pierreqi/r_code_50k · Datasets at Hugging Face

69f454d7126e516be1657a8b5b2c7c7021e2836e

3aa7718eda2b2e76efde2e9d41beb4fbd6115c79

/man/remove_accents.Rd

5aa52868a6db29c2121417b26e57f37d7b222a72

[]

no_license

alinemsm/rslp

9c0e929323830615dceb0a9de1e619209ec6f549

be0a04855d8cca473bc36ae69349510b593ac6a9

refs/heads/master

2021-05-01T06:22:23.372303

2016-10-14T12:31:54

null

0

null

UTF-8

R

false

true

292

rd

remove_accents.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/zzz.R \name{remove_accents} \alias{remove_accents} \title{Remove Acccents} \usage{ remove_accents(s) } \arguments{ \item{s}{the string you want to remove accents} } \description{ A wrappper for stringi package. }

9f841c39115e2751a56d99b3492c4e1c73343ace

2d24c72abe89c38bc13682ca2a048ffa97dcf9c3

/Bohn_Kleemann_Bambauer_Blatt9.R

a81bdf74762a841a9ad9bff79c6c8cfb2a9a2d31

[]

no_license

curala70/StatistikSS2017

e42c93fe62255774727e0e44cad6ca97328fc008

b535c398ee1ad6ec6a0434b759ace077712108ac

refs/heads/master

2021-01-20T02:05:54.611177

2017-06-27T08:45:11

89,371,542

0

1

null

UTF-8

R

false

1,649

r

Bohn_Kleemann_Bambauer_Blatt9.R

# a) KSS <- function(x) { x = sort(x) n = length(x) d = c() for (i in 1:n-1) { Fx = (1/n)*sum(x<=x[i]) d[i] = max(abs(Fx - pnorm(x[i])), abs(Fx - pnorm(x[i+1]))) } d[n] = abs(1-pnorm(x[n])) return (max(d)) } # b) # create samples of size n, each 100 times x10 = list() for (i in 1:100) { x10[[i]] = rnorm(10) } x40 = c() for (i in 1:100) { x40[[i]] = rnorm(40) } x160 = c() for (i in 1:100) { x160[[i]] = rnorm(160) } x640 = c() for (i in 1:100) { x640[[i]] = rnorm(640) } # compute KSS for each sample of size n=10 and store in d10 d10 = c() for (i in 1:100) { d10[i] = KSS(x10[[i]]) } # compute KSS for each sample of size n=40 and store in d40 d40 = c() for (i in 1:100) { d40[i] = KSS(x40[[i]]) } # compute KSS for each sample of size n=160 and store in d160 d160 = c() for (i in 1:100) { d160[i] = KSS(x160[[i]]) } # compute KSS for each sample of size n=640 and store in d640 d640 = c() for (i in 1:100) { d640[i] = KSS(x640[[i]]) } # create headline - hier d10 oder x10? Mittelwert wovon? MW10=round(mean(d10)*sqrt(10),digits=2) MW40=round(mean(d40)*sqrt(40),digits=2) MW160=round(mean(d160)*sqrt(160),digits=2) MW640=round(mean(d640)*sqrt(640),digits=2) maintext=paste("n10:",MW10,";n40:",MW40,";n160:",MW160,";n640:",MW640) # plot samples plot(1:100, d10, col='black', type='p',main=maintext,xlab="Index",ylab="KSS",ylim=c(0,1)) points(1:100, d40, col='red') points(1:100, d160, col='green') points(1:100, d640, col='blue') # create legend legend("topright", c("n=10","n=40","n=160", "n=640"), lty=c(1,1), lwd=c(2,2), col=c("black","red","green","blue"))

f5da4739cb9ac7315d12e86cd5e0a877fec75862

8399dd26135eb99332c0d0cb456f42ee4ec63cb5

/congress109.R

6bdc7124fb23bba5670bea87d441031afab79e85

[]

no_license

tiffblahthegiraffe/STA380-class-of-2019

fafa4ba321c5f1945b1e2ce38a5f68934b6c0ff1

84040d91ecdca3d42285b087ce94e22496bf1aa0

refs/heads/master

2020-03-25T00:38:21.157438

2018-08-15T22:35:44

143,196,278

0

null

UTF-8

R

false

1,784

r

congress109.R

library(ggplot2) countdata = read.csv(url("https://raw.githubusercontent.com/jgscott/STA380/master/data/congress109.csv"), header=TRUE, row.names=1) memberdata = read.csv(url("https://raw.githubusercontent.com/jgscott/STA380/master/data/congress109members.csv"), header=TRUE, row.names=1) # First normalize phrase counts to phrase frequencies. # (often a sensible first step for count data, before z-scoring) Z = countdata/rowSums(countdata) # PCA pc2 = prcomp(Z, scale=TRUE, rank=2) #rank = 2 ??? #there will be PC529 loadings = pc2$rotation scores = pc2$x # Question 1: where do the observations land in PC space? # a biplot shows the first two PCs qplot(scores[,1], scores[,2], color=memberdata$party, xlab='Component 1', ylab='Component 2') # Confusingly, the default color mapping has Democrats as red and republicans as blue. This might be confusing, so let's fix that: qplot(scores[,1], scores[,2], color=memberdata$party, xlab='Component 1', ylab='Component 2') + scale_color_manual(values=c("blue", "grey", "red")) # Interpretation: the first PC axis primarily gas Republicans as positive numbers and Democrats as negative numbers # PC1 left: Demoncrate; right: Republicant # PC2 ambiguous, hard to interpretate # Question 2: how are the individual PCs loaded on the original variables? # Which X serve more important?? # The top words associated with each component o1 = order(loadings[,1], decreasing=TRUE) # sort Xs in PC1 from highest positive to hightest negative colnames(Z)[head(o1,25)] #most positive, might be the topics more discuss by Republicants colnames(Z)[tail(o1,25)] #the most Demoncrative phrases o2 = order(loadings[,2], decreasing=TRUE)# sort Xs in PC2 from highest positive to hightest negative colnames(Z)[head(o2,25)] colnames(Z)[tail(o2,25)]

8e9cddcbfdcc85159d855fba9322f54231d9d349

52c1f08ce14e5542ff36f7d5ae4b8f1da966508d

/MadingleyPlots/R/PlotMassDensity.R

1a747930f4dbe48a7d18c82126adce62da1af960

[]

no_license

timnewbold/MadingleyPlots

5e61b75df743408e277456f9daba54ffe0ef1a5f

03cca3b67eea3b4de46d14774e9814237507e841

refs/heads/master

2020-07-23T11:57:21.421452

2017-06-29T08:17:16

73,809,091

0

null

UTF-8

R

false

4,832

r

PlotMassDensity.R

PlotMassDensity <- function(resultsDir,plotName = "MassDensity", outDir=NULL, label=NULL, whichCells=NULL,endTimeStep=NULL, numTimeSteps=12, vars=c("herbivore abundance", "omnivore abundance", "carnivore abundance"), cols=c("#66a61e", "#7570b3", "#d95f02"), xlims = NULL, returnResults=FALSE){ initialization <- read.csv(paste(resultsDir,"/SimulationControlParameters.csv",sep="")) cellsize <- as.numeric(paste(initialization$Value[which(initialization$Parameter=="Grid Cell Size")])) locations <- read.csv(paste(resultsDir,"/SpecificLocations.csv",sep="")) cohortDefs <- read.csv(paste(resultsDir,"/CohortFunctionalGroupDefinitions.csv",sep="")) maxPossibleMass <- max(cohortDefs$PROPERTY_Maximum.mass) minPossibleMass <- min(cohortDefs$PROPERTY_Minimum.mass) .Log("Finding Madingley mass-bins output files\n") files <- .ListMassBinsFiles(resultsDir) if(!is.null(whichCells)){ files <- files[sapply(paste("Cell",whichCells-1,sep=""),FUN = function(x) return(grep(x,files)))] } # Find the unique cells in these simulations cells.re<-regexpr("Cell[0-9]+",files) cells<-as.list(unique(substr(files,cells.re,cells.re+ attr(cells.re,"match.length")-1))) # Find the simulation numbers sims.re<-regexpr("_[0-9]+_",files) sims<-as.list(unique(substr(files,sims.re,sims.re+ attr(sims.re,"match.length")-1))) if(is.null(label)){ label<-unique(substr(files,1,sims.re-1)) stopifnot(length(label)==1) label<-label[1] } else { label <- paste("MassBinsOutputs_",label,sep="") } .Log(paste("Found results for ",length(cells)," cells\n",sep="")) .Log(paste("Found results for ",length(sims)," simulations\n",sep="")) .Log("Getting basic information about simulations\n") sds.path<-paste("msds:nc?file=",resultsDir,"/",label,sims[1],cells[1], ".nc",sep="") data<-open.sds(sds.path) allTimes<-get.sds(data,"Time step") if (is.null(endTimeStep)) endTimeStep <- tail(allTimes,1) times <- (endTimeStep-numTimeSteps+1):endTimeStep massBins <- get.sds(data,"Mass bin") massBinsMidPoints <- 10^(log10(c(minPossibleMass,massBins[-1]))+diff(log10(c( minPossibleMass,massBins[-1],maxPossibleMass)))/2) latitudes <- locations$Latitude if(!is.null(whichCells)){ latitudes <- latitudes[whichCells] } cell_areas <- DegreeCellAreaKM(lat = latitudes,height = cellsize,width = cellsize) names(cell_areas) <- cells if (is.null(xlims)){ xlims <- range(massBinsMidPoints) } .Log("Initializing plot\n") if(!is.null(outDir)){ pdf(paste(outDir,plotName,".pdf",sep=""), width = 17.5/2.54,height = (5/2.54)*length(cells)) } par(mfrow=c(length(cells),3)) par(las=1) par(tck=-0.01) par(mar=c(2.8,3.3,0.2,0.2)) .Log("Plotting\n") ret <- lapply(cells,FUN=function(cell){ # Create a list of matrices to hold the results for each specified variable allResults<-list() for (i in 1:length(vars)){ allResults[[i]]<-array(data = NA,dim = c(length(sims),length(massBins),length(allTimes))) } names(allResults)<-vars # Loop over simulations in the ensemble s<-1 for (sim in sims){ sds.path<-paste("msds:nc?file=",resultsDir,"/",label,sim,cell, ".nc",sep="") data<-open.sds(sds.path) # Populate the results matrices for (var in vars){ allResults[var][[1]][s,,]<-exp(get.sds(data,paste( "Log ",var," in mass bins",sep="")))/cell_areas[cell] } s<-s+1 } resultsTimesMean <- lapply(allResults,function(x){ return(apply(x[,,times,drop=FALSE],MARGIN=c(1,2),FUN=mean,na.rm=TRUE)) }) resultsSimMean <- lapply(resultsTimesMean,function(x){ return(apply(x,MARGIN=2,FUN=mean,na.rm=TRUE)) }) v <- 1 r <- list() for (var in vars){ par(mgp=c(1.2,0.2,0)) plot(massBinsMidPoints,resultsSimMean[[var]],log="xy",type="l", col=cols[v],xlim=xlims,xlab="Current body mass (g)", yaxt="n",ylab=NA) par(mgp=c(2.5,0.2,0)) axis(2) title(ylab=Hmisc::capitalize(var)) v <- v+1 r[[var]] <- data.frame(mass=massBinsMidPoints, density=resultsSimMean[[var]]) } return(r) }) if(!is.null(outDir)) invisible(dev.off()) if (returnResults){ return(ret) } }

