pierreqi/r_code_50k · Datasets at Hugging Face

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/src/08_ts_decomposition.R

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ian-flores/suicidesPR

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refs/heads/master

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08_ts_decomposition.R

library(fs) library(lubridate) library(tidyverse) library(ggfortify) data_files <- dir_ls('data/mortality_data_2000_2008/', type = 'file') mortality <- map_df(data_files, read_csv, col_types = cols(.default = "c")) ts_mortality <- mortality %>% filter(typedeath == '2') %>% select(yeardeath, monthdeath) %>% group_by(yeardeath, monthdeath) %>% count() %>% ungroup() %>% mutate(date = ymd(paste(yeardeath, monthdeath, '01', sep = '-'))) %>% filter(!is.na(date), date < ymd('2010-01-01')) %>% arrange(date) %>% select(date, n) ts_mortality %>% ggplot(aes(x = date, y =n)) + geom_line() ts_mortality <- ts(ts_mortality$n, start = c(2000, 1), frequency = 12) loess_decomp <- stl(ts_mortality, s.window = 'periodic') loess_decomp %>% autoplot(colour = 'brown') + theme_light() + labs(y = 'Suicide cases', x = 'Date', title = 'How do suicide cases vary by time in Puerto Rico?', subtitle = 'LOESS Decomposition from 2000 to 2008', caption = 'Graph prepared by Ian Flores Siaca')

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/man/fitch.Rd

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syerramilli/R-sysid

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fitch.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/estpoly.R \name{fitch} \alias{fitch} \title{Fit Characteristics} \usage{ fitch(x) } \arguments{ \item{x}{the estimated model} } \value{ A list containing the following elements \item{MSE}{Mean Square Error measure of how well the response of the model fits the estimation data} \item{FPE}{Final Prediction Error} \item{FitPer}{Normalized root mean squared error (NRMSE) measure of how well the response of the model fits the estimation data, expressed as a percentage.} \item{AIC}{Raw Akaike Information Citeria (AIC) measure of model quality} \item{AICc}{Small sample-size corrected AIC} \item{nAIC}{Normalized AIC} \item{BIC}{Bayesian Information Criteria (BIC)} } \description{ Returns quantitative assessment of the estimated model as a list }

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/man/surveySurvival.Rd

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dougkinzey/Grym

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surveySurvival.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/Grym.R \name{surveySurvival} \alias{surveySurvival} \title{Survival to a survey period.} \usage{ surveySurvival(yr, cls, s1, s2, Ms, M, Fs = 0, F = 0, rcls = 1) } \arguments{ \item{yr}{vector of survey year} \item{cls}{vector of survey age class} \item{s1}{vector of the first time step in the survey} \item{s2}{vector of the final time step in the survey} \item{Ms}{matrix of \emph{unscaled} integrated natural mortality} \item{M}{vector of annual natural mortalities} \item{Fs}{matrix of \emph{unscaled} integrated fishing mortality} \item{F}{vector of annual fishing mortalities} \item{rcls}{the reference age class to adjust to} } \value{ Returns a vector of the mean survival from time of recruitment to the survey period. } \description{ Compute the scalings required to adjust surveyed age class abundances to initial abundances at a reference age. } \details{ Given the age class, the year and the time steps within the year at which the age class was surveyed, this function computes the total survival from the start of the year in which the cohort were in the reference age class to the survey period. If the surveyed age class is younger than the reference class, the reciporical of the total survival from the survey period to the start of the year that the cohort will be in the reference class is computed. If there is inter-annual variability in natural or fishing mortality, the survey years must be labelled so that \code{yr==1} corresponds to the first element of the vector of \code{M} and/or \code{F}, and it is no possible to compute the survival for cohorts that recruit before year 1. If there is no inter-annual variablity in natural or fishing mortality, the survey year is irrelevant. } \examples{ ## Daily time steps and 7 age classes nsteps <- 365 Ages <- 2:8 Days <- seq(from=0, to=1, length=nsteps+1) h <- 1/nsteps ## Constant intra-annual natural mortality ms <- matrix(data=1, nrow=nsteps+1, ncol=length(x=Ages)) ms <- ms/mean(x=trapz(fs=ms, h=h)) Ms <- ctrapz(fs=ms, h=h) ## Survey year, period and age classes svy <- data.frame(yr=3:5, s1=c(190, 220, 150), s2=c(201, 231, 161)) svy <- cbind(svy[rep(x=1:3, each=7), ], cls=1:7) head(svy) ## Constant mortality M <- 0.2 ## Survival to the survey period from age class 1 surveySurvival(yr=svy$yr, cls=svy$cls, s1=svy$s1, s2=svy$s2, Ms=Ms, M=M) ## Survival to the survey period from age class 3 surveySurvival(yr=svy$yr, cls=svy$cls, s1=svy$s1, s2=svy$s2, Ms=Ms, M=M, rcls=3) ## Variable mortality M <- rgamma(n=10, shape=20, rate=100) M ## Survival cannot be projected outside the period for which mortality ## is specified. surveySurvival(yr=svy$yr, cls=svy$cls, s1=svy$s1, s2=svy$s2, Ms=Ms, M=M) }

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arey0pushpa/dcnf-autarky

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query30_query05_1344n.R

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bertozzivill/infx572_fall16

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data_table_exercise_filled.r

############################################################################## ## data.table exercise ## Author: Amelia Bertozzi-Villa ############################################################################## library(data.table) ## Creating a data.table -------------------------------------------------------------- ## Create a data.table named DT that is ten elements long, with the following columns: ## V1: the integers 1-10 ## V2: the letters A and B, repeating ## V3: the integers 1-5, repeating ## V4: the following vector: c(4.55, 2.6, 90.9, 21.2, 4.81, 77.1, 4.4, 8.43, 5.09, 2.33) custom_vector <- c(4.55, 2.6, 90.9, 21.2, 4.81, 77.1, 4.4, 8.43, 5.09, 2.33) DT <- data.table(V1=1:10, V2=c("A", "B"), V3=1:5, V4=custom_vector) ## Subsetting on rows (i) -------------------------------------------------------------- ## Select all rows in which V3 is equal to 4 DT[V3==4] ## Select all rows in which V3 is equal to 3 or 4 DT[V3 %in% 3:4] DT[V3==3 | V3==4] # equivalent ## Select all rows in which V2 is equal to A DT[V2=="A"] ## Note: can also put a comma at the end of each of these statements (e.g. DT[V3==4,]) ## Subsetting on columns (j) -------------------------------------------------------------- ## Select column V1 DT[, V1] ## Select columns V1 and V4 DT[, .(V1, V4)] DT[, list(V1, V4)] # equivalent ## Take the sum of column V4 DT[, sum(V4)] ## Return the sum of column V4 (named 'sum') and the maximum value of column V1 (named 'max') DT[, .(sum=sum(V4), max=max(V1))] ## Doing (j) *by* group -------------------------------------------------------------- ## Take the sum of column V4, by column V2 DT[, .(sum(V4)), by=V2] ## Do the same as above, but name the summed column "sum_V4" DT[, .(sum_V4=sum(V4)), by=V2] ## Do the same as above, but take the sum by column V2 and V3 DT[, .(sum_V4=sum(V4)), by=.(V2, V3)] ## Adding/Updating columns -------------------------------------------------------------- ## Create a new column, V5, equal to the minimum value of V4. DT[, V5:=min(V4)] ## Do the same thing, but grouping by column V2. DT[, V5:=min(V4), by=V2] ## Delete column V5. DT[, V5:= NULL] ## Create a new column, V6, equal to the standard deviation of V4, ## AND a new column, V7, equal to the sum of V3, grouped by V2. ## Note: do this in a single command. DT[, c("V6", "V7") := .(sd(V4), sum(V3)), by=V2] ## or : DT[, ':=' (V6=sd(V4), V7=sum(V3)), by=V2] ## Delete column V6 and V7. DT[, c("V6", "V7") := NULL]

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/scripts/skills_extraction.R

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PPPeck313/team_tidy

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skills_extraction.R

library(httr) library(jsonlite) library(tidyverse) library(stringr) get_token <- function(client_id, secret, scope){ url <- "https://auth.emsicloud.com/connect/token" payload <- str_interp("client_id=${client_id}&client_secret=${secret}&grant_type=client_credentials&scope=${scope}") encode <- "form" response <- VERB("POST", url, body = payload, add_headers(Content_Type = 'application/x-www-form-urlencoded'), content_type("application/x-www-form-urlencoded"), encode = encode) token_text <- content(response, "text") token_json <- fromJSON(token_text) access_token <- token_json$access_token return(access_token) } get_skills <- function(description, confidence_threshold, access_token){ url <- "https://emsiservices.com/skills/versions/latest/extract" clean_description <- description %>% str_replace_all("\n","") %>% str_replace_all("\r","") payload <- str_c("{ \"text\": \"... ", clean_description, " ...\", \"confidenceThreshold\": ", confidence_threshold, " }") token_string <- str_interp('authorization: Bearer ${access_token}') encode <- "json" response <- VERB("POST", url, body = payload, add_headers( Authorization = token_string, Content_Type = 'application/json'), content_type("application/json"), encode=encode ) response_text <- content(response, "text") response_json <- fromJSON(response_text) skill_type <- response_json$data$skill$type$name skill_name <- response_json$data$skill$name skill_df <- as_tibble(skill_name) colnames(skill_df) <- c("skill") skill_df <- skill_df %>% mutate( type = skill_type ) return(skill_df) } create_skills_df <- function(job_title, company_name, state, description, confidence_threshold, access_token){ base_df <- tibble( job_title = character(), company_name = character(), state = character(), description = character(), skill = character(), type = character() ) skills_df <- get_skills(description, confidence_threshold, access_token) if (length(skills_df)==0){ print("error with job") return(base_df) } skills_df <- skills_df %>% mutate( job_title = job_title, company_name = company_name, state = state, description = description ) %>% select(job_title, company_name, state, description, skill, type) return(skills_df) } get_dataset_skills <- function(df, confidence_threshold, access_token){ base_df <- tibble( job_title = character(), company_name = character(), state = character(), description = character(), skill = character(), type = character() ) for (row in 1:nrow(df)){ job_title = df[row,"job_title"][[1]] company_name = df[row,"company_name"][[1]] state = df[row,"state"][[1]] description = df[row,"description"][[1]] print(c(job_title, company_name, state,description)) skills_df = create_skills_df(job_title, company_name, state, description, confidence_threshold, access_token) base_df <- bind_rows(base_df, skills_df) } return(base_df) }

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/plot1.R

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DDHGITHUB/ExData_Plotting1

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plot1.R

## Read in table DF <- read.table("household_power_consumption.txt", header=TRUE, as.is = T , sep=";") ## Convert Data field to Date format DF$DD<-as.Date(DF$Date,"%d/%m/%Y") ## Filter only 2 days needed DF.select = DF[(DF$DD == "2007-02-01" | DF$DD == "2007-02-02"),] ## make GAP field a number DF.select$Global_active_power<-as.numeric(DF.select$Global_active_power) ## make the histogram hist(DF.select$Global_active_power,col="Red", main = "Global Active Power", xlab="Global Active Power (kilowatts)") # Make a png dev.copy(png, file="plot1.png", width=480, height=480) dev.off()

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/data-raw/corn_110110.R

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BrunoProgramming/BBOToolkit

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corn_110110.R

corn_110110 <- read_fwf('data-raw/XCBT_C_FUT_110110.TXT', fwf_widths(c(8,6,8,1,3,1,4,5,7,1,7,1,1,1,1,1,1,2,1,1,1,1,1,6 ), col_names = c("TradeDate", "TradeTime", "TradeSeq#", "SessionInd", "TickerSym", "FOIInd", "DeliveryDate", "TrQuantity", "Strike Price", "StrikePrDecLoc", "TrPrice", "TRPRDecLoc", "ASKBID", "IndicativeQuote", "MarketQuote", "CloseOpen", "OpenException", "PostClose", "CancelCode", "InsertedPrice", "FastLast", "Cabinet", "Book", "EntryDate")), col_types = cols("i", "c", "i", "c", "c", "c", "i", "i", "i", "i", "i", "i", "c", "c", "c", "c", "c", "c", "c", "c", "c", "c", "c", "i")) save(corn_110110, file = "data/corn_110110.rda") # Path to 2012-2013 in larger file format. Two years in one file. #'C:/Users/mallorym/BBOCORNDATA/2012Jan-2013Nov_txt/BBO_CBT_20120102-20131130_9552_00.txt'

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/data/genthat_extracted_code/nivm/tests/nicqTestChecks.R

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surayaaramli/typeRrh

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nicqTestChecks.R

library(nivm) ## following gives diff in prop outside CI. ## This is because ic!=ceiling(nc*q) ## Now gives warning #x<-nicqTest(20,g=nimDiffOR,delta0=.1,q=.2,nc=200,nt=300, # ic=round(600*.2),conf.int=TRUE) #x ## check that it works without specifying ic #x<-nicqTest(20,g=nimDiffOR,delta0=.1,q=.2,nc=200, # nt=300,conf.int=TRUE) #x ## check that alternative="greater" works ## x=114 barely rejects at 0.025 level #x<-nicqTest(114,g=nimDiffOR,delta0=.1,q=.2,nc=200, # nt=300,conf.int=TRUE,alternative="greater") ## x=113 barely fails to reject at 0.025 level #x<-nicqTest(113,g=nimDiffOR,delta0=.1,q=.2,nc=200, # nt=300,conf.int=TRUE,alternative="greater")

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henriqueineves/Data-Science-Coursera

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plot4.R

##Code for the second Peer Review assigments of course 4 Exploratory data analysis #Loaging the packages: library(zip); library(ggplot2) #Download, unzip the file, openning the data: if (!file.exists("NEI_data.zip")){ download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip", destfile = "NEI_data.zip", method = "curl") }else{ print("File already exists") } unzip("NEI_data.zip", exdir = "./NEI_Data") #Since both archives are in RDS format, to open them: summary_data <- readRDS("./NEI_Data/summarySCC_PM25.rds") source_code <- readRDS("./NEI_Data/Source_Classification_Code.rds") #Transform the year in a factor: summary_data <- transform(summary_data, year = as.factor(year)) summary_data <- transform(summary_data, SCC = as.factor(SCC)) #Plot4: #Getting all the data involved in coal: coal <- source_code$SCC[grep("[Cc]oal", source_code$EI.Sector)] sub_coal <- summary_data[summary_data$SCC %in% coal, ] sum_coal <- aggregate(sub_coal$Emissions, by = list(Year = sub_coal$year), FUN = sum) #Plotting png("plot4.png", width = 480, height = 480) barplot(sum_coal$x, names.arg = sum_coal$Year, ylab = "PM25 Emission by Coal", xlab = "Year") dev.off()

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dleutnant/tsconvert

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write_xts.R

#' Writes xts objects to file #' @title Write xts objects to file #' @param xts The xts object to write. #' @param file A connection, or a character string naming the file to write to. #' If file is "", print to the standard output connection. #' @param format The time format. #' @param sep The field separator string. Values within each row of x are #' separated by this string. #' @param dec The string to use for decimal points in numeric or complex #' columns: must be a single character. #' @rdname write_xts #' @export #' @seealso \code{\link[xts]{xts}}. write_xts <- function(xts, file="", format= "%Y-%m-%d %H:%M:%S", sep=";", dec = ".") { utils::write.table(data.frame(Index = format.POSIXct(zoo::index(xts), format = format), zoo::coredata(xts)), file = file, dec = dec, sep = sep, row.names = FALSE, quote = FALSE) }

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SIS.R

alp.m <- 0.000006 #transmission rate:male person-1 day-1 alp.f <- 0.0000009 #transmission rate:female person-1 day-1 gam.m <- 0.05 #recovery rate:male day-1 gam.f <- 0.007 #recovery rate:female day-1 Sm <- 14000 #susceptible males Sf <- 9000 #susceptible females Im <- 1000 #infected males If <- 1000 #infected females Sm.hist <- c() #Initialize vectors to hold pop. size as time goes by Sf.hist <- c() Im.hist <- c() If.hist <- c() for (day in 1:2000) { #2000 day time period Sm.hist[day] <- Sm #Each time step will update current value of pop. sizes Sf.hist[day] <- Sf Im.hist[day] <- Im If.hist[day] <-If delta.Sm <- (gam.m*Im-alp.m*Sm*If) #Equations for change in number of susceptible delta.Sf <- (gam.f*If-alp.f*Sf*Im) delta.Im <- (alp.m*Sm*If-gam.m*Im) #Equations for change in number of infected delta.If <- (alp.f*Sf*Im-gam.f*If) Sm <- Sm + delta.Sm #Update population sizes Sf <- Sf + delta.Sf Im <- Im + delta.Im If <- If + delta.If Sm <- max(Sm,0) #Make sure population sizes stay in the positive Sf <- max(Sf,0) Im <- max(Im,0) If <- max(If,0) } plot(Sm.hist, type="l", ylim=c(0,14000), xlab="Time (days)", ylab="Number of individuals") #Plot each pop. pool lines(Sf.hist,col=2) lines(Im.hist,col=3) lines(If.hist,col=4)

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x=c(3,5,8,4,1) if("8" %in% x){ print("Yes") } if("2" %in% x){ print("yes") }else{ print("no") } num=8 if((num %% 2) == 0) { print(paste(num,"is Even")) } else { print(paste(num,"is Odd")) } x=c("Yash Verma") i=1 repeat { print(x) i=i+1 if (i>5) { break } } x=c("Yash Verma") i=1 while (i<5) { print(x) i=i+1 } i=1 while (i<11) { print(i) i=i+1 } num=10 if(num < 0) { print("Enter a positive number") } else { sum = 0 while(num > 0) { sum = sum + num num = num - 1 } print(paste("The sum is", sum)) } x=c(1,2,3,4,5) for (2 in x) { print("yes") } x <- c(2,5,3,9,8,11,6) count <- 0 for (val in x) { if(val %% 2 == 0) count = count+1 } print(count) i=1 for (i in 1:10) { print(i) } i=1 sum=0 for (i in 1:10) { sum = sum+i } print(sum) fruit <- c('Apple', 'Orange', 'Passion fruit', 'Banana') for ( i in fruit){ print(i) }

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/code/figures/Hummingbird Range Graph.R

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austinspence/hbird_transplant

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Hummingbird Range Graph.R

#### Experimental Design Figure -------------------- # Austin Spence # October 12th, 2017 par(mfrow=c(1, 2)) #par(mfrow=c(1, 1)) ### Range Plot plot(c(1:5), 1:5, type = "n", ylab = "Elevation (m)", xlab = NA, axes = FALSE, main="Hummingbird Range Along Sierra Nevada Mountain") Axis(side=2, at = 1:5, labels=c("0", "1000", "2000", "3000", "4000"), tick = TRUE) polygon(1:5, c(1, 3, 5, 3, 1)) #mountain polygon(1:5, c(1, 3, 3, 3, 1), density = c(10, 20)) #Anna's Range legend("topright", title="Species", c("Anna","Calliope"), cex = 0.8, density = c(30, 0) ) ### Historic Range Plot plot(c(1:5), 1:5, type = "n", ylab = "Elevation (m)", xlab = NA, axes = FALSE, main="Hummingbird Range Along Sierra Nevada Mountain") Axis(side=2, at = 1:5, labels=c("0", "1000", "2000", "3000", "4000"), tick = TRUE) polygon(1:5, c(1, 3, 5, 3, 1)) #mountain polygon(c(1, 1.5, 2.5, 4.5, 5), c(1, 2, 2, 2, 1), density = c(10, 20)) #Anna's Range legend("topright", title="Species", c("Anna","Calliope"), cex = 0.8, density = c(30, 0) ) ## Experiment Plot plot(c(1:5), 1:5, type = "n", ylab = "Elevation (m)", xlab = NA, axes = FALSE, main="Acclimatization Experiment") Axis(side=1, at = c(1,3,5), labels=c("Capture", "Acclimatization", "End"), tick = TRUE) Axis(side=2, at = 1:5, labels=c("0", "1000", "2000", "3000", "4000"), tick = TRUE) segments(x0 = 1, y0 = 2, x1 = 4.5, y1 = 2, col = "red") segments(x0 = 4.5, y0 = 2, x1 = 4.5, y1 = 4.98, col = "red") segments(x0 = 4.5, y0 = 4.98, x1 = 5, y1 = 4.98, col = "red") segments(x0 = 1, y0 = 2.01, x1 = 1.5, y1 = 2.01, col = "black") segments(x0 = 1.5, y0 = 2.01, x1 = 1.5, y1 = 5, col = "black") segments(x0 = 1.5, y0 = 5, x1 = 5, y1 = 5, col = "black") points(x = 1.3, y = 2, pch = 15) points(x = 4.7, y = 5, pch = 15) points(x = 1.7, y = 5, pch = 15) points(x = 5, y = 5, pch = 16) text(1.1, 5, "B") ### Current Range Plot with Overlapping Ranges plot(c(1:5), 1:5, type = "n", ylab = "Elevation (m)", xlab = NA, axes = FALSE, main= "Anna's and Calliope Hummingbird Range Along Sierra Nevada Mountain") Axis(side=2, at = 1:5, labels=c("0", "1000", "2000", "3000", "4000"), tick = TRUE) polygon(1:5, c(1, 3, 5, 3, 1), col = "darkviolet", border = NA) #mountain polygon(c(1, 2.25, 3, 3.76, 5), c(1, 3.5, 3.5, 3.5, 1), col = "pink", border = NA) #Anna's Range polygon(c(1.5, 2.24, 3, 3.76, 4.5), c(2, 3.5, 3.5, 3.5, 2), col = "darkviolet", border = NA, density = (3.5), lwd = 5) legend("topright", title="Species", c("Calliope", "Anna"), cex = .8, fill = c("darkviolet", "pink")) text(1.1, 5, "A") ### Current Range Plot with Anna's Only plot(c(1:5), 1:5, type = "n", ylab = "Elevation (m)", xlab = NA, axes = FALSE, main= "Anna's Hummingbird Range Along Sierra Nevada Mountain") Axis(side=2, at = 1:5, labels=c("0", "1000", "2000", "3000", "4000"), tick = TRUE) polygon(1:5, c(1, 3, 5, 3, 1)) #mountain polygon(c(1, 2.25, 3, 3.76, 5), c(1, 3.5, 3.5, 3.5, 1), col = "pink") #Anna's Range

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/R/data_gen.R

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liangyuanhu/CIMTx

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data_gen.R

#' Data generation function #' #'This function generates data to test different causal inference methods. #' @param n total number of units for simulation #' @param scenario simulation scenario 1 or scenario 2 #' @param ratio ratio of units in the treatment groups #' @param overlap levels of covariate overlap: Please select: weak, strong, moderate #' @param all_confounder TRUE or FALSE. overlap is lacking for a variable that is not predictive of the outcome (all_confounder equals to TRUE) or situations when it is lacking for a true confounder (all_confounder equals to FALSE) #' #' @return list with the 5 elements. Nested within each list, it contains #' \item{n:}{Number of units for simulation} #' \item{trt_ind:}{A data frame with number of rows equals to n and 11 columns} #' \item{Y:}{Observed binary outcome for 3 treatments} #' \item{Yobs:}{Observed binary outcome} #' \item{Est:}{True ATE/ATT for RD/RR/OR} #' @export data_gen #' #' @examples #' library(CIMTx) #' set.seed(3242019) #' idata = data_gen(n = 120, ratio =1,scenario = 1) data_gen <- function(n, scenario, ratio, overlap, all_confounder){ if (scenario == 1) { data_gen_result <- data_gen_p1(n, ratio,all_confounder=FALSE) } if (scenario == 2) { data_gen_result <- data_gen_p1(n, overlap, all_confounder) } return(data_gen_result) }

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/R/PTMscape_main_functions.R

