pierreqi/r_code_50k · Datasets at Hugging Face

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wrspathrow.Rd

\docType{package} \name{wrspathrow} \alias{wrspathrow} \alias{wrspathrow-package} \title{wrspathrow} \description{ wrspathrow }

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vsrimurthy/EPFR

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rquaternion.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/EPFR.r \name{rquaternion} \alias{rquaternion} \title{rquaternion} \usage{ rquaternion(x) } \arguments{ \item{x}{= number of quaternions desired} } \description{ n x 4 matrix of randomly generated number of unit size } \keyword{rquaternion}

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cran/qdap

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proportions.end_mark_by.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/end_mark.R \name{proportions.end_mark_by} \alias{proportions.end_mark_by} \title{Question Counts} \usage{ \method{proportions}{end_mark_by}(x, ...) } \arguments{ \item{x}{The end_mark_by object.} \item{\ldots}{ignored} } \description{ View \code{\link[qdap]{end_mark_by}} proportions. } \details{ end_mark_by Method for proportions }

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/resources/cellcounts/houseman.R

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AST87/godmc

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houseman.R

# Adapted from https://gist.github.com/brentp/5058805 # Make a rank transformed methylation set # Make a cell-count-adjusted and rank transformed methylation set # These values will not be 0-1 source('resources/cellcounts/wbcInference-V112.R') ## arguments <- commandArgs(T) methylationfile <- arguments[1] cellcountfile <- arguments[2] rnmethdatafile <- arguments[3] ccrnmethdatafile <- arguments[4] ccrnsquaredmethdatafile <- arguments[5] nthreads <- as.numeric(arguments[6]) message("Reading methylation data...") load(methylationfile) if(cellcountfile != "NULL") { cellcounts <- read.table(cellcountfile, he=T) stopifnot(all(cellcounts$IID == colnames(norm.beta))) cellcounts <- as.matrix(subset(cellcounts, select=-c(IID))) } # Get inverse rank transformed data, no cell count adjustment dat <- inverse.rank.transform(norm.beta, nthreads) write.table(round(dat, 3), file=rnmethdatafile, row=TRUE, col=TRUE, qu=FALSE, sep="\t") # adjust for cell counts if(cellcounts != "NULL") { dat <- adjust.beta(norm.beta, cellcounts, mc.cores=nthreads) # and rank transformed data of cell countadjusted betas dat <- inverse.rank.transform(dat) write.table(round(dat, 3), file=ccrnmethdatafile, row=TRUE, col=TRUE, qu=FALSE, sep="\t") } else { system(paste("cp -v", rnmethdatafile, ccrnmethdatafile)) } # Get squared z values of cell count adjusted data dat <- dat^2 write.table(round(dat, 3), file=ccrnsquaredmethdatafile, row=TRUE, col=TRUE, qu=FALSE, sep="\t")

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/Research Paper_Soichi/code/T5 with Tests.R

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skajikaji/hello-world

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2021-01-17T11:57:05.140970

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T5 with Tests.R

#rm(list=c("D", "lag.fhpolrigaug", "lag.fhpolrigaug.")) require(plyr) #T5.C1 TwoSLS=plm(fhpolrigaug~lag(lrgdpch)+factor(year)+factor(country)|.-lag(lrgdpch)+lag(nsave, 2)+factor(year)+factor(country), data=Five, index = c("code","year"),subset = sample =="1", model = "within", effect="individual") #Extract observations used in Two stage least squaure regression model. A = model.frame(TwoSLS) #rename the variables Five_inst = rename(A, c("factor(year)" = "year", "factor(country)" = "country")) F_pols_inst = plm(fhpolrigaug ~ lag.lrgdpch.+factor(year), data = Five_inst,index = c("country","year"), model = "pooling") summary(F_pols_inst) #Test for multicollinearity by using pooled data #VIF(Variance inflation factior) vif(F_pols_inst) #Breusch-Godfrey test pbgtest(F_pols_inst) #Breusch-Pagan test for homoskedastisity plmtest(F_pols_inst, type="bp") #T5.C2 F_folswod_inst = plm(fhpolrigaug ~ lag.lrgdpch.+factor(year)+factor(country), data = Five_inst, index = c("country","year"), model = "within", effect = "individual") summary(F_folswod_inst) #Breusch-Godfrey test pbgtest(F_folswod_inst) #Breusch-Pagan test for homoskedastisity plmtest(F_folswod_inst, type="bp") #T5.C3 #To make a observation data set used in column (5) TwoSLS_1=plm(fhpolrigaug~lag(lrgdpch)+lag(fhpolrigaug)+factor(year)+factor(country)|.-lag(lrgdpch)+lag(nsave, 2)+factor(year)+factor(country), data=Five, index = c("code","year"),subset = sample =="1", model = "within", effect="individual") #Extract observations used in Two stage least squaure regression model. B = model.frame(TwoSLS_1) #Rename the variables in data B Five_inst_1 = rename(B, c("factor(year)" = "year", "factor(country)" = "country")) F_fols_inst = plm(fhpolrigaug ~ lag.fhpolrigaug.+lag.lrgdpch.+factor(country)+factor(year), data = Five_inst_1, index = c("country","year"), model = "within", effect = "individual") summary(F_fols_inst) #Breusch-Godfrey test pbgtest(F_fols_inst) #Breusch-Pagan test for homoskedastisity plmtest(F_fols_inst, type="bp") #T5.C4 # First stage regression Firststage_4=plm(lag(lrgdpch)~lag(nsave, 2)+factor(year)+factor(country), data = Five, subset = sample == "1", na.action = na.omit, index = c("code","year"), model = "within", effect="individual") summary(Firststage_4) # 2SLS regression by hand #fit_lrgdpch=predict(Firststage) #length(fit_lrgdpch) #Five$fit_lrgdpch=predict(Firststage) TwoSLS=plm(fhpolrigaug~lag(lrgdpch)+factor(year)+factor(country)|.-lag(lrgdpch)+lag(nsave, 2)+factor(year)+factor(country), data=Five, index = c("code","year"),subset = sample =="1", model = "within", effect="individual") # Second Stage: Regress outcome on x and fitted values from the first stage summary(TwoSLS) #Breusch-Godfrey test pbgtest(TwoSLS) #Breusch-Pagan test for homoskedastisity plmtest(TwoSLS, type="bp") #T5. C5 TwoSLS_5=plm(fhpolrigaug~lag(fhpolrigaug)+lag(lrgdpch)+factor(year)+factor(country)|.-lag(lrgdpch)+lag(fhpolrigaug)+lag(nsave, 2)+factor(year)+factor(country), data=Five, index = c("code","year"),subset = sample =="1", model = "within", effect="individual") # Second Stage: Regress outcome on x and fitted values from the first stage summary(TwoSLS_5) Firststage_5=plm(lag(lrgdpch)~lag(fhpolrigaug)+lag(nsave, 2)+factor(year)+factor(country), data = Five, subset = sample == "1", na.action = na.omit, index = c("code","year"), model = "within", effect="individual") summary(Firststage_5) #Breusch-Godfrey test pbgtest(TwoSLS_5) #Breusch-Pagan test for homoskedastisity plmtest(TwoSLS_5, type="bp") #T5. C7 TwoSLS_7=plm(fhpolrigaug~lag(lrgdpch)+lag(laborshare)+factor(year)+factor(country)|.-lag(lrgdpch)+lag(laborshare)+lag(nsave, 2)+factor(year)+factor(country), data=Five, index = c("code","year"),subset = sample =="1", model = "within", effect="individual") # Second Stage: Regress outcome on x and fitted values from the first stage summary(TwoSLS_7) #Breusch-Godfrey test pbgtest(TwoSLS_7) #Breusch-Pagan test for homoskedastisity plmtest(TwoSLS_7, type="bp") #T5. C8 TwoSLS_8=plm(fhpolrigaug~lag(fhpolrigaug, 1:3)+lag(lrgdpch)+factor(year)+factor(country)|.-lag(lrgdpch)+lag(fhpolrigaug)+lag(nsave, 2)+factor(year)+factor(country), data=Five, index = c("code","year"),subset = sample =="1", model = "within", effect="individual") # Second Stage: Regress outcome on x and fitted values from the first stage summary(TwoSLS_8) (R <- matrix(c(1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0),byrow=TRUE,nrow=3)) (r <- c(0,0,0)) linearHypothesis(TwoSLS_8, hypothesis.matrix=R, rhs = r) (R <- matrix(c(1,0,0,0,0,0,0,0,0,0,0), byrow=TRUE, nrow = 1)) (r = c(0)) linearHypothesis(TwoSLS_8, hypothesis.matrix=R, rhs = r) #Breusch-Godfrey test pbgtest(TwoSLS_8) #Breusch-Pagan test for homoskedastisity plmtest(TwoSLS_8, type="bp") #T5. C9 TwoSLS_9=plm(fhpolrigaug~lag(lrgdpch)+factor(year)+factor(country)|.-lag(lrgdpch)+lag(nsave, 2:3)+factor(year)+factor(country), data=Five, index = c("code","year"),subset = sample =="1", model = "within", effect="individual") # Second Stage: Regress outcome on x and fitted values from the first stage summary(TwoSLS_9) #Breusch-Godfrey test pbgtest(TwoSLS_9) #Breusch-Pagan test for homoskedastisity plmtest(TwoSLS_9, type="bp")

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/man/SlagPreprosess.Rd

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Rapporteket/Hjerneslag

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SlagPreprosess.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/SlagPreprosesser.R \name{SlagPreprosess} \alias{SlagPreprosess} \title{Preprosesser data fra Hjerneslag} \usage{ SlagPreprosess(RegData = RegData, reshID = reshID) } \arguments{ \item{RegData}{En dataramme med alle nødvendige variabler fra registeret} \item{reshID}{Parameter følger fra innlogging helseregister.no og angir hvilken enhet i spesialisthelsetjenesten brukeren tilhører} } \value{ Data En list med det filtrerte datasettet og sykehusnavnet som tilsvarer reshID } \description{ Denne funksjonen definerer variabler og fjerner ikke-ferdigstilte registreringer }

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/man/decomp.Rd

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xiangzhou09/rwrmed

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decomp.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/decomp.R \name{decomp} \alias{decomp} \title{Causal Effect Decomposition Based on a Fitted \code{rwrmed} Model} \usage{ decomp(object, a0 = 0, a1 = 1, m = 0, bootstrap = TRUE, rep = 250) } \arguments{ \item{object}{An object of class \code{rwrmed}.} \item{a0}{The baseline level of treatment.} \item{a1}{The level of treatment to be contrasted with the baseline.} \item{m}{The level of the mediator at which the CDE is evaluated.} \item{bootstrap}{Whether to compute standard errors and 95% confidence intervals using the nonparametric bootstrap.} \item{rep}{Number of bootstrap replications if \code{bootstrap = TRUE}. Default is 250.} } \value{ A list of two elements. \item{twocomp}{Two component decomposition of the rATE into rNDE and rNIE.} \item{fourcomp}{Four component decomposition of the rATE into CDE, rINTREF, rPIE, and rINTMED.} } \description{ \code{decomp} is a function that implements causal effect decomposition based on a fitted \code{rwrmed} model. It returns a two-component decomposition of the total effect into the randomized interventional analogues of the natural direct effect (rNDE) and the natural indirect effect (rNIE). It also returns a four-component decomposition of the total effect into the controlled direct effect (CDE) and the randomized analogues of the reference interaction effect (rINTREF), the mediated interaction effect (rINTMED), and the pure indirect effect (rPIE). } \seealso{ \code{\link{rwrmed}} for implementing the regression-with-residuals (RWR) approach to causal mediation. a0 = 0; a1 = 1; m = 0; bootstrap = TRUE; rep = 250 }

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/plot3.R

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NancyChang/ExData_Project2

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plot3.R

#download zip file and unzip the files into working directory temp <- tempfile() download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip",temp) list.files(unzip(temp,exdir=".",overwrite=TRUE)) unlink(temp) #check if the files exist and read the files into R if (file.exists('./summarySCC_PM25.rds')) { NEI <- readRDS("summarySCC_PM25.rds") } if (file.exists('./Source_Classification_Code.rds')) { SCC <- readRDS("Source_Classification_Code.rds") } #extract data only in Baltimore and make the table for plot3 using ggplot2 library(ggplot2) NEI_baltimore <- subset(NEI, NEI$fips == "24510") #remove NA and infinite elements in the data frame NEI_baltimore$logEmission<- na.omit(log(NEI_baltimore$Emissions)) NEI_baltimore$logEmission<- log(NEI_baltimore$Emissions) #remove NA/NaN/Inf in 'y'axis for plotting NEI_baltimore$logEmission[!is.finite(NEI_baltimore$logEmission)] <- 0 #plot the bar chart and save as png file par(mar=c(4,4,4,4)) png(filename='project2_plots/plot3.png',width=480,height=480,units='px') plot <- ggplot(NEI_baltimore,aes(year,logEmission)) plot + geom_point(aes(color=type),size=2) + geom_smooth(aes(color=type),method="lm")+labs(title="Comparing Emissions from Four Type Sources in Baltimore City", size=6) +labs(x = "Year", y = expression("log"*PM[2.5])) dev.off()

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/R/io.R

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assaron/r-utils

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io.R

options(stringsAsFactors=F) #' @export read.table.smart <- function(path, ...) { fields <- list(...) conn <- file(path) header <- readLines(conn, n=1) close(conn) sep <- "\t" for (s in c("\t", " ", ",")) { if (grepl(s, header)) { sep <- s break } } res <- as.data.table(read.table(path, sep=sep, header=T, stringsAsFactors=F)) oldnames <- character(0) newnames <- character(0) for (field in names(fields)) { if (field %in% colnames(res)) { next } z <- na.omit( match( tolower(c(field, fields[[field]])), tolower(colnames(res)))) if (length(z) == 0) { next } oldnames <- c(oldnames, colnames(res)[z]) newnames <- c(newnames, field) } setnames(res, oldnames, newnames) res } #' @export read.tsv <- function(file, header=T, sep="\t", quote="", comment.char="", check.names=FALSE, ...) { args <- list(...) res <- read.table(file, header=header, sep=sep, quote=quote, comment.char=comment.char, check.names=check.names, stringsAsFactors=FALSE, ...) if ((!"row.names" %in% names(args)) && (colnames(res)[1] == "")) { rownames(res) <- res[, 1] res[[1]] <- NULL } res } #' @export write.tsv <- function(table, dir, file=NULL, gzip=FALSE, row.names=NA, col.names=NA, ...) { name <- deparse(substitute(table)) table <- as.data.frame(table) if (is.null(file)) { file <- file.path(dir, paste0(name, ".tsv", if (gzip) ".gz")) } if (is.na(row.names)) { row.names <- is.character(attr(table, "row.names")) } if (!row.names && is.na(col.names)) { col.names=T } for (c in colnames(table)) { if (is.character(table[[c]])) { table[[c]] <- sub("#", "", table[[c]]) } } if (gzip) { file <- gzfile(file, "w") } write.table(table, file, quote=F, row.names=row.names, col.names=col.names, sep="\t") if (gzip) { close(file) } } #' Reads gtf file to data.table #' @param file Path to file #' @import data.table #' @export read.gtf <- function(file, attrs.to.extract=c("gene_id", "transcript_id", "gene_type", "gene_name"), features.to.extract=NULL) { res <- fread(file, header=F, col.names = c("chr", "source", "feature", "start", "end", "score", "strand", "frame", "attribute")) if (!is.null(features.to.extract)) { res <- res[feature %in% features.to.extract,] } attrlist <- strsplit(res$attribute, "; *") attrlist_length <- sapply(attrlist, length) attrtable <- data.table(rn=rep(seq_along(attrlist), attrlist_length), raw=unlist(attrlist)) attrtable[, name := gsub(" .*", "", raw)] attrtable[, value := gsub(".* ", "", raw)] attrtable[, value := gsub('^"(.*)"$', "\\1", value)] attrtable[, raw := NULL] all_attrs <- unique(attrtable$name) attrmatrix <- matrix(nrow = length(attrlist), ncol=length(all_attrs)) colnames(attrmatrix) <- all_attrs attrtable[, name := match(name, all_attrs)] attrmatrix[cbind(attrtable$rn, attrtable$name)] <- attrtable$value res[, attribute := NULL] res <- cbind(res, attrmatrix) res }

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travs/datasciencecoursera

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library(shiny) shinyUI(pageWithSidebar( headerPanel("Population of Newfoundland"), sidebarPanel( p("Click on a gender, select a year on the slider and watch the plot change!"), p("The graph on the right represents the population of Newfoundland across age groups."), radioButtons("gen", "Gender:", choices=list("Male" = "Male", "Female" = "Female", "Total" = "Total"), selected="Total" ), sliderInput("y", "Year:", value = 2012, min = 2010, max = 2012, format = "####") ), mainPanel( plotOutput("pop_plot") ) ))

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gaelberon/ExploData-Week4-ProgrammingAssignment

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plot2.R

# COURSERA - DATA SCIENCE # EXPLORATORY DATA - WEEK 4 # PROGRAMMING ASSIGNMENT ## The R file 'plot2.R' aims to answer the following question: ## "Have total emissions from PM2.5 decreased in the Baltimore City, Maryland ## (fips == "24510") from 1999 to 2008?" ## summarySCC_PM25.rds (PM2.5 Emissions Data): This file contains a data frame ## with all of the PM2.5 emissions data for 1999, 2002, 2005, and 2008. ## For each year, the table contains number of tons of PM2.5 emitted from a ## specific type of source for the entire year. Here are the first few rows ## ## List of variables: ## fips: A five-digit number (represented as a string) indicating the U.S. ## county ## SCC: The name of the source as indicated by a digit string (see source code ## classification table) ## Pollutant: A string indicating the pollutant ## Emissions: Amount of PM2.5 emitted, in tons ## type: The type of source (point, non-point, on-road, or non-road) ## year: The year of emissions recorded ## Source_Classification_Code.rds (Source Classification Code Table): This table ## provides a mapping from the SCC digit strings in the Emissions table to the ## actual name of the PM2.5 source. The sources are categorized in a few ## different ways from more general to more specific and you may choose to ## explore whatever categories you think are most useful. For example, source ## “10100101” is known as “Ext Comb /Electric Gen /Anthracite Coal /Pulverized ## Coal”. ################################################################################ ## 1. Read the input files ## NB. This first line will likely take a few seconds. Be patient! NEI <- readRDS("summarySCC_PM25.rds") #SCC <- readRDS("Source_Classification_Code.rds") ################################################################################ ## 2. Produce a barplot to answer the question: ## "Have total emissions from PM2.5 decreased in the Baltimore City, Maryland ## (fips == "24510") from 1999 to 2008? ## Use the base plotting system to make a plot answering this question." ## Compute dataset for Baltimore sumPerYearBaltimore <- with(NEI[NEI$fips == "24510", ], tapply(Emissions, year, sum, na.rm = TRUE)) ## Initialize a plotting area with 1 row and 1 column par(mfrow = c(1,1)) ## Plot dataset for Baltimore barplot(height = sumPerYearBaltimore, names.arg = names(sumPerYearBaltimore), main = "PM2.5 Emissions in Baltimore, Maryland", xlab = "Year", ylab = "Amount of PM2.5 emitted (tons)") ################################################################################ ## 3. Store the plot into the file plot2.png dev.copy(png, file = 'plot2.png', width = 480, height = 480, units = "px") ######################################################################## ## 4. Close the 'png file' device dev.off()

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getName.environment.Rd

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Do not modify this file since it was automatically generated from: % % getName.environment.R % % by the Rdoc compiler part of the R.oo package. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \name{getName.environment} \alias{getName.environment} \title{Gets the name of an environment} \description{ Gets the name of an environment, e.g. \code{"R_GlobalEnv"} or \code{"0x01ddd060"}. } \usage{ \method{getName}{environment}(env, ...) } \arguments{ \item{env}{An \code{\link[base]{environment}}.} \item{...}{Not used.} } \value{ Returns a \code{\link[base]{character}} string. } \examples{ name <- getName(globalenv()) print(name) stopifnot(identical(name, "R_GlobalEnv")) getName(new.env()) } \author{Henrik Bengtsson} \seealso{ \code{\link[base:environment]{environmentName}()}. } \keyword{programming} \keyword{methods}

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/R/predict.knn.R

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predict.knn.R

predict.knn <- function(object, test, train, theyre.the.same = FALSE, return.classifications = FALSE, verbose = 0, ...) { # # predict.knn: predict from an object with class "knn." # # Arguments: object: object of class knn. # test: Items to be predicted # train: Things to use to do prediction # theyre.the.same: Are test and train the same? Then use leave-one-out cv # return.classifcations: If true, return the classifications of the test set # verbose: Level of verbosity # # First check to see whether this training set is pure. If so this is easy: if # theyre the same, the error rate is zero; if not it's the proportion of test set # items whose classes are different from the training set's unique class. In either # class every item's classification is the training set one. # if(all(object$which == FALSE) || (any(names(object) == "pure") && object$ pure == TRUE) || length(unique(train[, 1])) == 1) { train.11 <- as.character(as.vector(train[1, 1])) if(theyre.the.same) { if(return.classifications) return(list(rate = 0, classifications = rep( train.11, nrow(train)))) else return(list(rate = 0)) } else if(return.classifications) { return(list(rate = mean(test[, 1] != train[1, 1]), classifications = rep(train.11, nrow(test)))) } else return(list(rate = mean(test[, 1] != train[1, 1]))) } # # Grab the true classes of the training set from column 1. If that column is a factor, # save the levels and then convert that column to numeric. # if(is.factor(train[, 1])) { class.is.factor <- TRUE labels <- levels(train[, 1]) train[, 1] <- as.integer(unclass(train[, 1])) - 1 } else { class.is.factor <- FALSE labels <- unique(train[, 1]) } train <- as.matrix(train) if(theyre.the.same && !missing(train)) { warning("They're the same, so I'm ignoring the 'test' argument" ) test <- 0 } else { if(is.factor(test[, 1])) { class.is.factor <- TRUE labels <- levels(test[, 1]) test[, 1] <- as.integer(unclass(test[, 1])) - 1 } else { class.is.factor <- FALSE labels <- unique(test[, 1]) } test <- as.matrix(test) } number.of.classes <- length(labels) k.vec <- object$best.k k.length <- 1 return.all.rates <- 0 best.error.rate <- 1.1 best.k.index <- -1 # Will come back zero-based, so add 1 which <- c(TRUE, object$which) scaled <- object$scaled col.sds <- object$col.sds if(return.classifications == TRUE) classifications <- numeric(nrow(test)) else classifications <- 0 backward <- 0 status <- 1 # # # Get "filename" (which will only be used if verbose > 0) # pr <- Sys.getenv("R_HOME") pr.chars <- substring(pr, 1:nchar(pr), 1:nchar(pr)) backslash <- pr.chars == "\\" pr.chars[backslash] <- "/" pr <- paste(pr.chars, collapse = "") filename <- paste(pr, "/status.txt", sep = "") # # # Call the DLL, and save the result. # thang <- .C("knnvar", as.double(train), as.integer(c(nrow(train), ncol(train))), as.double(test), as.integer(c(nrow(test), ncol(test))), as.integer(number.of.classes), as.integer(k.vec), as.integer(k.length), as.integer(theyre.the.same), rate = as.double(best.error.rate), as.integer(return.all.rates), best.k.index = as.integer(best.k.index), which = as.double(which), scaled = as.integer(scaled), col.sds = as.double(col.sds), as.integer(return.classifications), classifications = as.integer(classifications), backward = as.integer(backward), as.integer (0), as.integer(verbose), filename, status = as.integer(status), PACKAGE="knnTree") # # Produce the output list, which contains the rate plus, if return.classifications # is true, a vector of predictions. Factorize if necessary. # status <- thang$status if(status != 0) warning(paste("Uh-oh; bad status", status, "returned")) if(return.classifications) { if(class.is.factor) classes <- factor(thang$classifications, labels = labels, levels = 0:(length(labels) - 1)) else classes <- thang$classifications out <- list(rate = thang$rate, classifications = classes) } else out <- list(rate = thang$rate) # return(out) }

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rankhospital.R

## Rank hospitals according to deathrate and return specific rank ## For readability a lot of the statements are broken into multiple lines rankhospital <- function(state, outcome, num = "best") { ## Initialize parameters we want to be sure are dataframes inputData <- data.frame() stateData <- data.frame() rankedData <- data.frame() outputdata<-data.frame() rank<-0 numhosp<-0 ## Read the input data inputData <- read.csv("outcome-of-care-measures.csv", na.strings = c("Not Available"), colClasses = "character") ## Check that state and outcome are valid if (!state %in% inputData[,7]) stop("invalid state") if (outcome == "heart attack") colnum<-11 else if (outcome == "heart failure") colnum<-17 else if (outcome == "pneumonia") colnum<-23 else stop("invalid outcome") ## get the number of hospitals in state ## Subset data according to input parameter state stateData <- subset(inputData, inputData[,7]==state) ## Now stateData needs to be ordered by rank after mortality rates ## but first mortality rates must be converted to numerics and NA's removed ## Using <=100 is to cheat subset into accepting a logical statement since ## rate cannot exceed 100 percent stateData[,colnum] <- as.numeric(stateData[,colnum]) ## Order the rankedData after rate and hospitalname so that we can return the ## hospital with the name that comes first alphabetically outputData <- stateData[order(stateData[,colnum],stateData[,2]),] numhosp <- length(outputData[,2]) if (num == "best"){ outputData <- outputData[1,2] } else if (num == "worst"){ outputData <- stateData[order(stateData[,colnum], stateData[,2], decreasing = TRUE),] outputData <- outputData[1,2] } else if ( num > 0 & num <= numhosp){ rank <- num outputData<-outputData[rank,2] } else outputData<-NA outputData }

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arules-analysis.R

library(readr) library(arules) library(dplyr) df <- read_csv('/Users/macher1/Documents/SANDBOX/compilation-analysis/dataset_after_encoding.csv', col_types = list( OPENVSWITCH = col_factor(c("0", "1", "2")) )) # df <- read_csv('/Users/macher1/Documents/SANDBOX/tuxml-datasets/config_bdd40000-60000.csv') #df <- read_csv('/Users/macher1/Documents/SANDBOX/tuxml-datasets/config_bdd60000-90000.csv') df <- Filter(function(x)(length(unique(x))>1), df) # df['compilation_failure'] <- df['vmlinux'] == -1 #df2 <- Filter(function(x)(Negate(is.numeric(x))), df1) # df <- df %>% dplyr::select_if(Negate(is.numeric)) # df['date'] <- NULL rules <- apriori (data=df, parameter=list (supp=0.0000000001, conf = 1.0)) #, appearance = list (default="lhs", rhs="compilation_failure"), control = list (verbose=F)) # rules_conf <- sort (rules, by="support", decreasing=TRUE) rules_failure <- subset(rules, subset = rhs %in% "compile_success") tristate <- c(0, 1, 2) parse_factor(df, levels = tristate)

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/R/fit_house.R

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fit_house.R

library(tidyverse) library(rstan) options(mc.cores = parallel::detectCores()) rstan_options(auto_write = TRUE) source("R/process_polls.R") #source("R/house_data.R") prior_data <- read_csv("data/prior_data.csv") gen_res <- read_csv("data/gen_res.csv") if(!exists("exec_date")) exec_date <- Sys.Date() generic_ballot <- process_538_gb() %>% filter(end_date <= exec_date, end_date > '2020-01-01') house_races <- process_538_house() %>% filter(end_date <= exec_date, end_date > '2020-01-01') %>% distinct() write_csv(house_races, "data/house_races.csv") district_polls <- house_races %>% #mutate(dem = round(dem + .4*Other), rep = round(rep + .4*Other), # Other = round(0.2*Other)) %>% right_join(prior_data %>% distinct(state, district)) %>% mutate(state_district = paste(state, district, sep = "-")) %>% arrange(state, district) %>% mutate(district_id = match(state_district, unique(state_district)), days_out = ifelse(is.na(days_out), 365, days_out)) state_district <- unique(district_polls$state_district) lean <- prior_data %>% select(-inc) %>% spread(party, p_lean1) %>% mutate(state_district = paste(state, district, sep = "-")) %>% arrange(state, district) %>% mutate(district_id = match(state_district, unique(state_district))) %>% select(republican, democrat, other) %>% mutate(democrat = ifelse(is.na(democrat), -gen_res$gen_t[gen_res$party == 'democrat'], democrat), republican = ifelse(is.na(republican), -gen_res$gen_t[gen_res$party == 'republican'], republican), other = ifelse(is.na(other), -gen_res$gen_t[gen_res$party == 'other'], other)) %>% as.matrix() # Counts for each option in each district poll y_r <- district_polls %>% select(rep, dem, Other) %>% as.matrix() %>% apply(., 2, function(x) ifelse(is.na(x), 0, x)) # Counts for each option in each GB poll y_g <- generic_ballot %>% #mutate(dem = round(dem + .4*Other), rep = round(rep + .4*Other), # Other = round(0.2*Other)) %>% select(rep, dem, Other) %>% as.matrix() %>% apply(., 2, function(x) ifelse(is.na(x), 0, x)) # Number of district polls n_polls_r <- nrow(y_r) # Number of GB polls n_polls_g <- nrow(y_g) # Number of candidates n_options <- ncol(y_g) # Days out from election (for weighting) days_out_r <- as.numeric(district_polls$days_out) days_out_g <- as.numeric(generic_ballot$days_out) region_id <- district_polls$district_id n_regions <- n_distinct(region_id) load('data/house_error') load('data/house_corr') region_error <- rep(0.01, n_regions) # Combine into list model_data <- list(n_options = n_options, n_regions = n_regions, n_options = n_options, n_polls_r = n_polls_r, n_polls_g = n_polls_g, y_g = y_g, y_r = y_r, region_id = region_id, lean = lean, days_out_g = days_out_g, days_out_r = days_out_r, non_samp_error = house_error, non_samp_corr = house_corr, region_error = region_error, decay_param = 40, prior_g = c(0.45, 0.53, 0.02), prior_sd_g = c(0.01, 0.01, 0.01)) #start <- Sys.time() fit_house <- stan("stan/yapa.stan", data = model_data, chains = 10, iter = 2000) #print(Sys.time() - start) # results ----------------------------------------------------------------- ef_house <- extract(fit_house) colMeans(ef_house$res_g) # Poll averages ----------------------------------------------------------- # National poll_averages_gb_today <- data_frame( date = exec_date, lower_rep = quantile(ef_house$res_g[, 1], 0.1), mean_rep = quantile(ef_house$res_g[, 1], 0.5), upper_rep = quantile(ef_house$res_g[, 1], 0.9), lower_dem = quantile(ef_house$res_g[, 2], 0.1), mean_dem = quantile(ef_house$res_g[, 2], 0.5), upper_dem = quantile(ef_house$res_g[, 2], 0.9), lower_other = quantile(ef_house$res_g[, 3], 0.1), mean_other = quantile(ef_house$res_g[, 3], 0.5), upper_other = quantile(ef_house$res_g[, 3], 0.9) ) %>% gather(metric, value, -date) %>% mutate(candidate = sapply(strsplit(metric, "_"), tail, 1), metric = sapply(strsplit(metric, "_"), head, 1)) %>% spread(metric, value) # Append to tracking data poll_averages_gb <- read_csv("results/poll_averages_gb.csv") poll_averages_gb <- poll_averages_gb %>% filter(date != exec_date) %>% rbind(poll_averages_gb_today) write_csv(poll_averages_gb, "results/poll_averages_gb.csv") # District tmp_district <- vector("list", nrow(colMeans(ef_house$res_r))) for(s in 1:length(tmp_district)) { tmp_district[[s]] <- data_frame( date = exec_date, district = unique(district_polls$state_district)[s], lower_rep = quantile(ef_house$res_r[, s, 1], 0.1), mean_rep = quantile(ef_house$res_r[, s, 1], 0.5), upper_rep = quantile(ef_house$res_r[, s, 1], 0.9), lower_dem = quantile(ef_house$res_r[, s, 2], 0.1), mean_dem = quantile(ef_house$res_r[, s, 2], 0.5), upper_dem = quantile(ef_house$res_r[, s, 2], 0.9), lower_other = quantile(ef_house$res_r[, s, 3], 0.1), mean_other = quantile(ef_house$res_r[, s, 3], 0.5), upper_other = quantile(ef_house$res_r[, s, 3], 0.9) ) } district_averages_today <- do.call(rbind, tmp_district) %>% gather(metric, value, -date, -district) %>% mutate(candidate = sapply(strsplit(metric, "_"), tail, 1), metric = sapply(strsplit(metric, "_"), head, 1)) %>% spread(metric, value) # Append to tracking data district_averages <- read_csv("results/district_averages.csv") district_averages <- district_averages %>% filter(date != exec_date) %>% rbind(district_averages_today) write_csv(district_averages, "results/district_averages.csv") # Simulated results ------------------------------------------------------- # State # Results means_rep <- apply(ef_house$res_r, 2, function(x) mean(x[, 1])) quantiles_rep <- apply(ef_house$res_r, 2, function(x) quantile(x[, 1], c(0.1, 0.9))) means_dem <- apply(ef_house$res_r, 2, function(x) mean(x[, 2])) quantiles_dem <- apply(ef_house$res_r, 2, function(x) quantile(x[, 2], c(0.1, 0.9))) results_dem <- data_frame( district = state_district, lower = quantiles_dem[1, ], mean = means_dem, upper = quantiles_dem[2, ], cand = 'dem') results_rep <- data_frame( district = state_district, lower = quantiles_rep[1, ], mean = means_rep, upper = quantiles_rep[2, ], cand = 'rep') # Save save(results_dem, file = "results/res_r_dem") save(results_rep, file = "results/res_r_rep") # Formatted Table district_results <- results_dem %>% rename(`Lower Dem` = lower, `Upper Dem` = upper, `Mean Dem` = mean) %>% select(-cand) %>% left_join(results_rep %>% rename(`Lower Rep` = lower, `Upper Rep` = upper, `Mean Rep` = mean) %>% select(-cand)) %>% rename(District = district) %>% mutate_if(is.numeric, function(x) paste0(round(x*100), "%")) save(district_results, file = "results/district_results") # P-win -------------------------------------------------------------------- # Probability of winning the district p_dem <- round(apply(ef_house$res_r, 2, function(x) mean(x[, 2] > x[, 1])), 3) names(p_dem) <- state_district p_dem <- data.frame(p_dem) %>% tibble::rownames_to_column("district") %>% mutate(state = str_split(district, '-')[[1]][1], no = str_split(district, '-')[[1]][2]) %>% arrange(state, no) save(p_dem, file = "results/district_p_dem") # Simulate electoral college ---------------------------------------------- res_sims <- matrix(0, nrow = dim(ef_house$res_r)[1], ncol = dim(ef_house$res_r)[3]) for(i in 1:dim(ef_house$res_r)[1]) { winner <- apply(ef_house$res_r[i, , ], 1, function(x) which(x == max(x))) for(s in 1:dim(ef_house$res_r)[2]) { res_sims[i, winner[s]] <- res_sims[i, winner[s]] + 1 } } save(res_sims, file = "results/senate_res_sims") # Create data frame of results for tracking house_ts_today <- data_frame( date = exec_date, lower_rep = quantile(res_sims[, 1], 0.05), mean_rep = mean(res_sims[, 1]), upper_rep = quantile(res_sims[, 1], 0.95), lower_dem = quantile(res_sims[, 2], 0.05), mean_dem = mean(res_sims[, 2]), upper_dem = quantile(res_sims[, 2], 0.95) ) # Append to tracking data house_ts <- read_csv("results/house_ts.csv") house_ts <- house_ts %>% filter(date != exec_date) %>% rbind(house_ts_today) write_csv(house_ts, "results/house_ts.csv") # State simulations ------------------------------------------------------- house_simulations <- data_frame( value = round(c(c(ef_house$res_r[, , 1]), c(ef_house$res_r[, , 2]), c(ef_house$res_r[, , 3])), 3), state = rep(rep(state_district, each = dim(ef_house$res_r)[1]), times = 3), party = rep(c("rep", "dem", "other"), each = dim(ef_house$res_r)[1]*n_regions) ) %>% group_by(state, party) %>% mutate(mean = round(mean(value), 3)) %>% ungroup() %>% arrange(state) save(house_simulations, file = "results/house_simulations") tmp_district <- vector("list", n_distinct(state_district)) for(d in 1:length(tmp_district)) { tmp_district[[d]] <- data_frame( date = exec_date, district = state_district[d], lower_rep = quantile(ef_house$res_r[, d, 1], 0.1), mean_rep = quantile(ef_house$res_r[, d, 1], 0.5), upper_rep = quantile(ef_house$res_r[, d, 1], 0.9), lower_dem = quantile(ef_house$res_r[, d, 2], 0.1), mean_dem = quantile(ef_house$res_r[, d, 2], 0.5), upper_dem = quantile(ef_house$res_r[, d, 2], 0.9) ) } district_ts_today <- do.call(rbind, tmp_district) # Append to state tracking data district_ts <- read_csv("results/district_ts.csv") district_ts <- district_ts %>% filter(date != exec_date) %>% rbind(district_ts_today) write_csv(district_ts, "results/district_ts.csv")

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# 15-2. 변수 타입 # 변수에는 여러 가지 타입(Type, 속성)이 있음 # • 함수에 따라 적용 가능한 변수 타입 다름 # • 분석 전에 변수 타입이 무엇인지 확인 필요 # • 함수 실행했을 때 오류 발생 또는 예상과 다른 결과가 출력되면 변수 타입 확인 후 함수에 맞게 변경 # • 1. 연속 변수(Continuous Variable) - Numeric 타입 # – 값이 연속적이고 크기를 의미 # – 더하기 빼기, 평균 구하기 등 산술 가능 # – ex) 키, 몸무게, 소득 # • 2. 범주 변수(Categorical Variable) - Factor 타입 # – 값이 대상을 분류하는 의미를 지님 # – 산술 불가능 # – ex) 성별, 거주지 # 변수 Data Type 예 # -------------------------------------------------------------------------- # 연속 변수 Numeric 키(..., 151, 152, ...), 몸무게(..., 58, 59, ...) # 범주 변수 Factor 성별(1, 2), 지역(1, 2, 3, 4) # 변수 타입 간 차이 알아보기 var1 = c(1,2,3,1,2) # numeric 변수 생성 var2 = factor(c(1,2,3,1,2)) # factor 변수 생성 var1 # numeric 변수 출력 var2 # factor 변수 출력 var1 +2 # numeric 변수로 연산 var2 +2 # factor 변수로 연산 # 변수 타입 확인하기 class(var1) class(var2) # factor 변수의 구성 범주 확인하기 levels(var1) levels(var2) # 문자로 구성된 factor 변수 var3 = c("a", "b", "b", "c") # 문자 변수 생성 var4 = factor(c("a", "b", "b", "c")) # 문자로 된 factor 변수 생성 var3 var4 class(var3) class(var4) # 함수마다 적용 가능한 변수 타입이 다르다 mean(var1) mean(var2) # 변수 타입 바꾸기 var2 = as.numeric(var2) # numeric 타입으로 변환 mean(var2) # 함수 재적용 class(var2) # 타입 확인 levels(var2) # 범주 확인 # 변환 함수(Coercion Function) # 함수 기능 # -------------------------------------- # as.numeric() numeric으로 변환 # as.factor() factor로 변환 # as.character() character로 변환 # as.Date() Date로 변환 # as.data.frame() Data Frame으로 변환 # 혼자서 해보기 # • Q1. drv 변수의 타입을 확인해 보세요. class(mpg$drv) # • Q2. drv 변수를 as.factor()를 이용해 factor 타입으로 변환한 후 다시 타입을 확인해 보세요. as.factor(mpg$drv) class(mpg$drv) # • Q3. drv가 어떤 범주로 구성되는지 확인해 보세요. levels(mpg$drv)

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/man/PopFlyAnalysis.Rd

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BGD-UAB/iMKT

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PopFlyAnalysis.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/PopFlyAnalysis.R \name{PopFlyAnalysis} \alias{PopFlyAnalysis} \title{iMKT using PopFly data} \usage{ PopFlyAnalysis( genes = c("gene1", "gene2", "..."), pops = c("pop1", "pop2", "..."), cutoff = 0.05, recomb = TRUE/FALSE, bins = 0, test = c("standardMKT", "imputedMKT", "FWW", "aMKT"), xlow = 0, xhigh = 1, plot = FALSE ) } \arguments{ \item{genes}{list of genes to analyze} \item{pops}{list of populations to analyze} \item{recomb}{group genes according to recombination values (TRUE/FALSE)} \item{bins}{number of recombination bins to compute (mandatory if recomb=TRUE)} \item{test}{which test to perform. Options include: standardMKT (default), imputedMKT, FWW, aMKT} \item{xlow}{lower limit for asymptotic alpha fit (default=0)} \item{xhigh}{higher limit for asymptotic alpha fit (default=1)} \item{plot}{report plot (optional). Default is FALSE} \item{cutoffs}{list of cutofs to perform FWW and/or imputedMKT} } \value{ List of lists with the default test output for each selected population (and recombination bin when defined) } \description{ Perform any MKT method using a subset of PopFly data defined by custom genes and populations lists } \details{ Execute any MKT method (standardMKT, FWW, imputedMKT, aMKT) using a subset of PopFly data defined by custom genes and populations lists. It uses the dataframe PopFlyData, which can be already loaded in the workspace (using loadPopFly()) or is directly loaded when executing this function. It also allows deciding whether to analyze genes groupped by recombination bins or not, using recombination rate estimates from Comeron et al. 2012 Plos Genetics. } \examples{ ## List of genes mygenes = c('FBgn0053196', 'FBgn0086906', 'FBgn0261836', 'FBgn0031617', 'FBgn0260965', 'FBgn0028899', 'FBgn0052580', 'FBgn0036181', 'FBgn0263077', 'FBgn0013733', 'FBgn0031857', 'FBgn0037836') ## Perform analyses PopFlyAnalysis(genes=mygenes, pops='RAL', recomb=FALSE, test='aMKT', xlow=0, xhigh=0.9, plot=TRUE) PopFlyAnalysis(genes=mygenes, pops=c('RAL','ZI'), recomb=TRUE, bins=3, test='imputedMKT', plot=FALSE) } \keyword{PopData}

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/mrsb_annotate_orthology_2015-07-10.R

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msaul/three_species_orthology

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mrsb_annotate_orthology_2015-07-10.R

# 2015-07-10 # Michael C. Saul # msaul [at] illinois.edu # MRSB orthology annotation load("~/Desktop/mrsb/orthology/OrthoDB/three_species_OrthoDB_parse_2015-04-07.Rdata") stopifnot(require("biomaRt")) # Setting biomaRt to generate annotations from Ensembl gene IDs (the row names) mm_maRt = useMart(biomart = "ENSEMBL_MART_ENSEMBL", host = "may2012.archive.ensembl.org", dataset = "mmusculus_gene_ensembl") mm_maRt_filter = "ensembl_peptide_id" mm_maRt_attributes = c("ensembl_peptide_id","ensembl_gene_id") mm_mrsb_maRt_query = unlist(strsplit(threespeciesTriplets$mouse_protein_ids, ";")) # Grabbing biomart data to annotate the analyzed files mm_mrsb_biomaRt = getBM(mm_maRt_attributes, mm_maRt_filter, mm_mrsb_maRt_query, mm_maRt) mm_peptides_not_in_old_annotation = mm_mrsb_maRt_query[which(!mm_mrsb_maRt_query %in% mm_mrsb_biomaRt$ensembl_peptide_id)] # Setting biomaRt to generate annotations from new Ensembl peptide IDs (the row names) mm_new_maRt = useMart(biomart = "ENSEMBL_MART_ENSEMBL", host = "feb2014.archive.ensembl.org", dataset = "mmusculus_gene_ensembl") mm_mrsb_new_biomaRt = getBM(mm_maRt_attributes, mm_maRt_filter, mm_peptides_not_in_old_annotation, mm_new_maRt) mm_gene_annotations = rbind(mm_mrsb_biomaRt, mm_mrsb_new_biomaRt) sb_maRt = useMart(biomart = "ENSEMBL_MART_ENSEMBL", host = "feb2014.archive.ensembl.org", dataset = "gaculeatus_gene_ensembl") sb_maRt_filter = "ensembl_peptide_id" sb_maRt_attributes = c("ensembl_peptide_id","ensembl_gene_id") sb_mrsb_maRt_query = unlist(strsplit(threespeciesTriplets$stickleback_protein_ids, ";")) sb_mrsb_new_biomaRt = getBM(sb_maRt_attributes, sb_maRt_filter, sb_mrsb_maRt_query, sb_maRt) sb_gene_annotations = sb_mrsb_new_biomaRt threespeciesTriplets$bee_gene_ids = as.character(gsub("-PA","",threespeciesTriplets$bee_protein_ids)) threespeciesTriplets$mouse_gene_ids = as.character(threespeciesTriplets$mouse_protein_ids) threespeciesTriplets$stickleback_gene_ids = as.character(threespeciesTriplets$stickleback_protein_ids) for (i in 1:nrow(mm_gene_annotations)) { current_gene = mm_gene_annotations[i,"ensembl_gene_id"] current_peptide = mm_gene_annotations[i,"ensembl_peptide_id"] threespeciesTriplets$mouse_gene_ids = gsub(current_peptide, current_gene, threespeciesTriplets$mouse_gene_ids) } for (i in 1:nrow(sb_gene_annotations)) { current_gene = sb_gene_annotations[i,"ensembl_gene_id"] current_peptide = sb_gene_annotations[i,"ensembl_peptide_id"] threespeciesTriplets$stickleback_gene_ids = gsub(current_peptide, current_gene, threespeciesTriplets$stickleback_gene_ids) } orthology_edges = as.data.frame(matrix(nrow = 0, ncol = 5)) colnames(orthology_edges) = c("orthology_group","species_1","species_2","gene_species_1","gene_species_2") for (i in 1:nrow(threespeciesTriplets)) { current_orthology_group = threespeciesTriplets[i,"odb8_og_id"] current_bee_genes = unlist(strsplit(threespeciesTriplets[i,"bee_gene_ids"],";")) current_mouse_genes = unlist(strsplit(threespeciesTriplets[i,"mouse_gene_ids"],";")) current_stickleback_genes = unlist(strsplit(threespeciesTriplets[i,"stickleback_gene_ids"],";")) l_bee = length(current_bee_genes) l_mouse = length(current_mouse_genes) l_stickleback = length(current_stickleback_genes) for (j in 1:l_bee) { for (k in 1:l_mouse) { current_df = data.frame(orthology_group = current_orthology_group, species_1 = "honeybee", species_2 = "mouse", gene_species_1 = current_bee_genes[j], gene_species_2 = current_mouse_genes[k]) orthology_edges = rbind(orthology_edges, current_df) } } for (j in 1:l_bee) { for (k in 1:l_stickleback) { current_df = data.frame(orthology_group = current_orthology_group, species_1 = "honeybee", species_2 = "stickleback", gene_species_1 = current_bee_genes[j], gene_species_2 = current_stickleback_genes[k]) orthology_edges = rbind(orthology_edges, current_df) } } for (j in 1:l_mouse) { for (k in 1:l_stickleback) { current_df = data.frame(orthology_group = current_orthology_group, species_1 = "mouse", species_2 = "stickleback", gene_species_1 = current_mouse_genes[j], gene_species_2 = current_stickleback_genes[k]) orthology_edges = rbind(orthology_edges, current_df) } } }

