum(diag(mat))} rm.1d<-function(mat,indices){ sqrt(tr((mat %*% mat)[indices,indices] %*% solve(mat[indices,indices]))/tr(mat)) } dimension<-length(dim(indices)) if (dimension > 1){ rm.2d<-function(mat,subsets){ apply(subsets,1,function(indices){rm.1d(mat,indices)}) } if (dimension > 2) { rm.3d<-function(mat,array3d){ apply(array3d,3,function(subsets){rm.2d(mat,subsets)}) } output<-rm.3d(mat,indices) } if (dimension == 2) {output<-rm.2d(mat,indices)} } if (dimension < 2) {output<-rm.1d(mat,indices)} output }

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mahesh-ml/R-data-analysis

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Module4.R

#Module 4 Problem 1 Divide 743 by 2 and obtain the result such that the output gives us a value without the decimal point. a<- 743%/%2 a #Module 4 Problem 2 Print a 3*4 array of three dimensions, which has the input of vectors sequencing from 13554:13590 and index the second row and third column element of third dimension of the array. a <- matrix(data=c(13554:13590),nrow=3,ncol=4) a a[2,3:3] #Module 4 Problem 3: What is the command to install a package in R and how do you invoke it? install.packages('readxl') library('readxl') #Module 4 Problem 4: Create an if statement that prints the name of the team that won. # Where Team A scored 678 (out of 700), Team B scored 666 (out of 700). a_score <- 678/700 b_score <- 666/700 if (a_score>b_score) { print('team a won') }else { print('team b won') } #Module 4 Problem 5: Check whether the given number is positive and divisible by 5 or not using conditional statements #Given number: 468 given_number <- 468 if(given_number>0) { print("given number is positive") x(given_number) } else { print("given number is negative") x(given_number) } x <-function(a) { if(a %% 5 ==0) { print("given number is divisible by 5") } else { print("given number is not divisible by 5") } } #Module 4 Problem 6: calculate normalization normalized_value <- function(a) { return ((a - min(a)) / (max(a) - min(a))) } a <- c(33,434,242,434354, 545,54,56,56,4534,5342,24,5,65,65,767, 8,78,79,79,64,635,3,4,35,57,678,5,86, 86,457,546,34,345,34,3,4,65,6,57,347) a #Module 4 Problem 7: calculate normalization normalized_value(a) #Module 4 Problem 8: Achieve a Boxplot, Histogram and scatter plot on a given data ‘Q1’. Use any column/columns to get the respective outputs. data <- read_csv(file.choose()) data boxplot(data$workex) hist(data$workex) plot(data$gmat,data$workex,xlab = 'GMAT' , ylab='WORKEX')

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andre-lie/course-6-data-wrangling

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2-1-2-reshaping-data.R

# original wide data library(tidyverse) path <- system.file("extdata",package="dslabs") filename <- file.path(path, "fertility-two-countries-example.csv") wide_data <-read_csv(filename) # tidy data from dslabs library(dslabs) data("gapminder") tidy_data <- gapminder %>% filter(country %in% c("South Korea","Germany")) %>% select(country,year,fertility) # gather wide data to make new tidy data new_tidy_data <- wide_data %>% gather(year,fertility, `1960`:`2015`) head(new_tidy_data) # gather all columns except country new_tidy_data <- wide_data %>% gather(year,fertility, -country) # gather treats column names as characters by default class(tidy_data$year) class(new_tidy_data$year) # convert gathered column names to numeric new_tidy_data <- wide_data %>% gather(year, fertility, - country, convert=TRUE) class(new_tidy_data$year) # ggplot works on new tidy data new_tidy_data %>% ggplot(aes(year,fertility,color=country)) + geom_point() # spread tidy data to generate wide data new_wide_data <- new_tidy_data %>% spread(year, fertility) select(new_wide_data,country, `1960`:`1967`)

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plotMultiHist.Rd

\name{plotMultiHist} \alias{plotMultiHist} \title{ Plot multiple histograms in a single plot } \description{ This function plots density or cumulative distribution function of multiple histograms in a single plot, using lines. } \usage{ plotMultiHist( data, nBreaks = 100, col = 1:length(data), scaleBy = c("area", "max", "none"), cumulative = FALSE, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{data}{ A list in which each component corresponds to a separate histogram and is a vector of values to be shown in each histogram. } \item{nBreaks}{ Number of breaks in the combined plot. } \item{col}{ Color of the lines. Should be a vector of the same length as \code{data}. } \item{scaleBy}{ Method to make the different histograms comparable. The counts are scaled such that either the total area or the maximum are the same for all histograms, or the histograms are shown without scaling. } \item{cumulative}{ Logical: should the cumulative distribution be shown instead of the density? } \item{\dots}{ Other graphical arguments. } } \value{ Invisibly, \item{x}{A list with one component per histogram (component of \code{data}), giving the bin midpoints} \item{y}{A list with one component per histogram (component of \code{data}), giving the scaled bin counts} } \author{ Peter Langfelder } \note{ This function is still experimental and behavior may change in the future. } \seealso{ \code{\link{hist}} } \examples{ data = list(rnorm(1000), rnorm(10000) + 2); plotMultiHist(data, xlab = "value", ylab = "scaled density") } \keyword{misc}% __ONLY ONE__ keyword per line

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predsu_pred.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/predSurv_tools.R \name{predsu_pred} \alias{predsu_pred} \title{Predict survival time} \usage{ predsu_pred(prob_ints, condition_t = 0, option = c("upper", "lower")) } \description{ Predict survival time conditioning on censoring time based on interval probabilities }

