pierreqi/r_code_50k · Datasets at Hugging Face

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getMeasurements.Rd.R

library(shapeR) ### Name: getMeasurements ### Title: Get simple shape variables, filtered according to filter ### Aliases: getMeasurements ### ** Examples data(shape) # Calculate the mean otolith area for each fish population # The results are in square mm since the calibration ('cal') column # in the data file is in pixels (1 mm/pixel). tapply(getMeasurements(shape)$otolith.area, getMasterlist(shape)$pop,mean)

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phyloHeights.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/phyloHeights.R \name{phyloHeights} \alias{phyloHeights} \title{phyloHeights} \usage{ phyloHeights(tree) } \description{ Essentially the same function as heights.phylo from geiger }

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geo_utils.r

state_code_from_fips <- function(x) { floor(as.integer(as.character(x))/1000) }

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Ex4.20.R

#Chapter 4- Vector Spaces #Change of Basis #Page No.153 / 4-33 #Prob 7 #4.9.7 #clear console cat("\014") #clear variables rm(list=ls(all=TRUE)) pv<-c(.7,.1,.1,.2,.8,.2,.1,.1,.7) p=matrix(pv,3,3,TRUE) print(p) PI=p-diag(3) print(PI) zm=matrix(0,3,1,TRUE) PIx=cbind(PI,zm) print(PIx) PIx[,c(1,3)]=PIx[,c(3,1)] print(PIx) PIx=PIx*10 print(PIx) PIx[2,]=PIx[2,]-2*PIx[1,] PIx[3,]=PIx[3,]+3*PIx[1,] PIx[2,]=PIx[2,]/(-4) PIx[3,]=PIx[3,]-4*PIx[2,] PIx[1,]=PIx[1,]-1*PIx[2,] print(round(PIx)) print('x1=x3') print('x2=2x3') print('x3 is free') print('[x1 x2 x3]=x3[1 2 1]') print('the entries in [1 2 1] sum to 4') q=(1/4)*matrix(c(1,2,1),3,1,TRUE) print(q)

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/aws-api/plotBeeDat.R

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yannikbehr/arduino_project

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plotBeeDat.R

#!/usr/bin/env Rscript dateStart = as.Date('2023-04-23') dateEnd = Sys.Date() data = character() while (dateStart<=dateEnd){ month = substr(format(dateStart, '%B'), 1, 3) fname=paste("data/", format(dateStart, '%d_'), month, sep="") readDat=T if (dateStart < dateEnd && file.exists(fname)){ dat <- readRDS(fname, refhook = NULL) readDat=F } if (readDat){ cat(format(dateStart,'%A %B %d, %Y\n')) dat = system(paste("./query_by_date.sh ", format(dateStart, '%d/'), month, " | grep WeightKg | cut -f 2- ", sep=""), intern=T) cat(paste(dat[length(dat)], "\n", sep="")) } if (dateStart < dateEnd ){ saveRDS(dat, file = fname, ascii = FALSE, version = NULL,compress = TRUE, refhook = NULL) } data = c(data, dat) dateStart = dateStart+1 } datF = as.data.frame(strsplit(data, "\t"), stringsAsFactors=F) dd = lapply(datF[2, ], function(d){strptime(strsplit(strsplit(d, "_")[[1]][2], " ")[[1]][1],format='%d/%B/%Y:%H:%M:%S')}) mydates <- do.call("c", dd) df = data.frame(times = mydates, val=as.numeric(datF[1, ]), stringsAsFactors=F, row.names=NULL) idx = mydates > strptime("03/June/2021:06:7:38",format='%d/%B/%Y:%H:%M:%S') & datF[1, ] != 1000 #idx = mydates > strptime("20/June/2021:06:7:38",format='%d/%B/%Y:%H:%M:%S') & datF[1, ] != 1000 mydates = mydates + 2 * 3600 # times seem to be two hours off for som reason df = df[idx, ] library(ggplot2) pdf("GewichtsKurve.pdf") ggplot(data=df, aes(x=times, y=val))+geom_line() + geom_smooth() + ylab("Gewicht [g]") + xlab("Zeitpunkt") #ggplot(data=df, aes(x=dates, y=values))+geom_line() #p = ggplot(data=df, aes(x=dates, y=values))+geom_point(size=1.5) #p = p + geom_line() #p = p + scale_x_date(date_breaks = '3 day', date_labels = '%b %d')+ #p #plot(mydates[idx], datF[1, idx], type="l", xlab="Zeit", ylab="Gewicht [g]") dev.off()

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/Age discrimination permutation test.R

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wangrenfeng0/SMU-Data-Science

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Age discrimination permutation test.R

fired = c(34,37, 37, 38, 41, 42, 43, 44, 44, 45, 45, 45, 46, 48, 49, 53, 53, 54, 54, 55, 56) notfired = c(27, 33, 36, 37, 38, 38, 39, 42, 42, 43, 43, 44, 44, 44, 45, 45, 45, 45, 46, 46, 47, 47, 48, 48, 49, 49, 51, 51, 52, 54) #hist(fired) #hist(notfired) library(tidyverse) obersved_diff=mean(fired)-mean(notfired) obersved_diff fired=data.frame(Value=fired, Status='Fired') notfired=data.frame(Value=notfired, Status='Notfired') employee=rbind(fired,notfired) employee xbarDiffHolder = numeric(10000) for (i in 1:10000) { scramble_employee=sample(employee$Status, 51) employeeTemp=employee employeeTemp$Status=scramble_employee xbars=employeeTemp %>% group_by(Status) %>% summarize(mean=mean(Value)) xbars xbarNminusT=xbars[2,2]-xbars[1,2] xbarNminusT xbarDiffHolder[i]=xbarNminusT$mean } ggplot(mapping=aes(x=xbarDiffHolder), color='black')+geom_histogram(bins=25, color='black', fill='blue') num_more_extreme = sum((xbarDiffHolder) >= 1.92381) num_more_extreme pvalue = num_more_extreme / 10000 pvalue Fired = c(34, 37, 37, 38, 41, 42, 43, 44, 44, 45, 45, 45, 46, 48, 49, 53, 53, 54, 54, 55, 56) Not_fired = c(27, 33, 36, 37, 38, 38, 39, 42, 42, 43, 43, 44, 44, 44, 45, 45, 45, 45, 46, 46, 47, 47, 48, 48, 49, 49, 51, 51, 52, 54) #t.test(x = Fired, y = Not_fired, conf.int = .95, var.equal = TRUE, alternative = "two.sided") label1=rep('Fired',21) label2=rep('Not_fired', 30) label=as.factor(c(label1, label2)) people=data.frame(age=c(Fired, Not_fired), status=label) people graphics.off() par(mar = rep(2, 4)) par(mfrow=c(2,2)) hist(Fired,xlab='Age', main='Fired') hist(Not_fired, xlab='Age', main='Not Fired') qqnorm(Fired, main='Fired') qqline(Fired) qqnorm(Not_fired, main='Not_fired') qqline(Not_fired) people %>% ggplot(aes(x=status, y=age))+geom_boxplot()

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cran/CoRpower

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plotVElatCont.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plotVElatCont.R \name{plotVElatCont} \alias{plotVElatCont} \title{Plotting Treatment (Vaccine) Efficacy Curves for Different Correlate of Risk Relative Risks for Continuous Biomarkers} \usage{ plotVElatCont(outComputePower, outDir = NULL) } \arguments{ \item{outComputePower}{a list of lists of length \code{1} containing output from \code{\link{computePower}} or a character string specifying the \code{.RData} file containing \code{\link{computePower}} output} \item{outDir}{a character string specifying path to output \code{.RData} file, necessary if \cr\code{outComputePower} is a character string. Default is \code{NULL}.} } \value{ None. The function is called solely for plot generation. } \description{ Plots the treatment (vaccine) efficacy curve for the true latent biomarker for eight different values of the latent correlate of risk relative risk and the lowest vaccine efficacy level for the true biomarker. All curves assume \code{rho=1}, and treatment (vaccine) efficacy ranges from 0 to 1. The legend is completely determined by the function. } \details{ \code{\link{computePower}} function input parameter \code{VElowest} must have length greater than or equal to eight for all eight scenarios to have unique RRc and VElowest. Otherwise, only \code{length(VElowest)} unique VE curves will be displayed. When interpreting the output of the function, the null hypothesis corresponds to a flat curve where vaccine efficacy for all values of the true latent biomarker is equal to the overall vaccine efficacy. Increasing departures from the null hypothesis correspond to increasingly variable and steep VE curves. The output assumes the overall placebo-group endpoint risk between \eqn{\tau} and \eqn{\tau_{max}} is constant for all values of the latent and observed biomarker and that there is no measurement error (\eqn{\rho=1}). When this is the case, an association of the biomarker with infection risk in the vaccine group (a correlate of risk) is equivalent to an association of the biomarker with treatment (vaccine) efficacy. The function's plot can also be interpreted in conjunction with the output of the \code{\link{plotPowerCont}} function by matching the CoR relative risk in the two plots and examining power compared to VE. This sheds light on the importance of overall VE on power and further enables correlates of risk results to be interpreted in terms of potential correlates of efficacy/protection. } \examples{ # Example scenario with continuous biomarker, where values of rho are varied # Set input parameters for computePower function nCasesTx <- 10 nControlsTx <- 300 nCasesTxWithS <- 10 controlCaseRatio <- 3 VEoverall <- 0.75 risk0 <- 0.034 PlatVElowest <- 0.2 VElowest <- seq(0, VEoverall, len=8) Plat0 <- P0 <- 0.2 Plat2 <- P2 <- 0.6 M <- 13 alpha <- 0.05 sigma2obs <- 1 rho <- 1 biomType <- "continuous" # Output from computePower function is stored in an object as a list pwr <- computePower(nCasesTx=nCasesTx, nControlsTx=nControlsTx, nCasesTxWithS=nCasesTxWithS, controlCaseRatio=controlCaseRatio, risk0=risk0, VEoverall=VEoverall, PlatVElowest=PlatVElowest, VElowest=VElowest, Plat0=Plat0, Plat2=Plat2, P0=P0, P2=P2, M=M, alpha=alpha, sigma2obs=sigma2obs, rho=rho, biomType=biomType) # Set parameters for plotPowerCont function # outComputePower is a list containing output from the computePower function outComputePower <- pwr plotVElatCont(outComputePower=outComputePower) \dontrun{ # Output from computePower function is saved in an RData file computePower(..., saveDir = "myDir", saveFile = "myFile.RData") # outComputePower is a character string specifying the file containing the computePower output # outDir is a character string specifying the outComputePower file directory outComputePower <- "myFile.RData" outDir <- "~/myDir" plotVElatCont(outComputePower, outDir=outDir) } } \seealso{ \code{\link{computePower}}, \code{\link{plotPowerCont}} }

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e39cfbf4789808a3d7fcd04b08a92ab1_HoltWintersNew.R

# Modelled on the HoltWinters() function but with more conventional # initialization. # Written by Zhenyu Zhou. 21 October 2012 HoltWintersZZ <- function(x, # smoothing parameters alpha = NULL, # level beta = NULL, # trend gamma = NULL, # seasonal component seasonal = c("additive", "multiplicative"), exponential = FALSE, # exponential phi = NULL, # damp lambda = NULL, # box-cox biasadj = FALSE, # adjusted back-transformed mean for box-cox warnings = TRUE # return optimization warnings ) { x <- as.ts(x) seasonal <- match.arg(seasonal) m <- frequency(x) lenx <- length(x) if (!is.null(lambda)) { x <- BoxCox(x, lambda) } if (is.null(phi) || !is.numeric(phi)) { phi <- 1 } if (!is.null(alpha) && !is.numeric(alpha)) { stop("cannot fit models without level ('alpha' must not be 0 or FALSE).") } if (!all(is.null(c(alpha, beta, gamma))) && any(c(alpha, beta, gamma) < 0 || c(alpha, beta, gamma) > 1)) { stop("'alpha', 'beta' and 'gamma' must be within the unit interval.") } if ((is.null(gamma) || gamma > 0)) { if (seasonal == "multiplicative" && any(x <= 0)) { stop("data must be positive for multiplicative Holt-Winters.") } } if (m <= 1) { gamma <- FALSE } ## initialise l0, b0, s0 if (!is.null(gamma) && is.logical(gamma) && !gamma) { seasonal <- "none" l.start <- x[1L] s.start <- 0 if (is.null(beta) || !is.logical(beta) || beta) { if (!exponential) { b.start <- x[2L] - x[1L] } else { b.start <- x[2L] / x[1L] } } } else { ## seasonal Holt-Winters l.start <- mean(x[1:m]) b.start <- (mean(x[m + (1:m)]) - l.start) / m if (seasonal == "additive") { s.start <- x[1:m] - l.start } else { s.start <- x[1:m] / l.start } } # initialise smoothing parameters # lower=c(rep(0.0001,3), 0.8) # upper=c(rep(0.9999,3),0.98) lower <- c(0, 0, 0, 0) upper <- c(1, 1, 1, 1) if (!is.null(beta) && is.logical(beta) && !beta) { trendtype <- "N" } else if (exponential) { trendtype <- "M" } else { trendtype <- "A" } if (seasonal == "none") { seasontype <- "N" } else if (seasonal == "multiplicative") { seasontype <- "M" } else { seasontype <- "A" } ## initialise smoothing parameter optim.start <- initparam( alpha = alpha, beta = beta, gamma = gamma, phi = 1, trendtype = trendtype, seasontype = seasontype, damped = FALSE, lower = lower, upper = upper, m = m ) # if(!is.na(optim.start["alpha"])) # alpha2 <- optim.start["alpha"] # else # alpha2 <- alpha # if(!is.na(optim.start["beta"])) # beta2 <- optim.start["beta"] # else # beta2 <- beta # if(!is.na(optim.start["gamma"])) # gamma2 <- optim.start["gamma"] # else # gamma2 <- gamma # if(!check.param(alpha = alpha2,beta = beta2, gamma = gamma2,phi=1,lower,upper,bounds="haha",m=m)) # { # print(paste("alpha=", alpha2, "beta=",beta2, "gamma=",gamma2)) # stop("Parameters out of range") # } ################################################################################### # optimisation: alpha, beta, gamma, if any of them is null, then optimise them error <- function(p, select) { if (select[1] > 0) { alpha <- p[1L] } if (select[2] > 0) { beta <- p[1L + select[1]] } if (select[3] > 0) { gamma <- p[1L + select[1] + select[2]] } zzhw( x, lenx = lenx, alpha = alpha, beta = beta, gamma = gamma, seasonal = seasonal, m = m, dotrend = (!is.logical(beta) || beta), doseasonal = (!is.logical(gamma) || gamma), exponential = exponential, phi = phi, l.start = l.start, b.start = b.start, s.start = s.start )$SSE } select <- as.numeric(c(is.null(alpha), is.null(beta), is.null(gamma))) if (sum(select) > 0) # There are parameters to optimize { sol <- optim(optim.start, error, method = "L-BFGS-B", lower = lower[select], upper = upper[select], select = select) if (sol$convergence || any(sol$par < 0 | sol$par > 1)) { if (sol$convergence > 50) { if (warnings) { warning(gettextf("optimization difficulties: %s", sol$message), domain = NA) } } else { stop("optimization failure") } } if (select[1] > 0) { alpha <- sol$p[1L] } if (select[2] > 0) { beta <- sol$p[1L + select[1]] } if (select[3] > 0) { gamma <- sol$p[1L + select[1] + select[2]] } } final.fit <- zzhw( x, lenx = lenx, alpha = alpha, beta = beta, gamma = gamma, seasonal = seasonal, m = m, dotrend = (!is.logical(beta) || beta), doseasonal = (!is.logical(gamma) || gamma), exponential = exponential, phi = phi, l.start = l.start, b.start = b.start, s.start = s.start ) tspx <- tsp(x) fitted <- ts(final.fit$fitted, frequency = m, start = tspx[1]) res <- ts(final.fit$residuals, frequency = m, start = tspx[1]) if (!is.null(lambda)) { fitted <- InvBoxCox(fitted, lambda, biasadj, var(final.fit$residuals)) attr(lambda, "biasadj") <- biasadj } states <- matrix(final.fit$level, ncol = 1) colnames(states) <- "l" if (trendtype != "N") { states <- cbind(states, b = final.fit$trend) } if (seasontype != "N") { nr <- nrow(states) nc <- ncol(states) for (i in 1:m) states <- cbind(states, final.fit$season[(m - i) + (1:nr)]) colnames(states)[nc + (1:m)] <- paste("s", 1:m, sep = "") } states <- ts(states, frequency = m, start = tspx[1] - 1 / m) # Package output as HoltWinters class # structure(list(fitted = fitted, # x = x, # alpha = alpha, # beta = beta, # gamma = gamma, # coefficients = c(a = final.fit$level[lenx], # b = if (!is.logical(beta) || beta) final.fit$trend[lenx], # s = if (!is.logical(gamma) || gamma) final.fit$season[lenx - m + 1L:m]), # seasonal = seasonal, # exponential = exponential, # SSE = final.fit$SSE, # call = match.call(), # level = final.fit$level, # trend = final.fit$trend, # season = final.fit$season, # phi = phi # ), # class = "HoltWinters" # ) # Package output as ets class damped <- (phi < 1.0) if (seasonal == "additive") { # This should not happen components <- c("A", trendtype, seasontype, damped) } else if (seasonal == "multiplicative") { components <- c("M", trendtype, seasontype, damped) } else if (seasonal == "none" & exponential) { components <- c("M", trendtype, seasontype, damped) } else { # if(seasonal=="none" & !exponential) components <- c("A", trendtype, seasontype, damped) } initstate <- states[1, ] param <- alpha names(param) <- "alpha" if (trendtype != "N") { param <- c(param, beta = beta) names(param)[length(param)] <- "beta" } if (seasontype != "N") { param <- c(param, gamma = gamma) names(param)[length(param)] <- "gamma" } if (damped) { param <- c(param, phi = phi) names(param)[length(param)] <- "phi" } if (components[1] == "A") { sigma2 <- mean(res ^ 2) } else { sigma2 <- mean((res / fitted) ^ 2) } structure( list( fitted = fitted, residuals = res, components = components, x = x, par = c(param, initstate), initstate = initstate, states = states, SSE = final.fit$SSE, sigma2 = sigma2, call = match.call(), m = m ), class = "ets" ) } ################################################################################### # filter function zzhw <- function(x, lenx, alpha=NULL, beta=NULL, gamma=NULL, seasonal="additive", m, dotrend=FALSE, doseasonal=FALSE, l.start=NULL, exponential = NULL, phi=NULL, b.start=NULL, s.start=NULL) { if (exponential != TRUE || is.null(exponential)) { exponential <- FALSE } if (is.null(phi) || !is.numeric(phi)) { phi <- 1 } # initialise array of l, b, s level <- trend <- season <- xfit <- residuals <- numeric(lenx) SSE <- 0 if (!dotrend) { beta <- 0 b.start <- 0 } if (!doseasonal) { gamma <- 0 s.start[1:length(s.start)] <- ifelse(seasonal == "additive", 0, 1) } lastlevel <- level0 <- l.start lasttrend <- trend0 <- b.start season0 <- s.start for (i in 1:lenx) { # definel l(t-1) if (i > 1) { lastlevel <- level[i - 1] } # define b(t-1) if (i > 1) { lasttrend <- trend[i - 1] } # define s(t-m) if (i > m) { lastseason <- season[i - m] } else { lastseason <- season0[i] } if (is.na(lastseason)) { lastseason <- ifelse(seasonal == "additive", 0, 1) } # stop((lastlevel + phi*lasttrend)*lastseason) # forecast for this period i if (seasonal == "additive") { if (!exponential) { xhat <- lastlevel + phi * lasttrend + lastseason } else { xhat <- lastlevel * lasttrend ^ phi + lastseason } } else { if (!exponential) { xhat <- (lastlevel + phi * lasttrend) * lastseason } else { xhat <- lastlevel * lasttrend ^ phi * lastseason } } xfit[i] <- xhat res <- x[i] - xhat residuals[i] <- res SSE <- SSE + res * res # calculate level[i] if (seasonal == "additive") { if (!exponential) { level[i] <- alpha * (x[i] - lastseason) + (1 - alpha) * (lastlevel + phi * lasttrend) } else { level[i] <- alpha * (x[i] - lastseason) + (1 - alpha) * (lastlevel * lasttrend ^ phi) } } else { if (!exponential) { level[i] <- alpha * (x[i] / lastseason) + (1 - alpha) * (lastlevel + phi * lasttrend) } else { level[i] <- alpha * (x[i] / lastseason) + (1 - alpha) * (lastlevel * lasttrend ^ phi) } } # calculate trend[i] if (!exponential) { trend[i] <- beta * (level[i] - lastlevel) + (1 - beta) * phi * lasttrend } else { trend[i] <- beta * (level[i] / lastlevel) + (1 - beta) * lasttrend ^ phi } # calculate season[i] if (seasonal == "additive") { if (!exponential) { season[i] <- gamma * (x[i] - lastlevel - phi * lasttrend) + (1 - gamma) * lastseason } else { season[i] <- gamma * (x[i] - lastlevel * lasttrend ^ phi) + (1 - gamma) * lastseason } } else { if (!exponential) { season[i] <- gamma * (x[i] / (lastlevel + phi * lasttrend)) + (1 - gamma) * lastseason } else { season[i] <- gamma * (x[i] / (lastlevel * lasttrend ^ phi)) + (1 - gamma) * lastseason } } } list( SSE = SSE, fitted = xfit, residuals = residuals, level = c(level0, level), trend = c(trend0, trend), season = c(season0, season), phi = phi ) } #' Exponential smoothing forecasts #' #' Returns forecasts and other information for exponential smoothing forecasts #' applied to \code{y}. #' #' ses, holt and hw are simply convenient wrapper functions for #' \code{forecast(ets(...))}. #' #' @param y a numeric vector or time series of class \code{ts} #' @param h Number of periods for forecasting. #' @param damped If TRUE, use a damped trend. #' @param seasonal Type of seasonality in \code{hw} model. "additive" or #' "multiplicative" #' @param level Confidence level for prediction intervals. #' @param fan If TRUE, level is set to seq(51,99,by=3). This is suitable for #' fan plots. #' @param initial Method used for selecting initial state values. If #' \code{optimal}, the initial values are optimized along with the smoothing #' parameters using \code{\link{ets}}. If \code{simple}, the initial values are #' set to values obtained using simple calculations on the first few #' observations. See Hyndman & Athanasopoulos (2014) for details. #' @param exponential If TRUE, an exponential trend is fitted. Otherwise, the #' trend is (locally) linear. #' @param alpha Value of smoothing parameter for the level. If \code{NULL}, it #' will be estimated. #' @param beta Value of smoothing parameter for the trend. If \code{NULL}, it #' will be estimated. #' @param gamma Value of smoothing parameter for the seasonal component. If #' \code{NULL}, it will be estimated. #' @param phi Value of damping parameter if \code{damped=TRUE}. If \code{NULL}, #' it will be estimated. #' @param lambda Box-Cox transformation parameter. Ignored if NULL. Otherwise, #' data transformed before model is estimated. When \code{lambda=TRUE}, #' \code{additive.only} is set to FALSE. #' @param biasadj Use adjusted back-transformed mean for Box-Cox #' transformations. If TRUE, point forecasts and fitted values are mean #' forecast. Otherwise, these points can be considered the median of the #' forecast densities. #' @param x Deprecated. Included for backwards compatibility. #' @param ... Other arguments passed to \code{forecast.ets}. #' @return An object of class "\code{forecast}". #' #' The function \code{summary} is used to obtain and print a summary of the #' results, while the function \code{plot} produces a plot of the forecasts and #' prediction intervals. #' #' The generic accessor functions \code{fitted.values} and \code{residuals} #' extract useful features of the value returned by \code{ets} and associated #' functions. #' #' An object of class \code{"forecast"} is a list containing at least the #' following elements: \item{model}{A list containing information about the #' fitted model} \item{method}{The name of the forecasting method as a #' character string} \item{mean}{Point forecasts as a time series} #' \item{lower}{Lower limits for prediction intervals} \item{upper}{Upper #' limits for prediction intervals} \item{level}{The confidence values #' associated with the prediction intervals} \item{x}{The original time series #' (either \code{object} itself or the time series used to create the model #' stored as \code{object}).} \item{residuals}{Residuals from the fitted #' model.} \item{fitted}{Fitted values (one-step forecasts)} #' @author Rob J Hyndman #' @seealso \code{\link{ets}}, \code{\link[stats]{HoltWinters}}, #' \code{\link{rwf}}, \code{\link[stats]{arima}}. #' @references Hyndman, R.J., Koehler, A.B., Ord, J.K., Snyder, R.D. (2008) #' \emph{Forecasting with exponential smoothing: the state space approach}, #' Springer-Verlag: New York. \url{http://www.exponentialsmoothing.net}. #' #' Hyndman, R.J., Athanasopoulos (2014) \emph{Forecasting: principles and #' practice}, OTexts: Melbourne, Australia. \url{http://www.otexts.org/fpp}. #' @keywords ts #' @examples #' #' fcast <- holt(airmiles) #' plot(fcast) #' deaths.fcast <- hw(USAccDeaths,h=48) #' plot(deaths.fcast) #' #' @export ses <- function(y, h = 10, level = c(80, 95), fan = FALSE, initial=c("optimal", "simple"), alpha=NULL, lambda=NULL, biasadj=FALSE, x=y, ...) { initial <- match.arg(initial) if (initial == "optimal") { fcast <- forecast(ets(x, "ANN", alpha = alpha, opt.crit = "mse", lambda = lambda, biasadj = biasadj), h, level = level, fan = fan, ...) } else { fcast <- forecast(HoltWintersZZ(x, alpha = alpha, beta = FALSE, gamma = FALSE, lambda = lambda, biasadj = biasadj), h, level = level, fan = fan, ...) } fcast$method <- fcast$model$method <- "Simple exponential smoothing" fcast$model$call <- match.call() fcast$series <- deparse(substitute(y)) return(fcast) } #' @rdname ses #' @export holt <- function(y, h = 10, damped = FALSE, level = c(80, 95), fan = FALSE, initial=c("optimal", "simple"), exponential=FALSE, alpha=NULL, beta=NULL, phi=NULL, lambda=NULL, biasadj=FALSE, x=y, ...) { initial <- match.arg(initial) if (length(y) <= 1L) { stop("I need at least two observations to estimate trend.") } if (initial == "optimal" | damped) { if (exponential) { fcast <- forecast(ets(x, "MMN", alpha = alpha, beta = beta, phi = phi, damped = damped, opt.crit = "mse", lambda = lambda, biasadj = biasadj), h, level = level, fan = fan, ...) } else { fcast <- forecast(ets(x, "AAN", alpha = alpha, beta = beta, phi = phi, damped = damped, opt.crit = "mse", lambda = lambda, biasadj = biasadj), h, level = level, fan = fan, ...) } } else { fcast <- forecast( HoltWintersZZ(x, alpha = alpha, beta = beta, gamma = FALSE, phi = phi, exponential = exponential, lambda = lambda, biasadj = biasadj), h, level = level, fan = fan, ... ) } if (damped) { fcast$method <- "Damped Holt's method" if (initial == "simple") { warning("Damped Holt's method requires optimal initialization") } } else { fcast$method <- "Holt's method" } if (exponential) { fcast$method <- paste(fcast$method, "with exponential trend") } fcast$model$method <- fcast$method fcast$model$call <- match.call() fcast$series <- deparse(substitute(y)) return(fcast) } #' @rdname ses #' @export hw <- function(y, h = 2 * frequency(x), seasonal = c("additive", "multiplicative"), damped = FALSE, level = c(80, 95), fan = FALSE, initial=c("optimal", "simple"), exponential=FALSE, alpha=NULL, beta=NULL, gamma=NULL, phi=NULL, lambda=NULL, biasadj=FALSE, x=y, ...) { initial <- match.arg(initial) seasonal <- match.arg(seasonal) m <- frequency(x) if (m <= 1L) { stop("The time series should have frequency greater than 1.") } if (length(y) < m + 3) { stop(paste("I need at least", m + 3, "observations to estimate seasonality.")) } if (initial == "optimal" | damped) { if (seasonal == "additive" & exponential) { stop("Forbidden model combination") } else if (seasonal == "additive" & !exponential) { fcast <- forecast(ets(x, "AAA", alpha = alpha, beta = beta, gamma = gamma, phi = phi, damped = damped, opt.crit = "mse", lambda = lambda, biasadj = biasadj), h, level = level, fan = fan, ...) } else if (seasonal != "additive" & exponential) { fcast <- forecast(ets(x, "MMM", alpha = alpha, beta = beta, gamma = gamma, phi = phi, damped = damped, opt.crit = "mse", lambda = lambda, biasadj = biasadj), h, level = level, fan = fan, ...) } else { # if(seasonal!="additive" & !exponential) fcast <- forecast(ets(x, "MAM", alpha = alpha, beta = beta, gamma = gamma, phi = phi, damped = damped, opt.crit = "mse", lambda = lambda, biasadj = biasadj), h, level = level, fan = fan, ...) } } else { fcast <- forecast( HoltWintersZZ(x, alpha = alpha, beta = beta, gamma = gamma, phi = phi, seasonal = seasonal, exponential = exponential, lambda = lambda, biasadj = biasadj), h, level = level, fan = fan, ... ) } if (seasonal == "additive") { fcast$method <- "Holt-Winters' additive method" } else { fcast$method <- "Holt-Winters' multiplicative method" } if (exponential) { fcast$method <- paste(fcast$method, "with exponential trend") } if (damped) { fcast$method <- paste("Damped", fcast$method) if (initial == "simple") { warning("Damped methods require optimal initialization") } } fcast$model$method <- fcast$method fcast$model$call <- match.call() fcast$series <- deparse(substitute(y)) return(fcast) }

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/cache/Ryo-N7|soccer_ggplots|World Cup 2018__RMarkdown__worldcup_goal_plots.R

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Ryo-N7|soccer_ggplots|World Cup 2018__RMarkdown__worldcup_goal_plots.R

## ----setup, include=FALSE------------------------------------------------ knitr::opts_chunk$set(echo = TRUE) ## ----echo=FALSE, fig.width=8, fig.height=5------------------------------- library(ggsoccer) library(ggplot2) point_data <- data.frame(x = c( 100, 83, 100, 83, 88.5, 100, 83, 83, 100, 100), y = c( 0, 21, 21, 38, 50, 50, 62, 80, 80, 100), label = c("100, 0", "83, 21", "100, 21", "83, 38", "88.5, 50", "100, 50", "83, 62", "83, 80", "100, 80", "100, 100")) field <- ggplot(point_data) + annotate_pitch() + theme_pitch(aspect_ratio = NULL) + coord_flip() + geom_point(aes(x = x, y = y), size = 1.5) + geom_text(aes(x= x, y = y, label = label), vjust = 1.5, color = "red") ggsave(field, filename = "field.png", width = 8, height = 5) ## ----first goal, fig.height=6, fig.width=8------------------------------- library(ggplot2) library(dplyr) library(ggsoccer) library(ggimage) library(extrafont) # loadfonts() # 2 1 pass_data <- data.frame(x = c( 84, 82, 94), y = c( 6, 32, 35), x2 = c(77, 84, 83), y2 = c(13, 8 , 32.5)) # corner kick + golovin cross curve_data <- data.frame(x = c(100, 76), y = c(0, 19), x2 = c(94, 94), y2 = c(35, 60)) # Gazinsky header ball_data <- data.frame(x = c(94), y = c(60), x2 = c(99.2), y2 = c(47.5)) # soccer ball image goal_img <- data.frame(x = 100, y = 47) %>% mutate(image = "https://d30y9cdsu7xlg0.cloudfront.net/png/43563-200.png") # golovin + zhirkov movement movement_data <- data.frame(x = c(83, 98), y = c(24.25, 2), x2 = c(77, 88), y2 = c(21, 6)) saudi_data <- data.frame( x = c(96.5), y = c(35), label = "M. Al-Breik" ) g <- ggplot(pass_data) + annotate_pitch() + geom_segment(aes(x = x, y = y, xend = x2, yend = y2), arrow = arrow(length = unit(0.25, "cm"), type = "closed")) + geom_segment(data = ball_data, aes(x = x, y = y, xend = x2, yend = y2), linetype = "dashed", size = 0.85, color = "red") + geom_segment(data = movement_data, aes(x = x, y = y, xend = x2, yend = y2), linetype = "dashed", size = 1.2, color = "black") + geom_curve(data = curve_data, aes(x = x, y = y, xend = x2, yend = y2), curvature = 0.25, arrow = arrow(length = unit(0.25, "cm"), type = "closed")) + geom_image(data = goal_img, aes(x = x, y = y, image = image), size = 0.035) + theme_pitch() + theme(text = element_text(family = "Dusha V5")) + coord_flip(xlim = c(49, 101), ylim = c(-1, 101)) + ggtitle(label = "Russia (5) vs. (0) Saudi Arabia", subtitle = "First goal, Yuri Gazinsky (12th Minute)") + labs(caption = "By Ryo Nakagawara (@R_by_Ryo)") + geom_label(aes(x = 94, y = 60, label = "Gazinsky"), hjust = -0.1, color = "red", family = "Dusha V5") + geom_label(aes(x = 83, y = 23, label = "Golovin"), hjust = -0.05, color = "red", family = "Dusha V5") + geom_label(aes(x = 75, y = 11, label = "Golovin"), hjust = -0.1, color = "red", family = "Dusha V5") + geom_label(aes(x = 98, y = 0, label = "Zhirkov"), vjust = -0.3, color = "red", family = "Dusha V5") + geom_label(aes(x = 84, y = 6, label = "Zhirkov"), vjust = -0.3, color = "red", family = "Dusha V5") + geom_label( data = saudi_data, aes(x = x, y = y, label = label), color = "darkgreen", family = "Dusha V5") + annotate("text", x = 69, y = 65, family = "Dusha V5", label = "After a poor corner kick clearance\n from Saudi Arabia, Golovin picks up the loose ball, \n exchanges a give-and-go pass with Zhirkov\n before finding Gazinsky with a beautiful cross!") ggsave(g, filename = "gazinsky_goal.png", height = 6, width = 8) ## ----complete gazinsky gganimate, fig.width=8, fig.height=6-------------- library(ggplot2) library(dplyr) library(ggsoccer) library(ggimage) library(extrafont) library(gganimate) # loadfonts() # data pass_data <- data.frame( x = c(100, 94, 82, 82.5, 84, 76.5, 75.5, 94, 99.2), # pass balls y = c(0, 35, 31, 22, 8, 13, 19, 60, 47.5), time = c(1, 2, 3, 4, 5, 6, 7, 8, 9)) golovin_movement <- data.frame( x = c(78, 80, 80, 80, 75.5, 74.5, 73.5, 73, 73), #75, 74, 73 y = c(30, 30, 27, 25, 10, 9, 15, 15, 15), label = "Golovin", time = c(1, 2, 3, 4, 5, 6, 7, 8, 9) ) zhirkov_movement <- data.frame( x = c(98, 90, 84, 84, 84, 84, 84, 84, 84), y = c( 0, 2, 2, 2, 2, 2, 2, 2, 2), label = "Zhirkov", time = c(1, 2, 3, 4, 5, 6, 7, 8, 9) ) gazinsky_movement <- data.frame( x = c(92), y = c(66.8), label = "Gazinsky", time = c(6, 7, 8, 9) ) # segment golovin should only appear 4-5? # segment zhirkov should only appear 1-3? segment_data <- data.frame( x = c(77.5, 98), y = c(22, 2), xend = c(75, 84), yend = c(15, 3), linetype = c("dashed", "dashed"), color = c("black", "black"), size = c(1.2, 1.25) ) saudi_data <- data.frame( x = c(95), y = c(35), label = "M. Al-Breik" ) ### soccer ball ball_data <- tribble( ~x, ~y, ~time, 100, 0, 1, 94, 35, 2, 82, 31, 3, 82.5, 25, 4, 84, 6, 5, 77, 13, 6, 76, 19, 7, 94, 60, 8, 99.2, 47.5, 9, ) gazin_ani <- ggplot(pass_data) + annotate_pitch() + theme_pitch() + coord_flip(xlim = c(49, 101), ylim = c(-1, 101)) + geom_segment(data = segment_data, aes(x = x, y = y, xend = xend, yend = yend), size = segment_data$size, color = segment_data$color, linetype = c("dashed", "dashed")) + geom_label( data = saudi_data, aes(x = x, y = y, label = label), color = "darkgreen") + geom_label(data = zhirkov_movement, aes(x = x, y = y, frame = time, label = label), color = "red") + geom_label(data = golovin_movement, aes(x = x, y = y, frame = time, label = label), color = "red") + geom_label( data = gazinsky_movement, aes(x = x, y = y, label = label), color = "red") + ggimage::geom_emoji( data = ball_data, aes(x = x, y = y, frame = time), image = "26bd", size = 0.035) + ggtitle(label = "Russia (5) vs. (0) Saudi Arabia", subtitle = "First goal, Yuri Gazinsky (12th Minute)") + labs(caption = "By Ryo Nakagawara (@R_by_Ryo)") + annotate("text", x = 69, y = 65, family = "Dusha V5", label = "After a poor corner kick clearance\n from Saudi Arabia, Golovin picks up the loose ball, \n exchanges a give-and-go pass with Zhirkov\n before finding Gazinsky with a beautiful cross!") + theme(text = element_text(family = "Dusha V5")) gganimate(gazin_ani, width = 8, height = 6, title_frame = FALSE, "gazin_ggani_final.gif") ## ----complete gazinskiy tweenr, fig.width=8, fig.height=6---------------- library(ggplot2) library(dplyr) library(ggsoccer) library(ggimage) library(extrafont) library(gganimate) library(tweenr) library(purrr) # loadfonts() # data pass_data <- data.frame( x = c(100, 94, 82, 82.5, 84, 76.5, 75.5, 94, 99.2), # pass balls y = c(0, 35, 31, 22, 8, 13, 19, 60, 47.5), time = c(1, 2, 3, 4, 5, 6, 7, 8, 9)) golovin_movement <- data.frame( x = c(78, 80, 80, 80, 75.5, 74.5, 73.5, 73, 73), #75, 74, 73 y = c(30, 30, 27, 25, 10, 9, 15, 15, 15), label = "Golovin", time = c(1, 2, 3, 4, 5, 6, 7, 8, 9) ) zhirkov_movement <- data.frame( x = c(98, 90, 84, 84, 84, 84, 84, 84, 84), y = c( 0, 2, 2, 2, 2, 2, 2, 2, 2), label = "Zhirkov", time = c(1, 2, 3, 4, 5, 6, 7, 8, 9) ) gazinsky_movement <- data.frame( x = c(92), y = c(66.8), label = "Gazinsky", time = c(6, 7, 8, 9) ) # saudi defender saudi_data <- data.frame( x = c(95), y = c(35), label = "M. Al-Breik" ) ### soccer ball ball_data <- tribble( ~x, ~y, ~time, 100, 0, 1, 94, 35, 2, 82, 31, 3, 82.5, 25, 4, 84, 6, 5, 77, 13, 6, 76, 19, 7, 94, 60, 8, 99.2, 47.5, 9, ) ### ball movement b_list <- ball_data %>% pmap(data.frame) ball_tween <- b_list %>% tween_states(tweenlength = 0.5, statelength = 0.00000001, ease = "linear", nframes = 75) ### Golovin golovin_movement_list <- golovin_movement %>% pmap(data.frame) golovin_tween <- golovin_movement_list %>% tween_states(tweenlength = 0.5, statelength = 0.00000001, ease = "linear", nframes = 75) golovin_tween <- golovin_tween %>% mutate(label = "Golovin") ### Zhirkov zhirkov_movement_list <- zhirkov_movement %>% pmap(data.frame) zhirkov_tween <- zhirkov_movement_list %>% tween_states(tweenlength = 0.5, statelength = 0.00000001, ease = "linear", nframes = 75) zhirkov_tween <- zhirkov_tween %>% mutate(label = "Zhirkov") ### PLOT gazin_move <- ggplot(pass_data) + annotate_pitch() + theme_pitch() + coord_flip(xlim = c(49, 101), ylim = c(-1, 101)) + geom_label( data = saudi_data, aes(x = x, y = y, label = label), color = "darkgreen") + geom_label(data = zhirkov_tween, aes(x = x, y = y, frame = .frame, label = label), color = "red") + geom_label(data = golovin_tween, aes(x = x, y = y, frame = .frame, label = label), color = "red") + geom_label( data = gazinsky_movement, aes(x = x, y = y, label = label), color = "red") + ggimage::geom_emoji( data = ball_tween, aes(x = x, y = y, frame = .frame), image = "26bd", size = 0.035) + ggtitle(label = "Russia (5) vs. (0) Saudi Arabia", subtitle = "First goal, Yuri Gazinsky (12th Minute)") + labs(caption = "By Ryo Nakagawara (@R_by_Ryo)") + annotate("text", x = 69, y = 65, family = "Dusha V5", label = "After a poor corner kick clearance\n from Saudi Arabia, Golovin picks up the loose ball, \n exchanges a give-and-go pass with Zhirkov\n before finding Gazinsky with a beautiful cross!") + theme(text = element_text(family = "Dusha V5")) gganimate(gazin_move, width = 8, height = 6, title_frame = FALSE, interval = 0.25, "gazinsky_goal_final.gif") gganimate(gazin_move, width = 8, height = 6, title_frame = FALSE, interval = 0.25, "gazin_move.mp4") ## ----cristiano hat trick, fig.height=5, fig.width=7---------------------- library(ggplot2) library(ggsoccer) library(extrafont) library(emoGG) library(ggimage) # loadfonts() # Official WC 2018 Font: "Dusha" # http://fifa2018wiki.com/fifa-2018-font-typeface-download-dusha-font-ttf/509/ emoji_search("soccer") # "26bd" goals_data <- data.frame(x = c(88, 80, 71), y = c(50, 48, 54), label = c(1, 2, 3)) curve_data <- data.frame(x = c(88, 71), y = c(50, 54), xend = c(100, 100), yend = c(54, 54)) annotation_data <- data.frame( hjust = c(0.5, 0.5, 0.5, 0, 0, 0), label = c("Portugal (3) vs. Spain (3)", "Cristiano's Hattrick (4', 44', 88')", "by Ryo Nakagawara (@R_by_Ryo)", "1. Fouled by Nacho in the box,\nCristiano confidently strokes the ball\ninto the right corner from the spot.", "2. Guedes lays it off to Cristiano whose\nstrong shot is uncharacteristically\nfumbled by De Gea into the net.", "In the final minutes of the game,\nCristiano wins a freekick against Pique\nand curls it beautifully over the wall."), x = c(110, 105, 53, 76, 66, 66), y = c(30, 20, 85, 5, 5, 55) ) flag_data <- data.frame( image = c("PT", "ES"), x = c(110, 110), y = c(19.1, 51.1) ) # PLOT cr <- ggplot(goals_data) + annotate_pitch() + theme_pitch() + theme(text = element_text(family = "Dusha V5"), legend.position = "none") + coord_flip(xlim = c(55, 112), ylim = c(-1, 101)) + geom_segment(x = 80, y = 48, xend = 97, yend = 48) + # 2nd geom_segment(x = 97, y = 48, xend = 100, yend = 45.5, arrow = arrow(length = unit(0.25, "cm"), type = "closed")) + # degea fumble geom_curve(data = curve_data, aes(x = x, y = y, xend = xend, yend = yend), # FREEKICK curvature = 0.3, arrow = arrow(length = unit(0.25, "cm"), type = "closed")) + geom_text(data = annotation_data, family = "Dusha V5", aes(x = x, y = y, hjust = hjust, label = label), size = c(6.5, 4.5, 3, 3.5, 3.5, 3.5)) + geom_flag(data = flag_data, aes(x = x, y = y, image = image), size = c(0.08, 0.08)) + # Portugal + Spain Flag ggimage::geom_emoji(aes(x = 105, y = c(45, 50, 55)), image = "26bd", size = 0.035) + geom_point(aes(x = x, y = y), shape = 21, size = 7, color = "black", fill = "white") + geom_text(aes(x = x, y = y, label = label, family = "Dusha V5")) ggsave(cr, filename = "cr_hattrick.png", height = 5, width = 7) ## ----osako winner, fig.height = 5, fig.width = 7------------------------- library(ggplot2) library(dplyr) library(ggsoccer) library(extrafont) library(ggimage) cornerkick_data <- data.frame(x = 99, y = 0.3, x2 = 94, y2 = 47) osako_gol <- data.frame(x = 94, y = 49, x2 = 100, y2 = 55.5) player_label <- data.frame(x = c(92, 99), y = c(49, 2)) wc_logo <- data.frame(x = 107, y = 85) %>% mutate(image = "https://upload.wikimedia.org/wikipedia/en/thumb/6/67/2018_FIFA_World_Cup.svg/1200px-2018_FIFA_World_Cup.svg.png") g <- ggplot(osako_gol) + annotate_pitch() + theme_pitch() + theme(text = element_text(family = "Dusha V5")) + coord_flip(xlim = c(55, 112), ylim = c(-1, 101)) + geom_curve(data = cornerkick_data, aes(x = x, y = y, xend = x2, yend = y2), curvature = -0.15, arrow = arrow(length = unit(0.25, "cm"), type = "closed")) + geom_segment(aes(x = x, y = y, xend = x2, yend = y2), arrow = arrow(length = unit(0.25, "cm"), type = "closed")) + geom_label(data = player_label, aes(x = x, y = y), label = c("Osako", "Honda"), family = "Dusha V5") + geom_point(aes(x = 98, y = 50), size = 3, color = "green") + geom_text(aes(x = 99.7, y = 50), size = 5, label = "???", family = "Dusha V5") + annotate(geom = "text", family = "Dusha V5", hjust = c(0.5, 0.5, 0.5, 0.5, 0.5), size = c(6.5, 4.5, 4, 3.5, 3), label = c("Japan (2) vs. Colombia (1)", "Kagawa (PEN 6'), Quintero (39'), Osako (73')", "Japan press their man advantage, substitute Honda\ndelivers a delicious corner kick for Osako to (somehow) tower over\nColombia's defense and flick a header into the far corner!", "Bonus: Ospina looking confused and\ndoing a lil' two-step-or-god-knows-what.", "by Ryo Nakagawara (@R_by_Ryo)"), x = c(110, 105, 70, 92, 53), y = c(30, 30, 45, 81, 85)) + ggimage::geom_flag(aes(image = "JP"), # Japan Flag x = 110, y = 13, size = 0.08) + ggimage::geom_flag(aes(image = "CO"), # Colombia Flag x = 110, y = 53, size = 0.08) + ggimage::geom_emoji(aes(x = 95, y = 50), image = "26bd", size = 0.035) + geom_image(data = wc_logo, aes(x = x, y = y, image = image), size = 0.17) + theme(plot.margin=grid::unit(c(0,0,0,0), "mm")) ggsave(g, filename = "osako_winner.png", height = 5, width = 7) ## ----osako anim---------------------------------------------------------- library(ggplot2) library(dplyr) library(ggsoccer) library(extrafont) library(emoGG) library(ggimage) library(gganimate) ball_data <- data.frame(x = c(99, 94, 100), y = c(0.3, 47, 55.5), time = c(1, 2, 3)) player_label <- data.frame(x = c(92, 97), y = c(48, 0)) wc_logo <- data.frame(x = 107, y = 85) %>% mutate(image = "https://upload.wikimedia.org/wikipedia/en/thumb/6/67/2018_FIFA_World_Cup.svg/1200px-2018_FIFA_World_Cup.svg.png") g <- ggplot(ball_data) + annotate_pitch() + theme_pitch() + theme(text = element_text(family = "Dusha V5")) + coord_flip(xlim = c(55, 112), ylim = c(-1, 101)) + geom_label(data = player_label, aes(x = x, y = y), label = c("Osako", "Honda"), family = "Dusha V5") + geom_point(aes(x = 98, y = 50), size = 3, color = "green") + geom_text(aes(x = 99.7, y = 50), size = 5, label = "???", family = "Dusha V5") + annotate(geom = "text", family = "Dusha V5", hjust = c(0.5, 0.5, 0.5, 0.5, 0.5), size = c(6.5, 4.5, 4, 3.5, 3), label = c("Japan (2) vs. Colombia (1)", "Kagawa (PEN 6'), Quintero (39'), Osako (73')", "Japan press their man advantage, substitute Honda\ndelivers a delicious corner kick for Osako to (somehow) tower over\nColombia's defense and flick a header into the far corner!", "Bonus: Ospina looking confused and\ndoing a lil' two-step-or-god-knows-what.", "by Ryo Nakagawara (@R_by_Ryo)"), x = c(110, 105, 70, 92, 53), y = c(30, 30, 45, 81, 85)) + ggimage::geom_flag(aes(image = "JP"), # Japan Flag x = 110, y = 13, size = 0.08) + ggimage::geom_flag(aes(image = "CO"), # Colombia Flag x = 110, y = 53, size = 0.08) + ggimage::geom_emoji(aes(x = x, y = y, frame = time), image = "26bd", size = 0.035) + geom_image(data = wc_logo, aes(x = x, y = y, image = image), size = 0.17) + theme(plot.margin=grid::unit(c(0,0,0,0), "mm")) g gganimate(g, "osako_ani.gif") ## ----osako tween, fig.height = 5, fig.width = 7-------------------------- # TWEEN library(ggplot2) library(dplyr) library(ggsoccer) library(extrafont) library(emoGG) library(ggimage) library(gganimate) library(purrr) library(tweenr) player_label <- data.frame(x = c(92, 97), y = c(48, 0)) wc_logo <- data.frame(x = 107, y = 85) %>% mutate(image = "https://upload.wikimedia.org/wikipedia/en/thumb/6/67/2018_FIFA_World_Cup.svg/1200px-2018_FIFA_World_Cup.svg.png") # tweenr the ball movement data ball_data <- data.frame(x = c(99, 94, 100), y = c(0.3, 47, 55.5)) ball_list <- ball_data %>% pmap(data.frame) osako_tween <- ball_list %>% tween_states(tweenlength = 1.5, statelength = 0.01, ease = "quadratic-out", nframes = 50) g2 <- ggplot(osako_tween) + annotate_pitch() + theme_pitch() + theme(text = element_text(family = "Dusha V5")) + coord_flip(xlim = c(55, 112), ylim = c(-1, 101)) + geom_label(data = player_label, aes(x = x, y = y), label = c("Osako", "Honda"), family = "Dusha V5") + geom_point(aes(x = 98, y = 50), size = 3, color = "green") + annotate(geom = "text", family = "Dusha V5", hjust = c(0.5, 0.5, 0.5, 0.5), size = c(6.5, 4.5, 5, 3), label = c("Japan (2) vs. Colombia (1)", "Kagawa (PEN 6'), Quintero (39'), Osako (73')", "Japan press their man advantage, substitute Honda\ndelivers a delicious corner kick for Osako to (somehow) tower over\nColombia's defense and flick a header into the far corner!", "by Ryo Nakagawara (@R_by_Ryo)"), x = c(110, 105, 70, 53), y = c(30, 30, 47, 85)) + ggimage::geom_emoji(aes(x = x, y = y, frame = .frame), image = "26bd", size = 0.035) + ggimage::geom_flag(aes(image = "JP"), # Japan Flag x = 110, y = 13, size = 0.08) + ggimage::geom_flag(aes(image = "CO"), # Colombia Flag x = 110, y = 53, size = 0.08) + geom_image(data = wc_logo, aes(x = x, y = y, image = image), size = 0.17) + theme(plot.margin=grid::unit(c(0,0,0,0), "mm")) g2 gganimate(g2, ani.width = 800, ani.height = 500, interval = 0.5, "osako_tween.gif") gganimate(g2, title_frame = FALSE, width = 700, height = 500, interval = 0.01, "osako_tween_final.gif") ## ----offside data, fig.height=6, fig.width=4----------------------------- library(ggplot2) library(dplyr) library(ggsoccer) library(ggimage) library(extrafont) library(gganimate) library(tweenr) library(purrr) library(countrycode) # library(StatsBombR) # loadfonts() # flags flag_data <- data.frame( x = c( 48, 87), y = c(107, 107), team = c("japan", "senegal") ) %>% mutate( image = team %>% countrycode(., origin = "country.name", destination = "iso2c") ) %>% select(-team) # PLAYERS # JAPAN: x, y (blue) Senegal: x2, y2 (lightgreen) # push 2, 3 frames up above box >>> add 5, 6 as frames?? trap_data <- data.frame( time = c(1, 2, 3, 4, 5), # ball trajectory x = c(70, 70, 70, 87, 95), # pass balls y = c(85, 85, 85, 52, 33), # offside bar #xo = c(83, 81.2, 79, 77.5, 70), xoend = c(83.8, 81.8, 79, 78.5, 71), yo = c( 5, 5, 5, 5, 5), yoend = c(95, 95, 95, 95, 95), # players: japan jx = c(83, 81, 77, 75, 70), jy = c(rep(65, 5)), jx2 = c(83, 81.8, 78.5, 77, 70), jy2 = c(rep(60.5, 5)), jx3 = c(83, 81, 76.5, 75, 71), jy3 = c(rep(55, 5)), jx4 = c(83, 81.2, 76.3, 75, 70), jy4 = c(rep(52, 5)), jx5 = c(82.8, 81, 77, 74, 70), jy5 = c(rep(49, 5)), jx6 = c(83, 81.8, 77, 74, 70), jy6 = c(rep(45, 5)), jx7 = c(83.8, 81, 79, 77.5, 70), jy7 = c(rep(40, 5)), # players: senegal sx = c(83, 84, 84, 84, 84), sy = c(rep(33, 5)), sx2 = c(83, 85, 87, 92, 95), sy2 = c(38, 37, 35, 34, 33), sx3 = c(83, 84, 84, 83, 83), sy3 = c(rep(41, 5)), sx4 = c(83, 84, 83, 78, 78), sy4 = c(rep(45, 5)), sx5 = c(83, 84, 87, 88, 89), sy5 = c(rep(52, 5)), sx6 = c(83, 85, 84, 84, 83), sy6 = c(rep(69, 5)) ) # fix focus field issue with coord_fixed() + aspect_ratio = NULL in theme_pitch() g <- ggplot(trap_data) + annotate_pitch() + theme_pitch(aspect_ratio = NULL) + coord_fixed(xlim = c(30, 101), ylim = c(-5, 131)) + # offside line geom_segment(aes(x = xoend, y = yo, xend = xoend, yend = yoend, frame = time), color = "black", size = 1.3) + # start at 83 just use geom_segment instead # japan geom_point(aes(x = jx, y = jy, frame = time), size = 4, color = "blue") + geom_point(aes(x = jx2, y = jy2, frame = time), size = 4, color = "blue") + geom_point(aes(x = jx3, y = jy3, frame = time), size = 4, color = "blue") + geom_point(aes(x = jx4, y = jy4, frame = time), size = 4, color = "blue") + geom_point(aes(x = jx5, y = jy5, frame = time), size = 4, color = "blue") + geom_point(aes(x = jx6, y = jy6, frame = time), size = 4, color = "blue") + geom_point(aes(x = jx7, y = jy7, frame = time), size = 4, color = "blue") + # senegal geom_point(aes(x = sx, y = sy, frame = time), size = 4, color = "green") + geom_point(aes(x = sx2, y = sy2, frame = time), size = 4, color = "green") + geom_point(aes(x = sx3, y = sy3, frame = time), size = 4, color = "green") + geom_point(aes(x = sx4, y = sy4, frame = time), size = 4, color = "green") + geom_point(aes(x = sx5, y = sy5, frame = time), size = 4, color = "green") + geom_point(aes(x = sx6, y = sy6, frame = time), size = 4, color = "green") + # free kick spot (reference) geom_point(aes(x = 70, y = 85), color = "blue", size = 1.2) + annotate(geom = "text", family = "Dusha V5", hjust = c(0, 0, 0, 0.5), size = c(4.5, 3, 5.5, 3), label = c("Japan (2) vs. Senegal (2)", "Mane (11'), Inui (33'), Wague (71'), Honda (78')", "The Perfect Offside Trap", "by Ryo Nakagawara\n(@R_by_Ryo)"), x = c(30, 30, 30, 94), y = c(117, 108, 125, -3)) + ggimage::geom_flag(data = flag_data, aes(x = x, y = y, image = image), size = c(0.08, 0.08)) + ggimage::geom_emoji(aes(x = x, y = y, frame = time), image = "26bd", size = 0.035) g ## ----offside gganimate, fig.height=6, fig.width=4------------------------ # vline for offside line # x1 Ja, x2 Sen # sligh twiggle before kick? # goalkeeper position gganimate(g, "g_ani.gif") ## ----tweenr offside final, fig.height=10, fig.width=8-------------------- library(ggplot2) library(dplyr) library(ggsoccer) library(ggimage) library(extrafont) library(gganimate) library(tweenr) library(purrr) library(countrycode) # library(StatsBombR) # loadfonts() # PLAYERS # JAPAN: x, y (blue) Senegal: x2, y2 (lightgreen) trap_data <- data.frame( time = c(1, 2, 3, 4, 5), # ball trajectory x = c(70, 70, 70, 87, 95), # pass balls y = c(85, 85, 85, 52, 33), # offside bar #xo = c(83, 81.2, 79, 77.5, 70), xoend = c(83.8, 81.8, 79, 78.5, 71), yo = c( 5, 5, 5, 5, 5), yoend = c(95, 95, 95, 95, 95), # players: japan jx = c(83, 81, 77, 75, 70), jy = c(rep(65, 5)), jx2 = c(83, 81.8, 78.5, 77, 70), jy2 = c(rep(60.5, 5)), jx3 = c(83, 81, 76.5, 75, 71), jy3 = c(rep(55, 5)), jx4 = c(83, 81.2, 76.3, 75, 70), jy4 = c(rep(52, 5)), jx5 = c(82.8, 81, 77, 74, 70), jy5 = c(rep(49, 5)), jx6 = c(83, 81.8, 77, 74, 70), jy6 = c(rep(45, 5)), jx7 = c(83.8, 81, 79, 77.5, 70), jy7 = c(rep(40, 5)), # players: senegal sx = c(83, 84, 84, 84, 84), sy = c(rep(33, 5)), sx2 = c(83, 85, 87, 92, 95), sy2 = c(38, 37, 35, 34, 33), sx3 = c(83, 84, 84, 83, 83), sy3 = c(rep(41, 5)), sx4 = c(83, 84, 83, 78, 78), sy4 = c(rep(45, 5)), sx5 = c(83, 84, 87, 88, 89), sy5 = c(rep(52, 5)), sx6 = c(83, 85, 84, 84, 83), sy6 = c(rep(69, 5)) ) # flags flag_data <- data.frame( x = c( 42, 72), y = c(107, 107), team = c("japan", "senegal") ) %>% mutate( image = team %>% countrycode(., origin = "country.name", destination = "iso2c") ) %>% select(-team) # extra players: goalkeeper_data <- data.frame( x = c(98), y = c(50) ) senegal_data <- data.frame( x = c(55, 55, 68.5), y = c(50, 60, 87) ) # create list of dfs offside_list <- trap_data %>% pmap(data.frame) # tweenr offside_tween <- offside_list %>% tween_states(tweenlength = 0.5, statelength = 0.00000001, ease = "linear", nframes = 50) # PLOT g2 <- ggplot(offside_tween) + annotate_pitch() + theme_pitch(aspect_ratio = NULL) + coord_fixed(xlim = c(30, 101), ylim = c(-5, 117)) + # offside line geom_segment(aes(x = xoend, y = yo, xend = xoend, yend = yoend, frame = .frame), color = "black", size = 1.3) + # start at 83 just use geom_segment instead # japan geom_point(aes(x = jx, y = jy, frame = .frame), size = 4, color = "blue") + geom_point(aes(x = jx2, y = jy2, frame = .frame), size = 4, color = "blue") + geom_point(aes(x = jx3, y = jy3, frame = .frame), size = 4, color = "blue") + geom_point(aes(x = jx4, y = jy4, frame = .frame), size = 4, color = "blue") + geom_point(aes(x = jx5, y = jy5, frame = .frame), size = 4, color = "blue") + geom_point(aes(x = jx6, y = jy6, frame = .frame), size = 4, color = "blue") + geom_point(aes(x = jx7, y = jy7, frame = .frame), size = 4, color = "blue") + # senegal geom_point(aes(x = sx, y = sy, frame = .frame), size = 4, color = "green") + geom_point(aes(x = sx2, y = sy2, frame = .frame), size = 4, color = "green") + geom_point(aes(x = sx3, y = sy3, frame = .frame), size = 4, color = "green") + geom_point(aes(x = sx4, y = sy4, frame = .frame), size = 4, color = "green") + geom_point(aes(x = sx5, y = sy5, frame = .frame), size = 4, color = "green") + geom_point(aes(x = sx6, y = sy6, frame = .frame), size = 4, color = "green") + # free kick spot (reference) geom_point(aes(x = 70, y = 85), color = "black", size = 1.2) + # goalkeeper geom_point(data = goalkeeper_data, aes(x = x, y = y), size = 4, color = "blue") + # senegal defenders geom_point(data = senegal_data, aes(x = x, y = y), size = 4, color = "green") + annotate( geom = "text", family = "Dusha V5", hjust = c(0, 0, 0.5), size = c(6, 6.5, 3), label = c("Japan (2) vs. Senegal (2)", "The Perfect Offside Trap", "by Ryo Nakagawara\n(@R_by_Ryo)"), x = c(30, 30, 94), y = c(107, 115, -3)) + ggimage::geom_flag(data = flag_data, aes(x = c(48, 90), y = c(107, 107), image = image), size = c(0.07, 0.07)) + ggimage::geom_emoji(aes(x = x, y = y, frame = .frame), image = "26bd", size = 0.035) gganimate(g2, interval = 0.001, height = 10, width = 8, "offside_final.gif", title_frame = FALSE) ## ----meme---------------------------------------------------------------- library(memery) img <- ("https://imgflip.com/s/meme/Roll-Safe-Think-About-It.jpg") meme_labs <- c("you can't lose the aerial battle", "if you set an offside trap") meme(img, meme_labs, "offside_meme.png")