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/FA.R

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simaonogueira101/CLSBE_BusinessStatistics-R_Assignements

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FA.R

# Turn off scientific notation options(scipen = 999) # Install required packages # install.packages("lm.beta") # Load required packages library(lm.beta) # Load DF # FAdatabase <- read.csv("WA_ESSDatabase.csv", header = TRUE) dependent_var <- "trstplc" iteration_num <- 4 # Columns to be excluded from analysis excluded_columns <- c( # Dependent variable dependent_var, # DF info variables "name", "essround", "edition", "proddate", "idno", "cntry", "region", "regunit", "inwtm", # Other unusable variables "ctzshipc", "cntbrthc", "livecnta", "lnghom1", "lnghom2", "fbrncntb", "mbrncntb", "hhmmb", "agea", "yrbrn", "icpart1", "pdjobyr", "emplno", "eduyrs", "njbspv", "wkhct", "wkhtotp", "isco08", "prtvtal", "wrkorg", "prtclal", "rlgdnal", "rlgdeal", "cldgng", "rshpscz", "rshpsfi", "lvgptnea", "mbtru", "edulvlb", "edulvlpb", "mainact", "mnactp" ) debug_columns <- c( "trstplc", "trstlgl", "mnactp", "tporgwk" ) # Columns to be used in analysis # good_columns <- debug_columns good_columns <- colnames(FAdatabase)[! colnames(FAdatabase) %in% excluded_columns] # Initiate model variable model <- c(dependent_var) # Function to clean database cleanDatabase <- function (database, variables) { # Determine type of scale and clean column for (variable in variables) { max_answer <- max(database[variable], na.rm = TRUE) if (max_answer > 9) { database[variable][database[variable] > 10 | database[variable] == ""] <- NA } else if (max_answer > 6) { database[variable][database[variable] > 6 | database[variable] == ""] <- NA } # Remove rows with NA values database <- database[!is.na(database[variable]), ] } return(database) } progress_total <- iteration_num * length(good_columns) - iteration_num + 1 progress_current <- 0 # Iterate to find each variable for the model for (i in 1:iteration_num) { # Create empty results array results <- data.frame( variable = character(0), p_value_individual = numeric(0), r_squared_overall = numeric(0), p_value_overall = numeric(0) ) for (column in good_columns[! good_columns %in% model]) { # Duplicate DF into a temporary object temp_database <- data.frame(FAdatabase) proposed_model <- c(model, column) temp_database <- cleanDatabase(temp_database, proposed_model) current_model <- "" for (i in 1:length(model)) { if(i < 2) { current_model <- paste( "temp_database$", current_model, toString(model[i]), " ~ ", sep = "" ) } else { current_model <- paste( current_model, "temp_database$", toString(model[i]), " + ", sep = "" ) } } current_model <- paste( current_model, "temp_database$", toString(column), sep = "" ) # Temporary model analysis temp_model <- lm(formula(current_model)) temp_summary <- summary(lm.beta(temp_model)) temp_p_value_individual <- format(round(as.numeric( temp_summary$coefficients[length(temp_summary$coefficients)] ), 10), nsmall = 10) temp_r_squared_overall <- as.numeric(temp_summary$r.squared) temp_p_value_overall <- format(round(as.numeric(pf( temp_summary$fstatistic[[1]], temp_summary$fstatistic[[2]], temp_summary$fstatistic[[3]], lower.tail = FALSE )), 10), nsmall = 10) # Record results temp_results <- c( column, temp_p_value_individual, temp_r_squared_overall, temp_p_value_overall ) results[nrow(results) + 1,] <- temp_results # Update progress indicator print(paste( "Progress: ", format(round(progress_current / progress_total * 100, 0),nsmall = 0), "%", sep = "" )) progress_current <- progress_current + 1 } # Order result based on PRE results <- results[order(results$r_square, decreasing = TRUE), ] # Remove results with no statistical significance results <- results[results$p_value_individual < 0.05 & results$p_value_overall < 0.05, ] head(results, 10) # Record winning variable in model print("-----------------") model <- c(model, results[1, 1]) print(paste("Variable #", i, ": ", results[1, 1], sep="")) print(paste("Current R-Squared: ", results[1, 3], sep="")) print("-----------------") } createFormula <- function (model, database) { formula <- "" for (i in 1:length(model)) { if(i == 1) { formula <- paste( formula, database, "$", toString(model[i]), " ~ ", sep = "" ) } else if (i < length(model)) { formula <- paste( formula, database, "$", toString(model[i]), " + ", sep = "" ) } else { formula <- paste( formula, database, "$", toString(model[i]), sep = "" ) } } return(formula) } adapted_variables <- c( dependent_var, "wkdcorga", "iprspot", "impfree", "tporgwk" ) all_variables <- c( model, adapted_variables ) final_database <- clean_database(FAdatabase, all_variables) optimized_model <- createFormula(model, "final_database") adapted_model <- createFormula(adapted_variables, "final_database") optimized_summary <- summary(lm.beta(lm(optimized_model))) adapted_summary <- summary(lm.beta(lm(adapted_model))) pre_change <- optimized_summary$r.squared - adapted_summary$r.squared

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/IAM65/man/iamArgs.rd

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florencebriton/IAM_Dvt

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refs/heads/master

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iamArgs.rd

\name{iamArgs-class} \docType{class} \alias{iamArgs-class} \title{Class "iamArgs"} \description{ToDo} \section{Slots}{ \describe{ \item{\code{desc}:}{object description} \item{\code{arguments}:}{ToDo} \item{\code{specific}:}{ToDo} } } \author{Mathieu Merzereaud} \examples{ showClass("iamArgs") } \keyword{classes}

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/man/designPlot.Rd

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cran/dae

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refs/heads/master

2023-08-18T19:51:15.580290

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designPlot.Rd

\name{designPlot} \alias{designPlot} \title{A graphical representation of an experimental design using labels stored in a matrix.} \description{This function uses labels, usually derived from treatment and blocking factors from an experimental design and stored in a matrix, to build a graphical representation of the matrix, highlighting the position of certain labels . It is a modified version of the function supplied with DiGGer. It includes more control over the labelling of the rows and columns of the design and allows for more flexible plotting of designs with unequal block size.} \usage{ designPlot(designMatrix, labels = NULL, altlabels = NULL, plotlabels = TRUE, rtitle = NULL, ctitle = NULL, rlabelsreverse = FALSE, clabelsreverse = FALSE, font = 1, chardivisor = 2, rchardivisor = 1, cchardivisor = 1, cellfillcolour = NA, plotcellboundary = TRUE, rcellpropn = 1, ccellpropn = 1, blocksequence = FALSE, blockdefinition = NULL, blocklinecolour = 1, blocklinewidth = 2, rotate = FALSE, new = TRUE, ...) } \arguments{ \item{designMatrix}{A \code{\link{matrix}} containing a set of numerics or characters being the labels as they have been assigned to the cells of the grid represented by the \code{\link{matrix}}.} \item{labels}{A \code{\link{numeric}} or \code{\link{character}} vector giving the cells in \code{designMatrix} that are to be plotted in this call to \code{designPlot}. If \code{NULL} then all the cells are plotted. What is actually plotted for a cell is controlled jointly by \code{labels}, \code{plotlabels}, \code{altlabels}, \code{plotcellboundary} and \code{cellfillcolour}. If \code{plotlabels} is TRUE and \code{altlabels} is \code{NULL} then \code{labels} are plotted in the cells, unless \code{labels} is \code{NULL} when the labels in \code{designMatrix} are plotted. Whatever is being plotted, \code{altlabels} and \code{cellfillcolour} must have an appropriate number of values. See \code{\link{text}} for more information on specifying the labels.} \item{altlabels}{Either a \code{\link{character}} vector containing an alternative set of labels for the \code{labels} currently being plotted or a single \code{\link{integer}} specifying an alternative symbol to be used in plotting cells when \code{plotlabels} is \code{TRUE}. The length of \code{altlabels} must be one or the same length as \code{labels}, unless \code{labels} is \code{NULL} in which case it must equal the number of unique labels in \code{designMatrix}. If \code{altlabels} is \code{NULL}, the labels specified in \code{labels} are plotted when \code{plotlabels} is \code{TRUE}. If \code{labels} is also \code{NULL}, the labels in \code{designMatrix} are plotted. See \code{\link{text}} for more information on specifying the labels.} \item{plotlabels}{A \code{\link{logical}} to indicate whether labels are to be plotted in the cells. If TRUE, print all labels or the specific labels listed in \code{labels}. If FALSE, no labels are printed in the cells.} \item{rtitle}{A \code{\link{character}} string to use as a title for rows of the plot. If \code{rtitle} is \code{NULL} then no title is plotted.} \item{ctitle}{A \code{\link{character}} string to use as a title for columns of the plot. If \code{ctitle} is \code{NULL} then no title is plotted.} \item{rlabelsreverse}{A \code{\link{logical}} indicating whether to reverse the row labels.} \item{clabelsreverse}{A \code{\link{logical}} indicating whether to reverse the column labels.} \item{font}{An \code{\link{integer}} specifying the font to be used for row and column labelling. See \code{\link{par}} for further details.} \item{chardivisor}{A \code{\link{numeric}} that changes the size of text and symbols in the cells by dividing the default size by it.} \item{rchardivisor}{A \code{\link{numeric}} that changes the size of the labels of the rows of the design by dividing the default size by it.} \item{cchardivisor}{A \code{\link{numeric}} that changes the size of the labels of the columns of the design by dividing the default size by it.} \item{cellfillcolour}{A \code{\link{character}} string specifying the colour of the fill for the cells to be plotted in this call. If there is only one colour then all cells being plotted with that colour. If there is more than one colour then, unless \code{labels} is \code{NULL}, the number of colours must at least equal the number of labels and then the fill colours will be matched, one for one from the first colour, with the labels. If \code{labels} is \code{NULL} then the number of colours must at least equal the number of unique labels in \code{designMatrix}. The default, \code{NA}, is to leave ther cells unfilled. See also \code{Colour specification} under the \code{\link{par}} function.} \item{plotcellboundary}{A \code{\link{logical}} indicting whether a boundary is to plotted around a cell.} \item{rcellpropn}{a value between 0 and 1 giving the proportion of the standard row size of a cell size to be plotted as a cell.} \item{ccellpropn}{a value between 0 and 1 giving the proportion of the standard column size of a cell size to be plotted as a cell.} \item{blocksequence}{A \code{\link{logical}} that determines whether block numbers are repetitions or sequences of block numbers.} \item{blockdefinition}{A \code{\link{matrix}} of block sizes: \itemize{ \item if there is only one row, then the first element is interpreted as the no. rows in each block and blocks with this number of rows are to be repeated across the rows of the design. \item if there is more than one row, then each row of the matrix specifies a block, with the sequence of rows in the matrix specifying a corresponding sequence of blocks down the rows of the design.} Similarly, a single value for a column specifies a repetition of blocks of that size across the columns of the design, while several column values specifies a sequence of blocks across the columns of the size specified.} \item{blocklinecolour}{A \code{\link{character}} string specifying the colour of the block boundary. See also \code{Colour specification} under the \code{\link{par}} function.} \item{blocklinewidth}{A \code{\link{numeric}} giving the width of the block boundary to be plotted.} \item{rotate}{A \code{\link{logical}} which, if \code{TRUE}, results in the matrix being rotated 90 degrees for plotting.} \item{new}{A \code{\link{logical}} indicating if a new plot is to be produced or the current plot is added to.} \item{...}{further arguments passed to \code{\link{polygon}} in plotting the cell.} } \value{no values are returned, but a plot is produced.} \references{Coombes, N. E. (2009). \emph{DiGGer design search tool in R}. \url{http://nswdpibiom.org/austatgen/software/}} \author{Chris Brien} \seealso{\code{\link{blockboundaryPlot}}, \code{\link{designPlotlabels}}, \code{\link{designLatinSqrSys}}, \code{\link{designRandomize}}, \code{\link{designAnatomy}} in package \pkg{dae}. \cr Also, \code{\link{par}}, \code{\link{polygon}}, \code{DiGGer}} \examples{\dontrun{ designPlot(des.mat, labels=1:4, cellfillcolour="lightblue", new=TRUE, plotcellboundary = TRUE, chardivisor=3, rtitle="Lanes", ctitle="Positions", rcellpropn = 1, ccellpropn=1) designPlot(des.mat, labels=5:87, plotlabels=TRUE, cellfillcolour="grey", new=FALSE, plotcellboundary = TRUE, chardivisor=3) designPlot(des.mat, labels=88:434, plotlabels=TRUE, cellfillcolour="lightgreen", new=FALSE, plotcellboundary = TRUE, chardivisor=3, blocksequence=TRUE, blockdefinition=cbind(4,10,12), blocklinewidth=3, blockcolour="blue")}} \keyword{design} \keyword{plot}

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fdc11ac8fd91a0e3f384c5be41422900379e768f

/covid_shiny_v5.1_summary_dashboard_dep.R

cdf3e6cc510c52c135b16bbb5c0ac44e091efcca

[]

no_license

kylebennison/covid-19-dashboard

a4c28c12415bb3d068609c6ac7d06889890e065b

44347bc145fc4e20f8c1d5769d3d12ee9f4c1ad4

refs/heads/master

2022-11-08T19:22:21.650759

2020-07-02T16:20:06

268,882,880

0

null

2020-07-01T16:14:07

2020-06-02T18:50:50

R

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46,830

r

covid_shiny_v5.1_summary_dashboard_dep.R

### Load necessary packages ### library(shiny) library(tidyverse) library(lubridate) library(scales) library(DT) library(jsonlite) library(plotly) library(htmlwidgets) library(gt) ### Load theme ### #Staturdays Colors staturdays_col_list <- c( lightest_blue = "#5c6272", lighter_blue = "#4c5872", light_blue = "#394871", medium_blue = "#22345a", dark_blue = "#041e42", orange = "#de703b", sign = "#1e1e1e", white = "#FFFFFF" ) staturdays_colors <- function(...) { cols <- c(...) if (is.null(cols)) return (staturdays_col_list) staturdays_col_list[cols] } staturdays_theme <- theme(plot.caption = element_text(size = 12, hjust = 1, color = staturdays_colors("orange")), plot.title = element_text(color = staturdays_colors("dark_blue"), size = 30, face = "bold"), plot.subtitle = element_text(color = staturdays_colors("lightest_blue"), size = 20), axis.text = element_text(color = staturdays_colors("lightest_blue"), size = 15), axis.title = element_text(color = staturdays_colors("lightest_blue"), size = 15), legend.title = element_text(color = staturdays_colors("lightest_blue"), size = 15), legend.text = element_text(color = staturdays_colors("lightest_blue"), size = 15) ) # User two action buttons and reactiveValues to let users select data source between cases and deaths ### Load in Data ### # Johns Hopkins Case and Death Timeseries by County ----------------------- data_source <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_US.csv" data_csv <- read.csv(data_source, header = TRUE, sep = ",") # Read in csv file from the web data_csv <- data_csv %>% pivot_longer(cols = -c(1:11), names_to = "Date") # Move Dates to one column data_csv <- data_csv %>% mutate(Date = str_remove(data_csv$Date, "X")) # Remove the X from the date data_csv <- data_csv %>% mutate(date = mdy(data_csv$Date)) # Convert to actual date values data_csv <- data_csv %>% select(-Date) # Get rid of unnecessary Date column covid_data_cases <- data_csv covid_data_cases <- covid_data_cases %>% mutate(New_Cases = case_when( lag(Combined_Key, n = 1L) == Combined_Key ~ value - lag(value, 1L), TRUE ~ as.integer(0) ) ) covid_data_cases <- covid_data_cases %>% rename(Cumulative_Cases = value) data_source_deaths <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_US.csv" data_csv_deaths <- read.csv(data_source_deaths, header = TRUE, sep = ",") # Read in csv file from the web data_csv_deaths <- data_csv_deaths %>% pivot_longer(cols = -c(1:12), names_to = "Date") # Move Dates to one column data_csv_deaths <- data_csv_deaths %>% mutate(Date = str_remove(data_csv_deaths$Date, "X")) # Remove the X from the date data_csv_deaths <- data_csv_deaths %>% mutate(date = mdy(data_csv_deaths$Date)) # Convert to actual date values data_csv_deaths <- data_csv_deaths %>% select(-Date) # Get rid of unnecessary Date column covid_data_deaths <- data_csv_deaths %>% mutate(New_Deaths = case_when( lag(Combined_Key, n = 1L) == Combined_Key ~ value - lag(value, 1L), TRUE ~ as.integer(0) ) ) covid_data_deaths <- covid_data_deaths %>% rename(Cumulative_Deaths = value) covid_data <- left_join(covid_data_cases, covid_data_deaths, by = c("Province_State", "Combined_Key", "date")) covid_data <- covid_data %>% mutate(New_Cases_Per_Cap = (New_Cases / Population) * 100000, Cum_Cases_Per_Cap = (Cumulative_Cases / Population) * 100000, New_Deaths_Per_Cap = (New_Deaths / Population) * 100000, Cum_Deaths_Per_Cap = (Cumulative_Deaths / Population) * 100000) # State and US Summaries -------------------------------------------------- covid_data_state <- covid_data %>% group_by(Province_State, date) %>% summarise(New_Cases = sum(New_Cases), New_Deaths = sum(New_Deaths), Cumulative_Cases = sum(Cumulative_Cases), Cumulative_Deaths = sum(Cumulative_Deaths), Population = sum(Population)) %>% mutate(New_Cases_Per_Cap = (New_Cases / Population) * 100000, Cum_Cases_Per_Cap = (Cumulative_Cases / Population) * 100000, New_Deaths_Per_Cap = (Cumulative_Cases / Population) * 100000, Cum_Deaths_Per_Cap = (Cumulative_Cases / Population) * 100000) covid_data_us <- covid_data_state %>% group_by(date) %>% summarise(New_Cases = sum(New_Cases), New_Deaths = sum(New_Deaths), Cumulative_Cases = sum(Cumulative_Cases), Cumulative_Deaths = sum(Cumulative_Deaths), Population = sum(Population)) %>% mutate(New_Cases_Per_Cap = (New_Cases / Population) * 100000, Cum_Cases_Per_Cap = (Cumulative_Cases / Population) * 100000, New_Deaths_Per_Cap = (Cumulative_Cases / Population) * 100000, Cum_Deaths_Per_Cap = (Cumulative_Cases / Population) * 100000) # Global Data ------------------------------------------------------------- #Global Confirmed Cases data_source_global <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv" data_csv <- read.csv(data_source_global, header = TRUE, sep = ",") # Read in csv file from the web data_csv <- data_csv %>% pivot_longer(cols = -c(1:4), names_to = "Date") # Move Dates to one column data_csv <- data_csv %>% mutate(Date = str_remove(data_csv$Date, "X")) # Remove the X from the date data_csv <- data_csv %>% mutate(date = mdy(data_csv$Date)) # Convert to actual date values data_csv <- data_csv %>% select(-Date) # Get rid of unnecessary Date column covid_data_cases <- data_csv covid_data_cases <- covid_data_cases %>% group_by(Country.Region, date) %>% summarise(Cumulative_Cases = as.integer(sum(value))) covid_data_cases <- covid_data_cases %>% mutate(New_Cases = case_when( lag(Country.Region, n = 1L) == Country.Region ~ Cumulative_Cases - lag(Cumulative_Cases, 1L), TRUE ~ as.integer(0) ) ) data_source_global_deaths <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv" data_csv <- read.csv(data_source_global_deaths, header = TRUE, sep = ",") # Read in csv file from the web data_csv <- data_csv %>% pivot_longer(cols = -c(1:4), names_to = "Date") # Move Dates to one column data_csv <- data_csv %>% mutate(Date = str_remove(data_csv$Date, "X")) # Remove the X from the date data_csv <- data_csv %>% mutate(date = mdy(data_csv$Date)) # Convert to actual date values data_csv <- data_csv %>% select(-Date) # Get rid of unnecessary Date column covid_data_deaths <- data_csv covid_data_deaths <- covid_data_deaths %>% group_by(Country.Region, date) %>% summarise(Cumulative_Deaths = as.integer(sum(value))) covid_data_deaths <- covid_data_deaths %>% mutate(New_Deaths = case_when( lag(Country.Region, n = 1L) == Country.Region ~ Cumulative_Deaths - lag(Cumulative_Deaths, 1L), TRUE ~ as.integer(0) ) ) covid_data_global <- left_join(covid_data_cases, covid_data_deaths) data_source_global_recovered <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv" data_csv <- read.csv(data_source_global_recovered, header = TRUE, sep = ",") # Read in csv file from the web data_csv <- data_csv %>% pivot_longer(cols = -c(1:4), names_to = "Date") # Move Dates to one column data_csv <- data_csv %>% mutate(Date = str_remove(data_csv$Date, "X")) # Remove the X from the date data_csv <- data_csv %>% mutate(date = mdy(data_csv$Date)) # Convert to actual date values data_csv <- data_csv %>% select(-Date) # Get rid of unnecessary Date column covid_data_recovered <- data_csv covid_data_recovered <- covid_data_recovered %>% group_by(Country.Region, date) %>% summarise(Cumulative_Recovered = as.integer(sum(value))) covid_data_recovered <- covid_data_recovered %>% mutate(New_Recovered = case_when( lag(Country.Region, n = 1L) == Country.Region ~ Cumulative_Recovered - lag(Cumulative_Recovered, 1L), TRUE ~ as.integer(0) ) ) covid_data_global <- left_join(covid_data_global, covid_data_recovered) covid_data_global_longer <- covid_data_global %>% pivot_longer(cols = c(Cumulative_Deaths, Cumulative_Recovered, New_Deaths, New_Recovered), names_to = "Case_Outcome", values_to = "Count") # Calculate Recovery Rate covid_data_global_recoveries <- covid_data_global %>% group_by(Country.Region) %>% summarise(Total_Deaths = max(Cumulative_Deaths), Total_Recovered = max(Cumulative_Recovered), Recovery_Rate = Total_Recovered/(Total_Deaths+Total_Recovered)) # Testing Data Source --------------------------------------------------------- covid_state_daily_source <- "https://covidtracking.com/api/v1/states/daily.json" covid_state_daily <- jsonlite::fromJSON(covid_state_daily_source) #update date covid_state_daily <- covid_state_daily %>% mutate(date = ymd(date)) #update state names state_names <- as.data.frame(cbind(state.abb,state.name)) # #Fix non-US states AS, GU, etc. # covid_state_daily %>% # filter(is.na(state.name) == T) %>% # count(state) state_names <- state_names %>% rbind(tribble( ~ state.abb, ~ state.name, "AS", "American Samoa", "DC", "District of Columbia", "GU", "Guam", "MP", "Northern Mariana Islands", "PR", "Puerto Rico", "VI", "Virgin Islands" ) ) covid_state_daily <- left_join(covid_state_daily, state_names, by = c("state" = "state.abb")) # Hospital Data (Beds and ICUs in use by Date and State) covid_state_daily_longer <- covid_state_daily %>% pivot_longer(cols = c(hospitalizedCurrently, inIcuCurrently, onVentilatorCurrently), names_to = ("current_hospitalization_severity")) # Join in population data to show % of pop tested state_populations <- select(covid_data_state, c(Province_State, Population)) %>% distinct() covid_state_daily_pop <- left_join(covid_state_daily, state_populations, by = c("state.name" = "Province_State")) # Total Tests Done by Each State temp_rank <- covid_state_daily_pop %>% filter(date == max(date)) %>% group_by(state.name) %>% summarise(percent_tests_pos = positive / posNeg, percent_pop_tested = posNeg / Population, percent_pop_positive = positive / Population, positive, negative, total_tests = posNeg) %>% arrange(desc(percent_pop_positive)) covid_state_daily_rank <- temp_rank %>% mutate(percent_tests_pos_rank = row_number(desc(percent_tests_pos)), total_positive_rank = row_number(desc(positive)), total_testing_rank = row_number(desc(total_tests))) %>% arrange(percent_tests_pos_rank) # WOW and MOM changes in cases, hospitalizations, and deaths # Monthly covid_state_daily_MOM <- covid_state_daily %>% mutate(week = epiweek(date), month = month(date, label = TRUE, abbr = FALSE), day = mday(date)) datemax <- max(covid_state_daily_MOM$date) # need to get current max date then filter previous months to do MTD calculation covid_state_daily_MOM <- covid_state_daily_MOM %>% filter(day <= day(datemax)) %>% # Make data MTD for each month group_by(state.name,month) %>% summarise(monthly_cases = sum(positiveIncrease), monthly_hospitalizations = sum(hospitalizedIncrease), monthly_deaths = sum(deathIncrease)) %>% arrange(desc(state.name, month)) %>% summarise(state.name, month, MOM_Cases = case_when(state.name == lag(state.name, 1L) ~ ((monthly_cases - lag(monthly_cases, 1L))/lag(monthly_cases, 1L)), TRUE ~ 0 ), MOM_Hospitalizations = case_when(state.name == lag(state.name, 1L) ~ ((monthly_hospitalizations - lag(monthly_hospitalizations, 1L))/lag(monthly_hospitalizations, 1L)), TRUE ~ 0 ), MOM_Deaths = case_when(state.name == lag(state.name, 1L) ~ ((monthly_deaths - lag(monthly_deaths, 1L))/lag(monthly_deaths, 1L)), TRUE ~ 0 )) %>% filter(month == max(month)) # Weekly covid_state_daily_WOW <- covid_state_daily %>% mutate(week = epiweek(date), month = month(date)) %>% group_by(state.name,week) %>% summarise(weekly_cases = sum(positiveIncrease), weekly_hospitalizations = sum(hospitalizedIncrease), weekly_deaths = sum(deathIncrease)) %>% arrange(desc(state.name, week)) %>% summarise(state.name, week, WOW_Cases = case_when(state.name == lag(state.name, 1L) ~ ((weekly_cases - lag(weekly_cases, 1L))/lag(weekly_cases, 1L)), TRUE ~ 0 ), WOW_Hospitalizations = case_when(state.name == lag(state.name, 1L) ~ ((weekly_hospitalizations - lag(weekly_hospitalizations, 1L))/lag(weekly_hospitalizations, 1L)), TRUE ~ 0 ), WOW_Deaths = case_when(state.name == lag(state.name, 1L) ~ ((weekly_deaths - lag(weekly_deaths, 1L))/lag(weekly_deaths, 1L)), TRUE ~ 0 )) %>% filter(week == max(week) - 1) # Join tables covid_state_daily_WOW_MOM <- covid_state_daily_MOM %>% left_join(covid_state_daily_WOW, by = "state.name") # Start Shiny App --------------------------------------------------------- # UI ---------------------------------------------------------------------- ui <- navbarPage(title = "COVID-19 Case Tracker", tabPanel("Summary", fluidPage( sidebarLayout( sidebarPanel( plotOutput(outputId = "top10_counties_summary"), plotOutput(outputId = "top10_states_summary") ), mainPanel( tags$h2("States With New Case Peak in the Past Week"), dataTableOutput(outputId = "recent_peaks_state", width = "100%"), plotOutput(outputId = "total_tests"), plotOutput(outputId = "summary_pct_pos_tests"), tags$h2("% Increase in Cases, Hospitalizations, and Deaths by State"), tags$h3("Current Month-to-Date and Most Recent Week"), dataTableOutput(outputId = "WOW_MOM_state", width = "100%"), tags$p("A shiny app by ", tags$a("Kyle Bennison", href="https://www.linkedin.com/in/kylebennison", target="_blank"), " - ", tags$a("@kylebeni012", href="https://www.twitter.com/kylebeni012", target="_blank")), tags$p("Data - ", tags$a("Johns Hopkins CSSE" , href="https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series", target="_blank")) ) ) ) ), tabPanel("County", fluidPage( sidebarLayout( sidebarPanel( selectizeInput(inputId = "casetype", label = "Select Cases to Display", choices = c("New Cases", "Total Cases")), selectizeInput(inputId = "county", label = "Select a county", choices = unique(covid_data$Combined_Key), selected = NULL, multiple = TRUE, options = list(maxItems = 3, placeholder = "Select up to 3 counties") ), tags$h6("Optionally, you can type the state name to filter the list down to just the counties in that state.", style = "margin-bottom:30px; color:#545454"), dateRangeInput(inputId = "daterange", label = "Select Date Range", start = today()-60, end = max(covid_data$date), min = "2020-01-15"), plotOutput(outputId = "top10_counties") ), mainPanel( plotOutput(outputId = "cases"), plotOutput(outputId = "deaths"), tags$p("A shiny app by ", tags$a("Kyle Bennison", href="https://www.linkedin.com/in/kylebennison", target="_blank"), " - ", tags$a("@kylebeni012", href="https://www.twitter.com/kylebeni012", target="_blank")), tags$p("Data - ", tags$a("Johns Hopkins CSSE" , href="https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series", target="_blank")) ) ) ) ), navbarMenu("State", tabPanel("Cases and Deaths", fluidPage( sidebarLayout( sidebarPanel( selectizeInput(inputId = "casetype_state", label = "Select Cases to Display", choices = c("New Cases", "Total Cases")), selectizeInput(inputId = "state", label = "Select a state", choices = unique(covid_data$Province_State), selected = NULL, multiple = TRUE, options = list(maxItems = 3, placeholder = "Select up to 3 states") ), dateRangeInput(inputId = "daterange_state", label = "Select Date Range", start = today()-60, end = max(covid_data$date), min = "2020-01-15"), plotOutput(outputId = "top10_states") ), mainPanel( plotOutput(outputId = "cases_state"), plotOutput(outputId = "deaths_state"), tags$p("A shiny app by ", tags$a("Kyle Bennison", href="https://www.linkedin.com/in/kylebennison", target="_blank"), " - ", tags$a("@kylebeni012", href="https://www.twitter.com/kylebeni012", target="_blank")), tags$p("Data - ", tags$a("Johns Hopkins CSSE" , href="https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series", target="_blank")) ) ) ) ), tabPanel("Testing and Hospitalizations", fluidPage( tags$h2("Detailed Testing Data by State"), dataTableOutput(outputId = "total_testing_dt_state", width = "100%"), tags$p("Data - ", tags$a("COVID Tracking Project" , href="https://covidtracking.com/api", target="_blank")), tags$hr(), dateRangeInput(inputId = "daterange_testing", label = "Select Date Range", start = today()-60, end = max(covid_state_daily$date), min = "2020-01-15"), selectizeInput(inputId = "testing_state", label = "Select a state", choices = unique(covid_state_daily$state.name), selected = NULL, multiple = FALSE, options = list(maxItems = 1, placeholder = "Select a state") ), plotOutput(outputId = "hospitalizations_state"), tags$p("Data - ", tags$a("COVID Tracking Project" , href="https://covidtracking.com/api", target="_blank")), plotOutput(outputId = "testing_state"), tags$p("Data - ", tags$a("COVID Tracking Project" , href="https://covidtracking.com/api", target="_blank")), selectizeInput(inputId = "pos_test_input_state", label = "Select a state", choices = unique(covid_state_daily$state.name), selected = NULL, multiple = TRUE, options = list(maxItems = 3, placeholder = "Select up to 3 states") ), plotOutput(outputId = "pos_tests_state"), tags$p("Data - ", tags$a("COVID Tracking Project" , href="https://covidtracking.com/api", target="_blank")), tags$p("A shiny app by ", tags$a("Kyle Bennison", href="https://www.linkedin.com/in/kylebennison", target="_blank"), " - ", tags$a("@kylebeni012", href="https://www.twitter.com/kylebeni012", target="_blank")) ) ) ), tabPanel("US", fluidPage( sidebarLayout( sidebarPanel( selectizeInput(inputId = "casetype_US", label = "Select Cases to Display", choices = c("New Cases", "Total Cases")), dateRangeInput(inputId = "daterange_US", label = "Select Date Range", start = today()-60, end = max(covid_data$date), min = "2020-01-15") ), mainPanel( plotOutput(outputId = "cases_US"), plotOutput(outputId = "deaths_US"), tags$p("A shiny app by ", tags$a("Kyle Bennison", href="https://www.linkedin.com/in/kylebennison", target="_blank"), " - ", tags$a("@kylebeni012", href="https://www.twitter.com/kylebeni012", target="_blank")), tags$p("Data - ", tags$a("Johns Hopkins CSSE" , href="https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series", target="_blank")) ) ) ) ), tabPanel("Global", fluidPage( sidebarLayout( sidebarPanel( selectizeInput(inputId = "casetype_global", label = "Select Cases to Display", choices = c("New Cases", "Total Cases")), selectizeInput(inputId = "country", label = "Select a country", choices = unique(covid_data_global$Country.Region), selected = NULL, multiple = TRUE, options = list(maxItems = 1, placeholder = "Select a country") ), dateRangeInput(inputId = "daterange_global", label = "Select Date Range", start = today()-60, end = max(covid_data_global$date), min = "2020-01-15") ), mainPanel( plotOutput(outputId = "cases_global"), plotOutput(outputId = "deaths_global"), tags$hr(), tags$h2("Recovery Rate by Country"), dataTableOutput(outputId = "recovery_rate_global"), tags$p("A shiny app by ", tags$a("Kyle Bennison", href="https://www.linkedin.com/in/kylebennison", target="_blank"), " - ", tags$a("@kylebeni012", href="https://www.twitter.com/kylebeni012", target="_blank")), tags$p("Data - ", tags$a("Johns Hopkins CSSE" , href="https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series", target="_blank")) ) ) ) ) ) # Server ------------------------------------------------------------------ server <- function(input, output) { # Summary Output ---------------------------------------------------------- output$top10_counties_summary <- renderPlot( { covid_data %>% filter(date == max(covid_data$date)) %>% top_n(10, New_Cases) %>% ggplot() + geom_col(aes(x = Combined_Key, y = New_Cases), fill = staturdays_colors("lightest_blue")) + labs(title = "Counties with Most\nNew Cases Today", subtitle = paste0("Data as of ", format.Date(max(covid_data$date), "%B %d, %Y")), x = "County", y = "Number of Cases", caption = "@kylebeni012 | @staturdays") + staturdays_theme + theme(plot.title = element_text(color = staturdays_colors("dark_blue"), size = 15, face = "bold"), plot.subtitle = element_text(size = 10)) + scale_y_continuous(labels = comma) + theme(axis.text.x = element_text(angle = 90)) + scale_x_discrete(labels = function(x) str_remove(x, ", US")) } ) output$top10_states_summary <- renderPlot( { covid_data_state %>% ungroup() %>% filter(date == max(date)) %>% slice_max(n = 10, order_by = New_Cases) %>% ggplot() + geom_col(aes(x = Province_State, y = New_Cases), fill = staturdays_colors("light_blue")) + labs(title = "States with Most\nNew Cases Today", subtitle = paste0("Data as of ", format.Date(max(covid_data_state$date), "%B %d, %Y")), x = "State", y = "Number of Cases", caption = "@kylebeni012 | @staturdays") + staturdays_theme + theme(plot.title = element_text(color = staturdays_colors("dark_blue"), size = 15, face = "bold"), plot.subtitle = element_text(size = 10)) + scale_y_continuous(labels = comma) + theme(axis.text.x = element_text(angle = 90)) } ) output$recent_peaks_state <- renderDataTable( { datatable({covid_data_state %>% group_by(Province_State) %>% slice_max(New_Cases) %>% filter(New_Cases != 0, today()-date <= 7) %>% select(1:3)}, colnames = c("State", "Date of Peak", "New Cases"), caption = paste0("Data as of ", format.Date(max(covid_data_state$date), "%B %d, %Y")), options = list(scrollX = TRUE, pageLength = 50)) %>% DT::formatRound(3, digits = 0) %>% DT::formatDate(2) } ) output$total_tests <- renderPlot( { covid_state_daily %>% filter(date >= today() - 30) %>% group_by(date) %>% summarise(total_tests = sum(totalTestResultsIncrease)) %>% ggplot(aes(x = date, y = total_tests)) + geom_col(fill = staturdays_colors("orange")) + labs(title = "New Tests in US by Day", subtitle = paste0("Data as of ", format.Date(max(covid_state_daily$date), "%B %d, %Y")), x = "Date", y = "Number of Tests", caption = "@kylebeni012 | @staturdays") + staturdays_theme + theme(plot.title = element_text(color = staturdays_colors("dark_blue"), size = 15, face = "bold"), plot.subtitle = element_text(size = 10)) + scale_y_continuous(labels = comma) } ) output$summary_pct_pos_tests <- renderPlot( { covid_state_daily %>% filter(date >= today() - 60) %>% group_by(date) %>% summarise(pct_positive = (sum(positiveIncrease)/sum(totalTestResultsIncrease))) %>% ggplot(aes(x = date, y = pct_positive)) + geom_line(colour = staturdays_colors("light_blue")) + labs(title = "Positive Test Rate in the US by Day", subtitle = paste0("Data as of ", format.Date(max(covid_state_daily$date), "%B %d, %Y")), x = "Date", y = "Positive Test Percentage", caption = "@kylebeni012 | @staturdays") + staturdays_theme + theme(plot.title = element_text(color = staturdays_colors("dark_blue"), size = 15, face = "bold"), plot.subtitle = element_text(size = 10)) + scale_y_continuous(labels = percent) } ) output$WOW_MOM_state <- renderDataTable( { datatable( covid_state_daily_WOW_MOM, colnames = c("State", "Month", "Monthly Cases", "Monthly Hospitalizations", "Monthly Deaths", "Week of Year", "Weekly Cases", "Weekly Hospitalizations", "Weekly Deaths"), caption = paste0("Data as of ", format.Date(today() - 1, "%B %d, %Y")), options = list(pageLength = 60, scrollX = TRUE, columnDefs = list(list(className = 'dt-left', targets = 0:8))), rownames = FALSE) %>% DT::formatPercentage(c(3:5, 7:9)) } ) # County Output ----------------------------------------------------------- output$top10_counties <- renderPlot( { covid_data %>% filter(date == max(covid_data$date)) %>% top_n(10, New_Cases) %>% ggplot() + geom_col(aes(x = Combined_Key, y = New_Cases), fill = staturdays_colors("orange")) + labs(title = "Counties with Most\nNew Cases Today", subtitle = paste0("Data as of ", format.Date(max(covid_data$date), "%B %d, %Y")), x = "County", y = "Number of Cases", caption = "@kylebeni012 | @staturdays") + staturdays_theme + theme(plot.title = element_text(color = staturdays_colors("dark_blue"), size = 15, face = "bold"), plot.subtitle = element_text(size = 10)) + scale_y_continuous(labels = comma) + theme(axis.text.x = element_text(angle = 90)) + scale_x_discrete(labels = function(x) str_remove(x, ", US")) } ) output$cases <- renderPlot( { covid_data %>% filter(Combined_Key %in% c(input$county), date >= input$daterange[1] & date <= input$daterange[2]) %>% ggplot() + { if(input$casetype == "New Cases") geom_line(aes(y = New_Cases, x = date, colour = Combined_Key), na.rm = T)} + { if(input$casetype == "Total Cases") geom_line(aes(y = Cumulative_Cases, x = date, colour = Combined_Key), na.rm = T)} + labs(title = input$casetype, subtitle = paste0("Data as of ", format.Date(max(covid_data$date), "%B %d, %Y")), x = "Date", y = "Number of Cases", color = "County", caption = "@kylebeni012 | @staturdays") + staturdays_theme + theme(legend.position = "bottom") + guides(color = guide_legend(nrow = 3, byrow = TRUE)) + expand_limits(y = 0) + scale_y_continuous(labels = comma) } ) output$deaths <- renderPlot( { covid_data %>% filter(Combined_Key %in% c(input$county), date >= input$daterange[1] & date <= input$daterange[2]) %>% ggplot() + { if(input$casetype == "New Cases") geom_line(aes(y = New_Deaths, x = date, colour = Combined_Key), na.rm = T)} + { if(input$casetype == "Total Cases") geom_line(aes(y = Cumulative_Deaths, x = date, colour = Combined_Key), na.rm = T)} + labs(title = paste0(str_remove(input$casetype, " Cases"), " Deaths"), subtitle = paste0("Data as of ", format.Date(max(covid_data$date), "%B %d, %Y")), x = "Date", y = "Number of Deaths", color = "County", caption = "@kylebeni012 | @staturdays") + staturdays_theme + theme(legend.position = "bottom") + guides(color = guide_legend(nrow = 3, byrow = TRUE)) + expand_limits(y = 0) + scale_y_continuous(labels = comma) } ) # State Cases Output ------------------------------------------------------ output$top10_states <- renderPlot( { covid_data_state %>% ungroup() %>% filter(date == max(date)) %>% slice_max(n = 10, order_by = New_Cases) %>% ggplot() + geom_col(aes(x = Province_State, y = New_Cases), fill = staturdays_colors("orange")) + labs(title = "States with Most\nNew Cases Today", subtitle = paste0("Data as of ", format.Date(max(covid_data_state$date), "%B %d, %Y")), x = "State", y = "Number of Cases", caption = "@kylebeni012 | @staturdays") + staturdays_theme + theme(plot.title = element_text(color = staturdays_colors("dark_blue"), size = 15, face = "bold"), plot.subtitle = element_text(size = 10)) + scale_y_continuous(labels = comma) + theme(axis.text.x = element_text(angle = 90)) } ) output$cases_state <- renderPlot( { covid_data_state %>% filter(Province_State %in% c(input$state), date >= input$daterange_state[1] & date <= input$daterange_state[2]) %>% ggplot() + { if(input$casetype_state == "New Cases") geom_line(aes(y = New_Cases, x = date, colour = Province_State), na.rm = T)} + { if(input$casetype_state == "Total Cases") geom_line(aes(y = Cumulative_Cases, x = date, colour = Province_State), na.rm = T)} + labs(title = input$casetype_state, subtitle = paste0("Data as of ", format.Date(max(covid_data_state$date), "%B %d, %Y")), x = "Date", y = "Number of Cases", color = "State", caption = "@kylebeni012 | @staturdays") + staturdays_theme + theme(legend.position = "bottom") + guides(color = guide_legend(nrow = 3, byrow = TRUE)) + expand_limits(y = 0) + scale_y_continuous(labels = comma) } ) output$deaths_state <- renderPlot( { covid_data_state %>% filter(Province_State %in% c(input$state), date >= input$daterange_state[1] & date <= input$daterange_state[2]) %>% ggplot() + { if(input$casetype_state == "New Cases") geom_line(aes(y = New_Deaths, x = date, colour = Province_State), na.rm = T)} + { if(input$casetype_state == "Total Cases") geom_line(aes(y = Cumulative_Deaths, x = date, colour = Province_State), na.rm = T)} + labs(title = paste0(str_remove(input$casetype_state, " Cases"), " Deaths"), subtitle = paste0("Data as of ", format.Date(max(covid_data_state$date), "%B %d, %Y")), x = "Date", y = "Number of Deaths", color = "State", caption = "@kylebeni012 | @staturdays") + staturdays_theme + theme(legend.position = "bottom") + guides(color = guide_legend(nrow = 3, byrow = TRUE)) + expand_limits(y = 0) + scale_y_continuous(labels = comma) } ) # State Testing Output ---------------------------------------------------- output$hospitalizations_state <- renderPlot( { covid_state_daily_longer %>% filter(state.name %in% c(input$testing_state), date >= input$daterange_testing[1] & date <= input$daterange_testing[2]) %>% ggplot() + geom_line(aes(y = value, x = date, color = current_hospitalization_severity), na.rm = T) + labs(title = "Current Hospitalization Data", subtitle = paste0("Data as of ", format.Date(max(covid_state_daily_longer$date), "%B %d, %Y")), x = "Date", y = "Number of Patients", color = "Severity Level", caption = "@kylebeni012 | @staturdays") + staturdays_theme + theme(legend.position = "bottom", plot.title = element_text(size = 20)) + guides(color = guide_legend(nrow = 3, byrow = TRUE)) + expand_limits(y = 0) + scale_y_continuous(labels = comma) + scale_color_viridis_d(labels = c("Hospitalized", "In ICU", "On Ventilator")) } ) output$testing_state <- renderPlot( { if(input$testing_state == ""){ return(covid_state_daily %>% ggplot() + labs(title = "Current Testing Data", subtitle = paste0("Data as of ", format.Date(max(covid_state_daily$date), "%B %d, %Y")), x = "Date", y = "Number of Tests", color = "Test Outcome", caption = "@kylebeni012 | @staturdays") + staturdays_theme + theme(legend.position = "bottom", plot.title = element_text(size = 20)) + guides(color = guide_legend(nrow = 3, byrow = TRUE)) + expand_limits(y = 0) + scale_y_continuous(labels = comma) + scale_color_viridis_d(labels = c("Negative", "Positive"), option = "C", begin = .3, end = .7)) } else { covid_state_daily %>% filter(state.name %in% c(input$testing_state), date >= input$daterange_testing[1] & date <= input$daterange_testing[2]) %>% ggplot() + geom_line(aes(y = positiveIncrease, x = date, color = "Positive"), na.rm = T) + geom_line(aes(y = negativeIncrease, x = date, color = "Negative"), na.rm = T) + labs(title = "Current Testing Data", subtitle = paste0("Data as of ", format.Date(max(covid_state_daily$date), "%B %d, %Y")), x = "Date", y = "Number of Tests", color = "Test Outcome", caption = "@kylebeni012 | @staturdays") + staturdays_theme + theme(legend.position = "bottom", plot.title = element_text(size = 20)) + guides(color = guide_legend(nrow = 3, byrow = TRUE)) + expand_limits(y = 0) + scale_y_continuous(labels = comma) + scale_color_viridis_d(labels = c("Negative", "Positive"), option = "C", begin = .3, end = .7) #First label is getting applied via alphabetical order of colors above } } ) output$pos_tests_state <- renderPlot( { covid_state_daily %>% group_by(state.name, date) %>% summarise(percent_positive_test = positiveIncrease/(sum(positiveIncrease, negativeIncrease))) %>% filter(state.name %in% c(input$pos_test_input_state), date >= input$daterange_testing[1] & date <= input$daterange_testing[2]) %>% ggplot() + geom_smooth(aes(y = percent_positive_test, x = date, colour = state.name), na.rm = T) + labs(title = "Positive Test Percentage", subtitle = paste0("Data as of ", format.Date(max(covid_state_daily$date), "%B %d, %Y")), x = "Date", y = "Percent of Tests Positive", color = "State", caption = "@kylebeni012 | @staturdays") + staturdays_theme + guides(color = guide_legend(nrow = 3, byrow = TRUE)) + theme(legend.position = "bottom", plot.title = element_text(size = 20)) + expand_limits(y = 0) + scale_y_continuous(labels = percent) + scale_color_viridis_d() } ) output$total_testing_dt_state <- renderDataTable( { datatable(covid_state_daily_rank, colnames = c("State", "Percent of Tests Positive", "Percent of Population Tested", "Percent of Population Positive", "Positive Results", "Negative Results", "Total Tests", "Ranking - Percent of Tests Positive", "Ranking - Total Positives", "Ranking - Total Tests"), caption = paste0("Data as of ", format.Date(max(covid_state_daily_pop$date), "%B %d, %Y")), options = list(scrollX = TRUE)) %>% formatPercentage(2:4, digits = 2) %>% DT::formatRound(5:7, digits = 0) } ) # US Output --------------------------------------------------------------- output$cases_US <- renderPlot( { covid_data_us %>% filter(date >= input$daterange_US[1] & date <= input$daterange_US[2]) %>% ggplot() + { if(input$casetype_US == "New Cases") geom_line(aes(y = New_Cases, x = date), na.rm = T)} + { if(input$casetype_US == "Total Cases") geom_line(aes(y = Cumulative_Cases, x = date), na.rm = T)} + labs(title = input$casetype_US, subtitle = paste0("Data as of ", format.Date(max(covid_data_us$date), "%B %d, %Y")), x = "Date", y = "Number of Cases", color = "State", caption = "@kylebeni012 | @staturdays") + staturdays_theme + theme(legend.position = "bottom") + guides(color = guide_legend(nrow = 3, byrow = TRUE)) + expand_limits(y = 0) + scale_y_continuous(labels = comma) } ) output$deaths_US <- renderPlot( { covid_data_us %>% filter(date >= input$daterange_US[1] & date <= input$daterange_US[2]) %>% ggplot() + { if(input$casetype_US == "New Cases") geom_line(aes(y = New_Deaths, x = date), na.rm = T)} + { if(input$casetype_US == "Total Cases") geom_line(aes(y = Cumulative_Deaths, x = date), na.rm = T)} + labs(title = paste0(str_remove(input$casetype_US, " Cases"), " Deaths"), subtitle = paste0("Data as of ", format.Date(max(covid_data_us$date), "%B %d, %Y")), x = "Date", y = "Number of Deaths", color = "State", caption = "@kylebeni012 | @staturdays") + staturdays_theme + theme(legend.position = "bottom") + guides(color = guide_legend(nrow = 3, byrow = TRUE)) + expand_limits(y = 0) + scale_y_continuous(labels = comma) } ) # Global Output ----------------------------------------------------------- output$cases_global <- renderPlot( { covid_data_global %>% filter(Country.Region %in% c(input$country), date >= input$daterange_global[1] & date <= input$daterange_global[2]) %>% ggplot() + { if(input$casetype_global == "New Cases") geom_line(aes(y = New_Cases, x = date, colour = Country.Region), na.rm = T)} + { if(input$casetype_global == "Total Cases") geom_line(aes(y = Cumulative_Cases, x = date, colour = Country.Region), na.rm = T)} + labs(title = input$casetype_global, subtitle = paste0("Data as of ", format.Date(max(covid_data_global$date), "%B %d, %Y")), x = "Date", y = "Number of Cases", color = "Country", caption = "@kylebeni012 | @staturdays") + staturdays_theme + theme(legend.position = "bottom") + guides(color = guide_legend(nrow = 3, byrow = TRUE)) + expand_limits(y = 0) + scale_y_continuous(labels = comma) } ) output$deaths_global <- renderPlot( { global_recover_1 <- if (identical(input$casetype_global, "New Cases")) { filter(covid_data_global_longer,Case_Outcome %in% c("New_Deaths", "New_Recovered")) } else if (identical(input$casetype_global, "Total Cases")) { filter(covid_data_global_longer,Case_Outcome %in% c("Cumulative_Deaths", "Cumulative_Recovered")) } global_recover_2 <- global_recover_1 %>% filter(Country.Region %in% c(input$country), date >= input$daterange_global[1] & date <= input$daterange_global[2]) global_recover_2 %>% ggplot() + geom_line(aes(y = Count, x = date, colour = Country.Region, linetype = Case_Outcome), na.rm = T) + labs(title = paste0(str_remove(input$casetype_global, " Cases"), " Outcomes"), subtitle = paste0("Data as of ", format.Date(max(covid_data_global$date), "%B %d, %Y")), x = "Date", y = "Number of Outcomes", color = "Country", linetype = "Case Outcome", caption = "@kylebeni012 | @staturdays") + staturdays_theme + theme(legend.position = c(0.75, 0.75), legend.spacing = unit(0, units = "points")) + guides(color = guide_legend(nrow = 1, byrow = TRUE), linetype = guide_legend(nrow = 2, byrow = TRUE)) + expand_limits(y = 0) + scale_y_continuous(labels = comma) + { if(input$casetype_global == "New Cases") scale_linetype_manual(labels = c("New Deaths", "New Recoveries"), values = c("solid", "dashed")) } + { if(input$casetype_global == "Total Cases") scale_linetype_manual(labels = c("Total Deaths", "Total Recoveries"), values = c("solid", "dashed")) } } ) output$recovery_rate_global <- renderDataTable( datatable(covid_data_global_recoveries, colnames = c("Country", "Total Deaths", "Total Recoveries", "Recovery Rate"), caption = "Total Recoveries Divided By Recoveries Plus Deaths") %>% formatPercentage(4, digits = 2) %>% DT::formatRound(2:3, digits = 0) ) } # Run App ----------------------------------------------------------------- shinyApp(ui = ui, server = server)

