pierreqi/r_code_50k · Datasets at Hugging Face

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/r_scripts/interpreting_regression_coefficients.R

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refs/heads/master

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interpreting_regression_coefficients.R

hsb <- read.csv("datasets/hsb_comb_full.csv") names(hsb) # Let's go with the first school, and the first 5 student-level variables hsb <- hsb[hsb$schoolid == hsb$schoolid[1], 1:5] summary(hsb) # Mathach, ses and female seem to have some variability # Let's predict math achievement using female (dummy), ses (continuous) lm(mathach ~ female + ses, hsb)

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/fuzzedpackages/gRain/man/finding.Rd

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akhikolla/testpackages

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2023-02-18T03:50:28.288006

2021-01-18T13:23:32

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finding.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/finding.R \name{finding} \alias{finding} \alias{setFinding} \alias{retractFinding} \alias{getFinding} \alias{pFinding} \title{Set, retrieve, and retract finding in Bayesian network.} \usage{ setFinding(object, nodes = NULL, states = NULL, flist = NULL, propagate = TRUE) } \arguments{ \item{object}{A "grain" object} \item{nodes}{A vector of nodes} \item{states}{A vector of states (of the nodes given by 'nodes')} \item{flist}{An alternative way of specifying findings, see examples below.} \item{propagate}{Should the network be propagated?} } \description{ Set, retrieve, and retract finding in Bayesian network. NOTICE: The functions described here are kept only for backward compatibility; please use the corresponding evidence-functions in the future. } \note{ NOTICE: The functions described here are kept only for backward compatibility; please use the corresponding evidence-functions in the future: \code{setEvidence()} is an improvement of \code{setFinding()} (and as such \code{setFinding} is obsolete). Users are recommended to use \code{setEvidence()} in the future. \code{setEvidence()} allows to specification of "hard evidence" (specific values for variables) and likelihood evidence (also known as virtual evidence) for variables. The syntax of \code{setEvidence()} may change in the future. } \examples{ ## setFindings yn <- c("yes", "no") a <- cptable(~asia, values=c(1,99),levels=yn) t.a <- cptable(~tub+asia, values=c(5,95,1,99),levels=yn) s <- cptable(~smoke, values=c(5,5), levels=yn) l.s <- cptable(~lung+smoke, values=c(1,9,1,99), levels=yn) b.s <- cptable(~bronc+smoke, values=c(6,4,3,7), levels=yn) e.lt <- cptable(~either+lung+tub,values=c(1,0,1,0,1,0,0,1),levels=yn) x.e <- cptable(~xray+either, values=c(98,2,5,95), levels=yn) d.be <- cptable(~dysp+bronc+either, values=c(9,1,7,3,8,2,1,9), levels=yn) chest.cpt <- compileCPT(a, t.a, s, l.s, b.s, e.lt, x.e, d.be) chest.bn <- grain(chest.cpt) ## These two forms are equivalent bn1 <- setFinding(chest.bn, nodes=c("chest", "xray"), states=c("yes", "yes")) bn2 <- setFinding(chest.bn, flist=list(c("chest", "yes"), c("xray", "yes"))) getFinding(bn1) getFinding(bn2) pFinding(bn1) pFinding(bn2) bn1 <- retractFinding(bn1, nodes="asia") bn2 <- retractFinding(bn2, nodes="asia") getFinding(bn1) getFinding(bn2) pFinding(bn1) pFinding(bn2) } \references{ Søren Højsgaard (2012). Graphical Independence Networks with the gRain Package for R. Journal of Statistical Software, 46(10), 1-26. \url{http://www.jstatsoft.org/v46/i10/}. } \seealso{ \code{\link{setEvidence}}, \code{\link{getEvidence}}, \code{\link{retractEvidence}}, \code{\link{pEvidence}}, \code{\link{querygrain}} } \author{ Søren Højsgaard, \email{sorenh@math.aau.dk} } \keyword{models} \keyword{utilities}

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/inst/variancecomponents/baseball/demo_gibbsflow_sis.R

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jeremyhengjm/GibbsFlow

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refs/heads/master

2021-06-27T13:17:50.151264

2021-02-12T12:08:06

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demo_gibbsflow_sis.R

rm(list = ls()) library(GibbsFlow) library(tictoc) library(ggplot2) # prior prior <- list() prior$logdensity <- function(x) as.numeric(baseball_artificial_logprior(x)) prior$gradlogdensity <- function(x) baseball_gradlogprior_artificial(x) prior$rinit <- function(n) baseball_sample_artificial_prior(n) # likelihood likelihood <- list() likelihood$logdensity <- function(x) as.numeric(baseball_logprior(x) + baseball_loglikelihood(x) - baseball_artificial_logprior(x)) likelihood$gradlogdensity <- function(x) baseball_gradlogprior(x) + baseball_gradloglikelihood(x) - baseball_gradlogprior_artificial(x) # define functions to compute gibbs flow (and optionally velocity) exponent <- 2 compute_gibbsflow <- function(stepsize, lambda, lambda_next, derivative_lambda, x, logdensity) baseball_gibbsflow(stepsize, lambda, lambda_next, derivative_lambda, x, logdensity) gibbsvelocity <- function(t, x) as.matrix(baseball_gibbsvelocity(t, x, exponent)) # smc settings nparticles <- 2^7 nsteps <- 50 timegrid <- seq(0, 1, length.out = nsteps) lambda <- timegrid^exponent derivative_lambda <- exponent * timegrid^(exponent - 1) # run sampler tic() smc <- run_gibbsflow_sis(prior, likelihood, nparticles, timegrid, lambda, derivative_lambda, compute_gibbsflow, gibbsvelocity) toc() # ess plot ess.df <- data.frame(time = 1:nsteps, ess = smc$ess * 100 / nparticles) ggplot(ess.df, aes(x = time, y = ess)) + geom_line() + labs(x = "time", y = "ESS%") + ylim(c(0, 100)) # normalizing constant plot normconst.df <- data.frame(time = 1:nsteps, normconst = smc$log_normconst) ggplot() + geom_line(data = normconst.df, aes(x = time, y = normconst), colour = "blue") + labs(x = "time", y = "log normalizing constant") # norm of gibbs velocity normvelocity.df <- data.frame(time = timegrid, lower = apply(smc$normvelocity, 2, function(x) quantile(x, probs = 0.25)), median = apply(smc$normvelocity, 2, median), upper = apply(smc$normvelocity, 2, function(x) quantile(x, probs = 0.75))) gnormvelocity <- ggplot(normvelocity.df, aes(x = time, y = median, ymin = lower, ymax = upper)) gnormvelocity <- gnormvelocity + geom_pointrange(alpha = 0.5) + xlim(0, 1) + # scale_y_continuous(breaks = c(0, 40, 80, 120)) + xlab("time") + ylab("norm of Gibbs velocity") gnormvelocity ggsave(filename = "~/Dropbox/GibbsFlow/draft_v3/vcmodel_baseball_normvelocity_gfsis.pdf", plot = gnormvelocity, device = "pdf", width = 6, height = 6)

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/inst/examples/retired/ex-mk_intervalplot.R

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gmlang/ezplot

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2022-09-20T11:43:50.578034

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ex-mk_intervalplot.R

library(ezplot) library(tidyr) library(dplyr) # ex1 dat = films %>% select(year_cat, budget) %>% group_by(year_cat) %>% summarise(mid = median(budget), lwr = min(budget), upr = max(budget)) dat plt = mk_intervalplot(dat) title = "Budget Range from 1913 to 2014" p = plt(xvar="year_cat", yvar="mid", ymin_var="lwr", ymax_var="upr", ylab="budget ($)", main=title) scale_axis(p, scale = "log10") # ex2 fit = lm(log10(boxoffice) ~ year_cat, data=films) pred = predict(fit, films, interval="prediction") dat = data.frame(year_cat=films$year_cat, pred) plt = mk_intervalplot(dat) p = plt("year_cat", "fit", ymin_var="lwr", ymax_var="upr", ylab="predicted log10(budget) ($)", main="Budget Prediction Using year_cat") p

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/cachematrix.R

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k29jm7e/ProgrammingAssignment2

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2021-01-11T00:52:52.329274

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cachematrix.R

## The following two functions are used to create a matrix and cash its inverse by ## storing them in a special object aswell as retreiving the invers of the matrix ## from the cash. ## The following function creates a special "matrix" object that can cache its inverse. makeCacheMatrix <- function(x = matrix()) { + inv <- NULL + set <- function(y) { + x <<- y + inv <<- NULL + } + get <- function() x + setInverse <- function(inverse) inv <<- inverse + getInverse <- function() inv + list(set = set, + get = get, + setInverse = setInverse, + getInverse = getInverse) } ## The following function computes the inverse of the special ## matrix" returned by the first function `makeCacheMatrix`. If the inverse has ## already been calculated (and the matrix has not changed), then ## `cacheSolve` should retrieve the inverse from the cache. ## This function assumes that the matrix is always invertible. cacheSolve <- function(x, ...) { ## Return a matrix that is the inverse of 'x' + inv <- x$getInverse() + if (!is.null(inv)) { + message("getting cached data") + return(inv) + } + mat <- x$get() + inv <- solve(mat, ...) + x$setInverse(inv) + inv }

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/plot3.R

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TerryDuan/ExData_Plotting1

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refs/heads/master

2021-01-15T08:20:06.655869

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plot3.R

##the wording dir is set to default, so need redirect to where raw data located plot3 <- function(){ data <- read.csv("~/myR/data/household_power_consumption.txt",sep=";", stringsAsFactors = FALSE) data2<- tbl_df(data) data3<-data2[data2["Date"] == "1/2/2007" | data2["Date"] == "2/2/2007",] data3<-data3[data3["Global_active_power"] != "?",] cols <- c("Date", "Time") data3$Day <- apply(data3[,cols],1, paste, collapse = " ") data3$Day <- strptime(data3$Day, "%d/%m/%Y %H:%M:%S") Sub1 <- as.numeric(data3$Sub_metering_1) plot(data3$Day, data3$Sub_metering_1, ylim = range(Sub1),lines(data3$Day, data3$Sub_metering_1) , pch = "", xlab = "", ylab = "Global Active Power (kilowatts)") par(new = TRUE) plot(data3$Day, data3$Sub_metering_3, ylim = range(Sub1),lines(data3$Day, data3$Sub_metering_3, col = "blue") , pch = "", xlab = "", ylab = "Global Active Power (kilowatts)") par(new = TRUE) plot(data3$Day, data3$Sub_metering_2, ylim = range(Sub1),lines(data3$Day, data3$Sub_metering_2, col = "red") , pch = "", xlab = "", ylab = "Global Active Power (kilowatts)") legend("topright", legend = c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"),lty = c(1,1,1), col = c("black", "red", "blue")) dev.copy(png, "plot3.png") dev.off() }

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/synthetic_expr/synthetic_mscls_2.R

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bargavjayaraman/secure_model_aggregation

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refs/heads/master

2022-09-11T01:32:21.456279

2020-05-28T13:38:20

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synthetic_mscls_2.R

# misclassification rate in synthetic dataset for centralized load("syndata.Rdata") Nexpr = 20 misclsfctn = rep(0,Nexpr) #misclassification rate load("../../centr20000_100.Rdata") for(expr_no in 1:Nexpr){ betahat = betas[[expr_no]][[5]] betahat = as.numeric(betahat) # if using 40 parties, should be <=t[2], 60 parties for t[3], 80 for t[4] ... #betahat[abs(betahat) <= t[1]/2 ] = 0 # if using 40 parties, should be muhat[[expr_no]][, 2], 60 for muhat[[expr_no]][, 3]... difference = apply(data[[1]], 2, '-', muhat[[expr_no]][, 5]) predict = (crossprod(betahat, difference) > 0) e1 = sum(predict == F) # if using 40 parties, should be muhat[[expr_no]][, 2], 60 for muhat[[expr_no]][, 3]... difference = apply(data[[2]], 2, '-', muhat[[expr_no]][, 5]) predict = (crossprod(betahat, difference) > 0) e2 = sum(predict == T) misclsfctn[expr_no] = (e1 + e2) / (10000) } cat( mean(misclsfctn) ) cat('\n') cat( sd(misclsfctn) )

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/man/climexp_to_sef.Rd

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cran/dataresqc

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refs/heads/master

2023-04-13T07:42:08.328276

2023-04-02T21:00:02

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climexp_to_sef.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/conversions.R \name{climexp_to_sef} \alias{climexp_to_sef} \title{Download a GHCN-Daily data file from the Climate Explorer and convert it into the Station Exchange Format} \usage{ climexp_to_sef(url, outpath) } \arguments{ \item{url}{Character string giving the url of the data file.} \item{outpath}{Character string giving the path where to save the file.} } \description{ Download a GHCN-Daily data file from the Climate Explorer and convert it into the Station Exchange Format } \author{ Yuri Brugnara }

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/bin/create_phased_maplist.R

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czheluo/Polymap

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refs/heads/master

2020-05-05T07:18:09.433180

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create_phased_maplist.R

create_phased_maplist <- function(maplist, dosage_matrix.conv, dosage_matrix.orig = NULL, remove_markers = NULL, N_linkages = 2, lower_bound = 0.05, ploidy = 4, ploidy2 = NULL, marker_assignment.1, marker_assignment.2, parent1 = P1, parent2 = P2, original_coding = TRUE, log = NULL, verbose = FALSE) { vector.to.matrix <- function(x, n.columns){ if(length(x)>n.columns){ x<-c(x, rep("", n.columns-length(x)%%n.columns)) } else { n.columns <- length(x) } x.m <- matrix(x, ncol=n.columns, byrow=T) colnames(x.m)<-rep("_", n.columns) return(x.m) } test_dosage_matrix <- function(dosage_matrix){ if(class(dosage_matrix) == "data.frame"){ warning("dosage_matrix should be a matrix, now it's a data.frame.") message("Trying to convert it to matrix, assuming markernames are in the first column..") rownames(dosage_matrix) <- dosage_matrix[,1] dosage_matrix <- as.matrix(dosage_matrix[,-1]) class(dosage_matrix) <- "integer" } else if(class(dosage_matrix) == "matrix"){ rn <- rownames(dosage_matrix) cn <- colnames(dosage_matrix) if(is.null(rn)) stop("The rownames of dosage_matrix should contain markernames. Now NULL") if(is.null(cn)) stop("The columnnames of dosage_matrix should contain genotype names. Now NULL") if(!(typeof(dosage_matrix)=="integer" | typeof(dosage_matrix)=="double")){ warning("dosage_matrix should be integer or numeric. Trying to convert it.. ") class(dosage_matrix) <- "integer" } } else { stop("dosage_matrix should be a matrix of integers. See the manual of this function for more information.") } return(dosage_matrix) } marker_data_summary <- function(dosage_matrix, ploidy = 4, pairing = c("random", "preferential"), parent1 = P1, parent2 = P2, progeny_incompat_cutoff = 0.1, verbose = TRUE, log = NULL) { dosage_matrix <- test_dosage_matrix(dosage_matrix) if (is.null(log)) { log.conn <- stdout() } else { matc <- match.call() write.logheader(matc, log) log.conn <- file(log, "a") } pardos <- dosage_matrix[, c(parent1, parent2)] if(any(is.na(pardos))){ NAmark <- rownames(pardos)[is.na(pardos[,parent1]) | is.na(pardos[,parent2])] warning("There are parental scores with missing values. These are not considered in the analysis. It is recommended to remove those before proceeding to further steps.") dosage_matrix <- dosage_matrix[!rownames(dosage_matrix) %in% NAmark, ] if(verbose) write(paste(c("\nThe following marker have missing values in their parental scores:", NAmark, "\n"), collapse = "\n\n"), file = log.conn) } pairing <- match.arg(pairing) add_P_to_table <- function(table) { #add parent info to table colnames(table) <- paste0("P2_", colnames(table)) rownames(table) <- paste0("P1_", rownames(table)) return(table) } test_row <- function(x, lu, parpos = c(1, 2)) { #analyse offspring incompatibility for a marker #with lu as lookup table for maximum and minimum offspring dosages progeny <- x[-parpos] partype <- lu$pmin == min(x[parpos]) & lu$pmax == max(x[parpos]) min <- lu[partype, "min"] max <- lu[partype, "max"] return(!is.na(progeny) & progeny >= min & progeny <= max) } ####################################### nm <- nrow(dosage_matrix) end_col <- ncol(dosage_matrix) if(verbose) write("Calculating parental info...", stdout()) # contingency table number of markers parental_info <- table(as.factor(dosage_matrix[, parent1]), as.factor(dosage_matrix[, parent2])) parental_info <- add_P_to_table(parental_info) #Checking offspring compatability if(verbose) write("Checking compatability between parental and offspring scores...", stdout()) parpos <- which(colnames(dosage_matrix) %in% c(parent1, parent2)) progeny <- dosage_matrix[,-parpos] nr_offspring <- ncol(progeny) seg.fname <- paste0("seg_p", ploidy, "_", pairing) seg <- get(seg.fname)#,envir=getNamespace("polymapR")) segpar <- seg[, c("dosage1", "dosage2")] colnames(segpar) <- c("pmax", "pmin") segoff <- seg[, 3:ncol(seg)] segoff <- segoff > 0 segpos <- c(0:ploidy) lu_min_max <- apply(segoff, 1, function(x) { a <- segpos[x] min <- min(a) max <- max(a) return(c(min, max)) }) rownames(lu_min_max) <- c("min", "max") lu <- cbind(segpar, t(lu_min_max)) expected_dosage <- apply(dosage_matrix, 1, test_row, lu = lu, parpos = parpos) #NA should be "TRUE", now "FALSE" expected_dosage <- t(expected_dosage) if(length(which(is.na(progeny))) > 0) expected_dosage[is.na(progeny)] <- TRUE #two factorial table of parental dosages with percentage of "FALSE" per factor combination progeny_incompat <- colSums(!expected_dosage) na_progeny <- colSums(is.na(progeny)) perc_incompat <- progeny_incompat / (nrow(expected_dosage) - na_progeny) progeny_incompat <- colnames(progeny)[perc_incompat > progeny_incompat_cutoff] nr_incompat <- rowSums(!expected_dosage) offspring_incompat <- tapply( nr_incompat, list(dosage_matrix[, parent1], dosage_matrix[, parent2]), FUN = function(x) sum(x) / (length(x) * nr_offspring) * 100 ) offspring_incompat <- round(offspring_incompat, 2) offspring_incompat <- add_P_to_table(offspring_incompat) summary <- list(parental_info, offspring_incompat, progeny_incompat) names(summary) <- c("parental_info", "offspring_incompatible", "progeny_incompatible") for (i in c(1, 2)) { if(verbose) { write(paste0("\n####", names(summary)[i], "\n"), file = log.conn) #sink(log.conn) write(knitr::kable(summary[[i]]), log.conn) } #suppressWarnings(sink()) } if(verbose) write("\n####Incompatible individuals:\n", log.conn) if (length(progeny_incompat) == 0 & verbose) write("None\n", log.conn) if(verbose) write(summary$progeny_incompatible, log.conn) if (!is.null(log)) close(log.conn) return(summary) } #marker_data_summary() if(original_coding & is.null(dosage_matrix.orig)) stop("Uncoverted dosage matrix should also be specified if original_coding = TRUE") mapped_markers <- unlist(lapply(maplist, function(x) as.character(x$marker))) if(!all(mapped_markers %in% rownames(dosage_matrix.conv))) stop("Not all markers on map have corresponding dosages! If duplicated markers were added back to maps, make sure to use an appropriate dosage matrix!") if (is.null(log)) { log.conn <- stdout() } else { matc <- match.call() write.logheader(matc, log) log.conn <- file(log, "a") } if(is.null(ploidy2)) ploidy2 <- ploidy if(ploidy == ploidy2){ palindromes <- rownames(dosage_matrix.conv)[which(dosage_matrix.conv[,parent1] != dosage_matrix.conv[,parent2] & abs(dosage_matrix.conv[,parent1] - (0.5*ploidy)) == abs(dosage_matrix.conv[,parent2]-(0.5*ploidy2)))] ## If there are any unconverted palindromes, convert them: if(any(dosage_matrix.conv[palindromes,parent1] > dosage_matrix.conv[palindromes,parent2])) dosage_matrix.conv[palindromes[dosage_matrix.conv[palindromes,parent1] > dosage_matrix.conv[palindromes,parent2]],] <- ploidy - dosage_matrix.conv[palindromes[dosage_matrix.conv[palindromes,parent1] > dosage_matrix.conv[palindromes,parent2]],] } # Begin by separating the SxN and NxS linkages: SxN_assigned <- marker_assignment.1[marker_assignment.1[,parent1]==1 & marker_assignment.1[,parent2]==0,] p1_assigned <- marker_assignment.1[-match(rownames(SxN_assigned),rownames(marker_assignment.1)),] NxS_assigned <- marker_assignment.2[marker_assignment.2[,parent1]==0 & marker_assignment.2[,parent2]==1,] p2_assigned <- marker_assignment.2[-match(rownames(NxS_assigned),rownames(marker_assignment.2)),] #Use only the markers with at least N_linkages significant linkages P1unlinked <- rownames(p1_assigned)[apply(p1_assigned[,3+grep("LG",colnames(p1_assigned)[4:ncol(p1_assigned)]),drop = FALSE],1,max)<N_linkages] P2unlinked <- rownames(p2_assigned)[apply(p2_assigned[,3+grep("LG",colnames(p2_assigned)[4:ncol(p2_assigned)]),drop = FALSE],1,max)<N_linkages] if(verbose) { removed.m1 <- vector.to.matrix(P1unlinked, n.columns = 4) removed.m2 <- vector.to.matrix(P2unlinked, n.columns = 4) if(nrow(removed.m1) > 0){ write(paste("\nThe following P1 markers had less than", N_linkages,"significant linkages:\n_______________________________________\n"),log.conn) write(knitr::kable(removed.m1,format="markdown"), log.conn) } if(nrow(removed.m2) > 0){ write(paste("\n\nThe following P2 markers had less than", N_linkages,"significant linkages:\n_______________________________________\n"),log.conn) write(knitr::kable(removed.m2,format="markdown"), log.conn) write("\n", log.conn) } } if(length(P1unlinked) > 0) p1_assigned <- p1_assigned[-match(P1unlinked,rownames(p1_assigned)),] if(length(P2unlinked) > 0) p2_assigned <- p2_assigned[-match(P2unlinked,rownames(p2_assigned)),] # Only select markers for which the number of homologue assignments match the seg type: p1cols <- 3+grep("Hom",colnames(p1_assigned)[4:ncol(p1_assigned)]) p2cols <- 3+grep("Hom",colnames(p2_assigned)[4:ncol(p2_assigned)]) P1rates <- p1_assigned[,p1cols]/rowSums(p1_assigned[,p1cols], na.rm = TRUE) P2rates <- p2_assigned[,p2cols]/rowSums(p2_assigned[,p2cols], na.rm = TRUE) P1rates[P1rates < lower_bound] <- 0 P2rates[P2rates < lower_bound] <- 0 P1linked <- apply(P1rates,1,function(x) length(which(x!=0))) P2linked <- apply(P2rates,1,function(x) length(which(x!=0))) p1.markers <- rownames(p1_assigned[p1_assigned[,parent1]!=0,]) p2.markers <- rownames(p2_assigned[p2_assigned[,parent2]!=0,]) ## Assuming markers are converted here; have to treat palindrome markers in P2 carefully: P1different <- rownames(p1_assigned[rownames(p1_assigned) %in% p1.markers & p1_assigned[,parent1] != P1linked,]) P2different <- rownames(p2_assigned[setdiff(which(rownames(p2_assigned) %in% p2.markers & p2_assigned[,parent2] != P2linked), which(rownames(p2_assigned) %in% palindromes & ploidy2 - p2_assigned[,parent2] == P2linked)),]) if(verbose) { removed.m1 <- if(!is.null(P1different)) { vector.to.matrix(P1different, n.columns = 4) } else matrix(,nrow=0,ncol=1) #catching error removed.m2 <- if(!is.null(P2different)){ vector.to.matrix(P2different, n.columns = 4) } else matrix(,nrow=0,ncol=1) #catching error if(nrow(removed.m1) > 0){ write(paste("\nThe following markers did not have the expected assignment in P1:\n_______________________________________\n"),log.conn) write(knitr::kable(removed.m1,format="markdown"), log.conn) } if(nrow(removed.m2) > 0){ write(paste("\n\nThe following markers did not have the expected assignment in P2:\n_______________________________________\n"),log.conn) write(knitr::kable(removed.m2,format="markdown"), log.conn) write("\n", log.conn) } } P1rates <- P1rates[!rownames(p1_assigned) %in% P1different,] P2rates <- P2rates[!rownames(p2_assigned) %in% P2different,] #Update p1_assigned and p2_assigned p1_assigned <- p1_assigned[!rownames(p1_assigned) %in% P1different,] p2_assigned <- p2_assigned[!rownames(p2_assigned) %in% P2different,] rownames(P1rates) <- rownames(p1_assigned) rownames(P2rates) <- rownames(p2_assigned) # return simplex x nulliplex markers p1_assigned <- rbind(SxN_assigned,p1_assigned) p2_assigned <- rbind(NxS_assigned,p2_assigned) P1rates <- rbind(SxN_assigned[,p1cols],P1rates) P2rates <- rbind(NxS_assigned[,p2cols],P2rates) # Remove the bi-parental markers that are not assigned in both parents (what about unconverted markers here? Logical test is only looks for a nulliplex parent.) bip1 <- rownames(p1_assigned[rowSums(p1_assigned[,c(parent1,parent2)]!=0)==2,]) bip2 <- rownames(p2_assigned[rowSums(p2_assigned[,c(parent1,parent2)]!=0)==2,]) BiP_different <- c(setdiff(bip1,intersect(bip1,bip2)),setdiff(bip2,intersect(bip1,bip2))) if (verbose & !is.null(BiP_different)) { removed.m <- vector.to.matrix(BiP_different, n.columns = 4) if(nrow(removed.m) > 0){ write(paste("\nThe following markers did not have the expected assignment across both parents:\n_______________________________________\n"),log.conn) write(knitr::kable(removed.m,format="markdown"), log.conn) write("\n", log.conn) } } P1rates <- P1rates[!rownames(p1_assigned) %in% setdiff(bip1,intersect(bip1,bip2)),] P2rates <- P2rates[!rownames(p2_assigned) %in% setdiff(bip2,intersect(bip1,bip2)),] #Update p1_assigned and p2_assigned p1_assigned <- p1_assigned[!rownames(p1_assigned) %in% setdiff(bip1,intersect(bip1,bip2)),] p2_assigned <- p2_assigned[!rownames(p2_assigned) %in% setdiff(bip2,intersect(bip1,bip2)),] ALL_assigned <- unique(c(rownames(p1_assigned),rownames(p2_assigned))) # Make up the output maplist.out <- lapply(seq(length(maplist)),function(mapn) { map <- maplist[[mapn]] map <- map[map$marker%in%ALL_assigned,] outmap <- map[,c("marker","position")] hom_mat <- sapply(1:nrow(outmap), function(r){ a <- rep(0, ploidy+ploidy2) temp <- P1rates[match(as.character(outmap$marker[r]),rownames(P1rates)),] if(length(which(temp!=0)) > 0) a[(1:ploidy)[which(temp!=0)]] <- 1 temp <- P2rates[match(outmap$marker[r],rownames(P2rates)),] if(length(which(temp!=0)) > 0) a[((ploidy+1):(ploidy+ploidy2))[which(temp!=0)]] <- 1 return(a) }) hom_mat <- t(hom_mat) colnames(hom_mat) <- paste0("h",seq(1,ploidy+ploidy2)) # correct palindrome markers: if(any(outmap$marker %in% palindromes)){ hom_mat[outmap$marker %in% palindromes,(ploidy+1):(ploidy+ploidy2)] <- (hom_mat[outmap$marker %in% palindromes,(ploidy+1):(ploidy+ploidy2)] + 1) %% 2 } # recode using the original coding: if(original_coding){ orig_parents <- dosage_matrix.orig[match(outmap$marker,rownames(dosage_matrix.orig)),c(parent1,parent2)] orig_mat <- hom_mat for(r in 1:nrow(orig_mat)){ if(sum(hom_mat[r,1:ploidy]) != orig_parents[r,1]) orig_mat[r,1:ploidy] <- (hom_mat[r,1:ploidy]+1)%%2 if(sum(hom_mat[r,(ploidy+1):(ploidy+ploidy2)]) != orig_parents[r,2]) orig_mat[r,(ploidy+1):(ploidy+ploidy2)] <- (hom_mat[r,(ploidy+1):(ploidy+ploidy2)]+1)%%2 } outmap <- cbind(outmap,orig_mat) } else{ outmap <- cbind(outmap,hom_mat) } return(outmap) } ) names(maplist.out) <- names(maplist) phased_markers <- unlist(lapply(maplist.out, function(x) as.character(x$marker))) if(original_coding){ mapped.dosages <- dosage_matrix.orig[mapped_markers,] } else{ mapped.dosages <- dosage_matrix.conv[mapped_markers,] } if(verbose){ mds.b4 <- marker_data_summary(dosage_matrix = mapped.dosages, ploidy = (ploidy+ploidy2)/2, pairing = "random", verbose = FALSE) mds.aft <- marker_data_summary(dosage_matrix = dosage_matrix.conv[phased_markers,], ploidy = (ploidy+ploidy2)/2, pairing = "random", verbose = FALSE) write(paste("\nMapped marker breakdown before phasing:\n_______________________________________\n"),log.conn) write(knitr::kable(mds.b4$parental_info,format="markdown"), log.conn) write("\n", log.conn) write(paste("\nPhased marker breakdown:\n_______________________________________\n"),log.conn) write(knitr::kable(mds.aft$parental_info,format="markdown"), log.conn) write("\n", log.conn) } ## Run a final check to make sure that the phased marker dosages equal the original marker dosages: phased.dose <- do.call(rbind,lapply(maplist.out, function(x) { temp <- cbind(rowSums(x[,paste0("h",1:ploidy)]), rowSums(x[,paste0("h",(ploidy + 1):(ploidy + ploidy2))])) rownames(temp) <- x[,"marker"] return(temp) })) orig.dose <- mapped.dosages[rownames(phased.dose),c(parent1,parent2)] conflicting <- which(rowSums(phased.dose == orig.dose) != 2) if(length(conflicting) > 0){ warning("Not all phased markers matched original parental dosage. \nPerhaps unconverted marker dosages were supplied as converted dosages by mistake? \nThe following conflicts were detected and removed:") warn.df <- cbind(orig.dose[conflicting,],phased.dose[conflicting,]) colnames(warn.df) <- c("P1_original","P2_original","P1_phased","P2_phased") write(knitr::kable(warn.df,format="markdown"), log.conn) ## Simply remove these markers from the output: rem.markers <- rownames(phased.dose)[conflicting] maplist.out <- lapply(maplist.out, function(x) x[!x$marker %in% rem.markers,]) } if(!is.null(log)) close(log.conn) return(maplist.out) }

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/2-SymiinChow/01DiscreteTimedynrExamples/CFA/GenDataCFA.r

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ktw5691/dynamic-time-imps2018

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GenDataCFA.r

#To simulate data for dynamic factor analysis model with auto- and cross-regressions for 2 factors, 2lags (0 and 1 lag) each rm(list=ls(all=TRUE)) nt=1 # number of time points ne=2 # number of states ny=6 # number of observed nx=0 # number of fixed regressors np=500 # number of subjects filey=paste0('CFA.dat') # output file for obs y npad=1 # start up ist=npad+1 ntt=nt+npad # S S=matrix(c( 1,0, 1.2,0, .8,0, 0,1, 0,.9, 0,1.1 ),ny,ne,byrow=TRUE) # Q Q=matrix(c( 2.5,.6, .6,2.5 ),ne,ne,byrow=TRUE) # H #H=matrix(c( # .5,-.3, # -.2,.4),ne,ne,byrow=TRUE) H = matrix(rep(0,4),ncol=ne) # R R=diag(c(.8,.6,2,1,1.5,2)) # c c=matrix(c(0,0),ne,1,byrow=TRUE) # d d=matrix(c(3,2,4,5,3,4),ny,1,byrow=TRUE) ## Z # states a t=0 a0=matrix(c(0,0),ne,1,byrow=TRUE) # cholesky of Q & R (assumes these are positive definite) Qs = Q Rs = R if (sum(diag(Q))> 0) Qs = chol(Q) if (sum(diag(R))> 0) Rs = chol(R) # innov z residuals e a=matrix(0,ntt,ne) y=matrix(0,ntt,ny) x=matrix(0,ntt,nx) yall=matrix(0,nt*np,ny) all = matrix(0,nt*np,ne) for (j in 1:np){ a[1,1:ne] = a0 for (i in 2:ntt) { ztmp=t(rnorm(ne)%*%Qs) etmp=t(rnorm(ny)%*%Rs) atmp=as.matrix(a[i-1,1:ne]) atmp=H%*%atmp+ztmp+c a[i,1:ne]=t(atmp) ytmp=S%*%atmp+etmp+d y[i,1:ny]=ytmp } yall[ (1+(j-1)*nt):(j*nt),1:ny] = y[(ist:ntt),1:ny] all[ (1+(j-1)*nt):(j*nt),1:ne] = a[(ist:ntt),1:ne] } # yx=yall nyx=nx+ny if (nx>0) { yx=cbind(y,x) } write.table(yx,file=filey,append=FALSE,col.names = FALSE,row.names = FALSE)

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/itemset_mining.r

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benjaminvdb/mining_recipe_data

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refs/heads/master

2023-01-28T07:18:24.358564

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itemset_mining.r

require(arules) require(arulesViz) require(tikzDevice) base_dir = '/Users/benny/Repositories/recipes/paper' tables_dir = file.path(base_dir, 'tables') plots_dir = file.path(base_dir, 'plots') saveTikz <- function(plt, filename, width = 4.9823, ratio = 1.618) { height <- width/ratio filename <- file.path(plots_dir, filename) tikz(file = filename, width = width, height = height) replayPlot(plt) dev.off() } # Load data filename <- '/Users/benny/Repositories/recipes/data/recipes.single' Recipes = read.transactions(filename, format='single', sep=',', cols=seq(1, 2)) # Create summary summary(Recipes) # Mine rules using Apriori rules <- apriori(Recipes, parameter=list(support=0.02, confidence=0.5)) # Top 3 rules according to lift inspect(head(sort(rules, by ="lift"), 10)) top10 <- as(head(sort(rules, by ="lift"), 10), 'data.frame') write.table(top10, file.path(tables_dir, 'rules_top10.dat'), sep = ';', col.names = TRUE, row.names = FALSE) # Scatter plot plot(rules) # The quality() function prints out quality scores for rules head(quality(rules)) # Two-key plot plots support against confidence, with the 'order' # indicated by color, which is the number of items plot(rules, shading="order", control=list(main = "Two-key plot")) # Interactive plot sel <- plot(rules, measure=c("support", "lift"), shading="confidence", interactive=TRUE) # Select rules with confidence > 0.9 subrules <- rules[quality(rules)$confidence > 0.9] plot(subrules, method="matrix", measure="lift") # reordering rows and columns in the matrix such that rules with similar values of the interest measure are presented closer together plot(subrules, method="matrix", measure="lift", control=list(reorder=TRUE)) # Same thing, interactive plot(subrules, method="matrix", measure="lift", control=list(reorder=TRUE), interactive=TRUE) # Plot in 3D (less intuitive!) plot(subrules, method="matrix3D", measure="lift", control=list(reorder=TRUE)) # Two measures combined in one coloring grid plot(subrules, method="matrix", measure=c("lift", "support"), control=list(reorder=TRUE)) plot(subrules, method="matrix", measure=c("confidence", "support"), control=list(reorder=TRUE)) # Grouping statistically dependent consequents (LHS) allows to plot many more rules many_rules <- apriori(Recipes, parameter=list(support=0.01, confidence=0.3)) plot(many_rules, method="grouped") # Select some rules with high lift subrules2 <- head(sort(rules, by="lift"), 20) # Plotting makes things cluttered... #plot(subrules2, method="graph") # ... while vertices = itemsets and edges = rules is pretty nice plot(subrules2, method="graph", control=list(type="itemsets")) # Export to Gephi!! # NOTE: here we quickly found there seem to be two clusterd ('hartig' en 'zoetig'?) saveAsGraph(head(sort(rules, by="lift"),200), file="rules2.graphml") plot(subrules2, method="paracoord", control=list(reorder=TRUE)) # Double decker plot oneRule <- sample(rules, 1) inspect(oneRule) plot(oneRule, method="doubledecker", data = Recipes) set.seed(1234) s <- sample(Recipes, 2000) d <- dissimilarity(s, method = "Jaccard") library("cluster") clustering <- pam(d, k = 16) plot(clustering) # Prediction based on clustering allLabels <- predict(s[clustering$medoids], Recipes, method = "Jaccard") cluster <- split(Recipes, allLabels) itemFrequencyPlot(cluster[[1]], population = s, support = 0.05) itemFrequencyPlot(cluster[[2]], population = s, support = 0.05) # Sweet pastries? itemFrequencyPlot(cluster[[3]], population = s, support = 0.05) # Greek? itemFrequencyPlot(cluster[[4]], population = s, support = 0.05) itemFrequencyPlot(cluster[[5]], population = s, support = 0.05) # Apple based sweet pasties? itemFrequencyPlot(cluster[[6]], population = s, support = 0.05) itemFrequencyPlot(cluster[[7]], population = s, support = 0.05) itemFrequencyPlot(cluster[[8]], population = s, support = 0.05) clustering <- pam(d, k = 2) allLabels <- predict(s[clustering$medoids], Recipes, method = "Jaccard") cluster <- split(Recipes, allLabels) itemFrequencyPlot(cluster[[1]], population = s, support = 0.05) # Hartig itemFrequencyPlot(cluster[[2]], population = s, support = 0.05) # Zoet # Supplement a recipe chickenRules <- subset(rules, subset = rhs %in% "chicken") # Cool result: # 461 {carrot,celery stalks} => {chicken} 0.01029268 0.5436782 2.993976 require(ggplot2) require(RColorBrewer) require(plyr) # Plot ingredient distribution y <- sort(itemFrequency(Recipes, type = 'abs'), decreasing = TRUE) n <- length(y) x <- 1:n # Data data <- data.frame(x=x, y=y, group='Data') # Fit linear line on logarithmic data fit <- lm(log(y) ~ x, data=data.frame(x=x, y=y)) fitvals <- exp(fit$fitted.values) data2 <- data.frame(x=x, y=fitvals, group='Regression') # Plot library(tikzDevice) plots_dir = '/Users/benny/Repositories/recipes/paper/plots' phi <- 1.618 width <- 4.9823 height <- width/phi filename <- file.path(plots_dir, 'ingredient_frequencies.tex') tikz(file = filename, width = width, height = height) ggplot() + aes(x=x, y=y, color=group) + geom_point(data=data, size=.5) + geom_line(data=data2, linetype='dashed', size=.8) + scale_y_log10() + scale_color_brewer(palette = 'Set1') + ggtitle('Ingredient frequencies on a logarithmic scale') + labs(x='Ingredients', y='Frequency') + theme(plot.title = element_text(size=12), legend.title = element_blank(), legend.justification=c(1,1), legend.position=c(1,1)) dev.off() # Save table mod_stargazer <- function(output.file, ...) { output <- capture.output(stargazer(...)) cat(paste(output, collapse = "\n"), file=output.file, sep="\n", append=FALSE) } tables_dir <- '/Users/benny/Repositories/recipes/paper/tables' top <- sort(itemFrequency(Recipes, type='abs'), decreasing = TRUE) topN <- top[1:10] t <- data.frame(Ingredient=names(topN), Frequency=unname(topN), Relative=unname(topN)/sum(top)) filename <- file.path(tables_dir, 'ingredients_top10.tex') mod_stargazer(filename, t, summary=FALSE, digit.separator=' ') filename <- filename <- file.path(tables_dir, 'ingredients_top10.dat') write.table(t, file = filename, quote = FALSE, sep = ";", row.names = FALSE, col.names = TRUE) library(party) f <- function(v) {v <= 1000} a <- as(Recipes[1:2000], 'matrix') b <- cbind(a, sapply(1:2000, f)) dimnames <- attr(b, 'dimnames') dimnames[[2]][404] <- 'class' attr(b, 'dimnames') <- dimnames data = data.frame(b) #tree <- ctree(class ~ pepper + salt, data = data) tinfo <- as(transactionInfo(Recipes), 'list')[[1]] # Get list of index -> tid tid_to_index <- hashmap(tinfo, sapply(1:length(tinfo), toString)) good_tids <- unlist(recipes_good@data@Dimnames[[2]]) bad_tids <- unlist(recipes_bad@data@Dimnames[[2]]) GoodRecipes <- Recipes[tid_to_index[[good_tids]]] BadRecipes <- Recipes[tid_to_index[[bad_tids]]] good <- as(GoodRecipes, 'matrix') bad <- as(BadRecipes, 'matrix') good <- cbind(good, 1) bad <- cbind(bad, 2) data <- rbind(good, bad) dimnames <- attr(data, 'dimnames') dimnames[[2]][404] <- 'class' attr(data, 'dimnames') <- dimnames write.csv(data, 'good_bad.csv') # Frequency of item pairs X <- as(Recipes, 'matrix') X <- sapply(as.data.frame(X), as.numeric) out <- crossprod(X) # Same as: t(X) %*% X diag(out) <- 0 library("recommenderlab") algorithms <- list("random items" = list(name = "RANDOM", param = NULL), "popular items" = list(name = "POPULAR", param = NULL), "association rules (0.001)" = list(name = "AR", param = list(support = 0.001,confidence=0.1, maxlen=3))) #"association rules (0.01)" = list(name = "AR", param = list(support = 0.01)), #"association rules (0.05)" = list(name = "AR", param = list(support = 0.05)), #"association rules (0.1)" = list(name = "AR", param = list(support = 0.1)), #"item-based CF (k=3)" = list(name = "IBCF", param = list(k = 3)), #"item-based CF (k=5)" = list(name = "IBCF", param = list(k = 5)), #"item-based CF (k=10)" = list(name = "IBCF", param = list(k = 10)), "item-based CF (k=20)" = list(name = "IBCF", param = list(k = 20)), #"item-based CF (k=30)" = list(name = "IBCF", param = list(k = 30)), "item-based CF (k=40)" = list(name = "IBCF", param = list(k = 40)), #"item-based CF (k=50)" = list(name = "IBCF", param = list(k = 50)), "item-based CF (k=200)" = list(name = "IBCF", param = list(k = 200))) #"item-based CF (k=40)" = list(name = "IBCF", param = list(k = 40, method='dice')), #"item-based CF (k=200)" = list(name = "IBCF", param = list(k = 200, method='dice'))) #"item-based CF (k=402)" = list(name = "IBCF", param = list(k = 402))) #"user-based CF (Jaccard)" = list(name = "UBCF", param = list(nn = 50, method = 'jaccard'))) #"user-based CF (Pearson)" = list(name = "UBCF", param = list(nn = 50, method = 'pearson'))) Recipes_binary <- as(Recipes, 'binaryRatingMatrix') Recipes_binary <- Recipes_binary[rowCounts(Recipes_binary) > 5] scheme <- evaluationScheme(Recipes_binary, method="split", train=.9, k=1, given=2) results2 <- evaluate(scheme, algorithms, progress = TRUE, type = "topNList", n=c(1,3,5,10)) nms <- c('Random items', 'Popular items', 'AR s=0.01', 'AR s=0.05', 'AR s=0.1', 'IBCF k=20', 'IBCF k=40', 'IBCF k=200') names(results2) <- nms plot(results2, annotate=c(1,3,7)) title('ROC curve for ingredient recommendation') plt <- recordPlot() saveTikz(plt, 'ingredients_recommendations_given2.tex')

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FatmaElBadry/ProgrammingAssignment2

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cachematrix.R

## Week 3 Assignment;cachematrix;Developed By: Fatma ElBadry ## Put comments here that give an overall description of what your ## functions do ## Function "makeCacheMatrix" gets a matrix as an input, And has a list of functions that can do the following: ## 1.set the value of the matrix, ## 2.get the value of the matrix, ## 3.set the inverse Matrix and ## 4.get the inverse Matrix. ## The matrix object can cache its own object. ## Write a short comment describing this function ## This function creates a special "matrix" object that can cache its inverse makeCacheMatrix <- function(x = matrix()) { #take the matrix as an input invMatrix <- NULL # 1.set the value of the Matrix setMatrix <- function(y) { x <<- y invMatrix <<- NULL } # 2.get the value of the Matrix getMatrix <- function() x # 3.set the value of the invertible matrix setInverse <- function(inverse) invMatrix <<- inverse # 4.get the value of the invertible matrix getInverse <- function() invMatrix ##define the below list in order to refer to the functions with the $ operator list(setMatrix = setMatrix, getMatrix = getMatrix, setInverse = setInverse, getInverse = getInverse) } ## Write a short comment describing this function ## The function "cacheSolve" takes the output of makeCacheMatrix(matrix) as an # input and checks inverse matrix from makeCacheMatrix(matrix) has any value in it or not. # In case inverse matrix from makeCacheMatrix((matrix) is empty, it gets the original matrix data from # and set the invertible matrix by using the solve function. # In case inverse matrix from makeCacheMatrix((matrix) has some value in it (always works # after running the code 1st time), it returns the cached object cacheSolve <- function(x, ...) { ## Return a matrix that is the inverse of 'x' invMatrix <- x$getInverse() if(!is.null(invMatrix)) { #if inverse matrix is not NULL return(invMatrix) #return the invertible matrix } #if value of the invertible matrix is NULL then MatrixData <- x$getMatrix() #get the original Matrix Data invMatrix <- solve(MatrixData, ...) #use solve function to inverse the matrix x$setInverse(invMatrix) #set the invertible matrix return(invMatrix) #return the invertible matrix }

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straussalbee/ShinyStability

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ui.R

library(shiny) #NK repertoire stability data visualization app stability <- read.table("StabilityData",header=TRUE,colClasses=c(rep("factor",4),"numeric","factor")) dataset <- stability #Define UI shinyUI(fluidPage( title = "Human NK Cell Repertoire Stability", plotOutput('plot',width = "900px",height="600px"), hr(), fluidRow( column(3, h4("Human NK Cell Repertoire Stability"), br(), checkboxInput('jitter', 'Jitter'), checkboxInput('smooth', 'Smooth') ), column(4, offset = 1, selectInput('x', 'X', names(dataset),selected="Timepoint"), selectInput('y', 'Y', names(dataset), selected="Marker"), selectInput('facet_row', 'Facet Row',c(None='.', names(stability[sapply(stability, is.factor)]))), selectInput('facet_col', 'Facet Column',c(None='.', names(stability[sapply(stability, is.factor)])),selected="Donor") ), column(4, selectInput('color', 'Color', c('None', names(dataset)),selected="Type"), selectInput('size', 'Size', c('None', names(dataset)),selected="Frequency") ) ) ))

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/cran/paws.compute/man/emrserverless_list_job_runs.Rd

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paws-r/paws

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emrserverless_list_job_runs.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/emrserverless_operations.R \name{emrserverless_list_job_runs} \alias{emrserverless_list_job_runs} \title{Lists job runs based on a set of parameters} \usage{ emrserverless_list_job_runs( applicationId, nextToken = NULL, maxResults = NULL, createdAtAfter = NULL, createdAtBefore = NULL, states = NULL ) } \arguments{ \item{applicationId}{[required] The ID of the application for which to list the job run.} \item{nextToken}{The token for the next set of job run results.} \item{maxResults}{The maximum number of job runs that can be listed.} \item{createdAtAfter}{The lower bound of the option to filter by creation date and time.} \item{createdAtBefore}{The upper bound of the option to filter by creation date and time.} \item{states}{An optional filter for job run states. Note that if this filter contains multiple states, the resulting list will be grouped by the state.} } \description{ Lists job runs based on a set of parameters. See \url{https://www.paws-r-sdk.com/docs/emrserverless_list_job_runs/} for full documentation. } \keyword{internal}

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/man/FuzzyData-class.Rd

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edwardchu86/FuzzyAHP

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FuzzyData-class.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/class-FuzzyData.R \docType{class} \name{FuzzyData-class} \alias{FuzzyData-class} \title{Class "FuzzyData"} \description{ An S4 class to represent fuzzy data. } \section{Slots}{ \describe{ \item{\code{fnMin}}{A numeric vector of minimal values of fuzzy data.} \item{\code{fnModal}}{A numeric vector of modal values of fuzzy data.} \item{\code{fnMax}}{A numeric vector of maximal values of fuzzy data.} }}

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TomHarrop/honeybee-genotype-pipeline

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plot_ihist.R

#!/usr/bin/env Rscript log <- file(snakemake@log[[1]], open = "wt") sink(log, type = "message") sink(log, type = "output", append = TRUE) library(data.table) library(ggplot2) ReadIhist <- function(ihist_file){ fread(ihist_file, skip = 5)[`#InsertSize` <= 1000] } CalculateMeanInsert <- function(ihist_dt){ my_rle <- ihist_dt[, structure( list(lengths = Count, values = `#InsertSize`), class = "rle")] my_mean <- mean(inverse.rle(my_rle)) as.integer(round(my_mean, 0)) } # read files ihist_files <- snakemake@input[["ihist_files"]] names(ihist_files) <- sub(".ihist", "", basename(ihist_files)) # combine ihist_list <- lapply(ihist_files, ReadIhist) ihist_data <- rbindlist(ihist_list, idcol = "sample") # mean per sample mean_dt <- ihist_data[, .(meansize = CalculateMeanInsert(.SD)), by = sample] # configure plot y_pos <- ihist_data[, max(Count) * 0.95] vd <- viridisLite::viridis(3) # plot gp <- ggplot(ihist_data, aes(x = `#InsertSize`, y = Count)) + theme_grey(base_size = 6) + facet_wrap(~ sample) + xlab("Mapped insert size") + geom_vline(mapping = aes(xintercept = meansize), data = mean_dt, linetype = 2, colour = vd[[2]])+ geom_text(mapping = aes(x = meansize + 10, y = y_pos, label = meansize), hjust = "inward", colour = vd[[2]], data = mean_dt, size = 2) + geom_area(fill = alpha(vd[[1]], 0.5)) ggsave(snakemake@output[["plot"]], gp, device = cairo_pdf, width = 10, height = 7.5, units = "in") sessionInfo()