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ginnyintifa/PTMscape

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2021-11-24T13:28:44.458778

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PTMscape_main_functions.R

### do every thing without decoy # pssm_generation --------------------------------------------------------- pssm_generation = function(candidate_Rds_name, center_position, output_label) { candidate = readRDS(candidate_Rds_name) pos_window <- candidate %>% dplyr::filter(label == "positive") %>% dplyr::select(window) ### to check if size and center position aligns if(center_position == (nchar(pos_window$window[1])+1)/2) { noc_pos_window = pos_window$window str_sub(noc_pos_window, center_position, center_position) <- "" }else{ warning("window size error!") } #neg_windows = noc_candi_window pos_windows = noc_pos_window positive_base_matrix = get_base_freq_matrix(pos_windows) pssm=positive_base_matrix pssm[is.infinite(pssm)]=0 saveRDS(pssm, file = paste0(output_label,"_pssm.Rds")) pssm_df = data.frame(AA =c("A","R","N","D","C","Q","E","G","H","I", "L","K","M","F","P","S","T","W","Y","V","X","U"), pssm) write.table(pssm_df, paste0(output_label,"_pssm.tsv"), quote = F, sep = "\t", row.names = F) } # window_formation -------------------------------------------------------- window_formation = function(mod_site, flanking_size, prot_seqs, prot_ids, positive_info, output_label) { candidate = get_all_candidate(mod_site, flanking_size,prot_seqs, prot_ids, positive_info) saveRDS(candidate, file = paste0(output_label,"_candidate.Rds")) write.table(candidate, paste0(output_label,"_candidate.tsv"), row.names = F, quote = F, sep = "\t") } window_formation_no_positive = function(mod_site, flanking_size, prot_seqs, prot_ids, output_label) { candidate = get_all_candidate_no_positive(mod_site, flanking_size,prot_seqs, prot_ids) saveRDS(candidate, file = paste0(output_label,"_candidate.Rds")) write.table(candidate, paste0(output_label,"_candidate.tsv"), row.names = F, quote = F, sep = "\t") } # pssm_feature_extraction ------------------------------------------------- ## there is no way to get the protein mean of pssm features pssm_feature_extraction = function(candidate_Rds_name, pssm_Rds_name, center_position, output_label) { candidate = readRDS(candidate_Rds_name) pssm = readRDS(pssm_Rds_name) candi_window <- candidate %>% dplyr::filter(label == "negative") %>% dplyr::select(window) # cat("candi size: ",nrow(candi_window),"\n") ### to check if size and center position aligns if(center_position == (nchar(candi_window$window[1])+1)/2) { ### remove the center site noc_candi_window = candi_window$window str_sub(noc_candi_window, center_position, center_position) <- "" }else{ warning("window size error!") } noc_candi_pssm = t(sapply(1:length(noc_candi_window), function(x) { get_pssm_feature(pssm,noc_candi_window[x]) })) saveRDS(noc_candi_pssm, file = paste0(output_label, "_noc_candi_pssm_matrix.Rds")) pos_window <- candidate %>% dplyr::filter(label == "positive") %>% dplyr::select(window) #cat("pos size: ",nrow(pos_window),"\n") if(nrow(pos_window)>0) { if(center_position == (nchar(pos_window$window[1])+1)/2) { ### remove the center site noc_pos_window = pos_window$window str_sub(noc_pos_window, center_position, center_position) <- "" }else{ warning("window size error!") } noc_pos_pssm = t(sapply(1:length(noc_pos_window), function(x) { get_pssm_feature(pssm,noc_pos_window[x]) })) saveRDS(noc_pos_pssm, file = paste0(output_label, "_noc_pos_pssm_matrix.Rds")) } } # aaindex_feature_extraction ---------------------------------------------- ### input: Rdata for candidate and decoy, the file of aaindex properties ### output: Rdata of the aaindex matrix aaindex_feature_extraction = function( aaindex_property, candidate_Rds_name, center_position, output_label) { ### for each protein get the protein mean of the features candidate = readRDS(candidate_Rds_name) candi = candidate%>%dplyr::filter(label == "negative") candi_window <- candidate %>% dplyr::filter(label == "negative") %>% dplyr::select(window) pos = candidate%>%dplyr::filter(label == "positive") pos_window <- candidate %>% dplyr::filter(label == "positive") %>% dplyr::select(window) rm(candidate) ### to check if size and center position aligns if(center_position == (nchar(candi_window$window[1])+1)/2) { noc_candi_window = candi_window$window str_sub(noc_candi_window, center_position, center_position) <- "" noc_candi_cluster = get_matrix_all(noc_candi_window, aaindex_property) saveRDS(noc_candi_cluster,file = paste0(output_label,"_noc_candi_cluster_matrix.Rds")) # cat("get window aaindex","\n") }else{ warning("window size error!") } if(nrow(pos_window)>0) { if(center_position == (nchar(pos_window$window[1])+1)/2) { noc_pos_window = pos_window$window str_sub(noc_pos_window, center_position, center_position) <- "" noc_pos_cluster = get_matrix_all(noc_pos_window, aaindex_property) saveRDS(noc_pos_cluster,file = paste0(output_label,"_noc_pos_cluster_matrix.Rds")) #cat("get window aaindex","\n") }else{ warning("window size error!") } } } # spider_feature_extraction ----------------------------------------------- spider_feature_joining_without_mean = function(candidate_Rds_name, extracted_spider_Rds_name, spider_which_retain, spider_which_logit, center_position, #spider_which_center, output_label) { protID_pos_spider_site_specific = readRDS(extracted_spider_Rds_name) ###for each position there is 4 spider properties. ### so the four properties for the center site are in column center_start = 4*(center_position-1)+1 spider_which_center = c(center_start:(center_start+3)) candidate = readRDS(candidate_Rds_name) pos = candidate%>%dplyr::filter(label == "positive") candi = candidate%>%dplyr::filter(label == "negative") #cat("see mem change","\n") #cat(mem_change(rm(candidate)),"\n") #### pos pos_site_specific = dplyr::left_join(pos,protID_pos_spider_site_specific,by = c("protID", "pos")) rm(pos) #### the only correct way to get from the back last_col = ncol(pos_site_specific) get_col = c((last_col-100+1):last_col) pos_site_specific_matrix = as.matrix(pos_site_specific[,get_col]) rm(pos_site_specific) noc_pos_structure = pos_site_specific_matrix[, -spider_which_center] saveRDS(noc_pos_structure, file = paste0(output_label, "_noc_pos_structure_matrix.Rds")) rm(noc_pos_structure) # cat("pos_processed","\n") #### candi candi_site_specific = dplyr::left_join(candi,protID_pos_spider_site_specific,by = c("protID", "pos")) rm(candi) candi_site_specific_matrix = as.matrix(candi_site_specific[,get_col]) rm(candi_site_specific) noc_candi_structure = candi_site_specific_matrix[, -spider_which_center] saveRDS(noc_candi_structure, file = paste0(output_label, "_noc_candi_structure_matrix.Rds")) rm(noc_candi_structure) #cat("candi_processed","\n") } # feature_combining ------------------------------------------------------- combine_all_features = function(pos_aaindex = NULL, candi_aaindex, pos_spider = NULL, candi_spider, pos_pssm = NULL, candi_pssm, output_label) { noc_not_na_candi_cluster = readRDS(candi_aaindex) noc_not_na_candi_structure = readRDS(candi_spider) noc_not_na_candi_pssm = readRDS(candi_pssm) noc_not_na_candi_feature = cbind(noc_not_na_candi_cluster, noc_not_na_candi_structure, noc_not_na_candi_pssm) col_mean = colMeans(noc_not_na_candi_feature, na.rm = T) cat("Take care of NA","\n") for(i in 1:ncol(noc_not_na_candi_feature)) { this_na = which(is.na(noc_not_na_candi_feature[,i])) noc_not_na_candi_feature[this_na,i] = col_mean[i] } saveRDS(noc_not_na_candi_feature, file = paste0(output_label, "_noc_not_na_candi_feature.Rds")) rm(noc_not_na_candi_feature) if(!is.null(pos_aaindex)) { noc_not_na_pos_cluster = readRDS(pos_aaindex) noc_not_na_pos_structure = readRDS(pos_spider) noc_not_na_pos_pssm = readRDS(pos_pssm) noc_not_na_pos_feature = cbind(noc_not_na_pos_cluster, noc_not_na_pos_structure, noc_not_na_pos_pssm) col_mean = colMeans(noc_not_na_pos_feature, na.rm = T) for(i in 1:ncol(noc_not_na_pos_feature)) { this_na = which(is.na(noc_not_na_pos_feature[,i])) noc_not_na_pos_feature[this_na,i] = col_mean[i] } saveRDS(noc_not_na_pos_feature, file = paste0(output_label, "_noc_not_na_pos_feature.Rds")) rm(noc_not_na_pos_feature) } } combine_all_features_no_spider = function(pos_aaindex = NULL, candi_aaindex, pos_pssm = NULL, candi_pssm, output_label) { noc_not_na_candi_cluster = readRDS(candi_aaindex) noc_not_na_candi_pssm = readRDS(candi_pssm) noc_not_na_candi_feature = cbind(noc_not_na_candi_cluster, noc_not_na_candi_pssm) col_mean = colMeans(noc_not_na_candi_feature, na.rm = T) for(i in 1:ncol(noc_not_na_candi_feature)) { this_na = which(is.na(noc_not_na_candi_feature[,i])) noc_not_na_candi_feature[this_na,i] = col_mean[i] } saveRDS(noc_not_na_candi_feature, file = paste0(output_label, "_noc_not_na_candi_feature.Rds")) rm(noc_not_na_candi_feature) if(!is.null(pos_aaindex)) { noc_not_na_pos_cluster = readRDS(pos_aaindex) noc_not_na_pos_pssm = readRDS(pos_pssm) noc_not_na_pos_feature = cbind(noc_not_na_pos_cluster, noc_not_na_pos_pssm) col_mean = colMeans(noc_not_na_pos_feature, na.rm = T) for(i in 1:ncol(noc_not_na_pos_feature)) { this_na = which(is.na(noc_not_na_pos_feature[,i])) noc_not_na_pos_feature[this_na,i] = col_mean[i] } saveRDS(noc_not_na_pos_feature, file = paste0(output_label, "_noc_not_na_pos_feature.Rds")) rm(noc_not_na_pos_feature) } } ### more process with the negative data set , actually the negative dataset is either decoy or candidate ### and the process can be two forms now, 1 knn cleaning, 2 just randomly sample the same number as the positive set # negative_selection ------------------------------------------------------ balanced_size_sampling = function(pos_matrix_Rds_name, neg_matrix_Rds_name, output_label) { pos_matrix = readRDS(pos_matrix_Rds_name) neg_matrix = readRDS(neg_matrix_Rds_name) pos_size = nrow(pos_matrix) neg_size = nrow(neg_matrix) set.seed(123) chosen_neg = sample(neg_size, pos_size) balanced_neg = neg_matrix[chosen_neg, ] saveRDS(balanced_neg, file = paste0(output_label, "_balanced_neg_feature.Rds")) } balanced_size_sampling_after_combining_pos_neg = function(feature_matrix_Rds_name, feature_label_Rds_name, output_label) { feature_matrix = readRDS(feature_matrix_Rds_name) feature_label = readRDS(feature_label_Rds_name) pos_ind = which(feature_label == 1) neg_ind = which(feature_label == 0) pos_feature_matrix = feature_matrix[pos_ind,] neg_feature_matrix = feature_matrix[neg_ind,] rm(feature_matrix) pos_size = nrow(pos_feature_matrix) neg_size = nrow(neg_feature_matrix) if (pos_size<neg_size) { set.seed(123) chosen_neg_ind = sample(neg_size, pos_size) chosen_neg = neg_feature_matrix[chosen_neg_ind, ] }else{ chosen_neg = neg_feature_matrix } balanced_feature_matrix = rbind(pos_feature_matrix, chosen_neg) balanced_feature_label = c(rep(1,pos_size),rep(0, nrow(chosen_neg))) saveRDS(balanced_feature_matrix, file = paste0(output_label, "_balanced_feature_matrix.Rds")) saveRDS(balanced_feature_label, file = paste0(output_label, "_balanced_feature_label.Rds")) write.table(balanced_feature_label, paste0(output_label, "_balanced_feature_label.tsv"), quote = F, sep = "\t", row.names = F) } # pos_neg_combination ----------------------------------------------------- combine_pos_neg = function(pos_feature_matrix_Rds_name, neg_feature_matrix_Rds_name, output_label) { pos_feature_matrix = readRDS(pos_feature_matrix_Rds_name) neg_feature_matrix = readRDS(neg_feature_matrix_Rds_name) ### rbind data pos_neg_feature_matrix = rbind(pos_feature_matrix, neg_feature_matrix) col_mean = colMeans(pos_neg_feature_matrix, na.rm = T) for(i in 1:ncol(pos_neg_feature_matrix)) { this_na = which(is.na(pos_neg_feature_matrix[,i])) pos_neg_feature_matrix[this_na,i] = col_mean[i] } saveRDS(pos_neg_feature_matrix, file = paste0(output_label, "_feature_matrix.Rds")) write.table(pos_neg_feature_matrix, paste0(output_label, "_feature_matrix.tsv"), row.names = F, col.names = F, sep = "\t", quote = F) rm(pos_neg_feature_matrix) pos_neg_feature_label = c(rep(1, nrow(pos_feature_matrix)), rep(0, nrow(neg_feature_matrix))) saveRDS(pos_neg_feature_label, file = paste0(output_label, "_feature_label.Rds")) write.table(pos_neg_feature_label, paste0(output_label, "_feature_label.tsv"), row.names = F, col.names = F, sep = "\t", quote = F) rm(pos_neg_feature_label) } # n_fold_cv_without_decoy ------------------------------------------------- construct_n_fold_cv_without_decoy = function(pos_candi_feature_matrix_Rds_name,pos_candi_feature_label_Rds_name, n, output_label) { # pos_candi_feature_matrix_Rds_name = "glcnac_s_wp_feature_matrix.Rds" # pos_candi_feature_label_Rds_name = "glcnac_s_wp_feature_label.Rds" # n = 2 # output_label = "tg" # pos_candi_feature_matrix = readRDS(pos_candi_feature_matrix_Rds_name) pos_candi_feature_label = readRDS(pos_candi_feature_label_Rds_name) ### so the output should be n pairs of training/test sets ### for simplicity I want to use Caret package, it is not always good to build your own wheel pos_size = length(which(pos_candi_feature_label==1)) pos_feature_matrix = pos_candi_feature_matrix[1:pos_size,] pos_seq = c(1:pos_size) candi_size = length(which(pos_candi_feature_label==0)) candi_feature_matrix = pos_candi_feature_matrix[((pos_size+1):(pos_size+candi_size)),] candi_seq = c(1:candi_size) set.seed(123) pos_folds = caret::createFolds(pos_seq, n, FALSE ) set.seed(123) candi_folds = caret::createFolds(candi_seq, n, FALSE) for(i in 1:n) { test_pos_ind = which(pos_folds==i) test_candi_ind = which(candi_folds==i) saveRDS(test_pos_ind, file = paste0(output_label,"_test_pos_ind_",i,".Rds")) saveRDS(test_candi_ind, file = paste0(output_label,"_test_candi_ind_",i,".Rds")) #cat(test_pos_ind,"\n") #cat(test_candi_ind,"\n") train_pos_ind = pos_seq[-test_pos_ind] train_candi_ind = candi_seq[-test_candi_ind] saveRDS(train_pos_ind, file = paste0(output_label,"_train_pos_ind_",i,".Rds")) saveRDS(train_candi_ind, file = paste0(output_label,"_train_candi_ind_",i,".Rds")) #cat(train_pos_ind,"\n") #cat(train_candi_ind,"\n") test_feature = rbind(pos_feature_matrix[test_pos_ind,],candi_feature_matrix[test_candi_ind,]) # write.table(test_feature, paste0(output_label,"_test_feature_" ,i,".tsv"), # row.names = F, col.names = F, sep = "\t", quote = F) saveRDS(test_feature, file = paste0(output_label,"_test_feature_" ,i,".Rds")) rm(test_feature) train_feature = rbind(pos_feature_matrix[train_pos_ind,],candi_feature_matrix[train_candi_ind,]) #write.table(train_feature, paste0(output_label,"_train_feature_" ,i,".tsv"), # row.names = F, col.names = F, sep = "\t", quote = F) saveRDS(train_feature, file = paste0(output_label,"_train_feature_" ,i,".Rds")) rm(train_feature) test_label = c(rep(1,length(test_pos_ind)), rep(0, length(test_candi_ind))) write.table(test_label, paste0(output_label,"_test_label_" ,i,".tsv"), row.names = F, col.names = F, sep = "\t", quote = F) saveRDS(test_label, file = paste0(output_label,"_test_label_" ,i,".Rds")) train_label = c(rep(1,length(train_pos_ind)), rep(0, length(train_candi_ind))) write.table(train_label, paste0(output_label,"_train_label_" ,i,".tsv"), row.names = F, col.names = F, sep = "\t", quote = F) saveRDS(train_label, file = paste0(output_label,"_train_label_" ,i,".Rds")) } } # scale the data ---------------------------------------------------------- scale_train_test = function(feature_train_name, feature_test_name, n_fold, upper_bound, lower_bound, output_label_fortrain, output_label_fortest) { for(i in 1:n_fold) { feature_train_matrix = readRDS(paste0(feature_train_name, "_",i,".Rds")) feature_test_matrix = readRDS(paste0(feature_test_name, "_",i,".Rds")) get_train_range = scale_train(feature_train_matrix, upper_bound, lower_bound, paste0(output_label_fortrain,"_",i)) scale_test(feature_test_matrix, get_train_range, upper_bound, lower_bound, paste0(output_label_fortest,"_",i)) } } scale_train_test_single = function(feature_train_name, feature_test_name, upper_bound, lower_bound, output_label_fortrain, output_label_fortest) { feature_train_matrix = readRDS(feature_train_name) feature_test_matrix = readRDS(feature_test_name) get_train_range = scale_train(feature_train_matrix, upper_bound, lower_bound, output_label_fortrain) scale_test(feature_test_matrix, get_train_range, upper_bound, lower_bound, output_label_fortest) } scale_train_single = function(feature_train_name, upper_bound, lower_bound, output_label_fortrain) { feature_train_matrix = readRDS(feature_train_name) it = scale_train(feature_train_matrix, upper_bound, lower_bound, output_label_fortrain) } # libsvm formating -------------------------------------------------------- libsvm_formating_single = function(train_feature_name, train_label_name, test_feature_name, test_label_name, output_label) { feature_train_out_Rds_name = paste0(output_label, "_train_feature") feature_train_out_tsv_name = paste0(output_label, "_train_feature") feature_test_out_Rds_name = paste0(output_label, "_test_feature") feature_test_out_tsv_name = paste0(output_label, "_test_feature") libsvm_formating(train_feature_name, train_label_name, feature_train_out_Rds_name, feature_train_out_tsv_name) libsvm_formating(test_feature_name, test_label_name, feature_test_out_Rds_name, feature_test_out_tsv_name) } # PCA plots --------------------------------------------------------------- plot_plsda_for_two_types = function(pos_feature_Rds_name, candi_feature_Rds_name, output_label) { # pos_feature_Rds_name = "py/py_nr_size25_nms_noc_not_na_pos_feature.Rds" # candi_feature_Rds_name = "py/py_nr_size25_nms_noc_not_na_candi_feature.Rds" # output_label = "zpy" # output_label = "zpy" # pos_feature = readRDS(pos_feature_Rds_name) candi_feature = readRDS(candi_feature_Rds_name) pos_size = nrow(pos_feature) candi_size = nrow(candi_feature) ### limit pos size to 5000 if(pos_size>5000) { set.seed(123) sel_pos = pos_feature[sample(pos_size,5000),] }else{ sel_pos = pos_feature } pos_size = nrow(sel_pos) if(candi_size>=pos_size) { set.seed(123) sel_candi = candi_feature[sample(candi_size,pos_size),] }else{ sel_candi = candi_feature } sel_feature = rbind(sel_pos, sel_candi) type_label = as.factor(c(rep("positive",nrow(sel_pos)), rep("negative", nrow(sel_candi)))) get_plsda_pos_candi(sel_feature, type_label, paste0(output_label,"_plsda_plot_two.pdf")) } plot_plsda_for_two_types_with_score_selection= function(pos_feature_Rds_name, candi_feature_Rds_name, pos_score_Rds_name, candi_score_Rds_name, candi_score_threshold, output_label) { # pos_feature_Rds_name = "py/py_nr_size25_nms_noc_not_na_pos_feature.Rds" # candi_feature_Rds_name = "py/py_nr_size25_nms_noc_not_na_candi_feature.Rds" # output_label = "zpy" # output_label = "zpy" # pos_feature = readRDS(pos_feature_Rds_name) candi_feature = readRDS(candi_feature_Rds_name) pos_score = readRDS(pos_score_Rds_name) candi_score = readRDS(candi_score_Rds_name) pos_size = nrow(pos_feature) candi_size = nrow(candi_feature) ### limit pos size to 5000 if(pos_size>5000) { set.seed(123) sel_pos_ind = sample(pos_size,5000) sel_pos = pos_feature[sel_pos_ind,] sel_pos_score = pos_score[sel_pos_ind,] }else{ sel_pos = pos_feature sel_pos_score = pos_score } pos_size = nrow(sel_pos) if(candi_size>=pos_size) { set.seed(123) sel_candi_ind = sample(candi_size,pos_size) sel_candi = candi_feature[sel_candi_ind,] sel_candi_score = candi_score[sel_candi_ind,] }else{ sel_candi = candi_feature sel_candi_score = candi_score } sel_feature = rbind(sel_pos, sel_candi) candi_score_label = rep("not_selected",nrow(sel_candi)) candi_score_label[which(candi_score>= candi_score_threshold)] = "selected" score_label = as.factor(c(rep("selected", nrow(sel_pos)), candi_score_label)) type_label = as.factor(c(rep("positive",nrow(sel_pos)), rep("negative", nrow(sel_candi)))) get_plsda_pos_candi_with_score_selection(sel_feature, type_label, score_label, paste0(output_label,"_plsda_plot_two_score.pdf")) } # AUC_calculation --------------------------------------------------------- # MCC calculation for prediction score threshold -------------------------- ### modifiy this so that it can hold k fold rather than 2-fold only # get_score_threshold_for_whole_proteome_mode ----------------------------- get_score_threshold = function(prediction_score_file_names, test_label_file_names, specificity_level, output_label) { # prediction_score_path = "/data/ginny/liblinear-2.11/test_package/ps_cv_predict/" # test_label_path = "/data/ginny/test_package/pred/ps_cv_predict/" # # prediction_score_file_names = "ps_0103_predict.txt" # test_label_file_names = "ps_0103_test_label.txt" # output_label = "ps_0103" # specificity_level = 0.99 score_files = readRDS(prediction_score_file_names) label_files = readRDS(test_label_file_names) ### need to think about how I can combine them together all_pred_df = data.frame(rbindlist(lapply(1:length(score_files), function(i) { this_predict = data.table::fread(score_files[i], stringsAsFactors = F, header = T) this_label = data.table::fread(label_files[i], stringsAsFactors = F, header = F) pred_df = cbind(this_predict, this_label) old_cn = colnames(pred_df) new_cn = c("pred_label","pos_score","neg_score","true_label") new_cn[which(old_cn == "labels")] = "pred_label" new_cn[which(old_cn == "1")] = "pos_score" new_cn[which(old_cn == "0")] = "neg_score" colnames(pred_df) = new_cn return(pred_df %>% dplyr::select(pos_score, true_label)) })), stringsAsFactors = F) candi_score = all_pred_df %>% dplyr::filter(true_label == 0) %>% dplyr::select(pos_score) positive_score = all_pred_df %>% dplyr::filter(true_label == 1) %>% dplyr::select(pos_score) saveRDS(candi_score$pos_score, file = paste0(output_label, "_candi_score.Rds")) saveRDS(positive_score$pos_score, file = paste0(output_label, "_positive_score.Rds")) #### then output AUC and specificity and MCC etc total_label = all_pred_df$true_label total_score = all_pred_df$pos_score #### combine all the positive together and all the candidate together # roc_test = roc(total_label, total_score) # cat("AUC", roc_test$auc,"\n") # record_mcc = rep(0,999) record_spec = rep(0,999) for(i in 1:999) { cutoff = i/1000 tp = sum(total_label==1 & total_score>cutoff) tn = sum(total_label==0 & total_score<=cutoff) fp = sum(total_label==0 & total_score>cutoff) fn = sum(total_label==1 & total_score<=cutoff) # cat(cutoff, tp, tn, fp, fn, "\n") this_spec = tn/(tn+fp) this_mcc = calculate_MCC(tp,fp, fn, tn) # cat(this_mcc, "\n") record_mcc[i] = this_mcc record_spec[i] = this_spec } max_mcc = max(record_mcc, na.rm = T) score_max_mcc = which.max(record_mcc)/1000 get_spec = abs(record_spec-specificity_level) score_which_spec = which.min(get_spec)/1000 cat("at specificity level wanted, score cutoff is: ", score_which_spec,"\n") #cat("at specificity level wanted, how many sites are predicted? ", # length(which(candi_score$pos_score>score_which_spec)), "\n") cat("threshold at best MCC ", score_max_mcc, "\n" ) cat("best MCC", max_mcc,"\n") btp = sum(total_label==1 & total_score>score_max_mcc) btn = sum(total_label==0 & total_score<=score_max_mcc) bfp = sum(total_label==0 & total_score>score_max_mcc) bfn = sum(total_label==1 & total_score<=score_max_mcc) sens = btp/(btp+bfn) spec = btn/(btn+bfp) #cat("sens and spec at best MCC ", sens,"\t", spec, "\n" ) #cat("how many candidate predicted: ", length(which(candi_score>score_max_mcc)), "\n") # pdf(paste0(output_label,"_roc.pdf"), useDingbats = F) # plot(roc_test) # dev.off() # pdf(paste0(output_label,"_candidate_score_hist.pdf"), useDingbats = F) hist(candi_score$pos_score,breaks = 50, main = "predicted_score_on_candidate_sites") dev.off() pdf(paste0(output_label,"_positive_score_hist.pdf"), useDingbats = F) hist(positive_score$pos_score,breaks = 50, main = "predicted_score_on_positive_sites") dev.off() pdf(paste0(output_label,"_both_score_dens.pdf"), useDingbats = F) pd = density(positive_score$pos_score) cd = density(candi_score$pos_score) ymax = max(c(pd$y, cd$y)) plot(cd, ylim = c(0,ymax), main = "candidate_pos_score", col = "blue") lines(pd, col = "red") dev.off() return(score_which_spec) } assemble_window_score_cv = function(candidate_df_Rds, positive_index_file_names, candi_index_file_names, positive_score_Rds, candi_score_Rds, score_threshold, id_convert, output_label) { # candidate_df_Rds = "ps_wp_52_candidate.Rds" # positive_index_file_names = "ps_wp_52_pos_index_names.Rds" # candi_index_file_names = "ps_wp_52_candi_index_names.Rds" # positive_score_Rds = "ps_wp_52_positive_score.Rds" # candi_score_Rds = "ps_wp_52_candi_score.Rds" # score_threshold = 0.683 # output_label = "test_0125" candidate_df = readRDS(candidate_df_Rds) ### now get the order correct positive_score = readRDS(positive_score_Rds) candi_score = readRDS(candi_score_Rds) #### tidy up the order of index and score and combine them together ### only need to look at test indices positive_df = candidate_df%>%dplyr::filter(label == "positive") candi_df = candidate_df%>%dplyr::filter(label == "negative") ###start from here tomorrow ### get the order of the index and the order of score in the same way all_positive_ind = readRDS(positive_index_file_names) all_candi_ind = readRDS(candi_index_file_names) tidy_positive_ind = data.frame(rbindlist( lapply( 1:length(all_positive_ind), function(i) { this_positive_ind = readRDS(all_positive_ind[i]) return(data.frame(ind = this_positive_ind, stringsAsFactors = F)) })), stringsAsFactors = F) positive_df_order = positive_df[tidy_positive_ind$ind,] positive_df_order_score = data.frame(positive_df_order, pred_score = positive_score) rm(positive_df_order) tidy_candi_ind = data.frame(rbindlist( lapply( 1:length(all_candi_ind), function(i) { this_candi_ind = readRDS(all_candi_ind[i]) return(data.frame(ind = this_candi_ind, stringsAsFactors = F)) })), stringsAsFactors = F) candi_df_order = candi_df[tidy_candi_ind$ind,] candi_df_order_score = data.frame(candi_df_order, pred_score = candi_score) rm(candi_df_order) df_score = rbind(positive_df_order_score, candi_df_order_score) rm(positive_df_order_score, candi_df_order_score) colnames(id_convert) = c("protID","gene_name") df_score_label = df_score %>% dplyr::mutate(pred_label = "negative") %>% dplyr::mutate(pred_label = replace(pred_label, pred_score >= score_threshold, "positive")) %>% dplyr::mutate(prediction_label = pred_label) %>% dplyr::mutate(combined_label = replace(pred_label, label == "positive", "positive")) %>% dplyr::mutate(known_label = label)%>% dplyr::mutate(threshold = score_threshold)%>% dplyr::left_join(id_convert) %>% dplyr::arrange(protID, pos) %>% dplyr::select(protID, gene_name, pos, window, pred_score, threshold,prediction_label, known_label, combined_label) rm(df_score) write.table(df_score_label,paste0(output_label, "_window_score_df.tsv"), sep = "\t", quote = F, row.names = F, na = "") saveRDS(df_score_label, file = paste0(output_label, "_window_score_df.Rds")) } assemble_window_score_target = function(prediction_score_file, predict_candidate_df_Rds, id_convert, score_threshold, output_label) { #### simply combine # # prediction_score_file = "/data/ginny/liblinear-2.11/test_package/ps_predict_test_predict.tsv" # # predict_candidate_df_Rds = "ps_predict_candidate.Rds" # # score_threshold = 0.683 # # output_label = "ps_predict" # # pred_score_df = data.table::fread(prediction_score_file, stringsAsFactors = F, header = T) ### I think it is safer to code it in a matching manner old_cn = colnames(pred_score_df) new_cn = c("pred_label","pos_score","neg_score") new_cn[which(old_cn == "labels")] = "pred_label" new_cn[which(old_cn == "1")] = "pos_score" new_cn[which(old_cn == "0")] = "neg_score" colnames(pred_score_df) = new_cn pred_df = readRDS(predict_candidate_df_Rds) colnames(id_convert) = c("protID","gene_name") pred_df_score_label = pred_df %>% dplyr::mutate(pred_score = pred_score_df$pos_score) %>% dplyr::mutate(pred_label = "negative") %>% dplyr::mutate(pred_label = replace(pred_label, pred_score >= score_threshold, "positive")) %>% dplyr::mutate(prediction_label = pred_label) %>% dplyr::mutate(combined_label = prediction_label)%>% dplyr::mutate(known_label = label)%>% dplyr::mutate(threshold = score_threshold) %>% dplyr::left_join(id_convert) %>% dplyr::arrange(protID, pos) %>% dplyr::select(protID, gene_name, pos, window, pred_score, threshold, prediction_label, known_label, combined_label) write.table(pred_df_score_label,paste0(output_label, "_window_score_df.tsv"), sep = "\t", quote = F, row.names = F, na = "") saveRDS(pred_df_score_label, file = paste0(output_label, "_window_score_df.Rds")) } # select_before_feature_extraction ---------------------------------------- select_equal_size_candidate_decoy = function(candidate_Rds_name, output_label) { candidate_df = readRDS(candidate_Rds_name) candi_df <- candidate_df%>%dplyr::filter(label == "negative") candi_nrow = nrow(candi_df) pos_df <- candidate_df%>%dplyr::filter(label == "positive") pos_nrow = nrow(pos_df) set.seed(123) choose_candi = sample(candi_nrow, pos_nrow) choose_candi_df = candi_df[choose_candi,] choose_candidate_df = rbind(pos_df, choose_candi_df) write.table(choose_candidate_df, paste0(output_label, "_candidate.tsv"), quote = F, row.names = F, sep = "\t") saveRDS(choose_candidate_df, paste0(output_label, "_candidate.Rds")) } ### use R to control the terminal extract_site_specific_features_new=function(feature_data, to_extract, to_logit, center_position) { #feature_data = half_spider_matrix1 # set.seed(123) # feature_data = matrix(abs(rnorm(10*250)),nrow = 10, ncol = 250) #center_position = 13 #to_extract = c(1,6,8,9) #to_logit = c(8,9) logit_from_extract = which(to_extract %in% to_logit) ##### extract first total_feature_length = 2*(center_position-1)+1 extractbs = matrix(rep(seq(0, 10*(total_feature_length-1),10),length(to_extract)), nrow=total_feature_length, ncol=length(to_extract)) add_extractbs = sapply(1:length(to_extract), function(x) extractbs[,x]+to_extract[x]) vec_add_extractbs = sort(as.vector(add_extractbs)) extract_feature_data = feature_data[,vec_add_extractbs, with = F] rm(feature_data) ##### log second ef = length(to_extract) logbs = matrix(rep(seq(0, ef*(total_feature_length-1),ef),length(logit_from_extract)), nrow=total_feature_length, ncol=length(logit_from_extract)) add_logbs = sapply(1:length(logit_from_extract), function(x) logbs[,x]+logit_from_extract[x]) vec_add_logbs = sort(as.vector(add_logbs)) ### arrange a matrix to get all the columns need to be loggit extract_feature_data = as.matrix(extract_feature_data) tar = extract_feature_data[, vec_add_logbs] tar[which(tar<0.001)]=0.001 tar[which(tar>0.999)]=0.999 logitit = function(p1){return(log(p1/(1-p1)))} logit_tar = logitit(tar) ### place back these columns to the orignal data extract_feature_data[,vec_add_logbs] = logit_tar return(extract_feature_data) } # window score assembly ---------------------------------------------- #### ok the following two functions are still in the test procedure # prediction_annotation --------------------------------------------------- domain_subcellular_mapping = function(pos_window_score_Rds, candi_window_score_Rds, dm_df_Rds, sc_df_Rds, output_label) { # pos_window_score_Rds = "glcnac_s_pred/glcnac_s_pred_pos_window_score.Rds" # candi_window_score_Rds = "glcnac_s_pred/glcnac_s_pred_candi_window_score.Rds" # # dm_df_Rds = "domain_df_pure.Rds" # sc_df_Rds = "subcellular_location_df_pure.Rds" # # output_label = "glcnac_s_try" # output_label = "glcnac_s_try" # dm_df = readRDS(dm_df_Rds) sc_df = readRDS(sc_df_Rds) pos_window_score = readRDS(pos_window_score_Rds) pos_each_domain = map_domain(dm_df, pos_window_score) pos_each_subcellular = map_subcellular_location(sc_df, pos_window_score) pos_info_df = data.frame(pos_window_score, domain = pos_each_domain, subcellular = pos_each_subcellular, stringsAsFactors = F) saveRDS(pos_info_df, file = paste0(output_label, "_pos_info_df.Rds")) write.table(pos_info_df, paste0(output_label,"_pos_info_df.tsv"), quote = F, row.names = F, sep = "\t") rm(pos_info_df) rm(pos_window_score) rm(pos_each_domain) rm(pos_each_subcellular) candi_window_score = readRDS(candi_window_score_Rds) candi_each_domain = map_domain(dm_df, candi_window_score) candi_each_subcellular = map_subcellular_location(sc_df, candi_window_score) candi_info_df = data.frame(candi_window_score, domain = candi_each_domain, subcellular = candi_each_subcellular, stringsAsFactors = F) saveRDS(candi_info_df, file = paste0(output_label, "_candi_info_df.Rds")) write.table(candi_info_df, paste0(output_label,"_candi_info_df.tsv"), quote = F, row.names = F, sep = "\t") } retrieve_domain_all_mod = function(mod_names, mod_pos_score, mod_candi_score, domain_name, output_label) { ### try to use rbindlist all_retrieve = data.frame(rbindlist(lapply(1:length(mod_names), function(i) { mod_name = mod_names[i] pos_info = readRDS(paste0(mod_name, "_pred_pos_info_df.Rds")) candi_info = readRDS(paste0(mod_name, "_pred_candi_info_df.Rds")) pos_score = mod_pos_score[i] candi_score = mod_candi_score[i] this_mod_retrieve = retrieve_for_each_mod(pos_info, candi_info, pos_score,candi_score, mod_name, domain_name) return(this_mod_retrieve) }))) all_retrieve_df = all_retrieve %>% dplyr::arrange(protID, pos) saveRDS(all_retrieve_df, file = paste0(output_label, "_",domain_name,"_retrieve.Rds")) write.table(all_retrieve_df, paste0(output_label, "_",domain_name,"_retrieve.tsv"), quote = F, row.names= F, sep = "\t") } create_gglogo_plot = function(candidate_df_Rds, output_label) { candidate_df = readRDS(candidate_df_Rds) # candidate_df = ps_candidate # output_label = "try_new" pos_windows = candidate_df %>% dplyr::filter(label == "positive") %>% dplyr::select(window) delete_center = paste0(substr(pos_windows$window,1,12),substr(pos_windows$window,14,25)) pos_windows$window = delete_center # pdf(paste0(output_label,"_logoPlot.pdf"), useDingbats = F) ggplot(data = ggfortify(pos_windows, "window", method = "frequency")) + geom_logo(aes(x=position, y=info, group=element, label=element, fill=interaction(Water, Polarity)), alpha = 0.6) + scale_fill_brewer(palette="Paired") + theme(legend.position = "bottom") #dev.off() ggsave(filename = paste0(output_label,"_logoPlot.pdf"),device = "pdf", width = 10, height = 8) } get_average_weights_of_two = function(first_train_model_file, second_train_model_file, full_feature, arrange_feature, output_label) { weight_matrix = matrix(0, nrow = length(full_feature), ncol = 2) first_weight_file = readLines(first_train_model_file) first_ws = as.numeric(first_weight_file[7:length(first_weight_file)]) weight_matrix[,1] = first_ws second_weight_file = readLines(second_train_model_file) second_ws = as.numeric(second_weight_file[7:length(second_weight_file)]) weight_matrix[,2] = second_ws ave_weights = rowMeans(weight_matrix) ws_df = data.frame(name = full_feature, weights = ave_weights, abs_weights = abs(ave_weights), stringsAsFactors = F) name_arr_ws_df = arrange_feature%>%dplyr::left_join(ws_df, by = c("arrange_feature" ="name")) write.table(name_arr_ws_df,paste0(output_label,"_arrange_name_coeff_df.tsv"), sep = "\t", row.names = F, quote = F) } plot_weights = function(coef_file, plot_name) { #coef_file = "/Users/ginny/PTMtopographer_2017_08/classifier_performance/model_weight/ps_arrange_name_coeff_df.tsv" coef_df = data.table::fread(coef_file, header = T, stringsAsFactors = F) col_hydrophobicity = rep("skyblue", 8) col_aaindex = rep("purple", 45) col_ASA = rep("green",24 ) col_HSE = rep("yellow",24 ) col_pC = rep("red",24 ) col_pH = rep("orange",24 ) col_pssm = rep("pink",24) cols = c(col_hydrophobicity, col_aaindex, col_ASA, col_HSE, col_pC, col_pH, col_pssm) # max_y = max(abs(coef_df$weights)) pdf(paste0(plot_name,"_weights_bar.pdf"), useDingbats = F) barplot(coef_df$weights, main = plot_name, col = cols, ylim = c(-0.55, 0.55)) # abline (h = 0, col = "green", lty = 2) # abline(v= 8, col = "red", lty = 2) # abline(v= 53, col = "red", lty = 2) # abline(v= 101, col = "red", lty = 2) # abline(v= 149, col = "red", lty = 2) # dev.off() } calculate_seq_pairs = function(anchor_mod_site, cross_mod_site, distance, anchor_mod, cross_mod, anchor_new_merge_Rds, cross_new_merge_Rds, output_label) { anchor_new_merge = readRDS(anchor_new_merge_Rds) cross_new_merge = readRDS(cross_new_merge_Rds) cn_anchor = colnames(anchor_new_merge) cn_anchor[which(grepl(paste0(anchor_mod, "_label"), cn_anchor))] = "anchor_label" colnames(anchor_new_merge) = cn_anchor cn_cross = colnames(cross_new_merge) cn_cross[which(grepl(paste0(cross_mod, "_label"), cn_cross))] = "cross_label" colnames(cross_new_merge) = cn_cross anchor_domain_list =unique(anchor_new_merge$domain) anchor_domain_list = unique( unlist(strsplit(anchor_domain_list, split = " "))) cross_domain_list = unique(cross_new_merge$domain) cross_domain_list = unique( unlist(strsplit(cross_domain_list, split = " "))) common_domain = intersect(anchor_domain_list, cross_domain_list) common_domain = common_domain[!is.na(common_domain)] output_df = data.frame(domain_name = common_domain, a = 0, b=0, c=0, d=0) domain_prot_df = data.frame(domain_name = common_domain, protIDs = character(length(common_domain)), stringsAsFactors = F) for(i in 1:length(common_domain)) { if(i%%100==0) cat(i, "\n") domain_prot_df$protIDs[i] = NA #which(common_domain == "CTP_synth_N") a =0;b=0;c=0;d=0 ### because some domains are connected by blanks in a row this_domain = common_domain[i] this_anchor_domain = anchor_new_merge %>% dplyr:: filter(grepl(paste0("\\b",this_domain,"\\b") ,domain)) this_cross_domain = cross_new_merge %>% dplyr:: filter(grepl(paste0("\\b",this_domain,"\\b"), domain)) if(nrow(this_anchor_domain)>0 & nrow(this_cross_domain)>0) { ### crosstalk happen within the same protein and the same domain this_anchor_proteins = unique(this_anchor_domain$protID) this_cross_proteins = unique(this_cross_domain$protID) common_proteins = intersect(this_anchor_proteins, this_cross_proteins) if(length(common_proteins)>0) { domain_crosstalk_in_prot = c("") for(j in 1:length(common_proteins)) { this_prot_anchor_domain = this_anchor_domain %>% filter(protID == common_proteins[j]) this_prot_cross_domain = this_cross_domain %>% filter(protID == common_proteins[j]) ### get the pairs of anchor cross positions for analysis anchor_cross_pair_pos = data.frame(anchor_position = numeric(), cross_position = numeric()) anchor_pos = this_prot_anchor_domain %>% dplyr::select(pos) anchor_pos = anchor_pos$pos cross_pos = this_prot_cross_domain %>% dplyr::select(pos) cross_pos = cross_pos$pos for(p in 1:length(anchor_pos)) { dis = abs(anchor_pos[p]-cross_pos) find_cross = which(dis>0 & dis<=distance) if(length(find_cross)>0) { anchor_position = rep(anchor_pos[p], length(find_cross)) cross_position = cross_pos[find_cross] pos_pair = cbind(anchor_position, cross_position) anchor_cross_pair_pos = rbind(anchor_cross_pair_pos, pos_pair) } } if(nrow(anchor_cross_pair_pos)>0) { get_anchor_match = match(anchor_cross_pair_pos$anchor_position, this_prot_anchor_domain$pos) get_cross_match = match(anchor_cross_pair_pos$cross_position, this_prot_cross_domain$pos) anchor_match_label = this_prot_anchor_domain$anchor_label[get_anchor_match] cross_match_label = this_prot_cross_domain$cross_label[get_cross_match] add_to_a = which(anchor_match_label == T & cross_match_label == T) add_to_b = which(anchor_match_label == F & cross_match_label == T) add_to_c = which(anchor_match_label == T & cross_match_label == F) add_to_d = which(anchor_match_label == F & cross_match_label == F) a = a+length(add_to_a) b = b+length(add_to_b) c = c+length(add_to_c) d = d+length(add_to_d) if(length(add_to_a)>0) { domain_crosstalk_in_prot = c(domain_crosstalk_in_prot, common_proteins[j]) domain_prot_df$protIDs[i] = paste(domain_crosstalk_in_prot, collapse = " ") } } } } } output_df$a[i] = a output_df$b[i] = b output_df$c[i] = c output_df$d[i] = d } saveRDS(output_df, file = paste0(output_label,"_tbt_table.Rds")) write.table(output_df, paste0(output_label, "_tbt_table.tsv"), sep = "\t", quote = F, row.names = F) saveRDS(domain_prot_df, file = paste0(output_label, "_domain_prot_match.Rds")) write.table(domain_prot_df, paste0(output_label, "_domain_prot_match.tsv"), sep = "\t", quote = F, row.names = F) get_tbt = output_df %>% dplyr::group_by(domain_name) %>% dplyr::summarise(both_positive = sum(a), cross_positive = sum(b), anchor_positive = sum(c), both_negative = sum(d)) colnames(get_tbt) = c("domain", "both_positive",paste0(cross_mod, "_positive"), paste0(anchor_mod,"_positive"), "both_negative") ### calculate fisher's exact test on this fisher_p = rep(0, nrow(get_tbt)) or = rep(0, nrow(get_tbt)) for(i in 1:nrow(get_tbt)) { fm = matrix(as.numeric(get_tbt[i,c(2:5)]), nrow = 2, ncol = 2, byrow = T) ft = fisher.test(fm, alternative = "g") fisher_p[i] = ft$p.value or[i] = ft$estimate } tbt_p = get_tbt %>% dplyr::mutate(fisher_pvalue = fisher_p)%>% dplyr::mutate(odds_ratio = or)%>% dplyr::arrange(fisher_pvalue) write.table(tbt_p, paste0(output_label, "_test.tsv"), quote = F, row.names = F, sep = "\t") } calculate_seq_pairs_negative = function(compete_mod_site, anchor_mod, cross_mod, new_merge_Rds, output_label) { new_merge = readRDS(new_merge_Rds) cn_merge = colnames(new_merge) cn_merge[which(grepl(paste0(anchor_mod, "_label"), cn_merge))] = "anchor_label" cn_merge[which(grepl(paste0(cross_mod, "_label"), cn_merge))] = "cross_label" colnames(new_merge) = cn_merge domain_list =unique(new_merge$domain) domain_list = unique( unlist(strsplit(domain_list, split = " "))) common_domain = domain_list[!is.na(domain_list)] output_df = data.frame(domain_name = common_domain, a = 0, b=0, c=0, d=0) domain_prot_df = data.frame(domain_name = common_domain, protIDs = character(length(common_domain)), stringsAsFactors = F) for(i in 1:length(common_domain)) { if(i%%100==0) cat(i, "\n") domain_prot_df$protIDs[i] = NA a =0;b=0;c=0;d=0 this_domain = common_domain[i] this_domain_df = new_merge %>% dplyr:: filter(grepl(paste0("\\b",this_domain,"\\b") ,domain)) if(nrow(this_domain_df)>0 ) { anchor_match_label = this_domain_df$anchor_label cross_match_label = this_domain_df$cross_label a = length(which(anchor_match_label == T & cross_match_label == T)) b = length(which(anchor_match_label == F & cross_match_label == T)) c = length(which(anchor_match_label == T & cross_match_label == F)) d = length(which(anchor_match_label == F & cross_match_label == F)) have_cross = this_domain_df %>% dplyr::filter(anchor_label == T, cross_label == T) %>% dplyr::select(protID) if(nrow(have_cross)>0) domain_prot_df$protIDs[i] = paste(unique(have_cross$protID), collapse = " ") } output_df$a[i] = a output_df$b[i] = b output_df$c[i] = c output_df$d[i] = d } saveRDS(domain_prot_df, file = paste0(output_label, "_domain_prot_match.Rds")) write.table(domain_prot_df, paste0(output_label, "_domain_prot_match.tsv"), sep = "\t", quote = F, row.names = F) saveRDS(output_df, file = paste0(output_label,"_tbt_table.Rds")) write.table(output_df, paste0(output_label, "_tbt_table.tsv"), sep = "\t", quote = F, row.names = F) get_tbt = output_df %>% dplyr::group_by(domain_name) %>% dplyr::summarise(both_positive = sum(a), cross_positive = sum(b), anchor_positive = sum(c), both_negative = sum(d)) colnames(get_tbt) = c("domain", "both_positive",paste0(cross_mod, "_positive"), paste0(anchor_mod,"_positive"), "both_negative") ### calculate fisher's exact test on this fisher_p = rep(0, nrow(get_tbt)) or = rep(0, nrow(get_tbt)) for(i in 1:nrow(get_tbt)) { fm = matrix(as.numeric(get_tbt[i,c(2:5)]), nrow = 2, ncol = 2, byrow = T) ft = fisher.test(fm, alternative = "g") fisher_p[i] = ft$p.value or[i] = ft$estimate } tbt_p = get_tbt %>% dplyr::mutate(fisher_pvalue = fisher_p)%>% dplyr::mutate(odds_ratio = or)%>% dplyr::arrange(fisher_pvalue) write.table(tbt_p, paste0(output_label, "_test.tsv"), quote = F, row.names = F, sep = "\t") } calculate_individual_ptm = function(mod_type, new_merge_Rds, output_label) { new_merge = readRDS(new_merge_Rds) cn_merge = colnames(new_merge) cn_merge[which(grepl(paste0(mod_type, "_label"), cn_merge))] = "anchor_label" colnames(new_merge) = cn_merge num_positive_ptm = sum(new_merge$anchor_label) domain_list = unique(new_merge$domain) domain_list = domain_list[!is.na(domain_list)] domain_list = unique( unlist(strsplit(domain_list, split = " "))) output_df = data.frame(domain = domain_list, a = 0, b=0, c=0, d=0) domain_prot_df = data.frame(domain_name = domain_list, protIDs = character(length(domain_list)), stringsAsFactors = F) for(i in 1:length(domain_list)) { if(i%%1000 == 0) cat(i, "\n") domain_prot_df$protIDs[i] = NA this_domain_rows = new_merge %>% dplyr::filter(grepl(paste0("\\b",domain_list[i],"\\b"), domain)) have_ptm_domain = this_domain_rows %>% dplyr::filter(anchor_label == T) if(nrow(have_ptm_domain)>0) domain_prot_df$protIDs[i] = paste(unique(have_ptm_domain$protID), collapse = " ") a= sum(this_domain_rows$anchor_label) b = num_positive_ptm - a c = nrow(this_domain_rows) - a d = nrow(new_merge) - a - b - c output_df$a[i] = a output_df$b[i] = b output_df$c[i] = c output_df$d[i] = d } saveRDS(domain_prot_df, file = paste0(output_label, "_domain_prot_match.Rds")) write.table(domain_prot_df, paste0(output_label, "_domain_prot_match.tsv"), sep = "\t", quote = F, row.names = F) colnames(output_df) = c("domain", "InDomain_positive","OutDomain_positive", "InDomain_negative", "OutDomain_negative") saveRDS(output_df, file = paste0(output_label,"_tbt_table.Rds")) write.table(output_df, paste0(output_label, "_tbt_table.tsv"), sep = "\t", quote = F, row.names = F) chisq_p = rep(0, nrow(output_df)) or = rep(0, nrow(output_df)) for(i in 1:nrow(output_df)) { if(i%%100 == 0) cat(i, "\n") fm = matrix(as.numeric(output_df[i,c(2:5)]), nrow = 2, ncol = 2, byrow = T) ft = chisq.test(fm, simulate.p.value = T) chisq_p[i] = ft$p.value or[i] = output_df[i,2]*output_df[i,5]/output_df[i,3]/output_df[i,4] } tbt_p = output_df %>% dplyr::mutate(chisq_pvalue = chisq_p)%>% dplyr::mutate(odds_ratio = or)%>% dplyr::arrange(chisq_pvalue) write.table(tbt_p, paste0(output_label, "_test.tsv"), quote = F, row.names = F, sep = "\t") } map_domain_subcellular = function(window_score_label_Rds, dm_df_Rds, sc_df_Rds, output_label) { # window_score_label_Rds = "ps_0103_window_score_df.Rds" # dm_df_Rds = "domain_df_pure.Rds" # sc_df_Rds = "subcellular_location_df_pure.Rds" # output_label = "ps_0103" dm_df = readRDS(dm_df_Rds) sc_df = readRDS(sc_df_Rds) window_score_label = readRDS(window_score_label_Rds) each_domain = map_domain(dm_df, window_score_label) each_subcellular = map_subcellular_location(sc_df, window_score_label) info_df = data.frame(window_score_label, domain = each_domain, subcellular = each_subcellular, stringsAsFactors = F) saveRDS(info_df, file = paste0(output_label, "_mapped_df.Rds")) write.table(info_df, paste0(output_label,"_mapped_df.tsv"), quote = F, row.names = F, sep = "\t",na = "") } # chisq_test_for_single_ptm ----------------------------------------------- calculate_tbt_single_ptm = function(mapped_window_score_label_Rds, output_label) { #mapped_window_score_label_Rds = "ps_0103_mapped_df.Rds" mapped_window_score_label = readRDS(mapped_window_score_label_Rds) colnames(mapped_window_score_label) = c("protID","gene_name","pos","window","pred_score","threshold", "prediction_label","known_label","combined_label", "domain","subcellular") cn_merge = colnames(mapped_window_score_label) cn_merge[which(grepl("combined_label", cn_merge))] = "anchor_label" colnames(mapped_window_score_label) = cn_merge ### recode positive negative to TURE and FALSE mapped_window_score_label = mapped_window_score_label %>% dplyr::mutate(anchor_label = replace(anchor_label, anchor_label == "positive", TRUE)) %>% dplyr::mutate(anchor_label = as.logical(replace(anchor_label, anchor_label == "negative", FALSE))) num_positive_ptm = sum(mapped_window_score_label$anchor_label) domain_list = unique(mapped_window_score_label$domain) domain_list = domain_list[!is.na(domain_list)] domain_list = unique(unlist(strsplit(domain_list, split = " "))) output_df = data.frame(domain = domain_list, a = 0, b=0, c=0, d=0) domain_prot_df = data.frame(domain_name = domain_list, protIDs = character(length(domain_list)), stringsAsFactors = F) for(i in 1:length(domain_list)) { # if(i%%1000 == 0) # cat(i, "\n") # domain_prot_df$protIDs[i] = NA this_domain_rows = mapped_window_score_label %>% dplyr::filter(grepl(paste0("\\b",domain_list[i],"\\b"), domain)) have_ptm_domain = this_domain_rows %>% dplyr::filter(anchor_label == T) if(nrow(have_ptm_domain)>0) domain_prot_df$protIDs[i] = paste(unique(have_ptm_domain$protID), collapse = " ") a= sum(this_domain_rows$anchor_label) b = num_positive_ptm - a c = nrow(this_domain_rows) - a d = nrow(mapped_window_score_label) - a - b - c output_df$a[i] = a output_df$b[i] = b output_df$c[i] = c output_df$d[i] = d } saveRDS(domain_prot_df, file = paste0(output_label, "_domain_prot_match.Rds")) write.table(domain_prot_df, paste0(output_label, "_domain_prot_match.tsv"), sep = "\t", quote = F, row.names = F) colnames(output_df) = c("domain", "InDomain_positive","OutDomain_positive", "InDomain_negative", "OutDomain_negative") saveRDS(output_df, file = paste0(output_label,"_tbt_table.Rds")) write.table(output_df, paste0(output_label, "_tbt_table.tsv"), sep = "\t", quote = F, row.names = F) chisq_p = rep(0, nrow(output_df)) or = rep(0, nrow(output_df)) adjusted_or = rep(0,nrow(output_df)) for(i in 1:nrow(output_df)) { # if(i%%100 == 0) # cat(i, "\n") fm = matrix(as.numeric(output_df[i,c(2:5)]), nrow = 2, ncol = 2, byrow = T) set.seed(123) ft = chisq.test(fm, simulate.p.value = T) chisq_p[i] = ft$p.value or[i] = output_df[i,2]*output_df[i,5]/output_df[i,3]/output_df[i,4] adjusted_or[i] = (output_df[i,2]+0.5)*(output_df[i,5]+0.5)/(output_df[i,3]+0.5)/(output_df[i,4]+0.5) } # qv = qvalue(p = chisq_p) tbt_p = output_df %>% dplyr::mutate(chisq_pvalue = chisq_p)%>% dplyr::mutate(odds_ratio = or)%>% #dplyr::mutate(qvalue = qv$qvalues)%>% dplyr::mutate(adjusted_odds_ratio = adjusted_or)%>% dplyr::arrange(chisq_pvalue) write.table(tbt_p, paste0(output_label, "_test.tsv"), quote = F, row.names = F, sep = "\t") }

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#' Airfoil data #' #' Here goes a description of the data. #' #' Here goes a more detailed description of the data. #' There are 1503 observations and 6 variables: #' \code{y}, \code{frequency}, \code{angle}, \code{chord_length}, #' \code{velocity}, and \code{thickness}. #' #' @docType data #' #' @usage data(airfoil) #' #' @format An object of class \code{"data.frame"}. #' #' @references Brooks, T. F., Pope, D. S., and Marcolini, M. A. (1989). Airfoil self-noise and prediction. NASA Reference Publication-1218, document id: 9890016302. #' #' @source The UCI Archive https://archive.ics.uci.edu/ml/datasets/Airfoil+Self-Noise, #' #' @examples #' data(airfoil) "airfoil"