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WeiSong-bio/roryk-bcbioSinglecell

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readCellRanger.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/readCellRanger.R \name{readCellRanger} \alias{readCellRanger} \title{Read 10X Genomics Cell Ranger Data} \usage{ readCellRanger(uploadDir, sampleMetadataFile = NULL, refdataDir = NULL, interestingGroups = "sampleName", transgeneNames = NULL, spikeNames = NULL, ...) } \arguments{ \item{uploadDir}{Path to Cell Ranger output directory. This directory path must contain \code{filtered_gene_bc_matrices*} as a child directory.} \item{sampleMetadataFile}{Sample barcode metadata file. Optional for runs with demultiplixed index barcodes (e.g. SureCell), but otherwise required for runs with multipliexed FASTQs containing multiple index barcodes (e.g. inDrop).} \item{refdataDir}{Directory path to Cell Ranger reference annotation data.} \item{interestingGroups}{Character vector of interesting groups. Must be formatted in camel case and intersect with \code{\link[=sampleData]{sampleData()}} colnames.} \item{transgeneNames}{\code{character} vector indicating which \code{\link[=assay]{assay()}} rows denote transgenes (e.g. EGFP, TDTOMATO).} \item{spikeNames}{\code{character} vector indicating which \code{\link[=assay]{assay()}} rows denote spike-in sequences (e.g. ERCCs).} \item{...}{Additional arguments, to be stashed in the \code{\link[=metadata]{metadata()}} slot.} } \value{ \code{SingleCellExperiment}. } \description{ Read \href{https://www.10xgenomics.com/software/}{10x Genomics Chromium} cell counts from \code{barcodes.tsv}, \code{genes.tsv}, and \code{matrix.mtx} files. } \section{Directory structure}{ Cell Ranger can vary in its output directory structure, but we're requiring a single, consistent data structure for datasets containing multiple samples. Note that Cell Ranger data may not always contain per sample subdirectories, or the "\code{outs}" subdirectory. We may make this more flexible in the future, but for now we're making this strict to ensure reproducibility. \preformatted{ file.path( "<uploadDir>", "<sampleName>", "outs", "filtered_gene_bc_matrices*", "outs", "<genomeBuild>", "matrix.mtx" ) } } \section{Sample metadata}{ A user-supplied sample metadata file defined by \code{sampleMetadataFile} is required for multiplexed datasets. Otherwise this can be left \code{NULL}, and minimal sample data will be used, based on the directory names. } \section{Reference data}{ We strongly recommend supplying the corresponding reference data required for Cell Ranger with the \code{refdataDir} argument. When set, the function will detect the \code{organism}, \code{ensemblRelease}, and \code{genomeBuild} automatically, based on the 10X \code{refdataDir} YAML metadata. Additionally, it will convert the gene annotations defined in the GTF file into a \code{GRanges} object, which get slotted in \code{\link[=rowRanges]{rowRanges()}}. Otherwise, the function will attempt to use the most current annotations available from Ensembl, and some gene IDs may not match, due to deprecation in the current Ensembl release. } \examples{ uploadDir <- system.file("extdata/cellranger", package = "bcbioSingleCell") x <- readCellRanger(uploadDir) show(x) } \seealso{ Other Read Functions: \code{\link{readCellTypeMarkers}} } \author{ Michael Steinbaugh }

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/data_analysis/computer_classes/data_visualisation.R

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data_visualisation.R

#------------------------------------------------------------------------------ # Tutorial 2: Data visualisation #------------------------------------------------------------------------------ setwd("~/Dropbox/ucd/data_analysis/") # Set working directory par(las=1,bty="n") # Plot settings # In this tutorial we are going to have a look at a number of different # probability distributions. # We are going to look at the characteristics of these distributions and # see how weel these fit observed data. # However, we first start with generating random data before we move on # to real life data. #------------------------------------------------------------------------------ #### 1) Generating probability distributions #### # We start with generating some random data. # The normal distribution is often a good vantage point: set.seed(42);x<-rnorm(1000) # NB - 'set.seed' used for replicable results # When working with data, one of the first things you do is examining the # properties by looking and the descriptive statistics and plotting the data. # This can often reveal relevant information such as the presence of missing # values or whether the data is skewed. # Important aspect to be considered when further processing the data and using # it for more formal statistical analysis. # For our randomly distributed data, # let's have a look at the summary statistics: summary(x) sd(x) # Plot the data in a histogram, adding the mean as a vertical line hist(x) # You can use 'prob=TRUE' to get probabilities rather than frequencies. abline(v=mean(x),col="red") ## Q1: Generate the following distributions, all with size 1000: # Normal distribution with mean 2 and sd 1; # Uniform distribution with min 0 and max 10; # Poisson distribution with mean 2; # Gamma distribution with mean 2. # See '?Distributions' for help. # Plot the results using the code given below. par(mfrow=c(2,2)) # 2x2 plot hist(Normal);hist(Uniform);hist(Poisson);hist(Gamma) # Note use of ';' dev.off() # To remove plot and reset settings ## Q2; For each distribution, think of real life example where the data would # fit that distribution. # A recent study has shown that the Dutch men are the tallest in the world. # http://www.bbc.com/news/science-environment-36888541 # The average height of the Dutch man is 182.5 cm with a standard deviation of # 6 cm. # Generate the height distribution for a population the size of 's-Hertogenbosch # (150,000) and plot the data in a histogram indicating where the mean is. ## Q3: How many man will be at least 188.5 cm tall? ## Q4: The average Dutch woman has a height of 169 cm with a standard deviation # of 2 cm. # On a population of 8.5 million Dutch women, how many will be at least as tall # as the average Dutch man? # Hint: you have to use the 'pnorm' function here, check '?pnorm'. # You can take the square root using 'sqrt'. #------------------------------------------------------------------------------ #******************# #### Tukey Time #### #******************# # John Tukey (1915-2000) was an American mathematician who made a number of # important contributions to the field of statistics. # For instance, he is responsible for laying out the foundations of # exploratory data analysis, a central theme in this part of the course. # One of his most famous contributions was the boxplot. # The boxplot is a method to graphically display the distribution of the data, # specifically focusing on the quartiles. # Let's use the boxplot to visually summarise the height data you just generated. boxplot(x,horizontal=TRUE) # Can change orientation using 'horizontal=TRUE' # In a boxplot, the box shows the range of the data between the first and third # quartile, where the thick black line indicates the median. # The whiskers in this case extend to 1.5 of the Inter Quartile Range (Q3-Q1) # of the lower or upper quartile. # The dots outside of the whiskers represent the outliers. # To get a summary we can get Tukey's Five Numbers, which will give the # minimum, lower-hinge, median, upper-hing, and maximum. fivenum(x) # The boxplot is useful to compare data across groups. # Let's look at the average height of women again aross groups of different # size. ## Q5: What indication of sample size does the boxplot give? index<-c(rep(1,30),rep(2,200),rep(3,1500),rep(4,20000)) # Generate groups data<-c(rnorm(30,169,2),rnorm(200,169,2), rnorm(1500,169,2),rnorm(20000,169,2)) # Generate data boxplot(data~index) #------------------------------------------------------------------------------ #### 2) The size of whales #### # From Dutch women we move on to the size of whales. # You should have downloaded a csv-file called 'whales.csv'. # The data is taken from a paper called "How Large Should Whales Be?", which # of course is an important scientific question. # http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0053967 # The data consists of estimates of whale size for different species. # Load said file using the following command: whales<-read.csv("whales.csv",header=TRUE,sep=",",stringsAsFactors=FALSE) # Let's check the data we just loaded str(whales) head(whales) # A useful command in R is 'unique'. # Let's find out the number of different groups and families. length(unique(whales$group)) length(unique(whales$family)) ## Q6: Examine the distribution of the size of whales. # Do you think that the average size is a good measure of centrality? # Explain why or why not. # We can use the boxplot to summarise the size distribution per group. # Due to the length of the names we first need to adjust some of the plot # settings par(mar=c(4,10,2,2),las=1)# 'mar' sets graph margins (bottom,right,top,left). boxplot(mass_kg~family,whales,horizontal=TRUE) # Figure shows that there is quite some variation. # The large scale differences obscure details for some families. # One thing we could do is use a log-scale. # This is easily implemented in the plot function specifying the 'log' argument. options(scipen=4) # To keep axis without scientific notations boxplot(mass_kg~family,whales,horizontal=TRUE,log="x") ## Q7: Examine why the Physeteridae family produces such a peculiar boxplot. #------------------------------------------------------------------------------ #### 3) A classic: Prussian cavalry officers kicked to death by horses #### # This is a classic example in statistics based on the work by # Ladislaus Bortkiewicz (1868-1931), a Russian economist and statistician, # whose parents were Polish and lived and worked mostly in Germany. # For his most famous work he used data on the number of officers in the # Prussian cavalry which were kicked to deaths. # The data spans 20 years and covers 14 corps, corps as in military units. prussia<-read.csv("prussian.csv",stringsAsFactors=FALSE) ## Q8: Examine the fatality data and discuss what type of distribution would # best fit the data. # We continue by calculating the proportion of observations with size X. # So we're interested in what percentage of the observations have 0 fatalities, # the percentage with 1 fatality and so on. table(prussia$deaths) # Frequency of fatalities V<-as.vector(table(prussia$deaths)) # Vectorise the table perc<-V/sum(V)*100 # Calculate proportion dev.off() # Reset plot area plot(0:4,perc,type="b",ylim=c(0,60), xlab="Number of fatalities",ylab="Percentage") # Plot the results ## Q9: Use your anwer for question 8 to predict the proportion of # observations of size X. # Add your result to the existing plot using the code given below. # Does your distribution fit the data well? lines(0:4,'YOUR PREDICTIONS',lty=2,type="b") # Let's examine the toal number of fatalities per year. # Aggregating the data to annual level. kicks.yr<-aggregate(deaths~year,prussia,sum) plot(kicks.yr$year,kicks.yr$deaths,type="b", xlim=c(1875,1894),ylim=c(0,20)) # Use 'xlim' and 'ylim' to adjust axis ## Q10: Suppose you're a young cavalry officer in the Prussian army. # Which corps would you rather avoid being enlisted to? # How much larger is the average fatality rate for this corps compared to all # cavalry units? # And which corps have below average fatality rates?

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/scripts/Non_cen_zeros.R

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Non_cen_zeros.R

temp_flag = data.frame(eventida = 1,lagoslakeid = 1,variable="noah",value=1,flag="first_row") for(i in 1:length(variable.cols)) { sensor.col = which(names(Data)==paste(names(Data)[variable.cols[i]],"_censorcode",sep="")) #if(i != 11) { #Can't remember why I excluded tdn in the past...i think the algorithm crapped out { var_data <- Data[,c(event.col, lakeid.col,variable.cols[i],sensor.col)] %>% drop_na() %>% filter((!!sym(variable.names[i])) == 0) var_data = var_data if(nrow(var_data)>0){ names(var_data)[4]="censor_code" var_data <- var_data %>% filter(censor_code=="NC4" | censor_code == "" | is.na(censor_code)==TRUE | censor_code == "NC3") if(nrow(var_data)>0) { temp.out <- data.frame(eventida = var_data[,1], lagoslakeid = var_data[,2], variable = variable.names[i], value = var_data[,3], flag = "non_cen_zero") temp_flag<- rbind(temp_flag,temp.out) } } } } temp_flag <- temp_flag %>% filter(variable != "noah") %>% filter(variable !="secchi")

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library(testthat) library(NMDer) library("BSgenome.Mmusculus.UCSC.mm10") test_check("NMDer")

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## subset observations realte to coal NEI_coal<- NEI[which(NEI$SCC %in% SCC[grep("coal",SCC$Short.Name,ignore.case = TRUE),"SCC"]),] g<-ggplot(NEI_coal,aes(year,Emissions)) g+geom_line(stat = "summary",fun.y="sum")+ labs(y="Emissions from coal combustion-related sources",x="Year (1999 - 2008)")

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data<-read.table('conc_min.csv',TRUE,sep=",") data day<-data[,1] obmintemp<-data[,2] fcmintemp<-data[,3] summary(obmintemp) summary(fcmintemp) sd(obmintemp,na.rm=TRUE) sd(fcmintemp) var(obmintemp,na.rm=TRUE) var(fcmintemp) m<-mean(obmintemp,na.rm=TRUE) std<-sqrt(var(obmintemp,na.rm=TRUE)) m1<-mean(fcmintemp) std1<-sqrt(var(fcmintemp)) hist(obmintemp, prob=TRUE, main="Histogram of observed minimum temperature", xlab="Temperature, (degrees celcius)", col="orange", las=1) curve(dnorm(x, mean=m, sd=std), col="darkblue",lwd=2,add=TRUE,yaxt="n") hist(fcmintemp, prob=TRUE, main="Histogram of forecast minimum temperature", xlab="Temperature, (degrees celcius)", col="pink", las=1) curve(dnorm(x, mean=m1, sd=std1), col="darkblue",lwd=2,add=TRUE,yaxt="n") #for a students t test: t.test(obmintemp,fcmintemp,var.equal=TRUE) #for an f test: var.test(obmintemp,fcmintemp,conf.level = 0.9) ?var.test #for welchs t test: t.test(obmintemp,fcmintemp,alternative="two.sided",var.equal=FALSE) #correlation: cor(obmintemp,fcmintemp,use="complete.obs") #method=c("spearman")) cor.test(obmintemp,fcmintemp,use="complete.obs") ?cor fit<-lm(fcmintemp~obmintemp)#not sure if i can use as lin reg means one depends on other summary(fit) plot(obmintemp,fcmintemp, main="Scattergraph of observed minimum temperature and forcast minimum temperature", xlab="Observed min temperature, (degrees celcius)", ylab="Forecast min temperature, (degrees celcius)", col="blue", las=1) abline(lm(fcmintemp~obmintemp), col="darkred") residuals(fit) qqnorm(residuals(fit)) qqnorm(obmintemp, main="Normal Q-Q plot for observed data") qqnorm(fcmintemp, main="Normal Q-Q plot for forecast data") obmintemp-fcmintemp error<-obmintemp-fcmintemp rmse <- function(error) { sqrt(mean(error^2,na.rm=TRUE)) } rmse(error) #turned pair into na values from this point fcmintemp[140]=NA obmintemp[1]=3.9 lowobtemp<-(obmintemp<=0) highobtemp<-obmintemp>0 lowfctemp<-fcmintemp<=0 highfctemp<-fcmintemp>0 aa<-lowobtemp&lowfctemp a<-length(which((aa==TRUE))) bb<-highobtemp&lowfctemp b<-length(which((bb==TRUE))) cc<-lowobtemp&highfctemp c<-length(which((cc==TRUE))) dd<-highobtemp&highfctemp d<-length(which((dd==TRUE))) length(which((lowobtemp==TRUE))) length(which((highobtemp==TRUE))) length(which((lowfctemp==TRUE))) length(which((highfctemp==TRUE))) 58+305 a b c d proba<-a/364 probb<-b/364 probc<-c/364 probd<-d/364 #skill scores and measures of accuracy phi<-((a*d)-(b*c))/sqrt((a+b)*(c+d)*(a+c)*(b+d)) pod<-a/(a+c) far<-b/(a+b) csi<-a/(a+b+c) accuracy<-(a+d)/(a+b+c+d) bias<-(a+b)/(a+c) pofd<-b/(b+d) heidke<-(2*((a*d)-(b*c)))/((a+b)*(c+d)*(a+c)*(b+d)) hk<-((a*d)-(b*c))/((b+d)*(a+c)) clayton<-((a*d)-(b*c))/((a+b)*(c+d)) aref<-((a+b)*(a+c))/364 ets<-(a-aref)/(a+b+c-aref) phi pod far csi accuracy bias pofd heidke hk clayton ets #PERSISTENCE data2<-read.table('new.txt') persistence<-data2[,1] obmintemp[1]=NA lowpertemp<-persistence<=0 highpertemp<-persistence>0 aa2<-lowobtemp&lowpertemp a2<-length(which((aa2==TRUE))) bb2<-highobtemp&lowpertemp b2<-length(which((bb2==TRUE))) cc2<-lowobtemp&highpertemp c2<-length(which((cc2==TRUE))) dd2<-highobtemp&highpertemp d2<-276 length(which((lowobtemp==TRUE))) length(which((highobtemp==TRUE))) length(which((lowpertemp==TRUE))) length(which((highpertemp==TRUE))) phi2<-((a2*d2)-(b2*c2))/sqrt((a2+b2)*(c2+d2)*(a2+c2)*(b2+d2)) pod2<-a2/(a2+c2) far2<-b2/(a2+b2) csi2<-a2/(a2+b2+c2) accuracy2<-(a2+d2)/(a2+b2+c2+d2) bias2<-(a2+b2)/(a2+c2) pofd2<-b2/(b2+d2) heidke2<-(2*((a2*d2)-(b2*c2)))/((a2+b2)*(c2+d2)*(a2+c2)*(b2+d2)) hk2<-((a2*d2)-(b2*c2))/((b2+d2)*(a2+c2)) clayton2<-((a2*d2)-(b2*c2))/((a2+b2)*(c2+d2)) aref2<-((a2+b2)*(a2+c2))/363 ets2<-(a2-aref2)/(a2+b2+c2-aref2) phi2 pod2 far2 csi2 accuracy2 bias2 pofd2 heidke2 hk2 clayton2 ets2 #CLIMATE problow<-59/364 #problow<-length(which((lowobtemp==TRUE)))/364 climate<-sample(c(0,1),365,replace=TRUE,prob=c(1-problow,problow)) climate climate[140]=NA lowclimtemp<-climate==1 highclimtemp<-climate==0 aa3<-lowobtemp&lowclimtemp a3<-length(which((aa3==TRUE)))+1 bb3<-highobtemp&lowclimtemp b3<-length(which((bb3==TRUE))) cc3<-lowobtemp&highclimtemp c3<-length(which((cc3==TRUE))) dd3<-highobtemp&highclimtemp d3<-length(which((dd3==TRUE))) length(which((lowclimtemp==TRUE))) length(which((highclimtemp==TRUE))) a3 b3 c3 d3 phi3<-((a3*d3)-(b3*c3))/sqrt((a3+b3)*(c3+d3)*(a3+c3)*(b3+d3)) pod3<-a3/(a3+c3) far3<-b3/(a3+b3) csi3<-a3/(a3+b3+c3) accuracy3<-(a3+d3)/(a3+b3+c3+d3) bias3<-(a3+b3)/(a3+c3) pofd3<-b3/(b3+d3) heidke3<-(2*((a3*d3)-(b3*c3)))/((a3+b3)*(c3+d3)*(a3+c3)*(b3+d3)) hk3<-((a3*d3)-(b3*c3))/((b3+d3)*(a3+c3)) clayton3<-((a3*d3)-(b3*c3))/((a3+b3)*(c3+d3)) aref3<-((a3+b3)*(a3+c3))/364 ets3<-(a3-aref3)/(a3+b3+c3-aref3) phi3 pod3 far3 csi3 accuracy3 bias3 pofd3 heidke3 hk3 clayton3 ets3 skillscore1<-(34-31)/(59-31) #compare forecast to persistence skillscore2<-(34-12)/(59-12) #compare forecast to climatology

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setwd('D:/Desktop/Itwill ws/Pproject/연습') # https://otexts.com/fppkr/graphics-exercises.html install.packages('ggfortify') library(forecast) library(ggfortify) library(ggplot2) library(ggplot) tute1 <- read.csv('tute1.csv',header=T) View(tute1) myts <- ts(tute1[,-1],start=1981,frequency = 4) autoplot(myts,facets=T) retail <- readxl::read_excel('retail.xlsx',skip=1) View(retail) myts_r <- ts(retail[,'A3349873A'],frequency = 12,start=c(1982,4)) autoplot(myts_r) ggseasonplot(myts_r) ggsubseriesplot(myts_r) gglagplot(myts_r) ggAcf(myts_r)

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testlist <- list(lambda = NaN, logq = numeric(0), nu = c(1.53063836115601e-18, 1.53063836115601e-18, NaN, NaN, 5.43230922486616e-312, 3.23785921002061e-319, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), p = numeric(0), tol = 0) result <- do.call(COMPoissonReg:::qzicmp_cpp,testlist) str(result)

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#' @importFrom stats model.response model.frame #' @importFrom Formula model.part higherMomentsIV_IIV <- function(F.formula, data, g=NULL, iiv, ...){ # Catch ellispsis l.iivregressors <- list(...) # Check inputs --------------------------------------------------------------------------------------------- check_err_msg(checkinput_highermomentsiv_g(g=g)) check_err_msg(checkinput_highermomentsiv_iiv(iiv=iiv)) check_err_msg(checkinput_highermomentsiv_iivVSg(g=g, iiv=iiv)) check_err_msg(checkinput_highermomentsiv_iivregressors(l.iivregressors=l.iivregressors, F.formula=F.formula, iiv=iiv)) # discard given exogenous regressors and g if not needed because otherwise description is wrong if(iiv %in% c("y2", "p2", "yp")){ l.iivregressors <- list() g <- NULL } # Read out needed data ------------------------------------------------------------------------------------- # To multiply: Read out as matrices as data.frames allow no element-wise mutliplication if dimensions do # not match. For iiv = gp, gy this may be the case when col(g)>1 names.exo.regs <- unique(unlist(l.iivregressors)) mf <- model.frame(F.formula, rhs=1, lhs=1, data=data) vec.data.endo <- model.part(F.formula, data=mf, rhs=2, lhs = 0, drop=TRUE) # endo data from rhs 2 AS VECTOR # The exogenous regressors are only the ones specified in the IIV() part, not all exogenous ones # Therefore read out the data by names. If none (NULL) results in zero-row data.frame df.data.exo.iiv <- mf[, names.exo.regs, drop=FALSE] vec.data.y <- model.response(mf) # Calculate internal IVs ----------------------------------------------------------------------------------- # determine g function, if needed if(!is.null(g)) fct.g <- switch(g, "x2" = function(x){x^2}, "x3" = function(x){x^3}, "lnx" = function(x){log(x)}, "1/x" = function(x){1/x}) # Col-wise de-mean helper function de.mean <- function(x){ if(length(dim(x)) > 1){ # >1 col (data.frame,...). # Use sweep contrary to apply because it again returns data.frame # return(apply(x, MARGIN = 2, FUN = function(x){x-mean(x)})) return(sweep(x = x, MARGIN = 2, STATS = colMeans(x=x, na.rm = TRUE), FUN = "-")) }else{ # vector return(x-mean(x)) } } # IIV calculations df.IIV <- data.frame(res.iiv = switch(EXPR = iiv, # to allow element-wise multiplication for data.frames. # The vector data HAS to be the first input "g" = de.mean(fct.g(df.data.exo.iiv)), # IIV1 "gp" = de.mean(fct.g(df.data.exo.iiv)) * de.mean(vec.data.endo), # IIV2 "gy" = de.mean(fct.g(df.data.exo.iiv)) * de.mean(vec.data.y), # IIV3 "yp" = de.mean(vec.data.y) * de.mean(vec.data.endo), # IIV4 "p2" = de.mean(vec.data.endo)^2, # IIV5 "y2" = de.mean(vec.data.y)^2)) # IIV6 # Naming ------------------------------------------------------------------------------------------------ # Rename after IIV() # Cannot make in single paste() cmd as double . are introduced for emtpy chars colnames.iiv <- paste0("IIV.is",iiv) colnames.iiv <- if(is.null(g)) colnames.iiv else paste0(colnames.iiv,".gis",g) colnames.iiv <- if(!length(names.exo.regs)) colnames.iiv else paste0(colnames.iiv,".regis",names.exo.regs) # Keep make.names for the case g=1/x and too be sure its always correct colnames(df.IIV) <- make.names(colnames.iiv) # Readable description desc.iiv <- paste0("IIV(iiv=",iiv) desc.iiv <- if(is.null(g)) desc.iiv else paste0(desc.iiv,",g=",g) desc.iiv <- if(!length(names.exo.regs)) desc.iiv else paste0(desc.iiv,",",paste(names.exo.regs,collapse = ",")) desc.iiv <- paste0(desc.iiv,")") # Return ------------------------------------------------------------------------------------------------ # Return list with readable description and IIV as data.frame return(list(desc.IIV = desc.iiv, df.IIV = df.IIV)) }

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/evaluate.r \name{generate_DIAG} \alias{generate_DIAG} \title{Generate sample diagnosis data} \usage{ generate_DIAG(cmshcc_map, size = 100, seed = 2, max_dx = 10) } \arguments{ \item{cmshcc_map}{data frame is the CMSHCC diagnosis to HCC mapping in question (Required)} \item{size}{integer is the number of rows of diagnoses that are required (default 100)} \item{seed}{integer is the random seed starting value for reproducability (default 2)} \item{max_dx}{integer is the maximum number of diagnoses that a beneficiary can have (default 10)} } \value{ data frame DIAG is the list of HICNO and ICD diagnoses } \description{ Generate sample diagnosis data }

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# ------------------------------------ # Author: Andreas Alfons # Erasmus University Rotterdam # ------------------------------------ #' (Robust) CCA via alternating series of grid searches #' #' Perform canoncial correlation analysis via projection pursuit based on #' alternating series of grid searches in two-dimensional subspaces of each #' data set, with a focus on robust and nonparametric methods. #' #' The algorithm is based on alternating series of grid searches in #' two-dimensional subspaces of each data set. In each grid search, #' \code{nGrid} grid points on the unit circle in the corresponding plane are #' obtained, and the directions from the center to each of the grid points are #' examined. In the first iteration, equispaced grid points in the interval #' \eqn{[-\pi/2, \pi/2)}{[-pi/2, pi/2)} are used. In each subsequent #' iteration, the angles are halved such that the interval #' \eqn{[-\pi/4, \pi/4)}{[-pi/4, pi/4)} is used in the second iteration and so #' on. If only one data set is multivariate, the algorithm simplifies #' to iterative grid searches in two-dimensional subspaces of the corresponding #' data set. #' #' In the basic algorithm, the order of the variables in a series of grid #' searches for each of the data sets is determined by the average absolute #' correlations with the variables of the respective other data set. Since #' this requires to compute the full \eqn{(p \times q)}{(p x q)} matrix of #' absolute correlations, where \eqn{p} denotes the number of variables of #' \code{x} and \eqn{q} the number of variables of \code{y}, a faster #' modification is available as well. In this modification, the average #' absolute correlations are computed over only a subset of the variables of #' the respective other data set. It is thereby possible to use randomly #' selected subsets of variables, or to specify the subsets of variables #' directly. #' #' Note that also the data sets are ordered according to the maximum average #' absolute correlation with the respective other data set to ensure symmetry #' of the algorithm. #' #' For higher order canonical correlations, the data are first transformed into #' suitable subspaces. Then the alternate grid algorithm is applied to the #' reduced data and the results are back-transformed to the original space. #' #' @aliases print.cca #' #' @param x,y each can be a numeric vector, matrix or data frame. #' @param k an integer giving the number of canonical variables to compute. #' @param method a character string specifying the correlation functional to #' maximize. Possible values are \code{"spearman"} for the Spearman #' correlation, \code{"kendall"} for the Kendall correlation, \code{"quadrant"} #' for the quadrant correlation, \code{"M"} for the correlation based on a #' bivariate M-estimator of location and scatter with a Huber loss function, or #' \code{"pearson"} for the classical Pearson correlation (see #' \code{\link{corFunctions}}). #' @param control a list of additional arguments to be passed to the specified #' correlation functional. If supplied, this takes precedence over additional #' arguments supplied via the \code{\dots} argument. #' @param nIterations,maxiter an integer giving the maximum number of #' iterations. #' @param nAlternate,maxalter an integer giving the maximum number of #' alternate series of grid searches in each iteration. #' @param nGrid,splitcircle an integer giving the number of equally spaced #' grid points on the unit circle to use in each grid search. #' @param select optional; either an integer vector of length two or a list #' containing two index vectors. In the first case, the first integer gives #' the number of variables of \code{x} to be randomly selected for determining #' the order of the variables of \code{y} in the corresponding series of grid #' searches, and vice versa for the second integer. In the latter case, the #' first list element gives the indices of the variables of \code{x} to be used #' for determining the order of the variables of \code{y}, and vice versa for #' the second integer (see \dQuote{Details}). #' @param tol,zero.tol a small positive numeric value to be used for #' determining convergence. #' @param standardize a logical indicating whether the data should be #' (robustly) standardized. #' @param fallback logical indicating whether a fallback mode for robust #' standardization should be used. If a correlation functional other than the #' Pearson correlation is maximized, the first attempt for standardizing the #' data is via median and MAD. In the fallback mode, variables whose MADs are #' zero (e.g., dummy variables) are standardized via mean and standard #' deviation. Note that if the Pearson correlation is maximized, #' standardization is always done via mean and standard deviation. #' @param seed optional initial seed for the random number generator (see #' \code{\link{.Random.seed}}). This is only used if \code{select} specifies #' the numbers of variables of each data set to be randomly selected for #' determining the order of the variables of the respective other data set. #' @param \dots additional arguments to be passed to the specified correlation #' functional. Currently, this is only relevant for the M-estimator. For #' Spearman, Kendall and quadrant correlation, consistency at the normal model #' is always forced. #' #' @return An object of class \code{"cca"} with the following components: #' \item{cor}{a numeric vector giving the canonical correlation measures.} #' \item{A}{a numeric matrix in which the columns contain the canonical vectors #' for \code{x}.} #' \item{B}{a numeric matrix in which the columns contain the canonical vectors #' for \code{y}.} #' \item{centerX}{a numeric vector giving the center estimates used in #' standardization of \code{x}.} #' \item{centerY}{a numeric vector giving the center estimates used in #' standardization of \code{y}.} #' \item{scaleX}{a numeric vector giving the scale estimates used in #' standardization of \code{x}.} #' \item{scaleY}{a numeric vector giving the scale estimates used in #' standardization of \code{y}.} #' \item{call}{the matched function call.} #' #' @note \code{CCAgrid} is a simple wrapper function for \code{ccaGrid} for #' more compatibility with package \pkg{pcaPP} concerning function and argument #' names. #' #' @author Andreas Alfons #' #' @seealso \code{\link{ccaProj}}, \code{\link{maxCorGrid}}, #' \code{\link{corFunctions}} #' #' @examples #' data("diabetes") #' x <- diabetes$x #' y <- diabetes$y #' #' ## Spearman correlation #' ccaGrid(x, y, method = "spearman") #' #' ## Pearson correlation #' ccaGrid(x, y, method = "pearson") #' #' @keywords multivariate robust #' #' @importFrom Rcpp evalCpp #' @import robustbase #' @useDynLib ccaPP, .registration = TRUE #' @export ccaGrid <- function(x, y, k = 1, method = c("spearman", "kendall", "quadrant", "M", "pearson"), control = list(...), nIterations = 10, nAlternate = 10, nGrid = 25, select = NULL, tol = 1e-06, standardize = TRUE, fallback = FALSE, seed = NULL, ...) { ## initializations matchedCall <- match.call() ## define list of control arguments for algorithm nIterations <- as.integer(nIterations) nAlternate <- as.integer(nAlternate) nGrid <- as.integer(nGrid) tol <- as.numeric(tol) ppControl <- list(nIterations=nIterations, nAlternate=nAlternate, nGrid=nGrid, select=select, tol=tol) ## call workhorse function cca <- ccaPP(x, y, k, method=method, corControl=control, algorithm="grid", ppControl=ppControl, standardize=standardize, fallback=fallback, seed=seed) cca$call <- matchedCall cca } ## wrapper function for more compatibility with package pcaPP #' @rdname ccaGrid #' @export CCAgrid <- function(x, y, k = 1, method = c("spearman", "kendall", "quadrant", "M", "pearson"), maxiter = 10, maxalter = 10, splitcircle = 25, select=NULL, zero.tol = 1e-06, standardize = TRUE, fallback = FALSE, seed = NULL, ...) { ## initializations matchedCall <- match.call() ## call ccaGrid() cca <- ccaGrid(x, y, k=k, method=method, nIterations=maxiter, nAlternate=maxalter, nGrid=splitcircle, select=select, tol=zero.tol, standardize=standardize, fallback=fallback, seed=seed, ...) cca$call <- matchedCall cca } #' (Robust) CCA via projections through the data points #' #' Perform canoncial correlation analysis via projection pursuit based on #' projections through the data points, with a focus on robust and #' nonparametric methods. #' #' First the candidate projection directions are defined for each data set #' from the respective center through each data point. Then the algorithm #' scans all \eqn{n^2} possible combinations for the maximum correlation, #' where \eqn{n} is the number of observations. #' #' For higher order canonical correlations, the data are first transformed into #' suitable subspaces. Then the alternate grid algorithm is applied to the #' reduced data and the results are back-transformed to the original space. #' #' @param x,y each can be a numeric vector, matrix or data frame. #' @param k an integer giving the number of canonical variables to compute. #' @param method a character string specifying the correlation functional to #' maximize. Possible values are \code{"spearman"} for the Spearman #' correlation, \code{"kendall"} for the Kendall correlation, \code{"quadrant"} #' for the quadrant correlation, \code{"M"} for the correlation based on a #' bivariate M-estimator of location and scatter with a Huber loss function, or #' \code{"pearson"} for the classical Pearson correlation (see #' \code{\link{corFunctions}}). #' @param control a list of additional arguments to be passed to the specified #' correlation functional. If supplied, this takes precedence over additional #' arguments supplied via the \code{\dots} argument. #' @param standardize a logical indicating whether the data should be #' (robustly) standardized. #' @param useL1Median a logical indicating whether the \eqn{L_{1}}{L1} medians #' should be used as the centers of the data sets in standardization (defaults #' to \code{TRUE}). If \code{FALSE}, the columnwise centers are used instead #' (columnwise means if \code{method} is \code{"pearson"} and columnwise #' medians otherwise). #' @param fallback logical indicating whether a fallback mode for robust #' standardization should be used. If a correlation functional other than the #' Pearson correlation is maximized, the first attempt for standardizing the #' data is via median and MAD. In the fallback mode, variables whose MADs are #' zero (e.g., dummy variables) are standardized via mean and standard #' deviation. Note that if the Pearson correlation is maximized, #' standardization is always done via mean and standard deviation. #' @param \dots additional arguments to be passed to the specified correlation #' functional. Currently, this is only relevant for the M-estimator. For #' Spearman, Kendall and quadrant correlation, consistency at the normal model #' is always forced. #' #' @return An object of class \code{"cca"} with the following components: #' \item{cor}{a numeric vector giving the canonical correlation #' measures.} #' \item{A}{a numeric matrix in which the columns contain the canonical vectors #' for \code{x}.} #' \item{B}{a numeric matrix in which the columns contain the canonical vectors #' for \code{y}.} #' \item{centerX}{a numeric vector giving the center estimates used in #' standardization of \code{x}.} #' \item{centerY}{a numeric vector giving the center estimates used in #' standardization of \code{y}.} #' \item{scaleX}{a numeric vector giving the scale estimates used in #' standardization of \code{x}.} #' \item{scaleY}{a numeric vector giving the scale estimates used in #' standardization of \code{y}.} #' \item{call}{the matched function call.} #' #' @note \code{CCAproj} is a simple wrapper function for \code{ccaProj} for #' more compatibility with package \pkg{pcaPP} concerning function names. #' #' @author Andreas Alfons #' #' @seealso \code{\link{ccaGrid}}, \code{\link{maxCorProj}}, #' \code{\link{corFunctions}} #' #' @examples #' data("diabetes") #' x <- diabetes$x #' y <- diabetes$y #' #' ## Spearman correlation #' ccaProj(x, y, method = "spearman") #' #' ## Pearson correlation #' ccaProj(x, y, method = "pearson") #' #' @keywords multivariate robust #' #' @importFrom Rcpp evalCpp #' @import pcaPP #' @import robustbase #' @useDynLib ccaPP, .registration = TRUE #' @export ccaProj <- function(x, y, k = 1, method = c("spearman", "kendall", "quadrant", "M", "pearson"), control = list(...), standardize = TRUE, useL1Median = TRUE, fallback = FALSE, ...) { ## initializations matchedCall <- match.call() ## define list of control arguments for algorithm ppControl <- list(useL1Median=isTRUE(useL1Median)) ## call workhorse function cca <- ccaPP(x, y, k, method=method, corControl=control, algorithm="proj", ppControl=ppControl, standardize=standardize, fallback=fallback) cca$call <- matchedCall cca } ## wrapper function for more compatibility with package pcaPP #' @rdname ccaProj #' @export CCAproj <- function(x, y, k = 1, method = c("spearman", "kendall", "quadrant", "M", "pearson"), standardize = TRUE, useL1Median = TRUE, fallback = FALSE, ...) { ## initializations matchedCall <- match.call() ## call ccaProj() cca <- ccaProj(x, y, k=k, method=method, standardize=standardize, useL1Median=useL1Median, fallback=fallback, ...) cca$call <- matchedCall cca } ## workhorse function ccaPP <- function(x, y, k = 1, method = c("spearman", "kendall", "quadrant", "M", "pearson"), corControl, forceConsistency = TRUE, algorithm = c("grid", "proj"), ppControl, standardize = TRUE, fallback = FALSE, seed = NULL) { ## initializations x <- as.matrix(x) y <- as.matrix(y) n <- nrow(x) if(nrow(y) != n) { stop("'x' and 'y' must have the same number of observations") } p <- ncol(x) q <- ncol(y) # check number of canonical variables to compute k <- rep(as.integer(k), length.out=1) if(is.na(k) || k < 0) k <- formals()$k k <- min(k, p, q) ## prepare the data and call C++ function if(n == 0 || p == 0 || q == 0 || k == 0) { # zero dimension A <- B <- matrix(numeric(), 0, 0) cca <- list(cor=NA, A=A, B=B) } else { # check high-dimensional data if(k > 1 && (n <= p+1 || n <= q+1)) { k <- 1 warning("higher-order canonical correlations not yet implemented", "for high-dimensional data") } # check method and get list of control arguments method <- match.arg(method) corControl <- getCorControl(method, corControl, forceConsistency) # additional checks for grid search algorithm if(algorithm == "grid") { # check subset of variables to be used for determining the order of # the variables from the respective other data set select <- ppControl$select ppControl$select <- NULL if(!is.null(select)) { if(is.list(select)) { # make sure select is a list with two index vectors and # drop invalid indices from each vector select <- rep(select, length.out=2) select <- mapply(function(indices, max) { indices <- as.integer(indices) indices[which(indices > 0 & indices <= max)] - 1 }, select, c(p, q)) valid <- sapply(select, length) > 0 # add the two index vectors to control object if(all(valid)) { ppControl$selectX <- select[[1]] ppControl$selectY <- select[[2]] } else select <- NULL } else { # check number of indices to sample select <- rep(as.integer(select), length.out=2) valid <- !is.na(select) & select > 0 & select < c(p, q) if(all(valid)) { # generate index vectors and add them to control object if(!is.null(seed)) set.seed(seed) ppControl$selectX <- sample.int(p, select[1]) - 1 ppControl$selectY <- sample.int(q, select[2]) - 1 } else select <- NULL } } if(is.null(select)) { ppControl$selectX <- ppControl$selectY <- integer() } } # call C++ function cca <- .Call("R_ccaPP", R_x=x, R_y=y, R_k=k, R_method=method, R_corControl=corControl, R_algorithm=algorithm, R_ppControl=ppControl, R_standardize=isTRUE(standardize), R_fallback=isTRUE(fallback), PACKAGE="ccaPP") drop <- c("cor", "centerX", "centerY", "scaleX", "scaleY") cca[drop] <- lapply(cca[drop], drop) } ## assign class and return results class(cca) <- "cca" cca }