ab0a3ed2c99c98f201b846b9e3573df323122fc6

5289bb29b4f7d11b01f327761ece631de13d8ac9

/R/helpers.R

71edf67f54cdcd6ffd367db35b2b07755631a5a4

[]

no_license

mironcat/conpac

ec018115aed26a09afb520f05e4052703c15637a

b1d3f45ca2e3702cc5f89389ac5ab338117e737a

refs/heads/master

2023-04-30T11:23:53.140232

2021-05-21T20:00:05

352,006,472

0

null

UTF-8

R

false

1,830

r

helpers.R

getDatedMarkers <- function(formattedcpcht) { dmarkers <- formattedcpcht %>% filter(CLADE=='[dated' | CLADE=='AGE' )%>% rename ( DATLEV=FAD)%>% separate( col=EVENT,sep = '=',into=c('EVENT','AGE'))%>% #разделяем колонку EVENT на две используя в качестве разделителя '=' mutate( AGE=as.numeric(AGE))%>% #преобразуем тип колонки AGE в числовой тип arrange( desc(AGE)) %>% #сортируем select(ID, EVENT, DATLEV, AGE) return (dmarkers) } get_intervals_by_params<- function (start,finish, step, age=FALSE){ # start=14 # finish=200 # step=8 if (age==FALSE) { st <- seq(start, finish-step, by=step) #заполнение начальных координат отрезков en <- seq(start+step, finish, by=step) #заполнение конечных координат отрезков } if (age==TRUE) { en <- seq(start, finish-step, by=step*-1) #заполнение начальных координат отрезков st <- seq(start+step, finish, by=step*-1) #заполнение конечных координат отрезков } return ( data.frame(num=c(1:numrow),st=st,en=en,mid=(st+en)/2) ) #data: num,st,en,mid } splitRangesToBins_by_int <- function(intervals, dat) { divdindat<-dat[0,]%>%mutate(int=NULL) #create empty tibble for (i in 1:nrow(intervals)) { #interate intervals int<-intervals[i,] #select first intervals st<-int$st en<-int$en dat.tt<-dat%>%filter( (max_ma<=st & max_ma>=en) | (min_ma<=st & min_ma>=en) | (max_ma>=st & min_ma<=en) )#filter dat by params dat.tt<-mutate(dat.tt,int=int$num) #set interval nuber divdindat<- bind_rows(divdindat, dat.tt) #connect filtered data together } return (divdindat) }

de1f93579dbbb3a0c60ee537a6a8884c114d931c

f43377cd5c921dd609770789e2be686cdd012917

/scripts/Thermal_reaction_norms.R

1f30c3d58cf8eedaeae6bde1258236c1cbd82acb

[]