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chiser/Strokelitude-Scripts

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TorquePlotting.R

######################## A script for plotting data from torque measurements ## source the functions needed source("StrokePrepFunctions.R") tTraces <- flyTorqueImport() ## query the user for start and end seconds for the excerpts print("Please enter the starting time for the excerpts.") startTime <- scan(n=1) print("Please enter the end time for the excerpts.") endTime <- scan(n=1) for(ind in 1:length(tTraces)){ png(filename = paste("torqueTrace",ind,".png", sep = ""), width = 1920) plot(x = tTraces[[ind]]$Time, y = tTraces[[ind]]$Trace, type = "l", main = paste("Torque Trace",ind), xlab = "Time (sec)", ylab = "Yaw Torque") # graphics.off() #uncommenting the above solved the error I had running the script without it: Error in plot.window(): endliche xlim werte nötig. #another possibility would be to try to hardcore the xlim between reasonable values: not easy because they vary quite a bit. In addition in the plots I see the last fly is plotted wrong: probably because of the conversion of numbers to string(factors) and then to numbers again flyTraceExcerptPlot(tTraces[[ind]]$Trace, tTraces[[ind]]$Time, startTime, endTime, filename = paste("torqueTraceExcerpt", ind, ".png")) #dev.off() } graphics.off()

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cran/AnalyzeFMRI

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threeDto4D.Rd

\name{threeDto4D} \alias{threeDto4D} \title{threeDto4D} \description{To read tm functionnal images files in ANALYZE or NIFTI format, and concatenate them to obtain one 4D image file in Analyze (hdr/img pair) or Nifti format (hdr/img pair or single nii) which is written on disk. Note that this function outputs the files in the format sent in. If desired, one can use the function \code{analyze2nifti} to create NIFTI files from ANALYZE files.} \usage{threeDto4D(outputfile,path.in=NULL,prefix=NULL,regexp=NULL,times=NULL, list.of.in.files=NULL,path.out=NULL,is.nii.pair=FALSE,hdr.number=1)} \arguments{\item{outputfile}{character. Name of the outputfile without extension} \item{path.in}{character with the path to the directory containing the image files} \item{prefix}{character. common prefix to each file} \item{regexp}{character. Regular expression to get all the files} \item{times}{vector. numbers of the image files to retrieve} \item{list.of.in.files}{names of img files to concatenate (with full path)} \item{path.out}{where to write the output hdr/img pair files. Will be taken as path.in if not provided.} \item{is.nii.pair}{logical. Should we write a signle nii NIFTI file or a hdr/img NIFTI pair file} \item{hdr.number}{Number of the original 3D Analyze or NIFTI image file from which to take the header that should serve as the final header of the newly 4D created image file} } \value{None.} \seealso{ \code{\link{twoDto4D}} \code{\link{fourDto2D}} } \examples{ # path.fonc <- "/network/home/lafayep/Stage/Data/map284/functional/ # MondrianApril2007/preprocessing/1801/smoothed/" # threeDto4D("essai",path.in=path.fonc,prefix="su1801_",regexp="????.img",times=1:120) } \keyword{utilities}

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DaniBoo/cyanobacteria_project

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rinput.R

library(ape) testtree <- read.tree("12758_0.txt") unrooted_tr <- unroot(testtree) write.tree(unrooted_tr, file="12758_0_unrooted.txt")

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surayaaramli/typeRrh

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mean.geometric.Rd.R

library(PerformanceAnalytics) ### Name: mean.geometric ### Title: calculate attributes relative to the mean of the observation ### series given, including geometric, stderr, LCL and UCL ### Aliases: mean.geometric mean.utils mean.UCL mean.LCL mean.stderr ### mean.stderr mean.LCL mean.UCL ### ** Examples data(edhec) mean.geometric(edhec[,"Funds of Funds"]) mean.stderr(edhec[,"Funds of Funds"]) mean.UCL(edhec[,"Funds of Funds"]) mean.LCL(edhec[,"Funds of Funds"])

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grambank/grambank-analysed

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assigning_AUTOTYP_areas.R

source("global_variables.R") source("fun_def_h_load.R") h_load(c("fields", "tidyverse")) #Script written by Hedvig Skirgård cat("Matching all languages in Grambank to an AUTOTYP-area.\n") #combining the tables languages and values from glottolog_df-cldf into one wide dataframe. #this can be replaced with any list of Language_IDs, long and lat if (!file.exists("output/non_GB_datasets/glottolog-cldf_wide_df.tsv")) { source("make_glottolog-cldf_table.R") } glottolog_df <- read_tsv("output/non_GB_datasets/glottolog-cldf_wide_df.tsv",col_types = cols()) %>% dplyr::select(Language_ID, Longitude, Latitude) %>% filter(!is.na(Longitude)) ##Adding in areas of linguistic contact from AUTOTYP AUTOTYP_FN <- "../autotyp-data/data/csv/Register.csv" cat("Fetching AUTOTYP data from", AUTOTYP_FN, ".\n") AUTOTYP <- read_csv(AUTOTYP_FN ,col_types = cols()) %>% dplyr::select(Language_ID = Glottocode, Area, Longitude, Latitude) %>% group_by(Language_ID) %>% sample_n(1) #when a language is assigned to more than one area, pick randomly. #This next bit where we find the autotyp areas of languages was written by Seán Roberts # We know the autotyp-area of languages in autotyp and their long lat. We don't know the autotyp area of languages in grambank. We also can't be sure that the long lat of languoids with the same glottoids in autotyp and grambank_df have the exact identical long lat. First let's make two datasets, one for autotyp languages (hence lgs where we know the area) and those that we wish to know about, the grambank ones. lgs_with_known_area <- as.matrix(AUTOTYP[!is.na(AUTOTYP$Area),c("Longitude","Latitude")]) rownames(lgs_with_known_area) <- AUTOTYP[!is.na(AUTOTYP$Area),]$Language_ID known_areas <- AUTOTYP %>% dplyr::filter(!is.na(Area)) %>% dplyr::select(Language_ID, Area) %>% distinct() %>% dplyr::select(AUTOTYP_Language_ID = Language_ID, everything()) rm(AUTOTYP) lgs_with_unknown_area <- as.matrix(glottolog_df[,c("Longitude","Latitude")]) rownames(lgs_with_unknown_area) <- glottolog_df$Language_ID # For missing, find area of closest language cat("Calculating the geographical distance between languages with known AUTOTYP-areas and those without a matched AUTOTYP-area.\n") atDist <- rdist.earth(lgs_with_known_area,lgs_with_unknown_area, miles = FALSE) rm(lgs_with_known_area, lgs_with_unknown_area) df_matched_up <- as.data.frame(unlist(apply(atDist, 2, function(x){names(which.min(x))})), stringsAsFactors = F) %>% dplyr::rename(AUTOTYP_Language_ID = `unlist(apply(atDist, 2, function(x) { names(which.min(x)) }))`) cat("Matching languages without known AUTOTYP-area to the AUTOTYP-area of its closest neighbour with has a known AUTOTYP-area.\n") glottolog_df_with_AUTOTYP <- df_matched_up %>% tibble::rownames_to_column("Language_ID") %>% full_join(known_areas, by = "AUTOTYP_Language_ID") %>% right_join(glottolog_df, by = "Language_ID") %>% dplyr::select(-AUTOTYP_Language_ID) %>% dplyr::rename(AUTOTYP_area = Area) glottolog_df_with_AUTOTYP %>% write_tsv("output/non_GB_datasets/glottolog_AUTOTYP_areas.tsv")

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/Duke-Data Analysis/Lab3a.R

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JPruitt/Coursera

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Lab3a.R

load(url("http://s3.amazonaws.com/assets.datacamp.com/course/dasi/ames.RData")) head(ames) names(ames) dim(ames) area<-ames$Gr.Liv.Area price<-ames$SalePrice summary(area) hist(area) samp0<-sample(area, 50) hist(samp0) samp1<-sample(area, 50) hist(samp1) mean(samp1) # The vector 'sample_means50' is initialized with NA values sample_means50 = rep(NA, 5000) # The for loop runs 5000 times, with 'i' taking values 1 up to 5000 for (i in 1:5000) { # Take a random sample of size 50 samp = sample(area, 50) # Store the mean of the sample in the 'sample_means50' vector on the ith # place sample_means50[i] = mean(samp) # Print the counter 'i' print(i) } # Print the first few random medians head(sample_means50) sample_means_small<-rep(NA, 100) for (i in 1:100){ samp=sample(area, 50) sample_means_small[i]<-mean(samp) } sample_means_small # Initialize the sample distributions: sample_means10 = rep(NA, 5000) sample_means100 = rep(NA, 5000) # Run the for loop: for (i in 1:5000) { samp = sample(area, 10) sample_means10[i] = mean(samp) samp = sample(area, 100) sample_means100[i] = mean(samp) } # Take a look at the results: head(sample_means10) head(sample_means50) # was already loaded head(sample_means100) # Divide the plot in 3 rows: par(mfrow = c(3, 1)) # Define the limits for the x-axis: xlimits = range(sample_means10) # Draw the histograms: hist(sample_means10, breaks=20, xlim=xlimits) hist(sample_means50, breaks=20, xlim=xlimits) hist(sample_means100, breaks=20, xlim=xlimits) # Take a sample of size 50 from 'price': sample_50<-sample(price, 50) # Print the mean: mean(sample_50) sample_means50<-rep(NA, 5000) sample_means150<-rep(NA, 5000) for (i in 1:5000){ samp=sample(price, 50) sample_means50[i]<-mean(samp) samp=sample(price, 150) sample_means150[i]<-mean(samp) } par(mfrow=c(2,1)) hist(sample_means50) hist(sample_means150)

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/Graph generators/ROCdraw.R

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DebolinaHalderLina/CRISPRpred_plus_plus

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ROCdraw.R

library(ROCR) #data(ROCR.simple) #newob1= read.csv(file.choose()) #newob2= read.csv(file.choose()) #newob3= read.csv(file.choose()) #newob4= read.csv(file.choose()) #newob5= read.csv(file.choose()) #observed = read.csv(file.choose()) preds <- cbind(p1 = newob1$x, p1 = newob2$x, p1 = newob3$x,p1 = newob4$x,p1 = newob5$x) n <- 5 # you have n models colors <- c('red', 'blue','green','orange','black') # 2 colors for (i in 1:n) { plot(performance(prediction(preds[,i],observed$result),"mat",), add=(i!=1),col=colors[i],lwd=2) } #for (i in 1:n) { #plot(performance(prediction(preds[,i],observed$score_drug_gene_threshold),"mat"), # add=(i!=1),col=colors[3],lwd=2) #plot(performance(prediction(preds[,i],observed$result),"sens","spec"), #add=(i!=1),col=colors[i],lwd=2) #} #for (i in 1:n) { #plot(performance(prediction(preds[,i],observed$score_drug_gene_threshold),"mat"), #add=(i!=1),col=colors[2],lwd=2) #plot(performance(prediction(preds[,i],observed$result),"prec","rec"), #add=(i!=1),col=colors[i],lwd=2) #}

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alexanderrobitzsch/miceadds

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ma.wtd.quantileNA.R

## File Name: ma.wtd.quantileNA.R ## File Version: 0.14 #*** weighted quantile ma.wtd.quantileNA <- function( data, weights=NULL, vars=NULL, type=7, probs=seq(0,1,.25) ) { require_namespace("TAM") #*** pre-processing res <- ma_wtd_stat_prepare_data(data=data, weights=weights, vars=vars ) data <- res$data weights <- res$weights M <- length(data) #*** weighted quantile V <- ncol(data[[1]]) PP <- length(probs) res <- matrix( NA, nrow=M, ncol=V*PP ) for (ii in 1:M){ data1 <- data[[ii]] for (vv in 1:V){ M1 <- TAM::weighted_quantile(x=data1[,vv], w=weights, type=type, probs=probs ) res[ii, 1:PP + (vv-1)*PP ] <- M1 } } res <- colMeans(res) res <- matrix( res, nrow=PP, ncol=V, byrow=FALSE) colnames(res) <- colnames(data[[1]]) rownames(res) <- paste0(100*probs,"%") return(res) }

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cran/lancet.iraqmortality

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prep.houses.Rd

\name{prep.houses} \alias{prep.houses} \title{Loads up houses dataset from the mortality.zip file} \description{ Returns a data frame containing renamed and cleaned up variables of the 'houses' from the provided mortality.zip file in the data directory. See the package vignette for a description of these variables. If no mortality.zip is provided, then this function will not work. } \usage{ prep.houses() } \value{ Returns a data frame with information on the 1,849 households interviewed in Burnham et al (2006). Descibed as the \bold{houses} data frame in the vignette. } \seealso{ \code{\link{prep.deaths}} } \author{Arjun Ravi Narayan, David Kane} \keyword{datasets}

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pruning.r

#' Given that we want to remove a set of \eq{l \subset L}, the algorithm goes as #' follows: #' #' 1. Match the node ids with position. #' 2. Sort it to be decreasing, so the most inner nodes show first, #' 3. Increase the list: #' a. Compute the geodesic matrix G, #' b. For i in l do, j != i: #' If G(i,j)!=0, then add it to the list #' #' \code{sapply(1:(3-1), function(x) sapply(x:3, c, x)[,-1,drop=FALSE])} #' #' b. Degine tags(n). For k in p, For s in p[k]: #' 1. If G[s] != 0, then tag() = 1 #' #' Two things to do: 1 remove them from the #' #' #' # A simple example of how prune works------------------------------------------- #' # Simulating a nice tree #' set.seed(1213) #' x <- sim_tree(4) #' #' # Setting up the plot envir #' oldpar <- par(no.readonly = TRUE) #' par(mfrow = c(3,2), mai=rep(.5,4), cex=1, xpd=NA, omi=c(0,0,1,0)) #' #' # Plotting #' plot(x, main = "Full tree", show.node.label = TRUE) #' plot(prune(x, 5), main="removing 5", show.node.label = TRUE) #' plot(prune(x, 6), main="removing 6", show.node.label = TRUE) #' plot(prune(x, 4), main="removing 4", show.node.label = TRUE) #' plot(prune(x, 3), main="removing 3", show.node.label = TRUE) #' plot(prune(x, c(4,6,3)), main="removing (4,6,3)", show.node.label = TRUE) #' #' # Adding a title #' par(mai=rep(1,4), omi=rep(0,4), mfrow=c(1,1), new=FALSE) #' title("Prunning trees with -prune-") #' par(oldpar) #' #' @name prune NULL #' @export #' @rdname prune prune <- function(x, ids, ...) UseMethod("prune") #' @export #' @rdname prune prune.po_tree <- function(x, ids) { # 1. Identify which will be removed ------------------------------------------ # Getting the unique set, and sorting it ids <- sort(unique(ids)) n <- length(attr(x, "labels")) # Matching to actual labels if (is.character(ids)) ids <- match(ids, getlabels(x)) - 1L # Checking the lengths if (any(is.na(ids))) stop("Some -ids- don't match any leafs of the tree.") if (any(ids > (n - 1))) stop("Out of range: Some -ids- are out of range (above n-1).") if (any(ids < 1)) stop("Out of range: Some -ids- are out of range (below 1). Root node cannot be removed.") # 2. Computing Geodesics to extend the list ---------------------------------- nodes_ids <- ids G <- approx_geodesic(x, undirected = FALSE, warn = FALSE) # Which others should be removed for (l in ids) nodes_ids <- c(nodes_ids, which(G[l + 1L, ] > 0) - 1L) # Getting the final list and checking if it leaves at least 2 nodes nodes_ids <- sort(unique(nodes_ids)) if ( (n - length(nodes_ids)) < 2 ) stop("You are removing all but the root node, and that's not a tree.") # 3. Marking the ones that need to be removed -------------------------------- old_ids <- 0L:(n - 1L) new_ids <- rep(0L, n) new_ids[nodes_ids+1L] <- 1L new_ids <- old_ids - cumsum(new_ids) old_labels <- attr(x, "labels") # 4. Removing everything that won't be used any more ------------------------- # From the edgelist edges_ids <- which(!(x[,1] %in% nodes_ids) & !(x[,2] %in% nodes_ids)) x <- x[edges_ids,,drop=FALSE] # 4. Relabeling -------------------------------------------------------------- x[,1] <- new_ids[match(x[,1], old_ids)] x[,2] <- new_ids[match(x[,2], old_ids)] attr(x, "labels") <- old_labels[-(nodes_ids + 1L)] # 5. Re computing the offspring ---------------------------------------------- attr(x, "offspring") <- list_offspring(x) structure(x, class= "po_tree") }

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/scripts/voxceleb.R

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cdo03c/Audio_Age_Classifier

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voxceleb.R

#Set working directory setwd("~/Documents/Github/Audio_Age_Classifier") # Clear workspace rm(list=ls()) #Load packages library(rvest) #Tests if data is downloaded and download if it doesn't exist if(!file.exists("./data/voxceleb.zip")){download.file(url = "http://www.robots.ox.ac.uk/~vgg/data/voxceleb/voxceleb1.zip", destfile = "./data/voxceleb.zip")} #Unzips the data unzip("./data/voxceleb.zip", exdir = "./data") #Creates list of file dirs = list.dirs('./data/voxceleb1_txt') dirs = dirs[-1] files = paste(dir,list.files(dir)) for(dir in dirs[2:length(dirs)]){ files = c(files, paste(dir,list.files(dir))) } ###USE REGEX TO EXTRACT CELEBRITY NAME AND YOUTUBE ID ###USE RVEST TO EXTRACT THE CELEBRITY'S BIRTH DATE FROM WIKIPEDIA #Create data frame of survivor seasons wiki = read_html("https://en.wikipedia.org/wiki/Survivor_(U.S._TV_series)") #USE REVEST TO EXTRACT PUBLICATION DATE FOR VIDEO FROM YOUTUBE #SUBTRACT DIFFERENCE BETWEEN AGE AND PUBLICATION DATE FOR AGE ESTIMATE FOR CELEBRITY IN VIDEO

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/data/genthat_extracted_code/spatial.gev.bma/examples/spatial.gev.bma.Rd.R

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surayaaramli/typeRrh

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spatial.gev.bma.Rd.R

library(spatial.gev.bma) ### Name: spatial.gev.bma ### Title: Run an MCMC to fit a hierarchical spatial generalized extreme ### value (GEV) model with the option for Bayesian model averaging (BMA) ### Aliases: spatial.gev.bma ### ** Examples data(norway) attach(norway) ##To replicate our results, change 2 to 2e5 below a <- spatial.gev.bma(Y.list,X,S,2)

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/lib/functions.R

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joebrew/controlflu2016

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refs/heads/master

2021-01-20T18:33:39.383334

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functions.R

# Read and clean absenteeism data read_and_clean_absenteeism_data <- function(file = 'data/scrubbed_data_for_joe_correct_ids.csv'){ # Package requirements require(readr) require(plyr) require(dplyr) # Read file # df <- read_csv(file) df <- read.csv(file) # Clean up grade df$grade[df$grade %in% c('30', '31')] <- '3' # Clean up date of birth df$dob <- substr(df$dob, start = 1, stop = 10) # Make date objects df$absenceDate <- as.Date(df$absenceDate, format = '%m/%d/%Y') df$dob <- as.Date(df$dob) # Clean up race ethnicity df$race <- ifelse(df$raceEthnicity == 'A', 'Asian', ifelse(df$raceEthnicity == 'B', 'Black', ifelse(df$raceEthnicity == 'H', 'Hispanic', ifelse(df$raceEthnicity == 'I', 'Indigenous', ifelse(df$raceEthnicity == 'M', 'Mixed', ifelse(df$raceEthnicity == 'W', 'White', NA)))))) # Clean up lunch status #1 Applied Not Eligible #0 Did not apply #2 Eligible for free lunch #6 Eligible for free lunch/Direct Certified/Decline #9 Eligible for free lunch/Direct Certified #3 Eligible Reduced #4 Enrolled USDA approved Prov 2 school #Z Unknown df$lunch <- ifelse(df$lunchStatus %in% c(0, 1), 'Not free/reduced', ifelse(df$lunchStatus %in% c(2, 3, 9), 'Free/reduced', NA)) # Remove useless columns and clean up names df <- df %>% dplyr::select(studentNumber, lunch, race, schoolName, grade, dob, absenceDate) df <- df %>% dplyr::rename(id = studentNumber, school = schoolName, date = absenceDate) # Make id character df$id <- as.character(df$id) return(df) } # Read and clean school immunization data read_and_clean_immunization_data <- function(directory = 'data/immunization_data/'){ # Packages require(readr) require(dplyr) # Snapshot the current working directory cwd <- getwd() # And set to new working directory setwd(directory) # Read in files <- dir() results_list <- list() counter <- 0 for (i in 1:length(files)){ this_file <- suppressWarnings(read_csv(files[i])) this_file <- data.frame(this_file) # Snapshot the column names if the first if(i == 1){ this_file <- this_file %>% dplyr::select(-`NA.`) assign('column_names', names(this_file), envir = .GlobalEnv) } else { for (j in 1:length(column_names)){ this_column <- column_names[j] if(!this_column %in% names(this_file)){ this_file[,this_column] <- NA } } this_file <- this_file[,column_names] } # Remove any NA row this_file <- this_file %>% filter(!is.na(this_file$student_id)) # Make all characters for(j in 1:length(column_names)){ this_file[,j] <- as.character(this_file[,j]) } results_list[[i]] <- this_file message(paste0('Just finished reading in data for: ', this_file$school_name[1], '\n', 'has ', nrow(this_file), ' rows.\n\n')) counter <- counter + nrow(this_file) } rm(column_names, envir = .GlobalEnv) df <- do.call('rbind', results_list) # Set back to original working directory setwd(cwd) # Clean up the dataset df$consent_form_return <- ifelse(df$consent_form_return %in% c('y', 'y\`', 'Y', 'yes'), TRUE, FALSE) df$consent_form_yes <- ifelse(df$consent_form_yn %in% c('n', 'N', 'no', 'No'), FALSE, ifelse(df$consent_form_yn %in% c('y', 'Y', 'yes'), TRUE, NA)) df$vaccine_date <- as.Date(df$vaccine_date) df$vfc_priv <- ifelse(df$vfc_priv %in% c('peiv', 'pri', 'PRI', 'pric', 'priv', 'Priv', 'prtiv', 'vpri'), 'Private', ifelse(df$vfc_priv %in% c('fc', 'vc', 'vf', 'vfc', 'VFC'), 'VFC', NA)) df$refusal <- ifelse(df$refusal %in% c('1', 'ref', 'REF', 'y', 'Y', 'yes'), TRUE, FALSE) df$absence <- ifelse(is.na(df$absence), FALSE, TRUE) df$vaccine <- ifelse(df$vaccine %in% c('0', '?'), FALSE, ifelse(is.na(df$vaccine), TRUE, TRUE)) # Reduce columns df <- df %>% dplyr::select(school_name, grade, student_id, consent_form_return, consent_form_yes, vaccine, vaccine_date) %>% dplyr::rename(id = student_id) # Make data.frame df <- data.frame(df) # Remove those for whom student id appears to be a birthday df <- df[!grepl('/', df$id),] df <- df[!grepl('-', df$id),] # Make id character df$id <- as.character(df$id) # Remove duplicates # arrange so that yesses come first # (this is justified since "no" is default, and any yes most likely indicates # a true yes) df <- df %>% arrange(desc(consent_form_return), desc(consent_form_yes), desc(vaccine)) df <- df[!duplicated(df$id),] # Fix date screw up df$vaccine_date[df$vaccine_date <= '2015-01-01'] <- '2015-10-01' return(df) } # Create panel data create_panel_data <- function(ab, students){ # Manually create date range date_range <- seq(as.Date('2015-08-25'), as.Date('2016-05-29'), by = 1) # Make a dataframe df <- data.frame(date = date_range) df$day <- weekdays(df$date) # Remove weekends df <- df %>% filter(!day %in% c('Saturday', 'Sunday')) # Remove christmas break, etc. (dates for which there are 0 absences) df <- df %>% filter(! date %in% df$date[!df$date %in% sort(unique(ab$date))]) # Create an expanded grid of all dates with all students df <- expand.grid(date = df$date, id = students$id) # Join the expanded grid to absences df <- left_join(df, ab %>% mutate(absent = TRUE) %>% dplyr::select(id, date, absent), by = c('date', 'id')) # If not absent, assume present df$absent[is.na(df$absent)] <- FALSE # Return return(df) } # Make pretty table make_pretty <- function(x){ require(Hmisc) require(DT) the_names <- capitalize(gsub('_', ' ', toupper(names(x)))) x <- data.frame(x) for (j in 1:ncol(x)){ if(class(x[,j]) %in% c('numeric', 'integer')){ x[,j] <- round(x[,j], digits = 2) } else { x[,j] <- toupper(x[,j]) } } for (j in 1:ncol(x)){ if(grepl('rate', names(x)[j])){ x[,j] <- paste0(x[,j], '%') } } names(x) <- the_names DT::datatable(x) }

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refs/heads/master

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ex5_3.r

# Group members (Name, Student ID, E-Mail): # 1. Baldomero Valdez, Valenzuela, 2905175, baldmer.w@gmail.com # 2. Omar Trinidad Gutierrez Mendez, 2850441, omar.vpa@gmail.com # 3. Shinho Kang, 2890169, wis.shinho.kang@gmail.com data(iris) # shuffle the dataset and get training and test dataset shuffled.iris <- iris[sample(1:nrow(iris)), ] test.ds <- shuffled.iris[1:30,] training.ds <- shuffled.iris[31:150,] png(filename="task3.png") par(mfrow=c(4, 3)) labels = names(iris)[-5] indexes = c(1:4) for (x in indexes) { for (y in indexes) { if (x != y) { a = training.ds[,x] b = training.ds[,y] plot(a~b, pch = 22, bg = c('red', 'green', 'blue')[unclass(iris$Species)], xlab = labels[x], ylab = labels[y] # xlim = c(0,7), # ylim = c(0,7) ) model = lm(a~b) abline(model, col='brown') } } } dev.off() # Because each of the plots show a correlation between the columns we can # conclude that one of the predictors can be expressed as a linear combination # of the others.

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/MarketBasket_Recommender.R

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santiagovama/R

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refs/heads/master

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MarketBasket_Recommender.R

# Association Rules for Market Basket Analysis (R) # http://rpubs.com/Adhislacy/281337 # https://github.com/krupanss/Market-Basket-Analysis-R/blob/master/MarketBasketAnalysis.Rmd # https://educationalresearchtechniques.com/2016/08/01/market-basket-analysis-in-r/ # http://www.rpubs.com/Mughundhan/268460 -> use this first # https://stackoverflow.com/questions/17313450/how-to-convert-data-frame-to-transactions-for-arules # https://stackoverflow.com/questions/45578516/r-aggregate-and-collapse-several-cells-into-one # https://stackoverflow.com/questions/15933958/collapse-concatenate-aggregate-a-column-to-a-single-comma-separated-string-w # http://rstatistics.net/association-mining-with-r/ # https://rpubs.com/cheverne/associationrules_marketbasketanalysis # https://rstudio-pubs-static.s3.amazonaws.com/267119_9a033b870b9641198b19134b7e61fe56.html -> ECLAT # https://benjnmoss.wordpress.com/2017/02/13/market-basket-analysis-in-alteryx/ # https://synerise.com/data-mining-how-to-analyze-customers-market-baskets-to-increase-sales/# # http://r-train.ru/apriori-tips-and-tricks/ library(arules) # association rules library(arulesViz) # data visualization of association rules library(RColorBrewer) # color palettes for plots library(tidyverse) library(lubridate) # read sample data into dataframe raw_data <- read_csv("https://raw.githubusercontent.com/kh7393/Market-Basket/master/Online%20Retail_new.csv") raw_data <- raw_data %>% mutate(InvoiceDate = format(InvoiceDate, "%H:%M:%S")) %>% mutate(InvoiceDate = make_datetime(day, month, year, hour, minute)) # show countries raw_data1 <- raw_data %>% select(Country, InvoiceNo) %>% group_by(Country) %>% summarize(InvoiceNo, n()) # Remove the canceled/refunded orders # Remove rows with invalid product description # select the rows with relevant data for analysis transaction_detail <- aggregate(raw_data$AirlineDescription ~ raw_data$InvoiceNo, FUN=paste,collapse=',') # remove column for invoice number transaction_itemsets<-transaction_detail[,-1] # convert to transactions object for market basket analysis write(transaction_itemsets,"itemsets2.csv") itemsets_txn<-read.transactions("itemsets2.csv",format="basket",rm.duplicates=TRUE,sep=",") # show the dimensions of the transactions object print(dim(itemsets_txn)) print(dim(itemsets_txn)[1]) # X no. market baskets for flight trips print(dim(itemsets_txn)[2]) # X no. of initial product/items # summary of dataset including most frequent items, itemset/transaction length distribution summary(itemsets_txn) # find the top 15 items itemFrequencyPlot(itemsets_txn, topN=15) # exploratory plotting - examine frequency for each item with support greater than 0.025 pdf(file="fig_market_basket_initial_item_support.pdf", width = 8.5, height = 11) itemFrequencyPlot(itemsets_txn, support = 0.025, cex.names=0.8, xlim = c(0,0.3), type = "relative", horiz = TRUE, col = "dark red", las = 1, xlab = paste("Proportion of Market Baskets Containing Item", "\n(Item Relative Frequency or Support)")) dev.off() pdf(file="fig_market_basket_final_item_support.pdf", width = 8.5, height = 11) itemFrequencyPlot(itemsets_txn, support = 0.025, cex.names=1.0, xlim = c(0,0.5), type = "relative", horiz = TRUE, col = "blue", las = 1, xlab = paste("Proportion of Market Baskets Containing Item", "\n(Item Relative Frequency or Support)")) dev.off() # obtain large set of association rules for items by category and all shoppers # this is done by setting very low criteria for support and confidence first.rules <- apriori(itemsets_txn, parameter = list(support = 0.001, confidence = 0.05)) print(summary(first.rules)) # yields 69,921 rules... too many # for splitting LHS & RHS Firstitemsets_txnrules_df <- as(first.rules, "data.frame") Firstitemsets_txnrules_df <- Firstitemsets_txnrules_df %>% separate(rules, c("LHS", "RHS"), sep = "=>") # select association rules using thresholds for support and confidence # yields 344 rules second.rules <- apriori(itemsets_txn, parameter = list(support = 0.025, confidence = 0.05)) print(summary(second.rules)) Seconditemsets_txnrules_df <- Firstitemsets_txnrules_df %>% filter(support >= 0.025 & confidence >= 0.05) # data visualization of association rules in scatter plot # pdf(file="fig_market_basket_rules.pdf", width = 8.5, height = 8.5) plot(second.rules, control=list(jitter=2, col = rev(brewer.pal(9, "Greens")[4:9])), shading = "lift") # dev.off() # grouped matrix of rules # pdf(file="fig_market_basket_rules_matrix.pdf", width = 8.5, height = 8.5) plot(second.rules, method="grouped", control=list(col = rev(brewer.pal(9, "Greens")[4:9]))) # dev.off() # this needs fixing top.second.rules <- head(sort(second.rules, decreasing = TRUE, by = "lift"), 10) pdf(file="fig_market_basket_farmer_rules.pdf", width = 11, height = 8.5) plot(top.second.rules, method="graph", control=list(type="items"), shading = "lift") dev.off() # plot(second.rules,method="graph",interactive=TRUE,shading=NA) itemsets_txnrules_df <- as(second.rules, "data.frame") itemsets_txnrules_df <- itemsets_txnrules_df %>% separate(rules, c("LHS", "RHS"), sep = "=>") %>% mutate(InverseConfidence = (support * lift) / confidence) # final table of recommended rules to use filtered by max confidence # option to sort by lift # if LHS is bought then RHS is purchased rules_final <- itemsets_txnrules_df %>% filter(confidence > InverseConfidence) # %>% # arrange(desc(lift)) # non-case sensitive filter filteredrules_df <- rules_final %>% filter(grepl("hotel", RHS, ignore.case = TRUE))

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/plot1.R

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avishekbasak/ExData_Plotting1

b1d2b0c42b74094357cefe6fd374084aaa914845

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plot1.R

file.Name <- "household_power_consumption.txt" file.path <- "~/R_Workspace/exploratory/ExData_Plotting1/" #downlad and unzip file, if file doesn't exist file.full <- paste(file.path,file.Name, sep="") if(!file.exists(file.full)){ download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip","exploratory/ExData_Plotting1/householdpowerconsumption.zip",method = "curl") unzip("exploratory/ExData_Plotting1/householdpowerconsumption.zip",exdir = "exploratory/ExData_Plotting1/") } #read data data.full <- read.csv(file.full, header = TRUE, sep = ";") #format the data data.full$Date <- as.Date(data.full$Date, format = "%d/%m/%Y") #subset of data data.small <- data.full[data.full$Date == "2007-02-01"| data.full$Date=="2007-02-02",] #format the numeric field Global_active_power data.small$Global_active_power <- as.numeric(as.character(data.small$Global_active_power)) #set the png file png(filename=paste(file.path,"plot1.png",sep=""),width = 480, height = 480, units = "px") #create the histogram hist(data.small$Global_active_power,col="red",xlab="Global Active Power (kilowatts)",main="Global Active Power") dev.off()

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/code/ggplot.R

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no_license

frugeles/hackathon2017

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refs/heads/master

2021-08-15T04:03:31.180279

2017-11-17T09:46:39

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ggplot.R

#### Libraries #### library(ggplot2) library(RColorBrewer) library(dplyr) library(rgdal) library(rgeos) library(leaflet) library(ggmap) library(sp) #### Leaflet #### data_path = './data/' load(paste0(data_path,'calls_Final.RData')) nb_calls_district <- calls_Final %>% group_by(districtID) %>% summarise(colorTest=n()*100/nrow(calls_Final)) # load Seattle data load(file = paste0(data_path,'Seattle.RData')) test <- right_join(nb_calls_district,Seattle@data,by=c('districtID'='OBJECTID')) Seattle@data <- test bins <- quantile(nb_calls_district$colorTest, c(0, .25, .5, .75, 1)) pal <- colorBin("RdYlBu", domain = Seattle@data$colorTest, bins = bins, na.color = "grey40", reverse = T) centr.STL <- gCentroid(Seattle)@coords l <- leaflet(options = leafletOptions(minZoom = 5, maxZoom = 14)) %>% addProviderTiles("Esri.WorldImagery") %>% setView(centr.STL[1], centr.STL[2], zoom = 11) %>% addLegend(pal = pal, values = round(Seattle@data$colorTest, 1), opacity = 0.7, position = "bottomright", title = "Percentage of total calls to 911") l %>% addPolygons(data=Seattle, weight = 1, fill = ~colorTest, fillColor = ~pal(colorTest), opacity=1, fillOpacity = 0.6, color=grey(0.5), ## USE POPUP popup = ~as.character( paste(S_HOOD, L_HOOD, "<br>", "Percentage =", round(colorTest, 2))) )

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comb_output_table.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/comb_output_table.R \name{comb_output_table} \alias{comb_output_table} \title{Combine file-based table output} \usage{ comb_output_table(pattern, ...) } \arguments{ \item{pattern}{Pattern to be passed to \code{\link{Sys.glob}}} \item{...}{Additional parameters passed to \code{\link{read.table}}} } \description{ Attempts to load all files matching a certain pattern and combine them vertically (rbind). Returns the result. If no files match the pattern, then an empty data.frame is returned. } \author{ Luke Winslow }

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/Clustering/Heirachical/2_hierarch_cluster_vehicals.R

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[]

no_license

ashukumar12d/ML-with-R

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2_hierarch_cluster_vehicals.R

# To illustrate interpretation of the dendogram, we'll look at a cluster analysis # performed on a set of cars. head(mtcars) # Find the distance matrix d <- dist(as.matrix(mtcars)) # find distance matrix print(d) # apply hirarchical clustering - The hclust function in R # uses the complete linkage method for hierarchical clustering by default. hc <- hclust(d) print(hc) # plot the dendrogram plot(hc) # Simple plotting

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trackingCode.R

library(plyr) compare.foam<-function(cDir,goldFile='glpor_1.csv',kevinFile='clpor_2.csv') { kbubs<-read.csv(paste(cDir,kevinFile,sep='/'),skip=1) gbubs<-read.csv(paste(cDir,goldFile,sep='/'),skip=1) gbubs<-compare.foam.clean(gbubs) kbubs<-compare.foam.clean(kbubs) compare.frames(gbubs,kbubs) } # calculate the bubble to bubble spacing calc.track.statistics<-function(in.roi) ddply(in.roi,.(sample),function(c.sample) data.frame(mean_velocity=mean(c.sample$DIR_Z), mean_obj_spacing=(with(c.sample,rng(POS_X)*rng(POS_Y)*rng(POS_Z))/nrow(c.sample))^(0.33), sd_vel_x=sd(c.sample$DIR_X), sd_vel_y=sd(c.sample$DIR_Y), sd_vel_z=sd(c.sample$DIR_Z) )) # fancy edge data reader read.edge<-function(x) { edge.data<-read.csv(x,skip=1) names(edge.data)[1]<-"Component.1" edge.data } # Add MChain to edges edges.append.mchain<-function(in.edges,in.bubbles) { sub.bubbles<-in.bubbles[,names(in.bubbles) %in% c("sample","LACUNA_NUMBER","MChain")] colnames(sub.bubbles)[colnames(sub.bubbles)=="MChain"]<-"MChain.1" o.merge1<-merge(in.edges,sub.bubbles, by.x=c("sample","Component.1"),by.y=c("sample","LACUNA_NUMBER")) sub.bubbles<-in.bubbles[,names(in.bubbles) %in% c("sample","LACUNA_NUMBER","MChain")] colnames(sub.bubbles)[colnames(sub.bubbles)=="MChain"]<-"MChain.2" out.df<-merge(o.merge1,sub.bubbles, by.x=c("sample","Component.2"),by.y=c("sample","LACUNA_NUMBER")) mc1a<-out.df$MChain.1 mc2a<-out.df$MChain.2 switch.els<-which(mc1a>mc2a) out.df$MChain.1[switch.els]<-mc2a[switch.els] out.df$MChain.2[switch.els]<-mc1a[switch.els] out.df } # calculate statistics chain.life.stats.fn<-function(in.data,include.orig=F) ddply(in.data,.(MChain),function(c.chain) { disp.val<-sqrt(with(c.chain,sum(DIR_X)^2+sum(DIR_Y)^2+sum(DIR_Z)^2)) leng.val<-with(c.chain,sum(sqrt(DIR_X^2+DIR_Y^2+DIR_Z^2))) new.cols<-data.frame(sample=c.chain$sample, min.sample=min(c.chain$sample), max.sample=max(c.chain$sample), cnt.sample=length(unique(c.chain$sample)), cnt.chain=nrow(c.chain), mean.dist=mean(c.chain$M_MATCH_DIST), max.dist=max(c.chain$M_MATCH_DIST), dir.disp=disp.val, dir.length=leng.val, disp.to.leng=disp.val/leng.val) if(include.orig) { cbind(c.chain,new.cols) } else { new.cols } }) # calculate the bubble life stats from the chains bubble.life.stats.fn<-function(in.chains,chain.life.stats,sample.vec) { out.val<-ddply(in.chains,.(MChain.1,MChain.2),function(c.edge) { a.chain<-c.edge$MChain.1[1] b.chain<-c.edge$MChain.2[1] sample.range<-subset(chain.life.stats,MChain %in% c(a.chain,b.chain)) sample.cnt<-ddply(sample.range,.(sample),function(c.sample) data.frame(cnt=nrow(c.sample))) both.present<-intersect(subset(sample.cnt,cnt>1)$sample,sample.vec) # max of the min and the min of the max make the smallest range data.frame(c.edge,min.sample=min(both.present),max.sample=max(both.present),cnt.sample=length(both.present)) }) out.val$range.sample<-out.val$max.sample-out.val$min.sample out.val } bubble.samples.exists.fn<-function(edge.chains,chain.life.stats,sample.vec) { # calculate the full lifetime information bubble.life.full<-bubble.life.stats.fn(edge.chains,chain.life.stats,sample.vec) # only the possible topological events # the bubbles must have been mutually alive more than 2 frames # the number of number of frames they are connected much be less than the mutual lifetime bubble.life.good<-subset(bubble.life.full,range.sample>=2 & Connections<=(range.sample+1)) # give the bubbles an id bubble.life.good$id<-as.factor(paste(bubble.life.good$MChain.1,bubble.life.good$MChain.2)) bubble.samples.exists<-ddply(bubble.life.good,.(MChain.1,MChain.2,id),function(c.edge) { a.chain<-c.edge$MChain.1[1] b.chain<-c.edge$MChain.2[1] sample.range<-subset(chain.life.stats,MChain %in% c(a.chain,b.chain)) sample.cnt<-ddply(sample.range,.(sample),function(c.sample) data.frame(cnt=nrow(c.sample))) both.present<-intersect(subset(sample.cnt,cnt>1)$sample,sample.vec) data.frame(sample=both.present) }) all.bubbles.topo<-rbind(cbind(bubble.life.good[,names(bubble.life.good) %in% c("MChain.1","MChain.2","id","sample","Voxels")],connection="Touching"),cbind(bubble.samples.exists,Voxels=0,connection="Separated")) # remove all the extra empties ddply(all.bubbles.topo,.(id,sample),function(c.edge) { o.val<-subset(c.edge,connection=="Touching") if (nrow(o.val)<1) o.val<-c.edge o.val }) } # merge two data files after tracking into the same file mergedata<-function(goldData,matchData,prefix="M_",as.diff=F) { outData<-data.frame(goldData) for (cCol in names(matchData)) { outData[[paste(prefix,cCol,sep="")]]=matchData[[cCol]] } as.diff.data(outData,m.prefix=prefix) } as.diff.data<-function(mergedData,m.prefix="M_",d.prefix="D_",sample.col.name="sample") { all.cols<-names(mergedData) keep.original<-which(laply(all.cols, function(x) { length(grep(m.prefix,x))<1 } ) ) original.cols<-all.cols[keep.original] keep.numeric<-which(laply(original.cols, function(x) { is.numeric(mergedData[,x]) } ) ) numeric.cols<-all.cols[keep.numeric] out.data<-mergedData[,(names(mergedData) %in% original.cols)] # normalize the fields by their velocity (D_sample) if (sample.col.name %in% original.cols) { dsample.vec<-mergedData[[paste(m.prefix,sample.col.name,sep="")]]-mergedData[[sample.col.name]] } else { dsample.vec<-1 } for(c.col in numeric.cols) { # add differential column new.name<-switch(c.col, POS_X={"DIR_X"}, POS_Y={"DIR_Y"}, POS_Z={"DIR_Z"}, paste(d.prefix,c.col,sep="") ) old.name<-paste(m.prefix,c.col,sep="") cur.out.col<-mergedData[[old.name]]-mergedData[[c.col]] if (c.col!=sample.col.name) cur.out.col=cur.out.col/dsample.vec out.data[[new.name]]<-cur.out.col } cbind(out.data,M_MATCH_DIST=mergedData$M_MATCH_DIST,BIJ_MATCH=mergedData$M_BIJ_MATCHED) } edge.status.change<-function(ic.edge) { # sort the list appropriately c.edge<-ic.edge[order(ic.edge$sample),] c.info<-c.edge[,c("sample","connection")] # true if the bubbles are touching c.info$connection<-c.info$connection=="Touching" # shift the list by one forwards to have the connection before # in each column c.info$cxn.before<-c(NA,c.info$connection[-nrow(c.info)]) # shift the list by one backwards c.info$cxn.after<-c(c.info$connection[-1],NA) # there are actually 4 possibilities # was.created means it was created between t-1 and t # will.created means it will be created between t and t+1 # was.destroyed means it was destroyed between t-1 and t # will.destroyed means it will be destoryed between t and t+1 c.info$was.created<-(!c.info$cxn.before & c.info$connection) c.info$will.created<-(!c.info$connection & c.info$cxn.after) c.info$was.destroyed<-(c.info$cxn.before & !c.info$connection) c.info$will.destroyed<-(c.info$connection & !c.info$cxn.after) out.cols<-c.info[,c("was.created","will.created","was.destroyed","will.destroyed")] cbind(c.edge,out.cols) } # Converts a topology into a list of status changes for each eedge topo2status.change<-function(in.topo,parallel=T) ddply(in.topo,.(id),edge.status.change,.parallel=parallel) # Add position (or other columns to the edge file) # it can be used like this edge.w.pos<-edges.append.pos(bubbles.join,mini.edges) edges.append.pos<-function(in.bubbles,in.edges,time.col="sample", bubble.col="MChain",bubble.col1="MChain.1",bubble.col2="MChain.2") { join.cols<-c(time.col,bubble.col) link.left<-merge(in.edges,in.bubbles,all.x=T,all.y=F,by.x=c(time.col,bubble.col1),by.y=join.cols,sort=F,suffixes=c(".edge","")) merge(link.left,in.bubbles,all.x=T,all.y=F,by.x=c(time.col,bubble.col2),by.y=join.cols,sort=F,suffixes=c(".start",".end")) } # add chain (time-independent bubble identifier) tracking.add.chains<-function(in.data,check.bij=F) { if (check.bij) sub.bubbles<-subset(in.data,BIJ_MATCH) else sub.bubbles<-in.data if(nrow(sub.bubbles)>0) { sub.bubbles$Chain<-c(1:nrow(sub.bubbles)) # Unique Bubble ID bubbles.forward<-data.frame(sample=sub.bubbles$sample+sub.bubbles$D_sample, LACUNA_NUMBER=sub.bubbles$LACUNA_NUMBER+sub.bubbles$D_LACUNA_NUMBER, Chain=sub.bubbles$Chain) bubbles.mapping<-merge(sub.bubbles[,names(sub.bubbles) %in% c("sample","Chain","LACUNA_NUMBER")], bubbles.forward,by=c("sample","LACUNA_NUMBER")) bubble.mapping.proper<-mapply(list, bubbles.mapping$Chain.x, bubbles.mapping$Chain.y, SIMPLIFY=F) bubbles.mapping.full<-1:max(sub.bubbles$Chain) for(c in bubble.mapping.proper) { cx<-c[[1]] cy<-c[[2]] min.ch<-c(cx,cy,bubbles.mapping.full[cx],bubbles.mapping.full[cy]) min.val<-min(min.ch[!is.na(min.ch)]) bubbles.mapping.full[cx]<-min.val bubbles.mapping.full[cy]<-min.val } cbind(sub.bubbles,MChain=bubbles.mapping.full[sub.bubbles$Chain]) } else { cbind(sub.bubbles,MChain=c(),Chain=c()) } } # combine the edges with the bubble file to have chains id's instead of components and unique names process.edges<-function(in.edges,in.bubbles) { edges.join<-edges.append.mchain(in.edges,in.bubbles) rows.to.swap<-which(edges.join$MChain.2>edges.join$MChain.1) edges.join2<-edges.join[rows.to.swap,] edges.join[rows.to.swap,]$MChain.1<-edges.join2$MChain.2 edges.join[rows.to.swap,]$MChain.2<-edges.join2$MChain.1 # Edge lifetime information edges.join.stats<-ddply(edges.join,.(MChain.1,MChain.2),function(x) {cbind(x, Range=max(x$sample)-min(x$sample), Start.Frame=min(x$sample), Final.Frame=max(x$sample), Connections=nrow(x) )}) edges.join.stats$name<-paste(edges.join.stats$MChain.1,edges.join.stats$MChain.2) edges.join.stats$id<-as.numeric(as.factor(edges.join.stats$name)) edges.join.stats2<-ddply(edges.join.stats,.(MChain.1,MChain.2),function(x) { cbind(x,n.sample=x$sample-min(x$sample),x.sample=(x$sample-min(x$sample))/(max(x$sample)-min(x$sample))) }) edges.join.stats2 } edges.missing<-function(in.edges,in.bubbles) { sub.bubbles<-ddply(in.bubbles[,names(in.bubbles) %in% c("sample","MChain")],.(MChain),function(c.chain) { data.frame(start.sample=min(c.chain$sample),final.sample=max(c.chain$sample)) }) ddply(in.edges,.(id),function(c.edge) { c.row<-c.edge[1,!(names(c.edge) %in% c("sample"))] c1<-c.row$MChain.1 c2<-c.row$MChain.2 rel.samples<-subset(sub.bubbles,MChain==c1 | MChain==c2) sample.vals<-c(max(rel.samples$start.sample):min(rel.samples$final.sample)) # from the highest starting frame to the lowest ending frame cbind(c.row,sample=sample.vals,connected=(sample.vals %in% c.edge$sample)) }) } #' Match objects #' @author Kevin Mader (kevin.mader@gmail.com) #' #' #' @param groundTruth is the frame to compare to #' @param susData is the current frame #' @param maxVolDifference is the largest allowable difference in volume before maxVolPenalty is added #' @param in.offset the offset to apply to groundTruth before comparing to susData #' @param do.bij run the bijective comparison as well #' @param x.weight weight to scale the x distance with #' @param dist.fun a custom distance metric to use matchObjects<-function(groundTruth,susData,maxVolDifference=0.5, maxVolPenalty=5000^2,in.offset=c(0,0,0), do.bij=T,x.weight=1,y.weight=1,z.weight=1, dist.fun=NA) { gmatch<-c() gdist<-c() gincl<-c() if(is.na(dist.fun)) { # if it is not present if (!is.na(maxVolPenalty)) { # use maxVolPenalty dist.fun<-function(bubMat,cPos,offset) { (maxVolPenalty*((abs(bubMat$VOLUME-cPos$VOLUME)/cPos$VOLUME)>maxVolDifference)+x.weight*(bubMat$POS_X-offset[1]-cPos$POS_X)**2+y.weight*(bubMat$POS_Y-offset[2]-cPos$POS_Y)**2+z.weight*(bubMat$POS_Z-offset[3]-cPos$POS_Z)**2) } } else { # skip it # leave volume out dist.fun<-function(bubMat,cPos,offset) { x.weight*(bubMat$POS_X-offset[1]-cPos$POS_X)**2+y.weight*(bubMat$POS_Y-offset[2]-cPos$POS_Y)**2+z.weight*(bubMat$POS_Z-offset[3]-cPos$POS_Z)**2 } } } for (i in 1:dim(groundTruth)[1]) { cVec<-dist.fun(susData,groundTruth[i,],in.offset) cDist<-min(cVec) gdist[i]<-sqrt(cDist) # perform square root operation before saving and only on one value gmatch[i]<-which(cVec==cDist)[1] } mData<-susData[gmatch,] mData$MATCH_DIST<-gdist if (do.bij) { # Check the reverse for (i in 1:length(gmatch)) { c.susbubble<-gmatch[i] # distance from matched bubble to all bubbles in ground truth cVec<-dist.fun(groundTruth,susData[c.susbubble,],-1*in.offset) cDist<-min(cVec) gincl[i]<-(i==which(cVec==cDist)[1]) } mData$BIJ_MATCHED<-gincl } mData } # allow old function to continue working compare.foam.frames<-function(...) compare.frames(...) # compare two frames and forward parameters to the matchObjects function compare.frames<-function(gbubs,kbubs,as.diff=F,...) { kmatch<-matchObjects(gbubs,kbubs,...) fData<-mergedata(gbubs,kmatch,as.diff=as.diff) fData } # takes a tracked data experiment with sample columns and calculates the birth and death bubble.life.check<-function(in.data) { ddply(in.data,.(sample),function(x) { c.sample<-x$sample[1] n.sample<-x$sample[1]+x$D_sample[1] n.bubbles<-unique(subset(in.data,sample==n.sample)$LACUNA_NUMBER) dies=!((x$LACUNA_NUMBER+x$D_LACUNA_NUMBER) %in% n.bubbles) l.bubbles.list<-subset(in.data,sample+D_sample==c.sample) l.bubbles<-unique(l.bubbles.list$LACUNA_NUMBER+l.bubbles.list$D_LACUNA_NUMBER) born=!(x$LACUNA_NUMBER %in% l.bubbles) cbind(x,dies=dies,born=born) }) } plot.t1.event<-function(edges.tracked,keep.event,with.frames=F,all.frames=F, x.name="POS_X",y.name="POS_Z",x.label=NA,y.label=NA) { important.edges<-edges.tracked$important.edges edge.info<-edges.tracked$edge.info good.roi.data<-edges.tracked$obj.list cur.event<-subset(important.edges,event.name==keep.event) keep.chains<-unique(c(cur.event$MChain.1,cur.event$MChain.2)) keep.frames<-c((min(cur.event$sample)-1):(max(cur.event$sample)+1)) sub.edges<-subset(edge.info,(MChain.1 %in% keep.chains) | (MChain.2 %in% keep.chains)) sub.edges<-subset(sub.edges,((MChain.1 %in% keep.chains) & (MChain.2 %in% keep.chains)) | (was.created | was.destroyed)) selected.links<-edges.append.pos(good.roi.data,sub.edges) selected.chains<-subset(good.roi.data[with(good.roi.data, order(sample)), ],MChain %in% keep.chains) if (!all.frames) { print(keep.frames) selected.links<-subset(selected.links,sample %in% keep.frames) if (with.frames) selected.chains<-subset(selected.chains,sample %in% keep.frames) } selected.links$involved<-with(selected.links,(MChain.1 %in% keep.chains) & (MChain.2 %in% keep.chains)) selected.links$edge.length<-with(selected.links,sqrt((POS_X.start-POS_X.end)^2+(POS_Y.start-POS_Y.end)^2+(POS_Z.start-POS_Z.end)^2)) selected.links$type="No Event" selected.links[which(selected.links$was.created),]$type<-"Was Created" selected.links[which(selected.links$will.created),]$type<-"Will Created" selected.links[which(selected.links$was.destroyed),]$type<-"Was Destroyed" selected.links[which(selected.links$will.destroyed),]$type<-"Will Destroyed" ss<-function(var) paste(var,".start",sep="") se<-function(var) paste(var,".end",sep="") if (with.frames) { o.plot<-ggplot(selected.links)+ geom_segment(aes_string(x=ss(x.name),y=ss(y.name),xend=se(x.name),yend=se(y.name), linetype="connection",alpha="involved",color="type"))+ geom_point(data=selected.chains,aes_string(x=x.name,y=y.name),alpha=1,color="red")+ labs(color="Edge Event")+facet_wrap(~sample) } else { o.plot<-ggplot(selected.links)+ geom_segment(aes_string(x=ss(x.name),y=ss(y.name),xend=se(x.name),yend=se(y.name), linetype="connection",alpha="involved"))+ geom_point(data=selected.chains,aes_string(x=x.name,y=y.name),alpha=1,color="red")+ geom_path(data=selected.chains,aes_string(x=x.name,y=y.name,group="MChain",color="as.factor(MChain)"),alpha=1)+ labs(color="Chain") } if(is.na(x.label)) x.label<-x.name if(is.na(y.label)) y.label<-y.name o.plot+theme_bw(20)+labs(x=x.label,y=y.label,alpha="Involved",linetype="Connected") } #' Edge Tracking Function #' @author Kevin Mader (kevin.mader@gmail.com) #' Tracks a list of data.frames using the compare.frames function #' and standard tracking, offset tracking, and adaptive offset tracking #' Tracking Function track.edges<-function(in.objs,in.edges,keep.all.events=F,parallel=F) { edge.chain<-process.edges(in.edges,in.objs) chain.life.stats<-chain.life.stats.fn(in.objs) sample.vec<-unique(in.objs$sample) # just get a summary (we can more carefully analyze later) obj.life.stats<-bubble.life.stats.fn(edge.chain,chain.life.stats,sample.vec) all.bubbles.topo<-bubble.samples.exists.fn(edge.chain,chain.life.stats,sample.vec) edge.info<-topo2status.change(all.bubbles.topo,parallel=parallel) # keep only the interesting events edge.info.interesting<-subset(edge.info,was.created | will.created | was.destroyed | will.destroyed) # combine the list together as chain1 and chain2 singlechain.edge.info<-rbind(cbind(edge.info.interesting,MChain=edge.info.interesting$MChain.1), cbind(edge.info.interesting,MChain=edge.info.interesting$MChain.2)) important.edges<-ddply(singlechain.edge.info,.(sample,MChain),function(c.bubble.frame) { sum.stats<-colSums(c.bubble.frame[,c("was.created","will.created","was.destroyed","will.destroyed")],na.rm=T) event.count<-sum(sum.stats) event.name<-paste("S",c.bubble.frame$sample[1],"_",paste(unique(c.bubble.frame$id),collapse=",",sep=""),sep="") if ((sum.stats["was.created"]>0) & (sum.stats["was.destroyed"]>0)) { was.events<-subset(c.bubble.frame,was.created | was.destroyed) } else { was.events<-c.bubble.frame[0,] } if ((sum.stats["will.created"]>0) & (sum.stats["will.destroyed"]>0)) { will.events<-subset(c.bubble.frame,will.created | will.destroyed) } else { will.events<-c.bubble.frame[0,] } out.mat<-rbind(was.events,will.events) if (nrow(out.mat)>0) out.mat<-cbind(out.mat,event.count=event.count,event.name=event.name) out.mat }) important.edges<-important.edges[order(-important.edges$event.count),] list(important.edges=important.edges,edge.info=edge.info,obj.list=in.objs,obj.life.stats=obj.life.stats) } #' @author Kevin Mader (kevin.mader@gmail.com) #' Tracks a list of data.frames using the compare.frames function #' and standard tracking, offset tracking, and adaptive offset tracking #' #' #' @param inData the list of data.frames containing the samples #' @param offset is the offset to use for the offset run #' @param run.offset if the offset analysis should be run #' @param run.adaptive if the adaptive analysis should be run #' @param ... parameters to be passed onto the compare.frames function track.frames<-function(inData,offset,run.offset=T,run.adaptive=T,parallel=F,...) { track.fcn<-function(x,in.offset=c(0,0,0)) { cbind(compare.frames(x[[1]],x[[2]],as.diff=T,in.offset=in.offset,...),Frame=x[[3]]) } # Track function adaptive track.fcn.adp<-function(x,in.offset=c(0,0,0)) { pre.match<-compare.frames(x[[1]],x[[2]],in.offset=in.offset,...) pre.offset<-colMeans(pre.match)[c("DIR_X","DIR_Y","DIR_Z")] cbind(compare.frames(x[[1]],x[[2]],as.diff=T,in.offset=pre.offset,...),Frame=x[[3]]) } staggered.data<-mapply(list, inData[-length(inData)], inData[-1],1:(length(inData)-1), SIMPLIFY=F) track.data<-ldply(staggered.data, track.fcn,.parallel=parallel) if(run.offset) { track.data.fix<-ldply(staggered.data, function(x) track.fcn(x,offset),.parallel=parallel) if(nrow(track.data.fix)<1) run.offset<-F } if(run.adaptive) { track.data.adp<-ldply(staggered.data, function(x) track.fcn.adp(x,offset),.parallel=parallel) if(nrow(track.data.adp)<1) run.adaptive<-F } # functions to apply before combinining # 1) life check # 2) under quantile for match distance # 2) add chain numbers based on remaining bubbles preproc.fcn<-function(...) { alive.bubbles<-bubble.life.check(...) track.one<-tracking.add.chains(alive.bubbles) chain.life.stats.fn(track.one,include.orig=T) } all.tracks<-cbind(preproc.fcn(track.data),Matching="No Offset") if(run.offset) all.tracks<-rbind(all.tracks,cbind(preproc.fcn(track.data.fix),Matching="Fix Offset")) if(run.adaptive) all.tracks<-rbind(all.tracks,cbind(preproc.fcn(track.data.adp),Matching="Adaptive Offset")) all.tracks }