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/align_scf.R

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jhavsmith/mimicry

694737f08a77550dd2f48512440806df7b26bbe3

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refs/heads/master

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align_scf.R

load("scf_table.RDATA") load("freq_matrix_SureSelect.RDATA") load("Mapp_scf.RDATA") load("Null_scf.RDATA") load("scf_map.RDATA") scf.map = data.frame(matrix(nrow=nrow(freq.matrix),ncol=9)) index=1 Null.scf = 0 Mapp.scf = 0 for (i in 1:nrow(scf.table)) { index.b = index-1 scf.matches = freq.matrix[which(freq.matrix[,1]==scf.table[i,4]),] if (length(scf.matches)==0) {print(paste("NULL",as.character(i)));Null.scf=c(Null.scf,i);next} print(i) Mapp.scf = c(Mapp.scf,i) if (length(scf.matches)!=8) { end.row=nrow(scf.matches) new.pos=scf.matches[,2] alleles=scf.matches[,c(3:4)] freqs=scf.matches[,c(5:8)] new.index = nrow(scf.matches) } if (length(scf.matches)==8) { end.row=1;new.pos=scf.matches[2] alleles=scf.matches[3:4] freqs=scf.matches[5:8] new.index = 1 } scf.map[c((index):((index.b)+end.row)),1] = scf.table[i,1] scf.map[c((index):((index.b)+end.row)),2] = scf.table[i,4] scf.map[c((index):((index.b)+end.row)),3] = (as.numeric(scf.table[i,2])-1)+as.numeric(new.pos) scf.map[c((index):((index.b)+end.row)),c(4:5)] = alleles scf.map[c((index):((index.b)+end.row)),c(6:9)] = as.numeric(freqs) index = index+new.index } scf.d = scf.map scf.d[which(scf.map[,1]=="chr1_unmapped"),1] = "chr1" scf.d[which(scf.map[,1]=="chr2_unmapped"),1] = "chr2" scf.d[which(scf.map[,1]=="chr3_unmapped"),1] = "chr3" scf.d[which(scf.map[,1]=="chr6_unmapped"),1] = "chr6" scf.d[which(scf.map[,1]=="chr7_unmapped"),1] = "chr7" scf.d[which(scf.map[,1]=="chr8_unmapped"),1] = "chr8" scf.d[which(scf.map[,1]=="chr11_unmapped"),1] = "chr11" scf.d[which(scf.map[,1]=="chr12_unmapped"),1] = "chr12" scf.d[which(scf.map[,1]=="chr13_unmapped"),1] = "chr13" scf.d[which(scf.map[,1]=="chr15_unmapped"),1] = "chr15" scf.d[which(scf.map[,1]=="chr16_unmapped"),1] = "chr16" scf.d[which(scf.map[,1]=="chr17_unmapped"),1] = "chr17" scf.d[which(scf.map[,1]=="chr18_unmapped"),1] = "chr18" scf.d[which(scf.map[,1]=="chr19_unmapped"),1] = "chr19" scf.d[which(scf.map[,1]=="chr20_unmapped"),1] = "chr20" scf.d[which(scf.map[,1]=="chrZ_unmapped"),1] = "chrZ" og.freqs=as.numeric(matrix[,1]) og.cons = round(og.freqs) og.pol = og.freqs og.pol[which(og.cons==0)] = 1 - og.freqs[which(og.cons==0)] #polarizing no.chrom.d.pol=matrix no.chrom.d.pol[,c(2:4)]= abs(matrix[,1]-matrix[,c(2:4)]) no.chrom.d.pol[,1] = og.pol filler = no.chrom.d.pol for (i in 1:ncol(filler)) { filler[,i] = 0 } no.chrom.d.pol = cbind(filler,no.chrom.d.pol) no.chrom.d.pol[,2] = as.numeric(no.chrom.d[,2]) #window.tables for divergence ol = window.table[22:23,] bg = window.table[-c(22:23),] window.list = list("ol"=ol,"bg"=bg) #get rid of list table=scf.d.pol matrix = (matrix(nrow=nrow(table),ncol=4)) for (j in 1:4) { matrix[,j] = as.numeric(paste(unlist(table[,j+4]))) } blank.matrix=(matrix(nrow=nrow(table),ncol=4)) matrix = cbind(blank.matrix,matrix) matrix[,2] = as.numeric(paste(unlist(table[,2]))) scp -r joelsmith@beast.uchicago.edu:kronforst_lab/agl_project/scf_d.RDATA scf_d.RDATA factor = c(1:21) plot.table = cbind(factor,d.table[1:21,3]) pdf(file="agl_plot.pdf",width=8,height=6,pointsize = 10) plot(plot.table,col = rgb(0,0,1,.7),lwd=10,ylim=c(-.2,.2),xlim=c(1,23)) points(cbind(c(22:23),d.table[22:23,3]),col = rgb(0,1,0,.7),lwd=10,ylim=c(-.2,.2),xlim=c(1,23)) abline(h=0,lwd=2) dev.off() png(file="~/kronforst_lab/figures/agl_D.png",width=550,height=550) plot(c(1:279),d.table[,3],ylim=c(-.7,.5),xlim=c(0,300),ylab="D",xlab="scaffold") points(c(278:279),d.table[278:279,3],col="red",lwd=3,ylim=c(-.7,.5),xlim=c(0,300),ylab="") abline(h=0) dev.off() png(file="~/kronforst_lab/figures/agl_SNPs.png",width=550,height=550) plot(c(1:279),d.table[,2]-d.table[,1],ylab="# of SNPs",xlab="scaffold",xlim=c(0,300),ylim=c(0,80000)) points(c(278:279),d.table[278:279,2]-d.table[278:279,1],lwd=3,col="red",xlim=c(0,300),ylim=c(0,80000)) dev.off()

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/R/30_gadm_getBbox.R

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Epiconcept-Paris/GADMTools

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2021-01-17T13:05:39.092878

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30_gadm_getBbox.R

# gadm_getBB.gadm_sf ------------------------------------------------------ # ========================================================================= gadm_getBbox.gadm_sf <- function(x) { sf::st_bbox(x$sf) } # gadm_getBbox.gadm_sp ------------------------------------------------------ # ========================================================================= gadm_getBbox.gadm_sp <- function(x) { bbox(x$spdf) }

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/R/dfrankcop.R

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AlexanderRitz/copR

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refs/heads/master

2020-06-28T03:20:51.019617

2019-09-23T18:27:50

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dfrankcop.R

#' Construction of Frank pdf #' #' @inheritParams dcop #' #' @export dcop.frankcop <- function(copula, u = NULL) { if (is.null(copula$distribution$pdf)) { d <- copula$dimension theta <- copula$parameter if (is.null(copula$distribution$cdf)) { stop("Supplied copula object does not contain a cdf expresssion") } else { pdf <- copula$distribution$cdf for (i in 1:d) { pdf <- stats::D(pdf, paste("u", i, sep = "")) } if (is.null(u)) { return(parse( text = paste( as.character(pdf)[2], as.character(pdf)[1], as.character(pdf)[3], sep = "" ) )) } else { if (length(u) == d) { if (!any(is.na(u))) { if (any(u >= 1) || any(u <= 0)) { return(0) } for (i in 1:d) { assign(paste("u", i, sep = ""), u[i]) } eval(pdf) } else { stop( "Supplied u contains missing values!" ) } } else { stop( "Supplied data vector not of appropriate length. Has to be of the same dimension as the supplied copula." ) } } } } else if (is.null(u)) { return(copula$distribution$pdf) } else { d <- copula$dimension if (length(u) == d) { if (!any(is.na(u))) { if (any(u >= 1) || any(u <= 0)) { return(0) } for (i in 1:d) { assign(paste("u", i, sep = ""), u[i]) } theta <- copula$parameter eval(copula$distribution$pdf) } else { stop( "Supplied u contains missing values!" ) } } else { stop( "Supplied data vector not of appropriate length. Has to be of the same dimension as the supplied copula." ) } } }

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/lib/plot_theme.R

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bfrggit/R-sc

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refs/heads/master

2020-04-20T22:31:15.788936

2019-01-17T00:06:46

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plot_theme.R

# plot_theme.R # # Created: 2018-10-08 # Updated: 2019-01-16 # Author: Charles Zhu # # Color theme for plots # derived from plot_theme_no_move.R # if(!exists("EX_PLOT_THEME_R")) { EX_PLOT_THEME_R <<- TRUE library(ggplot2) ggplot_theme <<- theme_light() + theme(axis.text = element_text(size = 16, color = "black")) + theme(axis.title = element_text(size = 18)) + theme(legend.text = element_text(size = 16, color = "black")) + theme(legend.title = element_text(size = 18)) + # element_blank()) theme(legend.background = element_rect( linetype = "solid", color = "gray30")) + theme(legend.justification = c(1, 0)) + theme(legend.position = c(1, 0)) scale_names_selector <<- c( "minimal" = "Minimal", "all" = "All", # "nodal" = "Nodal", "local" = "Local", # "nodal_lim" = "Nodal bnd.", "local_lim" = "Local bnd.", "interval_1" = "TTNI-driven" ) scale_color_selector <<- scale_color_manual( name = "Selectors", labels = scale_names_selector, values = c( "minimal" = "brown", "all" = "tomato", # "nodal" = "purple", "local" = "mediumpurple", # "nodal_lim" = "blue", "local_lim" = "skyblue", "interval_1" = "black" ) ) scale_shape_selector <<- scale_shape_manual( name = "Selectors", labels = scale_names_selector, values = c( "minimal" = 16, "all" = 18, # "nodal" = 2, "local" = 17, # "nodal_lim" = 0, "local_lim" = 15, "interval_1" = 11 ) ) } # ENDIF

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/data/genthat_extracted_code/gdata/examples/case.Rd.R

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surayaaramli/typeRrh

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refs/heads/master

2023-05-05T04:05:31.617869

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case.Rd.R

library(gdata) ### Name: case ### Title: Map elements of a vector according to the provided 'cases' ### Aliases: case ### Keywords: manip ### ** Examples ## default = NA case( c(1,1,4,3), "a"=1, "b"=2, "c"=3) ## default = "foo" case( c(1,1,4,3), "a"=1, "b"=2, "c"=3, default="foo" )

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/server.R

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hoangphand/shiny-weed-stocks

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refs/heads/master

2021-10-25T05:23:24.572909

2019-04-02T00:01:43

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server.R

library(shiny) library(corrplot) source("dataUtils.R") source("plotUtils.R") source("predictionUtils.R") duration = NULL server <- function(input, output, session) { destinationFromURL <- reactive({ destination <- parseQueryString(session$clientData$url_search)$destination }) selectedSymbolsFromURL <- reactive({ rawQuerySymbols <- parseQueryString(session$clientData$url_search)$selectedSymbols as.integer(unlist(strsplit(rawQuerySymbols, split=","))) }) periodSelectionFromURL <- reactive({ rawPeriodSelection <- parseQueryString(session$clientData$url_search)$periodSelection }) dateRangeFromURL <- reactive({ fromDate <- parseQueryString(session$clientData$url_search)$fromDate toDate <- parseQueryString(session$clientData$url_search)$toDate c(fromDate, toDate) }) userPredictionFromURL <- reactive({ rawUserPrediction <- strtoi(parseQueryString(session$clientData$url_search)$userPrediction, base = 0L) }) investmentDurationFromURL <- reactive({ rawNoOfDays <- strtoi(parseQueryString(session$clientData$url_search)$investmentDuration, base = 0L) }) investmentAmountFromURL <- reactive({ rawInitialAmount <- strtoi(parseQueryString(session$clientData$url_search)$investmentAmount, base = 0L) }) symbolWeightsFromURL <- reactive({ rawQuerySymbolWeights <- parseQueryString(session$clientData$url_search)$symbolWeights as.integer(unlist(strsplit(rawQuerySymbolWeights, split=","))) }) dataOfSelectedSymbols <- reactive({ if (is.null(periodSelectionFromURL())) { dateRange <- dateRangeFromURL() fromDate <- as.character.Date(dateRange[1]) toDate <- as.character.Date(dateRange[2]) readSymbolListPriceByDateRange(selectedSymbolsFromURL(), fromDate, toDate) # readSymbolListDataByDateRange(selectedSymbolsFromURL(), fromDate, toDate) } else { readSymbolListDataByPeriod(selectedSymbolsFromURL(), periodSelectionFromURL()) } }) output$homePair <- renderPlot({ if (!is.null(destinationFromURL()) & length(destinationFromURL()) > 0) { if (destinationFromURL() == 'home') { validate( need(length(selectedSymbolsFromURL()) >= 2, "Please select at least 2 symbols") ) pairs(dataOfSelectedSymbols()[, -1], pch = 16) } } }) output$homeCorr <- renderPlot({ if (!is.null(destinationFromURL()) & length(destinationFromURL()) > 0) { if (destinationFromURL() == 'home') { validate( need(length(selectedSymbolsFromURL()) >= 2, "Please select at least 2 symbols") ) corMatrix = cor(dataOfSelectedSymbols()[,-1], use='pair') corrplot(corMatrix, method = "number") } } }) output$homeIndividualSymbols <- renderPlot({ if (!is.null(destinationFromURL()) & length(destinationFromURL()) > 0) { if (destinationFromURL() == 'home') { validate( need(length(selectedSymbolsFromURL()) >= 2, "Please select at least 2 symbols") ) plotMultilinesSameGraph(dataOfSelectedSymbols()) } } }) output$predictionUserInput <- renderPlot({ if (!is.null(destinationFromURL()) & length(destinationFromURL()) > 0) { if (destinationFromURL() == 'prediction') { userPrediction = userPredictionFromURL() investmentDuration = investmentDurationFromURL() investmentAmount = investmentAmountFromURL() selectedSymbols = selectedSymbolsFromURL() symbolWeights = symbolWeightsFromURL() portfolioValues = getPortfolioValues(userPrediction, selectedSymbols, symbolWeights, investmentDuration, investmentAmount) portfolioValuesToDisplay = calculatePortfolioValues(portfolioValues, investmentDuration) output$predictionResult <- renderUI({ tagList( h4(paste("The original investment was $", format(round(investmentAmount), big.mark = ",", scientific = F), "placed on", as.character(as.Date(Sys.Date())))), h4(paste("As of", as.character(as.Date(Sys.Date() + investmentDuration))), ", we think that:"), h4(paste("You have a", sprintf(" %.1f %%", 100 * portfolioValuesToDisplay[4]), "chance of having less than $", format(round(portfolioValuesToDisplay[1]), big.mark = ",", scientific = F))), h4(paste("You also have a", sprintf(" %.1f %%", 100 * portfolioValuesToDisplay[4]), "chance of having more than $", format(round(portfolioValuesToDisplay[3]), big.mark = ",", scientific = F))), h4(paste("Our best guess is that you will have about $", format(round(portfolioValuesToDisplay[2]), big.mark = ",", scientific = F)))) }) plotPortfolioValues(portfolioValues, investmentAmount, investmentDuration) } } }) # output$dataTable <- renderDataTable(dataOfSelectedSymbols()) # output$table <- renderTable(dataOfSelectedSymbols()[, -1]) # output$dataTable <- renderDataTable(dateRangeFromURL()) # output$table <- renderTable(dateRangeFromURL()) output$value <- renderText({ }) }

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/cachematrix.R

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jaygoody101/ProgrammingAssignment2

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refs/heads/master

2021-01-01T19:48:32.415561

2017-07-29T02:28:45

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2017-07-28T22:35:25

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cachematrix.R

## Pair of functions to invert a matrix and cache the result for subsequent use ## Using example "mean" function as a template: ## change vectors to matrices where appropriate ## change "NULL" setting to "NaN" ## change evaluate call from Mean to Solve ## Creates list of functions to store cached version of a matrix inverse makeCacheMatrix <- function(x = matrix()) { mInv <- as.matrix(NaN) set <- function(y) { x <<- y mInv <<- as.matrix(NaN) } get <- function() x setInv <- function(pInv) mInv <<- pInv getInv <- function() mInv list(set = set, get = get, setInv = setInv, getInv = getInv) } ## Checks for existence of cached matrix inverse. ## If present, uses it; if not, inverts input matrix cacheSolve <- function(x, ...) { ## Return a matrix that is the inverse of 'x' mInv <- x$getInv() if(!is.nan(mInv[1,1])) { message("getting cached data") return(mInv) } data <- x$get() mInv <- solve(data, ...) mInv <-as.matrix(x$setInv(mInv)) mInv }

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/man/Ctry.msci.sql.Rd

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Turnado-dx/EPFR

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refs/heads/master

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Ctry.msci.sql.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/EPFR.r \name{Ctry.msci.sql} \alias{Ctry.msci.sql} \title{Ctry.msci.sql} \usage{ Ctry.msci.sql(fcn, x, y, n) } \arguments{ \item{fcn}{= function to convert from yyyymm to yyyymmdd} \item{x}{= output of Ctry.msci} \item{y}{= single two-character country code} \item{n}{= date field such as DayEnding or WeightDate} } \description{ SQL query to get date restriction } \seealso{ Other Ctry: \code{\link{Ctry.info}}, \code{\link{Ctry.msci.index.changes}}, \code{\link{Ctry.msci.members.rng}}, \code{\link{Ctry.msci.members}}, \code{\link{Ctry.msci}}, \code{\link{Ctry.to.CtryGrp}} } \keyword{Ctry.msci.sql}

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/R/read_pnadcovid19.R

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rodrigoesborges/PNADcovidIBGE

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refs/heads/master

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read_pnadcovid19.R

#' Read PNAD-COVID19 microdata #' @import readr dplyr magrittr #' @param microdata A text file containing microdata from PNAD-COVID19 survey. The file must be downloaded from \url{ftp://ftp.ibge.gov.br/Trabalho_e_Rendimento/Pesquisa_Nacional_por_Amostra_de_Domicilios_PNAD_COVID19/Microdados/} #' @param vars Character vector of the name of the variables you want to keep for analysys. \code{default} is to keep all variables #' @return A tibble with the survey design variables and selected variables. #' @examples #' input_path <- pnadcovid19_example("input_example.xlsx") #' data_path <- pnadcovid19_example("exampledata.txt") #' pnadc.df <- read_pnadcovid19(data_path,input_path,vars="VD4002") #' @export read_pnadcovid19 <- function(microdata,vars=NULL) { ########## reading data data_pnadcovid19 <- suppressWarnings(readr::read_csv(microdata)) input <- names(data_pnadcovid19) ########## creating columns specification if(!is.null(vars)){ if(any(!(vars %in% input))) { missvar=vars[!(vars %in% input)] warning(paste("Variables", paste(missvar,collapse = ", "), "not present in dataset\n")) } data_pnadcovid19 %<>% select("UPA","Estrato","V1027","posest","V1029","V1031","V1030",vars) } return(data_pnadcovid19) }

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/swarmAnal2020.R

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guisantagui/MSK_repo_new

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refs/heads/master

2021-07-03T23:22:31.328607

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swarmAnal2020.R

setwd("C:/Users/Guillem/Documents/PhD/comput/wrkng_dirs_clean/swarmAnalysis") swarmDat <- read.csv("C:/Users/Guillem/Documents/PhD/comput/pics/allSwarms.csv") swarmDat$Strains <- gsub("H5707", "H5708", swarmDat$Strains) swarmDat$Strains <- gsub("M6057", "M6075", swarmDat$Strains) # Normalize each experiment to the mean value of PA14 (for each measure) for(i in 1:length(exps)){ expDat <- swarmDat[swarmDat$exp == exps[i], ] PA14 <- expDat[gsub("_.*", "", expDat$Strains) == "PA14", 2:8] PA14_mean <- apply(PA14, 2, mean) for(j in 2:8){ expDat[, j] <- expDat[, j]/PA14_mean[j - 1] } swarmDat[swarmDat$exp == exps[i], ] <- expDat } # PCA for looking what variable among the ones we used for measuring the swarming is most separated in metabolites PCA. if(!require(factoextra)) install.packages("factoextra") library(factoextra) if(!require(readxl)) install.packages("readxl") library(readxl) # Load metabolomics data and dictionary load("C:/Users/Guillem/Documents/PhD/comput/wrkng_dirs_clean/normMetAnal/oldDataGood/ccmn_norm_mets_good_old.RData") load("C:/Users/Guillem/Documents/PhD/comput/wrkng_dirs_clean/dictionary/dictionary.RData") dictionary$Consensus[match(colnames(ccmn_norm_mets_good_old), dictionary$`Old Data Names`)] ccmnNormMets <- ccmn_norm_mets_good_old colnames(ccmnNormMets) <- dictionary$Consensus[match(colnames(ccmn_norm_mets_good_old), dictionary$`Old Data Names`)] ccmnNormMets <- ccmnNormMets[, !is.na(colnames(ccmnNormMets))] groups <- unique(gsub("_.*", replacement = "", rownames(ccmnNormMets))) getSwarmDataMeans <- function(swarmMat){ groups <- unique(gsub("\\_.*", replacement = "", swarmMat$Strains)) meanDF <- data.frame() for(i in seq_along(groups)){ subMat <- swarmMat[grep(groups[i], swarmMat$Strains), ] means <- apply(subMat[, 2:8], 2, mean) meanDF <- rbind.data.frame(meanDF, means) } meanDF <- cbind.data.frame(groups, meanDF) colnames(meanDF) <- colnames(swarmMat)[1:8] return(meanDF) } swarmDatMeans <- getSwarmDataMeans(swarmDat) save(swarmDatMeans, file = "swarmDatMeans.RData") swarmDatMeansFilt <- swarmDatMeans[match(gsub("\\_.*|(PA14).*", rownames(ccmnNormMets), rep = "\\1"), swarmDatMeans$Strains), ] swarmDatMeansFilt pcaMets <- prcomp(ccmnNormMets) fviz_eig(pcaMets) tiff("pcaSwarmMetsArea.tiff", res = 300, height = 5000, width = 5000) fviz_pca_ind(pcaMets, col.ind = swarmDatMeansFilt$Area, gradient.cols = c("#003CFF", "#66FF00", "#FF0000"), legend.title = "Area") dev.off() tiff("pcaSwarmMetsAreaPercentage.tiff", res = 300, height = 5000, width = 5000) fviz_pca_ind(pcaMets, col.ind = swarmDatMeansFilt$AreaPercentage, gradient.cols = c("#003CFF", "#66FF00", "#FF0000"), legend.title = "Area Percentage") dev.off() tiff("pcaSwarmMetsCircularity.tiff", res = 300, height = 5000, width = 5000) fviz_pca_ind(pcaMets, col.ind = swarmDatMeansFilt$Circularity, gradient.cols = c("#003CFF", "#66FF00", "#FF0000"), legend.title = "Circularity") dev.off() tiff("pcaSwarmMetsPerimeter.tiff", res = 300, height = 5000, width = 5000) fviz_pca_ind(pcaMets, col.ind = swarmDatMeansFilt$Perimeter, gradient.cols = c("#003CFF", "#66FF00", "#FF0000"), legend.title = "Perimeter") dev.off() tiff("pcaSwarmMetsMaxLength.tiff", res = 300, height = 5000, width = 5000) fviz_pca_ind(pcaMets, col.ind = swarmDatMeansFilt$MaxLength, gradient.cols = c("#003CFF", "#66FF00", "#FF0000"), legend.title = "Maximum Length") dev.off() # Obtain bidirectional correlations between swarming variables. corMat <- matrix(ncol = ncol(swarmDat) - 2, nrow = ncol(swarmDat) - 2) rownames(corMat) <- colnames(swarmDat[, 2:(ncol(swarmDat) - 1)]) colnames(corMat) <- colnames(swarmDat[, 2:(ncol(swarmDat) - 1)]) for(i in 1:ncol(corMat)){ for(j in 1:nrow(corMat)){ corMat[i, j] <- cor(swarmDat[[rownames(corMat)[j]]], swarmDat[[colnames(corMat)[i]]]) } } corMat # Do PCA with the different measures of swarming. We first normalize them by dividing each column by its # highest value. That way all values range from 0 to 1. THe idea is to get a linear combination of the # measures that spreads most of the variability of the strains and that we could use as the response # variable for the supervised analysis. save(swarmDat, file = "swarmDat.RData") m <- swarmDatMeans[, 2:8] m <- apply(m, 2, function(x) x/max(x)) rownames(m) <- make.unique(gsub(".tif", replacement = "", swarmDatMeans$Strains)) pcaSwarms <- prcomp(m) tiff("pcaSwarmMeasures.tiff", res = 300, height = 5000, width = 5000) fviz_pca_ind(pcaSwarms#, #col.ind = swarmDatMeansFilt$MaxLength, #gradient.cols = c("#003CFF", "#66FF00", "#FF0000"), #legend.title = "Maximum Length" ) dev.off() tiff("biplotSwarmMeasures.tiff", res = 300, height = 5000, width = 5000) fviz_pca_biplot(pcaSwarms) dev.off() m <- swarmDatMeans[, c(4, 6)] m <- apply(m, 2, function(x) x/max(x)) rownames(m) <- make.unique(gsub(".tif", replacement = "", swarmDatMeans$Strains)) pcaSwarms <- prcomp(m) tiff("biplotSwarmMeasures_CircAreaPerc.tiff", res = 300, height = 5000, width = 5000) fviz_pca_biplot(pcaSwarms) dev.off() distManh <- dist(m, method = "canberra") c <- cmdscale(distManh, eig=TRUE, x.ret=TRUE) mds.var.per <- round(c$eig/sum(c$eig)*100, 1) mds.var.per ## now make a fancy looking plot that shows the MDS axes and the variation: mds.values <- c$points mds.data <- data.frame(Sample=rownames(mds.values), X=mds.values[,1], Y=mds.values[,2]) mds.data ggplot(data=mds.data, aes(x=X, y=Y, label=Sample)) + geom_text() + theme_bw() + xlab(paste("MDS1 - ", mds.var.per[1], "%", sep="")) + ylab(paste("MDS2 - ", mds.var.per[2], "%", sep="")) + ggtitle("PCOA Swarm Data") library(ggplot2) pcomp1 <- pcaSwarms$x[, 1] groups <- unique(gsub("\\_.*", replacement = "", names(pcomp1))) pcomp1Means <- c() for(i in seq_along(groups)){ stMean <- mean(pcomp1[grep(groups[i], names(pcomp1))]) pcomp1Means <- c(pcomp1Means, stMean) } names(pcomp1Means) <- groups pcomp1MeansFilt <- pcomp1Means[names(pcomp1Means) %in% rownames(ccmnNormMets)] match(gsub("\\_.*|(PA14).*", rownames(ccmnNormMets), rep = "\\1"), names(pcomp1Means)) pcomp1MeansFilt <- pcomp1Means[match(gsub("\\_.*|(PA14).*", rownames(ccmnNormMets), rep = "\\1"), names(pcomp1Means))] tiff("pcaSwarmMetsPrComp1.tiff", res = 300, height = 5000, width = 5000) fviz_pca_ind(pcaMets, col.ind = pcomp1MeansFilt, gradient.cols = c("#FF0000", "#66FF00", "#003CFF"), legend.title = "PrComp1") dev.off() pcomp1Means save(pcomp1Means, file = "pcomp1AreaPercCircularity.RData") # The metabolites that are more correlated with swarming, according to OPLS-DA, are succinate and formyl-methionine. plot(log(swarmDatMeansFilt$AreaPercentage), ccmnNormMets$`Formyl-methionine`) plot(log(swarmDatMeansFilt$AreaPercentage), ccmnNormMets$succinate) metSwarm <- cbind.data.frame(ccmnNormMets, log(swarmDatMeansFilt$AreaPercentage)) colnames(metSwarm)[ncol(metSwarm)] <- "logAreaPct" colnames(metSwarm) <- make.names(colnames(metSwarm)) # Let's do a PCA of the five metabolites that have highest loadings in OPLS-DA model to get the linear combination of these in # x-axis. pcaSelMets <- prcomp(ccmnNormMets[, c("Formyl-methionine", "2-aminoadipate-6-semialdehyde", "Methylcitrate 1", "succinate", "Acetylhomoserine 2" )]) fviz_pca_biplot(pcaSelMets) # Obtain values of PC1 and add to metSwarm dataframe metSwarm <- cbind.data.frame(metSwarm, pcaSelMets$x[, 1]) colnames(metSwarm)[ncol(metSwarm)] <- "prComp1SelMets" if(!require(ggpubr)) install.packages("ggpubr") library(ggplot2) if(!require(ggpmisc)) BiocManager::install("ggpmisc") library(ggpmisc) ggscatter(metSwarm, x = "logAreaPct", y = "Formyl.methionine", add = "reg.line") + stat_cor(aes(label = paste(..rr.label.., ..p.label.., sep = "~`,`~"), label.x = 10)) + stat_regline_equation(label.x = 3, label.y = 13) ggscatter(metSwarm, x = "logAreaPct", y = "succinate", add = "reg.line", label = rownames(metSwarm)) + stat_cor(aes(label = paste(..rr.label.., ..p.label.., sep = "~`,`~"), label.x = 9)) + stat_regline_equation(label.x = 3, label.y = 10) ggscatter(metSwarm, x = "logAreaPct", y = "prComp1SelMets", add = "reg.line") + stat_cor(aes(label = paste(..rr.label.., ..p.label.., sep = "~`,`~"), label.x = 10, label.y = 3)) + stat_regline_equation(label.x = 3) cor(log(swarmDatMeansFilt$AreaPercentage), pcaSelMets$x[, 1]) plot(log(swarmDatMeansFilt$AreaPercentage), pcaSelMets$x[, 1]) cor(log(swarmDatMeansFilt$AreaPercentage), ccmnNormMets$`Formyl-methionine`) cor(log(swarmDatMeansFilt$AreaPercentage), ccmnNormMets$succinate)

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/contornos.R

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Francisco-Pacopaco/Tesis

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refs/heads/main

2023-05-15T02:11:40.149977

2021-06-03T15:58:11

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contornos.R

####MAs graficas para la tesis library(lattice) library(plotly) library(ggplot2) library(ContourFunctions) library(shape) #Graficas para las funciones de activación t<-seq(from=-10,to=10,by=0.001) sigmoid<-function(z){ 1/(1+exp(-z)) } x11() plot(t,sigmoid(t),col="green",type = "l",ylab = " ",xlab = "", lwd=2) abline(h=0) abline(v=0) ReLU<-function(z){ 1/2*(z+abs(z)) } x11() plot(t,ReLU(t),col="purple",type = "l",ylab = " ",xlab = "", lwd=2) abline(h=0) abline(v=0) x11() plot(t,tanh(t),col="tomato",type = "l",ylab = " ",xlab = "",lwd=2) abline(h=0) abline(v=0) mini<-function(z){ (1/2)*(z+1-abs(1-z)) } maxi<-function(z){ (1/2)*(z-1+abs(z+1)) } hardtanh<-function(z){ maxi(mini(z)) } x11() plot(t,hardtanh(t),col="blue",type = "l",ylab = " ",xlab = "", lwd=2) abline(h=0) abline(v=0) #Este es el paraboliode paraboliode<-function(x,y){ (2/3)*x^2+2*y^2 } x<-seq(-1,1, length=20) y<-seq(-1,1, length=20) z<-outer(x,y,paraboliode) x11() wireframe(z, data = NULL, drape = TRUE,, xlab=expression('w'[1]), ylab=expression('w'[2]), colorkey=FALSE, zlab='Error', main='Función de perdida', scales = list( arrows = FALSE, col="black"), par.settings = list(axis.line = list(col = 'transparent')), strip.border = list(col = 'transparent'), strip.background = list(col = 'transparent')) x11() cf_grid(x, y, z, lines_only=TRUE, bar = FALSE, main='Contornos de nivel', levels = c(0.2,0.4,0.6,1,1.4,1.8,2.2)) points(0.5,0.5,pch=19,col='tomato') text(0.5,0.46,"mínimo") Arrows(0.2,0.2,0.2,0.24,arr.type="triangle",arr.width=0.2,col = "aquamarine3") Arrows(0.2,0.27,0.24,0.32,arr.type="triangle",arr.width=0.2,col = "aquamarine3") Arrows(0.26,0.35,0.31,0.4,arr.type="triangle",arr.width=0.2,col = "aquamarine3") x11() cf_grid(x, y, z, lines_only=TRUE, bar = FALSE, main='Contornos de nivel', levels = c(0.2,0.4,0.6,1,1.4,1.8,2.2)) points(0.5,0.5,pch=19,col='tomato') text(0.5,0.46,"mínimo") Arrows(0.1,0.1,0.3,0.6,arr.type="triangle",arr.width=0.2,col = "chocolate3") Arrows(0.3,0.6,0.7,0.15,arr.type="triangle",arr.width=0.2,col = "chocolate3") Arrows(0.7,0.15,0.9,0.7,arr.type="triangle",arr.width=0.2,col = "chocolate3") #######Funcion timo<-function(a,b,c){ (a+4*b+c)*(1/6) } timo2<-function(a,b){ ((b-a)*(1/6))**2 } print(timo(1,3,5)) print(timo2(1,5)) ##### contorno<-function(x,y){ 800+10*x+7*y-8.5*(x**2)-5*(y**2)+4*x*y } x1<-seq(0,10, length=100) y1<-seq(0,10, length=100) z1<-outer(x1,y1,contorno) x11() cf_grid(x1, y1, z1, lines_only=TRUE, bar = FALSE, main='Contornos de nivel', levels = c(800,750,700,625,550,475,400,325,250,175,100,25)) ####Modelo polinomial varias variables modelop<-function(T,C){ -1105.56+8.0242*T+22.994*C-0.0142*T**2-0.20502*C**2-0.062*T*C } x2<-seq(180,260, length=100) y2<-seq(15,30, length=100) z2<-outer(x2,y2,modelop) x11() cf_grid(x2, y2, z2, lines_only=TRUE, bar = FALSE, main='Contornos de nivel', levels = c(80,75,70,62.5,55,47.5,40,32.5,25,17.5,10)) x11() wireframe(z2, data = NULL, drape = TRUE,, xlab=expression('T'), ylab=expression('C'), colorkey=FALSE, zlab='y', main='Superficie de Respuesta', scales = list( arrows = FALSE, col="black"), par.settings = list(axis.line = list(col = 'transparent')), strip.border = list(col = 'transparent'), strip.background = list(col = 'transparent')) temperatura<-c(200,250,200,250, 189.65, 260.35,225,225,225,225,225,225) concentracion<-c(15,15,25,25,20,20,12.93,27.07,20,20,20,20) respuesta<-c(43,78,69,73,48,76,65,74,76,79,83,81) unos<-c() for (i in 1:length(temperatura)) { unos[i]=1 } matriz<-cbind(unos,temperatura,concentracion,temperatura**2,concentracion**2,temperatura*concentracion) beta<-t(matriz)%*%matriz beta<-solve(beta) beta<-beta%*%t(matriz)%*%respuesta B<-c(-0.0142,-0.062/2) B1<-c(-0.062/2,-0.20502) B<-cbind(B,B1) lin<-c(8.0242,22.994) pt_cr<-(-1/2)*solve(B)%*%lin val_cr<-beta[1]+(1/2)*t(pt_cr)%*%lin pro<-eigen(B) val_pro<-pro$values vec_pro<-pro$vectors norm_vec <- function(x) sqrt(sum(x^2)) print(norm_vec(vec_pro[,1])) temp1<-temperatura-pt_cr[1,1] conc1<-concentracion-pt_cr[2,1] aux1<-cbind(temp1,conc1) w<-t(vec_pro)%*%t(aux1) print(val_pro)

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microbenchmark::microbenchmark( recursiva = potenciaRecursiva(2, 10, FALSE), log = potenciaLog(2, 10, FALSE) )

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#' @title sxtLength #' @description Calculate length of multiple vectors. #' @param ... vectors. #' @author Xiaotao Shen #' @export sxtLength <- function(...) { data <- list(...) cat(unlist(lapply(data, length))) }

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# Exercise 3: using the pipe operator # Install (if needed) and load the "dplyr" library #install.packages("dplyr") library("dplyr") # Install (if needed) and load the "fueleconomy" package #install.packages('devtools') #devtools::install_github("hadley/fueleconomy") library(fueleconomy) # Which 2015 Acura model has the best hwy MGH? (Use dplyr, but without method # chaining or pipes--use temporary variables!) acura_2015 <- filter(vehicles, make == 'Acura', year == 2015) max_hwy_acura_2015 <- filter(acura_2015, hwy == max(hwy)) temp_vars_best_model <- select(max_hwy_acura_2015, model) # Which 2015 Acura model has the best hwy MPG? (Use dplyr, nesting functions) nested_best_model <- select(filter(filter(vehicles, make == 'Acura', year == 2015), hwy == max(hwy)), model) # Which 2015 Acura model has the best hwy MPG? (Use dplyr and the pipe operator) pipe_best_model <- filter(vehicles, make == 'Acura', year == 2015) %>% filter(hwy == max(hwy)) %>% select(model) ### Bonus # Write 3 functions, one for each approach. Then, # Test how long it takes to perform each one 1000 times system.time(for (i in 1:1000) temp_vars_best_model()) system.time(for (i in 1:1000) nested_best_model()) system.time(for (i in 1:1000) pipe_best_model())

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sentimentAnalysis_PositiveAndNegativeWords_2018.r

# date: 28 Nov 2018 # sentiment analysis in R #check to see whether all libraries are installed properly. #install.packages("janeaustenr") library(janeaustenr) library(dplyr) library(stringr) #install.packages("tidytext") library(tidytext) library(ggplot2) library(tidyr) original_books <- austen_books() %>% group_by(book) %>% mutate(linenumber = row_number(), chapter = cumsum(str_detect(text, regex("^chapter [\\divxlc]", ignore_case = TRUE)))) %>% ungroup() View(original_books) tidy_books <- original_books %>% unnest_tokens(word, text) #make a list of words from the paragraphs View(tidy_books) data("stop_words") cleaned_books <- tidy_books %>% anti_join(stop_words) # anti_join() returns all rows from x where there are not matching values in y, keeping just columns from x. cleaned_books %>% count(word, sort = TRUE) # consider the joy-words in Emma using the nrc lexicon. nrcjoy <- get_sentiments("nrc") %>% filter(sentiment == "joy") tidy_books %>% filter(book == "Emma") %>% semi_join(nrcjoy) %>% count(word, sort = TRUE) bing <- get_sentiments("bing") bing # the listing of words according to being of a positive of negative nature. #determine the sentiment fluxuations in the books. janeaustensentiment <- tidy_books %>% inner_join(bing) %>% count(book, index = linenumber %/% 80, sentiment) %>% spread(sentiment, n, fill = 0) %>% mutate(sentiment = positive - negative) # plot the sentiment in each book ggplot(janeaustensentiment, aes(index, sentiment, fill = book)) + geom_bar(stat = "identity", show.legend = FALSE) + facet_wrap(~book, ncol = 2, scales = "free_x") # what are the common good and bad words? bing_word_counts <- tidy_books %>% inner_join(bing) %>% count(word, sentiment, sort = TRUE) %>% ungroup() bing_word_counts bing_word_counts %>% filter(n > 150) %>% mutate(n = ifelse(sentiment == "negative", -n, n)) %>% mutate(word = reorder(word, n)) %>% ggplot(aes(word, n, fill = sentiment)) + geom_bar(stat = "identity") + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + ylab("Contribution to sentiment")

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unmc_fgsea.R

library(fgsea) library(tidyverse) library(data.table) library(ggplot2) setwd("/Users/yli1/unmc01") #hcq_vs_con res<-read.table("hcq_vs_con_fgsea.txt") ranks <- deframe(res) pathways.kegg<- gmtPathways("c2.cp.kegg.v7.2.symbols.gmt") pathways<-gmtPathways("c2.cp.v7.2.symbols.gtm") fgseaRes_kegg<- fgsea(pathways = pathways.kegg, stats = ranks, minSize = 1,maxSize = Inf, nPermSimple = 100000) fgseaRes<- fgsea(pathways = pathways, stats = ranks, minSize = 1,maxSize = Inf, nPermSimple = 100000) fwrite(fgseaRes_kegg, file="hcq_vs_con_kegg01.txt", sep="\t", sep2=c("", " ", "")) fwrite(fgseaRes, file="hcq_vs_con_all01.txt", sep="\t", sep2=c("", " ", "")) plotEnrichment(pathways.kegg[["KEGG_SYSTEMIC_LUPUS_ERYTHEMATOSUS"]], ranks) + labs(title="SYSTEMIC_LUPUS_ERYTHEMATOSUS") #pcq_vs_con res<-read.table("pcq_vs_con_fgsea.txt") ranks <- deframe(res) fgseaRes_kegg<- fgsea(pathways = pathways.kegg, stats = ranks, minSize = 1,maxSize = Inf, nPermSimple = 100000) fgseaRes<- fgsea(pathways = pathways, stats = ranks, minSize = 1,maxSize = Inf, nPermSimple = 100000) fwrite(fgseaRes_kegg, file="pcq_vs_con_kegg01.txt", sep="\t", sep2=c("", " ", "")) fwrite(fgseaRes, file="pcq_vs_con_all01.txt", sep="\t", sep2=c("", " ", "")) plotEnrichment(pathways.kegg[["KEGG_SYSTEMIC_LUPUS_ERYTHEMATOSUS"]], ranks) + labs(title="SYSTEMIC_LUPUS_ERYTHEMATOSUS") plotEnrichment(pathways.kegg[["KEGG_LEISHMANIA_INFECTION"]], ranks) + labs(title="LEISHMANIA_INFECTION") #phpma_vs_con res<-read.table("phpma_vs_con_fgsea.txt") ranks <- deframe(res) fgseaRes_kegg<- fgsea(pathways = pathways.kegg, stats = ranks, minSize = 1,maxSize = Inf, nPermSimple = 100000) fgseaRes<- fgsea(pathways = pathways, stats = ranks, minSize = 1,maxSize = Inf, nPermSimple = 100000) fwrite(fgseaRes_kegg, file="phpma_vs_con_kegg01.txt", sep="\t", sep2=c("", " ", "")) fwrite(fgseaRes, file="phpma_vs_con_all01.txt", sep="\t", sep2=c("", " ", ""))

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test-04-rmarkdown.R

context("rmarkdown templates") test_that("knit doc", { # check there is still a template under inst template <- "perlbrewr" package <- "perlbrewr" template_path = system.file("rmarkdown", "templates", template, package = package) expect_true(nzchar(template_path)) # create a draft # Have to supply full template path. For some reason system.file() call in # rmarkdown::draft() cannot find package, although the above does. # Passing package = NULL, ensures template is used verbatim. draft <- rmarkdown::draft(file = file.path(tempdir(), "perlbrewr.Rmd"), template = template_path, package = NULL, create_dir = FALSE, edit = FALSE) # knit using rmarkdown rendering output <- rmarkdown::render(input = draft, output_file = file.path(tempdir(), "perlbrewr.md"), output_format = "github_document", quiet = TRUE) # test output content <- readLines(output) lib_lines <- content[grepl(pattern = "perl-5\\.26\\.0@template", content)] expect_equal(length(lib_lines), 3) # warning(paste0(content, collapse = "\n")) })

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merge_overall_model.R

setwd('/data/zhangh24/breast_cancer_data_analysis/') logodds_overall <- NULL var_overall <- NULL for(i1 in 1:32){ load(paste0("/data/zhangh24/breast_cancer_data_analysis/risk_prediction/Nasim_new_prs/result/overall_log_odds_",i1,".Rdata")) logodds_overall <- c(logodds_overall,result[[1]]) var_overall <- c(var_overall, result[[2]]) } load(paste0("./risk_prediction/Nasim_new_prs/result/discover_snp_id.rdata")) final_result <- data.frame(snp_id[,1:3], logodds_overall, var_overall, stringsAsFactors = F) save(final_result,file= "./risk_prediction/Nasim_new_prs/result/32_overall_logodds.Rdata")

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# shiny project about checkee, input month year city output average waiting time library(shiny) shinyUI(pageWithSidebar( headerPanel("Average Waiting days for USA Visa Administrative Processing in China"), sidebarPanel( numericInput('id1','Year',2008,min = 2008,max = 2015,step = 1), numericInput('id2','Month',0,min = 1,max = 12,step = 1), radioButtons("id3",label=h5("Visa Type"), choices = list("F1" = "F1", "B1" = "B1", "J1" = "J1", "H1" = "H1"), selected = 1), radioButtons("id4",label=h5("Embassy Location"), choices = list("BeiJing" = "BeiJing", "ShangHai" = "ShangHai", "ChengDu" = "ChengDu", "GuangZhou" = "GuangZhou", "ShenYang" = "ShenYang"), selected = 1), radioButtons("id5",label=h5("Visa Entry"), choices = list("New" = "New", "Renewal" = "Renewal"), selected = 1), submitButton('submit') ), mainPanel( h3('App Description:'), p('Data sourse: ',a('Check Reporter',href = 'http://www.checkee.info/main.php?dispdate=')), p('Calculate the average waiting days for Administrative Processing also known as Check, based on Month, Year, Embassy Location, Visa type and entry.'), p('Data range from 2008/11 to 2015/7. NaN output means lack of data for selected Input.'), h3('Result of prediction'), h4('Your interview year'), verbatimTextOutput("oid1"), h4('Your interview month'), verbatimTextOutput("oid2"), h4('Your visa type'), verbatimTextOutput("oid3"), h4('Embassy Location'), verbatimTextOutput("oid4"), h4('Your Visa Entry'), verbatimTextOutput("oid5"), h4('Average waiting time in days'), verbatimTextOutput("prediction") ) ))

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calcular_fitness_poblacion <- function(poblacion, funcion_objetivo, optimizacion, verbose = TRUE, ...) { # Esta función devuelve el fitness de cada individuo de una población. # # ARGUMENTOS # ============================================================================ # poblacion: matriz que representa la población de individuos. # funcion_objetivo: nombre de la función que se desea optimizar. Debe de haber # sido definida previamente. # optimizacion: "maximizar" o "minimizar". Dependiendo de esto, la relación # del fitness es directamente o indirectamente proporcional # al valor de la función. # verbose: mostrar información del proceso por pantalla. # # RETORNO # ============================================================================ # Vector con el fitness de todos los individuos de la población. El orden de # los valores se corresponde con el orden de las filas de la matriz población. # CÁLCULO DEL FITNESS DE CADA INDIVIDUO DE LA POBLACIÓN # ---------------------------------------------------------------------------- # Vector donde almacenar el fitness de cada individuo. fitness_poblacion <- rep(NA, times = nrow(poblacion)) for (i in 1:nrow(poblacion)) { individuo <- poblacion[i, ] fitness_individuo <- calcular_fitness_individuo( individuo = individuo, funcion_objetivo = funcion_objetivo, optimizacion = optimizacion, verbose = verbose ) fitness_poblacion[i] <- fitness_individuo } # MEJOR INDIVIDUO DE LA POBLACIÓN # ---------------------------------------------------------------------------- # Se identifica el mejor individuo de toda la población, el de mayor # fitness. indice_mejor_individuo <- which.max(fitness_poblacion) # Se identifica el valor de la función objetivo para el mejor individuo. if (optimizacion == "maximizar") { valor_funcion <- fitness_poblacion[indice_mejor_individuo] } else { valor_funcion <- -1*fitness_poblacion[indice_mejor_individuo] } # INFORMACIÓN DEL PROCESO (VERBOSE) # ---------------------------------------------------------------------------- if (verbose) { cat("------------------", "\n") cat("Población evaluada", "\n") cat("------------------", "\n") cat("Optimización =", optimizacion, "\n") cat("Mejor fitness encontrado =", fitness_poblacion[indice_mejor_individuo], "\n") cat("Mejor solución encontrada =", paste(poblacion[indice_mejor_individuo,], collapse = " "), "\n") cat("Valor función objetivo =", valor_funcion, "\n") cat("\n") } return(fitness_poblacion) }

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#-*- R -*- library(nlme) pdf(file = 'ch01.pdf') options( width = 65, digits = 5 ) options( contrasts = c(unordered = "contr.helmert", ordered = "contr.poly") ) # Chapter 1 Linear Mixed-Effects Models: Basic Concepts and Examples # 1.1 A Simple Example of Random Effects Rail fm1Rail.lm <- lm( travel ~ 1, data = Rail ) fm1Rail.lm fm2Rail.lm <- lm( travel ~ Rail - 1, data = Rail ) fm2Rail.lm fm1Rail.lme <- lme(travel ~ 1, data = Rail, random = ~ 1 | Rail) summary( fm1Rail.lme ) fm1Rail.lmeML <- update( fm1Rail.lme, method = "ML" ) summary( fm1Rail.lmeML ) plot( fm1Rail.lme ) # produces Figure 1.4 intervals( fm1Rail.lme ) anova( fm1Rail.lme ) # 1.2 A Randomized Block Design plot.design( ergoStool ) # produces Figure 1.6 contrasts( ergoStool$Type ) ergoStool1 <- ergoStool[ ergoStool$Subject == "1", ] model.matrix( effort ~ Type, ergoStool1 ) # X matrix for Subject 1 fm1Stool <- lme(effort ~ Type, data = ergoStool, random = ~ 1 | Subject) summary( fm1Stool ) anova( fm1Stool ) options( contrasts = c( factor = "contr.treatment", ordered = "contr.poly" ) ) contrasts( ergoStool$Type ) fm2Stool <- lme(effort ~ Type, data = ergoStool, random = ~ 1 | Subject) summary( fm2Stool ) anova( fm2Stool ) model.matrix( effort ~ Type - 1, ergoStool1 ) fm3Stool <- lme(effort ~ Type - 1, data = ergoStool, random = ~ 1 | Subject) summary( fm3Stool ) anova( fm3Stool ) intervals( fm1Stool ) plot( fm1Stool, # produces Figure 1.8 form = resid(., type = "p") ~ fitted(.) | Subject, abline = 0 ) # 1.3 Mixed-effects Models for Replicated, Blocked Designs with(Machines, interaction.plot( Machine, Worker, score, las = 1)) # Figure 1.10 fm1Machine <- lme( score ~ Machine, data = Machines, random = ~ 1 | Worker ) fm1Machine fm2Machine <- update( fm1Machine, random = ~ 1 | Worker/Machine ) fm2Machine anova( fm1Machine, fm2Machine ) ## delete selected rows from the Machines data MachinesUnbal <- Machines[ -c(2,3,6,8,9,12,19,20,27,33), ] ## check that the result is indeed unbalanced table(MachinesUnbal$Machine, MachinesUnbal$Worker) fm1MachinesU <- lme( score ~ Machine, data = MachinesUnbal, random = ~ 1 | Worker/Machine ) fm1MachinesU intervals( fm1MachinesU ) fm4Stool <- lme( effort ~ Type, ergoStool, ~ 1 | Subject/Type ) if (interactive()) intervals( fm4Stool ) (fm1Stool$sigma)^2 (fm4Stool$sigma)^2 + 0.79621^2 Machine1 <- Machines[ Machines$Worker == "1", ] model.matrix( score ~ Machine, Machine1 ) # fixed-effects X_i model.matrix( ~ Machine - 1, Machine1 ) # random-effects Z_i fm3Machine <- update( fm1Machine, random = ~Machine - 1 |Worker) summary( fm3Machine ) anova( fm1Machine, fm2Machine, fm3Machine ) # 1.4 An Analysis of Covariance Model names( Orthodont ) levels( Orthodont$Sex ) OrthoFem <- Orthodont[ Orthodont$Sex == "Female", ] fm1OrthF.lis <- lmList( distance ~ age, data = OrthoFem ) coef( fm1OrthF.lis ) intervals( fm1OrthF.lis ) plot( intervals ( fm1OrthF.lis ) ) # produces Figure 1.12 fm2OrthF.lis <- update( fm1OrthF.lis, distance ~ I( age - 11 ) ) plot( intervals( fm2OrthF.lis ) ) # produces Figure 1.13 fm1OrthF <- lme( distance ~ age, data = OrthoFem, random = ~ 1 | Subject ) summary( fm1OrthF ) fm1OrthFM <- update( fm1OrthF, method = "ML" ) summary( fm1OrthFM ) fm2OrthF <- update( fm1OrthF, random = ~ age | Subject ) anova( fm1OrthF, fm2OrthF ) random.effects( fm1OrthF ) ranef( fm1OrthFM ) coef( fm1OrthF ) plot( compareFits(coef(fm1OrthF), coef(fm1OrthFM))) # Figure 1.15 plot( augPred(fm1OrthF), aspect = "xy", grid = TRUE ) # Figure 1.16 # 1.5 Models for Nested Classification Factors fm1Pixel <- lme( pixel ~ day + I(day^2), data = Pixel, random = list( Dog = ~ day, Side = ~ 1 ) ) intervals( fm1Pixel ) plot( augPred( fm1Pixel ) ) # produces Figure 1.18 VarCorr( fm1Pixel ) summary( fm1Pixel ) fm2Pixel <- update( fm1Pixel, random = ~ day | Dog) anova( fm1Pixel, fm2Pixel ) fm3Pixel <- update( fm1Pixel, random = ~ 1 | Dog/Side ) anova( fm1Pixel, fm3Pixel ) fm4Pixel <- update( fm1Pixel, pixel ~ day + I(day^2) + Side ) summary( fm4Pixel ) # 1.6 A Split-Plot Experiment fm1Oats <- lme( yield ~ ordered(nitro) * Variety, data = Oats, random = ~ 1 | Block/Variety ) anova( fm1Oats ) fm2Oats <- update( fm1Oats, yield ~ ordered(nitro) + Variety ) anova( fm2Oats ) summary( fm2Oats ) fm3Oats <- update( fm1Oats, yield ~ ordered( nitro ) ) summary( fm3Oats ) fm4Oats <- lme( yield ~ nitro, data = Oats, random = ~ 1 | Block/Variety ) summary( fm4Oats ) VarCorr( fm4Oats ) intervals( fm4Oats ) plot(augPred(fm4Oats), aspect = 2.5, layout = c(6, 3), between = list(x = c(0, 0, 0.5, 0, 0))) # produces Figure 1.21 # cleanup proc.time() q("no")

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\name{AmigoDot.to.graphNEL} \alias{AmigoDot.to.graphNEL} \title{ Converts an AmigoDot S4 object to a graphNEL object. } \description{ Converts an AmigoDot S4 object to a graphNEL object. } \usage{ AmigoDot.to.graphNEL(object) } \arguments{ \item{object}{ is a AmigoDot S4 object. } } \value{ \item{gNEL}{ is a graphNEL object. } } \author{ Markus Schroeder <mschroed@jimmy.harvard.edu> } \examples{ ## set GO ID's and color goIDs <- c("GO:0051130","GO:0019912","GO:0005783") color <- c("lightblue","red","yellow") dd <- getAmigoTree(goIDs=goIDs,color=color, filename="example",picType="dot",saveResult=FALSE) tt <- readAmigoDot(object=dd) AmigoDot.to.graphNEL(tt) }