no_license

siyuChen540/PFT_thermal_response

0e5692f2984c8697501a73f551f32a1099816e67

74598788f0647bac2f9ec3492f1aa7d482616bbf

refs/heads/master

2023-08-29T02:59:51.547675

2021-09-14T16:10:54

null

0

null

UTF-8

R

false

21,999

r

Thermal_reaction_norms.R

# Code for Anderson et al. (2021) # Marine Phytoplankton Functional Types Exhibit Diverse Responses to Thermal Change # Stephanie I. Anderson updated 09/14/2020 # Contains: ## Phytoplankton functional type thermal reaction norms (Figure 1) ## Exponential curve fits ## Q10 calculations (Table 1) ## Comparison of thermal dependencies (Figure 2) ## Extended Figures 3 & 5 # Load packages library(ggplot2) library(quantreg) library(lme4) library(dplyr) library(data.table) library(cowplot) # Load data isolates <- read.csv("data/derived_traits.csv") rates <- read.csv("data/growth_rates.csv") source("scripts/nbcurve.R") source("scripts/custom_theme.R") ######################################################################### ##### Thermal reaction norms ##### diatom<-subset(isolates, group=="diatoms") cyano<-subset(isolates, group=="cyanobacteria") dino<-subset(isolates, group=="dinoflagellates") coccolithophores<-subset(isolates, group=="coccolithophores") x<-seq(-2, 40, by=0.01) # temperature sequence ########## Diatoms ########## # Fit 99th quantile regression bissd <- rq(ln.r~temperature, data=subset(rates, group=="diatoms"),tau=0.99,ci=T) cf.bd <- coef(bissd) #extract coefficients # Calculate confidence intervals ## He & Hu (2002) method = "mcmb" uses the Markov chain marginal bootstrap QR.b <- boot.rq(cbind(1,subset(rates, group=="diatoms")$temperature), subset(rates, group=="diatoms")$ln.r,tau=0.99, R=10000, method="mcmb") ci_d <- t(apply(QR.b$B, 2, quantile, c(0.025,0.975))) # plotting thermal performance curves dev.off() for(j in 1){ pdf("figures/Diatom_TPC.pdf", width = 5.8, height = 4) plot.new() plot.window(c(-2,40),c(0,3)) axis(1, 10*(-2:40), mgp=c(1,0.5,0)) axis(2, 0.5*(0:6), mgp=c(1,0.5,0)) box() for(i in 1:nrow(diatom)){ o=diatom[i, "mu.c.opt.list"] w=diatom[i, "mu.wlist"] a=diatom[i, "mu.alist"] b=diatom[i, "mu.blist"] curve(nbcurve(x=x,opt=o,w=w,a=a,b=b),-2,40, col=alpha("black",alpha=0.6),ylim=c(0,3), lty=1, add=T,xlab="", ylab="", cex=1.5) } # Add regression tempd <- seq(min(subset(rates, group=="diatoms")$temperature), max(subset(rates, group=="diatoms")$temperature), by=0.1) y1 <- c(exp(ci_d[1,2]+ci_d[2,2]*tempd)) y2 <- c(exp(ci_d[1,1]+ci_d[2,1]*tempd)) polygon(c(tempd, rev(tempd)),c(y1, rev(y2)),col=alpha("#026cb1",alpha=0.2), border=FALSE) curve(exp(cf.bd[[1]]+cf.bd[[2]]*x),min(subset(rates, group=="diatoms")$temperature),max(subset(rates, group=="diatoms")$temperature),add=T,col='#026cb1',lwd=2.5) # Eppley, 1972 curve(0.59*exp(0.0633*x),-2,40,add=T,col='grey30',lwd=2.5, lty=2) # Eppley, 1972 # add plot labels title(xlab=(expression(bold("Temperature (ºC)"))), ylab=(expression(bold("Specific Growth Rate (d"^"-1" *")"))), line=1.5, cex.lab=1) title(main=expression(bold("Diatoms")), line=-1, adj=0.05, cex=0.9) text(-1.6, 2.4, paste0("n=", length(diatom$isolate.code)), adj=c(0,0)) text(-1.6, 2.1, paste0("N=", length(subset(rates, group=="diatoms")$isolate.code)), adj=c(0,0)) dev.off() } ########## Cyanobacteria ########## # Fit 99th quantile regression bissc_b<-rq(ln.r~temperature, data=subset(rates, group=="cyanobacteria"),tau=0.99,ci=T) cf.bcb<-coef(bissc_b) #extract coefficients # Calculate confidence intervals QR.c <- boot.rq(cbind(1,subset(rates, group=="cyanobacteria")$temperature), subset(rates, group=="cyanobacteria")$ln.r,tau=0.99, R=10000, method="mcmb") ci_cy<-t(apply(QR.c$B, 2, quantile, c(0.025,0.975))) # plotting thermal performance curves dev.off() for(j in 1){ pdf("figures/Cyanobacteria_TPC.pdf", width = 5.8, height = 4) plot.new() plot.window(c(-2,40),c(0,3)) axis(1, 10*(-2:40), mgp=c(1,0.5,0)) axis(2, 0.5*(0:6), mgp=c(1,0.5,0)) box() for(i in 1:nrow(cyano)){ o=cyano[i, "mu.c.opt.list"] w=cyano[i, "mu.wlist"] a=cyano[i, "mu.alist"] b=cyano[i, "mu.blist"] curve(nbcurve(x=x,opt=o,w=w,a=a,b=b),-2,40,ylim=c(0,3), col=alpha("black",alpha=0.6),lty=1, xlab="", ylab="", add=T, cex=1.5) } # Add regression tempc <- seq(min(subset(rates, group=="cyanobacteria")$temperature),max(subset(rates, group=="cyanobacteria")$temperature), by=0.1) y1_cy <- c(exp(ci_cy[1,2]+ci_cy[2,2]*tempc)) y2_cy <- c(exp(ci_cy[1,1]+ci_cy[2,1]*tempc)) polygon(c(tempc, rev(tempc)),c(y1_cy, rev(y2_cy)),col=alpha("#ec3a25",alpha=0.2), border=FALSE) curve(exp(cf.bcb[[1]]+cf.bcb[[2]]*x),min(subset(rates, group=="cyanobacteria")$temperature),max(subset(rates, group=="cyanobacteria")$temperature),add=T,col='#ec3a25',lwd=2.5) # Eppley, 1972 curve(0.59*exp(0.0633*x),-2,40,add=T,col='grey30',lwd=2.5, lty=2) # Eppley, 1972 # add plot labels title(xlab=(expression(bold("Temperature (ºC)"))), ylab=(expression(bold("Specific Growth Rate (d"^"-1" *")"))), line=1.5, cex.lab=1) title(main=expression(bold("Cyanobacteria")), line=-1, adj=0.05, cex=0.9) text(-1.6, 2.4, paste0("n=", length(cyano$isolate.code)), adj=c(0,0)) text(-1.6, 2.1, paste0("N=", length(subset(rates, group=="cyanobacteria")$isolate.code)),adj=c(0,0)) dev.off() } ########## Dinoflagellates ########## # Fit 99th quantile regression bissdi<-rq(ln.r~temperature, data=subset(rates, group=="dinoflagellates"),tau=0.99,ci=T) cf.di<-coef(bissdi) #extract coefficients # Calculate confidence intervals QR.d <- boot.rq(cbind(1,subset(rates, group=="dinoflagellates")$temperature), subset(rates, group=="dinoflagellates")$ln.r,tau=0.99, R=10000, method="mcmb") ci_df<-t(apply(QR.d$B, 2, quantile, c(0.025,0.975))) # plotting thermal performance curves dev.off() for(j in 1){ pdf("figures/Dinoflagellate_TPC.pdf", width = 5.8, height = 4) plot.new() plot.window(c(-2,40),c(0,3)) axis(1, 10*(-2:40), mgp=c(1,0.5,0)) axis(2, 0.5*(0:6), mgp=c(1,0.5,0)) box() for(i in 1:nrow(dino)){ o=dino[i, "mu.c.opt.list"] w=dino[i, "mu.wlist"] a=dino[i, "mu.alist"] b=dino[i, "mu.blist"] curve(nbcurve(x=x,opt=o,w=w,a=a,b=b),-2,40,ylim=c(0,3),col=alpha("black",alpha=0.6), lty=1, xlab="", ylab="", add=T, cex=1.5) } # Add regression tempdi <- seq(min(subset(rates, group=="dinoflagellates")$temperature), max(subset(rates, group=="dinoflagellates")$temperature), by=0.1) y1_df <- c(exp(ci_df[1,2]+ci_df[2,2]*tempdi)) y2_df <- c(exp(ci_df[1,1]+ci_df[2,1]*tempdi)) polygon(c(tempdi, rev(tempdi)),c(y1_df, rev(y2_df)),col=alpha("#3ea127",alpha=0.2), border=FALSE) curve(exp(cf.di[[1]]+cf.di[[2]]*x),min(subset(rates, group=="dinoflagellates")$temperature), max(subset(rates, group=="dinoflagellates")$temperature),add=T,col='#3ea127',lwd=2.5) # Eppley, 1972 curve(0.59*exp(0.0633*x),-2,40,add=T,col='grey30',lwd=2.5, lty=2) # Eppley, 1972 # add plot labels title(xlab=(expression(bold("Temperature (ºC)"))), ylab=(expression(bold("Specific Growth Rate (d"^"-1" *")"))), line=1.5, cex.lab=1) title(main=expression(bold("Dinoflagellates")), line=-1, adj=0.05, cex=0.9) text(-1.6, 2.4, paste0("n=", length(dino$isolate.code)), adj=c(0,0)) text(-1.6, 2.1, paste0("N=", length(subset(rates, group=="dinoflagellates")$isolate.code)), adj=c(0,0)) dev.off() } ########## Coccolithophores ########## # Fit 99th quantile regression bissco<-rq(ln.r~temperature, data=subset(rates, group=="coccolithophores"),tau=0.99,ci=T) #weights=wts cf.co<-coef(bissco) #extract coefficients # Calculate confidence intervals QR.c <- boot.rq(cbind(1,subset(rates, group=="coccolithophores")$temperature), subset(rates, group=="coccolithophores")$ln.r,tau=0.99, R=10000, method="mcmb") ci_co<-t(apply(QR.c$B, 2, quantile, c(0.025,0.975))) # plotting thermal performance curves dev.off() for(j in 1){ pdf("figures/Coccolithophore_TPC.pdf", width = 5.8, height = 4) plot.new() plot.window(c(-2,40),c(0,3)) axis(1, 10*(-2:40), mgp=c(1,0.5,0)) axis(2, 0.5*(0:6), mgp=c(1,0.5,0)) box() for(i in 1:nrow(coccolithophores)){ o=coccolithophores[i, "mu.c.opt.list"] w=coccolithophores[i, "mu.wlist"] a=coccolithophores[i, "mu.alist"] b=coccolithophores[i, "mu.blist"] curve(nbcurve(x=x,opt=o,w=w,a=a,b=b),-2,40,ylim=c(0,3), col=alpha("black",alpha=0.6), lty=1, add=T, xlab="", ylab="", cex=1.5) } # Add regression tempco<-seq(min(subset(rates, group=="coccolithophores")$temperature), max(subset(rates, group=="coccolithophores")$temperature), by=0.1) y1_co <- c(exp(ci_co[1,2]+ci_co[2,2]*tempco)) y2_co <- c(exp(ci_co[1,1]+ci_co[2,1]*tempco)) polygon(c(tempco, rev(tempco)),c(y1_co, rev(y2_co)),col=alpha("orange",alpha=0.2), border=FALSE) curve(exp(cf.co[[1]]+cf.co[[2]]*x),min(subset(rates, group=="coccolithophores")$temperature), max(subset(rates, group=="coccolithophores")$temperature),add=T,col='orange',lwd=2.5) # Eppley, 1972 curve(0.59*exp(0.0633*x),-2,40,add=T,col='grey30',lwd=2.5, lty=2) # Eppley, 1972 # add plot labels title(xlab=(expression(bold("Temperature (ºC)"))), ylab=(expression(bold("Specific Growth Rate (d"^"-1" *")"))), line=1.5, cex.lab=1) title(main=expression(bold("Coccolithophores")), line=-1, adj=0.05, cex=0.9) text(-1.6, 2.4, paste0("n=", length(coccolithophores$isolate.code)), adj=c(0,0)) text(-1.6, 2.1, paste0("N=", length(subset(rates, group=="coccolithophores")$isolate.code)), adj=c(0,0)) dev.off() } ########## All PFTs ########## ## (Extended Figure 3) # Fit 99th quantile regression biss<-rq(ln.r~temperature, data=rates, tau=0.99,ci=T) cf.b<-coef(biss) #extract coefficients # Calculate confidence intervals QR.all <- boot.rq(cbind(1,rates$temperature), rates$ln.r,tau=0.99, R=10000, method="mcmb") ci<-t(apply(QR.all$B, 2, quantile, c(0.025,0.975))) # plotting thermal performance curves dev.off() for(j in 1){ pdf("figures/Extended_Figure3.pdf", width = 5.8, height = 4) plot.new() plot.window(c(-2,40),c(0,3)) axis(1, 10*(-2:40), mgp=c(1,0.5,0)) axis(2, 0.5*(0:6), mgp=c(1,0.5,0)) box() for(i in 1:nrow(isolates)){ o=isolates[i, "mu.c.opt.list"] w=isolates[i, "mu.wlist"] a=isolates[i, "mu.alist"] b=isolates[i, "mu.blist"] curve(nbcurve(x=x,opt=o,w=w,a=a,b=b),-2,40,ylim=c(0,3), col=alpha("black",alpha=0.6), lty=1, add=T, xlab="", ylab="", cex=1.5) } # Add regression temp<-seq(min(rates$temperature), max(rates$temperature), by=0.1) y1 <- c(exp(ci[1,2]+ci[2,2]*temp)) y2 <- c(exp(ci[1,1]+ci[2,1]*temp)) polygon(c(temp, rev(temp)),c(y1, rev(y2)),col=alpha("orangered1",alpha=0.2), border=FALSE) curve(exp(cf.b[[1]]+cf.b[[2]]*x),min(temp),max(temp),add=T,col='orangered1',lwd=2.5) # Eppley, 1972 curve(0.59*exp(0.0633*x),-2,40,add=T,col='grey30',lwd=2.5, lty=2) # Eppley, 1972 # add plot labels title(xlab=(expression(bold("Temperature (ºC)"))), ylab=(expression(bold("Specific Growth Rate (d"^"-1" *")"))), line=1.5) title(main=expression(bold("All PFTs")), line=-1, adj=0.05, cex=0.9) text(-1.6, 2.4, paste0("n=", length(isolates$isolate.code)), adj=c(0,0)) text(-1.6, 2.1, paste0("N=", length(rates$isolate.code)), adj=c(0,0)) dev.off() } ########## Q10 Temperature Coefficient & Activation Energy ############ # Activation Energy k = 8.617333262145*(10^(-5)) # boltzman constant Ea<-function(b){ #slope (b) b*k*(273)^2 } ### Table 1 ### group <- c('all.groups','coccolithophores','cyanobacteria', 'diatoms','dinoflagellates') table1 <- as.data.frame(group) # Number of unique isolates table1$n <- rbind(length(isolates$isolate.code),length(coccolithophores$isolate.code),length(cyano$isolate.code), length(diatom$isolate.code),length(dino$isolate.code)) # Number of growth rate measurements table1$N <- rbind(length(rates$isolate.code),length(subset(rates, group=="coccolithophores")$isolate.code), length(subset(rates, group=="cyanobacteria")$isolate.code),length(subset(rates, group=="diatoms")$isolate.code), length(subset(rates, group=="dinoflagellates")$isolate.code)) #Coefficients for exponential curves (calculated above) table1$a <- rbind(round(cf.b[[1]],3), round(cf.co[[1]],3), round(cf.bcb[[1]],3), round(cf.bd[[1]],3), round(cf.di[[1]],3)) table1$a_ci <- rbind(paste0("[",round(ci[[1]],3),", ",round(ci[[3]],3),"]"), paste0("[",round(ci_co[[1]],3),", ",signif(ci_co[[3]],3),"]"), paste0("[",round(ci_cy[[1]],3),", ",round(ci_cy[[3]],3),"]"), paste0("[",round(ci_d[[1]],3),", ",round(ci_d[[3]],3),"]"), paste0("[",round(ci_df[[1]],3),", ",round(ci_df[[3]],3),"]")) table1$b <- rbind(round(cf.b[[2]],3), round(cf.co[[2]],3), round(cf.bcb[[2]],3), round(cf.bd[[2]],3), round(cf.di[[2]],3)) table1$b_ci <- rbind(paste0("[",round(ci[[2]],3),", ",round(ci[[4]],3),"]"), paste0("[",round(ci_co[[2]],3),", ",round(ci_co[[4]],3),"]"), paste0("[",round(ci_cy[[2]],3),", ",round(ci_cy[[4]],3),"]"), paste0("[",round(ci_d[[2]],3),", ",round(ci_d[[4]],3),"]"), paste0("[",round(ci_df[[2]],3),", ",round(ci_df[[4]],3),"]")) # calculated variables table1$intercept = exp(table1$a) # y-intercept table1$int_ci <- rbind(paste0("[",round(exp(ci[[1]]),3),", ",round(exp(ci[[3]]),3),"]"), paste0("[",round(exp(ci_co[[1]]),3),", ",signif(exp(ci_co[[3]]),3),"]"), paste0("[",round(exp(ci_cy[[1]]),3),", ",round(exp(ci_cy[[3]]),3),"]"), paste0("[",round(exp(ci_d[[1]]),3),", ",round(exp(ci_d[[3]]),3),"]"), paste0("[",round(exp(ci_df[[1]]),3),", ",round(exp(ci_df[[3]]),3),"]")) table1$Q10 = exp(table1$b*10) # Q10 table1$Ea = Ea(table1$b) # acivation energy table1$umax20 = exp(table1$a+table1$b*20) # maximum growth at 20ºC write.csv(table1, "output/table1.csv") ###### Extended Data Figure 5 ###### cocco<-subset(rates, group =='coccolithophores') cyano<-subset(rates, group =='cyanobacteria') diatoms<-subset(rates, group =='diatoms') dinos<-subset(rates, group =='dinoflagellates') pdf("figures/Extended_Figure5.pdf", width = 7.2, height = 4.5) x=tempco par(mfrow=c(2,2), mar=c(0.5,3.5, 3.5, 0.5)) plot(cocco$temperature, cocco$r, xlim=c(-2, 40), ylim=c(0,3), xaxt='n',xlab='', ylab='', pch=20, col=alpha("black", 0.4)) axis(side=1,labels=F) curve(exp(cf.co[[1]]+cf.co[[2]]*x),min(x), max(x),add=T,col='orange',lwd=2.5) polygon(c(tempco, rev(tempco)),c(y1_co, rev(y2_co)),col=alpha("orange",alpha=0.2), border=FALSE) curve(0.59*exp(0.0633*x),-2,40,add=T,col='grey30',lwd=2.5, lty=2) title(main=expression(bold("Coccolithophores")), line=-1, adj=0.05, cex=1) text(-1.6, 2.3, paste0("n=", length(unique(coccolithophores$isolate.code))), adj=c(0,0)) text(-1.6, 1.9, paste0("N=", length(subset(rates, group=="coccolithophores")$isolate.code)), adj=c(0,0)) x=tempc par(mar=c(0.5,0.5,3.5,3.5)) plot(cyano$temperature, cyano$r, xlim=c(-2, 40), ylim=c(0,3), xaxt='n', yaxt='n',xlab='', ylab='', pch=20, col=alpha("black", 0.4)) axis(side=1,labels=F) curve(exp(cf.bcb[[1]]+cf.bcb[[2]]*x),min(x), max(x),add=T,col=colors[2], lwd=2.5) polygon(c(tempc, rev(tempc)),c(y1_cy, rev(y2_cy)),col=alpha(colors[2], alpha=0.2), border=FALSE) curve(0.59*exp(0.0633*x),-2,40,add=T,col='grey30',lwd=2.5, lty=2) title(main=expression(bold("Cyanobacteria")), line=-1, adj=0.05, cex=1) text(-1.6, 2.3, paste0("n=", length(unique(cyano$isolate.code))), adj=c(0,0)) text(-1.6, 1.9, paste0("N=", length(subset(rates,group=="cyanobacteria")$isolate.code)), adj=c(0,0)) x=tempd par(mar=c(3.5, 3.5,0.5,0.5)) plot(diatoms$temperature, diatoms$r, xlim=c(-2, 40), ylim=c(0,3), xlab='', ylab='', pch=20, col=alpha("black", 0.4)) curve(exp(cf.bd[[1]]+cf.bd[[2]]*x),min(x), max(x),add=T,col=colors[3],lwd=2.5) polygon(c(tempd, rev(tempd)),c(y1, rev(y2)),col=alpha(colors[3],alpha=0.2), border=FALSE) curve(0.59*exp(0.0633*x),-2,40,add=T,col='grey30',lwd=2.5, lty=2) title(main=expression(bold("Diatoms")), line=-1, adj=0.05, cex=1) text(-1.6, 2.3, paste0("n=", length(unique(diatoms$isolate.code))), adj=c(0,0)) text(-1.6, 1.9, paste0("N=", length(subset(rates, group=="diatoms")$isolate.code)), adj=c(0,0)) x=tempdi par(mar=c(3.5, 0.5,0.5,3.5)) plot(dinos$temperature, dinos$r, xlim=c(-2, 40), ylim=c(0,3), xlab='', ylab='', yaxt='n',pch=20, col=alpha("black", 0.4)) curve(exp(cf.di[[1]]+cf.di[[2]]*x),min(x), max(x),add=T,col=colors[4],lwd=2.5) polygon(c(tempdi, rev(tempdi)),c(y1_df, rev(y2_df)),col=alpha(colors[4],alpha=0.2), border=FALSE) curve(0.59*exp(0.0633*x),-2,40,add=T,col='grey30',lwd=2.5, lty=2) title(main=expression(bold("Dinoflagellates")), line=-1, adj=0.05, cex=1) text(-1.6, 2.3, paste0("n=", length(unique(dinos$isolate.code))), adj=c(0,0)) text(-1.6, 1.9, paste0("N=", length(subset(rates, group=="dinoflagellates")$isolate.code)), adj=c(0,0)) mtext(expression(bold("Temperature (ºC)")), side = 1, outer = TRUE, line = -1.5) mtext(expression(bold("Specific Growth Rate (d"^"-1" *")")), side = 2, outer = TRUE, line = -1.5) dev.off() ######################################################################### ###### Calculate the Rate of Change for Reaction Norms ######################### # calculate the change in growth estimating change from lower 20% to max growth growth.change.inc <- vector() growth.change.dec <- vector() lower <- vector() upper <- vector() for(i in 1:nrow(isolates)){ o=isolates[i, "mu.c.opt.list"] w=isolates[i, "mu.wlist"] a=isolates[i, "mu.alist"] b=isolates[i, "mu.blist"] min=isolates[i, "tmin"] max=isolates[i, "tmax"] mumax = isolates[i, "mu.g.opt.val.list"] topt=isolates[i, "mu.g.opt.list"] #find 20% of µmax target = mumax * 0.20 x1=seq(min,topt, by=0.001) x2=seq(topt,max, by=0.001) #find the temperature values that result in the target rates lowerbound <- x1[which(abs(nbcurve(x1,o,w,a,b)-target)==min(abs(nbcurve(x1,o,w,a,b)-target)))] upperbound <- x2[which(abs(nbcurve(x2,o,w,a,b)-target)==min(abs(nbcurve(x2,o,w,a,b)-target)))] lower<-append(lower, lowerbound) upper<-append(upper, upperbound) inc = (mumax-target)/(topt-lowerbound) dec = abs((target-mumax)/(upperbound-topt)) growth.change.inc<-rbind(growth.change.inc, inc) growth.change.dec<-rbind(growth.change.dec, dec) } isolates$lowerbound = lower isolates$upperbound = upper isolates$growth.change.inc = growth.change.inc isolates$growth.change.dec = growth.change.dec write.csv(isolates, "output/Isolate_growth_bounds.csv") ##### Figure 2A: Exponential curve comparison #### inset<- ggplot(data.frame(x = c(-2, 25)), aes(x = x)) + stat_function(fun = nbcurve, args=list(8.19,31.2,0.21,0.1), color="grey30")+ ylim(0, 0.8)+theme_classic()+ geom_hline(yintercept=0.1566, linetype=2, color="grey60")+ geom_hline(yintercept=0.1566*5, linetype=2, color="grey60")+ theme(axis.title = element_blank(), axis.text = element_blank(), axis.ticks = element_blank(), plot.margin = unit(c(0, 0.4, 0, 4), "lines"), plot.background = element_blank())+ geom_text(x=-9, y=0.1566, label=expression(µ["20%max"]), color="black", size=3)+ geom_text(x=-7, y=0.1566*5, label=expression(µ["max"]), color="black", size=3)+ coord_cartesian(clip = "off") inset rofc<- ggplot(data=isolates)+ geom_boxplot(aes(x=group, y=growth.change.inc), position=position_nudge(x=-0.22),width=0.4, color="black")+ geom_boxplot(aes(x=group, y=growth.change.dec), fill="grey60",color="black", width=0.4, position=position_nudge(x=0.22))+ labs(x="", y=expression(bold(paste( "Change in Performance (|", "µ", "|/ºC)"))))+ scale_color_manual(values=colors)+ scale_fill_manual(values=colors)+ guides(fill=FALSE, color=FALSE)+ scale_x_discrete(labels=c("CO", "CY", "DT", "DF"))+ annotation_custom( ggplotGrob(inset), xmin = 1.75, xmax =Inf, ymin = 0.4, ymax = 0.6)+ y rofc ##### Figure 2B: Exponential curve comparison #### # Color palette colors <- c("orange", "#ec3a25","#026cb1","#3ea127","grey30", "#033175", "#84e04c", "#fd843d" ) # primary colors x=seq(-2, 40, by=0.1) envel<- ggplot(data.frame(x = c(-2, 35)), aes(x = x)) + stat_function(fun = dQ, aes(color="Diatoms", linetype="Diatoms"), lwd=0.8, xlim = c(min(tempd), max(tempd)))+ geom_ribbon(data=data.frame(cbind(tempd, y1, y2)), aes(ymax=y2, ymin=y1, x=tempd), alpha=0.1, fill=colors[[3]])+ stat_function(fun = cQ, aes(color="Cyanobacteria", linetype="Cyanobacteria"), lwd=0.8, xlim = c(min(tempc), max(tempc)))+ geom_ribbon(data=data.frame(cbind(tempc, y1_cy, y2_cy)), aes(ymax=y2_cy, ymin=y1_cy, x=tempc), alpha=0.1, fill=colors[[2]])+ stat_function(fun = coQ, aes(color="Coccolithophores", linetype="Coccolithophores"), lwd=0.8, xlim = c(min(tempco), max(tempco)))+ geom_ribbon(data=data.frame(cbind(tempco, y1_co, y2_co)), aes(ymax=y2_co, ymin=y1_co, x=tempco), alpha=0.1, fill=colors[[1]])+ stat_function(fun = diQ, aes(color="Dinoflagellates", linetype="Dinoflagellates"), lwd=0.8, xlim = c(min(tempdi), max(tempdi)))+ geom_ribbon(data=data.frame(cbind(tempdi, y1_df, y2_df)), aes(ymax=y2_df, ymin=y1_df, x=tempdi), alpha=0.1, fill=colors[[4]])+ stat_function(fun = ep, aes(color="Eppley (1972)", linetype="Eppley (1972)"), lwd=0.8)+ labs(x="Temperature (ºC)", y=expression(bold("Specific Growth Rate (d"^"-1" *")")), color="")+ scale_colour_manual("Groups", values=colors)+ scale_linetype_manual(values=c(1,1,1,1,2), guide=FALSE)+ guides(color=guide_legend(override.aes = list(linetype = c(1,1,1,1,2))))+ #overrides color so legend lines are dashed coord_cartesian(ylim = c(0.1,2.9))+ y+theme(legend.position =c(0.20, 0.825), legend.text = element_text(size=9), legend.title = element_blank()) envel ### Save Figure 2 ### plot_grid(rofc, envel, labels =letters[1:2]) ggsave("figures/Figure2.pdf", width = 8.3, height = 4.2)