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iProFun.eFDR.R

#' Summarize the association probabilities for an outcome. #' #' Summarize the association probabilities for an outcome across different association patterns. #' The summation includes variables with missingness in some data types. #' @export iProFun.sum.prob.1y #' @param prob The posterior probabilieis of association patterns across multiple data types, #' including association patterns with missing data in some of the data types. The output of iProFun.prob can be used here. #' @param which.y The index for one outcome data type that the summation is for. #' @param NoProbXIndex The index for the predictor data type(s) that are not considered #' for calculating posterior probabilities of association patterns. #' @return #' \item{prob.all:}{The summarized posterior probabilities for all outcome variables in the data type of interest #' (regardless of missingness in other data types).} #' #' # function to get prob for 1y, with missing iProFun.sum.prob.1y <- function (prob, which.y=1, NoProbXIndex=NULL) { xlength=length(prob) x.prob=setdiff(1:xlength, NoProbXIndex) # calculate prob on 1y prob.all <- vector("list", length(x.prob)) for (p in 1:length(x.prob)) { k= x.prob[p] Q_index=prob[[k]]$Config[,which.y] prob.all[[p]]=apply(prob[[k]]$PostProb[,which(Q_index==1)], 1, sum) config.miss=prob[[k]]$Config.miss if (is.null(config.miss)==F) { for (j in 1:length(config.miss)) { #print(j) Q_index1= config.miss[[j]][, colnames(config.miss[[j]])==as.character(which.y)] prob.temp=prob[[k]]$PostProb.miss[[j]] if (length(which(Q_index1==1))==1 ) { prob.miss1=prob.temp[,which(Q_index1==1)] prob.all[[p]][row.match(data.frame(prob[[k]]$xName.miss[[j]]), data.frame(prob[[k]]$xName_J))]=prob.miss1 } if (length(which(Q_index1==1))>1 & nrow(prob.temp)>1) { prob.miss1=apply(as.matrix(prob.temp[,which(Q_index1==1)]), 1, sum) prob.all[[p]][row.match(data.frame(prob[[k]]$xName.miss[[j]]), data.frame(prob[[k]]$xName_J))]=prob.miss1 } } }# end (j), which fills the list of missing values } return(prob.all) } #' Fast estimation of FDR without permutation procedure for one data type #' #' @export estFDR #' @param ProbPattern1y1x Posterior probabilities for associations between one predictor data type and one outcome data type. #' @param grids grids specify the searching grids to find significant associations #' @return #' \item{estFDR:}{Estimated FDR values by different posterior probability cutoffs considered in grids} estFDR<-function(ProbPattern1y1x, grids = seq(0.01, 0.99, by=0.01)) { # estFDR= sum_i (1-Prob_i) 1(Prob_i>lambda) over No. Prob_i>lambda estFDR=NULL for (i in 1:length(grids) ) { denominator= sum(ProbPattern1y1x>grids[i], na.rm=T) numerator = sum ( (1-ProbPattern1y1x)*(ProbPattern1y1x>grids[i]), na.rm=T) estFDR=c(estFDR, numerator/denominator) } return(estFDR) } #' iProFun eFDR assessment for one outcome data type. #' #'iProFun empirical false discovery rate (eFDR) assessment for one outcome data type. #' @export iProFun.eFDR.1y #' @param yList yList is a list of data matrix for outcomes. #' @param xList xList is a list of data matrix for predictors. #' @param covariates covariates is a list of data matrix for covariate. #' @param pi1 pi1 is pre-specified prior of proportion of non-null statistics. It cane be a number in (0, 1) or a vector #' of numbers with length of ylist. #' @param var.ID var.ID gives the variable name (e.g. gene/protein name) to match different data types. #' If IDs are not specified, the first columns will be considered as ID variable. #' @param var.ID.additional var.ID.additional allows to output additional variable names from the input. #' Often helpful if multiple rows (e.g. probes) are considered per gene to allow clear index of the rows. #' @param permutate_number Number of permutation, default 10 #' @param reg.all The regression summary (unformatted) such as from iProFun.reg. #' @param which.y The index for one outcome data type that the eFDR assessment is for. #' @param NoProbXIndex NoProbXIndex allows users to provide the index for the predictor data type(s) that are not considered #' for calculating posterior probabilities of association patterns. #' @param fdr norminal false discover rate, default at 0.1 #' @param PostCut PostCut specifies minimal posterior probabilty cutoff to be considered as #' significant, default as 0.75 #' @param filter filter is a vector with the same length of xList, taking values of 1, -1, 0 or NULL. #' "NULL" is default imposes no filtering. 1" indicates that an association is considered for #' significance only if its significant associations are positive across all outcome platforms. "-1" indicates #' that an association is considered #' for significance only if its significant associations are negative across all outcome platforms. "0" indicates #' that an association is considered for significance only if its significant association across all outcome #' platforms preserve consistent directions (either positive or negative). #' @param grids Grids specify the searching grids for significant associations #' @param seed seed allows users to externally assign seed to replicate results. #' @return #' \item{eFDR.grid:}{eFDR by the grid of posterior probability cutoffs.} #' \item{fdr_cutPob:}{the cutoff values for pre-specified eFDR rate and the posterior probabilities for a pair of #' data types based on permutation.} #' \item{No.Identified.filter:}{the number of identified variables for each pair of data types.} #' \item{No.Identified.no.filter:}{the number of identified variables for each pair of data types.} #' \item{Gene_fdr:}{A table summarizing the posterior probabilities (PostProb), the eFDR (eFDR.no.filter), #' the significance under different criteria (nominal FDR, PostProb cutoffs and filter) for each variable (e.g. gene) #' under consideration.} #' iProFun.eFDR.1y= function(reg.all, which.y, yList, xList, covariates, pi1, NoProbXIndex=NULL,filter=NULL, permutate_number=10, grids = seq(0.01, 0.99, by=0.01), fdr = 0.1, PostCut=0.75, var.ID=c("Gene_ID"), var.ID.additional=NULL, seed=NULL) { # NoProbXIndex=NULL;filter=c(1,0); # permutate_number=1; # grids = seq(0.01, 0.99, by=0.01); # fdr = 0.1; PostCut=0.75; # var.ID=c("Gene_ID"); # var.ID.additional=NULL; seed=NULL xlength=length(xList) x.prob=setdiff(1:xlength, NoProbXIndex) xlength2=length(x.prob) # Obtain posterior probabiliy from original data - including missing sum=multi.omic.reg.summary(reg.out.list=reg.all, var.ID=var.ID) prob=iProFun.prob(Reg.Sum=sum, NoProbXIndex=NoProbXIndex, pi1=pi1) prob1y=iProFun.sum.prob.1y(prob=prob, which.y=which.y, NoProbXIndex=NoProbXIndex) count_orig_grid=lapply(1: xlength2, function(p) sapply(1:length(grids), function(f) sum(prob1y[[p]]>grids[f], na.rm=T))) # permutation perm.reg.all=reg.all permProb1y=vector("list", xlength2) for (perm in 1: permutate_number) { print(c("perm", perm)) if (is.null(seed)){seed.p=NULL} if (is.null(seed)==F) {seed.p =as.integer((seed+perm)*978)} ft1=iProFun.reg.1y(yList.1y=yList[[which.y]], xList=xList, covariates.1y=covariates[[which.y]], permutation=T, var.ID=var.ID, var.ID.additional=var.ID.additional, seed=seed.p) perm.reg.all[[which.y]]=ft1 sum1=multi.omic.reg.summary(reg.out.list=perm.reg.all, var.ID=var.ID) prob1=iProFun.prob(Reg.Sum=sum1, NoProbXIndex=NoProbXIndex, pi1=pi1) perm1y=iProFun.sum.prob.1y(prob=prob1, which.y=which.y, NoProbXIndex=NoProbXIndex) for (p in 1:xlength2) { permProb1y[[p]]=cbind(permProb1y[[p]], perm1y[[p]]) } if( perm%%10 ==0) {print(paste0("Completed Permutation ", perm-9, " to ", perm))} } # calculate the number of genes significant at each threshold level count_perm_grid=lapply(1:xlength2, function(p) sapply(1:length(grids), function(f) sum(permProb1y[[p]]>grids[f], na.rm=T)/permutate_number )) # calculate the eFDR on a grid of prob eFDR.grid=lapply(1:xlength2, function(p) count_perm_grid[[p]]/count_orig_grid[[p]]) AboveCut=which(grids>=PostCut) grid_PostCut= AboveCut[which.min(abs(AboveCut- PostCut))] fdr_cut=lapply(eFDR.grid, function(f) max(min(which(f<fdr), na.rm=T), grid_PostCut)) fdr_cutPob=lapply(1:xlength2, function(f) grids[fdr_cut[[f]]] ) No.Identified.no.filter=lapply(1:xlength2, function(f) count_orig_grid[[f]] [fdr_cut[[f]] ]) # calculate eFDR based on cutoff eFDR.no.filter=vector("list", xlength2); for (f in 1:xlength2) { t=length(prob1y[[f]]) t2=rep(NA, t) t2[which(is.na(prob1y[[f]])==F)]=sapply(which(is.na(prob1y[[f]])==F), function(g) tail(which(prob1y[[f]][g]>grids), n=1)) t2[sapply(t2, function(x) length(x)==0)] <- NA t2=unlist(t2) eFDR.no.filter[[f]]=eFDR.grid[[f]][t2] eFDR.no.filter[[f]][which(is.na(prob1y[[f]])==F & is.na(t2))] =1 } # add filter x_filter_gene=vector("list", xlength2) for (j in 1: xlength2){ k=x.prob[j] betas_J=prob[[k]]$betas_J if (is.null(filter[j]) ){ # no requirement x_filter_gene[[j]]=seq(1, nrow(betas_J)) } else { temp1=which(sapply(1:nrow(betas_J), function(f) any(betas_J[f,][(eFDR.no.filter[[j]][f]<fdr)]<=0, na.rm=T))==F) temp2=which(sapply(1:nrow(betas_J), function(f) any(betas_J[f,][(eFDR.no.filter[[j]][f]<fdr)]>=0, na.rm=T))==F) if (filter[j] == 1) {x_filter_gene[[j]]= temp1} # all positive beta among signifiant results if (filter[j] == -1) { x_filter_gene[[j]]=temp2} # all negative beta among signifiant results if (filter[j] == 0) {x_filter_gene[[j]] = sort( union(temp1, temp2))} # all positive or all negative } } No.Identified.filter=lapply(1: xlength2, function(p) sum(prob1y[[p]][x_filter_gene[[p]]]>fdr_cutPob[[p]], na.rm=T)) Gene_fdr=vector("list", xlength2); for (j in 1:xlength2) { k=x.prob[j] # no filter temp1=temp2=rep(0, nrow(prob[[k]]$xName_J)) temp1[is.na(prob1y[[j]])]=temp2[is.na(prob1y[[j]])]=NA temp1[which(prob1y[[j]]>fdr_cutPob[[j]])]=1 # filter sig=intersect(x_filter_gene[[j]], which(prob1y[[j]]>fdr_cutPob[[j]])) temp2[sig]=1 Gene_fdr[[j]]=data.frame(xName=prob[[k]]$xName_J, PostProb=prob1y[[j]], eFDR.no.filter=eFDR.no.filter[[j]], sig.no.filter=temp1, sig.filter=temp2) } eFDR_result=list(eFDR.grid=eFDR.grid, fdr_cutPob=fdr_cutPob, No.Identified.filter=No.Identified.filter, No.Identified.no.filter=No.Identified.no.filter, Gene_fdr=Gene_fdr) return(eFDR_result) } #' iProFun eFDR assessment based on permutation for multiple outcome data type. #' #' iProFun empirical false discovery rate (eFDR) assessment based on permutation for multiple outcome data type. #' @export iProFun.eFDR #' @param yList yList is a list of data matrix for outcomes. #' @param xList xList is a list of data matrix for predictors. #' @param covariates covariates is a list of data matrix for covariate. #' @param pi1 pi1 is pre-specified prior of proportion of non-null statistics. It cane be a number in (0, 1) or a vector #' of numbers with length of ylist. #' @param var.ID var.ID gives the variable name (e.g. gene/protein name) to match different data types. #' If IDs are not specified, the first columns will be considered as ID variable. #' @param var.ID.additional var.ID.additional allows to output additional variable names from the input. #' Often helpful if multiple rows (e.g. probes) are considered per gene to allow clear index of the rows. #' @param permutate_number Number of permutation, default 10 #' @param reg.all The regression summary (unformatted) such as from iProFun.reg. #' @param NoProbXIndex NoProbXIndex allows users to provide the index for the predictor data type(s) that are not considered #' for calculating posterior probabilities of association patterns. #' @param fdr norminal false discover rate, default at 0.1 #' @param PostCut PostCut specifies minimal posterior probabilty cutoff to be considered as #' significant, default as 0.75 #' @param filter filter is a vector with the same length of xList, taking values of 1, -1, 0 or NULL. #' "NULL" is default imposes no filtering. 1" indicates that an association is considered for #' significance only if its significant associations are positive across all outcome platforms. "-1" indicates #' that an association is considered #' for significance only if its significant associations are negative across all outcome platforms. "0" indicates #' that an association is considered for significance only if its significant association across all outcome #' platforms preserve consistent directions (either positive or negative). #' @param grids Grids specify the searching grids for significant associations #' @param seed seed allows users to externally assign seed to replicate results. #' @return #' \item{eFDR.grid:}{eFDR by the grid of posterior probability cutoffs.} #' \item{fdr_cutPob:}{the cutoff values for pre-specified eFDR rate and the posterior probabilities for a pair of #' data types based on permutation.} #' \item{No.Identified.filter:}{the number of identified variables for each pair of data types.} #' \item{No.Identified.no.filter:}{the number of identified variables for each pair of data types.} #' \item{Gene_fdr:}{A table summarizing the posterior probabilities (PostProb), the eFDR (eFDR.no.filter), #' the significance under different criteria (nominal FDR, PostProb cutoffs and filter) for each variable (e.g. gene) #' under consideration.} iProFun.eFDR= function(reg.all, yList, xList, covariates, pi1, NoProbXIndex=NULL,filter=NULL, permutate_number=10, grids = seq(0.01, 0.99, by=0.01), fdr = 0.1, PostCut=0.75, var.ID=c("Gene_ID"), var.ID.additional=c("phospho_ID", "Hybridization", "chr"), seed=NULL) { ylength=length(yList) xlength=length(xList) x.prob=setdiff(1:xlength, NoProbXIndex) xlength2=length(x.prob) eFDR.grid=fdr_cutPob= No.Identified.filter=No.Identified.no.filter= PostProb=eFDR.no.filter=sig.no.filter=sig.filter=xNames=vector("list", xlength2); for (q in 1:ylength) { print(c("Outcome", q)) if (is.null(seed)){seed.qq=NULL} if (is.null(seed)==F) {seed.qq =as.integer((seed+q)*4109)} eFDR1=iProFun.eFDR.1y(reg.all=reg.all, which.y=q, yList=yList, xList=xList, covariates=covariates, pi1=pi1, NoProbXIndex=NoProbXIndex,filter=filter, permutate_number=permutate_number, grids = grids,fdr =fdr, PostCut=PostCut, var.ID=var.ID, var.ID.additional=var.ID.additional, seed=seed.qq) for (p in 1:xlength2 ) { eFDR.grid[[p]]= cbind(eFDR.grid[[p]], eFDR1$eFDR.grid[[p]]); fdr_cutPob[[p]]= cbind(fdr_cutPob[[p]], eFDR1$fdr_cutPob[[p]]); No.Identified.filter[[p]]= cbind(No.Identified.filter[[p]], eFDR1$No.Identified.filter[[p]]); No.Identified.no.filter[[p]]= cbind(No.Identified.no.filter[[p]], eFDR1$No.Identified.no.filter[[p]]); PostProb[[p]]= cbind(PostProb[[p]], eFDR1$Gene_fdr[[p]]$PostProb); eFDR.no.filter[[p]]= cbind(eFDR.no.filter[[p]], eFDR1$Gene_fdr[[p]]$eFDR.no.filter); sig.no.filter[[p]]= cbind(sig.no.filter[[p]], eFDR1$Gene_fdr[[p]]$sig.no.filter); sig.filter[[p]]= cbind(sig.filter[[p]], eFDR1$Gene_fdr[[p]]$sig.filter); rownames(eFDR.grid[[p]])=grids xNames[[p]]=eFDR1$Gene_fdr[[p]][,setdiff(colnames(eFDR1$Gene_fdr[[p]]), c("PostProb", "eFDR.no.filter", "sig.no.filter", "sig.filter"))] } } eFDR_result=list(eFDR.grid=eFDR.grid, fdr_cutPob=fdr_cutPob, No.Identified.no.filter=No.Identified.no.filter, No.Identified.filter=No.Identified.filter, PostProb=PostProb, eFDR.no.filter=eFDR.no.filter, sig.no.filter=sig.no.filter, sig.filter=sig.filter, xNames=xNames) return(eFDR_result) }