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shonil24/Applied-Analytics

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6.R

library(readr) library(dplyr) library(epitools) ames <- read_csv("F:/MS/Sem 1/AA/week6/ames.csv") population <- ames$area sample <- sample(population, 60) # Q1 summary(sample) hist(sample) # Every student will have different values and mean # Q2 mean.ci <- function(x, conf = 0.95) { alpha <- 1- conf t_crit <- qt(p = (1-alpha/2), df = (length(x)-1) ) lb <- mean(x) - t_crit * ( sd(x) / sqrt (length(x) ) ) ub <- mean(x) + t_crit * ( sd(x) / sqrt (length(x) ) ) return(c(lb,ub)) } # mean.ci is function which is being defining # Q3 mean.ci(sample) # Q4 mean.ci(sample, conf = 0.9) #sorry maybe let me rephrase my question with an example. I am trying to understand the correlation between significance level and confidence level by using a t test. Let me propose a situation where a new drug is introduced, to find out the if the drug is effective, I decide to use the t test at 95% confidence level, which is the alpha at 5%. we test on patients. H0 = no effect, H1 = effective. if the result of the t-test is p-value > 5%, theoretically we reject H1, and conclude the new drug is ineffective. My question is, because the p value is greater than 5% so that means, more that 5% of the mean of the sampling distribution is completely out of the 95% confidence interval range, therefore, I can also say, the drug is only effective to less than 95% of the sample, am I correct? # Q5 t.test(sample, conf = 0.95) t.test(sample, conf = 0.9) samp_mean <- rep(NA, 100) samp_sd <- rep(NA, 100) lower_vector <- rep(NA, 100) upper_vector <- rep(NA, 100) n <- 60 for(i in 1:100){ samp <- sample(population, n) samp_mean[i] <- mean(samp) samp_sd[i] <- sd(samp) ci<- mean.ci(samp) lower_vector[i] <-ci[1] upper_vector[i] <-ci[2] } plot_ci <- function(lo, hi, m) { par(mar=c(2, 1, 1, 1), mgp=c(2.7, 0.7, 0)) k <- length(lo) ci.max <- max(rowSums(matrix(c(-1*lo,hi),ncol=2))) xR <- m + ci.max*c(-1, 1) yR <- c(0, 41*k/40) plot(xR, yR, type='n', xlab='', ylab='', axes=FALSE) abline(v=m, lty=2, col='#00000088') axis(1, at=m, paste("mu = ",round(m,4)), cex.axis=1.15) #axis(2) for(i in 1:k){ x <- mean(c(hi[i],lo[i])) ci <- c(lo[i],hi[i]) if((m < hi[i] & m > lo[i])==FALSE){ col <- "#F05133" points(x, i, cex=1.4, col=col) # points(x, i, pch=20, cex=1.2, col=col) lines(ci, rep(i, 2), col=col, lwd=5) } else{ col <- 1 points(x, i, pch=20, cex=1.2, col=col) lines(ci, rep(i, 2), col=col) } } } plot_ci(lower_vector, upper_vector, mean(population)) cdc <- read_csv("F:/MS/Sem 1/AA/week6/cdc.csv") # Q7 cdc$exerany <- factor(cdc$exerany, levels = c(1,0), labels = c("Yes", "No")) cdc$exerany %>% table() %>% prop.table() # Q8 prop.ci <- function(x,n, conf = 0.95) { alpha <- 1- conf prop=x/n z_crit <- qnorm(p = (1-alpha/2) ) lb <- prop - z_crit *sqrt (prop*(1-prop)/n) ub <- prop + z_crit * sqrt (prop*(1-prop)/n) return(c(lb,ub)) } # Q9 #p=x/n which means x=14914, n = total of both #rounde decimal points at the end prop.ci(14914, 14914+5086, conf = 0.95) # Q10 binom.approx(14914, 14914+5086, conf.level = 0.95)

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library(kernlab) library(readr) library(caret) setwd("C:/Amit/Upgrad/SVM_dataset/SVM Dataset1") ## Read the training data set mnist_dataset <- read.csv("mnist_train.csv" , header = F) ###Structure of the dataset str(mnist_dataset) ## Examine few records head(mnist_dataset) #Exploring the data summary(mnist_dataset) ## Check if there is any missing value in data set column_null_check <- sapply (mnist_dataset , function(x) sum(is.na(x) )) which(column_null_check >0 ) ## No null value column_min_value <- sapply (mnist_dataset , function(x) min(x) ) which(column_min_value < 0) ## None which(column_min_value > 0) ## None length(which(column_min_value == 0)) ## 785. All min value is 0 column_max_value <- sapply (mnist_dataset , function(x) max(x) ) which(column_max_value > 255) ## None ## So all the values are within range of 0 and 255. This is within permissable limit. So no outlier is there ## Check if there is any duplicate data in mnist_dataset data set which(duplicated(mnist_dataset)) ## 0 ## Plot couple of digits to check how images are looking flip <- function(matrix){ apply(matrix, 2, rev) } par(mfrow=c(3,3)) for (i in 20:28){ digit <- flip(matrix(rev(as.numeric(mnist_dataset[i,-c(1, 786)])), nrow = 28)) #look at one digit image(digit, col = grey.colors(255)) } par(mfrow=c(3,3)) for (i in 5000:5008){ digit <- flip(matrix(rev(as.numeric(mnist_dataset[i,-c(1, 786)])), nrow = 28)) #look at one digit image(digit, col = grey.colors(255)) } par(mfrow=c(3,3)) for (i in 10000:10008){ digit <- flip(matrix(rev(as.numeric(mnist_dataset[i,-c(1, 786)])), nrow = 28)) #look at one digit image(digit, col = grey.colors(255)) } # Changing output variable "V1" to factor type mnist_dataset$V1<-factor(mnist_dataset$V1) ## Since data set is huge. SO taking 10% sample of mnist_dataset dataset to train the model train.indices = sample(1:nrow(mnist_dataset), 0.1*nrow(mnist_dataset)) train = mnist_dataset[train.indices, ] ## Make sure we have enough representation of each digits in the sample taken table ( train$V1) ## Load the test data set mnist_test_dataset <- read.csv("mnist_test.csv" , header = F) ## Take 50% of test data set for model test test.indices = sample(1:nrow(mnist_test_dataset), 0.5*nrow(mnist_test_dataset)) test = mnist_test_dataset[test.indices, ] test$V1<-factor(test$V1) #Constructing Model #Using Linear Kernel Model_linear <- ksvm(V1~ ., data = train, scale = FALSE, kernel = "vanilladot") ## Predict the output Eval_linear<- predict(Model_linear, test) confusionMatrix(Eval_linear,test$V1) ## Overall Accuracy : 0.915. But sensitivity is too low for few digit e.g. 3, 5 and 8 ( close to 80%). ### So we need to move from linear to RBF #Using RBF Kernel Model_RBF <- ksvm(V1~ ., data = train, scale = FALSE, kernel = "rbfdot") Eval_RBF<- predict(Model_RBF, test) #confusion matrix - RBF Kernel confusionMatrix(Eval_RBF,test$V1) ### Overall Accuracy increased to .95 . Accuracy for all digits increased to more than 90% now. ## So RBF kernel is better than linear one. ##################################################################### #Hyperparameter tuning and Cross Validation - Non-Linear - SVM ###################################################################### # We will use the train function from caret package to perform Cross Validation. #traincontrol function Controls the computational nuances of the train function. # i.e. method = CV means Cross Validation. # Number = 2 implies Number of folds in CV. trainControl <- trainControl(method="cv", number=5) # Metric <- "Accuracy" implies our Evaluation metric is Accuracy. metric <- "Accuracy" #Expand.grid functions takes set of hyperparameters, that we shall pass to our model. set.seed(8) grid <- expand.grid(.sigma=c(0.50e-07, 1.50e-07, 2.50e-07), .C=c(1,2) ) # Performing 5-fold cross validation fit.svm_radial <- train(V1~., data=train, method="svmRadial", metric=metric, tuneGrid=grid, trControl=trainControl) # Printing cross validation result print(fit.svm_radial) # Best tune at sigma = 2.5e-07 and C = 2 for maximum accuracy ## Since sigma is very low, data is not very linear # Plotting model results plot(fit.svm_radial) ###################################################################### # Checking overfitting - Non-Linear - SVM ###################################################################### # Validating the model results on test data evaluate_non_linear<- predict(fit.svm_radial, test) confusionMatrix(evaluate_non_linear, test$V1) # Accuracy - 97.4% # Sensitivity - Around 95% for all digits # Specificity - Around 99%% for all digits

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testthat.R

context("ds_polygon") test_that("regionalization of polygon data (ds_polygon)", { data("socioGrid") socioGrid$class <- ds_polygon(socioGrid, k = 7, disjoint = TRUE, plot = TRUE, explain = FALSE) expect_equal( head(socioGrid$class), c(6, 5, 3, 1, 2, 6)) }) context("ds_points") test_that("regionalization of point data (ds_points)", { data("realEstate") realEstate$class <- ds_points(realEstate, k = 5, explain = FALSE) expect_equal( head(realEstate$class), c(1, 5, 4, 2, 4, 4)) }) context("regionalize") test_that("regionalization of point data (regionalize)", { data("realEstate") realEstate$class <- regionalize(realEstate, k = 5, explain = FALSE) expect_equal( head(realEstate$class), c(1, 5, 4, 2, 4, 4)) }) test_that("regionalization of polygon data (regionalize)", { data("socioGrid") socioGrid$class <- regionalize(socioGrid, k = 7, disjoint = TRUE, plot = TRUE, explain = FALSE) expect_equal( head(socioGrid$class), c(6, 5, 3, 1, 2, 6)) }) #Need tests for accuracy

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predict.coco.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/methods.R \name{predict.coco} \alias{predict.coco} \title{Make predictions from a coco object} \usage{ \method{predict}{coco}( object, newx, s = NULL, lambda.pred = NULL, type = c("response", "coefficients"), ... ) } \arguments{ \item{object}{Fitted \code{coco} model object} \item{newx}{matrix of new values for \code{x} at which predictions are to be made. Do not include the intercept (this function takes care of that). Must be a matrix. This argument is not used for \code{type=c("coefficients")}. This matrix must have the same number of columns originally supplied to the \code{coco} fitting function.} \item{s}{Value(s) of the penalty parameter \code{lambda} at which predictions are required. Default is the entire sequence used to create the model.} \item{lambda.pred}{Value(s) of the penalty parameter \code{lambda} at which coefficients are extracted to calculate the response based on the matrix of new values \code{newx}. Default is \code{lambda.sd}.} \item{type}{Type of prediction required. Type \code{"coefficients"} computes the coefficients at the requested values for \code{s}. Type \code{response} computes the response based on the covariates values in \code{newx}, for coefficients corresponding to \code{lambda} value in \code{lambda.pred}} \item{...}{currently ignored} } \value{ The object returned depends on type. } \description{ Similar to other predict methods, this functions predicts fitted values, logits, coefficients and more from a fitted \code{coco} object. }

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library(shiny) fluidPage( tags$head( tags$style(HTML(" .shiny-text-output { color:navy; font-size:large; font-weight:bold; } ")) ), headerPanel( h1("MONEYBALL", style='color:navy') ), h3("Estimating Runs Scored by a Baseball Team", style='color:navy'), p(paste("This application uses a regression model built with data", "from 2000 - 2008 for every major-league baseball team.", "It applies this model, which predicts the number", "of runs scored by a team. The application also gives", "the 95% confidence interval of the prediction.") ), p(paste("Use the sliders to adjust the inputs, such as singles,", "doubles, etc. Notice the effects on the number of runs scored", "by the team and the resulting confidence interval for the prediction.") ), sidebarLayout( sidebarPanel( fluidRow( column(6, sliderInput("singles", "Singles:", min = 0, max = 5000, value = 995, step = 1), sliderInput("doubles", "Doubles:", min = 0, max = 1500, value = 309, step = 1), sliderInput("triples", "Triples:", min = 0, max = 100, value = 34, step = 1), sliderInput("homeruns", "Home Runs:", min = 0, max = 600, value = 236, step = 1) ), column(6, sliderInput("walks", "Walks:", min = 0, max = 2000, value = 608, step = 1), sliderInput("hitbypitch", "Hit by Pitch:", min = 0, max = 300, value = 93, step = 1), sliderInput("sacrificeflies", "Sacrifice Flies:", min = 0, max = 150, value = 52, step = 1), sliderInput("stolenbases", "Stolen Bases:", min = 0, max = 150, value = 47, step = 1), sliderInput("caughtstealing", "Caught Stealing:", min = 0, max = 150, value = 43, step = 1) ) ), width = 8 ), mainPanel( fluidRow( column(12, h4('Estimated runs scored based on your input:'), textOutput('estimatedRuns'), br(), h4('95% confidence interval:'), textOutput('ci') ) ), width=4 )), br(), p(style='color:purple',"Data and data model from ", a("R in a Nutshell", href="http://web.udl.es/Biomath/Bioestadistica/R/Manuals/r_in_a_nutshell.pdf"), "by Joseph Adler, 2010.") )

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pca.reg2.Rd

% Generated by roxygen2 (4.1.1.9000): do not edit by hand % Please edit documentation in R/pca.reg2.R \name{pca.reg2} \alias{pca.reg2} \title{Linear Regression on PCA objects 2} \usage{ pca.reg2(pca.object, sp.name, group, plot = TRUE) } \arguments{ \item{pca.object}{PCA object from rda function (vegan).} \item{sp.name}{Name of species vector of interest.} \item{group}{Name of vector to be used to specify groups within the data.} \item{plot}{Plot the regression lines Defaults to TRUE.} } \description{ This functions allows one to perform a linear regression on groups of PCA points to determine the trajectory or trend of points within a specified group. The regression lines for each group are then used to determine the correlation values between these lines and a specified species vector. The output is a dataframe containing correlation values for each group when compared to the specified species vector. } \examples{ data(ffg.cast) ffg.hel <- decostand(ffg.cast[,-1:-4], 'hellinger') pca.hel <- rda(ffg.hel) pca.hel.scores <- scores(pca.hel, display='sites', scaling=1) pca.hel.plot <- pca.plot(pca.hel, ffg.cast$CU, ffg.cast$CU, ffg.hel, cex=1.5) text(pca.hel.scores, labels=ffg.cast$YR.POSTREST, pos=3) lines(pca.hel.scores[-4:-6,], col='red') lines(pca.hel.scores[-1:-3,], col='green') pca.reg2(pca.hel, 'SHREDDER', ffg.cast$CU, plot=TRUE) } \keyword{vegan}

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/srs-cran/src/models/arima/ArimaModelPrices.R

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ArimaModelPrices.R

# TODO: Add comment # # Author: ajinkya library(forecast) arimaPricePrediction <- function(ret,predictionsignals) { winsize <- 30 x <- as.ts(ret$daily.returns) start <- 1 end <- nrow(ret) pred <- nrow(ret) pred <-NULL for(i in 1:nrow(ret)+1) { index <- start + winsize window <- ret[start:index,] x <- as.ts(window$daily.returns) fit <- auto.arima(x) f <- forecast(fit,h=1) predictedrange <- cbind(f$lower,f$upper,f$mean) pred<- rbind(pred,predictedrange) start <- start + 1 index <- index + 1 } ret1 <- as.data.frame(cbind(pred,ret)) for(i in 1:4) { ret1[,i] <- (round(ret1[,i])) } predicted.price.percent <- nrow(ret1) # arima and random forest integration for(i in 1:nrow(ret1)) { if(predictionsignals[i] == "buy") { predicted.price.percent[i] <- (ret1[i,3]+ret1[i,4])/2 } if(predictionsignals[i] == "hold") { predicted.price.percent[i] <- (ret1[i,2]+ret1[i,3])/2 } if(predictionsignals[i] == "sell") { predicted.price.percent[i] <- (ret1[i,1]+ret1[i,2])/2 } } return (predicted.price.percent) # code below is retired as of now. #plot(f) #fit1 <- bats(x) #f1<-forecast(fit1) #plot(f1) #fit2 <- nnetar(x) #f2<-forecast(fit2,h=200) #plot(f2) }

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make_f_alldata.R

##makes a kinship matrix when there is no missing data. Takes frequency data, not # of copies. Later would be good to add a check for this. This function also drops an individual to deal with losing a degree of freedom when mean centering. The input table has rows as individuals, loci as columns. make_f_alldata <- function(myG){ myEs = 1/(colMeans(myG)*(1-colMeans(myG))) myEsr = replace(myEs, myEs==Inf, 0) myS = matrix(0,nrow=dim(myG)[2], ncol=dim(myG)[2]) diag(myS) = myEsr myM = dim(myG)[1] myT = matrix(data = -1/myM, nrow=myM-1, ncol=myM) diag(myT) = (myM-1)/myM myK = dim(myG)[2] myF = (1/(myK-1)) * myT %*% myG %*% myS %*% t(myG) %*% t(myT) return(myF) }

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Chapter5_knn_cv.R

## This script uses the caret package to estimate a cross validated KNN classifier using the Auto dataset. library("caret") library("ISLR") library("ggplot2") library("MASS") library("class") library("gridExtra") library("doMC") registerDoMC(cores = 4) data(Auto) View(Auto) Auto$mpg01[Auto$mpg < median(Auto$mpg)] = 0 Auto$mpg01[Auto$mpg > median(Auto$mpg)] = 1 Auto$mpg01 = as.factor(Auto$mpg01) Auto$cylinders = as.factor(Auto$cylinders) Auto$year = as.factor(Auto$year) set.seed(107) train_ind = createDataPartition(y = Auto$mpg01, p = 0.75, list = FALSE) Auto.train = Auto[train_ind, ] Auto.test = Auto[-train_ind, ] Auto.train = Auto.train[, -c(1,8,9)] Auto.test = Auto.test[, -c(1,8,9)] #knn using caret set.seed(400) ptm = proc.time() ctrl = trainControl(method = "repeatedcv", repeats = 3) knnfit = train(mpg01 ~ ., data = Auto.train, method = "knn", trControl = ctrl, preProcess = c("center", "scale"), tuneLength = 20) proc.time() - ptm knnfit plot(knnfit) knnpred = predict(knnfit, newdata = Auto.test) confusionMatrix(knnpred, Auto.test$mpg01) TeER.knn = mean(knnpred != Auto.test$mpg01) #Choose own k values ptm = proc.time() ctrl = trainControl(method = "repeatedcv", repeats = 3) knngrid = expand.grid(k = 1:10) knnfit = train(mpg01 ~ ., data = Auto.train, method = "knn", trControl = ctrl, preProcess = c("center", "scale"), tuneGrid= knngrid) proc.time() - ptm knnfit plot(knnfit) knnpred = predict(knnfit, newdata = Auto.test) confusionMatrix(knnpred, Auto.test$mpg01) TeER.knn = mean(knnpred != Auto.test$mpg01) #Experiment with different cross validation methods: #Just k-fold cross validation ptm = proc.time() ctrl = trainControl(method = "cv") knnfit = train(mpg01 ~ ., data = Auto.train, method = "knn", trControl = ctrl, preProcess = c("center", "scale"), tuneLength = 20) proc.time() - ptm knnfit plot(knnfit) #LOOCV ptm = proc.time() ctrl = trainControl(method = "LOOCV") knnfit = train(mpg01 ~ ., data = Auto.train, method = "knn", trControl = ctrl, preProcess = c("center", "scale"), tuneLength = 20) proc.time() - ptm knnfit plot(knnfit) knnpred = predict(knnfit, newdata = Auto.test) confusionMatrix(knnpred, Auto.test$mpg01) TeER.knn = mean(knnpred != Auto.test$mpg01)

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#' Side by Side Histogram and Box Plots #' #' @param dat The dataset #' @param x The variable in the dataset #' #' @return nothing #' @export #' #' @examples #' nothing hist_box_plots <- function(dat, x){ hist(dat[, x], main = paste0("Histogram of ",x), xlab=x, ylab="Frequency", col = "blue") boxplot(dat[, x], main = paste0("Boxplot of ",x), xlab=x, col = "blue") } #' Stacked Histograms #' #' @param dat_a The dataset for stack 1 #' @param dat_b The dataset for stack 2 #' @param x The variable in the dataset #' #' @return nothing #' @export #' #' @examples #' nothing stacked_bar_plots <- function(dat_a, dat_b, x){ hist(dat_a[, x], main = paste0("Stacked Histogram of ",x), xlab=x, ylab="Frequency", col=rgb(.5,.8,1,0.5)) hist(dat_b[, x], col = rgb(1,.5,.4,.5), add=T) legend("topright", c("Alert", "Not Alert"), col=c(rgb(.5,.8,1,0.5), rgb(1,.5,.4,.5)), lwd=10) box() }

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/manuscript/SuperExactTest/SuperExactTest.r

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SuperExactTest.r

#install.packages("SuperExactTest") #install.packages("openxlsx", dependencies = TRUE) #install.packages("dplyr") #install.packages("tidyverse") library(SuperExactTest) library("dplyr") require(openxlsx) library(tidyverse) xlsx_sheet <- read.xlsx("Fig1_Venn_David.xlsx", sheet = 1, startRow = 1, colNames = FALSE) cats <- unique(xlsx_sheet$X2) data <- lapply(cats, function(x,s) xlsx_sheet[xlsx_sheet$X2 == x,]$X1) Result <- supertest(data,n=19608) sink("Fig1_Venn_David.txt") "Fig1_Venn_David.xlsx" "Sets:" cats "Result:" summary(Result) sink(file=NULL) xlsx_sheet <- read.xlsx("Fig2_Venn_David.xlsx", sheet = 1, startRow = 1, colNames = FALSE) cats <- unique(xlsx_sheet$X2) data <- lapply(cats, function(x,s) xlsx_sheet[xlsx_sheet$X2 == x,]$X1) Result <- supertest(data,n=20993) sink("Fig2_Venn_David.txt") "Fig2_Venn_David.xlsx" "Sets:" cats "Result:" summary(Result) sink(file=NULL) xlsx_sheet <- read.xlsx("Fig3_David_Venn.xlsx", sheet = 1, startRow = 1, colNames = FALSE) cats <- unique(xlsx_sheet$X2) data <- lapply(cats, function(x,s) xlsx_sheet[xlsx_sheet$X2 == x,]$X1) Result <- supertest(data,n=19608) sink("Fig3_David_Venn.txt") "Fig3_David_Venn.xlsx" "Sets:" cats "Result:" summary(Result) sink(file=NULL)

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amirmasoudabdol/DeclareDesign

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## ----echo=FALSE, warning=FALSE, message=FALSE---------------------------- set.seed(42) library(DeclareDesign) options(digits=2) my_population <- declare_population(N = 1000, income = rnorm(N), age = sample(18:95, N, replace = TRUE)) pop <- my_population() my_potential_outcomes <- declare_potential_outcomes( formula = Y ~ .25 * Z + .01 * age * Z) pop_pos <- my_potential_outcomes(pop) my_sampling <- declare_sampling(n = 250) smp <- my_sampling(pop_pos) my_assignment <- declare_assignment(m = 25) smp <- my_assignment(smp) my_estimand <- declare_estimand(ATE = mean(Y_Z_1 - Y_Z_0)) smp <- reveal_outcomes(smp) ## ----echo=TRUE, results="hide"------------------------------------------- m_arm_trial <- function(numb){ my_population <- declare_population( N = numb, income = rnorm(N), age = sample(18:95, N, replace = T)) my_potential_outcomes <- declare_potential_outcomes( formula = Y ~ .25 * Z + .01 * age * Z) my_sampling <- declare_sampling(n = 250) my_assignment <- declare_assignment(m = 25) my_estimand <- declare_estimand(ATE = mean(Y_Z_1 - Y_Z_0)) my_estimator_dim <- declare_estimator(Y ~ Z, estimand = my_estimand) my_design <- declare_design(my_population, my_potential_outcomes, my_estimand, my_sampling, my_assignment, reveal_outcomes, my_estimator_dim) return(my_design) } my_1000_design <- fill_out(template = m_arm_trial, numb = 1000) head(draw_data(my_1000_design)) ## ----echo=FALSE---------------------------------------------------------- knitr::kable(head(draw_data(my_1000_design))) ## ----echo=TRUE, results="hide"------------------------------------------- my_potential_outcomes_continuous <- declare_potential_outcomes( formula = Y ~ .25 * Z + .01 * age * Z, conditions = seq(0, 1, by = .1)) continuous_treatment_function <- function(data){ data$Z <- sample(seq(0, 1, by = .1), size = nrow(data), replace = TRUE) data } my_assignment_continuous <- declare_assignment(handler = continuous_treatment_function) my_design <- declare_design(my_population(), my_potential_outcomes_continuous, my_assignment_continuous, reveal_outcomes) head(draw_data(my_design)) ## ----echo=FALSE---------------------------------------------------------- knitr::kable(head(draw_data(my_design))) ## ----echo=TRUE, results="hide"------------------------------------------- my_potential_outcomes_attrition <- declare_potential_outcomes( formula = R ~ rbinom(n = N, size = 1, prob = pnorm(Y_Z_0))) my_design <- declare_design(my_population(), my_potential_outcomes, my_potential_outcomes_attrition, my_assignment, reveal_outcomes(outcome_variables = "R"), reveal_outcomes(attrition_variables = "R")) head(draw_data(my_design)[, c("ID", "Y_Z_0", "Y_Z_1", "R_Z_0", "R_Z_1", "Z", "R", "Y")]) ## ----echo=FALSE---------------------------------------------------------- knitr::kable(head(draw_data(my_design)[, c("ID", "Y_Z_0", "Y_Z_1", "R_Z_0", "R_Z_1", "Z", "R", "Y")])) ## ----echo=TRUE, results="hide"------------------------------------------- stochastic_population <- declare_population( N = sample(500:1000, 1), income = rnorm(N), age = sample(18:95, N, replace = TRUE)) c(nrow(stochastic_population()), nrow(stochastic_population()), nrow(stochastic_population()))