45de62a61cde9f02dc798b8db55def235f65b0d6

03cb2887a235ba8038a8244f6a144af06a653e60

/R/get_peaks_chromatograms.R

f4e3750bd8c7ce65f9fc35ace93c047107a26c9a

[ "MIT" ]

permissive

Roestlab/DrawAlignR

8724825fbf266d682183370988bc1bebcdc0f028

14990d47a6212e47a68327200c73714a6db15c78

refs/heads/master

2020-11-25T12:03:40.924982

2020-04-09T03:45:33

228,648,957

5

0

MIT

2020-04-09T03:45:34

2019-12-17T15:44:30

R

UTF-8

R

false

8,942

r

get_peaks_chromatograms.R

#' Extract XICs of all transitions requested in chromIndices. #' #' Extracts XICs using mz object. Generally Savitzky–Golay filter is used, however, filter can be turned-off as well. #' @author Shubham Gupta, \email{shubh.gupta@mail.utoronto.ca} #' #' ORCID: 0000-0003-3500-8152 #' #' License: (c) Author (2019) + MIT #' Date: 2019-12-13 #' @param mz (mzRpwiz object) #' @param chromIndices (vector of Integers) Indices of chromatograms to be extracted. #' @param XICfilter (string) This must be one of the strings "sgolay", "none". #' @param SgolayFiltOrd (integer) It defines the polynomial order of filer. #' @param SgolayFiltLen (integer) Must be an odd number. It defines the length of filter. #' @return A list of data-frames. Each data frame has elution time and intensity of fragment-ion XIC. #' @importFrom parallel mclapply detectCores #' @examples #' dataPath <- system.file("extdata", package = "DIAlignR") #' mzmlName<-paste0(dataPath,"/mzml/hroest_K120809_Strep10%PlasmaBiolRepl2_R04_SW_filt.chrom.mzML") #' mz <- mzR::openMSfile(mzmlName, backend = "pwiz") #' chromIndices <- c(37L, 38L, 39L, 40L, 41L, 42L) #' \dontrun{ #' XIC_group <- extractXIC_group(mz, chromIndices, SgolayFiltOrd = 4, SgolayFiltLen = 13) #' } extractXIC_group <- function(mz, chromIndices, XICfilter = "sgolay", SgolayFiltOrd = 4, SgolayFiltLen = 9){ if( any(class(mz)=="mzRpwiz") ){ message("[DrawAlignR::extractXIC_group] Calling mzR to extract XICs\n") XIC_group <- lapply( seq_along(chromIndices), function(i) { rawChrom <- mzR::chromatograms(mz, chromIndices[i]) # Savitzky-Golay filter to smooth chromatograms, filter order p = 3, filter length n = 13 if(XICfilter == "sgolay"){ rawChrom[,2] <- signal::sgolayfilt(rawChrom[,2], p = SgolayFiltOrd, n = SgolayFiltLen) } return(rawChrom) } ) } else if ( is.data.frame(mz) ) { # TODO Need to add a better check. message("[DrawAlignR::extractXIC_group] Calling mstools to extract XICs\n") XIC_group <- mstools::getChromatogramDataPoints_( filename = ".sqMass", chromIndices, id_type = "chromatogramIndex", name_time = "time", name_intensity = "paste0('X', data_row$FRAGMENT_ID)", mzPntrs = mz, SgolayFiltOrd = SgolayFiltOrd, SgolayFiltLen = SgolayFiltLen ) names(XIC_group) <- NULL } message(sprintf("[DrawAlignR::extractXIC_group] Lenth of XIC_group: %s\n", length(XIC_group))) return(XIC_group) } #' Extract XICs of all analytes from oswFiles #' #' For all the analytes requested, it fetches chromatogram indices from oswFiles and #' extract chromatograms from mzML files. #' #' @author Shubham Gupta, \email{shubh.gupta@mail.utoronto.ca} #' #' ORCID: 0000-0003-3500-8152 #' #' License: (c) Author (2019) + MIT #' Date: 2019-12-13 #' @param dataPath (char) path to mzml and osw directory. #' @param runs (vector of string) names of mzML files without extension. Names of the vector must be a combination of "run" and an iteger e.g. "run2". #' @param oswFiles (list of data-frames) it is output from getOswFiles function. #' @param analytes (string) analyte is as PRECURSOR.GROUP_LABEL or as PEPTIDE.MODIFIED_SEQUENCE and PRECURSOR.CHARGE from osw file. #' @param XICfilter (string) this must be one of the strings "sgolay", "none". #' @param SgolayFiltOrd (integer) it defines the polynomial order of filer. #' @param SgolayFiltLen (integer) must be an odd number. It defines the length of filter. #' @param mzPntrs A list of mzRpwiz. #' @return A list of list of data-frames. Each data frame has elution time and intensity of fragment-ion XIC. #' #' @seealso \code{\link{getOswFiles}, \link{getRunNames}} #' @examples #' dataPath <- system.file("extdata", package = "DIAlignR") #' filenames <- DIAlignR::getRunNames(dataPath = dataPath) #' runs <- c("run1" = "hroest_K120809_Strep0%PlasmaBiolRepl2_R04_SW_filt", #' "run0" = "hroest_K120808_Strep10%PlasmaBiolRepl1_R03_SW_filt") #' oswFiles <- DIAlignR::getOswFiles(dataPath, filenames) #' analytes <- "QFNNTDIVLLEDFQK_3" #' XICs <- getXICs4AlignObj(dataPath, runs, oswFiles, analytes) #' @export getXICs4AlignObj <- function(dataPath, runs, oswFiles, analytes, XICfilter = "sgolay", SgolayFiltOrd = 4, SgolayFiltLen = 9, mzPntrs = NULL){ if(is.null(mzPntrs)){ mzPntrs <- getMZMLpointers(dataPath, runs) } XICs <- vector("list", length(runs)) names(XICs) <- names(runs) for(i in seq_along(runs)){ runname = names(runs)[i] message("Fetching XICs from ", runname, " ", runs[[runname]]) XICs[[i]] <- lapply(seq_along(analytes), function(j){ analyte <- analytes[j] chromIndices <- selectChromIndices(oswFiles, runname = runname, analyte = analyte) if(is.null(chromIndices)){ warning("Chromatogram indices for ", analyte, " are missing in ", runs[[runname]]) message("Skipping ", analyte) XIC_group <- NULL } else { XIC_group <- extractXIC_group(mzPntrs[[runname]]$mz, chromIndices, XICfilter, SgolayFiltOrd, SgolayFiltLen) } XIC_group }) names(XICs[[i]]) <- analytes } rm(mzPntrs) XICs } #' Get XICs of all analytes #' #' For all the analytes requested in runs, it first creates oswFiles, then, fetches chromatogram indices from oswFiles and #' extract chromatograms from mzML files. #' #' @importFrom dplyr %>% #' @author Shubham Gupta, \email{shubh.gupta@mail.utoronto.ca} #' #' ORCID: 0000-0003-3500-8152 #' #' License: (c) Author (2019) + MIT #' Date: 2019-12-13 #' #' @param analytes (string) An analyte is as PRECURSOR.GROUP_LABEL or as PEPTIDE.MODIFIED_SEQUENCE and PRECURSOR.CHARGE from osw file. #' @param runs (A vector of string) Names of mzml file without extension. Vector must have names as shown in the example. #' @param dataPath (char) Path to mzml and osw directory. #' @param maxFdrQuery (numeric) A numeric value between 0 and 1. It is used to filter features from osw file which have SCORE_MS2.QVALUE less than itself. #' @param XICfilter (string) This must be one of the strings "sgolay", "none". #' @param SgolayFiltOrd (integer) It defines the polynomial order of filer. #' @param SgolayFiltLen (integer) Must be an odd number. It defines the length of filter. #' @param runType (char) This must be one of the strings "DIA_proteomics", "DIA_Metabolomics". #' @param oswMerged (logical) TRUE for experiment-wide FDR and FALSE for run-specific FDR by pyprophet. #' @param nameCutPattern (string) regex expression to fetch mzML file name from RUN.FILENAME columns of osw files. #' @param analyteInGroupLabel (logical) TRUE for getting analytes as PRECURSOR.GROUP_LABEL from osw file. #' @param mzPntrs A list of mzRpwiz. #' @return A list of list. Each list contains XIC-group for that run. XIC-group is a list of dataframe that has elution time and intensity of fragment-ion XIC. #' #' @seealso \code{\link{getOswFiles}, \link{getRunNames}} #' @examples #' dataPath <- system.file("extdata", package = "DIAlignR") #' runs <- c("hroest_K120808_Strep10%PlasmaBiolRepl1_R03_SW_filt", #' "hroest_K120809_Strep10%PlasmaBiolRepl2_R04_SW_filt") #' XICs <- getXICs(analytes = c("QFNNTDIVLLEDFQK_3"), runs = runs, dataPath = dataPath) #' @export getXICs <- function(analytes, runs, dataPath = ".", maxFdrQuery = 1.0, XICfilter = "sgolay", SgolayFiltOrd = 4, SgolayFiltLen = 9, runType = "DIA_proteomics", oswMerged = TRUE, nameCutPattern = "(.*)(/)(.*)", chrom_ext=".chrom.mzML", analyteInGroupLabel = FALSE, mzPntrs=NULL){ if( (SgolayFiltLen %% 2) != 1){ print("SgolayFiltLen can only be odd number") return(NULL) } # Get filenames from .merged.osw file and check if names are consistent between osw and mzML files. filenames <- getRunNames(dataPath = dataPath, oswMerged = oswMerged, nameCutPattern = nameCutPattern, chrom_ext = chrom_ext) filenames <- filenames[filenames$runs %in% runs,] # Get Chromatogram indices for each peptide in each run. oswFiles <- getOswFiles(dataPath, filenames, maxFdrQuery = maxFdrQuery, analyteFDR = 1.00, oswMerged = oswMerged, analytes = analytes, runType = runType, analyteInGroupLabel = analyteInGroupLabel) refAnalytes <- getAnalytesName(oswFiles, commonAnalytes = FALSE) analytesFound <- intersect(analytes, refAnalytes) analytesNotFound <- setdiff(analytes, analytesFound) if(length(analytesNotFound)>0){ message("Analytes ", paste(analytesNotFound, ", "), "not found.") } ####################### Get XICs ########################################## runs <- filenames$runs names(runs) <- rownames(filenames) # Get Chromatogram for each peptide in each run. message("Fetching Extracted-ion chromatograms from runs") XICs <- getXICs4AlignObj(dataPath, runs, oswFiles, analytesFound, XICfilter, SgolayFiltOrd, SgolayFiltLen, mzPntrs=mzPntrs) names(XICs) <- filenames$runs XICs }