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/RcppExports.R \name{ExpectationGaussianSufficientStatistics} \alias{ExpectationGaussianSufficientStatistics} \title{Gaussian transform natural parameters to E[T(X)] parameters.} \usage{ ExpectationGaussianSufficientStatistics(eta) } \arguments{ \item{eta}{The natural parameter vector.} } \description{ Gaussian transform natural parameters to E[T(X)] parameters. }

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# Notebook's basic functions # # You can learn more about package authoring with RStudio at: # # http://r-pkgs.had.co.nz/ # # Some useful keyboard shortcuts for package authoring: # # Build and Reload Package: 'Cmd + Shift + B' # Check Package: 'Cmd + Shift + E' # Test Package: 'Cmd + Shift + T' #' Return a Notebook #' #' If \code{id=NULL} the function runs an new empty notebook, else the notebook given as argument is run. #' #' @param id id of the notebook #' #' @return A shinyApp object which run in browser (see https://shiny.rstudio.com/reference/shiny/latest/shinyApp.html for more information). #' #' @examples #' runNotebook() #' #' @import shiny #' @import shinydashboard #' @export runNotebook <- function(id=NULL, port=1994) { message("Start notebook...") # add notebook name/id in the future shinyApp(ui=notebookUI, server=notebookServer, options = list(port=port)) } #' Notebook UI #' #' If \code{id=NULL} the function runs an new empty notebook, else the notebook given as argument is run. #' #' @param id id of the notebook #' #' @return A shinyApp object which run in browser (see https://shiny.rstudio.com/reference/shiny/latest/shinyApp.html for more information). #' #' @examples #' runNotebook() #' #' @import shiny #' @import shinydashboard #' @export notebookUI <- function(request){ header <- dashboardHeader(title = "{ ShinyNotebook }", tags$li(tags$a(tags$span(icon("plus"), style="margin-right:10px")," Cell", `class`="dropdown-toggle action-button shiny-bound-input", `id`="addCellBtn"), `class`="dropdown"), tags$li(tags$a(tags$span(icon("save")), `class`="dropdown-toggle action-button shiny-bound-input", `id`="._bookmark_"), `class`="dropdown")) sidebar <- dashboardSidebar( sidebarMenu(style="position:fixed; width:230px; height:calc(100vh - 50px); overflow-y:scroll", div(id='end_menu_out_treat') ) ) body <- dashboardBody() return(dashboardPage(header, sidebar, body)) } #' Notebook Server #' #' If \code{id=NULL} the function runs an new empty notebook, else the notebook given as argument is run. #' #' @param id id of the notebook #' #' @return A shinyApp object which run in browser (see https://shiny.rstudio.com/reference/shiny/latest/shinyApp.html for more information). #' #' @examples #' runNotebook() #' #' @import shiny #' @import shinydashboard #' @export notebookServer <- function(input, output, session){ # get_page <- function(session = shiny::getDefaultReactiveDomain()) { # session$userData$shiny.router.page()$path # } enableBookmarking(store = "server") # NotebookSession # NotebookSession is an Reference Class (see Reference Class documentation) # which is pass through all the modules of the session through the session$userData$NS variable # # This object can be seen as a slight wrapper to shiny library. # It allows to easily share variable between notebook cells and bookmark/restore notebooks. session$userData$NS <- NotebookSession$new(reactive=reactiveValues(), static=list(), private.reactive=reactiveValues(), private.static=list()) session$userData$NS$private.reactive[["cellCount"]] <- 0 session$userData$NS$private.reactive[["cellNames"]] <- c() session$userData$NS$private.reactive[["SessionCells"]] <- list() # Liste de Cell SessionCells$new(id=NULL, name=NULL) # UI button # The notebook UI is quite minimalist, thus there arren't a lot of observeEvent() # The few ones are defined bellow. # They concern the following buttons : addCellBtn, ... observeEvent(input[['addCellBtn']], { # Compute cell id new_cell_id <- session$userData$NS$private.reactive[["cellCount"]] + 1 session$userData$NS$private.reactive[["cellCount"]] <- new_cell_id session$userData$NS$private.reactive[["cellNames"]] <- c(session$userData$NS$private.reactive[["cellNames"]], paste("Cell", new_cell_id)) # session$userData$NS$private.reactive[["SessionCells"]]$addCell(id = new_cell_id, name = paste("Cell", new_cell_id)) print(NS(session$ns(new_cell_id))("ok")) session$userData$NS$private.reactive[["SessionCells"]][[new_cell_id]] <- session.new_cell <- CellSession$new(id=new_cell_id, userData=list(), bookmark = list(), ns = NS(session$ns(new_cell_id))) # Insert cell in UI session$userData$NS$insert_cell_UI(new_cell_id, session.new_cell) }) # Bookmarking is slightly refined with NotebookSession # # Bookmarking is very convenient in shiny as it automates bookmark procedure. # However, bookmarking has some intrinsinc limitations : can't handle UI's inserted through insertUI(), ... # Thus bookmarking need to be revised in the NotebookSession framework. # # Currently onBookmark and onRestore call session bookmarking are defined below. # Future improvements : make onBookmark() and onRestore() wrappers # # > Bookmark NotebookSession # called if the session is being bookmarked (bookmark button) onBookmark(function(state){ # 1. Bookmark NotebookSession state$values$NS <- session$userData$NS$bookmark_state() # 2. Bookmark cells for (bk_id in names(cell.session$bookmarkIds)){ if (cell.session$bookmarkIds[[bk_id]] == "textInput"){ print("wesh") print(input[[cell.session(bk_id)]]) cell.session$bookmark[[bk_id]] <- input[[cell.session(bk_id)]] } } }) # # > Restore NotebookSession # called if the session is being restored from a bookmark onRestore(function(state){ # 1. Restore NotebookSession session$userData$NS$restore_from_bookmarked_state(state) session$userData$NS$static$test <- "QLF" # 2. Restore cells lapply(1:session$userData$NS$private.reactive[["cellCount"]], function(cell_id){ session$userData$NS$insert_cell_UI(cell_id) }) }) }

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SEB113_CSA.R

# ===== START OF SCRIPT ===== # SEB 113 # Collaborative Scientific Article (CSA) # === Group member === # # Last Name: Siow # First Name: Yun Kai # Student No.: 9598138 # Course code: SE50 # ===== Dependency check ===== ## pacman, the package manager to check whether ## dependency libraries are installed, and load it if it does if (!require("pacman")) install.packages("pacman") # install pacman itself if it's not already pacman::p_load(ggplot2, dplyr, openair, GGally, reshape2, broom, lubridate, ggmap, gridExtra) # pacman to install/load libraries # latest ggplot2 needed to render subtitle, devtools::install_github("hadley/ggplot2") # ===== Dependency check ===== ## read raw csv file from cwd # air.quality.clinton.raw <- read.csv(file="data/clinton-aq-2015.csv", as.is=T, head=T) # Or read directly from data source. url <- "http://www.ehp.qld.gov.au/data-sets/air-quality/clinton-aq-2015.csv" air.quality.clinton.raw <- read.csv(file=url, as.is=T, head=T) ## Have a look on the first few data entries in the raw data frame head(air.quality.clinton.raw) ## Dimension of raw data frame dim(air.quality.clinton.raw) # it has 8760 entries and 17 variables ## Quick way to summarize the raw data frame summary(air.quality.clinton.raw) ## ==================== ## ## DATA WRANGLING ## ==================== ## # Lubridate to deal with date and time. Convert them to single variable colume # with POSIXct format so that R can understand it. # Overwrite the old "Date" variable. air.quality.clinton.raw$Date <- dmy_hm(paste(air.quality.clinton.raw$Date, air.quality.clinton.raw$Time)) # get rid of the old "Time" variable colume by assigning NULL to it # air.quality.clinton.raw$Time <- NULL # Lubridate to add few extra colume: day, month, year, yday, day_of_week air.quality.clinton.raw <- mutate(air.quality.clinton.raw, # day = day(Date), month = month(Date, label=T), # year = year(Date), # yday = yday(Date), day_of_week = wday(Date, label=T)) ## define data of interest, rearrange the seq data.of.interest <- c("Date", "Time", "month", "day_of_week", "PM2.5..ug.m.3.", "Wind.Speed..m.s.", "Wind.Direction..degTN.") air.quality.clinton <- subset(air.quality.clinton.raw, select = data.of.interest) ## Rename variable name names(air.quality.clinton) <- c("date", "time", "month", "day_of_week", "pm2.5", "ws", "wd") head(air.quality.clinton) ## Assign breakboint for cutting and labelling ## 0 and 360 is for NORTH breaks = c(0, seq(22.5, 337.5, by=45), 360) ## cut function dplyr to divides the range of feeded data into ## intervals and codes the values in "Direction" such as NE, E ... ## according to which interval they fall. ## Turn the continuous variable to categorial variable wd.label <- cut(air.quality.clinton$wd, breaks = breaks, dig.lab= 4, labels = c("N", "NE", "E", "SE", "S", "SW", "W", "NW", "N"), include.lowest = TRUE) ## Create new variable log.pm2.5 and wd.label air.quality.clinton <- mutate(air.quality.clinton, wd.label = wd.label) %>% mutate(log.pm2.5 = log(pm2.5)) %>% na.omit %>% filter(!is.nan(log.pm2.5) & !is.infinite(log.pm2.5)) # Check the levels of wd.label levels(air.quality.clinton$wd.label) # regroup both "N" level into only one level, should be only 8 instead of 9 levels(air.quality.clinton$wd.label) <- c("N", "NE", "E", "SE", "S", "SW", "W", "NW", "N") ## Check the range of produced log.pm2.5 range(air.quality.clinton$log.pm2.5) ## Check the latest data frame head(air.quality.clinton) ## summary(air.quality.clinton) ## ==================== ## ## Exploratory Data Visualisation ## ==================== ## # summarise variables summary(air.quality.clinton[,c(6,8,9)]) # Plot the pm2.5 concentration in air in histrogram pm1 <- ggplot(data=air.quality.clinton, aes(x=pm2.5)) + geom_histogram(binwidth = 0.1, col="#CD5D67", fill="#CD5D67", alpha=0.4) + theme_bw() + theme(plot.title = element_text(lineheight=.8, face="bold"), strip.background = element_rect(fill = "white", colour = "white")) + labs(title=expression(paste("Before: ",PM[2.5]," data skewed")), subtitle="bla bla bla", x= expression(paste(PM[2.5]," (",mu,g,m^-3,")")), y= "Count") # Plot the pm2.5 concentration in air in histrogram with log scale pm2 <- ggplot(data=air.quality.clinton, aes(x=log.pm2.5)) + geom_histogram(binwidth = 0.1, col="#61C9A8", fill="#61C9A8", alpha=0.7) + theme_bw() + theme(plot.title = element_text(lineheight=.8, face="bold"), strip.background = element_rect(fill = "white", colour = "white")) + labs(title="After log transformation", subtitle="bla bla bla", x= expression(paste(log," ",PM[2.5]," (",mu,g,m^-3,")")), y= "") grid.arrange(pm1, pm2, ncol=2) # Plot the wind direction in pie chart (wind_direction.pie <- ggplot(data=air.quality.clinton, aes(x=wd.label, fill = factor(wd.label))) + geom_bar(width = 1, color="white", alpha=0.7) + # rotation of the pie chart to 5.89 radian, clockwise. coord_polar(start = 5.89, direction=1) + theme_bw() + theme(legend.position="none") + labs(fill="Wind\nDirection", title = "Frequency of various wind direction", subtitle = "Site location: Latitude: -23.8701; Longitude: 151.2216", x = "Wind Direction", y = "Count") ) # histogram of ws (ws.histogram <- ggplot(data=air.quality.clinton, aes(x=ws)) + geom_histogram(binwidth = 0.1, col="#1789FC", fill="#1789FC", alpha=0.6) + theme_bw() + theme(plot.title = element_text(lineheight=.8, face="bold"), strip.background = element_rect(fill = "white", colour = "white")) + labs(title="Wind speed distribution", subtitle="Site location: Latitude: -23.8701; Longitude: 151.2216", x= expression(paste("Wind Speed (",ms^-1,")")), y= "Count") ) # ws vs wd ws_wd.plot <- ggplot(data=air.quality.clinton, aes(x=wd.label, y=ws)) + geom_boxplot(outlier.colour = NULL, aes_string(colour="wd.label", fill="wd.label")) + theme_bw() + theme(legend.position="none", plot.title = element_text(lineheight=.8, face="bold"), strip.background = element_rect(fill = "white", colour = "white")) + labs(title = "Wind speed vs Wind direction", subtitle = "further split it into 8 different wind direction facet", y = expression(paste("Wind speed, ", ms^-1)), x = "Wind Direction") # use the details of the plot, to derive the coordinates of where the median line is, # and then add colour to it using geom_segment. dat <- ggplot_build(ws_wd.plot)$data[[1]] (ws_wd.plot <- ws_wd.plot + geom_segment(data=dat, aes(x=xmin, xend=xmax, y=middle, yend=middle), colour="white", size=0.8) ) # Plot the pm2.5 concentration varies according to wind direction in boxplot pm_wd.plot <- ggplot(data=air.quality.clinton, aes(x=wd.label, y=log.pm2.5)) + geom_boxplot(outlier.colour = NULL, aes_string(colour="wd.label", fill="wd.label")) + theme_bw() + theme(legend.position="none", plot.title = element_text(lineheight=.8, face="bold"), strip.background = element_rect(fill = "white", colour = "white")) + labs(title = "PM2.5 concentration varies according to wind direction", subtitle = "Site location: Latitude: -23.8701; Longitude: 151.2216", x = "Wind Direction", y = expression(paste(log," ",PM[2.5]," (",mu,g,m^-3,")"))) # use the details of the plot, to derive the coordinates of where the median line is, # and then add colour to it using geom_segment. dat <- ggplot_build(pm_wd.plot)$data[[1]] (pm_wd.plot <- pm_wd.plot + geom_segment(data=dat, aes(x=xmin, xend=xmax, y=middle, yend=middle), colour="white", size=0.8) ) # ws and pm (pm_ws.plot <- ggplot(data=air.quality.clinton, aes(x=ws, y=log.pm2.5)) + geom_point( size=8, alpha=0.1, color="#545E56") + geom_smooth(method="lm", se=FALSE, color="#FF6978", alpha=0.6, size=0.5) + theme_bw() + theme(legend.position="none", plot.title = element_text(lineheight=.8, face="bold"), strip.background = element_rect(fill = "white", colour = "white")) + labs(title = "PM2.5 concentration plot against Wind Speed", subtitle = "", x = expression(paste("Wind Speed (",ms^-1,")")), y = expression(paste(log," ",PM[2.5]," (",mu,g,m^-3,")"))) ) # Look up table for facet_wrap label Direction <- c( N = "North", NE = "North East", E = "East", SE = "South East", S = "South", SW = "South West", W = "West", NW = "North West", N = "North" ) # global labeller used for facet label global_labeller <- labeller( wd.label = Direction ) # Facet plot, log PM2.5 concentration varies according to wind speed and wind direction (pm_ws_wd.plot <- ggplot(data=air.quality.clinton, aes(x=ws, y=log.pm2.5)) + geom_point(aes(color=wd.label), size=4, alpha=0.3) + geom_smooth(method="lm", se=FALSE, color="grey40", alpha=0.6, size=0.5) + facet_wrap(~wd.label, nrow = 2, labeller = global_labeller) + theme_bw() + theme(legend.position="none", plot.title = element_text(lineheight=.8, face="bold"), strip.background = element_rect(fill = "white", colour = "white")) + labs(title = "PM2.5 concentration varies with Wind speed and Wind Direction", subtitle = "further split it into 8 different wind direction facet", x = expression(paste("Wind Speed (",ms^-1,")")), y = expression(paste(log," ",PM[2.5]," (",mu,g,m^-3,")")) ) ) ## ==================== ## ## Fitting a Linear Model with Multiple Explanatory Variables ## ==================== ## ## fitting four model to see what happen to estimates # 1. PM2.5 ~ Wind Speed # 2. PM2.5 ~ Wind direction[s] # 3. PM2.5 ~ Wind Speed + Wind direction[s] # 4. PM2.5 ~ Wind speed * wind direction[s] (PM2.5 ~ WS + WD + WS x WD) # 5. PM2.5 ~ Wind speed : wind direction[s] (PM2.5 ~ WS x WD) # ======================================== # ## 1. log.pm2.5 ~ Wind Speed (y = B_0 + B_1X1) lm.pm_ws <- lm(data=air.quality.clinton, log.pm2.5 ~ ws) lm.pm_ws # summary of lm summary(lm.pm_ws) # Confident Interval tidy(lm.pm_ws, conf.int = T) # Check lm whether the residuals are normally distributed df.fort.pm_ws <- fortify(lm.pm_ws) head(df.fort.pm_ws) # ======================================== # ## 2. log.pm2.5 ~ Wind direction (y = B_0 + B_2X2) (lm.pm_wd <- lm(data=air.quality.clinton, log.pm2.5 ~ wd.label - 1)) (lm.pm_wd.intercept <- lm(data=air.quality.clinton, log.pm2.5 ~ wd.label)) # summary of lm summary(lm.pm_wd) summary(lm.pm_wd.intercept) # Confident Interval tidy(lm.pm_wd, conf.int = T) # Check lm whether the residuals are normally distributed df.fort.pm_wd <- fortify(lm.pm_wd) head(df.fort.pm_wd) # ======================================== # ## 3. log.pm2.5 ~ Wind Speed + Wind direction (y = B_0 + B_1X1 + B_2X2) (lm.pm_ws_wd <- lm(data=air.quality.clinton, log.pm2.5 ~ ws+wd.label - 1)) (lm.pm_ws_wd.intercept <- lm(data=air.quality.clinton, log.pm2.5 ~ ws+wd.label)) # summary of lm summary(lm.pm_ws_wd) summary(lm.pm_ws_wd.intercept) # Confident Interval tidy(lm.pm_ws_wd, conf.int = T) # Check lm whether the residuals are normally distributed df.fort.pm_ws_wd <- fortify(lm.pm_ws_wd) head(df.fort.pm_ws_wd) # =========== JUST THE INTERACTION TERMs ========= # ## 4. log.pm2.5 ~ Wind speed : wind direction. (log.pm2.5 ~ WS x WD) (y = B1X1Z1 + B2X1Z2 + ....) (lm.pm_wswd <- lm(data=air.quality.clinton, log.pm2.5 ~ ws:wd.label -1)) (lm.pm_wswd.intercept <- lm(data=air.quality.clinton, log.pm2.5 ~ ws:wd.label)) # summary of lm summary(lm.pm_wswd) summary(lm.pm_wswd.intercept) # for r2 ONLY # Confident Interval tidy(lm.pm_wswd, conf.int = T) # Check lm whether the residuals are normally distributed df.fort.pm_wswd <- fortify(lm.pm_wswd) head(df.fort.pm_wswd) # =========== WD AND THE INTERACTION TERM, THE BEST ========= # ## 5. log.pm2.5 ~ Wind direction + Wind speed : wind direction. (log.pm2.5 ~ WD + WS x WD) (y = B_0 + WD + B_1X1Z1 + ....) (lm.pm_wd_wswd <- lm(data=air.quality.clinton, log.pm2.5 ~ wd.label-1 + ws:wd.label)) (lm.pm_wd_wswd.intercept <- lm(data=air.quality.clinton, log.pm2.5 ~ wd.label + ws:wd.label)) # summary of lm summary(lm.pm_wd_wswd) summary(lm.pm_wd_wswd.intercept) # for r2 ONLY # Confident Interval tidy(lm.pm_wd_wswd, conf.int = T) # Check lm whether the residuals are normally distributed df.fort.pm_wd_wswd <- fortify(lm.pm_wd_wswd) head(df.fort.pm_wd_wswd) # =========== Both terms and with Interaction term ============ # ## 6. log.pm2.5 ~ Wind speed * wind direction (y = B1X1 + B2X2 + .... + BiX1X2 + BiX1Xi) (lm.pm_ws_wd_wswd <- lm(data=air.quality.clinton, log.pm2.5 ~ ws*wd.label-1)) (lm.pm_ws_wd_wswd.intercept <- lm(data=air.quality.clinton, log.pm2.5 ~ ws*wd.label)) # summary of lm summary(lm.pm_ws_wd_wswd) summary(lm.pm_ws_wd_wswd.intercept) # Confident Interval tidy(lm.pm_ws_wd_wswd, conf.int = T) # Check lm whether the residuals are normally distributed df.fort.pm_ws_wd_wswd <- fortify(lm.pm_ws_wd_wswd) head(df.fort.pm_ws_wd_wswd) # Model fitness # What is the coefficient of determination, R2, for these models? r2.check <- function(lm){ r2 <- broom::glance(lm)$r.squared return(r2) } # another function to check r2, for linear model with no intercept # r2.noint.check <- function(lm){ # 1 - sum(residuals(lm)^2) / sum((air.quality.clinton$log.pm2.5 - mean(air.quality.clinton$log.pm2.5))^2) # } # df to visualise all the model's r2 Model <- c("M1", "M2", "M3", "M4", "M5", "M6") R2s <- c(r2.check(lm.pm_ws), r2.check(lm.pm_wd.intercept), r2.check(lm.pm_ws_wd.intercept), r2.check(lm.pm_wswd.intercept), r2.check(lm.pm_wd_wswd.intercept), r2.check(lm.pm_ws_wd_wswd.intercept) ) (models.fitness.df <- data.frame(cbind(Model, R2s))) ## ========== Diagnostics for residuals ========== ## # residuals of each model into one big dataframe dat.resid <- data.frame(M1 = df.fort.pm_ws$.resid, M2 = df.fort.pm_wd$.resid, M3 = df.fort.pm_ws_wd$.resid, M4 = df.fort.pm_wswd$.resid, M5 = df.fort.pm_wd_wswd$.resid, M6 = df.fort.pm_ws_wd_wswd$.resid) head(dat.resid) # melt the data, and make a histogram # facet_wrap layer, to split the data into its four models dat.resid.melt <- melt(dat.resid) # plot all.resid.histogram <- ggplot(dat.resid.melt, aes(x = value)) + geom_histogram(binwidth = 0.5, col="white", alpha=0.7, aes(y = ..density.., fill=variable)) + facet_wrap(~variable) + stat_function(fun = dnorm, colour = "grey50", size=1) + theme_bw() + theme(legend.position="none", plot.title = element_text(lineheight=.8, face="bold"), strip.background = element_rect(fill = "white", colour = "white")) + labs(title="Residuals of each model against normal distribution", subtitle="bla bla bla", x="Residual", y="Density") # Residual histogram Resid.histogram <- ggplot(df.fort.pm_wd_wswd, aes(x = .resid)) + geom_histogram(binwidth = 0.5, col="white", alpha=0.7, fill="#4981D1", aes(y = ..density..)) + stat_function(fun = dnorm, colour = "grey50", size=1) + theme_bw() + theme(legend.position="none", plot.title = element_text(lineheight=.8, face="bold"), strip.background = element_rect(fill = "white", colour = "white")) + labs(title="Residuals from the model 5 plot against Normal Distribution", subtitle="Histogram of the residuals with density on the y axis with assumption of normal distribution of the residuals", x="Residual", y="Density") # Model 5 # Make a scatter plot of the fitted values vs the residuals, # including a smooth line of best fit to determine whether the residuals have a mean of zero homoplot <- ggplot(df.fort.pm_wd_wswd, aes(y=.resid, x=.fitted)) + geom_point(aes(color=wd.label), alpha=0.3) + geom_smooth(se=FALSE, color="blue", alpha=0.6, size=0.5) + theme_bw() + theme(plot.title = element_text(lineheight=.8, face="bold"), strip.background = element_rect(fill = "white", colour = "white")) + labs(title="Homogeneity of errors", subtitle="Residuals doesn't look like the variance stays the same as we move from left to right along the fitted values axis.", x="Fitted value", y="Residuals") # Does it look like there’s unexplained variation in the residuals? Why or why not? # Make a quantile-quantile plot of the standardised residuals from the interaction model. qqplot <- ggplot(df.fort.pm_wd_wswd, aes(sample=.stdresid)) + stat_qq(geom="point", color="#00CC99", size=5, alpha=0.3) + # geom_abline(xintercept=0, slope=1, lty=2) geom_abline(intercept=0, slope=1, lty=2, color="#FF1654", size=0.8) + # xintercept depreciated theme_bw() + theme(legend.position="none", plot.title = element_text(lineheight=.8, face="bold")) + labs(title="Normality of the residuals, \nquantile-quantile (QQ) plot of the standardised residuals.", subtitle="The quantiles of the values are plotted against the quantiles of a standard Normal.", x="Theoretical (Z ~ N(0,1))", y="Sample") # Create a scatterplot of the fitted vs observed data (yhat vs y), # including a line showing where the yhat and y are equal. goodnessplot <- ggplot(data=df.fort.pm_wd_wswd, aes(y=log.pm2.5, x=.fitted)) + geom_point(aes(color=wd.label), alpha=0.3, size=3) + geom_abline(intercept=0, slope=1) + theme_bw() + theme(plot.title = element_text(lineheight=.8, face="bold"), strip.background = element_rect(fill = "white", colour = "white")) + labs(title="Fitted vs observed data (yhat vs y)", subtitle="Line showing where the yhat and y are equal.", x="Fitted value (yhat)", y=expression(paste(log," ",PM[2.5]," (observed data)"))) # ==== ANOVA ==== # Use the F test via the anova() function to compare both model # model 4 and model 5 has diff r2, but close. Anova to compare both of them # model 4 nested inside of model 5 # use the fitted model with intercept when using anova comparison models.fitness.df anova(lm.pm_wswd.intercept, lm.pm_wd_wswd.intercept) # Model 5 vs. Model 6, model 5 nested in model 6 # Same r2 but model 6 has extra term (ws) anova(lm.pm_wd_wswd.intercept, lm.pm_ws_wd_wswd.intercept) # =========== ggmap ========= # # ggmap clinton = get_map(location = c(lon = 151.2216, lat = -23.8701), zoom = 16) clinton.satelite = get_map(location = c(lon = 151.2216, lat = -23.8701), zoom = 16, maptype = "satellite") # google map layer sensor_map <- ggmap(clinton, extent="device") + geom_point(aes(x=151.2216, y=-23.8701), color="red", size=7, alpha=0.05) + theme_bw() + annotate("text", x=151.2216, y=-23.8701, label = "Sensor", colour = I("red"), size = 3.5) + labs(title = "Instrument location at Clinton", subtitle = "Site location: Latitude: -23.8701; Longitude: 151.2216", x = "Longitude", y = "Latitude") # save objects -------------------- # create a new directory for the report stuff save.image(file="data/SEB113_CSA_Objects.RData", safe = TRUE) # ===== END OF SCRIPT =====

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/as-tsibble.R \name{is_regular} \alias{is_regular} \alias{is.regular} \alias{is.regular} \alias{is_ordered} \title{\code{is_regular} checks if a tsibble is spaced at regular time or not; \code{is_ordered} checks if a tsibble is ordered by key and index.} \usage{ is_regular(x) is.regular(x) is_ordered(x) } \arguments{ \item{x}{A tsibble object.} } \description{ \code{is_regular} checks if a tsibble is spaced at regular time or not; \code{is_ordered} checks if a tsibble is ordered by key and index. } \examples{ data(pedestrian) is_regular(pedestrian) is_ordered(pedestrian) }

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#dummy variables df=mtcars df$cyl=factor(df$cyl) df$am=factor(df$am) summary(df$cyl) summary(df$am) summary (df) m1=lm(mpg~wt+cyl+am,data=df) summary(m1) predict(m1,newdata=data.frame(wt=c(2,2),cyl=factor(c(4,6)),am=factor(c(1,1)))) y4=33.9908+-3.2*wt+0 y6=33.9908+-3.2*wt+-4.2*cyl6(=1) y8=33.9908+-3.2*wt+-6.07*cyl8(=1)

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#debug branch setwd("I:\\VisTrails\\VisTrails_SAHM_x64_debug\\VisTrails\\vistrails\\packages\\sahm_MarianDev\\pySAHM\\Resources\\R_Modules") ScriptPath="I:\\VisTrails\\VisTrails_SAHM_x64_debug\\VisTrails\\vistrails\\packages\\sahm_MarianDev\\pySAHM\\Resources\\R_Modules" dir.path<-"I:\\VisTrails\\VisTrails_SAHM_x32_debug\\VisTrails\\vistrails\\packages\\TestingRCode2\\TestSuite\\AcrossModelPerformance\\Debug10.29" #master branch setwd("I:\\VisTrails\\VisTrails_SAHM_x64_debug\\VisTrails\\vistrails\\packages\\sahm\\pySAHM\\Resources\\R_Modules") ScriptPath="I:\\VisTrails\\VisTrails_SAHM_x64_debug\\VisTrails\\vistrails\\packages\\sahm\\pySAHM\\Resources\\R_Modules" dir.path<-"I:\\VisTrails\\VisTrails_SAHM_x32_debug\\VisTrails\\vistrails\\packages\\TestingRCode2\\TestSuite\\AcrossModelPerformance\\Master10.15" #For Model tests source("LoadRequiredCode.r") source("MARS.helper.fcts.r") source("GLM.helper.fcts.r") source("BRT.helper.fcts.r") source("RF.helper.fcts.r") #For Apply Model Tests source("EvaluateNewData.r") #For PairsExplore and parameter inspection source("PairsExplore.r") source("Predictor.inspection.r") source("my.panel.smooth.binary.r") #For Data Splitting source("TestTrainSplit.r") source("CrossValidationSplit.r") file.list<-list.files("I:\\VisTrails\\VisTrails_SAHM_x32_debug\\VisTrails\\vistrails\\packages\\TestingRCode2\\TestSuite") rc=c(rep("responseCount",times=3),rep("responseBinary",times=10)) input.file<-vector() input.file=c("I:\\VisTrails\\VisTrails_SAHM_x32_debug\\VisTrails\\vistrails\\packages\\TestingRCode2\\TestSuite\\CountFactorCVEvaluation.csv", "I:\\VisTrails\\VisTrails_SAHM_x32_debug\\VisTrails\\vistrails\\packages\\TestingRCode2\\TestSuite\\CountFactorEvaluation.csv", "I:\\VisTrails\\VisTrails_SAHM_x32_debug\\VisTrails\\vistrails\\packages\\TestingRCode2\\TestSuite\\CountFactorSplit.csv", "I:\\VisTrails\\VisTrails_SAHM_x32_debug\\VisTrails\\vistrails\\packages\\TestingRCode2\\TestSuite\\ElithPsdoAbs.csv", "I:\\VisTrails\\VisTrails_SAHM_x32_debug\\VisTrails\\vistrails\\packages\\TestingRCode2\\TestSuite\\ElithSyntheticPresAbsLargeDat.csv", "I:\\VisTrails\\VisTrails_SAHM_x32_debug\\VisTrails\\vistrails\\packages\\TestingRCode2\\TestSuite\\ElithSynthPresAbsTestTrainEval.csv", "I:\\VisTrails\\VisTrails_SAHM_x32_debug\\VisTrails\\vistrails\\packages\\TestingRCode2\\TestSuite\\PresAbsEval.csv", "I:\\VisTrails\\VisTrails_SAHM_x32_debug\\VisTrails\\vistrails\\packages\\TestingRCode2\\TestSuite\\PresAbsFactorCVEvaluation.csv", "I:\\VisTrails\\VisTrails_SAHM_x32_debug\\VisTrails\\vistrails\\packages\\TestingRCode2\\TestSuite\\PresAbsNonSpatial.csv", "I:\\VisTrails\\VisTrails_SAHM_x32_debug\\VisTrails\\vistrails\\packages\\TestingRCode2\\TestSuite\\PresAbsNoSplit.csv", "I:\\VisTrails\\VisTrails_SAHM_x32_debug\\VisTrails\\vistrails\\packages\\TestingRCode2\\TestSuite\\PresAbsSelectionEval.csv", "I:\\VisTrails\\VisTrails_SAHM_x32_debug\\VisTrails\\vistrails\\packages\\TestingRCode2\\TestSuite\\UsedAvailableSp1CV.csv", "I:\\VisTrails\\VisTrails_SAHM_x32_debug\\VisTrails\\vistrails\\packages\\TestingRCode2\\TestSuite\\UsedAvailableSp1NoCV.csv") predictor<-c("NDVI_annualMinimumValue_2005","NDVI_browndownrates1_2009","romoveg_rc_categorical","Temperature","Noise2Rast","NDVI_amplitudes1_2006","ppt_1971_2000_06_800m", "NDVI_annualMeanValue_2006","NDVI_greenuprates1_2003") responseCol<-c(rep("responseBinary",times=6),rep("responseCount",times=1)) #I'm cutting these out of the standard test suite because they take a long time to run #and only test whether we run well on large datasets or big tiffs #"C:/VisTrails/mtalbert_20110504T132851/readMaTests/CanadaThistleNewFormat.csv" #"C:/VisTrails/mtalbert_20110504T132851/readMaTests/LargeSplit.csv" #add a missing data csv and maybe a couple with pseudo absence output.dir<-vector() output.dir[1]<-paste(dir.path,"\\rf",sep="") output.dir[2]<-paste(dir.path,"\\brt",sep="") output.dir[3]<-paste(dir.path,"\\mars",sep="") output.dir[4]<-paste(dir.path,"\\glm",sep="") output.dir[5]<-paste(dir.path,"\\maxlike",sep="") ######## Model Fit Test ########### ##BRT for(i in 1:length(input.file)){ try(FitModels(ma.name=input.file[i], tif.dir=NULL,output.dir=output.dir[2], response.col=rc[i],make.p.tif=T,make.binary.tif=F,n.folds=3,simp.method="cross-validation",tc=NULL,alpha=1, family = "bernoulli",max.trees = 10000,tolerance.method = "auto", tolerance = 0.001,seed=1,opt.methods=2, simp.method="cross-validation",debug.mode=T,responseCurveForm="pdf",script.name="brt", learning.rate =NULL, bag.fraction = 0.5,prev.stratify = TRUE, max.trees = NULL,opt.methods=2,MESS=F)) try(rm(out),silent=TRUE) } ##MARS for(i in 1:length(input.file)){ try(FitModels(ma.name=input.file[i], tif.dir=NULL,output.dir=output.dir[3], response.col=rc[i],make.p.tif=T,make.binary.tif=T, mars.degree=1,mars.penalty=2,debug.mode=T,responseCurveForm="pdf",script.name="mars",opt.methods=2,MESS=TRUE)) } ##GLM for(i in 1:length(input.file)){ try(FitModels(ma.name=input.file[i], tif.dir=NULL, output.dir=output.dir[4], response.col=rc[i],make.p.tif=T,make.binary.tif=F, simp.method="AIC",debug.mode=T,responseCurveForm="pdf",script.name="glm",MESS=FALSE,opt.methods=2,squared.terms=TRUE)) } ### Random Forest for(i in 1:length(input.file)){ proximity=NULL try(FitModels(ma.name=input.file[i], tif.dir=NULL, output.dir=output.dir[1], response.col=rc[i],make.p.tif=T,make.binary.tif=F, debug.mode=T,opt.methods=2,script.name="rf", responseCurveForm="pdf",xtest=NULL,ytest=NULL,n.trees=1000,mtry=NULL, samp.replace=FALSE,sampsize=NULL,nodesize=NULL,maxnodes=NULL,importance=FALSE, localImp=FALSE,nPerm=1,proximity=NULL,oob.prox=proximity,norm.votes=TRUE, do.trace=FALSE,keep.forest=NULL,keep.inbag=FALSE,MESS=F,seed=1)) } ### Maxlike Formula="~bio_06_2000_2km + bio_14_2000_4km + NDVI_annualMaximumValue_2009 + NDVI_greenuprates1_2003 + NDVI_peakdates1_2003" for(i in 1:2){ try(FitModels(ma.name=input.file[i], tif.dir=NULL, output.dir=output.dir[5], response.col=rc[i], make.p.tif=T,make.binary.tif=T, debug.mode=T,responseCurveForm="pdf",script.name="maxlike", opt.methods=2,MESS=T,Formula=Formula,UseTiffs=FALSE)) } ### Pairs Explore Tests ##### source("PairsExplore.r") source("PairsExploreHelperFcts.r") source("read.dat.r") source("chk.libs.r") source("read.dat.r") source("my.panel.smooth.binary.r") source("Predictor.inspection.r") for(i in 1:length(predictor)){ if(i==1) { try(Pairs.Explore(num.plots=5, min.cor=.5, input.file=input.file[i], output.file=paste(dir.path,"\\",i,"Par1",".jpg",sep=""), response.col=rc[i], pres=TRUE, absn=TRUE, bgd=TRUE)) try(Pairs.Explore(num.plots=10, min.cor=.5, input.file=input.file[i], output.file=paste(dir.path,"\\",i,"Par2",".jpg",sep=""), response.col=rc[i], pres=TRUE, absn=FALSE, bgd=FALSE, cors.w.highest=TRUE)) try(Predictor.inspection(predictor[i], input.file=input.file[i], output.dir=dir.path, response.col=rc[i], pres=TRUE, absn=TRUE, bgd=TRUE)) } try(Pairs.Explore(num.plots=15, min.cor=min.cor, input.file=input.file[i], output.file=paste(dir.path,"\\",i,".jpg",sep=""), response.col=rc[i], pres=TRUE, absn=TRUE, bgd=TRUE)) try(Predictor.inspection(predictor[i], input.file[i], output.dir=paste(dir.path,"\\",sep=""), response.col=rc[i], pres=TRUE, absn=TRUE, bgd=TRUE)) } input.file<-"I:\\VisTrails\\VisTrails_SAHM_x32_debug\\VisTrails\\vistrails\\packages\\TestingRCode2\\TestSuite\\PairsExploreManyPredictors.csv" for (i in 1:25){ try(Pairs.Explore(num.plots=i, min.cor=.5, input.file=input.file, output.file=paste(dir.path,"\\",i,"NumPlotsTest",".jpg",sep=""), response.col=rc[4], pres=TRUE, absn=TRUE, bgd=TRUE )) } ### Apply Model Test input.workspace=list( for(i in 1:length(input.workspace){ EvaluateNewData(workspace=paste(output.dir,"modelWorkspace",sep="\\"),out.dir=output.dir,b.tif=TRUE,p.tif=TRUE,mess=TRUE,new.tifs="I:\\VisTrails\\WorkingFiles\\workspace\\_applyModel\\Error\\MergedDataset_10.csv",produce.metrics=TRUE) } ### Data Splitting Tests