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plotMeanTemp.R

res <- dbSendQuery(conn = con, statement = "select s.latitude, s.longitude, avg(ds.mean_temp_fahr) from stations s, daily_summaries ds WHERE ds.station_id = s.station_id and ds.wban_id = s.wban_id GROUP BY s.latitude, s.longitude") print("Fetching average temperature query results...") avgTempLocationFrame <- dbFetch(res) dbClearResult(res) baseMap <- ggplot() + borders("world", colour="gray15", fill="gray90") meanTempMap <- baseMap + geom_point(aes(x=avgTempLocationFrame$longitude, y=avgTempLocationFrame$latitude, color= avgTempLocationFrame$avg), alpha=0.95, size=3) + scale_color_gradient(low="navy", high="red", na.value = "navy", limits=c(25,100), name="Temperature") + ggtitle("Average Temperature 1973-2014") + theme(plot.title = element_text(size=18, face="bold", vjust=2), axis.ticks.y = element_blank(), axis.text.y = element_blank(), axis.ticks.x = element_blank(), axis.text.x = element_blank()) + labs(x="", y="") plot(meanTempMap) ggsave(filename='mean_temperature_map.png', path=plotOutputPath, width=17.2, height=10.7)

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/hw03/code/make-teams-table.R

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make-teams-table.R

#================================================================================= #title: make-teams-table # description: a short description of what the script is about # input(s): what are the inputs required by the script? # output(s): what are the outputs created when running the script? #================================================================================= setwd("C:/Users/eriko/stat133/stat133-hws-fall17/hw03/code") library(dplyr) #Raw data and dictionaries dat1 <- read.csv("../data/nba2017-roster.csv") dat2 <- read.csv("../data/nba2017-stats.csv") #Adding new variables dat2 <- mutate(dat2,missed_fg=dat2$field_goals_atts - dat2$field_goals_made, missed_ft=dat2$points1_atts-dat2$points1_made, points= 3*dat2$points3_made +2*dat2$points2_made+dat2$points1_made, rebounds = dat2$off_rebounds + dat2$def_rebounds, efficiency= (points+rebounds+dat2$assist+dat2$steals+ dat2$blocks- missed_fg - missed_ft - dat2$turnovers) /dat2$games_played) sink(file = "../output/efficiency-summary.txt") summary(dat2) sink() #Merging Tables dat <- merge(dat1,dat2) colnames(dat)[20] <- "free_throws" #Creating nba2017-teams.csv team <- summarize(group_by(dat,team),Experience=round(sum(experience),digits = 2), Salary=sum(round(salary*(10^-6),digits = 2)), Points3=sum(points3_made),Points2=sum(points2_made), Free_throws=sum(free_throws),Points=sum(points), Off_rebounds=sum(off_rebounds),Def_rebounds=sum(def_rebounds), Assists=sum(assists),Steals=sum(steals),Blocks=sum(blocks), Turnovers=sum(turnovers),Fouls=sum(fouls), Efficiency=sum(efficiency)) team <- data.frame(team,row.names = team$team) sink(file = "../data/teams-summary.txt") summary(team) sink() write.csv(team,file = "../data/nba2017-teams.csv") #Some graphics ##use stars() to get a star plot of the teams stars(team[ ,-1],labels = as.character(team$team)) ##use ggplot() to get a scatterplot of experience and salary pdf(file = "../image/experience_salary.pdf") ggplot(team,aes(x=Experience,y=Salary))+geom_point()+geom_text(aes(label=team)) dev.off()

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/tests/testthat/test-ip_to_binary.R

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test-ip_to_binary.R

x <- ip_address(c("192.168.0.1", "2001:db8::8a2e:370:7334", NA)) test_that("binary encoding/decoding works", { expect_snapshot(error = TRUE, { ip_to_binary("hello") }) expect_snapshot(error = TRUE, { binary_to_ip(x) }) expect_type(ip_to_binary(x), "character") expect_equal(ip_to_binary(x), c( "11000000101010000000000000000001", "00100000000000010000110110111000000000000000000000000000000000000000000000000000100010100010111000000011011100000111001100110100", NA_character_ )) expect_equal(binary_to_ip(ip_to_binary(x)), x) # zero padding expect_equal(ip_to_binary(ip_address("0.0.0.0")), strrep("0", 32)) expect_equal(ip_to_binary(ip_address("::")), strrep("0", 128)) expect_warning(binary_to_ip(strrep("a", 32)), "contains non-binary characters") expect_warning(binary_to_ip("11000000"), "incorrect number of bits") expect_warning(binary_to_ip("110000001010100000000000000000010"), "incorrect number of bits") })

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sim_simulate_read_count.R

library(optparse) option_list <- list( make_option(c("-p", "--param"), type="character", default=NULL, help="input YAML telling the parameters of simulation", metavar="character"), make_option(c("-o", "--output_prefix"), type="character", default=NULL, help="prefix of output", metavar="character"), make_option(c("-y", "--genotype"), type="character", default=NULL, help="simulated gene file", metavar="character"), make_option(c("-g", "--gene"), type="character", default=NULL, help="simulated genotype file", metavar="character"), make_option(c("-t", "--type"), type="character", default='single', help="specify [single] or [multi] to perform single-snp or milti-snp simulation", metavar="character") ) opt_parser <- OptionParser(option_list=option_list) opt <- parse_args(opt_parser) library(mixqtl) ## load parameter param = yaml::read_yaml(opt$param) # set.seed(param$seed) ## Pre-fixed parameters L_read = param$L_read if(opt$type == 'single') { betas = log(eval(parse(text = param$betas))) # effect size (log fold change) } # load the gene gene_param = readRDS(opt$gene) gene = create_gene(gene_param$L_gene, gene_param$library_dist, gene_param$theta_dist, gene_param$f, gene_param$maf, gene_param$p) # load genotypes genotype = readRDS(opt$genotype) if(opt$type == 'single') { fG = genotype$fG geno = genotype$genotype } else if(opt$type == 'multi') { maf = colMeans(rbind(genotype$h1, genotype$h2), na.rm = T) betas = create_betas( maf = maf, genetic_var = c(param$genetic_var_l, param$genetic_var_h), ncausal = c(param$ncausal_l, param$ncausal_h) ) } # Simulate reads if(opt$type == 'single') { data_collector = list() for(beta in betas) { df = simulate_read_count(gene, geno, beta, L_read, param$y_dist) data_collector[[length(data_collector) + 1]] = list(data = df, fG = fG, beta = beta) } } else if(opt$type == 'multi') { data_collector = list() data_collector[[1]] = simulate_read_count_multi( gene = gene, genotype = genotype, betas = rep(0, length(betas)), L_read = L_read, param$y_dist ) data_collector[[2]] = simulate_read_count_multi( gene = gene, genotype = genotype, betas = betas, L_read = L_read, param$y_dist ) } saveRDS(data_collector, opt$output)

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/project/plot1.R

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plot1.R

# Creates plot1.png aimed at answering the following question: # ---- # 1. Have total emissions from PM2.5 decreased in the United States from 1999 to 2008? # Using the base plotting system, make a plot showing the total PM2.5 emission # from all sources for each of the years 1999, 2002, 2005, and 2008. # --- # Preparations setwd("~/git/coursera_exdata_project_emissions") # Read data set NEI <- readRDS("exdata-data-NEI_data/summarySCC_PM25.rds") SCC <- readRDS("exdata-data-NEI_data/Source_Classification_Code.rds") # Aggregate yearly totals NEI.total.emmissions.by.year <- aggregate(NEI$Emissions, by=list(NEI$year), FUN=sum) names(NEI.total.emmissions.by.year) <- c("year", "total_emissions") # Plot data, add linear fit and write to file png(filename="plot1.png") plot(NEI.total.emmissions.by.year$year, NEI.total.emmissions.by.year$total_emissions, xlab="year", ylab="PM2.5 emissions (tons)", main="Yearly PM2.5 emissions USA (measured total)") abline(lm(NEI.total.emmissions.by.year$total_emissions ~ NEI.total.emmissions.by.year$year),col="red",lwd=1.5) dev.off()

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/pkgs/debias/R/MLEw3p_secant.r

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MLEw3p_secant.r

## MLEw3p_secant ## ## This is a prototype function pending port to C++ using RcppArmadillo. ## This function optimizes the MLE for the three parameter Weibull distribution ## for a given dataset. Data may contain both failures and suspensions. ## The method of optimization is similar to the best method found to optimize the ## R_squared from MRR fitting for the third parameter. A discrete Newton method, also called ## the secant method is used to identify the root of the derivative of the MLE~t0 function. ## In this case the derivative is numerically determined by a two point method. ## The 3-parameter Weibull MLE optimization is known to present instability with some ## data. This is a particular challenge that this routine has been designed to handle. ## It is expected that when instability is encountered the function will terminate gracefully ## at the point at which MLE fitting calculations fail, providing output of last successful ## calculation. ## ## (C) David Silkworth 2013 ## ## This program is free software; you can redistribute it and/or modify it ## under the terms of the GNU General Public License as published by the ## Free Software Foundation; either version 2, or (at your option) any ## later version. ## ## These functions are distributed in the hope that they will be useful, ## but WITHOUT ANY WARRANTY; without even the implied warranty of ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the ## GNU General Public License for more details. ## ## You should have received a copy of the GNU General Public License ## along with this program; if not, a copy is available at ## http://www.r-project.org/Licenses/ MLEw3p_secant<- function (x, s = NULL,limit=10^-5,listout=FALSE) { ## two-point derivative function dLLdx<-function(data,Nf,vstart,limit) { fit1<-.Call("MLEw2p", data, Nf,c(vstart, limit), PACKAGE = "debias") fit2<-.Call("MLEw2p", data+0.1*limit, Nf,c(vstart, limit), PACKAGE = "debias") dLLdx<-(fit1[3]-fit2[3])/(0.1*limit) attr(dLLdx,"fitpar")<-fit1 dLLdx } # ## a starting position is simply constructed here (quicker than MRR?) data<-c(x,s) v <- var(log(data)) shape <- 1.2/sqrt(v) vstart <- shape Nf<-length(x) warning=FALSE ## Tao Pang's original variable labels from FORTRAN are used where possible DL<-limit ## Introduce constraints for the 3p Weibull C1<-min(x) maxit<-100 ## initial step is based on min(x)*.1 DX<-C1*0.1 X0<-0.0 istep<-0 X1<-X0+DX if(X1>C1) {X1<-X0+0.9*(C1-X0)} FX0<-dLLdx(data,Nf,vstart,limit) ## introduce a new start estimate based on last fit vstart<-attributes(FX0)[[1]][2]*0.5 ## modify data by X1 for next slope reading ms<-NULL mx<-x-X1 if(length(s)>0) { for(i in 1:length(s) ) { if((s[i]-X1)>0 ) {ms<-c(ms,s[i]-X1)} } } FX1<-dLLdx(c(mx,ms),Nf,vstart,limit) vstart<-attributes(FX1)[[1]][2]*0.5 ## FX1 will contain slope sign information to be used only one time to find X2 D<- abs(FX1-FX0) X2<-X1+abs(X1-X0)*FX1/D if(X2>C1) {X2<-X1+0.9*(C1-X1)} X0<-X1 X1<-X2 DX<-X1-X0 istep<-istep+1 ## Detail output to be available with listout==TRUE DF<-data.frame(steps=istep,root=X0,error=DX,deriv=FX1) while(abs(DX)>DL&&istep<maxit) { FX0<-FX1 ## modify data by X1 for next slope reading ms<-NULL mx<-x-X1 if(length(s)>0) { for(i in 1:length(s) ) { if((s[i]-X1)>0 ) {ms<-c(ms,s[i]-X1)} } } FX1<-dLLdx(c(mx,ms),Nf,vstart,limit) if(is.nan(FX1)) { FX1<-FX0 warning=TRUE break } vstart<-attributes(FX1)[[1]][2]*0.5 ## FX1 will contain slope information only one time D<- abs(FX1-FX0) X2<-X1+abs(X1-X0)*FX1/D if(X2>C1) {X2<-X1+0.9*(C1-X1)} X0<-X1 X1<-X2 DX<-X1-X0 istep<-istep+1 DFline<-data.frame(steps=istep,root=X0,error=DX,deriv=FX1) DF<-rbind(DF,DFline) } fit<-attributes(FX1)[[1]] outvec<-c(Eta=fit[1],Beta=fit[2],t0=X0,LL=fit[3]) if(warning==TRUE) { warn="optimization unstable" attr(outvec,"warning")<-warn } if(listout==TRUE) { outlist<-list(outvec,DF) return(outlist) }else{ return(outvec) } }

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/4.1 - RD plots (rdrobust).R

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4.1 - RD plots (rdrobust).R

############################################################################################################ # ANÁLISE EMPÍRICA - GRÁFICOS RDPLOTS ############################################################################################################ rm(list = ls()) # Pacotes: library(dplyr) library(ggplot2) library(tidyr) library(readr) library(stringr) library(htmltools) library(htmlwidgets) library(zeallot) library(readxl) library(gdata) library(stringi) library(Hmisc) library(gmodels) library(export) library(ggpubr) # Pacotes Específicos para se trabalhar com RDD: library(rdd) # Pacote mais básico library(rddtools) # Pacote com muitas opções de testes de placebo e sensibilidade das estimações e estimação paramétrica library(rdrobust) # Pacote mais abrangente library(rddapp) # Pacote com interface em Shiny, reduz a necessidade de se saber programar. # Opções: options(scipen = 999) # Desabilita notação scientífica. Para voltar ao padrão -> options(scipen = 1) # Definindo o diretório onde se encontram as bases de dados prontas para análise empírica: setwd("C:/Users/joseg_000.PC-JE/Documents/Dissertação - Dados/Bases de Dados Prontas") # ---------------------------------------------------------------------------------------------------------------------------------- # Baixando as bases de dados (ESCOLHER A QUE FOR USAR) # load("bases_prontas_ECD") load("bases_prontas_ED") # Definindo o diretório para o R salvar as imagens geradas: # setwd("C:/Users/joseg_000.PC-JE/Google Drive/FGV-EPGE/Dissertação/Imagens/Partidos de Esquerda, Centro e Direita/Rdplots") setwd("C:/Users/joseg_000.PC-JE/Google Drive/FGV-EPGE/Dissertação/Imagens/Partidos de Esquerda e Direita/Rdplots") # Analisando quais partidos estão sendo considerados: table(base_pronta_mandato$ideologia_partido_eleito) summarytools::freq(base_pronta_mandato$ideologia_partido_eleito) summarytools::descr(base_pronta_mandato$margem_vitoria_esquerda, transpose = T, stats = "common", round.digits = 3) # ------------------------------------------------------------------------------------------------------- # ANÁLISE GRÁFICA DO EFEITO CAUSAL # ------------------------------------------------------------------------------------------------------- # RDD: Análise Gráfica - RD Plots --------------------------------------------------- # Primeiramente, iremos plotar um gráfico que relaciona a forcing variable com os outcomes de interesse, para # termos uma primeira ideia se há indícios de efeito causal dos partidos de esquerda: # Realizando a análise gráfica utilizando o Pacote rdrobust: attach(base_pronta_mandato) # RDPLOTS # Outcome: despesa geral: png("despesa_geral_pc.png", width = 1600, height = 837) rdplot(y = despesa_geral_pc, x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Total expenditures per capita", title = "") # Polinômio de quarta ordem dev.off() png("despesa_geral_pib.png", width = 1600, height = 837) rdplot(y = despesa_geral_pib, x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Total expenditures as a share of income", title = "") # Polinômio de quarta ordem dev.off() # ------------------------------------------------------------------------------------------------------ # Outcome: Receitas Totais per capita: png("total_receitas_pc.png", width = 1600, height = 837) rdplot(y = total_receitas_pc, x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Current revenues per capita", title = "") # Polinômio de quarta ordem dev.off() png("total_receitas_pib.png", width = 1600, height = 837) rdplot(y = total_receitas_pib, x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Current revenues as a share of income", title = "") # Polinômio de quarta ordem dev.off() # ---------------------------------------------------------------------------------------------------------- # Outcome: Receita tributária per capita png("receita_tributaria_pc.png", width = 1600, height = 837) rdplot(y = receita_tributaria_pc, x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Tax revenues per capita", title = "") # Polinômio de quarta ordem dev.off() png("receita_tributaria_pib.png", width = 1600, height = 837) rdplot(y = receita_tributaria_pib, x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Tax revenues as a share of income", title = "") # Polinômio de quarta ordem dev.off() # ---------------------------------------------------------------------------------------------------------- # Outcome: Total de servidores públicos por mil habitantes: png("total_servidores_mil.png", width = 1600, height = 837) rdplot(y = total_servidores_mil, x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Total Public Employees (per 1000 residents)", title = "") # Polinômio de quarta ordem dev.off() # --------------------------------------------------------------------------------------------------------- # Outcome: Servidores comissionados por mil habitantes: png("servidores_comissionados_mil.png", width = 1600, height = 837) rdplot(y = servidores_comissionados_mil, x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Employees holding commissioned positions (per 1000 residents)", title = "") # Polinômio de quarta ordem dev.off() # Outcome: despesa com educação png("educacao_pc.png", width = 1600, height = 837) rdplot(y = educacao_pc, x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Expenditures with education per capita", title = "") # Polinômio de quarta ordem dev.off() png("educacao_pib.png", width = 1600, height = 837) rdplot(y = educacao_pib, x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Expenditures with education as a share of income", title = "") # Polinômio de quarta ordem dev.off() png("educacao_prop.png", width = 1600, height = 837) rdplot(y = educacao_prop, x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Expenditures with education (share of total)", title = "") # Polinômio de quarta ordem dev.off() # ---------------------------------------------------------------------------------------------------------- # Outcome: despesa com saúde png("saude_pc.png", width = 1600, height = 837) rdplot(y = saude_pc, x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Expenditures with health per capita", title = "") # Polinômio de quarta ordem dev.off() png("saude_pib.png", width = 1600, height = 837) rdplot(y = saude_pib, x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Expenditures with health as a share of income", title = "") # Polinômio de quarta ordem dev.off() png("saude_prop.png", width = 1600, height = 837) rdplot(y = saude_prop, x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Expenditures with health (share of total)", title = "") # Polinômio de quarta ordem dev.off() # ---------------------------------------------------------------------------------------------------------- # Outcome: despesa com assistência social png("assistencia_social_pc.png", width = 1600, height = 837) rdplot(y = assistencia_social_pc, x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Expenditures with social assistance per capita", title = "") # Polinômio de quarta ordem dev.off() png("assistencia_social_pib.png", width = 1600, height = 837) rdplot(y = assistencia_social_pib, x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Expenditures with social assistance as a share of income", title = "") # Polinômio de quarta ordem dev.off() png("assistencia_social_prop.png", width = 1600, height = 837) rdplot(y = assistencia_social_prop, x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Expenditures with social assistance (share of total)", title = "") # Polinômio de quarta ordem dev.off() ######################################################################################################### ######################################################################################################### ######################################################################################################### # RDPLOTS - Agora tranasformando as variáveis que representam o tamanho de governo em log: # Outcome: despesa geral: png("log_despesa_geral_pc.png", width = 1600, height = 837) rdplot(y = log(despesa_geral_pc), x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Log total expenditures per capita", title = "") # Polinômio de quarta ordem dev.off() png("log_despesa_geral_pib.png", width = 1600, height = 837) rdplot(y = log(despesa_geral_pib), x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Log total expenditures as a share of income", title = "") # Polinômio de quarta ordem dev.off() # -------------------------------------------------------------------------------------------------------- # Outcome: Receitas Totais per capita: png("log_total_receitas_pc.png", width = 1600, height = 837) rdplot(y = log(total_receitas_pc), x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Log total revenues per capita", title = "") # Polinômio de quarta ordem dev.off() png("log_total_receitas_pib.png", width = 1600, height = 837) rdplot(y = log(total_receitas_pib), x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Log total revenues as a share of income", title = "") # Polinômio de quarta ordem dev.off() # ------------------------------------------------------------------------------------------------------- # Outcome: Receita tributária per capita png("log_receita_tributaria_pc.png", width = 1600, height = 837) rdplot(y = log(receita_tributaria_pc), x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Log tax revenues per capita", title = "") # Polinômio de quarta ordem dev.off() png("log_receita_tributaria_pib.png", width = 1600, height = 837) rdplot(y = log(receita_tributaria_pib), x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Log tax revenues as a share of income", title = "") # Polinômio de quarta ordem dev.off() # ------------------------------------------------------------------------------------------------------ # Outcome: Total de servidores públicos por mil habitantes: png("log_total_servidores_mil.png", width = 1600, height = 837) rdplot(y = log(total_servidores_mil + 0.00001), x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Log total public employees (per 1000 residents)", title = "") # Polinômio de quarta ordem dev.off() # ------------------------------------------------------------------------------------------------------ # Outcome: Servidores comissionados por mil habitantes: png("log_servidores_comissionados_mil.png", width = 1600, height = 837) rdplot(y = log(servidores_comissionados_mil + 0.00001), x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Log total employees holding commissioned positions (per 1000 residents)", title = "") # Polinômio de quarta ordem dev.off() # ------------------------------------------------------------------------------------------------------ # Outcome: despesa com educação png("log_educacao_pc.png", width = 1600, height = 837) rdplot(y = log(educacao_pc), x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Log expenditures with education per capita", title = "") # Polinômio de quarta ordem dev.off() png("log_educacao_pib.png", width = 1600, height = 837) rdplot(y = log(educacao_pib), x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Log expenditures with education as a share of income", title = "") # Polinômio de quarta ordem dev.off() png("log_educacao_prop.png", width = 1600, height = 837) rdplot(y = educacao_prop, x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Expenditures with education (share of total)", title = "") # Polinômio de quarta ordem dev.off() # ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- # Outcome: despesa com saúde png("log_saude_pc.png", width = 1600, height = 837) rdplot(y = log(saude_pc), x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Log expenditures with health per capita", title = "") # Polinômio de quarta ordem dev.off() png("log_saude_pib.png", width = 1600, height = 837) rdplot(y = log(saude_pib), x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Log expenditures with health as a share of income", title = "") # Polinômio de quarta ordem dev.off() png("log_saude_prop.png", width = 1600, height = 837) rdplot(y = saude_prop, x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Expenditures with health (share of total)", title = "") # Polinômio de quarta ordem dev.off() # ----------------------------------------------------------------------------------------------------------- # Outcome: despesa com assistência social png("log_assistencia_social_pc.png", width = 1600, height = 837) rdplot(y = log(assistencia_social_pc + 0.00001), x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Log expenditures with social assistance per capita", title = "") # Polinômio de quarta ordem dev.off() png("log_assistencia_social_pib.png", width = 1600, height = 837) rdplot(y = log(assistencia_social_pib + 0.00001), x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Log expenditures with social assistance as a share of income", title = "") # Polinômio de quarta ordem dev.off() png("log_assistencia_social_prop.png", width = 1600, height = 837) rdplot(y = assistencia_social_prop, x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Expenditures with social assistance (share of total)", title = "") # Polinômio de quarta ordem dev.off() ########################################################################################################## ########################################################################################################## ########################################################################################################## # Gerando somente os RDplots que serão adicionados ao trabalho final: # ECD rdplot(y = log(despesa_geral_pc), x = margem_vitoria_esquerda, subset = log(despesa_geral_pc) > 7.15, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Log total expenditures per capita", title = "") rdplot(y = log(total_receitas_pc), x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Log total revenues per capita", title = "") rdplot(y = log(receita_tributaria_pc), x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Log tax revenues per capita", title = "") rdplot(y = log(servidores_comissionados_mil + 0.00001), x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Log commission employees (per 1000 residents)", title = "") rdplot(y = educacao_prop, x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Expenditures with education (share of total)", title = "") rdplot(y = saude_prop, x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Expenditures with health (share of total)", title = "") rdplot(y = assistencia_social_prop, x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Expenditures with social assistance (share of total)", title = "") rdplot(y = urbanismo_prop, x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Expenditures with urbanism (share of total)", title = "") # ED rdplot(y = log(despesa_geral_pc), x = margem_vitoria_esquerda, subset = log(despesa_geral_pc) > 7.15, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Log total expenditures per capita", title = "") rdplot(y = log(total_receitas_pc), x = margem_vitoria_esquerda, subset = log(total_receitas_pc) > 7.88, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Log total revenues per capita", title = "") rdplot(y = log(receita_tributaria_pc), x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Log tax revenues per capita", title = "") rdplot(y = log(servidores_comissionados_mil + 0.00001), x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Log commission employees (per 1000 residents)", title = "") rdplot(y = educacao_prop, x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Expenditures with education (share of total)", title = "") rdplot(y = saude_prop, x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Expenditures with health (share of total)", title = "") rdplot(y = assistencia_social_prop, x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Expenditures with social assistance (share of total)", title = "") rdplot(y = urbanismo_prop, x = margem_vitoria_esquerda, nbins = 30, p = 3, col.lines = "red", x.label = "Left Vote share margin of victory", y.label = "Expenditures with urbanism (share of total)", title = "") detach(base_pronta_mandato)

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/inst/scripts/analysis/plots/hbonds/AHdist_CXL_ARG_bfac20.R

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refs/heads/master

2021-01-19T03:54:05.386349

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2016-06-16T23:00:32

2015-11-28T03:28:34

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r

AHdist_CXL_ARG_bfac20.R

# -*- tab-width:2;indent-tabs-mode:t;show-trailing-whitespace:t;rm-trailing-spaces:t -*- # vi: set ts=2 noet: # # (c) Copyright Rosetta Commons Member Institutions. # (c) This file is part of the Rosetta software suite and is made available under license. # (c) The Rosetta software is developed by the contributing members of the Rosetta Commons. # (c) For more information, see http://www.rosettacommons.org. Questions about this can be # (c) addressed to University of Washington UW TechTransfer, email: license@u.washington.edu. library(ggplot2) feature_analyses <- c(feature_analyses, methods::new("FeaturesAnalysis", id = "AHdist_CXL_ARG_bfac20", author = "Matthew O'Meara", brief_description = "", feature_reporter_dependencies = c("StructureFeatures", "HBondFeatures"), run=function(self, sample_sources, output_dir, output_formats){ sele <-" CREATE TEMPORARY TABLE max_residue_bfactors AS SELECT hb_pdb_site.struct_id as struct_id, hb_pdb_site.resNum as resNum, MAX( hb_pdb_site.heavy_atom_temperature ) as max_temp FROM hbond_sites_pdb as hb_pdb_site GROUP BY hb_pdb_site.struct_id, hb_pdb_site.resNum; CREATE TEMPORARY TABLE sc_hbond_card AS SELECT don_site.struct_id AS struct_id, don_site.resNum AS don_resNum, acc_site.resNum AS acc_resNum, COUNT( hbond.hbond_id ) AS num_sc_hbs FROM hbonds as hbond, hbond_sites as don_site, hbond_sites as acc_site WHERE hbond.struct_id = don_site.struct_id and hbond.don_id = don_site.site_id and hbond.struct_id = acc_site.struct_id and hbond.acc_id = acc_site.site_id and don_site.HBChemType != 'hbdon_PBA' AND acc_site.HBChemType != 'hbacc_PBA' GROUP BY hbond.struct_id, don_site.resNum, acc_site.resNum; CREATE TEMPORARY TABLE arg_cxl_hbonds AS SELECT hbond.hbond_id AS hbond_id, hbond.struct_id AS struct_id, hbond.don_id AS don_id, hbond.acc_id AS acc_id FROM hbonds AS hbond, hbond_sites AS don_site, hbond_sites AS acc_site WHERE hbond.struct_id = don_site.struct_id AND hbond.don_id = don_site.site_id AND hbond.struct_id = acc_site.struct_id AND hbond.acc_id = acc_site.site_id AND (don_site.HBChemType = 'hbdon_GDE' OR don_site.HBChemType = 'hbdon_GDH' ) AND acc_site.HBChemType = 'hbacc_CXL'; CREATE TEMPORARY TABLE arg_cxl_hbond_temps AS SELECT hbond.hbond_id AS hbond_id, hbond.struct_id AS struct_id, hbond.don_id AS don_id, hbond.acc_id AS acc_id, don_site_pdb.resNum AS don_resNum, acc_site_pdb.resNum AS acc_resNum, don_max_temp.max_temp AS don_temp, acc_max_temp.max_temp AS acc_temp FROM arg_cxl_hbonds AS hbond, hbond_sites_pdb AS don_site_pdb, hbond_sites_pdb AS acc_site_pdb, max_residue_bfactors AS don_max_temp, max_residue_bfactors AS acc_max_temp WHERE hbond.struct_id = don_site_pdb.struct_id AND hbond.don_id = don_site_pdb.site_id AND hbond.struct_id = acc_site_pdb.struct_id AND hbond.acc_id = acc_site_pdb.site_id AND don_max_temp.struct_id = hbond.struct_id AND don_max_temp.resNum = don_site_pdb.resNum AND acc_max_temp.struct_id = hbond.struct_id AND acc_max_temp.resNum = acc_site_pdb.resNum; SELECT structure.tag, don_site_pdb.chain, don_site_pdb.resNum, don_site_pdb.iCode, acc_site_pdb.chain, acc_site_pdb.resNum, acc_site_pdb.iCode, geom.AHdist, n_sc_hbonds.num_sc_hbs FROM arg_cxl_hbond_temps AS hbond, hbond_geom_coords AS geom, sc_hbond_card as n_sc_hbonds, hbond_sites_pdb AS don_site_pdb, hbond_sites_pdb AS acc_site_pdb, structures AS structure WHERE hbond.don_temp < 20 AND hbond.acc_temp < 20 AND n_sc_hbonds.struct_id = hbond.struct_id AND n_sc_hbonds.don_resNum = hbond.don_resNum AND n_sc_hbonds.acc_resNum = hbond.acc_resNum AND geom.struct_id = hbond.struct_id AND geom.hbond_id = hbond.hbond_id AND don_site_pdb.struct_id = hbond.struct_id AND don_site_pdb.site_id = hbond.don_id AND acc_site_pdb.struct_id = hbond.struct_id AND acc_site_pdb.site_id = hbond.acc_id AND structure.struct_id = hbond.struct_id;" #SELECT # geom.AHdist, # n_sc_hbonds.num_sc_hbsFROM # hbonds AS hbond # JOIN hbond_sites AS don_site ON hbond.struct_id = don_site.struct_id AND hbond.don_id = don_site.site_id # JOIN hbond_sites AS acc_site ON hbond.struct_id = acc_site.struct_id AND hbond.acc_id = acc_site.site_id # JOIN hbond_sites_pdb AS don_site_pdb ON hbond.struct_id = don_site_pdb.struct_id AND hbond.don_id = don_site_pdb.site_id # JOIN hbond_sites_pdb AS acc_site_pdb ON hbond.struct_id = acc_site_pdb.struct_id AND hbond.acc_id = acc_site_pdb.site_id # JOIN max_residue_bfactors AS don_temperature ON don_site_pdb.struct_id AND don_site_pdb.resNum = don_temperature.resNum # JOIN max_residue_bfactors AS acc_temperature ON acc_site_pdb.struct_id AND acc_site_pdb.resNum = acc_temperature.resNum, # sc_hbond_card AS n_sc_hbonds, # hbond_geom_coords AS geom #WHERE # (don_site.HBChemType = 'hbdon_GDE' OR don_site.HBChemType = 'hbdon_GDH') AND # acc_site.HBChemType = 'hbacc_CXL' AND # don_temperature.max_temp < 20 AND # acc_temperature.max_temp < 20 AND # hbond.acc_id = acc_site.site_id AND # n_sc_hbonds.struct_id = hbond.struct_id AND # n_sc_hbonds.don_resNum = don_site.resNum AND # n_sc_hbonds.acc_resNum = acc_site.resNum AND # hbond.struct_id = geom.struct_id AND # hbond.hbond_id = geom.hbond_id;" f <- query_sample_sources(sample_sources, sele) sele <- " DROP TABLE max_residue_bfactors; DROP TABLE sc_hbond_card; DROP TABLE arg_cxl_hbonds; DROP TABLE arg_cxl_hbond_temps;" query_sample_sources(sample_sources, sele, warn_zero_rows=F) # PRESERVE THIS #SELECT # geom.AHdist, # n_sc_hbonds.num_sc_hbs #FROM # hbonds AS hbond, # hbond_sites AS don_site, # hbond_sites AS acc_site, # hbond_sites_pdb AS don_site_pdb, # hbond_sites_pdb AS acc_site_pdb, # sc_hbond_card AS n_sc_hbonds, # max_residue_bfactors AS don_temperature, # max_residue_bfactors as acc_temperature, # hbond_geom_coords AS geom #WHERE # hbond.struct_id = don_site.struct_id AND # hbond.don_id = don_site.site_id AND # hbond.struct_id = acc_site.struct_id AND # (don_site.HBChemType = 'hbdon_GDE' OR don_site.HBChemType = 'hbdon_GDH') AND # acc_site.HBChemType = 'hbacc_CXL' AND # don_site_pdb.struct_id = hbond.struct_id AND don_site_pdb.site_id = hbond.don_id AND # acc_site_pdb.struct_id = hbond.struct_id AND acc_site_pdb.site_id = hbond.acc_id AND # hbond.struct_id = don_temperature.struct_id AND don_site_pdb.resNum = don_temperature.resNum AND # hbond.struct_id = acc_temperature.struct_id AND acc_site_pdb.resNum = acc_temperature.resNum AND # don_temperature.max_temp < 20 AND # acc_temperature.max_temp < 20 AND # hbond.acc_id = acc_site.site_id AND # n_sc_hbonds.struct_id = hbond.struct_id AND # n_sc_hbonds.don_resNum = don_site.resNum AND # n_sc_hbonds.acc_resNum = acc_site.resNum AND # hbond.struct_id = geom.struct_id AND # hbond.hbond_id = geom.hbond_id; #f$don_chem_type_name <- don_chem_type_name_linear(f$don_chem_type) #f$acc_chem_type_name <- acc_chem_type_name_linear(f$acc_chem_type) #f <- na.omit(f, method("r")) dens <- estimate_density_1d( f, c("sample_source", "num_sc_hbs" ), "AHdist", weight_fun = radial_3d_normalization) plot_id <- "AHdist_CXL_ARG_bcut20" p <- ggplot(data=dens) + theme_bw() + geom_line(aes(x=x, y=y, colour=sample_source)) + geom_indicator(aes(indicator=counts, colour=sample_source, group=sample_source)) + facet_wrap( ~ num_sc_hbs, nrow=1 ) + ggtitle("Hydrogen Bonds A-H Distance between ASP/GLU and ARG with Bfactors < 20\nnormalized for equal weight per unit distance\nBy Number of sc hbonds between the acceptor and donor residues") + scale_y_continuous("FeatureDensity", limits=c(0,6), breaks=c(1,3,5)) + scale_x_continuous(expression(paste('Acceptor -- Proton Distance (', ring(A), ')')), limits=c(1.4,2.7), breaks=c(1.6, 1.9, 2.2, 2.6)) if(nrow(sample_sources) <= 3){ p <- p + theme(legend.position="bottom", legend.direction="horizontal") } save_plots(self, plot_id, sample_sources, output_dir, output_formats) })) # end FeaturesAnalysis

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/hinv_matrix_asreml.R \name{hinv_matrix_asreml} \alias{hinv_matrix_asreml} \title{Build the H-inverse-matrix from G-inverse-matrix and pedigree-full and pedigree-genotype#'} \usage{ hinv_matrix_asreml(M012, ped_full) } \arguments{ \item{ped_full}{It contains the full pedigree, it has three columns:ID,Sire,Dam} \item{G_mat}{It is the matrix which has rownames and colnames(ID)} \item{ped_geno}{It contains the pedigree that has the genotype, it is a part of the ped_full pedigree} } \value{ The H-inverse-matrix form the formula } \description{ Build the H-inverse-matrix from G-inverse-matrix and pedigree-full and pedigree-genotype#' } \examples{ # paper:Legarra A, Aguilar I, Misztal I. A relationship matrix including full pedigree and genomic information.[J]. Journal of Dairy Science, 2009, 92(9):4656-63. ped_full <- data.frame(ID=9:17,Sire=c(1,3,5,7,9,11,11,13,13),Dam=c(2,4,6,8,10,12,4,15,14)) ped_full G <- matrix(0.7,4,4) diag(G) <- 1 rownames(G) <- colnames(G) <- 9:12 G #another example library(MASS) animal <- 13:26 data.11.1 <- data.frame(animal, sire = c(0,0,13,15,15,14,14,14,1,14,14,14,14,14), dam = c(0,0,4,2,5,6,9,9,3,8,11,10,7,12), mean = rep(1,length(animal)), EDC = c(558,722,300,73,52,87,64,103,13,125,93,66,75,33), fat_DYD = c(9.0,13.4,12.7,15.4,5.9,7.7,10.2,4.8,7.6,8.8,9.8,9.2,11.5,13.3), SNP1 = c(2,1,1,0,0,1,0,0,2,0,0,1,0,1), SNP2 = c(0,0,1,0,1,1,0,1,0,0,1,0,0,0),SNP3 = c(1,0,2,2,1,0,1,1,0,0,1,0,0,1), SNP4 = c(1,0,1,1,2,1,1,0,0,1,0,0,1,1), SNP5 = c(0,0,1,0,0,0,0,0,0,1,0,1,1,0), SNP6 = c(0,2,0,1,0,2,2,1,1,2,1,1,2,2), SNP7 = c(0,0,0,0,0,0,0,0,2,0,0,0,0,0), SNP8 = c(2,2,2,2,2,2,2,2,2,2,2,2,2,1), SNP9 = c(1,1,1,2,1,2,2,2,1,0,2,0,1,0), SNP10 = c(2,0,2,1,2,1,0,0,2,0,1,0,0,0)) rm(list="animal") animal <- 1:26 sire <- c(rep(0,12), data.11.1$sire) dam <- c(rep(0,12), data.11.1$dam) ped_full <- data.frame(animal, sire, dam) rm(list=c("animal","dam","sire")) M <- data.11.1[6:14, c(1, 7:16)] rownames(M) <- M[, 1] M012 <- as.matrix(M[, -1]) hinv_matrix(M012,ped_full) \%>\% round(3) }

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library(tidyverse) library(ggmjs) plot_original <- economics %>% ggplot(aes(x = date, y = uempmed)) %+% geom_line(color = 'steelblue') %+% xlab('') %+% ylab('') %+% ggtitle('The long term unemployed', 'Median duration of unemployment, in weeks, 1967 to 2015') plot_mjs <- plot_original %+% theme_mjs()

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final_project.R

# MATH 110 Final Project # titanic <- read.csv("titanic.csv", header = T, stringsAsFactors = F) install.packages('corrplot') install.packages('ggthemes') library(ggplot2) library(ggthemes) library(dplyr) library(corrplot) survival_factor <- function(df, variable) { survive <- df %>% mutate(Class = ifelse(Survived == 0, 'Died', 'Survived')) %>% group_by_(variable, 'Class') %>% summarise(count = n()) p <- ggplot(survive, aes(y=survive$count, x=survive[[variable]], color=survive$Class, fill=survive$Class)) p <- p + geom_bar(stat="identity", position="dodge") + labs(color="Class", fill="Class", y="Number of Passenger",x=variable) print(p) } survival_factor(titanic, 'Pclass') survival_factor(titanic, 'Sex') survival_factor(titanic, 'Parch') class_fare <- function(df) { m <- rep(NA, 3) for (i in 1:length(m)) { class_df <- dplyr::filter(df, Pclass == i) m[i] <- mean(class_df$Fare) } fare_df <- data.frame(Class=1:3, Avg.Fare=m, stringsAsFactors = F) return (fare_df) } survival_two_factors <- function(df, f1, f2) { ggplot(df, aes(df[[f1]], fill = factor(df$Survived))) + geom_bar(stat = "count")+ theme_few() + xlab("Pclass") + facet_grid(.~df[[f2]])+ ylab("Count") + scale_fill_discrete(name = "Survived") + ggtitle("Pclass vs Sex vs Survived") } survival_two_factors(titanic, 'Pclass', 'Sex') survival_three_factors <- function(df, Age, Sex, Pclass){ p <- ggplot(df, aes(x = Age, y = Sex)) + geom_jitter(aes(colour = factor(Survived))) + theme_few() p <- p + theme(legend.title = element_blank())+ facet_wrap(~Pclass) p <- p + labs(x = "Age", y = "Sex", title = "Survivor factors: Class vs Sex vs Age") p <- p + scale_fill_discrete(name = "Survived") + scale_x_continuous(name="Age",limits=c(0, 81)) print(p) } survival_three_factors(titanic, 'Age', 'Sex', 'Pclass') survival_title <- function(df) { df$Title <- gsub('(.*, )|(\\..*)', '', df$Name) officer <- c('Capt', 'Col', 'Don', 'Dr', 'Major', 'Rev') royalty <- c('Dona', 'Lady', 'the Countess','Sir', 'Jonkheer') # Reassign titles df$Title[df$Title == 'Mlle'] <- 'Miss' df$Title[df$Title == 'Ms'] <- 'Miss' df$Title[df$Title == 'Mme'] <- 'Mrs' df$Title[df$Title %in% royalty] <- 'Royalty' df$Title[df$Title %in% officer] <- 'Officer' df$Surname <- sapply(df$Name, function(x) strsplit(x, split = '[,.]')[[1]][1]) p <- ggplot(df, aes(Title,fill = factor(Survived))) + geom_bar(stat = "count")+ xlab('Title') + ylab("Count") p <- p + scale_fill_discrete(name = " Survived") + ggtitle("Title vs Survived")+ theme_few() print(p) } survival_title(titanic) # Survival: Family Size survival_familysize <- function(df) { df$Fsize <- df$SibSp + df$Parch + 1 p <- ggplot(df, aes(x = Fsize, fill = factor(Survived))) + geom_bar(stat='count', position='dodge') p <- p + scale_x_continuous(breaks=c(1:11)) + xlab('Family Size') + ylab("Count") p <- p + theme_few()+scale_fill_discrete(name = "Survived") p <- p + ggtitle("Family Size vs Survived") print(p) } survival_familysize(titanic) survival_correlation <- function(df) { df <- titanic df$Fsize <- df$SibSp + df$Parch + 1 df$FsizeD[df$Fsize == 1] <- 'Alone' df$FsizeD[df$Fsize < 5 & df$Fsize > 1] <- 'Small' df$FsizeD[df$Fsize > 4] <- 'Big' df$Title <- gsub('(.*, )|(\\..*)', '', df$Name) officer <- c('Capt', 'Col', 'Don', 'Dr', 'Major', 'Rev') royalty <- c('Dona', 'Lady', 'the Countess','Sir', 'Jonkheer') # Reassign titles df$Title[df$Title == 'Mlle'] <- 'Miss' df$Title[df$Title == 'Ms'] <- 'Miss' df$Title[df$Title == 'Mme'] <- 'Mrs' df$Title[df$Title %in% royalty] <- 'Royalty' df$Title[df$Title %in% officer] <- 'Officer' corr_data <- df corr_data$Sex <- revalue(corr_data$Sex, c("male" = 1, "female" = 2)) corr_data$Title <- revalue(corr_data$Title, c("Mr" = 1, "Master" = 2,"Officer" = 3, "Mrs" = 4,"Royalty" = 5,"Miss" = 6)) corr_data$FsizeD <- revalue(corr_data$FsizeD, c("Small" = 1, "Alone" = 2, "Big" = 3)) corr_data$FsizeD <- as.numeric(corr_data$FsizeD) corr_data$Sex <- as.numeric(corr_data$Sex) corr_data$Title <- as.numeric(corr_data$Title) corr_data$Pclass <- as.numeric(corr_data$Pclass) corr_data$Survived <- as.numeric(corr_data$Survived) corr_data <-corr_data[,c("Survived", "Pclass", "Sex", "FsizeD", "Fare", "Title")] str(corr_data) mcorr_data <- cor(corr_data) corrplot(mcorr_data,method="circle") } survival_correlation(titanic)

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library(phylosim) ### Name: getRateParamList.TN93 ### Title: Get the rate parameters ### Aliases: getRateParamList.TN93 TN93.getRateParamList ### getRateParamList,TN93-method ### ** Examples # create TN93 object p<-TN93() # set/get rate parameters setRateParamList(p,list( "Alpha1"=1, "Alpha2"=2, "Beta"=0.5 )) getRateParamList(p) # set/get rate parameters via virtual field p$rateParamList<-list( "Alpha1"=1, "Alpha2"=1, "Beta"=3 ) p$rateParamList # get object summary summary(p)

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rm(list=ls()) #set work directory path setwd("directory path") #predict price using 'toyota' dataset rawdata <- read.csv('toyota.csv', header = TRUE, na.strings="") rawdata <- subset(rawdata, select = c(Price,Age_08_04,KM,Fuel_Type,HP,Color)) #catergorize cars with their Fuel Types unique_type <- unique(rawdata$Fuel_Type) #create dummy dataframe type_dummy = as.data.frame(matrix(0,nrow(rawdata), length(unique_type))) #read toyota dataset's Fuel Type and write into dummy dataframe for(i in 1:(length(unique_type))){ tmp = unique_type[i] tmp_idx = which(rawdata$Fuel_Type == tmp) type_dummy[tmp_idx,i] = 1 colnames(type_dummy)[i] = sprintf("type_%s",tmp) } rawdata <- rawdata[c(4,2,3,1,5,6)] prdata = rawdata[,-1] prdata = cbind(type_dummy,prdata) #data partition trn_ratio = 0.7 trn_idx = sample(1:nrow(prdata), round(trn_ratio * nrow(prdata))) tst_idx = setdiff(1:nrow(prdata), trn_idx) trn_data = prdata[trn_idx,] tst_data = prdata[tst_idx,] #linear regression fit_lr = lm(Price ~ ., data=trn_data) fit_lr summary(fit_lr) pred_lr = predict(fit_lr, tst_data) mse_lr = mean((tst_data$Price-pred_lr)^2) #Step Wise step_lr = step(fit_lr, direction = "both") summary(step_lr) pred_step = predict(step_lr, tst_data) mse_step = mean((tst_data$Price-pred_step)^2) # Plot both and Compare par(mfrow = c(1,2)) plot(tst_data$Price, pred_lr) plot(tst_data$Price, pred_step)

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cartogram.R

library(qlcVisualize) library(qlcData) library(rworldmap) library(spatstat) library(maptools) library(rgdal) library(rmapshaper) # glottolog data(glottolog) g <- glottolog langs <- g[ !is.na(g$longitude) ,] coords <- langs[, c("longitude","latitude")] rownames(coords) <- rownames(langs) # proj4 # scan("http://spatialreference.org/ref/sr-org/gall-peters-orthographic-projection/proj4/", what="character",sep="\n") pacific <- "+proj=longlat +lon_wrap=155 +lon_0=155 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +no_defs" atlantic <- "+proj=longlat +lon_wrap=8 +lon_0=8 +ellps=WGS84 +datum=WGS84 +no_defs" gall <- "+proj=cea +lon_0=0 +lat_ts=45 +x_0=0 +y_0=0 +ellps=WGS84 +units=m +no_defs " mollweide <- "+proj=moll +lon_wrap=155 +lon_0=155 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs " mollweide_atlantic <- "+proj=moll +lon_wrap=9 +lon_0=9 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs " wgs <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0" # ===== simple worldmap # w <- getMap("less islands") # w <- w[-which(w$continent == "Antarctica"), ] # w <- unionSpatialPolygons(w,rep(1,length(w))) # some languages are missing from the polygon (8.7 %) # test <- inside.owin(coords[,1],coords[,2],w = as.owin(w)) # sum(!test)/length(test) makePoly <- function(point, size = .1) { p <- cbind(c(point[1]+size, point[1]+size, point[1]-size, point[1]-size) , c(point[2]-size, point[2]+size, point[2]+size, point[2]-size)) return(p) } # add <- sapply(which(!test),function(x){makePoly(as.numeric(coords[x,]))},simplify=F) # add <- Polygons(sapply(add,Polygon), "add") # add <- SpatialPolygons(list(add)) # poly <- SpatialPolygons(c(slot(w,"polygons"),slot(add,"polygons")),proj4string = CRS(wgs)) # poly <- spTransform(poly, CRS(pacific)) # this map is manually edited for better visibility # IDs <- sapply(slot(poly, "polygons"), function(x) slot(x, "ID")) # df <- data.frame(rep(0, length(IDs)), row.names=IDs) # poly <- SpatialPolygonsDataFrame(poly, df) # writeOGR(poly,"../gis", "world2","ESRI Shapefile") # ========== Edited map w <- readOGR("gis2/world.shp") # w <- spTransform(w,CRS(atlantic)) # shifted coordinates! # coords$longitude[coords$longitude < -172] <- coords$longitude[coords$longitude < -172] + 360 coords$longitude[coords$longitude < -35] <- coords$longitude[coords$longitude < -35] + 360 # test again: less languages are missing from the polygon (1.9 %) test <- inside.owin(coords[,1],coords[,2],w = as.owin(w)) sum(!test)/length(test) # add the missing ones add <- sapply(which(!test),function(x){makePoly(as.numeric(coords[x,]), size = .4)},simplify=F) add <- Polygons(sapply(add,Polygon, hole=FALSE), "add") add <- SpatialPolygons(list(add),proj4string = CRS(wgs)) w <- SpatialPolygons(c(w@polygons,add@polygons),proj4string = CRS(wgs)) w <- unionSpatialPolygons(w,rep(1,length(w))) # voronoi v <- voronoi(coords,as.owin(w)) # conversion poly <- as(v, "SpatialPolygons") proj4string(poly) <- CRS(wgs) poly <- spTransform(poly, CRS(mollweide)) # cartogram poly <- SpatialPolygonsDataFrame(poly, data.frame(weight = rep(1, length(poly)))) # this works very slowly and the result has overlapping polygons. not nice # library(cartogram) # cart <- cartogram(poly,"weight", threshold = .05, itermax = 60) # alternative. Parameter "res" influences the strength of the deformation library(getcartr) cart <- quick.carto(poly, poly$weight, res = 1000, gapdens = 0.5) # back conversion regions <- slot(cart, "polygons") regions <- lapply(regions, function(x) { SpatialPolygons(list(x)) }) windows <- lapply(regions, as.owin) map <- tess(tiles = windows) save(cart,v,langs,map,file="~/Desktop/cart5.Rdata") # cols <- rep("grey",nrow(langs)) # cols[langs$stock == "Nuclear Trans New Guinea"] <- "blue" # cols[langs$stock == "Turkic"] <- "red" # cols[langs$stock == "Arawakan"] <- "green" # cols[langs$stock == "Atlantic-Congo"] <- "orange" # cols[langs$stock == "Afro-Asiatic"] <- "black" # cols[langs$stock == "Mayan"] <- "purple" # vmap(map,col=cols, border = NA, outer.border = NA)