97ef015ad4866d02ced6a87d98624b5fa9f69bec

0dd9227755c5b154d2184e712d53f4bacc02305c

/man/wait_for_dir.Rd

33d96d4140194359249f93936428ef9650fb7056

[ "MIT" ]

permissive

imbs-hl/MDRDist

811b68cad2877c83d6d4ac9c08e04a63141757b1

2aa6838aeeb6291b76971b34169230abcd28a909

refs/heads/master

2023-09-02T08:34:07.942115

2017-07-05T10:51:59

85,298,779

0

null

UTF-8

R

false

true

604

rd

wait_for_dir.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/supporting_functions.R \name{wait_for_dir} \alias{wait_for_dir} \title{Waiting until recently created directory appears} \usage{ wait_for_dir(Dir, max_wait = 30, timeout = 1) } \arguments{ \item{Dir}{path to the directory which we are waiting for} \item{max_wait}{timeout until assertion will be raised, if dir does not appear} \item{timeout}{timestep between two attempts to look for the dir} } \value{ nothing but certainty, that a directory is callable } \description{ Waiting until recently created directory appears }

4897d314e934878e15c20846bd738a1efdde53ec

ffdea92d4315e4363dd4ae673a1a6adf82a761b5

/data/genthat_extracted_code/ahaz/examples/sorlie.Rd.R

86690da2a25ac1a09079096ce7207a55f685e231

[]

no_license

surayaaramli/typeRrh

d257ac8905c49123f4ccd4e377ee3dfc84d1636c

66e6996f31961bc8b9aafe1a6a6098327b66bf71

refs/heads/master

2023-05-05T04:05:31.617869

2019-04-25T22:10:06

null

0

null

UTF-8

R

false

147

r

sorlie.Rd.R

library(ahaz) ### Name: sorlie ### Title: Sorlie gene expressions ### Aliases: sorlie ### Keywords: datasets ### ** Examples data(sorlie)

e3718a21d9659ba4e70923f1dee4771c006c84d6

99fd08dac3a1bb59df57983ee5c737fc3fa3d721

/main__summarize_industry_assignment.R

060892eb917be681c2bc0705f1b5c1188d33f6d5

[]

no_license

nareal/CRSP-Data-Summary-Statistics-by-Industry-

b01307836e1237b6460263e874e2c4687d2d816d

31bd5652d2d64a29b27eb3cf63ba1cdeb2773ccd

refs/heads/master

2021-01-15T22:41:40.908879

2011-11-22T02:06:11

null

0

null

UTF-8

R

false

743

r

main__summarize_industry_assignment.R

rm(list=ls()) library(foreign) library(reshape) library(plyr) library(matlab) library(rjson) library(RColorBrewer) library(ggplot2) library(tikzDevice) library(classInt) source("summarize_industry_assignment.R") ## plot_number_of_firms() ## plot_number_of_firms_per_industry(industry_classification = "mg1999") ## plot_number_of_firms_per_industry(industry_classification = "ff1988") ## plot_number_of_firms_per_sub_industry() ## plot_distribution_of_excess_returns_by_industry(industry_classification = "mg1999") ## plot_distribution_of_excess_returns_by_industry(industry_classification = "ff1988") ## plot_market_cap_by_industry(industry_classification = "mg1999") plot_market_cap_by_industry(industry_classification = "ff1988")

a3564286c60930ab1e531d2f2475aaae0649042f

d690af5c19bb0d6b723e1b8f1687794b4e0f8830

/tests/testthat/test-stats-nls.R

37c2e5331e7f4461adb681eeed90be2f9e69bb15

[ "MIT" ]

permissive

roldanalex/safepredict

03113c5095518fef7c007c7e98342ecf15c0f9dc

05c3b9c8770583221a73b7b68f88805402630f5f

refs/heads/master

2021-10-09T11:32:24.866936

2018-12-27T06:11:45

null

0

null

UTF-8

R

false

831

r

test-stats-nls.R

context("test-stats-nls") fit <- nls(demand ~ SSasympOrig(Time, A, lrc), data = BOD) test_that("function signature", { check_safepredict_signature(safe_predict.nls) }) test_that("input validation", { expect_error( safe_predict(fit), "argument \"new_data\" is missing, with no default" ) expect_error( safe_predict(fit, BOD, type = "infinite fun space"), "`type` should be one of: \"response\"" ) expect_warning( safe_predict(fit, BOD, bad_arg = 0.2), "Some components of ... were not used: bad_arg" ) }) ## checks on returned predictions test_that("default type", { default_preds <- safe_predict(fit, BOD) check_predict_output(default_preds, BOD, type = "response") }) test_that("type = \"response\"", { check_predict(safe_predict.nls, fit, BOD, "demand", type = "response") })

8c449b7631a711f8eabd3c0a2035b4fb04f6a40c

f42a7b41b6acd4dac40234ff2d939c938f6f2d53

/man/earlyReduction.Rd

3f542af466342771deeae151c78f2bb1cfb2de65

[ "MIT" ]

permissive

ttriche/bayesCC

2927f9228782b9c9814f3bdf5e31c36a50e5e794

627a88a5af1b07b4923ecba42174d4c148df29c2

refs/heads/master

2023-06-26T19:57:56.436883

2023-05-11T18:51:53

44,848,488

24

4

null

UTF-8

R

false

true

2,137

rd

earlyReduction.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/earlyReduction.R \name{earlyReduction} \alias{earlyReduction} \title{do dimension reduction (via NMF or SVD) before Bayesian consensus clustering} \usage{ earlyReduction( mat, how = c("NMF", "SVD"), mat2 = NULL, joint = FALSE, findK = FALSE, howNA = c("both", "column", "row"), viaCV = FALSE, pctNA = 0.2 ) } \arguments{ \item{mat}{a matrix to decompose (columns are samples, rows are features)} \item{how}{one of "NMF" or "SVD"; SVD is likely to be much faster} \item{mat2}{a 2nd matrix to reduce (optional; for joint factorization)} \item{joint}{if using NMF, should joint factorization be attempted?} \item{findK}{if using marginal NMF, should the optimal rank(s) be sought?} \item{howNA}{for rank finding, add NAs column-wise, row-wise, or both?} \item{viaCV}{for rank finding, should five-fold CV be used when imputing?} \item{fracNA}{for rank finding, what fraction of the data should be NA'ed?} } \value{ a list with W, H, and K for each matrix if using NMF, or a list with D, U, and V for each matrix if using SVD. } \description{ if NMF, the rank can be estimated by 5xCV on NAs, though this can be slow. the underlying rationale is that whatever rank K best recovers artificially missing data (knocked out column-wise, row-wise, or randomly across both) is the best estimable rank we are likely to recover. In order to stabilize the estimate of K, we can run 5x cross-validation and rotate the NAs (set at a default of 20% of the entries to facilitate sampling without replacement). } \details{ joint NMF can also be requested (as in Wang et al., Bioinformatics 2015, doi: 10.1093/bioinformatics/btu679) but in this case the ranks can only be estimated marginally. Joint rank estimation (and, by extension, optimal joint imputation for linked views) is an open research topic as best as we can tell. if anyone wants to send a patch we will gladly apply it and a great many people will probably start using it thereafter. if SVD, the rank will be whatever the data supports (i.e. min(nrow, ncol)). }

6b206a2af2f52692bc67107fc872dd6c6afe656b

ffdea92d4315e4363dd4ae673a1a6adf82a761b5

/data/genthat_extracted_code/letsR/examples/lets.subsetPAM.Rd.R

b2532f0fd3fe3066d62a3b66377ae9cbd0c69ba7

[]

no_license

surayaaramli/typeRrh

d257ac8905c49123f4ccd4e377ee3dfc84d1636c

66e6996f31961bc8b9aafe1a6a6098327b66bf71

refs/heads/master

2023-05-05T04:05:31.617869

2019-04-25T22:10:06

null

0

null

UTF-8

R

false

528

r

lets.subsetPAM.Rd.R

library(letsR) ### Name: lets.subsetPAM ### Title: Subset a PresenceAbsence object based on species names ### Aliases: lets.subsetPAM ### ** Examples ## Not run: ##D data(PAM) ##D # PAM before subset ##D plot(PAM, xlab = "Longitude", ylab = "Latitude", ##D main = "Phyllomedusa species richness") ##D ##D # Subset PAM to the first 20 species ##D PAMsub <- lets.subsetPAM(PAM, PAM[[3]][1:20]) ##D plot(PAMsub, xlab = "Longitude", ylab = "Latitude", ##D main = "Phyllomedusa species richness") ## End(Not run)