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/run_analysis.R

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ilabiga/Getting_and_Cleaning_Data_Course_Project

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## load the package dplyr library(dplyr) ## before running this script please save it into the directory where the data folder UCI HAR Dataset has been saved ## now set the R working directory equal to that directory with the setwd(dir) comamnd ## READ DATA ## read 'features.txt' and 'activity_labels.txt' ## inside the directory 'UCI HAR Dataset': features <- read.table("UCI HAR Dataset/features.txt") activityLabels <- read.table("UCI HAR Dataset/activity_labels.txt") ## read 'subject_train.txt', 'y_train.txt' and 'X_train.txt' ## inside the directory 'UCI HAR Dataset/train': subjectTrain <- read.table("UCI HAR Dataset/train/subject_train.txt") yTrain <- read.table("UCI HAR Dataset/train/y_train.txt") xTrain <- read.table("UCI HAR Dataset/train/X_train.txt") ## read 'subject_test.txt', 'y_test.txt' and 'X_test.txt' ## inside the directory 'UCI HAR Dataset/test': subjectTest <- read.table("UCI HAR Dataset/test/subject_test.txt") yTest <- read.table("UCI HAR Dataset/test/y_test.txt") xTest <- read.table("UCI HAR Dataset/test/X_test.txt") ## DATA PREPARATION ## append subjectTest after subjectTrain: subject <- rbind(subjectTrain,subjectTest) ## left join between yTrain/yTest and activityLabels; the corresponding ## activity name is associated to each experiment (= each row): yTrainActivity <- left_join(yTrain,activityLabels) yTestActivity <- left_join(yTest,activityLabels) ## append yTestActivity after yTrainActivity: activity <- rbind(yTrainActivity,yTestActivity) ## 1) MERGE THE TRAINING AND TEST SETS TO CREATE ONE DATA SET ## append xTest after xTrain and use ## descriptive feature names as column names xDataSet <- rbind(xTrain,xTest) colnames(xDataSet) <- features$V2 ## 2) EXTRACTS ONLY THE MEASUREMENTS ON THE MEAN AND STANDARD DEVIATION ## search for the text "meaN()" or "std()" inside the feature names: mean <- grepl("mean()",features$V2) std <- grepl("std()",features$V2) extraction <- mean | std ## column extraction: xDataSetExtract <- xDataSet[,extraction] ## 3) USES DESCRIPTIVE ACTIVITY NAMES THE ACTIVITIES IN THE DATA SET ## add columns containing the subject ID and the activity name ## assicuated ti each experiment: DataAll <- cbind(subject, activity[,2],xDataSetExtract) ## 4) APPROPIATELY LABELS THE DATA SET WITH DESCRIPTIVE VARIABLE NAMES ## adjust column names: colnames(DataAll) <- c("ID_Subject","Activity",colnames(xDataSetExtract)) ## 5) CREATE A FINAL TIDY DATA SET WITH THE AVERAGE OF EACH VARIABLE ## FOR EACH ACTIVITY AND EACH SUBJECT ## aggregation on activity and subject and average calculation: DataFinal <-aggregate(DataAll[,3:81], by=list(DataAll[,1],DataAll[,2]),FUN=mean, na.rm=TRUE) ## adjust column names: colnames(DataFinal) <- c("Subject","Activity",colnames(xDataSetExtract)) ## Export the final data set: write.table(DataFinal, "C:/Users/UL16971/Downloads/Dati-R/Corso-Getting-Cleaning-Data/Project/UCI HAR Dataset/OutputData.txt", sep="\t", row.name=FALSE)

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#' Master controlling the workers #' #' exchanging messages between the master and workers works the following way: #' * we have submitted a job where we don't know when it will start up #' * it starts, sends is a message list(id=0) indicating it is ready #' * we send it the function definition and common data #' * we also send it the first data set to work on #' * when we get any id > 0, it is a result that we store #' * and send the next data set/index to work on #' * when computatons are complete, we send id=0 to the worker #' * it responds with id=-1 (and usage stats) and shuts down #' #' @param qsys Instance of QSys object #' @param iter Objects to be iterated in each function call #' @param rettype Return type of function #' @param fail_on_error If an error occurs on the workers, continue or fail? #' @param chunk_size Number of function calls to chunk together #' defaults to 100 chunks per worker or max. 500 kb per chunk #' @param timeout Maximum time in seconds to wait for worker (default: Inf) #' @param max_calls_worker Maxmimum number of function calls that will be sent to one worker #' @param verbose Print progress messages #' @return A list of whatever `fun` returned #' @keywords internal master = function(qsys, iter, rettype="list", fail_on_error=TRUE, chunk_size=NA, timeout=Inf, max_calls_worker=Inf, verbose=TRUE) { # prepare empty variables for managing results n_calls = nrow(iter) job_result = rep(vec_lookup[[rettype]], n_calls) submit_index = 1:chunk_size jobs_running = 0 cond_msgs = list() n_errors = 0 n_warnings = 0 shutdown = FALSE kill_workers = FALSE on.exit(qsys$finalize()) if (verbose) { message("Running ", format(n_calls, big.mark=",", scientific=FALSE), " calculations (", qsys$data_num, " objs/", format(qsys$data_size, big.mark=",", units="Mb"), " common; ", chunk_size, " calls/chunk) ...") pb = progress::progress_bar$new(total = n_calls, format = "[:bar] :percent (:wup/:wtot wrk) eta: :eta") pb$tick(0, tokens=list(wtot=qsys$workers, wup=qsys$workers_running)) } # main event loop while((!shutdown && submit_index[1] <= n_calls) || jobs_running > 0) { msg = qsys$receive_data(timeout=timeout) if (is.null(msg)) { # timeout reached if (shutdown) { kill_workers = TRUE break } else stop("Socket timeout reached, likely due to a worker crash") } if (verbose) pb$tick(length(msg$result), tokens=list(wtot=qsys$workers, wup=qsys$workers_running)) # process the result data if we got some if (!is.null(msg$result)) { call_id = names(msg$result) jobs_running = jobs_running - length(call_id) job_result[as.integer(call_id)] = msg$result n_warnings = n_warnings + length(msg$warnings) n_errors = n_errors + length(msg$errors) if (n_errors > 0 && fail_on_error == TRUE) { shutdown = TRUE timeout = getOption("clustermq.error.timeout", min(timeout, 30)) } new_msgs = c(msg$errors, msg$warnings) if (length(new_msgs) > 0 && length(cond_msgs) < 50) cond_msgs = c(cond_msgs, new_msgs[order(names(new_msgs))]) } if (shutdown || (!is.null(msg$n_calls) && msg$n_calls >= max_calls_worker)) { qsys$send_shutdown_worker() next } if (msg$token != qsys$data_token) { qsys$send_common_data() } else if (submit_index[1] <= n_calls) { # if we have work, send it to the worker submit_index = submit_index[submit_index <= n_calls] qsys$send_job_data(chunk = chunk(iter, submit_index)) jobs_running = jobs_running + length(submit_index) submit_index = submit_index + chunk_size # adapt chunk size towards end of processing cs = ceiling((n_calls - submit_index[1]) / qsys$workers_running) if (cs < chunk_size) { chunk_size = max(cs, 1) submit_index = submit_index[1:chunk_size] } } else if (qsys$reusable) { qsys$send_wait() } else { # or else shut it down qsys$send_shutdown_worker() } } if (!kill_workers && (qsys$reusable || qsys$cleanup(quiet=!verbose))) on.exit(NULL) summarize_result(job_result, n_errors, n_warnings, cond_msgs, min(submit_index)-1, fail_on_error) }

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01_48_spineplot.R

# main idea: creating spineplots # width of bars = frequency of X # height of bars = frequency of y # Y must be a factor and is the dependent variable spineplot(ChickWeight$weight, ChickWeight$Diet) # spineplot(x,y) # interesting observations # Height of bars indicates obs per diet. Diet 1 has more obs # Width of bars indicates obs per weight. More chicks are weighed between 50 and 100 # or... spineplot(Diet ~ weight, data = ChickWeight) # spineplot(y ~ x) # bells and whistles spineplot(Diet ~ weight, data = ChickWeight, breaks = fivenum(ChickWeight$weight), col = c(5:8), xlab = "Chicken Weight", ylab = "Chicken Diet")

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/Artificial Intelligence/Next Word Auto Completion/task6_Next_Word.R

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xchromosome219/Portfolio

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task6_Next_Word.R

library(data.table) path<-"./data" bigram <- readRDS(paste0(path,"/smle.n2.join.rds",sep="")) trigram <- readRDS(paste0(path,"/smle.n3.join.rds",sep="")) quadrigram <- readRDS(paste0(path,"/smle.n4.join.rds",sep="")) wordBank <- readRDS(paste0(path,"/wordBank.rds",sep="")) wordBank$word <- as.character(wordBank$word) splitString <- function(text) { text <- gsub("[^A-Za-z\']", "", text) text <- tolower(text) text <- text[text != ""] if (length(text) == 3) { gram <- 3 string3 <- paste0(tail(text, 3), collapse = " ") string2 <- paste0(tail(text, 2), collapse = " ") string1 <- paste0(tail(text, 1), collapse = " ") } else if (length(text) == 2){ gram <- 2 string3 <- NA string2 <- paste0(tail(text, 2), collapse = " ") string1 <- paste0(tail(text, 1), collapse = " ") } else { gram <- 1 string3 <- NA string2 <- NA string1 <- paste0(tail(text, 1), collapse = " ") } return(c(gram, string1, string2, string3)) } Backoff.1 <- function(string1, idx = 0) { print("GRAM1") result <- c() defult <- c("the", "to", "and", "a", "of") if (length(grep(string1, bigram$input)) != 0) { # string is in the DB print("GRAM1-1") if(idx != 0) { # called from Backedoff.3, return single value only outputX <- paste0("output", idx) outputX <- bigram[input == string1 & rank == idx, ]$output outputX <- ifelse(length(outputX) == 0, defult[idx], outputX) return(outputX) } for (i in seq(1, 4)) { # obtain top 5 choices outputX <- paste0("output", i) outputX <- bigram[input == string1 & rank == i, ]$output outputX <- ifelse(length(outputX) == 0, defult[i], outputX) result <- c(result, outputX) } } else if (idx != 0) { # getting only one missing value from backoff.1 print("GRAM1-2") return(defult[idx]) } else { print("GRAM1-3") return(defult) } return(result) } Backoff.2 <- function(string1, string2, idx = 0) { print("GRAM2") result <- c() if (length(grep(string2, trigram$input)) != 0) { # string is in the DB print("GRAM2-1") print(idx) if(idx != 0) { # called from Backedoff.3, return single value only outputX <- paste0("output", idx) outputX <- trigram[input == string2 & rank == idx, ]$output outputX <- ifelse(length(outputX) == 0, Backoff.1(string1, idx=idx), outputX) return(outputX) } for (i in seq(1, 4)) { # obtain top 5 choices outputX <- paste0("output", i) outputX <- trigram[input == string2 & rank == i, ]$output outputX <- ifelse(length(outputX) == 0, Backoff.1(string1, idx=i), outputX) result <- c(result, outputX) } } else if (idx != 0) { # getting only one missing value from backoff.1 print("GRAM2-2") return(Backoff.1(string1, idx)) } else { # string isn't in the DB, move to backoff.1 print("GRAM2-3") return(Backoff.1(string1, 0)) } return(result) } Backoff.3 <- function(string1, string2, string3) { print("GRAM3") result <- c() if (length(grep(string3, quadrigram$input)) != 0) { # string is in the DB for (i in seq(1, 4)) { # obtain top 5 choices print("GRAM3-2") outputX <- paste0("output", i) outputX <- quadrigram[input == string3 & rank == i, ]$output outputX <- ifelse(length(outputX) == 0, Backoff.2(string1, string2, idx=i), outputX) result <- c(result, outputX) } } else # string isn't in the DB, move to Backoff.2 return(Backoff.2(string1, string2, 0)) return(result) } predictWord <- function(text) { text <- splitString(text) if (text[1] == 3){ t<-Backoff.3(text[2], text[3], text[4]) print(class(t)) return(t) } else if (text[1] == 2) { return(Backoff.2(text[2], text[3])) } else return(Backoff.1(text[2])) } predictWord2 <- function(text) { text <- gsub("[^A-Za-z\']", "", text) text <- tolower(text) index <- grepl(paste0("^", text), wordBank$word) result <- wordBank[index, ][1:4] return(result) } text <- tail(unlist(strsplit("asd asd sad sad adsade2", split = ' ')), 3) predictWord(text) text <- tail(unlist(strsplit("i would like b", split = ' ')), 3) predictWord(text) text <- tail(unlist(strsplit("i would like to get the fir", split = ' ')), 3) predictWord(text) text <- tail(unlist(strsplit("i", split = ' ')), 1) predictWord2(text) text <- tail(unlist(strsplit("i would", split = ' ')), 1) predictWord2(text) text <- tail(unlist(strsplit("i would like", split = ' ')), 1) predictWord2(text) text <- tail(unlist(strsplit("i would like to g", split = ' ')), 1) predictWord2(text) text <- tail(unlist(strsplit("i would like to get the fir", split = ' ')), 1) predictWord2(text)

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Venks88/RepData_PeerAssessment1

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plot1.R

##setting the working directory setwd("C:/Users/Venkata/Downloads/repdata_data_activity") library(knitr) knit2html(input = "PA1_template.Rmd")

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adamnyberg/machine-learning

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setwd("/Users/Simon/Documents/TDDE01/tdde01/Simon/lab4_assignment1") library(readxl) library(tree) library(boot) set.seed(12345) data=read_excel("xls_state.xls") #1.1 reorder_data = data[with(data, order(data$MET)), ] plot(reorder_data$MET,reorder_data$EX, ylab = "EX", xlab = "MET", main="1.1 EX vs MET") #---->regression tree model #1.2 MET = reorder_data$MET # make output look better model = tree(reorder_data$EX~MET, data=reorder_data, control = tree.control(minsize = 8, nobs = nrow(reorder_data))) model.cv <- cv.tree(object = model) #plot(model.cv$size,model.cv$dev,'b') #Best (lowest deviance) value is 3. See plot. model.pruned <- prune.tree(model,best=3) plot(model.pruned) text(model.pruned, cex=.75) pred_data = predict(model.pruned,newdata = reorder_data) plot(reorder_data$MET,reorder_data$EX, ylab = "EX", xlab = "MET", main="1.2 Original and fitted data") points(reorder_data$MET, pred_data, col="blue") legend("top", lty=c(1,1), col=c("black","blue"), legend = c("Original", "Fitted")) residuals = reorder_data$EX-pred_data hist(residuals) #1.3 # computing bootstrap samples bootstrap=function(data, ind){ boot_data=data[ind,]# extract bootstrap sample res=tree(EX~MET, data=boot_data, control = tree.control(minsize = 8, nobs = nrow(reorder_data))) #predict values for all Area values from the original model.pruned = prune.tree(res,best=3) EX_pred=predict(model.pruned,newdata=reorder_data) return(EX_pred) } res=boot(reorder_data, bootstrap, R=1000) #make bootstrap e = envelope(res, level=0.95) plot(reorder_data$MET, reorder_data$EX, pch=21, bg="orange", main="Non-parametric bootstrap", xlab="MET", ylab="EX") legend("top", lty=c(1,1,1), col=c("orange","black", "blue"), legend = c("Data", "Model", "Confidence bands")) points(reorder_data$MET,pred_data,type="l") #plot fitted line #plot cofidence bands points(reorder_data$MET,e$point[2,], type="l", col="blue") points(reorder_data$MET,e$point[1,], type="l", col="blue") #bumpy, tries to fit every data point #reliable, inside the confidence bands #1.4 mle=tree(reorder_data$EX~reorder_data$MET, data=reorder_data, control = tree.control(minsize = 8, nobs = nrow(reorder_data))) mle.pruned=prune.tree(mle,best=3) rng=function(data, mle) { data1=data.frame(EX=reorder_data$EX, MET=reorder_data$MET) n=length(data$EX) print(data1) #generate new EX p = predict(mle,newdata = data1) residuals = data1$EX-p data1$EX=rnorm(n,p,sd(residuals)) return(data1) } f1=function(data1){ #print(data1) res=tree(EX~MET, data=data1, control = tree.control(minsize = 8, nobs = nrow(reorder_data))) #predict values for all Area values from the original data model.pruned = prune.tree(res,best=3) EX_pred=predict(model.pruned,newdata=reorder_data) return(EX_pred) } res_parametric=boot(reorder_data, statistic=f1, R=1000, mle=mle.pruned,ran.gen=rng, sim="parametric") e_parametric = envelope(res_parametric, level=0.95) plot(reorder_data$MET, reorder_data$EX, pch=21, bg="orange", main="Bootstrap comparison", xlab = "MET", ylab = "EX",ylim=c(100,600)) legend("top", lty=c(1,1), col=c("pink", "black", "blue","red"), legend = c("Prediction bands", "Predictions", "Non-parametric confidence bands", "Parametric confidence bands")) points(reorder_data$MET,pred_data,type="l") #plot fitted line #plot cofidence bands points(reorder_data$MET,e_parametric$point[2,], type="l", col="red") points(reorder_data$MET,e_parametric$point[1,], type="l", col="red") points(reorder_data$MET,e$point[2,], type="l", col="blue") points(reorder_data$MET,e$point[1,], type="l", col="blue") #Prediction boundary bootstrap.prediction <- function(data) { model = tree(EX ~ MET, data=data, control = tree.control(nobs = nrow(data), minsize = 8)) model.pruned = prune.tree(model,best=3) EX_pred = predict(model.pruned,data) n = length(data$EX) ndata = rnorm(n , EX_pred,sd(resid(model.pruned))) return(ndata) } bootstrap3 = boot(reorder_data, bootstrap.prediction,R=1500, mle=mle.pruned, ran.gen=rng, sim="parametric") e_pred_bands = envelope(bootstrap3,level=0.95) points(reorder_data$MET, e_pred_bands$point[2,], type="l", col="pink") points(reorder_data$MET, e_pred_bands$point[1,], type="l", col="pink")

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test_that("Julia", { skip_if_not(JuliaConnectoR::juliaSetupOk(), "Julia setup is not OK") julia <- Jack_julia() # numerical #### x <- c("1/2", "2/3", "5") xq <- gmp::as.bigq(x) lambda <- c(2, 1, 1) # jack alpha <- "2/3" alphaq <- gmp::as.bigq(alpha) expect_equal( julia$Jack(x, lambda, alpha), Jack(xq, lambda, alphaq) ) # zonal expect_equal( julia$Zonal(x, lambda), Zonal(xq, lambda) ) # zonalQ expect_equal( julia$ZonalQ(x, lambda), ZonalQ(xq, lambda) ) # Schur expect_equal( julia$Schur(x, lambda), Schur(xq, lambda) ) # polynomials #### n <- 3 lambda <- c(3, 2) # jack alpha <- "2/3" alphaq <- gmp::as.bigq(alpha) mvpol_julia <- julia$JackPol(n, lambda, alpha, poly = "mvp") gmpol_julia <- julia$JackPol(n, lambda, alpha, poly = "qspray") gmpol_r <- JackPol(n, lambda, alphaq) #expect_true(mvpEqual(mvpol_julia, gmpoly::gmpoly2mvp(gmpol_julia))) expect_true(gmpol_r == gmpol_julia) # zonal mvpol_julia <- julia$ZonalPol(n, lambda, poly = "mvp") gmpol_julia <- julia$ZonalPol(n, lambda, poly = "qspray") gmpol_r <- ZonalPol(n, lambda) #expect_true(mvpEqual(mvpol_julia, gmpoly::gmpoly2mvp(gmpol_julia))) expect_true(gmpol_r == gmpol_julia) # zonalq mvpol_julia <- julia$ZonalQPol(n, lambda, poly = "mvp") gmpol_julia <- julia$ZonalQPol(n, lambda, poly = "qspray") gmpol_r <- ZonalQPol(n, lambda) #expect_true(mvpEqual(mvpol_julia, gmpoly::gmpoly2mvp(gmpol_julia))) expect_true(gmpol_r == gmpol_julia) # schur mvpol_julia <- julia$SchurPol(n, lambda, poly = "mvp") gmpol_julia <- julia$SchurPol(n, lambda, poly = "qspray") gmpol_r <- SchurPol(n, lambda) #expect_true(mvpEqual(mvpol_julia, gmpoly::gmpoly2mvp(gmpol_julia))) expect_true(gmpol_r == gmpol_julia) # as.function mvpol <- julia$JackPol(m = 2, lambda = c(3, 1), alpha = "2/5", poly = "mvp") gmpol <- julia$JackPol(m = 2, lambda = c(3, 1), alpha = "2/5", poly = "qspray") f <- as.function(mvpol) y1 <- f("2/3", "7/5") y2 <- as.character(qspray::evalQspray(gmpol, c("2/3", "7/5"))) expect_equal(y1, y2) # JuliaConnectoR::stopJulia() })

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levelfun.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/helpers.R \name{levelfun} \alias{levelfun} \title{Parse merMod levels} \usage{ levelfun(x, nl.n, allow.new.levels = FALSE) } \description{ Parse merMod levels } \keyword{internal}

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func6.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/NguyenToolsRfunctions.R \name{func6} \alias{func6} \title{Highlevel check function} \usage{ func6(x) } \arguments{ \item{x}{object} } \value{ object } \description{ Checks and throws error if not numeric, finit, zero lenth, NA, NAN } \examples{ func6(NA) }

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rkvidyar/Human-Activity-Recognition-Using-Smartphones-Dataset---Tidy-Data-Project

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Tidy R Run Analysis Script.R

X_test <- read.table("X_test.txt", header=FALSE, fill=TRUE) y_test <- read.table("y_test.txt", header=FALSE) subject_test <- read.table("subject_test.txt", header=FALSE) subject_train <- read.table("subject_train.txt", header=FALSE) X_train <- read.table("X_train.txt", header=FALSE, fill=TRUE) y_train <- read.table("y_train.txt", header=FALSE) features <- read.table("features.txt", header=FALSE, stringsAsFactors=FALSE) activity <- read.table("activity_labels.txt", header=FALSE, stringsAsFactors=FALSE) X_bind <- rbind(X_test, X_train) y_bind <- rbind(y_test, y_train) subject_bind <- rbind(subject_test, subject_train) avector <- as.vector(features[ , 2]) avector2 <- make.names(avector, unique = TRUE) colnames(X_bind) <- avector2 selectVector <- grep("mean|std", names(X_bind), ignore.case=FALSE) X_bind1 <- X_bind[ , selectVector] selectVectorF <- !grepl("meanFreq", names(X_bind1), ignore.case=FALSE) table(selectVectorF) X_bind2 <- X_bind1[ , selectVectorF] pre_tidy1 <- cbind(y_bind, X_bind2) pre_tidy2 <- cbind(subject_bind, pre_tidy1) merged_Data = merge(pre_tidy2, activity, by.x=2, by.y=1, all=TRUE) pre_tidy3 <- merged_Data[ , c(2, 69, 3:68)] colnames(pre_tidy3)[1] <- "subject.id" colnames(pre_tidy3)[2] <- "activity.label" colnames(pre_tidy3) <- tolower(names(pre_tidy3)) freq <- sub("^f+", "freq.", names(pre_tidy3)) time <- sub("^t+", "time.", freq) body <- sub("body+", "body.", time) body2 <- sub("body.body+", "body.", body) gravity <- sub("gravity+", "gravity.", body2) jerk <- sub("jerk+", ".jerk", gravity) mag <- sub("mag+", ".mag", jerk) periods <- sub("\\.\\.", "", mag) colnames(pre_tidy3) <- periods tidy1 <- pre_tidy3 install.packages("dplyr") library(dplyr) tidy1 <- tbl_df(tidy1) tidy2 <- group_by(tidy1, subject.id, activity.label) %>% summarise_each(funs(mean), "time.body.acc.mean.x" : "freq.body.gyro.jerk.mag.std") write.table(tidy2, file="tidy2RM.txt", row.names=FALSE, col.names=TRUE, sep=" ", quote=FALSE) tidy_data <- read.table("tidy2RM.txt", header = TRUE) View(tidy_data)

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na_interp.r

#' Linearly interpolate NA values #' #' \code{na_interp} linearly interpolates NA values found in vector y with #' respect to index x (e.g. timestamp). #' #' @param y numeric vector in which to fill bracketed NA values #' @param x vector giving index of y. NULL if y is equally spaced #' #' @importFrom stats approx #' @importFrom utils head tail #' #' @export na_interp <- function (y, x = NULL) { if (is.null(x)) x <- 1:length(y) nona <- which(!is.na(y)) start <- head(nona, 1) end <- tail(nona, 1) if (length(end - start) < 1) return(y) xsub <- x[start:end] ysub <- y[start:end] idx <- which(is.na(ysub)) ysub[idx] <- approx(xsub, ysub, xout = xsub[idx], method = "linear")$y y[start:end] <- ysub return(y) }

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outsider.devtools.R

#' outsider.devtools: Build 'outsider' Modules #' #' Tools, resources and information for making it easier to build your own #' outsider modules. #' #' For more information visit the outsider website #' (\url{https://docs.ropensci.org/outsider.devtools/}). #' #' @docType package #' @name outsider.devtools #' @importFrom outsider.base cat_line char func stat #' @importFrom outsider module_uninstall NULL

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/quickPlot.R \name{.quickPlottables-class} \alias{.quickPlottables-class} \alias{quickPlottables} \title{\code{quickPlot} classes} \description{ \pkg{quickPlot} offers a type of plotting that is modular. Users of NetLogoR may find this useful for simulation modeling. We have put in place the required methods and imported the appropriate classes to use the \code{quickPlot::Plot} function. Users can still use \code{plot} from the \pkg{graphics} package, but it is not modular. } \details{ This adds \code{agentMatrix} to the \code{.quickPlottables}, \code{.quickObjects}, and \code{spatialObjects}. This adds \code{worldMatrix} to the \code{.quickPlottables}, \code{.quickObjects}, \code{spatialObjects} and \code{griddedClasses}. } \section{Slots}{ \describe{ \item{\code{members}}{\code{\link[=.quickPlotObjects]{.quickPlotObjects()}} and \code{\link[=.quickPlot]{.quickPlot()}}} }} \seealso{ \code{\link[=quickPlotClasses]{quickPlotClasses()}} } \author{ Eliot McIntire } \keyword{internal}

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Revisions.R

### Revisions library(survival) library(eha) library(ggplot2) library(survminer) ### Missing sample descriptives #summary(ht.df[!ccs,c('AFQT89','SAMPLE_SEX','Child_SES','Adult_SES','SES_Education_USE','SES_OccStatus_USE','SES_Income_USE')]) sum(ht.df[!ccs,]$SAMPLE_SEX=='MALE') sum(ht.df[!ccs,]$SAMPLE_SEX=='FEMALE') ## IQ aggregate(AFQT89 ~ SAMPLE_SEX, data=ht.df[!ccs,], FUN=mean) aggregate(AFQT89 ~ SAMPLE_SEX, data=ht.df[!ccs,], FUN=sd) ## Child SES aggregate(Child_SES ~ SAMPLE_SEX, data=ht.df[!ccs,], FUN=mean) aggregate(Child_SES ~ SAMPLE_SEX, data=ht.df[!ccs,], FUN=sd) ## Adult SES aggregate(Adult_SES ~ SAMPLE_SEX, data=ht.df[!ccs,], FUN=mean) aggregate(Adult_SES ~ SAMPLE_SEX, data=ht.df[!ccs,], FUN=sd) ## Income aggregate(SES_Income_USE ~ SAMPLE_SEX, data=ht.df[!ccs,], FUN=mean) aggregate(SES_Income_USE ~ SAMPLE_SEX, data=ht.df[!ccs,], FUN=sd) ## Education aggregate(SES_Education_USE ~ SAMPLE_SEX, data=ht.df[!ccs,], FUN=mean) aggregate(SES_Education_USE ~ SAMPLE_SEX, data=ht.df[!ccs,], FUN=sd) ## Occupation Status aggregate(SES_OccStatus_USE ~ SAMPLE_SEX, data=ht.df[!ccs,], FUN=mean) aggregate(SES_OccStatus_USE ~ SAMPLE_SEX, data=ht.df[!ccs,], FUN=sd) ## Hypertension # table(ht.df[ccs,]$hasHT,ht.df[!ccs,]$SAMPLE_SEX) # # hist(ht.df[ccs,]$HTage50t[ht.df[ccs,]$hasHT]) ## FIXED: aggregate(recordTime ~ SAMPLE_SEX, data=ht.df[!ccs,][ht.df[!ccs,]$hasHT,], FUN=mean) aggregate(recordTime ~ SAMPLE_SEX, data=ht.df[!ccs,][ht.df[!ccs,]$hasHT,], FUN=sd) ### Comparing analytic and non-analytic means (aggregate(AFQT89 ~ SAMPLE_SEX, data=ht.df[!ccs,], FUN=mean) - aggregate(AFQT89 ~ SAMPLE_SEX, data=ht.df[ccs,], FUN=mean))/aggregate(AFQT89 ~ SAMPLE_SEX, data=ht.df[!ccs,], FUN=sd) (aggregate(Child_SES ~ SAMPLE_SEX, data=ht.df[!ccs,], FUN=mean) - aggregate(Child_SES ~ SAMPLE_SEX, data=ht.df[ccs,], FUN=mean))/aggregate(Child_SES ~ SAMPLE_SEX, data=ht.df[!ccs,], FUN=sd) (aggregate(Adult_SES ~ SAMPLE_SEX, data=ht.df[!ccs,], FUN=mean) - aggregate(Adult_SES ~ SAMPLE_SEX, data=ht.df[ccs,], FUN=mean))/aggregate(Adult_SES ~ SAMPLE_SEX, data=ht.df[!ccs,], FUN=sd) (aggregate(SES_Income_USE ~ SAMPLE_SEX, data=ht.df[!ccs,], FUN=mean) - aggregate(SES_Income_USE ~ SAMPLE_SEX, data=ht.df[ccs,], FUN=mean))/aggregate(SES_Income_USE ~ SAMPLE_SEX, data=ht.df[!ccs,], FUN=sd) (aggregate(SES_Education_USE ~ SAMPLE_SEX, data=ht.df[!ccs,], FUN=mean) - aggregate(SES_Education_USE ~ SAMPLE_SEX, data=ht.df[ccs,], FUN=mean))/aggregate(SES_Education_USE ~ SAMPLE_SEX, data=ht.df[!ccs,], FUN=sd) (aggregate(SES_OccStatus_USE ~ SAMPLE_SEX, data=ht.df[!ccs,], FUN=mean) - aggregate(SES_OccStatus_USE ~ SAMPLE_SEX, data=ht.df[ccs,], FUN=mean))/aggregate(SES_OccStatus_USE ~ SAMPLE_SEX, data=ht.df[!ccs,], FUN=sd) ### More details on education and income summary(ht.df$SES_Education_a[ccs]) ht.df$Edu_int = as.integer(ht.df$SES_Education_a) table(ht.df$Edu_int[ccs], useNA='ifany') mean(ht.df$Edu_int[ccs]) summary(ht.df$SES_Income_a[ccs]) summary(ht.df$SES_Income_b[ccs]) # B seems to be higher in a few fringe cases #ht.df$Inc_dollars = ht.df$ View(ht.df[ccs,c('SES_Income_a','SES_Income_b') ]) cor(ht.df[ccs,c('SES_Income_a','SES_Income_b')]) mean(ht.df[ccs,c('SES_Income_b')]) ### Partner status - sensitivity models partn.14 = read.csv('./PartnerStat2014.csv') colnames(partn.14)[c(2,6)] = c('caseid','partner.status') partn.df = merge(ht.df, partn.14[,c('caseid','partner.status')], by.x='CASEID_1979',by.y='caseid') table(partn.df$partner.status, useNA='ifany') partn.df$partner.status[partn.df$partner.status==-999] = NA partn.df$partner.status[partn.df$partner.status==33] = 1 # just 'partner's aft.gd3.4.partn = aftreg(y.ccs ~ SAMPLE_SEX * AFQT89 + age_1979 + Child_SES + SES_Income_USE + partner.status ,data = partn.df[ccs,], dist='loglogistic') aft.gd3.4.partn$n aft.gd3.4$n aft.gd3.4.partn$loglik extractAIC(aft.gd3.4.partn) summary(aft.gd3.4.partn) ## nothing at all... # aft.gd3.8.partn = aftreg(y.ccs ~ SAMPLE_SEX * AFQT89 + age_1979 + Child_SES + Adult_SES # + SES_Income_USE*SAMPLE_SEX + partner.status # ,data = partn.df[ccs,], dist='loglogistic') # extractAIC(aft.gd3.8.partn) # summary(aft.gd3.8.partn) ### Split by sex - sensitivity models M.ccs = ((ht.df$SAMPLE_SEX=='MALE')&ccs) head(y.ccs[ht.df[ccs,]$SAMPLE_SEX=='MALE']) aft.gd3.3.M = aftreg(y.ccs[ht.df[ccs,]$SAMPLE_SEX=='MALE'] ~ AFQT89 + age_1979 + Child_SES + + Adult_SES , data = ht.df[ccs&(ht.df$SAMPLE_SEX=='MALE'),], dist='loglogistic') aft.gd3.3.F = aftreg(y.ccs[ht.df[ccs,]$SAMPLE_SEX=='FEMALE'] ~ AFQT89 + age_1979 + Child_SES + + Adult_SES , data = ht.df[ccs&(ht.df$SAMPLE_SEX=='FEMALE'),], dist='loglogistic') summary(aft.gd3.3.M) summary(aft.gd3.3.F) ### Make Figure 2 *taller* ggfit = survfit(Surv(recordTime, hasHT) ~ ccs.df$sex_tert, data=ccs.df) g <- ggsurvplot(ggfit, conf.int=T,censor=F , linetype = c(1,1,2,2,3,3) #, color = c(1,2,1,2,1,2) , palette = c('dodgerblue','violetred1','dodgerblue','violetred1','dodgerblue','violetred1') , legend = 'none' #c(0.2, 0.2) #'none' , xlim = c(19,55), ylim = c(0.5,1) , break.x.by = 5 ) g = g + xlab('Age') +ylab('Proportion that remains normotensive') g # ggfit = survfit(Surv(recordTime, hasHT) ~ ht.df$IQtert, data=ht.df) # # g <- ggsurvplot(ggfit, conf.int=T,censor=F # , linetype = c(1,2,3) # #, color = c(1,2,1,2,1,2) # , palette = c('dodgerblue','violetred1','green','violetred1','dodgerblue','violetred1') # , legend = 'none' #c(0.2, 0.2) #'none' # , xlim = c(19,54), ylim = c(0.5,1) # , break.x.by = 5 # ) # # g = g + xlab('Age') +ylab('Proportion that remains normotensive') # # g

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/code/workspace_scripts/ddm_measure_labels.R

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zenkavi/SRO_DDM_Analyses

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refs/heads/master

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ddm_measure_labels.R

if(!exists('from_gh')){ from_gh=TRUE } if(from_gh){ input_path = 'https://raw.githubusercontent.com/zenkavi/SRO_DDM_Analyses/master/input/' }else{ input_path = '/Users/zeynepenkavi/Dropbox/PoldrackLab/SRO_DDM_Analyses/input/' } measure_labels <- read.csv(paste0(input_path, 'measure_labels.csv')) measure_labels = measure_labels %>% select(-measure_description) %>% filter(ddm_task == 1) %>% select(-ddm_task) %>% filter(rt_acc != "other") %>% mutate(dv = as.character(dv), overall_difference = factor(overall_difference,levels = c("overall", "difference", "condition"), labels = c("non-contrast", "contrast", "condition")), ddm_raw = ifelse(raw_fit == "raw", "raw", "ddm")) %>% separate(dv, c("task_group", "var"), sep = "\\.", remove=FALSE, extra = "merge")

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/reverse_sequences.R

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kbodulic/eunapius_lncRNA

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refs/heads/master

2023-01-03T01:54:58.292377

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reverse_sequences.R

args <- commandArgs(trailingOnly = TRUE) library(Biostrings) library(IRanges) library(GenomicRanges) library(BSgenome) setwd(".") #reversing the rev_comp hits #Arguments: 1 - concatanated fasta files of transcripts with similarities sequences_list<-list() for(i in list.files()) { sequences_list<-c(sequences_list,readDNAStringSet(i,format = "fasta")) } for (i in 1:length(sequences_list)){ for(j in 1:length(sequences_list[[i]])) { if(score(pairwiseAlignment(sequences_list[[i]][j],sequences_list[[i]][length(sequences_list[[i]])])) < score(pairwiseAlignment(sequences_list[[i]][j],reverseComplement(sequences_list[[i]][length(sequences_list[[i]])])))) { sequences_list[[i]][length(sequences_list[[i]])]<-reverseComplement(sequences_list[[i]][length(sequences_list[[i]])]) } } writeXStringSet(x = sequences_list[[i]],filepath = paste(names(sequences_list[[i]][length(sequences_list[[i]])]),"R.fa",sep="_"),) }

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/part3/R 러닝/0613그래프 평균 줄 표시or 구글빕이용.R

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wisdom009/R

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refs/heads/master

2020-06-03T19:03:28.896231

2019-06-17T08:30:42

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0613그래프 평균 줄 표시or 구글빕이용.R

setwd('D:/workspace/R_date/Part2/Stage3_StructuredData') count = read.csv("연도별요양기관별보험청구건수_2001_2013_세로.csv", stringsAsFactors= F) count colname <- count$년도 colname v1 <- count[,2]/100000 v2 <- count[,3]/100000 v3 <- count[,4]/100000 v4 <- count[,5]/100000 v5 <- count[,6]/100000 v6 <- count[,7]/100000 v7 <- count[,8]/100000 v8 <- count[,9]/100000 v9 <- count[,10]/100000 v10 <- count[,11]/100000 plot(v1, xlab="",ylab="",ylim = c(0,10000), axes=F, col="violet", type = "o", lwd=2, main=paste("연도별 금액", "\n" , "출처:검보심")) # 년도 axis(1, at=1:10, label = colname, las=2) #금액 axis(2,las=1) # 각 년도별 금액 그래프 lines(v2, col = "blue",type="o",lwd=2) lines(v3, col = "red",type="o",lwd=2) lines(v4, col = "black",type="o",lwd=2) lines(v5, col = "orange",type="o",lwd=2) lines(v6, col = "cyan",type="o",lwd=2) lines(v7, col = "yellow",type="o",lwd=2) lines(v8, col = "brown",type="o",lwd=2) lines(v9, col = "green",type="o",lwd=2) lines(v10, col = "navy",type="o",lwd=2) abline(h=seq(0,15000, 10000), v=seq(1,100,1),lty=3,lwd=0.2) col = names(count[1,2:11]) colors=c("blue","red","black","orange","cyan","yellow", "brown","green","navy") legend(1,10000,col,cex=0.8,col=colors,lty=1,lwd=2,bg="white") count2$ ggplot(count2) # -----3-5 --------------------------------------------------------- library(ggplot2) library(reshape2) library() windowsFonts(malgun = windowsFont("맑은 고딕")) count=read.csv("연도별요양기관별보험청구금액_2004_2013_세로.csv") count count2 = melt(count, id = c('년도'), variable.names ='병원종류', value.name = '금액') count2 = as.data.frame(count) count$금액 = 금액/1000000 options(2) ggplot(count, aes(x=년도, y="금액", fill='병원종류', color='병원종류')) + geom_line(linetype=1, size=1) + geom_point(size=3) + geom_hline(yintercept = seq(0,8000,1000), lty='dotted', size=0.1)+ theme_classic(base_family = "malgum", base_size = 10)+ ggtitle(paste('연도별 기관 보험청구금액','\n', '(단위:백만원)'))+ theme(plot.title = element_text(family="malgun", face= "bold", hjust = 0.5, size = 15, color = "darkblue")) # 연습문제 야구 bb= read.csv("야구성적.csv") bb ggplot(bb, aes(x=선수명,y=연봉대비출루율))+ geom_bar(stat = 'identity') mean_ops = mean(bb$연봉대비출루율) ggplot(bb, aes(x=선수명, y=연봉대비출루율))+ geom_bar(stat = 'identity', fill=palete) + geom_text(aes(y=연봉대비출루율+0.8, label=연봉대비출루율), # 그래프 바 위에 있는 텍스트 color="black", size=3)+ theme(axis.text.x=element_text(angle=45, hjust=1, vjust=1, colour="black", size=9)) + ggtitle('프로야구선수 밥값은 하고 있나?') + theme(plot.title = element_text(face = "bold", hjust = 0.5, size = 15, color = "darkblue")) + geom_hline(yintercept=mean_obp, color='purple', linetype = 'dashed') # 평균 값을 보여주는 선 pie(value, labels=label, radius=0.1,cex=0.6) #구글 차트 library(dplyr) library(googleVis) data = read.csv("2013년_서울_구별_주요과목별병원현황_구글용.csv") data has = gvisColumnChart(data, options = list(title="지역별 병원현황", height=400,weight=500)) plot(has) header= has$html$header header = gsub('charset=utf-8', 'charset=euc-kr',header) has$html$header = header plot(has)

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/data/genthat_extracted_code/rbmn/examples/rm8nd4adja.Rd.R

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surayaaramli/typeRrh

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rm8nd4adja.Rd.R

library(rbmn) ### Name: rm8nd4adja ### Title: removes somes nodes from an adjacency matrix ### Aliases: rm8nd4adja ### Keywords: utilities PKEYWORDS ### ** Examples rm8nd4adja(rbmn0adja.04, "1.1");

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/R/functions/archive/proc.vba.survey.R

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geryan/rfst

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refs/heads/master

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proc.vba.survey.R

proc.vba2 <- function(x, project.crs, vba.crs = 4283, cutoff.date = "2009-03-01"){ source(file = "R/functions/read.vba.R") library(dplyr) z <- read.vba(x) %>% dplyr::rename("date" = `Survey Start Date`, "lon" = `Longitude GDA94`, "lat" = `Latitude GDA94`, "count" = `Total Count`, "sm" = `Survey method`) %>% dplyr::select(sm, date, lon, lat, count) %>% mutate(date = dmy(date)) %>% filter(date > ymd(cutoff.date)) %>% mutate(PA = ifelse(count != 0 | is.na(count), 1, 0)) %>% dplyr::select(lon, lat, PA, date, sm) %>% dplyr::arrange(date, PA, lon, lat) %>% st_as_sf(coords = c("lon", "lat"), crs = vba.crs) %>% st_transform(crs = project.crs) z <- z[grep(pattern = "spotlight", x = z$sm, ignore.case = TRUE),] %>% mutate(PA = 0) %>% dplyr::select(PA, date, geometry) return(z) }

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/man/hello.Rd

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uk-gov-mirror/ukgovdatascience.testpackage