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/RInvest/stocks.R

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refs/heads/master

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stocks.R

source("utils.R") load.stocks.data <- function(file) { data <- read.table(file, header = T, sep = ",", as.is = T, fileEncoding = "utf-8", colClasses = c("factor", "character", "Date", "numeric", "numeric", "numeric"), col.names = c("Code", "Name", "Date", "PE", "DPE", "PB")) data <- sqldf("select Date, Code, Name, PE, DPE, PB from data order by Date") percentiles <- apply(data[, c("PE", "DPE", "PB")], 2, get.percentile, percentile.length = 240, only.last = T) data <- transform(data, PEM = median(get.data.year(PE, 1), na.rm = T), DEPM = median(get.data.year(DPE, 1), na.rm = T), PBM = median(get.data.year(PB, 1), na.rm = T), ROE = round(PB / PE, 2), PEP = percentiles[, 1], DPEP = percentiles[, 2], PBP = percentiles[, 3]) return(data) } calculate_spread <- function(x, y, beta) { return(y - beta * x) } calculate_beta_and_level <- function(x, y) { dx <- diff(x) dy <- diff(y) r <- prcomp( ~ dx + dy) beta <- r$rotation[2, 1] / r$rotation[1, 1] spread <- calculate_spread(x, y, beta) names(spread) <- "spread" level <- mean(spread, na.rm = T) outL <- list() outL$spread <- spread outL$beta <- beta outL$level <- level return(outL) } calculate_buy_sell_signals <- function(spread, beta, level, lower_threshold, upper_threshold) { buy_signals <- ifelse(spread <= level - lower_threshold, 1, 0) sell_signals <- ifelse(spread >= level + upper_threshold, 1, 0) output <- cbind(spread, buy_signals, sell_signals) colnames(output) <- c("spread", "buy_signals", "sell_signals") return(output) } folder <- paste(getwd(), "/zz/", sep = "") files <- list.files(folder, pattern = "\\.csv$") i <- 1 pb.df <- NULL for (file in files) { cat("processing ", file, "\r\n", sep = "") name.postfix <- substr(file, 1, 6) stocks.whole.name <- paste("stocks.whole", name.postfix, sep = "") stocks.whole <- load.stocks.data(paste(folder, file, sep = "")) assign(stocks.whole.name, stocks.whole, pos = .GlobalEnv) if (i == 1) { pb.df <- sqldf(paste("SELECT Date FROM [stocks.whole]", sep = "")) } pb.df <- sqldf(paste("SELECT df1.*, df2.PB AS s", name.postfix, " FROM [pb.df] df1 JOIN [stocks.whole] df2 USING(Date)", sep = "")) i <- i + 1 } rownames(pb.df) <- pb.df[[1]] pb.df[[1]] <- NULL pb.xts = as.xts(pb.df) x <- pb.xts[, 7] y <- pb.xts[, 8] dF <- cbind(x, y) names(dF) <- c("x", "y") run_regression <- function(dF) { return (coef(lm(y ~x -1 ,data = as.data.frame(dF)))) } rolling_beta <- function(z, width) { rollapply(z, width = width, FUN = run_regression, by.column = FALSE, align = "right") } betas <- rolling_beta(diff(dF), 10) data <- merge(betas, dF) data$spread <- data$y - lag(betas, 1) * data$x returns <- diff(dF) / dF return_beta <- rolling_beta(returns, 10) data$spreadR <-diff(data$y) / data$y - return_beta * diff(data$x) / data$x threshold <- sd(data$spread, na.rm = T)