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bioclimvars.R

#' bio1: Calculates mean annual temperature #' #' @description `bio1` is used to calculated mean annual temperature. #' #' @param temps a vector of temperatures, normally for one year (see details). #' @param tme a `POSIXlt` object representing the date and time of each `temps` value. #' Ignored if method unspecified. #' @param method an optional character string describing the method used to #' calculate mean annual temperature. Options are "anuclim", "dailymaxmin" or unspecified #' (see details). #' @return a single numeric value of mean annual temperature. #' @export #' #' @details If `method` is "anuclim" temperatures are aggregated by month, and #' spline intepolated to weekly before mean annual temperature is calculated, #' replicating the method used by http://www.worldclim.org/. If `method` is #' "dailymaxmin", daily mean temperatures are calculated as the mean of daily #' maximum and minimum temperatures and annual mean calculated from daily #' means. Otherwise the mean of `temps` is returned. If using `anuclim` method #' and data span more than one year, data are aggregated by unique month #' irrespective of year and one value returned. If using `dailymaxmin` method #' and data span more than one year, calculations will be performed on all data #' and a single value returned. #' #' @seealso the [tmecreate()] function can be used to create a POSIXlt object. #' #' @examples #' temps <- 10 * sin(c(0:1459) / (pi * 150)) + rnorm(1460) #' tme <- tmecreate(2010, 6) #' plot(temps~as.POSIXct(tme), type = "l", xlab = "Month", ylab = "Temperature") #' bio1(temps, tme) #' bio1(temps, tme, method = "anuclim") #' bio1(temps, tme, method = "dailymaxmin") #' bio1 <- function(temps, tme, method = "") { if (is.na(sd(temps, na.rm = TRUE))) tmean <- NA else { if (method == "anuclim") { if (length(unique(tme$year)) > 1) warna() tmth <- aggregate(temps, by = list(tme$mon), mean, na.rm = TRUE)$x twk <- spline(tmth, n = length(tmth) / 12 * 52)$y tmean <- mean(twk, na.rm = TRUE) } else if (method == "dailymaxmin") { if (length(unique(tme$year)) > 1) warnb() tmx <- aggregate(temps, by = list(tme$yday), max, na.rm = TRUE)$x tmn <- aggregate(temps, by = list(tme$yday), min, na.rm = TRUE)$x tme <- (tmx + tmn) /2 tmean <- mean(tme, na.rm = TRUE) } else { if (length(unique(tme$year)) > 1) warna() tmean <- mean(temps, na.rm = TRUE) } } tmean } #' bio2: Calculates mean annual diurnal temperature range #' #' @description `bio2` is used to calculate the mean annual diurnal range in #' temperature (range mean of the maximum-minimum). #' #' @param temps a vector of temperatures, normally for one year (see details). #' @param tme a `POSIXlt` object representing the date and time of each `temps` value. #' @param method an optional character string describing the method used to #' mean annual diurnal temperature range. Options are "anuclim" or unspecified (see #' details). #' #' @return a single numeric value of mean diurnal temperature range. #' @export #' #' @details If using `anuclim` method and data span more than one year, data are #' aggregated by unique month irrespective of year and one value returned. If #' `method` is "anuclim" temperatures are aggregated by month and spline #' intepolated to weekly before mean diurnal temperature range is calculated, #' replicating the method used by http://www.worldclim.org/. If left #' unspecified and time interval is <= daily, the mean difference between the #' daily maximum and minimum values is calculated. #' #' @seealso the [tmecreate()] function can be used to create a POSIXlt object. #' #' @examples #' temps <- 10 * sin(c(0:1459) / (pi * 150)) + rnorm(1460) #' tme <- tmecreate(2010, 6) #' plot(temps~as.POSIXct(tme), type = "l", xlab = "Month", ylab = "Temperature") #' bio2(temps, tme) #' bio2(temps, tme, method = "anuclim") #' bio2 <- function(temps, tme, method = "") { if (is.na(sd(temps, na.rm = TRUE))) tdtr <- NA else { if (as.numeric(tme[2]) - as.numeric(tme[1]) > 86400 & method != "anuclim") { warning ("time interval > daily. Using anuclim method") method <- "anuclim" } if (method == "anuclim") { if (length(unique(tme$year)) > 1) warna() tmthmx <- aggregate(temps, by = list(tme$mon), max, na.rm = TRUE)$x tmthmn <- aggregate(temps, by = list(tme$mon), min, na.rm = TRUE)$x twkmx <- spline(tmthmx, n = length(tmthmx) / 12 * 52)$y twkmn <- spline(tmthmn, n = length(tmthmn) / 12 * 52)$y tdtr <- mean((twkmx - twkmn), na.rm = TRUE) } else { if (length(unique(tme$year)) > 1) warnb() tmx <- aggregate(temps, by = list(tme$yday), max, na.rm = TRUE)$x tmn <- aggregate(temps, by = list(tme$yday), min, na.rm = TRUE)$x dtr <- tmx - tmn tdtr <- mean(dtr, na.rm = TRUE) } } tdtr } #' bio4: Calculates temperature seasonality #' #' @description `bio4` calculates the variation in temperature over a given year #' as the coeeficient of variation in the mean temperatures #' #' @param temps a vector of temperatures, normally for one year (see details). #' @param tme a `POSIXlt` object representing the date and time of each `temps` value. #' @param method an optional character string describing the method used to #' calculate temperature seasonality. Options are "anuclim" or unspecified (see #' details). #' #' @return a single numeric value representng annual temperature seasonality. #' @export #' #' @details If method is "anuclim" temperatures are aggregated by month and #' monthly averages are calculated and spline intepolated to weekly values. #' Temperature seasonality is calculated as the standard deviation of weekly #' mean temperatures as a percentage of the mean of those temperatures. If #' using "anuclim" method and data span more than one year, data are aggregated #' by unique month irrespective of year and one value returned. If method is #' not specified, calculation is based on the standard deviation of the mean of #' all temperatures as a percentage of the mean of all temperatures. If method #' is not specified and data span more than one year, calculations will be #' performed on all data and a single value returned. For all calculations, the #' mean in degrees Kelvin is used. #' #' @seealso the [tmecreate()] function can be used to create a POSIXlt object. #' #' @examples #' temps <- 10 * sin(c(0:1459) / (pi * 150)) + rnorm(1460) #' tme <- tmecreate(2010, 6) #' plot(temps~as.POSIXct(tme), type = "l", xlab = "Month", ylab = "Temperature") #' bio4(temps, tme) #' bio4(temps, tme, method = "anuclim") bio4 <- function(temps, tme, method = "") { if (is.na(sd(temps, na.rm = TRUE))) tcv <- NA else { if (method == "anuclim") { if (length(unique(tme$year)) > 1) warna() tmth <- aggregate(temps, by = list(tme$mon), mean, na.rm = TRUE)$x twk <- spline(tmth, n = length(tmth) / 12 * 52)$y tcv <- sd(twk, na.rm = TRUE) / mean(twk, na.rm = TRUE)*100 } else { if (length(unique(tme$year)) > 1) warnb() tcv <- sd(temps, na.rm = TRUE) / mean(temps, na.rm = TRUE)*100 } } tcv } #' bio5: Calculates maximum temperature of the warmest period of the year #' #' @description `bio5` is used to calculate the maximum weekly or monthly temperature in each year #' #' @param temps a vector of temperatures, normally for one year (see details). #' @param tme a `POSIXlt` object representing the date and time of each `temps` value. #' @param method an optional character string describing the method to calculate #' maximum temperature in the warmest period. Options are "anuclimmean", #' "anuclimmax" or unspecified (see details). #' #' @return a single numeric value of maximum temperature (weekly or monthly) for each year. #' @export #' #' @details If method is "anuclimmean", mean monthly temperatures are spline #' interpolated to a weekly time period and the maximum weekly value returned. #' If method is "anuclimmax", monthly maximum temperatures are spline #' interpolated to a weekly time period and the maximum weekly value returned. #' If method is unspecified, the maximum temperature across all values for each year is returned. #' #' @seealso the [tmecreate()] function can be used to create a POSIXlt object. #' #' @examples #' temps <- 10 * sin(c(0:1459) / (pi * 150)) + rnorm(1460) #' tme <- tmecreate(2010, 6) #' plot(temps~as.POSIXct(tme), type = "l", xlab = "Month", ylab = "Temperature") #' bio5(temps, tme) #' bio5(temps, tme, method = "anuclimmean") #' bio5(temps, tme, method = "anuclimmax") #' bio5 <- function(temps, tme, method = "") { if (is.na(sd(temps, na.rm = TRUE))) tmx <- NA else { if (method == "anuclimmean") { if (length(unique(tme$year)) > 1) warna() tmth <- aggregate(temps, by = list(tme$mon), mean, na.rm = TRUE)$x twk <- spline(tmth, n = length(tmth) / 12 * 52)$y tmx <- max(twk, na.rm = TRUE) } else if (method == "anuclimmax") { if (length(unique(tme$year)) > 1) warna() tmth <- aggregate(temps, by = list(tme$mon), max, na.rm = TRUE)$x twk <- spline(tmth, n = length(tmth) / 12 * 52)$y tmx <- max(twk, na.rm = TRUE) } else { if (length(unique(tme$year)) > 1) warnb() tmx <- max(temps, na.rm = TRUE) } } tmx } #' bio6: Calculates minimum temperature of the coldest period of the year #' #' @description `bio6` is used to calculate the minimum temperature value across #' all months of the year. #' #' @param temps a vector of temperatures, normally for one year (see details). #' @param tme a `POSIXlt` object representing the date and time of each `temps` value. #' @param method an optional character string describing the method to calculate #' minimum temperature. Options are "anuclimmean", "anuclimmin" or unspecified (see #' details). #' #' @return a single numeric value of minimum temperature for the defiend period. #' @export #' #' @details "anuclimmean" splines mean monthly temperature to weekly time period #' across all months and returns the mean minimum weekly temperature. Anuclimmax #' splines minimum monthly temperatures to weekly time period and returns the #' minimum weekly temperature. If left unspecified, the minimum temperature #' across all values of each year is returned. #' #' @seealso the [tmecreate()] function can be used to create a POSIXlt object. #' #' @examples #' temps <- 10 * sin(c(0:1459) / (pi * 150)) + rnorm(1460) #' tme <- tmecreate(2010, 6) #' plot(temps~as.POSIXct(tme), type = "l", xlab = "Month", ylab = "Temperature") #' bio6(temps, tme) #' bio6(temps, tme, method = "anuclimmean") #' bio6(temps, tme, method = "anuclimmin") bio6 <- function(temps, tme, method = "") { if (is.na(sd(temps, na.rm = TRUE))) tmn <- NA else { if (method == "anuclimmean") { if (length(unique(tme$year)) > 1) warna() tmth <- aggregate(temps, by = list(tme$mon), mean, na.rm = TRUE)$x twk <- spline(tmth, n = length(tmth) / 12 * 52)$y tmn <- min(twk, na.rm = TRUE) } else if (method == "anuclimmin") { if (length(unique(tme$year)) > 1) warna() tmth <- aggregate(temps, by = list(tme$mon), min, na.rm = TRUE)$x twk <- spline(tmth, n = length(tmth) / 12 * 52)$y tmn <- min(twk, na.rm = TRUE) } else { if (length(unique(tme$year)) > 1) warnb() tmn <- min(temps, na.rm = TRUE) } } tmn } #' bio7: Annual temperature range #' #' @description `bio7` is used to calculate the annual range in temperature #' #' @param temps a vector of temperatures, normally for one year (see details). #' @param tme a `POSIXlt` object representing the date and time of each `temps` value. #' @param method An optional character string describing the method used to #' calculate maximum and minimum temperatures. Options are "anuclimmean", #' "anuclimmax" or unspecified (see details). #' #' @return a single numeric value of annnual temperature range (maximum-minimum #' temperature values). #' @export #' #' @details If method is "anuclimmean", mean monthly temperatures are spline #' interpolated to a weekly time period and range calculated from the maximum #' and minimum weeekly temperature values. If method is "anuclimmaxmin", #' maximum and minimum monthly temperatues are spline interpolated to a weekly #' time period and range is calculated from the maximum and minimum mean weekly #' temperature values. If left unspecified, the range is calculated by #' subtracting the maximum and minimum temperature alues for each year. To #' satisfy the requirements for `tme`, a POSIXlt object can be created using #' the `tmecreate` wrapper function. This calculation should return the value of #' bio5(temps, tme)-bio6(temps, tme) when methods remain the same. #' #' @seealso the [tmecreate()] function can be used to create a POSIXlt object. #' #' @examples #' temps <- 10 * sin(c(0:1459) / (pi * 150)) + rnorm(1460) #' tme <- tmecreate(2010, 6) #' plot(temps~as.POSIXct(tme), type = "l", xlab = "Month", ylab = "Temperature") #' bio7(temps, tme) #' bio7(temps, tme, method = "anuclimmean") #' bio7(temps, tme, method = "anuclimmaxmin") #' bio7 <- function(temps, tme, method = "") { if (is.na(sd(temps, na.rm = TRUE))) tanr <- NA else { if (method == "anuclimmean") { if (length(unique(tme$year)) > 1) warna() tmth <- aggregate(temps, by = list(tme$mon), mean, na.rm = TRUE)$x twk <- spline(tmth, n = length(tmth) / 12 * 52)$y tanr <- max(twk, na.rm = TRUE) - min(twk, na.rm = TRUE) } else if (method == "anuclimmaxmin") { if (length(unique(tme$year)) > 1) warna() tmthmx <- aggregate(temps, by = list(tme$mon), max, na.rm = TRUE)$x tmthmn <- aggregate(temps, by = list(tme$mon), min, na.rm = TRUE)$x twkmx <- spline(tmthmx, n = length(tmth) / 12 * 52)$y twkmn <- spline(tmthmn, n = length(tmth) / 12 * 52)$y tanr <- max(twkmx, na.rm = TRUE) - min(twkmn, na.rm = TRUE) } else { if (length(unique(tme$year)) > 1) warna() tanr <- max(temps, na.rm = TRUE) - min(temps, na.rm = TRUE) } } tanr } #' bio3: Calculates isothermality #' #' @description `bio3` is used to calculate isothermality (day-to-night #' temperature oscillations relative to annual oscillations). #' #' @param temps a vector of temperatures, normally for one year (see details). #' @param tme a `POSIXlt` object representing the date and time of each `temps` value. #' @param method An optional character string describing the method used to #' calculate isothermality. Options include "anuclimmean", "anuclimmaxmin" or #' unspecified (see details). #' #' @return a single numeric value representing isothermality for each year. #' @export #' #' @details #' If method is "anuclimmean", bio3 is calculated using "anuclim" method. #' Temperatures are aggregated by month and spline intepolated to weekly before #' mean diurnal temperature range is calculated, replicating the method used by #' http://www.worldclim.org/. #' #' If method is "anuclimaxmin", bio3 is calculated using "anuclimmaxmin" method. #' Maximum and minimum monthly temperatues are spline interpolated to a weekly #' time period and range is calculated from the maximum and minimum mean weekly #' temperature values. #' #' If using method "anuclimmean" or "anuclimmaxmin" and data spans more than one #' year, data are aggregated by unique month irrespective of year and one value #' returned. #' #' If method is left unspecified, bio3 is calculated using the mean of daily #' maximum and minimum temperature. If data spans more than one year, data are #' aggregated by unique month irrespective of year and one value returned. If #' method is unspecified, bio7 is calculated using the maximum temperature value #' for the year.If data spans more than one year, bio7 calculations are performed #' on all data and single value returned. #' #' @seealso [bio2()] and [bio7()] for calculating diurnal and annual temperature ranges. #' [tmecreate()] for creating a 'POSIXlt' object. #' #' @examples #' temps <- 10 * sin(c(0:1459) / (pi * 150)) + rnorm(1460) #' tme <- tmecreate(2010, 24) #' plot(temps~as.POSIXct(tme), type = "l", xlab = "Month", ylab = "Temperature") #' bio3(temps, tme) #' bio3(temps, tme, method = "anuclimmean") #' bio3(temps, tme, method = "anuclimmaxmin") #' bio3 <- function(temps, tme, method = "") { if (is.na(sd(temps, na.rm = TRUE))) tiso <- NA else { if (method == "anuclimmean") { if (length(unique(tme$year)) > 1) warna() tiso <- bio2(temps, tme, "anuclim") / bio7(temps, tme, "anuclimean") } else if (method == "anuclimmmaxmin") { if (length(unique(tme$year)) > 1) warna() tiso <- bio2(temps, tme, "anuclim") / bio7(temps, tme, "anuclimmaxmin") } else { if (length(unique(tme$year)) > 1) warnb() tiso <- bio2(temps, tme) / bio7(temps, tme) } } tiso } #' bio8: Calculates mean temperature of Wettest quarter #' #' @description `bio8` is used to calculate the mean temperature in the wettest #' quarter of the year #' #' @param temps a vector of temperatures, normally for one year (see details). #' @param prec a vector of precipitation values, normally for one year (see #' details). #' @param tme1 a `POSIXlt` object representing the date and time of each `temps` value. #' @param tme2 a `POSIXlt` object representing the date and time of each `prec` value. #' @param method An optional character string describing the methods used to #' calculate mean temperature in the wettest quarter. Options are "anuclim" or unspecified #' (see details). #' #' @return a single numeric value of mean temperature of the wettest quarter of #' the year. #' @export #' #' @details If method is "anuclim", mean monthly temperature and total monthly #' precipitation is calculated and then spline interpolated to a weekly time #' period. Precipitation is calculated for each 13-week period and the mean #' temperature for the wettest period returned. If data spans more than one #' year, data are aggregated by unique month irrespective of year and one value #' returned. If method is unspecified, the mean temperature of the wettest #' quarter is calculated using all `temps` values and precipitation per quarter #' is calculated using the time interval for measurements. If data span more #' than one year, calculations are performed on all data and a single value #' returned. #' #' @seealso the [tmecreate()] function can be used to create a POSIXlt object. #' #' @examples #' temps <- 10 * sin(c(0:1459) / (pi * 150)) + rnorm(1460) #' tme1 <- tmecreate(2010, 6) #' plot(temps~as.POSIXct(tme1), type = "l", xlab = "Month", ylab = "Temperature") #' prec <- (10 * sin(c(0:364) * (pi / -360)) + rnorm(365) + 12) #' tme2 <- tmecreate(2010, 24) #' plot(prec~as.POSIXct(tme2), type = "l", xlab = "Month", ylab = "Precipitation") #' bio8(temps, prec, tme1, tme2) #' bio8(temps, prec, tme1, tme2, method = "anuclim") #' bio8 <- function(temps, prec, tme1, tme2, method = "") { if (is.na(sd(prec, na.rm = TRUE)) | is.na(sd(temps, na.rm = TRUE))) twet <- NA else { if (method == "anuclim") { if (length(unique(tme1$year)) > 1) warna() if (length(unique(tme2$year)) > 1) warna() tmth <- aggregate(temps, by = list(tme1$mon), mean, na.rm = TRUE)$x twk <- spline(tmth, n = length(tmth) / 12 * 52)$y pmth <- aggregate(prec, by = list(tme2$mon), sum, na.rm = TRUE)$x pwk <- spline(pmth, n = length(pmth) / 12 * 52)$y * 12 / 52 pwk[which(pwk < 0)] <- 0 qtr <- function(i) { ptw <- c(pwk, pwk) psu <- sum(ptw[i: (i + 12)], na.rm = TRUE) psu } wq <- sapply(c(1:length(pwk)), qtr) i <- which(wq == max(wq, na.rm = TRUE))[1] twk2 <- c(twk, twk) twet <- mean(twk2[i:(i + 12)], na.rm = TRUE) } else { if (length(unique(tme1$year)) > 1) warnb() qtr <- function(i, int) { prec2 <- c(prec, prec) psu <- sum(prec2[i: (i + int)], na.rm = TRUE) psu } id <- (as.numeric(tme2[2]) - as.numeric(tme2[1])) / 86400 dd1 <- 24/(24/(1/id)) int <- 91 / id wq <- sapply(c(1:length(prec)), qtr, int) i <- which(wq == max(wq, na.rm = TRUE))[1] tid <-(as.numeric(tme1[2]) - as.numeric(tme1[1])) / 86400 dd2 <- 24/(24/(1/tid)) tint <- 91 / tid ti <- i*(dd1*dd2) tte <- c(temps, temps) twet <- mean(tte[ti:(ti + tint)], na.rm = TRUE) } } twet } #' bio9: Calculates mean temperature of the driest quarter #' #' @description `bio9` is used to calculate the mean temperature of the driest #' quarter of the year #' #' @param temps a vector of temperatures, normally for one year (see details). #' @param prec a vector of precipitation values, normally for one year (see #' details). #' @param tme1 a `POSIXlt` object representing the date and time of each `temps` value. #' @param tme2 a `POSIXlt` object representing the date and time of each `prec` value. #' @param method character string describing the method used to calculate mean temperature #' of the driest quarter. Options are "anuclim" or unspecified (see details). #' #' @return a single numeric value of mean temperature of the wettest quarter of #' the year. #' @export #' #' @details If method is "anuclim", mean monthly temperature values are #' calculated and spline interpolated to a weekly time period. Precipitation #' values are summed for all months and then spline interpolated to a weekly #' time period. Mean temeprature of the driest 13-week period is returned. #' Otherwise, annual precipitation values are used to calculate precipitation #' in the driest three-month period and mean temperature in this period #' returned. #' #' @seealso the [tmecreate()] function can be used to create a POSIXlt object. #' #' @examples #' temps <- 10 * sin(c(0:1459) / (pi * 150)) + rnorm(1460) #' tme1 <- tmecreate(2010, 6) #' plot(temps~as.POSIXct(tme), type = "l", xlab = "Month", ylab = "Temperature") #' prec <- (10 * sin(c(0:364) * (pi / -360)) + rnorm(365) + 12) #' tme2 <- tmecreate(2010, 24) #' plot(prec~as.POSIXct(tme2), type = "l", xlab = "Month", ylab = "Precipitation") #' bio9(temps, prec, tme1, tme2) #' bio9(temps, prec, tme1, tme2, method = "anuclim") #' bio9 <- function(temps, prec, tme1, tme2, method = "") { if (is.na(sd(prec, na.rm = TRUE)) | is.na(sd(temps, na.rm = TRUE))) tdry <- NA else { if (method == "anuclim") { if (length(unique(tme1$year)) > 1) warna() if (length(unique(tme2$year)) > 1) warna() tmth <- aggregate(temps, by = list(tme1$mon), mean, na.rm = TRUE)$x twk <- spline(tmth, n = length(tmth) / 12 * 52)$y pmth <- aggregate(prec, by = list(tme2$mon), sum, na.rm = TRUE)$x pwk <- spline(pmth, n = length(pmth) / 12 * 52)$y * 12 / 52 pwk[which(pwk < 0)] <- 0 qtr <- function(i) { ptw <- c(pwk, pwk) psu <- sum(ptw[i: (i + 12)], na.rm = TRUE) psu } dq <- sapply(c(1:length(pwk)), qtr) i <- which(dq == min(dq, na.rm = TRUE))[1] twk2 <- c(twk, twk) tdry <- mean(twk2[i:(i + 12)], na.rm = TRUE) } else { if (length(unique(tme1$year)) > 1) warnb() qtr <- function(i, int) { prec2 <- c(prec, prec) psu <- sum(prec2[i: (i + int)], na.rm = TRUE) psu } id <- (as.numeric(tme2[2]) - as.numeric(tme2[1])) / 86400 dd1 <- 24/(24/1/id) int <- 91 / id dq <- sapply(c(1:length(prec)), qtr, int) i <- which(dq == min(dq, na.rm = TRUE))[1] tid <-(as.numeric(tme1[2]) - as.numeric(tme1[1])) / 86400 dd2 <- 24/(24/1/tid) tint <- 91 / tid ti <- i*(dd1*dd2) tte <- c(temps, temps) tdry <- mean(tte[ti:(ti + tint)], na.rm = TRUE) } } tdry } #' bio10: Calculates mean temperature of the Warmest quarter #' #' @description `bio10` is used to calculate the mean temperature of the warmest #' quarter (three months) of the year #' #' @param temps a vector of temperatures, normally for one year (see details). #' @param tme a `POSIXlt` object representing the date and time of each `temps` value. #' @param method An optional character string describing the method used to #' calculate mean temperature of the warmest quarter. Options are "anuclim" or #' unspecified (see details). #' #' @return a single numeric value of mean temperature in the warmest quarter of #' the year. #' @export #' #' @details If method is "anuclim", warmest quarter is determined to the nearest #' week. Mean monthly temperature values are calculated and spline interpolated #' to a weekly time period. Precipitation values are summed for all months and #' then spline interpolated to a weekly time period. Otherwise, the mean #' temperature of the warmest 3-month period is calculated from annual values. #' #' @seealso the [tmecreate()] function can be used to create a POSIXlt object. #' #' @examples #' temps <- 10 * sin(c(0:1459) / (pi * 150)) + rnorm(1460) #' tme <- tmecreate(2010, 6) #' plot(temps~as.POSIXct(tme), type = "l", xlab = "Month", ylab = "Temperature") #' bio10(temps, tme) #' bio10(temps, tme, method = "anuclim") #' bio10 <- function(temps, tme, method = "") { if (is.na(sd(temps, na.rm = TRUE))) thot <- NA else { if (method == "anuclim") { if (length(unique(tme$year)) > 1) warna() tmth <- aggregate(temps, by = list(tme$mon), mean, na.rm = TRUE)$x twk <- spline(tmth, n = length(tmth) / 12 * 52)$y qtr <- function(i) { tw <- c(twk, twk) me <- mean(tw[i: (i + 12)], na.rm = TRUE) me } hq <- sapply(c(1:length(twk)), qtr) i <- which(hq == max(hq, na.rm = TRUE))[1] twk2 <- c(twk, twk) thot <- mean(twk2[i:(i + 12)], na.rm = TRUE) } else { if (length(unique(tme$year)) > 1) warnb() qtr <- function(i, int) { tw <- c(temps, temps) me <- mean(tw[i: (i + int)], na.rm = TRUE) me } id <- (as.numeric(tme[2]) - as.numeric(tme[1])) / 86400 int <- 91 / id hq <- sapply(c(1:length(temps)), qtr, int) i <- which(hq == max(hq, na.rm = TRUE))[1] tte <- c(temps, temps) thot <- mean(tte[i:(i + int)], na.rm = TRUE) } } thot } #' bio11: Calculates mean temperature of the coldest quarter #' #' @description `bio11` is used to calculate the mean temperature of the coldest #' quarter (three months) of the year #' #' @param temps a vector of temperatures, normally for one year (see details). #' @param prec a vector of precipitation values, normally for one year (see #' details). #' @param tme a `POSIXlt` object representing the date and time of each `temps` value. #' @param method An optional character vector describing the method used to calculate #' the mean temperature of the coldest quarter. Options are "anuclim" or unpsecified (see #' details). #' #' @return a single numeric value of mean temperature of the warmest quarter of #' the year. #' @export #' #' @details If method is "anuclim", mean monthly temperature values are #' calculated and spline interpolated to a weekly time period. Mean temperature of the coldest 13-week period is #' determined. If method is left unspecified, mean temperature of the coldest 3-month (91-day) period is #' calculated from annual temperature values. #' #' @seealso the [tmecreate()] function can be used to create a POSIXlt object. #' #' @examples #' temps <- 10 * sin(c(0:1459) / (pi * 150)) + rnorm(1460) #' tme <- tmecreate(2010, 6) #' plot(temps~as.POSIXct(tme), type = "l", xlab = "Month", ylab = "Temperature") #' bio11(temps, tme) #' bio11(temps, tme, method = "anuclim") #' bio11 <- function(temps, tme, method = "") { if (is.na(sd(temps, na.rm = TRUE))) tcold <- NA else { if (method == "anuclim") { if (length(unique(tme$year)) > 1) warna() tmth <- aggregate(temps, by = list(tme$mon), mean, na.rm = TRUE)$x twk <- spline(tmth, n = length(tmth) / 12 * 52)$y qtr <- function(i) { tw <- c(twk, twk) me <- mean(tw[i: (i + 12)], na.rm = TRUE) me } cq <- sapply(c(1:length(twk)), qtr) i <- which(cq == min(cq, na.rm = TRUE))[1] twk2 <- c(twk, twk) tcold <- mean(twk2[i:(i + 12)], na.rm = TRUE) } else { if (length(unique(tme$year)) > 1) warnb() qtr <- function(i, int) { tw <- c(temps, temps) me <- mean(tw[i: (i + int)], na.rm = TRUE) me } id <- (as.numeric(tme[2]) - as.numeric(tme[1])) / 86400 int <- 91 / id cq <- sapply(c(1:length(temps)), qtr, int) i <- which(cq == min(cq, na.rm = TRUE))[1] tte <- c(temps, temps) tcold <- mean(tte[i:(i + int)], na.rm = TRUE) } } tcold } #' bio12: Calculates total annual precipitation #' @description `bio12` is used to calculate total precipitation in the year #' @param prec a vector of precipitation values, normally for one year (see #' details). #' @param tme a `POSIXlt` object representing the date and time of each `temps` value. #' @param method An optional character string describing the method used to #' calculate total annual precipitation. Options are "anuclim" or unspecified (see #' details). #' #' @return a single numeric value of total annual precipitation. #' @export #' #' @details If method is "anuclim", monthly precipitation values are spline #' interpolated to a weekly time period and the total for each year returned. #' Otherwise, all precipitation values for each year are summed. #' #' @seealso the [tmecreate()] function can be used to create a POSIXlt object. #' #' @examples #' prec <- (10 * sin(c(0:364) * (pi / -360)) + rnorm(365) + 12) #' tme <- tmecreate(2010, 24) #' plot(prec~as.POSIXct(tme), type = "l", xlab = "Month", ylab = "Precipitation") #' bio12(prec, tme) #' bio12(prec, tme, method="anuclim") #' bio12 <- function(prec, tme, method = "") { if (is.na(sd(prec, na.rm = TRUE))) map <- NA else { if (method == "anuclim") { if (length(unique(tme$year)) > 1) warna() pmth <- aggregate(prec, by = list(tme$mon), sum, na.rm = TRUE)$x pwk <- spline(pmth, n = length(pmth) / 12 * 52)$y * 12 / 52 pwk[which(pwk < 0)] <- 0 map <- sum(pwk, na.rm = TRUE) / length(unique(tme$year)) } else { if (length(unique(tme$year)) > 1) warnb() map <- sum(prec, na.rm = TRUE) / length(unique(tme$year)) } } map } #' bio13: Calculates precipitation of the wettest period #' #' @description `bio13` is used to calculate the precipitation of the wettest #' week or month of the year, depending on the time step. #' #' @param prec a vector of precipitation values, normally for one year (see #' details). #' @param tme a `POSIXlt` object representing the date and time of each `prec` value. #' @param method An optional character string describing how the maximum weekly or #' monthly precipitation is calculated. Options are "week" and "month" (see #' details). #' #' @return a single numeric value of total precipitation in the wettest week or month of the year. #' @export #' #' @details #' If method is "week", monthly precipitation values are spline interpolated to a #' weekly time period and the maximum weekly precipitation is returned. If method #' is "month", monthly precipitation values are summed and the maximum monthly #' precipitation is returned. #' #' If data span more than one year, data are aggregated by unique month #' irrespective of year and one value returned. #' #' @seealso the [tmecreate()] function can be used to create a POSIXlt object. #' #' @examples #' prec <- (10 * sin(c(0:364) * (pi / -360)) + rnorm(365) + 12) #' tme <- tmecreate(2010, 24) #' plot(prec~as.POSIXct(tme), type = "l", xlab = "Month", ylab = "Precipitation") #' bio13(prec, tme, method="week") #' bio13(prec, tme, method="month") #' bio13(prec, tme) #' bio13 <- function(prec, tme, method = "week") { if (is.na(sd(prec, na.rm = TRUE))) wp <- NA else { if (method == "month"){ if (length(unique(tme$year)) > 1) warna() pmth <- aggregate(prec, by = list(tme$mon), sum, na.rm = TRUE)$x pmth[which(pmth < 0)] <- 0 wp <- max(pmth, na.rm = TRUE) } else { if(method == "week"){ if (length(unique(tme$year)) > 1) warna() pmth <- aggregate(prec, by = list(tme$mon), sum, na.rm = TRUE)$x pwk <- spline(pmth, n = length(pmth) / 12 * 52)$y * 12 / 52 pwk[which(pwk < 0)] <- 0 wp <- max(pwk, na.rm = TRUE) } else { if (length(unique(tme$year)) > 1) warnb() pmth <- aggregate(prec, by = list(tme$mon), sum, na.rm = TRUE)$x wp <- max(pmth, na.rm = TRUE) } } } wp } #' bio14: Calculates precipitation of the driest period #' #' @description `bio14` is used to calculate the precipitation in the driest #' period of the year #' @param prec a vector of precipitation values, normally for one year (see #' details). #' @param tme a `POSIXlt` object representing the date and time of each `prec` value. #' @param method an optional character string describing how the minimum weekly #' or monthly precipitation is calculated. Options include"week", "month" or unspecified #' (see details). #' #' @return a single numeric value of total precipitation in the driest week or #' month of the year. #' @export #' #' @details If method is "week" or left unspecified, monthly precipitation values #' are spline interpolated to a weekly time period and the minimum weekly #' precipitation is returned. If method is "month", the minimum monthly #' precipitation is returned. #' #' @seealso the [tmecreate()] function can be used to create a POSIXlt object. #' #' @examples #' prec <- (10 * sin(c(0:364) * (pi / -360)) + rnorm(365) + 12) #' tme <- tmecreate(2010, 24) #' plot(prec~as.POSIXct(tme), type = "l", xlab = "Month", ylab = "Precipitation") #' bio14(prec, tme) #' bio14(prec, tme, method="week") #' bio14(prec, tme, method="month") #' bio14 <- function(prec, tme, method = "week") { if (is.na(sd(prec, na.rm = TRUE))) dp <- NA else { if (method == "month"){ if (length(unique(tme$year)) > 1) warna() pmth <- aggregate(prec, by = list(tme$mon), sum, na.rm = TRUE)$x pmth[which(pmth < 0)] <- 0 dp <- min(pmth, na.rm = TRUE) } else { if(method == "week"){ if (length(unique(tme$year)) > 1) warna() pmth <- aggregate(prec, by = list(tme$mon), sum, na.rm = TRUE)$x pwk <- spline(pmth, n = length(pmth) / 12 * 52)$y * 12 / 52 pwk[which(pwk < 0)] <- 0 dp <- min(pwk, na.rm = TRUE) } else { if (length(unique(tme$year)) > 1) warnb() pmth <- aggregate(prec, by = list(tme$mon), sum, na.rm = TRUE)$x dp <- min(pmth, na.rm = TRUE) } } } dp } #' bio15: Calculates precipitation seasonality #' #' @description `bio15` is used to calculate precipitation seasonality, which is #' the standard deviation of weekly or monthly precipitation values as a #' percentage of the mean of those values. #' #' @param prec a vector of precipitation values, normally for one year (see #' details). #' @param tme a `POSIXlt` object representing the date and time of each `prec` value. #' @param method an optional character string describing the method used to #' calculate precipitation seasonality. Options include "anuclim" or unspecified (see #' details). #' #' @return a single numeric value representing precipitation seasonality. #' @export #' #' @details #' If method is "anuclim", monthly precipitation is spline interpolated to a #' weekly time period and precipitation seasonality calculated using these #' values, replicating the method used by http://www.worldclim.org/. Otherwise, #' precipitation seasonality is calculated using yearly values. #' #' If using `anuclim` method and data span more than one year, data are #' aggregated by unique month irrespective of year and one value returned. If #' method is left unspecified and data span more than one year, calculations #' will be performed on all data and a single value returned. #' #' @seealso the [tmecreate()] function can be used to create a POSIXlt object. #' #' @examples #' prec <- (10 * sin(c(0:364) * (pi / -360)) + rnorm(365) + 12) #' tme <- tmecreate(2010, 24) #' plot(prec~as.POSIXct(tme), type = "l", xlab = "Month", ylab = "Precipitation") #' bio15(prec, tme, method="week") #' bio15(prec, tme, method="month") #' #' bio15 <- function(prec, tme, method = "anuclim") { if (is.na(sd(prec, na.rm = TRUE))) cvp <- NA else { if (method == "anuclim"){ if (length(unique(tme$year)) > 1) warna() pmth <- aggregate(prec, by = list(tme$mon), sum, na.rm = TRUE)$x pwk <- spline(pmth, n = length(pmth) / 12 * 52)$y * 12 / 52 pwk[which(pwk <= 0)] <- 1 cvp <- (sd(pwk, na.rm = TRUE) / mean(pwk, na.rm = TRUE)) * 100 } else { if (length(unique(tme$year)) > 1) warnb() cvp <- (sd(prec, na.rm = TRUE) / mean (prec, na.rm = TRUE)) * 100 } } cvp } #' bio16: Calculates precipitation of the wettest quarter #' #' @description `bio16` is used to calculate the total precipitation of the #' wettest quarter of the year #' #' @param prec a vector of precipitation values, normally for one year (see #' details). #' @param tme a `POSIXlt` object representing the date and time of each `prec` value. #' @param method an optional character string describing how precipitation of the wettest #' quarter is calculated. Options include "anuclim" or unspecified (see details). #' #' @return a single numeric value for precipitation in the wettest quarter of the year. #' @export #' #' @details If method is "anuclim", monthly precipitation is spline interpolated #' to a weekly time period. Precipitation for each 13-week period is calculated #' and total precipitation in the wettest quarter returned. If data span more #' than one year, data are aggregated by unique month irrespective of year and #' one value returned. Otherwise, precipitation for each three-month (91-day) #' period is calculated and total precipitation in the wettest quarter #' returned. If data span more than one year, calculations are performed on all #' data and a single value returned. #' #' @seealso the [tmecreate()] function can be used to create a POSIXlt object. #' #' @examples #' prec <- (10 * sin(c(0:364) * (pi / -360)) + rnorm(365) + 12) #' tme <- tmecreate(2010, 24) #' plot(prec~as.POSIXct(tme), type = "l", xlab = "Month", ylab = "Precipitation") #' bio16(prec, tme) #' bio16(prec, tme, method="anuclim") #' bio16 <- function(prec, tme, method = "") { if (is.na(sd(prec, na.rm = TRUE))) pwet <- NA else { if (method == "anuclim") { if (length(unique(tme$year)) > 1) warna() pmth <- aggregate(prec, by = list(tme$mon), sum, na.rm = TRUE)$x pwk <- spline(pmth, n = length(pmth) / 12 * 52)$y * 12 / 52 pwk[which(pwk < 0)] <- 0 qtr <- function(i) { pw <- c(pwk, pwk) su <- sum(pw[i: (i + 12)], na.rm = TRUE) su } wq <- sapply(c(1:length(pwk)), qtr) i <- which(wq == max(wq, na.rm = TRUE))[1] pwk2 <- c(pwk, pwk) pwet <- sum(pwk2[i:(i + 12)], na.rm = TRUE) } else { if (length(unique(tme$year)) > 1) warnb() qtr <- function(i, int) { pw <- c(prec, prec) su <- sum(pw[i: (i + int)], na.rm = TRUE) su } id <- (as.numeric(tme[2]) - as.numeric(tme[1])) / 86400 int <- 91 / id wq <- sapply(c(1:length(prec)), qtr, int) i <- which(wq == max(wq, na.rm = TRUE))[1] pre2 <- c(prec, prec) pwet <- sum(pre2[i:(i + int)], na.rm = TRUE) } } pwet } #' bio17: Calculates precipitation of the driest quarter #' #' @description `bio17` is used to calculate the precipitation in the driest #' quarter of the year #' #' @param prec a vector of precipitation values, normally for one year (see #' details). #' @param tme a `POSIXlt` object representing the date and time of each `prec` value. #' @param method an optional character string describing how quarterly #' precipitation is calculated. Options include "anuclim" or unspecified (see details). #' #' @return a single numeric value of precipitation of the driest quarter. #' @export #' #' @details If method is "anuclim", monthly precipitation is spline interpolated #' to a weekly time period and precipitation of each 13-week period is #' calculated. The precipitation in the driest quarter is then found. If data #' spans more than one year, data are aggregated by unique month irrespective #' of year and one value returned. Otherwise, precipitation in each three-month #' period is calculated and total precipitation in the driest quarter #' returned. If data span more than one year, calculations are performed on all #' data and single value returned. #' #' @seealso the [tmecreate()] function can be used to create a POSIXlt object. #' #' @examples #' prec <- (10 * sin(c(0:364) * (pi / -360)) + rnorm(365) + 12) #' tme <- tmecreate(2010, 24) #' plot(prec~as.POSIXct(tme), type = "l", xlab = "Month", ylab = "Precipitation") #' bio17(prec, tme) #' bio17(prec, tme, method="anuclim") #' bio17 <- function(prec, tme, method = "") { if (is.na(sd(prec, na.rm = TRUE))) pdry <- NA else { if (method == "anuclim") { if (length(unique(tme$year)) > 1) warna() pmth <- aggregate(prec, by = list(tme$mon), sum, na.rm = TRUE)$x pwk <- spline(pmth, n = length(pmth) / 12 * 52)$y * 12 / 52 pwk[which(pwk < 0)] <- 0 qtr <- function(i) { pw <- c(pwk, pwk) su <- sum(pw[i: (i + 12)], na.rm = TRUE) su } dq <- sapply(c(1:length(pwk)), qtr) i <- which(dq == min(dq, na.rm = TRUE))[1] pwk2 <- c(pwk, pwk) pdry <- sum(pwk2[i:(i + 12)], na.rm = TRUE) } else { if (length(unique(tme$year)) > 1) warnb() qtr <- function(i, int) { pw <- c(prec, prec) su <- sum(pw[i: (i + int)], na.rm = TRUE) su } id <- (as.numeric(tme[2]) - as.numeric(tme[1])) / 86400 int <- 91 / id dq <- sapply(c(1:length(prec)), qtr, int) i <- which(dq == min(dq, na.rm = TRUE))[1] pre2 <- c(prec, prec) pdry <- sum(pre2[i:(i + int)], na.rm = TRUE) } } pdry } #' bio18: Precipitation of the warmest quarter #' #' @description `bio18` is used to calculate the precipitation in the warmest #' quarter of the year #' #' @param temps a vector of temperature values, normally for one year (see #' details). #' @param prec a vector of precipitation values, normally for one year (see #' details). #' @param tme1 a `POSIXlt` object representing the date and time of each `temps` value. #' @param tme2 a `POSIXlt` object representing the date and time of each `prec` value. #' @param method an optional character string describing how quarterly #' temperature and precipitation are calculated. Options are "anuclim" or unspecified #' (see details). #' #' @return a single numeric value of precipitation in the warmest quarter of the #' year. #' @export #' #' @seealso the [tmecreate()] function can be used to create a POSIXlt object. #' #' @details If method is "anuclim", monthly mean temperature and total monthly #' precipitation are interpolated to a weekly time period before calculating #' mean temperature of each 13-week period. The precipitation in the warmest #' quarter is then found. If data span more than one year, data are aggregated #' by unique month irrespective of year and one value returned. If method is #' unspecified, the mean temperature in each three-month period is calculated #' and precipitation in the coldest quarter returned. If data span more than #' one year, calculations are performed on all data and single value returned. #' #' @examples #' prec <- (10 * sin(c(0:364) * (pi / -360)) + rnorm(365) + 12) #' temps <- 10 * sin(c(0:1459) / (pi * 150)) + rnorm(1460) #' tme1 <- tmecreate(2010, 6) #' tme2<- tmecreate(2010, 24) #' plot(temps~as.POSIXct(tme1), type = "l", xlab = "Month", ylab = "Temperature") #' bio18(temps, prec, tme1, tme2) #' bio18(temps, prec, tme1, tme2, method="anuclim") bio18 <- function(temps, prec, tme1, tme2, method = "") { if (is.na(sd(prec, na.rm = TRUE)) | is.na(sd(temps, na.rm = TRUE))) pwarm <- NA else { if (method == "anuclim") { if (length(unique(tme1$year)) > 1) warna() if (length(unique(tme2$year)) > 1) warna() tmth <- aggregate(temps, by = list(tme1$mon), mean, na.rm = TRUE)$x twk <- spline(tmth, n = length(tmth) / 12 * 52)$y pmth <- aggregate(prec, by = list(tme2$mon), sum, na.rm = TRUE)$x pwk <- spline(pmth, n = length(pmth) / 12 * 52)$y * 12 / 52 pwk[which(pwk < 0)] <- 0 qtr <- function(i) { tw <- c(twk, twk) me <- mean(tw[i: (i + 12)], na.rm = TRUE) me } wq <- sapply(c(1:length(twk)), qtr) i <- which(wq == max(wq, na.rm = TRUE))[1] pwk2 <- c(pwk, pwk) pwarm <- sum(pwk2[i:(i + 12)], na.rm = TRUE) } else { if (length(unique(tme1$year)) > 1) warnb() if (length(unique(tme2$year)) > 1) warnb() qtr <- function(i, int) { tw <- c(temps, temps) me <- mean(tw[i: (i + int)], na.rm = TRUE) me } id <- (as.numeric(tme1[2]) - as.numeric(tme1[1])) / 86400 dd1 <- 24/(24/(1/id)) int <- 91 / id wq <- sapply(c(1:length(temps)), qtr, int) i <- which(wq == max(wq, na.rm = TRUE))[1] pid <-(as.numeric(tme2[2]) - as.numeric(tme2[1])) / 86400 dd2 <- 24/(24/(1/pid)) pint <- 91 / pid pi <- i*(dd2/dd1) pre2 <- c(prec, prec) pwarm <- sum(pre2[pi:(pi + pint)], na.rm = TRUE) } } pwarm } #' bio19: Precipitation of the coldest quarter #' #' @description `bio19` is used to calculate the precipitation in the coldest #' quarter of the year. #' #' @param temps a vector of temperature values, normally for one year (see #' details) #' @param prec a vector of precipitation values, normally for one year (see #' details). #' @param tme1 a `POSIXlt` object representing the date and time of each `temps` value. #' @param tme2 a `POSIXlt` object representing the date and time of each `prec` value. #' @param method an optional character string describing how quarterly mean #' temperature and precipitation are calculated. Options are "anuclim" or unspecified #' (see details). #' #' @return a single numeric value of precipitation in the coldest quarter of the #' year. #' @export #' #' @seealso the [tmecreate()] function can be used to create a POSIXlt object. #' #' @details If method is "anuclim", monthly mean temeprature and total monthly #' precipitation are interpolated to weekly time period before calculating mean #' temperature for each 13-week period. The precipitation in the coldest #' quarter is then calculated. If data spans more than one year, data are #' aggregated by unique month irrespective of year and one value returned If #' method is left unspecified, the mean temperature in each three-month period #' is calculated and precipitation in the coldest quarter returned. If data #' spans more than one year, calculations are performed on all data and single #' value returned. #' #' @examples #' #' prec <- 10 * sin(c(0:364) * (pi / -360)) + (rnorm(365) + 12) #' temps <- 10 * sin(c(0:1459) / (pi * 150)) + rnorm(1460) #' tme1 <- tmecreate(2010, 6) #' tme2<- tmecreate(2010, 24) #' plot(prec~as.POSIXct(tme), type = "l", xlab = "Month", ylab = "Precipitation") #' bio19(temps, prec, tme1, tme2) #' bio19(temps, prec, tme1, tme2, method="anuclim") bio19 <- function(temps, prec, tme1, tme2, method = "") { if (is.na(sd(prec, na.rm = TRUE)) | is.na(sd(temps, na.rm = TRUE))) pcld <- NA else { if (method == "anuclim") { if (length(unique(tme1$year)) > 1) warna() if (length(unique(tme2$year)) > 1) warna() tmth <- aggregate(temps, by = list(tme1$mon), mean, na.rm = TRUE)$x twk <- spline(tmth, n = length(tmth) / 12 * 52)$y pmth <- aggregate(prec, by = list(tme2$mon), sum, na.rm = TRUE)$x pwk <- spline(pmth, n = length(pmth) / 12 * 52)$y * 12 / 52 pwk[which(pwk < 0)] <- 0 qtr <- function(i) { tw <- c(twk, twk) me <- mean(tw[i: (i + 12)], na.rm = TRUE) me } cq <- sapply(c(1:length(twk)), qtr) i <- which(cq == min(cq, na.rm = TRUE))[1] pwk2 <- c(pwk, pwk) pcld <- sum(pwk2[i:(i + 12)], na.rm = TRUE) } else { if (length(unique(tme1$year)) > 1) warnb() if (length(unique(tme2$year)) > 1) warnb() qtr <- function(i, int) { tw <- c(temps, temps) me <- mean(tw[i: (i + int)], na.rm = TRUE) me } id <- (as.numeric(tme1[2]) - as.numeric(tme1[1])) / 86400 dd1 <- 24/(24/(1/id)) int <- 91 / id cq <- sapply(c(1:length(temps)), qtr, int) i <- which(cq == min(cq, na.rm = TRUE))[1] pid <-(as.numeric(tme2[2]) - as.numeric(tme2[1])) / 86400 dd2 <- 24/(24/(1/pid)) pint <- 91 / pid pi <- i*(dd2/dd1) pre2 <- c(prec, prec) pcld <- sum(pre2[pi:(pi + pint)], na.rm = TRUE) } } pcld }

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/man/event_term.Rd

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/event_vector.R \name{event_term} \alias{event_term} \title{Create an event model term from a named list of variables.} \usage{ event_term(evlist, onsets, blockids, durations = 1, subset = NULL) } \arguments{ \item{evlist}{A list of named variables.} \item{onsets}{A vector of onset times for the experimental events in seconds.} \item{blockids}{A vector of block numbers associated with each onset.} \item{durations}{A vector of event durations (default is 1).} \item{subset}{A logical vector indicating the subset of onsets to retain (default is NULL).} } \value{ A list containing the following components: \itemize{ \item varname: A character string representing the variable names, concatenated with colons. \item events: A list of event variables. \item subset: A logical vector indicating the retained onsets. \item event_table: A tibble containing event information. \item onsets: A vector of onset times. \item blockids: A vector of block numbers. \item durations: A vector of event durations. } } \description{ This function generates an event model term from a list of named variables, along with their onsets, block IDs, and durations. Optionally, a subset of onsets can be retained. } \examples{ x1 <- factor(rep(letters[1:3], 10)) x2 <- factor(rep(1:3, each=10)) eterm <- event_term(list(x1=x1,x2=x2), onsets=seq(1,100,length.out=30), blockids=rep(1,30)) x1 <- rnorm(30) x2 <- factor(rep(1:3, each=10)) eterm <- event_term(list(x1=x1,x2=x2), onsets=seq(1,100,length.out=30), blockids=rep(1,30), subset=x1>0) }

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/man/as_search.Rd

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as_search.Rd

% Generated by roxygen2 (4.1.1): do not edit by hand % Please edit documentation in R/as_search.R \name{as_search} \alias{as_search} \title{Search articles} \usage{ as_search(q, fq = NULL, sort = NULL, begin_date = NULL, end_date = NULL, key = NULL, fl = NULL, hl = FALSE, page = 0, facet_field = NULL, facet_filter = NULL, ..., callopts = list()) } \arguments{ \item{q}{Search query term. Search is performed on the article body, headline and byline.} \item{fq}{Filtered search query using standard Lucene syntax. The filter query can be specified with or without a limiting field: label. See fq_fields for the filter query fields.} \item{sort}{(character) Default NULL. One of newest or oldest . By default, search results are sorted by their relevance to the query term (q). Use the sort parameter to sort by pub_date.} \item{begin_date}{Begin date - Restricts responses to results with publication dates of the date specified or later. In the form YYYYMMDD} \item{end_date}{End date - Restricts responses to results with publication dates of the date specified or earlier. In the form YYYYMMDD} \item{key}{your New York Times API key; pass in, or loads from .Rprofile as \code{nytimes_as_key}, or from .Renviron as \code{NYTIMES_AS_KEY}} \item{fl}{Fields to get back, as vector. See Details for the.} \item{hl}{(logical) Highlight or not, default is FALSE} \item{page}{Page number. The value of page corresponds to a set of 10 results (it does not indicate the starting number of the result set). For example, page=0 corresponds to records 0-9. To return records 10-19, set page to 1, not 10.} \item{facet_field}{(character) Specifies the sets of facet values to include in the facets array at the end of response, which collects the facet values from all the search results. By default no facet fields will be returned. See details for options.} \item{facet_filter}{(logical) Fields to facet on, as vector. When set to true, facet counts will respect any applied filters (fq, date range, etc.) in addition to the main query term. To filter facet counts, specifying at least one facet_field is required.} \item{...}{Futher args pass into query} \item{callopts}{Curl options (debugging tools mostly) passed to \code{\link[httr]{GET}}} } \description{ Search articles } \details{ fl parameter options are: web_url, snippet, lead_paragraph, abstract, print_page, blog, source, multimedia, headline, keywords, pub_date, document_type, news_desk, byline, type_of_material, _id, word_count. facet_field param options are: section_name, document_type, type_of_material, source, day_of_week } \examples{ \dontrun{ as_search(q="bailout", begin_date = "20081001", end_date = '20081201') as_search(q="bailout", facet_field = 'section_name', begin_date = "20081001", end_date = '20081201', fl = 'word_count') as_search(q="money", fq = 'The New York Times') as_search(q="money", fq = 'news_desk:("Sports" "Foreign")') as_search(q="bailout", hl = TRUE) library('httr') as_search("iowa caucus", callopts = verbose()) } } \references{ \url{http://developer.nytimes.com/docs/article_search_api/} }

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refs/heads/master

2021-04-02T23:07:17.373647

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library(dplyr); library(ggplot2); df <- read.csv("saidas.11.05.2020/papiallrapl"); k <- df %>% select(counter,size,memtype,temp,opt,value) %>% group_by(counter,size,memtype,temp,opt) %>% summarise(mean=mean(value), n=n(), sd=sd(value), se=sd/sqrt(n), ic=2.576*se) write.csv(k, "pp.csv") exit k <- k[k$size=="huge",] k <- k[k$counter=="PAPI_TOT_CYC",] ggplot(k, aes(x=temp, y = mean)) + geom_point() + geom_errorbar(aes(ymin=mean-ic, ymax=mean+ic), color="grey") + xlab("Versão") + ylab(k$counter) + #scale_y_continuous(expand = c(0,0)) + scale_y_continuous(trans="log10") + scale_fill_grey() + facet_wrap(~opt+memtype) + #theme_bw() + theme(axis.text.x = element_text(angle = 90, hjust = 1))