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/CS498AML/HW1/HW1-PartB.R

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HW1-PartB.R

setwd('C:/SravansData/mcsds/aml') wdat<-read.csv('pima-indians-diabetes.data.txt', header=FALSE) library(klaR) library(caret) bigx<-wdat[,-c(9)] # all the features from colum 1 to 8 and 9th contains the label bigy<-wdat[,9] #9th colum contains the label nbx<-bigx for (i in c(3, 4, 6, 8)) {vw<-bigx[, i]==0 #making the features 3,4,6,8 as NA when they are 0 nbx[vw, i]=NA } trscore<-array(dim=10) #declaring an array of 10 for the scores tescore<-array(dim=10) #declaring the array of 10 for test scores for (wi in 1:10) # for loop for 10 iteratiors for train and test split {wtd<-createDataPartition(y=bigy, p=.8, list=FALSE) #Splitling the data by createdatapartition of Cartet package into 80% train and 20% test ntrbx<-nbx[wtd, ] #nbx data frame has all features ntrby<-bigy[wtd] # ntrby has the labels trposflag<-ntrby>0 #trposflag contains true is the label is 1 else it contains false ptregs<-ntrbx[trposflag, ] #all the rows from ntrbx which has postive label ntregs<-ntrbx[!trposflag,] #all the rows from ntrbx which has negative label ntebx<-nbx[-wtd, ] # ntebx contains the test data or the remaning 20% of the data nteby<-bigy[-wtd] #the actual labels for the test data ptrmean<-sapply(ptregs, mean, na.rm=TRUE) #calcuating the mean of all the eight features which has the postive label ntrmean<-sapply(ntregs, mean, na.rm=TRUE) #calculating the mean of the eight features which have negative label ptrsd<-sapply(ptregs, sd, na.rm=TRUE) #calculating the standard deviation of the eight features which have the postive label ntrsd<-sapply(ntregs, sd, na.rm=TRUE) # calculating the standard deviation of the eight features which have the negative label ptroffsets<-t(t(ntrbx)-ptrmean) # subtracting the mean from the training features for postive label ptrscales<-t(t(ptroffsets)/ptrsd) #divdiving the offsets by standard deviation for postive label ptrlogs<--(1/2)*rowSums(apply(ptrscales,c(1, 2), function(x)x^2), na.rm=TRUE)-sum(log(ptrsd)) # log likelihoods using normal distribution for postive label of diabetes ntroffsets<-t(t(ntrbx)-ntrmean) # subtracting the mean from the training features for Negative label ntrscales<-t(t(ntroffsets)/ntrsd) #divdiving the offsets by standard deviation for Negative label ntrlogs<--(1/2)*rowSums(apply(ntrscales,c(1, 2), function(x)x^2), na.rm=TRUE)-sum(log(ntrsd)) # log likelihoods using normal distribution for Negative label of diabetes lvwtr<-ptrlogs>ntrlogs #rows classfied as diabetes postive by the classifier gotrighttr<-lvwtr==ntrby #comparing the results with the actual training label trscore[wi]<-sum(gotrighttr)/(sum(gotrighttr)+sum(!gotrighttr)) # the accuarcy of the classification for the training set pteoffsets<-t(t(ntebx)-ptrmean) #normalize the test data with mean of training ptescales<-t(t(pteoffsets)/ptrsd) #normalize the test data with the standard deviation of training ptelogs<--(1/2)*rowSums(apply(ptescales,c(1, 2), function(x)x^2), na.rm=TRUE)-sum(log(ptrsd)) # log likehoods using normal distribution for postive labels of test data nteoffsets<-t(t(ntebx)-ntrmean) #normalize the test data with mean of training for negative label ntescales<-t(t(nteoffsets)/ntrsd) #normalize the test data with the standard deviation of training for negative label ntelogs<--(1/2)*rowSums(apply(ntescales,c(1, 2), function(x)x^2), na.rm=TRUE)-sum(log(ntrsd)) # log likehoods using normal distribution for negative labels of test data lvwte<-ptelogs>ntelogs #rows classfied as diabetes postive by the classifier for test data gotright<-lvwte==nteby #comparing the results with the actual testing label tescore[wi]<-sum(gotright)/(sum(gotright)+sum(!gotright)) # the accuarcy of the classification for the test set } print(paste(mean(trscore), "mean accuracy of Training data")) print(paste(mean(tescore), "mean accuracy of Testting data")) #> mean(trscore) #[1] 0.735935 #> mean(tescore) #[1] 0.7418301

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/Function/F_Shuffle_test_sp_model.r

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F_Shuffle_test_sp_model.r

# Base code by Margaret Short, transformed into function by Filhol S. on 3rd MAy 2012 # Do we need a spatial model? # # Idea: # # 1) Reorder the data many times, calculate and plot empirical # semivariograms. # # 2) Overlay our empirical semivariogram. Compare. # Test.sp.model <- function(my.vario,my.geodata,trend.tec,titl){ library(geoR) plot(my.vario, main = "", pts.range.cex = c(1,3), type='b',cex.lab=1.5, cex.axis=1.5) title(main=titl) my.longs <- my.geodata$coords[,1] my.lats <- my.geodata$coords[,2] my.new.geo <- my.geodata$data #my.log.my.geodata <- my.geodata$data # remember, logged my.geodata earlier n <- length( my.geodata$coords ) for( i in 1:100 ) { my.new.order <- sample( 1:n, size=n, repl=FALSE ) my.reordered <- my.new.geo[my.new.order] my.reordered.geo <- as.geodata(cbind(my.longs,my.lats,my.reordered),coords.col = 1:2, data.col = 3) my.v.new <- variog(my.reordered.geo,trend=trend.tec,estimator.type="modulus") lines(my.v.new, col="gray") } lines(my.vario,lwd=3) # overlay our semi-v'gram in thick, black } # Example: # Test.sp.model(my.robust.vario,my.geodata,"2nd","b")

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/man/IntLik-package.Rd

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IntLik-package.Rd

\name{IntLik-package} \alias{IntLik-package} \alias{IntLik} \docType{package} \title{ Numerical Integration for Integrated Likelihood } \description{ This package calculates the integrated likelihood numerically. Given the Likelihood function and the prior function, this package integrates out the nuisance parameters by Metropolis-Hastings (MCMC) Algorithm. } \details{ \tabular{ll}{ Package: \tab IntLik\cr Type: \tab Package\cr Version: \tab 1.0\cr Date: \tab 2012-01-25\cr License: \tab GPL\cr } } \author{ Zhenyu Zhao \email{zhenyuzhao2014@u.northwestern.edu} } \references{ Chib, S. and Jeliazkov, I. (2001) Marginal likelihood from the Metropolis-Hastings Output. Journal of the American Statistical Association. 96, 270-281 Severini, T.A. (2007) Integrated likelihood functions for non-Bayesian inference. Biometrika. 94 529-542 } \keyword{ package } \examples{ ##Integrated Likelihood for Ratio of Normal Mean (Example 2 in Severini 2007) ##Generating Data n=10 u1=4 u2=1/5 x=rnorm(1,u1,sqrt(1/n)) y=rnorm(1,u2,sqrt(1/n)) ##Calculate MLE for the start value psi_hat=x/y lambda_hat=(x*psi_hat+y)/(psi_hat^2+1) #Define prior function prior=function(lambda,psi){ dnorm((psi^2+1)*lambda/(psi*psi_hat+1),mean=0,sd=1)*(psi^2+1)/(psi*psi_hat+1) } #Define Likelihood L=function(psi,lambda){ L=n/2/pi*exp(-n/2*((x-psi*lambda)^2+(y-lambda)^2)) L } #Estimate the Integrated Likelihood evaluated at a sequence of psi ILik(L,prior, start=lambda_hat, seq(psi_hat-10,psi_hat+10,1), 1, "Normal") }

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/IndepDyads/R/HRS/1_Variable_Recode.R

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1_Variable_Recode.R

# -------------------------------------- rm(list=ls(all.names = TRUE)) # This script recodes most variables, makes some new ones. # This is the step after extracting from the RAND version 2016v1 # file in H0_RAND_reshape.R # -------------------------------------- # source("R/1_Variable_Recode.R") # Sets working directory for Tim's machines library(here) library(lubridate) library(data.table) library(tidyverse) #--------------------------------------------------------- # start utility function preamble #---------------------------------------------------------- # convert yes no coded questions into binary convertYN <- function(x){ x <- as.character(x) xx <- rep(NA, length(x)) xx[grepl("yes", x)] <- 1 xx[grepl("no", x)] <- 0 invisible(xx) } #----------------------------------------------------- # convert odd binary with first and second try into single binary # TR: changed Aug 4, 2016. No more intermediate values: percent incorrect. convertCI <- function(x){ xx <- rep(NA, length(x)) x <- as.character(x) xx[x == "1.correct"] <- 0 xx[x == "2.correct, 1st try"] <- 0 #xx[x == "1.correct, 2nd try"] <- .5 xx[x == "1.correct, 2nd try"] <- 0 xx[x == "0.incorrect"] <- 1 invisible(as.numeric(xx)) } #----------------------------------------------------- # convert CESD variables into binary. # 1 = yes or most of the time convertCESD <- function(x){ if (class(x) == "numeric"){ return(x) } x <- as.character(x) xx <- rep(NA, length(x)) xx[x == "0.no"] <- 0 xx[x == "4. none or almost none"] <- 0 xx[x == "4.none or almost none"] <- 0 # TR: changed Aug 4, 2016. No more intermediate values: percent incorrect. # xx[x == "3. some of the time"] <- .5 # xx[x == "2. most of the time" ] <- .75 xx[x == "3. some of the time"] <- 0 xx[x == "3.some of the time"] <- 0 xx[x == "2. most of the time" ] <- 1 xx[x == "2.most of the time" ] <- 1 xx[x == "1.yes"] <- 1 xx[x == "1. all or almost all"] <- 1 xx[x == "1.all or almost all"] <- 1 xx } #----------------------------------------------------- # convert dates to R internal format convertDates <- function(Dat){ # can't be done with apply because we can't have Date class matrices. # all date columns can be detected with the pattern _dt, it turns out. DateInd <- grep(pattern = "_dt",colnames(Dat)) for (i in DateInd){ Dat[,i] <- as.Date(Dat[, i], origin = "1960-1-1") } invisible(Dat) } #----------------------------------------------------- # two functions to get exact years lived and left getThanoAge <- function(Date, DeathDate){ out <- rep(NA, length(Date)) Ind <- !is.na(Date) out[Ind] <- lubridate::decimal_date(DeathDate[Ind]) - lubridate::decimal_date(Date[Ind]) out } getChronoAge <- function(Date, BirthDate){ out <- rep(NA, length(Date)) Ind <- !is.na(Date) & !is.na(BirthDate) out[Ind] <- lubridate::decimal_date(Date[Ind]) - lubridate::decimal_date(BirthDate[Ind]) out } # -------------------------------# # Weight imputation function # # see code for annotation # # -------------------------------# imputeWeights <- function(wt,intv_dt){ # positive weights, also used for indexing ind <- wt > 0 # if all weights 0, replace w NA if (all(wt == 0)){ wt2 <- NA * wt return(wt2) } # if only one valid weight, all later # observations will keep that weight. if (sum(ind) == 1){ wt2 <- approx(x = intv_dt[ind], y = wt[ind], xout = intv_dt, rule = 1:2, method = "constant", f = .5)$y } # if at least two valid observations, we # interpolate linearly for any missing, # but extrapolate (rightward) with constant if (sum(ind)>=2){ wt2 <- approx(x = intv_dt[ind], y = wt[ind], xout = intv_dt, rule = 1:2, method = "linear")$y } return(wt2) } # end utility function preamble #---------------------------------------------------------- #---------------------------------------------------------- # load in long files from PHC Dat <- readRDS(here::here("IndepDyads","Data","RAND_2016v1_long.rds")) varnames <- readRDS( here::here("IndepDyads","Data","varnames.rds")) # varnames[!varnames %in% colnames(Dat)] # remove missed interviews Dat <- Dat[!is.na(Dat$intv_dt), ] # change all factors to character (to be later recoded in some instances) factor.columns <- sapply(Dat, is.factor) Dat[,factor.columns] <- apply(Dat[,factor.columns], 2, as.character) # make sex column easier to use: Dat$sex <- ifelse(Dat$sex == "1.male","m","f") # reduce to deceased-only Dat$dead <- ifelse(is.na(Dat$d_dt), 0, 1) Dat <- Dat[Dat$dead == 1, ] #(dead_cases <- nrow(Dat)) # 82609 # stats for paper #nrow(Dat) #dead_cases / length(unique(Dat$id)) # as of 2016v1: 5.368548 # as of vP: 4.822015 / person on average #hist(rle(sort(Dat$id))$lengths) # convert dates to native R format Dat <- convertDates(Dat) # --------------------------------------------------# # merge weights (big assumption here: # # weights in institutions are valid and # # comparable with weights outside institutions. # # soooo annoying ppl in institutions don't have # # comparable weights. # # --------------------------------------------------# # TR: update: since for the bootstrap method we resample # using weights on first appearance, this is innocuous # (no one starts HRS in nursing home) Dat$nh_wt[is.na(Dat$nh_wt)] <- 0 Dat$p_wt <- Dat$p_wt + Dat$nh_wt # --------------------------------------------------# # now we do weight interpolation/extrapolation # # --------------------------------------------------# Dat <- data.table(Dat) Dat <- Dat[, p_wt2 := imputeWeights(p_wt,intv_dt), by = list(id) ] Dat <- Dat[!is.na(Dat$p_wt2),] #(cases_with_weights <- nrow(Dat)) #cases_with_weights / length(unique(Dat$id)) # RAND 2016v1: 5.201893 # RAND vP: 4.683211 # --------------------------------------------------# # calculate thanatological ages # # --------------------------------------------------# Dat$ta <- getThanoAge(Dat$intv_dt, Dat$d_dt) Dat$ca <- getChronoAge(Dat$intv_dt, Dat$b_dt) Dat$la_int <- floor(Dat$ta + Dat$ca) # hist(Dat$ca - (Dat$age /12) ) # there is one individual with an NA b_dt, and NA age, # but thano age is known # --------------------------------------------------# # locate yes/no, correct/incorrect columns # # --------------------------------------------------# YNcols <- apply(Dat, 2, function(x){ xx <- unique(x) length(xx) <= 4 & any(grepl("yes",xx)) }) CIcols <- apply(Dat, 2, function(x){ xx <- unique(x) length(xx) <= 5 & any(grepl("correct",xx)) }) # which columns are these anyway? #colnames(Dat)[YNcols] #colnames(Dat)[CIcols] # convert to binary Dat <- data.frame(Dat) Dat[YNcols] <- lapply(Dat[YNcols], convertYN) Dat[CIcols] <- lapply(Dat[CIcols], convertCI) Dat <- Dat %>% mutate( srhfairpoor = case_when(srh == "1.excellent" ~ 0L, srh == "2.very good" ~ 0L, srh == "3.good" ~ 0L, srh == "4.fair" ~ 1L, srh == "5.poor" ~ 1L, is.na(srh) ~ as.integer(NA)), srhpoor = case_when( srh == "1.excellent" ~ 0L, srh == "2.very good" ~ 0L, srh == "3.good" ~ 0L, srh == "4.fair" ~ 0L, srh == "5.poor" ~ 1L, is.na(srh) ~ as.integer(NA)), srmfairpoor = case_when(srm == "1.excellent" ~ 0L, srm == "2.very good" ~ 0L, srm == "3.good" ~ 0L, srm == "4.fair" ~ 1L, srm == "5.poor" ~ 1L, is.na(srm) ~ as.integer(NA)), srmpoor = case_when( srm == "1.excellent" ~ 0L, srm == "2.very good" ~ 0L, srm == "3.good" ~ 0L, srm == "4.fair" ~ 0L, srm == "5.poor" ~ 1L, is.na(srm) ~ as.integer(NA)), pastmem = case_when( pastmem == "3.worse" ~ 1L, pastmem == "2.same" ~ 0L, pastmem == "1.better" ~ 0L, is.na(pastmem) ~ as.integer(NA))) # do cesd questions (1 bad, 0 good) cesdquestions <- colnames(Dat)[grepl("cesd", colnames(Dat))] cesdquestions <- cesdquestions[cesdquestions != "cesd"] Dat[cesdquestions] <- lapply(Dat[cesdquestions],convertCESD) # cesd_enjoy is flipped yet again, because 1 is 'yes I enjoyed life', # --------------------------------------------------------------- # various recodings in a mutate call Dat <- Dat %>% mutate(# cesd_enjoy and cesd_happy flip so that high is bad cesd_enjoy = 1 - cesd_enjoy, cesd_happy = 1 - cesd_happy, # total word recall (max 20) twr = 1 - twr / 20, # vocab: 1 worst 0 best vocab = 1 - vocab / 10, # total mental: 1 worst, 0 best (max 15) tm = 1 - tm /15, # delayed word recall (max 10) dwr = 1 - dwr / 10, # immediate word recall (max 10) iwr = 1 - iwr / 10, # mobility index, break at 1 mob = ifelse(mob > 1, 1, 0), # large muscle difficulty index, lg_mus = ifelse(lg_mus > 1, 1, 0), # Gross motor difficulty index gross_mot = ifelse(gross_mot > 1, 1, 0), # Fine motor difficulty index fine_mot = ifelse(fine_mot > 0, 1, 0), # serial 7s hist(Dat$serial7s) # < 4 serial7s = ifelse(serial7s < 4, 1, 0), # Number of chronic conditions > 2 table(Dat$cc) cc = ifelse(cc > 2, 1, 0), # Drinking days/week alc_days = ifelse(alc_days > 1, 1, 0), # alc_drinks > 0 alc_drinks = ifelse(alc_drinks > 0, 1, 0), # adl 3 > 0 adl3 = ifelse(adl3 > 0, 1, 0), # adl 5 cut points adl5_1 = ifelse(adl5 > 0, 1, 0), adl5_2 = ifelse(adl5 > 1, 1, 0), adl5_3 = ifelse(adl5 > 2, 1, 0), # iadl3 (any) iadl3 = ifelse(iadl3 > 0, 1, 0), # iadl 5 cut points iadl5_1 = ifelse(iadl5 > 0, 1, 0), iadl5_2 = ifelse(iadl5 > 1, 1, 0), iadl5_3 = ifelse(iadl5 > 2, 1, 0), # Depression score 2+ cesd = ifelse(cesd > 1, 1, 0), # define underweight, obese, normalweight underweight = ifelse(bmi < 18.5, 1, 0), obese = ifelse(bmi > 30, 1, 0), # nursing home yes or no? nh_nights = ifelse(nh_nights > 0, 1, 0), nh_stays = ifelse(nh_stays > 0, 1, 0), # hospital night yes or no? hosp_nights = ifelse(hosp_nights > 0, 1, 0), hosp_stays = ifelse(hosp_stays > 0, 1, 0), # doc visits standardize ref period, need helper column wave3p = ifelse(wave > 2, 2, 1), doc_visits = floor(doc_visits / wave3p), doc_visits = ifelse(doc_visits > 8, 1, 0), wave3p = NULL ) # Everything else we estimate prevalence not_fit <- c("id", "cohort", "sex", "hisp", "race", "b_mo", "b_yr", "b_dt", "d_mo", "d_yr", "d_dt", "edu_yrs", "m_edu", "f_edu", "b_pl", "s_wt", "wave", "adl5", "age", "alz", "bmi", "iadl_calc", "div", "reg", "dem", "iadl5", "intv", "intv_dt", "mar", "srh", "srm", "nh_wt", "p_wt", "dead", "p_wt2", "ta", "ca", "la_int","normalweight") varnames_fit <- colnames(Dat)[!colnames(Dat)%in%not_fit] # let's just make sure this will work: stopifnot(all(varnames_fit %in% colnames(Dat))) # make sure all will work as prevalence: stopifnot(all( sapply(varnames_fit, function(vn, Dat){ max(Dat[[vn]], na.rm = TRUE) }, Dat = Dat) == 1) ) # all in [0,1] stopifnot(all( sapply(varnames_fit, function(vn, Dat){ diff(range(na.omit(Dat[[vn]]))) }, Dat = Dat) == 1) ) # these are the ones that should be fit as of now. names(varnames_fit) <- NULL saveRDS(varnames_fit, file = here::here("IndepDyads","Data","varnames_fit.rds")) # use quasibinom to fit due to these three variables. #varnames_fit[which(sapply(varnames_fit, function(vn, Dat){ # length(unique(na.omit(Dat[[vn]]))) # }, Dat = Dat) > 2)] #"vocab" "tm" "twr" # ------------------- # check cases by wave ( tapering in recent waves because selected down to deaths..) #checkwaves <- function(var,Dat){ # table(Dat[[var]],Dat[["wave"]]) #} #checkwaves("adl3_",Dat) #checkwaves("adl5_",Dat) #checkwaves("iadl3_",Dat) #checkwaves("iadl5_",Dat) #checkwaves("cesd",Dat) # ------------------- # ------------------------------------------------------- # for binning purposes, akin to 'completed age' Dat$tafloor <- floor(Dat$ta) Dat$cafloor <- floor(Dat$ca) # I guess actual interview date could be some weeks prior to registered # interview date? There are two negative thano ages at wave 4 otherwise, but # still rather close. Likely died shortly after interview. #(Dat[Dat$tafloor < 0, ]) # there is one individual with an erroneous death date (or id!), throwing out. Dat <- Dat[Dat$ta > -1, ] Dat$tafloor[Dat$tafloor < 0] <- 0 # We use higher bin widths fur purposes of visualizing raw data, # Just diagnostics. larger widths help cancel out noise. This # Such binning can be done as an alternative to the loess smoothing, # where we take weighted means in cells. It'd probably make sense # to keep the final year of life in a single year width, but the # general pattern ought to remain visible. Dat$cafloor2 <- Dat$cafloor - Dat$cafloor %% 2 Dat$tafloor2 <- Dat$tafloor - Dat$tafloor %% 2 Dat$cafloor3 <- Dat$cafloor - Dat$cafloor %% 3 Dat$tafloor3 <- Dat$tafloor - Dat$tafloor %% 3 #---------------------------------------------- # save out, so this doesn't need to be re-run every time saveRDS(Dat,file = here::here("IndepDyads","Data","RAND_2016v1_long.rds")) #graphics.off() # next step would be CreateMatrices.R, usually #lapply(Dat[varnames_check],unique) # how many NAs per variable? #varnames_check[!varnames_check%in%colnames(Dat)] # NAs <- lapply(Dat[varnames],function(x){ # sum(is.na(x)) # }) #sort(unlist(NAs) / nrow(Dat)) rm(list = ls(all.names = TRUE)) #will clear all objects includes hidden objects. gc() # end # ------------------------------------------------------------------------- # Last checked 7-Nov-2019 # varnames <- c(adl3_ = "adl3", adl5_ = "adl5", iadl3_ = "iadl3", iadl5_ = "iadl5", # cesd = "cesd", lim_work = "lim_work", srh = "srh", bmi = "bmi", # back = "back", hosp = "hosp", hosp_stays = "hosp_stays", hosp_nights = "hosp_nights", # nh = "nh", nh_stays = "nh_stays", nh_nights = "nh_nights", nh_now = "nh_now", # doc = "doc", doc_visits = "doc_visits", hhc = "hhc", meds = "meds", # surg = "surg", dent = "dent", shf = "shf", adl_walk = "adl_walk", # adl_dress = "adl_dress", adl_bath = "adl_bath", adl_eat = "adl_eat", # adl_bed = "adl_bed", adl_toilet = "adl_toilet", iadl_map = "iadl_map", # iadl_money = "iadl_money", iadl_meds = "iadl_meds", iadl_shop = "iadl_shop", # iadl_meals = "iadl_meals", mob = "mob", lg_mus = "lg_mus", gross_mot = "gross_mot", # fine_mot = "fine_mot", bp = "bp", diab = "diab", cancer = "cancer", # lung = "lung", heart = "heart", stroke = "stroke", psych = "psych", # arth = "arth", cc = "cc", alc_ev = "alc_ev", alc_days = "alc_days", # alc_drinks = "alc_drinks", smoke_ev = "smoke_ev", smoke_cur = "smoke_cur", # cesd_depr = "cesd_depr", cesd_eff = "cesd_eff", cesd_sleep = "cesd_sleep", # cesd_lone = "cesd_lone", cesd_sad = "cesd_sad", cesd_going = "cesd_going", # cesd_enjoy = "cesd_enjoy", srm = "srm", pastmem = "pastmem", # serial7s = "serial7s", serial7s = "bwc20", name_mo = "name_mo", name_dmo = "name_dmo", # name_yr = "name_yr", name_dwk = "name_dwk", name_sci = "name_sci", # name_cac = "name_cac", name_pres = "name_pres", name_vp = "name_vp", # vocab = "vocab", tm = "tm") # saveRDS(varnames, file = here::here("IndepDyads","Data","varnames.rds"))

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## This function will create a list of functions on the matrix(x) makeCacheMatrix <- function(x = matrix()) { mInv <<- NULL ## function to set matrix, assumption if we set new matrix we also reset inverse to NULL set <- function(y) { x <<- y mInv <<- NULL } ## function to get matrix get <- function() x ## function to set inverse matrix setinverse <- function(inverse) mInv <<- inverse ## function to get inverse matrix getinverse <- function() mInv list(set = set, get = get, setinverse = setinverse, getinverse = getinverse) } ## CacheSolve is function that returns a matrix that is the inverse of 'x' ## input is the list retrieved from makeCacheMatrix cacheSolve <- function(x) { ## retrieve current cached inverse for x inverse <- x$getinverse() ## check if getinverse exists (ie is not NULL) and in that case retrieve the cached data if(!is.null(inverse)) { message("getting cached data") return(inverse) } else ## inverse doesn't exists and calculate inverse { message("getting new calculated data") ## retrieve matrix data <- x$get() ## calculate inverse inverse <- solve(data) ## cache calculated inverse x$setinverse(inverse) ## print inverse inverse } }

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GENEAdistance.Rd.R

library(GENEAclassify) ### Name: GENEAdistance ### Title: Find the mean distance between two steps detected ### Aliases: GENEAdistance ### Keywords: internal ### ** Examples x1 <- c(20, 15, 10) GENEAdistance(x1) x2 <- c(300, 255, 111) GENEAdistance(x2)

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testCBIND.r

callIds <- c('mcp1','mcp5','mcp7') assetIds <- c("AUD", "CAD", "EUR", "GBP", "HKD", "JPY" ,"SGD","USD") call.num <- length(callIds) asset <- length(assetIds) base.mat <- matrix(0,nrow=call.num,ncol=asset.num, dimnames = list(callIds,assetIds)) eli.mat <- base.mat group.callIds <- c('mcp1','mcp1','mcp1','mcp5','mcp5','mcp5','mcp5''mcp7') group.assetIds <- c("AUD", "CAD", "EUR", "GBP", "HKD", "JPY" ,"SGD","USD") eli.mat[cbind(group.callIds ,group.assetIds)] <- 1 print(eli.mat)

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corr_bestlag.R

#' Computes best lag correlation #' #' This function computes correlation based on best picked lags. The lags indicate delayed changes. #' #' @param data a matrix or data frame with rows representing genes and columns #' representing different timepoints. If data is a data frame, the gene names #' can be specified using the \code{row.names()}. #' @param max.lag a integer value of the maximum lags allowed in the dataset, #' if null, defaults to the floor of the number of timepoints divided by 4 #' @param timepoints a vector of time points used in the dataset #' @param C a numeric value of C used in computing weighted correlation, #' if null, a default is computed based on the penalty argument #' @param penalty a factor with two levels high and low penalty on the weighted correlation #' @param iter an integer indicating the number of C values to test for low penalty #' @return a list containing weighted correlation and best lags used in each row #' #' @examples #' corr.bestlag(array(rnorm(30), c(5, 6)), max.lag = 1, #' timepoints = c(0, 5, 10, 15, 20, 25), C = 10, penalty = "high") #' corr.bestlag(array(runif(40, 0, 20), c(4, 10)), #' timepoints = c(0, 0.5, 1.5, 3, 6, 12, 18, 26, 39, 50), penalty = "high") #' corr.bestlag(matrix(data = rexp(n = 40, 2), nrow = 8), #' timepoints = c(0, 5, 15, 20, 40), penalty = "low", iter = 5) #' #' @importFrom stats var #' #' @author Thevaa Chandereng, Anthony Gitter #' #' @export corr.bestlag corr.bestlag <- function(data, timepoints, max.lag = NULL, C = NULL, penalty = "high", iter = 10){ #fixing the max lag if it is NULL if(is.null(max.lag)){ max.lag <- floor(length(timepoints) / 4) } #ADDED test cases to see if C > 0 if(!is.null(C)){ stopifnot(C > 0) } #checking the condition stopifnot(dim(data)[2] == length(timepoints), max.lag <= length(timepoints) / 4, is.numeric(iter), penalty == "high" | penalty == "low", max.lag %% 1 == 0, iter %% 1 == 0, iter > 1, max.lag >= 1) #checking for 0 variance if(any(apply(data, 1, var) == 0)){stop("At least one of the genes has 0 variance!")} #finding the values of C values <- findC(timepoints, max.lag, iter = iter) #if C is already given, the matrix is computed based on that value if(is.numeric(C)){ lags <- best.lag(data, max.lag = max.lag, timepoints, C = C) new.data <- prep.data(data, lags, timepoints) return(list(corr = comp.corr(new.data$data, new.data$time, C = C), lags = lags, C = C)) } # if C is not given but penalty is high, the matrix is computed based on first C value from values else if(penalty == "high"){ lags <- best.lag(data, max.lag = max.lag, timepoints, C = values[1]) new.data <- prep.data(data, lags, timepoints) return(list(corr = comp.corr(new.data$data, new.data$time, C = values[1]), lags = lags, C = values[1])) } #if penalty is low, the matrix is computed for all 10 values in values vector else if(penalty == "low"){ clustdiff <- rep(NA, length(values) - 1) allcorr <- NULL alllags <- NULL for(v in 1:length(values)){ lags <- best.lag(data, max.lag = max.lag, timepoints, C = values[v]) new.data <- prep.data(data, lags, timepoints) result <- list(corr = comp.corr(new.data$data, new.data$time, C = values[v]), lags = lags) allcorr[[v]] <- result$corr alllags[[v]] <- result$lags } # the adjacent similarity matrix for(j in 1:(length(values) - 1)){ clustdiff[j] <- sum((as.vector(allcorr[[j + 1]]) - as.vector(allcorr[[j]]))^2) } #the best C matrix is picked based on the smallest different with the adjacent similarity matrix return(list(corr = allcorr[[which.min(clustdiff) + 1]], lags = alllags[[which.min(clustdiff) + 1]], C = values[which.min(clustdiff) + 1] )) } }

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calculate_ozcbi_forest.R

#' Calculate the Forest OzCBI from numeric input variables #' #' `r lifecycle::badge("stable")` #' #' @details The Forest OzCBI is calculated from the following components: #' #' * Stratum 1 fraction of cover: always 1 (100%), therefore never captured. #' * Stratum {2..5} fcov: surface covered by the stratum in quarter steps #' from 0 to 1: 0.0, 0.25, 0.5, 0.75, 1.0 #' * Stratum scores (CBI): The average grading from all non-null gradings out of #' all captured variables for each stratum. #' The variables are named with a stratum prefix. #' E.g., all variables for stratum 1 are prefixed `stratum_1_surface_s1_`. #' #' ### Overall index: OzCBI #' The OzCBI is calculated as: #' #' (sum of stratum scores) / (sum of stratum fraction of cover) #' #' ### Variables not used in OzCBI formula #' Some variables are captured, but not used for OzCBI calculation. #' They provide context and metadata, and include representative photos. #' #' @param stratum_1_surface_p_s1_unburnt A numeric grade from 0.0 to 3.0. #' Unburnt area. #' The approximate percentage of area not burned. #' @param stratum_1_surface_p_s1_duff A numeric grade from 0.0 to 3.0. #' Duff condition (crushed sticks and leaves). #' Broken leaf pieces that form a type of mulch under the litter layer. #' If necessary, scrape down to mineral soil to see how deeply the char has #' penetrated. #' @param stratum_3_elevated_p_s3_crown #' A numeric grade from 0.0 to 3.0. #' Intact original crown cover. #' How much original crown is intact? #' @param straum_4_intermediate_p_s4_crown #' A numeric grade from 0.0 to 3.0. #' Intact original crown cover. #' How much original crown is intact? #' @param straum_4_intermediate_p_s4_char #' A numeric grade from 0.0 to 3.0. #' Char height. #' Fraction of total stratum height charred. #' @param stratum_5_overstorey_p_s5_crown #' A numeric grade from 0.0 to 3.0. #' Intact original crown cover. #' How much original crown is intact? #' @param stratum_5_overstorey_p_s5_litter #' A numeric grade from 0.0 to 3.0. #' Ground surface covered by leaves that have fallen after the burn, #' not unburned patches. #' @param stratum_5_overstorey_p_s5_char #' A numeric grade from 0.0 to 3.0. #' Char height. #' Fraction of total stratum height charred. #' @param stratum_1_surface_s1_fcov #' The fraction of coverage of stratum 1, default: 1. This value #' is never captured in the digital form as it always is 1 (100%). #' The variable is however provided here to allow different values. #' @param stratum_3_elevated_p_s3_fcov #' The fraction of coverage of stratum 2 in quarter steps #' from 0.0 to 1.0. Default: 0. #' @param straum_4_intermediate_p_s4_fcov #' The fraction of coverage of stratum 2 in quarter steps #' from 0.0 to 1.0. Default: 0. #' @param stratum_5_overstorey_p_s5_fcov #' The fraction of coverage of stratum 2 in quarter steps #' from 0.0 to 1.0. Default: 0. #' @param verbose Whether to display diagnostic messages, default: FALSE. #' @family ozcbi #' @export #' @examples #' # With missing variables #' calculate_ozcbi_forest( #' stratum_1_surface_p_s1_unburnt = 1, #' verbose = TRUE #' ) #' #' # With complete variables, all set to 1 #' calculate_ozcbi_forest( #' stratum_1_surface_p_s1_unburnt = 1, #' stratum_1_surface_p_s1_duff = 1, #' stratum_3_elevated_p_s3_crown = 1, #' straum_4_intermediate_p_s4_crown = 1, #' straum_4_intermediate_p_s4_char = 1, #' stratum_5_overstorey_p_s5_crown = 1, #' stratum_5_overstorey_p_s5_litter = 1, #' stratum_5_overstorey_p_s5_char = 1, #' stratum_1_surface_s1_fcov = 1, #' stratum_3_elevated_p_s3_fcov = 0, #' straum_4_intermediate_p_s4_fcov = 0, #' stratum_5_overstorey_p_s5_fcov = 0, #' verbose = TRUE #' ) calculate_ozcbi_forest <- function( stratum_1_surface_p_s1_unburnt = NA_real_, stratum_1_surface_p_s1_duff = NA_real_, stratum_3_elevated_p_s3_crown = NA_real_, straum_4_intermediate_p_s4_crown = NA_real_, straum_4_intermediate_p_s4_char = NA_real_, stratum_5_overstorey_p_s5_crown = NA_real_, stratum_5_overstorey_p_s5_litter = NA_real_, stratum_5_overstorey_p_s5_char = NA_real_, stratum_1_surface_s1_fcov = 1, stratum_3_elevated_p_s3_fcov = 0, straum_4_intermediate_p_s4_fcov = 0, stratum_5_overstorey_p_s5_fcov = 0, verbose = FALSE) { # -------------------------------------------------------------------------- # # Stratum 1 # s1_cbi <- c( stratum_1_surface_p_s1_unburnt, stratum_1_surface_p_s1_duff ) %>% purrr::discard(is.na) %>% mean() s1_score <- s1_cbi * stratum_1_surface_s1_fcov if (verbose == TRUE) { ruODK::ru_msg_info( glue::glue( "Stratum 1: CBI {s1_cbi} * FCOV ", "{stratum_1_surface_s1_fcov} = Score {s1_score}" ) ) } # -------------------------------------------------------------------------- # # Stratum 2 is unused # # s2_cbi <- c( # stratum_2_near_surface_s2_area_unburnt, # stratum_2_near_surface_s2_grass_trees_with_skirts, # stratum_2_near_surface_s2_unburnt_shrub_density, # stratum_2_near_surface_s2_fcov_regenerating_plants # ) %>% # purrr::discard(is.na) %>% # mean() # s2_score <- s2_cbi * stratum_2_near_surface_s2_fcov # if (verbose == TRUE) # ruODK::ru_msg_info( # glue::glue( # "Stratum 2: CBI {s2_cbi} * FCOV ", # "{stratum_2_near_surface_s2_fcov} = Score {s2_score}" # ) # ) # # -------------------------------------------------------------------------- # # Stratum 3 s3_cbi <- stratum_3_elevated_p_s3_crown s3_score <- s3_cbi * stratum_3_elevated_p_s3_fcov if (verbose == TRUE) { ruODK::ru_msg_info( glue::glue( "Stratum 3: CBI {s3_cbi} * FCOV ", "{stratum_3_elevated_p_s3_fcov} = Score {s3_score}" ) ) } # -------------------------------------------------------------------------- # # Stratum 4 s4_cbi <- c( straum_4_intermediate_p_s4_crown, straum_4_intermediate_p_s4_char ) %>% purrr::discard(is.na) %>% mean() s4_score <- s4_cbi * straum_4_intermediate_p_s4_fcov if (verbose == TRUE) { ruODK::ru_msg_info( glue::glue( "Stratum 4: CBI {s4_cbi} * FCOV ", "{straum_4_intermediate_p_s4_fcov} = Score {s4_score}" ) ) } # -------------------------------------------------------------------------- # # Stratum 5 s5_cbi <- c( stratum_5_overstorey_p_s5_crown, stratum_5_overstorey_p_s5_litter, stratum_5_overstorey_p_s5_char ) %>% purrr::discard(is.na) %>% mean() s5_score <- s5_cbi * stratum_5_overstorey_p_s5_fcov if (verbose == TRUE) { ruODK::ru_msg_info( glue::glue( "Stratum 5: CBI {s5_cbi} * FCOV ", "{stratum_5_overstorey_p_s5_fcov} = Score {s5_score}" ) ) } # OzCBI score_sum <- sum( s1_score, s3_score, s4_score, s5_score, na.rm = TRUE ) fcov_sum <- sum( stratum_1_surface_s1_fcov, stratum_3_elevated_p_s3_fcov, straum_4_intermediate_p_s4_fcov, stratum_5_overstorey_p_s5_fcov ) ozcbi <- score_sum / fcov_sum if (verbose == TRUE) { ruODK::ru_msg_success( glue::glue( "OzCBI: {ozcbi} = Score sum {score_sum} / FCOV sums {fcov_sum}" ) ) } ozcbi } # usethis::use_test("calculate_ozcbi")

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#Page Number: 55 library(plyr) dataset <- data.frame(Distinct_entry = c(8,9,0,9,1,4,3,7)) n <- length(dataset$Distinct_entry) y <- count(dataset,'Distinct_entry') print(y) z <- table(dataset) cat("Mode is",names(z)[which(z==max(z))] )

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/genejam-freshen.R \name{freshenGenes} \alias{freshenGenes} \title{Freshen gene annotations using Bioconductor annotation data} \usage{ freshenGenes( x, ann_lib = c("", "org.Hs.eg.db"), try_list = c("SYMBOL2EG", "ACCNUM2EG", "ALIAS2EG"), final = c("SYMBOL"), split = "[ ]*[,/;]+[ ]*", sep = ",", handle_multiple = c("first_try", "first_hit", "all", "best_each"), empty_rule = c("empty", "original", "na"), include_source = FALSE, protect_inline_sep = TRUE, intermediate = "intermediate", ignore.case = FALSE, verbose = FALSE, ... ) } \arguments{ \item{x}{character vector or \code{data.frame} with one or most columns containing gene symbols.} \item{ann_lib}{character vector indicating the name or names of the Bioconductor annotation library to use when looking up gene nomenclature.} \item{try_list}{character vector indicating one or more names of annotations to use for the input gene symbols in \code{x}. The annotation should typically return the Entrez gene ID, usually given by \code{'2EG'} at the end of the name. For example \code{SYMBOL2EG} will be used with ann_lib \code{"org.Hs.eg.db"} to produce annotation name \code{"org.Hs.egSYMBOL2EG"}. Note that when the \code{'2EG'} form of annotation does not exist (or another suitable suffix defined in argument \code{"revmap_suffix"} in \code{get_anno_db()}), it will be derived using \code{AnnotationDbi::revmap()}. For example if \code{"org.Hs.egALIAS"} is requested, but only \code{"org.Hs.egALIAS2EG"} is available, then \code{AnnotationDbi::revmap(org.Hs.egALIAS2EG)} is used to create the equivalent of \code{"org.Hs.egALIAS"}.} \item{final}{character vector to use for the final conversion step. When \code{final} is \code{NULL} no conversion is performed. When \code{final} contains multiple values, each value is returned in the output. For example, \code{final=c("SYMBOL","GENENAME")} will return a column \code{"SYMBOL"} and a column \code{"GENENAME"}.} \item{split}{character value used to separate delimited values in \code{x} by the function \code{base::strsplit()}. The default will split values separated by comma \verb{,} semicolon \verb{;} or forward slash \code{/}, and will trim whitespace before and after these delimiters.} \item{sep}{character value used to concatenate multiple entries in the same field. The default \code{sep=","} will comma-delimit multiple entries in the same field.} \item{handle_multiple}{character value indicating how to handle multiple values: \code{"first_hit"} will query each column of \code{x} until it finds the first possible returning match, and will ignore all subsequent possible matches for that row in \code{x}. For example, if one row in \code{x} contains multiple values, only the first match will be used. \code{"first_try"} will return the first match from \code{try_list} for all columns in \code{x} that contain a match. For example, if one row in \code{x} contains two values, the first match from \code{try_list} using one or both columns in \code{x} will be maintained. Subsequent entries in \code{try_list} will not be attempted for rows that already have a match. \code{"all"} will return all possible matches for all entries in \code{x} using all items in \code{try_list}.} \item{empty_rule}{character value indicating how to handle entries which did not have a match, and are therefore empty: \code{"original"} will use the original entry as the output field; \code{"empty"} will leave the entry blank.} \item{include_source}{logical indicating whether to include a column that shows the colname and source matched. For example, if column \code{"original_gene"} matched \code{"SYMBOL2EG"} in \code{"org.Hs.eg.db"} there will be a column \code{"found_source"} with value \code{"original_gene.org.Hs.egSYMBOL2EG"}.} \item{protect_inline_sep}{logical indicating whether to protect inline characters in \code{sep}, to prevent them from being used to split single values into multiple values. For example, \code{"GENENAME"} returns the full gene name, which often contains comma \code{","} characters. These commas do not separate multiple separate values, so they should not be used to split a string like \code{"H4 clustered histone 10, pseudogene"} into two strings \code{"H4 clustered histone 10"} and \code{"pseudogene"}.} \item{intermediate}{\code{character} string with colname in \code{x} that contains intermediate values. These values are expected from output of the first step in the workflow, for example \code{"SYMBOL2EG"} returns Entrez gene values, so if the input \code{x} already contains some of these values in a column, assign that colname to \code{intermediate}.} \item{ignore.case}{\code{logical} indicating whether to use case-insensitive matching when \code{ignore.case=TRUE}, otherwise the default \code{ignore.case=FALSE} will perform default \code{mget()} which requires the upper and lowercase characters are an identical match. When \code{ignore.case=TRUE} this function calls \code{genejam::imget()}.} \item{verbose}{logical indicating whether to print verbose output.} } \value{ \code{data.frame} with one or more columns indicating the input data, then a column \code{"intermediate"} containing the Entrez gene ID that was matched, then one column for each item in \code{final}, by default \code{"SYMBOL"}. } \description{ Freshen gene annotations using Bioconductor annotation data } \details{ This function takes a vector or \code{data.frame} of gene symbols, and uses Bioconductor annotation methods to find the most current official gene symbol. The annotation process runs in two basic steps: \enumerate{ \item \strong{Convert the input gene to Entrez gene ID}. \item \strong{Convert Entrez gene ID to official gene symbol}. } \subsection{Step 1. Convert to Entrez gene ID}{ The first step uses an ordered list of annotations, with the assumption that the first match is usually the best, and most specific. By default, the order is: \itemize{ \item \code{"org.Hs.egSYMBOL2EG"} -- almost always 1-to-1 match \item \code{"org.Hs.egACCNUM2EG"} -- mostly a 1-to-1 match \item \code{"org.Hs.egALIAS2EG"} -- sometimes a 1-to-1 match, sometimes 1-to-many } When multiple Entrez gene ID values are matched, they are all retained. See argument \code{handle_multiple} for custom options. } \subsection{Step 2. Use Entrez gene ID to return official annotation}{ The second step converts the Entrez gene ID (or multiple IDs) to the official gene symbol, by default using \code{"org.Hs.egSYMBOL"}. The second step may optionally include multiple annotation types, each of which will be returned. Some common examples: \itemize{ \item \code{"org.Hs.egSYMBOL"} -- official Entrez gene symbol \item \code{"org.Hs.egALIAS"} -- set of recognized aliases for an Entrez gene. \item \code{"org.Hs.egGENENAME"} -- official Entrez long gene name } For each step, the annotation matched can be returned, as an audit trail to see which annotation was available for each input entry. Note that if the input data already contains Entrez gene ID values, you can define that colname with argument \code{intermediate}. } \subsection{Case-insensitive search}{ For case-insensitive search, which is particularly useful in non-human organisms because they often use mixed-case, use the argument \code{ignore.case=TRUE}. In our benchmark tests it appears to add roughly 0.1 seconds per annotation, regardless of the number of input entries. This appears to be the time it takes to spool the list of annotation keys stored in the SQLite database, and may therefore be dependent upon the size of the annotation file. } } \examples{ if (suppressPackageStartupMessages(require(org.Hs.eg.db))) { cat("\nBasic usage\n"); print(freshenGenes(c("APOE", "CCN2", "CTGF"))); } if (suppressPackageStartupMessages(require(org.Hs.eg.db))) { ## Optionally show the annotation source matched cat("\nOptionally show the annotation source matched\n"); print(freshenGenes(c("APOE", "CCN2", "CTGF"), include_source=TRUE)); } if (suppressPackageStartupMessages(require(org.Hs.eg.db))) { ## Show comma-delimited genes cat("\nInput genes are comma-delimited\n"); print(freshenGenes(c("APOE", "CCN2", "CTGF", "CCN2,CTGF"))); } if (suppressPackageStartupMessages(require(org.Hs.eg.db))) { ## Optionally include more than SYMBOL in the output cat("\nCustom output to include SYMBOL, ALIAS, GENENAME\n"); print(freshenGenes(c("APOE", "HIST1H1C"), final=c("SYMBOL", "ALIAS", "GENENAME"))); } if (suppressPackageStartupMessages(require(org.Hs.eg.db))) { ## More advanced, match affymetrix probesets if (suppressPackageStartupMessages(require(hgu133plus2.db))) { cat("\nAdvanced example including Affymetrix probesets.\n"); print(freshenGenes(c("227047_x_at","APOE","HIST1H1D","NM_003166,U08032"), include_source=TRUE, try_list=c("hgu133plus2ENTREZID","REFSEQ2EG","SYMBOL2EG","ACCNUM2EG","ALIAS2EG"), final=c("SYMBOL","GENENAME"))) } } } \seealso{ Other genejam: \code{\link{freshenGenes2}()}, \code{\link{freshenGenes3}()}, \code{\link{get_anno_db}()}, \code{\link{is_empty}()} } \concept{genejam}

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\name{nikkei.df} \docType{data} \alias{nikkei.df} \title{ Nikkei Stock Market Index (data.frame Object) January 4, 1994-March 25, 2004 } \description{ The \code{nikkei.df} dataframe provides the daily closing value for the Nikkei index from January 1994 to March 2004 in its nikkei@Data slot. QRMlib's R-version 1.4.2 and above supplies data in both timeSeries and data.frame versions. } \usage{ data(nikkei.df) } \format{ This dataframe object contains the prices for the index at nikkei.df[,2] and the corresponding dates at nikkei.df\$DATE. The dataframe can be converted to a timeSeries by calling the ConvertDFToTimeSeries() method in functionsUtility.R. } \seealso{ \code{\link{nikkei}} } \keyword{datasets}

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plot_resolution_effect <- function(mod_res) { res_effect <- mod_res %>% mutate(obs = map_dbl(rounded_data, nrow)) %>% ggplot(aes(resolution, r2)) + geom_point(aes(size = obs), shape = 21, fill = "steelblue") + geom_smooth(se = F) + scale_y_continuous(limits = c(0, NA), name = expression(R^2)) + scale_size_continuous(name = "Observations",range = c(2,7), breaks = seq(0,3000, by = 500)) + theme(axis.title.y = element_text(angle = 0)) + labs(x = "Lat/Long Resolution", title = "Model: Fish ~ Fishing") }

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#import data first library(readxl) income_data <- read_excel("C:/Users/karen/Downloads/UIC/ids572/hw2/income.data.xlsx", + col_names = FALSE) #add title for each column names(income_data)<-c("hh_income","Sex","martial status","age","education","occupation","timeinSF","dualincomes", "hh_size","under18","hh_status","home_type","ethinic_class","lang_speak") #clean outliers #clean missing values first summary(income_data) #found that some of the data are characters , including the "NA"-NEED TO MAKE THEM LOGICAL TYPE income_data[income_data=="NA"]<-NA #double check again sum(!complete.cases(income_data)) #now start to find out all of them library(mice) md.pattern(income_data) #only 6876 cases are completed #visualize that first library("VIM") na_plot<-aggr(income_data,col=c('blue','red'),numbers=TRUE,prop=TRUE,sortVars=TRUE,labels=names(income_data),cex.axis=1,gap=0, ylab=c("histrogram of missing values","pattern")) #10% of data in timeinSF are missing now followed by the hh_size #inputing the missing values using mice #transform categorical var to factor first income_data$`martial status`<-as.factor(income_data$`martial status`) income_data$education<-as.factor(income_data$education) income_data$occupation<-as.factor(income_data$occupation) income_data$timeinSF<-as.factor(income_data$timeinSF) income_data$hh_size<-as.factor(income_data$hh_size) income_data$hh_status<-as.factor(income_data$hh_status) income_data$home_type<-as.factor(income_data$home_type) income_data$ethinic_class<-as.factor(income_data$ethinic_class) income_data$lang_speak<-as.factor(income_data$lang_speak) #now we could start the imputation init = mice(income_data, maxit=0) meth = init$method predM = init$predictorMatrix #now set different imputation for different variables #ordinal meth[c("timeinSF","hh_size","education")]='polyreg' #nominal: lang_speak,home_type,"hh_status","martial status","occupation","ethic_class", try pmm firsst meth[c("lang_speak","hh_status","martial status","occupation","ethinic_class","home_type")]='pmm' #marital summary(income_data$`martial status`) #hh_type summary(income_data$home_type) #lang_speak summary(income_data$lang_speak) #hh_status #after imputation imputation<-mice(income_data,m=9,method=meth,predictorMatrix = predM) complete(imputation) #income ready to use now income.data<-complete(imputation)

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/shinyDND.R \docType{package} \name{shinyDND} \alias{shinyDND} \alias{shinyDND-package} \title{ShinyDND: A package for creating drag and drop elements in Shiny.} \description{ The ShinyDND package provides three categories of important functions: dragUI, dropUI, and dragSetUI. } \section{ShinyDND functions}{ The ShinyDND functions ... }

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#' Find set of node ids in graph #' #' This function allows you to get a vector of node ids matching your input vector with strings of node identifiers. Identifiers not found in the network are ignored. #' @param seed_graph igraph object of the network to be searched. #' @param seedvec vector with strings of node identifiers. #' @return rowindex vector of integers with the found node ids #' @export seedrows=function(seed_graph,seedvec){ vertexlist=unlist(igraph::vertex_attr(seed_graph)) rowindex=which(is.element(vertexlist,seedvec)) }