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/backup/CCode/SHM/SHM_CM.R

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refs/heads/master

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r

SHM_CM.R

filename="M_SHM_A_FULL" library(rjags) library(qfa) library(qfaBayes) Control<-c("Adam_cdc13-1_SDLV2_REP1.txt","Adam_cdc13-1_SDLV2_REP2.txt","Adam_cdc13-1_SDLV2_REP3.txt","Adam_cdc13-1_SDLV2_REP4.txt") DescripControl<-"ExptDescriptionCDC13.txt" a<-rod.read(files=Control,inoctimes=DescripControl) qfa.variables(a) Screen<-unique(a$Screen.Name) Treat<-27 MPlate<-(unique(a$MasterPlate.Number)) a<-funcREMOVE(a,Screen,Treat,MPlate) Row<-paste(a$Row) Col<-paste(a$Col) for (i in 1:nrow(a)){ if (nchar(Row[i])<2){Row[i]=paste(0,Row[i],sep="")} if (nchar(Col[i])<2){Col[i]=paste(0,Col[i],sep="")} } a$ID<-paste(a$Barcode,a$MasterPlate.Number,Row,Col,sep="") ORFuni=unique(a$ORF)######## funcIDlist<-function(x){ a$ID[a$ORF==x] } funcStrip<-function(x,i){x[1:i]} IDstrip=sapply(ORFuni,funcIDlist) IDstrip=lapply(IDstrip,unique) IDstrip=lapply(IDstrip,i=6,funcStrip) IDstrip=unlist(IDstrip) IDstrip=na.omit(IDstrip) a<-a[a$ID%in%IDstrip,] ######### a<-a[order(a$ORF,a$ID,a$Expt.Time), ] Scaling=TRUE IDuni<-unique(a$ID) ORFuni=unique(a$ORF)######## gene<-unlist(lapply(ORFuni,funcGENE,data=a)) N<-length(ORFuni);M<-length(IDuni) NoORF_a<-unlist(lapply(ORFuni,funcNoORF,data=a))#no of repeats each orf NoTime_a<-c(0,unlist(lapply(IDuni,funcNoTime,data=a)))# 0+ no of NoSum_a<-c(0,unlist(lapply(1:N,funcNoSum,NoORF_vec=NoORF_a))) dimr<-max(NoORF_a);dimc<-max(NoTime_a) y<-funcXY(a$Growth,M,N,NoTime_a,NoSum_a,dimr,dimc) x<-funcXY(a$Expt.Time,M,N,NoTime_a,NoSum_a,dimr,dimc) QFA.I<-list("NoORF"=c(NoORF_a),"NoTime"=c(NoTime_a)[-1],"NoSum"=c(NoSum_a),"N"= N,"M"=M,"gene"=gene) if (Scaling==TRUE){y<-funcSCALING(a,y)} QFA.D<-list(y=y,x=x,ORFuni=ORFuni) x[is.na(x)]=-999 y[is.na(y)]=-999 xx<-aperm(x,c(2,1,3)) yy<-aperm(y,c(2,1,3)) write.table(file="xdata.txt",c(xx)) write.table(file="ydata.txt",c(yy)) write.table(file="NoORFdata.txt",c(NoORF_a)) write.table(file="NoTIMEdata.txt",c(NoTime_a)[-1]) write.table(file="LMNmaxdata.txt",c(N,max(NoORF_a),max(NoTime_a),length(y),length(NoTime_a[-1]))) ################################################# ################################################# QFA.P<-list( sigma_K=3, phi_K=0.5, eta_K_o=0.5, psi_K_o=1, sigma_r=-1, phi_r=0.1, eta_r_o=1, psi_r_o=1, eta_nu=-1, psi_nu=1, K_mu=log(0.2192928), eta_K_p=1, r_mu=log(2.5), eta_r_p=1, nu_mu=log(31), eta_nu_p=1, P_mu=log(0.0002), eta_P=1/0.01 ) write(" model { for (i in 1:N){ for (j in 1:NoORF[i]){ for (l in 1:NoTime[(NoSum[i]+j)]){ y[j,l,i] ~ dnorm(y.hat[j,l,i], exp(nu_l[i])) y.hat[j,l,i] <- (K_lm[(NoSum[i]+j)]*P*exp(r_lm[(NoSum[i]+j)]*x[j,l,i]))/(K_lm[(NoSum[i]+j)]+P*(exp(r_lm[(NoSum[i]+j)]*x[j,l,i])-1)) } K_lm[(NoSum[i]+j)]<- exp(K_lm_L[(NoSum[i]+j)]) K_lm_L[(NoSum[i]+j)] ~ dnorm(K_o_l[i],exp(tau_K_l[i])) r_lm[(NoSum[i]+j)]<- exp(min(3.5,r_lm_L[(NoSum[i]+j)])) r_lm_L[(NoSum[i]+j)] ~ dnorm(r_o_l[i],exp(tau_r_l[i])) } K_o_l[i] ~ dnorm( K_p, exp(sigma_K_o) ) r_o_l[i] ~ dnorm( r_p, exp(sigma_r_o) ) nu_l[i] ~ dnorm(nu_p, exp(sigma_nu) ) tau_K_l[i]~dnorm(sigma_K,phi_K) tau_r_l[i]~dnorm(sigma_r,phi_r) } K_p ~ dnorm(K_mu,eta_K_p) r_p ~ dnorm(r_mu,eta_r_p) nu_p ~ dnorm(nu_mu,eta_nu_p) P<-exp(P_L) P_L ~ dnorm(P_mu,eta_P) sigma_nu~dnorm(eta_nu,psi_nu) sigma_K_o ~ dnorm(eta_K_o,psi_K_o) sigma_r_o ~ dnorm(eta_r_o,psi_r_o) } ","model1.bug") jags <- jags.model('model1.bug', data = list('x' = QFA.D$x, 'y' = QFA.D$y, 'N' = QFA.I$N, 'NoTime' = QFA.I$NoTime, 'NoORF' = QFA.I$NoORF, 'NoSum' = QFA.I$NoSum, 'sigma_K'=QFA.P$sigma_K, 'phi_K'=QFA.P$phi_K, 'sigma_r'=QFA.P$sigma_r, 'phi_r'=QFA.P$phi_r, 'eta_K_o'=QFA.P$eta_K_o, 'psi_K_o'=QFA.P$psi_K_o, 'eta_r_o'=QFA.P$eta_r_o, 'psi_r_o'=QFA.P$psi_r_o, 'eta_nu'=QFA.P$eta_nu, 'psi_nu'=QFA.P$psi_nu, 'K_mu'=QFA.P$K_mu, 'eta_K_p'=QFA.P$eta_K_p, 'r_mu'=QFA.P$r_mu, 'eta_r_p'=QFA.P$eta_r_p, 'nu_mu'=QFA.P$nu_mu, 'eta_nu_p'=QFA.P$eta_nu_p, 'P_mu'=QFA.P$P_mu, 'eta_P'=QFA.P$eta_P ), n.chains = 1, n.adapt = 100) samp<-coda.samples(jags, c('K_lm_L', 'tau_K_l', 'K_o_l', 'sigma_K_o', 'K_p', 'P_L', 'r_lm_L', 'tau_r_l', 'r_o_l', 'sigma_r_o', 'r_p', 'nu_l', 'sigma_nu', 'nu_p'), 1000,thin=1) save(samp,file=paste(filename,"_F0.R",sep="")) update(jags,10000) date() samp<-coda.samples(jags, c('K_lm_L', 'tau_K_l', 'K_o_l', 'sigma_K_o', 'K_p', 'P_L', 'r_lm_L', 'tau_r_l', 'r_o_l', 'sigma_r_o', 'r_p', 'nu_l', 'sigma_nu', 'nu_p'), 20000,thin=20) save(samp,file=paste(filename,"_F1.R",sep="")) samp<-coda.samples(jags, c('K_lm_L', 'tau_K_l', 'K_o_l', 'sigma_K_o', 'K_p', 'P_L', 'r_lm_L', 'tau_r_l', 'r_o_l', 'sigma_r_o', 'r_p', 'nu_l', 'sigma_nu', 'nu_p'), 100000,thin=10) save(samp,file=paste(filename,"_F2.R",sep="")) stop() L=N=4294 M=17308 pdf(file="SHMtestplot_C_4294.pdf") #K_lm[%i] for (i in 1:M){ j=i plot(density(aa[,j]),main=paste(colnames(samp)[i],t.test((aa[,j]),samp[,i])$p.value));lines(density(samp[,i]),col=2); par(mfrow=c(2,1)) plot(c(aa[,j]),main=paste(mean(aa[,j])-mean(samp[,i])),type="l");plot(samp[,i],col=2,type="l"); par(mfrow=c(1,1)) } dev.off() #tau_K_l[%i] j=M+1 for (i in (2*M+3*N+8):(2*M+4*N+7)){ plot(density(aa[,j]),main=paste(colnames(samp)[i],t.test((aa[,j]),samp[,i])$p.value));lines(density(samp[,i]),col=2); par(mfrow=c(2,1)) plot(c(aa[,j]),main=paste(mean(aa[,j])-mean(samp[,i])),type="l");plot(samp[,i],col=2,type="l"); par(mfrow=c(1,1)) } #"K_o_l[%i] j=M+N+1 for (i in (M+1):(M+N)){ plot(density(aa[,j]),main=paste(colnames(samp)[i],t.test((aa[,j]),samp[,i])$p.value));lines(density(samp[,i]),col=2); par(mfrow=c(2,1)) plot(c(aa[,j]),main=paste(mean(aa[,j])-mean(samp[,i])),type="l");plot(samp[,i],col=2,type="l"); par(mfrow=c(1,1)) } #sigma_K_o "); i=2*M+3*N+5 j=M+2*N+1 plot(density(aa[,j]),main=paste(colnames(samp)[i],t.test((aa[,j]),samp[,i])$p.value));lines(density(samp[,i]),col=2); par(mfrow=c(2,1)) plot(c(aa[,j]),main=paste(mean(aa[,j])-mean(samp[,i])),type="l");plot(samp[,i],col=2,type="l"); par(mfrow=c(1,1)) #K_p "); i=M+1+N j=M+2*N+2 plot(density(aa[,j]),main=paste(colnames(samp)[i],t.test((aa[,j]),samp[,i])$p.value));lines(density(samp[,i]),col=2); par(mfrow=c(2,1)) plot(c(aa[,j]),main=paste(mean(aa[,j])-mean(samp[,i])),type="l");plot(samp[,i],col=2,type="l"); par(mfrow=c(1,1)) #"P_l "); i=(M+N+2) j=M+2*N+3 plot(density(aa[,j]),main=paste(colnames(samp)[i],t.test((aa[,j]),samp[,i])$p.value));lines(density(samp[,i]),col=2); par(mfrow=c(2,1)) plot(c(aa[,j]),main=paste(mean(aa[,j])-mean(samp[,i])),type="l");plot(samp[,i],col=2,type="l"); par(mfrow=c(1,1)) #r_lm[%i] j=M+2*N+4 for (i in (M+2*N+4):(2*M+2*N+3)){ plot(density(aa[,j]),main=paste(colnames(samp)[i],t.test((aa[,j]),samp[,i])$p.value));lines(density(samp[,i]),col=2); par(mfrow=c(2,1)) plot(c(aa[,j]),main=paste(mean(aa[,j])-mean(samp[,i])),type="l");plot(samp[,i],col=2,type="l"); par(mfrow=c(1,1)) j=j+1 } #tau_r_l[%i] ",l); j=2*M+2*N+4 for (i in (2*M+4*N+8):(2*M+5*N+7)){ plot(density(aa[,j]),main=paste(colnames(samp)[i],t.test((aa[,j]),samp[,i])$p.value));lines(density(samp[,i]),col=2); par(mfrow=c(2,1)) plot(c(aa[,j]),main=paste(mean(aa[,j])-mean(samp[,i])),type="l");plot(samp[,i],col=2,type="l"); par(mfrow=c(1,1)) j=j+1 } #r_o_l[%i] ",l); j=2*M+3*N+4 for (i in (2*M+2*N+4):(2*M+3*N+3)){ plot(density(aa[,j]),main=paste(colnames(samp)[i],t.test((aa[,j]),samp[,i])$p.value));lines(density(samp[,i]),col=2); par(mfrow=c(2,1)) plot(c(aa[,j]),main=paste(mean(aa[,j])-mean(samp[,i])),type="l");plot(samp[,i],col=2,type="l"); par(mfrow=c(1,1))j=j+1 } #sigma_r_o "); i=2*M+3*N+7 j=2*M+4*N+4 plot(density(aa[,j]),main=paste(colnames(samp)[i],t.test((aa[,j]),samp[,i])$p.value));lines(density(samp[,i]),col=2); par(mfrow=c(2,1)) plot(c(aa[,j]),main=paste(mean(aa[,j])-mean(samp[,i])),type="l");plot(samp[,i],col=2,type="l"); par(mfrow=c(1,1)) #r_p "); i=2*M+3*N+4 j=2*M+4*N+5 plot(density(aa[,j]),main=paste(colnames(samp)[i],t.test((aa[,j]),samp[,i])$p.value));lines(density(samp[,i]),col=2); par(mfrow=c(2,1)) plot(c(aa[,j]),main=paste(mean(aa[,j])-mean(samp[,i])),type="l");plot(samp[,i],col=2,type="l"); par(mfrow=c(1,1)) #"nu_l[%i] ",l); j=2*M+4*N+6 for (i in (M+N+3):(M+2*N+2)){ plot(density(aa[,j]),main=paste(colnames(samp)[i],t.test((aa[,j]),samp[,i])$p.value));lines(density(samp[,i]),col=2); par(mfrow=c(2,1)) plot(c(aa[,j]),main=paste(mean(aa[,j])-mean(samp[,i])),type="l");plot(samp[,i],col=2,type="l"); par(mfrow=c(1,1)) j=j+1 } #sigma_nu "); i=2*M+3*N+6 j=2*M+5*N+6 plot(density(aa[,j]),main=paste(colnames(samp)[i],t.test((aa[,j]),samp[,i])$p.value));lines(density(samp[,i]),col=2); par(mfrow=c(2,1)) plot(c(aa[,j]),main=paste(mean(aa[,j])-mean(samp[,i])),type="l");plot(samp[,i],col=2,type="l"); par(mfrow=c(1,1)) #nu_p "); i=M+2*N+3 j=2*M+5*N+7 plot(density(aa[,j]),main=paste(colnames(samp)[i],t.test((aa[,j]),samp[,i])$p.value));lines(density(samp[,i]),col=2); par(mfrow=c(2,1)) plot(c(aa[,j]),main=paste(mean(aa[,j])-mean(samp[,i])),type="l");plot(samp[,i],col=2,type="l"); par(mfrow=c(1,1)) dev.off() i=M+N+3;j=i+N+1; colnames(samp)[i];names(aa)[j]

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/man/residuals.ccrm.Rd

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cran/iRegression

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residuals.ccrm.Rd

\name{residuals.ccrm} \alias{residuals.ccrm} \title{Extract Constrained Centre and Range Method Residuals} \description{Returns the residuals from an object class \code{ccrm}.} \usage{ \method{residuals}{ccrm}(object, ...) } \arguments{ \item{object}{ an object class \code{ccrm}. } \item{\dots}{ other arguments. } } \value{ Residuals extracted from the object class \code{ccrm}. } \seealso{ \code{\link{ccrm}} } \keyword{regression}

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manuelmunoz/datasciencecoursera

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complete.R

complete <- function(directory, id = 1:332) { # Initializing counters h <- 0 suma <- 0 contador <- 0 final <- data.frame() # Retrieving list of data files ficheros <- dir("specdata") # Going through data files for(h in id) { # Dumping a single file in a data frame datos <- read.csv(paste("/home/manuel/datasciencecoursera/", directory, "/", ficheros[h], sep = "")) resultado <- subset(datos, (!is.na(datos[, 2])) & (!is.na(datos[, 3]))) final <- rbind(final, c(h, nrow(resultado))) } final <- setNames(final, c("id","nobs")) final }

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/man/ComputeCropCalendar.Rd

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bluegulcy/aquacrop

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refs/heads/master

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ComputeCropCalendar.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ComputeCropCalendar.R \name{ComputeCropCalendar} \alias{ComputeCropCalendar} \title{Compute additional parameters needed to define crop} \usage{ ComputeCropCalendar(Crop, CropName, CropChoices, Weather, ClockStruct) } \arguments{ \item{Crop}{list} \item{CropName}{list with crops names} \item{CropChoices}{crops to be analysed} \item{Weather}{dataset with weather data} \item{ClockStruct}{crop calendar} } \value{ \code{Crop}. } \description{ Compute additional parameters needed to define crop } \examples{ ComputeCropCalendar(Crop, CropName, CropChoices, Weather, ClockStruct) }

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/.Rproj.user/E300CC8A/sources/per/t/5DF6BD11-contents

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DaoudaTandiangDjiba/ExamEdacySalingPricePredictions