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/code/daejeon_linear.R

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refs/heads/master

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daejeon_linear.R

######daegeon_modeling_linear regression##### #예측값이 양수->log transform ####전체를이용한분석#### ###original_rmse### library(Metrics) set.seed(2019) sqrt(mean((predict(lm(mean(log(new_n))~1.,data=train),newdata = test)-log(test$new_n))^2)) library(Metrics) rmse(predict(lm(mean(log(new_n))~1.,data=train),newdata = test),log(test$new_n)) ##0.764992 summary(train) ###simple_linear### lm_all<-lm(log(new_n)~.,data=train) lm_all_summary<-summary(lm_all) write.csv(lm_all_summary$coefficients,"lm_all_summary.csv") #train error fitted_lm_all<-ifelse(lm_all$fitted.values<=log(5),log(5),lm_all$fitted.values) rmse(fitted_lm_all,log(train$new_n)) #0.4337806 0.4331446 0.4337169 #test error pred_lm_all<-predict(lm_all,test) pred_lm_all<-ifelse(pred_lm_all<=log(5),log(5),pred_lm_all) rmse(log(test$new_n),(pred_lm_all)) #0.5229548 0.5236249 0.524703 obs_lm_all<-as.data.frame(cbind(log(test$new_n),pred_lm_all)) colnames(obs_lm_all)<-c("real","pred_lm_all") library(ggplot2) ggplot(data=obs_lm_all) + geom_point(mapping=aes(x=exp(real),y=exp(pred_lm_all)))+geom_abline(intercept= 0, slope=1, color='blue', size = 1.5)+ggtitle("daejeon_lm_all")+theme(plot.title = element_text(family = "serif", face = "bold", hjust = 0.5, size = 25)) +xlab("obs")+ylab("pred") ggsave("pred_lm_all.jpg") #5이하인것들 5로일괄적용 #pred_lm_all<-ifelse(pred_lm_all<=log(5),log(5),pred_lm_all) #ggsave("pred_lm_all_5.jpg") ######구별로나누어분석#### #daedeok lm_daedeok<-lm(log(new_n)~.,data=train_daedeok) lm_daedeok_summary<-summary(lm_daedeok) write.csv(lm_daedeok_summary$coefficients,"lm_daedeok_summary.csv") #train error fitted_lm_daedeok<-ifelse(lm_daedeok$fitted.values<=log(5),log(5),lm_daedeok$fitted.values) rmse(fitted_lm_daedeok,log(train_daedeok$new_n)) #0.4655092 0.4645112 0.465376 #test error pred_lm_daedeok<-predict(lm_daedeok,test_daedeok) pred_lm_daedeok<-ifelse(pred_lm_daedeok<=log(5),log(5),pred_lm_daedeok) rmse(log(test_daedeok$new_n),pred_lm_daedeok) #0.6291933 0.5939541 0.5976513 a<-as.data.frame(cbind(log(test_daedeok$new_n),pred_lm_daedeok)) colnames(a)<-c("real","pred_lm_daedeok") library(ggplot2) ggplot(data=exp(a))+geom_point(mapping=aes(x=real,y=pred_lm_daedeok))+geom_abline(intercept= 0, slope=1, color='blue', size = 1.5)+ggtitle("daejeon_lm_daedeok")+theme(plot.title = element_text(family = "serif", face = "bold", hjust = 0.5, size = 25))+xlab("obs")+ylab("pred") ggsave("pred_lm_daedeok.jpg") #dong lm_dong<-lm(log(new_n)~.,data=train_dong) lm_dong_summary<-summary(lm_dong) write.csv(lm_dong_summary$coefficients,"lm_dong_summary.csv") #train error fitted_lm_dong<-ifelse(lm_dong$fitted.values<=log(5),log(5),lm_dong$fitted.values) rmse(fitted_lm_dong,log(train_dong$new_n)) #0.4256949 0.4246244 0.4249205 #test error pred_lm_dong<-predict(lm_dong,test_dong) pred_lm_dong<-ifelse(pred_lm_dong<=log(5),log(5),pred_lm_dong) rmse(log(test_dong$new_n),pred_lm_dong) #0.4093099 0.4101709 0.4097641 a<-as.data.frame(cbind(log(test_dong$new_n),pred_lm_dong)) colnames(a)<-c("real","pred_lm_dong") library(ggplot2) ggplot(data=exp(a))+geom_point(mapping=aes(x=real,y=pred_lm_dong))+geom_abline(intercept= 0, slope=1, color='blue', size = 1.5)+ggtitle("daejeon_lm_dong")+theme(plot.title = element_text(family = "serif", face = "bold", hjust = 0.5, size = 25))+xlab("obs")+ylab("pred") ggsave("pred_lm_dong.jpg") #seo lm_seo<-lm(log(new_n)~.,data=train_seo) lm_seo_summary<-summary(lm_seo) write.csv(lm_seo_summary$coefficients,"lm_seo_summary.csv") #train error fitted_lm_seo<-ifelse(lm_seo$fitted.values<=log(5),log(5),lm_seo$fitted.values) rmse(fitted_lm_seo,log(train_seo$new_n)) #0.3902521 0.3892703 0.3899579 #test error pred_lm_seo<-predict(lm_seo,test_seo) pred_lm_seo<-ifelse(pred_lm_seo<=log(5),log(5),pred_lm_seo) rmse(log(test_seo$new_n),pred_lm_seo) #0.4815595 0.4815223 0.4825923 a<-as.data.frame(cbind(log(test_seo$new_n),pred_lm_seo)) colnames(a)<-c("real","pred_lm_seo") library(ggplot2) ggplot(data=exp(a))+geom_point(mapping=aes(x=real,y=pred_lm_seo))+geom_abline(intercept= 0, slope=1, color='blue', size = 1.5)+ggtitle("daejeon_lm_seo")+theme(plot.title = element_text(family = "serif", face = "bold", hjust = 0.5, size = 25))+xlab("obs")+ylab("pred") ggsave("pred_lm_seo.jpg") #yuseong lm_yuseong<-lm(log(new_n)~.,data=train_yuseong) lm_yuseong_summary<-summary(lm_yuseong) write.csv(lm_yuseong_summary$coefficients,"lm_yuseong_summary.csv") #train error fitted_lm_yuseong<-ifelse(lm_yuseong$fitted.values<=log(5),log(5),lm_yuseong$fitted.values) rmse(fitted_lm_yuseong,log(train_yuseong$new_n)) #0.3947048 0.3942627 0.396508 #test error pred_lm_yuseong<-predict(lm_yuseong,test_yuseong) pred_lm_yuseong<-ifelse(pred_lm_yuseong<=log(5),log(5),pred_lm_yuseong) rmse(log(test_yuseong$new_n),pred_lm_yuseong) #0.4287863 0.4277372 0.4315214 a<-as.data.frame(cbind(log(test_yuseong$new_n),pred_lm_yuseong)) colnames(a)<-c("real","pred_lm_yuseong") library(ggplot2) ggplot(data=exp(a))+geom_point(mapping=aes(x=real,y=pred_lm_yuseong))+geom_abline(intercept= 0, slope=1, color='blue', size = 1.5)+ggtitle("daejeon_lm_yuseong")+theme(plot.title = element_text(family = "serif", face = "bold", hjust = 0.5, size = 25))+xlab("obs")+ylab("pred") ggsave("pred_lm_yuseong.jpg") #jung lm_jung<-lm(log(new_n)~.,data=train_jung) lm_jung_summary<-summary(lm_jung) write.csv(lm_jung_summary$coefficients,"lm_jung_summary.csv") #train error fitted_lm_jung<-ifelse(lm_jung$fitted.values<=log(5),log(5),lm_jung$fitted.values) rmse(fitted_lm_jung,log(train_jung$new_n)) #0.4032288 0.4025624 0.4028036 #test error pred_lm_jung<-predict(lm_jung,test_jung) pred_lm_jung<-ifelse(pred_lm_jung<=log(5),log(5),pred_lm_jung) rmse(log(test_jung$new_n),pred_lm_jung) #0.4571876 0.4580719 0.4586944 a<-as.data.frame(cbind(log(test_jung$new_n),pred_lm_jung)) colnames(a)<-c("real","pred_lm_jung") library(ggplot2) ggplot(data=exp(a))+geom_point(mapping=aes(x=real,y=pred_lm_jung))+geom_abline(intercept= 0, slope=1, color='blue', size = 1.5)+ggtitle("daejeon_lm_jung")+theme(plot.title = element_text(family = "serif", face = "bold", hjust = 0.5, size = 25))+xlab("obs")+ylab("pred") ggsave("pred_lm_jung.jpg")

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/mxnet_generated.R \name{mx.symbol.gamma} \alias{mx.symbol.gamma} \title{gamma:Returns the gamma function (extension of the factorial function \ to the reals), computed element-wise on the input array.} \usage{ mx.symbol.gamma(...) } \arguments{ \item{data}{NDArray-or-Symbol The input array.} \item{name}{string, optional Name of the resulting symbol.} } \value{ out The result mx.symbol } \description{ The storage type of ``gamma`` output is always dense }

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process_mr_results.R

# Our pipeline is largely based on # https://academic.oup.com/ije/article/47/4/1264/5046668, Box 3 # Aka the Rücker model-selection framework setwd("~/Desktop/rivaslab/biomarkers/resubmission1/") library(data.table) # read the trait name info trait_names = fread("./mr_rg_traits.txt", data.table = F,stringsAsFactors = F, header= F) rnames = trait_names[,1] trait_names = trait_names[,-1] names(trait_names) = rnames # read the results simplify_name<-function(s,trait_names){ if(is.element(s,rownames(trait_names))){ return(trait_names[s]) } arr = strsplit(s,split="/|,") return(arr[[1]][length(arr[[1]])]) } raw_results = fread("./all.results.restructured.txt",stringsAsFactors = F,data.table = F) pleio_res = fread("./all.intercepts.restructured.txt",stringsAsFactors = F,data.table = F) rownames(pleio_res) = paste(pleio_res$exposure,pleio_res$outcome,sep=",") pleio_res$exposure = sapply(pleio_res$exposure,simplify_name,trait_names=trait_names) pleio_res$outcome = sapply(pleio_res$outcome,simplify_name,trait_names=trait_names) het_res = fread("./all.heterogeneity.restructured.txt",stringsAsFactors = F,data.table = F) het_res$exposure = sapply(het_res$exposure,simplify_name,trait_names=trait_names) het_res$outcome = sapply(het_res$outcome,simplify_name,trait_names=trait_names) # # HDL vs. CAD # check_HDL_res<-function(x){ # inds1 = grepl("HDL",x[,"exposure"]) # inds2 = grepl("CAD",x[,"outcome"]) | grepl("cardio",x[,"outcome"]) # inds = inds1 & inds2 # return(x[inds,]) # } # check_HDL_res(raw_results) # check_HDL_res(het_res) # # Diabetes # check_diab_res<-function(x){ # inds1 = grepl("T2D",x[,"outcome"]) # inds2 = grepl("gluc|hdl|gly",x[,"exposure"],ignore.case = T) # inds = inds1 & inds2 # x = x[inds,c("exposure","method","b","pval","nsnp")] # x[,1] = sapply(x[,1], function(x){ # arr = strsplit(x,split='\\/')[[1]]; # arr[length(arr)] # } # ) # x[,2] = gsub("Inverse variance weighted","IVW",x[,2]) # x[,2] = gsub("multiplicative random effects","mRE",x[,2]) # x[,2] = gsub("bootstrap","b",x[,2]) # x[,2] = gsub("fixed effects","FE",x[,2]) # return(x) # } # check_diab_res(raw_results) # Exclude some unwanted pairs before the analysis exclude_pairs<-function(x){ xrows = apply(x,1,paste,sep=",",collapse=",") # remove ApoB without adjustment for statins, results_to_exclude = grepl("Apolipoprotein_B_white_british",xrows,ignore.case = T) | grepl("CKDGen_eGFRdecline",xrows,ignore.case = T) | grepl("Telomere",xrows,ignore.case = T) | grepl("EPIC",xrows,ignore.case = F) return(x[!results_to_exclude,]) } raw_results = exclude_pairs(raw_results) pleio_res = exclude_pairs(pleio_res) het_res = exclude_pairs(het_res) # Define the thresolds Q_p_thr = 0.01 FDR_level = 0.05 # p_het_thr = 0.01 # separate the main results by method method2res = list() for(method in unique(raw_results$method)){ method2res[[method]] = raw_results[raw_results$method==method,] } names(method2res) = gsub("Inverse variance weighted","IVW",names(method2res)) names(method2res) = gsub("random effects","RE",names(method2res)) names(method2res) = gsub("fixed effects","FE",names(method2res)) method2res = lapply(method2res, function(x){rownames(x) = paste(x$exposure,x$outcome,sep=","); x$exposure = sapply(x$exposure,simplify_name,trait_names=trait_names); x$outcome = sapply(x$outcome,simplify_name,trait_names=trait_names);x}) # Add the FDR to the methods for(mname in names(method2res)){ q_values = p.adjust(method2res[[mname]]$pval,method="fdr") method2res[[mname]] = cbind(method2res[[mname]],q_values) } ######################################################################## ######################################################################## # Method comparison: # plot the raw p-values par(mfrow=c(2,3)) for(method in names(method2res)){ mname = strsplit(method,split="\$|\$")[[1]] mname = paste(mname,collapse="\n") hist(method2res[[method]]$pval, main=mname,xlab="P-value",cex.main=1) } # Method similarity shared_pairs = rownames(method2res[[1]]) for(method in names(method2res)){ shared_pairs = intersect(shared_pairs,rownames(method2res[[method]])) } pval_mat = sapply(method2res,function(x,y)x[y,"pval"],y=shared_pairs) pval_mat = cbind(pval_mat,pleio_res[shared_pairs,]$pval) colnames(pval_mat)[ncol(pval_mat)] = "Egger_pleio_p" scaled_b_mat = sapply(method2res,function(x,y)x[y,"scaled.b"],y=shared_pairs) library(ggcorrplot) pval_corrs = cor(pval_mat,method="spearman") print(ggcorrplot(t(pval_corrs),lab=T,lab_size=2.5,hc.order = F) + ggtitle("P-value, spearman") + theme(plot.title = element_text(hjust = 0.5,size=20))) # remove rows with NAs scaled_b_mat = scaled_b_mat[!apply(is.na(scaled_b_mat),1,any),] b_corrs = cor(scaled_b_mat,method="spearman") print(ggcorrplot(t(b_corrs),lab=T,lab_size=2.5,hc.order = F) + ggtitle("Scaled beta, spearman") + theme(plot.title = element_text(hjust = 0.5,size=20))) dev.off() ######################################################################## # Adjust for FDR - get the number of results per method get_adjusted_results<-function(x,sig=0.01){ return(x[p.adjust(x$pval,method="fdr")<sig,]) } method2adjres = lapply(method2res,get_adjusted_results) par(mar=c(5,10,5,5)) barplot(sapply(method2adjres,nrow),las=2,horiz = T,xlab="Number of pairs (0.01 FDR)") dev.off() ######################################################################## # Scheme for selecting the models and their results based on # the Rücker model-selection framework # In our case (comparisons above) we used Egger with bootstrap to increas power. # We then adapt the analysis of the heterogeneity as follows: # For inignificant IVW Q scores, use the beta and p-value from IVW+FE. # For significant Q scores use the beta valuse use IVW+ME. # If the difference between the Egger Q and the IVW+FE Q is significant, we use the # beta and p-value from Egger. # IVW ivw_fe_results = method2res$`IVW (FE)` ivw_fe_q_results = het_res[het_res$method == "Inverse variance weighted",] ivw_me_results = method2res$IVW rownames(ivw_fe_q_results) = paste(ivw_fe_q_results$exposure,ivw_fe_q_results$outcome,sep=",") rownames(ivw_fe_results) = paste(ivw_fe_results$exposure,ivw_fe_results$outcome,sep=",") rownames(ivw_me_results) = paste(ivw_me_results$exposure,ivw_me_results$outcome,sep=",") ivw_shared_pairs = intersect(rownames(ivw_fe_q_results),rownames(ivw_fe_results)) ivw_fe_results = ivw_fe_results[ivw_shared_pairs,] ivw_fe_q_results = ivw_fe_q_results[ivw_shared_pairs,] ivw_me_results = ivw_me_results[ivw_shared_pairs,] is_ivw_fe_q_significant = ivw_fe_q_results$Q_pval < Q_p_thr names(is_ivw_fe_q_significant) = rownames(ivw_fe_q_results) ivw_merged_results = rbind( ivw_fe_results[!is_ivw_fe_q_significant,], ivw_me_results[is_ivw_fe_q_significant,] ) # Egger # make sure that Egger and EggerB are ordered correctly: (sanity check) all(method2res$`MR Egger`[,1:4] == method2res$`MR Egger (bootstrap)`[,1:4]) egger_results = method2res$`MR Egger` egger_b_inds = egger_results$nsnp > 30 egger_results[egger_b_inds,] = method2res$`MR Egger (bootstrap)`[egger_b_inds,] egger_q_results = het_res[het_res$method == "MR Egger",] rownames(egger_results) = paste(egger_results$exposure,egger_results$outcome,sep=",") rownames(egger_q_results) = paste(egger_q_results$exposure,egger_q_results$outcome,sep=",") # Methods' rownames do not perfectly fit but all Egger pairs are in the IVW pairs egger_q_diffs = ivw_fe_q_results[rownames(egger_q_results),"Q"] - egger_q_results$Q egger_q_diffs_pval = pchisq(egger_q_diffs,1,lower.tail = F) table(egger_q_diffs_pval > Q_p_thr) egger_pairs = rownames(egger_q_results)[egger_q_diffs_pval < Q_p_thr] egger_pairs = egger_pairs[egger_q_results[egger_pairs,"Q_pval"] > 1e-100] ivw_egger_merged_results = ivw_merged_results ivw_egger_merged_results[egger_pairs,] = egger_results[egger_pairs,] # Get the current significant results selected_merged_results = p.adjust(ivw_egger_merged_results$pval,method="fdr") < FDR_level table(selected_merged_results) selected_merged_results = ivw_egger_merged_results[selected_merged_results,] rownames(selected_merged_results) selected_merged_results = selected_merged_results[,-c(1:2)] # Fix some of the names for(j in 1:2){ selected_merged_results[,j] = gsub("_all$","",selected_merged_results[,j],ignore.case = T) selected_merged_results[,j] = gsub("^int_","",selected_merged_results[,j],ignore.case = T) selected_merged_results[,j] = gsub("noncancer_illness_code_","", selected_merged_results[,j],ignore.case = T) selected_merged_results[,j] = gsub("diagnosed_by_doctor_","", selected_merged_results[,j],ignore.case = T) selected_merged_results[,j] = gsub("vascularheart","", selected_merged_results[,j],ignore.case = T) selected_merged_results[,j] = gsub("^\\s","",selected_merged_results[,j],ignore.case = T) selected_merged_results[,j] = gsub("\\s$","",selected_merged_results[,j],ignore.case = T) selected_merged_results[,j] = gsub("^_","",selected_merged_results[,j],ignore.case = T) # transform "_" to " " selected_merged_results[,j] = gsub("_"," ",selected_merged_results[,j],ignore.case = T) # remove white british.1cm selected_merged_results[,j] = gsub(" white british.1cm","", selected_merged_results[,j],ignore.case = T) # Extra formatting selected_merged_results[grepl("diabetes",selected_merged_results[,j],ignore.case = T),j] = "Diabetes" selected_merged_results[grepl("MEGASTROKE",selected_merged_results[,j],ignore.case = T),j] = "Stroke" selected_merged_results[grepl("stroke",selected_merged_results[,j],ignore.case = T),j] = "Stroke" selected_merged_results[grepl("T2D",selected_merged_results[,j],ignore.case = T),j] = "Diabetes" selected_merged_results[,1] = gsub("\\s+adjstatins","",selected_merged_results[,1],ignore.case = T) selected_merged_results[,j] = gsub(" diagnosed by doctor","",selected_merged_results[,j],ignore.case = T) selected_merged_results[grepl("HYPOTHYROIDISM",selected_merged_results[,j],ignore.case = T),j] = "Hypothyroidism" # Fix some of the nodes (after manual inspection) selected_merged_results[selected_merged_results[,j]=="Fracture bones",j] = "Fractured bones" } unique_pairs = unique(selected_merged_results[,2:1]) rownames(unique_pairs) = NULL write.table(unique_pairs,sep="\t",quote=F,row.names = F) # Add alternative beta scores selected_merged_results[["abs(b)"]] = abs(selected_merged_results$scaled.b) selected_merged_results[["log(b^2)"]] = log(selected_merged_results$scaled.b^2,base=10) v = as.numeric(selected_merged_results$b>0) v[v==0]=-1 selected_merged_results[["Effect_sign"]] = v # Node attr for cytoscape allnodes = union(selected_merged_results[,1],selected_merged_results[,2]) m = cbind(allnodes,is.element(allnodes,set=selected_merged_results$outcome)) colnames(m) = c("node","is_outcome") write.table(m,file="node_info.txt" ,sep="\t",row.names = F,col.names = T,quote = F) write.table(selected_merged_results, file="selected_results_fdr0.05_Q0.01.txt" ,sep="\t",row.names = F,col.names = T,quote = F) # ########################################################################## # ########################################################################## # ########################################################################## # # Old analysis that focuses on diseases # # Filter the original network using "disease" regex # disease_reg = c("angina","disease","cancer","bone","diabetes","alz","asthma", # "gout","hypothr","hypothyroidism","multiple","pain","lupus", # "stroke","CAD","celiac","amd","oma","degeneration","scz", # "microalbuminuria","eczema","vascular pro") # disease_reg = paste(disease_reg,collapse = "|") # selected_merged_results_disease = selected_merged_results[grepl( # disease_reg,selected_merged_results$outcome,ignore.case=T),] # unique(selected_merged_results_disease$outcome) # unique(selected_merged_results$outcome) # # # Print all the files (for the paper) # # # Disease, FDR 1% # # Add two more columns # selected_merged_results_disease$Effect_sign = # as.numeric(selected_merged_results_disease$scaled.b > 0) # selected_merged_results_disease$type_and_sign = # paste(selected_merged_results_disease$Effect_sign, # selected_merged_results_disease$edge_type,sep="") # # remove UKB # selected_merged_results_disease = selected_merged_results_disease[ # !grepl("ukb",selected_merged_results_disease$path,ignore.case = T), # ] # # remove not adjusted for statins # nonadj_to_rem = c("Apolipoprotein B","Cholesterol","LDL direct") # selected_merged_results_disease = selected_merged_results_disease[ # ! selected_merged_results_disease$exposure %in% nonadj_to_rem, # ] # # keep ischemic stroke only # selected_merged_results_disease = selected_merged_results_disease[ # selected_merged_results_disease$title !="Any stroke", # ] # write.table(selected_merged_results_disease, # file="selected_results_disease_fdr0.01_pleio0.01.txt" # ,sep="\t",row.names = F,col.names = T,quote = F)

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/biobank_vs_loo_effects.R

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############################################################## ##### Biobanks VS. Leave-that-biobank-out meta-analyses ###### ############################################################## library(data.table) library(ggplot2) library(dplyr) library(broom) library(readxl) library(ggrepel) library(writexl) library(tidyverse) library(purrr) # set working directory output_dir="" setwd(output_dir) ###################### ###### Load data ##### ###################### results_dir = "" # load biobank results biobank_files <- list.files(results_dir, pattern=glob2rx("*BBONLY.txt"), full.names=TRUE) list.biobanks <- lapply(biobank_files,function(x) fread(x, select=c('SNP_ID', 'CHR', 'POS', 'Allele1', 'Allele2', 'biobank_BETA', 'biobank_SE', 'biobank_p.value'))) list.biobanks.names <- list('BBJ', 'CKB', 'DECODE', 'ESTBB', 'FinnGen', 'GNH', 'GS', 'HUNT', 'Lifelines', 'MGB', 'QSKIN', 'TWB') names(list.biobanks) <- list.biobanks.names biobank_meta_files <- list.files(results_dir, pattern=glob2rx("*BBMETA.txt"), full.names=TRUE) list.bb_meta <- lapply(biobank_meta_files, function(x) fread(x, select=c(1:8))) list.bb_meta.names <- list('BioMe', 'BioVU', 'CCPM', 'MGI', 'UCLA', 'UKBB') names(list.bb_meta) <- list.bb_meta.names # load LOO meta-analyses loo_files <- list.files(results_dir, pattern=glob2rx("*LOO.txt"), full.names=TRUE) list.loo <- lapply(loo_files,function(x) fread(x, select=c('SNP_ID','CHR', 'POS', 'REF', 'ALT', 'inv_var_meta_beta', 'inv_var_meta_sebeta', 'inv_var_meta_p'))) list.loo.names <- list('BBJ', 'BIOME', 'BIOVU', 'CCPM', 'CKB', 'DECODE', 'ESTBB', 'FG', 'GNH', 'GS', 'HUNT', 'LIFELINES', 'MGB', 'MGI', 'QSKIN', 'TWB', 'UCLA', 'UKBB') names(list.loo) <- list.loo.names # load top hits top_hits <- read_excel("") top_hits <- top_hits %>% select(SNP, CHR, POS, REF, ALT, all_inv_var_meta_p) # load sample sizes sample_sizes <- read_excel("biobanks_sampling_prevalence.xlsx") ######################## ###### Format data ##### ######################## # combine all biobank-specific meta-analyses into one list list.all.biobanks <- c(list.bb_meta, list.biobanks) list.all.biobanks <- lapply(list.all.biobanks, setNames, nm = c('SNP_ID','CHR', 'POS', 'REF', 'ALT', 'biobank_beta', 'biobank_sebeta', 'biobank_p')) list.all.biobanks <- list.all.biobanks[order(names(list.all.biobanks))] list.loo <- list.loo[order(names(list.loo))] # align to LOO risk allele add_risk_allele <- function(x, beta_col, ALT_col, REF_col){ x %>% mutate(risk_allele = ifelse(beta_col > 0, ALT_col, REF_col), risk_allele_beta = ifelse(beta_col > 0, beta_col, -(beta_col))) } list.loo <- lapply(list.loo, add_risk_allele, beta_col=inv_var_meta_beta, ALT_col=ALT, REF_col=REF) list.loo <- lapply(list.loo, setNames, nm = c('SNP_ID', 'CHR', 'POS', 'REF', 'ALT', 'inv_var_meta_beta', 'inv_var_meta_sebeta', 'inv_var_meta_p ', 'risk_allele_LOO', 'risk_allele_beta_LOO')) align_alleles <- function(loo, biobank, beta_col, ALT_col, REF_col){ merge <- right_join(loo,biobank, by='SNP_ID') merge <- merge %>% mutate(matched_allele_BIOBANK = ifelse(ALT_col == risk_allele_LOO, ALT_col, REF_col), matched_allele_beta_BIOBANK = ifelse(ALT_col == risk_allele_LOO, beta_col, -(beta_col))) } list.aligned <- Map(align_alleles, list.loo, list.all.biobanks, beta_col=biobank_beta, ALT_col=ALT.y, REF_col=REF.y) # add column with biobank names biobank_names <- c(names(list.all.biobanks)[order(names(list.all.biobanks))]) list.aligned <- Map(function(x,y){x <- x %>% mutate(biobank = y)}, list.aligned, biobank_names) df <- Reduce('rbind',list.aligned) ################################################### ##### Plot ratios of biobank vs. LOO effects ###### ################################################### # compute beta ratios df <- df %>% mutate(beta_ratio = matched_allele_beta_BIOBANK / risk_allele_beta_LOO) averages <- df %>% group_by(biobank) %>% summarise(average = mean(beta_ratio), n=n()) # plot average beta ratios output_name='.jpeg' text_size=25 barplot_ratios <- ggplot(averages, aes(x=biobank, y=average)) + geom_bar(stat="identity", fill="steelblue3", width=0.5) + geom_hline(yintercept = 1.0, linetype='dashed', size=1) + ylab(paste0("Average SNP effect in biobank over", "\n", "leave-that-biobank-out meta-analysis")) + theme_classic() + theme(text=element_text(size=text_size),axis.text.x=element_text(angle=65, hjust=1), axis.title.y=element_text(size=20)) ggsave(barplot_ratios, height = 9, width = 12, dpi = 300, filename=output_name) ################################### ##### Plot Deming Regressions ##### ################################### df1 <- lapply(list.aligned, as.data.frame) ggplotDemingRegression <- function(fit, df, param) { require(ggplot2) deming_slope = unlist(fit$coefficients[2]) p=ggplot(fit$model, aes_string(x = names(fit$model)[2], y = names(fit$model)[1])) + geom_errorbar(aes(ymin=df$lowV, ymax=df$highV), width=.005, alpha=0.8) + geom_errorbarh(aes( xmin=df$lowH,xmax=df$highH), height=.005, alpha=0.8) + geom_abline(slope=deming_slope, intercept=0, color = "orangered3") + theme_bw() + theme(plot.title = element_text(hjust = 0.5,size=18), axis.text=element_text(size=10), axis.title=element_text(size=10,face="bold"), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black"), legend.position = "none", plot.margin=grid::unit(c(5,5,5,5), "mm")) + geom_point(size=0.5) + geom_abline(intercept = 0, slope=1, col="grey3", show.legend = T, linetype = "dashed") + xlab(param$xlabtext) + ylab(param$ylabtext)+ scale_x_continuous(expand=c(0,0), limits=c(param$MinVal-0.1,param$MaxVal+0.1)) + scale_y_continuous(expand=c(0,0), limits=c(param$MinVal-0.1,param$MaxVal+0.1)) + coord_cartesian(xlim=c(param$MinVal,param$MaxVal), ylim=c(param$MinVal,param$MaxVal)) + labs(title = paste( " Slope =",signif(deming_slope, 2) ) ) ggsave(p,filename=param$pdffile, dpi=300,width = 5, height = 5) } # deming regression models for each biobank fit_deming <- function(dat){ require(deming) require(devtools) demingfit <- deming(risk_allele_beta_LOO ~ matched_allele_beta_BIOBANK + 0, data=dat, xstd=biobank_sebeta, ystd=inv_var_meta_sebeta) } # plot parameters ggplotDemingRegression_input_step1 <- function(data){ data <- data %>% mutate(lowH = matched_allele_beta_BIOBANK - 1.96*biobank_sebeta, highH = matched_allele_beta_BIOBANK + 1.96*biobank_sebeta, lowV = risk_allele_beta_LOO - 1.96*inv_var_meta_sebeta, highV = risk_allele_beta_LOO + 1.96*inv_var_meta_sebeta) } ggplotDemingRegression_input_step2 <- function(data, biobank_name){ xlabtext <- paste("Effect sizes reported by", biobank_name, sep=" ") ylabtext <- paste("Effect sizes reported by LOO meta-analysis excluding", biobank_name, sep=" ") MinVal <- min(0, min(data$lowH)-0.01, min(data$lowV)-0.01) MaxVal <- max(max(data$highH)+0.01, max(data$highV)+0.01) pdffile <- paste0('tophits_bb_loo_effectsize_comparison_DemingRegression_allSNPs', biobank_name, '.jpeg') input.df <- data.frame(xlabtext, ylabtext, as.numeric(MaxVal), as.numeric(MinVal), pdffile) names(input.df) <- c('xlabtext', 'ylabtext', 'MaxVal', 'MinVal', 'pdffile') return(input.df) } # plot list.deming <- lapply(df1, FUN=fit_deming) df.deming <- lapply(df1, ggplotDemingRegression_input_step1) plot.param <- Map(ggplotDemingRegression_input_step2, df.deming, biobank_names) Map(ggplotDemingRegression, fit=list.deming, df=df.deming, param=plot.param)

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/mult.R

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michel-briand/emath

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896185ed58b325d116762108e09adbe3559e189c

refs/heads/master

2020-03-29T23:42:53.141295

2018-09-26T20:19:13

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mult.R

# # This is a Shiny web application. You can run the application by clicking # the 'Run App' button above. # # Find out more about building applications with Shiny here: # # http://shiny.rstudio.com/ # # # This application is a small workshop for mathematics. # It is inspired from the Micmaths video (https://youtu.be/-X49VQgi86E). # It displays a representation of multiplication on the circle. # Playing with the number and the modulo, one creates flower petals. # # (c) 2018 Michel Briand # Creative Commons Attribution-ShareAlike 4.0 International License # http://creativecommons.org/licenses/by-sa/4.0/ library(shiny) library(ggplot2) # Define UI for application that draws a histogram ui <- fluidPage( # Application title titlePanel("Multiplication"), fluidRow( column(12, span(style="font-style: italic;font-size: 1em;", p(style="margin:0px", "Ce petit atelier mathématique est inspiré par la vidéo de Micmaths :"), tags$a(href="https://youtu.be/-X49VQgi86E", "La face cachée des tables de multiplication", target="_blank",rel="noopener noreferrer", style="align: center;"), p(style="margin:0px", "En choisissant la table de multiplication et le modulo, vous pouvez visualiser des pétales de fleur...") ) ), column(12, br()) # blank ), # Sidebar with a slider input for number and modulo sidebarLayout( sidebarPanel( sliderInput("nombre", "Table de multiplication :", min = 1, max = 500, value = 2), sliderInput("modulo", "Modulo :", min = 0, max = 500, value = 10) ), # Show a plot of the generated circle and lines mainPanel( plotOutput("distPlot", width = "50vw", height = "50vw") ) ), fluidRow( column(12, br(),br(), p("(c) 2018 Michel Briand"), tags$a(rel="license", href="http://creativecommons.org/licenses/by-sa/4.0/", "Creative Commons Attribution-ShareAlike 4.0 International License"), img(alt="Creative Commons License",style="border-width:0",src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png"), br(),br() ) ) ) # Define server logic required to draw a histogram server <- function(input, output) { output$distPlot <- renderPlot({ n <- input$nombre m <- input$modulo theta <- 2*pi/m N <- 100 tt <- seq(0, 2*pi, length.out = N) i <- seq(0, m-1) circleFun <- function(center = c(0,0),diameter = 1){ r = diameter / 2 xx <- center[1] + r * cos(tt) yy <- center[2] + r * sin(tt) return(data.frame(x = xx, y = yy)) } startFun <- function(a) { return(a*theta) } endFun <- function(a) { return(n*a*theta) } linesFun <- function(center = c(0,0),diameter = 1){ r = diameter / 2 # do not use lapply which returns a list, use this to have a vector: s <- sapply(i, startFun, simplify = TRUE) e <- sapply(i, endFun, simplify = TRUE) tt <- c(rbind(s, e)) xx <- center[1] + r * cos(tt) yy <- center[2] + r * sin(tt) return(data.frame(x = xx, y = yy)) } dat <- circleFun(c(0,0), 2) dat2 <- linesFun(c(0,0), 2) g <- ggplot(asp = 1, dat,aes(x,y)) + geom_path() + geom_path(data=dat2) return(g) }) } # Run the application shinyApp(ui = ui, server = server)

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devxia/NativeRNAseqComplexTranscriptome

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refs/heads/master

2022-02-28T18:59:15.892634

2019-10-29T19:13:54

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gff2gtf.R

args <- (commandArgs(trailingOnly = TRUE)) for (i in 1:length(args)) { eval(parse(text = args[[i]])) } print(gff) print(gtf) suppressPackageStartupMessages({ library(rtracklayer) library(readr) library(withr) }) ## Filter out exons that can not be handled by gffcompare x <- readr::read_tsv(gff, col_names = FALSE, col_types = "cccddcccc") dim(x) message("Excluding the following lines:") x[x$X5 - x$X4 >= 30000, ] x <- x[x$X5 - x$X4 < 30000, ] dim(x) withr::with_options(c(scipen = 100), write.table(x, file = gsub("\\.gff$", ".fixed.gff", gff), quote = FALSE, sep = "\t", row.names = FALSE, col.names = FALSE)) x <- rtracklayer::import(gsub("\\.gff$", ".fixed.gff", gff)) x$transcript_id <- as.character(x$group) x$group <- NULL rtracklayer::export(x, gtf) date() sessionInfo()

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/personal/baby_names.R

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mdgbeck/projects

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6e8205dcc139a5d2335e35ba667f056e2f569e10

refs/heads/master

2022-05-30T02:10:34.793217

2022-05-18T18:38:41

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baby_names.R

library(tidyverse) library(lubridate) library(babynames) library(mdgr) library(scales) names <- babynames %>% filter(year >= 1950 & sex == "F" & n > 15) %>% group_by(name) %>% mutate(n_years = n_distinct(year), prev = lag(prop), change = prop - prev, perc_chg = 100 * change / prop) %>% filter(n_years == max(n_years)) name_var <- names %>% group_by(name) %>% summarize(n = sum(n), yrs = n_distinct(year), avg = mean(perc_chg, na.rm = TRUE), med = median(perc_chg, na.rm = TRUE), sd_perc = sd(perc_chg, na.rm = TRUE)) %>% mutate(abs_avg = abs(avg), abs_med = abs(med)) %>% filter(yrs == n_distinct(names$year)) %>% #arrange(desc(n)) %>% #arrange(abs(sd_perc)) arrange(sd_perc) names %>% filter(name %in% name_var$name) %>% ggplot(aes(x=year, y = prop)) + geom_line(aes(group = name), color = "gray75", size = .5) + geom_line(data = filter(names, #name %in% c("abc")), name %in% name_var$name[name_var$abs_avg < 2][1:5]), aes(color = name), size = 1.25) + #coord_cartesian(ylim = c(0, .02)) + theme_mdgr() # boy girl names boys <- babynames %>% filter(year >= 1980 & sex == 'M' & n > 50) %>% group_by(name) %>% filter(n_distinct(year) == max(n_distinct(year))) %>% select(year, name, n_boys = n, prop_boys = prop) girls <- babynames %>% filter(year >= 1980 & sex == 'F' & n > 50) %>% group_by(name) %>% filter(n_distinct(year) == max(n_distinct(year))) %>% select(year, name, n_girls = n, prop_girls = prop) bg <- boys %>% inner_join(girls, by = c('year', 'name')) %>% mutate( perc_girl = n_girls / (n_boys + n_girls), mid = abs(.5 - perc_girl) ) bg_names <- bg %>% group_by(name) %>% summarize( n_boys = sum(n_boys), n_girls = sum(n_girls), n = n_boys + n_girls, perc_girl_total = n_girls / n, mid_total = abs(.5 - perc_girl_total), var_girl = var(perc_girl) ) %>% arrange(desc(n)) var_names <- bg_names %>% arrange(desc(var_girl)) # plot of highest variance names (boy / girl change) bg %>% filter(name %in% bg_names$name[1:1000]) %>% ggplot(aes(year, perc_girl)) + geom_line(aes(group = name), color = "gray75", size = .5, show.legend = NA) + geom_line(data = filter(bg, name %in% var_names$name[1:10]), aes(color = name), size = 1.25) + scale_y_continuous(label = percent) + theme_mdgr() # overall popularity of name plot_data <- babynames %>% filter(name %in% var_names$name[1:5] & year > 1980) ggplot(plot_data, aes(year, prop)) + geom_line(data = filter(plot_data, sex == 'F'), aes(group = name), color = 'pink') + geom_line(data = filter(plot_data, sex == 'M'), aes(group = name), color = 'skyblue') + geom_point(data = filter(plot_data, sex == 'F'), aes(color = name)) + geom_point(data = filter(plot_data, sex == 'M'), aes(color = name)) + scale_y_continuous(label = percent) + theme_mdgr() write_csv(arrange(filter(bg_names, perc_girl_total > .05), mid_total), 'personal/names_bg1.csv', na="") girl2017 <- babynames %>% filter(year == 2017 & sex == 'F') %>% arrange(desc(n)) %>% slice(1:1000) %>% write_csv(., 'personal/names_2017.csv')

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/R/add.missing.expression.R

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alexjcornish/DiseaseCellTypes

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refs/heads/master

2021-01-19T08:46:26.242215

2015-09-02T09:03:56

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add.missing.expression.R

add.missing.expression <- function( expression, genes, score.missing ) { # add genes in 'genes' missing in 'expression' to 'expression' with score 'score.missing' # remove genes in 'expression' not represented in 'genes' contexts <- colnames(expression) n.contexts <- ncol(expression) if (is.null(rownames(expression))) stop("genes not found as rownames in expression") genes.found <- genes[genes %in% rownames(expression)] genes.missing <- genes[!genes %in% rownames(expression)] expression.data <- array(as.numeric(expression[genes.found, ]), dim=c(length(genes.found), n.contexts), dimnames=list(genes.found, contexts)) expression.no.data <- array(score.missing, dim=c(length(genes.missing), n.contexts), dimnames=list(genes.missing, contexts)) rbind(expression.data, expression.no.data) }