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/logistic_reg_adj_diff.R \name{logistic_reg_adj_diff} \alias{logistic_reg_adj_diff} \title{Logistic regression adjusted differences} \usage{ logistic_reg_adj_diff( data, variable, by, adj.vars, conf.level, type, ci_type = c("sd", "centile"), boot_n = 250, ... ) } \arguments{ \item{data}{a data frame} \item{variable}{string of binary variable in \verb{data=}} \item{by}{string of the \verb{by=} variable name} \item{adj.vars}{character vector of variable names to adjust model for} \item{conf.level}{Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.} \item{type}{string indicating the summary type} \item{ci_type}{string dictation bootstrap method for CI estimation. Must be one of \code{c("sd", "centile")}.} \item{boot_n}{number of bootstrap iterations to use. In most cases, it is reasonable to used 250 for the \code{"sd"} method and 5000 for the \code{"centile"} method.} \item{...}{not used} } \value{ tibble with difference estimate } \description{ This function works with \code{gtsummary::add_difference()} to calculate adjusted differences and confidence intervals based on results from a logistic regression model. Adjustment covariates are set to the mean to estimate the adjusted difference. The function uses bootstrap methods to estimate the adjusted difference between two groups. The CI is estimate by either using the SD from the bootstrap difference estimates and calculating the CI assuming normality or using the centiles of the bootstrapped differences as the confidence limits The function can also be used in \code{add_p()}, and if you do, be sure to set \code{boot_n = 1} to avoid long, unused computation. } \section{Example Output}{ \if{html}{Example 1} \if{html}{\figure{logistic_reg_adj_diff_ex1.png}{options: width=80\%}} \if{html}{Example 2} \if{html}{\figure{logistic_reg_adj_diff_ex2.png}{options: width=80\%}} } \examples{ library(gtsummary) tbl <- tbl_summary(trial, by = trt, include = response, missing = "no") # Example 1 ----------------------------------------------------------------- logistic_reg_adj_diff_ex1 <- tbl \%>\% add_difference( test = everything() ~ logistic_reg_adj_diff, adj.vars = "stage" ) # Example 2 ----------------------------------------------------------------- # Use the centile method, and # change the number of bootstrap resamples to perform logistic_reg_adj_diff_ex2 <- tbl \%>\% add_difference( test = everything() ~ purrr::partial(logistic_reg_adj_diff, ci_type = "centile", boot_n = 100), adj.vars = "stage" ) } \seealso{ Other gtsummary-related functions: \code{\link{add_inline_forest_plot}()}, \code{\link{add_sparkline}()}, \code{\link{as_ggplot}()}, \code{\link{bold_italicize_group_labels}()}, \code{\link{style_tbl_compact}()}, \code{\link{tbl_likert}()}, \code{\link{theme_gtsummary_msk}()} } \concept{gtsummary-related functions}

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# General Data Analysis data <- read.csv("general_data.csv", header=TRUE, sep = ";") # Profit Per Click data["PRP"] <- (data["Revenue"]-data["Spend"])/data["Clicks"] # Revenue per order data["RPO"] <- data["Revenue"]/data["Orders"] # Correlation library(corrplot) part <- cbind(data[,2],data[,3],data[,6],data[,9]) colnames(part) <- c("Imp","Clicks","Orders","Revenue") corrplot(cor(part), method = "ellipse") # Performance plot(cbind(c(1:12),data["ROAS"]), type = "l") # Other plots plot(cbind(c(1:12),scale(data["PRP"])), ylab = "", type = "l", main = "Comparison", xlab = "Every Month") lines(cbind(c(1:12), scale(data["RPO"])), lty = 2) legend("topleft", c("Profit Per Click", "Revenue Per Order"), lty = c(1,2), cex=0.75) # Key Words Analysis keywords <- read.csv("Keywords.csv", header = TRUE, sep = ";") keywords <- as.data.frame(keywords) keywords <- keywords[-9,] keywords <- keywords[-10,] row.names(keywords) <- keywords[,1] # Profit Per Click keywords["PRP"] <- (keywords["Revenue"] - keywords["Spend"])/keywords["Clicks"] plot(cbind(keywords["Clicks"],keywords["PRP"])) abline(h = 0) identify(cbind(keywords["Clicks"],keywords["PRP"])) legend("topleft", c("2:Acme Tennis", "3:Acme Tennis Balls", "4:Tennis Balls", "6:Buy Tennis Balls"), cex = 0.75) name <- c("Acme","Acme Tennis","Acme Tennis Balls","Tennis Balls","Bouncy Tennis Balls","Buy Tennis Balls", "Free Balls","Tennis Raquets","Sports Equipment","Andre Agassi") rownames(key) <- name colnames(key) <- c("Imp", "Clicks", "Orders") key_pr <- prcomp(key, rtex = TRUE) # Ridge Regression library(glmnet) X <- cbind(as.matrix(data["Imp"]),as.matrix(data["Clicks"]),as.matrix(data["Orders"])) fit <- glmnet(X, as.matrix(data["Revenue"]), family = "gaussian", alpha = 0) cv.fit <- cv.glmnet(X, as.matrix(data["Revenue"]), type.measure = "mse", nfolds = 12, grouped=FALSE) coef(fit, s = cv.fit$lambda.min) ad_1 <- c(1160023, 5796, 1129) ad_2 <- c(1070790, 4554, 1273) predict(fit, newx = rbind(ad_1,ad_2), s = cv.fit$lambda.min) cbind(predict(fit, newx = X, s = cv.fit$lambda.min), as.matrix(data["Revenue"]))

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#' Check the data file and provide fixes if available #' @param dataset an object of the type data.frame #' @param refreshRate the refresh rate used during the motion capture (in hertz) #' @param time.unit the unit of measurement in which time is expressed in the 'time' column of the dataset given to the function. 1 = seconds, 10 = deciseconds, 100 = centiseconds, 1000 = milliseconds, ... Default to 1000 #' @examples #' libraries() #' #' ### restoring missing columns #' #' head(rtgData_bad) # dataset provided by this package #' rtgChecked <- data.check(rtgData_bad) # subjName is given without quotes. When asked to type the subject name, run the next line as is #' test_subject #' head(rtgChecked) #' #' ### time.unit #' #' rtgData <- data.check(rtgData) # dataset provided by this package #' # time column in rtgData is in milliseconds. Note that data.check allows to specify different time units as well #' head(rtgData) #' #' # instead, should the dataset have time in seconds #' # the function will return frameT as a vector of NAs #' data(rtgData) # reload dataset #' rtgData$time <- rtgData$time / 1000 # change time to seconds #' rtgData <- data.check(rtgData) #' rtgData$frameT # always check that frameT looks good #' #' # use time.unit to fix it #' data(rtgData) # reload dataset #' rtgData$time <- rtgData$time / 1000 # change time to seconds #' rtgData <- data.check(rtgData, time.unit = 1) #' rtgData$frameT #' #' @export data.check <- function(dataset, refreshRate = 85, time.unit = 1, check.only = F, ...) { # assign refreshRate & time.unit to global environment for looping inside ddply (temporary) assign("refreshRate", refreshRate, envir = kinesis_parameters) assign("time.unit", time.unit, envir = kinesis_parameters) # get required columns reqCols <- kinesis_parameters$dataCols # look for missing columns missingCols <- reqCols[!reqCols %in% names(dataset)] #### Fix missing columns (if any) if (length(missingCols) > 0) { cat("The following columns do not exist:\n") cat(missingCols, sep = ", ") if(!check.only) { cat("\n\nFixing...\n\n") # Fix subjName if (reqCols[1] %in% missingCols) { cat("Please type subject name:\n") dataset$subjName <- readline() names(dataset)[names(dataset) == "subjName"] <- reqCols[1] cat(reqCols[1], " added.\n", sep = "") } # Fix frameN if (reqCols[2] %in% missingCols) { dataset <- kin.frameN(dataset) cat(reqCols[2], " added.\n", sep = "") } # Fix time if (reqCols[3] %in% missingCols) { dataset <- kin.time(dataset, kinesis_parameters$refreshRate, kinesis_parameters$time.unit) cat(reqCols[3], " added.\n", sep = "") } # Fix trialN if (reqCols[5] %in% missingCols) { # if trialN is missing, it is assumed that there is one trial dataset$trialN <- 1 names(dataset)[names(dataset) == "trialN"] <- reqCols[5] cat(reqCols[5], " added.\n", sep = "") } # Fix deltaTime if (reqCols[4] %in% missingCols) { # if time does not exists, create deltaTime if(reqCols[3] %in% missingCols){ dataset <- eval(substitute( ddply(dataset, .(trialN), mutate, frameT = kinesis_parameters$time.unit / kinesis_parameters$refreshRate) , list(trialN = as.name(kinesis_parameters$dataCols[5])))) } else { # else deltaTime is delta time dataset <- eval(substitute( ddply(dataset, .(trialN), mutate, frameT = c(NA, diff(time))) , list(trialN = as.name(kinesis_parameters$dataCols[5]), time = as.name(kinesis_parameters$dataCols[3])))) } names(dataset)[names(dataset) == "frameT"] <- reqCols[4] cat(reqCols[4], " added.\n", sep = "") } cat("\nDatabase fixed successfully.") } else { opt <- options(show.error.messages=FALSE) on.exit(options(opt)) stop() } } else { cat("\nDatabase looks good.") } return(dataset) }

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/Mockup/r/win-library/3.5/emov/analysis/analysis.R

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Praedo4/The-Eye-Tracking-Interface

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analysis.R

# sample analysis of eye movement data using emov in R # by Simon Schwab library(calibrate) # textxy setwd("~/Work/code/emov/pkg/R") source("emov.R") # install.packages("circular") #library("circular") # read raw data file data = emov.read_iviewsamples( "~/Data/nscenes/natural_scenes_samples.txt", 46) # handle missing data: Iview has 0 for missing data data$L.Raw.X..px.[data$L.Raw.X..px. == 0] = NA data$L.Raw.Y..px.[data$L.Raw.Y..px. == 0] = NA data$L.POR.X..mm.[data$L.POR.X..mm. == 0] = NA data$L.POR.Y..mm.[data$L.POR.Y..mm. == 0] = NA data$L.GVEC.X[data$L.GVEC.X == 0] = NA data$L.GVEC.Y[data$L.GVEC.Y == 0] = NA data$L.GVEC.Z[data$L.GVEC.Z == 0] = NA # select channels to use data = data.frame(t = data$Time, x = data$L.POR.X..mm, y = -data$L.POR.Y..mm) # filter data, 1 deg is 14.4 mm, filtering > 750 deg/s (10800 mm/s) flt = emov.filter(data$x, data$y, 10500/200) data$x = flt$x data$y = flt$y # cart2sphere #data = emov.cart2sphere(data$L.GVEC.X, data$L.GVEC.Y, data$L.GVEC.Z) #data = data.frame(x=deg(data$az), y=-deg(data$elev)) # trial segmentation n = 12 # number of trials idx = c() # index start = 1 for (i in 1:n) { idx = c(idx, start, start - 1 + 2000) start = start + 2000 } idx <- matrix(idx, nrow=n, ncol=2, byrow=TRUE) idx <- data.frame(start=idx[,1], end=idx[,2]) # easy to access # fixation detecton for each trial max_disp = 19.0 # in cm, 28.8 cm (2 deg) min_dur = 80/1000*200 fix = emov.idt(data$t, data$x, data$y, max_disp, min_dur) # fixation segmentation for easy ploting fixseg = list() for (i in 1:n) { start = data$t[idx[i,1]] end = data$t[idx[i,2]] fixseg[[i]] = fix[fix$start >= start & fix$end <= end, ] } # Plot all trials, raw data and fixations #my_xlim = c(-35, 25) #my_ylim = c(-20, 15) my_xlim = c(0, 770) my_ylim = c(-680, -250) c = sqrt(80/pi) # constant, r=1 corresponds to fixation duration of 50 ms. par(mfcol=c(4,3)) for (i in 1:n) { plot(fixseg[[i]]$x, fixseg[[i]]$y, xlim=my_xlim, ylim=my_ylim, xlab=NA, ylab=NA, pch=19, cex=sqrt(fixseg[[i]][, 3] * 10^-3 * pi^-1) * c^-1, col='gray') textxy(fixseg[[i]]$x, fixseg[[i]]$y, 1:length(fixseg[[i]]$x), cx=1) par(new=TRUE) plot(fixseg[[i]]$x, fixseg[[i]]$y, xlim=my_xlim, ylim=my_ylim, xlab=NA, ylab=NA, cex=1) par(new=TRUE) plot(data$x[idx$start[i]:idx$end[i]], data$y[idx$start[i]:idx$end[i]], type="l", xlim=my_xlim, ylim=my_ylim, xlab="Horizontal (px)", ylab="Vertical (px)") } # Plot single trial par(mfcol=c(1,1)) nr = 1 plot(fixseg[[nr]]$x, fixseg[[nr]]$y, xlim=my_xlim, ylim=my_ylim, xlab=NA, ylab=NA, pch=19, cex=sqrt(fixseg[[nr]][, 3] * 10^-3 * pi^-1) * c^-1, col='gray') textxy(fixseg[[nr]]$x, fixseg[[nr]]$y, 1:length(fixseg[[nr]]$x), cx=1) par(new=TRUE) plot(fixseg[[nr]]$x, fixseg[[nr]]$y, xlim=my_xlim, ylim=my_ylim, xlab=NA, ylab=NA, cex=1) par(new=TRUE) plot(data$x[idx$start[nr]:idx$end[nr]], data$y[idx$start[nr]:idx$end[nr]], type="l", xlim=my_xlim, ylim=my_ylim, xlab="Horizontal (px)", ylab="Vertical (px)") # Plot stimuli # install.packages("jpeg") # library(jpeg) # # img = list() # img[[1]] <- readJPEG("/home/simon/Data/nscenes/stimuli/000.jpg") # img[[2]] <- readJPEG("/home/simon/Data/nscenes/stimuli/001.jpg") # img[[3]] <- readJPEG("/home/simon/Data/nscenes/stimuli/002.jpg") # img[[4]] <- readJPEG("/home/simon/Data/nscenes/stimuli/003.jpg") # img[[5]] <- readJPEG("/home/simon/Data/nscenes/stimuli/004.jpg") # img[[6]] <- readJPEG("/home/simon/Data/nscenes/stimuli/005.jpg") # img[[7]] <- readJPEG("/home/simon/Data/nscenes/stimuli/006.jpg") # img[[8]] <- readJPEG("/home/simon/Data/nscenes/stimuli/007.jpg") # img[[9]] <- readJPEG("/home/simon/Data/nscenes/stimuli/008.jpg") # img[[10]] <- readJPEG("/home/simon/Data/nscenes/stimuli/009.jpg") # img[[11]] <- readJPEG("/home/simon/Data/nscenes/stimuli/010.jpg") # img[[12]] <- readJPEG("/home/simon/Data/nscenes/stimuli/011.jpg") # # par(mfcol=c(4,3)) # # for (i in 1:n) { # plot(c(0,1), c(0,1)) # rasterImage(img[[i]], 0, 0, 1, 1) # }

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/Rlib/format.cbs.R

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gideonite/cn_pipeline

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2021-01-22T11:37:00.983072

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format.cbs.R

#Set up input files #Input is #cbs.output - the standard output from running CBS #segments.p.output - the output from running segments.p #outputfile - name of output file from running format.cbs #header - whether the first line in the output should be the column names format.cbs <- function(cbs.output,segments.p.output,outputfile,header=TRUE) { unique.names <- unique(segments.p.output[,1]) n <- length(unique.names) chroms.vector <- cbs.output$data[,1] positions.vector <- cbs.output$data[,2] for(i in 1:n) { new.output <- segments.p.output[segments.p.output[,1]==unique.names[i],] p <- nrow(new.output) new.data <- cbs.output$data[,i+2] previous.chrom <- new.output[1,2] previous.start <- new.output[1,3] previous.end <- new.output[1,4] previous.markers <- new.output[1,5] which.na <- which(is.na(new.data)) chroms.na <- chroms.vector[which.na] positions.na <- positions.vector[which.na] count.probes <- rep(0,p) sum.missing <- sum(chroms.na==previous.chrom & positions.na>=previous.start & positions.na<=previous.end) count.probes[1] <- previous.markers+sum.missing #Add count of informative markers for(j in 2:p) { new.chrom <- new.output[j,2] new.start <- new.output[j,3] new.end <- new.output[j,4] new.markers <- new.output[j,5] sum.missing <- sum(chroms.na==new.chrom & positions.na>=new.start & positions.na<=new.end) count.probes[j] <- new.markers+sum.missing } new.output <- cbind(new.output[,1:4],count.probes,new.output[,5:6],new.output[,8:10]) #Look in gaps between chromosomes new.matrix <- NULL previous.chrom <- new.output[1,2] previous.end <- new.output[1,3] previous.end <- new.output[1,4] which.missing <- which(chroms.na==previous.chrom & positions.na<=previous.start) if(length(which.missing)>0) { new.row <- c(unique.names[i],previous.chrom,min(positions.na[which.missing]),max(positions.na[which.missing]),length(which.missing),0,rep(NA,4)) if(length(new.matrix)==0) new.matrix <- new.row else new.matrix <- rbind(new.matrix,new.row) } for(j in 2:p) { new.chrom <- new.output[j,2] new.start <- new.output[j,3] new.end <- new.output[j,4] if (new.chrom==previous.chrom) { which.missing <- which(chroms.na==new.chrom & positions.na<=new.start & positions.na>=previous.end) if(length(which.missing)>0) { new.row <- c(unique.names[i],new.chrom,min(positions.na[which.missing]),max(positions.na[which.missing]),length(which.missing),0,rep(NA,4)) if(length(new.matrix)==0) new.matrix <- new.row else new.matrix <- rbind(new.matrix,new.row) } previous.chrom <- new.chrom previous.end <- new.end } else if(new.chrom!=previous.chrom) { which.missing <- which(chroms.na==previous.chrom & positions.na>=previous.end) if(length(which.missing)>0) { new.row <- c(unique.names[i],previous.chrom,min(positions.na[which.missing]),max(positions.na[which.missing]),length(which.missing),0,rep(NA,4)) if(length(new.matrix)==0) new.matrix <- new.row else new.matrix <- rbind(new.matrix,new.row) } which.missing <- which(chroms.na==new.chrom & positions.na<=new.start) if(length(which.missing)>0) { new.row <- c(unique.names[i],new.chrom,min(positions.na[which.missing]),max(positions.na[which.missing]),length(which.missing),0,rep(NA,4)) if(length(new.matrix)==0) new.matrix <- new.row else new.matrix <- rbind(new.matrix,new.row) } previous.chrom <- new.chrom previous.end <- new.end } } which.missing <- which(chroms.na==new.chrom & positions.na>=new.end) if(length(which.missing)>0) { new.row <- c(unique.names[i],new.chrom,min(positions.na[which.missing]),max(positions.na[which.missing]),length(which.missing),0,rep(NA,4)) if(length(new.matrix)==0) new.matrix <- new.row else new.matrix <- rbind(new.matrix,new.row) } # Now merge the two if(length(new.matrix)>0) { new.matrix <- matrix(new.matrix,ncol=10) new.matrix.chroms <- new.matrix[,2] # new.matrix.chroms <- as.numeric(new.matrix[,2]) new.matrix.ends <- as.numeric(new.matrix[,4]) for(j in 1:nrow(new.matrix)) { new.chrom <- new.matrix.chroms[j] new.end <- new.matrix.ends[j] if(new.chrom==new.output[1,2] & new.end<=as.numeric(new.output[1,3])) # if(new.chrom==as.numeric(new.output[1,2]) & new.end<=as.numeric(new.output[1,3])) { new.output <- rbind(new.matrix[j,],new.output) } else if (new.chrom==new.output[nrow(new.output),2] & new.end>=as.numeric(new.output[nrow(new.output),3])) # else if (new.chrom==as.numeric(new.output[nrow(new.output),2]) & new.end>=as.numeric(new.output[nrow(new.output),3])) { new.output <- rbind(new.output,new.matrix[j,]) } else { which.chrom <- which(new.output[,2]==new.chrom) no.match <- TRUE k <- 1 while(no.match & k<=length(which.chrom)) { new.position <- which.chrom[k] matrix.start <- as.numeric(new.output[new.position,3]) if(new.end<=matrix.start) { new.output <- rbind(new.output[1:(new.position-1),],new.matrix[j,],new.output[new.position:nrow(new.output),]) no.match <- FALSE } k <- k+1 } if(no.match) { new.output <- rbind(new.output[1:new.position,],new.matrix[j,],new.output[(new.position+1):nrow(new.output),]) } } } } left.threecolumns <- c(NA,NA,NA) for(j in 2:nrow(new.output)) { if(new.output[j-1,2]==new.output[j,2]) { left.threecolumns <- rbind(left.threecolumns,as.numeric(new.output[j-1,8:10])) } else { left.threecolumns <- rbind(left.threecolumns,c(NA,NA,NA)) } } new.output <- cbind(new.output[,1:7],left.threecolumns,new.output[,8:10]) if(i==1) output <- new.output else output <- rbind(output,new.output) } names(output) <- c("sample","chrom","loc.start","loc.end","num.mark","num.informative","seg.mean","pval","l.lcl","l.ucl","r.pval","r.lcl","r.ucl") write.table(output,outputfile,sep="\t",row.names=FALSE,col.names=header,quote=FALSE) }

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ong8181/eDNA-early-pooling

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F02_HelperFunctions.R

#### #### F02. Figure Helper functions #### # taxa name summarize function taxa_name_summarize <- function(ps_object, taxa_rank, top_taxa_n = 10, taxa_always_include = NULL){ tax_df <- as.data.frame(tax_table(ps_object)) if(is.null(tax_df$rep_tax)) tax_df$rep_tax <- "Undetermined" # Search Others and Undetermined taxa tax_col1 <- which(colnames(tax_df) == taxa_rank) # Target rank tax_col2 <- which(colnames(tax_df) == "species") # Highest resolution ## Search unidentified taxa (= taxa is null from the target rank to the highest resolution) rep_tax_cond1 <- tax_df[,taxa_rank] == "" & !is.na(tax_df[,taxa_rank]) rep_tax_cond2 <- apply(tax_df[,tax_col1:tax_col2] == "", 1, sum) == (tax_col2 - tax_col1) + 1 # Replace taxa names tax_df[!rep_tax_cond1, "rep_tax"] <- as.character(tax_df[!rep_tax_cond1, taxa_rank]) tax_df[rep_tax_cond1 & !rep_tax_cond2, "rep_tax"] <- "Others" # Re-import phyloseq object with revised tax_table ps_object2 <- phyloseq(otu_table(ps_object), sample_data(ps_object), tax_table(as.matrix(tax_df))) # Replace low abundance taxa name with Others taxa_abundance_rank <- aggregate(taxa_sums(ps_object2), by = list(tax_table(ps_object2)[,"rep_tax"]), sum) taxa_abundance_rank <- taxa_abundance_rank[order(taxa_abundance_rank$x, decreasing = T),] taxa_top <- taxa_abundance_rank[1:top_taxa_n,] if(is.null(taxa_always_include)) { include_taxa <- as.character(taxa_top[,1]) } else { include_taxa <- unique(c(as.character(taxa_top[,1]), taxa_always_include)) } low_tax <- is.na(match(tax_table(ps_object2)[,"rep_tax"], include_taxa)) tax_table(ps_object2)[low_tax,"rep_tax"] <- "Others" return(ps_object2) }

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raphaelgall/nobelprizestats

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source.R

#nobelprize analysis with statistical techniques. #explorative analysis, including machine learning, quantitative text analysis. #source file #libraries, installed only once. #neededpackages <- c("tm", "SnowballCC", "RColorBrewer", "ggplot2", "wordcloud", "biclust", "cluster", "igraph", "fpc", "psych", "ggvis") #install.packages(neededpackages, dependencies=TRUE) library('tm') library('wordcloud') library('SnowballC') library('ggplot2') library('cluster') library('fpc') library('psych') library('ggvis') #load texts for all years per prize peacedata <- file.path("nobelprize_pea/1940_2016") literaturedata <- file.path("nobelprize_lit/1949_2016") nobel_peace <- Corpus(DirSource(peacedata)) nobel_literature <- Corpus(DirSource(literaturedata)) #load texts for each decade separatly per prize peacedata_1940 <- file.path("nobelprize_pea/1940") peacedata_1950 <- file.path("nobelprize_pea/1950") peacedata_1960 <- file.path("nobelprize_pea/1960") peacedata_1970 <- file.path("nobelprize_pea/1970") peacedata_1980 <- file.path("nobelprize_pea/1980") peacedata_1990 <- file.path("nobelprize_pea/1990") peacedata_2000 <- file.path("nobelprize_pea/2000") peacedata_2010 <- file.path("nobelprize_pea/2010") nobel_peace_1940 <- Corpus(DirSource(peacedata_1940)) nobel_peace_1950 <- Corpus(DirSource(peacedata_1950)) nobel_peace_1960 <- Corpus(DirSource(peacedata_1960)) nobel_peace_1970 <- Corpus(DirSource(peacedata_1970)) nobel_peace_1980 <- Corpus(DirSource(peacedata_1980)) nobel_peace_1990 <- Corpus(DirSource(peacedata_1990)) nobel_peace_2000 <- Corpus(DirSource(peacedata_2000)) nobel_peace_2010 <- Corpus(DirSource(peacedata_2010)) literaturedata_1940 <- file.path("nobelprize_lit/1940") literaturedata_1950 <- file.path("nobelprize_lit/1950") literaturedata_1960 <- file.path("nobelprize_lit/1960") literaturedata_1970 <- file.path("nobelprize_lit/1970") literaturedata_1980 <- file.path("nobelprize_lit/1980") literaturedata_1990 <- file.path("nobelprize_lit/1990") literaturedata_2000 <- file.path("nobelprize_lit/2000") literaturedata_2010 <- file.path("nobelprize_lit/2010") nobel_literature_1940 <- Corpus(DirSource(literaturedata_1940)) nobel_literature_1950 <- Corpus(DirSource(literaturedata_1950)) nobel_literature_1960 <- Corpus(DirSource(literaturedata_1960)) nobel_literature_1970 <- Corpus(DirSource(literaturedata_1970)) nobel_literature_1980 <- Corpus(DirSource(literaturedata_1980)) nobel_literature_1990 <- Corpus(DirSource(literaturedata_1990)) nobel_literature_2000 <- Corpus(DirSource(literaturedata_2000)) nobel_literature_2010 <- Corpus(DirSource(literaturedata_2010)) #store speeches by decades in list nobel_peace_bydecade <- list( nobel_peace_1940, nobel_peace_1950, nobel_peace_1960, nobel_peace_1970, nobel_peace_1980, nobel_peace_1990, nobel_peace_2000, nobel_peace_2010 ) nobel_literature_bydecade <- list( nobel_literature_1940, nobel_literature_1950, nobel_literature_1960, nobel_literature_1970, nobel_literature_1980, nobel_literature_1990, nobel_literature_2000, nobel_literature_2010 )

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r

03-run-sra-2sim.R

cores <- parallel::detectCores() / 2 library(MSEtool) ############ Condition operating models with SRA_scope and data SRA_data <- readRDS("mse/scoping/SRA_data.rds") data_names <- c("Chist", "Index", "I_sd", "I_type", "length_bin", "s_CAA", "CAA", "CAL", "I_units") data_ind <- match(data_names, names(SRA_data)) OM_condition <- readRDS("mse/scoping/OM_2sim.rds") # Base SRA <- SRA_scope(OM_condition, data = SRA_data[data_ind], condition = "catch2", selectivity = rep("free", 2), s_selectivity = rep("logistic", 5), cores = 1, vul_par = SRA_data$vul_par, map_vul_par = matrix(NA, 80, 2), map_s_vul_par = SRA_data$map_s_vul_par, map_log_rec_dev = SRA_data$map_log_rec_dev, LWT = list(CAL = 0, CAA = 0)) ret <- retrospective(SRA, 11) saveRDS(list(SRA, ret), file = "mse/scoping/scoping_base.rds") SRA_list <- readRDS("mse/scoping/scoping_base.rds") SRA <- SRA_list[[1]]; ret <- SRA_list[[2]] plot(SRA, retro = ret, file = "mse/scoping/scoping_base", dir = getwd(), open_file = FALSE, f_name = SRA_data$f_name, s_name = SRA_data$s_name, MSY_ref = c(0.4, 0.8), render_args = list(output_format = "word_document")) # Upweight dogfish SRA2 <- SRA_scope(OM_condition, data = SRA_data[data_ind], condition = "catch2", selectivity = rep("free", 2), s_selectivity = rep("logistic", 5), cores = 1, vul_par = SRA_data$vul_par, map_vul_par = matrix(NA, 80, 2), map_s_vul_par = SRA_data$map_s_vul_par, map_log_rec_dev = SRA_data$map_log_rec_dev, LWT = list(CAL = 0, CAA = 0, Index = c(1, 4, 1, 1, 1))) ret2 <- retrospective(SRA2, 11) saveRDS(list(SRA2, ret2), file = "mse/scoping/scoping_upweight_dogfish.rds") SRA_list <- readRDS("mse/scoping/scoping_upweight_dogfish.rds") SRA2 <- SRA_list[[1]]; ret2 <- SRA_list[[2]] # plot for report ------------------------------------------------------------- # png(here::here("mse/figures/retrospective-equal-weighting.png"), width = 8, height = 5, # res = 220, units = "in") # par(mfcol = c(2, 3), mar = c(5, 4, 1, 1), oma = c(0, 0, 2.5, 0), cex = 0.7) # plot(ret) # dev.off() # # png(here::here("mse/figures/retrospective-upweight-dogfish-est-sel.png"), width = 8, height = 5, # res = 220, units = "in") # par(mfcol = c(2, 3), mar = c(5, 4, 1, 1), oma = c(0, 0, 2.5, 0), cex = 0.7) # plot(ret2) # dev.off() # ----------------------------------------------------------------------------- plot(SRA2, retro = ret2, file = "mse/scoping/scoping_upweight_dogfish", dir = getwd(), open_file = FALSE, f_name = SRA_data$f_name, s_name = SRA_data$s_name, MSY_ref = c(0.4, 0.8), render_args = list(output_format = "word_document")) # Upweight dogfish survey, fix HBLL sel from base base_s_vul_par <- c(SRA@mean_fit$report$s_LFS[1], SRA@mean_fit$report$s_L5[1]) s_vul_par <- matrix(c(base_s_vul_par, 0.5), 3, 5) map_s_vul_par <- matrix(NA, 3, 5) SRA3 <- SRA_scope(OM_condition, data = SRA_data[data_ind], condition = "catch2", selectivity = rep("free", 2), s_selectivity = rep("logistic", 5), cores = 1, vul_par = SRA_data$vul_par, map_vul_par = matrix(NA, 80, 2), s_vul_par = s_vul_par, map_s_vul_par = map_s_vul_par, map_log_rec_dev = SRA_data$map_log_rec_dev, LWT = list(CAL = 0, CAA = 0, Index = c(1, 4, 1, 1, 1))) ret3 <- retrospective(SRA3, 11) saveRDS(list(SRA3, ret3), file = "mse/scoping/scoping_updog_fixsel.rds") SRA_list <- readRDS("mse/scoping/scoping_updog_fixsel.rds") SRA3 <- SRA_list[[1]]; ret3 <- SRA_list[[2]] #' @param xfrac The fraction over from the left side. #' @param yfrac The fraction down from the top. #' @param label The text to label with. #' @param pos Position to pass to text() #' @param ... Anything extra to pass to text(), e.g. cex, col. add_label <- function(xfrac, yfrac, label, pos = 4, ...) { u <- par("usr") x <- u[1] + xfrac * (u[2] - u[1]) y <- u[4] - yfrac * (u[4] - u[3]) text(x, y, label, pos = pos, ...) } plot_retro_pbs <- function(retro, legend = TRUE, french = FALSE) { xlim <- range(as.numeric(dimnames(retro@TS)$Year)) nyr_label <- dimnames(retro@TS)$Peel color <- viridisLite::plasma(length(nyr_label)) Year_matrix <- matrix(as.numeric(dimnames(retro@TS)$Year), ncol = length(color), nrow = dim(retro@TS)[2], byrow = FALSE) # for(i in 1:length(retro@TS_var)) { for(i in 3) { matrix_to_plot <- t(retro@TS[, , i]) ylim <- c(0, 1.1 * max(matrix_to_plot, na.rm = TRUE)) if (!french) ylab <- attr(retro, "TS_lab")[i] if (french) ylab <- rosettafish::en2fr("Spawning biomass") plot(NULL, NULL, xlim = xlim, ylim = ylim, xlab = "Year", ylab = ylab, axes = FALSE) abline(h = 0, col = "grey") if(grepl("MSY", as.character(ylab))) abline(h = 1, lty = 3) matlines(Year_matrix, matrix_to_plot, col = color, lty = 1) if (legend) legend(1917, 4000, legend = nyr_label, lwd = 1, col = color, bty = "n", title = paste0(rosettafish::en2fr("Years removed", translate = french), ":"), y.intersp = 0.8) } } # plot for report ------------------------------------------------------------- png(here::here("mse/figures/retrospective-spawning-biomass.png"), width = 5, height = 5, res = 260, units = "in") par(mfcol = c(2, 1), mar = c(0, 4, 0, 0), oma = c(4, 0, 1, 1), cex = 0.7, yaxs = "i") plot_retro_pbs(ret, legend = FALSE) add_label(0.02, 0.06, "(A) Initial fit") box() axis(2, at = seq(0, 5000, 1000)) plot_retro_pbs(ret3) axis(2, at = seq(0, 4000, 1000)) axis(1) box() mtext("Year", side = 1, line = 2.5, cex = 0.8) add_label(0.02, 0.06, "(B) Base OM") nyr_label <- dimnames(ret@TS)$Peel dev.off() png(here::here("mse/figures-french/retrospective-spawning-biomass.png"), width = 5, height = 5, res = 260, units = "in") par(mfcol = c(2, 1), mar = c(0, 4, 0, 0), oma = c(4, 0, 1, 1), cex = 0.7, yaxs = "i") plot_retro_pbs(ret, legend = FALSE, french = TRUE) add_label(0.02, 0.06, "(A) Ajustement initial du modèle") box() axis(2, at = seq(0, 5000, 1000)) plot_retro_pbs(ret3, french = TRUE) axis(2, at = seq(0, 4000, 1000)) axis(1) box() mtext(rosettafish::en2fr("Year"), side = 1, line = 2.5, cex = 0.8) add_label(0.02, 0.06, "(B) ME de base") nyr_label <- dimnames(ret@TS)$Peel dev.off() # ----------------------------------------------------------------------------- # ----------------------------------------------------------------------------- plot(SRA3, retro = ret3, file = "mse/scoping/scoping_updog_fixsel", dir = getwd(), open_file = FALSE, f_name = SRA_data$f_name, s_name = SRA_data$s_name, MSY_ref = c(0.4, 0.8), render_args = list(output_format = "word_document")) compare_SRA(SRA, SRA2, SRA3, scenario = list(names = c("base", "upweight dogfish", "up.dog. fix HBLL sel"))) # Low catch - use gfdatabase estimates of commerical catch in 1986-2005 SRA_data2 <- SRA_data SRA_data2$Chist[match(1986:2005, SRA_data2$Year), 1] <- 0.5 * SRA_data2$Chist[match(1986:2005, SRA_data2$Year), 1] SRA4 <- SRA_scope(OM_condition, data = SRA_data2[data_ind], condition = "catch2", selectivity = rep("free", 2), s_selectivity = rep("logistic", 5), cores = 1, vul_par = SRA_data$vul_par, map_vul_par = matrix(NA, 80, 2), map_s_vul_par = SRA_data$map_s_vul_par, map_log_rec_dev = SRA_data$map_log_rec_dev, LWT = list(CAL = 0, CAA = 0, Index = c(1, 4, 1, 1, 1))) ret4 <- retrospective(SRA4, 11) saveRDS(list(SRA4, ret4), file = "mse/scoping/scoping_lowcatch.rds") SRA_list <- readRDS("mse/scoping/scoping_lowcatch.rds") SRA4 <- SRA_list[[1]]; ret4 <- SRA_list[[2]] plot(SRA4, retro = ret4, file = "mse/scoping/scoping_lowcatch", dir = getwd(), open_file = FALSE, f_name = SRA_data$f_name, s_name = SRA_data$s_name, MSY_ref = c(0.4, 0.8), render_args = list(output_format = "word_document")) # Low catch - fix HBLL sel from base SRA5 <- SRA_scope(OM_condition, data = SRA_data2[data_ind], condition = "catch2", selectivity = rep("free", 2), s_selectivity = rep("logistic", 5), cores = 1, vul_par = SRA_data$vul_par, map_vul_par = matrix(NA, 80, 2), s_vul_par = s_vul_par, map_s_vul_par = map_s_vul_par, map_log_rec_dev = SRA_data$map_log_rec_dev, LWT = list(CAL = 0, CAA = 0, Index = c(1, 4, 1, 1, 1))) ret5 <- retrospective(SRA5, 11) saveRDS(list(SRA5, ret5), file = "mse/scoping/scoping_lowcatch_fixsel.rds") SRA_list <- readRDS("mse/scoping/scoping_lowcatch_fixsel.rds") SRA5 <- SRA_list[[1]]; ret5 <- SRA_list[[2]] plot(SRA5, retro = ret5, file = "mse/scoping/scoping_lowcatch_fixsel", dir = getwd(), open_file = FALSE, f_name = SRA_data$f_name, s_name = SRA_data$s_name, MSY_ref = c(0.4, 0.8), render_args = list(output_format = "word_document")) ## Try to estimate fishery selectivity SRA_data$vul_par[1:2, ] <- c(50, 40, 30, 25) map_vul_par <- matrix(NA, 80, 2) map_vul_par[1:2, ] <- 1:4 SRA6 <- SRA_scope(OM_condition, data = SRA_data[data_ind], condition = "catch2", selectivity = rep("logistic", 2), s_selectivity = rep("logistic", 5), cores = 1, vul_par = SRA_data$vul_par, map_vul_par = map_vul_par, map_s_vul_par = SRA_data$map_s_vul_par, map_log_rec_dev = SRA_data$map_log_rec_dev, LWT = list(CAL = 1, CAA = 20, Index = c(1, 4, 1, 1, 1))) ret6 <- retrospective(SRA6, 11) saveRDS(list(SRA6, ret6), file = "mse/scoping/scoping_estfisherysel_esthbllsel.rds") SRA_list <- readRDS("mse/scoping/scoping_estfisherysel_esthbllsel.rds") SRA6 <- SRA_list[[1]]; ret6 <- SRA_list[[2]] plot(SRA6, retro = ret6, file = "mse/scoping/scoping_estfisherysel_esthbll_sel", dir = getwd(), open_file = FALSE, f_name = SRA_data$f_name, s_name = SRA_data$s_name, MSY_ref = c(0.4, 0.8), render_args = list(output_format = "word_document")) ## Compare plots compare_SRA(SRA, SRA2, SRA3, SRA4, SRA5, SRA6, scenario = list(names = c("base", "upweight dogfish", "up.dog. fix HBLL sel", "low catch", "low catch fix HBLL sel", "est fishery/HBLL sel")), filename = "mse/scoping/compare_scoping", dir = getwd(), open_file = FALSE, f_name = SRA_data$f_name, s_name = SRA_data$s_name, MSY_ref = c(0.4, 0.8), render_args = list(output_format = "word_document")) # Grid M and steepness library(dplyr) DLMtool::setup(8) LH_grid <- expand.grid(M = seq(0.02, 0.07, 0.01), h = seq(0.65, 0.75, 0.01)) OM_condition <- readRDS("mse/scoping/OM_2sim.rds") OM_condition@nsim <- nrow(LH_grid) OM_condition@cpars$M <- LH_grid$M OM_condition@cpars$h <- LH_grid$h Mat_age <- OM_condition@cpars$Mat_age[1,,1] OM_condition@cpars$Mat_age <- array(Mat_age, c(OM_condition@maxage, OM_condition@nyears + OM_condition@proyears, OM_condition@nsim)) %>% aperm(perm = c(3, 1, 2)) # Upweight dogfish SRA7 <- SRA_scope(OM_condition, data = SRA_data[data_ind], condition = "catch2", selectivity = rep("free", 2), s_selectivity = rep("logistic", 5), cores = cores, vul_par = SRA_data$vul_par, map_vul_par = matrix(NA, 80, 2), map_s_vul_par = SRA_data$map_s_vul_par, map_log_rec_dev = SRA_data$map_log_rec_dev, LWT = list(CAL = 0, CAA = 0, Index = c(1, 4, 1, 1, 1))) saveRDS(SRA7, file = "mse/scoping/profile_M_and_h.rds") SRA7 <- readRDS("mse/scoping/profile_M_and_h.rds") plot(SRA7, sims = LH_grid$h == 0.71, file = "mse/scoping/profile_M", dir = getwd(), open_file = FALSE, f_name = SRA_data$f_name, s_name = SRA_data$s_name, MSY_ref = c(0.4, 0.8), scenarios = list(names = paste0("M = 0.0", 2:7), col = 1:6)) # Upweight dog. fix HBLL sel base_s_vul_par <- c(SRA@mean_fit$report$s_LFS[1], SRA@mean_fit$report$s_L5[1]) s_vul_par <- matrix(c(base_s_vul_par, 0.5), 3, 5) map_s_vul_par <- matrix(NA, 3, 5) #### Episodic recruitment SRA <- readRDS("mse/OM/upweight_dogfish.rds") set.seed(324) sporadic_recruitment2 <- function(x, years = length(x), low_sigmaR = 0.4, high_sigmaR = 0.8) { require(dplyr) nhigh <- 25 high_ind <- sample(1:years, nhigh) new_samp <- rnorm(nhigh, -0.5 * high_sigmaR^2, high_sigmaR) %>% exp() x[high_ind] <- new_samp return(x) } new_Perr_y <- apply(SRA@OM@cpars$Perr_y[, 182:281], 1, sporadic_recruitment2) SRA@OM@cpars$Perr_y[, 182:281] <- t(new_Perr_y) saveRDS(SRA, file = "mse/OM/sporadic_recruitment.rds") # M = 0.02 OM_condition@cpars$M <- rep(0.02, OM@nsim) SRA <- SRA_scope(OM_condition, condition = "catch2", Chist = SRA_data$Chist, Index = SRA_data$Index, I_sd = SRA_data$I_sd, I_type = SRA_data$I_type, selectivity = rep("logistic", 2), s_selectivity = rep("logistic", 5), length_bin = 0.1 * SRA_data$length_bin, cores = cores, s_CAA = SRA_data$s_CAA, vul_par = SRA_data$vul_par, map_s_vul_par = SRA_data$map_s_vul_par, map_log_rec_dev = SRA_data$map_log_rec_dev) saveRDS(SRA, file = "mse/OM/lowM.rds") SRA <- readRDS("mse/OM/lowM.rds") plot(SRA, file = "mse/OM/OM_lowM", dir = getwd(), open_file = FALSE, f_name = SRA_data$f_name, s_name = SRA_data$s_name, MSY_ref = c(0.4, 0.8))