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5DF6BD11-contents

# # Import packages library(plyr) library(tidyverse) library(MASS) library(car) library(e1071) library(caret) library(cowplot) library(caTools) library(pROC) library(ggcorrplot) library(reshape) library(scales) library(xgboost) library(gbm) library(randomForest) # Load the train and test dataset train <- read.csv("train.csv", header=TRUE, stringsAsFactors = FALSE) test <- read.csv("test.csv",header=TRUE, stringsAsFactors = FALSE) # Check the variables View(train) View(test) str(train) str(test) names(train) names(test) # Add a "SalePrice" variable to the test set to allow for combining data sets summary(train$SalePrice) test.SalePrice <- data.frame(SalePrice = rep("None", nrow(test)), test[,]) # Combine data sets data.combined <- rbind(train, test.SalePrice) data.combined$SalePrice <- as.integer(data.combined$SalePrice) dim(data.combined) summary(data.combined$SalePrice) # Take a look to the saling price summary(data.combined$SalePrice) ggplot(data.combined[!is.na(data.combined$SalePrice),], aes(x = SalePrice)) + geom_histogram(binwidth = 10000) + xlab("SalePrice") + ylab("Total Count") + scale_x_continuous(breaks= seq(0, 800000, by=100000), labels = comma) # Check outliers options(repr.plot.width =4, repr.plot.height = 4) boxplot(data.combined$SalePrice)$out scale_x_continuous(breaks= seq(0, 800000, by=100000), labels = comma) # Missing data missing_data <- data.combined %>% summarise_all(funs(sum(is.na(.))/n())) View(missing_data) # Transform it as a dataframe with 2 columns missing_data <- gather(missing_data, key = "variables", value = "percent_missing") # visualize the missing data percent for each variables # Plot dimension options(repr.plot.width = 12, repr.plot.height = 8) # Missing data barplot Missingdata_barplot <- ggplot(missing_data, aes(x = reorder(variables, percent_missing), y = percent_missing)) + geom_bar(stat = "identity", fill = "red", aes(color = I('white')), size = 0.1)+ xlab('variables')+ coord_flip()+ theme_bw() Missingdata_barplot nacolumn <- which(colSums(is.na(data.combined)) > 0) sort(colSums(sapply(data.combined[nacolumn], is.na)), decreasing = TRUE) names(nacolumn) # 34 variables with NAs, NAs in SalePrice is from the test set. # Imputing missing values # Only Fence variable and PoolQC are needed for our Work table(data.combined$Fence) # The NAs value on the Fence means None data.combined$Fence[is.na(data.combined$Fence)] <- "No Fence" #The NAs value on the PoolQC means None table(data.combined$PoolQC) data.combined$PoolQC[is.na(data.combined$PoolQC)] <- "No Pool" #======================================New Dataset============================================================= data.combined_full <- data.combined[c(18,20,29,53,62,73,74,52,80,81)] str(data.combined_full) data.combined_full$ExterCond <- as.factor(data.combined_full$ExterCond) data.combined_full$PoolQC <- as.factor(data.combined_full$PoolQC) data.combined_full$Fence <- as.factor(data.combined_full$Fence) data.combined_full$SaleCondition <- as.factor(data.combined_full$SaleCondition) #====================================Distribution of variables================================================= # Distribution of the Bedroom variable ggplot(data.combined_full, aes(x = BedroomAbvGr )) + geom_bar() + xlab("Bedroom") + ylab("Total Count") + theme_bw() # Distribution of the Fence variable ggplot(data.combined_full, aes(x = Fence)) + geom_bar(fill = 'Blue') + xlab("Fence") + ylab("Total Count")+ theme_bw() # Distribution of the Sale condition varibale ggplot(data.combined_full, aes(x = SaleCondition )) + geom_bar() + xlab("Sale condition") + ylab("Total Count") + theme_bw() # Distribution of the Extern condition variable ggplot(data.combined_full, aes(x = ExterCond )) + geom_bar() + xlab("Extern condition") + ylab("Total Count") + theme_bw() # Distribution of the Pool quality variable ggplot(data.combined_full, aes(x = PoolQC )) + geom_bar() + xlab("Pool quality") + ylab("Total Count") + theme_bw() # =======================================Correlation with the SalePrice Variable==================================== options(repr.plot.width =6, repr.plot.height = 4) data.combined_full$BedroomAbvGr <- as.integer(data.combined_full$BedroomAbvGr) correlation <- round(cor(data.combined_full[1:1460,][,c("YearBuilt","OverallQual","KitchenAbvGr", "GarageCars","BedroomAbvGr","SalePrice")]), 1) ggcorrplot(correlation, title = "Correlation")+theme(plot.title = element_text(hjust = 0.5)) # Feature engineering data.combined_full$SalePrice2[data.combined_full$SalePrice >=34900 & data.combined_full$SalePrice <=129975 ] <- 'Cheap' data.combined_full$SalePrice2[data.combined_full$SalePrice > 129975 & data.combined_full$SalePrice <= 180921] <- 'Medium' data.combined_full$SalePrice2[data.combined_full$SalePrice > 180921 & data.combined_full$SalePrice <= 755000] <- 'Expensive' data.combined_full$SalePrice2 <- as.factor(data.combined_full$SalePrice2) # Distribution of factor variables by Saleprice ggplot(data.combined_full[1:1460,], aes(x = PoolQC, fill = SalePrice2)) + geom_bar() + facet_grid(~SalePrice2)+ xlab("Pool quality") + ylab("Total Count") + theme_bw() ggplot(data.combined_full[1:1460,], aes(x = BedroomAbvGr, fill = SalePrice2 )) + geom_bar() + facet_grid(~SalePrice2)+ xlab("Pool quality") + ylab("Total Count") + theme_bw() ggplot(data.combined_full[1:1460,], aes(x = Fence, fill = SalePrice2 )) + geom_bar() + facet_grid(~SalePrice2)+ xlab("Fence") + ylab("Total Count") + theme_bw() ggplot(data.combined_full[1:1460,], aes(x = SaleCondition, fill = SalePrice2 )) + geom_bar() + facet_grid(~SalePrice2)+ xlab("Fence") + ylab("Total Count") + theme_bw() ggplot(data.combined_full[1:1460,], aes(x = ExterCond, fill = SalePrice2 )) + geom_bar() + facet_grid(~SalePrice2)+ xlab("Extern condition") + ylab("Total Count") + theme_bw() # Data Preparation data.combined_full$SalePrice[is.na(data.combined$SalePrice)] <- "None" # ====================================Exploratory modeling============================================== # Fisrt Model: Random Forest set.seed(2018) data.combined_full$SalePrice <- as.factor(data.combined_full$SalePrice) RFModel <- randomForest(formula = SalePrice ~ OverallQual+YearBuilt+ExterCond+KitchenAbvGr+ GarageCars+PoolQC+Fence+BedroomAbvGr+SaleCondition, data = data.combined_full[0:1461,], ntree=500) Prediction <- predict(RFModel, data.combined_full[1461:2919,]) Prediction2 <- Prediction PreComp_RF <- Prediction2 Compl_testRF <- data.combined_full$SalePrice[1461:2919,] (M_RF_test <- as.matrix(table(Compl_testRF, PreComp_RF))) perf(Test_set$Cible, Test_set$Pred.rf) # Second Model: Gradient Boosting gbm.fit <- gbm( formula = SalePrice ~ OverallQual+YearBuilt+ExterCond+KitchenAbvGr+ GarageCars+PoolQC+Fence+BedroomAbvGr+SaleCondition, distribution = "gaussian", data = data.combined_full[1:1460,], n.trees = 10000, interaction.depth = 1, shrinkage = 0.001, cv.folds = 5, n.cores = NULL, # will use all cores by default verbose = FALSE ) formula("y ~ a+b+c") model.rf <- randomForest(formula(SalePrice ~ OverallQual+YearBuilt+ExterCond+KitchenAbvGr+GarageCars+PoolQC+Fence+ BedroomAbvGr+SaleCondition, data=data.combined_full[1:1460,], ntree=150)) #Splitting the data set.seed(123) indices = sample.split(telco_final$Churn, SplitRatio = 0.7) train = telco_final[indices,] test = telco_final[!(indices),] # Take a look to the saling price summary(data.combined$SalePrice) ggplot(data.combined[!is.na(data.combined$SalePrice),], aes(x = SalePrice)) + geom_histogram(binwidth = 10000) + xlab("SalePrice") + ylab("Total Count") + scale_x_continuous(breaks= seq(0, 800000, by=100000), labels = comma) # Check outliers options(repr.plot.width =4, repr.plot.height = 4) boxplot(data.combined$SalePrice)$out scale_x_continuous(breaks= seq(0, 800000, by=100000), labels = comma)

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/R/pcalc.R

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victoraLab/pcReasy

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refs/heads/main

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#' Calculate PC reaction #' #' Returns a efficient protocol for pc reaction setup. #' #' @param v The final volume desired for each PC reaction. Numeric. #' @param rxn The number of total reactions. Numeric. #' @param initialConc Data Frame containing the initial concentrations of each component. #' @param finalConc Data Frame containing the final concentrations of each component. #' @param pError Pipetting error to consider for each reaction. #' @importFrom dplyr bind_rows #' #' @export pcalc <- function(v = 10, rxn = NULL, initialConc = NULL, finalConc = NULL, pError = 0){ options(scipen=999) if(length(initialConc) == 0){ finalConc <- data.frame( buffer = 1, dNTP = 200, FPrimer = 10, RPrimer = 10, DNA = 10, Q5Poly = 0.02, optional = 0, row.names = "Q5" ) initialConc <- data.frame( buffer = 5, dNTP = 10000, FPrimer = 100, RPrimer = 100, DNA = 100, Q5Poly = 2, optional = 0, row.names = "Q5" ) rxn = 10 } res <- list() initialConc <- initialConc[,!initialConc == 0] finalConc <- finalConc [,!finalConc == 0] for(i in 1:ncol(initialConc)){ z <- initialConc[[i]]/finalConc[[i]] res[[i]] <- model.frame(y ~ I(x / z), data = data.frame(x = v, y = initialConc[[i]])) } res <- bind_rows(res) res <- data.frame(initialConc = as.vector(t(initialConc)), finalConc = as.vector(t(finalConc)), rxnVols = res[["I(x/z)"]], row.names = colnames(initialConc)) parc <- sum(res[["rxnVols"]]) water <- v - parc if(parc > v){ stop( paste( sprintf("Total volume cannot be bigger than %s", v), sprintf("Volume is at %s", parc), sep = "\n")) } pError <- 1 + pError res["Water",] <- c(0,0, water) res["Total",] <- c(0,0, water + parc) res[, sprintf("rxnVols%s_pError%s", rxn, pError-1)] <- (res[,"rxnVols"] * rxn) * pError #print(sprintf("The protocol for the reaction named: -- %s --", rownames(initialConc))) return(res) }

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/NGS/script_all.R

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igemsoftware2021/iGEM_Simulation