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fit.stem.R

#' Estimation using Spatio-Temporal Epidemic Model (STEM) #' #' This function implement the model fitting of the spatiotemporal epidemic model. #' @import mgcv #' @importFrom kr MGLM #' #' @param formula A STEM formula which is exactly like the formula for a GAM except that bivariate smooth terms, f(), can be added to the right hand side to specify that the predictor depends on bivariate smooth functions of predictors. #' @param location Location information used in the bivariate smooth function. #' @param V Vertices of a triangulation. #' @param Tr Triangles of a triangulation. #' @param d Degree of polynomials in bivariate smooth funciton, default is 2. #' @param r Smoothness parameter in bivariate smooth function, default is 1. #' @param family This is a family object specifying the distribution and link to use in fitting etc, default is ziP() (the zero-inflated poisson). #' @param data A data frame or list containing the model response variable and covariates required by the formula. #' @param offset The offset used to supply a model fitting. #' #' @return fit.stem <- function(cov.infec, cov.death, data = list(), b1.type = "cons", cov.type = "cons"){ dat.fit = data$dat.fit dat.fit$lI = log(dat.fit$I + 1) BQ2.infec = data$BQ2.infec P.infec = data$P.infec BQ2.death = data$BQ2.death P.death = data$P.death if(b1.type == "vary"){ tmp01 <- paste0("y.infec ~ 0 + BQ2_all.infec + BQ2_all.X.infec + Control2") }else{ tmp01 <- paste0("y.infec ~ 0 + BQ2_all.infec + lI + Control2") } if(cov.type == "vary"){ tmp02 <- paste0(' + s(',cov.infec,', bs = \'cr\', k = 4)', collapse = '') }else{ tmp02 <- paste0(' + ', cov.infec, collapse = '') } formula.infec = as.formula(paste0(tmp01, tmp02)) cat("formula.infec:", as.character(formula.infec), "\n") if(b1.type == "vary"){ mfit.stem.infec <- gam(formula.infec, family = ziP(), paraPen = list(BQ2_all.infec = list(P.infec), BQ2_all.X.infec = list(P.infec)), data = dat.fit, offset = log(dat.fit$S.ratio)) }else{ mfit.stem.infec <- gam(formula.infec, family = ziP(), paraPen = list(BQ2_all.infec = list(P.infec)), data = dat.fit, offset = log(dat.fit$S.ratio)) } tmp01 <- paste0("y.death ~ 0 + BQ2_all.death + lI + Control2") tmp02 <- paste0(' + ', cov.death, collapse = '') formula.death = as.formula(paste0(tmp01, tmp02)) cat("formula.death:", as.character(formula.death), "\n") mfit.stem.death <- gam(formula.death, family = ziP(), paraPen = list(BQ2_all.death = list(P.death)), data = dat.fit, offset = log(dat.fit$S.ratio)) list(mfit.stem.infec = mfit.stem.infec, mfit.stem.death = mfit.stem.death, b1.type = b1.type, cov.type = cov.type, BQ2.infec = BQ2.infec, BQ2.death = BQ2.death) }

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/Report_2.R

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DrMuhsin/Statistical-Inference

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Report_2.R

library(ggplot2) # data(ToothGrowth) summary(ToothGrowth) dose <- ToothGrowth$dose supp <- ToothGrowth$supp len <- ToothGrowth$len # Load the ToothGrowth data and perform some basic exploratory data analyses # Provide a basic summary of the data. # Use confidence intervals and hypothesis tests to compare tooth growth by supp and dose. (Use the techniques from class even if there's other approaches worth considering) # State your conclusions and the assumptions needed for your conclusions. par(mfrow = c(1,2)) p1 <- ggplot(ToothGrowth, aes(x = factor(dose), y = len, fill = factor(dose))) p1 + geom_boxplot() + guides(fill=FALSE) + facet_grid(. ~ supp) p2 <- ggplot(ToothGrowth, aes(x = factor(supp), y = len, fill = factor(supp))) p2 + geom_boxplot() + guides(fill=FALSE) + facet_grid(. ~ dose) # calculate the mean and sd of each dosage and supplement dosages_means <- aggregate(ToothGrowth$len, by = list(ToothGrowth$dose), FUN = mean) dosages_sds <- aggregate(ToothGrowth$len, by = list(ToothGrowth$dose), FUN = sd) Supplements_means <- aggregate(ToothGrowth$len, by = list(ToothGrowth$supp), FUN = mean) Supplements_sds <- aggregate(ToothGrowth$len, by = list(ToothGrowth$supp), FUN = sd) # split the data up by dosages d0.5 <- subset(ToothGrowth, dose == 0.5) d1.0 <- subset(ToothGrowth, dose == 1.0) d2.0 <- subset(ToothGrowth, dose == 2.0) # conduct a t test between supplements test0.5 <- t.test(len ~ supp, paired = FALSE, var.equal = FALSE, data = d0.5) test0.5$p.value; test0.5$conf test1.0 <- t.test(len ~ supp, paired = FALSE, var.equal = FALSE, data = d1.0) test1.0$p.value; test1.0$conf test2.0 <- t.test(len ~ supp, paired = FALSE, var.equal = FALSE, data = d2.0) test2.0$p.value; test2.0$conf

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/Lab2.R

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aphiratnimanussonkul/data-analysis

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Lab2.R

#Lab 2 #Sys.setlocale(locale = "Thai") #when readline can get input in thai language #1. Write an R program to take input from the user (name and age) and # display the values and their types. name <- readline(prompt = "Enter name: ") age <- as.integer(readline(prompt = "Enter age: ")) # class(age) , typeof(age) #class(n) show tpye of variable #as.integer() make sure input will be integer #age_numberic <- as.numeric(age) print(paste("Your name is :", name)) print(paste("youe age is : ", age)) # 2. Write an R program to create a sequence of numbers from 20 to 50 # and find the mean of numbers from 20 to 60 and sum of numbers from # 51 to 91. #create a sequence of numbers from 20 to 50 seq_num_20_50 <- seq(from = 20, to = 50, by = 1) #find the mean of numbers from 20 to 60 #create seq num 20 to 60 seq_num_20_60 <- seq(from = 20, to = 60, by = 1) mean_20to60 <- mean(seq_num_20_60) # sum of numbers from 51 to 91 #create seq num 51 to 91 seq_num_51_91 <- seq(from = 51, to = 91, by = 1) sum_51to90 <- sum(seq_num_51_91) # 3. Write an R program to create a vector which contains 100 random # integer values between -5 and 5 ran_5to5 <- c(runif(100, min = -5, max = 5)) #or # ran_5to5 <- c(sample(-5:5, 100, replace = T)) # 4. Write an R program to get the first 9 Fibonacci numbers and put them # into the vector,then put them into the matrix. recurse_fibonacci <- function(n) { if(n <= 1) { return(n) } else { return(recurse_fibonacci(n-1) + recurse_fibonacci(n-2)) } } nterms <- 9 #create list for store fibo from loop fibo_list = list() #create n terms fibo for(i in 0:(nterms-1)) { fibo_list[i+1] <- c(recurse_fibonacci(i)) } #convert list to vector fibo_9 <- unlist(fibo_list) fibo_matrix <- matrix(fibo_9) # 5. Write an R program to print the numbers from 44 to 100 and print # "Fizz" for multiples of 2, print "Buzz" for multiples of 3, and print # "FizzBuzz" for multiples of both. for (i in 44:100) { if (i %% 2 == 0 & i %% 3 == 0) { print(paste("i = ", i, " FizzBuzz")) } else if (i %% 2 == 0) { print(paste("i = ", i, " Fizz")) } else if (i %% 3 == 0) { print(paste("i = ", i, " Buzz")) } else { print(i) } } # 6. Write an R program to find the maximum and the minimum value of a # given vector. #create vector from random vec_random <- c(runif(100, min = -5, max = 10)) vec_random_min <- min(vec_random) vec_random_max <- max(vec_random) # 7. Write an R program to create three vectors a,b,c with 3 integers. # Combine the three vectors to become a 3×3 matrix where each column # represents a vector. Print the content of the matrix. #replace = F is Not random number repeate a <- c(sample(1:20, 3, replace = F)) b <- c(sample(1:20, 3, replace = F)) c <- c(sample(1:20, 3, replace = F)) matrix_3_3 <- cbind(a, b, c) print(matrix_3_3) # 8. Write an R program to create a vector of random numbers in normal # distribution and count occurrences of each value. vector_normal_dis <- c(rnorm(50, mean = 5, sd = 2)) # vector_normal_dis <- c(sample(1:25, 50, replace = T)) count = list() temp_vect_nor = list() index = 0 isRepeate = F for (i in vector_normal_dis) { print(i) if (index == 0) { index <- index + 1 print("index = 1") temp_vect_nor[index] <- i count[index] <- 1 } else { for (j in 1:index) { print(paste("j = ", temp_vect_nor[j])) if (temp_vect_nor[j] == i) { count[j] <- as.numeric(count[j]) + 1 isRepeate = T } } if (!isRepeate) { index <- index + 1 temp_vect_nor[index] <- i count[index] <- 1 } } isRepeate = F } #table() for count count_re <- table(vector_normal_dis) summary_repeate <- cbind(unlist(temp_vect_nor), unlist(count)) print(vector_normal_dis) # 9. Write an R program to read the .csv file and display the content. grades_set <- read.csv("C:\\cygwin64\\home\\AphiratNimanussonkul\\data-analysis\\grade_csv.csv", header = TRUE, sep = ",") print(grades_set) # 10. Write an R program to print row 2-3-4 of the .csv file. #[row, col] print(grades_set[2:4, ])

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Example 1 - Simulation.R

# Simulation with zero nuisance parameters n<-500 # 100, 200, 300, 400, 500 N<-1000 ME<-matrix(0, N, 2) A<-matrix(0, N, 2) B<-matrix(0, N, 2) ALPHA<-NULL # Simulation for (f in 1:N) { ## True paremeters beta1<-c(0,0) beta2<-c(0,0) tu<-1.5 a<-c(tu, tu) b<-c(tu, -tu) c<-c(0,0) sigma1<-c(1,1) sigma2<-c(1,1) alpha<-c(0.5,0.5) ## Sample based on HMM with $K_{0}=2$ Y<-NULL M<-NULL n1<-floor(n*alpha[1]) n2<-n-n1 X<-rnorm(n) M[1:n1]<-beta1[1]+a[1]*X[1:n1]+rnorm(n1)*sigma1[1] Y[1:n1]<-beta2[1]+c[1]*X[1:n1]+b[1]*M[1:n1]+rnorm(n1)*sigma2[1] M[(1+n1):n]<-beta1[2]+a[2]*X[(1+n1):n]+rnorm(n2)*sigma1[2] Y[(1+n1):n]<-beta2[2]+c[2]*X[(1+n1):n]+b[2]*M[(1+n1):n]+rnorm(n2)*sigma2[2] U<-rep(1, n) V<-matrix(c(U, X), ncol=2) Z<-matrix(c(U, X, M), ncol=3) # EM for Two Mixed Gaussians ## Initialization setting K<-2 beta1<-runif(K) beta2<-runif(K) a<-runif(K) b<-runif(K) c<-runif(K) d<-runif(1, 0.4, 0.6) alpha<-c(d,1-d) sigma1<-runif(K, 0.5, 1) sigma2<-runif(K, 0.5, 1) P<-matrix(rep(0,n*K),ncol=K) Q<-matrix(rep(0,n*K),ncol=K) R<-matrix(rep(0,n*K),ncol=K) H<-matrix(rep(0,n*K),ncol=K) ## Circle for (S in 1:200){ ## E-step for (j in 1:K){ for (i in 1:n){ P[i,j]<-sapply(M[i], dnorm, beta1[j]+a[j]*X[i], sigma1[j]) Q[i,j]<-sapply(Y[i], dnorm, beta2[j]+c[j]*X[i]+b[j]*M[i], sigma2[j]) H[i,j]<-alpha[j]*P[i,j]*Q[i,j] } } H<-H/rowSums(H) oldbeta1<-beta1 oldbeta2<-beta2 olda<-a oldb<-b oldc<-c oldalpha<-alpha oldsigma1<-sigma1 oldsigma2<-sigma2 ## M-step for (j in 1:K){ alpha[j]<-sum(H[,j])/sum(H) beta1[j]<-(solve(t(V)%*%diag(H[,j])%*%V)%*%t(V)%*%diag(H[,j])%*%M)[1] a[j]<-(solve(t(V)%*%diag(H[,j])%*%V)%*%t(V)%*%diag(H[,j])%*%M)[2] beta2[j]<-(solve(t(Z)%*%diag(H[,j])%*%Z)%*%t(Z)%*%diag(H[,j])%*%Y)[1] c[j]<-(solve(t(Z)%*%diag(H[,j])%*%Z)%*%t(Z)%*%diag(H[,j])%*%Y)[2] b[j]<-(solve(t(Z)%*%diag(H[,j])%*%Z)%*%t(Z)%*%diag(H[,j])%*%Y)[3] sigma1[j]<-{(M-c(beta1[j], a[j])%*%t(V))%*%diag(H[,j])%*%t(M-c(beta1[j], a[j])%*%t(V))/sum(H[,j])}^0.5 sigma2[j]<-{(Y-c(beta2[j], c[j], b[j])%*%t(Z))%*%diag(H[,j])%*%t((Y-c(beta2[j], c[j], b[j])%*%t(Z)))/sum(H[,j])}^0.5 } ## Change condition espsilo<-1e-5 if (sum(abs(a-olda)<espsilo) & sum(abs(b-oldb)<espsilo) & sum(abs(c-oldc)<espsilo) & sum(abs(alpha-oldalpha)<espsilo)& sum(abs(beta1-oldbeta1)<espsilo)& sum(abs(beta2-oldbeta2)<espsilo) ) break cat('S', S, 'a', a, 'b', b, 'c', c,'alpha', alpha, '\n') } # Interested Parameters' Estimation men1<-which.min(c(a[1]*b[1],a[2]*b[2])) men2<-which.max(c(a[1]*b[1],a[2]*b[2])) me1<-a[men1]*b[men1] me2<-a[men2]*b[men2] ME[f,]<-c(me1, me2) A[f,]<-c(a[men1], a[men2]) B[f,]<-c(b[men1], b[men2]) ALPHA[f]<-alpha[men1] } ## Mean and SD in Table 2 XXX<-ALPHA # XXX=ALPHA, A[, 1:2], B[, 1:2], and ME[, 1:2]. c(round(mean(XXX), digits=3), round(sd(XXX), digits=3))

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SupplementaryDataObservationContribution.R

#Find names of people who contributed to the ABBBS banding data used in the traits that decreas Thermal Stress paper rm(list = ls()) library(raster) indir<-'/Users/daisy/Google Drive/PhD/Data/Observaitons/Raw/egg' abbbsA<-read.csv(paste0(indir,"/ABBBS/pullusBandingRecordsPartA_dd.csv")) abbbsB<-read.csv(paste0(indir,"/ABBBS/pullusBandingRecordsPartB_dd.csv")) abbbs<-rbind(abbbsA,abbbsB) abbbs<-abbbs[!duplicated(abbbs[,c("SCIENTIFIC_NAME","Day","Month","Year","LAT","LON","BANDER")]),] abbbs$sourceName<-'ABBBS' abbbs$epoch<-as.numeric(as.Date(paste0(abbbs$Year,'-',abbbs$Month,'-',abbbs$Day))) abbbs$type<-with(abbbs, ifelse (AGE == "NESTLING","youngNest",'unknown')) abbbs$startUnknown<-with (abbbs, ifelse (type=='unknown', abbbs$epoch,NA)) abbbs$startYoung<-with (abbbs, ifelse (type=='startYoung', abbbs$epoch,NA)) abbbs$lat<-abbbs$LAT abbbs$lon<-abbbs$LON abbbs$Scientific.Name<-abbbs$SCIENTIFIC_NAME abbbs$sourceName <- 'ABBBS' abbbs<-abbbs[,c('Scientific.Name','lat','lon', 'sourceName', 'type' ,'startYoung','startUnknown','BANDER')] eggdat<-abbbs # returns string w/o leading or trailing whitespace trim <- function (x) gsub("^\\s+|\\s+$", "", x) #finish cleaning egdat eggdat$Scientific.Name<- capitalize(trim(gsub(' ', ' ', eggdat$Scientific.Name, fixed=TRUE)))#check for extra spaces and capatlize eggdat<-subset(eggdat, !is.na(lat) & !is.na(lon) & !is.na(Scientific.Name))#make sure lat and longs are given #make sure all latitudes are negative latfix<-subset(eggdat, lat > 0) latfix$lat<-latfix$lat*-1 latgood<-subset(eggdat, lat <= 0) eggdat<-rbind(latfix, latgood) #remove duplicates eggdat<-eggdat[!duplicated(eggdat),] #make sure years are as expected syn<-subset(read.csv('/Users/daisy/Google Drive/PhD/Data/Observaitons/Raw/namesResolved.csv'), use ==1) bad<-subset(read.csv('/Users/daisy/Google Drive/PhD/Data/Observaitons/Raw/namesResolved.csv'), use ==0) eggdat<-eggdat[eggdat$Scientific.Name %nin% bad$scn1,] fix<-eggdat[eggdat$Scientific.Name %in% syn$scn1,] nofix<-eggdat[eggdat$Scientific.Name %nin% syn$scn1,]#[c(1:8)] fix<-merge(fix,syn,by.x = 'Scientific.Name',by.y='scn1') fix$Scientific.Name <-fix$garnett_name fix<-fix[,c('Scientific.Name','lat','lon', 'sourceName', 'type' ,'startYoung','startUnknown','BANDER')] eggdat<-smartbind(fix,nofix) eggdat<-eggdat[!duplicated(eggdat),] #make sure data is within continental Australia aus<-raster("/Users/daisy/Google Drive/PhD/Data/Spatial/Climate/averageAnnualVPD.asc", crs = '+proj=longlat +datum=WGS84') xy<-cbind(eggdat$lon,eggdat$lat) eggdat$outsideAustralia<-is.na(extract(aus,xy)) eggdat<-subset(eggdat,outsideAustralia==FALSE) #see if they are species of interest inc<-read.csv('/Users/daisy/Google Drive/PhD/ThermalStress/tables/Table_S1_20161128.csv') #make sure only have species interested in species<-inc$Species eggdat<-eggdat[eggdat$Scientific.Name %in% species,] #keep observations of wanted species a<- trim(gsub(' ', ' ', eggdat$BANDER,fixed=TRUE))#remove white spaces a<-as.data.frame(unique(a))#list of names need to include a<-lapply(a, as.character)[[1]] write.csv(a,paste0("~/Google Drive/PhD/ThermalStress/tables/ABBBSBanders", as.Date(Sys.time()),".csv"),row.names=F)

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compute.PARz.R

#' Compute PAR at depth from surface irradiance (Ed0+) and profile of #' downwelling irradiance (EdZ) #' #'@param Depth is a vector of depth corresponding to the EdZ measurements #'@param waves is a vector of wavelength of EdZ and Ed0+ #'@param Edz is a matrix of EdZ (col=waves; row=Depth) after nomalization to Ed.0 variation #'@param Ed.0 is a is a vector surface irradiance #'@param f.PAR is a vector of numeric values corresponting to light fraction. #' The depth (in m) of each light fraction will be computed. #' Default is f.PAR=c(0.001, 0.01, 0.1,0.5) #'@param z.fixed is a vector of numeric values corresponting to depth at which light fraction will be calculated. #' Default is z.fixed=c(5,10,20,30,40) #' #'@return The program will return three vectors for #' 1) the depth of each PAR fraction requested (z.f.PAR); #' 2) the fraction of PAR at the depth requested (PAR.at.z); #' 3) the vector of PAR at all depth Z (PAR.z) in micro mol photon / m^2 #' #'@author Simon Belanger #'@export compute.PARz <- function(Depth, waves, Edz, Ed.0, f.PAR=c(0.001, 0.01, 0.1,0.5), z.fixed=c(5,10,20,30,40)) { # Convert microW/cm^-2.nm to Quanta/m-2.nm.s c = 2.9979e17 # in nm/sec h = 6.6255e-34 # in J sec Q0 = Ed.0 * waves / (h*c) * 0.01 # factor -0.01 to convert to W/m^-2.nm # integrated Q0[is.na(Q0)] <- 0 fx.linear <- approxfun(waves, Q0) PAR.0m = integrate(fx.linear, 380, 700, subdivisions=350, stop.on.error = FALSE)[1] PAR.0m = PAR.0m$value nz = length(Depth) PAR.z = rep(NA,nz) for (i in 1:nz){ QZ = Edz[i,] * waves / (h*c) * 10000/1e6 # # integrated QZ[is.na(QZ)] <- 0 fx.linear <- approxfun(waves, QZ) tmp = integrate(fx.linear, 380, 700, subdivisions=350, stop.on.error = FALSE)[1] PAR.z[i] = tmp$value } # Compute the depth of 90%, 50%, 30%, 10%, 1% and 0.1% PAR res= spline((PAR.z/PAR.0m), Depth, xout=f.PAR) z.f.PAR= cbind(f.PAR*100,res$y) z.f.PAR = as.data.frame(z.f.PAR[length(f.PAR):1,]) names(z.f.PAR) = c("%PAR", "z") # compute %PAR at fixed depth res= spline(Depth, (PAR.z/PAR.0m), xout=z.fixed) PAR.at.z= as.data.frame(cbind(z.fixed,res$y*100)) names(PAR.at.z) = c("z.fixed", "%PAR") return(list(z.f.PAR=z.f.PAR, PAR.z=PAR.z / 6.06E23 *1e6, PAR.at.z=PAR.at.z)) }

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functions.R

# Functions used for Data Analysis and Visualization Creations stored.procedure.from.db <- function(srv.nm, db.nm, procedure.nm) { db.con <- dbConnect(odbc::odbc(), driver = "SQL Server", server = srv.nm, database = db.nm, trusted_connection = "yes") w.tbl <- DBI::dbGetQuery(db.con, procedure.nm) odbc::dbDisconnect(db.con) as_tibble(w.tbl) return(w.tbl) } get.county.census <- function(c.type, c.yr, c.table, c.geo="county:033,035,053,061", c.state="state:53", l.yr = label.yr) { # Download Table from API tbl.values <- suppressWarnings(getCensus(name = c.type, vintage = c.yr, vars = c("NAME",paste0("group(",c.table,")")),region = c.geo, regionin = c.state) %>% select(ends_with(c("E","M"))) %>% select(-state) %>% rename(Geography=NAME) %>% pivot_longer(cols=contains("_"), names_to="name", values_to="value") %>% mutate(Geography = str_replace(Geography, ", Washington", ""))) # Get variable labels tbl.vars <- listCensusMetadata(name = c.type, vintage = l.yr, type = "variables", group = c.table) %>% filter(grepl("(E|M)$", name)) %>% select(name,label) %>% mutate(label = gsub("!!"," ", label), label = gsub("Margin of Error","MoE", label), label = gsub(" Total:",":", label)) # JOin values and labels tbl.values <- inner_join(tbl.values, tbl.vars, by="name") %>% select(-name) %>% pivot_wider(names_from = label) tbl.values[tbl.values == -555555555 ] <- 0 # Add total for region with calculated MoE for county to region aggregation region.moe <- suppressWarnings(tbl.values %>% select(contains("MoE")) %>% mutate(PSRC=1) %>% group_by(PSRC) %>% summarise_all(moe_sum)) region.tot <- tbl.values %>% select(!contains("MOE"),-Geography) %>% mutate(PSRC=1) %>% group_by(PSRC) %>% summarise_all(sum) region <- inner_join(region.tot,region.moe,by="PSRC") %>% mutate(Geography="Central Puget Sound") %>% select(-PSRC) # Append Region Total to table tbl.values <- bind_rows(tbl.values,region) return(tbl.values) } return.value <-function(data=results, c.geo=c, c.year=c.yr, acs.typ, c.tbl, c.val ) { r <- data[[c.year]][['tables']][[acs.typ]][[c.tbl]] %>% filter(Geography %in% c.geo) %>% pull(c.val) %>% sum() return(r) }

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/R/shiftDataByCol.R

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shiftDataByCol.R

# TODO: Add comment # # Author: kaiyin ############################################################################### shiftDataByCol = function(dat) { nr = nrow(dat) nc = ncol(dat) message("Shifting data col by col...") message(paste(" dat has", nr, "rows")) message(paste(" dat has", nc, "cols")) message(paste("Head of dat before shifting: ")) print(head(dat)) message(paste("Tail of dat before shifting: ")) print(tail(dat)) message("initialize a matrix newdat with same size as dat...") newdat = matrix(NA, nr, nc) for(i in 1:nc) { colShiftN = as.integer(colnames(dat)[i]) message(paste(" This col should be shifted by ", colShiftN, "according to colname")) newdat[1:(nr-colShiftN), i] = dat[(colShiftN+1):nr, 1] } message(paste("Head of dat after shifting: ")) print(head(newdat)) message(paste("Tail of dat after shifting: ")) print(tail(newdat)) message(paste("Setting colnames for new dat...")) colnames(newdat) = colnames(dat) print(head(newdat)) print(tail(newdat)) newdat }

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/SpatemR.R

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SpatemR.R

library(ggmap) library(httr) library(rjson) library(RCurl) library(grid) library(png) library(jpeg) # Abfragen der Instagarm Daten ## httr Rederiction URI full_url <- oauth_callback() full_url <- gsub("(.*localhost:[0-9]{1,5}/).*", x=full_url, replacement="\\1") print(full_url) #Zugangsdaten für den Instragram Client laden source("secret.R",chdir=T) ## Konfiguration der authentifizierten Verbindung instagram <- oauth_endpoint( authorize = "https://api.instagram.com/oauth/authorize", access = "https://api.instagram.com/oauth/access_token") myapp <- oauth_app(app_name, client_id, client_secret) ig_oauth <- oauth2.0_token(instagram, myapp,scope="basic", type = "application/x-www-form-urlencoded",cache=FALSE) tmp <- strsplit(toString(names(ig_oauth$credentials)), '"') token <- tmp[[1]][4] ## Daten abrufen username <- "Inventionate" user_info <- fromJSON(getURL(paste('https://api.instagram.com/v1/users/search?q=',username,'&access_token=',token,sep="")),unexpected.escape = "keep") id <- user_info$data[[1]]$id # Follower erfragen followers <- fromJSON(getURL(paste('https://api.instagram.com/v1/users/',id,'/follows?access_token=',token,sep="")),unexpected.escape = "keep") anniID <- followers$data[[1]]$id ### Kompletten eigenen Feed laden media <- fromJSON(getURL(paste('https://api.instagram.com/v1/users/',id,'/media/recent/?access_token=',token,'&count=33',sep="")),unexpected.escape = "keep") # Nutzernamen auslesen for (i in 1:length(media$data) ) { print(paste(i,': ',media$data[[i]]$user$full_name)) } ### Kompletten Feed eines anderen Nutzers laden mediaAnni <- fromJSON(getURL(paste('https://api.instagram.com/v1/users/',anniID,'/media/recent/?access_token=',token,'&count=33',sep="")),unexpected.escape = "keep") mediaAnni # Nutzernamen auslesen for (i in 1:length(mediaAnni$data) ) { print(paste(i,': ',mediaAnni$data[[i]]$user$full_name)) } # Koordinatenpunkte pro Nutzen plotten # Geokoordinaten der Bilder abrufen df = data.frame(no = 1:length(media$data)) for (i in 1:length(media$data) ) { if ( is.null(media$data[[i]]$location$name) ) { df$name[i] <- "Nicht benannt" } else { df$name[i] <- media$data[[i]]$location$name } df$latitude[i] <- media$data[[i]]$location$latitude df$longitude[i] <- media$data[[i]]$location$longitude date <- as.POSIXct(as.numeric(media$data[[i]]$created_time), origin="1970-01-01") df$created_date[i] <- format(date, "%d.%m.%Y") # Stunden: %H # Minuten: %M # Datum nach drei Kategorien ordnen hour <- as.numeric(format(date, "%H")) # Morgens: 5 bis 11 Uhr if (5 < hour && hour <= 11) { df$created_time[i] <- "morgens" } # Mittags: 11 bis 15 Uhr if (11 < hour && hour <= 15) { df$created_time[i] <- "mittags" } # Nachmittags: 15 bis 18 Uhr if (15 < hour && hour <= 18) { df$created_time[i] <- "nachmittags" } # Abends: 18 bis 23 Uhr if (18 < hour && hour <= 23) { df$created_time[i] <- "abends" } # Nachts: 23 bis 5 Uhr if (23 < hour || hour <= 5) { df$created_time[i] <- "nachts" } df$image[i] <- media$data[[i]]$images$standard_resolution$url } df # Geografische Analyse der sozialen Orte Chronologie ## Erstellen einer Karte für den geografischen Raum Karlsruhe myMap <- get_map(location = c(lat = 49.01345, lon = 8.39451), source="stamen", zoom=17, maptype="watercolor", crop=FALSE) map <- ggmap(myMap, maprange=FALSE, extent = 'device', base_layer=ggplot(aes(x = longitude, y = latitude), data=df)) # legend="topright" plot <- map + theme( legend.title = element_blank(), legend.text = element_text(size = 14) ) + geom_path(size=1.5, alpha=0.75, colour="darkred") + geom_point(aes(colour=created_time), shape=15, alpha = 1, size = 25) + guides(colour = guide_legend(override.aes = list(size=5))) plot # Bilder in Karte einfügen # Bilder abrufen for (i in 1:nrow(df) ) { myurl = toString(df$image[i]) z <- tempfile() download.file(myurl,z,mode="wb") assign( paste0("pic_",i,sep=""), readJPEG(z)) file.remove(z) # cleanup assign( paste0("g_",i,sep=""), rasterGrob(paste("pic_",i,sep=""), interpolate=T)) } # Bilder hinzufügen margin <- 0.0005 plot.pic <- plot for (i in 1:nrow(df)) { plot.pic <- plot.pic + inset(rasterGrob(get(paste("pic_",i,sep="")), interpolate=T), xmin=df$longitude[i]-margin, xmax=df$longitude[i]+margin, ymin=df$latitude[i]-margin,ymax=df$latitude[i]+margin) } plot.pic # Abstrakte Analyse der sozialen Orte Chronologie plot <- ggplot(df, aes(x=longitude, y=latitude)) + geom_path(size=1.5,alpha=0.75,linetype="solid",colour="black") + geom_point(aes(colour=created_time), shape=15, alpha = 0.85, size = 24) + guides(colour = guide_legend(override.aes = list(size=5))) + theme_minimal() + theme( legend.title = element_blank(), legend.text = element_text(size = 14) ) plot # Bilder in abstrakten Plot einfügen # Bilder abrufen for (i in 1:nrow(df) ) { myurl = toString(df$image[i]) z <- tempfile() download.file(myurl,z,mode="wb") assign( paste0("pic_",i,sep=""), readJPEG(z)) file.remove(z) # cleanup assign( paste0("g_",i,sep=""), rasterGrob(paste("pic_",i,sep=""), interpolate=T)) } # Bilder hinzufügen margin <- 0.05 plot.pic <- plot for (i in 1:nrow(df)) { plot.pic <- plot.pic + annotation_custom(rasterGrob(get(paste("pic_",i,sep="")), interpolate=T), xmin=df$longitude[i]-margin, xmax=df$longitude[i]+margin, ymin=df$latitude[i]-margin,ymax=df$latitude[i]+margin) } plot.pic

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Redspecialist/pagoda2

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RcppExports.R

# Generated by using Rcpp::compileAttributes() -> do not edit by hand # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 non0LogColLmS <- function(sY, X, ldepth, maxCells = 0L) { .Call('pagoda2_non0LogColLmS', PACKAGE = 'pagoda2', sY, X, ldepth, maxCells) } colMeanVarS <- function(sY, rowSel) { .Call('pagoda2_colMeanVarS', PACKAGE = 'pagoda2', sY, rowSel) } colSumByFac <- function(sY, rowSel) { .Call('pagoda2_colSumByFac', PACKAGE = 'pagoda2', sY, rowSel) } inplaceColMult <- function(sY, mult, rowSel) { .Call('pagoda2_inplaceColMult', PACKAGE = 'pagoda2', sY, mult, rowSel) } inplaceWinsorizeSparseCols <- function(sY, n) { .Call('pagoda2_inplaceWinsorizeSparseCols', PACKAGE = 'pagoda2', sY, n) } jsDist <- function(m) { .Call('pagoda2_jsDist', PACKAGE = 'pagoda2', m) } orderColumnRows <- function(p, i) { .Call('pagoda2_orderColumnRows', PACKAGE = 'pagoda2', p, i) } smatColVecCorr <- function(sY, sv, parallel = TRUE) { .Call('pagoda2_smatColVecCorr', PACKAGE = 'pagoda2', sY, sv, parallel) } arma_mat_cor <- function(m) { .Call('pagoda2_arma_mat_cor', PACKAGE = 'pagoda2', m) } hnswKnn <- function(m, efConstruction = 20L, indexThreadQty = 4L, searchMethod = 4L) { .Call('pagoda2_hnswKnn', PACKAGE = 'pagoda2', m, efConstruction, indexThreadQty, searchMethod) } hnswKnn2 <- function(m, k = 5L, nThreads = 30L, efConstruction = 20L, indexThreadQty = 4L, searchMethod = 4L, seed = -1L, verbose = TRUE) { .Call('pagoda2_hnswKnn2', PACKAGE = 'pagoda2', m, k, nThreads, efConstruction, indexThreadQty, searchMethod, seed, verbose) } hnswKnnJS <- function(m, k = 5L, nThreads = 20L, efConstruction = 20L, indexThreadQty = 4L, searchMethod = 4L, seed = -1L) { .Call('pagoda2_hnswKnnJS', PACKAGE = 'pagoda2', m, k, nThreads, efConstruction, indexThreadQty, searchMethod, seed) } hnswKnnLp <- function(m, k = 5L, nThreads = 30L, p = 2.0, efConstruction = 20L, indexThreadQty = 4L, searchMethod = 4L, seed = -1L, verbose = TRUE) { .Call('pagoda2_hnswKnnLp', PACKAGE = 'pagoda2', m, k, nThreads, p, efConstruction, indexThreadQty, searchMethod, seed, verbose) } hnswKnn3test <- function(m, k = 5L, multiplex = 1L, nqueries = 1000L, nThreads = 30L, efConstruction = 20L, indexThreadQty = 4L, searchMethod = 4L, seed = -1L, verbose = TRUE) { .Call('pagoda2_hnswKnn3test', PACKAGE = 'pagoda2', m, k, multiplex, nqueries, nThreads, efConstruction, indexThreadQty, searchMethod, seed, verbose) } matWCorr <- function(Mat, Matw) { .Call('pagoda2_matWCorr', PACKAGE = 'pagoda2', Mat, Matw) } winsorizeMatrix <- function(Mat, Trim) { .Call('pagoda2_winsorizeMatrix', PACKAGE = 'pagoda2', Mat, Trim) } plSemicompleteCor2 <- function(Pl) { .Call('pagoda2_plSemicompleteCor2', PACKAGE = 'pagoda2', Pl) } avg_rank <- function(x) { .Call('pagoda2_avg_rank', PACKAGE = 'pagoda2', x) } sparse_matrix_column_ranks <- function(sY) { .Call('pagoda2_sparse_matrix_column_ranks', PACKAGE = 'pagoda2', sY) } nearbyPointsGreedyCluster <- function(p, windowSize) { .Call('pagoda2_nearbyPointsGreedyCluster', PACKAGE = 'pagoda2', p, windowSize) } closestNPointsToSegments <- function(s, e, p, tss, N) { .Call('pagoda2_closestNPointsToSegments', PACKAGE = 'pagoda2', s, e, p, tss, N) } closestNSegmentsToPoints <- function(s, e, p, tss, N) { .Call('pagoda2_closestNSegmentsToPoints', PACKAGE = 'pagoda2', s, e, p, tss, N) }

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/dataspec.R \docType{data} \name{dataspec_p4} \alias{dataspec_p4} \alias{dataspec_p5} \alias{dataspec_pd} \title{Specific absorption coefficients for PROSPECT model} \format{ An object of class \code{matrix} (inherits from \code{array}) with 2101 rows and 3 columns. An object of class \code{matrix} (inherits from \code{array}) with 2101 rows and 5 columns. An object of class \code{matrix} (inherits from \code{array}) with 2101 rows and 6 columns. } \usage{ dataspec_p4 dataspec_p5 dataspec_pd } \description{ Specific absorption coefficients for PROSPECT model } \keyword{datasets}

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run_analysis.R

run_analysis <- function(){ #packages and librarys library(plyr) #set calling names dir1 <- (getwd()) print(paste("please make sure the UCI HAR Dataset folder is in", dir1) ) # read all data into working tables Xtest <- read.table("UCI HAR Dataset/test/X_test.txt") ytest <- read.table("UCI HAR Dataset/test/y_test.txt") Xtrain <- read.table("UCI HAR Dataset/train/X_train.txt") ytrain <- read.table("UCI HAR Dataset/train/y_train.txt") labels <- read.table("UCI HAR Dataset/activity_labels.txt") subject_train <- read.table("UCI HAR Dataset/train/subject_train.txt") subject_test <- read.table("UCI HAR Dataset/test/subject_test.txt") # create names for data columns names(Xtest) <- features[,2] names(Xtrain) <- features[,2] #create subject_* tables to merge with tidy info subject_test <- cbind(subject_test, "test") names(subject_test) <- c("subject" , "group-partition") subject_train <- cbind(subject_train, "train") names(subject_train) <- c("subject" , "group-partition") #create label identifyable names for y* files names(ytrain) <- "activity" names(ytest) <- "activity" names(labels) <- c("activity", "activity_name") ytrain <- join(ytrain, labels) ytest <- join (ytest, labels) #merge test and train (subject, X, y) tables bu columns Xtest <- cbind(ytest[,2], Xtest) Xtrain <- cbind(ytrain[,2], Xtrain) names(Xtest) <- c( "activity_name", names(Xtest[,2:562])) names(Xtrain) <- c( "activity_name", names(Xtrain[,2:562])) Xtest <- cbind(subject_test, Xtest) Xtrain <- cbind(subject_train, Xtrain) #merge train and test by rows Xtotal <- rbind(Xtest, Xtrain) # take subset of columns that have mean or std of some measurement. Because of # 'features_info.txt'I know "mean" or "std" will be on the column name (ignoring # cases). Include first three columns (subject number, activity, train or test) Xmean <- grep("MEAN", names(Xtotal), value = F, ignore.case = T) Xstd <- grep("std", names(Xtotal), value = F, ignore.case = T) z <- c(1,2,3, Xmean, Xstd) Xmeanstd <- Xtotal[,z] # to name the columns descriptively, following the rules on the first week # 4 lectures, I will change t for time (when at the beggining of the name), # f for freqdom (frequency domain according to features_info.txt), taking out # spaces, dots, underscores, end the other rules explain in the mentioned lecture names(Xmeanstd) <- sub("^t", "time", names(Xmeanstd)) names(Xmeanstd) <- sub("^f", "freqdom", names(Xmeanstd)) names(Xmeanstd) <- tolower(names(Xmeanstd)) names(Xmeanstd) <- sub("_", "-", names(Xmeanstd)) names(Xmeanstd) <- sub("-", "", names(Xmeanstd)) names(Xmeanstd) <- sub("()-", "", names(Xmeanstd)) # now, create new tidy data set with the average of each column for each subject # and each activity (names in activity-labels.txt). newTidy1 <- matrix(,180,89) #I need 180 rows as there are 30 subjects and 6 activities newTidy1 <- data.frame(newTidy1) names(newTidy1) <- names(Xmeanstd) multiplo = nrow(labels) # first, sequence for each subject for (i in seq_along(unique(Xmeanstd$subject))){ #now, sequence for each activity for (j in seq_along(unique(Xmeanstd$activityname))){ #create subset for each subject+activity subgroup p1 <- subset(Xmeanstd, Xmeanstd$subject == i & Xmeanstd$activityname == labels[j,2] ) #assign values to first three columns (subject, train or test, activity name) newTidy1[(i-1)*multiplo+j,1]<-i newTidy1[(i-1)*multiplo+j,3]<-as.character(labels[j,2]) newTidy1[(i-1)*multiplo+j,2]<-as.character(p1[1,2]) #now calculate the mean for each of the other columns of the subset and assign it newTidy1[(i-1)*multiplo+j,4:89]<-colMeans(p1[,4:89]) } } # I must export the data set to a file so I can attach it in the course project # url. I wil use coma as a separator so it can be read as a csv file. for (k in 4:89){ a <- paste("average",names(newTidy1[k]), sep = "") names(newTidy1)[k] <- a } write.table(newTidy1, file = "tidySamsung.txt", sep=",") return (newTidy1) }

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r

Lec1_05.R

################################################################ # # R을 활용한 통계분석 # # 5. 다중회귀분석 # ################################################################ #--------------------------------------------------------------- # 다중회귀분석 (Multiple Regression Model) #--------------------------------------------------------------- # programmer 20명 # salary가 experience(경력년수), score (직무적성검사성적)과 연관성을 갖는지 검증. df <- read.csv("data/salary.csv") head(df) summary(df) library(psych) pairs.panels(df) # salary ~ experience 상관계수 0.86 # 단순회귀 : 경력 증가시 연봉 증가 상관관계 model <- lm(salary ~ experience, data = df) summary(model) # 다중회귀 : 경력 증가시 적성검사 점수 증가로 인한 연봉 증가까지 포함된 관계 # experience ~ score 의 cor() = 0.34 # model <- lm(salary ~ ., data = df) model <- lm(salary ~ experience + score, data = df) summary(model) # 추정된 회귀식 # salary = 3.174 + 1.404 * experience + 0.251 * score # b1 : b2(score)가 일정하다고 할 때, experience가 1년 증가하면 salary가 $1,404 증가할 것으로 기대된다. # b2 : b1(experience)가 일정하다고 할 때, score가 1점 증가하면 salary가 $251 증가할 것으로 기대된다. #--------------------------------------------------------------- # 다중회귀분석 결과 해석 # (1) Adjusted R-squared # R-squared: 0.83 --> experience와 score가 salary 변동량의 83%를 설명한다. # But, 설명변수 갯수가 증가하면 결정계수도 증가 # --> 설명변수 갯수에 대한 패널티 적용한 결정계수 = Adjusted R-squared # (2) F-test # H0 : b1 = b2 = ...... = bk = 0 # 종속변수와 모든 독립(설명)변수 집합간에 유의한 관계가 존재하는지 검정 # b0 는 큰 의미가 없다. # (3) T-test # H0 : bi = 0 # 각 개별 독립변수의 유의성 검정 # (4) 잔차분석 --> Residuals plot / Normal Q-Q plot / Leverage plot #--------------------------------------------------------------- # 영향점이 있는 경우 plot(model) # --> Leverage plot에서 2번째 자료가 이상치 & 영향점 dcolor <- rep(1, length(df$salary)) dcolor[2] = 2 pairs(df, col = dcolor, pch = dcolor) # 2번 자료만 다르게 표시 # 영향점 제거는 주관적으로 판단하는 수밖에 없다. df2 <- df[-2, ] # 영향점 제거할 경우 pairs.panels(df2) # salary ~ experience 상관계수 높아짐(0.91). 다른 상관계수는 낮아짐. model2 <- lm(salary ~ experience + score, data = df2) summary(model2) # score 회귀계수가 유의하지 않다. #--------------------------------------------------------------- # 추정과 예측 # 경력 5년, 적성검사성적 80점인 사람과 경력 10년, 성적 70점인 사람의 연봉 예측 # 평균 연봉의 95% 신뢰구간 predict(model, data.frame("experience" = c(5,10), "score" = c(80,70)), interval = "confidence") # 새로운 한 명에 대한 95% 예측구간 predict(model, data.frame("experience" = c(5,10), "score" = c(80,70)), interval = "prediction") #--------------------------------------------------------------- # 다중공선성 (Multicollinearity) #--------------------------------------------------------------- # 독립변수들이 서로 높은 상관관계를 가지면 회귀계수의 정확한 추정이 어렵다. # ---> 모형 선택 방법론을 적용하여 가장 적절한 변수를 선택할 수 있다. # 30개 부서에서 부서당 35명의 직원 설문조사 # 데이터 숫자는 해당 질문에 긍정한 직원의 비율 attitude round(cor(attitude),3) pairs.panels(attitude) # cor : complaints + learning = 0.597 # cor : complaints + raises = 0.669 plot(attitude[ , c("rating", "complaints", "learning")]) a <- lm(rating ~ complaints + learning, data = attitude) summary(a) # learning의 t-test p-value 값을 보면 유의하지 않다. # 하지만 rating과 상관관계가 없는 것이 아니다. # complaints 와의 상관관계도 있기 때문에 rating 변수에 대한 역할이 작아보일 뿐이다. #--------------------------------------------------------------- # 모형 선택법 (Model Selection) = 설명변수 선택 #--------------------------------------------------------------- # *** 해당 업무분야에서 반드시 들어가야 하는 변수는 고정 !!! # (1) Forward selection # --- 가장 유의한 변수부터 하나씩 추가 (R-sq 기준) # --- 변수값의 작은 변동에도 결과가 크게 달라져 안정성 부족 # (2) Backward selection # --- 모든 변수를 넣고 가장 기여도가 낮은 것부터 하나씩 제거 # --- 전체 변수 정보를 이용하는 장점 # --- 변수의 갯수가 많은 경우 사용 어려움. 안정성 부족. # (3) Stepwise selection # --- Forward selection과 backward selection을 조합 # --- 새로운 변수 추가 후에 기존 변수의 중요도가 약화되면 그 변수 제거 # (4) All Subsets Regression # --- 모든 가능한 모형을 비교하여 최적의 모형선택 # --- 여러 모형 중 최소 AIC, BIC, Mallow’s Cp 또는 최대 adjusted R-sq를 갖는 모형을 선택 # --- 모형의 복잡도에 벌점을 주는 방법. AIC (Akaike information criterion), BIC (Bayesian ...) #--------------------------------------------------------------- # Backward selection out <- lm(rating ~ ., attitude) summary(out) anova(out) # 각 회귀계수 t검정 p-value 기준 선별. critical 제거. out2 <- lm(rating ~ . - critical, data = attitude) summary(out2) anova(out2) # raises 제거 # Backward selection 자동화 backward <- step(out, direction = "backward", trace = T) backward <- step(out, direction = "backward", trace = F) backward # 최종 선택된 회귀모형 : rating ~ complaints + learning backward$anova # critical, raises, privileges, advance 순으로 제거됨 #--------------------------------------------------------------- # Stepwise selection both <- step(out, direction = "both", trace = F) both both$anova #--------------------------------------------------------------- # All Subsets Regression library(leaps) leap <- regsubsets(rating ~ ., attitude, nbest = 5) # size당 5개의 최적 모형 저장 summary(leap) plot(leap) plot(leap, scale = "adjr2") # adjusted r-squred 기준 #--------------------------------------------------------------- # practice 5 #--------------------------------------------------------------- # hotel margin prediction data <- read.csv("data/laquinta.csv") summary(data) str(data) # 자료의 산점도 확인 round( cor(data), 3) pairs.panels(data) # 설명변수간의 correlation도 낮다. 종속변수와도 낮다. # 회귀모형 model <- lm(Margin ~ ., data) summary(model) # F-test 유의함. R-squared: 0.525. Distance, Enrollment 제외한 회귀계수 유의함. plot(model) # 잔차도 이상 없음 backward <- step(model, direction = "backward", trace = F) backward both <- step(model, direction = "both", trace = F) both # 최종 회귀모형 : Margin ~ Number + Nearest + Office.Space + Enrollment + Income # Coefficients: # (Intercept) Number Nearest Office.Space Enrollment Income # 37.128891 -0.007742 1.586923 0.019576 0.196385 0.421411 # 다음 조건을 가진 한 지역의 Margin을 95% 신뢰구간으로 예측 new <- data.frame("Number" = 3815, "Nearest" = 0.9, "Office.Space" = 476, "Enrollment" = 24.5, "Income" = 35, "Distance" = 11.2) new predict(model, new, interval = "prediction") # BIC 값을 최소로 하는 설명변수의 조합을 찾아 회귀식을 추정 regsub <- regsubsets(Margin ~ ., data, nbest = 5) plot(regsub) # 최종 회귀모형 : Margin ~ Number + Nearest + Office.Space + Income plot(regsub, scale="adjr2")

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/Old Package/R/conc_comp.R

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PHP2560-Statistical-Programming-R/r-package-courses-brown

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conc_comp.R

#' conc_comp Function #' #' This function takes two concentrations of interest as inputs and returns a #' table with all the required classes for both concentrations. #'conc.name1: a character string for the first concentration name as it appears in the #' list of Brown concentrations in the website: #' "https://bulletin.brown.edu/the-college/concentrations/". The input is not case sensitive. #' **conc.name is a character string, so it needs to go with "". #' conc.name2: a character string for the second concentration name as it appears in the #' list of Brown concentrations in the website: #' "https://bulletin.brown.edu/the-college/concentrations/". The input is not case sensitive. #' **conc.name is a character string, so it needs to go with "". #' Table of all the required courses for the concentration inputed. #' conc_comp("Economics", "Mathematics") #' conc_comp("mathematics", "Africana Studies") conc_comp <- function(conc_name1, conc_name2) { # This function takes the concentration of interest as an input and returns a # table with all the required classes for that particular concentration. The input is not # case sensitive. The function will run only if the input matches the concentration name listed # on the website. If the department does not display a table, a message will display this. # Compile a list of undergraduate concentrations available at Brown from the website, so # that if the concentrations are updated on the website, the list is also updated link <- html_session("https://bulletin.brown.edu/the-college/concentrations/") conc_list <- link %>% html_nodes("#textcontainer li") %>% # css selector for the entire list of concentration html_text() # select only the text conc_list <- as.vector(conc_list) ## Run the function only if the user's input matches the name listed in the concentration ## list, ignoring cases. match1 <- grepl(pattern=paste("^", conc_name1,"$", sep=""), conc_list, ignore.case=TRUE) match2 <- grepl(pattern=paste("^", conc_name2,"$", sep=""), conc_list, ignore.case=TRUE) # Index to find the link of the concentraton of interest i <- grep(pattern=paste("^", conc_name1,"$", sep=""), conc_list, ignore.case=TRUE) b <- grep(pattern=paste("^", conc_name2,"$", sep=""), conc_list, ignore.case=TRUE) if (any(match1==TRUE) && str_length(conc_name1) == str_length(conc_list[i]) && any(match2==TRUE) && str_length(conc_name2) == str_length(conc_list[b])) { # Pull up the website that has a list of all the undergraduate concentrations link <- html_session("https://bulletin.brown.edu/the-college/concentrations/") # Select the concentration of interest link_conc1 <- link %>% follow_link(i+36) link_conc2 <- link %>% follow_link(b+36) # Read the content of the link content1 <- read_html(link_conc1) content2 <- read_html(link_conc2) # Scrape the table link_table1 <- html_nodes(content1, 'table') link_table2 <- html_nodes(content2, 'table') # If the department doesn't display a table, an error "subscript out of bounds" appears. tryCatch will # ignore this error and allow the function to keep working scrape_table1 <- tryCatch(html_table(link_table1)[[1]], error=function(e) matrix(nrow=2, ncol=2)) scrape_table2 <- tryCatch(html_table(link_table2)[[1]], error=function(e) matrix(nrow=2, ncol=2)) # Create a table only if the table exists (i.e. if scrape table ≠ NA) if ((is.na(scrape_table1[1,1])== FALSE) && (is.na(scrape_table2[1,1]) == FALSE)) { # Convert the table into a dataframe classes <- scrape_table1$X1 class_name <- scrape_table1$X2 number_classes <- scrape_table1$X3 table_req1 <- data_frame(classes, class_name, number_classes) table_req1$number_classes[table_req1$number_classes == ""] <- " " space1 <- list("-", "-", "-") space2 <- list("-", "-", "-") name1 <- list("Concentration 1: ", "", "") name2 <- list("Concentration 2: ", "", "") table_req1s <- rbind(name1, table_req1) table_req1s <- rbind(table_req1s, space1) table_req1s <- rbind(table_req1s,space2) classes <- scrape_table2$X1 class_name <- scrape_table2$X2 number_classes <- scrape_table2$X3 table_req2 <- data_frame(classes, class_name, number_classes) table_req2 <- rbind(name2, table_req2) total <- rbind(table_req1s, table_req2) total$number_classes[total$number_classes == ""] <- " " table_req_2<-rename(total, "Class Code" = classes, "Class Name" = class_name, "Number of Classes" = number_classes) explain <- list("", "", "If the Class Number cell is empty or has a NA, refer to the category the class belongs to.") rbind(table_req_2, explain) #Course <- scrape_table2$X1 #Title <- scrape_table2$X2 #Credit <- scrape_table2$X3 # table_req2 <- data_frame(Course, Title, Credit) #total <- rbind(table_req1, table_req2) return(total) } else {stop('One of the concentrations does not have a table presented')} } else {stop('Please enter valid concentration names. Refer to the list of undergraduate concentrations offered at Brown at https://bulletin.brown.edu/the-college/concentrations/')} } a <- conc_comp("Economics", "chemistry")

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/r/learning/optimization/assign_memory_ahead.r