29d61a683e3c98a438e52dff949f826071235010

9cfbe86f685f8ef280899ca97bc425d1bfda5564

/0323_in_class.R

a087bb791dd652a7b3dc0ee002f52b5fc139178c

[]

no_license

kisumzzz/DataAnalyticsSpring2020

20f3b6fa7fc112efcf5d6731d6cd024e17a61a2b

deb9496c9156991e81ab88091f5f13bfa55ed974

refs/heads/master

2020-12-21T11:52:53.424495

2020-05-05T03:06:49

236,422,294

0

null

UTF-8

R

false

783

r

0323_in_class.R

data("USArrests") states=row.names(USArrests) states apply(USArrests , 2, mean) apply(USArrests , 2, var) pr.out=prcomp(USArrests, scale=TRUE) names(pr.out) pr.out$center pr.out$scale pr.out$rotation dim(pr.out$x) biplot(pr.out, scale=0) pr.out$sdev # PCA with iris dataset data("iris") head(iris) irisdata1 <- iris[,1:4] irisdata1 head(irisdata1) principal_components <- princomp(irisdata1, cor = TRUE, score = TRUE) summary(principal_components) plot(principal_components) plot(principal_components, type = "l") biplot(principal_components) install.packages('MASS') data(Boston, package="MASS") pca_out <- prcomp(Boston,scale. = T) pca_out plot(pca_out) help(biplot) biplot(pca_out, scale = 0) boston_pc <- pca_out$x boston_pc head(boston_pc) summary(boston_pc)

87a9ec7985912e0486649f35fa8f96031f85d6d6

852d3fb58551d0c612c1c40ebf6ef5ad4d78f92b

/Visualization/BitEpiVis.R

56d14810054c1daa74d0271debf1e4e2c9e79438

[ "MIT", "BSD-3-Clause" ]

permissive

aehrc/BitEpi

4a2e34a76453d66f18863d3ff0931c7f7c837953

8783f9664433c8de5f03b15a47cd5d7d81bb2e09

refs/heads/master

2021-08-06T00:54:44.046981

2021-07-27T07:35:41

211,199,347

8

4

NOASSERTION

2021-07-22T00:16:18

2019-09-26T23:43:50

C++

UTF-8

R

false

6,319

r

BitEpiVis.R

library(dplyr) library(RCy3) library(igraph) setwd('~/temp/cc/BitEpi') Color=list(SNP='red',PAIR='blue',TRIPLET='orange',QUADLET='green', OTHER='gray') # Nodes of the graph are SNPs and Interactions # Each SNP node could be connected to multiple Interaction Node # Each Interaction Node is conneced to the SNPs that are involved in that interaction. # This function name the interaction nodes by concatinating SNPS with # seprator. # the 2-SNP, 3-SNP, and 4-SNP names are added as 3 new column to the best dataframe # For Example if rs123, rs456 and rs789 Interact with each other then # the Interaction node is called rs123#rs456#rs789 AddInteractionNode = function(data) { x = as.data.frame(t(apply(select(data, SNP, PAIR), 1, sort))) data$nP = paste(x$V1, x$V2, sep = "#") x = as.data.frame(t(apply(select(data, SNP, TRIPLET_1, TRIPLET_2), 1, sort))) data$nT = paste(x$V1, x$V2, x$V3, sep = "#") x = as.data.frame(t(apply(select(data, SNP, QUADLET_1, QUADLET_2, QUADLET_3), 1, sort))) data$nQ = paste(x$V1, x$V2, x$V3, x$V4, sep = "#") return(data) } # list all the nodes (1-SNP, 2-SNP, 3-SNP, 4SNP) assing beta to the size and rank them by order NodeGen = function(dataX) { #1-SNP data = dataX data$Node = data$SNP data$order = 1 data$beta = data$SNP_B data$color = Color$SNP data = data[order(-data$SNP_A),] data$rank = seq.int(nrow(data)) nodes = select(data, Node, rank, beta, color, order) #2-SNP data = dataX data$Node = data$nP data$order = 2 data$beta = data$PAIR_B data$color = Color$PAIR data = data[order(data[,'Node'],-data[,'beta']),] data = data[!duplicated(data$Node),] data = data[order(-data$PAIR_A),] data$rank = seq.int(nrow(data)) nodes = rbind(nodes, select(data, Node, rank, beta, color, order)) #3-SNP data = dataX data$Node = data$nT data$order = 3 data$beta = data$TRIPLET_B data$color = Color$TRIPLET data = data[order(data[,'Node'],-data[,'beta']),] data = data[!duplicated(data$Node),] data = data[order(-data$TRIPLET_A),] data$rank = seq.int(nrow(data)) nodes = rbind(nodes, select(data, Node, rank, beta, color, order)) #4-SNP data = dataX data$Node = data$nQ data$order = 4 data$beta = data$QUADLET_B data$color = Color$QUADLET data = data[order(data[,'Node'],-data[,'beta']),] data = data[!duplicated(data$Node),] data = data[order(-data$QUADLET_A),] data$rank = seq.int(nrow(data)) nodes = rbind(nodes, select(data, Node, rank, beta, color, order)) return(nodes) } # list all edges between interactive nodes (2-SNP, 3-SNP and 4-SNP) and SNP nodes (1-SNP) EdgeGen = function(data) { edf = data.frame(source=character(), target=character()) for(i in 1:nrow(data)) { edf = rbind(edf, data.frame(source=data[i,"nP"], target=data[i,"SNP"])) edf = rbind(edf, data.frame(source=data[i,"nP"], target=data[i,"PAIR"])) edf = rbind(edf, data.frame(source=data[i,"nT"], target=data[i,"SNP"])) edf = rbind(edf, data.frame(source=data[i,"nT"], target=data[i,"TRIPLET_1"])) edf = rbind(edf, data.frame(source=data[i,"nT"], target=data[i,"TRIPLET_2"])) edf = rbind(edf, data.frame(source=data[i,"nQ"], target=data[i,"SNP"])) edf = rbind(edf, data.frame(source=data[i,"nQ"], target=data[i,"QUADLET_1"])) edf = rbind(edf, data.frame(source=data[i,"nQ"], target=data[i,"QUADLET_2"])) edf = rbind(edf, data.frame(source=data[i,"nQ"], target=data[i,"QUADLET_3"])) } return(edf) } # convert BitEpi Best file to nodes and edges BestToNodesAndEdges = function(bestFn) { # Read BitEpi "best" file into a data frame bestDf = read.csv(bestFn) bestDf = AddInteractionNode(bestDf) Nodes = NodeGen(bestDf) Edges = EdgeGen(bestDf) return(list(Nodes=Nodes, Edges=Edges)) } # query nodes and related edges QueryGraph = function(Graph, thr, minNodeSize, maxNodeSize) { if(minNodeSize >= maxNodeSize) { print("minNodeSize is greater or equal maxNodeSize") return(NULL,NULL) } allNodes = Graph$Nodes allEdges = Graph$Edges # select nodes to be in the graph s1 = allNodes %>% filter(order==1 & allNodes$rank<=thr$SNP) s2 = allNodes %>% filter(order==2 & allNodes$rank<=thr$PAIR) s3 = allNodes %>% filter(order==3 & allNodes$rank<=thr$TRIPLET) s4 = allNodes %>% filter(order==4 & allNodes$rank<=thr$QUADLET) selNodes = unique(rbind(s1,s2,s3,s4)) # select interaction nodes intNodes = selNodes %>% filter(order>1) # select all edges for intraction nodes intEdges = select(merge(x=allEdges, y=intNodes, by.x='source', by.y='Node'), source, target) intEdges = unique(intEdges) # select all target names for interaction nodes tarNames = unique(select(intEdges, target)) names(tarNames) = 'Node' #grab target nodes from all nodes tarNodes = merge(x=allNodes, y=tarNames, by='Node') #and merge them to dataset selNodes = unique(rbind(selNodes, tarNodes)) minBeta = min(selNodes$beta) maxBeta = max(selNodes$beta) rangeBeta = maxBeta - minBeta rangeSize = maxNodeSize - minNodeSize ratio = rangeSize/rangeBeta selNodes$size = ((selNodes$beta - minBeta) * ratio) + minNodeSize; selNodes[(selNodes$order==1) & (selNodes$rank>thr$SNP),]$color = Color$OTHER selNodes[(selNodes$order==1) & (selNodes$rank>thr$SNP),]$size = minNodeSize return(list(Nodes=selNodes, Edges=intEdges)) } DoItAll = function(bestFn, thr, minNodeSize, maxNodeSize) { # read best file into a graph GraphAll = BestToNodesAndEdges(bestFn) # query graph GraphSelected = QueryGraph(GraphAll, thr, minNodeSize, maxNodeSize) Edges = GraphSelected$Edges Nodes = GraphSelected$Nodes #plot graph Nodes$label = " " network = graph_from_data_frame(d=Edges, directed=FALSE, vertices = Nodes) plot(network, vertex.size=V(network)$size, vertex.label=V(network)$Node, vertex.color=V(network)$color, vertex.label=V(network)$label) cytoscapePing() createNetworkFromIgraph(network,"BitEpi Network", title = "BitEpi Graph") } thr=list(SNP=3,PAIR=3,TRIPLET=3,QUADLET=3) minNodeSize = 10 maxNodeSize = 35 #Sort by Alpha and but represent beta as node size in the plot DoItAll('sampleData/out.best.csv', thr, minNodeSize, maxNodeSize)

67b4c288cc691e1b72ce5df79a91ee5298726943

f5e25afe6fb3abdc9ea1ebe09ad383c21c83f92f

/R/errorbar.R

2f944886fdadac7639f37258657b78789528a0f8

[]

no_license

cran/phonTools

34a4527f0ab8cbe06947dab7aff6385bb56cd452

80e82b901140f715c0b4d2c55b214410a6498038

refs/heads/master

2016-09-16T04:11:26.219457

2015-07-30T00:00:00

17,698,512

2

0

null

UTF-8

R

false

598

r

errorbar.R

# Copyright (c) 2015 Santiago Barreda # All rights reserved. errorbars = function(x, y, top, bottom = top, length = .2, add = TRUE, ...){ if (add) arrows(x, y+top, x, y-bottom, angle=90, code=3, length=length, ...) if (!add){ plot (x,y,pch=16, ylim = range(y) + c(-top, bottom)) arrows(x, y+top, x, y-bottom, angle=90, code=3, length=length, ...) } } errorbar = function(x, y, top, bottom = top, length = .2, add = TRUE, ...){ cl = match.call() args = sapply (2:length(cl), function(x) cl[[x]]) names(args) = names(cl)[-1] do.call (errorbars, args) }