2b9e7778b19591382c1247a740cd01817193d24e

65cd986ba44482281761b8fed4028f87ebebcd12

/to be deleted/share_allocation/3_ctrfact_sim_reformat.r

e9de0fd7f7aafcc04966b6557a4c91838aab6b24

[]

no_license

Superet/Expenditure

07efeb48f700bec71bb1d1b380476dddceae76ee

f6e1655517b65106ea775768e935e94efa73dbaa

refs/heads/master

2021-07-14T18:09:50.415191

2020-06-01T19:54:30

38,344,163

0

null

UTF-8

R

false

7,843

r

3_ctrfact_sim_reformat.r

library(ggplot2) library(reshape2) library(Rcpp) library(RcppArmadillo) library(maxLik) library(evd) library(data.table) library(doParallel) library(foreach) # library(chebpol) library(nloptr) library(mgcv) options(error = quote({dump.frames(to.file = TRUE)})) seg_id <- as.numeric(Sys.getenv("PBS_ARRAY_INDEX")) cat("seg_id =", seg.id, "\.\n") args <- commandArgs(trailingOnly = TRUE) print(args) if(length(args)>0){ for(i in 1:length(args)){ eval(parse(text=args[[i]])) } } # setwd("~/Documents/Research/Store switching/processed data") # plot.wd <- '~/Desktop' # source("../Exercise/Multiple_discrete_continuous_model/0_Allocation_function.R") # source("../Exercise/main/share_allocation/ctrfact_sim_functions.r") # setwd("/home/brgordon/ccv103/Exercise/run") # setwd("/kellogg/users/marketing/2661703/Exercise/run") setwd("/sscc/home/c/ccv103/Exercise/run") run_id <- 4 plot.wd <- getwd() make_plot <- TRUE ww <- 10 ar <- .6 source("0_Allocation_function.R") source("ctrfact_sim_functions.r") # Load estimation data ver.date <- "2016-02-26" cpi.adj <- TRUE if(cpi.adj){ loadf <- paste("estrun_",run_id,"/MDCEV_cpi_est_seg",seg_id,"_", ver.date,".rdata",sep="") }else{ loadf <- paste("estrun_",run_id,"/MDCEV_est_seg",seg_id,"_", ver.date,".rdata",sep="") } loadf load(loadf) rm(list = intersect(ls(), c("gamfit", "shr","model_name", "tmpdat"))) # Set simulation parameters interp.method <- "spline" # Spline interpolation or Chebyshev interpolation exp.method <- "Utility" # Utility maximization or finding roots for first order condition trim.alpha <- 0.05 numsim <- 1000 #numsim1 #<- 1000 draw.par <- FALSE sim.omega <- FALSE fname <- paste("ctrfact_ref_seg",seg_id,sep="") if(draw.par){ fname <- paste(fname, "_pardraw", sep="") } if(sim.omega){fname <- paste(fname, "_simomega", sep="") } fname <- paste(fname, "_sim", numsim, "_", as.character(Sys.Date()), sep="") cat("Output file name is", fname, ".\n") ############################### # Prepare simulation elements # ############################### # Data required: parameters, income level, price, retail attributes, and random draws # For each simulation scenario, if income, price or retail attributes change, we need to re-simulate inclusive values. # Set simulation parameters lambda <- coef(sol.top2) shr.par <- coef(sol) #-----------------------# # Construct income data # # Take the households' income in 2007 as basis selyr <- 2007 tmp <- data.table(subset(mydata, year %in% selyr)) tmp <- tmp[,list(income = unique(income_midvalue)), by = list(household_code, year)] sim.data<- data.frame(tmp)[,c("household_code","income")] sim.unq <- data.frame(income2007 = unique(sim.data[,-1])) # Counterfactual scenario: income is lower by 10%. my.change <- .1 lnInc_08 <- lnInc + log(1 - my.change) sim.unq$income2008 <- (1 - my.change) * sim.unq$income2007 sim.unq$Inc07 <- log(sim.unq[,"income2007"]) sim.unq$Inc08 <- log(sim.unq[,"income2008"]) sim.unq <- sim.unq[order(sim.unq$income2007),] cat("dim(sim.unq) =", dim(sim.unq), "\n") #----------------------------# # Average price in year 2007 tmp <- dcast(subset(price_dat, year %in% selyr), scantrack_market_descr + year + biweek ~ channel_type, value.var = "bsk_price_paid_2004") price.07 <- setNames( colMeans(as.matrix(tmp[,4:(3+R)]), na.rm=T), fmt_name) cat("The average price level in 2007:\n"); print(price.07);cat("\n") # Average retail attributes in 2007 selcol <- c("size_index", "ln_upc_per_mod", "ln_num_module","overall_prvt") X_list07<- setNames( lapply(fmt_name, function(x) colMeans(as.matrix(subset(fmt_attr, channel_type == x & year%in% selyr)[,selcol]))), fmt_name) cat("The average retail attributes in 2007:\n"); print(do.call(rbind, X_list07)); cat("\n") # Compute delta psi = -log(-0.1)*alpha*X tmpX <- do.call(rbind, X_list07) d.psi <- tmpX %*% shr.par[paste("beta_", 5:8, sep="")] * log(.9) cat("Change of marginal utility (psi) of 10% income change:\n"); print(d.psi); cat("\n") # Expand X_list and price to match the nobs of income price.07 <- rep(1, nrow(sim.unq)) %*% matrix(price.07, nrow = 1) colnames(price.07) <- fmt_name X_list07 <- lapply(X_list07, function(x) {out <- rep(1, nrow(sim.unq)) %*% matrix(x, nrow = 1); colnames(out) <- names(x); return(out)}) #-------------------# # Take random draws # set.seed(666) eps_draw <- matrix(rgev(numsim*R, scale = exp(shr.par["ln_sigma"])), numsim, R) if(draw.par){ par_se <- c(sqrt(diag(vcov(sol.top2))), sqrt(diag(vcov(sol))) ) par_se[is.na(par_se)] <- 0 par_draw <- sapply(par_se, function(x) rnorm(numsim, mean = 0, sd = x)) }else{ par_draw <- NULL } ############## # Simulation # ############## if(interp.method == "spline"){ y.nodes <- quantile(mydata$dol, c(0:50)/50) y.nodes <- sort(unique(c(y.nodes , seq(600, 1000, 100)) )) }else{ # Set up Chebyshev interpolation GH_num_nodes<- 100 y.interval <- c(.1, 1000) y.nodes <- chebknots(GH_num_nodes, interval = y.interval)[[1]] } numnodes<- length(y.nodes) # Simulate expenditure and expenditure share. pct <- proc.time() sim.base07 <- SimWrapper_fn(omega_deriv, ln_inc = sim.unq$Inc07, lambda = lambda, param_est = shr.par, base = beta0_base, X_list = X_list07, price = price.07, eps_draw = eps_draw, method = exp.method, ret.sim = TRUE, par.draw = par_draw) use.time <- proc.time() - pct cat("2007 Baseline simulation finishes with", use.time[3]/60, "min.\n") pct <- proc.time() sim.base08 <- SimWrapper_fn(omega_deriv, ln_inc = sim.unq$Inc08, lambda = lambda, param_est = shr.par, base = beta0_base, X_list = X_list07, price = price.07, eps_draw = eps_draw, method = exp.method, ret.sim = TRUE, par.draw = par_draw) use.time <- proc.time() - pct cat("2008 Baseline simulation finishes with", use.time[3]/60, "min.\n") # -------------- # # Counterfactual # # Change only attributes # If retail A behaves the same as retail B ret.a <- "Discount Store" ret.b <- c("Grocery", "Dollar Store", "Warehouse Club") ref.sim <- setNames(vector("list", length(ret.b)), ret.b) for(i in 1:length(ret.b)){ pct <- proc.time() X.new <- X_list07 X.new[[ret.a]] <- X.new[[ret.b[i]]] # price.new <- price.07 # price.new[,ret.a] <- price.new[, ret.b] if(sim.omega){ ref.sim[[i]] <- SimOmega_fn(ln_inc = sim.unq[,"Inc08"], lambda = lambda, param_est = shr.par, base = beta0_base, X_list = X_list_new, price = price.07, lnInc_lv = lnInc_08, y.nodes = y.nodes, eps_draw = eps_draw, method = exp.method, interp.method = interp.method, ret.sim = TRUE, alpha = trim.alpha, par.draw = par_draw) }else{ ref.sim[[i]] <- SimWrapper_fn(omega_deriv = omega_deriv, ln_inc = sim.unq[,"Inc08"], lambda = lambda, param_est = shr.par, base = beta0_base, X_list = X.new, price = price.07, eps_draw = eps_draw, method = exp.method, ret.sim = TRUE, par.draw = par_draw) } use.time <- proc.time() - pct cat("Counterfactual finishes with", use.time[3]/60, "min.\n") } ################ # Save results # ################ rm(list = intersect(ls(), c("ar", "args", "cl", "ggtmp", "ggtmp1", "ggtmp2", "i", "lastFuncGrad", "lastFuncParam", "make_plot", "mycore", "myfix", "plot.wd", "s1_index", "sel", "selyr", "price", "sol", "sol.top", "sol.top2", "tmp", "tmp_coef", "tmp_price", "tmp1", "tmp2", "use.time", "ver.date", "var1", "ww", "f", "numnodes", "out", "out1", "pct", "tmpd1", "tmpd2", "tmpdat", "u", "W", "y", "y.nodes", "Allocation_constr_fn", "Allocation_fn", "Allocation_nlop_fn", "cheb.1d.basis", "cheb.basis", "chebfun", "exp_fn", "expFOC_fn", "incl_value_fn", "mysplfun", "mytrimfun", "param_assignR", "simExp_fn", "SimOmega_fn", "SimWrapper_fn", "solveExp_fn", "spl2dfun", "uP_fn", "uPGrad_fn", "X_list"))) save.image(paste("estrun_",run_id, "/", fname, ".rdata",sep="")) cat("This program is done.\n")

75220a794ffcd35a6782b19423a1da88139e6f96

e051cfb06eb74bc41448c523df8930f38d20aac6

/man/countReads-methods.Rd

61d9a157e7b6ac7a1187d661b0d37b009b31bc98

[]

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duydnguyen/tan-coverage

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countReads-methods.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/AllGenerics.R, R/methods-segvis.R \docType{methods} \name{countReads} \alias{countReads} \alias{countReads,segvis-method} \alias{countReads} \title{countReads method for segvis class} \usage{ countReads(object) \S4method{countReads}{segvis}(object) } \arguments{ \item{object}{segvis object} } \value{ The number of reads in the bam file considered for the \code{segvis} object } \description{ Counts the number of reads considered in object } \examples{ \dontrun{ countReads(segvis) } } \seealso{ \code{\link{segvis-class}} }

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/Exploratory_EBC.R

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DrMattG/ES_Conservation

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Exploratory_EBC.R

#Results: 557 #(from Web of Science Core Collection) #You searched for: TOPIC: ("evidence-based" "conservation") #Refined by: WEB OF SCIENCE CATEGORIES: ( ECOLOGY OR ENVIRONMENTAL SCIENCES OR BIODIVERSITY CONSERVATION OR ENVIRONMENTAL STUDIES OR ZOOLOGY ) #Timespan: All years. Indexes: SCI-EXPANDED, SSCI, A&HCI, ESCI. library(visNetwork) library(bibliometrix) library(igraph) library(here) library(tidytext) library(textmineR) library(tidyverse) library(reshape2) library(wordcloud) file1 <-paste0(here(),"/Data/EBC/EBC.bib") file2 <-paste0(here(),"/Data/EBC/EBC2.bib") M <- convert2df(file = c(file1,file2), dbsource = "isi", format = "bibtex") M results <- biblioAnalysis(M, sep = ";") options(width=100) S <- summary(object = results, k = 10, pause = FALSE) CR <- citations(M, field = "article", sep = ";") #cbind(CR$Cited[1:10]) A <- cocMatrix(M, Field = "CR", sep = ";") #NetMatrix <- biblioNetwork(M, analysis = "co-citation", network = "references", sep = ";") # Plot the network #net=networkPlot(NetMatrix, n = 30, Title = "Co-Citation Network", type = "fruchterman", size=T, remove.multiple=FALSE, labelsize=0.7,edgesize = 5) # Create a historical citation network #options(width=130) #histResults <- histNetwork(M, min.citations = 1, sep = ";") #net <- histPlot(histResults, n=30, size = 10, labelsize=5) Cites<-igraph::graph_from_incidence_matrix(A) V(Cites)$name V(Cites)$Year=sub("^.*([0-9]{4}).*", "\\1", V(Cites)$name) V(Cites)$Journal=sub(".*([0-9]{4})", "", V(Cites)$name) deg <- igraph::degree(Cites, mode="all") sort(deg) #plot(Cites, vertex.label=NA, vertex.size=deg) Cites = igraph::delete.vertices(Cites,igraph::degree(Cites)<5) Cites<-simplify(Cites) Cites<-delete.vertices(simplify(Cites), degree(Cites)==0) vn <- toVisNetworkData(Cites) vn$nodes$title<-vn$nodes$label unique(vn$nodes$Year) # #Repair some year values Early view #vn$nodes$Year[1]="2020" #vn$nodes$Year[2]="2020" #vn$nodes$Year[3]="2020" #vn$nodes$Year[4]="2020" vn$nodes$Year[5]="2020" vn$nodes$Year[6]="2020" vn$nodes$Year[7]="2020" # vn$nodes$Year[9]="2012" # vn$nodes$Year[10]="2020" vn$nodes$Year[11]="2020" vn$nodes$Year[12]="2020" vn$nodes$Year[19]="2020" vn$nodes$Year[20]="2020" vn$nodes$Year[41]="2020" vn$nodes$Year[659]="2013" vn$nodes$Year[699]="2000" # vn$nodes$Year[13]="2020" # vn$nodes$Year[14]="2020" # vn$nodes$Year[16]="2020" # vn$nodes$Year[17]="2020" # vn$nodes$Year[52]="2020" # vn$nodes$Year[53]="2020" vn$nodes$Journal<-gsub(", ", "",vn$nodes$Journal) vn$nodes$Journal<-trimws(vn$nodes$Journal, which = "both", whitespace = "[ \t\r\n]") unique(vn$nodes$Journal) # # vn$nodes$Journal[2]<-"OIKOS" # vn$nodes$Journal[3]<-"AMBIO" # vn$nodes$Journal[7]<-"URBAN ECOSYST" # vn$nodes$Journal[9]<-"CONSERV BIOL" # vn$nodes$Journal[10]<-"CONSERV BIOL" # vn$nodes$Journal[11]<-"CONSERV BIOL" # vn$nodes$Journal[12]<-"CONSERV BIOL" # vn$nodes$Journal[13]<-"CONSERV BIOL" # vn$nodes$Journal[14]<-"AMBIO" # vn$nodes$Journal[16]<-"PRIMATOL" # vn$nodes$Journal[17]<-"RESTOR ECOL" # vn$nodes$Journal[53]<-"SOC NAT RESOUR" # vn$nodes$Journal[52]<-"URBAN AFF" # # # # Nodes are sized by degree (the number of links to other packages) degree_value <- degree(Cites, mode = "all") vn$nodes$value <- degree_value[match(vn$nodes$id, names(degree_value))] # vn$nodes$x<-as.numeric(vn$nodes$Year) # # unique(vn$nodes$x) # # # vn$nodes$color<-ifelse(vn$nodes$x>=2015, "red", # ifelse(vn$nodes$x>=2010, "green", # ifelse(vn$nodes$x>=2005, "brown", # ifelse(vn$nodes$x>=2000,"green", # ifelse(vn$nodes$x>=1995, "yellow", "lightblue"))))) # #unique(vn$nodes$Year) which(vn$nodes$Year=="1968") vn$nodes$color="blue" vn$nodes$color[637]<-"red" vn$edges$arrows<-"to" visNetwork(nodes = vn$nodes, edges = vn$edges,main="EBC",height = "500px", width = "100%")%>% #visOptions(highlightNearest = TRUE)%>% visOptions(highlightNearest = list(enabled = TRUE, degree = 1)) %>% visNodes() %>% visSave(file =paste0(here(),"/plots/EBC.html"), selfcontained = T) #Find subgraphs c1 = cluster_fast_greedy(Cites) # modularity measure modularity(c1) coords = layout_with_fr(Cites) plot(c1, Cites, layout=coords, ) membership(c1) sizes(c1) vn$nodes$group=membership(c1) cols<-viridis::magma(11) plotrix::color.id(cols[3]) vn$nodes$color<-ifelse(vn$nodes$group==1, plotrix::color.id(cols[1]), ifelse(vn$nodes$group==2, plotrix::color.id(cols[2]), ifelse(vn$nodes$group==3, plotrix::color.id(cols[3]), ifelse(vn$nodes$group==4,plotrix::color.id(cols[4]), ifelse(vn$nodes$group==5,plotrix::color.id(cols[5]), ifelse(vn$nodes$group==6,plotrix::color.id(cols[6]), ifelse(vn$nodes$group==7,plotrix::color.id(cols[7]), ifelse(vn$nodes$group==8,plotrix::color.id(cols[8]), ifelse(vn$nodes$group==9,plotrix::color.id(cols[9]), ifelse(vn$nodes$group==10, plotrix::color.id(cols[10]), plotrix::color.id(cols[11]))))))))))) visNetwork(nodes = vn$nodes, edges = vn$edges,main="EBC",height = "500px", width = "100%")%>% visOptions(highlightNearest = TRUE)%>% visOptions(highlightNearest = list(enabled = TRUE, degree = 1)) %>% visNodes() %>% visSave(file =paste0(here(),"/plots/EBC_col.html"), selfcontained = T) # # # unique(vn$nodes$Journal) # # vn$nodes$color<-ifelse(vn$nodes$Journal=="CONSERV BIOL", "red","blue") # # visNetwork(nodes = vn$nodes, edges = vn$edges,main="Coexist",height = "500px", width = "100%")%>% # #visOptions(highlightNearest = TRUE)%>% # visOptions(highlightNearest = list(enabled = TRUE, degree = 2)) %>% # visNodes() %>% # visSave(file =paste0(here(),"/plots/Coexist_conb.html"), selfcontained = T) ############################################################################################################### # # # # extract the abstracts of main papers # # data=data.frame("Title"=as.character(M$TI),"Abstract"= as.character(M$AB), stringsAsFactors = FALSE) # text_df <- mutate(data, text = data$Abstract) # text_df<-text_df %>% # rowid_to_column() # text_cleaning_tokens<-text_df %>% # unnest_tokens(word, text) # text_cleaning_tokens$word <- gsub('[[:digit:]]+', '', text_cleaning_tokens$word) # text_cleaning_tokens$word <- gsub('[[:punct:]]+', '', text_cleaning_tokens$word) # text_cleaning_tokens <- text_cleaning_tokens %>% filter(!(nchar(word) == 1))%>% # anti_join(stop_words) # tokens <- text_cleaning_tokens %>% filter(!(word=="")) # tokens <- tokens %>% mutate(ind = row_number()) # tokens <- tokens %>% group_by(Title) %>% mutate(ind = row_number()) %>% # tidyr::spread(key = ind, value = word) # tokens [is.na(tokens)] <- "" # tokens <- tidyr::unite(tokens, text,-Title,sep =" " ) # tokens$text <- trimws(tokens$text) # # # #create DTM # dtm <- CreateDtm(tokens$text, # doc_names = tokens$Title, # ngram_window = c(1, 2)) # #explore the basic frequency # tf <- TermDocFreq(dtm = dtm) # original_tf <- tf %>% select(term, term_freq,doc_freq) # rownames(original_tf) <- 1:nrow(original_tf) # # Eliminate words appearing less than 2 times or in more than half of the # # documents # vocabulary <- tf$term[ tf$term_freq > 1 & tf$doc_freq < nrow(dtm) / 2 ] # dtm = dtm # # k_list <- seq(1, 40, by = 1) # model_dir <- paste0("models_", digest::digest(vocabulary, algo = "sha1")) # if (!dir.exists(model_dir)) dir.create(model_dir) # model_list <- TmParallelApply(X = k_list, FUN = function(k){ # filename = file.path(model_dir, paste0(k, "_topics.rda")) # # if (!file.exists(filename)) { # m <- FitLdaModel(dtm = dtm, k = k, iterations = 500) # m$k <- k # m$coherence <- CalcProbCoherence(phi = m$phi, dtm = dtm, M = 5) # save(m, file = filename) # } else { # load(filename) # } # # m # }, export=c("dtm", "model_dir")) # export only needed for Windows machines # #model tuning # #choosing the best model # coherence_mat <- data.frame(k = sapply(model_list, function(x) nrow(x$phi)), # coherence = sapply(model_list, function(x) mean(x$coherence)), # stringsAsFactors = FALSE) # ggplot(coherence_mat, aes(x = k, y = coherence)) + # geom_point() + # geom_line(group = 1)+ # ggtitle("Best Topic by Coherence Score") + theme_minimal() + # scale_x_continuous(breaks = seq(1,40,1)) + ylab("Coherence") # # model <- model_list[which.max(coherence_mat$coherence)][[ 1 ]] # model$top_terms <- GetTopTerms(phi = model$phi, M = 40) # top20_wide <- as.data.frame(model$top_terms) # # allterms <-data.frame(t(model$phi)) # # allterms$word <- rownames(allterms) # # rownames(allterms) <- 1:nrow(allterms) # # allterms <- melt(allterms,idvars = "word") # # allterms <- allterms %>% rename(topic = variable) # # FINAL_allterms <- allterms %>% group_by(topic) %>% arrange(desc(value)) # # # # model$topic_linguistic_dist <- CalcHellingerDist(model$phi) # model$hclust <- hclust(as.dist(model$topic_linguistic_dist), "ward.D") # model$hclust$labels <- paste(model$hclust$labels, model$labels[ , 1]) # plot(model$hclust) # # final_summary_words <- data.frame(top_terms = t(model$top_terms)) # final_summary_words$topic <- rownames(final_summary_words) # rownames(final_summary_words) <- 1:nrow(final_summary_words) # final_summary_words <- final_summary_words %>% melt(id.vars = c("topic")) # final_summary_words <- final_summary_words %>% rename(word = value) %>% select(-variable) # final_summary_words <- left_join(final_summary_words,allterms) # final_summary_words <- final_summary_words %>% group_by(topic,word) %>% # arrange(desc(value)) # final_summary_words <- final_summary_words %>% group_by(topic, word) %>% filter(row_number() == 1) %>% # ungroup() %>% tidyr::separate(topic, into =c("t","topic")) %>% select(-t) # word_topic_freq <- left_join(final_summary_words, original_tf, by = c("word" = "term")) # # for(i in 1:length(unique(final_summary_words$topic))) # { wordcloud(words = subset(final_summary_words ,topic == i)$word, freq = subset(final_summary_words ,topic == i)$value, min.freq = 1, # max.words=200, random.order=FALSE, rot.per=0.35, # colors=brewer.pal(8, "Dark2"))} # # dev.off() # # d<-dtm # p <- as.data.frame(predict(object = model, newdata = d, method = "dot")) # names(p) # # p[, "max"] <- apply(p, 1, max) file <-paste0(here(),"/Data/EBC/Cite100.bib") M <- convert2df(file = file, dbsource = "isi", format = "bibtex") M results <- biblioAnalysis(M, sep = ";") options(width=100) S <- summary(object = results, k = 10, pause = FALSE) CR <- citations(M, field = "article", sep = ";") #cbind(CR$Cited[1:10]) A <- cocMatrix(M, Field = "CR", sep = ";") Cites<-igraph::graph_from_incidence_matrix(A) V(Cites)$name V(Cites)$Year=sub("^.*([0-9]{4}).*", "\\1", V(Cites)$name) V(Cites)$Journal=sub(".*([0-9]{4})", "", V(Cites)$name) deg <- igraph::degree(Cites, mode="all") sort(deg) #plot(Cites, vertex.label=NA, vertex.size=deg) vn <- toVisNetworkData(Cites) vn$nodes$title<-vn$nodes$label # unique(vn$nodes$Year) # # #Repair some year values Early view # #vn$nodes$Year[1]="2020" # #vn$nodes$Year[2]="2020" # #vn$nodes$Year[3]="2020" # #vn$nodes$Year[4]="2020" # vn$nodes$Year[5]="2020" # vn$nodes$Year[6]="2020" # vn$nodes$Year[7]="2020" # # vn$nodes$Year[9]="2012" # # vn$nodes$Year[10]="2020" # vn$nodes$Year[11]="2020" # vn$nodes$Year[12]="2020" # vn$nodes$Year[19]="2020" # vn$nodes$Year[20]="2020" # vn$nodes$Year[41]="2020" # vn$nodes$Year[659]="2013" # vn$nodes$Year[699]="2000" # # vn$nodes$Year[13]="2020" # # vn$nodes$Year[14]="2020" # # vn$nodes$Year[16]="2020" # # vn$nodes$Year[17]="2020" # # vn$nodes$Year[52]="2020" # # vn$nodes$Year[53]="2020" # # vn$nodes$Journal<-gsub(", ", "",vn$nodes$Journal) # vn$nodes$Journal<-trimws(vn$nodes$Journal, which = "both", whitespace = "[ \t\r\n]") # unique(vn$nodes$Journal) # # # # vn$nodes$Journal[2]<-"OIKOS" # # vn$nodes$Journal[3]<-"AMBIO" # # vn$nodes$Journal[7]<-"URBAN ECOSYST" # # vn$nodes$Journal[9]<-"CONSERV BIOL" # # vn$nodes$Journal[10]<-"CONSERV BIOL" # # vn$nodes$Journal[11]<-"CONSERV BIOL" # # vn$nodes$Journal[12]<-"CONSERV BIOL" # # vn$nodes$Journal[13]<-"CONSERV BIOL" # # vn$nodes$Journal[14]<-"AMBIO" # # vn$nodes$Journal[16]<-"PRIMATOL" # # vn$nodes$Journal[17]<-"RESTOR ECOL" # # vn$nodes$Journal[53]<-"SOC NAT RESOUR" # # vn$nodes$Journal[52]<-"URBAN AFF" # # # # # # Nodes are sized by degree (the number of links to other packages) degree_value <- degree(Cites, mode = "all") vn$nodes$value <- degree_value[match(vn$nodes$id, names(degree_value))] # vn$nodes$x<-as.numeric(vn$nodes$Year) # # unique(vn$nodes$x) # # # vn$nodes$color<-ifelse(vn$nodes$x>=2015, "red", # ifelse(vn$nodes$x>=2010, "green", # ifelse(vn$nodes$x>=2005, "brown", # ifelse(vn$nodes$x>=2000,"green", # ifelse(vn$nodes$x>=1995, "yellow", "lightblue"))))) # #unique(vn$nodes$Year) #which(vn$nodes$Year=="1968") #vn$nodes$color="blue" #vn$nodes$color[637]<-"red" vn$edges$arrows<-"to" visNetwork(nodes = vn$nodes, edges = vn$edges,main="EBC cited 100+",height = "500px", width = "100%")%>% #visOptions(highlightNearest = TRUE)%>% visOptions(highlightNearest = list(enabled = TRUE, degree = 1)) %>% visNodes() %>% visSave(file =paste0(here(),"/plots/EBC_cited100.html"), selfcontained = T)

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shuffle_gtf.R

#!/usr/bin/env Rscript library(data.table) library(dplyr) library(GenomicRanges) library(rtracklayer) library(valr) ########### # GLOBALS # ########### os_gff_file <- snakemake@input[["os_gff_file"]] os_gtf_file <- snakemake@input[["os_gtf_file"]] seqlengths_file <- snakemake@input[["seqlengths_file"]] irgsp_gff_file <- snakemake@input[["irgsp_gff_file"]] osa1r7_gff_file <- snakemake@input[["osa1r7_gff_file"]] osa1_mirbase_gff_file <- snakemake@input[["osa1_mirbase_gff_file"]] tigr_repeats_fa <- snakemake@input[["tigr_repeats_fa"]] star_index_dir <- snakemake@params[["star_index_dir"]] cpus <- snakemake@threads[[1]] log_file <- snakemake@log[["log"]] shuffled_gff_file <- snakemake@output[["shuffled_gff"]] ######## # MAIN # ######## # set log log <- file(log_file, open = "wt") sink(log, type = "message") sink(log, append = TRUE, type = "output") # load tbl genome genome <- read_genome(seqlengths_file) # get genes os_gff_genes <- import.gff(os_gff_file, feature.type = "gene") # slop genes slopped_genes_tbl <- bed_slop(as.tbl_interval(os_gff_genes), genome, both = 100) # fix irgsp gff irgsp_tmp1 <- tempfile(fileext = ".gff") irgsp_tmp2 <- tempfile(fileext = ".gff") irgsp_tmp3 <- tempfile(fileext = ".gff") system2("sed", args = c("282d", irgsp_gff_file), stdout = irgsp_tmp1, stderr = log_file) system2("sed", args = c("537d", irgsp_tmp1), stdout = irgsp_tmp2, stderr = log_file) system2("sed", args = c("913d", irgsp_tmp2), stdout = irgsp_tmp3, stderr = log_file) irgsp_gff <- import.gff(irgsp_tmp3) # rename chromosomesq slr <- gsub("0(\\d)", "\\1", sub("chr", "Chr", seqlevels(irgsp_gff))) names(slr) <- seqlevels(irgsp_gff) seqlevels(irgsp_gff) <- slr irgsp_tbl <- as.tbl_interval(irgsp_gff) # load osa1r7 gff osa1r7_gff <- import.gff(osa1r7_gff_file) osa1r7_tbl <- as.tbl_interval(osa1r7_gff) # load osa.gff3 miRBase miRNAs osa1_mirbase_gff <- import.gff(osa1_mirbase_gff_file) osa1_mirbase_tbl <- as.tbl_interval(osa1_mirbase_gff) # simulate reads from tigr repeats wgsim1 <- tempfile(fileext = ".wgsim.1.fq") wgsim2 <- tempfile(fileext = ".wgsim.2.fq") system2("wgsim", args = c("-e", "0", "-1", "55", "-2", "55", "-r", "0", "-R", "0", "-X", "0", "-d", "0", "-s", "0", tigr_repeats_fa, wgsim1, wgsim2),, stdout = log_file, stderr = log_file) # map tigr repeats star_outdir <- tempdir() prefix <- paste(star_outdir, "TIGR_Oryza_Repeats.", sep = "/") system2("STAR", args = c("--runThreadN", cpus, "--genomeDir", star_index_dir, "--outSAMtype", "BAM SortedByCoordinate", "--outFilterMultimapNmax", "-1", "--outBAMcompression", "10 ", "--readFilesIn", wgsim1, wgsim2, "--outFileNamePrefix", prefix),, stdout = log_file, stderr = log_file) # convert BAM to bed rpt_bed <- tempfile(fileext = ".bed6") star_bamfile <- paste0(prefix, "Aligned.sortedByCoord.out.bam") system2("bedtools", args = c("bamtobed", "-i", star_bamfile), stdout = rpt_bed, stderr = log_file) # read bed hits rpt_hits <- import.bed(rpt_bed) rpt_tbl <- as.tbl_interval(rpt_hits) # merge with valr all_tbl <- dplyr::bind_rows(slopped_genes_tbl, irgsp_tbl, osa1r7_tbl, osa1_mirbase_tbl, rpt_tbl) merged_tbl <- bed_merge(all_tbl) # prepare a dummy GFF for shuffling os_gff_exons <- import.gff(os_gtf_file, feature.type = "exon", format = "gtf") grl <- GenomicRanges::reduce(split(os_gff_exons, elementMetadata(os_gff_exons)$gene_name)) gtf_reduced <- unlist(os_gff_exons, use.names = FALSE) # add metadata elementMetadata(gtf_reduced)$widths <- width(gtf_reduced) # calculate feature lengths with dplyr feature_length_tbl <- group_by(as.data.frame(gtf_reduced), gene_name) %>% summarize(length = sum(widths)) feature_lengths <- data.table(Length = feature_length_tbl$length, rn = feature_length_tbl$gene_name, key = "rn") to_shuffle <- feature_lengths[Length < quantile(feature_lengths$Length, 0.9), unique(rn)] # generate dummy ranges gene_chromosome <- unique( data.table(rn = os_gff_genes$Name, seqid = as.character(GenomeInfoDb::seqnames(os_gff_genes)), strand = as.character(rtracklayer::strand(os_gff_genes)), key = "rn")) dummy_gff_dt <- gene_chromosome[feature_lengths, .( chrom = seqid, source = 'phytozomev10', type = 'CDS', start = 1, end = Length, score = ".", strand, phase = ".", ID = rn )] dummy_gff_tbl <- as.tbl_interval(dummy_gff_dt) # shuffle shuffled_gtf <- bed_shuffle( dummy_gff_tbl %>% filter( (!chrom %in% c("ChrSy", "ChrUn")) & ID %in% to_shuffle), genome, excl = merged_tbl, within = TRUE, seed = 1) # convert to Granges shuffled_gr <- makeGRangesFromDataFrame(shuffled_gtf, keep.extra.columns=FALSE, ignore.strand=FALSE, seqinfo=NULL, seqnames.field="chrom", start.field="start", end.field="end", strand.field="strand") names(shuffled_gr) <- shuffled_gtf$ID shuffled_gr$type <- "CDS" # write output export(shuffled_gr, shuffled_gff_file, "gff3") # write log sessionInfo()

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r

rna-seq_three_lab.R

library("DESeq2") # load count matrix###################################################################################################################### setwd("/Users/chengl/Desktop/") Bartolomei=read.table("Bartolomei.txt",header=T,sep="\t") Dolinoy=read.table("Dolinoy.txt",header=T,sep="\t") Mutlu=read.table("Mutlu.txt",header=T,sep="\t") rownames(Bartolomei)=Bartolomei[,1] Bartolomei=Bartolomei[,-1] Bartolomei=Bartolomei[,-1] rownames(Dolinoy)=Dolinoy[,1] Dolinoy=Dolinoy[,-1] Dolinoy=Dolinoy[,-1] Dolinoy=Dolinoy[,-which(colnames(Dolinoy)=="T105c_Lead_F_Liver_5mo.R1")] rownames(Mutlu)=Mutlu[,1] Mutlu=Mutlu[,-1] Mutlu=Mutlu[,-1] countdata=cbind(Bartolomei,Dolinoy,Mutlu) # load experiment design##################################################################################################################### tt=read.table("BartolomeiLab_exp_design.txt",header=T,sep="\t") Tissue=factor(rep(1,17),label="Liver") tt=cbind(tt,Tissue) group1=tt[tt$SAMPLE%in%colnames(Bartolomei),2] sex1=tt[tt$SAMPLE%in%colnames(Bartolomei),3] tissue1=tt[tt$SAMPLE%in%colnames(Bartolomei),4] sex=c() group=c() tissue=c() for(i in 1:dim(Dolinoy)[2]) { if(length(grep("M",colnames(Dolinoy)[i],fixed=T))==1) { sex=c(sex,"MALE") } else { sex=c(sex,"FEMALE") } if(length(grep("Ctrl",colnames(Dolinoy)[i],fixed=T))==1) { group=c(group,"Ctrl") } else if(length(grep("Lead",colnames(Dolinoy)[i],fixed=T))==1) { group=c(group,"Lead") } else{ group=c(group,"DEHP") } if(length(grep("Liver",colnames(Dolinoy)[i],fixed=T))==1) { tissue=c(tissue,"Liver") } else { tissue=c(tissue,"Blood") } } sex2=sex group2=group tissue2=tissue tt=read.table("MutluLab_exp_design.txt",header=T,sep="\t") Tissue=factor(c(rep(0,24),rep(1,24),rep(2,24)),label=c("Lung","Liver","Heart")) tt=cbind(tt,Tissue) group3=tt[tt$Samples%in%colnames(Mutlu),3] sex3=tt[tt$Samples%in%colnames(Mutlu),4] tissue3=tt[tt$Samples%in%colnames(Mutlu),5] group=as.factor(c(as.character(group1),as.character(group2),as.character(group3))) sex=as.factor(c(as.character(sex1),as.character(sex2),as.character(sex3))) lab=factor(c(rep(0,ncol(Bartolomei)),rep(1,ncol(Dolinoy)),rep(2,ncol(Mutlu))),label=c("Bartolomei","Dolinoy","Mutlu")) tissue=as.factor(c(as.character(tissue1),as.character(tissue2),as.character(tissue3))) colData=data.frame(lab,sex,group,tissue) rownames(colData)=colnames(countdata) # create DESeq object and pre-filter##################################################################################################################### dds=DESeqDataSetFromMatrix(countData=countdata,colData=colData,design=~lab+sex+tissue) dds=dds[rowSums(fpm(dds,robust=F)>10)>10,] # transformation##################################################################################################################### rld=vst(dds,blind=FALSE) # distance analysis##################################################################################################################### library("pheatmap") library("RColorBrewer") library(grid) sampleDists=dist(t(assay(rld))) sampleDistMatrix=as.matrix(sampleDists) rownames(sampleDistMatrix)=tissue colnames(sampleDistMatrix)=lab colors=colorRampPalette(rev(brewer.pal(9,"Blues")))(255) png("distance_ALL.png",height=3700,width=3700,res=300) setHook("grid.newpage", function() pushViewport(viewport(x=1,y=1,width=0.9, height=0.9, name="vp", just=c("right","top"))), action="prepend") pheatmap(sampleDistMatrix,clustering_distance_rows=sampleDists,clustering_distance_cols=sampleDists,col=colors,main="Heatmap of similarity between ALL samples based on Euclidean distance") setHook("grid.newpage", NULL, "replace") grid.text("Index of samples", y=-0.02, gp=gpar(fontsize=12)) grid.text("Index of tissue", x=-0.03, rot=90, gp=gpar(fontsize=12)) dev.off() # correlation analysis##################################################################################################################### df=as.data.frame(colData(dds)[,c("sex","lab","tissue")]) png("correlation_ALL.png",height=3700,width=3700,res=300) pheatmap(cor(assay(rld)),annotation_col=df,show_colnames=F,main="Heatmap of correlation between ALL samples") dev.off() # PCA##################################################################################################################### library(ggplot2) pcaData=plotPCA(rld,intgroup=c("group","sex","lab","tissue"), returnData = TRUE) percentVar=round(100*attr(pcaData,"percentVar")) ggplot(pcaData,aes(x=PC1,y=PC2,size=lab,shape=sex,color=tissue))+ geom_point()+ scale_shape_manual(values=c(1,2,16))+ scale_size_manual(values=c(6,4,8))+ ggtitle("Principal component analysis with covariate of ALL samples")+ xlab(paste0("PC1: ",percentVar[1],"% variance"))+ ylab(paste0("PC2: ",percentVar[2],"% variance"))+ theme(plot.title=element_text(size=14,family="Tahoma",face="bold",hjust=0.5), text=element_text(size=12,family="Tahoma"), axis.title=element_text(face="bold"), axis.text.x=element_text(size=10,face="bold"), axis.text.y=element_text(size=10,face="bold"), legend.text=element_text(size=10,face="bold"), legend.title=element_text(size=10,face="bold"))

45df8c72c8464df49d3b8dfb5ecf8a67c03eb27c

24ec28988913ab689df0553e8492757d4f1f383a

/R/davidScoreLA.R

2258f96240ef749a98b9e5fa33dd614173adc9a7

[]

no_license

nmmarquez/linHierarchy

bb0222ec8563a490609b90cebe339fa3d3fd6c2e

97a27cec497d301a70c46c156ff7b2783de97ecf

refs/heads/master

2021-01-10T19:15:22.915490

2015-03-03T08:44:14

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2,025

r

davidScoreLA.R

#' Calculate the David's Score of players #' #' Calculates the David's Score of players in an "interData" object. #' @param intData object of class "interData" to calculate scores. #' @param corrected specify wether to use David's adjustment for chance. #' @param normalized specify wether to use a normalizing factor detailed in #' de Vries et al (2006). #' @details Using the methods outlined in Gamel et al. 2003 and de Vries et al. #' 2006 a David's score is calculated using interactions from intData. Adjusting #' the corrected parameter will modify the algorithm to use David's adjustment #' for chance. #' @return A 2 column data frame specifying the players used in the algorithm #' sorted by their corresponding david's score. #' @examples #' # generate generic data #' interactions <- data.frame (a = sample (letters [1:10], 100, T), #' b = sample (letters [1:10], 100, T), #' o = sample (c(-1,-1,0,1,1), 100, T), #' d = Sys.time () + runif (100, 40, 160)) #' # convert to interData object #' id1 <- intTableConv (interactions) #' # calculate David's Score #' davidScore (id1) #' # with David's adjustment for chance #' davidScore (id1, corrected = TRUE) #' @references Gammel et al. (2003) David's Score. Animal Behaviour. #' de Vries et al (2006). Measuring and testing the steepness of #' dominance hierarchies. Animal Behaviour. #' @export davidScore <- function (intData, corrected = FALSE, normalize = FALSE){ idError (intData); plyrs <- intData$players if (corrected){ Pmat <- Dij (intData) } else{ Pmat <- Pij (intData) } w <- rowSums (Pmat); l <- colSums (Pmat) w2 <- Pmat %*% w; l2 <- t (t(l) %*% Pmat) DS <- data.frame (players = plyrs, score = w + w2 - l - l2) DS <- DS [order (-DS$score),]; row.names (DS) <- 1:nrow(DS) if (normalize){ DS$score <- (DS$score + nrow (Pmat) * ((nrow (Pmat) - 1)/2))/nrow (Pmat) } DS }

785f5a9a33738d1fe77c37848acb3b4cf247e814

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/Code/06_Hunt_Prey_Kg_to_Kcal.R

58277ff0b4ce91dd8f348278b1f11eebafab9b34

[]