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refs/heads/master

2021-06-28T13:57:15.965212

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hello.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/hello.R \name{hello} \alias{hello} \title{Print hello world} \usage{ hello(x) } \arguments{ \item{x}{A character string to be concatencated in print statement} } \value{ Hello world } \description{ Prints hello world } \examples{ library(testpackage) hello('string') }

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/man/dds.Rd

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YTLogos/bcbioRNASeq

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refs/heads/master

2021-08-07T21:30:20.243822

2017-11-08T15:51:21

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dds.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/datasets.R \docType{data} \name{dds} \alias{dds} \title{Example \link{DESeqDataSet}} \format{An object of class \code{DESeqDataSet} with 505 rows and 4 columns.} \usage{ dds } \description{ Derived from \code{bcb}, the example \link{bcbioRNASeq} run. } \keyword{internal}

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/test_meth_elle.R

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AlexisAyme/estimateurs-robustes

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r

test_meth_elle.R

source("methode_ellipsoide.R") source("spectralMethod.R") source("contamination.R") #X= oneParameterContamination(n=1000,s=100,p=3,mean1=cbind(c(1,1,1)),mean2=cbind(c(3,3,3))) #X<- oneParameterContamination(n=10000,s=1000,p=32,mean1=cbind(rep(1,32)),mean2=cbind(rep(3,32))) #print (diakonikolasInequality(X,100,0.05)) # #print(system.time(muSp <- spectralMethod(X,C=0.7))) # #muEll =ellipsoideMethode(X,tau=0.2,s=100) # # # #muEmp= cbind(rowMeans(X)) # # #print(list( diffEll= norm(muEll-cbind(rep(1,3)),type="F"), # diffSp= norm(muSp-cbind(rep(1,3)),type="F"), # diffEmp= norm(muEmp-cbind(rep(1,3)),type="F"))) # traceResult <- function (X,s,mu,a,b,pt){ C= a *rep(1,pt) + ((b-a)/pt)*c(1:pt) res= rep(0,pt) muEmp= cbind(rowMeans(X)) for (i in 1:pt){ diffSp <- norm(spectralMethod(X,C[i])-mu,type="F") res[i] <- diffSp } plot(C,res,type="l",xlim=c(a,b),ylim=c(min(res),max(res)), xlab = "C", ylab = "distance to mu",main = "teststst") abline(b = laiInequality(X,s), a = 0, col = 2) text(0.5,0.5*laiInequality(X,s), "Lai inequality", col = 2, adj = c(-.1, -.1)) abline(b = 0, a = norm(muEmp-mu,type="F"), col = 3) text(1, norm(muEmp-mu,type="F"), "distance with mean emp", col = 3, adj = c(-.1, -.1)) print(list(distanceWithMuEmp= norm(muEmp-mu,type="F"))) } findConstante <- function(X,mu,a,b,s,eps){ repeat{ c <- (a+b)/2 if (b-a < eps){ return (c) } if (norm(spectralMethod(X,c)-mu,type="F")> c*laiInequality(X,s)){ a <- c }else { b <- c } } } # #X<- oneParameterContamination(n=10000,s=1000,p=32,mean1=cbind(rep(1,32)),mean2=cbind(rep(3,32))) #traceResult(X,1000,cbind(rep(1,32)),0.3,2,100) # #X<- oneParameterContamination(n=9000,s=500,p=32,mean1=cbind(rep(1,32)),mean2=cbind(rep(3,32))) #traceResult(X,500,cbind(rep(1,32)),0.3,2,100) # matMeans <- matrix (data= rep(1,32*3), ncol=3,nrow=32) mean1<- cbind(rep(1,32)) mean1[1:10,1]<- rep(2,10) mean2 <- cbind(rep(1,32)+ rnorm(32,0,5)) mean3 <- cbind(rep(3,32)) matMeans[,1]<- mean1 matMeans[,2]<- mean2 matMeans[,3]<- mean3 X <- uniParameterContamination(n=10000,s=500,p=32,mean1=cbind(rep(1,32)),matMeans = matMeans) print(findConstante (X,mu=cbind(rep(1,32)),a=0.3,b=2,s=500,eps=0.05)) #traceResult(X,500,cbind(rep(1,32)),0.3,2,100) X<- oneParameterContamination2(n=10000,s=1000,p=32,mean1=cbind(rep(1,32)),mean2=cbind(rep(1.5,32)),sd=0.3) traceResult(X,1000,cbind(rep(1,32)),0.3,2,100) #X<- oneParameterContamination(n=10000,s=1000,p=32,mean1=cbind(rep(1,32)),mean2=cbind(rep(1.5,32))) #traceResult(X,1000,cbind(rep(1,32)),0.3,2,100)

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/week3/run_analysis.R

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Gcabrera1/datasciencecoursera

53bc4850c6606be77c8005225354062ec66fd0fb

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refs/heads/master

2020-06-04T14:35:49.763956

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run_analysis.R

run_analysis <- function() { dat <- read.table("./test/X_test.txt") dat1 <- read.table("./train/X_train.txt") labels <- read.table("features.txt") dat3 <- rbind(dat, dat1) names(dat3) <- labels[,"V2"] filtro1 <- (labels[,"V2"] %like% "mean" | labels[,"V2"] %like% "std") prefinal <- dat3[,filtro1] final <- colMeans(prefinal, na.rm = TRUE) }

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/cachematrix.R

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no_license

u001624/ProgrammingAssignment2

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refs/heads/master

2021-01-17T18:12:33.516183

2014-10-26T10:41:34

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cachematrix.R

## Put comments here that give an overall description of what your ## functions do # Function makeCacheMatrix creates a cached matrix and a list of functions # Function cacheSolve find the inverse of the matrix, either if it has been cached ## Write a short comment describing this function # Function makeCacheMatrix creates a list containing a function to #1. set the value of the matrix #2. get the value of the matrix #3. set the value of the inverse of matrix #4. get the value of the inverse of matrix # these functions maniplulate a cached matrix makeCacheMatrix <- function(x = matrix()) { m <- NULL # creates a variable in the environment of the makeCacheMatrix() function, to store/cache a result # why isn't the cached x also need a variable in this environment, so a single function could used instead of forcing the calling program to know when to set a new result? set <- function(y) { # function used internally x <<- y # store the inputs in x but is x in the environment of the caller or this function ie. makeCacheMatrix parameter, which is a copy of a variable in the callers environment m <<- NULL # clear the previous 'cached' result } get <- function() {x} # this is callable from the object returned from makeCacheMatrix setInverseOfMatrix <- function(InverseOfMatrix) {m <<- InverseOfMatrix} # do NOT call setInverseOfMatrix() directly despite it being accessible getInverseOfMatrix <- function() {m} # return the 'cached' result list(set = set, get = get, # return a list, containing four functions setInverseOfMatrix = setInverseOfMatrix, getInverseOfMatrix = getInverseOfMatrix) } ## Write a short comment describing this function #Function cacheSolve calculates the inverse of matrix, which is initially created with the function makeCacheMatrix. #It first checks to see if the inverse of matrix has already been calculated. If so, it `get`s the inverse of matrix from the #cache and skips the computation. #Otherwise, it calculates the inverse of the matrix of data and sets the value of the inverse of matrix in the cache via the `setInverseOfMatrix` function. cacheSolve <- function(x, ...) { ## Return a matrix that is the inverse of 'x' m <- x$getInverseOfMatrix() if(!is.null(m)) { # cache contain something, a result, but where is the test that input x matches the non-existent cached/stored inputs? message("getting cached data") return(m) } data <- x$get() # store/cache the inputs. Why can't this simply be coded as data <- x ? m <- solve(data, ...) # process inputs to produce result x$setInverseOfMatrix(m) # store/cache result. Why isn't this simply m <<- m. Lexical Scoping would resolve both sides to the same variable, which isn't what is needed. m # return result }

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/R/backup/backup June 29/avs_models_evaluation.R

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lorenzol/kaggle_avs

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c61d2d43562dad2411bc22994f84b968f68e4ab5

refs/heads/master

2021-01-10T22:09:55.147687

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avs_models_evaluation.R

# Model Evaluation ################################################ #install.packages("e1071") #library(e1071) ## Logistic regression model glm.pred <- predict(glm.tune, test.batch) confusionMatrix(glm.pred, test.batch$repeater) ## Decison Tree ctree.pred <- predict(ctree.tune, test.batch) confusionMatrix(ctree.pred, test.batch$repeater) ## Boosted model ada.pred <- predict(ada.tune, test.batch) confusionMatrix(ada.pred, test.batch$repeater) ## Random Forest model rf.pred <- predict(rf.tune, test.batch) confusionMatrix(rf.pred, test.batch$repeater) ## SVM model svm.pred <- predict(svm.tune, test.batch) confusionMatrix(svm.pred, test.batch$repeater) ## KNN model knn.pred <- predict(knn.tune, test.batch) confusionMatrix(knn.pred, test.batch$repeater) ## AVG NNET model avnnet.pred <- predict(avnnet.tune, test.batch) confusionMatrix(avnnet.pred, test.batch$repeater) ## GBM model gbm.pred <- predict(gbm.tune, test.batch) confusionMatrix(gbm.pred, test.batch$repeater) ## glmnet model glmnet.pred <- predict(glmnet.tune, test.batch) confusionMatrix(glmnet.pred, test.batch$repeater) # ROC curves ################################################ ## Logistic regression model (BLACK curve) glm.probs <- predict(glm.tune, test.batch, type = "prob") glm.ROC <- roc(response = test.batch$repeater, predictor = glm.probs$t, levels = levels(test.batch$repeater)) plot(glm.ROC, type="S") glm.ROC ## CTree model (Grey curve) ctree.probs <- predict(ctree.tune, test.batch, type = "prob") ctree.ROC <- roc(response = test.batch$repeater, predictor = ctree.probs$t, levels = levels(test.batch$repeater)) plot(ctree.ROC, add=TRUE, col="grey") ctree.ROC ## Boosted model (GREEN curve) ada.probs <- predict(ada.tune, test.batch, type = "prob") ada.ROC <- roc(response = test.batch$repeater, predictor = ada.probs$t, levels = levels(test.batch$repeater)) plot(ada.ROC, add=TRUE, col="green") ada.ROC ## Random Forest model (RED curve) rf.probs <- predict(rf.tune, test.batch, type = "prob") rf.ROC <- roc(response = test.batch$repeater, predictor = rf.probs$t, levels = levels(test.batch$repeater)) plot(rf.ROC, add=TRUE, col="red") rf.ROC ## SVM model (BLUE curve) svm.probs <- predict(svm.tune, test.batch, type = "prob") svm.ROC <- roc(response = test.batch$repeater, predictor = svm.probs$t, levels = levels(test.batch$repeater)) plot(svm.ROC, add=TRUE, col="blue") svm.ROC ## KNN model (YELLOW curve) knn.probs <- predict(knn.tune, test.batch, type = "prob") knn.ROC <- roc(response = test.batch$repeater, predictor = knn.probs$t, levels = levels(test.batch$repeater)) plot(knn.ROC, add=TRUE, col="yellow") knn.ROC ## AVNNET model (ORANGE curve) avnnet.probs <- predict(avnnet.tune, test.batch, type = "prob") avnnet.ROC <- roc(response = test.batch$repeater, predictor = avnnet.probs$t, levels = levels(test.batch$repeater)) plot(avnnet.ROC, add=TRUE, col="orange") avnnet.ROC ## GBM model (PINK curve) gbm.probs <- predict(gbm.tune, test.batch, type = "prob") gbm.ROC <- roc(response = test.batch$repeater, predictor = gbm.probs$t, levels = levels(test.batch$repeater)) plot(gbm.ROC, add=TRUE, col="pink") gbm.ROC ## glmnet model (BROWN curve) glmnet.probs <- predict(glmnet.tune, test.batch, type = "prob") glmnet.ROC <- roc(response = test.batch$repeater, predictor = glmnet.probs$t, levels = levels(test.batch$repeater)) plot(glmnet.ROC, add=TRUE, col="brown") glmnet.ROC # graph which sums up the performance of the four models ######################################################## cv.values <- resamples(list(Logit = #glm.tune, #ctree = ctree.tune, ada.tune, RF = rf.tune, SVM = svm.tune, KNN = knn.tune, AVNNET = avnnet.tune, GBM = gbm.tune, GLMNET = glmnet.tune)) dotplot(cv.values, metric = "ROC")

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/Cleaning lynching data.R

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ZIBOWANGKANGYU/POLVILINDIA

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refs/heads/master

2023-06-01T04:21:50.635960

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Cleaning lynching data.R

# Mark Wang, kaw129@ucsd.edu # 11 April 2019 library(xlsx) library(ggplot2) library(sf) library(dplyr) library(tmap) Sys.setlocale("LC_TIME", "English") Sys.setenv(LANG = "en_US.UTF-8") # cleaning and importing lynching data lynchings_date<-read.xlsx(file = "lynchings.xlsx", sheetIndex = 2, encoding = "UTF-8") colnames(lynchings_date)[16]<-"Notes" lynchings_date<-lynchings_date[1:125,1:16] lynchings_date$DATE<-as.Date(paste(lynchings_date$MONTH, lynchings_date$DAY,lynchings_date$YEAR, sep=" "), "%B %d %Y") lynchings_date$STATE<-as.character(lynchings_date$STATE) lynchings_date$STATE[lynchings_date$STATE=="TELENGANA"]<-"TELANGANA" # cleaning and importing maps map_Tehsil<-st_read("India_Tehsil_Boundary.shp") map_Tehsil_df<-data.frame(map_Tehsil)%>%select(-geometry) map_District<-st_read("India_District_Boundary.shp") map_District_df<-data.frame(map_District)%>%select(-geometry) map_State<-st_read("India_State_Boundary.shp") map_State_df<-data.frame(map_State)%>%select(-geometry) # importing election data elections<-read.xlsx("elections.xlsx", encoding="UTF=8", sheetIndex = 2) colnames(elections)[1]<-"STATE" elections$STATE<-toupper(elections$STATE) elections$date_begining<-as.Date(as.character(elections$date_begining), "%d %B %Y") elections$date_ending<-as.Date(as.character(elections$date_ending), "%d %B %Y") elections$date_government<-as.Date(as.character(elections$date_government), "%d %B %Y") elections$STATE[elections$STATE=="JAMMU & KASHMIR"]<-"JAMMU AND KASHMIR" elections$STATE[elections$STATE=="ODISHA"]<-"ORISSA" # aggregate by month and state lynchings_date$ARRESTS_MADE<-as.numeric(as.character(lynchings_date$ARRESTS_MADE)) lynchings_date$STATE<-as.character(lynchings_date$STATE) lynchings_date$STATE[lynchings_date$STATE=="JAMMU & KASHMIR"]<-"JAMMU AND KASHMIR" lynchings_date$STATE[lynchings_date$STATE=="ODISHA"]<-"ORISSA" lynchings_date$STATE<-as.factor(as.character(lynchings_date$STATE)) # May need population data on this # Number of victims of important states ggplot(lynchings_date[lynchings_date$STATE=="UTTAR PRADESH",], aes(x=DATE))+ geom_point(aes(y=VICTIMS))+scale_y_continuous(labels = scales::number_format(accuracy = 1))+ labs(x="time", y="victims", title = "Cow lynching and number of victims in Uttar Pradesh")+ geom_rect(aes(xmin=as.Date("2017-02-11"), xmax=as.Date("2017-03-08"),ymin=0, ymax=Inf, fill="BJP win"), alpha = 0.5, show.legend = TRUE)+ scale_fill_manual("MLA election",values=c("orange")) ggplot(lynchings_date[lynchings_date$STATE=="HARYANA",], aes(x=DATE))+ geom_point(aes(y=VICTIMS))+scale_y_continuous(labels = scales::number_format(accuracy = 1))+ labs(x="time", y="victims", title = "Cow lynching and number of victims in Haryana")+ geom_vline(aes(xintercept=as.Date("2014-10-15"), color="BJP win"), size=1.5) + scale_color_manual("MLA election",values=c("orange")) # automize ggplot elections<-elections[order(elections$date_begining),] elections$winner<-"BJP losing" elections$winner[elections$Government=="NDA"]<-"BJP winning" cols <- c("BJP winning" = "orange", "BJP losing" = "green") cols_incumbent <- c("BJP" = "orange", "non-BJP" = "green") # plot states with cow lynching ggplot(lynchings_date, aes(x=DATE))+ geom_point(aes(y=VICTIMS))+scale_y_continuous(labels = scales::number_format(accuracy = 1))+ labs(x="time", y="victims")+xlim(as.Date("2012-6-1"), Sys.Date())+ geom_vline(data=elections[elections$STATE %in% lynchings_STATE$STATE,], mapping = aes(xintercept=date, color=winner), size=1.5, show.legend = TRUE)+ scale_color_manual("MLA election",values=cols)+facet_wrap(~STATE, ncol = 3)+labs(title = "State election cycle and cow lynching victim, 2012-2019") ggsave("state election cycle and cow lynching victim.pdf", width = 12, height = 10) # cow lynchings relative to elections on t=0 for (i in 1:nrow(lynchings_date)){ dif_vec<-lynchings_date$DATE[i]-elections$date_begining[elections$STATE==lynchings_date$STATE[i]] winner_vec<-elections$winner[elections$STATE==lynchings_date$STATE[i]] lynchings_date$date_gap_beginning[i]<-dif_vec[which.min(abs(dif_vec))] lynchings_date$winning_party[i]<-winner_vec[which.min(abs(dif_vec))] } for (i in 1:nrow(lynchings_date)){ dif_vec<-lynchings_date$DATE[i]-elections$date_ending[elections$STATE==lynchings_date$STATE[i]] lynchings_date$date_gap_ending[i]<-dif_vec[which.min(abs(dif_vec))] } for (i in 1:nrow(lynchings_date)){ dif_vec<-lynchings_date$DATE[i]-elections$date_government[elections$STATE==lynchings_date$STATE[i]] lynchings_date$date_gap_government[i]<-dif_vec[which.min(abs(dif_vec))] } lynchings_date$date_gap_government[lynchings_date$date_gap_government>lynchings_date$date_gap_beginning]<-NA # get rid of more than 2-year gaps ggplot()+ geom_density(data=lynchings_date[lynchings_date$winning_party=="BJP winning",], aes(x=date_gap_beginning, color=winning_party), size=1.5, bw=60)+ geom_density(data=lynchings_date[lynchings_date$winning_party=="BJP losing",], aes(x=date_gap_beginning, color=winning_party), size=1.5, bw=60)+labs(x="Days to MLA election", y="frequency of cow linching")+ labs(x="Days to MLA election (beginning)", y="frequency of cow linching", title="Density of caw lynching on the elction timeline, by winner", caption = "kernal bandwidth: 60 days")+ geom_vline(xintercept = 0, color="red", size=1.5)+ scale_color_manual("MLA election",values=cols)+xlim(-365, 365) ggsave("plots/summary/density of cow lynching on the election timeline, by winner1.jpg", width = 6, height = 5) ggplot()+ geom_density(data=lynchings_date[lynchings_date$winning_party=="BJP winning",], aes(x=date_gap_ending, color=winning_party), size=1.5, bw=60)+ geom_density(data=lynchings_date[lynchings_date$winning_party=="BJP losing",], aes(x=date_gap_ending, color=winning_party), size=1.5, bw=60)+labs(x="Days to MLA election", y="frequency of cow linching")+ labs(x="Days to MLA election (ending)", y="frequency of cow linching", title="Density of caw lynching on the elction timeline, by winner", caption = "kernal bandwidth: 60 days")+ geom_vline(xintercept = 0, color="red", size=1.5)+ scale_color_manual("MLA election",values=cols)+xlim(-365, 365) ggsave("plots/summary/density of cow lynching on the election timeline, by winner2.jpg", width = 6, height = 5) ggplot()+ geom_density(data=lynchings_date[lynchings_date$winning_party=="BJP winning",], aes(x=date_gap_government, color=winning_party), size=1.5, bw=60)+ geom_density(data=lynchings_date[lynchings_date$winning_party=="BJP losing",], aes(x=date_gap_government, color=winning_party), size=1.5, bw=60)+labs(x="Days to MLA election", y="frequency of cow linching")+ labs(x="Days to MLA election (new government)", y="frequency of cow linching", title="Density of caw lynching on the elction timeline, by winner", caption = "kernal bandwidth: 60 days")+ geom_vline(xintercept = 0, color="red", size=1.5)+ scale_color_manual("MLA election",values=cols)+xlim(-365, 365) ggsave("plots/summary/density of cow lynching on the election timeline, by winner3.jpg", width = 6, height = 5) # get the previous winning party for (i in 1:nrow(lynchings_date)){ if (lynchings_date$STATE[i]=="TELANGANA"){ dif_vec_pre<-elections$date_begining[elections$STATE=="ANDHRA PRADESH" & elections$date_begining<lynchings_date$DATE[i]]-lynchings_date$DATE[i] winner_vec_pre<-elections$winner[elections$STATE=="ANDHRA PRADESH" & elections$date_begining<lynchings_date$DATE[i]] lynchings_date$winning_party_pre[i]<-winner_vec_pre[which.min(abs(dif_vec_pre))] } else{ dif_vec_pre<-elections$date_begining[elections$STATE==lynchings_date$STATE[i] & elections$date_begining<lynchings_date$DATE[i]]-lynchings_date$DATE[i] winner_vec_pre<-elections$winner[elections$STATE==lynchings_date$STATE[i] & elections$date_begining<lynchings_date$DATE[i]] lynchings_date$winning_party_pre[i]<-winner_vec_pre[which.min(abs(dif_vec_pre))] } } lynchings_date$incumbent[lynchings_date$winning_party_pre=="BJP winning"]<-"BJP" lynchings_date$incumbent[lynchings_date$winning_party_pre=="BJP losing"]<-"non-BJP"

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/man/mfcluster-class.Rd

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tmrealphd/MF

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refs/heads/master

2021-01-21T02:36:34.942956

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mfcluster-class.Rd

\docType{class} \name{mfcluster-class} \alias{mfcluster-class} \title{Class mfcluster} \usage{ mfcluster$new(All, bycluster, excludedClusters, call, compare) } \description{ Class mfcluster is created from output of function MFClus } \section{Fields}{ \itemize{ \item{\code{All: }}{vector with elements: \itemize{ \item{\emph{w }}{Wilcoxon statistic} \item{\emph{u }}{Mann-Whitney statistic} \item{\emph{r }}{mean ridit} \item{\emph{n1 }}{size of group 1} \item{\emph{n2 }}{size of group 2} \item{\emph{mf }}{mitigated fraction} }} \item{\code{byCluster: }}{As for All, by clusters} \item{\code{excludedClusters: }}{character vector naming clusters excluded because of missing treatment} \item{\code{call: }}{the call to \code{MFClus}} \item{\code{compare: }}{character vector naming groups compared} } } \author{ Marie Vendettuoli \email{marie.c.vendettuoli@aphis.usda.gov} } \seealso{ \code{\link{MFClus}} } \keyword{documentation}

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/Twitter API for brand sentiment/02a_Score_Tweets.R

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siddharthksuri/data_analysis

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refs/heads/master

2021-03-13T01:21:39.402088

2017-05-16T16:28:11

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02a_Score_Tweets.R

setwd("D:/G Drive/04 Career/Interview Prep/THINX/Twitter") thinx= read.csv("thinxtweets.csv") lulu= read.csv("lulutweets.csv") #readymade dictionaries for scoring hlpos=scan("D:/G Drive/04 Career/Interview Prep/THINX/Twitter/dict/positive-words.txt" , what='character', comment.char=';') hlneg=scan("D:/G Drive/04 Career/Interview Prep/THINX/Twitter/dict/negative-words.txt" , what='character', comment.char=';') # This is a custom function written by Jeffrey Breen # score.sentiment(), must be loaded from a separate script trial<-c("accolade I recieved","thrilled to be here","beautiful is the world", "dismayed Yoda was","angry I am","unhappy we all are") score.sentiment(trial, hlpos, hlneg) #scoring the actual tweets thinx$sentiment = score.sentiment(thinx$text, hlpos, hlneg)$score lulu$sentiment = score.sentiment(lulu$text, hlpos, hlneg)$score #exploring the scores range(thinx$sentiment) range(lulu$sentiment) hist(thinx$sentiment) hist(lulu$sentiment) mean(thinx$sentiment) mean(lulu$sentiment) # Using sentimentr package by Tler Rinker # Returns a score for each sentence in string input. library(sqldf) library(sentimentr) thinx_rscore<-sentiment(thinx$text, polarity_dt = lexicon::hash_sentiment_jockers, valence_shifters_dt = lexicon::hash_valence_shifters, hyphen = "", amplifier.weight = 0.8, n.before = 5, n.after = 2, question.weight = 1, adversative.weight = 0.85, missing_value = 0) thinx_rscore<-sentiment(thinx$text) lulu_rscore<-sentiment(lulu$text) #aggregating the scores to tweet level. thinx_rscore<-sqldf("select sum(sentiment) from thinx_rscore group by element_id ") thinx<-cbind(thinx,thinx_rscore,"thinx") colnames(thinx)[19:20]<-c("sentiment2","company") #thinx$sentiment1<-NULL lulu_rscore<-sqldf("select sum(sentiment) from lulu_rscore group by element_id ") lulu<-cbind(lulu,lulu_rscore,"lulu") colnames(lulu)[19:20]<-c("sentiment2","company") #Some more stats just to see if anything odd shows up cor(thinx$sentiment, thinx$sentiment1) cor(lulu$sentiment, lulu$sentiment1) range(thinx$sentiment1) range(lulu$sentiment1) hist(thinx$sentiment1$`sum(sentiment)`) hist(lulu$sentiment1$`sum(sentiment)`) mean(thinx$sentiment1$`sum(sentiment)`) mean(lulu$sentiment1$`sum(sentiment)`) ### Output the file write.csv(lulu,"lulu_scored.csv") write.csv(thinx,"thinx_scored.csv") tweet_scored<-rbind(thinx,lulu) write.csv(tweet_scored,"tweets_scored.csv")

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/EPFR.r \name{bbk.fanChart} \alias{bbk.fanChart} \title{bbk.fanChart} \usage{ bbk.fanChart(x) } \arguments{ \item{x}{= "rets" part of the output of function bbk} } \description{ quintile fan charts } \seealso{ Other bbk: \code{\link{bbk.bin.rets.prd.summ}}, \code{\link{bbk.bin.rets.summ}}, \code{\link{bbk.bin.xRet}}, \code{\link{bbk.data}}, \code{\link{bbk.drawdown}}, \code{\link{bbk.fwdRet}}, \code{\link{bbk.histogram}}, \code{\link{bbk.holidays}}, \code{\link{bbk.matrix}}, \code{\link{bbk.summ}}, \code{\link{bbk.turnover}}, \code{\link{bbk}} } \keyword{bbk.fanChart}

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minus.R

node<-read.csv("nodedis.csv",header = T,row.names = 1) View(node) node<-scale(node,center = T,scale = T) write.csv(node,"nodeminus.csv")

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Oil and Gas withdraws each year in terms of state.R

#Oil and Gas withdraws each year in terms of State library(ggplot2) library(dplyr) #read tidy data dfnew <- read.table("oilTidyData.txt") dfnew <- data.frame(dfnew) #select top 10000 oil/ gas withdraw county data dfnew.oil <- arrange(dfnew, desc(oilwithdraw)) dfnew.oil <- dfnew.oil[1:10000,] dfnew.gas <- arrange(dfnew, desc(gaswithdraw)) dfnew.gas <- dfnew.gas[1:10000,] #draw point plot and line to indicate oil/ gas withdraws from year to year in terms of state gg <- ggplot(dfnew.oil, aes(x = year, y = oilwithdraw, colour = factor(Stabr))) gg + geom_point() + labs(title = "Oil Withdraw from 2000 to 2011", x = "Year", y = "Oil Withdraw") qq <- ggplot(dfnew.gas, aes(x = year, y = gaswithdraw, colour = factor(Stabr))) qq + geom_point() + labs(title = "Gas Withdraw from 2000 to 2011", x = "Year", y = "Gas Withdraw")

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\name{extractPointVgm} \alias{extractPointVgm} \title{ Extract point-scale variogram from deconvoluted ataKrigVgm. } \description{ Extract point-scale variogram from deconvoluted ataKrigVgm. } \usage{ extractPointVgm(g) } \arguments{ \item{g}{ deconvoluted ataKrigVgm object. } } \value{ a list of gstat vgm model. }

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05 - Statistical Programming 2.R

## ## ## ------------------------------------------- ## LOAD PACKAGES ## ------------------------------------------- ## ## ## Load any R package required for your code ## to successfully run! library(tidyverse) ## ;; setwd("") ## ------------------------------------------- ## READING DATA ## ------------------------------------------- ## ;; load("burlington.RData") ## -------------------------------------------------- ## -------------------------------------------------- ## ;; ## --------------------------------------------- ## Q1: -- add your code below ## --------------------------------------------- ## ;; ## 1.1 # Constructs the 95% bootstrap CI using R "replications" CI.cor <- function(raw, R = 10000, conf = 0.95){ n <- raw$n data <- cbind(raw$lsat, # Compile the data into a 2 column matrix raw$gpa) rho_star <- NULL # Initialize vector of covariances for(r in 1:R){ # For R Replications index_star <- 1:n %>% sample(size = n, replace = TRUE) # Get n resamples from data data_star <- data[index_star,] # Obtains the resamples rho_star[r] <- cor(data_star)[1,2] # Gets correl and stores in vector } # Computing estimate of bias and variance for bootstrap rho bias_rho_star <- mean(rho_star - cor(data)[1,2]) var_rho_star <- var(rho_star) # Define alpha confidence level alpha = 1 - conf # Obtain Normal Bootstrap CI CI_normal_low <- cor(data)[1,2] - bias_rho_star + qnorm(alpha/2) * sqrt(var_rho_star) CI_normal_high <- cor(data)[1,2] - bias_rho_star + qnorm(1-alpha/2) * sqrt(var_rho_star) # Obtain Percentile Bootstrap CI CI_low <- quantile(rho_star, alpha/2) CI_high <- quantile(rho_star, 1-alpha/2) # Tabulate the results for output results <- data.frame( "Lower Bound" = c(CI_normal_low, CI_low), "Upper Bound" = c(CI_normal_high, CI_high), row.names = c("Normal", "Percentile")) return(results) } # Construct Data raw.1 <- list(lsat = c(576, 635, 558, 578, 666, 580, 555, 661, 651, 605, 653, 575, 545, 572, 594), gpa = c(3.39, 3.30, 2.81, 3.03, 3.55, 3.07, 3.00, 3.43, 3.36, 3.13, 3.12, 2.74, 2.76, 2.88, 2.96), n = 15) # Call Function CI.cor(raw.1) ## 1.2 CI.var.ratio <- function(raw, R = 10000, conf = 0.95){ n <- raw$n data <- cbind(raw$lsat, # Compile the data into a 2 column matrix raw$gpa) vr_star <- NULL # Initialize vector of variance ratios for(r in 1:R){ # For R Replications index_star <- 1:n %>% sample(size = n, replace = TRUE) # Get n resamples from data data_star <- data[index_star,] # Obtains the resamples vr_star[r] <- var(data_star[,1])/var(data_star[,2]) # Gets var ratios } vr_hat = var(data[,1])/var(data[,2]) # Define for easier referencing # Computing estimate of bias and variance for bootstrap var ratios bias_vr_star <- mean(vr_star - vr_hat) var_vr_star <- var(vr_star) # Define alpha confidence level alpha = 1 - conf # Obtain Normal Bootstrap CI CI_normal_low <- vr_hat - bias_vr_star + qnorm(alpha/2) * sqrt(var_vr_star) CI_normal_high <- vr_hat - bias_vr_star + qnorm(1-alpha/2) * sqrt(var_vr_star) # Obtain Percentile Bootstrap CI CI_low <- quantile(vr_star, alpha/2) CI_high <- quantile(vr_star, 1-alpha/2) # Tabulate the results for output results <- data.frame( "Lower Bound" = c(CI_normal_low, CI_low), "Upper Bound" = c(CI_normal_high, CI_high), row.names = c("Normal", "Percentile")) return(results) } CI.var.ratio(raw.1) # Call Function ## ;; ## ------------------------------------------- ## Q2: -- add your code below ## ------------------------------------------- ## ;; # The given code for GDP fit.gpd <- function(x, thresh, tol.xi.limit=5e-2, ...){ llik.gpd <- function(par, x, thresh, tol.xi.limit=5e-2) { y <- x[x>thresh] sigma <- exp(par[1]) xi <- par[2] n <- length(y) if(abs(xi)<=tol.xi.limit) { llik <- n*log(sigma) + sum((y-thresh)/sigma) return(llik) } par.log.max <- log( pmax( 1+xi*(y-thresh)/sigma, 0 ) ) llik <- -n*log( sigma )-(1+( 1/xi ))*sum( par.log.max ) llik <- -ifelse( llik > -Inf, llik, -1e40 ) return(llik) } fit <- optim(par = c(0, 0), fn = llik.gpd, x=x, thresh=thresh, control=list( maxit=10000, ... )) sigmahat <- exp( fit$par[1] ) xihat <- fit$par[2] return(c(sigmahat, xihat)) } fit.gpd(c(0,0), x=burlington$Precipitation, thresh=quantile(burlington$Precipitation,0.8)) ## 2.1 # Write an inversion sampling function for GDP rgdp <- function(n, thresh, sigma, xi, tol.xi.limit = 5e-2){ z <- runif(n) # Generate uniform RV's if(abs(xi) < tol.xi.limit){ # Consider the limiting case x <- thresh - sigma*log(1-z) # Inverse of limiting CDF return(x) } else { x <- thresh + (sigma/xi) * ((1-z)^(-xi) - 1) return(x) } } # Parametric Bootstrap Function parboot.gpd <- function(data, thresh, R=10000){ n <- length(data) # Get length of data # Fit the data to GDP model <- fit.gpd(c(0,0), x=data, thresh=thresh) sigma <- model[1] xi <- model[2] # Initialize estimate of p.hat is ratio of "successes" over total trials p.hat <- length(data[data > thresh]) / length(data) mle <- c(sigma, xi, p.hat) # Store the MLE Estimators based on data theta.star <- matrix(ncol=3, nrow=R) # Intialize matrix to store parameter fits p <- p.hat # Initialize for first replication for(r in 1:R){ n.star <- rbinom(1, n, p) # Generate binomial random variables to get exceedence counts x <- rgdp(n.star, thresh = thresh, sigma = sigma, xi = xi) # Get GPD Samples mod.star <- fit.gpd(c(0,0), # Fit the samples to a pareto x=x, thresh=thresh) theta.star[r, 1] <- mod.star[1] # Store sigma in column 1 theta.star[r, 2] <- mod.star[2] # Store xi in column 2 theta.star[r, 3] <- p p <- n.star/n # Update p for next replication } # Compute Biases bias.sigma <- mean(theta.star[,1] - sigma) bias.xi <- mean(theta.star[,2] - xi) bias.p <- mean(theta.star[,3] - p.hat) biases <- c(bias.sigma, bias.xi, bias.p) # Store biases in vector # Compute standard errors se.sigma <- sd(theta.star[,1]) se.xi <- sd(theta.star[,2]) se.p <- sd(theta.star[,3]) se <- c(se.sigma, se.xi, se.p) return(list(mle = mle, bias = biases, # Return the named list se = se, distn = theta.star)) } answer2.1 <- parboot.gpd(data=burlington$Precipitation, thresh=quantile(burlington$Precipitation,0.9)) ## 2.2 # Non-Parametric Bootstrap Function npboot.gpd <- function(data, thresh, R=10000){ n <- length(data) # Get length of data # Get MLE estimates from original data mle.fit <- fit.gpd(c(0,0), x=data, thresh=thresh) sigma.hat <- mle.fit[1] xi.hat <- mle.fit[2] p.hat <- length(data[data > thresh]) / length(data) mle <- c(sigma.hat, xi.hat, p.hat) # Store the MLE Estimators based on data theta.star <- matrix(ncol=3, nrow=R) # Intialize matrix to store parameter fits for(r in 1:R){ # Get a sample from the original data data.star <- data %>% sample(size = n, replace = TRUE) # Fit the model to the sample data fit.star <- fit.gpd(c(0,0), x = data.star, thresh = thresh) theta.star[r,1] <- fit.star[1] # Store sigma theta.star[r,2] <- fit.star[2] # Store xi theta.star[r,3] <- length(data.star[data.star > thresh]) / n # Store p } # Compute Biases bias.sigma <- mean(theta.star[,1] - sigma.hat) bias.xi <- mean(theta.star[,2] - xi.hat) bias.p <- mean(theta.star[,3] - p.hat) biases <- c(bias.sigma, bias.xi, bias.p) # Store biases in vector # Compute standard errors se.sigma <- sd(theta.star[,1]) se.xi <- sd(theta.star[,2]) se.p <- sd(theta.star[,3]) se <- c(se.sigma, se.xi, se.p) return(list(mle = mle, bias = biases, # Return the named list se = se, distn = theta.star)) } answer2.2 <- npboot.gpd(data=burlington$Precipitation, thresh=quantile(burlington$Precipitation,0.9)) ## 2.3 thresh = quantile(burlington$Precipitation,0.9) # Define for future use # Non-parametric bootstrap for returns and 95% CI ret.level <- function(T, param = FALSE, thresh = quantile(burlington$Precipitation,0.9), alpha = 0.05){ if(param == TRUE) { gpd <- answer2.1 # Get parametric data } else { gpd <- answer2.2 # Get non-parametric data } # Obtain distribution using bootstrap distributions of GPD distn <- gpd$distn r.distn <- thresh + (distn[,1]/distn[,2]) * ((T*distn[,3])^distn[,2] - 1) # Compute Percentile Confidence Intervals CI_lower <- quantile(r.distn, alpha/2) CI_upper <- quantile(r.distn, 1-alpha/2) r.CI <- c(CI_lower, CI_upper) return(list(distn = r.distn, CI = r.CI)) } # Obtain parametric bootstrap for different T's r.p.100 <- ret.level(100, param = TRUE) r.p.500 <- ret.level(500, param = TRUE) r.p.1000 <- ret.level(1000, param = TRUE) r.p.10000 <- ret.level(10000, param = TRUE) # Obtain non-parametric bootstraps for different T's r.np.100 <- ret.level(100) r.np.500 <- ret.level(500) r.np.1000 <- ret.level(1000) r.np.10000 <- ret.level(10000) ## ;; ## ------------------------------------------- ## Q3: -- add your code below ;; ## ------------------------------------------- ## ;; ## 3.1 ## Posteriors in PDF file ## 3.2 # Initialize data pumps.data <- list(t = c(94.3, 15.7, 62.9, 126, 5.24, 31.4, 1.05, 1.05, 2.1, 10.5), x = c(5, 1, 5, 14, 3, 19, 1, 1, 4, 22), n = 10) # Define acceptance probability for alpha accept.alpha <- function(a0, a1, V=1, n=10, beta=1, theta=rep(1,10)) { return( (gamma(a0)/gamma(a1))^n * beta^(n*(a1-a0)) * prod(theta) ^ (a1-a0) * exp(-(a1-a0)) * a1/a0 ) } # Generate samples from the posterior using MCMC # Ndraw is the number of draws, nburn is how many to discard among first entries, # V is the variance of random walk on log a mcmc.pumps <- function(data, ndraw=100, nburn=0, # Default initial parameters theta0 = rep(0.5,10), beta0 = 1, alpha0 = 0.5, V = 1) { # Retrieve values from data n <- data$n t <- data$t x <- data$x # The parameters differ from each pump to pump, i think d.theta <- matrix(nrow = ndraw, ncol=n) # Intialize a matrix to store draws for theta d.others <- matrix(nrow = ndraw, ncol=2) # Initialize matrix for beta and alpha iter <- -nburn # For burning entries # Set initial parameters as the current parameters theta <- theta0 alpha <- alpha0 beta <- beta0 alpha.accept.count <- 0 while(iter < ndraw) { iter <- iter + 1 # Increase counter # Update thetas for(i in 1:n) { # Update done for each pump in turn # Update using conditional distributions theta[i] <- rgamma(1, shape = x[i] + alpha0, rate = beta0 + t[i]) } # Update beta beta <- rgamma(1, shape = n * alpha, rate = sum(theta) + 0.01) # Generate proposal alpha alpha.prop <- exp(log(alpha) + rnorm(1,0,V)) # Random walk on log alpha alpha.accept <- min(1, accept.alpha(a0 = alpha, # Get acceptance criterion a1 = alpha.prop, V = V, n = n, beta = beta, theta = theta)) if(runif(1) <= alpha.accept){ # If unif is below the acceptance criterion alpha <- alpha.prop # accept alpha if(iter > 0) { alpha.accept.count <- alpha.accept.count + 1 # Count accepted alphas } } # Record draws if burning is done if(iter > 0) { d.theta[iter,] <- theta d.others[iter,1] <- beta d.others[iter,2] <- alpha } } # Return Values return( list(theta = d.theta, parameters = d.others[,1:2], acceptance = alpha.accept.count / ndraw)) } answer3.2 <- mcmc.pumps(pumps.data) ## 3.3 # Predictive distribution for failure for ith pump predictive.pumps <- function(i, t, pumps.data = answer3.2, max.x = 30){ # Retrieve samples from posterior distribution theta <- pumps.data$theta[,i] x <- 1:max.x # Maximum number of failures to compute distribution for pred.dist <- NULL for(f in 1:length(x)){ # Prob of X failures, X=0,1,2,...,30 pred.dist[f] <- mean(dpois(f, lambda=theta*t)) # Predictive distribution } return(cbind("Failures" = x, # Return a table that lists the prob for x failures "Probability" = pred.dist)) } # Call the function for pump 1 and 94.3 time units for up to 25 failures answer3.3 <- predictive.pumps(1, 94.3, max.x = 25) plot(answer3.3) # Compare with expected probabilities based on data ## ;; ## ------------------------------------------- ## Q4: -- add your code below;; ## ------------------------------------------- ## ;; ## 4.1 accept.mu <- function(x, mu0, mu1){ terms <- ((10+(x-mu1)^2)/(10+(x-mu0)^2))^(-11/2) first.part <- prod(terms) second.part <- exp( -(1/2) * (mu1^2 - mu0^2)) return(first.part * second.part) } # MCMC for t-distribution using random walk # V is the sd of the normal candidate generator mcmc.t <- function(x, ndraw=1000, nburn=100, mu0=0, v=1){ mu <- mu0 # Set initial mu as current iter <- -nburn # Initialize for data discarding draws <- NULL # Initialize vector of draws while(iter < ndraw){ iter <- iter + 1 # update counter mu.prop <- rnorm(1, mu, v) # Normal Proposal acc.mu <- min(1, accept.mu(x, mu, mu.prop)) # Compute acceptance probability if(runif(1) <= acc.mu){ # If accepted, change the current mu mu <- mu.prop } if(iter > 0){ # Once burning is done draws[iter] <- mu } } # Return the draws return(draws) } # Generate t-distribution samples x <- rt(12, df = 10) # Generate samples from posterior distribution for mu answer4.1 <- mcmc.t(x) ## 4.2 mcmc.gibbs <- function(x, mu0=0, z0=1, ndraw = 1000, nburn=100){ iter <- -nburn # Initialize for burning entries n <- length(x) # Initialize variables mu <- mu0 z <- rep(z0, n) d.mu <- NULL # Initialize an empty vetor for mu d.z <- matrix(nrow = ndraw, ncol = n) # Initialize matrix for draws of z while(iter < ndraw){ iter <- iter + 1 # Increment counter # Update mu and z via conditional posteriors mu <- rnorm(1, mean = sum(x*z)/sum(z+1), sd = sqrt(sum(z+1))) for(i in 1:n) { z[i] <- rgamma(1, shape = 11/2, rate = (1/2)*(x[i]-mu)^2 + 5) } if(iter > 0){ # Record draws if done burning d.mu[iter] <- mu d.z[iter,] <- z } } # Return named list return(list(mu = d.mu, z = d.z)) } # Generate data that comes from t-distribution with 10 df x <- rt(12, df=10) plot(density(x)) answer4.2 <- mcmc.gibbs(x) plot(density(answer4.2$mu)) # Inspect the distribution for mu ## 4.3 # Predictive distribution of t predictive.t <- function(mu = answer4.2$mu){ x <- seq(-3,5, len=100) # points at which distribution will be evaluated n <- length(x) m <- length(mu) dist <- NULL for(i in 1:n){ # Evaluate the integral at each point dist[i] <- mean(dnorm(x[i], mean = mu, sd = sqrt(rgamma(m, shape = 11/2, rate = (1/2)*(x[i]-mu)^2 + 5)))) } return(cbind(x, dist)) } answer4.3 <- predictive.t() plot(density(answer4.3)) ## 4.4 ## Predictive Quantile predictive.quantile <- function(mu = answer4.2$mu, alpha = 0.05){ preds <- predictive.t(mu)[,2] # Runs the prediction distribution and gets the values # Compute CI bounds CI_lower <- quantile(preds, alpha/2) CI_upper <- quantile(preds, 1-alpha/2) return(c(CI_lower, CI_upper)) } predictive.quantile() ## ------------------------------------------------ ## ;; ## ------------------------------------------- ## DRAFT ## ------------------------------------------- ## ;; ## foo <- rnorm(100) ## ## ##