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jk983294/math

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assign_memory_ahead.r

set.seed(1234) k <- 1e+05 x <- rnorm(k) y <- 0 system.time(for (i in 1:length(x)) y[i] <- x[i]^2) # for & no memory allocation y <- numeric(k) system.time(for (i in 1:k) y[i] <- x[i]^2) # for & memory allocation y <- numeric(k) system.time(y <- x^2) # vectorization & memory allocation

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/BrCa_Age_Associated_TMA/Packages/biostatUtil/man/Xunivcoxph.Rd

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BCCRCMO/BrCa_AgeAssociations

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Xunivcoxph.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utils_coxph.R \name{Xunivcoxph} \alias{Xunivcoxph} \title{Univariate cox proprtional hazards model} \usage{ Xunivcoxph(mod, digits = 3) } \arguments{ \item{mod}{model fit object, returned from either \code{coxph} or \code{coxphf}.} \item{digits}{number of digits to round} } \value{ Hazard ratio and 95/% confidence interval } \description{ Concatenates hazard ratios and confidence limits for every covariate in a Cox model. } \examples{ library(survival) library(coxphf) # One predictor test1 <- list( time = c(4, 3, 1, 1, 2, 2, 3), status = c(1, 1, 1, 0, 1, 1, 0), x = c(0, 2, 1, 1, 1, 0, 0), sex = c(0, 0, 0, 0, 1, 1, 1) ) mod <- coxph(Surv(time, status) ~ x + strata(sex), test1) Xunivcoxph(mod) # Multiple predictors bladder1 <- bladder[bladder$enum < 5, ] mod <- coxph(Surv(stop, event) ~ (rx + size + number) * strata(enum) + cluster(id), bladder1) Xunivcoxph(mod, digits = 2) # Firth's correction test2 <- data.frame(list( start = c(1, 2, 5, 2, 1, 7, 3, 4, 8, 8), stop = c(2, 3, 6, 7, 8, 9, 9, 9, 14, 17), event = c(1, 1, 1, 1, 1, 1, 1, 0, 0, 0), x = c(1, 0, 0, 1, 0, 1, 1, 1, 0, 0) )) mod <- coxphf(formula = Surv(start, stop, event) ~ x, pl = FALSE, data = test2) Xunivcoxph(mod) } \author{ Aline Talhouk, Derek Chiu }

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/sample_gradebook.R

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restrellado/sample_gradebook

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sample_gradebook.R

# Create a gradebook dataset as an example library(tidyverse) # Creates random quiz scores #------------------------------------------------------------------------------ make_score <- function(min, max, n) { # Creates random quiz scores # Args: # min: lowest possible quiz score # max: highest possible quiz score # n: number of quiz scores # Returns: numeric vector sample(c(1:100), n, replace = TRUE) } # Make dataset #------------------------------------------------------------------------------ gb <- tibble( name = paste0("student_", c(1:25)), gender = sample(c("f", "m"), 25, replace = TRUE), quiz_1 = make_score(0, 100, 25), quiz_2 = make_score(0, 100, 25), quiz_3 = make_score(0, 100, 25), quiz_4 = make_score(0, 100, 25), quiz_5 = make_score(0, 100, 25) )

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/src/dataDownload.R

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leekgroup/networks_correction

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dataDownload.R

rm(list=ls()) ## load libraries library(recount) source("functions.R") source("config.R") ## create dir dir.create(datDir) ## download and scale reads download_study('SRP012682', type = 'rse-gene', outdir = datDir) load(paste(datDir, "rse_gene.Rdata", sep = "")) tissue.interest <- c("Subcutaneous", "Lung", "Thyroid", "Muscle", "Blood", "Artery - Tibial", "Nerve - Tibial", "Skin - Sun Exposed") ## scale counts by total coverage of the sample rse_gene <- scale_counts(rse_gene, by = "auc", round = FALSE) ## select samples included in analysis freeze rse_gene <- rse_gene[,which(colData(rse_gene)$smafrze=="USE ME")] ## only keep protein coding genes pc.genes <- read.delim(paste(datDir, "etc/protein_coding.txt", sep = ""), header = F) pc.genes <- pc.genes[!pc.genes$V1 %in% c("chrM","chrY"),] overlapping_genes <- read.delim(paste(datDir, "etc/ensembl_ids_overlapping_genes.txt", sep = ""), stringsAsFactors = F) rse_gene <- rse_gene[which(rownames(rse_gene) %in% pc.genes$V2),] rse_gene <- rse_gene[-which(rownames(rse_gene) %in% overlapping_genes$x),] ## summary print(paste("Unique runs", length(unique(colData(rse_gene)$run)))) print(paste("Unique sample ids",length(unique(colData(rse_gene)$sampid)))) print(paste("Are all run values in phenotypes same order as colnames in rse_gene?", all(colData(rse_gene)$run==colnames(rse_gene)))) ## split rse object print(paste("Now splitting rse object by tissues")) gtex.rse <- sapply(tissue.interest, function(x, y){ idx <- grep(x, colData(y)$smtsd) rse.dat <- y[,idx] rse.dat <- select.genes(rse.object = rse.dat, threshold = 0.1) counts <- SummarizedExperiment::assay(rse.dat, 1) # log2 transformation SummarizedExperiment::assay(rse.dat, 1) <- log2(counts+2) rse.dat }, rse_gene) rm(rse_gene) names(gtex.rse) <- c("Subcutaneous", "Lung", "Thyroid", "Muscle", "Blood", "Artery_tibial", "Nerve_tibial", "Skin") ## Exclude one sample with missing mapping annotations from skin samp.idx <- which(gtex.rse$Skin@colData$sampid == "GTEX-YF7O-2326-101833-SM-5CVN9") gtex.rse$Skin <- gtex.rse$Skin[,-samp.idx] ## select only those thyroid samples that have genotype information #sampid <- as.character(sapply(colData(gtex.rse[["Thyroid"]])$sampid, function(x){ #k <-strsplit(x, '-') #k <- paste(k[[1]][1],k[[1]][2], sep = ".") #k #} #) #) #genotype.samples <- read.delim("genotype_samples.txt") #dim(gtex.rse[["Thyroid"]]) #gtex.rse[["Thyroid"]] <- gtex.rse[["Thyroid"]][,which(sampid %in% colnames(genotype.samples))] ## tissue specific data summary print(paste("Number of genes,samples:")) for(i in 1:length(gtex.rse)){ print(paste(names(gtex.rse)[i])) print(paste(dim(gtex.rse[[i]]))) } ## save rdata - adipose sub, lung, thyroid, whole Blood save(gtex.rse, file = paste(datDir, "raw_protein_coding.Rdata",sep = ""))

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/Analisis Multivariable/Arbolesde Decisión/Tree - Redes Sociales.R

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mecomontes/Data-Science-Complete-path

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Tree - Redes Sociales.R

install.packages("rpart") install.packages("rpart.plot") install.packages("rattle") library(rattle) library(rpart) library(rpart.plot) getwd() setwd("D:/UdeM/Análisis Multivariante/Árboles") # Ejemplos con Datos de Redes Sociales T2016<-read.csv("Tree2016.csv",header=T, sep =";",na.strings = c("NA"," ","","NULL",".")) summary(T2016) names(T2016) T2015<-read.csv("Tree2015.csv",header=T, sep =";",na.strings = c("NA"," ","","NULL",".")) summary(T2015) names(T2015) ### Tree 2016 ModeloTree<- data.frame(T2016) tree<- rpart(Brand_Asset_R ~ ., data = ModeloTree, minbucket=4) rpart.plot(tree) ### Tree 2015 ModeloTree<- data.frame(T2015) tree<- rpart(Brand_Asset_R ~ ., data = ModeloTree, minbucket=3) rpart.plot(tree)

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/R/ggbiplot.R

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zdk123/compPLS

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2022-05-06T21:00:56.959067

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ggbiplot.R

################################################################ # Methods for making pretty biplots with ggplot2 package # # based on code by http://www.vince.vu/software/#ggbiplot #' Pretty biplots using ggplots #' @title methods for making biplots from various projection & classification models #' @param xobj The object to be plotted #' @rdname ggbiplot #' @export ggbiplot <- function(xobj, ...) { UseMethod("ggbiplot") } #' @rdname ggbiplot #' @method ggbiplot princomp #' @export ggbiplot.princomp <- function(xobj, ...) { nobs.factor <- sqrt(xobj$n.obs) d <- xobj$sdev scores <- sweep(xobj$scores, 2, 6/(d * nobs.factor), FUN = '*') ggbiplot.default(list(scores=scores, loadings=xobj$loadings), ...) } #' @rdname ggbiplot #' @method ggbiplot prcomp #' @export ggbiplot.prcomp <- function(xobj, ...) { nobs.factor <- sqrt(nrow(xobj$x) - 1) d <- xobj$sdev scores <- sweep(xobj$x, 2, 1/(d * nobs.factor), FUN = '*') loadings <- xobj$rotation ggbiplot.default(list(scores=scores, loadings=loadings), ...) } #' @rdname ggbiplot #' @method ggbiplot lda #' @export ggbiplot.lda <- function(xobj, ...) { xname <- xobj$call$x gname <- xobj$call[[3L]] X <- eval.parent(xname) g <- eval.parent(gname) means <- colMeans(xobj$means) X <- scale(X, center = means, scale = FALSE) x <- as.matrix(X) %*% xobj$scaling nobs.factor <- sqrt(nrow(x)) d <- apply(x, 2, sd) x <- sweep(x, 2, 25/(d * nobs.factor), FUN = '*') ggbiplot.default(list(scores=x, loadings=xobj$scaling), ...) } #' @rdname ggbiplot #' @method ggbiplot plsda #' @export ggbiplot.plsda <- function(xobj, Yplot=FALSE, ...) { if (Yplot) { scores <- xobj$Yscores loadings <- xobj$Yloadings } else { scores <- xobj$scores loadings <- xobj$loadings } nobs.factor <- sqrt(nrow(scores)) d <- apply(scores, 2, sd) means <- colMeans(scores) scores <- scale(scores, center = means, scale = FALSE) scores <- sweep(scores, 2, 6/(d * nobs.factor), FUN = '*') ggbiplot.default(list(scores=scores, loadings=loadings), ...) } #' @rdname ggbiplot #' @method ggbiplot splsda #' @export ggbiplot.splsda <- function(xobj, ...) { # means <- irispls$meanx # X <- scale(xobj$x, center = means, scale = FALSE) X <- xobj$x if (is.null(xobj$scores)) xobj$scores <- as.matrix(X[,xobj$A]) %*% as.matrix(xobj$projection) xobj$loadings <- xobj$projection ggbiplot.plsda(xobj, ...) } #' @rdname ggbiplot #' @method ggbiplot matrix #' @export ggbiplot.matrix ggbiplot.matrix <- function(xobj, ...) { scores <- xobj loadings <- matrix(NA, nrow(xobj), ncol(xobj)) ggbiplot.default(list(scores=scores, loadings=loadings), plot.loadings=FALSE, equalcoord=FALSE, ...) } #' @param grouping an optional grouping vector (ie - for coloring points) #' @param select index of components to be plotted (must be length 2) #' @param circle enclose points in a circle #' @param circle.prob controls circle diameter (scales data std dev) if \code{circle = TRUE} #' @param plot.loadings should loading vectors be plotted #' @param label.loadings text of loadings labels, taken from rownames of loadings (depends on class of \code{xobj}) #' @param label.offset absolute offset for loading labels, so labels don't cover loadings vectors #' @param scale.loadings scale length of loading vectors for plotting purposes #' @param col.loadings a single value of vector for color of loadings #' @param alpha controls relative transparency of various plot features #' @param col color factor for points #' @param group.ellipse enclose within-group points in an covariance ellipse #' @param scale.ellipse scale \code{group.ellipse} to 1 standard deviation #' @param group.cloud connect within-group points to a group mean point with a straight edge #' @param xlab label for x axis #' @param ylab label for y axis #' @param equalcoord equal coordinates, ie should the plot area be square? #' @param size point size #' @param size.loadings line width of loading vectors #' #' @details additional plotting attributes (eg colors, themes, etc) can be chained on in the usual way for ggplots #' @examples #' # an LDA example with iris data #' ldamod <- lda(iris[,1:4], grouping=iris[,5]) #' ggbiplot(ldamod, grouping=iris[,5], alpha=.7, group.cloud=TRUE) + theme_bw() #' @rdname ggbiplot #' @method ggbiplot default #' @importFrom ggplot2 ggplot geom_segment geom_point geom_text geom_path scale_x_continuous scale_y_continuous aes #' @export ggbiplot.default <- function(xobj, grouping, select=1:2, circle = FALSE, circle.prob = 0.69, plot.loadings=TRUE, label.loadings=FALSE, sub.loadings=1:nrow(xobj$loadings), label.offset=0, label.size=4.5, scale.loadings = 1, col.loadings=scales::muted("red"), alpha = 1, col=grouping, shape=NULL, group.ellipse=FALSE, scale.ellipse = 1, group.cloud = FALSE, xlab="", ylab="", equalcoord=TRUE, size=3, size.loadings=1, loadingsOnTop = FALSE) { ## get scores and loadings from xobj if (length(select) > 2) stop("Error: only 2d plots supported") if (length(select) < 2) stop("Error: need at least 2 coordinates/components") scores <- data.frame(xvar=xobj$scores[,select[1]], yvar=xobj$scores[,select[2]]) loadings <- data.frame(xvar=xobj$loadings[sub.loadings,select[1]], yvar=xobj$loadings[sub.loadings,select[2]]) # standardize scores (?) # Base plot g <- ggplot(data = scores, aes(x = xvar, y = yvar)) if (plot.loadings) { loadingslayer <- geom_segment(data = loadings*scale.loadings, aes(x = 0, y = 0, xend = xvar, yend = yvar), arrow = grid::arrow(length = grid::unit(1/2, 'picas')), size = size.loadings, color = col.loadings) } if (is.character(label.loadings) || label.loadings) { if (is.logical(label.loadings)) labs <- rownames(loadings) else labs <- label.loadings # compute angles from orig. ang <- atan2(loadings$yvar*scale.loadings, loadings$xvar*scale.loadings) hyp <- sqrt((loadings$yvar*scale.loadings)^2 + (loadings$xvar*scale.loadings)^2) labdat <- data.frame(newx=(hyp + label.offset)*cos(ang), newy=(hyp + label.offset)*sin(ang), label=labs) g <- g + geom_text(aes(x=newx, y=newy, label=label), data=labdat, size=label.size) } if (!missing(grouping)) { gind <- order(grouping) grouping <- grouping[gind] scores <- scores[gind,] df <- data.frame(xvar=scores$xvar, yvar=scores$yvar, grouping=grouping) if (!is.null(shape)) { aesfun <- aes(color = grouping, shape=shape) ; df$shape <- shape[gind] } else aesfun <- aes(color = grouping) scoreslayer <- geom_point(data = df, aesfun, alpha = alpha, size=size) } else { if (!missing(col)) { df <- data.frame(xvar=scores$xvar, yvar=scores$yvar, col=col) if (!is.null(shape)) { aesfun <- aes(color = col, shape=shape) ; df$shape <- shape } else aesfun <- aes(color = col) scoreslayer <- geom_point(data=df, aesfun, alpha = alpha, size=size) } else { if (!is.null(shape)) aesfun <- aes(shape=shape) else aesfun <- aes() scoreslayer <- geom_point(aesfun, alpha = alpha, size=size) } } if (plot.loadings) { if (!loadingsOnTop) g <- g + loadingslayer + scoreslayer else g <- g + scoreslayer + loadingslayer } else g <- g + scoreslayer if (group.ellipse && !missing(grouping)) { l <- 200 group.scores <- split(scores[,1:2], grouping) group.centers <- lapply(group.scores, colMeans) group.cov <- lapply(group.scores, cov) group.RR <- lapply(group.cov, chol) angles <- seq(0, 2*pi, length.out=l) ell.list <- lapply(group.RR, function(RR) scale.ellipse * cbind(cos(angles), sin(angles)) %*% RR) ellCntr <- lapply(1:length(ell.list), function(i) sweep(ell.list[[i]], 2, group.centers[[i]], "+")) names(ellCntr) <- names(ell.list) ell.df <- as.data.frame(do.call("rbind", ellCntr)) ell.df$grouping <- factor(rep(names(ellCntr), each=l), levels=names(ellCntr)) g <- g + geom_path(data = ell.df, aes(color = grouping, group = grouping)) } if (group.cloud && !missing(grouping)) { group.scores <- split(scores[,1:2], grouping) group.centers <- lapply(group.scores, colMeans) centers.df <- do.call('rbind', rep(group.centers, table(grouping))) rownames(centers.df) <- rownames(scores) colnames(centers.df) <- c("xcntr", "ycntr") loadCntr.df <- cbind(scores, centers.df, grouping) g <- g + geom_segment(data = loadCntr.df, aes(x = xcntr, y = ycntr, xend = xvar, yend = yvar, color = grouping), alpha=10^log(alpha/1.4)) } if (circle) { # scale circle radius r1 <- sqrt(qchisq(circle.prob, df = 2)) * max(scores$xvar^2)^(1/2) r2 <- sqrt(qchisq(circle.prob, df = 2)) * max(scores$yvar^2)^(1/2) theta <- c(seq(-pi, pi, length = 50), seq(pi, -pi, length = 50)) circdat <- data.frame(xvar = r1 * cos(theta), yvar = r2 * sin(theta)) g <- g + geom_path(aes(x=xvar, y=yvar), data = circdat, color = scales::muted('black'), size = 0.5, alpha = alpha/3) } if (equalcoord) { if (circle) { xrange <- range(circdat$xvar) yrange <- range(circdat$yvar) } else { xrange <- c(-max(abs(scores$xvar)), max(abs(scores$xvar))) yrange <- c(-max(abs(scores$yvar)), max(abs(scores$yvar))) } g <- g + scale_x_continuous(xlab, limits=xrange) + scale_y_continuous(ylab, limits=yrange) } else { g <- g + scale_x_continuous(xlab) + scale_y_continuous(ylab) } g }

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/FMI-Code/Exam-2019/Problem-4.R

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nicksinch/Probability-and-Statistics

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refs/heads/master

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Problem-4.R

# Problem - 4 Observations = rnbinom(200,1,1/13) shapiro.test(Observations) Shapiro-Wilk normality test data: Observations W = 0.7351, p-value < 2.2e-16 # ===> не са нормално разпределени , пускаме wilcox.test , защото искаме доверителен интервал wilcox.test(Observations, conf.level = 0.90, conf.int = T) Wilcoxon signed rank test with continuity correction data: Observations V = 16836, p-value < 2.2e-16 alternative hypothesis: true location is not equal to 0 90 percent confidence interval: 8.499967 10.500040 sample estimates: (pseudo)median 9.500013 # P(X=9) ; асото е 10та карта # emp dnbinom(9, 1, 1/13) # брой неуспехи до първия успех [1] 0.03742809 # X = 10 sum(Observations == 10) / length(Observations) [1] 0.05

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/new_proj/new_proj.R

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xuhuanyunxiao/jupyter-notebook

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refs/heads/master

2021-07-21T03:26:54.359203

2020-04-22T13:18:42

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new_proj.R

library(readxl) library(lavaan) library(semPlot) library(OpenMx) library(GGally) library(corrplot) file_folder <- "D:/XH/Python_Project/notebook/new_proj" # getwd() setwd(file_folder) data <- read_excel('data_sel_1.xlsx') model1 = 'Z ~ x1 + x2 + x3 + Y 2Y ~ x1 + x2' fit1 = cfa(model1, data = data1) summary(fit1, fit.measures = TRUE, standardized = TRUE, rsquare = TRUE) semPaths(fit1, 'std', layout = 'circle')

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/RGtk2/man/gtkFileChooserUnselectFilename.Rd

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lawremi/RGtk2

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refs/heads/master

2023-03-05T01:13:14.484107

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gtkFileChooserUnselectFilename.Rd

\alias{gtkFileChooserUnselectFilename} \name{gtkFileChooserUnselectFilename} \title{gtkFileChooserUnselectFilename} \description{Unselects a currently selected filename. If the filename is not in the current directory, does not exist, or is otherwise not currently selected, does nothing.} \usage{gtkFileChooserUnselectFilename(object, filename)} \arguments{ \item{\verb{object}}{a \code{\link{GtkFileChooser}}} \item{\verb{filename}}{the filename to unselect} } \details{Since 2.4} \author{Derived by RGtkGen from GTK+ documentation} \keyword{internal}