no_license

PacheCoLuis/ethnodogs

91b0e085925075013f081a58ed140dfc404a7c4b

eb454823a8146f133932b4d7e57d3fd49d664cc1

refs/heads/main

2023-03-06T00:12:59.452765

2021-02-17T05:35:28

312,133,468

0

null

2020-11-12T02:31:57

2020-11-12T01:20:10

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r

06_Hunt_Prey_Kg_to_Kcal.R

# SUBSISTENCE HUNTING PREY KG TO KCAL ####################### source("05_Subsist_Hunt_Sample.R") # Table 4: Prey captures recorded during fieldwork + Harvest (kg to kcal) ####### # Switch to longitudinal format, to deal with serial prey catches [1-5] prey.all <- reshape(harv_succ, varying=list(prey=c("Prey.Catch","Prey.Catch.2","Prey.Catch.3","Prey.Catch.4","Prey.Catch.5"), weight=c("PC.kg","PC.kg.2","PC.kg.3","PC.kg.4","PC.kg.5")), direction="long") colnames(prey.all)[which(colnames(prey.all)=="Harvest.kg..NAs.Prey....")] <- "PC.kg.guess" prey.all <- filter(prey.all,!is.na(PC.kg)) prey.all <- prey.all[order(prey.all$Prey.Number,prey.all$Trip.ID.by.date),] # kcal/kg estimates per prey type (successful trips) ####### # Hill & Hawkes (1983: p158), assume that 65% of the prey live weight is edible # a general conversion factor to obtain the edible kg (ek) is (65*PC.kg)/100 # ek should be multiplied by the amount of estimated cal/kg (ck) per prey # prey ek*ck [units: (cal/kg)*kg = cal = kcal] given the food calories # equivalence cal ~ kcal [1Cal = 1000calories = 1kcal] the units in # ( (65*PC.kg)/100 ) * (cal/kg) would be kcal (the OFT currency) sort(unique(prey.all$Prey.Catch)) prey.all$kcal <- rep(0,dim(prey.all)[1]) prey.all$kcal[prey.all$Prey.Catch=="bird"] <- ((65*prey.all$PC.kg[prey.all$Prey.Catch=="bird"])/100 )*1900 prey.all$kcal[prey.all$Prey.Catch=="chiic"] <- ((65*prey.all$PC.kg[prey.all$Prey.Catch=="chiic"])/100 )*3000 prey.all$kcal[prey.all$Prey.Catch=="ek en che"] <- ((65*prey.all$PC.kg[prey.all$Prey.Catch=="ek en che"])/100 )*3000 prey.all$kcal[prey.all$Prey.Catch=="garuba"] <- ((65*prey.all$PC.kg[prey.all$Prey.Catch=="garuba"])/100 )*1500 prey.all$kcal[prey.all$Prey.Catch=="halee"] <- ((65*prey.all$PC.kg[prey.all$Prey.Catch=="halee"])/100 )*3000 prey.all$kcal[prey.all$Prey.Catch=="keeh"] <- ((65*prey.all$PC.kg[prey.all$Prey.Catch=="keeh"])/100 )*1250 prey.all$kcal[prey.all$Prey.Catch=="kiib"] <- ((65*prey.all$PC.kg[prey.all$Prey.Catch=="kiib"])/100 )*3000 prey.all$kcal[prey.all$Prey.Catch=="kitam"] <- ((65*prey.all$PC.kg[prey.all$Prey.Catch=="kitam"])/100 )*3000 prey.all$kcal[prey.all$Prey.Catch=="wech"] <- ((65*prey.all$PC.kg[prey.all$Prey.Catch=="wech"])/100 )*3000 prey.all$kcal[prey.all$Prey.Catch=="yuk"] <- ((65*prey.all$PC.kg[prey.all$Prey.Catch=="yuk"])/100 )*1250 # Visualizing prey.all[,c("Prey.Catch", "PC.kg", "kcal")] # Saving write.csv(prey.all,"prey_all.csv",row.names=FALSE) # SUMMARIZING THE PREY CATCH KILOGRAMS, HUNTS WITHOUT/WITH DOGS # MIND: harv_succ$Prey.Number, coming from hsd$Prey.Number, indicates the # total number of captures per trip, do not use it to sum captures # by prey type --- count Prey.Catch instead prety.stats <- group_by(prey.all,Prey.Catch) %>% summarize(count=n(), round(mean(PC.kg),1),round(sd(PC.kg),1), length(which((Dogs.used=="No")==TRUE)), length(which((Dogs.used=="Yes")==TRUE))) colnames(prety.stats) <- c("Prey","N","Mean_Kg","SD","Without_dogs","With_dogs") prety.stats <- prety.stats[order(-prety.stats$Mean_Kg),] write.table(prety.stats, file="Table-4_Prey-KG_means_sd.csv", append = FALSE, quote=FALSE, sep=" , ", row.names=FALSE) prety.stats # CHECK for consistency, the total Ns hold equally sum(prety.stats$N) == sum(prety.stats$Without_dogs) + sum(prety.stats$With_dogs) # SUMMARIZING THE PREY CATCH KCAL, HUNTS WITHOUT/WITH DOGS prety.kcals <- group_by(prey.all,Prey.Catch) %>% summarize(count=n(), round(mean(kcal),0),round(sd(kcal),0), length(which((Dogs.used=="No")==TRUE)), length(which((Dogs.used=="Yes")==TRUE))) colnames(prety.kcals) <- c("Prey","N","Mean_Kcal","SD","Without_dogs","With_dogs") prety.kcals <- prety.kcals[order(-prety.kcals$Mean_Kcal),] write.table(prety.kcals, file="Table-4_Prey-KCAL_means_sd.csv", append = FALSE, quote=FALSE, sep=" , ", row.names=FALSE) prety.kcals # NOTE: on weight estimates and actual weight measurements # If 'Harvest.kg..NAs.Prey....' [here 'prey.all$PC.kg.guess'] values are # filled with characters it is a 'Harvest.guess'=="Yes" meaning that the # 'Harvest.kg' were obtained from the literature, in such cases hunters # reported prey type but could not estimate the approximate prey weight # In all other cases weights were either measured using a hanging scale # or estimated by hunters. Use 'PC.kg' associated to Source.Uniform==HA # records as a rough of the number of weights that hunters did estimate sort(names(table(prey.all$PC.kg.guess))) # table with 45 dimnames table(prey.all$PC.kg.guess)[1:37] names(table(prey.all$PC.kg.guess)[38:45]) # working with numeric values (kg estimates or measures) kg.estme <- data.frame(table(prey.all$PC.kg.guess)[1:37][names(table(prey.all$PC.kg.guess)[1:37])]) ; sum(kg.estme$Freq) # 85 cases for which PC.kg were hunters' estimates or actual measurements length(which(prey.all$Source.Uniform=="HA")) # ~68 of kg.estme could come from hunters' estimates, which # leaves ~17 cases with actual prey measurements (weights) # working with character values (kg guesses) kg.guess <- data.frame(table(prey.all$PC.kg.guess)[38:45][names(table(prey.all$PC.kg.guess)[38:45])]) ; sum(kg.guess$Freq) # 36 cases for which PC.kg were taken from Primack et al. (1997) or Koster (2008) # CHECK for consistency sum(kg.estme$Freq) + sum(kg.guess$Freq) == sum(table(prey.all$PC.kg.guess)) # Metrics: kcal, hours, prey, ethno-source ####### # kg or kcal per source type successful hunts (mean, sd, n) prety.sourc <- group_by(prey.all,Source.Uniform) %>% summarize(count=n(), round(sum(PC.kg))) colnames(prety.sourc) <- c("Source","Prey_N","Sum_Kg") prety.sourc prety.sokca <- group_by(prey.all,Source.Uniform) %>% summarize(count=n(), round(sum(kcal))) colnames(prety.sokca) <- c("Source","Prey_N","Sum_Kcal") prety.sokca # Hours per trip (mean, sd, n) round(mean(hsd$Hours.hunted,na.rm=TRUE),1) ; round(sd(hsd$Hours.hunted,na.rm=TRUE),1) # mean 4.9 sd 2.1 length(unique(hsd$Trip.ID.by.date))-length(unique(hsd[which(is.na(hsd$Hours.hunted)),"Trip.ID.by.date"])) # n = 136 dates; after omitting 49 dates for which 'Hours.hunted' are NA... length(unique(hsd[which(is.na(hsd$Hours.hunted) & hsd$Source.Uniform=="Notes"), "Trip.ID.by.date"])) # ...37 from 'Notes' length(unique(hsd[which(is.na(hsd$Hours.hunted) & hsd$Source.Uniform=="HA"), "Trip.ID.by.date"])) # ...12 from 'HA' # Evidence on the sources' disparities and more # BUT FIRST, recalling unsuccessful hunts when converting from wide to long format prey.suun <- reshape(harv, varying=list(prey=c("Prey.Catch","Prey.Catch.2","Prey.Catch.3","Prey.Catch.4","Prey.Catch.5"), weight=c("PC.kg","PC.kg.2","PC.kg.3","PC.kg.4","PC.kg.5")), direction="long") colnames(prey.suun)[which(colnames(prey.suun)=="Harvest.kg..NAs.Prey....")] <- "PC.kg.guess" prey.suun <- filter(prey.suun, Prey.Catch!="") # kcal/kg estimates per prey type (all trips) ####### # Hill & Hawkes (1983: p158), assume that 65% of the prey live weight is edible # a general conversion factor to obtain the edible kg (ek) is (65*PC.kg)/100 # ek should be multiplied by the amount of estimated cal/kg (ck) per prey # prey ek*ck [units: (cal/kg)*kg = cal = kcal] given the food calories # equivalence cal ~ kcal [1Cal = 1000calories = 1kcal] the units in # ( (65*PC.kg)/100 ) * (cal/kg) would be kcal (the OFT currency) sort(unique(prey.suun$Prey.Catch)) prey.suun$kcal <- rep(0,dim(prey.suun)[1]) prey.suun$kcal[prey.suun$Prey.Catch=="aim_PCS"] <- ((65*prey.suun$PC.kg[prey.suun$Prey.Catch=="aim_PCS"])/100 )*0 prey.suun$kcal[prey.suun$Prey.Catch=="bird"] <- ((65*prey.suun$PC.kg[prey.suun$Prey.Catch=="bird"])/100 )*1900 prey.suun$kcal[prey.suun$Prey.Catch=="chiic"] <- ((65*prey.suun$PC.kg[prey.suun$Prey.Catch=="chiic"])/100 )*3000 prey.suun$kcal[prey.suun$Prey.Catch=="ek en che"] <- ((65*prey.suun$PC.kg[prey.suun$Prey.Catch=="ek en che"])/100 )*3000 prey.suun$kcal[prey.suun$Prey.Catch=="garuba"] <- ((65*prey.suun$PC.kg[prey.suun$Prey.Catch=="garuba"])/100 )*1500 prey.suun$kcal[prey.suun$Prey.Catch=="halee"] <- ((65*prey.suun$PC.kg[prey.suun$Prey.Catch=="halee"])/100 )*3000 prey.suun$kcal[prey.suun$Prey.Catch=="keeh"] <- ((65*prey.suun$PC.kg[prey.suun$Prey.Catch=="keeh"])/100 )*1250 prey.suun$kcal[prey.suun$Prey.Catch=="kiib"] <- ((65*prey.suun$PC.kg[prey.suun$Prey.Catch=="kiib"])/100 )*3000 prey.suun$kcal[prey.suun$Prey.Catch=="kitam"] <- ((65*prey.suun$PC.kg[prey.suun$Prey.Catch=="kitam"])/100 )*3000 prey.suun$kcal[prey.suun$Prey.Catch=="wech"] <- ((65*prey.suun$PC.kg[prey.suun$Prey.Catch=="wech"])/100 )*3000 prey.suun$kcal[prey.suun$Prey.Catch=="yuk"] <- ((65*prey.suun$PC.kg[prey.suun$Prey.Catch=="yuk"])/100 )*1250 # Visualizing prey.suun[,c("Prey.Catch", "PC.kg", "kcal")] # Saving write.csv(prey.suun,"prey_suun.csv",row.names=FALSE) # SUMMARIZING THE PREY COUNTS PER SOURCE prety.suun <- group_by(prey.suun,Prey.Catch) %>% summarize(count=n(), length(which((Source.Uniform=="GPS/HR")==TRUE)), length(which((Source.Uniform=="HA")==TRUE)), length(which((Source.Uniform=="Notes")==TRUE))) colnames(prety.suun) <- c("Prey", "N", "GPS/HR", "HA", "Notes") write.table(prety.suun, file="Table_Prey-Source.csv", append = FALSE, quote=FALSE, sep=", ", row.names=FALSE) prety.suun # Recalling notes on prey missed #hsd[1:20,c("Trip.ID.by.date", "Prey.Number", "HarvShare_NotAccounted", "Prey.Catch", "Harvest.kg..NAs.Prey....","Prey.Catch.Spa")] #harv[which(harv$PC.kg==0),c("Trip.ID.by.date", "Prey.Number", "HarvShare_NotAccounted", "Prey.Catch", "Harvest.kg..NAs.Prey....","Prey.Catch.Spa")] prety.miss <- group_by(prey.suun[which(prey.suun$Prey.Number==0),],Prey.Catch.Spa) %>% summarize(count=n(), length(which((Source.Uniform=="GPS/HR")==TRUE)), length(which((Source.Uniform=="HA")==TRUE)), length(which((Source.Uniform=="Notes")==TRUE))) colnames(prety.miss) <- c("Prey.Miss.Spa", "N_Aimed", "GPS/HR", "HA", "Notes") write.csv(prety.miss, file="Table_PreyMissSpa-Source.csv", row.names=FALSE) prety.miss # Number of prey captured per trip ####### # Single capture trips (n = 79) dim(unique(prey.all[which(prey.all$Prey.Number==1),c("Trip.ID.by.date","Prey.Catch")]))[1] kill_sing <- prey.all[which(prey.all$Prey.Number==1), c("Trip.ID.by.date","Prey.Catch","Dogs.used","Day.Night")] ; kill_sing # Double capture trips (n = 17) Prey.Catch [Trip.ID.by.date] length(unique(prey.all[which(prey.all$Prey.Number==2),"Trip.ID.by.date"])) kill_doub <- prey.all[which(prey.all$Prey.Number==2), c("Trip.ID.by.date","Prey.Catch","Dogs.used","Day.Night")] ; kill_doub # --SAME x2 PREY # keeh [27] # halee [40] # kiib [93] # wech [118], wech [119], wech [155] # kitam [29], kitam [39], kitam [107], kitam [130], kitam [139], kitam [142] # --ARRAYS OF x2 PREY # halee-kiib [32], keeh-wech [135], kiib-wech [141], wech-halee [172], kitam-wech [195] # TEXT: We observed seventeen trips with double-captures: kitam (n=6), wech (n=3), # keeh (n=1), halee (n=1), and kiib (n=1). The rest of the double-capture cases were # for different species: halee-kiib, keeh-wech, kiib-wech, wech-halee, kitam-wech. All # double capture trips were diurnal, and only one of them [x2 wech, 119] was without dogs # Triple capture trips (n = 1) Prey.Catch [Trip.ID.by.date] length(unique(prey.all[which(prey.all$Prey.Number==3),"Trip.ID.by.date"])) kill_trip <- prey.all[which(prey.all$Prey.Number==3), c("Trip.ID.by.date","Prey.Catch","Dogs.used","Day.Night")] ; kill_trip # ...x3 halee [26] --- Prey.Catch [Trip.ID.by.date] # TEXT: A unique nocturnal and without dogs trip, with a triple-capture for halee # Quadruple capture trips (n = 0) Prey.Catch [Trip.ID.by.date] dim(unique(prey.all[which(prey.all$Prey.Number==4),c("Trip.ID.by.date","Prey.Catch")]))[1] kill_quad <- prey.all[which(prey.all$Prey.Number==4), c("Trip.ID.by.date","Prey.Catch","Dogs.used","Day.Night")] ; kill_quad # Quintuple capture trips (n = 1) Prey.Catch [Trip.ID.by.date] length(unique(prey.all[which(prey.all$Prey.Number==5),"Trip.ID.by.date"])) kill_quin <- prey.all[which(prey.all$Prey.Number==5), c("Trip.ID.by.date","Prey.Catch","Dogs.used","Day.Night")] ; kill_quin # TEXT: We registered a diurnal and with dogs trip with a quintuple-capture # for wech (a whole family) # Metrics for Figs 4 & 5 Hours~Prey + Harvest~Trip ####### # FIGURES 4 and 5 (start, bring pieces together) # ALL CASES # Omitting cases for which we do not know 'Hours.hunted' hrsprey <- filter(prey.all,!is.na(Hours.hunted)) hrsprey_na <- filter(prey.all,is.na(Hours.hunted)) length(unique(hrsprey_na$Trip.ID.by.date)) pny.uhrs <- sum(xtabs(~Source.Uniform+Prey.Catch,hrsprey_na)) ; pny.uhrs # 28 successful trips and 34 prey items for which Hours.hunted are NA # (see 21 unsuccesful trips at the start of BEHAVIORAL METRICS OF THE HUNTS # section; together they sum the 49 cases for which Hours.hunted are NA) # OMITTING the "garuba" and the "bird" pgb.khrs <- dim(filter(hrsprey,Prey.Catch=="garuba"|Prey.Catch=="bird"))[1] ; pgb.khrs # 2 prey items omitted (garuba and bird) with known Hours.hunted hrsprey <- filter(hrsprey,Prey.Catch!="garuba"&Prey.Catch!="bird") hrsprey$Prey.Catch <- droplevels(hrsprey$Prey.Catch) # NOTE: # The wild pigeon shot was a last chance to get wild meat on the way back home after a 12h # unsuccessful trip with dogs which did not chase or alerted hunters about the prey. # The iguana that hunters brought down from a tree after concluding routine agricultural # tasks and attending to their dogs' barks, was not chased in the bush. # FIG4a-b --- HOURS~PREY ####### # Getting the descriptive stats of trips for which we know Hours.hunted prety.hour <- group_by(hrsprey,Prey.Catch) %>% summarize(count=n(), length(unique(Trip.ID.by.date)), mean(Hours.hunted,na.rm=TRUE), sd(Hours.hunted,na.rm=TRUE)) colnames(prety.hour) <- c("Prey","count","N_Trips","Mean_Trips_Hrs","SD") prety.hour <- prety.hour[order(-prety.hour$Mean_Trips_Hrs),] write.table(prety.hour,file="Table_Prey-HOURS_means_sd.csv", append=FALSE, quote=FALSE, sep=",", row.names = FALSE) prety.hour sum(prety.hour$count) # 85 prey items, in hunting trips without and with dogs # WITHOUT DOGS CASES hrsprey_nd <- filter(hrsprey,Dogs.used=="No") hrsprey_nd$Prey.Catch <- droplevels(hrsprey_nd$Prey.Catch) boxplot.medians <- boxplot(hrsprey_nd$Hours.hunted~hrsprey_nd$Prey.Catch, plot=FALSE)$stats[3,] boxplot.names <- boxplot(hrsprey_nd$Hours.hunted~hrsprey_nd$Prey.Catch, plot=FALSE)$names names.ordered <- boxplot.names[order(boxplot.medians)] prey.ordered <- factor(hrsprey_nd$Prey.Catch, ordered=TRUE, levels=names.ordered) hpc <- boxplot(hrsprey_nd$Hours.hunted~prey.ordered,plot=0) pnd.khrs <- sum(hpc$n[1:5]) # 11 prey items without dogs, and known Hours.hunted # WITH DOGS CASES hrsprey_wd <- filter(hrsprey,Dogs.used=="Yes") hrsprey_wd$Prey.Catch <- droplevels(hrsprey_wd$Prey.Catch) boxplot.medians <- boxplot(hrsprey_wd$Hours.hunted~hrsprey_wd$Prey.Catch, plot=FALSE)$stats[3,] boxplot.names <- boxplot(hrsprey_wd$Hours.hunted~hrsprey_wd$Prey.Catch, plot=FALSE)$names names.ordered <- boxplot.names[order(boxplot.medians)] prey.ordered <- factor(hrsprey_wd$Prey.Catch, ordered=TRUE, levels=names.ordered) hpc <- boxplot(hrsprey_wd$Hours.hunted~prey.ordered,plot=0) pyd.khrs <- sum(hpc$n[1:5]) # 74 prey items with dogs, and known Hours.hunted # CHECK FOR CONSISTENCY: # Sum of prey items holds equal to total prey number pny.uhrs + pgb.khrs + pnd.khrs + pyd.khrs == sum(prety.stats$N) # FIG5a-b --- HARVEST~DOGS.USED ####### # UNSUCCESSFUL AND SUCCESSFUL HUNTS # KILLS IN TRIPS WITHOUT AND WITH DOGS doguse_suco <- boxplot(prey.suun$PC.kg[prey.suun$Prey.Catch!="aim_PCS"] ~ prey.suun$Dogs.used[prey.suun$Prey.Catch!="aim_PCS"], plot=0) # FAILS IN TRIPS WITHOUT AND WITH DOGS doguse_umsk <- boxplot(prey.suun$PC.kg[prey.suun$Prey.Catch=="aim_PCS"] ~ prey.suun$Dogs.used[prey.suun$Prey.Catch=="aim_PCS"], plot=0) # WITHOUT DOGS ALL TRIPS doguse_suco$n[1] ; length(unique(prey.suun[which(prey.suun$PC.kg>=0 & prey.suun$Dogs.used=="No"),"Trip.ID.by.date"])) # Kills: 17 prey items in 42 trips without dogs doguse_umsk$n[1] ; length(unique(prey.suun[which(prey.suun$PC.kg>=0 & prey.suun$Dogs.used=="No"),"Trip.ID.by.date"])) # Fails: 28 trips (~missed targets) in 42 trips without dogs # WITH DOGS ALL TRIPS doguse_suco$n[2] ; length(unique(prey.suun[which(prey.suun$PC.kg>=0 & prey.suun$Dogs.used=="Yes"),"Trip.ID.by.date"])) # Kills: 104 prey items in 143 trips with dogs doguse_umsk$n[2] ; length(unique(prey.suun[which(prey.suun$PC.kg>=0 & prey.suun$Dogs.used=="Yes"),"Trip.ID.by.date"])) # Fails: 59 trips (~missed targets) in 143 trips with dogs # WITHOUT DOGS (Fails [UNSUCCESSFUL] omitted) SUCCESSFUL TRIPS ONLY doguse_suco$n[1] ; length(unique(prey.suun[which(prey.suun$PC.kg>0 & prey.suun$Dogs.used=="No"),"Trip.ID.by.date"])) # 17 prey items in 14 trips without dogs # WITH DOGS (Fails [UNSUCCESSFUL] omitted) SUCCESSFUL TRIPS ONLY doguse_suco$n[2] ; length(unique(prey.suun[which(prey.suun$PC.kg>0 & prey.suun$Dogs.used=="Yes"),"Trip.ID.by.date"])) # 104 prey items in 84 trips with dogs # SUCCESSFUL HUNTS ONLY doguse_succ <- boxplot(prey.suun$PC.kg[prey.suun$PC.kg>0] ~ prey.suun $Dogs.used[prey.suun$PC.kg>0], plot=0) # SUM ALL KG PER TRIP INTO kg_sum AND OMIT Trip.ID.by.date REPETITIONS kgsum <- prey.suun %>% group_by(Trip.ID.by.date) %>% mutate(kg_sum=sum(PC.kg)) %>% select(Trip.ID.by.date,kg_sum,Prey.Number,Dogs.used,Source.Uniform) kgsum <- kgsum %>% distinct(Trip.ID.by.date,kg_sum,Prey.Number,Dogs.used,Source.Uniform) # SUM ALL KCAL PER TRIP INTO kcal_sum AND OMIT Trip.ID.by.date REPETITIONS kcalsum <- prey.suun %>% group_by(Trip.ID.by.date) %>% mutate(kcal_sum=sum(kcal)) %>% select(Trip.ID.by.date,kcal_sum,Prey.Number,Dogs.used,Source.Uniform) kcalsum <- kcalsum %>% distinct(Trip.ID.by.date,kcal_sum,Prey.Number,Dogs.used,Source.Uniform) #write.csv(kgsum,"kg_sum.csv",row.names=FALSE) #write.csv(kcalsum,"kcal_sum.csv",row.names=FALSE) # FIGURES 4 and 5 (end, compile postscripts) # Figure 4: Hunting trip duration by prey type captured ####### # Raw material produced here for later BW-work on art using Adobe Illustrator setEPS() postscript("F4_Hrs-Prey_perDogsUsed.eps") par(mfrow=c(1,2)) # Fig4a Without dogs boxplot.medians <- boxplot(hrsprey_nd$Hours.hunted~hrsprey_nd$Prey.Catch, plot=FALSE)$stats[3,] boxplot.names <- boxplot(hrsprey_nd$Hours.hunted~hrsprey_nd$Prey.Catch, plot=FALSE)$names names.ordered <- boxplot.names[order(boxplot.medians)] prey.ordered <- factor(hrsprey_nd$Prey.Catch, ordered=TRUE, levels=names.ordered) hpc <- boxplot(hrsprey_nd$Hours.hunted~prey.ordered,plot=0) par(mar=c(5,5.5,2.5,5)+0.1,mgp=c(3,1,0)) boxplot(hrsprey_nd$Hours.hunted~prey.ordered,varwidth=TRUE,horizontal=TRUE, yaxt="n",xlab="Hunting (hours)\nWithout dogs",frame.plot=FALSE,cex.axis=0.9) axis(side=2, las=2, at=c(1:5), labels = c( names.ordered[1], names.ordered[2], names.ordered[3], names.ordered[4], names.ordered[5]), tick=FALSE) axis(side=4, las=2, at=c(1:5), labels = c( paste("n = ",hpc$n[1]), paste("n = ",hpc$n[2]), paste("n = ",hpc$n[3]), paste("n = ",hpc$n[4]), paste("n = ",hpc$n[5])), tick=FALSE) # Fig4b With dogs boxplot.medians <- boxplot(hrsprey_wd$Hours.hunted~hrsprey_wd$Prey.Catch, plot=FALSE)$stats[3,] boxplot.names <- boxplot(hrsprey_wd$Hours.hunted~hrsprey_wd$Prey.Catch, plot=FALSE)$names names.ordered <- boxplot.names[order(boxplot.medians)] prey.ordered <- factor(hrsprey_wd$Prey.Catch, ordered=TRUE, levels=names.ordered) hpc <- boxplot(hrsprey_wd$Hours.hunted~prey.ordered,plot=0) par(mar=c(5,5.5,2.5,5)+0.1,mgp=c(3,1,0)) boxplot(hrsprey_wd$Hours.hunted~prey.ordered,varwidth=TRUE,horizontal=TRUE, yaxt="n",xlab="Hunting (hours)\nWith dogs",frame.plot=FALSE,cex.axis=0.9) axis(side=2, las=2, at=c(1:5), labels = c( names.ordered[1], names.ordered[2], names.ordered[3], names.ordered[4], names.ordered[5]), tick=FALSE) axis(side=4, las=2, at=c(1:5), labels = c( paste("n = ",hpc$n[1]), paste("n = ",hpc$n[2]), paste("n = ",hpc$n[3]), paste("n = ",hpc$n[4]), paste("n = ",hpc$n[5])), tick=FALSE) par(mar = c(5, 4, 4, 2) + 0.1, mgp = c(3, 1, 0), mfrow=c(1,1)) # default dev.off() # Inferential statistics Hours~Prey when nDogs or wDogs Used summary(lm(hrsprey_nd$Hours.hunted~hrsprey_nd$Prey.Catch)) summary(lm(hrsprey_wd$Hours.hunted~hrsprey_wd$Prey.Catch)) # Figure 5: Hunting trip harvest without and with dogs + t-Test ####### # Raw material produced here for later BW-work on art using Adobe Illustrator # Inferential statistics Harvest~Trip per Dogs Used # MIND: no Normal distribution found # All hunts huntsall <- lm(prey.suun$kcal ~ prey.suun$Dogs.used) summary(huntsall) # F = 4.1, df = 206, p = 0.04; Adjusted R squared = 0.015; kcal ~ 9933 + dogsY*5907 # Successful hunts only prey.succ <- prey.suun %>% filter(kcal>0) huntsucc <- lm(prey.succ$kcal ~ prey.succ$Dogs.used) summary(huntsucc) # F = 0.12, df = 119, p = 0.73; Adjusted R squared = -0.007; kcal ~ 26293 + dogsY*(-1467) # t-tests ####### # All hunts adpkcal <- kcalsum %>% filter(Dogs.used=="Yes") length(adpkcal$kcal_sum) # 143 adakcal <- kcalsum %>% filter(Dogs.used=="No") length(adakcal$kcal_sum) # 42 t.test(adpkcal$kcal_sum,adakcal$kcal_sum) # TEXT: t=2.15, df=79.3, p-value=0.03 95% CI 567 to 14259 # Means adpkcal = 18055 and adakcal = 10642 # Successful hunts sdpkcal <- kcalsum %>% filter(Dogs.used=="Yes",kcal_sum>0) length(sdpkcal$kcal_sum) sdakcal <- kcalsum %>% filter(Dogs.used=="No",kcal_sum>0) length(sdakcal$kcal_sum) t.test(sdpkcal$kcal_sum,sdakcal$kcal_sum) # TEXT: t=-0.21, df=19, p-value=0.83 95% CI -12990 to 10610 # Means sdpkcal = 30737 and sdakcal = 31927 round(mean(adpkcal$kcal_sum),1) # avg 18055.5 round(median(adpkcal$kcal_sum),1) # median 9750 round(sd(adpkcal$kcal_sum),1) # sd 22502.1 length(adpkcal$kcal_sum) # 143 trips round(mean(adakcal$kcal_sum),1) # avg 10642.5 round(median(adakcal$kcal_sum),1) # median 0 round(sd(adakcal$kcal_sum),1) # sd 18661.4 length(adakcal$kcal_sum) # 42 trips setEPS() postscript("F5_Harvest-Trip_perDogsUsed.eps") par(mfrow=c(2,1)) # Fig5a All trips (kcal>=0) par(mar=c(6,6,4,4)+0.1,mgp=c(4,1,0)) boxplot(kcalsum$kcal_sum ~ kcalsum$Dogs.used, varwidth=TRUE, las=1, xaxt="n", ylab="Harvest (kcal)", frame.plot=FALSE) axis(side = 1, at=c(1, 2), labels = c( paste0("Without dogs\nSample ", length(adakcal$kcal_sum), " Succ.Tr ", length(which(adakcal$kcal_sum>0)), "\n%Succ.Rt ", round((length(which(adakcal$kcal_sum>0))*100)/length(adakcal$kcal_sum),0), " Anim.Cap ", doguse_suco$n[1]), paste0("With dogs\nSample ", length(adpkcal$kcal_sum), " Succ.Tr ", length(which(adpkcal$kcal_sum>0)), "\n%Succ.Rt ", round((length(which(adpkcal$kcal_sum>0))*100)/length(adpkcal$kcal_sum),0), " Anim.Cap ", doguse_suco$n[2])), tick=FALSE, cex.axis=0.6) # Fig5b Successful trips (kcal>0) par(mar=c(6,6,4,4)+0.1,mgp=c(4,1,0)) boxplot(kcalsum$kcal_sum[kcalsum$kcal_sum>0] ~ kcalsum$Dogs.used[kcalsum$kcal_sum>0], varwidth=TRUE, las=1, xaxt="n", ylab="Harvest (kcal)",frame.plot=FALSE) axis(side = 1, at=c(1, 2), labels = c( paste0("Without dogs"), paste0("With dogs")), tick=FALSE, cex.axis=0.6) par(mar = c(5, 4, 4, 2) + 0.1, mgp = c(3, 1, 0), mfrow=c(1,1)) # default dev.off()

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5.1.SingleSample_Neu.R

library(scran) library(dplyr) library(Rtsne) library(plyr) library(BiocSingular) library(scater) library(batchelor) dataList <- readRDS("../data/Robjects/ExpressionList_QC.rds") m <- dataList[[1]] pD <- dataList[[2]] fD <- dataList[[3]] rm(dataList) pD$Replicate <- mapvalues(pD$SampleID, c("BRCA1_A","BRCA1_B","NEU_A","NEU_B","FF99WT_A","FF99WT_B","4T1"), c("A","B","A","B","A","B","A")) %>% factor(., levels = c("A","B")) pD$Condition <- mapvalues(pD$SampleID, c("BRCA1_A","BRCA1_B","NEU_A","NEU_B","FF99WT_A","FF99WT_B","4T1"), c("BRCA1","BRCA1","NEU","NEU","FF99WT","FF99WT","4T1")) %>% factor(., levels = c("BRCA1","NEU","FF99WT","4T1")) fD$keep <- rowMeans(m) > 0.01 m <- m[fD$keep, pD$PassAll] pD <- pD[pD$PassAll,] fD <- fD[fD$keep, ] rownames(m) <- fD$symbol rownames(pD) <- pD$barcode rownames(fD) <- fD$symbol #------------------------------------------- BRCA1 <- pD$barcode[pD$Condition %in% c("BRCA1")] FF99WT <- pD$barcode[pD$Condition %in% c("FF99WT")] NEU <- pD$barcode[pD$Condition %in% c("NEU")] Sample_4T1 <- pD$barcode[pD$Condition %in% c("4T1")] #-------------------------------------------- # NEU set.seed(1000) m_NEU <- m[,pD$barcode %in% NEU] pD_NEU <- pD[pD$barcode %in% NEU,] #======================================================= fD_A <- fD %>% dplyr::mutate(keep = rowMeans(m_NEU[,pD_NEU$Replicate == "A"])> 0.01) fD_B <- fD %>% dplyr::mutate(keep = rowMeans(m_NEU[,pD_NEU$Replicate == "B"])> 0.01) sce.NEU_A <- SingleCellExperiment(list(counts=as.matrix(m_NEU[fD_A$keep,pD_NEU$Replicate == "A"])), colData = DataFrame(pD_NEU[pD_NEU$Replicate == "A",]), rowData = DataFrame(fD_A[fD_A$keep,])) sce.NEU_B <- SingleCellExperiment(list(counts=as.matrix(m_NEU[fD_B$keep,pD_NEU$Replicate == "B"])), colData = DataFrame(pD_NEU[pD_NEU$Replicate == "B",]), rowData = DataFrame(fD_B[fD_B$keep,])) #------------------------- clusters <- quickCluster(sce.NEU_A, method ='igraph',use.ranks=FALSE, min.mean = 0.1) table(clusters) sce.NEU_A <- computeSumFactors(sce.NEU_A, min.mean = 0.1, clusters = clusters) summary(sizeFactors(sce.NEU_A)) sce.NEU_A <- normalize(sce.NEU_A) #table(sce.NEU$Replicate) #batch <- c(rep("1", each=2242), rep("2", each = 1912)) fit <- trendVar(sce.NEU_A, use.spikes = FALSE) dec <- decomposeVar(sce.NEU_A, fit) dec$Symbol <- rowData(sce.NEU_A)$symbol dec_A <- dec[order(dec$bio, decreasing = TRUE), ] hvg.A <- dec_A[which(dec_A$FDR <= 0.1 & dec_A$bio >=0),] set.seed(1000) sce.NEU_A <- runPCA(sce.NEU_A, feature_set= rownames(hvg.A),BSPARAM=IrlbaParam()) sce.NEU_A <- runTSNE(sce.NEU_A , use_dimred = "PCA") sce.NEU_A <- runUMAP(sce.NEU_A , use_dimred = "PCA") #rownames(sce.NEU_A) <- rowData(sce.NEU_A)$symbol plotTSNE(sce.NEU_A, colour_by="Cd14") snn.gr <- buildSNNGraph(sce.NEU_A, use.dimred="PCA", assay.type="logcounts", k=500) clusters <- igraph::cluster_louvain(snn.gr) table(clusters$membership) sce.NEU_A$Cluster <- factor(clusters$membership) plotTSNE(sce.NEU_A, colour_by="Cluster") markers <- findMarkers(sce.NEU_A, sce.NEU_A$Cluster, direction="up") library(xlsx) for (cluster in names(markers)) { write.xlsx(data.frame(markers[[cluster]]), row.names=TRUE, file="NEU_A__MarkerGenes.xlsx", sheetName=cluster, append = TRUE) gc() } #---------------------------- clusters <- quickCluster(sce.NEU_B, method ='igraph',use.ranks=FALSE, min.mean = 0.1) table(clusters) sce.NEU_B <- computeSumFactors(sce.NEU_B, min.mean = 0.1, clusters = clusters) summary(sizeFactors(sce.NEU_B)) sce.NEU_B <- normalize(sce.NEU_B) #table(sce.NEU$Replicate) #batch <- c(rep("1", each=2242), rep("2", each = 1912)) fit <- trendVar(sce.NEU_B, use.spikes = FALSE) dec <- decomposeVar(sce.NEU_B, fit) dec$Symbol <- rowData(sce.NEU_B)$symbol dec_B <- dec[order(dec$bio, decreasing = TRUE), ] hvg.B <- dec_B[which(dec_B$FDR <= 0.1 & dec_B$bio >=0.1),] sce.NEU_B <- runPCA(sce.NEU_B, feature_set= rownames(hvg.B),BSPARAM=IrlbaParam()) sce.NEU_B <- runTSNE(sce.NEU_B, use_dimred = "PCA") sce.NEU_B <- runUMAP(sce.NEU_B, use_dimred = "PCA") rownames(sce.NEU_B) <- rowData(sce.NEU_B)$symbol snn.gr <- buildSNNGraph(sce.NEU_B, use.dimred="PCA", assay.type="logcounts", k=300) clusters <- igraph::cluster_walktrap(snn.gr) table(clusters$membership) sce.NEU_B$Cluster <- factor(clusters$membership) plotTSNE(sce.NEU_B, colour_by="Cluster") plotTSNE(sce.NEU_B, colour_by="Cd14") markers <- findMarkers(sce.NEU_B, sce.NEU_B$Cluster, direction="up") for (cluster in names(markers)) { write.xlsx(data.frame(markers[[cluster]]), row.names=TRUE, file="NEU_B_MarkerGenes.xlsx", sheetName=cluster, append = TRUE) gc() } #--Plot Genes expression-------------- genes <- c("Cd14","Bcl3","Osmr","Nfkbia") plt <- list() for (gene in genes){ tmp = plotTSNE(sce.NEU_A, colour_by = gene )+ scale_fill_gradient2(name = gene, low='grey',high ='red')+ theme_bw()+ theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) plt[[gene]] <- tmp } multiplot(plotlist=plt, cols = 2) plotTSNE(sce.NEU_A, colour_by ="Cluster") #==CombineDataset============================================================= universe <- intersect(rownames(dec_A), rownames(dec_B)) mean.bio <- (dec_A[universe,"bio"] + dec_B[universe,"bio"])/2 chosen <- universe[mean.bio > 0] length(chosen) rescaled <- batchelor::multiBatchNorm( sce.NEU_A[universe,], sce.NEU_B[universe,] ) rescaled.NEU_A <- rescaled[[1]] rescaled.NEU_B <- rescaled[[2]] set.seed(1000) unc.NEU_A <- logcounts(rescaled.NEU_A)[chosen,] unc.NEU_B <- logcounts(rescaled.NEU_B)[chosen,] mnn.out <- batchelor::fastMNN( NEU_A=unc.NEU_A, NEU_B=unc.NEU_B, k=20, d=50, BSPARAM=IrlbaParam(deferred=TRUE) ) mnn.out sce.NEU <- mnn.out sce.NEU <- runTSNE(sce.NEU, use_dimred="corrected") plotTSNE(sce.NEU, colour_by="batch") + ggtitle("Corrected") assay(sce.NEU, "original") <- cbind(unc.NEU_A, unc.NEU_B) osce.NEU <- runPCA(sce.NEU, exprs_values = "original",ntop = Inf, BSPARAM=IrlbaParam()) osce.NEU <- runTSNE(osce.NEU, use_dimred = "PCA") plotTSNE(osce.NEU, colour_by = "batch") +ggtitle("Original") #osce.NEU <- runUMAP(osce.NEU, use_dimred = "PCA") #plotUMAP(osce.NEU, colour_by = "batch") sceList <- list("sce_A" = sce.NEU_A, "sce_B" = sce.NEU_B, "combine"=sce.NEU) saveRDS(sceList, "../data/Robjects/Neu_sceList.rds") #--Plot Gene expression----------------- genes <- c("Cd14","Bcl3","Osmr","Nfkbia","Aqp5","Kcnn4","Col9a1","Apoc1") plt <- list() for (gene in genes){ tmp = plotTSNE(sce.NEU, by_exprs_values="original", colour_by = gene )+scale_fill_gradient2(name = gene, low='grey',high ='red') plt[[gene]] <- tmp } multiplot(plotlist=plt, cols = 2) #--Clustering------------------------------------ snn.gr <- buildSNNGraph(sce.NEU, use.dimred="corrected", k=100) #k=100 3 cluster k=50: 4 cluster k=40 : 5clusters clusters <- igraph::cluster_louvain(snn.gr) table(clusters$membership, sce.NEU$batch) sce.NEU$Cluster <- factor(clusters$membership) plotTSNE(sce.NEU, colour_by="Cluster") #----------------------- markers <- findMarkers(sce.NEU, sce.NEU$Cluster,block = sce.NEU$batch, assay.type="original", direction="up") for (cluster in names(markers)) { write.xlsx(data.frame(markers[[cluster]]), row.names=TRUE, file="NEU_combine_3clusters_MarkerGenes.xlsx", sheetName=cluster, append = TRUE) gc() } #=============================================================================== # NEU_A <- CreateSeuratObject(counts =m_NEU[,pD_NEU$Replicate == "A"] , project = "A", min.cells = 5) # NEU_A$Rep <- "A" # NEU_A <- NormalizeData(NEU_A, verbose = FALSE) # NEU_A <- FindVariableFeatures(NEU_A, selection.method = "vst", nfeatures = 2000) # # Set up stimulated object # NEU_B <- CreateSeuratObject(counts = m_NEU[,pD_NEU$Replicate == "B"], project = "B", min.cells = 5) # NEU_B$Rep <- "B" # NEU_B <- NormalizeData(NEU_B, verbose = FALSE) # NEU_B <- FindVariableFeatures(NEU_B, selection.method = "vst", nfeatures = 2000) # anchors <- FindIntegrationAnchors(object.list = list(NEU_A, NEU_B), dims = 1:20) # Neu <- IntegrateData(anchorset = anchors, dims = 1:20) # DefaultAssay(Neu) <- "integrated" # Neu <- ScaleData(Neu, verbose = FALSE) # Neu <- RunPCA(Neu, npcs = 30, verbose=FALSE) # Neu <- RunUMAP(Neu, reduction = "pca", dims = 1:20) # Neu <- RunTSNE(Neu, reduction = "pca",dims = 1:20) # DimPlot(Neu, reduction = "tsne", group.by = "Rep") # Neu <- FindNeighbors(Neu, reduction ="pca", dims = 1:20) # Neu <- FindClusters(Neu, resolution = 0.2) # DimPlot(Neu, reduction = "tsne", label=T) # DefaultAssay(Neu) <- "RNA" # markers <- FindAllMarkers(Neu, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25) # markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_logFC) # FeaturePlot(Neu, features = c("Cd14")) # head(dec) # hvg.out_A <- dec[which(dec$FDR <= 0.1 & dec$bio >=0),] # sce.NEU_A <- runPCA(sce.NEU,feature_set= rownames(hvg.out), BSPARAM=IrlbaParam()) # sce.NEU <- runTSNE(sce.NEU, use_dimred = "PCA") # plotTSNE(sce.NEU, colour_by = "SampleID") # sce.NEU <- runUMAP(sce.NEU, use_dimred ="PCA") # plotUMAP(sce.NEU, colour_by = "SampleID") # snn.gr <- buildSNNGraph(sce.NEU, use.dimred="PCA") # clusters <- igraph::cluster_fast_greedy(snn.gr) # sce.NEU$Cluster <- factor(clusters$membership) # plotTSNE(sce.NEU, colour_by = "Cluster") # rownames(sce) <- rowData(sce)$symbol # sceList[[name]] <- sce # saveRDS(sceList, "SingleSamples_norm_sce.rds") # genes <- c("Tslp", "Ctla2a", 'H2-Ab1', "Il24", "Pdgfrl", 'Ly6a', "H2-Aa", "Slpi", "H2-Eb1") # plotTSNE(sceList[["NEU"]], colour_by ="Cluster") # plotUMAP(sce, colour_by ="Cluster") # rownames(sce) <- rowData(sce)$symbol # genes <- c("Mgp","Kctd1","Matn4","Cp","Mt1","Steap4","Selenop","Mt2") # plt <- list() # for (gene in genes){ # tmp = plotTSNE(sce.BRCA1, colour_by = gene )+scale_fill_gradient2(name = gene, low='grey',high ='red') # plt[[gene]] <- tmp # } # multiplot(plotlist=plt, cols = 4) # #======================================================================= # fD_NEU <- fD %>% dplyr::mutate(keep = rowMeans(m_NEU) > 0.01) # sce.NEU <- SingleCellExperiment(list(counts=as.matrix(m_NEU[fD_NEU$keep,])), # colData = DataFrame(pD_NEU), # rowData = DataFrame(fD_NEU[fD_NEU$keep,])) # clusters <- quickCluster(sce.NEU, method ='igraph',use.ranks=FALSE, min.mean = 0.1) # table(clusters) # sce.NEU <- computeSumFactors(sce.NEU, min.mean = 0.1, clusters = clusters) # summary(sizeFactors(sce.NEU)) # sce.NEU <- normalize(sce.NEU) # table(sce.NEU$Replicate) # batch <- c(rep("1", each=2242), rep("2", each = 1912)) # fit <- trendVar(sce.NEU, use.spikes = FALSE, block = batch) # dec <- decomposeVar(sce.NEU, fit) # dec$Symbol <- rowData(sce.NEU)$symbol # dec <- dec[order(dec$bio, decreasing = TRUE), ] # hvg <- dec[which(dec_A$FDR <= 0.1 & dec_A$bio >=0),] # sce.NEU <- runPCA(sce.NEU, BSPARAM=IrlbaParam()) # sce.NEU <- runTSNE(sce.NEU, use_dimred = "PCA") # plotTSNE(sce.NEU, colour_by = "SampleID") # plotTSNE(sce.BRCA1, colour_by="Cluster") # plotUMAP(sce.4T1, colour_by="Cluster") # snn.gr <- buildSNNGraph(sce.BRCA1, use.dimred="PCA", k = 50) # clusters <- igraph::cluster_fast_greedy(snn.gr) # table(clusters$membership) # sce.BRCA1$Cluster <- factor(clusters$membership) # jgc <- function() # { # .jcall("java/lang/System", method = "gc") # } # options(java.parameters = "-Xmx8g") # markers <- findMarkers(sce.BRCA1, sce.BRCA1$Cluster, direction="up") # library(xlsx) # for (cluster in names(markers)) { # write.xlsx(data.frame(markers[[cluster]]), row.names=TRUE, # file="BRCA1_MarkerGenes.xlsx", # sheetName=cluster, append = TRUE) # jgc() # } # sce.4T1 <- sceList[["4T1"]] # plotTSNE(sce.4T1, colour_by="Cluster") # plotUMAP(sce.4T1, colour_by="Cluster") # markers <- findMarkers(sce.4T1, sce.4T1$Cluster, direction="up") # for (cluster in names(markers)) { # write.xlsx(data.frame(markers[[cluster]]), row.names=TRUE, # file="4T1_MarkerGenes.xlsx", # sheetName=cluster, append = TRUE) # gc() # } # sce.NEU <- sceList[["NEU"]] # plotTSNE(sce.NEU, colour_by="Cluster") # plotUMAP(sce.NEU, colour_by="Cluster") # markers <- findMarkers(sce.NEU, sce.NEU$Cluster, direction="up") # for (cluster in names(markers)) { # write.xlsx(data.frame(markers[[cluster]]), row.names=TRUE, # file="Neu_MarkerGenes.xlsx", # sheetName=cluster, append = TRUE) # gc() # } # sce.FF99WT <- sceList[["FF99WT"]] # plotTSNE(sce.FF99WT, colour_by="Cluster") # snn.gr <- buildSNNGraph(sce.FF99WT, use.dimred="PCA", k = 50) # clusters <- igraph::cluster_fast_greedy(snn.gr) # table(clusters$membership) # sce.FF99WT$Cluster <- factor(clusters$membership) # plotTSNE(sce.FF99WT, colour_by="Cluster") # plotUMAP(sce.FF99WT, colour_by="Cluster") # markers <- findMarkers(sce.FF99WT, sce.FF99WT$Cluster, direction="up") # for (cluster in names(markers)) { # write.xlsx(data.frame(markers[[cluster]]), row.names=TRUE, # file="FF99WT_MarkerGenes.xlsx", # sheetName=cluster, append = TRUE) # gc() # } # #==================================================================== # top.markers_c <- c() # for (i in 1:3){ # marker.tmp <- markers[[i]] # top.tmp <- rownames(marker.tmp)[marker.tmp$Top <=10] # top.markers_c <- c(top.markers_c, top.tmp) # } # top.markers_c <- top.markers_c[!duplicated(top.markers_c)] # top.exprs <- logcounts(sce.BRCA1)[top.markers_c,,drop=FALSE] # heat.vals <- top.exprs - rowMeans(top.exprs) # pheatmap(heat.vals, cluster_cols=TRUE, # show_colnames = FALSE, # annotation_col=data.frame(Cluster = factor(sce.BRCA1$Cluster), # row.names=colnames(sce.BRCA1)), # fontsize_row = 7) # #------------------------------------------- # universe <- intersect(rownames(decList[[1]]), rownames(decList[[2]])) # mean.bio <- (decList[[1]][universe, "bio"] + decList[[2]][universe, "bio"])/2 # chosen <- universe[mean.bio >0] # length(chosen) # rescaled <- batchelor::multiBatchNorm(sceList[[1]][universe, ], sceList[[2]][universe,]) # #------------------------------------------- # repA <- logcounts(rescaled[[1]])[chosen,] # repB <- logcounts(rescaled[[2]])[chosen,] # mnn.out <- batchelor::fastMNN(repA = repA, repB=repB, k = 20, d = 50, BSPARAM=IrlbaParam(deferred=TRUE)) # dim(reducedDim(mnn.out,"corrected")) # Rle(mnn.out$batch) # metadata(mnn.out)$merge.info$pairs[[1]] # #------------------------------------- # sce <- mnn.out # assay(sce,"original") <- cbind(repA, repB) # sce <- SingleCellExpriment(list(logcounts = omat)) # reducedDim(sce, "MNN") <- mnn.out$corrected # sce$Batch <- as.character(mnn.out$batch) # sce # #--------------------------------------------- # osce <- runPCA(sce, exprs_values="original",ntop = Inf, BSPARAM=IrlbaParam()) # osce <- runTSNE(osce, use_dimred = "PCA") # ot <- plotTSNE(osce, colour_by="batch") + ggtitle("Original") # csce <- runTSNE(sce, use_dimred = "corrected") # ct <- plotTSNE(csce, colour_by = "batch") + ggtitle("Corrected") # multiplot(ot, ct, cols = 2) # metadata(mnn.out)$merge.info$lost.var

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/analysis.r

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mnunes/IMDb

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refs/heads/master

2021-06-22T19:30:05.636927

2017-08-19T22:47:53

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analysis.r

setwd("~/Documents/Lectures/UFRN/EST0113 - Introdução à Estatística e Probabilidade/Material/Unidade II/04 - Análises Gerais/") library(ggplot2) library(dplyr) ################### ### Game of Thrones # ler e combinar os dados got.season <- scan(file="got.season.dat") got.episode <- scan(file="got.episode.dat") got.rating <- scan(file="got.rating.dat") got <- cbind(got.season, got.episode, got.rating) got <- data.frame(got) colnames(got) <- c("temporada", "episodio", "rating") ordem <- sort(got.season*100 + got.episode, index.return=TRUE)$ix got <- got[ordem, ] got$episodio <- 1:dim(got)[1] got$temporada <- as.character(got$temporada) head(got) # plots ggplot(got, aes(x=episodio, y=rating, color=temporada)) + labs(title="Game of Thrones: Ratings por Temporada", x="Episódio", y="Rating", colour="Temporada") + geom_smooth(method=loess, se=FALSE) + geom_point(shape=1) + theme(plot.title = element_text(hjust = 0.5)) got %>% select(temporada, rating) %>% group_by(temporada) %>% summarise(media=mean(rating), mediana=median(rating), desvPad=sd(rating), maximo=max(rating), episodio_max=which.max(rating), minimo=min(rating), episodio_min=which.min(rating)) ggplot(got, aes(x=temporada, y=rating, color=temporada)) + labs(title="Game of Thrones: Ratings por Temporada", x="Temporada", y="Rating", colour="Temporada") + geom_boxplot() + theme(plot.title = element_text(hjust = 0.5)) #################### ### The Walking Dead # ler e combinar os dados twd.season <- scan(file="twd.season.dat") twd.episode <- scan(file="twd.episode.dat") twd.rating <- scan(file="twd.rating.dat") twd <- cbind(twd.season, twd.episode, twd.rating) twd <- data.frame(twd) colnames(twd) <- c("temporada", "episodio", "rating") ordem <- sort(twd.season*100 + twd.episode, index.return=TRUE)$ix twd <- twd[ordem, ] twd$episodio <- 1:dim(twd)[1] twd$temporada <- as.character(twd$temporada) head(twd) # plots ggplot(twd, aes(x=episodio, y=rating, color=temporada)) + labs(title="The Walking Dead: Ratings por Temporada", x="Episódio", y="Rating", colour="Temporada") + geom_smooth(method=loess, se=FALSE) + geom_point(shape=1) + theme(plot.title = element_text(hjust = 0.5)) twd %>% select(temporada, rating) %>% group_by(temporada) %>% summarise(media=mean(rating), mediana=median(rating), desvPad=sd(rating), maximo=max(rating), episodio_max=which.max(rating), minimo=min(rating), episodio_min=which.min(rating)) ggplot(twd, aes(x=temporada, y=rating, color=temporada)) + labs(title="The Walking Dead: Ratings por Temporada", x="Temporada", y="Rating", colour="Temporada") + geom_boxplot() + theme(plot.title = element_text(hjust = 0.5))

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/pkg/R/plotCatCol.R

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mxc19912008/tabplot

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refs/heads/master

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plotCatCol.R

plotCatCol <- function(tCol, tab, vpTitle, vpGraph, vpLegend, max_print_levels, text_NA, legend.lines, compare){ midspace <- .05 drawContours <- TRUE anyNA <- tail(tCol$categories, 1)=="missing" categories <- tCol$categories if (anyNA) categories <- categories[-length(categories)] nCategories <- length(categories) spread <- (nCategories > max_print_levels) ## determine color indices for categories palet <- if (tCol$palet_recycled) { rep(tCol$palet, length.out = nCategories) } else { colorRampPalette(tCol$palet)(nCategories) } if (anyNA) { palet[nCategories+1] <- tCol$colorNA } if (compare) { marks.x <- seq(0, 1, length.out=5) } mgrey <- "#D8D8D8" cellplot(2,1,vpGraph, { if (compare) grid.rect(gp = gpar(col=mgrey,fill = mgrey)) ## create large vector of colors (one color for each bin*category colorset <- rep(palet, each=tab$nBins) missings <- which(tCol$widths==0) if (drawContours) { cols <- colorset cols[missings] <- NA } else { cols <- NA } ## draw bins grid.rect( x = tCol$x, y = tab$rows$y , width = tCol$widths, height = tab$rows$heights , just=c("left","bottom") , gp = gpar(col=cols, fill = colorset, linejoin="mitre", lwd=0)) ## draw white rect at the right to correct for rounding errors during plotting # grid.rect(x = 1, y=-.005, width=0.1, height=1.01, just=c("left", "bottom"), # gp=gpar(col=NA, fill="white")) if (compare) grid.rect(width=midspace, gp = gpar(col="white", fill = "white")) }) ## draw legend cellplot(3,1, vpLegend, { nLegendSpread <- min(((legend.lines-1) %/% 2) + 1, max_print_levels, nCategories) nLegendSpreadRows <- nLegendSpread * 2 -1 nLegendRows <- ifelse(spread, nLegendSpreadRows, nCategories) + 2 * anyNA Layout2 <- grid.layout(nrow = nLegendRows, ncol = 1 + spread, widths=if(spread) c(0.25, 0.75) else {1}) cex <- min(1, 1 / (convertHeight(unit(1,"lines"), "npc", valueOnly=TRUE) * nLegendRows)) pushViewport(viewport(name="legendblocks", layout = Layout2, gp=gpar(cex=cex))) #print(current.vpPath()) grid.rect(gp=gpar(col=NA, fill="white")) if (spread) { if (tCol$rev_legend) { palet <- rev(palet) } cellplot(1:nLegendSpreadRows,1, NULL, { grid.rect( x = 0, y = seq(1, 0, length.out=nCategories+1)[-(nCategories+1)] , width = 0.8, height = 1/nCategories , just=c("left", "top") , gp = gpar(col=palet, fill = palet) ) }) labels <- rep("...", nLegendSpreadRows) labels[seq(1, nLegendSpreadRows, by=2)] <- tCol$categories[seq(1, nCategories - anyNA, length.out=nLegendSpread)] for (j in 1:nLegendSpreadRows) { k <- ifelse(tCol$rev_legend, (nLegendSpreadRows+1)-j, j) cellplot(j,2, NULL, { grid.text( labels[k] , x = 0 , just="left") }) } if (anyNA) { cellplot(nLegendRows, 1, NULL, { grid.rect( x = 0, y = 0.5, width = 0.8, height = 1 , just=c("left") , gp = gpar(col=palet[nCategories + 1], fill = palet[nCategories + 1]) ) }) cellplot(nLegendRows, 2, NULL, { grid.text( text_NA , x = 0 , just="left") }) } } else { for (j in 1:nCategories) { k <- ifelse(tCol$rev_legend, (nCategories + 1) - j, j) cellplot(j,1, NULL, { grid.rect( x = 0, y = 0.5, width = 0.2, height = 1 , just=c("left") , gp = gpar(col=palet[k], fill = palet[k]) ) grid.text( categories[k] , x = 0.25 , just="left") }) } if (anyNA) { cellplot(nLegendRows, 1, NULL, { grid.rect( x = 0, y = 0.5, width = 0.2, height = 1 , just=c("left") , gp = gpar(col=palet[nCategories + 1], fill = palet[nCategories + 1]) ) grid.text( text_NA , x = 0.25 , just="left") }) } } popViewport(1) }) }