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readLPIdata.KAIRA.Rd

\name{readLPIdata.KAIRA} \title{readLPIdata.KAIRA} \alias{readLPIdata.KAIRA} \description{Read one integration period of voltage level data. Transmitter samples are recorded with USRP at Tromso and receiver samples with KLP at Kilpisjarvi.} \usage{readLPIdata.KAIRA( LPIparam , intPeriod )} \arguments{ \item{ LPIparam }{ An LPI parameter list from \link{LPI.KAIRA} } \item{ intPeriod }{ Integration period number. Integration periods are counted in steps of LPIparam$timeres.s, the period number 1 starting at LPIparam$startTime.} } \value{ A list with the following contents \item{'RX1'}{ First receiver samples } \item{'RX2'}{ Second receiver samples. Will be identical with 'RX1' in autocovariance function estimation} \item{'TX1'}{ First transmitter samples} \item{'TX2'}{ Second transmitter samples. Will usually be identical with 'TX1', but may be different e.g. in orthogonal polarization experiments.} \item{success}{ TRUE if all requested data was successfully read, FALSE otherwise.} The elements "RX1", "RX2", "TX1", and "TX2" are lists themselves with elements \item{'cdata'}{ Complex data vector.} \item{'idata'}{ Logical vector, TRUE for samples that should be used in LPI.} \item{'ndata'}{ Number of samples in the data vectors.} } \details{ \describe{ \item{'LPIparam contents'}{ Following components of the LPI parameter list are used for selecting the correct signal samples \describe{ \item{'startTime'}{'beginTime' converted into POSIX format, i.e. second count from 1970-01-01 00:00:00. } \item{'dataStartTimes'}{A named vector with components 'RX1', 'RX2', 'TX1', and 'TX2'. Each element is samling time of the first sample of corresponding data type. The times are in seconds in POSIX format. } \item{'dataSampleFreqs'}{ A named vector with components 'RX1', 'RX2', 'TX1', and 'TX2'. Each element is the sample rate of the corresponding data type in Hz.} \item{'timeRes.s'}{ Analysis time resolution (incoherent integration period) in seconds. } \item{'dataFileLengths'}{A named vector with components 'RX1', 'RX2', 'TX1', and 'TX2'. Each element is the number of complex samples in one data file of the corresponding data type. } \item{'fileNamePrefix'}{A named vector with components 'RX1', 'RX2', 'TX1', and 'TX2'. Each element contains the file name prefix of the corresponding data type as a string. } } } } } \seealso{LPI.gdf , LPI} \author{Ilkka Virtanen (University of Oulu, Finland) \cr \email{ilkka.i.virtanen@oulu.fi}}

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/App-positivity/inst/main.R

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tranlm/positivity

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main.R

############################################################################### # Description: Master file that calls in all other functions # # Author: Linh Tran <tranlm@berkeley.edu> # Title: Handling positivity violations in longitudinal data # Date: 2015-01-08 ############################################################################### rm(list = ls()) ################# ## DESCRIPTION ## ################# # This study simulates positivity violations in a longitudinal setting and tries # out four different approaches at handling it: # # 1) Choose regimens where not a problem (eg switch immediately or never) # 2) Dynamic regimes, eg switch as soon as you come in after your assigned time # 3) Joint (partially stochastic) intervention on both # (i.e. leave visits random until time of assigned switch and then force someone to come in) # 4) Don't adjust for the covariate causing the problem (ie dont adjust for whether a patient comes in) # and could show that this might or might not cause a problem depending on whether coming to # clinic has a direct effect on outcome not via switch. ############# ## OPTIONS ## ############# ncores = 8 setwd("~/Dropbox/Studies/positivity") options("mc.cores" = ncores, stringsAsFactors=FALSE, digits=4) words = function(...) paste(substitute(list(...)))[-1] ############# ## LIBRARY ## ############# load.packages = words(foreach, iterators, snow, doSNOW) lapply(load.packages, require, character.only=T) ######################### ## SIMULATION SETTINGS ## ######################### n = 1000 sim = 1000 time.pt = 10 ZaffectsY = TRUE ######################## ## TRUE DISTRIBUTIONS ## ######################## #Time ordering: W, Y(t), L(t), Z(t), A(t), C(t) : W=(W1,W2) and L(t) = (L2(t),L1(t)) #n.b. Within L(t) there is no implied time-ordering...i.e. either of L2(t) or L1(t) can go first rexpit = function(x) rbinom(n=length(x), size=1, prob=x) QW1 = function(n) rnorm(n, mean=0, sd=1) QW2 = function(n) rep(plogis(.2), n) QL1.t = function(y, w1, prev_l1, prev_l2, prev_a) ifelse(y==1, prev_l1, 0.1 + 0.4*w1 + 0.6*prev_l1 - 0.7*prev_l2 - 0.45*prev_a - rnorm(length(w1), sd=0.5)) QL2.t = function(y, w1, w2, prev_l1, prev_l2, prev_a) ifelse(y==1, prev_l2, -0.55 + 0.5*w1 + 0.75*w2 + 0.1*prev_l1 + 0.3*prev_l2 - 0.75*prev_a - rnorm(length(w1), sd=0.5)) gZ.t = function(y, w1, w2, l1, l2, prev_a) ifelse(y==1, 0, plogis(2.8 - 0.5*w1 + 0.6*w2 + 0.7*l1 + 0.7*l2)) gA.t = function(y, w1, w2, l1, l2, prev_a, z) ifelse(y==1, prev_a, ifelse(z==0, prev_a, ifelse(prev_a==1, 1, plogis(-1.5 - 1.5*w1 + 0.75*w2 + 0.8*l1 + 0.8*l2)))) if(ZaffectsY) { QY.t = function(prev_y, w1, w2, prev_l1, prev_l2, prev_a, prev_z) ifelse(prev_y==1, 1, plogis(-1.8 + 1.2*w1 - 2.4*w2 - 1.6*prev_l1 - 1*prev_l2 - 1.9*prev_a - 1.25*prev_z)) } else { QY.t = function(prev_y, w1, w2, prev_l1, prev_l2, prev_a, prev_z) ifelse(prev_y==1, 1, plogis(-1.8 + 1.2*w1 - 2.4*w2 - 1.6*prev_l1 - 1*prev_l2 - 1.9*prev_a + 0*prev_z)) } # nb. Distribution is set up such that: # Y(0)=0 for everyone, ie. Everyone is alive at the beginning of follow-up # if Y(t)=1, then they don't come in for a visit (ie. Z(t)=0)) # if Y(t)=1, then all remaining covariate last values get carried forward # if Z(t)=0, then A(t-1) gets carried forward # if A(t-1)=1 then A(t)=1 ############# ## FOLDERS ## ############# subdirectories = list.dirs() if(!"./data" %in% subdirectories) system("mkdir ./data") if(!"./inst" %in% subdirectories) system("mkdir ./inst") if(!"./inst/results" %in% subdirectories) system("mkdir ./inst/results") ########### ## CODES ## ########### source("./R/generateData.R") source("./R/generatePsi.R") source("./R/plotPsi.R") ############################################################################### ############################# TRUE PARAMETER VALUES ########################### ############################################################################### skip = function() { ## STARTS PARALLEL CLUSTER ## set.seed(1989) superman <- makeCluster(ncores, type="SOCK") registerDoSNOW(superman) clusterExport(cl = superman, c("time.pt", "generate.data", "rexpit", "QW1", "QW2", "QL1.t", "QL2.t", "gZ.t", "gA.t", "QY.t")) cat("Cluster summary\n"); getDoParRegistered(); getDoParName(); getDoParWorkers(); getDoParVersion() ## CALCULATES TRUE VALUES ## truePsi = generatePsi() save(truePsi, file=paste0("./data/truePsi_", ifelse(ZaffectsY,"ZaffectsY","noZaffectsY"), ".Rda")) ## STOPS CLUSTER ## stopCluster(superman) ####################### ## PLOTS TRUE VALUES ## ####################### pdf(paste0("./inst/results/truePsi_", ifelse(ZaffectsY,"ZaffectsY","noZaffectsY"), ".pdf"), height=6, width=6) plotPsi(main=ifelse(ZaffectsY,"(a)","(b)")) dev.off() } ## LOADS PREVIOUSLY COMPUTED/SAVED VALUES ## load(file=paste0("./data/truePsi_", ifelse(ZaffectsY,"ZaffectsY","noZaffectsY"), ".Rda")) ############################################################################### ################################## SIMULATION ################################# ############################################################################### set.seed(1)

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Annotation.Rd.R

library(Rgb) ### Name: Annotation ### Title: Annotation track constructors ### Aliases: Annotation track.table.GTF track.exons.CCDS track.CNV.DGV ### track.genes.NCBI track.bands.UCSC ### ** Examples # From the "How-to" vignette, section "Custom annotation tracks" file <- system.file("extdata/Cosmic_ATM.gtf.gz", package="Rgb") tt <- track.table.GTF(file)

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/man/fine_map.Rd

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william-denault/mWaveQTL

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fine_map.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils.R \name{fine_map} \alias{fine_map} \title{Defintion of the regions with Bayes factor over a threshold.} \usage{ fine_map(res, lev_res, thresh, start, end, chr) } \arguments{ \item{res}{Output of mWaveQTL.} \item{lev_res}{the maximum level of resolution of the previous analysis.} \item{thresh}{numeric, Bayes factor threshold to defined the fine mapping. If missing set as 1.} \item{start}{numeric, start in base pair of the analyzed regions .} \item{chr}{numeric, end in base pair of the analyzed regions .} } \description{ internal function for fine mapping tool for output of the mWaveQTL function. } \details{ return a list of chr, start, end position, that correspond of the sub regions defined by the dyadic decomposition of wavelet that are associated with a Bayes factor over the defined threshold. }

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/R/NSSTDJD.R

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ivotebexreni/JADE

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NSSTDJD.R

# Method NSS.JD NSS.TD.JD <- function(X,...) UseMethod("NSS.TD.JD") # main function for NSS.TD.JD # # input: # x = data matrix # K = number of intervals, default 12, obsolete when n.cuts is provided. # Tau = lags used to compute the autocovariance matrices for each interval, default = 0:11 # n.cuts = Cut of for intervals, must be of the form c(1, ..., n) where n is the sample size. If NULL, then K is used # eps = eps for the JD # maxiter = maxiter for the JD # output # # list of class "bss" with components # W = unmixing matrix # k = lag used # n.cut = n.cut used # K = number of intervals used # S = sources as a time series object NSS.TD.JD.default <- function(X, K=12, Tau=0:11, n.cuts=NULL, eps = 1e-06, maxiter = 100, ...) { n <- nrow(X) MEAN <- colMeans(X) COV <- cov(X) EVD.COV <- eigen(COV, symmetric=TRUE) COV.sqrt.inv <- EVD.COV$vectors %*% tcrossprod(diag(sqrt(1/EVD.COV$values)),EVD.COV$vectors) X.C <- sweep(X,2,MEAN,"-") Y <- tcrossprod(X.C, COV.sqrt.inv) p <- ncol(X) if (is.null(n.cuts)) n.cuts <- ceiling(seq(1,n,length=K+1)) else K <- length(n.cuts)-1 N.cuts <- n.cuts + c(rep(0,K),1) L<- length(Tau) R <- array(0, dim=c(p,p,L*K)) ii<-1 for (i in 1:K){ Y.i<-Y[N.cuts[i]:(N.cuts[i+1]-1),] for (j in 1:L){ R[,,ii] <- M.x(Y.i, Tau=Tau[j]) ii<-ii+1 } } W <- crossprod(frjd(R, eps=eps, maxiter=maxiter)$V, COV.sqrt.inv) S <- tcrossprod(X.C,W) S <- ts(S, names=paste("Series",1:p)) RES <- list(W=W, k=Tau, n.cut=n.cuts, K=K, S=S) class(RES) <- "bss" RES } NSS.TD.JD.ts <- function(X, ...) { x <- as.matrix(X) RES <- NSS.TD.JD.default(x,...) S <- RES$S attr(S, "tsp") <- attr(X, "tsp") RES$S <- S RES } ################################################################## # helper function M.x # # Autocovariance matrix for a centered time series at lag Tau # # (symmetrized) # ################################################################## M.x <- function(X,Tau=0) { n<- nrow(X) Xt <- X[1:(n-Tau),] Xti <- X[(1+Tau):n,] Ri <- crossprod(Xt,Xti)/nrow(Xt) (Ri+t(Ri))/2 }

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/data/genthat_extracted_code/descomponer/examples/gdf.Rd.R

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gdf.Rd.R

library(descomponer) ### Name: gdf ### Title: Get Frequency Data ### Aliases: gdf ### Keywords: smooth ### ** Examples n<-100;x<-seq(0,24*pi,length=n);y<-sin(x)+rnorm(n,sd=.3) gdf(y)

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/fisherX2.R

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jamesbrownlow/LoadNORCdata

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fisherX2.R

fisher.two = function(x, y){ # x and y are factor variables levelsY = levels(y) n= length(levelsY) for (i in 1:(n-1)) for (j in (i+1):n) { print(c(levelsY[i],levelsY[j])) tempDF=data.frame(x,y) tempDFfiltered = filter(tempDF, y %in% c(levelsY[i],levelsY[j])) droplevels(tempDFfiltered$y) dropIndex = c(i,j) fisherTable = as.matrix(table(tempDFfiltered$x, tempDFfiltered$y)[,dropIndex]) print(fisherTable) fisherTest=fisher.test(fisherTable, conf.int = TRUE) print(fisherTest) } }

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/man/bin_search_.Rd

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bin_search_.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/selection.R \name{bin_search_} \alias{bin_search_} \title{Perform binary search for the smallest balance which is feasible} \usage{ bin_search_(eps, feasfunc) } \arguments{ \item{eps}{Sorted list of balance tolerances to try} \item{feasfunc}{Function which returns True if feasible} } \value{ smallest tolerance which is feasible } \description{ Perform binary search for the smallest balance which is feasible }

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/plot4.R

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MVB1/ExData_Plotting1

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plot4.R

# Exploratory Data Analysis # Week 1, Assignment 1 # Plot 4, MVB ## Start message("This script will generate Plot 4") DateStarted <- date() DateStarted getwd() ## Functions for download and decompression Download <- function () { message("Downloading zip file") fileUrl <- "http://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip" download.file(fileUrl, destfile="./Data.zip") message("Download completed") } Unzip <- function () { message("Decompressing file") message("This may take a few minutes") unzip(zipfile="./Data.zip") message("Decompression completed") } ## Check if file is present in working directory (download and decompression will be skipped if file is present) if(!file.exists("./household_power_consumption.txt")){ message("File is not present in working directory") Download() dateDownloaded <- date() dateDownloaded Unzip() } ## Read file message("Reading file") HPC1 <- read.table(file="./household_power_consumption.txt", header=TRUE, sep=";", na.strings="?") ## Inspect data str(HPC1) head(HPC1) ## Generate tidy data set with appropriate column names names(HPC1) <- tolower(names(HPC1)) names(HPC1) <- gsub("_", "", names(HPC1)) names(HPC1) <- gsub("1", "kitchen", names(HPC1)) names(HPC1) <- gsub("2", "laundry", names(HPC1)) names(HPC1) <- gsub("3", "waterheaterairconditioner", names(HPC1)) names(HPC1) ## Convert date variable HPC1$date <- as.Date(HPC1$date, format="%d/%m/%Y") class(HPC1$date) ## Select subset HPC2 <- subset(HPC1, date >= "2007-02-01" & date <= "2007-02-02") str(HPC2) head(HPC2) ## Create datetime variable ('chron' package is preferred, but not required) if("chron" %in% rownames(installed.packages()) == FALSE) { message("Package 'chron' is not installed") datetime <- paste(HPC2$date, HPC2$time) HPC2$datetime <- strptime(datetime, format="%Y-%m-%d %H:%M:%S") HPC2$time <- as.character(HPC2$time) str(HPC2) head(HPC2) } else { message("Package 'chron' is installed") library(chron) datetime <- paste(HPC2$date, HPC2$time) HPC2$datetime <- strptime(datetime, format="%Y-%m-%d %H:%M:%S") HPC2$time <- times(format(HPC2$datetime, "%H:%M:%S")) str(HPC2) head(HPC2) } ## Generate Plot 4 on screen (for visualization) par(mfcol=c(2, 2)) plot(HPC2$datetime, HPC2$globalactivepower, type="l", xlab="", ylab="Global Active Power", cex=0.9) plot(HPC2$datetime, HPC2$submeteringkitchen, type="n", xlab="", ylab="Energy sub metering", cex=0.9) lines(HPC2$datetime, HPC2$submeteringkitchen, col="black") lines(HPC2$datetime, HPC2$submeteringlaundry, col="red") lines(HPC2$datetime, HPC2$submeteringwaterheaterairconditioner, col="blue") legend("topright", bty="n", cex=0.9, legend=c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), lty=1, col=c("black", "red", "blue")) plot(HPC2$datetime, HPC2$voltage, type="l", xlab="datetime", ylab="Voltage", cex=0.9) plot(HPC2$datetime, HPC2$globalreactivepower, type="l", xlab="datetime", ylab="Global_reactive_power", cex=0.9) ## Create png file for Plot 4 png(filename="plot4.png", width=480, height=480, bg="transparent") par(mfcol=c(2, 2)) plot(HPC2$datetime, HPC2$globalactivepower, type="l", xlab="", ylab="Global Active Power", cex=0.9) plot(HPC2$datetime, HPC2$submeteringkitchen, type="n", xlab="", ylab="Energy sub metering", cex=0.9) lines(HPC2$datetime, HPC2$submeteringkitchen, col="black") lines(HPC2$datetime, HPC2$submeteringlaundry, col="red") lines(HPC2$datetime, HPC2$submeteringwaterheaterairconditioner, col="blue") legend("topright", bty="n", cex=0.9, legend=c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), lty=1, col=c("black", "red", "blue")) plot(HPC2$datetime, HPC2$voltage, type="l", xlab="datetime", ylab="Voltage", cex=0.9) plot(HPC2$datetime, HPC2$globalreactivepower, type="l", xlab="datetime", ylab="Global_reactive_power", cex=0.9) dev.off() ## Finish message("Plot 4 is generated") DateCompleted <- date() DateCompleted ## This script has been optimized for Windows 7 Professional and RStudio Version 0.98.1087

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jukent/climod

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distmap.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/distmap.R \name{distmap} \alias{distmap} \title{Construct a mapping between the distributions of two sets of values.} \usage{ distmap(x, y, densfun = KernSmooth::bkde, pgrid = ppoints(1000), truncate = FALSE, trim = NULL, na.rm = TRUE, ...) } \arguments{ \item{x}{A vector of values whose distribution is to be mapped.} \item{y}{A vector of values whose distribution is the target of the mapping.} \item{densfun}{A kernel density estimation function, such as \code{stats::density} or \code{KernSmooth::bkde}. This function must return an object that can be passed as input to \code{\link{pdf2cdf}}.} \item{pgrid}{A vector of probability values used for the mapping between CDFs. It need not be regular but must be strictly increasing.} \item{truncate}{Logical; if TRUE, truncates the kernel density estimates of the PDFs at the minimum value of the input data. This is useful for preventing the transfer function from generating negative values in the case of a variable bounded at zero like precipitation. Defaults to FALSE.} \item{trim}{a function used to omit unwanted values when constructing the mapping. This is useful for preventing overfitting of the tails in heavy-tailed variables like precipitation by trimming very extreme values using a function like \code{\link{lof1d}} or \code{\link{chauvenet}}. The function should take a vector of values as input and return the same vector with the unwanted values removed. Defaults to NULL.} \item{na.rm}{Logical; if TRUE (default), remove NA values before constructing distribution mapping.} \item{...}{Additional arguments to densfun.} } \value{ A list of class \code{distmap}, with the following elements: x,y: The input x and y data. pgrid: The vector of probabilities used to construct the mapping. xpdf,ypdf: The estimated PDFs of x and y. xq,yq: The quantiles of x and y corresponding to the probabilities in pgrid. transfer: A function that will transform x to have the same distribution as y. } \description{ Distribution mapping adjusts the individual values of a dataset, x, such that its statistical distribution matches that of a second dataset,y. This is accomplished by converting data values to probabilities using the CDF of the first distribution, and then from probabilities back to data values using the CDF of the second distribution. } \details{ The \code{distmap} function constructs this mapping and a transfer function that implements it non-parametrically, by integrating kernel density estimation of the PDFs using the trapezoid rule. It uses monotone Hermite splines for the construction to guarantee monotonicity of the transfer function. } \examples{ library(nor1mix) set.seed(222) x <- rnorMix(1e6, norMix(mu=c(-3,2),sigma=c(1,2),w=c(2,1))) y <- rnorMix(1e6, norMix(mu=c(-2,2))) dmap <- distmap(x,y) z <- predict(dmap) title = "PDFs; z = x mapped to match y" plot(NA, type="n", xlim=c(-10,10), ylim=c(0,0.33), main=title) lines(density(x), col="blue", lwd=2) lines(density(y), col="black", lwd=3) lines(density(z), col="red", lwd=2, lty=2) legend("topright", c("x","y","z"), col=c("blue","black","red"), lty=c(1,1,2)) dev.new() pp <- pnorm(seq(-2,2)) yr <- c(-5,5) par(mfrow=c(3,1)) plot(y[1:100],type="b",ylim=yr,xlab="") abline(h=quantile(y,pp), lty=c(4,3,2,3,4)) plot(x[1:100],type="b",col="red", ylim=yr, xlab="") abline(h=quantile(x,pp), col="red", lty=c(4,3,2,3,4)) plot(z[1:100],type="b",col="blue", ylim=yr, xlab="") abline(h=quantile(z,pp), col="blue", lty=c(4,3,2,3,4)) }

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/R/functions/fe_fe_icar_spaciotemporal_functions.R

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fe_fe_icar_spaciotemporal_functions.R

################################################################################################## ## Spatial: FE (icar); Temporal: FE (rw1); Spatio-temporal: ad ## FE.FE.ad.icar ################################################################################################## FE.FE.ad.icar <- function(){ for (j in 1:Ndiseases){ for (i in 1:Nareas){ for(l in 1:Nyears){ ## Observed of the mean for each area, disease and time O[i,j,l] ~ dpois(lambda[i,j,l]) ## Modeling of the mean for each area, disease and time log(lambda[i,j,l]) <- log(E[i,j,l]) + mu[j] + Theta[i,j] + Gam[l,j] ## Risk for each area, disease and time SMR[i,j,l] <- exp(mu[j] + Theta[i,j] + Gam[l,j]) smr.prob[i,j,l]<- step(SMR[i,j,l]-1) } ## Spatial effects Espat[i,j] <- exp(Theta[i,j]) espat.prob[i,j]<- step(Espat[i,j]-1) } ## Temporal effects for(l in 1:Nyears){ Etemp[l,j] <- exp(Gam[l,j]) etemp.prob[l,j] <- step(Etemp[l,j]-1) } } #################################### ## Prior distribution for the mean risk for all areas #################################### for (j in 1:Ndiseases){ mu[j] ~ dflat() } #################################### ## spatial #################################### ################## ## Theta = Phi * M; (IxJ) = (IxK)*(KxJ) ################## for (i in 1:Nareas){ for (j in 1:Ndiseases){ Theta[i,j] <- inprod2(tPhi[,i], M[,j]) } } ################## ## Phi (IxK, K=2J) ################## for (j in 1:Ndiseases){ Spatial[j, 1:Nareas] ~ car.normal(adj[], weights[], num[], 1) for (i in 1:Nareas){ tPhi[j,i] <- Spatial[j,i] } } ################## ## M-matrix (KxJ, K=2J) ################## for (i in 1:(1 * Ndiseases)) { for (j in 1:Ndiseases) { M[i,j] ~ dflat() } } ################## ## Sigma=M'M and correlations ################## # Sigma_b =t(M)%*%M for(i in 1:Ndiseases){ for(j in 1:Ndiseases){ Sigma.s[i,j] <- inprod2(M[,i], M[,j]) Corre.s[i,j] <- Sigma.s[i,j]/(pow(Sigma.s[i,i],0.5)*pow(Sigma.s[j,j],0.5)) } } #################################### # temporal #################################### ################## # Gamma = Phig * Mg; (txJ) = (txK)*(KxJ) ################## for(l in 1:Nyears){ for(j in 1:Ndiseases){ Gam[l,j]<- inprod2(tPhiG[,l], Mg[,j]) } } ################## ## PhiG (txK, K=2J) ################## for (j in 1:(Ndiseases)){ Temporal[j, 1:Nyears] ~ car.normal(adjt[], weightst[], numt[],1) for (l in 1:Nyears){ tPhiG[j,l] <-Temporal[j,l] } } ################## ## Mg-matrix (KxJ, K=2J) ################## for (j in 1:Ndiseases){ for (i in 1:(Ndiseases)){ Mg[i,j] ~ dflat() } } ################## ## Sigma=M'M and correlations ################## for(i in 1:Ndiseases){ for(j in 1:Ndiseases){ Sigma.t[i,j] <- inprod2(Mg[,i], Mg[,j]) Corre.t[i,j] <- Sigma.t[i,j]/(pow(Sigma.t[i,i],0.5)*pow(Sigma.t[j,j],0.5)) } } ###################################### ###################################### } ################################################################################################## ## Spatial: FE (icar); Temporal: FE (rw1); Spatio-temporal: Type I ## FE.FE.t1.icar ################################################################################################## FE.FE.t1.icar <- function(){ for (j in 1:Ndiseases){ for (i in 1:Nareas){ for(l in 1:Nyears){ ## Observed of the mean for each area, disease and time O[i,j,l] ~ dpois(lambda[i,j,l]) ## Modeling of the mean for each area, disease and time log(lambda[i,j,l]) <- log(E[i,j,l]) + mu[j] + Theta[i,j] + Gam[l,j] + sdZet[j] * Zet[i,j,l] ## Risk for each area, disease and time SMR[i,j,l] <- exp(mu[j] + Theta[i,j] + Gam[l,j] + sdZet[j] * Zet[i,j,l]) smr.prob[i,j,l]<- step(SMR[i,j,l]-1) ## Spatio-temporal effect Eint[i,j,l] <- exp(sdZet[j] * Zet[i,j,l]) eint.prob[i,j,l]<- step(Eint[i,j,l]-1) } ## Spatial effects Espat[i,j] <- exp(Theta[i,j]) espat.prob[i,j]<- step(Espat[i,j]-1) } ## Temporal effects for(l in 1:Nyears){ Etemp[l,j] <- exp(Gam[l,j]) etemp.prob[l,j] <- step(Etemp[l,j]-1) } } #################################### ## Prior distribution for the mean risk for all areas #################################### for (j in 1:Ndiseases){ mu[j] ~ dflat() } #################################### ## spatial #################################### ################## ## Theta = Phi * M; (IxJ) = (IxK)*(KxJ) ################## for (i in 1:Nareas){ for (j in 1:Ndiseases){ Theta[i,j] <- inprod2(tPhi[,i], M[,j]) } } ################## ## Phi (IxK, K=2J) ################## for (j in 1:Ndiseases){ Spatial[j, 1:Nareas] ~ car.normal(adj[], weights[], num[], 1) for (i in 1:Nareas){ tPhi[j,i] <- Spatial[j,i] } } ################## ## M-matrix (KxJ, K=2J) ################## for (i in 1:(1 * Ndiseases)) { for (j in 1:Ndiseases) { M[i,j] ~ dflat() } } ################## ## Sigma=M'M and correlations ################## # Sigma_b =t(M)%*%M for(i in 1:Ndiseases){ for(j in 1:Ndiseases){ Sigma.s[i,j] <- inprod2(M[,i], M[,j]) Corre.s[i,j] <- Sigma.s[i,j]/(pow(Sigma.s[i,i],0.5)*pow(Sigma.s[j,j],0.5)) } } #################################### # temporal #################################### ################## # Gamma = Phig * Mg; (txJ) = (txK)*(KxJ) ################## for(l in 1:Nyears){ for(j in 1:Ndiseases){ Gam[l,j]<- inprod2(tPhiG[,l], Mg[,j]) } } ################## ## PhiG (txK, K=2J) ################## for (j in 1:(Ndiseases)){ Temporal[j, 1:Nyears] ~ car.normal(adjt[], weightst[], numt[],1) for (l in 1:Nyears){ tPhiG[j,l] <-Temporal[j,l] } } ################## ## Mg-matrix (KxJ, K=2J) ################## for (j in 1:Ndiseases){ for (i in 1:(Ndiseases)){ Mg[i,j] ~ dflat() } } ################## ## Sigma=M'M and correlations ################## for(i in 1:Ndiseases){ for(j in 1:Ndiseases){ Sigma.t[i,j] <- inprod2(Mg[,i], Mg[,j]) Corre.t[i,j] <- Sigma.t[i,j]/(pow(Sigma.t[i,i],0.5)*pow(Sigma.t[j,j],0.5)) } } ###################################### # spatio-temporal (M-model) ###################################### ################## # Zeta = Phiz * Mz #(IxT) = (IxK)*(KxT) K=T ################## for(j in 1:Ndiseases){ for(i in 1:Nareas){ for(l in 1:Nyears){ Zet.aux[i,j,l] ~ dnorm(0,1) Zet[i,j,l] <- Zet.aux[i,j,l] } } } ################## # Prior distribution for the standard deviations ################## for(j in 1:Ndiseases){ sdZet[j] ~ dunif(0,100) } ###################################### ###################################### } ################################################################################################## # Spatial: FE (icar); Temporal: FE (rw1); Spatio-temporal: Type II # FE.FE.t2.icar ################################################################################################## FE.FE.t2.icar <- function(){ for (j in 1:Ndiseases){ for (i in 1:Nareas){ for(l in 1:Nyears){ ## Observed of the mean for each area, disease and time O[i,j,l] ~ dpois(lambda[i,j,l]) ## Modeling of the mean for each area, disease and time log(lambda[i,j,l]) <- log(E[i,j,l]) + mu[j] + Theta[i,j] + Gam[l,j] + sdZet[j] * Zet[i,j,l] ## Risk for each area, disease and time SMR[i,j,l] <- exp(mu[j] + Theta[i,j] + Gam[l,j] + sdZet[j] * Zet[i,j,l]) smr.prob[i,j,l]<- step(SMR[i,j,l]-1) ## Spatio-temporal effect Eint[i,j,l] <- exp(sdZet[j] * Zet[i,j,l]) eint.prob[i,j,l]<- step(Eint[i,j,l]-1) } ## Spatial effect Espat[i,j] <- exp(Theta[i,j]) espat.prob[i,j]<- step(Espat[i,j]-1) } ## Temporal effect for(l in 1:Nyears){ Etemp[l,j] <- exp(Gam[l,j]) etemp.prob[l,j] <- step(Etemp[l,j]-1) } } #################################### ## Prior distribution for the mean risk for all municipalities #################################### for (j in 1:Ndiseases){ mu[j] ~ dflat() } #################################### ## spatial #################################### ################## ## Theta = Phi * M; (IxJ) = (IxK)*(KxJ) ################## for (i in 1:Nareas){ for (j in 1:Ndiseases){ Theta[i,j] <- inprod2(tPhi[,i], M[,j]) } } ################## ## Phi (IxK, K=2J) ################## for (j in 1:Ndiseases){ Spatial[j, 1:Nareas] ~ car.normal(adj[], weights[], num[], 1) # structured for (i in 1:Nareas){ tPhi[j,i] <- Spatial[j,i] } } ################## ## M-matrix (KxJ, K=2J) ################## for (i in 1:(1 * Ndiseases)) { for (j in 1:Ndiseases) { M[i,j] ~ dflat() } } ################## ## Sigma=M'M and correlations ################## for(i in 1:Ndiseases){ for(j in 1:Ndiseases){ Sigma.s[i,j] <- inprod2(M[,i], M[,j]) Corre.s[i,j] <- Sigma.s[i,j]/(pow(Sigma.s[i,i],0.5)*pow(Sigma.s[j,j],0.5)) } } #################################### ## temporal #################################### ################## ## Gamma = Phig * Mg; (txJ) = (txK)*(KxJ) ################## for(l in 1:Nyears){ for(j in 1:Ndiseases){ Gam[l,j]<- inprod2(tPhiG[,l], Mg[,j]) } } ################## ## PhiG (txK, K=2J) ################## for (j in 1:(Ndiseases)){ Temporal[j, 1:Nyears] ~ car.normal(adjt[], weightst[], numt[],1) # structured for (l in 1:Nyears){ tPhiG[j,l] <-Temporal[j,l] } } ################## ## Mg-matrix (KxJ, K=2J) ################## for (j in 1:Ndiseases){ for (i in 1:(Ndiseases)){ Mg[i,j] ~ dflat() } } ################## ## Sigma=M'M and correlations ################## for(i in 1:Ndiseases){ for(j in 1:Ndiseases){ Sigma.t[i,j] <- inprod2(Mg[,i], Mg[,j]) Corre.t[i,j] <- Sigma.t[i,j]/(pow(Sigma.t[i,i],0.5)*pow(Sigma.t[j,j],0.5)) } } ###################################### ## spatio-temporal (M-model) ###################################### ################## ## Zet = Phi.z * M; (IxT) = (IxK)*(KxT) ################## for(j in 1:Ndiseases){ for (i in 1:Nareas){ for (l in 1:Nyears){ Zet[i,j,l] <- Mz[i,j,l] } } } ################## ## M-matrix (KxJ, K=2J) ################## for(j in 1:Ndiseases){ for (i in 1:(1*Nareas)){ for (l in 1:Nyears){ Mz[i,j,l]<- Mzaux[i,j,l] } } ## Mzaux for (i in 1:(Nareas)){ Temporal.z[i,j,1:Nyears] ~ car.normal(adj.zt[], weights.zt[], num.zt[],1) # structured for (l in 1:Nyears){ Mzaux[i,j,l] <- Temporal.z[i,j,l] } } } ################## ## Prior distribution for the standard deviations of the random effects ################## for(j in 1:Ndiseases){ sdZet[j] ~ dunif(0,100) } ###################################### ###################################### } ################################################################################################## ## Spatial: FE (icar); Temporal: FE (rw1); Spatio-temporal: Type III ## FE.FE.t3.icar ################################################################################################## FE.FE.t3.icar <- function(){ for (j in 1:Ndiseases){ for (i in 1:Nareas){ for(l in 1:Nyears){ ## Observed of the mean for each area, disease and time O[i,j,l] ~ dpois(lambda[i,j,l]) ## Modeling of the mean for each area, disease and time log(lambda[i,j,l]) <- log(E[i,j,l]) + mu[j] + Theta[i,j] + Gam[l,j] + sdZet[j] * Zet[i,j,l] ## Risk for each area, disease and time SMR[i,j,l] <- exp(mu[j] + Theta[i,j] + Gam[l,j] + sdZet[j] * Zet[i,j,l]) smr.prob[i,j,l]<- step(SMR[i,j,l]-1) ## Spatio-temporal effect Eint[i,j,l] <- exp(sdZet[j] * Zet[i,j,l]) eint.prob[i,j,l]<- step(Eint[i,j,l]-1) } ## Spatial effect Espat[i,j] <- exp(Theta[i,j]) espat.prob[i,j]<- step(Espat[i,j]-1) } ## Temporal effect for(l in 1:Nyears){ Etemp[l,j] <- exp(Gam[l,j]) etemp.prob[l,j] <- step(Etemp[l,j]-1) } } #################################### ## Prior distribution for the mean risk for all areas #################################### for (j in 1:Ndiseases){ mu[j] ~ dflat() } #################################### ## spatial #################################### ################## ## Theta = Phi * M; (IxJ) = (IxK)*(KxJ) ################## for (i in 1:Nareas){ for (j in 1:Ndiseases){ Theta[i,j] <- inprod2(tPhi[,i], M[,j]) } } ################## ## Phi (IxK, K=2J) ################## for (j in 1:Ndiseases){ Spatial[j, 1:Nareas] ~ car.normal(adj[], weights[], num[], 1) # structured for (i in 1:Nareas){ tPhi[j,i] <- Spatial[j,i] } } ################## ## M-matrix (KxJ, K=2J) ################## for (i in 1:(1 * Ndiseases)) { for (j in 1:Ndiseases) { M[i,j] ~ dflat() } } ################## ## Sigma=M'M and correlation ################## for(i in 1:Ndiseases){ for(j in 1:Ndiseases){ Sigma.s[i,j] <- inprod2(M[,i], M[,j]) Corre.s[i,j] <- Sigma.s[i,j]/(pow(Sigma.s[i,i],0.5)*pow(Sigma.s[j,j],0.5)) } } #################################### ## temporal #################################### ################## ## Gamma = Phig * Mg; (txJ) = (txK)*(KxJ) ################## for(l in 1:Nyears){ for(j in 1:Ndiseases){ Gam[l,j]<- inprod2(tPhiG[,l], Mg[,j]) } } ################## ## PhiG (txK, K=2J) ################## for (j in 1:(Ndiseases)){ Temporal[j, 1:Nyears] ~ car.normal(adjt[], weightst[], numt[],1) # structured for (l in 1:Nyears){ tPhiG[j,l] <-Temporal[j,l] } } ################## ## Mg-matrix (KxJ, K=2J) ################## for (j in 1:Ndiseases){ for (i in 1:(Ndiseases)){ Mg[i,j] ~ dflat() } } ################## ## Sigma=M'M and correlation ################## for(i in 1:Ndiseases){ for(j in 1:Ndiseases){ Sigma.t[i,j] <- inprod2(Mg[,i], Mg[,j]) Corre.t[i,j] <- Sigma.t[i,j]/(pow(Sigma.t[i,i],0.5)*pow(Sigma.t[j,j],0.5)) } } ###################################### ## spatio-temporal (M-model) ###################################### ################## ## Zet = Phi.z * M; (IxT) = (IxK)*(KxT) ################## for(j in 1:Ndiseases){ for (i in 1:Nareas){ for (l in 1:Nyears){ Zet[i,j,l] <- tPhi.z[l,j,i] } } } ################## # Phi.z (IxK, K=T) ################## for(j in 1:Ndiseases){ for (l in 1:Nyears){ Spatial.z[l, j, 1:Nareas] ~ car.normal(adj.zs[], weights.zs[], num.zs[],1) # structured for (i in 1:Nareas){ tPhi.z[l,j,i] <- Spatial.z[l,j,i] } } } ################## ## Prior distribution for the standard deviations of the random effects ################## for(j in 1:Ndiseases){ sdZet[j] ~ dunif(0,100) } ###################################### ###################################### } ################################################################################################## ## Spatial: FE (icar); Temporal: FE (rw1); Spatio-temporal: Type IV ## FE.FE.t4.icar ################################################################################################## FE.FE.t4.icar <- function(){ for (j in 1:Ndiseases){ for (i in 1:Nareas){ for(l in 1:Nyears){ ## Observed of the mean for each area, disease and time O[i,j,l] ~ dpois(lambda[i,j,l]) ## Modeling of the mean for each area, disease and time log(lambda[i,j,l]) <- log(E[i,j,l]) + mu[j] + Theta[i,j] + Gam[l,j] + sdZet[j] * Zet[i,j,l] ## Risk for each area, disease and time SMR[i,j,l] <- exp(mu[j] + Theta[i,j] + Gam[l,j] + sdZet[j] * Zet[i,j,l]) smr.prob[i,j,l]<- step(SMR[i,j,l]-1) ## Spatio-temporal effect Eint[i,j,l] <- exp(sdZet[j] * Zet[i,j,l]) eint.prob[i,j,l]<- step(Eint[i,j,l]-1) } ## Spatial effect Espat[i,j] <- exp(Theta[i,j]) espat.prob[i,j]<- step(Espat[i,j]-1) } ## Temporal effect for(l in 1:Nyears){ Etemp[l,j] <- exp(Gam[l,j]) etemp.prob[l,j] <- step(Etemp[l,j]-1) } } #################################### ## Prior distribution for the mean risk for all areas #################################### for (j in 1:Ndiseases){ mu[j] ~ dflat() } #################################### ## spatial #################################### ################## ## Theta = Phi * M; (IxJ) = (IxK)*(KxJ) ################## for (i in 1:Nareas){ for (j in 1:Ndiseases){ Theta[i,j] <- inprod2(tPhi[,i], M[,j]) } } ################## ## Phi (IxK, K=2J) ################## for (j in 1:Ndiseases){ Spatial[j, 1:Nareas] ~ car.normal(adj[], weights[], num[], 1) # structured for (i in 1:Nareas){ tPhi[j,i] <- Spatial[j,i] } } ################## ## M-matrix (KxJ, K=2J) ################## for (i in 1:(1 * Ndiseases)) { for (j in 1:Ndiseases) { M[i,j] ~ dflat() } } ################## ## Sigma=M'M and correlation ################## for(i in 1:Ndiseases){ for(j in 1:Ndiseases){ Sigma.s[i,j] <- inprod2(M[,i], M[,j]) Corre.s[i,j] <- Sigma.s[i,j]/(pow(Sigma.s[i,i],0.5)*pow(Sigma.s[j,j],0.5)) } } #################################### ## temporal #################################### ################## ## Gamma = Phig * Mg; (txJ) = (txK)*(KxJ) ################## for(l in 1:Nyears){ for(j in 1:Ndiseases){ Gam[l,j]<- inprod2(tPhiG[,l], Mg[,j]) } } ################## ## PhiG (txK, K=2J) ################## for (j in 1:(Ndiseases)){ Temporal[j, 1:Nyears] ~ car.normal(adjt[], weightst[], numt[],1) for (l in 1:Nyears){ tPhiG[j,l] <-Temporal[j,l] } } ################## ## Mg-matrix (KxJ, K=2J) ################## for (j in 1:Ndiseases){ for (i in 1:(Ndiseases)){ Mg[i,j] ~ dflat() } } ################## ## Sigma=M'M and correlation ################## for(i in 1:Ndiseases){ for(j in 1:Ndiseases){ Sigma.t[i,j] <- inprod2(Mg[,i], Mg[,j]) Corre.t[i,j] <- Sigma.t[i,j]/(pow(Sigma.t[i,i],0.5)*pow(Sigma.t[j,j],0.5)) } } ###################################### ## spatio-temporal (M-model) ###################################### ################## ## Zet = Phi.z * M; (IxT) = (IxK)*(KxT) ################## for(j in 1:Ndiseases){ for (i in 1:Nareas){ for (l in 1:Nyears){ Zet[i,j,l] <- inprod2(tPhi.z[,j,i], Mz[,l]) } } } ################## # Phi.z (IxK, K=T) ################## for(j in 1:Ndiseases){ for (l in 1:Nyears){ Spatial.z[l,j, 1:Nareas] ~ car.normal(adj.zs[], weights.zs[], num.zs[],1) # structured for (i in 1:Nareas){ tPhi.z[l,j,i] <- Spatial.z[l,j,i] } } } ################## ## M-matrix (KxJ, K=2J) ################## for (i in 1:(1*Nyears)){ for (l in 1:Nyears){ Mz[i,l]<- Mzz[i,l] } } ################## ## Prior distribution for the standard deviations of the random effects ################## for(j in 1:Ndiseases){ sdZet[j] ~ dunif(0,100) } ###################################### ###################################### } ################################################################################################## ##################################################################################################

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luiscartor/tambomaps-shinyapp