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script_all.R

library(ggplot2) name = c() pos = c() ref = c() var = c() read_mut = c() read_tot = c() file=list.files(pattern = "A122_10") for (i in file){ tmp = read.csv2(i, sep = "\t") name = c(name, rep(substring(i, 1,11), nrow(tmp))) pos = c(pos, tmp$Position) ref = c(ref, tmp$Ref) var = c(var, tmp$VarAllele) read_mut = c(read_mut, tmp$Reads2) read_tot = c(read_tot, tmp$Reads1 + tmp$Reads2) } data = data.frame(Sample = name, Position = pos, Ref = ref, Mutation = var, Occurences = read_mut, Depth = read_tot) data = data[data$Ref %in% c("A", "T", "C", "G"),] #write.csv2(data, file = "donneees_ngs_variantcall.csv", sep = ";", dec = ",") data$Sample = as.factor(data$Sample) gg = data.frame(data[((data$Ref %in% c('C', 'G')) & data$Mutation %in% c("T", "A") & data$Depth > 20),]) mutators = c("T7", "AID-T7", "pmCDAI-T7", "rAPOBEC1-T7", "TadA*-T7", "CGG", "AID-CGG", "pmCDA1-CGG", "rAPOBEC1-CGG", "TadA*-CGG", "evoAPOBEC1-BE4max-CGG", "evo-CDA1-BE4max-CGG", "ABE8.20-m-CGG", "evoAPOBEC1-BE4max-T7", "evo-CDA1-BE4max-T7", "ABE8.20-m-T7", "pSEVA221", "pSEVA471") id = c("100", "101", "102", "103", "104", "109", "110", "111", "112", "113", "114", "115", "116", "117", "118", "119", "221", "471") trans = data.frame(id = id, mut = mutators) nom = c() for (i in 1:nrow(gg)) { nom = c(nom, trans$mut[trans$id == substring(as.character(gg$Sample[i]), 9, 11)]) } gg[["nom"]] = nom ggplot(gg) + aes(x = nom, y = Occurences/Depth) + ggtitle("C:G -> T:A") + xlab("Mutator") + ylab("variant read / total") + geom_jitter() + geom_boxplot(alpha = 0.5, outlier.size=0, outlier.shape=NA) + theme(axis.text.x = element_text(angle=45, hjust = 1)) + scale_x_discrete(name ="Mutator", limits=c("pSEVA221", "T7", "AID-T7", "pmCDAI-T7", "rAPOBEC1-T7", "TadA*-T7", "evoAPOBEC1-BE4max-T7", "evo-CDA1-BE4max-T7", "ABE8.20-m-T7", "pSEVA471", "CGG", "AID-CGG", "pmCDA1-CGG", "rAPOBEC1-CGG", "TadA*-CGG", "evoAPOBEC1-BE4max-CGG", "evo-CDA1-BE4max-CGG", "ABE8.20-m-CGG")) gg2 = data nom = c() type = c() for (i in 1:nrow(gg2)) { nom = c(nom, trans$mut[trans$id == substring(as.character(gg2$Sample[i]), 9, 11)]) if (((data$Ref[i] %in% c('C', 'G')) & data$Mutation[i] %in% c("T", "A"))){ type = c(type, "C:G -> T:A") } else { type = c(type, "A:T -> G:C") } } gg2[["nom"]] = nom gg2[["type"]] = type ggplot(gg2) + aes(x = nom, fill = type) + ggtitle("total mutations") + xlab("Mutator") + ylab("mutations") + geom_bar() + theme(axis.text.x = element_text(angle=45, hjust = 1)) id = levels(as.factor(gg2$nom)) mut = c() idd = c() type = c() for (i in id){ mut = c(mut, sum(gg2$Occurences[gg2$type == "C:G -> T:A" & gg2$nom == i])/sum(gg2$Depth[gg2$type == "C:G -> T:A" & gg2$nom == i])) mut = c(mut, sum(gg2$Occurences[gg2$type == "A:T -> G:C" & gg2$nom == i])/sum(gg2$Depth[gg2$type == "A:T -> G:C" & gg2$nom == i])) type = c(type, "C:G -> T:A", "A:T -> G:C") idd = c(idd, i, i) } gg3 = data.frame(id =idd, mut = mut, type = type) ggplot(gg3) + aes(x = id, y = mut, fill = type) + ggtitle("Mutations of each type") + xlab("Mutator") + ylab("Proportion") + geom_col() + theme(axis.text.x = element_text(angle=45, hjust = 1)) ggplot(gg2[gg2$type == "A:T -> G:C",]) + aes(x = nom, y = Occurences/Depth) + ggtitle("A:T -> G:C") + xlab("Mutator") + ylab("variant read / total") + geom_jitter() + geom_boxplot(alpha = 0.5, outlier.size=0, outlier.shape=NA) + theme(axis.text.x = element_text(angle=45, hjust = 1)) + scale_x_discrete(name ="Mutator", limits=c("pSEVA221", "T7", "AID-T7", "pmCDAI-T7", "rAPOBEC1-T7", "TadA*-T7", "evoAPOBEC1-BE4max-T7", "evo-CDA1-BE4max-T7", "ABE8.20-m-T7", "pSEVA471", "CGG", "AID-CGG", "pmCDA1-CGG", "rAPOBEC1-CGG", "TadA*-CGG", "evoAPOBEC1-BE4max-CGG", "evo-CDA1-BE4max-CGG", "ABE8.20-m-CGG")) # mutations rate (validation) gg4 = gg2[gg2$nom %in% c("AID-T7", "pmCDAI-T7", "rAPOBEC1-T7", "TadA*-T7"),] id = c("AID-T7", "pmCDAI-T7", "rAPOBEC1-T7", "TadA*-T7", "AID-T7", "pmCDAI-T7", "rAPOBEC1-T7", "TadA*-T7") activ = c("C -> T", "C -> T", "C -> T", "T -> C", "G -> A", "G -> A", "G -> A", "A -> G") alpha = c() for (i in c("AID-T7", "pmCDAI-T7", "rAPOBEC1-T7")){ alpha = c(alpha, sum(gg4$Occurences[gg4$Ref == "C" & gg4$nom == i])/sum(gg4$Depth[gg4$Ref == "C" & gg4$nom == i])) } for (i in c("TadA*-T7")){ alpha = c(alpha, sum(gg4$Occurences[gg4$Ref == "T" & gg4$nom == i])/sum(gg4$Depth[gg4$Ref == "T" & gg4$nom == i])) } for (i in c("AID-T7", "pmCDAI-T7", "rAPOBEC1-T7")){ alpha = c(alpha, sum(gg4$Occurences[gg4$Ref == "G" & gg4$nom == i])/sum(gg4$Depth[gg4$Ref == "G" & gg4$nom == i])) } for (i in c("TadA*-T7")){ alpha = c(alpha, sum(gg4$Occurences[gg4$Ref == "A" & gg4$nom == i])/sum(gg4$Depth[gg4$Ref == "A" & gg4$nom == i])) } #plot gg4 = data.frame(Mutator = id, Activity = activ, Mutation_rate = alpha) ggplot(gg4) + aes(x = Mutator, y = Mutation_rate, fill = Activity) + geom_col(position = "dodge") + xlab("Mutator") + ylab("Mutation rate") + theme(axis.text.x = element_text(angle=45, hjust = 1)) + scale_x_discrete(name ="Mutator", limits=c("AID-T7", "pmCDAI-T7", "rAPOBEC1-T7", "TadA*-T7")) #graphe robustesse esti = c(0.00354, 0.0564, 0.00553, 0.00452, 0.00360, 0.00664, 0.00972, 0.000709) #estimated values (spanich publication) gg4[["Estimation"]] = esti ggplot(gg4) + aes(x = Estimation, y = Mutation_rate/1.23 - 0.0065) + xlab("Estimation at g = 3 (literature)") + ylab("Corrected estimation at g = 30") + geom_point() + geom_smooth(method = "lm") + geom_abline(slope = 1, intercept = 0, linetype="dashed", color = "red") # wo esti = c(0.00354, 0.00553, 0.00452, 0.00360, 0.00664, 0.00972, 0.000709) #estimated values (spanich publication) gg4 = gg4[-2,] gg4[["Estimation"]] = esti ggplot(gg4) + aes(x = Estimation, y = Mutation_rate) + ggtitle("Model accuracy") + xlab("Estimation at 3g (literature)") + ylab("Estimation at 30g (our NGS results)") + geom_point() + geom_smooth(method = "lm") # other estimationslimits=c("pSEVA221", "T7", "AID-T7", "pmCDAI-T7", "rAPOBEC1-T7", gg4 = gg2[gg2$nom %in% c("evoAPOBEC1-BE4max-T7", "evo-CDA1-BE4max-T7", "ABE8.20-m-T7"),] id = c("evoAPOBEC1-BE4max-T7", "evo-CDA1-BE4max-T7","ABE8.20-m-T7", "evoAPOBEC1-BE4max-T7", "evo-CDA1-BE4max-T7","ABE8.20-m-T7") activ = c("C -> T", "C -> T", "T -> C", "G -> A", "G -> A", "A -> G") alpha = c() for (i in c("evoAPOBEC1-BE4max-T7", "evo-CDA1-BE4max-T7")){ alpha = c(alpha, sum(gg4$Occurences[gg4$Ref == "C" & gg4$nom == i])/sum(gg4$Depth[gg4$Ref == "C" & gg4$nom == i])) } for (i in c("ABE8.20-m-T7")){ alpha = c(alpha, sum(gg4$Occurences[gg4$Ref == "T" & gg4$nom == i])/sum(gg4$Depth[gg4$Ref == "T" & gg4$nom == i])) } for (i in c("evoAPOBEC1-BE4max-T7", "evo-CDA1-BE4max-T7")){ alpha = c(alpha, sum(gg4$Occurences[gg4$Ref == "G" & gg4$nom == i])/sum(gg4$Depth[gg4$Ref == "G" & gg4$nom == i])) } for (i in c("ABE8.20-m-T7")){ alpha = c(alpha, sum(gg4$Occurences[gg4$Ref == "A" & gg4$nom == i])/sum(gg4$Depth[gg4$Ref == "A" & gg4$nom == i])) } alpha = alpha/1.23 -0.0065 # esti alpha # final gg4 = gg2[gg2$nom %in% c("AID-CGG", "pmCDA1-CGG", "rAPOBEC1-CGG", "TadA*-CGG", "evoAPOBEC1-BE4max-CGG","evo-CDA1-BE4max-CGG","ABE8.20-m-CGG"),] id = c("AID-CGG", "pmCDA1-CGG", "rAPOBEC1-CGG", "TadA*-CGG", "evoAPOBEC1-BE4max-CGG", "evo-CDA1-BE4max-CGG", "ABE8.20-m-CGG") activ = c("C -> T", "C -> T","C -> T", "T -> C", "C -> T","C -> T", "T -> C", "G -> A", "G -> A", "G -> A","A -> G", "G -> A", "G -> A","A -> G") alpha = c() for (i in c("AID-CGG", "pmCDA1-CGG", "rAPOBEC1-CGG")){ alpha = c(alpha, sum(gg4$Occurences[gg4$Ref == "C" & gg4$nom == i])/sum(gg4$Depth[gg4$Ref == "C" & gg4$nom == i])) } for (i in c("TadA*-CGG")){ alpha = c(alpha, sum(gg4$Occurences[gg4$Ref == "T" & gg4$nom == i])/sum(gg4$Depth[gg4$Ref == "T" & gg4$nom == i])) } for (i in c("evoAPOBEC1-BE4max-CGG", "evo-CDA1-BE4max-CGG")){ alpha = c(alpha, sum(gg4$Occurences[gg4$Ref == "C" & gg4$nom == i])/sum(gg4$Depth[gg4$Ref == "C" & gg4$nom == i])) } for (i in c("ABE8.20-m-CGG")){ alpha = c(alpha, sum(gg4$Occurences[gg4$Ref == "T" & gg4$nom == i])/sum(gg4$Depth[gg4$Ref == "T" & gg4$nom == i])) } for (i in c("AID-CGG", "pmCDA1-CGG", "rAPOBEC1-CGG")){ alpha = c(alpha, sum(gg4$Occurences[gg4$Ref == "G" & gg4$nom == i])/sum(gg4$Depth[gg4$Ref == "G" & gg4$nom == i])) } for (i in c("TadA*-CGG")){ alpha = c(alpha, sum(gg4$Occurences[gg4$Ref == "A" & gg4$nom == i])/sum(gg4$Depth[gg4$Ref == "A" & gg4$nom == i])) } for (i in c("evoAPOBEC1-BE4max-CGG", "evo-CDA1-BE4max-CGG")){ alpha = c(alpha, sum(gg4$Occurences[gg4$Ref == "G" & gg4$nom == i])/sum(gg4$Depth[gg4$Ref == "G" & gg4$nom == i])) } for (i in c("ABE8.20-m-CGG")){ alpha = c(alpha, sum(gg4$Occurences[gg4$Ref == "A" & gg4$nom == i])/sum(gg4$Depth[gg4$Ref == "A" & gg4$nom == i])) } # the graph gg0 = data.frame(Mutator = c("AID-T7", "pmCDAI-T7", "rAPOBEC1-T7", "TadA*-T7", "AID-T7", "pmCDAI-T7", "rAPOBEC1-T7", "TadA*-T7", "evoAPOBEC1-BE4max-T7", "evo-CDA1-BE4max-T7","ABE8.20-m-T7", "evoAPOBEC1-BE4max-T7", "evo-CDA1-BE4max-T7","ABE8.20-m-T7", "AID-CGG", "pmCDA1-CGG", "rAPOBEC1-CGG", "TadA*-CGG", "evoAPOBEC1-BE4max-CGG","evo-CDA1-BE4max-CGG","ABE8.20-m-CGG", "AID-CGG", "pmCDA1-CGG", "rAPOBEC1-CGG", "TadA*-CGG", "evoAPOBEC1-BE4max-CGG","evo-CDA1-BE4max-CGG","ABE8.20-m-CGG"), Activity = c("C -> T", "C -> T", "C -> T", "T -> C", "G -> A", "G -> A", "G -> A", "A -> G", "C -> T", "C -> T", "T -> C", "G -> A", "G -> A", "A -> G", "C -> T", "C -> T","C -> T", "T -> C", "C -> T","C -> T", "T -> C", "G -> A", "G -> A", "G -> A","A -> G", "G -> A", "G -> A","A -> G"), Mutation_rate = alpha) ggplot(gg0) + aes(x = Mutator, y = Mutation_rate, fill = Activity) + geom_col() + xlab("Mutator") + ylab("Mutation rate") + theme(axis.text.x = element_text(angle=45, hjust = 1)) + scale_x_discrete(name ="Mutator", limits=c("AID-T7", "pmCDAI-T7", "rAPOBEC1-T7", "TadA*-T7", "evoAPOBEC1-BE4max-T7", "evo-CDA1-BE4max-T7", "ABE8.20-m-T7", "", "AID-CGG", "pmCDA1-CGG", "rAPOBEC1-CGG", "TadA*-CGG", "evoAPOBEC1-BE4max-CGG", "evo-CDA1-BE4max-CGG", "ABE8.20-m-CGG"))