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/util/copy_high_performing_benchmark.R

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no_license

tadeze/ADMV

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fc4a0bf011f28355f5910c5d6d1f84cfed51a18c

refs/heads/master

2022-01-15T12:56:25.767940

2019-05-06T23:55:05

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copy_high_performing_benchmark.R

pathdir="/nfs/guille/bugid/adams/meta_analysis/results_summaries/" destination = "/nfs/guille/bugid/adams/ifTadesse/kddexperiment/group2/" ff = list.files(pathdir) aucs <- data.frame() for (bench in ff){ if(bench=="new_all" || bench=="all"){ next } for(idx in 290:300){ #idx = 300 bench_name = paste0(pathdir,bench,"/auc_",bench,".csv") res = read.csv(bench_name,T) res = res[order(res$iforest,decreasing=T),] if(bench %in% c("particle","gas","yeast","synthetic", "yearp")) next if(bench=="abalone"){ idx = idx +40 } cat(bench,"_",res$bench.id[idx],res$iforest[idx],"\n") filename = paste0("/nfs/guille/bugid/adams/meta_analysis/benchmarks/",bench,"/",res$bench.id[idx],".csv") xx = read.csv(filename) #print(nrow(xx)) aucs <- rbind(aucs,data.frame(res$bench.id[idx], res$iforest[idx], res$loda[idx],res$egmm[idx])) #cat(bench,"_",res$bench.id,"\n") file.copy(filename, destination) } } writet.table(aucs,"dataset_used_summary.csv",row.names=F,quote=F) #"/nfs/guille/bugid/adams/meta_analysis/results_summaries/abalone/auc_abalone.csv"

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/man/custom_distance.Rd

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vda-lab/stad

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f7ab25e6492c0360e2093e6f73bf005d087d56db

refs/heads/master

2020-04-25T20:53:38.508275

2020-03-22T13:32:05

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2

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2020-03-22T12:49:05

2019-02-28T07:31:07

R

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custom_distance.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/stad_with_lens.R \name{custom_distance} \alias{custom_distance} \title{Custom distance} \usage{ custom_distance(x, metric) } \arguments{ \item{x}{array variable. Dimension of the lens.} \item{metric}{string or array defining the metrics supported ("polar" or "euclidean").} } \value{ Returns a \code{dist} object with the distance of the filter. } \description{ Internal distance matrix for bivariate_split. Uses polar or euclidean metric. Returns a distance matrix as sum of the two independents dimensions. }

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/4_split.r

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no_license

DavidMoranPomes/census-profiling-and-income-prediction

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refs/heads/master

2020-06-17T20:45:01.995050

2019-07-09T17:13:26

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4_split.r

load('data/adult.rds') set.seed(1234) train <- sample(nrow(adult), 0.5*nrow(adult)) set <- logical(nrow(adult)) set[train] <- TRUE adult$Set <- factor(ifelse(set, 'Train', 'Test')) save(adult, file='data/adult_split.rds')

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/Using R/Text Mining/2/2.R

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no_license

dharmesh-coder/Data-Science

824b53a9b867e4eb268b18a5d03b1c8991491f83

913e6e1e2503cd2be94f458ce6add3afa2a63605

refs/heads/master

2023-04-19T20:58:39.355216

2021-05-15T17:00:51

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r

2.R

library(dplyr) library(rvest) library(NLP) library(tm) url <- "http://www.analytictech.com/mb021/mlk.htm" data <- read_html(url) text <- data %>% html_nodes("p") %>% html_text() text doc <- Corpus(VectorSource(text)) inspect(doc) doc <- tm_map(doc,removeNumbers) doc <- tm_map(doc,removeWords,stopwords("english")) doc <- tm_map(doc,stripWhitespace) doc <- tm_map(doc,tolower) dtm <- DocumentTermMatrix(doc) freq <- colSums(as.matrix(dtm)) ord <- order(freq,decreasing=TRUE) freq[head(ord,n=20)] findFreqTerms(dtm,lowfreq=5) findAssocs(dtm,terms='life',corlimit=0.7) removeSparseTerms(dtm,0.3)

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/R/dvinesim.R

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cran/CopulaREMADA

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dvinesim.R

dvinesim=function(nsim,param,qcond1,pcond1,tau2par1,qcond2,pcond2,tau2par2) { tau12=param[1] tau23=param[2] tau34=param[3] tau13.2=param[4] tau24.3=param[5] tau14.23=param[6] p = matrix(runif(nsim * 4), nsim, 4) th=matrix(0,4,4) th[1,2]=tau2par1(tau12) th[1,3]=tau2par2(tau23) th[1,4]=tau2par1(tau34) th[2,3]=tau2par.bvn(tau13.2) th[2,4]=tau2par.bvn(tau24.3) th[3,4]=tau2par.bvn(tau14.23) u1=p[,1] q11=p[,1] q22=p[,2] u2=qcond1(p[,2],p[,1],th[1,2]) q12=u2 v12=pcond1(u1,u2,th[1,2]) q33=p[,3] q23=qcondbvn(q33,v12,th[2,3]) q13=qcond2(q23,u2,th[1,3]) u3=q13 v13=pcond2(u2,u3,th[1,3]) v23=pcondbvn(v12,q23,th[2,3]) q44=p[,4] q34=qcondbvn(q44,v23,th[3,4]) q24=qcondbvn(q34,v13,th[2,4]) q14=qcond1(q24,u3,th[1,4]) u4=q14 cbind(u1,u2,u3,u4) }

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/2016-April/11-Optimization/3d-visulizations.R

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2021-01-20T21:06:12.488996

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3d-visulizations.R

library(plot3D) fun = function(x,y) { return(x+y) } x = seq(-2, 4, 0.5) y = seq(-2, 4, 0.5) f = outer(x,y,fun) windows(width=50, height=60) persp3D(x, y, f, xlab="x", ylab="y", zlab="f", theta = 30, phi = 10) X11() persp3D(x, y, f, xlab="x", ylab="y", zlab="f",color.palette = heat.colors, theta = 30, phi = 10, colkey = FALSE) X11() persp3D(x, y, f, xlab="x", ylab="y", zlab="f",color.palette = heat.colors, border = "#808080", theta = 30, phi = 10, colkey = FALSE, ticktype="detailed") x = c(-1,1) y = c(2,0) z = c(3,3) points3D(x,y,z, pch = 20, col = 'red', add = TRUE)

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/man/shift_index.Rd

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pawelru/sudoku_r_game

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shift_index.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/matrix.R \name{shift_index} \alias{shift_index} \title{Move elements from tail to the head of vector} \usage{ shift_index(x, n) } \arguments{ \item{x}{vecor to be shifted} \item{n}{shift value} } \value{ shifted vector } \description{ Move elements from tail to the head of vector }

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/Normal Contaminada - Poder Sinal.R

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ArthurCarneiroLeao/Monte-Carlo-Sinal

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2020-04-02T16:16:37.471734

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Normal Contaminada - Poder Sinal.R

#NormalContaminada-Poder Sinal(5%)# ### usando o grau de contaminação 5% e lamb = 3 library(BSDA) library(tidyverse) delta <- 0.05 lambda <- 3 r<-1000 theta.test<-c(seq(-2,2,0.05)) M <- length(theta.test) power <- numeric(M) nobs<-c(5, 10, 30, 80) #Vetor Para tamanhos de amostras diferentes power_nobs <- matrix(0,length(nobs),M) #criando o ambiente(matriz) para armazenamento cont <- 1 for (j in nobs){ for (i in 1:M) { theta<-theta.test[i] p_value <- replicate(r, expr = { x <- (1 - delta)*rnorm(j, theta, 1) + (delta*rnorm(j, theta, 1))/sqrt(lambda) SinalTest<-SIGN.test(x,mu=0) SinalTest$p.value }) power[i] <- mean(p_value <= 0.05) } power_nobs[cont,] <- power cont = cont+1 } x11() par(mfrow=c(2,2)) plot(theta.test, power_nobs[1,], type = "l", xlab = bquote(theta), ylab = "Poder", main = "n = 5") abline(v = 0.0, lwd = 2, col = "grey80", lty = 2) plot(theta.test, power_nobs[2,], type = "l", xlab = bquote(theta), ylab = "Poder", main = "n = 10") abline(v = 0.0, lwd = 2, col = "grey80", lty = 2) plot(theta.test, power_nobs[3,], type = "l", xlab = bquote(theta), ylab = "Poder", main = "n = 30") abline(v = 0.0, lwd = 2, col = "grey80", lty = 2) plot(theta.test, power_nobs[4,], type = "l", xlab = bquote(theta), ylab = "Poder", main = "n = 80") abline(v = 0.0, lwd = 2, col = "grey80", lty = 2) ### usando o grau de contaminação 10% e lamb = 3 delta <- 0.10 lambda <- 3 r<-1000 theta.test<-c(seq(-2,2,0.05)) M <- length(theta.test) power <- numeric(M) nobs<-c(5, 10, 30, 80) #Vetor Para tamanhos de amostras diferentes power_nobs <- matrix(0,length(nobs),M) #criando o ambiente(matriz) para armazenamento cont <- 1 for (j in nobs){ for (i in 1:M) { theta<-theta.test[i] p_value <- replicate(r, expr = { x <- (1 - delta)*rnorm(j, theta, 1) + (delta*rnorm(j, theta, 1))/sqrt(lambda) SinalTest<-SIGN.test(x,mu=0) SinalTest$p.value }) power[i] <- mean(p_value <= 0.05) } power_nobs[cont,] <- power cont = cont+1 } x11() par(mfrow=c(2,2)) plot(theta.test, power_nobs[1,], type = "l", xlab = bquote(theta), ylab = "Poder", main = "n = 5") abline(v = 0.0, lwd = 2, col = "grey80", lty = 2) plot(theta.test, power_nobs[2,], type = "l", xlab = bquote(theta), ylab = "Poder", main = "n = 10") abline(v = 0.0, lwd = 2, col = "grey80", lty = 2) plot(theta.test, power_nobs[3,], type = "l", xlab = bquote(theta), ylab = "Poder", main = "n = 30") abline(v = 0.0, lwd = 2, col = "grey80", lty = 2) plot(theta.test, power_nobs[4,], type = "l", xlab = bquote(theta), ylab = "Poder", main = "n = 80") abline(v = 0.0, lwd = 2, col = "grey80", lty = 2)

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/Programming Assignment 3/rankhospital.R

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JaMedina/DS_R_Programming

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rankhospital.R

rankhospital <- function(state, outcome, num="best"){ outcome_measures <- read.csv("outcome-of-care-measures.csv", colClasses = "character"); if(!state %in% outcome_measures$State){ stop("Invalid State."); } outcome_measures <- outcome_measures[outcome_measures$State==state,]; number_of_deaths <- numeric(); if(outcome == 'heart attack'){ number_of_deaths <- as.numeric(outcome_measures[,11]); } else if (outcome == 'heart failure'){ number_of_deaths <- as.numeric(outcome_measures[,17]); } else if (outcome == 'pneumonia'){ number_of_deaths <- as.numeric(outcome_measures[,23]); } else { stop("Invalud outcome"); } all_rankings <- rank(number_of_deaths, na.last = NA); if(num == "best"){ ranking <- 1; } else if (num == "worst"){ ranking <- length(all_rankings); } else if (num <= length(all_rankings)){ ranking <- num; } else { return(NA); } return (outcome_measures$Hospital.Name[order(number_of_deaths, outcome_measures$Hospital.Name)[ranking]]); }

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adjust_Rsq.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utility.R \encoding{UTF-8} \name{adjust_Rsq} \alias{adjust_Rsq} \title{Adjust a regression model R-squared for overfitting} \usage{ adjust_Rsq(Rsq, n, p, adjust = c("fisher", "pop", "cv")) } \arguments{ \item{Rsq}{Observed model R-squared} \item{n}{Sample size} \item{p}{Number of predictors} \item{adjust}{Which adjustment to apply. Options are "fisher" for the Adjusted R-squared method used in \code{\link[stats:lm]{stats::lm()}}, "pop" for the positive-part Pratt estimator of the population R-squared, and "cv" for the Browne/positive-part Pratt estimator of the cross-validity R-squared. Based on Shieh (2008), these are the estimators for the population and cross-validity R-squared values that show the least bias with a minimal increase in computational complexity.} } \value{ An adjusted R-squared value. } \description{ Estimate shrinkage for regression models } \examples{ adjust_Rsq(.55, 100, 6, adjust = "pop") } \references{ Shieh, G. (2008). Improved shrinkage estimation of squared multiple correlation coefficient and squared cross-validity coefficient. \emph{Organizational Research Methods, 11}(2), 387–407. \doi{10.1177/1094428106292901} }

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Day2.R

# Software Carpentry R Workshop - James's Code #doing some programming! number <- 37 if(number > 100) { print("greater than 100") } # The above produces no visible result cos 37 isn't > 100. number <- 37 if(number > 100) { print("greater than 100") } else { print("less than or equal to 100") } #Some notation: # Less than: < # Equal to: == # Not equal to: != number <- -3 if(number > 0) { print(1) } else if (number < 0) { print(-1) } else {print(0) } # And now... the ampersand. # 'a bit of r & coding' number1 <- -15 number2 <- 40 if(number1 >= 0 & number2 >= 0) { print("both numbers are positive") } else{ print("at least one number was negative") } # you could use this to figure out the boundaries of stages. # like finding which sites have no flowers at a certain coll. period # Loops! # Automating & doing repetetive tasks. numbers <- 1:10 # count to ten # don't forget to actually ctrl+r when you assign objects # for loop for(number in numbers) { print(number) } for(i in 1:10) { print(i) } # So R stores whatever number in the loop in 'i'. print(i) # It prints the last number that was assigned to 'i'. letter <- "z" print(letter) for(letter in c("a","b","c")) { print(letter) } print(letter) # 'c' was the last thing stored in the letter variable / the increment variable. # Socrative: Write a for loop that will calculate the sum of a vector of numbers and print it out at the end... without using 'sum()'. numbers <- c(4,8,16,23,42) sum <- 0 for(number in numbers) { sum <- sum+number print(sum) } # You can define objects, like 'number', within the loop # It will write over the value assignment each time the loop runs. # For example, here it is basically doing "number <- 0, number <- 0 + 4, # number <- 4+8, number <- 12+16 etc etc. # Now you can do the for loop and have it add the things, and run the print command # AFTER the loop close, so that you don't have a zillion numbers on the result. for(number in numbers) { sum <- sum+number } print(sum) #viz. # Functions # To see the source code of it, just run it with nothing. dim nrow # Farenheit to Kelvin function fahr_to_kelvin <- function(temp) { kelvin <- ((temp - 32) * (5/9) + 273.15) return(kelvin) # this tells the function what to send back when you run it } fahr_to_kelvin(32) fahr_to_kelvin(212) kelvin_to_celsius <- function(temp){ celsius <- temp - 273.15 return(celsius) } kelvin_to_celsius(0) # Socrative # Function to convert fahrenheit to kelvin fahr_to_kelvin <- function(temp) { temp <- ((temp - 32) * (5 / 9)) + 273.15 return(temp) } # Store the current temperature in F temp <- 73 # Get the temperature in kelvin kelvin_temp <- fahr_to_kelvin(temp) # Print the temperature print(temp) # Socrative: Write a function to convert a temperature in Celsius to Fahrenheit using # the formula: F = C * 9 / 5 + 32 celsius_to_fahr <- function(temp) { fahr <- ((temp) * (9/5) + 32) return(fahr) } celsius_to_fahr(0) celsius_to_fahr(100) celsius_to_fahr(37) # Day 2 afternoon: data manipulation with ~*Cera*~ # # RMarkdown install.packages(c("tidyr","dplyr","knitr","rmarkdown","formatR"))

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#Exploratory Data Analysis Assignment 1 #working data from plot1.R and plot2.R is used png(filename = "plot3.png", width = 480, height = 480) #open png device, set w x h in pixels (though 480 is typically default) par(pty="s") #make plot square #create plot and add submetering 1 line (default is black so don't need to specify) plot(workingdata$datetime, workingdata$Sub_metering_1, type = "l", ylab = "Energy sub metering", xlab = "") #add submetering 2 line in red lines(workingdata$datetime, workingdata$Sub_metering_2, col = "red") #add submetering 2 line in blue lines(workingdata$datetime, workingdata$Sub_metering_3, col = "blue") #add the legend in the top right corner legend("topright", lty = c(1,1,1), col = c("black", "red", "blue"), legend = c("Sub_metering_1","Sub_metering_2","Sub_metering_3")) dev.off() #closes device

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/Tides and lunar data scrape code.R

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2021-01-20T14:39:08.988677

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Tides and lunar data scrape code.R

# Scrape tide data library(rvest) #2006-2007 (2008 must be loaded seperately) tides_06_07 <- lapply(paste0("http://tides.mobilegeographics.com/calendar/year/3689.html?y=",2006:2007, "&m=4&d=1"), function(url){ url %>% read_html() %>% html_nodes("table") %>% html_table() }) tides_06_07_list <- do.call(rbind, tides_06_07) tides_06_07_df <- do.call(rbind, tides_06_07_list) # 2008 tides_08 <- read_html("http://tides.mobilegeographics.com/calendar/year/3689.html?y=2008&m=4&d=1") tides_08_tbls <- html_nodes(tides_08, "table") tides_08_list <- html_table(tides_08_tbls) # list of the tables tides_08_df <- ldply(tides_08_list, data.frame) # convert this into a big dataframe tides_08_df$Low.2 <- NULL tides_08_df$High.3 <- NULL # these two extra coloums were the reason I had to scrape 08 seperatly colnames(tides_08_df) <- colnames(tides_06_07_df) # some of the colnames didn't match (needed for rbind) # 2009-2016 tides_09_16 <- lapply(paste0("http://tides.mobilegeographics.com/calendar/year/3689.html?y=",2009:2016, "&m=4&d=1"), function(url){ url %>% read_html() %>% html_nodes("table") %>% html_table() }) tides_09_16_list <- do.call(rbind, tides_09_16) tides_09_16_df <- do.call(rbind, tides_09_16_list) # 2006-2016 merged together tides_all <- rbind( tides_06_07_df, tides_08_df, tides_09_16_df) tides_all$date <- seq(as.Date("2006/1/1"), as.Date("2016/12/31"), "days") tides_all$date <- format(tides_all$date, "%Y-%d-%m") tides_all$date <- as.Date(tides_all$date, "%Y-%d-%m" ) tides_all$year <- format(tides_all$date, "%Y") tides_all <- subset(tides_all, select = -c(Day,Sunrise,Sunset) ) tides_all <- tides_all[,c(8,7,6,1,2,3,4,5)] colnames(tides_all) <- c("year","date","event","high_1","low_1","high_2","low_2","high_3") tides_all$high_1 <- substring(tides_all$high_1,15,19) tides_all$low_1 <- substring(tides_all$low_1,15,19) tides_all$high_2 <- substring(tides_all$high_2,15,19) tides_all$low_2 <- substring(tides_all$low_2,15,19) tides_all$high_3 <- substring(tides_all$high_3,15,19) tides_all$high_1 <- as.numeric(tides_all$high_1)*100 tides_all$high_2 <- as.numeric(tides_all$high_2)*100 tides_all$high_3 <- as.numeric(tides_all$high_3)*100 tides_all$low_1 <- as.numeric(tides_all$low_1)*100 tides_all$low_2 <- as.numeric(tides_all$low_2)*100 tides_all$event <- gsub('First Quarter', 'fq', tides_all$event) tides_all$event <- gsub('Full Moon', 'fm', tides_all$event) tides_all$event <- gsub('Last Quarter', 'lq', tides_all$event) tides_all$event <- gsub('New Moon', 'nm', tides_all$event) # I want to have the highest high tides on a given day ht <- tides_all[,c(1,2,4,6,8)] ht$m= pmax(ht$high_1,ht$high_2,ht$high_3, na.rm=TRUE) #compare those two and give me the max ht$m = ht$m * 100 # add a coloum with the max tide height to tides_all tides_all$max_tide_height <- ht$m ## Scrape the lunar data # 2006-2016 moon_06 <- lapply(paste0("https://www.timeanddate.com/moon/mexico/mazatlan?month=",1:12,"&year=2006"), function(url){ url %>% read_html() %>% html_nodes("table") %>% html_table() }) moon_06_list <- do.call(rbind, moon_06) moon_06_df <- do.call(rbind, moon_06_list) moon_06_df <- moon_06_df[,c(1,8,11)] colnames(moon_06_df) <- c("date","time","illumination") dig <- c(1:31) moon_06_df=moon_06_df[moon_06_df$date %in% dig,] moon_06_df$date <- seq(as.Date("2006/1/1"), as.Date("2006/12/31"), "days") moon_07 <- lapply(paste0("https://www.timeanddate.com/moon/mexico/mazatlan?month=",1:12,"&year=2007"), function(url){ url %>% read_html() %>% html_nodes("table") %>% html_table() }) moon_07_list <- do.call(rbind, moon_07) moon_07_df <- do.call(rbind, moon_07_list) moon_07_df <- moon_07_df[,c(1,8,11)] colnames(moon_07_df) <- c("date","time","illumination") dig <- c(1:31) moon_07_df=moon_07_df[moon_07_df$date %in% dig,] # works, nice! moon_07_df$date <- seq(as.Date("2007/1/1"), as.Date("2007/12/31"), "days") moon_08 <- lapply(paste0("https://www.timeanddate.com/moon/mexico/mazatlan?month=",1:12,"&year=2008"), function(url){ url %>% read_html() %>% html_nodes("table") %>% html_table() }) moon_08_list <- do.call(rbind, moon_08) moon_08_df <- do.call(rbind, moon_08_list) moon_08_df <- moon_08_df[,c(1,8,11)] colnames(moon_08_df) <- c("date","time","illumination") dig <- c(1:31) moon_08_df=moon_08_df[moon_08_df$date %in% dig,] # works, nice! moon_08_df$date <- seq(as.Date("2008/1/1"), as.Date("2008/12/31"), "days") moon_09 <- lapply(paste0("https://www.timeanddate.com/moon/mexico/mazatlan?month=",1:12,"&year=2009"), function(url){ url %>% read_html() %>% html_nodes("table") %>% html_table() }) moon_09_list <- do.call(rbind, moon_09) moon_09_df <- do.call(rbind, moon_09_list) moon_09_df <- moon_09_df[,c(1,8,11)] colnames(moon_09_df) <- c("date","time","illumination") dig <- c(1:31) moon_09_df=moon_09_df[moon_09_df$date %in% dig,] # works, nice! moon_09_df$date <- seq(as.Date("2009/1/1"), as.Date("2009/12/31"), "days") View(moon_09_df) moon_10 <- lapply(paste0("https://www.timeanddate.com/moon/mexico/mazatlan?month=",1:12,"&year=2010"), function(url){ url %>% read_html() %>% html_nodes("table") %>% html_table() }) moon_10_list <- do.call(rbind, moon_10) moon_10_df <- do.call(rbind, moon_10_list) moon_10_df <- moon_10_df[,c(1,8,11)] colnames(moon_10_df) <- c("date","time","illumination") dig <- c(1:31) moon_10_df=moon_10_df[moon_10_df$date %in% dig,] # works, nice! moon_10_df$date <- seq(as.Date("2010/1/1"), as.Date("2010/12/31"), "days") View(moon_10_df) moon_11 <- lapply(paste0("https://www.timeanddate.com/moon/mexico/mazatlan?month=",1:12,"&year=2011"), function(url){ url %>% read_html() %>% html_nodes("table") %>% html_table() }) moon_11_list <- do.call(rbind, moon_11) moon_11_df <- do.call(rbind, moon_11_list) moon_11_df <- moon_11_df[,c(1,8,11)] colnames(moon_11_df) <- c("date","time","illumination") dig <- c(1:31) moon_11_df=moon_11_df[moon_11_df$date %in% dig,] # works, nice! moon_11_df$date <- seq(as.Date("2011/1/1"), as.Date("2011/12/31"), "days") View(moon_11_df) moon_12 <- lapply(paste0("https://www.timeanddate.com/moon/mexico/mazatlan?month=",1:12,"&year=2012"), function(url){ url %>% read_html() %>% html_nodes("table") %>% html_table() }) moon_12_list <- do.call(rbind, moon_12) moon_12_df <- do.call(rbind, moon_12_list) moon_12_df <- moon_12_df[,c(1,8,11)] colnames(moon_12_df) <- c("date","time","illumination") dig <- c(1:31) moon_12_df=moon_12_df[moon_12_df$date %in% dig,] # works, nice! moon_12_df$date <- seq(as.Date("2012/1/1"), as.Date("2012/12/31"), "days") View(moon_12_df) moon_13 <- lapply(paste0("https://www.timeanddate.com/moon/mexico/mazatlan?month=",1:12,"&year=2013"), function(url){ url %>% read_html() %>% html_nodes("table") %>% html_table() }) moon_13_list <- do.call(rbind, moon_13) moon_13_df <- do.call(rbind, moon_13_list) moon_13_df <- moon_13_df[,c(1,8,11)] colnames(moon_13_df) <- c("date","time","illumination") dig <- c(1:31) moon_13_df=moon_13_df[moon_13_df$date %in% dig,] # works, nice! moon_13_df$date <- seq(as.Date("2013/1/1"), as.Date("2013/12/31"), "days") View(moon_13_df) moon_14 <- lapply(paste0("https://www.timeanddate.com/moon/mexico/mazatlan?month=",1:12,"&year=2014"), function(url){ url %>% read_html() %>% html_nodes("table") %>% html_table() }) moon_14_list <- do.call(rbind, moon_14) moon_14_df <- do.call(rbind, moon_14_list) moon_14_df <- moon_14_df[,c(1,8,11)] colnames(moon_14_df) <- c("date","time","illumination") dig <- c(1:31) moon_14_df=moon_14_df[moon_14_df$date %in% dig,] # works, nice! moon_14_df$date <- seq(as.Date("2014/1/1"), as.Date("2014/12/31"), "days") View(moon_14_df) moon_15 <- lapply(paste0("https://www.timeanddate.com/moon/mexico/mazatlan?month=",1:12,"&year=2015"), function(url){ url %>% read_html() %>% html_nodes("table") %>% html_table() }) moon_15_list <- do.call(rbind, moon_15) moon_15_df <- do.call(rbind, moon_15_list) moon_15_df <- moon_15_df[,c(1,8,11)] colnames(moon_15_df) <- c("date","time","illumination") dig <- c(1:31) moon_15_df=moon_15_df[moon_15_df$date %in% dig,] # works, nice! moon_15_df$date <- seq(as.Date("2015/1/1"), as.Date("2015/12/31"), "days") View(moon_15_df) moon_16 <- lapply(paste0("https://www.timeanddate.com/moon/mexico/mazatlan?month=",1:12,"&year=2016"), function(url){ url %>% read_html() %>% html_nodes("table") %>% html_table() }) moon_16_list <- do.call(rbind, moon_16) moon_16_df <- do.call(rbind, moon_16_list) moon_16_df <- moon_16_df[,c(1,8,11)] colnames(moon_16_df) <- c("date","time","illumination") dig <- c(1:31) moon_16_df=moon_16_df[moon_16_df$date %in% dig,] # works, nice! moon_16_df$date <- seq(as.Date("2016/1/1"), as.Date("2016/12/31"), "days") View(moon_16_df) #2006-2016 merged moon_all <- rbind (moon_06_df, moon_07_df, moon_08_df , moon_09_df, moon_10_df, moon_11_df, moon_12_df, moon_13_df, moon_14_df, moon_15_df, moon_16_df) moon_all$time <- gsub("Moon does not pass the meridian on this day.","NA",moon_all$time ) moon_all$illumination <- gsub("Moon does not pass the meridian on this day.","NA",moon_all$illumination ) ## Interpolation for illumination at 12 pm Sys.setenv(TZ="UTC") moon_all$datetime<- as.POSIXct(paste(moon_all$date, moon_all$time), format="%Y-%m-%d %H:%M") moon_all$illumination <- gsub("%","",moon_all$illumination) moon_all$illumination <- gsub(",",".",moon_all$illumination) moon_all$illumination <- as.numeric(moon_all$illumination)/100 f <- approxfun(moon_all$datetime,moon_all$illumination) start <- as.POSIXct("2006-1-01 12:00:00", "%Y-%m-%d %H:%M:%S", tz="UTC") end <- as.POSIXct("2016-12-31 12:00:00", "%Y-%m-%d %H:%M:%S", tz="UTC") x <- seq(start, end, "days") moon_all$noon <- x moon_all$interpolated <- f(x) moon_all$year <- format(moon_all$date, "%Y") moon_all <- moon_all[,c(7,4,3,5,6)] colnames(moon_all) <- c("year","meridian_passing","illumination_mp","noon","illumination_noon")

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/rprof-write.R \name{write_rprof} \alias{write_rprof} \title{Write profiler data to an R profiler file} \usage{ write_rprof(ds, path) } \arguments{ \item{ds}{Profiler data, see \code{\link[=validate_profile]{validate_profile()}}} \item{path}{Target file name} } \description{ Use the profvis or proftools R packages to further analyze files created by this function. }

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library(readr) shelter_skim <- read_csv("Econ4670researchproject/shelter_skim.csv") shelter_skim$TotalEnrollments = NULL shelter_return <- subset(shelter_skim , StayedInShelterMoreThanOnce == 1) shelter_no_return <- subset(shelter_skim , StayedInShelterMoreThanOnce == 0) shelter_no <- shelter_no_return[1:328,] shelter_sample <- merge(shelter_no , shelter_return , all = TRUE) summary(shelter_sample) shelter_random <- shelter_sample[sample(nrow(shelter_sample)),] #Import Library library(e1071) #Contains the SVM Train <- shelter_random[1:460,] Test <- shelter_random[461:656,] #model linear model_l <- svm(StayedInShelterMoreThanOnce ~.,data=Train, kernel = "linear", type = "C-classification", gamma=0.2, cost=100) summary(model_l) #Predict Output preds_l <- predict(model_l,Test) table(preds_l, Test$StayedInShelterMoreThanOnce) #model radial model_r <- svm(StayedInShelterMoreThanOnce ~.,data=Train, kernel = "radial", type = "C-classification", gamma=0.2, cost=100) summary(model_r) #Predict Output preds_r <- predict(model_r,Test) table(preds_r, Test$StayedInShelterMoreThanOnce) #model polynomial model_p <- svm(StayedInShelterMoreThanOnce ~.,data=Train, kernel = "polynomial", type = "C-classification", gamma=0.2, cost=100) summary(model_p) #Predict Output preds_p <- predict(model_p,Test) table(preds_p, Test$StayedInShelterMoreThanOnce) count <- length(unique(shelter_skim$StayedInShelterMoreThanOnce)) count model_l$coefs #plot(model_l , shelter_random , )

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youtube datacleaner.R

cat("\014") options(warn=1) require(survey) require(dplyr) require(lattice) #read in, remove columns, from https://www.kaggle.com/datasnaek/youtube-new/data setwd("C:/Users/Yao/Desktop/you") youtubeRawUS <- read.csv(file="USvideos.csv", header=TRUE, sep=",") youtubeRawUS$country <- rep("US",nrow(youtubeRawUS)) youtubeRawUS <- youtubeRawUS[-c(3:4,7,12:16)] youtubeRawCA <- read.csv(file="CAvideos.csv", header=TRUE, sep=",") youtubeRawCA$country <- rep("CA",nrow(youtubeRawCA)) youtubeRawCA <- youtubeRawCA[-c(3:4,7,12:16)] youtubeRawDE <- read.csv(file="DEvideos.csv", header=TRUE, sep=",") youtubeRawDE$country <- rep("DE",nrow(youtubeRawDE)) youtubeRawDE <- youtubeRawDE[-c(3:4,7,12:16)] youtubeRawFR <- read.csv(file="FRvideos.csv", header=TRUE, sep=",") youtubeRawFR$country <- rep("FR",nrow(youtubeRawFR)) youtubeRawFR <- youtubeRawFR[-c(3:4,7,12:16)] youtubeRawGB <- read.csv(file="GBvideos.csv", header=TRUE, sep=",") youtubeRawGB$country <- rep("GB",nrow(youtubeRawGB)) youtubeRawGB <- youtubeRawGB[-c(3:4,7,12:16)] youtubeRaw<- rbind(youtubeRawUS, youtubeRawCA) youtubeRaw<- rbind(youtubeRaw, youtubeRawDE) youtubeRaw<- rbind(youtubeRaw, youtubeRawFR) youtubeRaw<- rbind(youtubeRaw, youtubeRawGB) head(youtubeRaw) youtubeRaw2 <- youtubeRaw #remove duplicates because they can be trending in multiple months, keep the least views to get on trending youtubeRaw2 = youtubeRaw2[order(youtubeRaw2[,'video_id'],youtubeRaw2[,'views']),] youtubeRaw2 = youtubeRaw2[!duplicated(youtubeRaw2$video_id),] head(youtubeRaw2) write.csv(youtubeRaw2, file = "youtubeRaw2.csv") #replace strata categories into real names youtubeRaw2$category_id2[youtubeRaw2$category_id == '1'] <- 'Film & Animation' youtubeRaw2$category_id2[youtubeRaw2$category_id == '2'] <- 'Autos & Vehicles' youtubeRaw2$category_id2[youtubeRaw2$category_id == '10'] <- 'Music' youtubeRaw2$category_id2[youtubeRaw2$category_id == '15'] <- 'Pets & Animals' youtubeRaw2$category_id2[youtubeRaw2$category_id == '17'] <- 'Sports' youtubeRaw2$category_id2[youtubeRaw2$category_id == '19'] <- 'Travel & Events' youtubeRaw2$category_id2[youtubeRaw2$category_id == '20'] <- 'Gaming' youtubeRaw2$category_id2[youtubeRaw2$category_id == '22'] <- 'People & Blogs' youtubeRaw2$category_id2[youtubeRaw2$category_id == '23'] <- 'Comedy' youtubeRaw2$category_id2[youtubeRaw2$category_id == '24'] <- 'Entertainment' youtubeRaw2$category_id2[youtubeRaw2$category_id == '25'] <- 'News & Politics' youtubeRaw2$category_id2[youtubeRaw2$category_id == '26'] <- 'Howto & Style' youtubeRaw2$category_id2[youtubeRaw2$category_id == '27'] <- 'Education' youtubeRaw2$category_id2[youtubeRaw2$category_id == '28'] <- 'Science & Technology' youtubeRaw2$category_id2[youtubeRaw2$category_id == '29'] <- 'Nonprofits & Activism' youtubeRaw2$category_id2[youtubeRaw2$category_id == '43'] <- 'Science & Technology' youtubeRaw2$category_id[youtubeRaw2$category_id == '43'] <- '28' #sanity check, remove column names head(youtubeRaw2) rownames(youtubeRaw2) <- c() youtubeRaw3 <- youtubeRaw2[order(youtubeRaw2$category_id2),] head(youtubeRaw3) write.csv(youtubeRaw3, file = "youtubeRaw3.csv", row.names=FALSE) #use sas for more youtubeRaw3.csv dataset stats #check initial distribution and number of rows boxplot(youtubeRaw3$views, main="Uncleaned Boxplot Distribution of Videos Views", xlab="Trending Videos", ylab="Number of Views") nrow(youtubeRaw3) bwplot(views ~ category_id2 , data = youtubeRaw3, scales=list(x=list(rot=45)), main="Uncleaned Boxplot Distribution of Videos Views", xlab="Trending Videos Categories", ylab="Number of Views") #solve for view count without cleaning data sum(as.numeric(youtubeRaw3$views)) max(youtubeRaw3$views) min(youtubeRaw3$views) mean(youtubeRaw3$views) #remove outliers more than 1.5 quant, save into new clean dataset remove_outliers <- function(x, na.rm = TRUE, ...) { qnt <- quantile(x, probs=c(.25, .75), na.rm = na.rm, ...) H <- 1.5 * IQR(x, na.rm = na.rm) y <- x y[x < (qnt[1] - H)] <- NA y[x > (qnt[2] + H)] <- NA y } youtubeClean <- youtubeRaw3 youtubeClean$views <- remove_outliers(youtubeRaw3$views) #check new distribution, only keep data within distribution boxplot(youtubeClean$views, main="Cleaned Boxplot Distribution of Videos Views", xlab="Trending Videos", ylab="Number of Views") bwplot(views ~ category_id2 , data = youtubeClean, scales=list(x=list(rot=45)), main="Cleaned Boxplot Distribution of Videos Views", xlab="Trending Videos Categories", ylab="Number of Views") youtubeClean2 <- youtubeClean[complete.cases(youtubeClean), ] write.csv(youtubeClean2, file = "youtubeClean2.csv", row.names=FALSE) #solve for actual target average view count and number of rows for strata sum(as.numeric(youtubeClean2$views)) nrow(youtubeClean2) max(youtubeClean2$views) min(youtubeClean2$views) mean(youtubeClean2$views) sd(youtubeClean2$views) #average amount of views for the outliers removed (sum(as.numeric(youtubeRaw3$views)) - sum(as.numeric(youtubeClean2$views))) / (nrow(youtubeRaw3) - nrow(youtubeClean2)) max(youtubeRaw3$views)-max(youtubeClean2$views) min(youtubeRaw3$views)-min(youtubeClean2$views) mean(youtubeRaw3$views)-mean(youtubeClean2$views) nrow(youtubeClean2)/nrow(youtubeRaw3) #use sas for more youtubeClean2.csv dataset stats #how do we choose MOE? currently, MOE = 5000 views #do we remove outliers per strata or for the whole dataset? currently, we remove for whole dataset #after removing outliers, 88% of the dataset is kept...do we ignore fpc adjustment? currently we ignore b/c more than 10% n0srs <- ceiling((1.96^2*sd(youtubeClean2$views)^2)/(5000^2)) n0srs #SRS function SrsMeanEstimate<-function(Seed, SampSize, printOutput= TRUE){ set.seed(Seed) youtubeClean2.SRSSampled = sample_n(youtubeClean2,SampSize) if(printOutput == TRUE){ print(nrow(youtubeClean2.SRSSampled)) print(bwplot(views ~ category_id2, data = youtubeClean2.SRSSampled, scales=list(x=list(rot=45)), main="SRS Boxplot Distribution of Videos Views", xlab="Trending Videos Categories", ylab="Number of Views")) } mydesign <- svydesign(id = ~1, data = youtubeClean2.SRSSampled) srsMean = svymean(~views, design = mydesign) srsSE = SE(srsMean) srsCI = confint(srsMean) rm(youtubeClean2.SRSSampled) rm(mydesign) return(list(as.numeric(srsMean[1]), as.numeric(srsSE), as.numeric(srsCI[1]), as.numeric(srsCI[2]) ) ) } srsMean <- SrsMeanEstimate(n0srs, n0srs) print(paste('The Mean Estimate =', srsMean[[1]])) print(paste('The Standard Error =', srsMean[[2]])) mean(youtubeClean2$views) #Proportional Strata PropMeanEstimate<-function(Seed, SampSize, printOutput= TRUE){ set.seed(Seed) # Identify Frequency of category_id2 Stratum PropFreq <- as.data.frame(table(youtubeClean2[,c("category_id2")])) names(PropFreq)[1] = 'category_id2' PropFreq PropFreq$N = nrow(youtubeClean2) PropFreq$p = PropFreq$Freq/PropFreq$N PropFreq$SampSizeh = (PropFreq$p * SampSize) PropFreq$SampSizehRounded = round(PropFreq$SampSizeh) youtubeClean2.PropSampled <- NULL for (i in 1:nrow(PropFreq)){ youtubeClean2.PropSampled<-rbind(youtubeClean2.PropSampled, sample_n(youtubeClean2[(youtubeClean2$category_id2 == PropFreq[i,"category_id2"]),] ,PropFreq[i,"SampSizehRounded"])) } if(printOutput == TRUE){ print(PropFreq) print(nrow(youtubeClean2.PropSampled)) print(bwplot(views ~ category_id2, data = youtubeClean2.PropSampled, scales=list(x=list(rot=45)), main="Prop Boxplot Distribution of Videos Views", xlab="Trending Videos Categories", ylab="Number of Views")) } mydesign <- svydesign(id = ~1, strata = ~category_id2, data = youtubeClean2.PropSampled) propMean = svymean(~views, design = mydesign) propSE = SE(propMean) propCI = confint(propMean) rm(youtubeClean2.PropSampled) rm(mydesign) propCI = confint(propMean) return(list(as.numeric(propMean[1]), as.numeric(propSE), as.numeric(propCI[1]), as.numeric(propCI[2]) ) ) } #adjusting the sample size calculation? propMean <- PropMeanEstimate(n0srs, n0srs) print(paste('The Mean Estimate =', propMean[[1]])) print(paste('The Standard Error =', propMean[[2]])) mean(youtubeClean2$views) #deff = se_complex/se_srs deffProp = as.numeric(propMean[[2]]/srsMean[[2]]) deffProp n0prop = ceiling(n0srs*deffProp) n0prop #prop adjusted for deff propMean <- PropMeanEstimate(n0srs, n0prop) print(paste('The Mean Estimate =', propMean[[1]])) print(paste('The Standard Error =', propMean[[2]])) #task 2 SeedList <- c(10000, 20000, 30000, 40000, 50000) df<- NULL #SRS Seed Executions for (seed in SeedList){ srsEstimate <- SrsMeanEstimate(seed, n0srs, FALSE) srsEstimate <- data.frame('SRS', seed, srsEstimate) names(srsEstimate) <- c("EstimateType","SeedValue", "MeanEstimate", "SE", "LowerCI", "UpperCI") df<- rbind(df,srsEstimate) } #Prop Seed Executions for (seed in SeedList){ PropEstimate <- PropMeanEstimate(seed, n0srs, FALSE) PropEstimate <- data.frame('Prop', seed, PropEstimate) names(PropEstimate) <- c("EstimateType","SeedValue", "MeanEstimate", "SE", "LowerCI", "UpperCI") df<- rbind(df,PropEstimate) } #Prop Seed Executions for (seed in SeedList){ PropEstimate <- PropMeanEstimate(seed, n0prop, FALSE) PropEstimate <- data.frame('Prop DE', seed, PropEstimate) names(PropEstimate) <- c("EstimateType","SeedValue", "MeanEstimate", "SE", "LowerCI", "UpperCI") df<- rbind(df,PropEstimate) } #Add True Mean Value, in-line with estimates df$TrueMeanValue <- mean(youtubeClean2$views) #Add Bool Value for whether the Conf Limit contains the True Mean Value df$WithinConfLimit <- df$LowerCI <= df$TrueMeanValue & df$UpperCI >= df$TrueMeanValue #Print Results print(df) winner = aggregate(df[, 3:7], list(df$EstimateType), mean) winner #Prop wins slightly #What is the percentage that the actual value is in the 95% confidence intervals for each design? #abs(94384.50-93891.7)/2565.645 = 19.20734% #how do you phrase it? true value is within 19.20734% of standard error for SRS estimation? winner$PercentFromTrueMean <- abs(winner$TrueMeanValue - winner$MeanEstimate)/winner$SE*100 print(winner)