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tambomap_app.R

# tambomap_app.R # by Luis Carrasco # on March 2021 # tambomap_app.R includes the Shiny IU and SERVER to run the tambomaps shiny application ############################################################################################################## # Load packages if(!require(shiny)) install.packages("shiny", repos = "http://cran.us.r-project.org") if(!require(leaflet)) install.packages("leaflet", repos = "http://cran.us.r-project.org") if(!require(raster)) install.packages("raster", repos = "http://cran.us.r-project.org") if(!require(ggplot2)) install.packages("ggplot2", repos = "http://cran.us.r-project.org") if(!require(RColorBrewer)) install.packages("EColorBrewer", repos = "http://cran.us.r-project.org") if(!require(shinyWidgets)) install.packages("shinyWidgets", repos = "http://cran.us.r-project.org") if(!require(shinydashboard)) install.packages("shinydashboard", repos = "http://cran.us.r-project.org") if(!require(shinythemes)) install.packages("shinythemes", repos = "http://cran.us.r-project.org") if(!require(plotly)) install.packages("plotly", repos = "http://cran.us.r-project.org") if(!require(dplyr)) install.packages("dplyr", repos = "http://cran.us.r-project.org") if(!require(rgdal)) install.packages("rgdal", repos = "http://cran.us.r-project.org") if(!require(leafem)) install.packages("leafem", repos = "http://cran.us.r-project.org") if(!require(mapview)) install.packages("mapview", repos = "http://cran.us.r-project.org") if(!require(leafpop)) install.packages("leafpop", repos = "http://cran.us.r-project.org") if(!require(lattice)) install.packages("lattice", repos = "http://cran.us.r-project.org") if(!require(stringr)) install.packages("stringr", repos = "http://cran.us.r-project.org") ############################################################################################################## # 1. DATA INPUTS options(max.print=100000) # INPUT Shapefiles # Shapefile of the GADM data for Japanese prefectures (does not include Okinawa) prefsshapefile <- readOGR(dsn='inputdata', layer='gadm36_JPN_1_noOkinawa_simp') # Shapefile of the GADM data for Japanese sub-prefectures (does not include Okinawa) subprefsshapefile <- readOGR(dsn='inputdata', layer='gadm36_JPN_2_noOkinawa_simp') # INPUT Data tables # Data on aggregated rice area at the prefecture and subprefecture levels prefsdata <- read.csv(file='inputdata/riceareabyprefecture.csv', header =TRUE) subprefsdata <- read.csv(file='inputdata/riceareabysubprefecture.csv', header =TRUE) # 2. DATA PRE-PROCESSING # Merge shapefiles with area change table: (function merge fails to keep polygon order to I use match function here) prefsshapefile@data = data.frame(prefsshapefile@data, prefsdata[match(prefsshapefile@data$NAME_1, prefsdata$NAME_1),]) subprefsshapefile@data = data.frame(subprefsshapefile@data, subprefsdata[match(subprefsshapefile@data$GID_2, subprefsdata$GID_2),]) # There are NA values for subN level percentage change (0 initial rice area), so: subprefsshapefile@data$percchange <- as.numeric(subprefsshapefile@data$percchange) # Area columns names areacolumns <- c("area198589","area199094","area199599","area200004", "area200509","area201014","area201519") # Value ranges to use in color scales prefsarearange <- c(min(prefsshapefile@data[,areacolumns],na.rm = T),max(prefsshapefile@data[,areacolumns],na.rm = T)) subprefsarearange <- c(min(subprefsshapefile@data[,areacolumns],na.rm = T),max(subprefsshapefile@data[,areacolumns],na.rm = T)) # Period names periods <- c("1985-89","1990-94","1995-99","2000-04","2005-09","2010-14","2015-19","Total Change") ############################################################################################################## # 3. LAYERS CREATION # Colour pallets # Create Diverging palette for percentage change in prefectures # vector of colors for values smaller than 0 (80 colors) rc1 <- colorRampPalette(colors = c("red", "white"), space = "Lab")(60) ## vector of colors for values larger than 0 (20 colors) rc2 <- colorRampPalette(colors = c("white", "blue"), space = "Lab")(10) ## Combine the two color palettes prefdivcols <- c(rc1, rc2) # Create Diverging palette for percentage change in subprefectures rc3 <- colorRampPalette(colors = c("red", "white"), space = "Lab")(100) ## vector of colors for values larger than 0 (20 colors) rc4 <- colorRampPalette(colors = c("white", "blue"), space = "Lab")(250) ## Combine the two color palettes subprefdivcols <- c(rc3, rc4) # CREATE BASEMAP basemap <- leaflet() %>% setView(lng = 138, lat = 38, zoom = 6) %>% # Adds order to layers (first background, then rasters, shapefiles, and lables) addMapPane("background_map", zIndex = 410) %>% # Level 1: bottom addMapPane("rastermaps", zIndex = 420) %>% addMapPane("prefslayer", zIndex = 430) %>% addMapPane("subprefslayer", zIndex = 440) %>% addMapPane("cartolabels", zIndex = 450) %>% # Adds carto DB maps (labels separated so that the are not overlapped by rasters/shapefiles) addProviderTiles("CartoDB.PositronNoLabels") %>% #addProviderTiles("CartoDB.PositronOnlyLabels", # options = leafletOptions(pane = "cartolabels"), # group = "cartolabels") %>% # Adds Ersri World Imagery map addProviderTiles(provider = "Esri.WorldImagery", group = "Esri World Imagery", #) %>% options = pathOptions(pane = "background_map")) %>% # Adds coords addMouseCoordinates() %>% # Adds background maps layer control addLayersControl( baseGroups = c("Carto", "Esri World Imagery"), options = layersControlOptions(collapsed = FALSE)) %>% # These widgets add title to LayersControl and put the base layer to the background htmlwidgets::onRender(" function() { $('.leaflet-control-layers-list').prepend('<label style=\"text-align:center\">Background Map</label>'); } function(el, x) { this.on('baselayerchange', function(e) { e.layer.bringToBack(); }) } ") ############################################################################################################## ### 4. SHINY UI ui <- bootstrapPage( tags$head(includeHTML("gtag.html")), navbarPage(theme = shinytheme("flatly"), collapsible = TRUE, HTML('<a style="text-decoration:none;cursor:default;color:#FFFFFF;" class="active" href="#">TAMBO Project</a>'), id="nav", windowTitle = "TAMBO Project", # FIRST PANNEL: MAPS tabPanel("Rice Mapping", div(class="outer", tags$head(includeCSS("styles.css")), leafletOutput("map", width="100%", height="100%"), absolutePanel(id = "controls", class = "panel panel-default", top = 75, left = 55, width = 350, fixed=TRUE, draggable = TRUE, height = "auto", sliderTextInput("periods","Select Period or Total Change" , choices = periods, selected = periods[1], grid = TRUE), #values which will be selected by default selectInput("mapselection", "Select Map", choices = list("Background only","Rice map","Rice paddy area (Prefecture-level)","Rice paddy area (County-level)"), selected = "Rice map"), # selectInput("colors", "Color Scheme for Area Maps", # rownames(subset(brewer.pal.info, category %in% c("seq", "div")))), checkboxInput("legend", "Show legend", TRUE), tags$br(), "Total Change represents lost rice fields, estimated from the difference between the period 1985-89 and 2015-19." ) ) ), # SECOND PANEL: DATA AND PLOTS tabPanel("Data and Plots", sidebarPanel( span(tags$h4("Visualize and download data"), style="color:#045a8d"), pickerInput("selectpref", "Select Prefecture", choices = c(prefsdata$NAME_1), selected = "All Japan"), pickerInput("selectcounty", "Select County" ,choices = NULL), ), mainPanel( tabsetPanel( tabPanel("Plots", br(), plotOutput("plot")), tabPanel("Tables", style = "overflow-y:scroll; max-height: 600px", br(), verbatimTextOutput("table"), downloadButton("downloadCsv", "Download as CSV") ) ) ) ), # THIRD PANEL: ABOUT tabPanel("About", tags$div( tags$h4("Tambo Project"), "The Tambo Project aims to map rice fields in Japan since the 80's. These data can be used for ecological or agricultural research, conservation, management and planning, etc. We suggest users to check the associated documentation and to understand the uncertainties and limitations of this dataset.", tags$br(),tags$br(),tags$h4("Raster layers"), "The rice raster layers were created by classfying landsat images, combining image temporal aggregation and phenology of rice fields in Google Earth Engine. Each layer represents an averaged representation of the distribution of rice fields over a period of 5 years. Methods for creating the rice layers are fully described here:", tags$br(), "Carrasco et al. 2022. Historical mapping of rice fields in Japan using phenology andtemporally aggregated Landsat images in Google Earth Engine. Under review.", tags$br(), "Original rice layers have a spatial resolution of 30m. In this app, rice layers are rendered using leaflet tiles and the resolution depends on the zoom level. Original rasters can be downloaded here:",tags$br(), "Extra post-processing was conducted for the presented rices layers: water bodies masking, and masking out rice pixels for Hokkaido cities were rice was never recorded in the last decades.", tags$br(), tags$br(),tags$h4("Rice paddy area aggregations"), "Polygon layers represent aggregated rice area based on the raster layers, at the prefecture and sub-prefectural levels.", tags$br(),tags$br(),tags$h4("Code"), "Code and input data used to generate this Shiny application are available on ",tags$a(href="https://github.com/luiscartor/rice-mapping-app/", "Github."), tags$br(),tags$br(),tags$h4("Contact"), "Luis Carrasco Tornero",tags$br(), "Laboratory of Biodiversity Sciences",tags$br(), "Graduate School of Agricultural and Life Sciences",tags$br(), "The University of Tokyo",tags$br(), "luiscartor@gmail.com" ) ) ) ) ############################################################################################################## ### 5. SHINY SERVER server <- function(input, output, session) { # Reactive expression to select which layer to render renderlayer <- reactive({ input$mapselection }) # This reactive expression represents the path to the rice map for certain period # The folder with tiles must be inside www folder in shiny project (only this solution works) # If working in web server (shinyapps), tiles are stored in personal website (luiscartor.github.io) ricemappath <- reactive({ # Reads tiles locally (impossible to upload to shinyapps.io) #paste("tambo",input$periods,"tiles/{z}/{x}/{y}.png",sep="") # Reads tiles from my github website paste("https://luiscartor.github.io/tiles/tanboproject/tambo",input$periods,"tiles/{z}/{x}/{y}.png",sep="") }) # Reactive expression to select period areayear <- reactive({ if(input$periods == "Total Change"){ "percchange" } else paste("area",gsub("-", "", input$periods),sep="") }) # Render basemap output$map <- renderLeaflet({ basemap }) # Background only option observe({ # Render when "background only" if(renderlayer() == "Background only"){ leafletProxy("map") %>% # Delete all layers clearGroup('rastermaps') %>% clearGroup('prefslayer') %>% clearGroup('subprefslayer') } }) # Rice maps observe({ # Render when "background only" option is not selected if(renderlayer() == "Rice map"){ leafletProxy("map") %>% # Adds rice maps # Only render rice maps if "background only" not selected clearGroup('rastermaps') %>% clearGroup('prefslayer') %>% clearGroup('subprefslayer') %>% addTiles(urlTemplate = ricemappath(), option = c(tileOptions(tms = T, minZoom = 5, maxZoom = 12), # This option fixes these tiles on top always pathOptions(pane = "rastermaps")), group = "rastermaps") } }) # Prefectures area maps observe({ if(renderlayer() == "Rice paddy area (Prefecture-level)"){ # Polygon popup information (Prefecture and rice area) if(input$periods == "Total Change"){ # Pallets for area change #pal <- colorBin("viridis", prefsshapefile@data$area198589, bins=round(seq(0,max(subprefsarearange),length.out=10),1), na.color = "#bdbdbd") pal <- colorNumeric(palette = prefdivcols, domain = c(-60,10), na.color = "transparent") # Popup for change popup <- paste0("Prefecture: ", prefsshapefile@data$NAME_1, " Rice area change: ", round(prefsshapefile@data$areachange,2), " km2", " Rice area percentage change: ", round(prefsshapefile@data$percchange,2), " %") } else{ # Palettes for area polygon maps #pal <- colorNumeric( palette="YlOrRd",domain = prefsshapefile@data[,areacolumns], na.color="transparent") pal <- colorBin("viridis", prefsarearange, bins=8, na.color = "#bdbdbd",pretty = FALSE) # Pop-up for area popup <- paste0("Prefecture: ", prefsshapefile@data$NAME_1, " Rice area: ", prefsshapefile@data[,areayear()], " km2") } leafletProxy("map", data = prefsshapefile) %>% # Adds prefectural rice-area maps clearGroup('rastermaps') %>% clearGroup('prefslayer') %>% clearGroup('subprefslayer') %>% # For practical reasons, we need to re-render the rice map when we select area layers addTiles(urlTemplate = ricemappath(), option = c(tileOptions(tms = T, minZoom = 6, maxZoom = 12), # This option fixes these tiles on top always pathOptions(pane = "rastermaps")), group = "rastermaps") %>% addPolygons(data = prefsshapefile, fillColor = ~pal(prefsshapefile@data[,areayear()]), fillOpacity = 0.7, color = "white", weight = 2, group = "prefslayer", option = pathOptions(pane = "prefslayer"), layerId = ~NAME_1, popup = popup) } }) # SUB-Prefectures area maps observe({ if(renderlayer() == "Rice paddy area (County-level)"){ # Polygon popup information (Sub prefecture and rice area) if(input$periods == "Total Change"){ # Pallets for area change #pal <- colorBin("YlOrRd", subprefsshapefile@data[,"areachange"], bins=10, na.color = "#bdbdbd") pal <- colorNumeric(palette = subprefdivcols, domain = c(-100,250), na.color = "transparent") popup <- paste0("Prefecture: ", subprefsshapefile@data$NAME_1, " County: ", subprefsshapefile@data$NAME_2, " Rice area change: ", round(subprefsshapefile@data$areachange,2), " km2", " Rice area percentage change: ", #if(is.numeric(subprefsshapefile@data$percchange)){ round(subprefsshapefile@data$percchange,2), " %") #} else # subprefsshapefile@data$percchange, " %") } else{ # Pallets for area polygon maps #pal <- colorBin("viridis", subprefsarearange, bins=10, na.color = "#bdbdbd") pal <- colorBin("viridis", subprefsarearange, bins = round(seq(0, sqrt(max(subprefsarearange)), length.out = 10)^2, 1), na.color = "#bdbdbd",pretty = FALSE) popup <- paste0("Prefecture: ", subprefsshapefile@data$NAME_1, " County: ", subprefsshapefile@data$NAME_2, " Rice area: ", subprefsshapefile@data[,areayear()], " km2") } leafletProxy("map", data = subprefsshapefile) %>% # Adds subprefectural rice-area maps clearGroup('rastermaps') %>% clearGroup('subprefslayer') %>% clearGroup('prefslayer') %>% # For practical reasons, we need to re-render the rice map when we select area layers addTiles(urlTemplate = ricemappath(), option = c(tileOptions(tms = T, minZoom = 6, maxZoom = 12), # This option fixes these tiles on top always pathOptions(pane = "rastermaps")), group = "rastermaps") %>% addPolygons(data = subprefsshapefile, fillColor = ~pal(subprefsshapefile@data[,areayear()]), fillOpacity = 0.7, color = "white", weight = 2, group = "subprefslayer", # adding layerId messes up the map option = pathOptions(pane = "subprefslayer"), popup = popup) } }) # Use a separate observer to recreate the legend as needed. observe({ proxy <- leafletProxy("map", data = prefsshapefile) # Remove any existing legend, and only if the legend is # enabled, create a new one. proxy %>% clearControls() if (input$legend & renderlayer() == "Rice paddy area (Prefecture-level)") { if(input$periods == "Total Change"){ pal <- colorNumeric(palette = prefdivcols, domain = c(-60,10), na.color = "transparent") proxy %>% addLegend(position = "bottomright", title = "Rice paddy area change (%)", pal = pal, values = ~c(-60,10)) } else{ #pal <- colorNumeric( palette="viridis", domain=prefsshapefile@data[,areacolumns], na.color="transparent") pal <- colorBin("viridis", prefsarearange, bins=round(seq(0,max(subprefsarearange),length.out=10),1), na.color = "#bdbdbd",pretty = FALSE) proxy %>% addLegend(position = "bottomright", title = "Rice paddy area (km2)", pal = pal, values = ~prefsarearange) } } # Legend for county-level area if (input$legend & renderlayer() == "Rice paddy area (County-level)") { if(input$periods == "Total Change"){ pal <- colorNumeric(palette = subprefdivcols, domain = c(-100,250), na.color = "transparent") proxy %>% addLegend(position = "bottomright", title = "Rice paddy area change (%)", pal = pal, values = ~c(-100,250)) } else{ #pal <- colorNumeric( palette="viridis", domain=subprefsshapefile@data[,areacolumns], na.color="transparent") pal <- colorBin("viridis", subprefsarearange, bins = round(seq(0, sqrt(max(subprefsarearange)), length.out = 10)^2, 1), na.color = "#bdbdbd",pretty = FALSE) proxy %>% addLegend(position = "bottomright", title = "Rice paddy area (km2)", pal = pal, values = ~subprefsarearange) } } }) # This observeEvent is needed in order to create a conditional seletInput (subprefecture level) for plot and data observeEvent(input$selectpref,{ updatePickerInput(session,'selectcounty', choices=as.character(c("No selection","All counties", subprefsshapefile@data[subprefsshapefile@data$NAME_1==input$selectpref,"NAME_2"]))) }) # PLOT ANT TABLE OUTPUTS # Reactive expression to select which prefecture prefselection <- reactive({ input$selectpref }) subprefselection <- reactive({ if (identical(input$selectcounty, "NULL")) NULL else input$selectcounty }) # PLOT OUTPUT output$plot <- renderPlot({ # Prefectural plots if(is.null(subprefselection())){ df <- data.frame(period = periods[1:7],area = as.numeric(prefsdata[prefsdata$NAME_1==prefselection(), c("area198589","area199094","area199599","area200004","area200509","area201014","area201519")])) areachange <- as.numeric(prefsdata[prefsdata$NAME_1==prefselection(),"areachange"]) percchange <- as.numeric(prefsdata[prefsdata$NAME_1==prefselection(),"percchange"]) ggplot(data = df,aes(x= period, y = area)) + scale_color_manual("blue") + geom_line(aes(group = 1),color='lightblue')+ geom_point(color='lightblue')+ ylab("Area "~(km^2))+ xlab("") + ggtitle(paste0("Prefecture: ",prefselection(),"\nTotal rice field area change: ", round(areachange,2)," km²", "\nChange percentage: ",round(percchange,2),"%")) + theme(axis.text=element_text(size=12),axis.title=element_text(size=14,face="bold"),plot.title = element_text(size=16)) } else if(subprefselection()=="All counties" | subprefselection()=="No selection"){ df <- data.frame(period = periods[1:7],area = as.numeric(prefsdata[prefsdata$NAME_1==prefselection(), c("area198589","area199094","area199599","area200004","area200509","area201014","area201519")])) areachange <- as.numeric(prefsdata[prefsdata$NAME_1==prefselection(),"areachange"]) percchange <- as.numeric(prefsdata[prefsdata$NAME_1==prefselection(),"percchange"]) ggplot(data = df,aes(x= period, y = area)) + scale_color_manual("blue") + geom_line(aes(group = 1),color='lightblue')+ geom_point(color='lightblue')+ ylab("Area "~(km^2))+ xlab("") + ggtitle(paste0("Prefecture: ",prefselection(),"\nTotal rice field area change: ", round(areachange,2)," km²", "\nChange percentage: ",round(percchange,2),"%")) + theme(axis.text=element_text(size=12),axis.title=element_text(size=14,face="bold"),plot.title = element_text(size=16)) } else{ df <- data.frame(period = periods[1:7],area = as.numeric(subprefsdata[subprefsdata$Prefecture==prefselection() & subprefsdata$Subprefecture==subprefselection(), c("area198589","area199094","area199599","area200004","area200509","area201014","area201519")])) areachange <- as.numeric(subprefsdata[subprefsdata$Prefecture==prefselection() & subprefsdata$Subprefecture==subprefselection(),"areachange"]) percchange <- as.numeric(subprefsdata[subprefsdata$Prefecture==prefselection() & subprefsdata$Subprefecture==subprefselection(),"percchange"]) ggplot(data = df,aes(x= period, y = area)) + scale_color_manual("blue") + geom_line(aes(group = 1),color='lightblue')+ geom_point(color='lightblue')+ ylab("Area "~(km^2))+ xlab("") + ggtitle(paste0("Prefecture: ",prefselection(),"\nTotal rice field area change: ", round(areachange,2)," km²", "\nChange percentage: ",round(percchange,2),"%")) + theme(axis.text=element_text(size=12),axis.title=element_text(size=14,face="bold"),plot.title = element_text(size=16)) } }) # TABLE OUTPUT preftablenames <- c("Prefecture","area1985-89","area1990-94","area1995-99","area2000-04", "area2005-09","area201-014","area2015-19","areachange","percentchange") subpreftablenames <- c("Prefecture","County","area1985-89","area1990-94","area1995-99","area2000-04", "area2005-09","area201-014","area2015-19","areachange","percentchange") datasetInput <- reactive({ if(is.null(subprefselection())){ if(prefselection()=="All Japan"){ mytable <- prefsdata names(mytable) <- preftablenames } else mytable <- prefsdata[prefsdata$NAME_1==prefselection(),] names(mytable) <- preftablenames } else if(prefselection()=="All Japan" & subprefselection()=="No selection" ){ mytable <- prefsdata names(mytable) <- preftablenames } else if(subprefselection()=="No selection"){ mytable <- prefsdata[prefsdata$NAME_1==prefselection(),] names(mytable) <- preftablenames } else if(subprefselection()=="All counties"){ if(prefselection()=="All Japan"){ mytable <- subprefsdata[,-3] } else mytable <- subprefsdata[subprefsdata$Prefecture==prefselection(),-3] } else { mytable <- subprefsdata[subprefsdata$Prefecture==prefselection() & subprefsdata$Subprefecture==subprefselection(),-3] } print(mytable, row.names = FALSE) }) # RENDER output table output$table <- renderPrint({ datasetInput() }) # DOWNLOAD table # Downloadable csv of selected dataset ---- output$downloadCsv <- downloadHandler( filename = function() { paste("TAMBOproject_riceareachanges", ".csv", sep = "") }, content = function(file) { write.csv(datasetInput(), file, row.names = FALSE) } ) } ############################################################################################################## shinyApp(ui, server)

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###wilcox.test #２標本検定 wilcox.test(x=da[,2], y=db[,2], var.equal=F) wilcox.test(x=dc[,2], y=dd[,2], var.equal=F) wilcox.test(x=de[,2], y=df[,2], var.equal=F) wilcox.test(x=da[,2], y=dc[,2], var.equal=F) wilcox.test(x=da[,2], y=de[,2], var.equal=F)

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library(SpikeInSubset) data(mas133) e <- exprs(mas133) plot(e[,1],e[,2],main=paste0("corr=",signif(cor(e[,1],e[,2]),3)),cex=0.5) k <- 3000 b <- 1000 #a buffer # correlation between first two samples polygon(c(-b,k,k,-b),c(-b,-b,k,k),col="red",density=0,border="red") # points in red box sum(e[,1] < k & e[,2] < k)/length(e[,1]) # plot log of values so tails do not dominate plot(log2(e[,1]),log2(e[,2]),main=paste0("corr=",signif(cor(log2(e[,1]),log2(e[,2])),2)),cex=0.5) k <- log2(k) b <- log2(0.5) polygon(c(b,k,k,b),c(b,b,k,k),col="red",density=0,border="red") sum(log2(e[,1]) < k & log2(e[,2]) < k)/length(e[,1]) # MA plot e <- log2(exprs(mas133)) plot((e[,1]+e[,2])/2,e[,2]-e[,1],cex=0.5) # standard deviation of log ratios sd(e[,2]-e[,1]) # > 2-fold changes sum(abs(e[,2]-e[,1]) > 1)

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operations.R

#' Projects the ranges in query into the ranges in target. #' #' Returns the ranges in target overlapping the ranges in query, adjusting #' their boundaries so that only the overlapping parts of the target ranges #' are returned. #' #' @param query The ranges to be projected. #' @param target The ranges to be projected against. #' #' @return The projection of the query ranges on the target ranges. #' @importFrom S4Vectors subjectHits queryHits #' @export project_ranges <- function(query, target) { hits = GenomicRanges::findOverlaps(target, query) new_start = pmax(start(query)[subjectHits(hits)], start(target)[queryHits(hits)]) new_end = pmin(end(query)[subjectHits(hits)], end(target)[queryHits(hits)]) ranges_df = data.frame(seqname=seqnames(target)[queryHits(hits)], start=new_start, end=new_end, strand=strand(target)[queryHits(hits)]) ranges_df = cbind(ranges_df, mcols(target)[queryHits(hits),], mcols(query)[subjectHits(hits),]) colnames(ranges_df) = c(colnames(ranges_df)[seq_len(4)], colnames(mcols(target)), colnames(mcols(query))) return(GRanges(ranges_df)) } #' Collapses a list of genomic ranges into a single set of unique, #' non-overlapping ranges. #' #' Ranges are prioritized in the input list order. So, if the first element #' of the list (A) covers the range 1-10, and the second element (B) covers #' the range 5-15, then the resulting ranges will have a 1-10 range named A, #' and a 11-15 range named 'B'. #' #' @param grl The ranges to be collapsed. #' #' @return The collapsed regions. #' @export collapse_regions <- function(grl) { # Resulting regions. collapsed.regions = list() # Keep track of the ranges that have already been assigned. combined.regions = GenomicRanges::GRanges() for(region.group in names(grl)) { # The ranges assigned to this element are all the specified ranges, # minus any range that has already been assigned. collapsed.regions[[region.group]] = GenomicRanges::setdiff(grl[[region.group]], combined.regions) collapsed.regions[[region.group]]$name = region.group # Add the newly assigned ranges to the set of assigned ranges. combined.regions = GenomicRanges::union(combined.regions, collapsed.regions[[region.group]]) } # Return a single set of ranges. return(unlist(GenomicRanges::GRangesList(collapsed.regions))) }

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fig1.R

# figure 1: reproduce Fig 5 from Gatti et al (2014) # # "We regressed log neutrophil counts on founder allele dosages at # each marker using a kinship correction with sex and log white blood cell counts as covariates" # load the data; 2nd phenotype is "NEUT" library(qtl2) set.seed(33003221) file <- "cache/do.rds" dir <- dirname(file) if(!dir.exists(dir)) { dir.create(dir) } if(file.exists(file)) { do <- readRDS(file) } else { do <- read_cross2("https://raw.githubusercontent.com/rqtl/qtl2data/master/DO_Gatti2014/do.zip") saveRDS(do, file) } # pseudomarker maps file <- "cache/maps_n_phe.RData" if(file.exists(file)) { load(file) } else { gmap <- insert_pseudomarkers(do$gmap, stepwidth="max", step=0.2) pmap <- interp_map(gmap, do$gmap, do$pmap) # phenotypes and covariates phe <- log10(do$pheno[,2]) covar <- cbind(sex=(do$is_female*1), wbc=log10(do$pheno[,1])) save(gmap, pmap, phe, covar, file=file) } # calculate genotype probabilities file <- "cache/probs.rds" if(file.exists(file)) { # pr <- readRDS(file) } else { pr <- calc_genoprob(do, gmap, error_prob=0.002, map_function="c-f", cores=0) saveRDS(pr, file) } # calculate allele dosages file <- "cache/aprobs.rds" if(file.exists(file)) { # apr <- readRDS(file) } else { apr <- genoprob_to_alleleprob(pr, cores=0) saveRDS(apr, file) } # calculate kinship file <- "cache/kinship.rds" if(file.exists(file)) { k <- readRDS(file) } else { k <- calc_kinship(apr, "loco", cores=0) saveRDS(k, file) } # genome scan with full model file <- "cache/out_full.rds" if(file.exists(file)) { out_full <- readRDS(file) } else { out_full <- scan1(pr, phe, k, addcovar=covar, cores=0) saveRDS(out_full, file) } # genome scan with additive model file <- "cache/out_add.rds" if(file.exists(file)) { out_add <- readRDS(file) } else { out_add <- scan1(apr, phe, k, addcovar=covar, cores=0) saveRDS(out_add, file) } # functions to query snps and genes qv <- create_variant_query_func("~/Data/CCdb/cc_variants.sqlite") qg <- create_gene_query_func("~/Data/CCdb/mouse_genes_mgi.sqlite") # GWAS at SNPs file <- "cache/out_snps.rds" if(file.exists(file)) { out_snps <- readRDS(file) } else { out_snps <- scan1snps(pr, pmap, phe, k, addcovar=covar, query_func=qv, cores=0) saveRDS(out_snps, file) } # estimate coefficients on chr 1 file <- "cache/coef_c1.rds" if(file.exists(file)) { co <- readRDS(file) } else { co <- scan1coef(apr[,1], phe, k[1], addcovar=covar) co[,1:8] <- co[,1:8] - rowMeans(co[,1:8]) saveRDS(co, file) } file <- "cache/blup_c1.rds" if(file.exists(file)) { blup <- readRDS(file) } else { blup <- scan1blup(apr[,1], phe, k[1], addcovar=covar, cores=0) saveRDS(blup, file) } # snp scan of a small region li <- lod_int(out_add, pmap, chr=1) file <- "cache/out_finemap_c1.rds" if(file.exists(file)) { out_finemap <- readRDS(file) } else { out_finemap <- scan1snps(pr, pmap, phe, k, addcovar=covar, query_func=qv, chr=1, start=li[1], end=li[3], keep_all_snps=TRUE, cores=0) saveRDS(out_finemap, file) } genes <- qg(chr=1, start=li[1], end=li[3]) # line colors altcolor <- "green4" linecolor <- "violetred" # subset genes to 1/2 sub <- sort(unique(c(sample(nrow(genes), nrow(genes)/2), match(c("Cxcr4", "Tmcc2"), genes$Name)))) genes <- genes[sub,] gene_col <- rep("gray40", nrow(genes)) names(gene_col) <- genes$Name gene_col[c("Cxcr4", "Tmcc2")] <- linecolor # load permutation results operm_full <- readRDS("Perms/operm_full.rds") operm_add <- readRDS("Perms/operm_add.rds") operm_snps <- readRDS("Perms/operm_snps.rds") # calculate thresholds thr_full <- summary(operm_full) thr_add <- summary(operm_add) thr_snps <- summary(operm_snps) # ylim ymx_full <- thr_full$A/thr_add$A*maxlod(out_add)*1.04 ymx_add <- maxlod(out_add)*1.04 ymx_snps <- thr_snps$A/thr_add$A*maxlod(out_add)*1.04 # make the plots panel_lab_adj <- c(0.12, 0.06) panel_lab_cex <- 1.3 res <- 256 for(figtype in c("png", "eps")) { if(figtype == "png") { png("../Figs/fig1.png", height=7.5*res, width=10*res, pointsize=14, res=res) } else { postscript("../Figs/fig1.eps", paper="special", height=7.5, width=10, horizontal=FALSE, onefile=FALSE) panel_lab_adj[1] <- 0.10 } layout(cbind(rep(1:3, each=4), c(4,4,4,5,5,6,6,6,7,7,7,7))) par(mar=c(2.1, 4.1, 1.6, 1.1)) plot(out_full, pmap, xlab="", ylim=c(0, ymx_full), altcol=altcolor) u <- par("usr") endA <- xpos_scan1(pmap, thechr=19, thepos=max(pmap[[19]]))+25/2 segments(u[1], thr_full$A, endA, thr_full$A, col=linecolor, lty=2) segments(endA, thr_full$X, u[2], thr_full$X, col=linecolor, lty=2) u <- par("usr") text(u[1]-diff(u[1:2])*panel_lab_adj[1], u[4]+diff(u[3:4])*panel_lab_adj[2], "A", font=2, xpd=TRUE, cex=panel_lab_cex) plot(out_add, pmap, xlab="", ylim=c(0, ymx_add), altcol=altcolor) u <- par("usr") segments(u[1], thr_add$A, endA, thr_add$A, col=linecolor, lty=2) segments(endA, thr_add$X, u[2], thr_add$X, col=linecolor, lty=2) text(u[1]-diff(u[1:2])*panel_lab_adj[1], u[4]+diff(u[3:4])*panel_lab_adj[2], "B", font=2, xpd=TRUE, cex=panel_lab_cex) plot(out_snps$lod, out_snps$snpinfo, altcol=altcolor, xlab="", ylim=c(0, ymx_snps)) u <- par("usr") segments(u[1], thr_snps$A, endA, thr_snps$A, col=linecolor, lty=2) segments(endA, thr_snps$X, u[2], thr_snps$X, col=linecolor, lty=2) text(u[1]-diff(u[1:2])*panel_lab_adj[1], u[4]+diff(u[3:4])*panel_lab_adj[2], "C", font=2, xpd=TRUE, cex=panel_lab_cex) par(mar=c(0.5,4.1,1.6,1.1)) ymx <- max(abs(blup[,1:8]))*1.04 * 1.6 mgp <- c(2.1, 0.3, 0) if(figtype=="eps") mgp <- c(2.8, 0.3, 0) plot_coefCC(blup, pmap, xaxt="n", ylim=c(-ymx, ymx), xlab="", mgp=mgp) legend("topleft", ncol=4, lwd=2, col=CCcolors, legend=names(CCcolors), bg="gray92") u <- par("usr") text(u[1]-diff(u[1:2])*panel_lab_adj[1], u[4]+diff(u[3:4])*panel_lab_adj[2], "D", font=2, xpd=TRUE, cex=panel_lab_cex) par(mar=c(3.1,4.1,0,1.1)) plot(out_add, pmap[1], xlab="", xaxt="n", mgp=mgp) axis(side=1, at=pretty(par("usr")[1:2]), tick=FALSE, mgp=c(0, 0.2, 0)) title(xlab="Chr 1 position (Mbp)", mgp=c(1.8, 0, 0)) par(mar=c(0.5,4.1,1.6,1.1)) plot(out_finemap$lod, out_finemap$snpinfo, show=TRUE, drop=1.5, xaxt="n", xlab="") u <- par("usr") text(u[1]-diff(u[1:2])*panel_lab_adj[1], u[4]+diff(u[3:4])*panel_lab_adj[2], "E", font=2, xpd=TRUE, cex=panel_lab_cex) par(mar=c(3.1,4.1,0,1.1)) plot_genes(genes, col=gene_col, xlab="", mgp=c(0,0.2,0), xlim=c(u[1], u[2]), xaxs="i") title(xlab="Chr 1 position (Mbp)", mgp=c(1.8, 0, 0)) dev.off() } # end loop over figure type

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# Get the data unzip("getdata_projectfiles_UCI HAR Dataset.zip") subjectTest<-read.table("~/Coursera/Data Cleaning/UCI HAR Dataset/test/subject_test.txt",sep="", header=FALSE, col.names="subjectId") subjectTrain<-read.table("~/Coursera/Data Cleaning/UCI HAR Dataset/train/subject_train.txt",sep="", header=FALSE, col.names="subjectId") activityTest<-read.table("~/Coursera/Data Cleaning/UCI HAR Dataset/test/y_test.txt",sep="", header=FALSE, col.names="activityId") activityTrain<-read.table("~/Coursera/Data Cleaning/UCI HAR Dataset/train/y_train.txt",sep="", header=FALSE, col.names="activityId") activityLabels<-read.table("~/Coursera/Data Cleaning/UCI HAR Dataset/activity_labels.txt",sep="", header=FALSE,col.names=c("activityId","activityDesc")) colHeaders<-read.table("~/Coursera/Data Cleaning/UCI HAR Dataset/features.txt") testSet<-read.table("~/Coursera/Data Cleaning/UCI HAR Dataset/test/X_test.txt",sep="", header=FALSE) trainSet<-read.table("~/Coursera/Data Cleaning/UCI HAR Dataset/train/X_train.txt",sep="", header=FALSE) # Label with variable Names colnames(testSet)<- colHeaders[,2] colnames(trainSet)<- colHeaders[,2] testSet <- cbind(subjectTest, activityTest, testSet) trainSet <- cbind(subjectTrain, activityTrain, trainSet) # Assign Activity Names testSet <- merge(activityLabels,testSet,by="activityId") trainSet <- merge(activityLabels,trainSet,by="activityId") # Merge Data sets mergedSet <- rbind(testSet,trainSet) # Extract only the mean and standard deviation for each measurement. subsetMean <- grepl("mean()",colnames(mergedSet), fixed=TRUE) subsetStd <- grepl("std()", colnames(mergedSet), fixed=TRUE) subsetAll <- as.logical(subsetMean + subsetStd) subsetAll[2:3] <- TRUE subMergedSet <- mergedSet[,subsetAll] # Create tidy data set tidySet <- aggregate(. ~ subMergedSet$subjectId+subMergedSet$activityDesc, data=subMergedSet ,FUN=mean) # Rename columns in new data set tidySet <- tidySet[,-c(3,4)] colnames(tidySet)[1:2] <- c("subjectId","activity") # Write output write.table(tidySet, "tidySet.txt", row.name=FALSE)

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/Fig3A_plotModuleEnrichment.r

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Fig3A_plotModuleEnrichment.r

library(ggplot2) library(plyr) setwd("...") dat=read.table("enrichmentPerModule_EGcell_NEG_v2_TADA0.5_updated.txt",header=T,sep='\t') dat_EG=dat[,c(1,2,3,4,5)] dat_EG=data.frame(dat_EG,rep(NA,dim(dat_EG)[1])) names(dat_EG)=c("Module","EG_all","NLG_all","OR","P","Category") dat_EG$Category[which(dat_EG$OR<1)] = "3NEG enrichment" dat_EG$Category[which(dat_EG$OR>1)] = "1EG enrichment" FDR_EG=p.adjust(dat_EG$P,method="BH") plotDat1=data.frame(substr(dat_EG$Module,1,3),-log10(dat_EG$P),-log10(FDR_EG),dat_EG$Category) names(plotDat1)=c("Module","Pval","FDR","Category") dat_TADA=dat[,c(1,6,7,8,9)] dat_TADA=data.frame(dat_TADA,rep('2Potential ASD gene enrichment',dim(dat_EG)[1])) names(dat_TADA)=c("Module","TADA0.5","nonTADA0.5","OR","P","Category") FDR_TADA=p.adjust(dat_TADA$P,method="BH") plotDat2=data.frame(substr(dat_TADA$Module,1,3),log10(dat_TADA$P),log10(FDR_TADA),dat_TADA$Category) names(plotDat2)=c("Module","Pval","FDR","Category") plotDat=rbind(plotDat1,plotDat2) names(plotDat)=c("Module","Pval","FDR","Category") plotDat$Category=factor(plotDat$Category,levels=c("1EG enrichment","2Potential ASD gene enrichment","3NEG enrichment")) orderedEGmods=substr(c("M01","M02","M07","M26","M22","M08","M37","M17","M25","M34","M14","M16","M10","M15","M18","M24","M33"),1,3) orderedNEGmods=substr(c("M04","M19","M12","M11","M06","M38","M09","M13","M30","M03","M32","M23","M29","M35","M27","M41","M05","M20","M36","M40","M31","M39","M21","M28"),1,3) plotDat$Module=factor(plotDat$Module,levels=c(rev(orderedEGmods),orderedNEGmods)) write.table(plotDat,file="enrichmentPerModule_EGcell_NLG_v2_TADA_FDR_updated.txt",sep='\t',quote=F,row.names=F) #myColors=c("darkgray","turquoise3","#FF6666","springgreen3") png("brainSpanModules_enrichment_EGcell_NEG_v2_TADA_1_largeFont_updated.png",height=7.5,width=10.5,units='in',res=500) ggplot(plotDat,aes(x= Module, y = FDR, fill=Category, order=Category)) + geom_bar(data=plotDat[which(plotDat$Category=="1EG enrichment" | plotDat$Category=="3NEG enrichment"),] , stat = "identity") + geom_bar(data=plotDat[which(plotDat$Category=="2Potential ASD gene enrichment"),], stat = "identity") + scale_fill_manual(name="",values=c("#F8766D","#00BA38","#00BFC4","black")) + geom_abline(intercept=-log10(0.1),slope=0, linetype="dashed",color="red") + geom_abline(intercept=log10(0.1),slope=0, linetype="dashed",color="red") + geom_abline(intercept=0,slope=0, linetype="solid",color="gray50") + theme_bw() + xlab("Module ID") + ylab("-log10(q)")+ ggtitle("")+ theme(plot.title=element_text(size=20),legend.text=element_text(size=14),axis.text=element_text(size=14),axis.title=element_text(size=18),axis.text.x = element_text(angle = 90, vjust = 0.5)) + scale_y_continuous(breaks=rev(c(40,35,30,25,20,15,10,5,1,0,-1,-5)),labels=rev(c("40","35","30","25","20","15","10","5","1","0","1","5"))) #+ coord_flip() dev.off()

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calculator.R

# Scientific calculator using functions written in R. # Select steps between the dashed lines and run in sequence: # Step 1--------------------------------------------------------------------------------------------------- add <- function(x, y) { return(x + y) } subtract <- function(x, y) { return(x - y) } multiply <- function(x, y) { return(x * y) } divide <- function(x, y) { return(x / y) } square <- function(x) { return(x * x) } cube <- function(x) { return(x * x * x) } squarert <- function(x) { return(sqrt(x)) } cosine <- function(x) { return(cos(x)) } sine <- function(x) { return(sin(x)) } tangent <- function(x) { return(tan(x)) } # take input from the user print("Select operation.") print("1.Add") print("2.Subtract") print("3.Multiply") print("4.Divide") print("5.Square") print("6.Cube") print("7.Squarert") print("8.Cosine") print("9.Sine") print("10.Tangent") choice = as.integer(readline(prompt="Enter choice[1/2/3/4/5/6/7/8/9/10]: ")) # Step 2-------------------------------------------------------------------------------------------------- num1 = as.integer(readline(prompt="Enter first number: ")) # For choices 5 - 10 inclusive skip step 3 # Step 3 ------------------------------------------------------------------------------------------------- num2 = as.integer(readline(prompt="Enter second number: ")) # Step 4 ------------------------------------------------------------------------------------------------- operator <- switch(choice,"+","-","*","/") result <- switch(choice, add(num1, num2), subtract(num1, num2), multiply(num1, num2), divide(num1, num2), square(num1), cube(num1), sqrt(num1), cos(num1), sin(num1), tan(num1)) print(paste(num1, operator, num2, "=", result)) # End -----------------------------------------------------------------------------------------------------

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils-funs.R \name{pvw} \alias{pvw} \title{pvw} \usage{ pvw( data, id_cols = NULL, names_from = name, names_prefix = "", names_sep = "_", names_repair = "check_unique", values_from = value, values_fill = NULL, values_fn = NULL ) } \description{ See \code{tidyr::\link[tidyr:pivot_wider]{pivot_wider}} for details. } \keyword{internal}