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sqlGetQuery.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/sqlGetQuery.R \name{sqlGetQuery} \alias{sqlGetQuery} \title{Gets result from sql query} \usage{ sqlGetQuery(server_name, query, param=c(), dt=FALSE, key=c(), ...) } \arguments{ \item{server_name}{character, name of the DB server from sqlServers list.} \item{query}{character, query structure with variant parameters between @'s, if they are.} \item{param}{character vector with the value of the parameters that would be used in query.} \item{dt}{boolean, If true, the results would be data table class.} \item{key}{character vector, it contains the names of the columns to be used as keys if return a data table.} \item{...}{inherit parameters used in dbGetQuery().} } \value{ A data frame or data table with query results. } \description{ Does query to a DB in a server from sqlServers and return its result as a data frame or data table. It calls dbGetQuery() from DBI package so the result is always free-d. Provide the option to make general queries and change its parameters easier, as in sqlGsub() from this package. } \details{ It ends the connection inmediately after getting the results. sqlServer is a list built-in sqlGetConn(). } \examples{ sqlWriteTable("local",mtcars,"mtcars") #create mtcars table in local server sqlListTables("local") q1="SELECT * FROM mtcars WHERE mpg>20" #decalrate query sqlGetQuery("local",q1) #do a simple query q2="SELECT @col@ FROM @table@ WHERE @condition@" #decalrate a mutable query p1=c(table="mtcars",col="*",condition="hp>100") p2=c(table="mtcars",col="wt",condition="wt>2.600") #and declarate the parameter to use in mutable query sqlGetQuery("local",q2,p1) sqlGetQuery("local",q2,p2,dt=T) # as data table } \seealso{ "sqlServerConn","setSqlServers()" and "sqlGsub()" documentation in toolkitEntel and "dbGetQuery()" from DBI for more details. }

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/PCA hierchial +k means scm.R

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surjithcm7/EXCEL-R-ASSIGNMENTS

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PCA hierchial +k means scm.R

library(readr) data<-read.csv(file.choose()) View(data) #since type column is irrelevant we can remove it mydata<-(data[-1]) View(mydata) attach(mydata) cor(mydata) #the correlation values are somewhere negative somewhere positive somewhere high #which implies the columns are correlated each other ################## model pca ######################## PCA<-princomp(mydata, cor = TRUE, scores = TRUE, covmat = NULL) summary(PCA) #here we can see that the first 3 components give us 92% information about the data loadings(PCA) plot(PCA) #here we can see that the first 5 componnts are much important. #out of those 1st component is the most important one biplot(PCA) plot(cumsum(PCA$sdev*PCA$sdev)*100/(sum(PCA$sdev*PCA$sdev)),type="b") # Showing the increase of variance with considering principal components # Which helps in choosing number of principal components #since we have to cluster using only 3 components we take those 3 only pca3<-PCA$scores[,1:3] mydata2<-cbind(mydata,pca3) View(mydata2) #now we have to perform clustering on both the datasets. #that is for before performing pca and after performing the pca. # Normalizing the data normdata<-scale(mydata[,1:(ncol(mydata))]) View(normdata) d<-dist(normdata,method="euclidian") fit<-hclust(d,method="complete") plot(fit) plot(fit,hang=-1) #choose k=sqrt(n/2)=9 rect.hclust(fit,k=9,border="blue") groups<-cutree(fit,k=10) membership<-as.matrix(groups) final<-data.frame(mydata,membership) View(final) final1<-final[,c(ncol(final),1:(ncol(final)-1))] View(final1) write.csv(final1, file="final wine hierarchial scm1.csv",row.names = F) getwd() aggregate(mydata,by=list(final1$membership),mean) #now we perform clustering on the data on which we have performed PCA normdata<-scale(mydata2[,14:(ncol(mydata2))]) View(normdata) d<-dist(normdata,method="euclidian") fit<-hclust(d,method="complete") plot(fit) plot(fit,hang=-1) #choose k=sqrt(n/2)=9 rect.hclust(fit,k=9,border="red") groups<-cutree(fit,k=10) membership<-as.matrix(groups) final<-data.frame(mydata,membership) View(final) final1<-final[,c(ncol(final),1:(ncol(final)-1))] View(final1) write.csv(final1, file="final wine hierarchial with pca scm.csv",row.names = F) getwd() aggregate(mydata,by=list(final1$membership),mean) #from this we can see that there is change in clustering before and after performing pca #membership values are changed for many rows ###############performing k means clustering############################ normdata<-scale(mydata[,1:(ncol(mydata))]) wss = NULL kmean1<- kmeans(normdata,10) str(kmean1) library(animation) kmeans.ani(mydata,9) wss = (nrow(normdata)-1)*sum(apply(normdata, 2, var)) for (i in 2:15) wss[i] = sum(kmeans(normdata, centers=i)$withinss) plot(1:15, wss, type="b", xlab="Number of Clusters", ylab="Within groups sum of squares") # Look for an "elbow" in the scree plot # title(sub = "K-Means Clustering Scree-Plot") twss <- NULL for (i in 2:15){ twss <- c(twss,kmeans(normdata,i)$tot.withinss) } twss plot(2:15,twss,type="o") kmean10 <- kmeans(normdata,10) str(kmean10) kmean11<-kmeans(normdata,11) str(kmean11) #using elbow curve we get that optimum no. of cluster is 11 which has minimum twss final2<- data.frame(mydata, kmean11$cluster) ncol(mydata) aggregate(mydata[,1:13],by=list(kmean11$cluster), FUN=mean) final1<-final[,c(ncol(final2),1:(ncol(final2)-1))] View(final1) write.csv(final1,file="wine kmean scm.csv",row.names= F) getwd() # now we perform kmeans clustering on the data on whichnwe have performed pca normdata<-scale(mydata2[,11:(ncol(mydata2))]) wss = NULL kmean1<- kmeans(normdata,9) str(kmean1) wss = (nrow(normdata)-1)*sum(apply(normdata, 2, var)) for (i in 2:15) wss[i] = sum(kmeans(normdata, centers=i)$withinss) plot(1:15, wss, type="b", xlab="Number of Clusters", ylab="Within groups sum of squares") # Look for an "elbow" in the scree plot # title(sub = "K-Means Clustering Scree-Plot") twss <- NULL for (i in 2:15){ twss <- c(twss,kmeans(normdata,i)$tot.withinss) } twss plot(2:15,twss,type="o") kmean3 <- kmeans(normdata,3) str(kmean3) kmean8<-kmeans(normdata,8) str(kmean8) kmean13<-kmeans(normdata,13) str(kmean13) #using elbow curve we get that optimum no. of cluster is 13 which has minimum twss final2<- data.frame(mydata, kmean13$cluster) aggregate(mydata[,1:13],by=list(kmean13$cluster), FUN=mean) final1<-final[,c(ncol(final2),1:(ncol(final2)-1))] View(final1) write.csv(final1,file="wine kmean with pca.csv",row.names= F) getwd() #here we can see that the optimum no. of clusters using k means differs for the data set #before we apply PCA no. of clusters was 11 and after the application of pca no. of clusters is 13. #also we can see that the membership changes before and after applying pca #so by applying PCA clustering groups changes in both hierchial and k means clustering.