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/rcode_realtor.R

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hannahjonesut/Realtor.com

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refs/heads/main

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2021-06-21T19:54:23

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rcode_realtor.R

library(dplyr) library(tidyverse) library(tidyr) library(gamlr) library(foreach) library(ggplot2) library(stringr) usa_data<- read.csv('https://raw.githubusercontent.com/hannahjonesut/Realtor.com/main/RDC_Inventory_Country_History_Assessment.csv?token=ASRSTUEJMVJZYFZFQAVYF2TAZ76HS') metro_data <- read.csv('https://raw.githubusercontent.com/hannahjonesut/Realtor.com/main/RDC_Inventory_Metro_History_Assessment.csv?token=ASRSTUHRS3O5BGP42ZDXU23AZ76JS') #hh rank is based on HH count in zip code, with 1 being greatest aka most dense #https://www.realtor.com/research/data/ metro_hh <- metro_data %>% mutate(rank_simple = ifelse(HouseholdRank>=1 & HouseholdRank<=5, 1, ifelse(HouseholdRank>5 & HouseholdRank<=10, 2, ifelse(HouseholdRank>10 & HouseholdRank<=15, 3, ifelse(HouseholdRank>15 & HouseholdRank<=20, 4, ifelse(HouseholdRank>20 & HouseholdRank<=25, 5, ifelse(HouseholdRank>25 & HouseholdRank<=30, 6, ifelse(HouseholdRank>30 & HouseholdRank<=35, 7, ifelse(HouseholdRank>35 & HouseholdRank<=40, 8, ifelse(HouseholdRank>40 & HouseholdRank<=45, 9, ifelse(HouseholdRank>45, 10, 0)))))))))))%>% group_by(rank_simple, month_date_yyyymm)%>% summarize( avg_listprice = mean(average_listing_price), new_list_count = mean(new_listing_count), days_on_mkt = mean(median_days_on_market)) ggplot(data = metro_hh)+ geom_smooth(aes(x = month_date_yyyymm, y = avg_listprice, color = as.factor(rank_simple)))+ labs(x="Date (YYYYMM)", y = "Average List Price", legend = "Household Rank (1 = most households, 10 = least households)", title = "Average List Price by Household Rank (2016 - 2021)") ggplot(data = metro_hh)+ geom_smooth(aes(x = month_date_yyyymm, y = new_list_count, color = as.factor(rank_simple)))+ labs(x="Date (YYYYMM)", y = "Total Number of Listings", legend = "Household Rank (1 = most households, 10 = least households)", title = "Total Listings by Household Rank (2016 - 2021)") usa_2020_2021 <- usa_data%>% filter(month_date_yyyymm >= 202001)%>% mutate(year = as.numeric(substr(month_date_yyyymm, 1, 4)), month = as.numeric(substr(month_date_yyyymm, 5, 6)), pct_inc = price_increased_count/total_listing_count) #look at percent of price increased over total listings ggplot(data = usa_2020_2021)+ geom_point(aes(x = median_days_on_market, y = pct_inc))+ facet_grid(cols = vars(month), rows = vars(year))+ labs(x="Median Days on Market by Month", y = "% of Listings that Increased Price by Year" , title = "Days on Market vs % Price Increased, Monthly") #price reduced freq as a fn of days on mkt-- when do people drop price? usa_2020_2021 <- usa_data%>% filter(month_date_yyyymm >= 202001)%>% mutate(year = as.numeric(substr(month_date_yyyymm, 1, 4)), month = as.numeric(substr(month_date_yyyymm, 5, 6)))%>% mutate(quarter = ifelse(month>0 & month <=3, 1, ifelse(month>3 & month <=6, 2, ifelse(month>6 & month <=9, 3, ifelse(month>9 & month<=12, 4, 0))))) %>% group_by(year, quarter)%>% summarize(pct_inc =mean(price_increased_count/total_listing_count), med_days_on_mkt = median(median_days_on_market)) #look at percent of price increased over total listings ggplot(data = usa_2020_2021)+ geom_point(aes(x = med_days_on_mkt, y = pct_inc))+ facet_grid(cols = vars(quarter), rows = vars(year))+ labs(x="Median Days on Market by Month", y = "% of Listings that Increased Price by Year" , title = "Days on Market vs % Price Increased, Monthly")

ced1b69196bdcc8fe3683a1ae79bd8b08ac412d7

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/man/data.math.Rd

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no_license

alexanderrobitzsch/sirt

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refs/heads/master

2023-08-31T14:50:52.255747

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%% File Name: data.math.Rd %% File Version: 0.15 \name{data.math} \alias{data.math} \docType{data} \title{ Dataset Mathematics } \description{ This is an example dataset involving Mathematics items for German fourth graders. Items are classified into several domains and subdomains (see Section Format). The dataset contains 664 students on 30 items. } \usage{data(data.math)} \format{ The dataset is a list. The list element \code{data} contains the dataset with the demographic variables student ID (\code{idstud}) and a dummy variable for female students (\code{female}). The remaining variables (starting with \code{M} in the name) are the mathematics items. \cr The item metadata are included in the list element \code{item} which contains item name (\code{item}) and the testlet label (\code{testlet}). An item not included in a testlet is indicated by \code{NA}. Each item is allocated to one and only competence domain (\code{domain}). \cr The format is: \code{List of 2} \cr \code{ $ data:'data.frame':} \cr \code{ ..$ idstud: int [1:664] 1001 1002 1003 ...} \cr \code{ ..$ female: int [1:664] 1 1 0 0 1 1 1 0 0 1 ...} \cr \code{ ..$ MA1 : int [1:664] 1 1 1 0 0 1 1 1 1 1 ...} \cr \code{ ..$ MA2 : int [1:664] 1 1 1 1 1 0 0 0 0 1 ...} \cr \code{ ..$ MA3 : int [1:664] 1 1 0 0 0 0 0 1 0 0 ...} \cr \code{ ..$ MA4 : int [1:664] 0 1 1 1 0 0 1 0 0 0 ...} \cr \code{ ..$ MB1 : int [1:664] 0 1 0 1 0 0 0 0 0 1 ...} \cr \code{ ..$ MB2 : int [1:664] 1 1 1 1 0 1 0 1 0 0 ...} \cr \code{ ..$ MB3 : int [1:664] 1 1 1 1 0 0 0 1 0 1 ...} \cr \code{ [...]} \cr \code{ ..$ MH3 : int [1:664] 1 1 0 1 0 0 1 0 1 0 ...} \cr \code{ ..$ MH4 : int [1:664] 0 1 1 1 0 0 0 0 1 0 ...} \cr \code{ ..$ MI1 : int [1:664] 1 1 0 1 0 1 0 0 1 0 ...} \cr \code{ ..$ MI2 : int [1:664] 1 1 0 0 0 1 1 0 1 1 ...} \cr \code{ ..$ MI3 : int [1:664] 0 1 0 1 0 0 0 0 0 0 ...} \cr \code{ $ item:'data.frame':} \cr \code{ ..$ item : Factor w/ 30 levels "MA1","MA2","MA3",..: 1 2 3 4 5 ...} \cr \code{ ..$ testlet : Factor w/ 9 levels "","MA","MB","MC",..: 2 2 2 2 3 3 ...} \cr \code{ ..$ domain : Factor w/ 3 levels "arithmetic","geometry",..: 1 1 1 ...} \cr \code{ ..$ subdomain: Factor w/ 9 levels "","addition",..: 2 2 2 2 7 7 ...} \cr } %% \keyword{datasets}

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local({ r <- getOption("repos") r["CRAN"] <- "https://cloud.r-project.org" options(repos = r) }) ## non CRAN packages remotes::install_github("https://github.com/rstudio-education/gradethis") remotes::install_github("StateOfTheR/optimLibR") remotes::install_github("mlverse/torch") remotes::install_github("Chabert-Liddell/MLVSBM") remotes::install_github("Demiperimetre/GREMLIN") remotes::install_github("rstudio/d3heatmap") remotes::install_github("jchiquet/PLNmodels") ## remotes::install_github("RamiKrispin/coronavirus") ## remotes::install_github("dreamRs/topogram") ## remotes::install_github("ropensci/rnaturalearthhires") ## CRAN packages not found in conda install.packages("rkeops") install.packages("sbm") install.packages("swirlify") install.packages("palmerpenguins") install.packages("ggiraph") install.packages("timevis") install.packages("ggraph") install.packages("fields") install.packages("slider") install.packages("fable") install.packages("fabletools") install.packages("ranger") ## Julia and co devtools::install_github("Non-Contradiction/JuliaCall") library(JuliaCall) julia <- julia_setup() install.packages("diffeqr")

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##' Construct a list with automatic names ##' ##' Constructs a lists from its arguments, automatically naming each ##' element of the list with the name of the argument. ##' ##' @param ... objects to add to the list. ##' ##' @return A list with named elements ##' ##' @examples ##' x <- 3 ##' y <- "a string" ##' z <- function(x){x^3 +4} ##' n <- namelist(x,y,z) ##' str(namelist) ##' ##' @export namelist <- function(...){ result <- list(...) names(result) <- as.list(substitute(list(...)))[-1L] return(result) } ### Copyright 2015 Univ. Corp for Atmos. Research ### Author: Seth McGinnis, mcginnis@ucar.edu

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# This checks out the barcodeRanks and defaultDrops functions. # library(DropletUtils); library(testthat); source("test-misc.R") # Mocking up some counts. set.seed(100) my.counts <- DropletUtils:::simCounts() totals <- Matrix::colSums(my.counts) test_that("barcodeRanks runs to completion", { limit <- 100 brout <- barcodeRanks(my.counts, lower=limit) expect_equal(brout$total, totals) expect_identical(brout$rank, rank(-totals, ties.method="average")) expect_true(all(is.na(brout$fitted[totals <= limit]))) # Trying again with a higher limit. limit2 <- 200 brout2 <- barcodeRanks(my.counts, lower=limit2) expect_identical(brout, brout2) # Specifying the boundaries. bounds <- c(200, 1000) brout3 <- barcodeRanks(my.counts, lower=limit, fit.bounds=bounds) is.okay <- totals > bounds[1] & totals < bounds[2] expect_true(all(is.na(brout3$fitted[!is.okay]))) expect_true(all(!is.na(brout3$fitted[is.okay]))) # Respecting column names. alt <- my.counts colnames(alt) <- sprintf("BARCODE_%i", seq_len(ncol(alt))) brout2 <- barcodeRanks(alt) expect_identical(rownames(brout2), colnames(alt)) expect_identical(names(brout2$rank), NULL) expect_identical(names(brout2$total), NULL) expect_identical(names(brout2$fitted), NULL) # Trying out silly inputs. expect_error(barcodeRanks(my.counts[,0]), "insufficient") expect_error(barcodeRanks(my.counts[0,]), "insufficient") }) test_that("barcodeRanks' excluder works correctly", { brout <- barcodeRanks(my.counts) keep <- brout$total >= 100 & !duplicated(brout$total) x <- log10(brout$rank[keep]) y <- log10(brout$total[keep]) o <- order(x) x <- x[o] y <- y[o] # Compares correctly to a reference. edge.out <- DropletUtils:::.find_curve_bounds(x=x, y=y, exclude.from=100) ref.out <- DropletUtils:::.find_curve_bounds(x=tail(x, -100), y=tail(y, -100), exclude.from=0) expect_identical(edge.out, ref.out+100) edge.outx <- DropletUtils:::.find_curve_bounds(x=x, y=y, exclude.from=200) ref.outx <- DropletUtils:::.find_curve_bounds(x=tail(x, -200), y=tail(y, -200), exclude.from=0) expect_false(identical(edge.outx, ref.outx+200)) # Proper edge behavior. edge.out2 <- DropletUtils:::.find_curve_bounds(x=x, y=y, exclude.from=0) expect_identical(edge.out[2], edge.out2[2]) expect_false(identical(edge.out[1], edge.out2[1])) edge.out3 <- DropletUtils:::.find_curve_bounds(x=x, y=y, exclude.from=Inf) expect_identical(unname(edge.out3[1]), length(y)-1) expect_identical(unname(edge.out3[2]), length(y)-1) # Works properly when put together. ref <- barcodeRanks(my.counts) brout <- barcodeRanks(my.counts, exclude.from=0) expect_false(identical(ref, brout)) brout2 <- barcodeRanks(my.counts, exclude.from=200) expect_false(identical(ref, brout2)) brout3 <- barcodeRanks(my.counts, exclude.from=Inf) expect_false(identical(ref, brout2)) }) test_that("defaultDrops runs to completion", { out <- defaultDrops(my.counts) # Should always call at least one cell (100th %ile cell) expect_true(sum(out)>0) out <- defaultDrops(my.counts, lower.prop=0) # should keep all non-zero cells. expect_true(all(out | totals==0)) out <- defaultDrops(my.counts, upper.quant=1, lower.prop=1) # as it's >, not >=. expect_true(!any(out)) # Works alright on silly inputs. expect_identical(logical(0), defaultDrops(my.counts[,0])) expect_identical(logical(ncol(my.counts)), defaultDrops(my.counts[0,])) })

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\name{emxCovariances} \alias{emxCovariances} \title{Create a set of covariances} \description{ This function creates a covariance matrix as an MxMatrix or MxPath object. } \usage{ emxCovariances(x, values, free, path=FALSE, type, name='Variances') } \arguments{ \item{x}{character vector. The names of the variables for which covariances are created.} \item{values}{numeric vector. See Details.} \item{free}{logical vector. See Details.} \item{path}{logical. Whether to return the MxPath object instead of the MxMatrix.} \item{type}{character. The kind of covariance structure to create. See Details.} \item{name}{The name of the matrix created.} } \details{ Possible values for the \code{type} argument are 'independent', 'full', and 'corr'. When \code{type='independent'}, the remaining arguments are passes to \code{\link{emxResiduals}}. The \code{values} and \code{free} arguments are only used when the \code{type} argument is 'independent'. For all other cases, they are ignored. When \code{type='full'}, a full covariance matrix is created. That is, a symmetric matrix is created with all unique elements freely estimated. The starting values for the variances are all 1; for the covariances, all 0.5. When \code{type='corr'}, a full correlation matrix is created. That is, a symmetric matrix is created with all unique elements not on the diagonal freely estimated. The starting values for the correlations are all 0.5. The variances are fixed at 1. } \value{ Depending on the value of the \code{path} argument, either an MxMatrix or and MxPath object that can be inspected, modified, and/or included in MxModel objects. } \seealso{ \link{emxFactorModel}, \link{emxGrowthModel} } %\references{ % %} \examples{ # Create a covariance matrix require(EasyMx) manVars <- paste0('x', 1:6) latVars <- paste0('F', 1:2) emxCovariances(manVars, type='full') emxCovariances(latVars, type='corr', path=TRUE) }

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annotate_tfbs_fun.R

function(seq,tfbs){ seqbp <- strsplit(seq, NULL)[[1]] seqL <- length(seqbp) #Remove known binding sites seqTruncbp <- seqbp for (i in 1:nrow(tfbs)){ s = tfbs[i,1] e = tfbs[i,2] seqTruncbp[s:e] <- 'F' } seqTrunc <- paste0(seqTruncbp,collapse = '') return(seqTrunc) }

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Cprop.test.Rd

\name{Cprop.test} \alias{Cprop.test} \title{ Test for proportions of one or two samples } \description{ Performs hypothesis testing and confidence interval for a proportion or difference of two proportions. The values of the samples necessary to perform the function are the number of successes and the number of trails. } \usage{ Cprop.test(ex, nx, ey = NULL, ny = NULL, p.null = 0.5, alternative = c("two.sided", "less", "greater"), conf.level = 0.95, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{ex}{numeric value that represents the number of successes of the first sample (see Details).} \item{nx}{numerical value representing the total number of trails of the first sample.} \item{ey}{ (optional) numerical value representing the number of success of the second sample (see Details).} \item{ny}{(optional) numerical value representing the total number of trails of the second sample.} \item{p.null}{numeric value that represents the value of the population proportion or the difference between the two population proportions, depending on whether there are one or two samples (see Details).} \item{alternative}{a character string specifying the alternative hypothesis, must be one of \code{"two.sided"} (default), \code{"greater"} or \code{"less"}. You can specify just the initial letter.} \item{conf.level}{confidence level of the interval.} \item{\dots}{further arguments to be passed to or from methods.} } \details{ So that the contrast can be made must be fulfilled that at least 1 hit. That is, in the case of a sample \code{ex} must be greater than or equal to 1 and in the case of two samples, \code{ex} or \code{ey} must be greater than or equal to 1. Furthermore, for the case of a sample value p.null must be strictly positive. } \value{ A list with class "htest" containing the following components: \item{statistic}{the value of the test statistic.} \item{parameter}{number of trails and value of the population proportion or the difference in population proportions.} \item{p.value}{the p-value for the test.} \item{conf.int}{a confidence interval for the proportion or for the difference in proportions, appropriate to the specified alternative hypothesis.} \item{estimate}{a value with the sample proportions.} \item{null.value}{the value of the null hypothesis.} \item{alternative}{a character string describing the alternative.} \item{method}{a character string indicating the method used, and whether Yates' continuity correction was applied.} \item{data.name}{a character string giving the names of the data.} } \seealso{ \code{\link{prop.test}} } \examples{ ## Proportion for a sample Cprop.test(1,6) # 1 success in 6 attempts #### With a data set: proportion of cars not manufactured in US data(cars93) #data set provided with the package exitos<-sum(cars93$USA == "nonUS") total<-length(cars93$USA) Cprop.test(ex=exitos, nx=total) ## Difference of proportions Cprop.test(1,6,3,15) # Sample 1: 1 success in 6 attempts # Sample 2: 3 success in 15 attempts #### With a data set: difference of proportions of cars not manufactured in US #### between manual and automatic exitosx<-sum(cars93$USA == "nonUS" & cars93$Manual == "Yes" ) totalx<-sum(cars93$Manual == "Yes") exitosy<-sum(cars93$USA == "nonUS" & cars93$Manual == "No" ) totaly<-sum(cars93$Manual == "No") Cprop.test(ex=exitosx, nx=totalx,ey=exitosy, ny=totaly) }

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setwd(normalizePath(dirname(R.utils::commandArgs(asValues=TRUE)$"f"))) source('../h2o-runit.R') test.rdoc_deep_learning.golden <- function(H2Oserver) { irisPath = system.file("extdata", "iris.csv", package = "h2o") iris.hex = h2o.uploadFile(H2Oserver, path = irisPath) indep <- names(iris.hex)[1:4] dep <- names(iris.hex)[5] h2o.deeplearning(x = indep, y = dep, training_frame = iris.hex, activation = "Tanh", epochs = 5, loss = "CrossEntropy") testEnd() } doTest("R Doc Deep Learning", test.rdoc_deep_learning.golden)

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report_06_Figure 6.4.25.3_advice_sheet_plots_2020.r

################################################################################# ################################## MIXFISH PLOTS ################################ ################################################################################# ## Paul Dolder ## 28/05/2015 ## Celtic Sea ## This script runs on the extract 'ca.csv' ## ## 31/05/2018 - PJD rewritten from fleets object ## ## It produces the landings by metier plot (for advice sheet annex) ## and the total landings pie plot (for advice sheet catch box) ## and outputs the figures for landings, discards, discard percentage ## and the landings by fleet (for advice sheet catch box) ## Includes stocks COD, HAD, WHG ## NEP3A currently excluded ## If the Species or Metiers change, the script will need to be changed to reflect ################################################################################# rm(list=ls()) res.path<-file.path("results/") plot.path<-file.path("plots/") library(reshape2) ; library(ggplot2) ;library(grid); library(data.table) library(FLCore); library(FLFleet) library(tidyr); library(dplyr) source("bootstrap/software/functions/FLFcube_FLCore_R31.R") source("bootstrap/software/functions/remove_validity_FLFleet.R") source("bootstrap/software/functions/funcs.R") load("results/02_Making_Fleets_Celtic_Sea_2020tier12nepnewLOUsingSAM_KW.RData") df <- slot.fleet(fleets, "landings") dfD <-slot.fleet(fleets, "discards") dfC <-slot.fleet(fleets, "catch") # merge catch categories df <- merge(df, dfD, all = T) df <- merge(df, dfC, all = T) rm(dfD, dfC) df$area <- substr(df$metier, 9, 14) df$metier<-substr(df$metier,1,7) colnames(df)[colnames(df)=="qname"] <-"stock" levels(df$stock)[levels(df$stock) %in% grep("nep", unique(df$stock), value = TRUE)]<-"nep.27.7bk" # aggregate it all up dfa<-aggregate(df[c("landings","discards","catch")],by=list(Year=df$year,Area= df$area,Stock = df$stock),sum, na.rm = T) # #for area df<-aggregate(df[c("landings","discards","catch")],by=list(Year=df$year,Metier = df$metier,Stock = df$stock),sum, na.rm = T) # melt df <-reshape2::melt(df,id=c("Year","Metier","Stock")) dfa<-reshape2::melt(dfa,id=c("Year","Area","Stock")) # order the dataframe df <-df[(order(df$Year,df$Metier,df$Stock)),] dfa <-dfa[(order(dfa$Year,dfa$Area,dfa$Stock)),] plot.path <- "plots" ###### PLOT CODE ##### none <- element_blank() ################################ ## LANDINGS BARPLOT BY METIER ## ################################ ## Make sure the stocks are labelled with numbers levels(df$Stock)[levels(df$Stock) == "cod.27.7e-k"] <-"1:cod.27.7e-k" levels(df$Stock)[levels(df$Stock) == "had.27.7b-k"] <- "2:had.27.7b-k" levels(df$Stock)[levels(df$Stock) == "meg.27.7b-k8abd"] <-"3:meg.27.7b-k8abd" levels(df$Stock)[levels(df$Stock) == "mon.27.78abd"] <- "4:mon.27.78abd" levels(df$Stock)[levels(df$Stock) == "sol.27.7fg"] <-"5:sol.27.7fg" levels(df$Stock)[levels(df$Stock) == "whg.27.7b-ce-k"] <- "6:whg.27.7b-ce-k" levels(df$Stock)[levels(df$Stock) == "nep.27.7bk"] <- "7:nep.27.7bk" df$Stock <- factor(df$Stock, levels = sort(levels(df$Stock))) levels(dfa$Stock)[levels(dfa$Stock) == "cod.27.7e-k"] <-"1:cod.27.7e-k" levels(dfa$Stock)[levels(dfa$Stock) == "had.27.7b-k"] <- "2:had.27.7b-k" levels(dfa$Stock)[levels(dfa$Stock) == "meg.27.7b-k8abd"] <-"3:meg.27.7b-k8abd" levels(dfa$Stock)[levels(dfa$Stock) == "mon.27.78abd"] <- "4:mon.27.78abd" levels(dfa$Stock)[levels(dfa$Stock) == "sol.27.7fg"] <-"5:sol.27.7fg" levels(dfa$Stock)[levels(dfa$Stock) == "whg.27.7b-ce-k"] <- "6:whg.27.7b-ce-k" levels(dfa$Stock)[levels(dfa$Stock) == "nep.27.7bk"] <- "7:nep.27.7bk" dfa$Stock <- factor(dfa$Stock, levels = sort(levels(dfa$Stock))) ## For other ares dfa$Area[dfa$Area %in% c("","27.7.a", "27.7.d")] <- "OTH" dfa <- dfa %>% group_by(Year, Area, Stock, variable) %>% summarise(value = sum(value)) pal <- pals::brewer.paired(12)[1:length(unique(df$Stock))] data.yr<-2019 p<-ggplot(df[(df$variable=="landings" & df$Year==data.yr),],aes(factor(Metier),value/1000,fill=Stock)) p + geom_bar(stat="identity",position = "stack") +# ylim(0,13)+ theme(panel.grid.major = none, panel.grid.minor = none) + theme(panel.background = none) + # scale_fill_grey(start=0,end=1)+ scale_fill_manual(values = pal) + theme(panel.border = none) + theme(axis.line = element_line(colour = "grey50")) + theme_bw() + xlab("Metiers used by mixed-fisheries model") + ylab("Landings ('000 tonnes)") + theme(axis.text.x = element_text(angle = 90,size = 8)) + theme(legend.key = element_rect(colour = "black"), legend.position=c(0.9,0.7),legend.key.size=unit(0.5,"cm"), legend.background = element_rect(colour="black", size=.5), legend.text=element_text(face="bold",size=8), legend.title=element_text(face="bold",size=8), legend.title.align=0.5) + theme(axis.text = element_text(lineheight=0.8, size=8,face="bold")) + theme(axis.title = element_text(size=12,face="bold")) + geom_bar(stat="identity",position = "stack",colour="black",show_guide=FALSE) + annotate("text", label=" ",x=5.5,y=18,fontface="bold",size=6) #label="Landings by species / metier") ggsave(file= "plots/Celtic Sea Figure 6.4.25.3_advice_sheet_landing_by_metier_plot.png",width=8,height=4.8) ########################### ## By area ########################### pa<-ggplot(dfa[(dfa$variable=="landings" & dfa$Year==data.yr),],aes(factor(Area),value/1000,fill=Stock)) pa + geom_bar(stat="identity",position = "stack") +# ylim(0,13)+ theme(panel.grid.major = none, panel.grid.minor = none) + theme(panel.background = none) + # scale_fill_grey(start=0,end=1)+ scale_fill_manual(values = pal) + theme(panel.border = none) + theme(axis.line = element_line(colour = "grey50")) + theme_bw() + xlab("Areas used by mixed-fisheries model") + ylab("Landings ('000 tonnes)") + theme(axis.text.x = element_text(angle = 90,size = 8)) + theme(legend.key = element_rect(colour = "black"), legend.position=c(0.1,0.7),legend.key.size=unit(0.5,"cm"), legend.background = element_rect(colour="black", size=.5), legend.text=element_text(face="bold",size=8), legend.title=element_text(face="bold",size=8), legend.title.align=0.5) + theme(axis.text = element_text(lineheight=0.8, size=8,face="bold")) + theme(axis.title = element_text(size=12,face="bold")) + geom_bar(stat="identity",position = "stack",colour="black",show_guide=FALSE) + annotate("text", label=" ",x=5.5,y=18,fontface="bold",size=6) #label="Landings by species / metier") ggsave(file= "plots/Celtic Sea Figure 6.4.25.3_advice_sheet_landing_by_area_plot.png",width=8,height=4.8) ################### #pie plot landings# ################### df2<-df[(df$variable=="landings" & df$Year==data.yr),] df2<-aggregate(df2["value"],by=list(Stock = df2$Stock),sum,na.rm=T) p<- ggplot(df2,aes(x="",y=value,fill=Stock)) p + geom_bar(width = 1,stat="identity") + scale_fill_manual(values = pal)+ coord_polar("y", start=0) + xlab("")+ylab("")+ theme(axis.text.x = element_blank(), panel.border= element_blank(), panel.background = element_blank()) + geom_bar(width = 1,stat="identity",colour="black",show_guide=FALSE) + theme(legend.key.size=unit(1,"cm"), legend.text=element_text(face="bold",size=10), legend.title=element_text(face="bold",size=10), legend.title.align=0.5, legend.background = element_rect(colour="black", size=.5)) + annotate("text",x=1.8,y=1,label="Total Landings by Stock",fontface="bold",size=10) ggsave(file= "plots/Celtic Sea Catch_distribution_figure_advice_sheet.png",width=11.7,height=8.3) ################################################ ## Landings, Discards totals for advice sheet ## ################################################ land<-df[(df$variable=="landings" & df$Year==data.yr),] disc<-df[(df$variable=="discards" & df$Year==data.yr),] print(paste("-----Landings =",round(sum(land$value,na.rm=T),0),paste("t -------"))) print(paste("-----Discards =",round(sum(disc$value,na.rm=T),0),paste("t -------"))) print(paste("-----Discard =",round(100*sum(disc$value,na.rm=T)/sum(sum(land$value,na.rm=T),sum(disc$value,na.rm=T)),0),paste("% -------"))) Fleet.summary.func<-function(x) { if (x %in% c("OTB_CRU","OTT_CRU","OTT_DEF","OTB_DEF","SSC_DEF","OTM_DEF")) return("Otter trawls and seines") if (x %in% c("TBB_DEF")) return("Beam trawls") if(x %in% c("GNS_DEF","GTR_DEF")) return("Gill and trammel nets") if(x %in% c("LLS_FIF")) return("Longlines") if (x %in% c("OTH","MIS_MIS")) return("Other gears") else return("THIS SHOULDN'T BE HERE!!") } ## and apply the function (assigning metiers to a fleet...) land$Fleet<-sapply(land$Metier,Fleet.summary.func) unique(land$Fleet) ## check that all metiers allocated to a fleet ## Fleet summary print(paste("-----Otter trawls and seines =",round(sum(land$value[(land$Fleet=="Otter trawls and seines")]),0),paste("t -------"),round(100*sum(land$value[(land$Fleet=="Otter trawls and seines")],na.rm=T)/sum(land$value,na.rm=T),0),paste("%"))) print(paste("-----Beam trawls =",round(sum(land$value[(land$Fleet=="Beam trawls")]),0),paste("t -------"),round(100*sum(land$value[(land$Fleet=="Beam trawls")],na.rm=T)/sum(land$value,na.rm=T),0),paste("%"))) print(paste("-----Gill and trammel nets =",round(sum(land$value[(land$Fleet=="Gill and trammel nets")]),0),paste("t -------"),round(100*sum(land$value[(land$Fleet=="Gill and trammel nets")],na.rm=T)/sum(land$value,na.rm=T),0),paste("%"))) print(paste("-----Longlines =",round(sum(land$value[(land$Fleet=="Longlines")]),0),paste("t -------"),round(100*sum(land$value[(land$Fleet=="Longlines")],na.rm=T)/sum(land$value,na.rm=T),0),paste("%"))) print(paste("-----Other gears =",round(sum(land$value[(land$Fleet=="Other gears")],na.rm=T),0),paste("t -------"),round(100*sum(land$value[(land$Fleet=="Other gears")],na.rm=T)/sum(land$value,na.rm=T),0),paste("%")))

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/scripts/modeling/help_functions/preparation&cleaning_functions.R

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preparation&cleaning_functions.R

# Define needed functions here ##-------------------------------------------------------------------- ## function to check "min=max" columns and get rid of such noVariation_filter <- function(data){ # read in data and return data without invalid columns data_out <- data[,!apply(data,2,function(x) min(x) == max(x))] return (data_out) } ##-------------------------------------------------------------------- ## function to check highly collinear columns and get rid of such ### NOTE: This function will filter out BOTH pairs... should not use... ### stackoverflow: https://stackoverflow.com/questions/18275639/remove-highly-correlated-variables collinear_filter <- function(data, threshold){ # plug in data you need check correlation and threshold of high correlation elimination # return a vector of features that are not highly correlated tmp <- cor(data) tmp[upper.tri(tmp)] <- 0 diag(tmp) <- 0 # print (tmp) data_out <- data[,!apply(tmp,2,function(x) any(abs(x) > threshold))] # print (data_out %>% head(5)) names_out <- names(data_out) return (names_out) } ##-------------------------------------------------------------------- ## function to select based on correlation with response corr_selection <- function(data, features, response, topn){ # input data, vector of all features and response and topn correlations you want # output dataframe with top selected features (and target) cor_df <- data.frame(feature = character(), cor = double()) for (fea in features){ cur_cor <- cor(data[[response]], data[[fea]]) cur_row <- data.frame(feature = fea, cor = cur_cor) cor_df <- rbind(cor_df, cur_row) } cor_df_sorted <- cor_df %>% arrange(desc(abs(cor))) cor_df_sorted_top <- cor_df_sorted %>% head(topn) # get top # select the features there all_f <- c(as.vector(cor_df_sorted_top$feature), response) data_small <- data %>% select(all_f) return (data_small) } ##-------------------------------------------------------------------- ## function to select based on random forest importance rf_selection <- function(data, response, topn){ # input data, vector of response (will be against all features) and topn correlations you want # output top n features (sorted) cur_formula <- paste(response, "~.", sep = "") rForest <- randomForest(formula = as.formula(cur_formula), data = data, mtry = 20, ntree = 1000,nodesize = 20, importance = TRUE) rf_importance <- data.frame(rForest$importance) rf_topn <- rf_importance %>% mutate(feature = rownames(rf_importance)) %>% arrange(desc(X.IncMSE)) %>% head(topn) all_f <- c(as.vector(rf_topn$feature))#, response) # data_small <- data %>% select(all_f) return (all_f) } ##-------------------------------------------------------------------- ## function to select based on xgboost importance xgb_selection <- function(fea_matrix, response_matrix, topn){ # input feature matrix, response matrix and number of n features to return # return list of topn features (sorted by importance) xgb_model <- xgboost(data = fea_matrix, label = response_matrix, eta = 0.3, nthread = 1, nrounds = 200, objective = "reg:linear", early_stopping_rounds = 3, verbose = 1) # importance xgbImp <- data.frame(xgb.importance(model = xgb_model)) top_fea <- xgbImp %>% arrange(desc(Gain)) %>% head(topn) all_f <- as.vector(top_fea$Feature) return (all_f) } ##-------------------------------------------------------------------- ## function to do "row-wise" feature engineering rw_fea_engineering <- function(data, response = ""){ # input data and all the features # output data + all features + engineered features + target # assuming no NAs in any of features # separate out the response dataframe (only needed for training) if (response != ""){ target_join <- data %>% select(!!sym(response)) %>% mutate(row_num = row_number()) data <- data %>% select(-!!sym(response)) } # generate new features with features dataframe data_w_features <- data %>% mutate(rowMean = rowMeans(.), rowMedian = apply(., 1, median), # , na.rm=TRUE rowMax = apply(., 1, max), # rowMin = apply(., 1, min), rowMean_n0 = apply(.,1, function(x) mean(x[x!=0])), # non-zero mean rowMin_n0 = apply(.,1, function(x) min(x[x!=0])), # non-zero min (will produce some inf) rowMedian_n0 = apply(.,1, function(x) median(x[x!=0])), # non-zero min count_n0 = apply(.,1, function(x) length(x[x!=0])) # count of non-zeros # -- can have more.. ) %>% replace(., is.na(.), -1) %>% # replace NA with -1 mutate(row_num = row_number()) data_w_features[mapply(is.infinite, data_w_features)] <- -1 # replace infinite with -1 # join back response and return if (response != ""){ # for training frame data_return <- data_w_features %>% left_join(target_join, by = "row_num") %>% select(-row_num) }else { # for testing frame data_return <- data_w_features %>% select(-row_num) } return (data_return) } ##-------------------------------------------------------------------- # evaluation function (calculating rmse) eval <- function(data, pred, actual, nrow = -1){ # input data, prediction, actual, and customized nrow (default will be nrow of data) data <- data %>% mutate(diff_2 = (!!sym(pred) - !!sym(actual))**2) if (nrow < 0){ rmse <- sqrt(sum(data$diff_2)/nrow(data)) }else{ ### cusomized nrow rmse <- sqrt(sum(data$diff_2)/nrow) } return (rmse) }

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Methods-accessors.R

#' Extract readInfo slot #' #' This generic function accesses the readInfo slot stored in an object #' derived from the Fast5Summary class. #' #' @param x Object of class \code{\linkS4class{Fast5Summary}} #' @return A data.frame with 5 columns #' @examples #' if( require(minionSummaryData) ) { #' data(s.typhi.rep2, package = 'minionSummaryData') #' readInfo( s.typhi.rep2 ) #' } setGeneric("readInfo", function(x) { standardGeneric("readInfo") }) #' @describeIn Fast5Summary Returns readInfo data.frame #' #' @include classes.R #' @export setMethod("readInfo", c(x = "Fast5Summary"), function(x) { x@readInfo } ) #' Extract eventData slot #' #' This generic function accesses the eventData slot stored in an object derived #' from the Fast5Summary class. #' #' @param x Object of class \code{\linkS4class{Fast5Summary}} #' @return A data.frame with 5 columns #' @examples #' if( require(minionSummaryData) ) { #' data(s.typhi.rep2, package = 'minionSummaryData') #' eventData( s.typhi.rep2 ) #' } setGeneric("eventData", function(x) { standardGeneric("eventData") }) #' @describeIn Fast5Summary Returns eventData data.frame #' #' @include classes.R #' @export setMethod("eventData", c(x = "Fast5Summary"), function(x) { x@eventData } ) #' Extract baseCalled slot #' #' This generic function accesses the baseCalled slot stored in an object #' derived from the Fast5Summary class. #' #' @param x Object of class \code{\linkS4class{Fast5Summary}} #' @return A data.frame with 6 columns #' @examples #' if( require(minionSummaryData) ) { #' data(s.typhi.rep2, package = 'minionSummaryData') #' baseCalled( s.typhi.rep2 ) #' } setGeneric("baseCalled", function(x) { standardGeneric("baseCalled") }) #' @describeIn Fast5Summary Returns baseCalled data.frame #' #' @include classes.R #' @export setMethod("baseCalled", c(x = "Fast5Summary"), function(x) { x@baseCalled } ) #' Extract fastq slot #' #' This generic function accesses the fastq slot stored in an object #' derived from the Fast5Summary class. #' #' @param x Object of class \code{\linkS4class{Fast5Summary}} #' @return A ShortReadQ object #' @examples #' if( require(minionSummaryData) ) { #' data(s.typhi.rep2, package = 'minionSummaryData') #' fastq( s.typhi.rep2 ) #' } setGeneric("fastq", function(x) { standardGeneric("fastq") }) #' @describeIn Fast5Summary Returns ShortReadQ object stored in fastq slot. #' #' @include classes.R #' @export setMethod("fastq", c(x = "Fast5Summary"), function(x) { x@fastq } )

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/man/IRACpm-package.Rd

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esplint/IRACpm

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IRACpm-package.Rd

\name{IRACpm-package} \alias{IRACpm-package} \alias{IRACpm} \alias{CD1} \alias{index1} \alias{epochs3} \alias{data1} \docType{package} \title{ Apply Distortion Correction to SPITZER IRAC Data } \description{ Applies a 7-8 order distortion correction to IRAC astrometric data from the Spitzer Space Telescope and includes a function for measuring apparent proper motions between different Epochs. } \details{ \tabular{ll}{ Package: \tab IRACpm\cr Type: \tab Package\cr Version: \tab 1.0\cr Date: \tab 2015-05-05\cr License: \tab GPL-2\cr Depends: \tab foreach, doMC, astro, R.utils\cr } Basic work flow for a data set to measure proper motion should follow this outline: 1) Read in files containing output from the Spitzer Science Center's APEX single frame module form MOPEX using read.in.data. 2) Measure image central world coordinates and rotations with CD.solver, CD.solver2, CD.solver3, or CD.solver4 3) Calculate average coordinates for each star of interest with calc.all.ch12 4) Repeat for other epochs 5) Run mucalc to measure apparent proper motions (If accurate relative astrometry is wanted without proper motions, just follow steps 1-3.) Example datasets and output for CD.solver is CD1, read.in.data is data1, and input data for mucalc is epochs3 To just convert pixel coordinates to World Coordinates using the distortion corrections measured follow the example listed below. } \author{ Taran Esplin Taran Esplin <tle918@psu.edu> } \keyword{ package } \examples{ data(CD1,ca_pix1,wa_pars1,data1) options(digits=10) #using a measured scale factor coor.calc(ca_pix1,wa_pars1,CD1[[1]][1,],-100,104,CD1[[2]],1) #estimating a scale factor from HMJD. coor.calc(ca_pix1,wa_pars1,CD1[[1]][1,],-100,104,data1,1) #the difference for this point in the array is ~2 mas }

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r

assess-flow.R

# R code to plot latitudinal profiles of (circular) mean flow direction, # along with both RMSE and (circular) correlation coefficients comparing # fused layers with both the raw ASTER and with the Canada DEM # # Jim Regetz # NCEAS library(raster) library(circular) datadir <- "/home/regetz/media/temp/terrain/flow" # create function to recode terraflow SFD values into degrees, with # 0=North and proceeding clockwise (this matches gdaldem's default # azimuth output for aspect calculation) recode <- function(r) { v <- values(r) v[v==0] <- NA v[v==1] <- 90 ## east v[v==2] <- 135 v[v==4] <- 180 ## south v[v==8] <- 225 v[v==16] <- 270 ## west v[v==32] <- 315 v[v==64] <- 0 ## north v[v==128] <- 45 r[] <- v return(r) } # load flow direction rasters, recoding on the fly sfd.aster <- recode(raster(file.path(datadir, "aster_300straddle_sfd.tif"))) sfd.srtm <- recode(raster(file.path(datadir, "srtm_150below_sfd.tif"))) sfd.uncor <- recode(raster(file.path(datadir, "fused_300straddle_sfd.tif"))) sfd.enblend <- recode(raster(file.path(datadir, "fused_300straddle_enblend_sfd.tif"))) sfd.bg <- recode(raster(file.path(datadir, "fused_300straddle_blendgau_sfd.tif"))) sfd.can <- recode(raster(file.path(datadir, "cdem_300straddle_sfd.tif"))) # extract raster latitudes for later lats300 <- yFromRow(sfd.aster, 1:nrow(sfd.aster)) lats150 <- yFromRow(sfd.srtm, 1:nrow(sfd.srtm)) # initialize output pdf device driver pdf("flowdir-assessment.pdf", height=8, width=11.5) # # plot latitudinal profiles of mean flow direction # # simple helper function to calculate row-wise means using circular # mean, patterned after circ.mean in the CircStats package rowMeansC <- function(r1, na.rm=TRUE) { m1 <- as.matrix(r1) m1[] <- (m1 * pi)/180 sinr <- rowSums(sin(m1), na.rm=na.rm) cosr <- rowSums(cos(m1), na.rm=na.rm) cmeans <- atan2(sinr, cosr) (cmeans * 180)/pi } par(mfrow=c(2,2), omi=c(1,1,1,1)) ylim <- c(-180, 180) plot(lats300, rowMeansC(sfd.can), type="l", yaxt="n", xlab="Latitude", ylab="Mean flow direction", ylim=ylim) axis(2, at=c(-180, -90, 0, 90, 180), labels=c("S", "W", "N", "E", "S")) text(min(lats300), min(ylim)+0.5, pos=4, font=3, labels="Original DEMs") lines(lats300, rowMeansC(sfd.aster), col="blue") lines(lats150, rowMeansC(sfd.srtm), col="red") legend("bottomright", legend=c("ASTER", "SRTM", "CDED"), col=c("blue", "red", "black"), lty=c(1, 1), bty="n") abline(v=60, col="red", lty=2) mtext(expression(paste("Latitudinal profiles of mean flow direction (", 125*degree, "W to ", 100*degree, "W)")), adj=0, line=2, font=2) #plot(lats300, rowMeans(as.matrix(sfd.uncor), na.rm=TRUE), type="l", # xlab="Latitude", ylab="Mean flow direction", ylim=ylim) #text(min(lats300), min(ylim)+0.5, pos=4, font=3, labels="uncorrected") #abline(v=60, col="red", lty=2) #mtext(expression(paste("Latitudinal profiles of mean flow direction (", # 125*degree, "W to ", 100*degree, "W)")), adj=0, line=2, font=2) plot(lats300, rowMeansC(sfd.uncor), type="l", yaxt="n", xlab="Latitude", ylab="Mean flow direction", ylim=ylim) axis(2, at=c(-180, -90, 0, 90, 180), labels=c("S", "W", "N", "E", "S")) text(min(lats300), min(ylim)+0.5, pos=4, font=3, labels="simple fused") abline(v=60, col="red", lty=2) plot(lats300, rowMeansC(sfd.enblend), type="l", yaxt="n", xlab="Latitude", ylab="Mean flow direction", ylim=ylim) axis(2, at=c(-180, -90, 0, 90, 180), labels=c("S", "W", "N", "E", "S")) text(min(lats300), min(ylim)+0.5, pos=4, font=3, labels="multires spline") abline(v=60, col="red", lty=2) plot(lats300, rowMeansC(sfd.bg), type="l", yaxt="n", xlab="Latitude", ylab="Mean flow direction", ylim=ylim) axis(2, at=c(-180, -90, 0, 90, 180), labels=c("S", "W", "N", "E", "S")) text(min(lats300), min(ylim)+0.5, pos=4, font=3, labels="gaussian blend") abline(v=60, col="red", lty=2) # # plot latitudinal profiles of RMSE # # simple helper function to calculate row-wise RMSEs, accounting for the # fact that flow dir values are circular (0-360), so the difference # between e.g. 5 and 355 should only be 10 rmse <- function(r1, r2, na.rm=TRUE, use) { diffs <- abs(as.matrix(r1) - as.matrix(r2)) if (!missing(use)) diffs[!use] <- NA diffs[] <- ifelse(diffs>180, 360-diffs, diffs) sqrt(rowMeans(diffs^2, na.rm=na.rm)) } par(mfrow=c(2,3), omi=c(1,1,1,1)) ylim <- c(0, 100) # ...with respect to ASTER plot(lats300, rmse(sfd.uncor, sfd.aster), type="l", xlab="Latitude", ylab="RMSE", ylim=ylim) lines(lats150, rmse(crop(sfd.uncor, extent(sfd.srtm)), sfd.srtm), col="blue") legend("topright", legend=c("ASTER", "SRTM"), col=c("black", "blue"), lty=c(1, 1), bty="n") text(min(lats300), max(ylim)-5, pos=4, font=3, labels="uncorrected") abline(v=60, col="red", lty=2) mtext(expression(paste( "Flowdir discrepancies with respect to separate ASTER/SRTM components (", 125*degree, "W to ", 100*degree, "W)")), adj=0, line=2, font=2) plot(lats300, rmse(sfd.enblend, sfd.aster), type="l", xlab="Latitude", ylab="RMSE", ylim=ylim) lines(lats150, rmse(crop(sfd.enblend, extent(sfd.srtm)), sfd.srtm), col="blue") legend("topright", legend=c("ASTER", "SRTM"), col=c("black", "blue"), lty=c(1, 1), bty="n") text(min(lats300), max(ylim)-5, pos=4, font=3, labels="exponential ramp") abline(v=60, col="red", lty=2) plot(lats300, rmse(sfd.bg, sfd.aster), type="l", xlab="Latitude", ylab="RMSE", ylim=ylim) lines(lats150, rmse(crop(sfd.bg, extent(sfd.srtm)), sfd.srtm), col="blue") legend("topright", legend=c("ASTER", "SRTM"), col=c("black", "blue"), lty=c(1, 1), bty="n") text(min(lats300), max(ylim)-5, pos=4, font=3, labels="gaussian blend") abline(v=60, col="red", lty=2) # ...with respect to CDEM plot(lats300, rmse(sfd.uncor, sfd.can), type="l", xlab="Latitude", ylab="RMSE", ylim=ylim) text(min(lats300), max(ylim)-5, pos=4, font=3, labels="uncorrected") abline(v=60, col="red", lty=2) mtext(expression(paste( "Flowdir discrepancies with respect to Canada DEM (", 125*degree, "W to ", 100*degree, "W)")), adj=0, line=2, font=2) plot(lats300, rmse(sfd.enblend, sfd.can), type="l", xlab="Latitude", ylab="RMSE", ylim=ylim) text(min(lats300), max(ylim)-5, pos=4, font=3, labels="exponential ramp") abline(v=60, col="red", lty=2) plot(lats300, rmse(sfd.bg, sfd.can), type="l", xlab="Latitude", ylab="RMSE", ylim=ylim) text(min(lats300), max(ylim)-5, pos=4, font=3, labels="gaussian blend") abline(v=60, col="red", lty=2) # # plot latitudinal profiles of correlation coefficients # # simple helper function to calculate row-wise *circular* correlation # coefficients corByLat <- function(r1, r2, rows) { if (missing(rows)) { rows <- 1:nrow(r1) } m1 <- circular(as.matrix(r1), units="degrees", rotation="clock") m2 <- circular(as.matrix(r2), units="degrees", rotation="clock") sapply(rows, function(row) { p <- cor.circular(m1[row,], m2[row,]) if (is.null(p)) NA else p }) } par(mfrow=c(2,3), omi=c(1,1,1,1)) ylim <- c(-1, 1) # ...with respect to ASTER plot(lats300, corByLat(sfd.uncor, sfd.aster), type="l", xlab="Latitude", ylab="Circular correlation", ylim=ylim) lines(lats150, corByLat(crop(sfd.uncor, extent(sfd.srtm)), sfd.srtm), col="blue") legend("bottomright", legend=c("ASTER", "SRTM"), col=c("black", "blue"), lty=c(1, 1), bty="n") text(min(lats300), min(ylim), pos=4, font=3, labels="simple fused") abline(v=60, col="red", lty=2) mtext(expression(paste( "Flow direction correlations with respect to separate ASTER/SRTM components (", 125*degree, "W to ", 100*degree, "W)")), adj=0, line=2, font=2) plot(lats300, corByLat(sfd.enblend, sfd.aster), type="l", xlab="Latitude", ylab="Circular correlation", ylim=ylim) lines(lats150, corByLat(crop(sfd.enblend, extent(sfd.srtm)), sfd.srtm), col="blue") legend("bottomright", legend=c("ASTER", "SRTM"), col=c("black", "blue"), lty=c(1, 1), bty="n") text(min(lats300), min(ylim), pos=4, font=3, labels="multires spline") abline(v=60, col="red", lty=2) plot(lats300, corByLat(sfd.bg, sfd.aster), type="l", xlab="Latitude", ylab="Circular correlation", ylim=ylim) lines(lats150, corByLat(crop(sfd.bg, extent(sfd.srtm)), sfd.srtm), col="blue") legend("bottomright", legend=c("ASTER", "SRTM"), col=c("black", "blue"), lty=c(1, 1), bty="n") text(min(lats300), min(ylim), pos=4, font=3, labels="gaussian blend") abline(v=60, col="red", lty=2) # ...with respect to CDEM plot(lats300, corByLat(sfd.uncor, sfd.can), type="l", xlab="Latitude", ylab="Circular correlation", ylim=ylim) text(min(lats300), min(ylim), pos=4, font=3, labels="simple fused") abline(v=60, col="red", lty=2) mtext(expression(paste( "Flow direction correlations with respect to Canada DEM (", 125*degree, "W to ", 100*degree, "W)")), adj=0, line=2, font=2) plot(lats300, corByLat(sfd.enblend, sfd.can), type="l", xlab="Latitude", ylab="Circular correlation", ylim=ylim) text(min(lats300), min(ylim), pos=4, font=3, labels="multires spline") abline(v=60, col="red", lty=2) plot(lats300, corByLat(sfd.bg, sfd.can), type="l", xlab="Latitude", ylab="Circular correlation", ylim=ylim) text(min(lats300), min(ylim), pos=4, font=3, labels="gaussian blend") abline(v=60, col="red", lty=2) # close pdf device driver dev.off()