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esl-j48ordinal.R

library(RWeka) library(partykit) library(abind) library(ggplot2) set.seed(2) # Cargar dataset esl esl <- read.arff('esl.arff') ordinal.label.encoder <- function(X, class.col, labels.ordered=NULL) { # Check parameters input if (!(is.data.frame(X)) || is.null(colnames(X))) { stop('X must be a dataframe with named columns') } if (length(class.col)!=1) { stop('Only one value allowed for "label.col"') } # Número de columna de clase class.column.num <- NA # Tomar el número de la columna de clase if (is.character(class.col)) { if (!class.col %in% colnames(X)) { stop(paste('"',class.col,'" not included in dataset')) } class.column.num <- which(colnames(X)==class.col) } else if (is.integer(class.col)) { if (class.col < 1 || class.col>ncol(X)) { stop('Column ',class.col,' not a valid column') } class.column.num <- class.col } else { stop('Only character or integers allowed for "class.col"') } # Las etiquetas pasadas deben de ser valores de etiqueta de la clase if (is.null(labels.ordered)) { # Tomar orden de los valores de la columna de clase si ningún orden es especificado if (is.ordered(X[,class.column.num]) || is.numeric(X[,class.column.num])) { labels.ordered <- sort(unique(X[,class.column.num])) } else { stop('Cannot deduce implicit order from class') } } else if (any(!labels.ordered %in% unique(X[,class.column.num]))) { stop('Any label in "labels.ordered" not a class of X') } # Nuevo conjunto de etiquetas de clase con información ordinal target <- matrix(data=rep(NA, nrow(X)*(length(labels.ordered)-1)), nrow=nrow(X)) # Para todas las etiquetas de la clase for (i in 1:(ncol(target))) { # Los índices para la etiqueta de clase i index.class <- X[,class.column.num]==labels.ordered[i] #Copiar las etiquetas de la anterior columna ordinal que representa # una clase de orden inferior if (i>1) { target[target[,i-1]==0,i] <- 0 } # Generar columna con 0's para la etiqueta i y 1's para el resto target[index.class,i] <- 0 target[is.na(target[,i]),i] <- 1 } # Convertir target en columnas de dataframe y agregarlo al df original target <- as.data.frame(target) target[] <- lapply(target, factor) colnames(target) <- paste(colnames(X)[class.column.num],'_', 1:ncol(target), sep='') # Reemplazar la columna de clase original por las ordinales X_new <- cbind(X[,-class.column.num], target) return(X_new) } # Convertir la clase en clases ordinales esl.ordinal <- ordinal.label.encoder(esl, 'out1') # De este modo, la columna de clase ha sido convertida en (nº clases-1 ) 8 # columnas de clases ordinales: out1_1,....,out1_8 colnames(esl.ordinal) # División en conjunto de train y test train=sample(1:nrow(esl.ordinal), nrow(esl.ordinal)-100) esl.test=esl.ordinal[-train ,] # Desarrollar modelo para cada clase ordinal #### Modelo m1 para out1_1 m1 = J48(out1_1~in1+in2+in3+in4,data = esl.ordinal,subset = train) m1 # Puesto que en el conjunto de train, sólo 2 instancias pertenecen # a la clase 1, la salida ordinal out1_1, sólo presenta 2 instancias # con valor 0, mientras el resto de instancias adopta el valor 1. # Como consecuencia, se ha originado un clasificador trivial que # que clasifica todas las instancias como pertenecientes a la clase 1 # de la salida out1_1, es decir, cualquier instancia es clasificada # como perteneciente a cualquier clase distinta de 1. # No se realiza una evaluación del modelo mediante validación cruzada # al existir únicamente 2 patrones pertenecientes a la clase 1. # El conjunto de test no presentaba ninguna instancia perteneciente a la clase 1. # Este clasificador trivial, ha clasificado todas las instancias como diferentes # a la clase 1, por lo que el porcentaje de aciertos es del 100% # Predecir todo el conjunto de test, tomando las probabilidades de pertenencia pred_m1 <- predict(m1, newdata = esl.test, 'probability') pred_m1 #### Modelo m2 para out1_2 m2 = J48(out1_2~in1+in2+in3+in4,data = esl.ordinal,subset = train) m2 # Dibujar el árbol obtenido plot(m2) # Evaluar el modelo sobre el conjunto de test eval_m2 <- evaluate_Weka_classifier(m2, numFolds = 5, class = TRUE) eval_m2 # Si bien el modelo generado presenta un rendimiento alto, hay que tener en cuenta que # Predecir todo el conjunto de test, tomando las probabilidades de pertenencia pred_m2 <- predict(m2, newdata = esl.test, 'probability') pred_m2 #### Modelo m3 para out1_3 m3 = J48(out1_3~in1+in2+in3+in4,data = esl.ordinal,subset = train) m3 plot(m3) # Evaluar el modelo sobre el conjunto de test eval_m3 <- evaluate_Weka_classifier(m3, numFolds = 5, class = TRUE) eval_m3 # Predecir todo el conjunto de test, tomando las probabilidades de pertenencia pred_m3 <- predict(m3, newdata = esl.test, 'probability') pred_m3 #### Modelo m4 para out1_4 m4 = J48(out1_4~in1+in2+in3+in4,data = esl.ordinal,subset = train) m4 plot(m4) # Evaluar el modelo sobre el conjunto de test eval_m4 <- evaluate_Weka_classifier(m4, numFolds = 5, class = TRUE) eval_m4 # Predecir todo el conjunto de test, tomando las probabilidades de pertenencia pred_m4 <- predict(m4, newdata = esl.test, 'probability') pred_m4 #### Modelo m5 para out1_5 m5 = J48(out1_5~in1+in2+in3+in4,data = esl.ordinal,subset = train) m5 plot(m5) # Evaluar el modelo sobre el conjunto de test eval_m5 <- evaluate_Weka_classifier(m5, numFolds = 5, class = TRUE) eval_m5 # Predecir todo el conjunto de test, tomando las probabilidades de pertenencia pred_m5 <- predict(m5, newdata = esl.test, 'probability') pred_m5 #### Modelo m6 para out1_6 m6 = J48(out1_6~in1+in2+in3+in4,data = esl.ordinal,subset = train) m6 plot(m6) # Evaluar el modelo sobre el conjunto de test eval_m6 <- evaluate_Weka_classifier(m6, numFolds = 5, class = TRUE) eval_m6 # Predecir todo el conjunto de test, tomando las probabilidades de pertenencia pred_m6 <- predict(m6, newdata = esl.test, 'probability') pred_m6 #### Modelo m7 para out1_7 m7 = J48(out1_7~in1+in2+in3+in4,data = esl.ordinal,subset = train) m7 plot(m7) # Evaluar el modelo sobre el conjunto de test eval_m7 <- evaluate_Weka_classifier(m7, numFolds = 5, class = TRUE) eval_m7 # Predecir todo el conjunto de test, tomando las probabilidades de pertenencia pred_m7 <- predict(m7, newdata = esl.test, 'probability') pred_m7 # Modelo m8 para out1_8 m8 = J48(out1_8~in1+in2+in3+in4,data = esl.ordinal,subset = train) m8 # Al igual que con el clasificador m1, este modelo la presencia de pocas # instancias pertencientes a la clase 9, lleva a la construcción de # un clasificador trivial que clasifica todos los patrones como # no pertenecientes a la clase 9, es decir, todos los patrones son clasificados # en la clase 0 de la columna out1_8 # Evaluar el modelo sobre el conjunto de test eval_m8 <- evaluate_Weka_classifier(m8, numFolds = 5, class = TRUE) eval_m8 # El clasificador ha clasificado erróneamente todos los patrones de la # clase 9 # Predecir todo el conjunto de test, tomando las probabilidades de pertenencia pred_m8 <- predict(m8, newdata = esl.test, 'probability') pred_m8 # Una vez realizadas todas las preciciones por parte de todos los clasificadores # se combinan todas las probabilidades haciendo uso de la probabilidad condicional # para conocer las probabilidades asignadas conjuntamente por los clasificadores a # todas las clases. compute.probability.from.ordinal <- function(P) { # Función que calcula la probabilidad de clases # a partir de un conjunto de probabilidades ordinales # dadas por un conjunto de clasificadores binarios # ordinales. # # Recibe: P: Array de (n_instancias x 2 x prob ordinales) # Calcular número de clases y el de instancias n_class <- dim(P)[3] + 1 n_inst <- dim(P)[1] # Matriz final con la probabilidades de cada instancia # para cada clase prob_class <- matrix(data = 1, ncol = n_class, nrow = n_inst) # Matriz auxiliar para calcular probabilidades condicionales aux_cond_prob <- matrix(data = 1, ncol = 1, nrow = n_inst) # La probabilidad de la clase 1 ya se conoce prob_class[,1] <- P[,1,1] for(i in 2:(n_class-1)) { # Añadir y calcular P(C>Ci-1) aux_cond_prob = aux_cond_prob*P[,2,i-1] # Calcular P(C=Ci) prob_class[,i] <- aux_cond_prob*P[,1,i] } # Asignar la probabilidad de la úlima clase prob_class[,ncol(prob_class)] <- P[,2,n_class-1]*aux_cond_prob return(prob_class) } # Combinar todas las predicciones probabilísticas en un array pred_all <- abind(pred_m1, pred_m2, pred_m3, pred_m4, pred_m5, pred_m6, pred_m7, pred_m8, along = 3) # Calcular las probabilidades reales de cada clase mediante # probabilidad condicional prob <- compute.probability.from.ordinal(pred_all) # Determinar las clases a las que han sido clasificados los patrones pred_class <- max.col(prob) # Calcular el score en test score <- mean(pred_class==esl[-train,]$out1) score # Conjuntamente, la predicción realizada alcanza un valor de accuraccy # de 0.62 sobre el conjunto de test # Analizamos la presencia de cada clase real en el conjunto de test # y la comparamos con la presencia de cada clase predicha # con ayuda de un diagrama de barras score_comparison <- data.frame(value=factor(esl[-train,]$out1), type='real') score_comparison <- rbind(score_comparison, data.frame(value=factor(pred_class), type='predicted')) ggplot2::ggplot(score_comparison, aes(value, fill = type)) + ggplot2::geom_bar(position = position_dodge(), colour = 'black') + xlab('Clase') + ylab('Número de patrones') + scale_y_continuous(breaks = scales::pretty_breaks(n=15)) # Se observa que el modelo conjunto generado, carece de capacidad predictiva # frente a la clase 1 y frente a la clase 9, lo que se explica por la presencia # de pocos patrones pertenecientes a estas clases en el dataset. # Comparamos el rendimiento de este modelo con un clasificador J48 no ordinal esl$out1 <- as.factor(esl$out1) m.no.ord <- J48(out1~in1+in2+in3+in4, data = esl,subset = train) m.no.ord # Evaluar el modelo sobre el conjunto de test eval_m.no.ord <- evaluate_Weka_classifier(m.no.ord, numFolds = 5, class = TRUE) eval_m.no.ord # Predecir todo el conjunto de test, tomando las probabilidades de pertenencia pred_m.no.ord <- predict(m.no.ord, newdata = esl[-train,]) score.no.ord <- mean(pred_m.no.ord==esl[-train,]$out1) score.no.ord # Se observa que el rendimiento obtenido con este modelo no ordinal, # es similar al del modelo ordinal. Nuevamente, deseamos comparar las # predicciones realizadas con la proporción real de clases score_comparison.no.ord <- data.frame(value=factor(esl[-train,]$out1), type='real') score_comparison.no.ord <- rbind(score_comparison.no.ord, data.frame(value=factor(pred_m.no.ord), type='predicted')) ggplot2::ggplot(score_comparison.no.ord, aes(value, fill = type)) + ggplot2::geom_bar(position = position_dodge(), colour = 'black') + xlab('Clase') + ylab('Número de patrones') + scale_y_continuous(breaks = scales::pretty_breaks(n=15)) # Al igual que sucede con el modelo ordinal, no se clasifica ningún # patrón en la clase 1 debido a la carencia de patrones de esta clase

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twolodzko/L1pack

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lad.Rd

\name{lad} \alias{lad} \title{Least absolute deviations regression} \description{ This function is used to fit linear models considering Laplace errors. } \usage{ lad(formula, data, method = c("BR", "EM"), subset, na.action, control, model = TRUE, x = FALSE, y = FALSE, contrasts = NULL) } \arguments{ \item{formula}{an object of class \code{"formula"}: a symbolic description of the model to be fitted.} \item{data}{an optional data frame containing the variables in the model. If not found in \code{data}, the variables are taken from \code{environment(formula)}, typically the environment from which \code{lad} is called.} \item{method}{character string specifying the algorithm to use. The default algorithm is the Barrodale and Roberts algorithm \code{method = "BR"}. Other possible value is \code{method = "EM"} for an EM algorithm using IRLS.} \item{subset}{an optional expression indicating the subset of the rows of data that should be used in the fit.} \item{na.action}{a function that indicates what should happen when the data contain NAs.} \item{control}{a list of control values for the estimation algorithm to replace the default values returned by the function \code{\link{l1pack.control}}.} \item{model, x, y}{logicals. If \code{TRUE} the corresponding components of the fit (the model frame, the model matrix, the response) are returned.} \item{contrasts}{an optional list. See the \code{contrasts.arg} of \code{model.matrix.default}.} } \value{ an object of class \code{lad} representing the linear model fit. Generic function \code{print}, show the results of the fit. } \author{The design was inspired by the R function \code{\link{lm}}.} \references{ Barrodale, I., and Roberts, F.D.K. (1974). Solution of an overdetermined system of equations in the L1 norm. \emph{Communications of the ACM} \bold{17}, 319-320. Phillips, R.F. (2002). Least absolute deviations estimation via the EM algorithm. \emph{Statistics and Computing} \bold{12}, 281-285. } \examples{ lad(stack.loss ~ ., data = stackloss, method = "EM") } \keyword{regression}

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/2020/Assignment-2020/Selected/Met-FE8828-Fan Wenqing/Assignment 4/assignment4-2.R

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leafyoung/fe8828

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assignment4-2.R

library(shiny) library(tidyverse) ui <- fluidPage( numericInput("r1", "Infection Rate", 0.05), numericInput("r2", "Specificity and Sensitivity", 0.95), plotOutput("p1"), h4("The percentage of an individual who tests negative is actually negative is:"), textOutput("t1") ) server <- function(input, output) { output$p1 <- renderPlot({ df_sensi <- full_join( tibble(x = 1:25, color = 'Actual Neg'), tibble(y = 1:20, color = 'Actual Neg'), by = 'color') n1 <- round(500*(input$r1)*(1 - input$r2)) n2 <- 500*(input$r1) - n1 n3 <- 500*(1 - input$r2) - n1 n4 <- 500 - n1 -n2 - n3 df_sensi['color'] <- c(rep('False Neg', n1), rep('Actual Pos', n2), rep('False Pos', n3), rep('Actual Neg', n4)) ggplot(df_sensi) + geom_point(aes(x, y,colour = color), size = 4, shape="circle") + theme_bw() + theme(axis.title.x=element_blank(), axis.title.y=element_blank(), axis.line=element_blank(), axis.text.x=element_blank(), axis.text.y=element_blank(), axis.ticks=element_blank()) }) output$t1 <- renderPrint({ n1 <- round(500*(input$r1)*(1 - input$r2)) n2 <- 500*(input$r1) - n1 n3 <- 500*(1 - input$r2) - n1 n4 <- 500 - n1 -n2 - n3 chance = (n3+n4)/(n1+n3+n4) *100 chance }) } # Run the application shinyApp(ui = ui, server = server)

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HARPgroup/vahydro

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l90_and_l30_year_comparison.R

# Upper and Middle Potomac cia table for model debugging # where is the extra water comingfrom in 2040 baseline flows? library("sqldf") library("stringr") #for str_remove() # Load Libraries basepath='/var/www/R'; site <- "http://deq2.bse.vt.edu/d.dh" #Specify the site of interest, either d.bet OR d.dh source("/var/www/R/config.local.private"); source(paste(basepath,'config.R',sep='/')) source(paste(hydro_tools_location,'/R/om_vahydro_metric_grid.R', sep = '')); folder <- "C:/Workspace/tmp/" df <- data.frame( 'model_version' = c('vahydro-1.0', 'vahydro-1.0'), 'runid' = c('runid_11', 'runid_11'), 'runlabel' = c('L90 Year', 'L30 Year'), 'metric' = c('l90_year', 'l30_year') ) wshed_data <- om_vahydro_metric_grid(metric, df) dsame <- sqldf( 'select count(*) from wshed_data as a where L90_Year = L30_Year ') ddiff <- sqldf( 'select count(*) from wshed_data as a where L90_Year <> L30_Year ') print( paste( "# of watersheds with L90 and L30 occuring in same year =", dsame, "and", ddiff, "watersheds had L90 and L30 in different years." ) ) df <- data.frame( 'model_version' = c('vahydro-1.0', 'vahydro-1.0', 'vahydro-1.0', 'vahydro-1.0', 'vahydro-1.0', 'vahydro-1.0'), 'runid' = c('runid_11', 'runid_13', 'runid_11', 'runid_13', 'runid_11', 'runid_13'), 'runlabel' = c('l30_2020', 'l30_2040', 'L90_2020', 'L90_2040', 'wdc_2020', 'wdc_2040'), 'metric' = c('l30_Qout', 'l30_Qout','l90_Qout','l90_Qout', 'wd_cumulative_mgd', 'wd_cumulative_mgd') ) wshed_data <- om_vahydro_metric_grid(metric, df) wshed_data <- sqldf( "select a.*, b.da from wshed_data as a left outer join da_data as b on (a.pid = b.pid) order by da ") # filter on watershed major/minor basin # where hydrocode like 'vahydrosw_wshed_P%' # and hydrocode not like 'vahydrosw_wshed_PL%' wshed_data$dl30 <- round((wshed_data$l30_2040 - wshed_data$l30_2020) / wshed_data$l30_2020,4) wshed_data$dl90 <- round((wshed_data$L90_2040 - wshed_data$L90_2020) / wshed_data$L90_2020,4) wshed_case <- sqldf( "select * from wshed_data where hydrocode not like '%0000' " ) wshed_wu <- sqldf( "select * from wshed_data where hydrocode not like '%0000' and wdc_2020 > 0 and wdc_2040 > 0 " ) ql90_all <- quantile(wshed_case$dl90, probs = c(0, 0.01,0.05, 0.1, 0.25, 0.5), na.rm=TRUE) ql30_all <- quantile(wshed_case$dl30, probs = c(0, 0.01,0.05, 0.1, 0.25, 0.5), na.rm=TRUE) ql90_haswd <- quantile(wshed_wu$dl90, probs = c(0, 0.01,0.05, 0.1, 0.25, 0.5), na.rm=TRUE) ql30_haswd <- quantile(wshed_wu$dl30, probs = c(0, 0.01,0.05, 0.1, 0.25, 0.5), na.rm=TRUE) ql_table <- as.data.frame(rbind(ql90_all, ql30_all, ql90_haswd, ql30_haswd)) names(ql_table) <- c( ) table_tex <- kable(sum_tbl,align = "l", booktabs = T,format = "latex",longtable =T, caption = "Unmet Demand Summaries, 2020 veruss 2040 demands.", label = "Unmet Demand Summary") %>% kable_styling(latex_options = "striped") %>% column_spec(2, width = "12em") table_tex <- gsub(pattern = "{table}[t]", repl = "{table}[H]", x = table_tex, fixed = T ) table_tex %>% cat(., file = paste0(export_path,"\\unmet_summary_tbl.tex"),sep="")

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padamu1/R_api_server

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api_server.R

library(plumber) r <- plumb("./api_test.R") r$run(port=8000)

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CSU-con-gen-bioinformatics-2020/unix-play-with-genomic-data-RGCheek

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tibbles.R

##Playing with tibbles #30 Jan 2020 library(tidyverse) #Make sure to be int he directory setwd("C:/Users/Rebecca/Dropbox/Course Work/Spring 2020/ConGen/unix-play-with-genomic-data-RGCheek") # comment tells it to ignore the comments from sam tools, which all starts with @ sammy <- read_tsv("sam/DPCh_plate1_A05_S5.sam", comment="@", col_names = FALSE) %>% view(sammy) #always pipe into the view to make sure the data makes sense print(sammy) #So pretty! #sam files don't have collumn names, so tibble just assigned some. #ask if there are any issues with reading in the data. problems(sammy)

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cran/survJamda.data

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2021-01-01T05:42:03.129385

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gse4335pheno.Rd

\name{gse4335pheno} \alias{gse4335pheno} \docType{data} \title{ Phenotype data of GSE4335. } \description{ Data such as patients' information, tumor characteristics, samples information related to GSE4335. } \usage{data(gse4335pheno)} \format{ A data frame with 115 observations on the following 17 variables. \describe{ \item{\code{Array_ID}}{a factor with levels \code{shac091} \code{shac092} \code{shac093} \code{shac100} \code{shac107} \code{shac110} \code{shac112} \code{shac113} \code{shaz104} \code{shaz110} \code{shaz112} \code{shaz114} \code{shaz116} \code{shaz121} \code{shaz124} \code{shaz125} \code{shaz126} \code{shaz129} \code{shaz131} \code{shaz132} \code{shaz133} \code{shaz134} \code{shaz135} \code{shaz140} \code{shbg109} \code{shbg110} \code{shbg128} \code{shby020} \code{shby021} \code{shby022} \code{shby028} \code{shby033} \code{shby035} \code{shby037} \code{shby038} \code{shby039} \code{shby040} \code{shby041} \code{shby042} \code{shby043} \code{shby046} \code{shby049} \code{shby050} \code{shby051} \code{shby236} \code{shby245} \code{shby249} \code{svcc100} \code{svcc101} \code{svcc104} \code{svcc105} \code{svcc106} \code{svcc107} \code{svcc1077} \code{svcc108} \code{svcc111} \code{svcc1114} \code{svcc115} \code{svcc118} \code{svcc119} \code{svcc120} \code{svcc122} \code{svcc124} \code{svcc130} \code{svcc131} \code{svcc132} \code{svcc134} \code{svcc137} \code{svcc51} \code{svcc53} \code{svcc55} \code{svcc61} \code{svcc68} \code{svcc76} \code{svcc78} \code{svcc81} \code{svcc83} \code{svcc84} \code{svcc87} \code{svcc88} \code{svcc89} \code{svcc92} \code{svcc93} \code{svcc96} \code{svcc98} \code{svcc99} \code{svj104} \code{svl002} \code{svl003} \code{svl006} \code{svl007} \code{svl012} \code{svl015} \code{svl016} \code{svl018} \code{svl020} \code{svl022} \code{svl026} \code{svl027} \code{svl029} \code{svl031} \code{svl033} \code{svl034} \code{svl035} \code{svl036} \code{svl037} \code{svl039} \code{svl041} \code{svl103} \code{svl106} \code{svl108} \code{svl109} \code{svl110} \code{svn007} \code{svn015}} \item{\code{Patient_ID}}{a factor with levels \code{new_york_1} \code{New_York_2} \code{New_York_3} \code{Norway_06FU} \code{Norway_10} \code{Norway_100} \code{Norway_101} \code{Norway_102} \code{Norway_104} \code{Norway_109} \code{Norway_11} \code{Norway_111} \code{Norway_112} \code{Norway_12} \code{Norway_14} \code{Norway_15} \code{Norway_16} \code{Norway_17} \code{Norway_18} \code{Norway_19} \code{Norway_2} \code{Norway_21} \code{Norway_22} \code{Norway_24} \code{Norway_26} \code{Norway_27} \code{Norway_29} \code{Norway_32} \code{Norway_37} \code{Norway_39} \code{Norway_4} \code{Norway_41} \code{Norway_43} \code{Norway_47} \code{Norway_48} \code{Norway_5} \code{Norway_51} \code{Norway_53} \code{Norway_55} \code{Norway_56} \code{Norway_57} \code{Norway_6} \code{Norway_61} \code{Norway_63} \code{Norway_64} \code{Norway_65} \code{Norway_7} \code{Norway_74} \code{Norway_75} \code{Norway_8} \code{Norway_80} \code{Norway_81} \code{Norway_83} \code{Norway_85} \code{Norway_90} \code{Norway_92} \code{Norway_95} \code{Norway_96} \code{Norway_98} \code{Norway_FU01} \code{Norway_FU02} \code{Norway_FU04} \code{Norway_FU05} \code{Norway_FU07} \code{Norway_FU08} \code{Norway_FU09} \code{Norway_FU10} \code{Norway_FU11} \code{Norway_FU12} \code{Norway_FU14} \code{Norway_FU15} \code{Norway_FU16} \code{Norway_FU17} \code{Norway_FU18} \code{Norway_FU19} \code{Norway_FU20} \code{Norway_FU22} \code{Norway_FU23} \code{Norway_FU24} \code{Norway_FU25} \code{Norway_FU26} \code{Norway_FU27} \code{Norway_FU29} \code{Norway_FU30} \code{Norway_FU35} \code{Norway_FU37} \code{Norway_FU39} \code{Norway_FU40} \code{Norway_FU41} \code{Norway_FU43} \code{Norway_FU44} \code{Norway_FU45} \code{Norway_H2} \code{Norway_H3} \code{Norway_H4} \code{Norway_H5} \code{Norway_H6} \code{Stanford_14} \code{Stanford_16} \code{Stanford_17} \code{Stanford_18} \code{Stanford_2} \code{Stanford_23} \code{Stanford_24} \code{Stanford_31} \code{Stanford_35} \code{Stanford_38} \code{Stanford_4} \code{Stanford_40} \code{Stanford_44} \code{Stanford_45} \code{Stanford_46} \code{Stanford_48} \code{Stanford_6} \code{Stanford_A}} \item{\code{Sample_ID}}{a factor with levels \code{BC102B-BE} \code{BC104A-BE} \code{BC105A-BE} \code{BC106B-BE} \code{BC107B-BE} \code{BC108A-BE} \code{BC110B-BE} \code{BC111A-BE} \code{BC111B-BE} \code{BC112B-BE} \code{BC114A-BE} \code{BC115B-BE} \code{BC116A-BE} \code{BC117A-BE} \code{BC118B-BE} \code{BC119A-BE} \code{BC120A-BE} \code{BC121B-BE} \code{BC123B-BE} \code{BC124A-BE} \code{BC1257} \code{BC125A-BE} \code{BC1369} \code{BC14} \code{BC16} \code{BC17} \code{BC18} \code{BC2} \code{BC201B-BE} \code{BC205A-BE} \code{BC206A-BE} \code{BC208A-BE} \code{BC210B-AF} \code{BC213B-BE} \code{BC214B-BE} \code{BC23} \code{BC24} \code{BC303B-BE} \code{BC305A-BE} \code{BC307B-BE} \code{BC308B-BE} \code{BC309A-BE} \code{BC31-0} \code{BC35-0} \code{BC38} \code{BC40} \code{BC402B-BE} \code{BC404B-BE} \code{BC405A-BE} \code{BC406A-2ndTUMOR} \code{BC44} \code{BC45} \code{BC46-LN46} \code{BC48-0} \code{BC4-LN4} \code{BC503B-BE} \code{BC6} \code{BC601A-BE} \code{BC605B-BE} \code{BC606B-AF} \code{BC608B-BE} \code{BC610A-BE} \code{BC702B-BE} \code{BC703B-BE} \code{BC704B-AF} \code{BC706A-BE} \code{BC708B-BE} \code{BC709B-BE} \code{BC710A-BE} \code{BC711B-BE} \code{BC713A-BE} \code{BC790} \code{BC805A-BE} \code{BC807A-BE} \code{BC808A-BE} \code{BC-A} \code{BC-HBC2} \code{BC-HBC3} \code{BC-HBC4-T1} \code{BC-HBC5} \code{BC-HBC6} \code{FU_01-BE} \code{FU_02-BE} \code{FU_04-BE} \code{FU_05-BE} \code{FU_06-BE} \code{FU_07-BE} \code{FU_08-BE} \code{FU_09-BE} \code{FU_10-BE} \code{FU_11-BE} \code{FU_12-BE} \code{FU_14-BE} \code{FU_15-BE} \code{FU_16-BE} \code{FU_17-AF} \code{FU_18-BE} \code{FU_19-BE} \code{FU_20-BE} \code{FU_22-BE} \code{FU_23-BE} \code{FU_24-BE} \code{FU_25-BE} \code{FU_26-BE} \code{FU_27-BE} \code{FU_29-BE} \code{FU_30-AF} \code{FU_35-BE} \code{FU_37-BE} \code{FU_39-BE} \code{FU_40-BE} \code{FU_41-BE} \code{FU_43-BE} \code{FU_44-BE} \code{FU_45-BE}} \item{\code{Age_at_diagnosis}}{a numeric vector} \item{\code{X.Status_0.A._1.AWD._2.DOD._3.DOC.}}{a numeric vector} \item{\code{Overall_survival_.months.}}{a numeric vector} \item{\code{Relapse.free_survival_.months.}}{a numeric vector} \item{\code{X._ER_status_.0.neg._1.pos..}}{a factor with levels \code{0} \code{1} \code{na}} \item{\code{T_.tumor_size.}}{a factor with levels \code{1} \code{2} \code{3} \code{4} \code{na}} \item{\code{N_.node_status.}}{a factor with levels \code{0} \code{1} \code{2} \code{na} \code{x}} \item{\code{M_.metastasis.}}{a numeric vector} \item{\code{Grade}}{a factor with levels \code{1} \code{2} \code{3} \code{na}} \item{\code{Histology}}{a factor with levels \code{DCIS} \code{Ductal} \code{Lobular} \code{Mucinous} \code{Papillary} \code{Pleomorph} \code{Undifferentiated}} \item{\code{reference_sample_batch_ID_}}{a factor with levels \code{CRA} \code{CRB} \code{CRD} \code{CRF} \code{CRG}} \item{\code{Microarray_batch..genes}}{a factor with levels \code{shac-23k} \code{shaz-49k} \code{shbg-49k} \code{shby-43k} \code{svcc-8k} \code{svj-8k} \code{svl-8k} \code{svn-8k}} \item{\code{Comments}}{a factor} \item{\code{Previously_published}}{a factor with levels \code{no} \code{yes}} } } \source{ \url{http://genome-www.stanford.edu/breast_cancer/robustness/data/SupplTable2.xls} } \references{ Yasrebi H, Sperisen P, Praz V, Bucher P, 2009 Can Survival Prediction Be Imp roved By Merging Gene Expression Data Sets?. PLoS ONE 4(10): e7431. doi:10.1371/journal.pone.0007431 } \seealso{ \code{\link{gse4335}} } \examples{ data(gse4335pheno) } \keyword{datasets}

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/FigS1_Grb2/2022_06_21_Grb2.R

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toettchlab/Farahani2022

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library(tidyverse) library(grid) library(gridExtra) library(reshape2) library(xlsx) rm(list = ls()) ########################################################################## # enter parameters frame_pre <- 10; # final frame before ligand addition time_ligand <- 9; # ligand added after this time point ########################################################################## # gather files setwd("/Users/payamfarahani/Documents/Python_R/RTK BIOSENSORS/Grb2/2022_06_21_Grb2") ZtSH2_names <- list.files(pattern="*ZtSH2.csv"); ZtSH2_list <- lapply(ZtSH2_names, read.csv); ZtSH2_names <- t(str_replace(ZtSH2_names,".csv","")); Grb2_names <- list.files(pattern="*Grb2.csv"); Grb2_list <- lapply(Grb2_names, read.csv); Grb2_names <- t(str_replace(Grb2_names,".csv","")); # create lists for dataframes list_raw <- list(); list_abs_decrease <- list(); list_rate_of_change <- list(); list_half_life <- list(); list <- list(); ########################################################################## # data analysis for (i in seq_along(ZtSH2_names)) { # raw values matrix_ZtSH2 <- ZtSH2_list[[i]]; colnames(matrix_ZtSH2) <- str_replace_all(colnames(matrix_ZtSH2), "cell.", ""); matrix_Grb2 <- Grb2_list[[i]]; colnames(matrix_Grb2) <- str_replace_all(colnames(matrix_Grb2), "cell.", ""); # melt raw reporter values matrix and classify observations by condition & date matrix_ZtSH2_melt <- melt(matrix_ZtSH2,id = c("time")); colnames(matrix_ZtSH2_melt) <- c("time", "cell", "reporter_raw"); matrix_ZtSH2_melt <- mutate(matrix_ZtSH2_melt, condition = str_replace(str_extract(ZtSH2_names[[i]],"[:alnum:]*_"),"_",""), date = str_extract(ZtSH2_names[[i]],"[:digit:]{8}"), reporter = str_replace_all(str_extract(ZtSH2_names[[i]],"_[:alnum:]{4}2"),"_","")); matrix_Grb2_melt <- melt(matrix_Grb2,id = c("time")); colnames(matrix_Grb2_melt) <- c("time", "cell", "reporter_raw"); matrix_Grb2_melt <- mutate(matrix_Grb2_melt, condition = str_replace(str_extract(Grb2_names[[i]],"[:alnum:]*_"),"_",""), date = str_extract(Grb2_names[[i]],"[:digit:]{8}"), reporter = str_replace_all(str_extract(Grb2_names[[i]],"_[:alnum:]{3}2"),"_","")); # combine matrices for ZtSH2 and Grb2 matrix <- bind_rows(matrix_ZtSH2_melt,matrix_Grb2_melt, .id = NULL) # normalize time to point of ligand addition matrix["time"] <- matrix["time"] - time_ligand; # calculate ZtSH2 response matrix <- matrix %>% group_by(cell,reporter) %>% mutate(reporter_norm_max = reporter_raw / mean(head(reporter_raw,frame_pre)), reporter_abs_decrease = (reporter_norm_max - 1) * -100); list[[i]] <- matrix; print(str_c(ZtSH2_names[[i]]," done")) } output <- rbind_list(list); ########################################################################## # plot themes theme_black <- theme_classic() + theme(legend.position="right", legend.background = element_rect(fill = 'black', colour = 'black'), legend.text = element_text(colour="white"), plot.background = element_rect(fill = 'black', colour = 'black'), panel.background = element_rect(fill = 'black', colour = 'black'), axis.text = element_text(colour="white"), axis.title = element_text(colour="white"), axis.line = element_line(colour="white"), text = element_text(family = "Arial")) + theme(aspect.ratio=1) theme_white <- theme_classic() + theme(legend.position="right", legend.background = element_rect(fill = 'white', colour = 'white'), legend.text = element_text(colour="black"), plot.background = element_rect(fill = 'white', colour = 'white'), panel.background = element_rect(fill = 'white', colour = 'white'), axis.text = element_text(colour="black"), axis.title = element_text(colour="black"), axis.line = element_line(colour="black"), text = element_text(family = "Arial")) + theme(aspect.ratio=1) ########################################################################## # activity vs. time (cd3e) dat <- filter(output, condition == "cd3e" & time <=15) %>% group_by(time,condition,reporter,date) %>% summarise(mean = mean(reporter_abs_decrease)) dat <- dat %>% group_by(time,condition,reporter) %>% summarise(mean_rep = mean(mean), sd = sd(mean)) p1 <- ggplot() + geom_ribbon(data=dat, aes(x = time, ymin = mean_rep - sd, ymax = mean_rep + sd, group = reporter, fill = reporter), alpha=0.2) + geom_line(data=dat, aes(x = time, y = mean_rep,group = reporter, color=reporter)) + scale_x_continuous(expand = c(0, 0), limits = c(-2, 15)) + scale_y_continuous(expand = c(0, 0), limits = c(-10, 80)) + labs(x = "time (min)", y = "RTK activity") p1 + theme_white ########################################################################## # activity vs. time (wt) dat <- filter(output, condition == "wt" & time <=15) %>% group_by(time,condition,reporter,date) %>% summarise(mean = mean(reporter_abs_decrease)) dat <- dat %>% group_by(time,condition,reporter) %>% summarise(mean_rep = mean(mean), sd = sd(mean)) p1 <- ggplot() + geom_ribbon(data=dat, aes(x = time, ymin = mean_rep - sd, ymax = mean_rep + sd, group = reporter, fill = reporter), alpha=0.2) + geom_line(data=dat, aes(x = time, y = mean_rep,group = reporter, color=reporter)) + scale_x_continuous(expand = c(0, 0), limits = c(-2, 15)) + scale_y_continuous(expand = c(0, 0), limits = c(-10, 80)) + labs(x = "time (min)", y = "RTK activity") p1 + theme_white ########################################################################## # boxplot dat <- filter(output,time == 5) x = c("ZtSH2", "Grb2") dat$reporter <- factor(dat$reporter, levels = x) p1 <- ggplot(dat, aes(x = interaction(condition,reporter), y = reporter_abs_decrease, fill = reporter)) + geom_boxplot(colour = "black") + labs(x = "reporter", y = "norm. cyt. intensity") p1 + theme_white

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/uebungserie-10/uebung10-recherche.R

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spip333/statistik-uebungen

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uebung10-recherche.R

########################### # Recherche Uebung 10 ########################### ?lm head(mtcars) y <- mtcars$wt x <- mtcars$hp plot(x, y) # model wt as function der hp wt.hp.model <- lm (wt~hp, data=mtcars) wt.hp.model$coefficients plot(wt.hp.model) coeffs <- coefficients(wt.hp.model) coeffs est.wt <- coeffs[1]+ 200 * coeffs[2] est.wt max(mtcars$hp) ?mtcars plot(x, y) lines(x2, coeffs[1] + x2*coeffs[2]) ?plot # gewicht bei 200hp: coeffs[1]+ 200 * coeffs[2] paste("a", "b", "c") newdata <- data.frame(hp=200) predict(wt.hp.model,newdata) hps <- c(110) newdata <- data.frame(hp=hps) predict(wt.hp.model,newdata) head(mtcars) model$fitted.values summary(wt.hp.model)$r.squared # Bestimmtheitsmass eruption.lm <- lm(eruptions ~ waiting, data=faithful) summary(eruption.lm) # Signifikanztest summary() ## Normalverteilte Residuen # Betrachten Sie das lineare Modell, welches das Gewicht eines Autos als Funktions # des Motorenleistung modelliert. Erstellen Sie ein Normal-Wahrscheinlichkeits-Diagramm # und testen Sie, ob die Residuen normalverteilt sind. plot(wt.hp.model, which=2) x <- c(1,2,3,4,5,6,7,8,9,10) y <- (x * 3) + 2 data <- as.data.frame(x, y) data$y <- y data plot(x2,y) plot(x2,y, type="line") mod <- lm(y~x, data=data) mod plot(mod, which=3)

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/plot3.R

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rickosborne/ExData_Plotting1

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plot3.R

#!/opt/local/bin/Rscript # Original code by Rick Osborne library(chron) library(data.table) source("get_power_data.R") OUT_FILE <- "plot3.png" all_data <- fread(DATA_FILE, sep=";", na.strings="?") relevant <- all_data[Date=="1/2/2007"|Date=="2/2/2007"] all_data <- NULL relevant[,DateTime:=chron(Date,Time, format=c(dates="d/m/Y",times="h:m:s"))] # relevant[,GAP:=as.numeric(Global_active_power)] relevant[,SM1:=as.numeric(Sub_metering_1)] relevant[,SM2:=as.numeric(Sub_metering_2)] relevant[,SM3:=as.numeric(Sub_metering_3)] png(filename=OUT_FILE, width=480, height=480, units="px") par(mar=c(2,4,1,2)) with(relevant, plot(DateTime, Sub_metering_1, type="n", xlab="", ylab="Energy sub metering", xaxt="n")) axis.Date(1, at=relevant$DateTime, format="%a") with(relevant, lines(DateTime, SM1, type="l", col="black")) with(relevant, lines(DateTime, SM2, type="l", col="red")) with(relevant, lines(DateTime, SM3, type="l", col="blue")) legend("topright", legend=c("Sub_metering_1","Sub_metering_2","Sub_metering_3"), col=c("black","red","blue"), pch="_") dev.off()

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cran/detector

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has_email_addresses.Rd

% Generated by roxygen2 (4.1.1): do not edit by hand % Please edit documentation in R/email_addresses.R \name{has_email_addresses} \alias{has_email_addresses} \title{Test if a character vector has any e-mail addresses.} \usage{ has_email_addresses(.x) } \arguments{ \item{.x}{A character vector.} } \value{ A logical value indicating if that string has any e-mail addresses. } \description{ Test if a character vector has any e-mail addresses. } \examples{ # Examples has_email_addresses("hello") # FALSE has_email_addresses("hello@world.edu") # TRUE }

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/gmse_06beta_landplacement_test.R

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jejoenje/gmse_vary

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gmse_06beta_landplacement_test.R

library(GMSE) ### All of these only run with 100x100 default landscape dimensions ### 4 stakeholders, no public land: test1 = gmse(time_max = 2, land_ownership = T, stakeholders = 8, plotting = FALSE, ownership_var = 0.9) image(test1$land[[1]][,,3]) table(test1$land[[1]][,,3])/sum(table(test1$land[[1]][,,3])) ov = seq(0,0.95,0.05) out8 = as.data.frame(NULL) stakeholders = 8 for(i in 1:length(ov)) { test = gmse(time_max = 2, land_ownership = T, stakeholders = stakeholders, plotting = FALSE, ownership_var = ov[i]) out8 = rbind(out8, c(min(table(test$land[[1]][,,3])),max(table(test$land[[1]][,,3])),sum(table(test$land[[1]][,,3])))) } names(out8) = c("lmin","lmax","ltotal") out16 = as.data.frame(NULL) stakeholders = 16 for(i in 1:length(ov)) { test = gmse(time_max = 2, land_ownership = T, stakeholders = stakeholders, plotting = FALSE, ownership_var = ov[i]) out16 = rbind(out16, c(min(table(test$land[[1]][,,3])),max(table(test$land[[1]][,,3])),sum(table(test$land[[1]][,,3])))) } names(out16) = c("lmin","lmax","ltotal") out32 = as.data.frame(NULL) stakeholders = 32 for(i in 1:length(ov)) { test = gmse(time_max = 2, land_ownership = T, stakeholders = stakeholders, plotting = FALSE, ownership_var = ov[i]) out32 = rbind(out32, c(min(table(test$land[[1]][,,3])),max(table(test$land[[1]][,,3])),sum(table(test$land[[1]][,,3])))) } names(out32) = c("lmin","lmax","ltotal") out8$rratio = (out8$lmax-out8$lmin)/out8$ltotal out16$rratio = (out16$lmax-out16$lmin)/out16$ltotal out32$rratio = (out32$lmax-out32$lmin)/out32$ltotal par(mfrow=c(1,1)) yl = max(c(out8$rratio, out16$rratio, out32$rratio)) plot(out8$rratio, type = "l", ylim = c(0,yl)) lines(out16$rratio, col = "red") lines(out32$rratio, col = "red") ### Tests with public land: test2 = gmse(time_max = 2, land_ownership = T, stakeholders = 4, plotting = FALSE, public_land = 0.2) image(test2$land[[1]][,,3]) table(test2$land[[1]][,,3])/sum(table(test2$land[[1]][,,3]))

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/R/item_upload_create.R

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item_upload_create.R

#' #'@title Upload file(s) and create a new item #' #'@param parent_id SB id of parent item for the newly created item #'@param files A string vector of paths to files to be uploaded #'@param session Session object from authenticate_sb #' #' #'@import httr #' #'@export item_upload_create = function(parent_id, files, session){ body = list() for(i in 1:length(files)){ if(!file.exists(files[i])){ stop('This file does not exist or cannot be accessed: ', files[i]) } body[[paste0('file', i)]] = upload_file(files[i]) } names(body) = rep('file', length(body)) url = paste0(pkg.env$url_upload_create, parent_id) r = POST(url, body=body, accept_json(), handle=session, query=list(title='title')) return(content(r)$id) }

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/R/sample.zi.r

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sample.zi.r

#'Generate random deviates for zero-inflated models #' @export #' #' @rdname sample.h sample.zi<-function(N,phi,distri='poisson',lambda=NA,r=NA,p=NA,alpha1=NA,alpha2=NA,n=NA) { ##check the validity of some inputs distri=tolower(distri)[1] ##so that all letters are in lower case. only use the first value if user inputs a vector but not a scalar. N=ceiling(N)[1] phi=phi[1] if(N<=0) stop('The sample size N is too small.') if((phi>=1)|(phi<=0)) stop('phi is not between 0 and 1 (not including 0 and 1).') if(!(distri%in%c('poisson','nb','bb','bnb'))) stop('please input a distribution name among poisson,nb,bb,bnb.') if(distri=="poisson"){ lambda=lambda[1] if(lambda<=0) stop('lambda is less or equal to 0.') } if(distri=="nb"){ r=ceiling(r[1]) p=p[1] if(r<=0) stop('r is too small.') if((p<=0)|(p>=1)) stop('p is not between 0 and 1 (not including 0 and 1).') } if((distri=="bb")|(distri=="bnb")){ alpha1=alpha1[1] alpha2=alpha2[1] if(alpha1<=0) stop('alpha1 is less or equal to 0.') if(alpha2<=0) stop('alpha2 is less or equal to 0.') } if(distri=="bb"){ n=ceiling(n[1]) if(n<=0) stop('n is too small.') } if(distri=="bnb"){ r=ceiling(r[1]) if(r<=0) stop('r is too small.') } ans=stats::rbinom(N,size=1,prob=phi) ##simulate Z_1,Z_2,...,Z_N from Bernoulli(phi). m=sum(ans==0) ##if Z_i=1, let X_i=0, if Z_i==0, generate new samples from original distribution. temp1=NULL if(m>0){ temp1=switch(distri,nb=stats::rnbinom(m,size=r,prob=p),bb=extraDistr::rbbinom(m,n,alpha1,alpha2),bnb=extraDistr::rbnbinom(m,r,alpha1,alpha2),stats::rpois(m,lambda)) } temp2=ans temp2[ans==1]=0 temp2[ans==0]=temp1 return(temp2) }

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plot_all.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plots.R \name{plot_all} \alias{plot_all} \title{Plot a list of dp objects, one by one} \usage{ plot_all(dp.list) } \arguments{ \item{dp.list}{A list of dp objects, see \code{dpload}} } \description{ Plot a list of dp objects, one by one. Press any key to move to the next dp object. Returns nothing. } \examples{ ## load several dp files dp.list <- dpload(dp.directory = system.file("extdata", package = "densitr")) ## trim the measurements \donttest{ if(interactive()){ plot_all(dp.list) }} } \seealso{ dptrim, dptrim_s, dptriml_s, }

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hw3.3.R

#hw3.3 flag = 0 x0 = 0 esp = .00000001 while(flag == 0) { x = x0 - (3*exp(x0) - 4*cos(x0))/(3*exp(x0) + 4*sin(x0)) tolerance = abs(x-x0) if (tolerance <= esp) { flag = 1 } else { x0 = x } } print(x)

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/man/Summary-BASiCS_Chain-method.Rd

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Summary-BASiCS_Chain-method.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/Methods.R \docType{methods} \name{Summary} \alias{Summary} \alias{Summary,BASiCS_Chain-method} \title{'Summary' method for BASiCS_Chain objects} \usage{ \S4method{Summary}{BASiCS_Chain}(x, prob = 0.95) } \arguments{ \item{x}{A \code{BASiCS_Chain} object.} \item{prob}{\code{prob} argument for \code{\link[coda]{HPDinterval}} function.} } \value{ An object of class \code{\link[BASiCS]{BASiCS_Summary-class}}. } \description{ For each of the BASiCS parameters (see Vallejos et al 2015), \code{Summary} returns the corresponding postior medians and limits of the high posterior density interval with probabilty equal to \code{prob}. } \examples{ Data = makeExampleBASiCS_Data() MCMC_Output <- BASiCS_MCMC(Data, N = 50, Thin = 2, Burn = 2) MCMC_Summary <- Summary(MCMC_Output) # See documentation of function BASiCS_MCMC } \author{ Catalina A. Vallejos \email{catalina.vallejos@mrc-bsu.cam.ac.uk} } \references{ Vallejos, Marioni and Richardson (2015). Bayesian Analysis of Single-Cell Sequencing data. }

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/plot4.R

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plot4.R

library(ggplot2) nei_file_name = "FNEI_data/summarySCC_PM25.rds" scc_file_name = "FNEI_data/summarySCC_PM25.rds" NEI <- readRDS(nei_file_name) SCC <- readRDS(scc_file_name) combustion <- grepl("comb", SCC$SCC.Level.One, ignore.case=TRUE) coal <- grepl("coal", SCC$SCC.Level.Four, ignore.case=TRUE) coal_combustion <- (combustion & coal) combustion_SCC <- SCC[coal_combustion,]$SCC combustion_NEI <- NEI[NEI$SCC %in% combustion_SCC,] png('plot4.png', width = 480, height = 480) plt <- ggplot(combustion_NEI, aes(factor(year), Emissions/10^5)) + geom_bar(stat = "identity", fill = "grey", width = 1) + labs(x = "Year", y = expression("Total PM"[2.5]*" Emission (10^5 tons)")) + ggtitle(expression("PM"[2.5]*" Coal Combustion Source Emissions Across US from 1999-2008")) print(plt) print(plt) dev.off()