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/fit_nodk.R \name{fit_nodk} \alias{fit_nodk} \title{Goodness of fit statistics for data without don't know} \usage{ fit_nodk(pre_test, pst_test, g, est.param) } \arguments{ \item{pre_test}{data.frame carrying pre_test items} \item{pst_test}{data.frame carrying pst_test items} \item{g}{estimates of \eqn{\gamma} produced from \code{\link{guesstimate}}} \item{est.param}{estimated parameters produced from \code{\link{guesstimate}}} } \value{ matrix with two rows: top row carrying chi-square value, and bottom row probability of observing that value } \description{ For data without Don't Know, chi-square goodness of fit between true and model based multivariate distribution } \details{ fit_nodk } \examples{ \dontrun{fit_nodk(pre_test, pst_test, g, est.param)} }

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Exercicio8.R

install.packages("gridExtra") library(xlsx) library(gridExtra) library(grid) ex8 <- read.xlsx("data/exercicio8.xls", sheetName = "Plan1") ex8 tabela <- table(ex8$Altura.dos.pacientes) tabela barplot(tabela, ylab = "Frequencia", ylim = c(0,3), main = "'Distribuicao de frequencia'") hist(ex8$Altura.dos.pacientes, main = "Histograma Ex 8", xlab = "Altura dos Pacientes")

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## Data tranformation library(tidyverse) library(readxl) # read data into R student <- read_excel("scholarships.xlsx", 1) address <- read_excel("scholarships.xlsx",2) scholarships <- read_excel("scholarships.xlsx",3) ## VLOOKUP (Spreadsheet) == left_join (R) ## Mutating join (left, inner, right, full) ## data pipeline (tranformation) student %>% left_join(address, by = "id") %>% inner_join(scholarships, by = "id") ## Filtering JOin (ani_join & semi_join) ## anti join - select student dont qualify for scholarships student %>% left_join(address, by = "id") %>% anti_join(scholarships, by = "id") student %>% left_join(address, by = "id") %>% semi_join(scholarships, by = "id") ## Review dplyr mtcars <- as_tibble(mtcars) mtcars %>% select(milePerGallon = mpg, horsePower = hp, wt, am) %>% filter(horsePower > 200) %>% mutate(milePerGallon = milePerGallon + 1, am = if_else(am == 0, "Auto", "Manual")) %>% arrange(am, desc(horsePower)) %>% summarise(avg_hp = mean(horsePower), sd_hp = sd(horsePower), n = n()) ## Group By + Summarise mtcars %>% mutate(am = if_else(am == 0, "Auto", "Manual")) %>% group_by(am) %>% summarise(avg_hp = mean(hp), sd_hp = sd(hp), n = n()) #join table library(nycflights13) result <- flights %>% filter(month == 9 & day == 9) %>% count(carrier) %>% arrange(desc(n)) %>% left_join(airlines, by = "carrier") %>% rename(carrier_name = name) #write/ export csv file write_csv(result, "learn-sql/nyc_summary.csv", ) # data wrangling 101 mtcars head(mtcars) tail(mtcars) summary(mtcars) # dplyr ## select columns you want mtcars %>% select(mpg, hp ,wt) %>% head(10) mtcars %>% select(mpg, 3, 5, am) mtcars %>% select( starts_with("a")) mtcars %>% select(contains("w")) # rename columns m <- mtcars %>% select(milePerGallon = mpg, housePower = hp, weight = wt) %>% head(10) # filter mtcars %>% select(milePerGallon = mpg, horsePower = hp, weight = wt) %>% filter(horsePower < 100 & weight < 2) # AND mtcars %>% select(milePerGallon = mpg, horsePower = hp, weight = wt) %>% filter(horsePower < 100 | weight < 2) # OR mtcars %>% select(milePerGallon = mpg, horsePower = hp, weight = wt, transmission = am) %>% filter(transmission != 0) # rownames to column mtcars <- mtcars %>% rownames_to_column() %>% rename(model = rowname) %>% tibble() # arrange (sort data) mtcars %>% select(mpg, hp, wt) %>% arrange(hp) # asc mtcars %>% select(mpg, hp, wt) %>% arrange(desc(hp)) # desc # mutate create new column mtcars %>% select(mpg, hp, wt, am) %>% mutate(hp_edit = hp + 5, wt_double = wt * 2, am = if_else(am == 0, "Auto", "Manual")) %>% filter(am == "Auto") # summarise data mtcars %>% select(mpg, am) %>% mutate(am = if_else(am == 0, "Auto", "Manual")) %>% group_by(am) %>% summarise(avg_mpg = mean(mpg), sum_mpg = sum(mpg), sd_mpg = sd(mpg), min_mpg = min(mpg), max_mpg = max(mpg)) library(skimr) mtcars <- mtcars %>% mutate(am = if_else(am == 0, "Auto", "Manual")) View(mtcars) mtcars %>% group_by(am) %>% skim() mtcars %>% filter(hp < 150) %>% select(mpg, hp, wt, am) %>% group_by(am) %>% skim()

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rpart_labels.Rd

\name{rpart_labels} \alias{rpart_labels} \title{Extract labels data frame from rpart object for plotting using ggplot.} \usage{ rpart_labels(model, splits = TRUE, label, FUN = text, all = FALSE, pretty = NULL, digits = getOption("digits") - 3, use.n = FALSE, fancy = FALSE, fwidth = 0.8, fheight = 0.8, ...) } \arguments{ \item{model}{object of class "rpart", e.g. the output of rpart()} \item{...}{ignored} } \value{ a list with two elements: $labels and $leaf_labels } \description{ Extract labels data frame from rpart object for plotting using ggplot. } \seealso{ \code{\link{ggdendrogram}} Other dendro_data methods: \code{\link{dendro_data.dendrogram}}, \code{\link{dendro_data.rpart}}, \code{\link{dendro_data.tree}}, \code{\link{dendrogram_data}} Other rpart functions: \code{\link{dendro_data.rpart}}, \code{\link{rpart_segments}} } \keyword{internal}

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kernelFactory.Rd

% Generated by roxygen2 (4.1.1): do not edit by hand % Please edit documentation in R/kernelFactory.R \name{kernelFactory} \alias{kernelFactory} \title{Binary classification with Kernel Factory} \usage{ kernelFactory(x = NULL, y = NULL, cp = 1, rp = round(log(nrow(x), 10)), method = "burn", ntree = 500, filter = 0.01, popSize = rp * cp * 7, iters = 80, mutationChance = 1/(rp * cp), elitism = max(1, round((rp * cp) * 0.05)), oversample = TRUE) } \arguments{ \item{x}{A data frame of predictors (numeric, integer or factor). Categorical variables need to be factors. Indicator values should not be too imbalanced because this might produce constants in the subsetting process.} \item{y}{A factor containing the response vector. Only \{0,1\} is allowed.} \item{cp}{The number of column partitions.} \item{rp}{The number of row partitions.} \item{method}{Can be one of the following: POLynomial kernel function (\code{pol}), LINear kernel function (\code{lin}), Radial Basis kernel Function \code{rbf}), random choice (random={pol, lin, rbf}) (\code{random}), burn- in choice of best function (burn={pol, lin, rbf }) (\code{burn}). Use \code{random} or \code{burn} if you don't know in advance which kernel function is best.} \item{ntree}{Number of trees in the Random Forest base classifiers.} \item{filter}{either NULL (deactivate) or a percentage denoting the minimum class size of dummy predictors. This parameter is used to remove near constants. For example if nrow(xTRAIN)=100, and filter=0.01 then all dummy predictors with any class size equal to 1 will be removed. Set this higher (e.g., 0.05 or 0.10) in case of errors.} \item{popSize}{Population size of the genetic algorithm.} \item{iters}{Number of generations of the genetic algorithm.} \item{mutationChance}{Mutationchance of the genetic algorithm.} \item{elitism}{Elitism parameter of the genetic algorithm.} \item{oversample}{Oversample the smallest class. This helps avoid problems related to the subsetting procedure (e.g., if rp is too high).} } \value{ An object of class \code{kernelFactory}, which is a list with the following elements: \item{trn}{Training data set.} \item{trnlst}{List of training partitions.} \item{rbfstre}{List of used kernel functions.} \item{rbfmtrX}{List of augmented kernel matrices.} \item{rsltsKF}{List of models.} \item{cpr}{Number of column partitions.} \item{rpr}{Number of row partitions.} \item{cntr}{Number of partitions.} \item{wghts}{Weights of the ensemble members.} \item{nmDtrn}{Vector indicating the numeric (and integer) features.} \item{rngs}{Ranges of numeric predictors.} \item{constants}{To exclude from newdata.} } \description{ \code{kernelFactory} implements an ensemble method for kernel machines (Ballings and Van den Poel, 2013). } \examples{ #Credit Approval data available at UCI Machine Learning Repository data(Credit) #take subset (for the purpose of a quick example) and train and test Credit <- Credit[1:100,] train.ind <- sample(nrow(Credit),round(0.5*nrow(Credit))) #Train Kernel Factory on training data kFmodel <- kernelFactory(x=Credit[train.ind,names(Credit)!= "Response"], y=Credit[train.ind,"Response"], method=random) #Deploy Kernel Factory to predict response for test data #predictedresponse <- predict(kFmodel, newdata=Credit[-train.ind,names(Credit)!= "Response"]) } \author{ Authors: Michel Ballings and Dirk Van den Poel, Maintainer: \email{Michel.Ballings@GMail.com} } \references{ Ballings, M. and Van den Poel, D. (2013), Kernel Factory: An Ensemble of Kernel Machines. Expert Systems With Applications, 40(8), 2904-2913. } \seealso{ \code{\link{predict.kernelFactory}} } \keyword{classification}

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data(geyser, package = "MASS") data = read.table("i0car.txt", header=TRUE, quote="\"") data[1:2,] library(crayon) cat(red$bold$bgGreen("mode(data) is ")) mode(data) cat(blue$bold$bgGreen("names(data) is ")) names(data) cat(yellow$bold$bgGreen("dim(data) is ")) dim(data) cat(red$bold$bgGreen("data$lp100km is ")) data$lp100km

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library(ape) testtree <- read.tree("13157_0.txt") unrooted_tr <- unroot(testtree) write.tree(unrooted_tr, file="13157_0_unrooted.txt")

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require(shiny) require(shinyWidgets) require(shinyBS) require(shinyjs) #required libraries require(markdown) require(DT) require(dplyr) require(Gviz) require(Homo.sapiens) require(OrganismDbi) require(stringr) require(condformat) require(shinyjs) require(bedr) require(rlist) require(Rsamtools) require(TxDb.Hsapiens.UCSC.hg19.knownGene) require(TxDb.Hsapiens.UCSC.hg38.knownGene) #require(BSgenome.Hsapiens.UCSC.hg19) #require(BSgenome.Hsapiens.UCSC.hg38) require(EnsDb.Hsapiens.v75) require(EnsDb.Hsapiens.v86) require(org.Hs.eg.db)

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plot1.R

# Martin Bontrager # Exploratory Data Analysis # Project 1 - Plot1 library(data.table) # Read power consumption data and subset to only two days: 1-2 Feb 2007 f <- "data/household_power_consumption.txt" DT <- fread(f, sep = ";", na.strings = "?", stringsAsFactors = FALSE, header = TRUE) DT$Date <- as.Date(DT$Date,format = "%d/%m/%Y") date1 <- as.Date("2007-02-01") date2 <- as.Date("2007-02-02") DT2 <- subset(DT, subset = (DT$Date >= date1 & DT$Date <= date2)) # Generate a histogram of Global Active Power for these dates png(filename = "plot1.png", width = 480, height = 480) DT2$Global_active_power <- as.numeric(DT2$Global_active_power) hist(DT2$Global_active_power, main = "Global Active Power", col = "red", xlab = "Global Active Power (kilowatts)") dev.off()

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TPL_QUERY.R

require(tpl) require(tpldata) t<-read.table("clipboard",header=F,sep="\t") tpl2<-tpl.get(t[,1]) write.table(tpl2,"D:/CWR/TPL_GBIF_9743.csv",sep="^",quote = F,row.names = F)

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ConvolutionalNeuralNetwork.R

####################################################################### ## Project: Image Processing Methods for Environment Classification ## Script purpose: Analyze and classify photos via Convolutional Neural Network ## Date: 2020-03-28 ## Author: Rahul Sharma ####################################################################### library(keras) library(EBImage) # Read metadata pm <- read.csv("C:\\Users\\Rahul\\Downloads\\photoMetaData.csv") y <- as.numeric(pm$category == "outdoor-day") # Read Images pics <- list() for (i in 1:800){ pics[[i]] <- readImage(paste0("C:\\Users\\Rahul\\Downloads\\columbiaImages\\", pm$name[i])) pics[[i]] <- resize(pics[[i]], 128, 128) } # Split indexes such that 80% of the data goes towards training, and remaining pictures for test train_index <- sample(1:length(pics), 0.8 * length(pics)) test_index <- setdiff(1:length(pics), train_index) # Split our picture data into test/train sets x.train <- pics[c(train_index)] x.test <- pics[c(test_index)] x.train <- combine(x.train) x.test <- combine(x.test) # Reorder dimensions x.train <- aperm(x.train, c(4, 1, 2, 3)) x.test <- aperm(x.test, c(4, 1, 2, 3)) # Create our reponse variables cat.train <- to_categorical(as.numeric(pm$category == "outdoor-day")[c(train_index)]) cat.test <- to_categorical(as.numeric(pm$category == "outdoor-day")[c(test_index)]) #Model model <- keras_model_sequential() %>% layer_conv_2d(filters = 16, kernel_size = c(3,3), activation = 'relu', input_shape = c(128,128,3)) %>% layer_conv_2d(filters = 16, kernel_size = c(3,3), activation = 'relu') %>% layer_max_pooling_2d(pool_size = c(2, 2)) %>% layer_conv_2d(filters = 32, kernel_size = c(3,3), activation = 'relu') %>% layer_conv_2d(filters = 32, kernel_size = c(3,3), activation = 'relu') %>% layer_max_pooling_2d(pool_size = c(2, 2)) %>% layer_dropout(rate = 0.25) %>% layer_flatten() %>% layer_dense(units = 10, activation = 'relu') %>% layer_dropout(rate = 0.5) %>% layer_dense(units = 2, activation = 'softmax') model %>% compile( loss = loss_categorical_crossentropy, optimizer = optimizer_adadelta(), metrics = c('accuracy') ) batch_size <- 64 epochs <- 16 # Train model model %>% fit( x.train, cat.train, batch_size = batch_size, epochs = epochs, validation_split = 0.2 ) # Determine our accuracy on test data score <- model %>% evaluate(x.test,cat.test) cat('Test loss: ', score$loss, "\n") cat('Test accuracy: ', score$acc, "\n")

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/plot1.R

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MridullS/exploratory_data_analysis

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plot1.R

library(dplyr) plot1 <- function() { file_read <- read.csv2('household_power_consumption.txt', dec='.', na.strings='?', stringsAsFactors=FALSE) start <- ymd('2007-02-01') end <- ymd('2007-02-03') file_read <- file_read %>% mutate(DateTime=dmy_hms(paste(Date, Time))) %>% select(DateTime, Global_active_power:Sub_metering_3) %>% filter(DateTime >= start, DateTime < end) png(filename='plot1.png', width=480, height=480, units='px') with(file_read, hist(file_read$Global_active_power, col='red', xlab='Global Active Power (kilowatts)', main='Global Active Power')) dev.off() }

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/SAL_BMU_Amazon/13-CRU_functions.R

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CIAT-DAPA/dapa-climate-change

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13-CRU_functions.R

require(raster) require(ncdf) require(rgdal) # require(ncdf4) # source("13-CRU_functions.R") CRU_cut <- function(baseDir="T:/gcm/cmip5/isi_mip", region=extent(-80, -66, -16, 5), outDir="Z:/DATA/WP2/03_Future_data/isi_mip_ft_0_5deg") { setwd(baseDir) if (!file.exists(outDir)) {dir.create(outDir, recursive = TRUE)} varList <- list("pre"="prec", "tmx"="tmax", "tmn"="tmin", "dtr"="dtr", "tmn"="tmean") for (v in 1:length(varList)){ if(!file.exists(paste0(outDir, "/", varList[[v]], "_1976_2005_climatology.nc"))){ nc <- paste0(baseDir, "/", names(varList[v]), "/cru_ts3.21.1901.2012.", names(varList[v]), ".dat.nc") oNc <- paste0(outDir, "/", varList[v], "_1961_2010_monthly.nc") ## Cut region and years cat("Cut region ", varList[[v]], "\n") system(paste("cdo -sellonlatbox,",region@xmin,",",region@xmax,",",region@ymin,",",region@ymax," -selyear,1961/2010 ", nc, " ", oNc, sep="")) ## Multiyear mean calcs cat("Clim calcs ", varList[[v]], "\n") system(paste("cdo -ymonavg -selyear,1986/2005 ", oNc, " ", outDir, "/", varList[[v]], "_1986_2005_climatology.nc", sep="")) system(paste("cdo -ymonavg -selyear,1971/2000 ", oNc, " ", outDir, "/", varList[[v]], "_1971_2000_climatology.nc", sep="")) system(paste("cdo -ymonavg -selyear,1976/2005 ", oNc, " ", outDir, "/", varList[[v]], "_1976_2005_climatology.nc", sep="")) } } } ## Cut CRU-TS 3.21 raw baseDir <- "S:/observed/gridded_products/cru-ts-v3-21/raw" region <- extent(-80, -66, -16, 5) outDir <- "Z:/DATA/WP2/02_Gridded_data/cru_0_5deg" otp <- CRU_cut(baseDir, region, outDir)

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RomeroBarata/IN1165-MCS-EL3

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ds-functions.R

predict.bagging_ola <- function(object, newdata, valdata, ...){ names(valdata)[ncol(valdata)] <- "Class" val_preds <- predict.bagging(object, valdata[, -ncol(valdata)]) knn_idx <- kknn::kknn(Class ~ ., train = valdata, test = newdata, k = 5, kernel = "rectangular")$C final_preds <- vector("logical", length = nrow(newdata)) for (i in seq_len(nrow(newdata))){ nns_idx <- knn_idx[i, ] nns_preds <- val_preds[nns_idx, ] nns_class <- unlist(valdata[nns_idx, "Class"], use.names = FALSE) overall_acc <- colMeans(nns_preds == nns_class) best_classifier <- which.max(overall_acc) final_preds[i] <- predict(object[[best_classifier]], newdata[i, ], type = "class") } levels(unlist(valdata[, ncol(valdata)], use.names = FALSE))[final_preds] } predict.bagging_lca <- function(object, newdata, valdata, ...){ names(valdata)[ncol(valdata)] <- "Class" val_preds <- predict.bagging(object, valdata[, -ncol(valdata)]) test_preds <- predict.bagging(object, newdata) knn_idx <- kknn::kknn(Class ~ ., train = valdata, test = newdata, k = 5, kernel = "rectangular")$C final_preds <- vector("logical", length = nrow(newdata)) for (i in seq_len(nrow(newdata))){ nns_idx <- knn_idx[i, ] nns_preds <- val_preds[nns_idx, ] nns_class <- unlist(valdata[nns_idx, "Class"], use.names = FALSE) f <- function(j){ current_pred <- test_preds[i, j] same_class <- current_pred == nns_class if (!any(same_class)) return(0) mean(nns_preds[same_class, j] == nns_class[same_class]) } local_acc <- vapply(seq_len(ncol(test_preds)), f, numeric(1)) best_classifier <- which.max(local_acc) final_preds[i] <- predict(object[[best_classifier]], newdata[i, ], type = "class") } levels(unlist(valdata[, ncol(valdata)], use.names = FALSE))[final_preds] } predict.bagging_dsknn <- function(object, newdata, valdata, ...){ names(valdata)[ncol(valdata)] <- "Class" val_preds <- predict.bagging(object, valdata[, -ncol(valdata)]) test_preds <- predict.bagging(object, newdata) knn_idx <- kknn::kknn(Class ~ ., train = valdata, test = newdata, k = 5, kernel = "rectangular")$C final_preds <- vector("logical", length = nrow(newdata)) for (i in seq_len(nrow(newdata))){ nns_idx <- knn_idx[i, ] nns_preds <- val_preds[nns_idx, ] nns_class <- unlist(valdata[nns_idx, "Class"], use.names = FALSE) f <- function(j){ current_pred <- test_preds[i, j] same_class <- current_pred == nns_class if (!any(same_class)) return(0) mean(nns_preds[same_class, j] == nns_class[same_class]) } local_acc <- vapply(seq_len(ncol(test_preds)), f, numeric(1)) # Highest accuracies best_classifiers <- order(local_acc, decreasing = TRUE)[1:5] # diversity_matrix <- diversityMatrix(val_preds[nns_idx, best_classifiers], valdata[nns_idx, ncol(valdata)], "doubleFault") best_idx <- order(colMeans(diversity_matrix))[1:3] best_classifiers <- best_classifiers[best_idx] preds <- predict.bagging(object[best_classifiers], newdata[i, ]) final_preds[i] <- names(which.max(table(preds))) } final_preds }

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/app2020/LakeAssessmentApp_v1/buildAppModules/2018IRversions/buildStationTable.R

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EmmaVJones/LakesAssessment2020

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buildStationTable.R

lake_filter <- filter(lakeStations, SIGLAKENAME == 'Claytor Lake') conventionals_Lake <- filter(conventionals, FDT_STA_ID %in% unique(lake_filter$FDT_STA_ID)) %>% left_join(dplyr::select(lakeStations, FDT_STA_ID, SEC, CLASS, SPSTDS,PWS, ID305B_1, ID305B_2, ID305B_3, STATION_TYPE_1, STATION_TYPE_2, STATION_TYPE_3, ID305B, SEC187, SIG_LAKE, USE, SIGLAKENAME, Chlorophyll_A_limit, TPhosphorus_limit, Assess_TYPE), by='FDT_STA_ID') AUData <- filter(conventionals_Lake, ID305B_1 %in% "VAW-N16L_NEW01A02" | #"VAW-N16L_NEW01A02" "VAW-N16L_NEW01B14" "VAW-N17L_PKC01A10" "VAW-N17L_PKC02A10" ID305B_2 %in% "VAW-N16L_NEW01A02" | ID305B_2 %in% "VAW-N16L_NEW01A02") %>% left_join(WQSvalues, by = 'CLASS') stationData <- filter(AUData, FDT_STA_ID %in% "9-NEW087.14") #"9-NEW087.14" "9-NEW089.34" point <- dplyr::select(stationData[1,], FDT_STA_ID:FDT_SPG_CODE, STA_LV2_CODE:ID305B_3, Latitude, Longitude ) %>% st_as_sf(coords = c("Longitude", "Latitude"), remove = F, # don't remove these lat/lon cols from df crs = 4269) # add projection, needs to be geographic for now bc entering lat/lng AU <- filter(lakeAU, ID305B %in% as.character(point$ID305B_1) | ID305B %in% as.character(point$ID305B_2) | ID305B %in% as.character(point$ID305B_3)) map1 <- mapview(AU,zcol = 'ID305B', label= AU$ID305B, layer.name = 'Assessment Unit (ID305B_1)', popup= popupTable(AU, zcol=c("ID305B","Acres","CYCLE","WATER_NAME"))) + mapview(point, color = 'yellow', lwd = 5, label= point$FDT_STA_ID, layer.name = c('Selected Station'), popup= popupTable(point, zcol=c("FDT_STA_ID","STA_DESC","ID305B_1", "ID305B_2", "ID305B_3"))) map1@map

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/tests/testthat/tests.R

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CharlesNaylor/walker

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tests.R

context("Test walker") test_that("arguments work as intended", { library(walker) expect_error(walker("aa")) expect_error(walker(rnorm(2) ~ 1:4)) expect_error(walker(rnorm(10) ~ 1)) expect_error(walker(y ~ 1)) expect_error(walker(rnorm(10) ~ 1, beta_prior = 0)) x <- 1:3 expect_identical(c(1,1,1,1:3), c(walker(1:3 ~ x, return_x_reg = TRUE))) }) test_that("stan side works", { y <- x <- 1:3 set.seed(1) expect_warning(fit <- walker(y ~ x, beta_prior = cbind(0, c(2, 2)), sigma_prior = cbind(0, c(2,2,2)), iter = 10, chains = 1, refresh = 0),NA) expect_equivalent(structure(c(0.575776440370937, 0.608739297869922, 0.600646410430753, 2.47394156830475, 0.503598122307422), .Dim = 5L, .Dimnames = structure(list( iterations = NULL), .Names = "iterations")), extract(fit, pars = "sigma_y")$sigma_y) set.seed(1) expect_warning(fit <- walker(y ~ x, naive = TRUE, beta_prior = cbind(0, c(2, 2)), sigma_prior = cbind(0, c(2,2,2)), iter = 10, chains = 1, refresh = 0),NA) expect_equivalent(structure(c(1.27535032198269, 1.27535032198269, 1.27535032198269, 1.07187289906723, 1.24949754280559), .Dim = 5L, .Dimnames = structure(list( iterations = NULL), .Names = "iterations")), extract(fit, pars = "sigma_y")$sigma_y) })

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lvjensen/PhysicsEdCoalition

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ByState.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ByState.R \name{ByState} \alias{ByState} \title{ByState} \usage{ ByState(Year, with_summary = TRUE) } \arguments{ \item{Year}{is digit numeric number of the year} \item{with_summary}{defaults to TRUE. Adds a summary (total) row and the bottom of the table.} } \description{ Creates an table of wrangled data from the Physics Teacher Education Coalition websites organized by State }

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/15-RStudio-essentials/2-Debugging/palindrome.R

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palindrome.R

# Extract nth (from left) digit of a number get_digit <- function(num, n) { # remove numbers on left, then numbers on right (num %% (10 ^ n)) %/% (10 ^ n) } # Indicate whether a positive number is a palindrome palindrome <- function(num) { digits <- floor(log(num, 10)) + 1 for (x in 1:((digits %/% 2))) { digit1 <- get_digit(num, x) digit2 <- get_digit(num, (digits + 1) - x) if (digit1 != digit2) return(FALSE) } return(TRUE) } # Find the largest palindrome that is the product of two 3-digit numbers biggest_palindrome <- function() { best <- 0 for (x in 100:999) { for (y in x:999) { candidate <- x * y if (candidate > best && palindrome(candidate)) { best <- candidate } } } best }

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/Scripts/MMM2019_Finalists.R

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trcsmallwood/MMM2019

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MMM2019_Finalists.R

##MMM2019_Finalists ##Calculate the proportion of brackets that pick each species as finalists and champions #Load in packages library(stringr) library(ggplot2) #Read in dataframe of predictions and results MMM_df <- read.csv("../Submissions/MMM2019_PredictionsSummary.csv", stringsAsFactors = F) #Extract finalists finalists_df <- data.frame(table(unlist(MMM_df[which(MMM_df$Round == "Semi Final"),-c(1:5)]))) names(finalists_df) <- c("Species", "Finalist") #Extract champions champions_df <- data.frame(table(unlist(MMM_df[which(MMM_df$Round == "Final"),-c(1:5)]))) names(champions_df) <- c("Species", "Champion") #Merge dataframes including all species combined_df <- merge(finalists_df, champions_df, by = "Species", all = T) #Assign zeros for finalists who don't become champions combined_df[which(is.na(combined_df$Champion) == T),3] <- 0 #Calculate Runners Up combined_df$RunnerUp <- combined_df$Finalist - combined_df$Champion #Calculate proportion of brackets which predicted each finalist, champion and runner up combined_prop_df <- data.frame("Species" = combined_df[,1], combined_df[,c(2:4)]/ apply(combined_df[,c(2:4)],2, sum)) #Reshape for plotting, including only champion and runner up columns combined_plot_df <- melt(combined_df[,-2], id.vars = "Species", variable.name = "Position", value.name = "Count") #Create output .pdf in relative dir pdf(file = "../Plots/MMM2019_Finalists.pdf", width=11.69, height=8.27) #Plot proportion of participants who predicted each species to be a champion or runner up ggplot(combined_plot_df, aes(x = Species, y = Count, fill = Position)) + geom_col(position = position_stack(reverse = T)) + theme_bw() + #Simple balck and white base theme #coord_flip() + theme(axis.ticks.length = unit(-0.2, "cm"), #Ticks marks inside axis.ticks.x = element_blank(), #No ticks on x axis axis.text.x = element_text(size = 8, margin=margin(10,10,0,10,"pt"), angle = 45, hjust =1), #x axis text size and spacing axis.text.y = element_text(size = 12, margin=margin(10,10,10,10,"pt")), #y axis text size and spacing panel.border = element_blank(), #No border axis.line.x = element_line(size = 0.5, color = "black"), axis.line.y = element_line(size = 0.5, color = "black"), #Axes colours and thickness axis.title.x = element_text(size = 14, margin=margin(0,0,0,0,"pt")), axis.title.y = element_text(size = 14, margin=margin(5,5,5,5,"pt")), #Axis titles size and space=ing panel.grid.major = element_blank(), panel.grid.minor = element_blank(), #No grid lines legend.position = "bottom", #Legend postion plot.margin = unit(c(0.5,0.2,0.1,0.1), "cm"), #Space around the outside, including space for the ends of the axes legend.title = element_text(size = 14), legend.text = element_text(size = 12)) + #Legend title and text size scale_y_continuous(name = "Number of Brackets", expand = c(0,0), breaks = c(0,5,10)) + #y axis title and limits scale_x_discrete("", labels = str_wrap(combined_prop_plot_df$Species, width = 20)) + #x axis title and include all rounds, not just those with scores scale_fill_manual("", values = c("gold", "grey70"), labels= c("Champion", "Runner Up")) + #Legend title and colour by preferred colours NULL #Close .pdf dev.off()

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Sarpwus/ExData_Plotting1

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## Load the dataset from the source directory ## assume we are the location of the file in the working directory library(sqldf) # I use sqldf package for reading the data source("load_hsepwr.R") png(file = "plot3.png", width = 480, height = 480, units = "px", bg = "transparent") with(hsepwr, plot(DateTime, Sub_metering_1, type = "l", col = "black", xlab = "", ylab = "Energy sub metering")) with(hsepwr, lines(DateTime, Sub_metering_2, col = "red")) with(hsepwr, lines(DateTime, Sub_metering_3, col = "blue")) legend("topright", col = c("black", "red", "blue"), c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), lwd = 1) dev.off()

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write_mallet_state.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/sampling_state.R \name{write_mallet_state} \alias{write_mallet_state} \title{Save the Gibbs sampling state to a file} \usage{ write_mallet_state(m, outfile = "state.gz") } \arguments{ \item{m}{the \code{mallet_model} model object} \item{outfile}{the output file name} } \description{ Saves the MALLET sampling state using MALLET's own state-output routine, which produces a ginormous gzipped textfile. } \seealso{ \code{\link{read_sampling_state}} }

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akhikolla/updated-only-Issues

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2023-04-13T08:22:15.699449

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1609959260-test.R

testlist <- list(x = structure(c(1.61100627174858e+126, NaN, 1.44202388027275e+135 ), .Dim = c(3L, 1L))) result <- do.call(bravo:::colSumSq_matrix,testlist) str(result)

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/CollectDealerInfor/ChevroletDealersLinks.R

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jpzhangvincent/Dealership-Scraping

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refs/heads/master

2021-01-12T14:25:19.400164

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r

ChevroletDealersLinks.R

#Collect all the nation-wide Chevrolet dealerships with information about name, address, website, inventory link and geo location install.packages("XML", repos = "http://cran.cnr.Berkeley.edu/") library(XML) install.packages("plyr", repos = "http://cran.cnr.Berkeley.edu/") library(plyr) load("zipdata.rdata") cities = unique(zipdata$city) #length(cities) cities = gsub(' ','',cities) #eg. url = "http://www.chevydealer.com/SanDiego/dealers" allsearchpages = unname(sapply(cities, function(city_str) paste0("http://www.chevydealer.com/", city_str,"/dealers"))) getdealerinfo<- function(url){ # print(url) tryCatch({ doc = htmlParse(url) dealerNameNodes = getNodeSet(doc,'//div[@class="dealer-name-and-address"]/a[@class="dealer-name"]/span/text()') dealerName = xmlSApply(dealerNameNodes,xmlValue,trim=T) roadNodes = getNodeSet(doc,'//div[@class="dealer-name-and-address"]/div[1]/text()') roadName = xmlSApply(roadNodes,xmlValue,trim=T)[-1] cityNodes = getNodeSet(doc,'//div[@class="cityStateZip"]/span[1]/text()') cityName = xmlSApply(cityNodes,xmlValue,trim=T) stateNodes = getNodeSet(doc,'//div[@class="cityStateZip"]/span[2]/text()') stateName = xmlSApply(stateNodes,xmlValue,trim=T) zipcodeNodes = getNodeSet(doc,'//div[@class="cityStateZip"]/span[3]/text()') zipcode = xmlSApply(zipcodeNodes,xmlValue,trim=T) dealerAddress = paste0(roadName,', ',cityName,', ',stateName,' ',zipcode) dealerWebsiteNodes = getNodeSet(doc,"//div[@class='dealer-name-and-address']/a") dealerWebsite = unlist(lapply(xmlSApply(dealerWebsiteNodes,xmlGetAttr,"href")[-c(1,2)],gsub,patter='(.*/).*',replacement='\\1')) dealerIVwebsiteNodes = getNodeSet(doc,"//a[contains(./text(),'View Inventory')]") dealerIVWebsite = unlist(lapply(xmlSApply(dealerIVwebsiteNodes,xmlGetAttr,"href"),gsub,patter='(.*)referrer.*',replacement='\\1')) dealerInventoryLink = paste0(dealerIVWebsite,'search=new') GeoNodes = getNodeSet(doc,'//div[@class="dealer-listing-item"]') Latitude = xmlSApply(GeoNodes,xmlGetAttr,"data-latitude") Longitude = xmlSApply(GeoNodes,xmlGetAttr,"data-longitude") df <- data.frame(dealerName, dealerAddress, dealerWebsite, zipcode, dealerInventoryLink, Latitude, Longitude, stringsAsFactors=F ) colnames(df) = c("Dealer","Address","Link","zipcode","IV_link", "Latitude", "Longitude") return(df) }, error = function(err){ return() }) } #url2 = "http://www.chevydealer.com/NewYork/dealers" chevroletDealers = ldply(allsearchpages, function(url){ out = try(getdealerinfo(url)) if(class(out)=='try-error') next; return(out) }, .progress = "text" ) ChevroletDealers = chevroletDealers[!duplicated(chevroletDealers$Dealer),] ChevroletDealers = merge(ChevroletDealers, zipdata) save(ChevroletDealers, file="chevroletDealers.rdata") head(ChevroletDealers) =============================#further cleaning require(RSelenium) RSelenium::startServer() remDr = remoteDriver(browserName = "firefox") remDr$open(silent = TRUE) getDealerInforInRS = function(doc){ doc = htmlParse(doc) dealerNameNodes = getNodeSet(doc,'//div[@class="dealer-name-and-address"]/a[@class="dealer-name"]/span/text()') dealerName = xmlSApply(dealerNameNodes,xmlValue,trim=T) roadNodes = getNodeSet(doc,'//div[@class="dealer-name-and-address"]/div[1]/text()') roadName = xmlSApply(roadNodes,xmlValue,trim=T)[-c(1,2,3,4)] cityNodes = getNodeSet(doc,'//div[@class="cityStateZip"]/span[1]/text()') cityName = xmlSApply(cityNodes,xmlValue,trim=T) stateNodes = getNodeSet(doc,'//div[@class="cityStateZip"]/span[2]/text()') stateName = xmlSApply(stateNodes,xmlValue,trim=T) zipcodeNodes = getNodeSet(doc,'//div[@class="cityStateZip"]/span[3]/text()') zipcode = xmlSApply(zipcodeNodes,xmlValue,trim=T) dealerAddress = paste0(roadName,', ',cityName,', ',stateName,' ',zipcode) dealerWebsiteNodes = getNodeSet(doc,"//div[@class='dealer-name-and-address']/a") dealerWebsite = unlist(lapply(xmlSApply(dealerWebsiteNodes,xmlGetAttr,"href")[-c(1,2)],gsub,patter='(.*/).*',replacement='\\1')) dealerIVwebsiteNodes = getNodeSet(doc,"//a[contains(./text(),'View Inventory')]") dealerIVWebsite = unlist(lapply(xmlSApply(dealerIVwebsiteNodes,xmlGetAttr,"href"),gsub,patter='(.*)referrer.*',replacement='\\1')) dealerInventoryLink = paste0(dealerIVWebsite,'search=new') GeoNodes = getNodeSet(doc,'//div[@class="dealer-listing-item"]') Latitude = xmlSApply(GeoNodes,xmlGetAttr,"data-latitude") Longitude = xmlSApply(GeoNodes,xmlGetAttr,"data-longitude") df <- data.frame(dealerName, dealerAddress, dealerWebsite, zipcode, dealerInventoryLink, Latitude, Longitude, stringsAsFactors=F ) colnames(df) = c("Dealer","Address","Link","zipcode","IV_link", "Latitude", "Longitude") return(df) } remDr$navigate(url) ndataset = list() i=0 for(zip in aa){ i = i+1 print(zip) webElem <- remDr$findElement(using = 'id',value="zip") webElem$sendKeysToElement(list(zip,"\uE007")) doc <- remDr$getPageSource()[[1]] ndataset[[i]] = getDealerInforInRS(doc) webElem$clearElement() }

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/data/genthat_extracted_code/libamtrack/examples/AT.SPC.spectrum.at.depth.step.Rd.R

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surayaaramli/typeRrh

d257ac8905c49123f4ccd4e377ee3dfc84d1636c

66e6996f31961bc8b9aafe1a6a6098327b66bf71

refs/heads/master

2023-05-05T04:05:31.617869

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r

AT.SPC.spectrum.at.depth.step.Rd.R

library(libamtrack) ### Name: AT.SPC.spectrum.at.depth.step ### Title: AT.SPC.spectrum.at.depth.step ### Aliases: AT.SPC.spectrum.at.depth.step ### ** Examples # None yet.