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/cal/rgl-plots.R

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wactbprot/svol

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rgl-plots.R

library("rgl") v2 <- read.table("data/ventil-kreis_2.txt" , skip=2 , sep=" " , row.names = NULL) v1 <- read.table("data/ventil-kreis_1.txt" , skip=2 , sep=" " , row.names=NULL) rgl.close() rgl.open() rgl.bg( sphere = FALSE , color=c("white","black") , back="lines") axes3d(box = TRUE , col=1 ) title3d(xlab= "x in mm" , ylab= "y in mm" , zlab= "z in mm" , pos=c(10,10,0) , col=1 ) ## Ursprung rgl.points(0,0,0 , col=1) ## Bezugskreis rgl.points(v1[, 3] , v1[, 4] , v1[, 5] , col=1) ## Scans scns <- c("SCN1" , "SCN3" , "SCN4" , "SCN5" , "SCN6" , "SCN7") col=1 for (scn in scns){ i <- which(v2[,1] == scn) rgl.points(v2[i, 3] , v2[i, 4] , v2[i, 5] , col=col ) col<- col+1 } s1 <- read.table("data/ventil-sitz_1.txt", skip=2, sep=" ", row.names=NULL) rgl.points(s1[, 3], s1[, 4], -s1[, 5] - 40, add=TRUE, col=1)

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/man/SDF.Rd

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wconstan/sapa

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rd

SDF.Rd

%% WARNING: This file was automatically generated from the associated %% sapa_sdf.mid file. Do NOT edit this Rd file to make a change. Instead, %% edit the sapa_sdf.mid file in the project MID directory. Once the %% sapa_sdf.mid file has been updated, this Rd file, and all other %% documentation (such as corresponding LaTeX, SGML and HTML documentation) %% should be regenerated using the mid.pl Perl script. %% R documentation for the SDF, as.matrix.SDF, plot.SDF, print.SDF functions \name{SDF} \alias{SDF} \alias{as.matrix.SDF} \alias{plot.SDF} \alias{print.SDF} \title{Nonparametric (cross) spectral density function estimation} \concept{spectral density function estimation} \usage{SDF(x, method="direct", taper.=NULL, window=NULL, n.taper=5, overlap=0.5, blocksize=NULL, single.sided=TRUE, sampling.interval=NULL, center=TRUE, recenter=FALSE, npad=2*numRows(x))} \description{Estimate the process (cross) spectral density function via nonparametric models.} \arguments{ \item{x}{a vector or matrix containing uniformly-sampled real-valued time series. If a \code{matrix}, each column should contain a different time series.} \item{blocksize}{an integer representing the number of points (width) of each block in the WOSA estimator scheme. Default: \code{floor(N/4)} where \code{N} is the number of samples in each series.} \item{center}{a logical value. If \code{TRUE}, the mean of each time series is recentered prior to estimating the SDF. Default: \code{TRUE}.} \item{method}{a character string denoting the method to use in estimating the SDF. Choices are \code{"direct"}, \code{"lag window"}, \code{"wosa"} (Welch's Overlapped Segment Averaging), \code{"multitaper"}. See \bold{DETAILS} for more information. Default: \code{"direct"}.} \item{n.taper}{an integer defining the number of tapers to use in a multitaper scheme. This value is overwritten if the \code{taper} input is of class \code{taper}. Default: \code{5}.} \item{npad}{an integer representing the total length of each time series to analyze after padding with zeros. This argument allows the user to control the spectral resolution of the SDF estimates: the normalized frequency interval is \eqn{\Delta f = 1 / \hbox{npad}}{deltaf=1/npad}. This argument must be set such that \eqn{\hbox{npad} > 2}{npad > 2}. Default: \code{2*numRows(x)}.} \item{overlap}{a numeric value on \eqn{[0,1]} denoting the fraction of window overlap for the WOSA estimator. Default: \code{0.5}.} \item{recenter}{a logical value. If \code{TRUE}, the mean of each time series is recentered after (posssibly) tapering the series prior to estimating the SDF. Default: \code{FALSE}.} \item{sampling.interval}{a numeric value representing the interval between samples in the input time series \code{x}. Default: \code{NULL}, which serves as a flag to obtain the sampling interval via the \code{deltat} function. If \code{x} is a list, the default sampling interval is \code{deltat(x[[1]])}. If \code{x} is an atomic vector (ala \code{isVectorAtomic}), then the default samplign interval is established ala \code{deltat(x)}. Finally, if the input series is a matrix, the sampling interval of the first series (assumed to be in the first column) is obtained ala \code{deltat(x[,1])}.} \item{single.sided}{a logical value. If \code{TRUE}, a single-sided SDF estimate is returned corresponding to the normalized frequency range of \eqn{[0,1/2]}. Otherwise, a double-sided SDF estimate corresponding to the normalized frequency interval \eqn{[-1/2,1/2]} is returned. Default: \code{TRUE}.} \item{taper.}{an object of class \code{taper} or a character string denoting the primary taper. If an object of class \code{taper}, the length of the taper is checked to ensure compatitbility with the input \code{x}. See \bold{DETAILS} for more information. The default values are a function of the \code{method} as follows: \describe{ \item{direct}{normalized rectangular taper} \item{lag window}{normalized Parzen window with a cutoff at \eqn{N/2} where \eqn{N} is the length of the time series.} \item{wosa}{normalized Hanning taper} \item{multitaper}{normalized Hanning taper}}} \item{window}{an object of class \code{taper} or a character string denoting the (secondary) window for the lag window estimator. If an object of class \code{taper}, the length of the taper is checked to ensure compatitbility with the input \code{x}. See \bold{DETAILS} for more information. Default: Normalized Hanning window.} } \value{ an object of class \code{SDF}. } \section{S3 METHODS}{ \describe{ \item{as.matrix}{converts the (cross-)SDF estimate(s) as a matrix. Optional arguments are passed directly to the \code{matrix} function during the conversion.} \item{plot}{plots the (cross-)SDF estimate(s). Optional arguments are: \describe{ \item{xscale}{a character string defining the scaling to perform on the (common) frequency vector of the SDF estimates. See the \code{scaleData} function for supported choices. Default: \code{"linear"}.} \item{yscale}{a character string defining the scaling to perform on the SDF estimates. See the \code{scaleData} function for supported choices. Default: \code{"linear"}.} \item{type}{a single character defining the plot type (ala the \code{par} function) of the SDF plots. Default: \code{ifelse(numRows(x) > 100, "l", "h")}.} \item{xlab}{a character string representing the x-axis label. Default: \code{"FREQUENCY (Hz)"}.} \item{ylab}{a (vector of) character string(s), one per (cross-)SDF estimate, representing the y-axis label(s). Default: in the multivariate case, the strings \code{"Sij"} are used for the y-axis labels, where i and j are the indices of the different variables. For example, if the user supplies a 2-column matrix for \code{x}, the labels \code{"S11"}, \code{"S12"}, and \code{"S22"} are used to label the y-axes of the corresponding (cross-)SDF plots. In the univariate case, the default string \code{"SDF"} prepended with a string describing the type of SDF performed (such as \code{"Multitaper"}) is used to label the y-axis.} \item{plot.mean}{a logical value. If \code{TRUE}, the SDF value at normalized frequency \eqn{f=0} is plotted for each SDF. This frequency is associated with the sample mean of the corresponding time series. A relatively large mean value dominates the spectral patterns in a plot and thus the corresponding frequency is typically not plotted. Default: \code{!attr(x,"center")}.} \item{n.plot}{an integer defining the maximum number of SDF plots to place onto a single graph. Default: \code{3}.} \item{FUN}{a post processing function to apply to the SDF values prior to plotting. Supported functions are \code{Mod}, \code{Im}, \code{Re} and \code{Arg}. See each of these functions for details. If the SDF is purely real (no cross-SDF is calculated), this argument is coerced to the \code{Mod} function. Default: \code{Mod}.} \item{add}{A logical value. If \code{TRUE}, the plot is added using the current \code{par()} layout. Otherwise a new plot is produced. Default: \code{FALSE}.} \item{...}{additional plot parameters passed directly to the \code{genPlot} function used to plot the SDF estimates.}}} \item{print}{prints the object. Available options are: \describe{ \item{justify}{text justification ala \code{prettPrintList}. Default: \code{"left"}.} \item{sep}{header separator ala \code{prettyPrintList}. Default: \code{":"}.} \item{...}{Additional print arguments sent directly to the \code{prettyPrintList} function.}}} } } \details{ % Let \eqn{X_t}{x(t)} be a uniformly sampled real-valued time series of length \eqn{N}, Let an estimate of the process spectral density function be denoted as \eqn{\hat{S}_X(f)}{S(f)} where \eqn{f} are frequencies on the interval \eqn{[-1/(2\Delta t),1/(2\Delta t)]}{-1/(2*deltat),1/(2*deltat)} where \eqn{\Delta t}{deltat} is the sampling interval. The supported SDF estimators are: \describe{ \item{direct}{The direct SDF estimator is defined as \eqn{\hat{S}_X^{(d)}(f) = | \sum_{t=0}^{N-1} h_t X_t e^{-i2\pi f t}|^2}{S(f)=|sum[t=0,...,N-1]{h(t)*x(t)*exp(-i*2*pi*f*t)}|^2}, where \eqn{\{h_t\}}{h(t)} is a data taper normalized such that \eqn{\sum_{t=0}^{N-1} h_t^2 = 1}{sum[t=0,...,N-1]{h(t)^2} = 1}. If \eqn{h_t=1/\sqrt{N}}{h(t)=1/sqrt(N)} then we obtain the definition of the periodogram \eqn{\hat{S}_X^{(p)}(f) = \frac{1}{N} | \sum_{t=0}^{N-1} X_t e^{-i2\pi f t}|^2}{S(f)=(1/N) * |sum[t=0,...,N-1]{x(t)*exp(-i*2*pi*f*t)}|^2}. See the \code{taper} function for more details on supported window types.} \item{lag window}{The lag window SDF estimator is defined as \eqn{\hat{S}_X^{(lw)}(f) = \sum_{\tau=-(N-1)}^{N-1} w_\tau \hat{s}_{X,\tau}^{(d)} e^{-i2\pi f \tau}}{S(f)=sum[k=-(N-1),...,(N-1)]{w(k)*s(k)*exp(-i*2*pi*f*k)}|^2}, where \eqn{\hat{s}_{X,\tau}^{(d)}}{s(k)} is the autocovariance sequence estimator corresponding to some direct spectral estimator (often the periodogram) and \eqn{w_\tau}{w(k)} is a lag window (popular choices are the Parzen, Papoulis, and Daniell windows). See the \code{taper} function for more details.} \item{wosa}{Welch's Overlapped Segment Averaging SDF estimator is defined as \deqn{ \hat S^{(wosa)} = {1\over N_B} \sum_{j=0}^{N_B-1} \hat S^{(d)}_{jN_O} (f) }{S(f)=(1/Nb)*sum[j=0,...,Nb-1]{S(j*No,f)}} where \deqn{ \hat S^{(d)}_{l}(f) \equiv \left| \sum_{t=0}^{N_S-1} h_t X_{t+l} e^{-i2\pi ft} \right|^2, \enskip 0 \le l \le N - N_S; }{S(l,f) =|sum[t=0,...,Ns-1]{h(t)*x(t+l)*exp(-i*2*pi*f*t)}|^2} Here, \eqn{N_O}{No} is a positive integer that controls how much overlap there is between segments and that must satisfy both \eqn{N_O \le N_S}{No <= Ns} and \eqn{N_O(N_B-1) = N-N_S}{No * (Nb - 1) = N - Ns}, while \eqn{\{ h_t \}}{h(t)} is a data taper appropriate for a series of length \eqn{N_S}{Ns} (i.e., \eqn{\sum_{t=0}^{N_S-1} h_t^2 = 1}{sum[t=0,...,Ns-1]{h_t^2} = 1}).} \item{multitaper}{A multitaper spectral estimator is given by \deqn{\hat S^{(mt)}_X(f)= {1\over K} \sum_{k=0}^{K-1} \left| \sum_{t=0}^{N-1} h_{k,t} X_t e^{-i2\pi ft} \right|^2, }{S(f) = (1/K) * sum[k=0,...,K-1] S(k,f)} where \eqn{S(k,f) = {|\sum_{t=0}^{N-1} h_{k,t} X_t \exp(-i 2 \pi f t)|}^2}{S(k,f) = |sum[t=0,...,N-1]{h(k,t) * X(t) * exp(-i*2*pi*f*t)}|^2} and \eqn{\{ h_{k,t} \}$, $k=0,\ldots,K-1}{h(k,t) for k=0,...,K-1}, is a set of \eqn{K} orthonormal data tapers. \deqn{\sum_{t=0}^{N-1} h_{k,t} h_{k',t} = \left\{ \begin{array}{ll} 1,& \mbox{if $k=k'$;}\\ 0,& \mbox{otherwise} \end{array} \right. }{See reference(s) for further details.} Popular choices for multitapers include sinusoidal tapers and discrete prolate spheroidal sequences (DPSS). See the \code{taper} function for more details.}} \bold{Cross spectral density function estimation:} If the input \code{x} is a matrix, where each column contains a different time series, then the results are returned in a matrix whose columns correspond to all possible unique combinations of cross-SDF estimates. For example, if \code{x} has three columns, then the output will be a matrix whose columns are \eqn{\{S_{11},S_{12},S_{13},S_{22},S_{23},S_{33}\}}{{S11, S12, S13, S22, S23, S33}} where \eqn{S_{ij}}{Sij} is the cross-SDF estimate of the \code{i}th and \code{j}th column of \code{x}. All cross-spectral density function estimates are returned as complex-valued series to maintain the phase relationships between components. For all \eqn{S_{ij}}{Sij} where \eqn{i=j}, however, the imaginary portions will be zero (up to a numerical noise limit). } \references{ Percival, Donald B. and Constantine, William L. B. (2005) ``Exact Simulation of Gaussian Time Series from Nonparametric Spectral Estimates with Application to Bootstrapping", \emph{Journal of Computational and Graphical Statistics}, accepted for publication. D.B. Percival and A. Walden (1993), \emph{Spectral Analysis for Physical Applications: Multitaper and Conventional Univariate Techniques}, Cambridge University Press, Cambridge, UK. } \seealso{ \code{\link{taper}}, \code{\link{ACVS}}.} \examples{ ## calculate various SDF estimates for the ## sunspots series. remove mean component for a ## better comparison. require(ifultools) data <- as.numeric(sunspots) methods <- c("direct","wosa","multitaper", "lag window") S <- lapply(methods, function(x, data) SDF(data, method=x), data) x <- attr(S[[1]], "frequency")[-1] y <- lapply(S,function(x) decibel(as.vector(x)[-1])) names(y) <- methods ## create a stack plot of the data stackPlot(x, y, col=1:4) ## calculate the cross-spectrum of the same ## series: all spectra should be the same in ## this case SDF(cbind(data,data), method="lag") ## calculate the SDF using npad=31 SDF(data, npad=31, method="multitaper") } \keyword{univar}

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library(GSA) #gmtfile <- system.file("extdata", "msigdb.v5.2.entrez.gmt",package="clusterProfiler") gmtfile <- system.file("extdata", "msigdb.v5.2.symbols.gmt",package="clusterProfiler") c2kegg <- read.gmt(gmtfile) #sum(gene %in% c2kegg$gene) ### Glioma - Gene load("D:/R_workspace/BioInformaticsGlioma/paramList10.Rdata") gene <- rownames(paramList$dfps) egmt <- enricher(gene, TERM2GENE=c2kegg,pvalueCutoff = 0.05) #head(egmt) #View(egmt@result[grep(pattern = "KEGG",x = rownames(egmt@result),ignore.case = F),]) reactome <- egmt@result[grep(pattern = "reactome",x = rownames(egmt@result),ignore.case = T),] kegg <- egmt@result[grep(pattern = "kegg",x = rownames(egmt@result),ignore.case = T),] biocarta <- egmt@result[grep(pattern = "biocarta",x = rownames(egmt@result),ignore.case = T),] # in c2 # estrarre i primi 30 pathway con p-value più alto # creare file per ogni pathway con i soli geni dei pazienti dim(egmt@result) View(egmt@result) plot(egmt@result$pvalue) c2 <- read.gmt('D:\\Download\\c2.all.v5.2.symbols.gmt') "VERHAAK_GLIOBLASTOMA_MESENCHYMAL" %in% c2$ont egmt_result <- egmt@result save(egmt_result, file="egmt_result.Rdata") # reactomeFinal <- reactome[which(reactome$Count >=10),] # keggFinal <- kegg[which(kegg$Count >=10),] # c6Final <- egmt@result[egmt@result$ID %in% c6 & egmt@result$Count >= 10,] save(reactomeFinal,file="reactomeFinal.Rdata") save(keggFinal,file="keggFinal.Rdata") save(c6Final,file="c6Final.Rdata")

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Covest.R

Covest <- function(par, Umod, TDmod, TUmod, TGmod, SdatCount, Sdat, ID) { # y0/y1/y2 are the jump time of h0/h1/h2 exclude censored beta0 <- par$beta0vec; h0 <- par$h0; y0 <- par$timeD; nbeta0 <- par$nbeta0; n0 <- par$n0 beta1 <- par$beta1vec; h1 <- par$h1; y1 <- par$timeU; nbeta1 <- par$nbeta1; n1 <- par$n1 beta2 <- par$beta2vec; h2 <- par$h2; y2 <- par$timeG; nbeta2 <- par$nbeta2; n2 <- par$n2 alpha <- par$alpvec; nalpha <- par$nalpha IND <- cumsum(c(nbeta0, n0, nbeta1, n1, nbeta2, n2, nalpha)) indbeta0 <- 1:IND[1]; indh0 <- (IND[1]+1):IND[2] indbeta1 <- (IND[2]+1):IND[3]; indh1 <- (IND[3]+1):IND[4] indbeta2 <- (IND[4]+1):IND[5]; indh2 <-(IND[5]+1):IND[6] indalpha <- (IND[6]+1):IND[7] npar <- max(indalpha) n <- dim(SdatCount)[1] nid <- dim(Sdat)[1]; d2L <- matrix(0, npar, npar) score1 <- matrix(0, nid, npar) score0 <- matrix(0, nid, npar) X <- as.matrix(cbind(intercept=rep(1,nid), model.frame(Umod, data=Sdat, na.action="na.pass")[,-1])) Xd <- model.matrix(TDmod, SdatCount)[,-1] Xe <- model.matrix(TUmod, Sdat)[,-1] temp <- as.matrix(model.frame(TDmod, data=SdatCount, na.action="na.pass")) StartD <- temp[,1]; EndD <- temp[,2]; DeltaD <- temp[,3] temp <- as.matrix(model.frame(TUmod, data=Sdat, na.action="na.pass")) SurvU <- temp[,1]; DeltaU <- temp[,2] temp <- as.matrix(model.frame(TGmod, data=SdatCount, na.action="na.pass")) Xg <- temp[,-(1:3)] StartG <- temp[,1]; EndG <- temp[,2]; DeltaG <- temp[,3] indID <- (matrix(SdatCount[,ID], n, nid, byrow=F)==matrix(Sdat[,ID], n, nid, byrow=T)) # estimating alpha PE1 <- exp(X%*%alpha)/(1+exp(X%*%alpha)) score1[, indalpha] <- matrix(1-PE1, nid, nalpha, byrow=FALSE)*(X) score0[, indalpha] <- matrix(0-PE1, nid, nalpha, byrow=FALSE)*(X) d2L[indalpha, indalpha] <- -(t(X)*matrix(PE1*(1-PE1),nalpha,nid,byrow=TRUE))%*%X # beta0 h0 out <- Covestsub(Xd, beta0, h0, y0, StartD, EndD, DeltaD, (SdatCount$Group%in%c(1,4)), 1-SdatCount$Ee) score0[, c(indbeta0, indh0)] <- t(indID)%*%out$score d2L[c(indbeta0, indh0), c(indbeta0, indh0)] <- out$dscore # beta1 h1 out <- Covestsub(Xe, beta1, h1, y1, rep(0,nid), SurvU, DeltaU, (Sdat$Group!=1), Sdat$Ee) score1[, c(indbeta1, indh1)] <- out$score d2L[c(indbeta1, indh1), c(indbeta1, indh1)] <- out$dscore # beta2 h2 out <- Covestsub(Xg, beta2, h2, y2, StartG, EndG, DeltaG, (SdatCount$Group%in% c(2,3)), SdatCount$Ee) score1[, c(indbeta2, indh2)] <- t(indID)%*%out$score d2L[c(indbeta2, indh2), c(indbeta2, indh2)] <- out$dscore # information matrix Ee <- Sdat$Ee EdLdL <- t(score1)%*%(as.vector(Ee)*score1)+t(score0)%*%(as.vector(1-Ee)*score0) EdL <- as.vector(Ee)*score1+as.vector(1-Ee)*score0 EdLEdL <- t(EdL)%*%EdL cov.est <- solve(-d2L-(EdLdL-EdLEdL)) Influence <- cov.est%*%t(EdL) return(list(cov.est=cov.est, Infl=Influence)) } Covestsub <- function(X, beta, hh0, yy0, Start, End, Delta, w, Ew) { XX<- X[w>0,] TStart<- Start[w>0] TEnd<- End[w>0] TDelta <- Delta[w>0] Tw <- w[w>0] ETw <- Ew[w>0] n <- length(TStart) nbeta <- length(beta) nh <- length(hh0) score <- matrix(0, n, nbeta+nh) dscore <- matrix(0, nbeta+nh, nbeta+nh) EXbeta <- exp(XX%*%beta) indY <- (matrix(TEnd, n, nh, byrow=FALSE)>=matrix(yy0,n,nh,byrow=TRUE))*(matrix(TStart, n, nh, byrow=FALSE)<matrix(yy0,n,nh,byrow=TRUE)) indY2 <- (matrix(TEnd, n, nh, byrow=FALSE)<matrix(c(yy0[-1],Inf),n,nh,byrow=TRUE)) HY <- indY%*%hh0 OneY <- (indY*indY2)%*%rep(1,nh) score[,1:nbeta] <- as.vector(TDelta*OneY)*XX-as.vector(Tw*HY*EXbeta)*XX score[,nbeta+(1:nh)] <- as.vector(TDelta)*indY*indY2%*%diag(1/hh0)-as.vector(Tw*EXbeta)*indY dscore[1:nbeta, 1:nbeta] <- -t(XX)%*%(matrix(ETw*HY*EXbeta,n,nbeta, byrow=FALSE)*XX) dscore[1:nbeta, nbeta+(1:nh)] <- -t(XX)%*%(matrix(ETw*EXbeta,n,nh,byrow=FALSE)*indY) dscore[nbeta+(1:nh), 1:nbeta] <- t(dscore[1:nbeta, nbeta+(1:nh)]) dscore[nbeta+(1:nh), nbeta+(1:nh)] <- -diag(apply(TDelta*indY*indY2,2,sum)/hh0^2) Tscore <- score score <- matrix(0, length(Start), nbeta+nh) score[w>0,] <- Tscore out=list(score=score, dscore=dscore) return(out) }

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df = read.csv('4. NSSO68 data set.csv') dim(df) names(df) summary(df) library(psych) unique(df$state) meg = df[df$state_1 == 'MEG',] # Data type of the subset data class(meg) any(is.na(meg)) # Shape of the Subset dim(meg) # To View the filtered data #View(meg) # Different columns names(meg) # Top 3 rows of the subset head(meg,3) # Bottom 3 rows of the subset tail(meg,3) # Unique Districts of the subset str(meg) #View(meg) # takes lot time to generate a report #create_report(meg) describe(meg) is.na(meg) sum(is.na(meg))

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/rprog031/tests/testthat/test_prog3.R

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library(rprog031) context("Outcomes frame created from data") outcome <- load_outcomes(outcome="pneumonia") test_that("loads_outcomes loads proper file and creates data frame" , { expect_equal(class(outcome), "data.frame") }) context("Finding the best hospital in the state") test_that("Best function exists and takes the proper arguments", { ## expect_error(best()) ## expect_error(best(state = "AK")) ## expect_error(best(outcome = "pneumonia")) ## expect_error(best(state = "AK", outcome = "herpies"), "invalid outcome", fixed=TRUE) ## expect_error(best(state = "XX", outcome = "heart attack"), "invalid state", fixed=TRUE) ## expect_equal(best("TX", "heart attack"), "CYPRESS FAIRBANKS MEDICAL CENTER") ## expect_equal(best("TX", "heart failure"), "FORT DUNCAN MEDICAL CENTER") ## expect_equal(best("MD", "heart attack"), "JOHNS HOPKINS HOSPITAL, THE") ## expect_equal(best("MD", "pneumonia"), "GREATER BALTIMORE MEDICAL CENTER") }) context("Return name of hospital in given state with given rank") test_that("rankhospital takes three arguments", { expect_error(rankhospital()) expect_equal(rankhospital("TX", "heart failure", 4), "DETAR HOSPITAL NAVARRO") expect_equal(rankhospital("MD", "heart attack", "worst"), "HARFORD MEMORIAL HOSPITAL") myfuncton <- function() {NA} #expect_equal(myfuncton(), NA ) }) context("Ranking each hospital in the state") test_that("rankall is a stupid function but it works", { })

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\name{acf} \alias{acf} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Auto- and Cross- Covariance and -Correlation Function Estimation} \description{ This function calls the acf function in the stats package and processes to drop lag-0 of the acf. It only works for univariate time series, so x below should be 1-dimensional. } \usage{ acf(x, lag.max = NULL, type = c("correlation", "covariance", "partial")[1], plot = TRUE, na.action = na.fail, demean = TRUE, drop.lag.0 = TRUE, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x}{a univariate or multivariate (not ccf) numeric time series object or a numeric vector or matrix, or an "acf" object.} \item{lag.max}{maximum number of lags at which to calculate the acf. Default is 10*log10(N/m) where N is the number of observations and m the number of series.} \item{type}{character string giving the type of acf to be computed. Allowed values are "correlation" (the default), "covariance" or "partial".} \item{plot}{ logical. If TRUE (the default) the acf is plotted.} \item{na.action}{function to be called to handle missing values. na.pass can be used.} \item{demean}{ logical. Should the covariances be about the sample means?} \item{drop.lag.0}{logical. Should lag 0 be dropped} \item{\dots}{further arguments to be passed to plot.acf.} } \value{ An object of class "acf", which is a list with the following elements: \item{lag}{ A three dimensional array containing the lags at which the acf is estimated.} \item{acf}{ An array with the same dimensions as lag containing the estimated acf.} \item{type}{ The type of correlation (same as the type argument).} \item{n.used}{ The number of observations in the time series.} \item{series}{ The name of the series x.} \item{snames}{ The series names for a multivariate time series.} } \references{ ~put references to the literature/web site here ~ } \author{Original authors of stats:::acf are: Paul Gilbert, Martyn Plummer, B.D. Ripley. This wrapper is written by Kung-Sik Chan} \seealso{\code{\link{plot.acf}}, \code{\link{ARMAacf}} for the exact autocorrelations of a given ARMA process.} \examples{ data(rwalk) model1=lm(rwalk~time(rwalk)) summary(model1) acf(rstudent(model1),main='') } \keyword{methods}

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\name{transform} \alias{transform} \title{S3 class: transform} \usage{ transform(type, ...) } \description{ This is a type of \code{\link{pipe}}. } \keyword{internal}

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names <- c("coye", "paul", "andrew", "judd") y <- c(9, 7, 6, 6) data.frame(chef = names, score = y) data.frame(chef = names, score = y) -> pr pr pr$chef names(pr) names(pr)[2] = "score1" pr pr$score2 <- c(3, 2, 1, 2) pr$spiciness <- c(3, 2, 1, 2) pr attach(pr) score1 score1 = score1 + 10 score1 pr$score1 mean(pr$score1) pr$score1 <- scale(pr$score1) pr$score1 pr$score2 = scale(pr$score2) pr pr$total_Score <- (pr$score1 + pr$score2) / 2 pr pr$above_av <- pr$total_Score > mean(pr$total_Score) pr pr$spiciness pr$spiciness <- factor(pr$spiciness, levels = c(1, 2, 3), labels = c("mild", "spicy", "extra spicy")) pr levels(pr$spiciness) levels(pr$spicines) <- c("mild", "medium", "hot") pr

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## Plotting global patterns of observed and SES Functional and phylogenetic diversity of raptors ## ### OBSERVED VALUES ### data_raptor.plots<-data_raptor[data_raptor$continent !="Antarctica",] # grouping some values to improve contrast in visualization # data_raptor.plots$mpd1<-data_raptor.plots$mpd data_raptor.plots$mpd1[data_raptor.plots$mpd1 <75] <- 74.9 data_raptor.plots$mntd1<-data_raptor.plots$mntd data_raptor.plots$mntd1[data_raptor.plots$mntd1 >95] <-96 data_raptor.plots$mntd1[data_raptor.plots$mntd1 < 12] <-11.9 data_raptor.plots$fdis_obs_niche2<-data_raptor.plots$fdis_obs_niche data_raptor.plots$fdis_obs_niche2[data_raptor.plots$fdis_obs_niche2 < 0.2] <-0.15 library(ggplot2) p.0 = plot_world_eqaul_area(color_polygon = "grey90") + viridis::scale_fill_viridis(direction = -1) + theme(legend.position = c(0.55,0.10), legend.direction = "horizontal", legend.key.height = unit(0.5, 'lines'), legend.key.width = unit(1.5, 'lines'), plot.margin = margin(-0.5, -0.1, -0.5, -0.1, "cm")) p_sprich = p.0 + geom_tile(data = filter(data_raptor.plots, !is.na(continent)), aes(x = x, y = y, fill = sr), inherit.aes = F) + labs(fill = 'SR') p_sprich p = plot_world_eqaul_area(color_polygon = "grey90", fill_polygon = "white") + viridis::scale_fill_viridis(direction = -1) + theme(legend.position = c(0.55,0), legend.direction = "horizontal", legend.key.height = unit(0.4, 'lines'), legend.key.width = unit(1.5, 'lines'), plot.margin = margin(-0.5, -0.1, -0.5, -0.1, "cm")) # plot pd uroot p_pduroot = p + geom_tile(data = data_raptor.plots, aes(x = x, y = y, fill = pd.uroot), inherit.aes = F) + labs(fill = 'PD') p_mpd = p + geom_tile(data = data_raptor.plots, aes(x = x, y = y, fill = mpd1), inherit.aes = F) + labs(fill = 'MPD ')+ # Change legend labels of continuous legend scale_fill_continuous(type = "viridis", direction= -1, breaks = c(80, 100, 120, 140, 160), labels = c("< 80", "100", "120", "140", "160")) p_mntd = p + geom_tile(data = data_raptor.plots, aes(x = x, y = y, fill = mntd1), inherit.aes = F) + labs(fill = 'MNTD ')+ # Change legend labels of continuous legend scale_fill_continuous(type = "viridis", direction= -1, breaks = c(10, 30, 50, 70, 90), labels = c("<10", "30", "50", "70", ">90")) #plot fd p_morph = p + geom_tile(data = data_raptor.plots, aes(x = x, y = y, fill = fdis_obs_morph), inherit.aes = F) + labs(fill = 'FD Morphology') p_fdis_niche = p + geom_tile(data = data_raptor.plots, aes(x = x, y = y, fill = fdis_obs_niche2), inherit.aes = F) + labs(fill = 'FD Niche ')+ scale_fill_continuous(type = "viridis", direction= -1, breaks = c(0, 0.15, 0.3, 0.45), labels = c("0.0", "0.15", "0.30", "0.45")) p_fdis_diet = p + geom_tile(data = data_raptor.plots, aes(x = x, y = y, fill = fdis_obs_diet), inherit.aes = F) + labs(fill = 'FD Diet ')+ # Change legend labels of continuous legend scale_fill_continuous(type = "viridis", direction= -1, breaks = c(0, 0.03, 0.06, 0.09), labels = c("0.0", "0.03", "0.06", "0.09")) p_fdis_forag = p + geom_tile(data = data_raptor.plots, aes(x = x, y = y, fill = fdis_obs_forag), inherit.aes = F) + labs(fill = 'FD Foraging ') p_fdis_dispersal = p + geom_tile(data = data_raptor.plots, aes(x = x, y = y, fill = fdis_obs_dispe), inherit.aes = F) + labs(fill = 'FD Vagility ') ## SES VALUES ### pz = plot_world_eqaul_area(color_polygon = "white") + scale_fill_gradient2(low = "#B2182B", mid = "white", high = "#2166AC") + theme(legend.position = c(0.55, 0), legend.direction = "horizontal", legend.key.height = unit(0.5, 'lines'), legend.key.width = unit(1.5, 'lines'), plot.margin = margin(-0.5, -0.1, -0.5, -0.1, "cm")) #phylogenetic p_pduroot_z = pz + geom_tile(data = data_raptor.plots, aes(x = x, y = y, fill = pd.uroot.z), inherit.aes = F) + labs(fill = 'SES PD') p_mpd_z = pz + geom_tile(data = data_raptor.plots, aes(x = x, y = y, fill = mpd.z), inherit.aes = F) + labs(fill = 'SES MPD') p_mntd_z = pz + geom_tile(data = data_raptor.plots, aes(x = x, y = y, fill = mntd.z), inherit.aes = F) + labs(fill = 'SES MNTD') p_morph_z = pz + geom_tile(data = data_raptor.plots, aes(x = x, y = y, fill = fdis_ses_morph), inherit.aes = F) + labs(fill = 'SES FD Morphology') p_fdis_niche_z = pz + geom_tile(data = data_raptor.plots, aes(x = x, y = y, fill = fdis_ses_niche), inherit.aes = F) + labs(fill = 'SES FD Niche') p_fdis_diet_z = pz + geom_tile(data = data_raptor.plots, aes(x = x, y = y, fill = fdis_ses_diet), inherit.aes = F) + labs(fill = 'SES FD Diet') p_fdis_forag_z = pz + geom_tile(data = data_raptor.plots, aes(x = x, y = y, fill = fdis_ses_forag), inherit.aes = F) + labs(fill = 'SES FD Foraging ') p_fdis_dispersal_z = pz + geom_tile(data = data_raptor.plots, aes(x = x, y = y, fill = fdis_ses_dispe), inherit.aes = F) + labs(fill = 'SES FD Vagility') ## FINAL FIGURES ## library(cowplot) p_obs = plot_grid(p_pduroot, p_mpd, p_mntd, p_morph, p_fdis_niche, p_fdis_diet, p_fdis_forag, p_fdis_dispersal, ncol = 2, labels = letters[2:9]) p_obs2 = plot_grid(p_sprich, p_obs, labels = c('a', ''), ncol = 1, rel_heights = c(0.3, 1)) #ggsave(filename = "Figures/newfig1_obs.pdf", plot = p_obs2, height = 15, width = 10) p_ses = plot_grid(p_pduroot_z, p_mpd_z, p_mntd_z, p_morph_z, p_fdis_niche_z, p_fdis_diet_z, p_fdis_forag_z, p_fdis_dispersal_z, ncol = 2, labels = letters[1:8]) #ggsave(filename = "Figures/newfig2_ses_alt.pdf", plot = p_ses, height = 15, width = 11)

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shinyServer(function(input, output){ output$grm_plot <- renderPlot({ D <- switch(input$D, "1" = 1, "2" = 1.702) p <- matrix(NA,nrow=N,4) for(j in 1:N){ #p[j,0] <- 0 p[j,1] <- Pfun(D=D, theta=thetas[j], delta=input$delta1, alpha=input$alpha) p[j,2] <- Pfun(D=D, theta=thetas[j], delta=input$delta2, alpha=input$alpha) p[j,3] <- Pfun(D=D, theta=thetas[j], delta=input$delta3, alpha=input$alpha) p[j,4] <- Pfun(D=D, theta=thetas[j], delta=input$delta4, alpha=input$alpha) #p[j,5] <- 1 } graphics::plot(NULL, ylab="P(X=m|theta)", xlab=expression(theta), main="Graded Response Model", xlim=c(-6,6), ylim=c(0,1)) lines(thetas, 1-p[,1], type="l", xlim=c(-6,6), col=2) lines(thetas, p[,1]-p[,2], type="l", xlim=c(-6,6), col=3) lines(thetas, p[,2]-p[,3], type="l", xlim=c(-6,6), col=4) lines(thetas, p[,3]-p[,4], type="l", xlim=c(-6,6), col=5) lines(thetas, p[,4]-0, type="l", xlim=c(-6,6), col=6) legend(legend=c("P(X=1|theta)", "P(X=2|theta)","P(X=3|theta)","P(X=4|theta)","P(X=5|theta)"), col=2:6, lty=1, "right") }) })

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/probMat.R \name{tables} \alias{tables} \title{Create blank tables} \usage{ tables(n, tdim) } \arguments{ \item{n}{number of tables} \item{tdim}{dimension of each table} } \description{ Create blank tables }

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library(dplyr) library(multidplyr) library(lubridate) library(stringr) library(future.apply) library(parallel) library(stringr) pred_locs<- read.csv("~/Data/West_prediction_locations.csv") pred_locs$Lon<- round(pred_locs$Lon, 5) pred_locs$Lat<- round(pred_locs$Lat, 5) Locs<- unique(pred_locs[,c("Lon", "Lat")]) #Need to round to 4 digits for AF # ##Active Fires # # AF1a<- read.csv("~/Data/fire_modis_25km_extract_final.csv") # AF1b<- read.csv("~/Data/fire_modis_50km_extract_final.csv") # AF1c<- read.csv("~/Data/fire_modis_100km_extract_final.csv") # AF1d<- read.csv("~/Data/fire_modis_500km_extract_final.csv") # # af1a<- data.frame(Lon = AF1a$Lon, Lat = AF1a$Lat, Date = AF1a$Date, Fires_25km = AF1a$fire_count) # af1b<- data.frame(Lon = AF1b$Lon, Lat = AF1b$Lat, Date = AF1b$Date, Fires_50km = AF1b$fire_count) # af1c<- data.frame(Lon = AF1c$Lon, Lat = AF1c$Lat, Date = AF1c$Date, Fires_100km = AF1c$fire_count) # af1d<- data.frame(Lon = AF1d$Lon, Lat = AF1d$Lat, Date = AF1d$Date, Fires_500km = AF1d$fire_count) # # AF<- Reduce(function(x,y) merge(x = x, y = y, by = c("Lon", "Lat", "Date"), all = TRUE), list(af1a, af1b, af1c, af1d)) # # AF[is.na(AF)]<- 0 # # AF$Date<- as.Date(AF$Date) # AF$Lon<- round(AF$Lon, 4) # AF$Lat<- round(AF$Lat, 4) # # AF_agg<- aggregate(. ~ Lon + Lat + Date, AF, mean) # AF<- AF_agg # AF_unique_locs<- unique(AF_agg[,c("Lon", "Lat")]) # # rm(list=c("AF", "AF1a", "AF1b", "AF1c", "AF1d", "af1a", "af1b", "af1c", "af1d")) # # ncores = detectCores() - 6 # AF_lags_template<- data.frame(Lon = numeric(), Lat = numeric(), Date = as.Date(character()), # Fires_lag0_25km = numeric(), Fires_lag0_50km = numeric(), # Fires_lag0_100km = numeric(), Fires_lag0_500km = numeric(), # Fires_lag1_25km = numeric(), Fires_lag1_50km = numeric(), # Fires_lag1_100km = numeric(), Fires_lag1_500km = numeric(), # Fires_lag2_25km = numeric(), Fires_lag2_50km = numeric(), # Fires_lag2_100km = numeric(), Fires_lag2_500km = numeric(), # Fires_lag3_25km = numeric(), Fires_lag3_50km = numeric(), # Fires_lag3_100km = numeric(), Fires_lag3_500km = numeric(), # Fires_lag4_25km = numeric(), Fires_lag4_50km = numeric(), # Fires_lag4_100km = numeric(), Fires_lag4_500km = numeric(), # Fires_lag5_25km = numeric(), Fires_lag5_50km = numeric(), # Fires_lag5_100km = numeric(), Fires_lag5_500km = numeric(), # Fires_lag6_25km = numeric(), Fires_lag6_50km = numeric(), # Fires_lag6_100km = numeric(), Fires_lag6_500km = numeric(), # Fires_lag7_25km = numeric(), Fires_lag7_50km = numeric(), # Fires_lag7_100km = numeric(), Fires_lag7_500km = numeric()) # # #Second try: # dates<- seq.Date(as.Date("2008-01-01"), as.Date("2018-12-31"), by = "day") # Date<- sort(rep(dates, dim(AF_unique_locs)[1])) # Lon<- rep(AF_unique_locs$Lon, length(dates)) # Lat<- rep(AF_unique_locs$Lat, length(dates)) # all_LLD<- data.frame(Lon, Lat, Date) # # ready_AF<- left_join(all_LLD, AF_agg, by = c("Lon", "Lat", "Date")) # num_Locs<- dim(AF_unique_locs)[1] # # merge_AF_lags<- function(these, j){ # these_AF_lags<- AF_lags_template # p<-1 # for(l in these){ # for(d in 1:length(dates)){ # lags<- 7 # if(dates[d] < "2008-01-08"){ # lags<- as.numeric(dates[d] - as.Date("2008-01-01") ) # } # these_AF_lags[p,1]<- ready_AF[l,"Lon"] # these_AF_lags[p,2]<- ready_AF[l,"Lat"] # these_AF_lags[p,3]<- dates[d] # my_vec<- c() # for(i in 0:lags){ # new<- ready_AF[(d-1-i)*num_Locs + l, 4:7] # new[is.na(new)]<- 0 # my_vec<- append(my_vec, new) # } # my_vec<- unlist(my_vec) # my_vec<- append(my_vec, rep(0, 32-length(my_vec))) # these_AF_lags[p,4:35]<- my_vec # p<- p+1 # } # } # write.csv(these_AF_lags, paste0("~/Data/AF/AF_lags4_",j,".csv"), row.names = FALSE) # return(these_AF_lags) # } # # my_seq<- seq(1, num_Locs, length.out = 300) # # loc_list<- c() # # for(j in my_seq){ # uniq_locs<- round(my_seq[j]):round(my_seq[j+1]-1) # loc_list<- append(loc_list, list(uniq_locs)) # } # # loc_list[[length(loc_list)]]<- append(loc_list[[length(loc_list)]], num_Locs) # # # options(future.globals.maxSize= 5000*1024^2) # # plan(multiprocess, workers = 8) ## Parallelize # this_list<- future_lapply(1:length(loc_list), function(j){merge_AF_lags(loc_list[[j]],j)}) # # save.image("With_AF_lags.RData") # ##THEN, IN TERMINAL: cat _____* > ______ # # ##Get unique: # AF_intermediate<- read.csv("~/Data/AF/AF_lags4.csv") # # #Remove text: # text_pos<- which((AF_intermediate$Lon == "Lon")&(AF_intermediate$Lat == "Lat")) # AF_intermediate<- AF_intermediate[-text_pos,] # # for(j in 1:(dim(AF_intermediate)[2]-1)){ # if(j != 3){ # AF_intermediate[,j]<- as.numeric(as.character(AF_intermediate[,j])) # }else{ # AF_intermediate[,j]<- as.Date(AF_intermediate[,j]) # } # } # # AF_final<- AF_intermediate # rm(list=c("AF_intermediate", "AF_unique_locs", "cluster", "ready_AF", # "j", "text_pos")) # save.image("AF_final.RData") #MAIAC # MAIAC<- read.csv("~/Data/MAIAC_extracted.csv") # maiac_locs<- unique(MAIAC[,c("Lon", "Lat")]) # maiac_locs<- apply(maiac_locs, MARGIN = 2, function(y){as.numeric(as.character(y))}) # maiac_locs<- maiac_locs[-1,] # maiac_locs<- data.frame(Lon = maiac_locs[,1], Lat = maiac_locs[,2]) # maiac_locs$Lon<- round(maiac_locs$Lon, 5) # maiac_locs$Lat<- round(maiac_locs$Lat, 5) # # MAIAC<- MAIAC[-1,] # MAIAC<- MAIAC[-1,] # MAIAC$Lon<- round(as.numeric(as.character(MAIAC$Lon)),5) # MAIAC$Lat<- round(as.numeric(as.character(MAIAC$Lat)),5) # # Locs_with_maiac<- data.frame(Lon = Locs$Lon, Lat = Locs$Lat, # M_lon = maiac_locs$Lon, M_lat = maiac_locs$Lat ) # #Note: Locs and maiac_locs are the same, but don't exactly match... rounding error somewhere along the line # # row.names(MAIAC)<- 1:dim(MAIAC)[1] # # MAIAC$Lon<- Locs_with_maiac[match(MAIAC$Lon, Locs_with_maiac$M_lon), "Lon"] # MAIAC$Lat<- Locs_with_maiac[match(MAIAC$Lat, Locs_with_maiac$M_lat), "Lat"] # # save.image("MAIAC.RData") load("MAIAC.RData") MAIAC$Lon<- round(MAIAC$Lon, 4) MAIAC$Lat<- round(MAIAC$Lat, 4) MAIAC$Date<- as.Date(MAIAC$Date) # ##NAM # files<- list.files("~/NAM/", pattern="Step5*", full.names = TRUE) # # for(s in State[-11]){ # for(f in files){ #f is the filename # data<- read.csv(f) # date_str<- strsplit(f, "Step5_")[[1]][2] #Used to be 2 # date<- strsplit(date_str, "_batch")[[1]][1] # names(data)[1:2]<- c("Lat", "Lon") # names(data)[length(names(data))]<- "Date" # data[,1:2]<- apply(data[,1:2], MARGIN = 2, # function(y) round(as.numeric(as.character(y)),5)) # nam<- inner_join(data[,c(1:2, 7:21)], Stat[which(Stat$State == s),c("Lon", "Lat", "State")], by = c("Lon", "Lat")) # write.csv(nam, paste0("~/NAM/nam_",s,"_", date, ".csv"), row.names = FALSE) # } # print(s) # } ##Then merge in terminal, read in, and remove header rows # for(y in 2008:2018){ # nam<- read.csv(paste0("~/Data/NAM_data/NAM_", y, ".csv")) # nam<- nam[,c(1:2,7:10, 14:19, 21)] # names(nam)<- c("Lat", "Lon", "HPBL_surface", "TMP_2m", "RH_2m", # "DPT_2m", "Ugrd_10m", "Vgrd_10m", "PRMSL_mean_sea_level", # "PRES_surface", "DZDT_850_mb", "DZDT_700_mb", "Date") # nam<- nam[which(nam$Lon != "Longitude"),] # nam$Lon<- round(as.numeric(as.character(nam$Lon)), 5) # nam$Lat<- round(as.numeric(as.character(nam$Lat)), 5) # # NAM<- inner_join(nam, Stat, by = c("Lon", "Lat")) # all_NAM<- rbind(all_NAM, NAM) # } # save.image("NAM.RData") ##NDVI # NDVI1<- read.csv("~/Data/ndvi_mod13a3_subset1_latlon.csv") # NDVI2<- read.csv("~/Data/ndvi_mod13a3_subset2_latlon.csv") # NDVI3<- read.csv("~/Data/ndvi_mod13a3_subset3_latlon.csv") # NDVI3<- NDVI3[,-1] # NDVI<- rbind(NDVI1, NDVI2, NDVI3) #Actually have all the data! # rm(list=c("NDVI1", "NDVI2", "NDVI3")) # # NDVI_final<- NDVI[,c("Longitude", "Latitude", "Date", "NDVI")] # names(NDVI_final)[1:2]<- c("Lon", "Lat") # NDVI_final[,c("Lon", "Lat", "NDVI")]<- apply(NDVI_final[,c("Lon", "Lat", "NDVI")], # MARGIN = 2, # function(y) round(as.numeric(y),5)) # NDVI_final$Date<- as.Date(NDVI_final$Date) # rm(list=setdiff(ls(), "NDVI_final")) # save.image("NDVI_final.RData") ##Stationary variables: #NLCD NLCD1<- read.csv("~/Data/nlcd_1km_extract.csv") NLCD2<- read.csv("~/Data/nlcd_5km_extract.csv") NLCD3<- read.csv("~/Data/nlcd_10km_extract.csv") NLCD1_agg<- aggregate(percent_urban_buffer ~ Lon + Lat, data = NLCD1, FUN = mean) NLCD2_agg<- aggregate(percent_urban_buffer ~ Lon + Lat, data = NLCD2, FUN = mean) NLCD3_agg<- aggregate(percent_urban_buffer ~ Lon + Lat, data = NLCD3, FUN = mean) NLCD<- Reduce(function(x,y) unique(inner_join(x, y, by = c("Lon", "Lat"))), list(NLCD1_agg, NLCD2_agg, NLCD3_agg)) NLCD$Lon<- round(as.numeric(as.character(NLCD$Lon)),5) NLCD$Lat<- round(as.numeric(as.character(NLCD$Lat)),5) names(NLCD)<- c("Lon", "Lat", "NLCD_1km", "NLCD_5km", "NLCD_10km") ##Population Density pop<- read.csv("~/Data/Pop_density.csv") pop$Lon<- round(pop$Lon, 5) pop$Lat<- round(pop$Lat, 5) pop_agg<- aggregate(. ~ Lon + Lat, pop[,c(2:3, 8)], mean) ##Highways HW<- read.csv("~/Data/Highways.csv") HW$Lon<- round(HW$Lon, 5) HW$Lat<- round(HW$Lat, 5) HW_vars<- c("Lon", "Lat", "A_100", "C_100", "Both_100", "A_250", "C_250", "Both_250", "A_500", "C_500", "Both_500", "A_1000", "C_1000", "Both_1000") HW_agg<- aggregate(. ~ Lon + Lat, HW[,HW_vars], mean) ##Elevation elev<- read.csv("~/Data/ned_extracted.csv") elev$Lon<- round(elev$Lon, 5) elev$Lat<- round(elev$Lat, 5) elev_agg<- aggregate(. ~ Lon + Lat, elev[,c(1:8)], mean) ##Merge stationary variables: Stat<- unique(Reduce(function(x,y){inner_join(x,y, by = c("Lon", "Lat"))}, list(pop_agg, NLCD, HW_agg, elev_agg))) write.csv(Stat, "Stationary_variables.csv", row.names = FALSE)