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/ui.R

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hanyangunivleejoohyun/r-test

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refs/heads/master

2020-05-23T21:50:52.097164

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ui.R

library(shiny) shinyUI(fluidPage( titlePanel("Wordcloud in Moby Dick example"), # 사이드바 UI sidebarLayout( sidebarPanel( textInput("word", label = h3("분석할 단어를 입력해주세요(example : dog)"), value=""), textInput("num", label = h3("문맥 분석할 앞,뒤 단어수를 입력해주세요."), value = "3"), sliderInput("scale", label="빈도수에 따라 표현될 글자 크기를 입력해주세요", min = 0.5,max = 7, value = c(0.5, 5)), numericInput("min_num", label = "워드 클라우드에 표현하고 싶은 최소 빈도수를 입력해주세요", value = 2,min=1), sliderInput("rot_per", label="값이 높을수록 수직으로 표현되는 단어의 수가 늘어납니다.", min = 0,max = 1, value =0.3), checkboxInput("random_order", label="단어 위치 임의 지정", value =F), actionButton("goButton", "Go!") ), mainPanel( # 메인 패널은 tab을 이용하여 여러자료를 표현할 수 있게했습니다. 들어가 있는 내용은 일단 임의로 넣었고 # 차후에 혹시라도 하나의 자료에 대해서 여러가지의 시각화를 하게되면 활용 가능할것 같습니다. tabsetPanel(type = "tabs", tabPanel("Plot", plotOutput("plot")), tabPanel("barplot", plotOutput("barplot")), tabPanel("table",h3("문맥 분석 결과 도출된 단어 빈도수 입니다."), tableOutput("table")) ) ) ) ) )

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/man/winsd.Rd

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shizidushu/fsrs

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refs/heads/master

2021-01-25T14:16:43.047809

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winsd.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/summation.R \name{winsd} \alias{winsd} \title{Winsorized sample standard deviation} \usage{ winsd(x, trim = 0.2, na.rm = FALSE) } \arguments{ \item{x}{An \R object. Currently there are methods for numeric/logical vectors and \link[=Dates]{date}, \link{date-time} and \link{time interval} objects. Complex vectors are allowed for \code{trim = 0}, only.} \item{trim}{the fraction (0 to 0.5) of observations to be trimmed from each end of \code{x} before the mean is computed. Values of trim outside that range are taken as the nearest endpoint. } \item{na.rm}{a logical value indicating whether \code{NA} values should be stripped before the computation proceeds.} } \description{ Winsorized sample standard deviation } \examples{ x <- c(1, 2, 8, 9, 10, 16, 18, 22, 27, 29, 35, 42) winsd(x) }

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/stl-async.R

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no_license

gianordoli/health-issues-server

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refs/heads/master

2021-07-11T17:05:43.287268

2017-10-05T13:59:14

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stl-async.R

needs(stlplus) vectorToTS <- function(data) { print("vectorToTs") ul <- unlist(strsplit(data,",")) dataMatrix <- matrix(ul, length(data), 2, T) # Retrieving first and last months and weeks firstDateRow <- head(dataMatrix[,c(1)], n=1) firstDate <- strsplit(toString(firstDateRow), "-") firstYear <- as.integer(firstDate[[1]][1]) firstMonth <- as.integer(firstDate[[1]][2]) lastDateRow <- tail(dataMatrix[,c(1)], n=1) lastDate <- strsplit(toString(lastDateRow), "-") lastYear <- as.integer(lastDate[[1]][1]) lastMonth <- as.integer(lastDate[[1]][2]) values <- dataMatrix[,c(2)] # Convert data to time series; using only second column (values) myTS <- ts(values, start=c(firstYear, firstMonth), end=c(lastYear, lastMonth), frequency=12) return(myTS) } myTS <- vectorToTS(input[[1]]) type <- input[[2]] mySTL <- stl(myTS, t.window = NULL, s.window="periodic", robust=TRUE) mySTL.DF <- as.data.frame(mySTL$time.series) if(type == 'seasonal') { response <- paste('seasonal:', toString(mySTL.DF$seasonal), collapse = "") } else if(type == 'trend') { response <- paste('trend:', toString(mySTL.DF$trend), collapse = "") } response

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/R/Sankey/Sankey diagrams.R

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no_license

samofashhurst/data-recipes

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refs/heads/master

2021-10-23T12:04:12.196235

2021-10-13T04:30:23

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Sankey diagrams.R

# This script creates a Sankey diagram, which can be used e.g. for seeing common paths between pages during a browser session # install.packages("networkD3") library(networkD3) # Set working dir to dir of script initial_wd = getwd() setwd(dirname(rstudioapi::getActiveDocumentContext()$path)) # Add names for the pages in the flow my_nodes <- c('Page 1','Page 2','Page 3','Drop-off') nodes <- data.frame(node=c(0:(length(my_nodes)-1)),name=my_nodes) # Load the page transition data: this should be a matrix of frequencies where rows represent 'from' pages # and columns represent 'to' pages temp = read.csv("sankey_prep.csv") temp<-as.matrix(temp) dimnames(temp)<-NULL # Create empty dataframe df<-data.frame(source=integer(),target=integer(),value=integer()) # For each combination of row and column, add one more row to the dataframe with source, target and value for(i in 1:nrow(temp)) { for(j in 1:ncol(temp)) { # Note that source/target must be zero-indexed for JavaScript to render the plot df[nrow(df)+1,] = list(i-1,j-1,temp[i,j]) } } # Create Sankey diagram n = networkD3::sankeyNetwork(Links = df, Nodes = nodes, Source = 'source', Target = 'target', Value = 'value', NodeID = 'name', units = 'pageviews', fontSize = 14, nodeWidth = 20) # Save plot as html, and render onscreen saveNetwork(n, "sankey.html", selfcontained = TRUE) n # Put working directory back setwd(initial_wd)

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/RegressionModel/Quiz1.R

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JohanLi1990/DataScienceCoursera

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refs/heads/master

2021-01-23T13:44:02.512726

2017-12-25T00:40:32

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Quiz1.R

#Quiz 1-1 x <- c(0.18, -1.54, 0.42, 0.95) w <- c(2, 1, 3, 1) for(i in c(0.3, 1.077, 0.0025, 0.1471)) { ans <- sum(w*(x-i)^2) print(ans) } # Quiz 1-2 x <- c(0.8, 0.47, 0.51, 0.73, 0.36, 0.58, 0.57, 0.85, 0.44, 0.42) y <- c(1.39, 0.72, 1.55, 0.48, 1.19, -1.59, 1.23, -0.65, 1.49, 0.05) fits <- lm(y ~ x-1) summary(fits) #Quiz 1-3 data("mtcars") fits <- lm(mpg ~ wt, mtcars) summary(fits) #Quiz 1-4 #ANS = 1 #Quiz 1-5 #Ans = 0.4 * 1.5 = 0.6 #Quiz 1-6 x <- c(8.58, 10.46, 9.01, 9.64, 8.86) mx <- mean(x) ms <- sd(x) (x[1] - mx[1])/ms[1] # -0.9718658 #Quiz 1-7 x <- c(0.8, 0.47, 0.51, 0.73, 0.36, 0.58, 0.57, 0.85, 0.44, 0.42) y <- c(1.39, 0.72, 1.55, 0.48, 1.19, -1.59, 1.23, -0.65, 1.49, 0.05) fits <- lm(y ~ x) fits$coefficients #Quiz 1-9 x <- c(0.8, 0.47, 0.51, 0.73, 0.36, 0.58, 0.57, 0.85, 0.44, 0.42) mean(x)

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/man/CosineDFV.Rd

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no_license

PengNi/dSimer

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refs/heads/master

2020-03-28T03:01:27.040649

2017-01-19T11:59:16

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CosineDFV.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/CosineDFV.R \name{CosineDFV} \alias{CosineDFV} \title{calculate disease similarity by using feature vectors} \usage{ CosineDFV(D1, D2, d2f, dcol = 2, fcol = 1, ccol = 3) } \arguments{ \item{D1}{a vector consists of disease ids/names} \item{D2}{another vector consists of disease ids/names} \item{d2f}{data.frame, contains term co-occurrences between features and diseases} \item{dcol}{integer, disease column number in d2f} \item{fcol}{integer, feature column number in d2f} \item{ccol}{integer, co-occurrences column number in d2f} } \value{ a matrix of disease disease similarity which rownames and colnames are the disease names } \description{ given two (lists of) disease names, this function will calculate cosine similarity between these diseases' feature vectors. } \examples{ ### this is a disease-symptom-cooccurrence sample, if you want to use ### the complete data, please use "data(d2s_hsdn)" command data(d2s_hsdn_sample) ds <- sample(unique(d2s_hsdn_sample[,2]), 10) simmat <- CosineDFV(ds, ds, d2s_hsdn_sample) } \author{ Zhihui Fei, Peng Ni, Min Li } \references{ Zhou X Z, Menche J, Barabasi A L, et al. Human symptoms-disease network[J]. Nature communications, 2014, 5. Van Driel M A, Bruggeman J, Vriend G, et al. A text-mining analysis of the human phenome[J]. European journal of human genetics, 2006, 14(5): 535-542. }

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/plot6.R

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ryanbthomas/EDAProj2

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refs/heads/master

2022-02-02T02:03:05.393310

2015-06-19T00:08:14

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plot6.R

#plot6 g <- ggplot(plot6_data) + aes(x=year,y=Emissions/1000, fill=fips) + geom_bar(stat="identity",position="dodge") + scale_fill_discrete(name="City",breaks=c("06037","24510"), labels=c("Los Angeles","Baltimore City")) g <- g + geom_p

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/Model Ensembling/Boosting/iris_XGBoost.R

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prathmesh31/machine_learning_classwork

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refs/heads/master

2020-03-24T03:37:34.664141

2018-07-26T10:55:59

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iris_XGBoost.R

data("iris") library(caret) set.seed(1992) intrain<-createDataPartition(y=iris$Species,p=0.7,list=FALSE) training <- iris[ intrain , ] validation <- iris[-intrain , ] library(xgboost) mat_train <- as.matrix(training[,-5]) mat_validation <- as.matrix(validation[,-5]) lbl_spec <- ifelse(training$Species=="setosa",0, ifelse(training$Species=="versicolor",1,2)) model.xgb <- xgboost(data=mat_train,label =lbl_spec ,nrounds = 9, num_class=3, objective="multi:softmax") pred.xgb <- predict(model.xgb,newdata=mat_validation) lbl_spec_val <- ifelse(validation$Species=="setosa",0, ifelse(validation$Species=="versicolor",1,2)) confusionMatrix(as.factor(pred.xgb),as.factor(lbl_spec_val))