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/analyze_msea.R

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XiaYangLabOrg/mergeomics

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analyze_msea.R

# Please excuse the roughness of these functions! I made these a long time ago while I was learning R and I will be making # efforts to improve the code hopefully soon. ## ----------------- DOWNSTREAM ANALYSIS OF MSEA RESULTS FROM MERGEOMICS ----------------- ## # # # Collection of functions dealing with downstream analysis of MSEA results # # # # Functions: # # # # 1) directOverlap - outputs exact module (gene set) matches between studies # # (i.e. "replication") # # 2) geneOverlap - does "looser" overlap analysis by % gene overlap between # # modules # # 3) SGMrepTable - makes table that records what SGM methods are being represented # # for each module # # 4) summaryTable - makes table for each SGM method detailing supersets and # # comprising modules # # # # Written by Jessica Ding # # # # ------------------------------------------------------------------------------------------# # Notes: # - These sets of functions are highly dependent on file names (case sensitive) # - If functions are done in the logical order that they were created, then this should not be # a problem. Problems may arise if you have file names in the folder that have string matches # to the string I am using to query the files to be analyzed. For example, a query for ".mod" # are for files like AD_Adipose_Subcutaneous.mod.txt. If you have a file in the folder that # is called "file.modified.txt" - it will capture this file and result in an error. #-----------------------------------------------------------------------------------------------------# # Abbreviation: SGM - SNP to gene mapping # This function does a % gene overlap for two studies for each # SGM method (as opposed to "direct overlap" - exact name # matching of gene sets/modules) # # Inputs: 1) results_Dir folder with MSEA results for each study with the SGM # as the common factor(s) between studies. file name format # should be "Study, SGM, .results.txt" (.result.txt is the # output extention of MSEA). # ex. "UKB_T2D.Adipose_Subcutaneous.results.txt" # 2) output_Dir folder to store results # 3) perc_gene_overlap minimum gene overlap to merge modules # 4) FDRcutoff usually 0.05 # 5) cohorts vector with study names that are reflected in the msea results # ex. c("DIAGRAMstage1_T2D", "UKB_T2D") # 6) modfile_path path to mod file, columns: 'MODULE' 'GENE' # 7) infofile_path path to info file, columns: 'MODULE' 'GENE' 'DESCR' # 8) study # # Outputs: 1) "combined_..." combined results file for each study # 2) "merged_..." mod and info file from module merging # 3) "analyzed_..." shows all supersets (does not include unmerged # modules) and study of origin. "OVERLAP" indicates # module came from all studies # 4) "analyzed_overlap_summary" a summary file of sets of modules that were merged with # different study representations (indicating new overlaps # based on % gene overlap) # 5) "shared_..." 'MODULE' 'METHOD' 'DESCR' file that can be used to merge # modules a second time given new overlaps # # Notes: 1) % gene overlap is set with "rmax" - ex. .3 is 30% gene overlap # 2) This function is written to consider multiple studies but all msea # results files must be in the same folder # 3) Directory path string parameters must end in "/" # 4) If DESCR unknown, put MODULE name # # Version April 2019 #---------------------------------------------------------------------------------------------------# geneOverlap <- function(results_Dir, output_Dir, perc_gene_overlap, fdr_cutoff=0.05, cohorts, modfile_path, infofile_path, study, type=NULL){ # MAKE COMBINED FILES FOR ALL STUDIES files = c() for(c in cohorts){ files = append(files, list.files(results_Dir)[grep(c, list.files(results_Dir))]) } ##get SGM methods. This assumes your results file is formatted - "cohort.SGM.results.txt" study_subset = files[grep(cohorts[1], files)] SGMs = c() for(f in study_subset){ SGMs = append(SGMs, unlist(strsplit(f, split = ".",fixed = TRUE))[2]) } SGMs = unique(SGMs) cat("The SNP to gene mappings used is/are:\n") print(SGMs) #this is to check that the SGMs are being extracted in the desired way ##combine studies for each SGM cat("Now combining studies together in one file for each SGM.\n") for(i in SGMs){ results = files[grep(i,files)] if(length(results)<2){ cat("The method ", i, " does not have a file for one of the studies.\n") } all = data.frame() for(j in results){ temp = read.delim(paste(results_Dir, j ,sep = ""), header = TRUE, stringsAsFactors = FALSE) if(is.null(type)){ temp = temp[temp$FDR<fdr_cutoff,] } temp=temp[!grepl("_ctrlA",temp$MODULE)&!grepl("_ctrlB",temp$MODULE),] if(nrow(temp)>0){ for(r in cohorts){ if(grepl(r, j)){ temp$STUDY = r } } } all = rbind(all, temp) } write.table(all, paste0(output_Dir,"combined_", i, ".txt"), row.names = FALSE, quote = FALSE, sep="\t") } combined_files = list.files(output_Dir) combined_files = combined_files[grep("combined_", combined_files)] cat("Now merging modules for % gene overlap...\n") # MERGE MODULES - this was modified 5.19.2019 and not currently tested for(l in combined_files){ cat("Now merging:\n") cat(l,"\n") # unfortunately hard coding this in... if(study=="UKB_Wojcik" & grepl("Pituitary_sQTL",l)) next df = read.delim(paste0(output_Dir,l), header = TRUE, stringsAsFactors = FALSE) if(sum(is.na(df$MODULE))>0){ cat("Skipping: ", l, " because there is no significance.\n") next } if(length(df$MODULE)<2){ cat("Skipping: ", l, " because there is only one or 0 significant modules meaning no overlap at all.\n") next } merge_modules(name = gsub("combined_","",gsub(".txt","",l)), modules_df = df, rcutoff = 0.30, output_Dir = output_Dir, modfile_path = modfile_path, infofile_path = infofile_path) } cat("Now analyzing for newly overlapped modules...\n") # FIND IF THERE WERE MERGED MODULES WITH DIFFERENT STUDY REPRESENTATION files1 = list.files(output_Dir) merged_mod_files = files1[grep("merged_",files1)] #works if no SGMs have "mod" in name merged_mod_files = merged_mod_files[grep(".mod", merged_mod_files)] uncommon_df = data.frame(stringsAsFactors = FALSE) for(m in merged_mod_files){ cat("Now inspecting ", m , "\n") file = read.delim(paste0(output_Dir,m), header = TRUE, stringsAsFactors = FALSE) temp = unique(file$OVERLAP[grep(",", file$OVERLAP)]) SGM = gsub("merged_","",gsub(".mod.txt","", m)) combined_file_name = combined_files[grep(SGM, combined_files)] combined_df = read.delim(paste0(output_Dir,combined_file_name), header = TRUE, stringsAsFactors = FALSE) study_reps = c() for(n in temp){ modules = unlist(strsplit(n,",")) study_rep = "" for(o in modules){ if(length(combined_df$STUDY[combined_df$MODULE==o])>1){ study_rep = paste(study_rep, "OVERLAP",sep = ", ") } else{ study_rep = paste(study_rep, combined_df$STUDY[combined_df$MODULE==o], sep = ", ") } } study_rep = substring(study_rep, 3) study_reps = append(study_reps, study_rep) } analyzed = data.frame("Merged Modules"= temp, "Study Representation"=study_reps) print(SGM) write.table(analyzed, paste0(output_Dir,"analyzed_", SGM, ".txt"), row.names = FALSE, quote = FALSE, sep = "\t") bool = c() for(p in study_reps){ p = gsub(" ", "", p) splitted = unlist(strsplit(p, ",")) if(sum(!splitted[1]==splitted)>0){ bool = append(bool, TRUE) } else{ bool = append(bool, FALSE) } } if(sum(bool)>0){ uncommon = analyzed[bool,] uncommon$SNPtoGeneMapping = SGM uncommon_df = rbind(uncommon_df, uncommon) } } #output summary of all different study representation "overlaps" write.table(uncommon_df, paste0(output_Dir,"analyzed_overlap_summary.txt"), row.names = FALSE, quote = FALSE, sep = "\t") #make overlap module files including all direct and quasi overlap modules for(s in SGMs){ cat("Now analyzing\n") cat(s, "\n") #get direct overlaps comb_file = read.delim(paste0(output_Dir,combined_files[grep(s, combined_files)]), header = TRUE, stringsAsFactors = FALSE) if(sum(is.na(comb_file$MODULE))>0){ cat("Skipping: ", l, " because there is no significance.\n") next } studies1 = unique(comb_file$STUDY) num = 1 shared_mods = c() while(num<length(studies1)){ if(num==1){ modules = comb_file$MODULE[comb_file$STUDY==studies1[num]] } else { modules = shared_mods } temp_modules = comb_file$MODULE[comb_file$STUDY==studies1[num+1]] shared_mods = intersect(modules,temp_modules) num = num + 1 } shared = data.frame(stringsAsFactors = FALSE) if(length(shared_mods>0)){ #if there are any direct overlaps shared = data.frame("MODULE" = shared_mods, "METHOD" = "direct") } uncommon_df = read.delim(paste0(output_Dir,"analyzed_overlap_summary.txt"), header = TRUE, stringsAsFactors = FALSE) #to convert back into strings if(sum(grepl(s, uncommon_df$SNPtoGeneMapping))>=1){ #if there are any quasi overlaps shared_quasi_mods = c() SGM_mod_rep = uncommon_df$Merged.Modules[grep(s, uncommon_df$SNPtoGeneMapping)] print(SGM_mod_rep) SGM_study_rep = uncommon_df$Study.Representation[grep(s, uncommon_df$SNPtoGeneMapping)] for(u in 1:length(SGM_mod_rep)){ SGM_merged_mod_rep = unlist(strsplit(SGM_mod_rep[u], split = ",")) SGM_merged_study_rep = unlist(strsplit(SGM_study_rep[u], split = ", ")) not_overlap = !(SGM_merged_study_rep=="OVERLAP") shared_quasi_mods = append(shared_quasi_mods, SGM_merged_mod_rep[not_overlap]) } shared_quasi = data.frame("MODULE"= shared_quasi_mods, "METHOD"= "quasi") if(length(shared_mods)>0){ #if both quasi and direct shared = rbind(shared, shared_quasi) } else{ # if only quasi shared = shared_quasi } } if(length(shared$MODULE)>0){ #annotate info = read.delim(infofile_path, header = TRUE, stringsAsFactors = FALSE) annot = c() for(v in shared$MODULE){ annot = append(annot, info$DESCR[info$MODULE==v]) } shared$DESCR = annot write.table(shared, paste0(output_Dir, "shared_", study, ".", s, ".txt"), row.names = FALSE, quote = FALSE, sep = "\t") } else{ next } shared = data.frame() } cat("Finished. shared_ files made.") } source("~/Desktop/Yang_Lab/Mergeomics/geneOntology.R") source("~/Desktop/Yang_Lab/resources/genesets/R-tomfunctions.R") #-----------------------------------------------------------------------------------------------------# # This function makes annotations for supersets that were obtained from module merge. # # Inputs: 1) results_Dir folder with ".mod.txt" files (output from module merge) # 2) ontologyfname path to ontology file # 3) study name to append to files # 4) trim vector of strings to "trim" off the mod.txt file name to get desired unique name # ex. UKB_AD.Adipose_Subcutaneous.mod.txt --> trim = c("UKB_AD.",".mod.txt") # # Outputs: 1) "supersets.study" folder contains superset annotations in .xls format # # Notes: If you get the error "Error in cbind(rep(modulesize, dim(fm)[1]), fm) : object 'fm' not found", the # offending module is printed before this error and this module must be taken out from the .mod.txt file # Version April 2019 #---------------------------------------------------------------------------------------------------# # ontologyfname = "~/Desktop/Yang_Lab/resources/genesets/MSigDB_Canonical_Pathways.txt" #--------------------------------------------------------------------------# # annotate_supersets <- function(results_Dir, ontologyfname, study="", trim){ #make trim argument that has a vector of things to trim to get the "SGM" and also so that you don't wind up with .mod.txt in subseqeunt result file names files = list.files(results_Dir) mod_files = files[grep(".mod.txt", files)] for(i in mod_files){ mod_file = read.delim(paste0(results_Dir,i), header = TRUE, stringsAsFactors = FALSE) if(sum(grepl(",", mod_file$MODULE))>0){ mod_file = mod_file[grep(",", mod_file$OVERLAP),] if(length(mod_files)>1){ SGM = i for(k in trim){ SGM = gsub(k, "", SGM) } SGM = gsub(study, "", SGM) name = paste0("supersets.", study, ".", SGM) } else{ mod_file = mod_file[grep(",", mod_file$OVERLAP),] name = paste0("supersets.", study) } file_name = paste0(results_Dir,name, ".txt") mod_file = mod_file[,c(1,2)] write.table(mod_file, file_name, row.names = FALSE, quote = FALSE, sep = "\t") geneOntology(work_Dir = results_Dir, output_Dir = results_Dir, ontologyfname = ontologyfname, file = file_name, name = name) } else{ next } } #input for geneOntology is a .txt file } summaryTable <- function(results_Dir, output_Dir, study, infofile_path){ #get SGMs files = list.files(results_Dir) mod_files = files[grep("mod", files)] SGMs = gsub("merged_", "", gsub(".mod.txt","",mod_files)) all_table = data.frame() for(i in SGMs){ table = data.frame(stringsAsFactors = FALSE) individual_table = data.frame() mod_file = read.delim(paste0(results_Dir,mod_files[grep(i, mod_files)]), header = TRUE, stringsAsFactors = FALSE) mods = unique(mod_file$OVERLAP) supersets = mods[grep(",", mods)] mod_names = unique(mod_file$MODULE) superset_names = mod_names[grep(",", mod_names)] superset_annotation = c() if(length(supersets)>0){ SGM_superset_table = data.frame() for(j in supersets){ superset_table = data.frame() sub_modules_DESCR = c() name = unique(mod_file$MODULE[mod_file$OVERLAP==j]) sub_modules = unlist(strsplit(j, split = ",")) for(l in sub_modules){ info = read.delim(infofile_path, header = TRUE, stringsAsFactors = FALSE) sub_modules_DESCR = append(sub_modules_DESCR, info$DESCR[info$MODULE==l]) } annot = read.delim(paste0(results_Dir,"supersets.",study,".", i,"/","a.supersets.", study, ".",i, "_Ontology_", name,".xls"), header = TRUE, stringsAsFactors = FALSE) #probably will cause problems size = c() annotation = c() if(is.null(annot$ModuleSize[1])){ annotation = append(annotation,"Unknown") size = append(size, "Error in annotation gave no module size") } else if(is.na(annot$ModuleSize[1])){ annotation = append(annotation,"Unknown") size = append(size, annot$ModuleSize[1]) } else if(sum(annot$pvalue_corrected<0.05)==0){ annotation = append(annotation,"Unknown") size = append(size, annot$ModuleSize[1]) } else{ sig_mods = annot[annot$pvalue_corrected<0.05,] max_mod_overlap_annot = annot$Gene.Category[annot$ModuleOverlap==max(annot$ModuleOverlap)] if(length(max_mod_overlap_annot)>1){ names = c() for(k in max_mod_overlap_annot){ names = paste(names, k, sep = ", ") #? } names = substring(names, 3) annotation = append(annotation, names) } else{ annotation = append(annotation,max_mod_overlap_annot) } size = append(size, annot$ModuleSize[1]) superset_table = data.frame("SGM" = i, "MODULE"= sub_modules, "DESCR" = sub_modules_DESCR, "SUPERSET"= annotation, "MODULE_SIZE" = size, stringsAsFactors = FALSE) } SGM_superset_table = rbind(SGM_superset_table, superset_table) } table = SGM_superset_table } individual_modules = mods[!grepl(",", mods)] if(length(individual_modules)>0){ size2 = c() individ_modules_DESCR = c() for(m in individual_modules){ individ_modules_DESCR = append(individ_modules_DESCR, info$DESCR[info$MODULE==m]) size2 = append(size2, sum(mod_file$MODULE==m)) } individual_table = data.frame("SGM" = i, "MODULE"= individual_modules, "DESCR" = individ_modules_DESCR, "SUPERSET" = "none", "MODULE_SIZE"=size2, stringsAsFactors = FALSE) table = rbind(table, individual_table) } if(grepl("quasi", study)){ shared_files = files[grep("shared_",files)] shared_file = shared_files[grep(i, shared_files)] shared = read.delim(paste0(results_Dir,shared_file), header=TRUE, stringsAsFactors = FALSE) methods = c() for(n in table$MODULE){ methods = append(methods, shared$METHOD[shared$MODULE==n]) } table$OVERLAP_METHOD = methods } write.table(table,paste0(output_Dir, "table_", study, ".", i, ".txt"), row.names = FALSE, quote = FALSE, sep = "\t") all_table = rbind(all_table, table) all_table= rbind(all_table,"") } #include SGMs in which there was only one module overlap (was included above^) shared_files = files[grep("shared_", files)] for(i in shared_files){ df = data.frame(stringsAsFactors = FALSE) file = read.delim(paste0(results_Dir, i), header = TRUE, stringsAsFactors = FALSE) if(length(file$MODULE)==1){ SGM = gsub(paste0("shared_", study, "."),"",i) SGM = gsub(".txt","", SGM) if(grepl("quasi",study)){ df = data.frame("SGM"=SGM, "MODULE"=file$MODULE, "DESCR"=file$DESCR, "SUPERSET" = "none", "MODULE_SIZE"="value", "OVERLAP_METHOD"=file$METHOD, stringsAsFactors = FALSE) } else{ info =read.delim("/Users/jessicading/Desktop/Yang_Lab/resources/genesets/all.info.txt", header = TRUE, stringsAsFactors = FALSE) df = data.frame("SGM"=SGM, "MODULE"=file$MODULE, "DESCR"=info$DESCR[info$MODULE==file$MODULE], "SUPERSET" = "none", "MODULE_SIZE"="value", stringsAsFactors = FALSE) } all_table = rbind(all_table, df) all_table = rbind(all_table,"") } else{ next } } #annotate coexpression modules modules = all_table$MODULE for(j in modules){ if(grepl("GTEXv7",j)){ descrip = c() if(j==""){ descrip = append(descrip, "") } else{ annot = read.delim(paste("~/Desktop/Yang_Lab/resources/genesets/reannotate/trim_coexpr/a.trim_coexpr_Ontology_", j, ".xls", sep = ""), header = TRUE, stringsAsFactors = FALSE) if(is.na(annot$ModuleSize[1])){ descrip = append(descrip, "Unknown") } else if(sum(annot$pvalue_corrected<0.05)==0){ descrip = append(descrip, "Unknown") } else{ sig_mods = annot$Gene.Category[annot$pvalue_corrected<0.05] if(length(sig_mods)>1){ names = c() for(k in sig_mods){ names = paste(names, k, sep = ", ") } names = substring(names, 3) } else{ names = sig_mods } descrip = append(descrip, names) } } all_table$DESCR[all_table$MODULE==j] = descrip } } all_table$MODULE_SIZE[is.na(all_table$MODULE_SIZE)]<-"" #-------------------------------------------------------# write.table(all_table, paste0(output_Dir, "table_summary_",study, ".txt"), row.names = FALSE, quote = FALSE, sep = "\t") } #set output_Dir to results folder mergedSummaryTable <- function(summary_table, study, output_Dir, modfile_path, infofile_path, ontologyfname, rcutoff){ mod_file = read.delim(modfile_path, header = TRUE, stringsAsFactors = FALSE) summary_tbl = read.delim(paste0(output_Dir,summary_table), header=TRUE, stringsAsFactors = FALSE) modules = summary_tbl$MODULE[!(summary_tbl$MODULE=="")] modules = unique(modules) modules_df = data.frame("MODULE"=modules) cat("The study being analyzed is", study, "\n") merge_modules(name = study, modules_df = modules_df, output_Dir = output_Dir, modfile_path = modfile_path, infofile_path = infofile_path, rcutoff = rcutoff) #annotate supersets annotate_supersets(results_Dir = output_Dir, ontologyfname = ontologyfname, study = study, trim = c("merged_", ".mod.txt")) merged = read.delim(paste0(output_Dir, "merged_",study, ".mod.txt"), header = TRUE, stringsAsFactors = FALSE) supersets = unique(merged$OVERLAP[grep(",", merged$OVERLAP)]) all_melted_df = data.frame(stringsAsFactors = FALSE) number = 1 index = c() aggregated_descr = c() for(i in supersets){ #maybe make function for this? break down supersets into individual modules name = unique(merged$MODULE[merged$OVERLAP==i]) sub_modules = unlist(strsplit(i, split = ",")) descriptions = c() sizes = c() for(k in sub_modules){ descriptions = append(descriptions,unique(summary_tbl$DESCR[summary_tbl$MODULE==k])) sizes = append(sizes, sum(mod_file$MODULE==k)) } temp = c() for(c in descriptions){ temp = paste(temp, c, sep = ", ") } temp = substring(temp, 3) aggregated_descr = append(aggregated_descr, temp) #for renaming melted_df = data.frame("ID" = paste0("S",number), "MODULE"=sub_modules, "DESCR" = descriptions, "INDIVID_SIZE" = sizes, "SUPERSET_SIZE" = extract_annotation(study = study, name = name, results_Dir = output_Dir)[,2], "SUPERSET"=i, "SUPERSET_DESCR"=extract_annotation(study = study, name = name, results_Dir = output_Dir)[,1], stringsAsFactors = FALSE) all_melted_df = rbind(all_melted_df, melted_df) index = append(index, nrow(all_melted_df)) number = number + 1 } superset_length = nrow(all_melted_df) standalone_modules = unique(merged$MODULE[!grepl(",", merged$MODULE)]) descriptions = c() sizes = c() for(j in standalone_modules){ descriptions = append(descriptions,unique(summary_tbl$DESCR[summary_tbl$MODULE==j])) sizes = append(sizes, sum(mod_file$MODULE==j)) } s_melted_df = data.frame("ID" = standalone_modules, "MODULE"=standalone_modules, "DESCR" = descriptions, "INDIVID_SIZE" = sizes, "SUPERSET_SIZE"= "none", "SUPERSET"="none", "SUPERSET_DESCR"= "none") all_melted_df = rbind(all_melted_df, s_melted_df) index = append(index, seq(from = (superset_length+1), to = nrow(all_melted_df), by = 1)) # ------- For further refined downstream analysis ------- # aggregated_descr = append(aggregated_descr, descriptions) renamed = data.frame("ID"= all_melted_df$ID[index], "MODULE" = all_melted_df$SUPERSET[index], "DESCR" = aggregated_descr, "RENAME" = "", "ANNOTATION" = all_melted_df$SUPERSET_DESCR[index]) write.table(all_melted_df, paste0(output_Dir, "30merged_summary_table.",study,".txt"), row.names = FALSE, quote = FALSE, sep = "\t") write.table(renamed, paste0(output_Dir, "30merged_summary_table_rename.",study,".txt"), row.names = FALSE, quote = FALSE, sep = "\t") } summaryTablev2 <- function(merged_table, merged_table_rename, summary_table, output_Dir){ merged_tbl = read.delim(merged_table, header = TRUE, stringsAsFactors = FALSE) merged_tbl_rename = read.delim(merged_table_rename, header = TRUE, stringsAsFactors = FALSE) summary_tbl = read.delim(summary_table, header = TRUE, stringsAsFactors = FALSE) modified_summary_tbl = summary_tbl # replace SUPERSET names from summary table with merged_tbl SUPERSET modules = summary_tbl$MODULE renames = c() ids = c() for(m in modules){ if(m==""){ renames = append(renames, "") ids = append(ids, "") next } if(merged_tbl$SUPERSET[merged_tbl$MODULE==m]=="none"){ if(sum(grepl(m, merged_tbl_rename$ID))==0){ renames = append(renames, merged_tbl_rename$RENAME[grep(m, merged_tbl_rename$MODULE)]) ids = append(ids, merged_tbl_rename$ID[grep(m, merged_tbl_rename$MODULE)]) next } if(merged_tbl_rename$RENAME[merged_tbl_rename$ID==m]!=""){ renames = append(renames, merged_tbl_rename$RENAME[merged_tbl_rename$ID==m]) ids = append(ids, "none") next } renames = append(renames, "none") ids = append(ids, "none") next } # get superset ID ID = merged_tbl$ID[merged_tbl$MODULE==m] ids = append(ids, ID) renames = append(renames, merged_tbl_rename$RENAME[merged_tbl_rename$ID==ID]) } modified_summary_tbl$SUPERSET_ID = ids modified_summary_tbl$RENAME = renames modified_summary_tbl = modified_summary_tbl[,c("SGM","MODULE","DESCR","SUPERSET_ID","RENAME","MODULE_SIZE","OVERLAP_METHOD")] write.table(modified_summary_tbl, paste0(output_Dir,"combined.renamed_summary_table.txt"), row.names = FALSE, quote = FALSE, sep = "\t") } #input table from output of summaryTable. #summary_table - path to table #1. merge all modules that were significant for all SGMs #2. annotate modules - pick one annotation in the end #3. extract comprising modules for each superset #4. go through each module for each SGM in summary_table and reference it to its superset (if any) - add new column that corresponds to its superset #5. do SGMrepTable function as I've written previously. #6. only did for quasi #set to results folder keys = c("gene2loci.050kb"=1,"all_48esnps"=2,"all_periph_35esnps"=4, "all_brain_13esnps"=3,"select_24esnps"=5, "periph_18esnps"=6, "brain_6esnps"=7,"Adipose_Subcutaneous"=8, "Adipose_Visceral_Omentum"=9, "Adrenal_Gland"=10, "Artery_Aorta"=11, "Artery_Tibial"=12,"Brain_Cerebellar_Hemisphere"=13,"Brain_Cerebellum"=14, "Brain_Cortex"=15, "Brain_Frontal_Cortex_BA9"=16, "Brain_Hippocampus"=17, "Brain_Hypothalamus"=18, "Colon_Sigmoid"=19, "Esophagus_Mucosa"=20, "Esophagus_Muscularis"=21, "Heart_Left_Ventricle"=22,"Liver"=23, "Muscle_Skeletal"=24,"Nerve_Tibial"=25, "Pancreas"=26, "Pituitary"=27, "Spleen"=28, "Stomach"=29, "Thyroid"=30, "Whole_Blood"=31) #module_categories is your merged table #Results directory must have table_summary_`study`_.txt - case sensitive SGMrepTable <- function(keys, results_Dir, infofile_path){ #do for both by superset and by module files = list.files(results_Dir) table_file = files[grep("renamed_table_summary",files)] merged_tbl_file = files[grep("merged_summary_table.AD_quasi", files)] #read in summary table summary_tbl = read.delim(paste0(results_Dir,table_file), header=TRUE, stringsAsFactors = FALSE) summary_tbl = summary_tbl[!(summary_tbl$SGM==""),] summary_tbl = summary_tbl[!(summary_tbl$SGM=="gene2loci.010kb"),] summary_tbl = summary_tbl[!(summary_tbl$SGM=="gene2loci.020kb"),] #read in merging of all modules (module_categories) mod_categories = read.delim(paste0(results_Dir,merged_tbl_file), header = TRUE, stringsAsFactors = FALSE) #for multiple superset names listed, convert to just showing the first one. modify = mod_categories$DESCR[mod_categories$SUPERSET!="none"] for(i in 1:length(modify)){ splitted = unlist(strsplit(modify[i], split = ", ")) modify[i] = splitted[1] } mod_categories$DESCR[mod_categories$SUPERSET!="none"] = modify mod_descr = unique(mod_categories$DESCR[mod_categories$SUPERSET!="none"]) #SUPERSETS combined_supersets = c() num_mod = c() for(j in mod_descr){ if(grepl(",", mod_categories$SUPERSET[mod_categories$DESCR==j][1])){ supersets = which(mod_categories$DESCR==j) position = 1 num_mod = append(num_mod, length(supersets)) for(k in supersets){ if(mod_categories$SUPERSET[k]=="none"){ supersets[position] = mod_categories$MODULE[k] # necessarily???? } else{ supersets[position] = mod_categories$SUPERSET[k] } position = position + 1 } supersets = unique(supersets) supersets = paste0(supersets, collapse = ",") } combined_supersets = append(combined_supersets, supersets) } combined_supersets = append(combined_supersets, mod_categories$MODULE[mod_categories$SUPERSET=="none"]) #INDIVIDUAL num_mod = append(num_mod, rep(1, length(mod_categories$MODULE[mod_categories$SUPERSET=="none"]))) number = 1 ids = c() for(b in combined_supersets){ name = paste0("S", number) if(grepl(",", b)){ ids = append(ids, name) number = number + 1 } else{ ids = append(ids, b) } } description = append(mod_descr, mod_categories$DESCR[mod_categories$SUPERSET=="none"]) new_df = data.frame("ID" = ids, "MODULE"=combined_supersets, "DESCR"=description, stringsAsFactors = FALSE) #check if was fine sign_map = c() sign_numMap = c() num_map = c() for(l in description){ temp2=c() for(m in 1:nrow(summary_tbl)){ if(l==mod_categories$DESCR[mod_categories$MODULE==summary_tbl$MODULE[m]]){ if(is.na(keys[summary_tbl$SGM[m]])){ mapping = summary_tbl$SGM[m] key = summary_tbl$SGM[m] } else{ mapping = summary_tbl$SGM[m] key = keys[summary_tbl$SGM[m]] } temp2 = paste(temp2, key, sep = ", ") } } temp2 = substring(temp2,3) foo2 = unlist(strsplit(temp2,", ")) foo2 = unique(foo2) foo2 = as.integer(foo2) foo2 = sort(foo2) num_map = append(num_map, length(foo2)) temp = names(keys[foo2]) temp = paste0(temp, collapse = ", ") temp2 = paste0(foo2, collapse = ", ") sign_map = append(sign_map, temp) sign_numMap = append(sign_numMap, temp2) } new_df$"Significant Mapping Description" = sign_map new_df$"Significant Mapping"= sign_numMap new_df$"Number of Significant Mappings" = num_map new_df$"Number of Significant Modules"= num_mod # fix problem of mapping to unknown modules for every module that had no annotation. might run into problems that # merged modules with no annotation will have "1" Significant modules when in fact it was a superset with multiple modules unknown = new_df[new_df$DESCR=="Unknown",] if(length(unknown$MODULE)>0){ for(d in 1:nrow(unknown)){ for(e in names(keys)){ if(grepl(e, unknown$MODULE[d])){ unknown$"Significant Mapping Description"[d]=e unknown$"Significant Mapping"[d] = keys[e] unknown$"Number of Significant Mappings"[d] = 1 unknown$"Number of Significant Modules"[d]= 1 } } } new_df = new_df[!(new_df$DESCR=="Unknown"),] new_df = rbind(new_df, unknown) } new_df_sorted = new_df[order(-new_df$"Number of Significant Mappings"),] study = gsub("table_summary_","",table_file) output_file_name = paste0(results_Dir, "SGMrep_table_", study) write.table(new_df_sorted, output_file_name, row.names = FALSE, quote = FALSE, sep="\t") info = read.delim(infofile_path, header = TRUE, stringsAsFactors = FALSE) #Create superset information table superset_ids = ids[1:length(mod_descr)] superset_info = data.frame(stringsAsFactors = FALSE) for(a in 1:length(mod_descr)){ sub_modules = unlist(strsplit(new_df$MODULE[new_df$DESCR==mod_descr[a]], split = ",")) temp_df = data.frame("SUPERSET_ID"=superset_ids[a], "MODULE"=sub_modules, stringsAsFactors = FALSE, "SUPERSET_DESCR"= mod_descr[a]) superset_info = rbind(superset_info, temp_df) } individual_df = data.frame("SUPERSET_ID"="none", "MODULE"=mod_categories$MODULE[mod_categories$SUPERSET=="none"], "SUPERSET_DESCR"="none") all_mod_info = rbind(superset_info, individual_df) sources = c() for(b in all_mod_info$MODULE){ sources = append(sources, info$SOURCE[info$MODULE==b]) } all_mod_info$SOURCE = sources descriptions = c() for(c in all_mod_info$MODULE){ descriptions = append(descriptions, info$DESCR[info$MODULE==c]) } all_mod_info$DESCR = descriptions all_mod_info = all_mod_info[, c(1,2,4,5,3)] write.table(all_mod_info, paste0(results_Dir, "mod.info_",study), row.names = FALSE, quote = FALSE, sep="\t") } SGMrepTablev2 <- function(keys, table_summary, results_Dir, study){ tbl_summary = read.delim(paste0(results_Dir, table_summary), header = TRUE, stringsAsFactors = FALSE) supersets = unique(tbl_summary$SUPERSET_ID[grep("S",tbl_summary$SUPERSET_ID)]) all_modules = append(supersets, unique(tbl_summary$MODULE[tbl_summary$SUPERSET=="none"])) mapping_names = c() key_map = c() num_map = c() descriptions = c() superset_size = c() for(l in all_modules){ temp = c() temp2 = c() for(m in 1:nrow(tbl_summary)){ if(sum(supersets==l)>0){ if(tbl_summary$SUPERSET_ID[m]==l){ if(is.na(keys[tbl_summary$SGM[m]])){ key = tbl_summary$SGM[m] } else{ key = keys[tbl_summary$SGM[m]] } temp = paste(temp, key, sep = ", ") } } else { if(tbl_summary$MODULE[m]==l){ if(is.na(keys[tbl_summary$SGM[m]])){ key = tbl_summary$SGM[m] } else{ key = keys[tbl_summary$SGM[m]] } temp = paste(temp, key, sep = ", ") } } } if(sum(supersets==l)>0){ descriptions = append(descriptions, tbl_summary$RENAME[tbl_summary$SUPERSET_ID==l][1]) } else if(unique(tbl_summary$RENAME[tbl_summary$MODULE==l])!="none"){ descriptions = append(descriptions, tbl_summary$RENAME[tbl_summary$MODULE==l][1]) } else{ descriptions = append(descriptions, tbl_summary$DESCR[tbl_summary$MODULE==l][1]) } if(sum(supersets==l)>0){ superset_size = append(superset_size, length(unique(tbl_summary$MODULE[tbl_summary$SUPERSET_ID==l]))) } else{ superset_size = append(superset_size, 1) } temp = substring(temp,3) foo = unlist(strsplit(temp,", ")) foo = unique(foo) foo = as.integer(foo) foo = sort(foo) num_map = append(num_map, length(foo)) temp2 = names(keys[foo]) # keep keys as vector temp2 = paste0(temp2, collapse = ", ") temp = paste0(foo, collapse = ", ") # now conjoin keys key_map = append(key_map, temp) mapping_names = append(mapping_names, temp2) } new_df = data.frame("MODULE"= all_modules, "DESCR" = descriptions, "Significant Mapping Description" = mapping_names, "Significant Mapping" = key_map, "Number_of_Significant_Mappings" = num_map, "Number of Modules" = superset_size, stringsAsFactors = FALSE) new_df_sorted = new_df[order(-new_df$Number_of_Significant_Mappings),] unknowns = new_df_sorted[new_df_sorted$DESCR=="Unknown",] new_df_sorted = new_df_sorted[!(new_df_sorted$DESCR=="Unknown"),] new_df_sorted = rbind(new_df_sorted, unknowns) # study = gsub("30renamed_table_summary_","",table_summary) output_file_name = paste0(results_Dir, "combined.SGMrep_table_", study,".txt") write.table(new_df_sorted, output_file_name, row.names = FALSE, quote = FALSE, sep="\t") } combineSGMrepTable <- function(results1, results2){ first = read.delim(results1, header = TRUE, stringsAsFactors = FALSE) second = read.delim(results2, header = TRUE, stringsAsFactors = FALSE) } #modified merge function that just takes in a list of modules - already filtered for FDR, no ctrl sets, etc. #include last "/" in output_Dir merge_modules <- function(name, modules_df, rcutoff, output_Dir, modfile_path, infofile_path){ plan = c() plan$folder = output_Dir plan$label = name plan$modfile= modfile_path plan$inffile= infofile_path #===================================================== pool=c() aa = modules_df if (nrow(aa) > 0) pool=unique(c(pool, as.character(aa[,"MODULE"]))) #===================================================== #=== Merge the modules before 2nd SSEA if (length(pool)>0){ meg.mods<- tool.read(plan$modfile) merged.modules <- pool moddata <- meg.mods[which(!is.na(match(meg.mods[,1], merged.modules))),] # Merge and trim overlapping modules. rmax <- rcutoff moddata$OVERLAP <- moddata$MODULE moddata <- tool.coalesce(items=moddata$GENE, groups=moddata$MODULE, rcutoff=rmax) #, ncore=500) moddata$MODULE <- moddata$CLUSTER moddata$GENE <- moddata$ITEM moddata$OVERLAP <- moddata$GROUPS moddata <- moddata[,c("MODULE", "GENE", "OVERLAP")] moddata <- unique(moddata) moddatainfo <- tool.read(plan$inffile) moddatainfo <- moddatainfo[which(!is.na(match(moddatainfo[,1], moddata[,1]))), ] # Mark modules with overlaps. for(j in which(moddata$MODULE != moddata$OVERLAP)){ moddatainfo[which(moddatainfo[,"MODULE"] == moddata[j,"MODULE"]), "MODULE"] <- paste(moddata[j,"MODULE"], "..", sep=",") moddata[j,"MODULE"] <- paste(moddata[j,"MODULE"], "..", sep=",") } # Save module info for 2nd SSEA and KDA. moddata <- unique(moddata) moddata[, 4] <- moddata[, 2]; names(moddata)[4] <- c("NODE") mdfile=paste0(name, ".mod.txt"); mifile=paste0(name, ".info.txt") write.table(moddata, paste0(plan$folder,"merged_", mdfile), #deleted "/" before "merged" sep='\t', col.names=T, row.names=F, quote=F) write.table(moddatainfo, paste0(plan$folder,"merged_", mifile), sep='\t', col.names=T, row.names=F, quote=F) } } #name includes ("supersets.",study,"." and maybe some other thing) extract_annotation <- function(study, name, results_Dir){ file_name = paste0(results_Dir,"supersets.",study,"/","a.supersets.", study, "_Ontology_", name,".xls") annot = read.delim(file_name, header = TRUE, stringsAsFactors = FALSE) size = c() annotation = c() if(is.null(annot$ModuleSize[1])){ annotation = append(annotation,"Unknown") size = append(size, "Error in annotation gave no module size") } else if(is.na(annot$ModuleSize[1])){ annotation = append(annotation,"Unknown") size = append(size, annot$ModuleSize[1]) } else if(sum(annot$pvalue_corrected<0.05)==0){ annotation = append(annotation,"Unknown") size = append(size, annot$ModuleSize[1]) } else{ sig_mods = annot[annot$pvalue_corrected<0.05,] #max_mod_overlap_annot = annot$Gene.Category[annot$ModuleOverlap==max(annot$ModuleOverlap)] #before wanted to get highest overlap max_mod_overlap_annot = sig_mods$Gene.Category if(length(max_mod_overlap_annot)>1){ names = c() for(k in max_mod_overlap_annot){ names = paste(names, k, sep = ", ") #? } names = substring(names, 3) annotation = append(annotation, names) } else{ annotation = append(annotation,max_mod_overlap_annot) } size = append(size, annot$ModuleSize[1]) } superset_table = data.frame("SUPERSET"= annotation, "MODULE_SIZE" = size, stringsAsFactors = FALSE) return(superset_table) #return(annotation) } # cohort is so that you only get one set of SGMs # trim is a vector of strings to trim off files to get the "SGM" # for example, things to trim include the study or any other common information ex. DIAGRAMstage1_T2D.Adipose_Subcutaneous.50.20.results.txt # the goal of the trim parameter is to reduce the example to "Adipose_Subcutaneous" # so your trim vector would be c("DIAGRAMstage1_T2D", ".50.20.results.txt") # because you just need to get SGMs and can get SGM files using "grep" and this is just an "intersect" or "replication" function, you don't need to put all studies replication <- function(results_Dir, cohort, study, trim=NULL){ files = list.files(results_Dir) #get SGMs subset = files[grep(cohort, files)] SGMs = subset for(i in trim){ SGMs = gsub(i, "", SGMs) } shared = c() for(j in SGMs){ results = files[grep(j, files)] iter = 1 for(k in results){ if(iter==1){ df = read.delim(paste0(results_Dir, k), header = TRUE, stringsAsFactors = FALSE) shared = df$MODULE } if(iter>1){ df = read.delim(paste0(results_Dir, k), header = TRUE, stringsAsFactors = FALSE) shared_temp = df$MODULE shared = intersect(shared,shared_temp) } iter = iter + 1 } shared_df = data.frame("MODULE"=shared) write.table(shared_df, paste0(results_Dir, "shared_", study,".", j, ".txt"), row.names = FALSE, quote = FALSE, sep="\t") } } combine_results <- function(folders, output_Dir){ # just to get traits and mapping files = list.files(folders[1]) mapping = c() traits = c() for(f in files){ mapping = append(mapping, unlist(strsplit(f, split = ".",fixed = TRUE))[2]) traits = append(traits, unlist(strsplit(f, split = ".",fixed = TRUE))[1]) } mapping = unique(mapping) traits = unique(traits) for(t in traits){ for(m in mapping){ combined = data.frame(stringsAsFactors = FALSE) files = c() for(f in folders){ file = list.files(f, full.names = TRUE)[grep(t, list.files(f))] file = file[grep(m, file)] # should just get one file from each folder if(length(file)>1){ cat("More than one similar file in ", f, "\n") } if(length(file)==0){ next } files = append(files, file) } if(is.null(files)) next for(f in files){ df <- read.delim(f, stringsAsFactors = FALSE) combined = rbind(combined, df) } combined = combined[!(combined$MODULE=="_ctrlA" | combined$MODULE=="_ctrlB"),] # will output the same name write.table(combined, paste0(output_Dir, tail(unlist(strsplit(files[1], split = "/")),n=1)), row.names = FALSE, quote = FALSE, sep="\t") } } # results = list.files(results_Dir) # files = results[grep(study, results)] # files = files[grep("results", files)] # coexpr_files = list.files(coexpr_Dir) # for(i in files){ # file = read.delim(paste0(results_Dir, i), header = TRUE, stringsAsFactors = FALSE) # file = file[file$FDR<=0.05,] # if(sum(grepl(i,coexpr_files))>0){ # coexpr = coexpr_files[grep(i, coexpr_files)] # coexpr_df = read.delim(paste0(coexpr_Dir,coexpr), header = TRUE, stringsAsFactors = FALSE) # coexpr_df = coexpr_df[coexpr_df$FDR<=0.05,] # combined = rbind(file, coexpr_df) # combined = combined[!(combined$MODULE=="_ctrlA" | combined$MODULE=="_ctrlB"),] # write.table(combined, paste0(output_Dir, i), row.names = FALSE, quote = FALSE, sep="\t") # next # } # file = file[!(file$MODULE=="_ctrlA" | file$MODULE=="_ctrlB"),] # write.table(file, paste0(output_Dir, i), row.names = FALSE, quote = FALSE, sep="\t") # } } combine_details <- function(canon_Dir, coexp_Dir, output_Dir){ files = list.files(canon_Dir) for(file in files){ if(sum(list.files(coexp_Dir)==file)==0){ mapping = file.copy(from = paste0(canon_Dir, file), to = paste0(output_Dir,file)) } else{ canon = read.delim(paste0(canon_Dir, file), stringsAsFactors = FALSE) coexp = read.delim(paste0(coexp_Dir, file), stringsAsFactors = FALSE) both = rbind(canon, coexp) write.table(both, paste0(output_Dir, file), row.names = FALSE, quote = FALSE, sep="\t") } } } runKDA <- function(nodes, network, trim = NULL){ cat("\nNow analyzing:", gsub(".txt","",nodes), "with", gsub(".txt","",network), "\n") job.kda <- list() job.kda$label<-"wKDA" network_name = gsub(".txt","",unlist(strsplit(network, split = "/"))[length(unlist(strsplit(network, split = "/")))]) nodes_name = nodes for(i in trim){ nodes_name = gsub(i,"",nodes_name) } name = paste(nodes_name, network_name,sep = "_") job.kda$folder<- name ## parent folder for results ## Input a network ## columns: TAIL HEAD WEIGHT job.kda$netfile <- network ## Input gene list ## columns: MODULE NODE job.kda$modfile <- nodes ## "0" means we do not consider edge weights while 1 is opposite. job.kda$edgefactor <- 0 ## The searching depth for the KDA job.kda$depth <- 1 ## 0 means we do not consider the directions of the regulatory interactions ## while 1 is opposite. job.kda$direction <- 1 job.kda$nperm <- 10000 moddata <- tool.read(job.kda$modfile) mod.names <- unique(moddata$MODULE) moddata <- moddata[which(!is.na(match(moddata$MODULE, mod.names))),] ## save this to a temporary file and set its path as new job.kda$modfile: tool.save(moddata, "subsetof.supersets.txt") job.kda$modfile <- "subsetof.supersets.txt" ## Run KDA job.kda <- kda.configure(job.kda) job.kda <- kda.start(job.kda) job.kda <- kda.prepare(job.kda) job.kda <- kda.analyze(job.kda) job.kda <- kda.finish(job.kda) job.kda <- kda2cytoscape(job.kda) } summarizeKDA <- function(KDA_folder, protein_descrip, name){ results = data.frame(stringsAsFactors = FALSE) KDs = data.frame(stringsAsFactors = FALSE) protein_names = read.delim(protein_descrip, header = TRUE, stringsAsFactors = FALSE, quote = "") for(l in list.files(KDA_folder)){ if(length(list.files(paste0("./",l, "/")))==1){ cat("No significant key drivers found for", l, "\n") next } else{ key_drivers = read.delim(paste0(KDA_folder,l,"/kda/wKDA.results.txt"), stringsAsFactors = FALSE) valued_indices = which(!is.na(key_drivers$NODE[1:5])) edges = read.delim(paste0(KDA_folder,l,"/cytoscape/kda2cytoscape.edges.txt"), stringsAsFactors = FALSE) edges = edges[!duplicated(edges[,c(1,2)]),] # makes an edge for each module but don't want this.. nodes = read.delim(paste0(KDA_folder,l,"/cytoscape/kda2cytoscape.nodes.txt"), stringsAsFactors = FALSE) temp = data.frame(stringsAsFactors = FALSE) # make network summary table temp = data.frame("NETWORK"=l, "KDs"=length(key_drivers$NODE), "KDs_p<0.05"=sum(key_drivers$P<0.05), "KDs_fdr<0.05"=sum(key_drivers$FDR<0.05), "topKDs"=concatenate(key_drivers$NODE[valued_indices], mysep = ", "), "topKDs_fdr"=concatenate(key_drivers$FDR[valued_indices], mysep = ", "), "n_nodes" = length(nodes$NODE), "n_edges"=length(edges$TAIL), "avg_degree"= (length(edges$TAIL)/length(unique(append(edges$TAIL, edges$HEAD)))), "perc_member"=(sum(grepl("chart", nodes$URL)))/length(nodes$NODE), stringsAsFactors = FALSE) results = rbind(results, temp) info = c() indices = which(!is.na(key_drivers$NODE[key_drivers$FDR<0.05])) for(m in key_drivers$NODE[indices]){ if(length(protein_names$annotation[protein_names$preferred_name==m])==0){ info = append(info, "Not annotated") } else if(length(protein_names$annotation[protein_names$preferred_name==m])>1){ info = append(info, concatenate(protein_names$annotation[protein_names$preferred_name==m], mysep = ",")) cat("This protein had more than one annotation:", m, "\n") } else{ info = append(info, protein_names$annotation[protein_names$preferred_name==m]) } } # make KD summary table kd_temp = data.frame("NETWORK"=l, "KDs"= key_drivers$NODE[indices], "Degrees" = key_drivers$N.neigh[indices], "P-value" = key_drivers$P[indices], "FDR" = key_drivers$FDR[indices], "INFO" = info, "MEMBER" = key_drivers$MEMBER[indices], stringsAsFactors = FALSE) KDs = rbind(KDs, kd_temp) } } write.table(results, paste0(name, "_results_summary.txt"), row.names = FALSE, quote = FALSE, sep="\t") write.table(KDs, paste0(name, "_KDs_summary.txt"), row.names = FALSE, quote = FALSE, sep="\t") } # this function outputs a new network in the folder with beginning "mod_" # this function has repetitive code.. will make better later # when modify nodes file to add GWAS information, have to delete, overwrite, or put in another folder the original nodes file trim_network <- function(folder,keep_only_orig_input = FALSE, keep_only_GWAS_hits = FALSE){ edges_file = list.files(folder)[grep("edges",list.files(folder))] edges = read.delim(paste0(folder,edges_file), stringsAsFactors = FALSE) nodes = read.delim(paste0(folder, list.files(folder)[grep("nodes",list.files(folder))]), stringsAsFactors = FALSE) prefixes = c("^MMT", "^NM_","^XM_","ENSMUST","^ri", "_at") # AK genes are long noncoding RNAs for(pre in prefixes){ edges = edges[!grepl(pre,edges$HEAD),] } if(keep_only_orig_input & !keep_only_GWAS_hits){ # keep only KDs and nodes with URLs # make list of KDs and URL nodes # keep edges that connect KD to member node keep_nodes = c() for(i in nodes$NODE){ if(nodes$SHAPE[nodes$NODE==i]!="Diamond" & nodes$URL[nodes$NODE==i]!=""){ keep_nodes = append(keep_nodes, i) } } total = data.frame(stringsAsFactors = FALSE) for(r in keep_nodes){ temp = edges[grep(paste0("^",r,"$"), edges$HEAD ),]# KDs in the TAIL column - keep KDs total = rbind(total, temp) } total = total[!duplicated(total),] total = total[!duplicated(total[,c(1,2)]),] write.table(total, paste0(folder, "mod_",edges_file), row.names = FALSE, quote = FALSE, sep="\t") return(total) } # should do a version that filters ONLY gwas hits so that genes that are not members can be included # though this happens rarely else if(keep_only_orig_input & keep_only_GWAS_hits){ # must have GWAS_hit_meta column, further filtering after filtering by module # keep only KDs and nodes with URLs # make list of KDs and URL nodes # keep edges that connect KD to member node keep_nodes = c() for(i in nodes$NODE){ if(nodes$SHAPE[nodes$NODE==i]!="Diamond" & nodes$URL[nodes$NODE==i]!="" & nodes$GWAS_hit_meta[nodes$NODE==i]!="no"){ keep_nodes = append(keep_nodes, i) } } total = data.frame(stringsAsFactors = FALSE) for(r in keep_nodes){ temp = edges[grep(paste0("^",r,"$"), edges$HEAD ),]# KDs in the TAIL column - keep KDs total = rbind(total, temp) } total = total[!duplicated(total),] total = total[!duplicated(total[,c(1,2)]),] write.table(total, paste0(folder, "mod_",edges_file), row.names = FALSE, quote = FALSE, sep="\t") return(total) } else if(!keep_only_orig_input & keep_only_GWAS_hits){ keep_nodes = c() for(i in nodes$NODE){ if(nodes$SHAPE[nodes$NODE==i]!="Diamond" & nodes$GWAS_hit_meta[nodes$NODE==i]!="no"){ keep_nodes = append(keep_nodes, i) } } total = data.frame(stringsAsFactors = FALSE) for(r in keep_nodes){ temp = edges[grep(paste0("^",r,"$"), edges$HEAD ),]# KDs in the TAIL column - keep KDs total = rbind(total, temp) } total = total[!duplicated(total),] total = total[!duplicated(total[,c(1,2)]),] write.table(total, paste0(folder, "mod_",edges_file), row.names = FALSE, quote = FALSE, sep="\t") return(total) } else{ write.table(edges, paste0(folder, "mod_",edges_file), row.names = FALSE, quote = FALSE, sep="\t") return(edges) } } # would be better to make reference that says all the genes that could be mapped from the significant values, not just for # individual mapping methods # also want to add the info in it was in both AD and T2D # ^ can do after this # do same nodes file on AD and T2D, then combine the T2D column result with AD, do if statements to make new column stating # whether there was any "overlap" add_GWAS_hit_info_network <- function(nodes, SGM, genes_folder, study){ nodes = read.delim(nodes, stringsAsFactors = FALSE) files = list.files(genes_folder) file = files[intersect(grep("noDescrip",files), grep(SGM,files))] df = read.delim(paste0(genes_folder,file), stringsAsFactors = FALSE) GWAS_hit = c() for(i in nodes$NODE){ if(is.character(df$Shared.genes)){ if(sum(unlist(strsplit(df$Shared.genes, split = ", "))==i)>0){ GWAS_hit = append(GWAS_hit, paste0("Replicated_GWAS_",study)) next } else if(sum(unlist(strsplit(df[,5], split = ", "))==i)>0){ GWAS_hit = append(GWAS_hit, paste0("GWAS_hit_", gsub("_genes","",colnames(df)[5]))) } else if(sum(unlist(strsplit(df[,6], split = ", "))==i)>0){ GWAS_hit = append(GWAS_hit, paste0("GWAS_hit_", gsub("_genes","",colnames(df)[6]))) } else{ GWAS_hit = append(GWAS_hit, "no") } } else{ GWAS_hit = append(GWAS_hit, "no") } } nodes$GWAS_hit = GWAS_hit return(nodes) } extract_annotationv2 <- function(module, set, results_Dir){ file_name = paste0(results_Dir, set, module,".xls") annot = read.delim(file_name, header = TRUE, stringsAsFactors = FALSE) size = c() annotation = c() if(is.null(annot$ModuleSize[1])){ annotation = append(annotation,"Unknown") size = append(size, "Error in annotation gave no module size") } else if(is.na(annot$ModuleSize[1])){ annotation = append(annotation,"Unknown") size = append(size, annot$ModuleSize[1]) } else if(sum(annot$pvalue_corrected<0.05)==0){ annotation = append(annotation,"Unknown") size = append(size, annot$ModuleSize[1]) } else{ sig_mods = annot[annot$pvalue_corrected<0.05,] #max_mod_overlap_annot = annot$Gene.Category[annot$ModuleOverlap==max(annot$ModuleOverlap)] #before wanted to get highest overlap max_mod_overlap_annot = sig_mods$Gene.Category if(length(max_mod_overlap_annot)>1){ names = c() for(k in max_mod_overlap_annot){ names = paste(names, k, sep = ", ") #? } names = substring(names, 3) annotation = append(annotation, names) } else{ annotation = append(annotation,max_mod_overlap_annot) } size = append(size, annot$ModuleSize[1]) } superset_table = data.frame("SUPERSET"= annotation, "MODULE_SIZE" = size, stringsAsFactors = FALSE) return(superset_table) #return(annotation) } trimSummaryTable <- function(summary_table, study, output_Dir){ new_summary_tbl = data.frame(stringsAsFactors = FALSE) summary_tbl = read.delim(summary_table, header = TRUE, stringsAsFactors = FALSE) SGMs = unique(summary_tbl$SGM) SGMs = SGMs[SGMs!=""] for(i in SGMs){ all_n_subset = data.frame(stringsAsFactors = FALSE) SGM_subset = summary_tbl[summary_tbl$SGM==i,] supers = SGM_subset$SUPERSET_ID[SGM_subset$SUPERSET_ID!="none"] supersets = unique(supers) for(j in supersets){ subset = SGM_subset[SGM_subset$SUPERSET_ID==j,] n_subset = data.frame("SGM"=i, "MODULE"=concatenate(subset$MODULE, mysep = ", "), "DESCR"=concatenate(subset$DESCR, mysep = ", "), "SUPERSET_ID"=subset$SUPERSET_ID[1], "RENAME"=subset$RENAME[1], "MODULE_SIZE"=subset$MODULE_SIZE[1], "OVERLAP_METHOD"=concatenate(subset$OVERLAP_METHOD, mysep = ", "), stringsAsFactors = FALSE) all_n_subset = rbind(all_n_subset, n_subset) } all_n_subset = rbind(all_n_subset, SGM_subset[SGM_subset$SUPERSET_ID=="none",]) new_summary_tbl = rbind(new_summary_tbl, all_n_subset) new_summary_tbl = rbind(new_summary_tbl,"") } write.table(new_summary_tbl, paste0(output_Dir, "trimmed_summary_table_",study,".txt"), row.names = FALSE, quote = FALSE, sep="\t") } enrichSupersetTable <- function(name1, sgm_rep_table1, name2, sgm_rep_table2, superset_info, output_Dir){ stbl_1 = read.delim(sgm_rep_table1, header = TRUE, stringsAsFactors = FALSE) stbl_2 = read.delim(sgm_rep_table2, header = TRUE, stringsAsFactors = FALSE) supersets = read.delim(superset_info, header = TRUE, stringsAsFactors = FALSE) modules = supersets$MODULE represent = c() # mapping1 = c() # mapping2 = c() for(i in modules){ if(sum(stbl_1$MODULE==i)>0 & sum(stbl_2$MODULE==i)>0){ represent = append(represent, paste(name1, name2, sep = ", ")) # mapping1 = append(mapping1, stbl_1$Significant.Mapping[stbl_1$MODULE==i]) # mapping2 = append(mapping2, stbl_2$Significant.Mapping[stbl_2$MODULE==i]) } else if(sum(stbl_1$MODULE==i)>0){ represent = append(represent, name1) # mapping1 = append(mapping1, stbl_1$Significant.Mapping[stbl_1$MODULE==i]) # mapping2 = append(mapping2, "") } else if(sum(stbl_2$MODULE==i)>0){ represent = append(represent, name2) # mapping1 = append(mapping1, "") # mapping2 = append(mapping2, stbl_2$Significant.Mapping[stbl_2$MODULE==i]) } else{ represent = append(represent, "") # mapping1 = append(mapping1, "") # mapping2 = append(mapping2, "") } } supersets$Representation = represent column1 = paste0(name1,"_Mapping") column2 = paste0(name2,"_Mapping") # supersets[,column1]= mapping1 # supersets[,column2] = mapping2 # study1 = supersets[supersets$Representation==name1,] # study2 = supersets[supersets$Representation==name2,] # both_studies = supersets[supersets$Representation==paste(name1,name2,sep = ", "),] # supersets_reorder = rbind(both_studies, study1) # supersets_reorder = rbind(supersets_reorder, study2) # supersets_reorder = supersets_reorder[,c(1,6,2,3,4,5,7,8)] supersets = supersets[,c(1,2,9,3,4,5,6,7,8)] write.table(supersets, paste0(output_Dir, "detailed_superset_info_rep.txt"), row.names = FALSE, quote = FALSE, sep="\t") # write.table(supersets_reorder, paste0(output_Dir, "superset_info_rep_reordered.txt"), # row.names = FALSE, quote = FALSE, sep="\t") } # get numbers # 2 studies # results_Dir is preprocess_quasi_overlap quantifyOverlap <- function(results_Dir, studies, quasi_Dir){ combined_files = list.files(results_Dir)[grep("combined_", list.files(results_Dir))] study1_num = c() study2_num = c() shared = c() # number shared_modules = c() # actual modules - at the end, unique to get number of unique shared modules all_study1_modules = c() # unique at end - overall across all mappings all_study2_modules = c() # at end after "uniquing", get num of shared modules, number of duplicated for(file in combined_files){ mapping = read.delim(paste0(results_Dir,file), stringsAsFactors = FALSE) study1_num = append(study1_num, length(mapping$STUDY[mapping$STUDY==studies[1]])) study2_num = append(study2_num, length(mapping$STUDY[mapping$STUDY==studies[2]])) shared = append(shared, sum(duplicated(mapping$MODULE))) shared_modules = append(shared_modules,mapping$MODULE[duplicated(mapping$MODULE)]) all_study1_modules = append(all_study1_modules, mapping$MODULE[mapping$STUDY==studies[1]]) all_study2_modules = append(all_study2_modules, mapping$MODULE[mapping$STUDY==studies[2]]) } quasi_overlapped = c() mappings = gsub("combined_","",gsub(".txt","",combined_files)) shared_files = list.files(quasi_Dir)[grep("shared_", list.files(quasi_Dir))] for(map in mappings){ if(sum(grepl(map,shared_files))==0) quasi_overlapped = append(quasi_overlapped, 0) else{ file = shared_files[grep(map, shared_files)] shared_file = read.delim(paste0(quasi_Dir, file), stringsAsFactors = FALSE) quasi_overlapped = append(quasi_overlapped, length(shared_file$MODULE)) } } superset_overlap = c() merged_files = list.files(quasi_Dir)[grep("merged_", list.files(quasi_Dir))] merged_info = merged_files[grep("info.txt", merged_files)] for(map in mappings){ if(sum(grepl(map, merged_info))==0) superset_overlap = c(superset_overlap, 0) else{ file = merged_info[grep(map, merged_info)] merged_file = read.delim(paste0(quasi_Dir,file), stringsAsFactors = FALSE) superset_overlap = append(superset_overlap, length(merged_file$MODULE)) } } quantified = data.frame("Mapping"= mappings,stringsAsFactors = FALSE) quantified[,studies[1]] = study1_num quantified[,studies[2]] = study2_num quantified$`Number of Shared Modules` = shared quantified$`Number of Shared Modules with Quasi Overlap` = quasi_overlapped quantified$`Number of Shared Supersets` = superset_overlap # address no merged modules because there was only 1 module quantified$`Number of Shared Supersets`[quantified$`Number of Shared Modules with Quasi Overlap`==1] <- 1 return(quantified) } # study1num = length(unique(all_study1_modules)) # study2num = length(unique(all_study2_modules)) # all = append(unique(all_study1_modules), unique(all_study2_modules)) # shared_overall = sum(duplicated(all)) # shared_byMapping = length(unique(shared_modules)) # take shared modules and get genes from each study. only direct. find consistent modules and unique ones for each different mappings. # method to get SGM is case specific. second to last thing separated by "." in the file # value_cutoff is association value - only show genes from SNPs that have values greater than the cutoff (lower p-value) # modify function such that it looks in the kbr.mod.txt file and checks if the shared genes are part of the pathways because in the details file, # it will output all the genes that are mapped to by that SNP but are not necessarily part of the pathway (but at least one will be) # ^^ changed this so it doesnt do that in this output! # ^^ make sure genes are actually in module - this is what parameter modfile is for # there is probably a better, modular way I could do this that can do more than 2 studies. createGenesFiles <- function(results_Dir, genes_Dir, output_Dir, studies, protein_descrip, value_cutoff, modfile){ modfile <- read.delim(modfile, stringsAsFactors = FALSE) shared_files = list.files(results_Dir)[grep("shared_", list.files(results_Dir))] all = data.frame(stringsAsFactors = FALSE) gene_files = c() for(s in studies){ gene_files = append(gene_files, list.files(genes_Dir)[grep(s, list.files(genes_Dir))]) } for(file in shared_files){ # direct overlap mapping = read.delim(paste0(results_Dir,file), stringsAsFactors = FALSE) SGM = unlist(strsplit(file, split = ".", fixed = TRUE))[2] cat(SGM,"\n") sig_modules = mapping$MODULE genes_s1 = c() genes_s2 = c() descrip_s1 = c() descrip_s2 = c() shared_genes = c() shared_descrip = c() all_long = data.frame(stringsAsFactors = FALSE) for(mod in sig_modules){ cat(mod,"\n") #gene_list1 = c() # to get shared genes #gene_list2 =c() # to get shared genes details = gene_files[grep(SGM, gene_files)] details_s1 = read.delim(paste0(genes_Dir,details[grep(studies[1], details)]), stringsAsFactors = FALSE) # this function will modify genes_s1 and descrip_s1 and return the genes gene_set1 <- retrieveGenes(genes_df = details_s1, genes = genes_s1, descrip = descrip_s1, mod = mod, modfile = modfile, value_cutoff = value_cutoff) genes_s1 = append(genes_s1, gene_set1[["Concatenated genes"]]) descrip_s1 = append(descrip_s1, gene_set1[["Concatenated descrip"]]) # get genes in list # if(sum(details_s1$MODULE==mod)==0){ # genes_s1 = append(genes_s1, "none") # descrip_s1 = append(descrip_s1, "none") # } # else{ # if(length(details_s1$GENE[details_s1$MODULE==mod & details_s1$VALUE>value_cutoff])==0){ # genes_s1 = append(genes_s1, "Genes not meeting association value threshold: ") # descrip_s1 = append(descrip_s1, "Genes not meeting association value threshold: ") # } # for(cell in details_s1$GENE[details_s1$MODULE==mod & details_s1$VALUE>value_cutoff]){ # if(grepl(",",cell)){ # gene_list1 = append(gene_list1, unlist(strsplit(cell,split = ","))) # } # else{ # gene_list1 = append(gene_list1, cell) # } # } # genes_s1 = append(genes_s1, concatenate(gene_list1, mysep = ", ")) # descrip_s1 = append(descrip_s1, concatenate(annotate_gene(gene_list1), mysep = ", ")) # } details_s2 = read.delim(paste0(genes_Dir,details[grep(studies[2], details)]), stringsAsFactors = FALSE) gene_set2 <- retrieveGenes(genes_df = details_s2, genes = genes_s2, descrip = descrip_s2, mod = mod, modfile = modfile, value_cutoff = value_cutoff) genes_s2 = append(genes_s2, gene_set2[["Concatenated genes"]]) descrip_s2 = append(descrip_s2, gene_set2[["Concatenated descrip"]]) # if(sum(details_s2$MODULE==mod)==0){ # genes_s2 = append(genes_s2, "none") # descrip_s2 = append(descrip_s2, "none") # } # else{ # for(cell in details_s2$GENE[details_s2$MODULE==mod & details_s1$VALUE>value_cutoff]){ # if(grepl(",",cell)){ # gene_list2 = append(gene_list2, unlist(strsplit(cell,split = ","))) # } # else{ # gene_list2 = append(gene_list2, cell) # } # } # genes_s2 = append(genes_s2, concatenate(gene_list2, mysep = ", ")) # descrip_s2 = append(descrip_s2, concatenate(annotate_gene(gene_list2), mysep = ", ")) # } shared_genes = append(shared_genes, concatenate(intersect(gene_set1[["Gene list"]], gene_set2[["Gene list"]]), mysep = ", ")) shared_descrip = append(shared_descrip, concatenate(annotate_gene(intersect(gene_set1[["Gene list"]], gene_set2[["Gene list"]])))) shared = intersect(gene_set1[["Gene list"]], gene_set2[["Gene list"]]) # for specifically gene2loci T2D, it is duplicating specificaly LDLC_Control 4 times??? (5 sets of the same shared_genes) # tried to debug, could not figure out why if(length(intersect(gene_set1[["Gene list"]], gene_set2[["Gene list"]]))>0){ genes_df_long = data.frame("Mapping"=SGM, "MODULE" = mod, "Shared genes" = shared, "Gene Descriptions" = annotate_gene(shared), stringsAsFactors = FALSE) cat(" ", length(shared), "\n") } all_long = rbind(all_long, genes_df_long[!duplicated(genes_df_long),]) #???? how are things getting duplicated? all_long = all_long[!duplicated(all_long),] } #ugh = all_long[duplicated(all_long),] write.table(all_long, paste0(output_Dir,"long_",SGM,"_overlap_genes.txt"), row.names = FALSE, quote = FALSE, sep = "\t") genes_df = data.frame("Mapping" = SGM, "MODULE" = mapping$MODULE, "DESCR"=mapping$DESCR, stringsAsFactors = FALSE) genes_df$`Overlap Method` = mapping$METHOD genes_df[,paste0(studies[1],"_genes")] = genes_s1 genes_df[,paste0(studies[2],"_genes")] = genes_s2 genes_df[,paste0(studies[1],"_genes_descrip")] = descrip_s1 genes_df[,paste0(studies[2],"_genes_descrip")] = descrip_s2 genes_df$`Shared genes` = shared_genes genes_df$`Shared genes description` = shared_descrip #**problem: overflow of cell so text file can't handle it, results in losing some columns (?) # do a version with no description write.table(genes_df[,c("Mapping","MODULE","DESCR","Overlap Method",paste0(studies[1],"_genes"), paste0(studies[2],"_genes"), "Shared genes")], paste0(output_Dir,"noDescrip_",SGM,"_overlap_genes.txt"), row.names = FALSE, quote = FALSE, sep = "\t") #write.table(genes_df, paste0(output_Dir,SGM,"_overlap_genes.txt"), row.names = FALSE, quote = FALSE, sep = "\t") #write.csv(genes_df, paste0(output_Dir,SGM,"_overlap_genes.txt"), row.names = FALSE, quote = FALSE, ) all = rbind(all, genes_df) } write.table(all, paste0(output_Dir,"All_mapping_Genes.txt"), row.names = FALSE, quote = FALSE, sep = "\t") } # this function modifies genes_s1! retrieveGenes <- function(genes_df, genes, descrip, mod, modfile, value_cutoff){ gene_list1 = c() # will modularize this later.... set = list() if(sum(genes_df$MODULE==mod)==0){ genes = "none" descrip = "none" set[["Gene list"]] = "" } else{ if(length(genes_df$GENE[genes_df$MODULE==mod & genes_df$VALUE>value_cutoff])==0){ genes = "Genes not meeting association value threshold: " descrip = "Genes not meeting association value threshold: " set[["Gene list"]] = "" } else{ for(cell in genes_df$GENE[genes_df$MODULE==mod & genes_df$VALUE>value_cutoff]){ if(grepl(",",cell)){ gene_list1 = append(gene_list1, unlist(strsplit(cell,split = ","))) } else{ gene_list1 = append(gene_list1, cell) } keep_indices = which(!is.na(match(gene_list1, modfile$GENE[modfile$MODULE==mod]))) gene_list1 = gene_list1[keep_indices] } genes = concatenate(gene_list1, mysep = ", ") descrip = concatenate(annotate_gene(gene_list1), mysep = ", ") set[["Gene list"]] = gene_list1 } } set[["Concatenated genes"]] = genes set[["Concatenated descrip"]] = descrip return(set) } # must have protein_names data frame # changed vector$GENE to vector annotate_gene <- function(vector){ info = c() for(m in vector){ if(length(protein_names$annotation[protein_names$preferred_name==m])==0){ if(length(protein_names$annotation[protein_names$preferred_name==gsub("[[:digit:]]","",m)])==0){ info = append(info, "Not annotated") } else{ info = append(info, paste0("Possible annotation: ", concatenate(protein_names$annotation[protein_names$preferred_name==gsub("[[:digit:]]","",m)], mysep = ","))) } } else if(length(protein_names$annotation[protein_names$preferred_name==m])>1){ info = append(info, concatenate(protein_names$annotation[protein_names$preferred_name==m], mysep = ",")) cat("This protein had more than one annotation:", m, "\n") } else{ info = append(info, protein_names$annotation[protein_names$preferred_name==m]) } } return(info) } replicate <- function(results_Dir, FDR_trim, FDR_cutoff, info_file){ files = list.files(results_Dir) mapping = c() traits = c() for(f in files){ mapping = append(mapping, unlist(strsplit(f, split = ".",fixed = TRUE))[2]) traits = append(traits, unlist(strsplit(f, split = ".",fixed = TRUE))[1]) } mapping = unique(mapping) traits = unique(traits) total = data.frame(stringsAsFactors = FALSE) for(map in mapping){ mapping_files = files[grep(map,files)] mapping_total = data.frame() for(file in mapping_files){ df = read.delim(paste0(results_Dir,file), stringsAsFactors = FALSE) df = df[df$FDR<FDR_trim,] df = df[!grepl("_ctrlA",df$MODULE),] df = df[!grepl("_ctrlB",df$MODULE),] if(length(df$MODULE)==0 | is.na(df$FDR[1])){ next } df$Trait = unlist(strsplit(file, split = ".", fixed = TRUE))[1] df$Mapping = unlist(strsplit(file, split = ".", fixed = TRUE))[2] mapping_total = rbind(mapping_total, df) } if(dim(mapping_total)[1]==0){ next } mapping_ls = list() for(trait in 1:length(traits)){ mapping_ls[[traits[trait]]] = c() mapping_ls[[paste0(traits[trait],"_FDR")]] = c() for(module in unique(mapping_total$MODULE)){ if(sum(mapping_total$Trait[mapping_total$MODULE==module]==traits[trait])==0){ mapping_ls[[traits[trait]]] = append(mapping_ls[[traits[trait]]], "NO") } else if(mapping_total$FDR[mapping_total$MODULE==module & mapping_total$Trait==traits[trait]]>FDR_cutoff){ mapping_ls[[traits[trait]]] = append(mapping_ls[[traits[trait]]], "NO") } else{ mapping_ls[[traits[trait]]] = append(mapping_ls[[traits[trait]]], "YES") } if(sum(mapping_total$Trait[mapping_total$MODULE==module]==traits[trait])>0){ mapping_ls[[paste0(traits[trait],"_FDR")]] = append(mapping_ls[[paste0(traits[trait],"_FDR")]], mapping_total$FDR[mapping_total$MODULE==module & mapping_total$Trait==traits[trait]]) } else{ mapping_ls[[paste0(traits[trait],"_FDR")]] = append(mapping_ls[[paste0(traits[trait],"_FDR")]], "NS") } } } mapping_df = data.frame("Mapping" = map, "MODULE"=unique(mapping_total$MODULE)) for(item in 1:length(mapping_ls)){ #mapping_df = cbind(mapping_df, mapping_ls[[item]]) mapping_df[,names(mapping_ls)[item]] = mapping_ls[[item]] } total = rbind(total, mapping_df) } # sum "YES"s in a row rownames(total) <- seq(length=nrow(total)) YESs = c() for(i in 1:nrow(total)){ YESs[i] = sum(total[i,]=="YES") } total["n_Overlap"] = YESs total = total[!(total$n_Overlap<2),] # get rid of no overlaps total = total[order(total$n_Overlap, decreasing = TRUE),] total = addDESCR(df = total, position_to_add = 3, info_file) return(total) } # add DESCR column to df at a position position_to_add # assumes df has MODULE column addDESCR <- function(df, position_to_add, info_file){ info <- read.delim(info_file, stringsAsFactors = FALSE) descr = c() for(i in df$MODULE){ descr = append(descr, info$DESCR[info$MODULE==i]) } df$DESCR = descr col_names = colnames(df) post_total = length(colnames(df))+1 before = 1:(position_to_add-1) after = (position_to_add):(post_total-1) # indices = c(before, post_total, after) df = df[,c(col_names[before], "DESCR",col_names[after])] return(df) } subset_replicate <- function(df, studies){ result = data.frame(stringsAsFactors = FALSE) for(i in studies){ temp = df[df[,i]=="YES",] result = rbind(result, temp) } } makeDf <- function(Dir){ files = list.files(Dir)[grep("shared_", list.files(Dir))] all = data.frame(stringsAsFactors = FALSE) for(foo in files){ SGM = unlist(strsplit(foo, split = ".", fixed = TRUE))[2] mods = read.delim(paste0(Dir,foo), stringsAsFactors = FALSE) mods$Mapping = SGM mods = mods[,c(4,1,3,2)] all = rbind(all, mods) } return(all) } describePathways <- function(modules_list, df, DESCR){ mappings = c() num = c() for(mod in modules_list){ num = append(num, length(df$Mapping[df$MODULE==mod])) mappings = append(mappings, concatenate(df$Mapping[df$MODULE==mod], mysep = ", ")) } describe = c() for(mod in modules_list){ describe = append(describe, all_DESCR$DESCR[all_DESCR$MODULE==mod]) if(length(all_DESCR$DESCR[all_DESCR$MODULE==mod])==0) cat(mod) } new_df = data.frame("MODULE"=modules_list, "DESCR"=describe, "Mapping" = mappings, "nMapping" = num, stringsAsFactors = FALSE) new_df = new_df[order(new_df$nMapping, decreasing = TRUE),] return(new_df) } # find consistent modules and unique ones for each different mappings. Hm, I already did this. # Purpose: # 1) consolidate results from all mappings # 2) take genes result file and append to each module in a long format. (each gene separated by comma) # 3) add gene information # nerve = read.delim("/Users/jessicading/Desktop/Yang_Lab/T2D_AD/Data/AD/quasi_overlap/supersets.AD_quasi.Nerve_Tibial.txt", header = TRUE, stringsAsFactors = FALSE) # nerve = nerve[!(nerve$MODULE=="Nerve_Tibial.GTEXv7.MEGENA_126,.."),] #this module was not able to be annotated!!! # write.table(nerve, "/Users/jessicading/Desktop/Yang_Lab/T2D_AD/Data/AD/quasi_overlap/supersets.AD_quasi.Nerve_Tibial1.txt", row.names = FALSE, quote = FALSE, sep = "\t") #"Nerve_Tibial.GTEXv7.MEGENA_126,.." THIS MODULE WAS NOT ABLE TO BE ANNOTATED IN DIRECT OVERLAP EITHER #if you get the error: "Error in cbind(rep(modulesize, dim(fm)[1]), fm) : object 'fm' not found" - FIND OFFENDING MODULE AND TAKE IT OUT #WORKFLOW FOR GENE OVERLAP (QUASI) #1. have files for both studies (ex. DIAGRAM and UKB) - combine canonical and coexpression # for AD, I put them in "canonical_coexpr". for T2D, I put them in "combined" (FOLDERS) #2. use geneOverlap function for to produce "shared_" files. all other files are used in this function to produce the "shared_" files in the end and have useful information #3. transfer "shared_" files to new folder. I called this "quasi_overlap" #4. merge modules in "shared_" files #5. annotatkee supersets resulting from step 4 using annotate_supersets function #6. do summaryTable function to get summary table! # setwd("/Users/jessicading/Desktop/Yang_Lab/T2D_AD/Data/AD_T2D/shared_quasi/") # for(i in files){ # file = read.delim(i, header = TRUE, stringsAsFactors = FALSE) # write.table(file, paste0("AD.",i), row.names = FALSE, quote = FALSE, sep = "\t") # } # # files = list.files("/Users/jessicading/Desktop/Yang_Lab/T2D_AD/Data/AD_T2D/shared_quasi/") # subset = files[grep("AD",files)] # SGMs = gsub("AD.shared_", "", gsub(".50.20.txt", "", subset)) # #took out Esophagus_Muscularis.GTEXv7.MEGENA_168,..

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# TODO: Add comment # # Author: Ben ############################################################################### source("rpecompare/builtin_models.R") source("rpecompare/basicmodel.R") # Build a histogram of G_{n,1}(p)=P(P(Chat_n(AX)=1|X)<=p) # Sample X, P(Chat_n(AX)=1|X) (by sampling) sample.g = function() { data = model.1()#basic.model(p = 5, d = 2) train = data$train test = data$test n_train = length(train$y) n_test = length(test$y) p = ncol(train)-1 d = 5 ############################### # Run RPE LDA with Haar measure-drawn/axis-aligned random projections rpelda.out = RPParallel(XTrain = data.matrix(train[-(p+1)]), YTrain = train$y, XTest = data.matrix(test[-(p+1)]), d = d, B1 = 100, B2 = 100, #projmethod = "axis", cores = 1) # Estimate G_{n,i} y.pred = rpelda.out[1:n_train, ] G.n.1.data = 2 - rowMeans(y.pred[train$y == "class.1", ], na.rm = TRUE) G.n.2.data = 2 - rowMeans(y.pred[train$y == "class.2", ], na.rm = TRUE) # Return them as data, not as ecdfs return(list(G.n.1.data, G.n.2.data)) } replicate.sample.g = function(r = 10) { library(parallel) cl = makeCluster(detectCores() - 1) clusterExport(cl, c("sample.g")) clusterEvalQ(cl, source("rpecompare/builtin_models.R")) clusterEvalQ(cl, source("rpecompare/basicmodel.R")) out = parSapply(cl, 1:r, function(i, ...) { return(sample.g()) } ) stopCluster(cl) return(out) } #out = replicate(10, sample.g()) out = replicate.sample.g() G.n.1 = ecdf(do.call(c, out[1,])) G.n.2 = ecdf(do.call(c, out[2,])) #png("G_ni_mini.png") #plot(G.n.1, col="red", xval=(1:100)/100) #lines(G.n.2, col="green", xval=(1:100)/100) #dev.off() R1 = do.call(c, out[1,]) png("r1_dist.png") hist(R1) dev.off()

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\name{pairwise.logfc} \alias{pairwise.logfc} \title{ Compute pairwise log-fold changes } \description{ The function computes pairwise log-fold changes. } \usage{ pairwise.logfc(x, g, ave = mean, log = TRUE, base = 2, ...) } \arguments{ \item{x}{ numeric vector. } \item{g}{ grouping vector or factor } \item{ave}{ function to compute the group averages. } \item{log}{ logical. Is the data logarithmic? } \item{base}{ If \code{log = TRUE}, the base which was used to compute the logarithms. } \item{\dots}{ optional arguments to \code{ave}. } } \details{ The function computes pairwise log-fold changes between groups, where the group values are aggregated using the function which is given by the argument \code{ave}. The implementation is in certain aspects analogously to \code{\link[stats]{pairwise.t.test}}. } \value{ Vector with pairwise log-fold changes. } %\references{ ~put references to the literature/web site here ~ } \author{ Matthias Kohl \email{Matthias.Kohl@stamats.de}} %\note{} \seealso{ \code{\link[stats]{pairwise.t.test}} } \examples{ set.seed(13) x <- rnorm(100) ## assumed as log2-data g <- factor(sample(1:4, 100, replace = TRUE)) levels(g) <- c("a", "b", "c", "d") pairwise.logfc(x, g) ## some small checks res <- by(x, list(g), mean) res[[1]] - res[[2]] # a vs. b res[[1]] - res[[3]] # a vs. c res[[1]] - res[[4]] # a vs. d res[[2]] - res[[3]] # b vs. c res[[2]] - res[[4]] # b vs. d res[[3]] - res[[4]] # c vs. d } \keyword{univar}

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# Code by Ameya Kulkarni # E-mail at echo blvmlbso@nbjm.fjotufjo.zv.fev | tr '[b-{' '[a-z]' # This code can be used to convert Stringdb Ensembl protein ids to HGNC symbols using biomaRt # The example code is shown for Human protein interaction data from experimental evidence only # Human Protein Interaction data file can be downloaded from https://stringdb-static.org/download/protein.links.full.v10.5.txt.gz # Protein pairs with experimental evidence of interaction can be extracted using # zgrep ^"9606\." protein.links.full.txt.gz | awk '($10 != 0) { print $1, $2, $10 }' > direct_experimental_data_human.txt library(biomaRt) library(dplyr) ppi <- read.table("path-to-direct_experimental_data_human.txt") # Modify using path to direct_experimental_data_human.txt ppi <- as.data.frame(sapply(ppi,gsub,pattern="9606.",replacement="")) ppi <- ppi[,-3] mart <- useDataset("hsapiens_gene_ensembl", useMart("ensembl")) P1 <- as.data.frame(ppi$V1) P2 <- as.data.frame(ppi$V2) P1_uniq <- unique(P1) G1_uniq <- getBM(filters= "ensembl_peptide_id", attributes= c("ensembl_peptide_id","hgnc_symbol"),values=P1_uniq,mart= mart) P2_uniq <- unique(P2) G2_uniq <- getBM(filters= "ensembl_peptide_id", attributes= c("ensembl_peptide_id","hgnc_symbol"),values=P2_uniq,mart= mart) colnames(P1) <- "ensembl_peptide_id" colnames(P2) <- "ensembl_peptide_id" join1 <- dplyr::left_join(as_tibble(P1)%>%mutate(ensembl_peptide_id=as.character(ensembl_peptide_id)), as_tibble(G1_uniq)%>%mutate(ensembl_peptide_id=as.character(ensembl_peptide_id)), by="ensembl_peptide_id") join2 <- dplyr::left_join(as_tibble(P2)%>%mutate(ensembl_peptide_id=as.character(ensembl_peptide_id)), as_tibble(G2_uniq)%>%mutate(ensembl_peptide_id=as.character(ensembl_peptide_id)), by="ensembl_peptide_id") ppi_join <- as.data.frame(cbind(join1$hgnc_symbol, join2$hgnc_symbol)) ppi_join <- ppi_join[complete.cases(ppi_join),]