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WGLP.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/WGLP.R \name{WGLP} \alias{WGLP} \title{Williams Transformation} \usage{ WGLP(s) } \arguments{ \item{s}{The run size of design B, where s is a prime.} } \value{ The return value is an LH(s,s-1) when s is a prime or LH(s, phi(s)) when s is not a prime. } \description{ \code{WGLP} is Williams transformations of linearly transformed good lattice points. This provides a choice of design B. } \details{ Under the L1-distance, Wang et al. (2018,Theorem 2) constructed an LH(s,s-1) when s is a prime, where LH(s,s-1) is a Latin hypercube of s runs for s-1 factors. If s is not a prime, the Williams transformation can generate designs with s runs and phi(s) factors, where phi(s) is the Euler function, that is, the number of positive integers smaller than and coprime to s. } \examples{ # Note that WGLP(7) produces an equi-distant LH(7,6) B <- WGLP(7) B <- WGLP(13) } \references{ Wenlong Li, Min-Qian Liu and Boxin Tang (2021). A method of constructing maximin distance designs. \emph{Biometrika}, published online. <doi:10.1093/biomet/asaa089> }

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/Resampling codes/q5.R

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somak135/Resampling-codes

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refs/heads/main

2023-05-22T08:48:42.868581

2021-06-14T16:26:02

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q5.R

##### Defining global things ### define Z(t) as function of t q = 4 n = 100 ## number of sample components T = seq(1, 4, length = 10) m = length(T) ##number of time points Z = function(t) { return(matrix(c(12-0.5*t^2, -2*t, log(3+1/t), exp(-t)), ncol = 1)) } sigma_e = 0.05 ### declare cutoff eta = 20 ### define paramters for Theta_i library(MASS) theta = c(4, 3, 5, 5) Sigma = matrix(c(0.75, -.27, .092, -.21, -.27, .86, .15, .049, .092, .15, .75, -.071, -.21, .049, -0.071, .24), nrow = q) ##### Simulate Simu = function(n, m, q, theta, Sigma, sigma_e) { Datmat = matrix(nrow = m, ncol = n) for(i in 1:n) { Theta = mvrnorm(1, theta, Sigma) for(j in 1:m){ Datmat[j, i] = t(Z(T[j])) %*% Theta + rnorm(1, mean = 0, sd = sigma_e) } } mylist = list("data" = Datmat) return(mylist) } #### Estimation of R-hat(t) Estim = function(n, m, q, S, Z, T) { Zmat = matrix(nrow = m, ncol = q) for(i in 1:m){ Zmat[i, ] = Z(T[i]) } Ybar = 0 for(i in 1:n){ Ybar = Ybar + S[, i] } Ybar = Ybar/n #### hat(theta)_n theta_hat = solve(t(Zmat) %*% Zmat) %*% t(Zmat) %*% Ybar #### hat(sigma)_e^2 sigma_e2_hat = 0 for(i in 1:n) { sigma_e2_hat = sigma_e2_hat + t(S[,i]) %*% S[,i] - t(S[,i]) %*% Zmat %*% solve(t(Zmat)%*%Zmat) %*% t(Zmat)%*%S[,i] } sigma_e2_hat = sigma_e2_hat/(n*(m-q)) #### hat(Sigma_Theta) Sigma_Theta = matrix(rep(0, q^2), nrow = q) for(i in 1:n) { X = solve(t(Zmat) %*% Zmat) %*% t(Zmat) %*% (S[,i]-Ybar) %*% t(S[,i]-Ybar) %*% Zmat %*% solve(t(Zmat) %*% Zmat) Sigma_Theta = Sigma_Theta + X } Sigma_Theta = Sigma_Theta/n - as.numeric(sigma_e2_hat) * solve(t(Zmat) %*% Zmat) #### hat(s(t)) st = c() for(i in 1:m) { x = sqrt(t(Zmat[i, ]) %*% Sigma_Theta %*% Zmat[i, ]) st = c(st, x) } ##### hat(R(t)) Rt = c() for(i in 1:m) { x = (t(Zmat[i, ]) %*% theta_hat - eta) / st[i] Rt = c(Rt, pnorm(x)) } mylist = list("theta_hat" = theta_hat, "sigma_e2_hat" = sigma_e2_hat, "Sigma_Theta_hat" = Sigma_Theta, "Rt" = Rt) return(mylist) } #### Now that we are done with Simulation and Estimation, lets jump into Jackknife and Bootstrap! vjack = function(n, m, S) { Jmat = matrix(nrow = n, ncol = m) for(i in 1:n) { S_new = S[, -i] result = Estim((n-1), m, q, S_new, Z, T) Jmat[i, ] = result$Rt } return(((n-1)^2/n) * diag(var(Jmat))) } vboot = function(B = 200, m, S) { Bmat = matrix(nrow = B, ncol = m) for(b in 1:B) { S_new = S[ , sample(n, n, replace = TRUE)] result = Estim(n, m, q, S_new, Z, T) Bmat[b, ] = result$Rt } return(((B-1)/B) * diag(var(Bmat))) } ### repeating Jackknife and Bootstrap multiple times N = 100; v_jack = c(); v_boot = c(); B = 2*n for(i in 1:N) { set.seed(i) S = Simu(n,m,q,theta, Sigma, sigma_e)$data v_jack = rbind(v_jack, vjack(n, m, S)) v_boot = rbind(v_boot, vboot(B, m, S)) } ##### Now trying to find the tedious one -- the linear estimate gradg = function(x1, x2, x3, i) { d = x2 - x1^2 - x3*(t(Zmat[i, ])%*%(solve(t(Zmat)%*%Zmat))%*%Zmat[i,]) g1 = dnorm((x1 - eta)/sqrt(d)) * (sqrt(d) + (x1 - eta)*x1/sqrt(d))/d g2 = dnorm((x1 - eta)/sqrt(d)) * (-0.5*(x1 - eta)/sqrt(d))/d g3 = dnorm((x1 - eta)/sqrt(d)) * (0.5*(x1 - eta)*(t(Zmat[i, ])%*%(solve(t(Zmat)%*%Zmat))%*%Zmat[i,])/sqrt(d))/d return(c(g1, g2, g3)) } N = 100; v_L = c() Zmat = matrix(nrow = m, ncol = q) for(i in 1:m){ Zmat[i, ] = Z(T[i]) } for(i in 1:N) { set.seed(i) S = Simu(n,m,q,theta, Sigma, sigma_e)$data M=solve(t(Zmat)%*%Zmat) K=Zmat%*%M%*%t(Zmat) v_linear = c() for(t in 1:m) { P=t(Zmat[t, ])%*%M%*%t(Zmat) f1<-function(v) { return(P%*%v) } x1=apply(S, 2, f1) x2=x1^2 f2<-function(v) { return(t(v)%*%v-t(v)%*%K%*%v) } x3=apply(S, 2, f2)/(m-q) X=cbind(x1, x2, x3) Sigma_hat_X=var(X) u=colMeans(X) g=gradg(u[1],u[2],u[3], t) v=(t(g)%*%Sigma_hat_X%*%g)/n v_linear = c(v_linear, v) } v_L = rbind(v_L, v_linear) } report_mean_matrix = matrix(nrow = m, ncol = 3) report_sd_matrix = matrix(nrow = m, ncol = 3) for(j in 1:m) { report_mean_matrix[j, 1] = mean(sqrt(v_jack[, j])) report_mean_matrix[j, 2] = mean(sqrt(v_boot[, j])) report_mean_matrix[j, 3] = mean(sqrt(v_L[, j])) report_sd_matrix[j, 1] = sd(sqrt(v_jack[, j])) report_sd_matrix[j, 2] = sd(sqrt(v_boot[, j])) report_sd_matrix[j, 3] = sd(sqrt(v_L[, j])) } report_mean_matrix report_sd_matrix library(beepr) beep(4)

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/app.R

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refs/heads/master

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app.R

library(readr) library(shiny) library(shinythemes) library(dplyr) library(ggplot2) library(visNetwork) library(igraph) hub_locations <- readRDS("Hub Locations.rds") ui <- fluidPage(theme = shinytheme("flatly"), #shinythemes::themeSelector(), titlePanel("BikeTown Trips Network"), #mainPanel( navlistPanel( "Introduction", tabPanel("Bikeshare background", p('Bike-sharing systems are becoming more and more prevalent in cities in both the United States and worldwide. A variety of systems exist, but a common "docked" setup consists of hundreds (or thousands) of bikes deployed at one of tens (or hundreds) of docking stations, alternately known as hubs or stands. People can unlock one of these bikes and ride them around, then leave them locked at a hub or on the street (depending on their payment plan). Both bikes and hubs are equipped with sensors that send and receive real-time feed data which can be accessed through an API. When a user borrows a bike, information on trip start time, location, user id, payment plan type, duration, end time, end location, and distance traveled are stored in a database. Some bike-sharing systems have publicly released anonymized journey data with fields as listed above; Austwick et. al analyzed data from London, UK; Boston, MA; Denver, CO; Minneapolis, MN; and Washington, DC. Recently, BikeTown, the bike-sharing system in Portland, OR, released all anonymized trip information from their inception, July 2016, to March 2018. Portland is a "semi-dockless" system in that trips can start or end at one of over 100 hubs or by parking the bike on the street (though there is a fee for not returning the bike to a hub and therefore most trips are from hub to hub).') ), tabPanel("Bikeshare as a network", h3('Adjacency Matrix approach'), p('It is logical to think about or model docked or semi-dockless bike-sharing systems with a networks approach. Hubs are well-defined, stationary places at which trips start/end, and trip data presents information on the flow of travelers between them (i.e., the edges connecting two nodes). Some trips did not start or end at a hub and were therefore not included in this analysis. The simplest networks approach would be to create a symmetric adjacency matrix where two hubs are connected if a trip took place between them. While this is is an okay start, it misses much of what makes bikesharing interesting. Though easily represented in network form, starting from an adjacency matrix the nature of trips in bikeshare networks requires that the network representation needs to be spatial, directed, temporal, weighted, with self-loops and non-sparsity (Austwick et. al), and additionally is non-planar in that edges between nodes intersect or overlap (Barthelemey). More detail on these characteristics is given below'), h3('Spatial approach'), p('Trips are inherently spatial, taking place in two-dimensional Euclidean space with a defined start- and end-point, which may or may not be the same. The network topology is intrinsically tied to the topology of the city and the location of points of interest within the city. The spatial layout of the city (and by extension bike trips) can be considered with a core-periphery structure approach with densely core nodes and sparsely-connected periphery nodes (Rombach et al). This can be identified in the network visualization below.'), h3('Temporal approach'), p('The temporal aspect of trips is present in several ways: the time of when trips start and end is not uniform throughout the day, week, or time of year but instead shows interesting patterns; likewise, the trips themselves have a non-zero duration, of importance when optimizing bike placement to not run out of available bikes. The majority of bikes are rented around 4-6PM, though the specific pattern of rides is different between weekdays and weekends or holidays; likewise, summer months see many more rides than the winter months do. Over time, the connectivity of the network increases as rides take place between hubs that had not been connected previously'), h3('Multiplex approach'), p('The users themselves have not yet been considered, but their attributes can have non-trivial effects on the network structure. There are many types of membership available to BikeTown users. The dataset provided to the public only has two membership types: "subscriber" or "casual". These users differ in their ridership habits, both in what time of day/week/year they ride, but also in the nodes they are likely to visit'), h3('Multigraph (directed graph) approach'), p('Given an appropriate amount of time for trips to occur, multiple trips will occur between hubs, and the flow of bikes between hubs has strong importance for the operational aspect of supplying bikes. On an aggregated level, which can be useful when dealing with thousands or millions of trips, the flows between hubs can be intuitively seen as a weighted property: while each trip has a weight of only one, on a larger scale the directed edge weight between hubs is the sum of the trips between the start and end hub. Which hubs see lots of traffic and which do not is an interesting and useful characteristic of the system.') ), tabPanel("Materials", h3('References'), p('Barthelemy, Marc. "Spatial Networks." Physics Reports, vol. 499, no. 1, 2011, pp. 1-101.'), p('Rombach, Puck, et al. "Core-Periphery Structure in Networks (Revisited)." SIAM Review, vol. 59, no. 3, 2017, pp. 619-646.'), p('Expert, Paul, et al. "Uncovering Space-Independent Communities in Spatial Networks." Proceedings of the National Academy of Sciences of the United States of America, vol. 108, no. 19, 2011, pp. 7663-8.'), p('Zaltz Austwick, Martin, et al. "The Structure of Spatial Networks and Communities in Bicycle Sharing Systems." PLoS ONE, vol. 8, no. 9, 2013, p. e74685.'), h3('Dataset'), a(href="https://www.biketownpdx.com/system-data", "BikeTown System Data", target = "_blank"), h3('Graphical interpretation packages'), strong('shiny'), p(), strong('igraph'), p(), strong('visNetwork') ), "Network", #tabsetPanel("Network Visualiztion", type = "tabs", tabPanel("Network Properties", h4("By default, the network shown is from the entire trips dataset. To dig deeper, filter by date or time."), h3("Network properties: "), p("Node size is proportional to the total degree of the node, while edge width is proportional to the out-degree. Edges are colored gray if they both nodes are in the same neighborhood (N, NE, NW, etc) and colored blue if they are not.") ), tabPanel("Dynamic Network", sidebarLayout( sidebarPanel(width = 2, position = "left", dateRangeInput("tripDate", label = "Trip Date Range", start = "2016-07-01", end = Sys.Date()), #sliderInput("tripDate", "Choose Date Range:", # min = as.Date("2016-07-19"), max = Sys.Date(), # value = c(as.Date("2016-07-19"), as.Date("2016-07-19")), # animate = TRUE), sliderInput("time", label = "Trip start time", min = 0, max = 23.5, step = 0.5, value = c(0,23.5)), radioButtons("weekend", "Weekend Trips", choices = c("Yes", "No", "Both"), selected = "Both") ), mainPanel( visNetworkOutput("network", height = "500", width = "800") ) )), tabPanel("Summary Statistics", tableOutput("avgDegree")), #), tabPanel("Degree Distributions", plotOutput("degreeDist"), plotOutput("components")), #tabPanel("Static Networks"), widths = c(2,10) ) ) server <- function(input, output){ network_data <- reactive({ #trips <- readRDS("C:/Users/Konrad/Desktop/Intro to Networks/Term Project/All Trips.rds") trips <- readRDS("All Trips.rds") filt_trips <- trips %>% filter(StartDate >= input$tripDate[1] & StartDate <= input$tripDate[2] & StartTime >= input$time[1] * 60 * 60 & StartTime < input$time[2] * 60 * 60 & !is.na(StartHub) & !is.na(EndHub)) to_from_trips <- filt_trips %>% group_by(StartHub, EndHub) %>% count() %>% select(from = StartHub, to = EndHub, weight = n) return(to_from_trips) }) network_graph <- reactive({ #od_matrix <- network_od() trips_data <- network_data() early_graph <- igraph::graph_from_edgelist(cbind(trips_data$from, trips_data$to), directed = TRUE) #early_graph <- igraph::graph_from_edgelist(cbind(early_june_trips$from, early_june_trips$to), directed = TRUE) #early_graph <- igraph::graph_from_adjacency_matrix(as.matrix(od_matrix), weighted = TRUE, mode = "directed") return(early_graph) }) network_vis_graph <- reactive({ early_graph <- network_graph() vis_early <- visIgraph(early_graph) #vis_early$x$nodes$id <- V(early_graph)$name #vis_early$x$nodes$label <- V(early_graph)$name vis_early$x$nodes <- vis_early$x$nodes %>% left_join(hub_locations, by = c("id" ="StartHub")) vis_early$x$nodes$value <- sqrt(degree(early_graph)) vis_early$x$nodes$color <- "#ff8d00" vis_early$x$nodes$title = paste0(vis_early$x$nodes$id, "<br>", "In-degree: ", degree(early_graph, mode = "in"), "<br>", "Out-degree: ", degree(early_graph, mode = "out")) #vis_early$x$edges <- vis_early$x$edges %>% # left_join(early_june_trips) %>% # mutate(width = weight) #degree(early_graph,) vis_early$x$edges$width = vis_early$x$edges$weight vis_early$x$edges$start_part <- substr(vis_early$x$edges$from, start = 1, stop = 2) vis_early$x$edges$end_part <- substr(vis_early$x$edges$to, start = 1, stop = 2) vis_early$x$edges$color <- NA vis_early$x$edges <- vis_early$x$edges %>% mutate(color = ifelse(start_part != end_part, "#b6bcc6", "blue")) #vis_early$x$edges$color.highlight.background <- "red" return(vis_early) }) network_summary <- reactive({ trips_data <- network_data() early_graph <- network_graph() #vis_early <- network_vis_graph() #od_matrix <- network_od() #edge_info <- vis_early$x$edges avg_connected_nodes <- trips_data %>% group_by(from) %>% count() %>% ungroup() %>% summarise(Mean = mean(n)) density = ecount(early_graph)/(vcount(early_graph)^2) summary_stats <- tibble(trips = sum(trips_data$weight), numNodes = vcount(early_graph), numEdges = ecount(early_graph), AvgEdgeWeight = mean(trips_data$weight), AvgDegree = avg_connected_nodes$Mean, AvgPathLength = average.path.length(early_graph, directed = TRUE), #Diameter = diameter(early_graph), Density = density, Clustering = transitivity(early_graph)) average.path.length(early_graph, directed = TRUE) return(summary_stats) }) output$avgDegree <- renderTable({ network_summary() }) output$network <- renderVisNetwork({ vis_early <- network_vis_graph() vis_early %>% visIgraphLayout(layout = "layout.norm", layoutMatrix = cbind(vis_early$x$nodes$Lon, -vis_early$x$nodes$Lat)) %>% visNodes(color = list(background = "#ff8d00", highlight = "black")) %>% #visEdges(color = "#b6bcc6", arrows = "none") %>% visEdges(arrows = "none") %>% visOptions(highlightNearest = TRUE) %>% visInteraction(dragNodes = FALSE, hover = TRUE, keyboard = TRUE) #visEvents(click = "function(nodes){ # Shiny.onInputChange('click', nodes.nodes[0]); # ;}" #) #visEvents(selectNode = "function(properties) { #alert('selected nodes ' + this.body.data.nodes.get(properties.nodes[0]).id);}") #visEvents(selectNode = "function myFunction() { # var popup = document.getElementById('myPopup'); # popup.classList.toggle('show');}") }) output$degreeDist <- renderPlot({ early_graph <- network_graph() my_data <- data.frame(Degree = degree(early_graph)) ggplot(my_data, aes(Degree)) + geom_histogram() + theme_minimal() }) output$components <- renderPlot({ early_graph <- network_graph() my_data <- data.frame(Var = c("Components", "Nodes"), Val= c(components(early_graph)$no, vcount(early_graph))) ggplot(my_data, aes(Var, Val)) + geom_bar(stat = "identity") + ylab("Count") + xlab("Variable") + theme_minimal() }) } shinyApp(ui = ui, server = server)

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/scripts/amerifluxFormatting.R

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USEPA/actonEC

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ceeab99bae03f9cdd5dcbd27865b43bfee9ecf07

refs/heads/master

2021-07-18T11:57:23.295626

2020-11-29T20:00:41

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amerifluxFormatting.R

#This script prepares a file in the format needed to submit to AmeriFlux #before running, load the following data frames (you can use loadPrelimOutputs.R): #epOutOrder, rbrTsub, campMet, vanni30min #file.edit("scriptsAndRmd/loadPrelimOutputs.R") USact<-mutate(epOutOrder, RDateTime_START = RDateTime, RDateTime_END = (RDateTime+30*60), FC_SSITC_TEST = qc_co2_flux, FCH4_SSITC_TEST = qc_ch4_flux, FETCH_70 = x_70, FETCH_90 = x_90, FETCH_FILTER = -9999, # 0 and 1 flag indicating direction that should be discarded and kept, respectively FETCH_MAX = x_peak, CH4 = ch4_mixing_ratio*1000, #nmolCH4 per mol CO2 = co2_mixing_ratio, #umol CO2 per mol CO2_SIGMA = sqrt(co2_var), FC = co2_flux, #umolCO2 m-2 s-1 FCH4 = ch4_flux*1000, #nmolCH4 m-2 s-1 H2O = h2o_mixing_ratio, #mmol mol-1 H2O_SIGMA = sqrt(h2o_var/1000), #h2o_var doesn't have units, and an # investigation (see co2FluxDiagnostics) reveals that # it is generally ~1000x the variance calculated from the raw # dataset, so probably in umol/mol, while h2o mixing ratio is # in mmol/mol. Likely not precisely a factor of 1000 due to processing steps. SC = co2_strg, SCH4 = ch4_strg*1000, #nmol/m2/s H = H, H_SSITC_TEST = qc_H, LE = LE, LE_SSITC_TEST = qc_LE, SH = H_strg, SLE = LE_strg, PA = air_pressure/1000, #kPa -- air_p_mean was being loaded in incorrectly RH = RH, T_SONIC = sonic_temperature-273.15, #C TA = air_temperature-273.15, #C VPD = VPD/100, #hPa P=-9999, #precipitation P_RAIN = -9999, #rainfall, from VWS NETRAD = -9999, #net radiation, W/m2, from our net radiometer PPFD_IN = -9999, #PPFD, incoming TS = -9999, #soil temperature, sed t?, from RBRs, ~1.6m TW_1 = -9999, #water T, from RBRs -0.1 TW_2 = -9999, #water T, from RBRs -0.25 TW_3 = -9999, #water T, from RBRs -0.5 TW_4 = -9999, #water T, from RBRs -0.75 TW_5 = -9999, #water T, from RBRs -1.0 TW_6 = -9999, #water T, from RBRs -1.25 WTD = -9999, MO_LENGTH = L, TAU = -Tau, #ameriflux sign convention: negative value of Tau indicates a downward transport of momentum flux TAU_SSITC_TEST = qc_Tau, U_SIGMA = sqrt(u_var), #rotated? USTAR = ustar, V_SIGMA = sqrt(v_var), W_SIGMA = sqrt(w_var), WD = wind_dir, WS = wind_speed, WS_MAX = max_wind_speed, ZL=zL)%>% select(RDateTime_START, RDateTime_END, FC_SSITC_TEST, FCH4_SSITC_TEST, FETCH_70, FETCH_90, FETCH_FILTER, FETCH_MAX, CH4, CO2, CO2_SIGMA, FC, FCH4, H2O, H2O_SIGMA, SC, SCH4, H, H_SSITC_TEST, LE, LE_SSITC_TEST, SH, SLE, PA, RH, T_SONIC, TA, VPD, P, P_RAIN, NETRAD, PPFD_IN, TS, TW_1, TW_2, TW_3, TW_4, TW_5, TW_6, WTD, MO_LENGTH, TAU, TAU_SSITC_TEST, U_SIGMA, USTAR, V_SIGMA, W_SIGMA, WD, WS, WS_MAX, ZL) ### filter values outside of plausible range (per email from Ameriflux Team): ggplot(USact, aes(RDateTime_START, NETRAD))+ geom_point()+ ylim(-9000, 2000) USact<-USact%>% mutate(CH4=replace(CH4, CH4< (-750), NA), CO2=replace(CO2, CO2>1570, NA), FC=replace(FC, abs(FC)>110, NA), FCH4=replace(FCH4, FCH4>5275, NA), H2O=replace(H2O, H2O>105, NA)) #merge RBRs amerifluxTime<-select(USact, RDateTime_START) amerifluxTime$RDateTime<-amerifluxTime$RDateTime_START amerifluxRBR<-left_join(amerifluxTime, rbrTsub, by = "RDateTime") amerifluxRBR2<-subset(amerifluxRBR, !duplicated(RDateTime)) #30693 USact<-subset(USact, !duplicated(RDateTime_START)) #30693 #give USact RBR values for TS, TW 1 thru 6 where available, -9999s where not avail USact<-mutate(USact, TW_1 = amerifluxRBR2$RBRmeanT_0.1, TW_2 = amerifluxRBR2$RBRmeanT_0.25, TW_3 = amerifluxRBR2$RBRmeanT_0.5, TW_4 = amerifluxRBR2$RBRmeanT_0.75, TW_5 = amerifluxRBR2$RBRmeanT_1, TW_6 = amerifluxRBR2$RBRmeanT_1.25, TS = amerifluxRBR2$RBRmeanT_1.6) # ggplot(campMet, aes(RDateTime, Rain_mm_tot))+ # geom_point(alpha=0.3) # ggplot(vanni30min, aes(RDateTime, dailyRain.vws))+ # geom_point() # ggplot(filter(vanni30min, RDateTime>"2017-10-01"), aes(RDateTime, rain30min))+ # geom_point() #merge precip & PAR & water level from VWS # also need to change precip from daily cumulative to 30-min # P=-9999, #precipitation # P_RAIN = -9999, #rainfall, from VWS # NETRAD = -9999, #net radiation, W/m2, from our net radiometer # PPFD_BC_IN = -9999, #PPFD, below canopy, incoming amerifluxVWS<-left_join(amerifluxTime, vanni30min, by="RDateTime") %>% #30709 subset(!duplicated(RDateTime)) #30693 #merge net radiation and precip from campbell suite amerifluxCampVWS<-left_join(amerifluxVWS, campMet, by="RDateTime")%>% subset(!duplicated(RDateTime)) #30693 # ggplot(amerifluxCampVWS, aes(rain30min, Rain_mm_tot))+ # geom_point(alpha=0.2)# # ggplot(filter(amerifluxCampVWS, RDateTime>"2018-04-15", # RDateTime<"2018-07-01"))+ # geom_point(aes(RDateTime, rain30min, color="VWS"), alpha=0.3)+ # geom_point(aes(RDateTime, Rain_mm_tot, color="tower"), alpha=0.3)+ # ylim(0, 5) ggplot(amerifluxCampVWS, aes(RDateTime, NR_Wm2_avg))+ geom_line() #give USact VWS values for PAR, precip, and water level (WTD) where avail, -9999s where not avail #also fill in fetch filter values here for(i in 1:length(USact$TS)){ USact$P[i] = if(!is.na(amerifluxCampVWS$rain30min[i])) amerifluxCampVWS$rain30min[i] else if(!is.na(amerifluxCampVWS$Rain_mm_tot[i])) amerifluxCampVWS$Rain_mm_tot[i] else -9999 USact$P_RAIN[i] = USact$P[i] USact$PPFD_IN[i] = ifelse(!is.na(amerifluxCampVWS$par.vws[i]), amerifluxCampVWS$par.vws[i], -9999) USact$WTD[i] = ifelse(!is.na(amerifluxCampVWS$levelAdj.vws[i]), #level adjust has the offset for the step change, plus 1 m to account for the depth difference between the flux footprint and the msmt site amerifluxCampVWS$waterLevel.vws[i], -9999) USact$NETRAD[i] = ifelse(!is.na(amerifluxCampVWS$NR_Wm2_avg[i]), amerifluxCampVWS$NR_Wm2_avg[i], -9999) USact$FETCH_FILTER[i]=ifelse(USact$RDateTime_START<"2018-05-01 00:00:00" & USact$WD>195 & USact$WD<330, 0, #value if winds are from the W at the dock 1) #value if aquatic tower -- no fetch filter } # ggplot(USact, aes(RDateTime_START, P))+ # geom_point(alpha=0.2)+ # ylim(0, 10) #change all na's to -9999's USact[is.na(USact)]<- -9999 USact[is.nan(USact)]<- -9999 USactNA<-USact USactNA[USact== -9999]<- NA sum(is.na(USactNA$FETCH_FILTER)) #check on outliers # ggplot(USactNA, aes(RDateTime_START, CO2))+ # geom_point() # ggplot(USactNA, aes(RDateTime_START, CH4))+ # geom_point() # ggplot(USactNA, aes(RDateTime_START, FC))+ # geom_point() # ggplot(USactNA, aes(RDateTime_START, FCH4))+ # geom_point() # ggplot(USactNA, aes(RDateTime_START, H2O))+ # geom_point() # ggplot(USactNA, aes(RDateTime_START, SC))+ # geom_point() # ggplot(USactNA, aes(RDateTime_START, SCH4))+ # geom_point() # ggplot(USactNA, aes(RDateTime_START, NETRAD))+ # geom_point() USactNA<-USactNA%>% mutate(NETRAD=replace(NETRAD, NETRAD< (-390), NA), SC = replace(SC, abs(SC)>200, NA), SCH4=replace(SCH4, abs(SCH4)>200, NA)) USactOF<-USactNA USactOF[is.na(USactOF)]<- -9999 #OF for outlier filtered sum(is.na(USactOF$NETRAD)) #2017 USactSub17<-filter(USactOF, RDateTime_START>"2017-01-25 18:30", RDateTime_START<"2017-12-31 19:00") #2018 USactSub18<-filter(USactOF, RDateTime_START>"2018-01-01 00:00", RDateTime_START<"2018-12-31 23:30") head(USactSub17$RDateTime_START) tail(USactSub17$RDateTime_END) head(USactSub18$RDateTime_START) tail(USactSub18$RDateTime_END) #check for missing HH periods: USactSub17$check<-c(1800, diff(as.numeric(USactSub17$RDateTime_START), 1)) summary(USactSub17$check) USactSub18$check<-c(1800, diff(as.numeric(USactSub18$RDateTime_START), 1)) summary(USactSub18$check) ggplot(filter(USactSub, RDateTime_START>"2017-01-01", RDateTime_START<"2017-02-01"), aes(RDateTime_START, check))+ geom_point() #change timestampts to YYYYMMDDHHMM format #strptime(USactSub$RDateTime_START, "%Y-%m-%d %H:%M:%S") USactSub17<-mutate(USactSub17, TIMESTAMP_START=format(strptime(RDateTime_START, "%Y-%m-%d %H:%M:%S"), "%Y%m%d%H%M"), TIMESTAMP_END=format(strptime(RDateTime_END, "%Y-%m-%d %H:%M:%S"), "%Y%m%d%H%M"))%>% select(TIMESTAMP_START, TIMESTAMP_END, FC_SSITC_TEST, FCH4_SSITC_TEST, FETCH_70, FETCH_90, FETCH_FILTER, FETCH_MAX, CH4, CO2, CO2_SIGMA, FC, FCH4, H2O, H2O_SIGMA, SC, SCH4, H, H_SSITC_TEST, LE, LE_SSITC_TEST, SH, SLE, PA, RH, T_SONIC, TA, VPD, P, P_RAIN, NETRAD, PPFD_IN, TS, TW_1, TW_2, TW_3, TW_4, TW_5, TW_6, WTD, MO_LENGTH, TAU, TAU_SSITC_TEST, U_SIGMA, USTAR, V_SIGMA, W_SIGMA, WD, WS, WS_MAX, ZL) head(USactSub17) USactSub18<-mutate(USactSub18, TIMESTAMP_START=format(strptime(RDateTime_START, "%Y-%m-%d %H:%M:%S"), "%Y%m%d%H%M"), TIMESTAMP_END=format(strptime(RDateTime_END, "%Y-%m-%d %H:%M:%S"), "%Y%m%d%H%M"))%>% select(TIMESTAMP_START, TIMESTAMP_END, FC_SSITC_TEST, FCH4_SSITC_TEST, FETCH_70, FETCH_90, FETCH_FILTER, FETCH_MAX, CH4, CO2, CO2_SIGMA, FC, FCH4, H2O, H2O_SIGMA, SC, SCH4, H, H_SSITC_TEST, LE, LE_SSITC_TEST, SH, SLE, PA, RH, T_SONIC, TA, VPD, P, P_RAIN, NETRAD, PPFD_IN, TS, TW_1, TW_2, TW_3, TW_4, TW_5, TW_6, WTD, MO_LENGTH, TAU, TAU_SSITC_TEST, U_SIGMA, USTAR, V_SIGMA, W_SIGMA, WD, WS, WS_MAX, ZL) head(USactSub18) #2017 # write.table(USactSub, # file=("output/US-Act_HH_201701260000_201712311800.csv"), # sep=",", # row.names=FALSE) write.table(USactSub17, file=("output/US-Act_HH_201701260300_201712311800.csv"), sep=",", row.names=FALSE) #2018 # write.table(USactSub, # file=("C_R_Projects/actonFluxProject/output/US-Act_HH_201801011130_201811131230.csv"), # sep=",", # row.names=FALSE) write.table(USactSub18, file=("output/US-Act_HH_201801011130_201811131230.csv"), sep=",", row.names=FALSE)

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/App Indices de Vulnerabilidad/Code/Mapa/Data Mapa.R

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# DATOS Mapa CANTONAL =========================== BDDMapReact = reactive({ periodos = c(2005,as.numeric(input$periodos),2019) pd = as.numeric(input$producto) BDDMap = data.frame() for(i in 1:length(IPC_canton)){ Fechas_aux = IPC_canton[[i]][,1] #Corregir # IPC_val_aux = IPC_canton[[i]][,pd+1] mav12 = MedMovBeta(IPC_canton[[i]][,2],n=12) IPC_val_aux = IPC_canton[[i]][,pd+1]/as.numeric(mav12$mvxRecup) #IPC Deflactado nombre_aux = names(IPC_canton)[i] BDDMap_aux = data.frame(ARCH_CANTON = nombre_aux, Fecha=Fechas_aux, Valor = IPC_val_aux) BDDMap = rbind(BDDMap, BDDMap_aux) } BDDMap = ArchCodCanton %>% dplyr::inner_join(BDDMap,by="ARCH_CANTON") %>% dplyr::select(COD_CANTON,CANTON,Fecha,Valor) return(BDDMap) }) # Datos Betas Cantonal ================================= BDDMapBetas = reactive({ # periodos = c(2005,as.numeric(input$periodos),2019) # print("Aqui se calcula Periodos !!!!!!!!!!!!") # print(periodos) pd = as.numeric(input$producto) BDDMap = data.frame() for(i in 1:length(IPC_canton)){ Fechas_aux = IPC_canton[[i]][,1] #Corregir mav12 = MedMovBeta(IPC_canton[[i]][,2],n=12) IPC_val_aux = IPC_canton[[i]][,pd+1]/as.numeric(mav12$mvxRecup) #IPC Deflactado nombre_aux = names(IPC_canton)[i] #Base del Canton i y producto pd ------------------- # periodos = c(2007,2010,2015) # periodos = c(2005,as.numeric(periodos),2019) periodos = c(2005,as.numeric(input$periodos),2019) Fecha = as.Date(IPC_canton[[1]]$Fecha, format = "%Y-%m-%d") Anio = as.numeric(format(Fecha, "%Y")) #;remove(Fecha) etiquetas = c() for (i in 1:(length(periodos) - 1)) { etiquetas[i] = paste0("Periodo: ", periodos[i], " - ", periodos[i + 1]) } PeriodoCorte = cut(Anio, breaks = periodos , labels = etiquetas , right = F) remove(Anio,periodos,Fecha,i) #---------------------------- BDD_aux = data.frame(Periodo = PeriodoCorte, Tmp = 1:length(Fechas_aux), Valor = IPC_val_aux # Valor = round(IPC_val_aux,digits = 5) # Valor = format(IPC_val_aux, scientific = TRUE) ) #mapa remove(mav12,Fechas_aux,IPC_val_aux) #Regresion de Panel -------------------------------- modelo = lm(data = BDD_aux, formula = Valor ~ Tmp*Periodo) # Betas del Modelo --------------------------------- resumen = data.frame(round(xtable(summary(modelo)),digits = 5)) names(resumen) = c("Estimación","Error Estándar","t-valor","Pr(>|t|)") remove(BDD_aux,modelo) b_nomb=startsWith(rownames(resumen),"Tmp") betas=resumen$`Estimación`[b_nomb] tablabetas=resumen[b_nomb,c(1,2)] remove(b_nomb) #Betas Acumulados (Sumado el pivote) tablabetas[2:length(tablabetas[,1]),1]=tablabetas[2:length(tablabetas[,1]),1]+tablabetas[1,1] tablabetas = data.frame(ARCH_CANTON = nombre_aux, #mapa Fecha = as.character(etiquetas), Valor = round(tablabetas[,1],digits = 6)) #Datos para MAPA ---------------------------------- BDDMap = rbind(BDDMap, tablabetas) } BDDMap = ArchCodCanton %>% dplyr::inner_join(BDDMap,by="ARCH_CANTON") %>% dplyr::select(COD_CANTON,CANTON,Fecha,Valor) return(BDDMap) }) # Datos Para Mapa de Provincia Sola ---------------------- BDDMapBetasProv = reactive({ BDDMap = BDDMapBetas() BDDMap = BDDMap %>% dplyr::inner_join(ArchCodCanton[,c("COD_CANTON","PROVINCIA")],by="COD_CANTON") %>% dplyr::filter(PROVINCIA == input$provincia) %>% dplyr::select(COD_CANTON,CANTON,Fecha,Valor) return(BDDMap) })

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# Generated by using Rcpp::compileAttributes() -> do not edit by hand # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 ComputeAlphaBeta <- function(y, x, WW, weight, Z, G) { .Call(`_bartik_weight_ComputeAlphaBeta`, y, x, WW, weight, Z, G) }

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## ---- echo = FALSE, message = FALSE------------------------------------------- library(beset) suppressPackageStartupMessages(library(tidyverse)) ## ----------------------------------------------------------------------------- set.seed(42) data <- cbind(swiss, matrix(replicate(5, rnorm(nrow(swiss))), ncol = 5)) names(data)[7:11] <- paste0("noise", names(data)[7:11]) ## ----------------------------------------------------------------------------- mod <- beset_elnet(Fertility ~ ., data) ## ---- fig.height=4, fig.width=5----------------------------------------------- plot(mod) ## ----------------------------------------------------------------------------- mod_sum <- summary(mod, oneSE = FALSE) mod_sum ## ----------------------------------------------------------------------------- summary(mod) ## ----------------------------------------------------------------------------- summary(mod, alpha = 0.01) ## ----------------------------------------------------------------------------- mod <- beset_elnet(Fertility ~ ., data, nest_cv = TRUE) ## ---- fig.height=4, fig.width=5----------------------------------------------- plot(mod) ## ----------------------------------------------------------------------------- mod_sum <- summary(mod) ## ----------------------------------------------------------------------------- summary(mod, robust = TRUE) ## ----------------------------------------------------------------------------- summary(mod, oneSE = FALSE) ## ----------------------------------------------------------------------------- summary(mod) %>% print(metric = "mse") ## ----------------------------------------------------------------------------- validate(mod, metric = "auto", oneSE = TRUE, alpha = NULL, lambda = NULL) ## ----------------------------------------------------------------------------- summary(prostate) ## ----------------------------------------------------------------------------- mod <- beset_elnet(tumor ~ ., data = prostate, family = "binomial", nest_cv = TRUE) summary(mod) ## ---- fig.height=4, fig.width=5----------------------------------------------- plot(mod) ## ---- fig.height=4, fig.width=5----------------------------------------------- plot(mod) + ylab("Log-loss") ## ---- fig.height=4, fig.width=5----------------------------------------------- plot(mod, metric = "auc") ## ----------------------------------------------------------------------------- summary(mod, metric = "auc") %>% print(metric = "auc")

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/construct.R \name{construct} \alias{construct} \title{Construct technical matrices} \usage{ construct() } \value{ WIOT A list containig input-output table and other parameters. Function adds/updates Leontieff Inverse L, technical coefficient matrix A, Final Demand matrix, value added per unit of gross output vector. } \description{ Construct technical matrices } \examples{ get_io(year = 2009, version = "B") construct() get_io(2008) construct() }

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library(doseLM) library(edgeR) se <- simData(FA=100) RUnit::checkEqualsNumeric(dim(se)[1]*dim(se)[2], 8000)

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format_REST_data.R

require(utils); require(plyr); library(lmerTest); library(car); require(data.table) require(psych) require(gdata) ### lists and data area_id_list <- as.character(c('14','15','16','17','18','19','20','21','22','39','40','41','43','44','45','46','47','48','49','50','51','52', '53','54','55','56','68','69','70','71','72','73','74','93','94','95','96','97','98','99','100','101')); # list of area_ids area_data <- read.table("area_data3.txt", header = TRUE) ### functions # convert REST matrix to individual data file for participant convert_participant_data <- function(participant, data, area_id_list = area_id_list) { participant_data <- data; colnames(participant_data)<- c(area_id_list) # change column names to be the area ids rownames(participant_data) <- c(area_id_list) participant_data[participant_data > 1] <- 1 diag(participant_data)<-NA participant_data[lower.tri(participant_data)]<-NA # get only half ind_p_data<-as.data.table(unmatrix(participant_data)); # transform from matrix to vector colnames(ind_p_data) <- "corr" ind_p_data$participant <- participant # add col for participant - from function input stimpairs <- combn(area_id_list,2); # compute the pairwise names from the "area_id_list" list of col names pairnames<-expand.grid(area_id_list,area_id_list) pairnames<-paste0(pairnames[,2],'_',pairnames[,1]) ind_p_data$area_id<-pairnames ind_p_data<-ind_p_data[complete.cases(ind_p_data),] return(ind_p_data) } # create one data file for all participants, based on participant list create_all_ind <- function(area_id_list) { all_ind <- c(); flist <- list.files(pattern = 'zFC*') for (i in 1:length(flist)) { # if (p < 10) { p_name <- paste("0", p, sep = "") } else { p_name <- p }; # data <- as.matrix(read.table(paste("zFCMap_Subject", p_name, ".txt", sep = ""))); data <- as.matrix(read.table(flist[i])); ind_data <- convert_participant_data(i, data, area_id_list); all_ind <- rbind(all_ind, ind_data) } return(all_ind) } # create unified data file with all relevant information add_cols_all_ind <- function(ind_data, area_data) { all_ind <- ind_data; all_ind$abs_corr <- abs(all_ind$corr); area_data <- area_data; all_ind <- merge(all_ind, area_data, by = "area_id"); return(all_ind) } ### Create one data file - *RUN THIS* ind_all <- add_cols_all_ind(create_all_ind(area_id_list), area_data); write.table(ind_all, file = 'all_data_18p_opn.txt', sep = ",", quote = FALSE, col.names = TRUE);

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plot_elements.R \name{plot_elements} \alias{plot_elements} \title{Get different plot elements} \usage{ plot_elements() } \description{ Get different plot elements }

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IRFinder.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/CollateData.R \name{IRFinder} \alias{IRFinder} \title{A wrapper function to call NxtIRF/IRFinder} \usage{ IRFinder( bamfiles = "Unsorted.bam", sample_names = "sample1", reference_path = "./Reference", output_path = "./IRFinder_Output", n_threads = 1, run_featureCounts = FALSE, localHub = FALSE, ah = AnnotationHub(localHub = localHub) ) } \arguments{ \item{bamfiles}{The file names of 1 or more BAM files} \item{sample_names}{The sample names of the given BAM files. Must be a vector of the same length as \code{bamfiles}} \item{reference_path}{The directory of the NxtIRF reference} \item{output_path}{The directory where NxtIRF/IRFinder output should be stored} \item{n_threads}{The number of threads to use. On Linux / Windows, this will use OpenMP from within the C++ subroutine. On Macs, BiocParallel MulticoreParam will be used on single-threaded NxtIRF/IRFinder} \item{run_featureCounts}{Whether this function will run \code{Rsubread::featureCounts()} on the BAM files. If so, the output will be saved to "main.FC.Rds" in the output directory as a list object} \item{localHub}{Set as TRUE to disable AnnotationHub online mode} \item{ah}{An AnnotationHub object.} } \value{ None. \code{IRFinder()} will save output to \code{output_path}. \cr\cr sample.txt.gz: The main IRFinder output file containing the quantitation of IR and splice junctions, as well as QC information\cr\cr sample.cov: Contains coverage information in compressed binary. This format is 5-10X faster than BigWig format (see \code{\link[=GetCoverage]{GetCoverage()}})\cr\cr main.FC.Rds: A single file containing gene counts for the whole dataset (only if \code{run_featureCounts == TRUE}) } \description{ This function calls IRFinder on one or more BAM files. } \examples{ \donttest{ # Run IRFinder on single BAM file, do not run featureCounts: IRFinder( bamfiles = "sample1.bam", sample_names = "sample1", reference_path = "./Reference", output_path = "./IRFinder_Output", run_featureCounts = FALSE ) # Run IRFinder on multiple BAM file, run featureCounts, use 4 threads: IRFinder( bamfiles = c("UT1.bam", "UT2.bam", "UT3.bam", "Rx1.bam", "Rx2.bam", "Rx3.bam"), sample_names = c("UT1", "UT2", "UT3", "Rx1", "Rx2", "Rx3"), reference_path = "./Reference", output_path = "./IRFinder_Output", run_featureCounts = TRUE, n_threads = 4 ) } }

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/Estatistica e Probabilidade/Lista de exercicios/Ex1.r

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Ex1.r

<<<<<<< HEAD #1. Crie uma sequencia de numeros de 1 a 90, de duas em duas unidades, utilizando a função seq do R. Armazene em uma variavel qualquer; #seq(de, ate, x(de x em x) a = seq(1,90,2) ======= #1. Crie uma sequencia de numeros de 1 a 90, de duas em duas unidades, utilizando a função seq do R. Armazene em uma variavel qualquer; #seq(de, ate, x(de x em x) a = seq(1,90,2) >>>>>>> 4d687118a90e6f1ef766281ce9447fa83af69c76 a

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/ds/df3.R

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df3.R

# Data Frame 3 #Properties #sdata rollno = c(10,11,12,13) name = c('Achal','Apoorva','Goldie','Hitesh') gender = c('M','F','M','M') sdata = data.frame(rollno, name, gender) sdata #Change Row and Colnmames colnames(sdata) = c("rollno1", "name1", "gender1") colnames(sdata) rownames(sdata) = c("ID1", "ID2", "ID3", "ID4") rownames(sdata) #Dimensions dim(sdata) dim(sdata)[1] #Number of rows dim(sdata)[2] #Number of columns #No of Rows & Colns nrow(sdata) ncol(sdata) length(sdata) #Changing Data attach(sdata) rollno1 = rollno1 - 5 rollno1 #reduce rollno by 5 (does not store in DF) sdata$rollno1 #Remove Colns/ Rows #Colns sdata[1] <- NULL #Rows rows_to_keep <- c(TRUE, FALSE, TRUE, FALSE) #Method1 df_limit = df[rows_to_keep, ] df_limit #Method2 df_limit2 <- df[ !rows_to_keep, ] df_limit2 #Threshold df_limit3 <- df[df$col1 > 40, ] df_limit3 # Add Rows & Columns to DF cbind(df, x)# x - same no of rows as df rbind(df, y) # y - same no of colns as df #Sort / Order / Rank #Order order(mtcars) #sort sort(mtcars) # error sort(mtcars[1, ]) # order row 1 by values sort(mtcars[ , 1]) # sort coln 1 sort(mtcars$mpg, decreasing=F) mtcars order(mtcars$mpg) mtcars[ order(mtcars$mpg), ] order( mtcars$mpg, mtcars[ , 2], decreasing=F) with( mtcars$mpg, order(mpg, cyl)) #rank rank(mtcars$mpg) rank( c(10, 7, 3, 4, 5)) # Options na.last=T , ties.method = c('average', 'first', 'random', 'max', 'min')) dplyr::arrange dplyr::arrange(mtcars, cyl, disp) dplyr::arrange(mtcars, desc(disp))

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/WK5 -3.R

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sonali4794/R-Code

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WK5 -3.R

library("tidyverse") library("dplyr") library("mosaic") set = c(12, 18,15,8,17,13,22,13,13,13,12,11,15,15,12,8,20,12,14,11,9,15,16,20,9,15,13,19,18,14) s = sum(set) size = length(set) a = s/size SE = sqrt(a/size) LN = a - 1.96*SE UN = a + 1.96*SE LN UN poissonsample = rpois(30, a) boot = do(1000)*{ btx = resample(poissonsample) mean(btx) } confint(boot) hist(boot$result)

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plot4.R

# plot4.R # set folder of project setwd("C:/EDA_PROJECT1") # if file not exist, download and unzip in subfolder "/data" if(!file.exists("data/household_power_consumption.txt")) { url <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip" download.file(url, destfile="data.zip") unzip(zipfile="data.zip", exdir="data") } # load file to workspace data1 <- read.table("C:/EDA_PROJECT1/data/household_power_consumption.txt", header=TRUE, sep=";", dec=".", stringsAsFactors=FALSE) # filter days 1 and 2 february 2007 data2<- subset(data1, (data1$Date == "1/2/2007" | data1$Date== "2/2/2007")) # create variable DateTime data2 <- transform(data2, DateTime=as.POSIXct(paste(Date, Time)), "%d/%m/%Y %H:%M:%S") # convert character to numeric data2$Sub_metering_1 <- as.numeric(as.character(data2$Sub_metering_1)) data2$Sub_metering_2 <- as.numeric(as.character(data2$Sub_metering_2)) data2$Sub_metering_3 <- as.numeric(as.character(data2$Sub_metering_3)) attach(data2) # use the variables of data2 # 4 graphics in 2 x 2 par(mfrow = c(2, 2)) # generate graphic top-left plot(DateTime, Global_active_power, type = "l", xlab = "", ylab = "Global Active Power") # generate graphic top-right plot(DateTime, Voltage, type = "l", xlab = "datetime", ylab = "Voltage") # generate graphic bottom-left plot(DateTime, Sub_metering_1, type="l", ylab= "Energy sub metering", xlab="") lines(DateTime, Sub_metering_2, type="l", col="red") lines(DateTime, Sub_metering_3, type="l", col="blue") legend("topright", c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), lty=1, col=c("black", "red", "blue")) # generate graphic bottom-right plot(DateTime, Global_reactive_power, type = "l", col = "black", xlab = "datetime", ylab = colnames(data2)[4]) # generate output dev.copy(png, file="plot4.png", with=480, height=480) dev.off() detach(data2)

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fpl.R

`fpl` <- function (x, a = 0.1, d = 2.4, e = 100, b = 1) d + (a - d)/(1 + (x/e)^b)

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/man/simplify_immgen_celltype.Rd

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ddiez/celltype

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simplify_immgen_celltype.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/predict.R \name{simplify_immgen_celltype} \alias{simplify_immgen_celltype} \alias{simplify_immgen_celltype.character} \title{simplify_immgen_celltype} \usage{ simplify_immgen_celltype(x) \method{simplify_immgen_celltype}{character}(x) } \arguments{ \item{x}{character vector with cell type names from Immgen.} } \description{ Simplifies cell type names in the immgen dataset by picking the upper level cell type in the hierarchy. }