88218dc79671a33dfc71ed2aa27acb818eef3e27

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/man/Specmodule.Rd

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JasonBason/Mosaic

f6fffa851155796d3f3b6838aba11b836130567c

5d8e487efbce2e0e95a58ef9b9c4766b45960c57

refs/heads/master

2020-03-22T20:38:07.682386

2018-06-01T03:35:12

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rd

Specmodule.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/interactive_plotting_modules.R \name{Specmodule} \alias{Specmodule} \title{Specmodule} \usage{ Specmodule(input, output, session, tag, set = list(spec = list(xrange = NULL, yrange = NULL, maxxrange = NULL, maxyrange = NULL, sel = NULL, mz = NULL, data = NULL, MS2 = T), layout = list(lw = 1, cex = 1, controls = F, ppm = 5, active = T, highlights = NULL, height = 550), msdata = NULL), keys) } \arguments{ \item{input}{} \item{output}{} \item{session}{} \item{tag}{id to be used in ns()} \item{set}{Import data from the shiny session} } \description{ server module for interactive mass spectrum view }

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/analyses/snv-callers/scripts/01-calculate_vaf_tmb.R

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[]

no_license

gonzolgarcia/OpenPBTA-analysis

da0118d5edfba585786e297d4d312245ab3643f1

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refs/heads/master

2020-08-07T12:38:00.508549

2019-10-07T15:51:30

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01-calculate_vaf_tmb.R

# Run variant caller evaluation for a given MAF file. # # C. Savonen for ALSF - CCDL # # 2019 # # Option descriptions # # -label : Label to be used for folder and all output. eg. 'strelka2'. Default is 'maf'. # -output : File path that specifies the folder where the output should go. # New folder will be created if it doesn't exist. Assumes file path is # given from top directory of 'OpenPBTA-analysis'. # --maf : Relative file path to MAF file to be analyzed. Can be .gz compressed. # Assumes file path is given from top directory of 'OpenPBTA-analysis'. # --metadata : Relative file path to MAF file to be analyzed. Can be .gz compressed. # Assumes file path is given from top directory of 'OpenPBTA-analysis'. # --annot_rds : Relative file path to annotation object RDS file to be analyzed. # Assumes file path is given from top directory of 'OpenPBTA-analysis'. # --bed_wgs : File path that specifies the caller-specific BED regions file. # Assumes from top directory, 'OpenPBTA-analysis'. # --bed_wxs : File path that specifies the WXS BED regions file. Assumes file path # is given from top directory of 'OpenPBTA-analysis' # --overwrite : If specified, will overwrite any files of the same name. Default is FALSE. # # Command line example: # # Rscript analyses/snv-callers/scripts/01-calculate_vaf_tmb.R \ # --label strelka2 \ # --output analyses/snv-callers/results \ # --maf scratch/snv_dummy_data/strelka2 \ # --metadata data/pbta-histologies.tsv \ # --bed_wgs data/WGS.hg38.mutect2.unpadded.bed \ # --bed_wxs data/WXS.hg38.100bp_padded.bed \ # --annot_rds scratch/hg38_genomic_region_annotation.rds ################################ Initial Set Up ################################ # Establish base dir root_dir <- rprojroot::find_root(rprojroot::has_dir(".git")) # Import special functions source(file.path(root_dir, "analyses", "snv-callers", "util", "wrangle_functions.R")) # Magrittr pipe `%>%` <- dplyr::`%>%` # Load library: library(optparse) ################################ Set up options ################################ # Set up optparse options option_list <- list( make_option( opt_str = c("-l", "--label"), type = "character", default = "maf", help = "Label to be used for folder and all output. eg. 'strelka2'. Default is 'maf'", metavar = "character" ), make_option( opt_str = c("-o", "--output"), type = "character", default = "none", help = "File path that specifies the folder where the output should go. Assumes from top directory, 'OpenPBTA-analysis'. New folder will be created if it doesn't exist.", metavar = "character" ), make_option( opt_str = "--maf", type = "character", default = "none", help = "Relative file path (assuming from top directory of 'OpenPBTA-analysis') to MAF file to be analyzed. Can be .gz compressed.", metavar = "character" ), make_option( opt_str = "--metadata", type = "character", default = "none", help = "Relative file path (assuming from top directory of 'OpenPBTA-analysis') to MAF file to be analyzed. Can be .gz compressed.", metavar = "character" ), make_option( opt_str = c("-a", "--annot_rds"), type = "character", default = "none", help = "Relative file path (assuming from top directory of 'OpenPBTA-analysis') to annotation object RDS file to be analyzed.", metavar = "character" ), make_option( opt_str = "--bed_wgs", type = "character", default = "none", help = "File path that specifies the caller-specific BED regions file. Assumes from top directory, 'OpenPBTA-analysis'", metavar = "character" ), make_option( opt_str = "--bed_wxs", type = "character", default = "none", help = "File path that specifies the WXS BED regions file. Assumes from top directory, 'OpenPBTA-analysis'", metavar = "character" ), make_option( opt_str = "--overwrite", action = "store_true", default = FALSE, help = "If TRUE, will overwrite any files of the same name. Default is FALSE", metavar = "character" ) ) # Parse options opt <- parse_args(OptionParser(option_list = option_list)) ########### Check that the files we need are in the paths specified ############ needed_files <- c(opt$maf, opt$metadata, opt$bed_wgs, opt$bed_wxs, opt$annot_rds, opt$cosmic) # Add root directory to the file paths needed_files <- file.path(root_dir, needed_files) # Get list of which files were found files_found <- file.exists(needed_files) # Report error if any of them aren't found if (!all(files_found)) { stop(paste("\n Could not find needed file(s):", needed_files[which(!files_found)], "Check your options and set up.", sep = "\n" )) } ################## Create output directories for this caller ################## # Caller specific results directory path caller_results_dir <- file.path(root_dir, opt$output) # Make caller specific results folder if (!dir.exists(caller_results_dir)) { dir.create(caller_results_dir, recursive = TRUE) } ####################### File paths for files we will create #################### vaf_file <- file.path(caller_results_dir, paste0(opt$label, "_vaf.tsv")) region_annot_file <- file.path(caller_results_dir, paste0(opt$label, "_region.tsv")) tmb_file <- file.path(caller_results_dir, paste0(opt$label, "_tmb.tsv")) # Declare metadata file name for this caller metadata_file <- file.path( caller_results_dir, paste0(opt$label, "_metadata_filtered.tsv") ) ##################### Check for files if overwrite is FALSE #################### # If overwrite is set to FALSE, check if these exist before continuing if (!opt$overwrite) { # Make a list of the output files output_files <- c(vaf_file, region_annot_file, tmb_file) # Find out which of these exist existing_files <- file.exists(output_files) # If all files exist; stop if (all(existing_files)) { stop(cat( "Stopping; --overwrite is not being used and all output files already exist: \n", vaf_file, "\n", region_annot_file, "\n", tmb_file )) } # If some files exist, print a warning: if (any(existing_files)) { warning(cat( "Some output files already exist and will not be overwritten unless you use --overwrite: \n", paste0(output_files[which(existing_files)], "\n") )) } } ########################### Set up this caller's data ########################## # Print progress message message(paste("Reading in", opt$maf, "MAF data...")) # Read in this MAF, skip the version number maf_df <- data.table::fread(opt$maf, skip = 1, data.table = FALSE) # Print progress message message(paste("Setting up", opt$label, "metadata...")) # Isolate metadata to only the samples that are in the datasets metadata <- readr::read_tsv(opt$metadata) %>% dplyr::filter(Kids_First_Biospecimen_ID %in% maf_df$Tumor_Sample_Barcode) %>% dplyr::distinct(Kids_First_Biospecimen_ID, .keep_all = TRUE) %>% dplyr::arrange() %>% dplyr::rename(Tumor_Sample_Barcode = Kids_First_Biospecimen_ID) %>% readr::write_tsv(metadata_file) # Print out completion message message(paste("Filtered metadata file saved to: \n", metadata_file)) # Make sure that we have metadata for all these samples. if (!all(unique(maf_df$Tumor_Sample_Barcode) %in% metadata$Tumor_Sample_Barcode)) { stop("There are samples in this MAF file that are not in the metadata.") } ################## Calculate VAF and set up other variables #################### # If the file exists or the overwrite option is not being used, calculate VAF if (file.exists(vaf_file) && !opt$overwrite) { # Stop if this file exists and overwrite is set to FALSE warning(cat( "The VAF file already exists: \n", vaf_file, "\n", "Use --overwrite if you want to overwrite it." )) } else { # Print out warning if this file is going to be overwritten if (file.exists(vaf_file)) { warning("Overwriting existing VAF file.") } # Print out progress message message(paste("Calculating VAF for", opt$label, "MAF data...")) # Use the premade function to calculate VAF this will also merge the metadata vaf_df <- set_up_maf(maf_df, metadata) %>% readr::write_tsv(vaf_file) # Print out completion message message(paste("VAF calculations saved to: \n", vaf_file)) } ######################### Annotate genomic regions ############################# # If the file exists or the overwrite option is not being used, run regional annotation analysis if (file.exists(region_annot_file) && !opt$overwrite) { # Stop if this file exists and overwrite is set to FALSE warning(cat( "The regional annotation file already exists: \n", region_annot_file, "\n", "Use --overwrite if you want to overwrite it." )) } else { # Print out warning if this file is going to be overwritten if (file.exists(vaf_file)) { warning("Overwriting existing regional annotation file.") } # Print out progress message message(paste("Annotating genomic regions for", opt$label, "MAF data...")) # Annotation genomic regions maf_annot <- annotr_maf(vaf_df, annotation_file = opt$annot_rds) %>% readr::write_tsv(region_annot_file) # Print out completion message message(paste("Genomic region annotations saved to:", region_annot_file)) } ############################# Calculate TMB #################################### # If the file exists or the overwrite option is not being used, run TMB calculations if (file.exists(region_annot_file) && !opt$overwrite) { # Stop if this file exists and overwrite is set to FALSE warning(cat( "The Tumor Mutation Burden file already exists: \n", tmb_file, "\n", "Use --overwrite if you want to overwrite it." )) } else { # Print out warning if this file is going to be overwritten if (file.exists(vaf_file)) { warning("Overwriting existing TMB file.") } # Print out progress message message(paste("Calculating TMB for", opt$label, "MAF data...")) # Set up BED region files for TMB calculations wgs_bed <- readr::read_tsv(opt$bed_wgs, col_names = FALSE) wxs_bed <- readr::read_tsv(opt$bed_wxs, col_names = FALSE) # Calculate size of genome surveyed wgs_genome_size <- sum(wgs_bed[, 3] - wgs_bed[, 2]) wxs_exome_size <- sum(wxs_bed[, 3] - wxs_bed[, 2]) # Print out these genome sizes cat( " WGS size in bp:", wgs_genome_size, "\n", "WXS size in bp:", wxs_exome_size, "\n" ) # Only do this step if you have WXS samples if (any(metadata$experimental_strategy == "WXS")) { # Filter out mutations for WXS that are outside of these BED regions. vaf_df <- wxs_bed_filter(vaf_df, wxs_bed_file = opt$bed_wxs) } # Calculate TMBs and write to TMB file tmb_df <- calculate_tmb(vaf_df, wgs_size = wgs_genome_size, wxs_size = wxs_exome_size ) %>% readr::write_tsv(tmb_file) # Print out completion message message(paste("TMB calculations saved to:", tmb_file)) }

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/Nitrogen_Algae/old_code/sim_ode.R

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jwerba14/Species-Traits

aa2b383ce0494bc6081dff0be879fc68ed24e9c2

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refs/heads/master

2022-10-13T10:57:54.711688

2020-06-12T01:57:21

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sim_ode.R

## simulation for ode library(rstan) rstan_options(auto_write = TRUE) options(mc.cores = parallel::detectCores()) sim_mod <- stan_model(file = "sim_mod.stan", model_name = "sim_mod", verbose = T) sim_out <- sampling(sim_mod, seed = 2, data = list(N = nrow(dat_27), y = dat_27[c(8,5)], t0 = 0, t_obs= seq(1,11), run_estimation = 0),chains=1,iter=4000, control = list(adapt_delta = 0.99, max_treedepth =18)) y_sim <- rstan::extract(sim_out, pars = "y_hat_n") y_sim <- data.frame(y_sim) true_param <- rstan::extract(sim_out) fit_sum_sim<- summary(sim_out) fsss<-data.frame(fit_sum_sim$summary[c(1:9),]) fsss1 <- fsss %>% mutate(param = rownames(fsss)) %>% gather(-c(n_eff, Rhat, param), key = "quantile", value = "value") fsss1 <- fsss1 %>% filter(quantile != "mean" & quantile != "se_mean" & quantile != "sd") %>% mutate(trial = "sim") fsss_med <- fsss1 %>% filter(quantile == "X50.") fsss_upr <- fsss1 %>% filter(quantile == "X75.") fsss_lwr <- fsss1 %>% filter(quantile == "X25.") simdat <- apply(y_sim$y_hat_n, 2:3,median) dgp_recapture_fit <- sampling(sim_mod, data = list(N = nrow(simdat), y = simdat, t0 = 0, t_obs= seq(1,11), run_estimation = 1),chains=1, control = list(adapt_delta = 0.99, max_treedepth =15)) fit_sum_check<- summary(dgp_recapture_fit) fsso<-data.frame(fit_sum_check$summary[c(1:9),]) fsso1 <- fsso %>% mutate(param = rownames(fsso)) %>% gather(-c(n_eff, Rhat, param), key = "quantile", value = "value") fsso1 <- fsso1 %>% filter(quantile != "mean" & quantile != "se_mean" & quantile != "sd") %>% mutate(trial = "recap") ndat <- rbind(fsso1,fsss1) ndat <- ndat %>% filter(param != "y0[1]" & param != "y0[2]" & param != "sigma[1]" & param != "sigma[2]") ggplot(ndat, aes(value, param)) + geom_point(aes(color=quantile, shape = trial))

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/hidrantesmanizales.R

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dechontaduro/DataScienceExamples

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refs/heads/master

2021-01-12T11:03:02.230015

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hidrantesmanizales.R

getData <- function(variableName, url, filename, ...){ if(!exists(variableName)){ if (!file.exists(filename)) { download.file(url, filename) } read.csv(filename, ...) } } geocodeAdddress <- function(address) { require(RJSONIO) url <- "http://maps.google.com/maps/api/geocode/json?address=" url <- URLencode(paste(url, address, "&sensor=false", sep = "")) x <- fromJSON(url, simplify = FALSE) if (x$status == "OK") { out <- c(x$results[[1]]$geometry$location$lng, x$results[[1]]$geometry$location$lat) } else { out <- NA } Sys.sleep(0.2) # API only allows 5 requests per second out } setwd("C:\\Users\\juanc\\Dropbox\\DataScience\\pruebas\\hidrantes") #rm(dataHidra) url <- "https://www.datos.gov.co/resource/ygcd-j498.csv" dataHidra <- getData("dataHidra", url, "hidrantesmanizales.csv") str(dataHidra) geo <- apply (dataHidra, 1, function(x) { geocodeAdddress(paste(x[2], ', Manizales, Colombia'))}) dataHidra$lat <- sapply(geo, function(x) x[2]) dataHidra$lon <- sapply(geo, function(x) x[1]) View(dataHidra) library(leaflet) library(dplyr) leaflet(data = dataHidra) %>% addTiles() %>% addMarkers(~lon, ~lat, popup = ~direccion, clusterOptions = markerClusterOptions())

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/BootSU2C-TCGA-HeatmapApp.R

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NateDee/YuLabSU2CHeat

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refs/heads/master

2021-05-01T20:00:51.485785

2018-03-23T15:26:40

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BootSU2C-TCGA-HeatmapApp.R

#Set working directory to SU2C_script setwd("R:/Medicine/Hematology-Oncology/Yu_Lab/Nate/scripts_and_tools/YuLab-SU2C-TCGA") #Create function to test for packages that are needed to run app installPkges <- function(pkg){ if(!pkg %in% installed.packages()) install.packages(pkg, repos = "https://mirror.las.iastate.edu/CRAN/") } #Required packages, "data.table", "gplots", "RColorBrewer", "shiny" print("Checking for required packages, installing if needed") installPkges("data.table") installPkges("gplots") installPkges("RColorBrewer") installPkges("shiny") #Load shiny library(shiny) #Load Heatmap app runApp("YuLab-SU2C-TCGA-HeatmapApp")

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hx_timeline.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/call.R \name{hx_timeline} \alias{hx_timeline} \title{Timeline} \usage{ hx_timeline(ids, resolution = c("D", "M", "W", "H")) } \arguments{ \item{ids}{A list or vector of article ids to query, see \code{\link{hx_articles}}.} \item{resolution}{The resolution of timeline. \code{H}: hour, \code{D}: day, \code{W}: week, \code{M}: month.} } \description{ Return timeline of tweets on given articles. } \examples{ \dontrun{ articles <- hx_articles("pizzagate") tl <- hx_timeline(articles$id[1:5]) } }

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#' Calculate mean of variables #' @description Calculate mean addressing item-level missing data using proration #' @param data Wide dataframe. #' @param id_str String of identifier variable. #' @param var_str String of variable to calculate mean for. #' @param session_str String of session number. #' @param n_min Minimum number of available scores to calculate mean. #' @param item_scores Add item scores after mean. #' @param sep seperator for variable names. #' @param sort_mean_item Logical, if TRUE and multiple sessions then output dataframe will be organised mean_timepoint followed by all items for that timepoint, if FALSE all means will come after id variable followed by all items. #' @export #' calc_mean <- function(data, id_str, var_str, session_str, n_min, item_scores = FALSE, short_var_name = TRUE, timepoint_str = "s" , sep = "_"){ session_str <- base::c(session_str) # Select variables based on variable names data_select_var <- data %>% dplyr::select(id_str, contains(var_str)) # Create emptty string for list of variable names var_names <- "" # Create tibble with only ids used to start joining at the end and also will be the return object data_join_start_end <- data %>% dplyr::select(id_str) # Start looping through list of variables for (i in 1:base::length(session_str)) { # Select all items from a sepecific session data_select_var_ses <- data_select_var %>% dplyr::select(id_str, contains(session_str[i])) # Get all variable names of items to inclode in rowMeans mutate var_names <- data_select_var %>% dplyr::select(contains(session_str[i])) %>% base::names() item_count <- base::length(var_names) # Extract session number or str from session_str # If no digit, take the letters, if there is a digit, take digit if (base::is.na(stringr::str_extract(session_str[i], "\\d+")) == TRUE) { session_str_var_name <- stringr::str_extract(session_str[i], "[:alpha:]+") } else if (base::is.na(stringr::str_extract(session_str[i], "\\d+")) == FALSE) { session_str_var_name <- stringr::str_extract(session_str[i], "\\d+") session_str_var_name <- base::paste0(timepoint_str, session_str_var_name) } # Create variable name for mean if (short_var_name == FALSE) { var_str_i <- base::paste0(var_str, sep, "mean", sep, session_str_var_name) } else if (short_var_name == TRUE) { var_str_i <- base::paste0(var_str, sep, session_str_var_name) } # # Calvulate number of available scores # data_select_var_ses_mean <- data_select_var_ses %>% # mutate(n = sum(is.na(variable))) # Calculate mean data_select_var_ses_mean <- data_select_var_ses %>% dplyr::mutate(!!var_str_i := psychdata:::calc_mean_n_min(.[ , 2:(item_count + 1)], n_min)) data_loop <- data_select_var_ses_mean %>% dplyr::select(id_str, !!var_str_i) # Here create dataframe, keep on adding to the same dataframe as looping through sessions # I'm not happy with this left_join approach but it works and I cant think of a better way right now if (item_scores == FALSE){ # If item_scores not asked for just add the data_loop (with the session means) data_join_start_end <- dplyr::left_join(data_join_start_end, data_loop, by = id_str) } else { data_join_start_end <- dplyr::left_join(data_join_start_end, data_loop, by = id_str) data_join_start_end <- dplyr::left_join(data_join_start_end, data_select_var_ses, by = id_str) } } return(data_join_start_end) }

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/createlms.R \name{fit_gamlss1} \alias{fit_gamlss1} \title{fit_gamlss1} \usage{ fit_gamlss1( data, age.min = 0, age.max = 80, age.int = 1/12, keep.models = F, dist = "BCCGo", formula = NULL, sigma.formula = ~1, nu.formula = ~1, tau.formula = ~1, method.pb = "ML" ) } \arguments{ \item{data}{dataframe as return by select_meas()} \item{age.min}{lower bound of age} \item{age.max}{upper bound of age} \item{age.int}{stepwidth of the age variable} \item{keep.models}{indicator whether or not models in each iteration should be kept} \item{dist}{distribution used for the fitting process, has to be one of BCCGo, BCPEo, BCTo as they are accepted by lms()} \item{formula}{formula for the location parameter} \item{sigma.formula}{formula for the sigma parameter} \item{nu.formula}{formula for the nu parameter} \item{tau.formula}{formula for the tau parameter} \item{method.pb}{GAIC or ML} } \value{ list containing a dataframe of the fitted lms parameter at the given age points and the fitted model } \description{ fit_gamlss } \details{ wrapper around the \code{\link[gamlss]{gamlss}} function from the gamlss package returns the fitted lms-parameter at given age points the function is called inside \code{\link{do_iterations}} and may not be called directly } \author{ Mandy Vogel }

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library(testthat) library(capsule) test_check("capsule")

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#ensure the file household_power_consumption is downloaded into your working directory #read the file inot R powerful<-read.table("household_power_consumption.txt",header=TRUE, sep=";") #combine Date and Time columns library(dplyr) verypowerful<-mutate(powerful,datetime=paste(Date,Time, sep=" ")) #convert datetime column to Date class and add to the data frame library(lubridate) asdate<-dmy_hms(verypowerful$datetime) verypowerful$DateTime<-asdate #convert the columns into numeric verypowerful$Global_Active_Power<-as.numeric(paste(verypowerful$Global_active_power)) verypowerful$Global_Reactive_Power<-as.numeric(paste(verypowerful$Global_reactive_power)) verypowerful$VOLTAGE<-as.numeric(paste(verypowerful$Voltage)) verypowerful$Global_Intensity<-as.numeric(paste(verypowerful$Global_intensity)) verypowerful$Sub_Metering_1<-as.numeric(paste(verypowerful$Sub_metering_1)) verypowerful$Sub_Metering_2<-as.numeric(paste(verypowerful$Sub_metering_2)) verypowerful$Sub_Metering_3<-as.numeric(paste(verypowerful$Sub_metering_3)) #filter only the 1st and 2nd of February powerful1<-select(verypowerful,DateTime,Global_Active_Power,Global_Reactive_Power,VOLTAGE,Global_Intensity,Sub_Metering_1,Sub_Metering_2,Sub_Metering_3) powerful2<-powerful1[grep("2007-02-01",powerful1$DateTime),] powerful3<-powerful1[grep("2007-02-02",powerful1$DateTime),] power4<-rbind(powerful2,powerful3) #melt the dataset to create long dataset library(reshape2) melted<- melt(power4, id.vars = c("DateTime", "Global_Active_Power","Global_Reactive_Power","VOLTAGE","Global_Intensity"), variable.name = "Sub_Metering_variable", value.name = "Sub_Metering_value") par(mfcol=c(1,1)) par(mar=c(2,4,2,2)) with(melted,plot(DateTime,Sub_Metering_value,ylab="Energy sub metering",type="n")) with(subset(melted,Sub_Metering_variable=="Sub_Metering_1"),lines(DateTime,Sub_Metering_value,pch=".",col="black")) with(subset(melted,Sub_Metering_variable=="Sub_Metering_2"),lines(DateTime,Sub_Metering_value,pch=".",col="red")) with(subset(melted,Sub_Metering_variable=="Sub_Metering_3"),lines(DateTime,Sub_Metering_value,pch=".",col="blue")) legend("topright",cex=0.6,lwd=c(1,1,1),col=c("black","red","blue"),legend=c("Sub_Metering_1","Sub_Metering_2","Sub_Metering_3")) #copy into PNG file dev.copy(png,file="Plot3.png") dev.off()

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## To install cummeRbund and DESeq2 (do it once) # source("https://bioconductor.org/biocLite.R") # biocLite("DESeq2") getwd() setwd('../Desktop/course_data/DESeq2/') library('DESeq2') data <- read.delim('../featureCounts/count_gene', skip=1, sep="\t") dim(data) head(data) colnames(data) count <- data[7:12] colnames(count) <- c('Con1_Rep1', 'Con1_Rep2', 'Con1_Rep3', 'Con2_Rep1', 'Con2_Rep2', 'Con2_Rep3') rownames(count) <- data$Geneid condition <- data.frame(c('con1', 'con1', 'con1', 'con2', 'con2', 'con2')) colnames(condition) <- 'group' rownames(condition) <- colnames(count) dds <- DESeqDataSetFromMatrix(count, condition, design = ~group) dds <- DESeq(dds) res <- results(dds) summary(res) summary(res, alpha=0.05) res_05 <- subset(res, padj <= 0.05) write.table(res_05, 'DESeq2.txt', quote=F, row.names=F, sep='\t') plotMA(res)

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/data.R \docType{data} \name{celdaCGMod} \alias{celdaCGMod} \title{celdaCGmod} \format{ A celda_CG object } \usage{ celdaCGMod } \description{ celda_CG model object generated from \code{celdaCGSim} using old \code{celda_CG} function. } \keyword{datasets}

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source("../../R/Rcheck.R") d <- read.jagsdata("litters-data.R") inits <- read.jagsdata("litters-init.R") m <- jags.model("litters.bug", d, inits, n.chains=2) update(m, 5000) x <- coda.samples(m, c("mu","theta"), n.iter=50000, thin=50) source("bench-test1.R") check.fun()

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# # This is the user-interface definition 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(shinydashboard) library(shinydashboardPlus) setwd('C:/Users/rbelhocine/Desktop/BD/Arcelor/R/') library(reticulate) source_python("Search_engine_and_topic_prediction.py") sidebar <- dashboardSidebar(width = 300, sidebarMenu( menuItem(span("Safety Assistant",style="font-size:18px;"), tabName = "safety", icon = icon("search")) ) ) body <- dashboardBody( tabItems( tabItem(tabName = "safety", hr() ,h1("Hello Safe.ly User ! I'm your Safety Assistant of the Future.", align="center") ,br() , HTML('<center><img src="photo.png" width="400"></center>') ,br() ,hr() ,br() ,fluidRow(column(12,tabBox( title=tagList(shiny::icon("search"), "Ask me a Question !"), width=12, fluidRow(column(12,align="center",textInput("quest", "", value = "You can write your question here.", placeholder = NULL))), fluidRow(column(12,align="center",actionButton("go", "Search")))))), fluidRow(column(12,tabBox(width=12, tabPanel("TopDoc",DT::dataTableOutput("top_documents")), tabPanel("TopAnswer", DT::dataTableOutput("top_answer"))))) ))) # Put them together into a dashboardPage dashboardPage(skin = "yellow", dashboardHeader(title = "Safe.Ly", titleWidth = 300), sidebar, body )

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library(tidyverse) library(ggplot2) h <- head s <- summary g <- glimpse t <- tail gen <- read.csv("Gender_StatsData.csv", stringsAsFactors = FALSE) indicator <- unique(gen$Indicator.Name) country <- unique(gen$?..Country.Name) gen1 <- gen %>% rename(Country = ?..Country.Name) topics_education <- c( "GDP (current US$)", "GDP per capita (Current US$)", "Government expenditure on education, total (% of GDP)", "Children out of school, primary, female", "Children out of school, primary, male", "Literacy rate, adult female (% of females ages 15 and above)", "Literacy rate, adult male (% of males ages 15 and above)", "Educational attainment, at least Bachelor's or equivalent, population 25+, female (%) (cumulative)", "Educational attainment, at least Bachelor's or equivalent, population 25+, male (%) (cumulative)", "Educational attainment, at least completed lower secondary, population 25+, female (%) (cumulative)", "Educational attainment, at least completed lower secondary, population 25+, male (%) (cumulative)", "Educational attainment, at least completed post-secondary, population 25+, female (%) (cumulative)", "Educational attainment, at least completed post-secondary, population 25+, male (%) (cumulative)", "Educational attainment, at least completed primary, population 25+ years, female (%) (cumulative)", "Educational attainment, at least completed primary, population 25+ years, male (%) (cumulative)", "Educational attainment, at least completed short-cycle tertiary, population 25+, female (%) (cumulative)", "Educational attainment, at least completed short-cycle tertiary, population 25+, male (%) (cumulative)", "Educational attainment, at least completed upper secondary, population 25+, female (%) (cumulative)", "Educational attainment, at least completed upper secondary, population 25+, male (%) (cumulative)", "Educational attainment, at least Master's or equivalent, population 25+, female (%) (cumulative)", "Educational attainment, at least Master's or equivalent, population 25+, male (%) (cumulative)", "Educational attainment, Doctoral or equivalent, population 25+, female (%) (cumulative)", "Educational attainment, Doctoral or equivalent, population 25+, male (%) (cumulative)") topics_employment <- c( "GDP (current US$)", "GDP per capita (Current US$)", "Employment in agriculture, female (% of female employment) (modeled ILO estimate)", "Employment in agriculture, male (% of male employment) (modeled ILO estimate)", "Employment in industry, female (% of female employment) (modeled ILO estimate)", "Employment in industry, male (% of male employment) (modeled ILO estimate)", "Employment in services, female (% of female employment) (modeled ILO estimate)", "Employment in services, male (% of male employment) (modeled ILO estimate)", "Proportion of seats held by women in national parliaments (%)", "Labor force with advanced education, female (% of female working-age population with advanced education)", "Labor force with advanced education, male (% of male working-age population with advanced education)", "Labor force with basic education, female (% of female working-age population with basic education)", "Labor force with basic education, male (% of male working-age population with basic education)", "Labor force with intermediate education, female (% of female working-age population with intermediate education)", "Labor force with intermediate education, male (% of male working-age population with intermediate education)", "Labor force, female", "Labor force, female (% of total labor force)", "Labor force, total", "Unemployment with advanced education, female (% of female labor force with advanced education)", "Unemployment with advanced education, male (% of male labor force with advanced education)", "Unemployment with basic education, female (% of female labor force with basic education)", "Unemployment with basic education, male (% of male labor force with basic education)", "Unemployment with intermediate education, female (% of female labor force with intermediate education)", "Unemployment with intermediate education, male (% of male labor force with intermediate education)", "Unemployment, female (% of female labor force) (modeled ILO estimate)", "Unemployment, female (% of female labor force) (national estimate)", "Unemployment, male (% of male labor force) (modeled ILO estimate)", "Unemployment, male (% of male labor force) (national estimate)", "Vulnerable employment, female (% of female employment) (modeled ILO estimate)", "Vulnerable employment, male (% of male employment) (modeled ILO estimate)", "Length of paid maternity leave (days)", "Maternity leave benefits (% of wages paid)", "Mothers are guaranteed an equivalent position after maternity leave (1=yes; 0=no)") topics_life <- c("GDP (current US$)", "GDP per capita (Current US$)", "Age at first marriage, female", "Age at first marriage, male", "Birth rate, crude (per 1,000 people)", "Death rate, crude (per 1,000 people)", "Completeness of birth registration, female (%)", "Completeness of birth registration, male (%)", "Suicide mortality rate, female (per 100,000 female population)", "Suicide mortality rate, male (per 100,000 male population)", "Fertility rate, total (births per woman)", "Wanted fertility rate (births per woman)" , "Sex ratio at birth (male births per female births)", "Proportion of time spent on unpaid domestic and care work, female (% of 24 hour day)", "Proportion of time spent on unpaid domestic and care work, male (% of 24 hour day)", "Total alcohol consumption per capita, female (liters of pure alcohol, projected estimates, female 15+ years of age)", "Total alcohol consumption per capita, male (liters of pure alcohol, projected estimates, male 15+ years of age)" )

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r

parse_dataset.R

#' @importFrom fs is_file #' @importFrom hdf5r H5File h5attr_names #' @importFrom Matrix sparseMatrix Matrix t parse_dataset <- function(x, loom_expression_layer = NULL) { assert_that( is.character(x), length(x) == 1, fs::is_file(x) || fs::is_link(x), msg = "--dataset should contain a pathname of a .loom or .h5 file. Add a '-h' flag for help." ) extra_input <- NULL expression <- NULL ########################## ### LOOM ### ########################## if (grepl("\\.loom$", x)) { file_h5 <- H5File$new(x, mode = "r") on.exit(file_h5$close_all()) assert_that(file_h5 %has_names% c("matrix", "row_attrs", "col_attrs", "layers")) counts <- file_h5[["matrix"]][,] %>% Matrix(sparse = TRUE) feature_paths <- paste0("row_attrs/", c("gene_names", "Gene")) cell_paths <- paste0("col_attrs/", c("cell_names", "CellID")) feature_exists <- map_lgl(feature_paths, file_h5$exists) %>% which() cell_exists <- map_lgl(cell_paths, file_h5$exists) %>% which() feature_ids <- if (length(feature_exists) == 1) { file_h5[[feature_paths[[feature_exists]]]][] } else { warning("feature IDs could not be found in the loom format!") paste("Feature", seq_len(ncol(counts))) } if (any(duplicated(feature_ids))) { stop("duplicated feature IDs found!") } cell_ids <- if (length(cell_exists) == 1) { file_h5[[cell_paths[[cell_exists]]]][] } else { warning("cell IDs could not be found in the loom format!") paste("Cell", seq_len(nrow(counts))) } if (any(duplicated(cell_ids))) { stop("duplicated cell IDs found!") } dimnames(counts) <- list(cell_ids, feature_ids) if (!is.null(loom_expression_layer)) { expression <- file_h5[[paste0("layers/", loom_expression_layer)]][,] %>% Matrix(sparse = TRUE) dimnames(expression) <- list(cell_ids, feature_ids) } } else if (grepl("\\.h5$", x)) { file_h5 <- H5File$new(x, mode = "r") on.exit(file_h5$close_all()) if (file_h5 %has_names% c("data", "names") && "object_class" %in% h5attr_names(file_h5)) { ########################## ### OWN H5 ### ########################## tmp <- dynutils::read_h5_(file_h5) counts <- tmp$counts expression <- tmp$expression extra_input <- list() if ("parameters" %in% names(tmp)) { extra_input$parameters <- tmp$parameters } if ("priors" %in% names(tmp)) { extra_input$priors <- tmp$priors } if ("prior_information" %in% names(tmp)) { extra_input$priors <- tmp$prior_information } if ("seed" %in% names(tmp)) { extra_input$seed <- tmp$seed } # add dataset prior if given if (any(c("milestone_percentages", "divergence_regions", "milestone_network", "progressions") %in% names(tmp))) { extra_input$priors$dataset <- tmp[c("milestone_network", "progressions", "milestone_percentages", "divergence_regions")] } } else if (file_h5 %has_names% "matrix" && file[["matrix"]] %has_names% c("barcodes", "data", "features", "indices", "indptr", "shape")) { ########################## ### CELLRANGER V3 ### ########################## subfile <- file_h5[["matrix"]] counts <- Matrix::sparseMatrix( i = subfile[["indices"]][], p = subfile[["indptr"]][], x = subfile[["data"]][], dims = subfile[["shape"]][], dimnames = list( subfile[["features/id"]][], subfile[["barcodes"]][] ), index1 = FALSE ) %>% Matrix::t() } else if (length(names(file_h5)) == 1 && file_h5[[names(file_h5)]] %has_names% c("barcodes", "data", "genes", "indices", "indptr", "shape")) { ########################## ### CELLRANGER V2 ### ########################## subfile <- file_h5[[names(file_h5)]] counts <- Matrix::sparseMatrix( i = subfile[["indices"]][], p = subfile[["indptr"]][], x = subfile[["data"]][], dims = subfile[["shape"]][], dimnames = list( subfile[["genes"]][], subfile[["barcodes"]][] ), index1 = FALSE ) %>% Matrix::t() } } if (is.null(expression)) { expression <- normalise(counts) } out <- lst(counts, expression) c(out, extra_input) } normalise <- function(counts) { # TODO: provide better normalisation :( # TODO: Also print out warning that better normalisation should be added expr <- counts expr@x <- log2(expr@x + 1) expr }

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/Data exploration/ui.R

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Ghaith701/Exploring-top-song-charts-in-each-decade

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refs/heads/main

2023-02-19T19:09:46.790883

2021-01-20T03:46:45

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ui.R

# ui.R library(plotly) library(shiny) library(shinythemes) shinyUI(fluidPage( theme = shinytheme("cosmo"), # Application title titlePanel("Top 50 Songs Each Decade"), navbarPage(title = "My project", tabPanel("About", titlePanel("My app"), sidebarLayout( sidebarPanel( h3("Describtion: "), h5("This application explores the top 50 songs in each decade in relation to the billboard songs data. The reason behind this application is to show the diversity and similarity in each decade. Two plots where made, Word Cloud plot and bubble chart plot.") ), mainPanel( h3("Plots used: "), h4("Word cloud"), h5("Is a plotting method to show the most used words in a text.",br(), "In this application , we explored the words of lyrics and showed the word cloud for each decade."), br(), h4("Bubble chart"), h5("Is an intresting plotting method have many dimmensions to control, such as; color, size, etc", br(), "In this application, we used the bubble chart to show the popularity of genres in each decade."), br(), h3("Refrences: "), h5("Application development by ", HTML("<a href = 'https://shiny.rstudio.com/articles/'>shiny</a>") ), h5("Billboard Weekly Hot 100 singles chart between 8/2/1958 and 12/28/2018", HTML("<a href = 'https://data.world/kcmillersean/billboard-hot-100-1958-2017'>Dataset</a>"), ".") ) ) ), tabPanel("Lyrics", # Sidebar with a slider input for the decade sidebarLayout( sidebarPanel( selectInput("Decade", "please select the decade", choices = c("1958 - 1969", "1970 - 1979", "1980 - 1989", "1990 - 1999", "2000 - 2009", "2010 - 2018")), br(), sliderInput("words", "Number of words", min = 50, max = 200, value = 100) ), # Show two plots in deffirent tabs mainPanel( h3("Most used words for decades"), plotOutput("distPlot2", width = "100%", height = "500px"), h5("This plot shows most used words for each decade inside of a word cloud.", align = "center") ) )), tabPanel("Genre", # Sidebar with a slider input for the decade sidebarLayout( sidebarPanel( selectInput("Decade1", "please select the decade", choices = c("1958 - 1969", "1970 - 1979", "1980 - 1989", "1990 - 1999", "2000 - 2009", "2010 - 2018", "All decades")) ), # Show two plots in deffirent tabs mainPanel( h3("Songs count for decades"), plotlyOutput("distPlot", width = "100%", height = "500px") ) ))) ) )

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/code/random_forest.building_energy.grid2.R

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DarrenCook/h2o

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refs/heads/bk

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2018-02-02T01:55:35

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random_forest.building_energy.grid2.R

g <- h2o.grid("randomForest", search_criteria = list( strategy = "RandomDiscrete", stopping_metric = "mse", stopping_tolerance = 0.001, stopping_rounds = 10, max_runtime_secs = 120 ), hyper_params = list( ntrees = c(50, 100, 150, 200, 250), mtries = c(2, 3, 4, 5), sample_rate = c(0.5, 0.632, 0.8, 0.95), col_sample_rate_per_tree = c(0.5, 0.9, 1.0) ), x = x, y = y, training_frame = train, nfolds = 5, max_depth = 40, stopping_metric = "deviance", stopping_tolerance = 0, stopping_rounds = 4, score_tree_interval = 3 )