3980ae7093f7e77dea6503b893a28c4e279bb3c0

f6f88407b149dfe2be1f46832ba4b3385ad7aada

/gdp_rates/r_scripts/assess_cumulative_impact.R

28e8d00548c28df02076d3ac975bdc7d5cda1829

[]

no_license

dstauffer11/colonization_effects

3ee84872c067844a303e8dad35b72baa078d8cc3

c8adf4472c4296a086505763e93f4a97a140edab

refs/heads/master

2022-11-14T13:42:37.441079

2020-06-26T14:53:36

275,174,879

1

0

null

UTF-8

R

false

8,360

r

assess_cumulative_impact.R

library(CausalImpact) library(ggplot2) library(rjson) library(rstan) library(bayesplot) library(ggplot2) library(CausalImpact) library(gridExtra) library(splines) options(mc.cores = parallel::detectCores()) country.year.neighbors.list <- fromJSON(file='data/country_year_neighbors_map.json') gdp.data <- read.csv('data/annual_growth.csv') T <- 20 T0 <- 10 J <- length(country.year.neighbors.list) N <- 0 X <- matrix(, nrow = 20, ncol = 0) Y <- c() indices <- c() codes <- c() neighbor_map = list() colonizers = c() for (country_code in names(country.year.neighbors.list)) { yin = country.year.neighbors.list[[country_code]] codes = c(codes, country_code) neighbor_map[[country_code]] = yin[[4]] colonizers = c(colonizers, yin[[5]]) first.year <- strtoi(yin[[1]]) independence.year <- strtoi(yin[[2]]) last.year <- strtoi(yin[[3]]) if ((first.year > independence.year - 9) | (last.year < independence.year + 10)) next first.year <- independence.year - 9 last.year <- independence.year + 10 countries <- c(country_code, yin[[4]]) local.gdp.data <- gdp.data[gdp.data$country_code %in% countries, ] local.gdp.data <- local.gdp.data[local.gdp.data$year >= first.year, ] local.gdp.data <- local.gdp.data[local.gdp.data$year <= last.year, ] y <- local.gdp.data[local.gdp.data$country_code == country_code, 'annual_growth'] Y <- append(Y, list(y)) indices <- c(indices, c(N+1, N+length(yin[[4]]))) N <- N + length(yin[[4]]) x.long <- local.gdp.data[local.gdp.data$country_code %in% yin[[4]], ] x.long <- subset(x.long, select=c('year', 'country_code', 'annual_growth')) x.wide <- reshape(x.long, direction = "wide", idvar = 'year', timevar = 'country_code') x <- x.wide[ , !(names(x.wide) %in% c('year'))] X <- cbind(X, data.matrix(x)) } #X = apply(X,2 , norm <- function(x){return (x - mean(x))}) # for (i in 1:J) { # Y[[i]] = Y[[i]] - mean(Y[[i]][1:T0]) # } colonizers_numeric = as.numeric(factor(colonizers)) C = max(colonizers_numeric) data = list( T=T, T0=T0, N=N, J=J, X=X, y=Y, indices=indices ) sm <- stan_model('stan_models/cumulative_impact.stan') fit <- sampling(sm, data=data, iter=5000, control=list(adapt_delta=0.95, max_treedepth=12), seed=1, chains=1, warmup=1000) sm_n <- stan_model('stan_models/cumulative_impact_normed.stan') fitn <- sampling(sm_n, data=data, iter=2000, control=list(adapt_delta=0.95, max_treedepth=12), seed=1, chains=4, warmup=1000) print(fit, pars=c('mean_effect', 's_effect', 'raw_effects', 'real_effects', 's_s_obs', 's_obs')) data_colonizers = list( T=T, T0=T0, N=N, J=J, X=X, y=Y, indices=indices, C=C, colonizer=colonizers_numeric ) smc <- stan_model('stan_models/multiple_lines_colonizers2.stan') fitc <- sampling(smc, data=data_colonizers, iter=2000, control=list(adapt_delta=0.9, max_treedepth=10), seed=5) # check tree depths check_treedepth(fit) # check energy check_energy(fit) # check chain mixing traceplot(fitn, pars=c('mean_effect', 's_effect', 'real_effects[1]', 's_obs[1]', 's_s_obs', 's_effect', 's_level[1]', 'neighbor_beta[10]')) mcmc_trace_highlight( fitn, pars = c('mean_effect', 's_effect', 'real_effects[1]', 's_obs[1]', 's_s_obs', 's_effect', 's_level[1]', 'neighbor_beta[10]'), alpha = 0.03, highlight = 2 ) traceplot(fitn, pars=c('neighbor_beta[1]', 'neighbor_beta[10]', 'neighbor_beta[50]', 'neighbor_beta[100]', 'neighbor_beta[200]', 'neighbor_beta[300]')) traceplot(fitc, pars=c('u[1,1]', 'u[1,15]', 'u[10,3]', 'u[10,5]', 'u[30,5]', 'u[40,20]')) traceplot(fitc, pars=c('mean_effect', 's_colonizer_effect_unif', 's_colonizer_effect', 'raw_colonizer_effects[1]', 'raw_colonizer_effects[2]', 'raw_colonizer_effects[3]', 'raw_colonizer_effects[4]', 'raw_colonizer_effects[5]')) pairs(fitn, pars=c('mean_effect', 's_effect', 'real_effects[1]', 'real_effects[10]', 'real_effects[30]')) pairs(fitn, pars=c('mean_effect', 's_effect', 's_obs[1]', 's_obs[10]', 's_obs[30]', 's_s_obs')) pairs(fitc, pars=c('mean_effect', 's_effect', 's_s_obs', 's_colonizer_effect', 'raw_colonizer_effects')) # Histogram of tree depths breaks = 0:13 sampler_params = get_sampler_params(fitn, inc_warmup=FALSE) treedepths = do.call(rbind, sampler_params)[, 'treedepth__'] treedepths_hist = hist(treedepths, breaks=breaks, plot=FALSE) par(mar=c(4, 4, 0.5, 0.5)) plot(treedepths_hist, main='', xlab='theta.1', yaxt='n', ann=FALSE) # Estimate distribution of mean effect from posterior samples mean_effect_draws = as.array(fitn, pars = c('mean_effect')) mcmc_dens(mean_effect_draws, pars=c('mean_effect')) + ggtitle('Estimated Effect of Indepence on Economic Growth') + xlab('Change in Growth Rate of GDP per Capita (2011 US$)') + ylab('Density') # Estimate effect of each colonizer colonizers_reducted = c('BEL', 'DEU', 'FRA', 'GBR', 'PRT', 'RUS') mean_effect_draws = as.array(fitc, pars = c('real_colonizer_effects')) mcmc_areas(-mean_effect_draws, regex_pars = "real_colonizer_effects\\[[1-6]\\]", prob=0.8) + ggtitle('Estimated Effect of Colonizer its Colonies') + xlab('Depression of Growth Rate') + ylab('Density') + xlim(-0.05, 0.05) + scale_y_discrete(labels=colonizers_reducted) # Estimate distribution of each indiviadual country's effects individual_effect_draws = as.matrix(fitn, pars = c('real_effects', 'mean_effect')) mcmc_intervals(individual_effect_draws) + scale_y_discrete(labels=c(codes, 'Overall')) + ggtitle('Estimated Independence Effect by Country') + xlab('Growth Rate Change') + theme( panel.grid.major = element_line(size = 0.1, linetype = 'solid', colour = "gray"), panel.grid.minor = element_line(size = 0.1, linetype = 'solid', colour = "gray") ) # check step sizes of sample sampler_params <- get_sampler_params(fit, inc_warmup=FALSE) stepsizes <- sapply(sampler_params, function(x) x[1,'stepsize__']) names(stepsizes) <- list("Chain 1", "Chain 2", "Chain 3" ,"Chain 4") stepsizes mean(stepsizes) # 2000 iterations, centered: 0.009013763 # check gradient evaluations n_gradients <- sapply(sampler_params, function(x) sum(x[,'n_leapfrog__'])) n_gradients sum(n_gradients) # 2000 ierations, centered: 2318880 stan_diag(fit, info = 'sample') color_scheme_set('darkgray') f <- extract(fit) ppc_dens_overlay(y = y, yrep = f$effect[1:T0, ]) draws <- as.array(fit, pars = c('beta', 's_obs', 's_slope', 's_level', 'effect')) np <- nuts_params(fit) mcmc_parcoord(draws, np=np) draws <- as.array(fit, pars = c('u')) np <- nuts_params(fit) mcmc_parcoord(draws, np=np) draws <- as.array(fit, pars = c('mean_effect', 's_effect', 'raw_effects')) np <- nuts_params(fit) mcmc_parcoord(draws, np=np) draws <- as.array(fit, pars = c('mean_effect', 's_effect', 's_s_obs')) np <- nuts_params(fit) mcmc_parcoord(draws, np=np) mcmc_scatter( as.matrix(fit), pars = c('s_obs', 's_slope'), np = nuts_params(fit), np_style = scatter_style_np(div_color = "green", div_alpha = 0.8) ) measure.impact <- function(country_code, yin) { first.year <- strtoi(yin[[1]]) independence.year <- strtoi(yin[[2]]) last.year <- strtoi(yin[[3]]) - 1 countries <- c(country_code, yin[[4]]) local.gdp.data <- gdp.data[gdp.data$country_code %in% countries, ] local.gdp.data <- local.gdp.data[local.gdp.data$year >= first.year, ] local.gdp.data <- local.gdp.data[local.gdp.data$year <= last.year, ] t <- as.Date(ISOdate(local.gdp.data[local.gdp.data$country_code == country_code, 'year'], 1, 1)) y <- local.gdp.data[local.gdp.data$country_code == country_code, 'annual_growth'] x.long <- local.gdp.data[local.gdp.data$country_code %in% yin[[4]], ] x.long <- subset(x.long, select=c('year', 'country_code', 'annual_growth')) x.wide <- reshape(x.long, direction = "wide", idvar = 'year', timevar = 'country_code') x <- x.wide[ , !(names(x.wide) %in% c('year'))] # print(x) simple.data <- zoo(cbind(y, x), t) pre.period <- as.Date(ISOdate(c(first.year, independence.year), 1, 1)) post.period <- as.Date(ISOdate(c(independence.year+1, last.year), 1, 1)) impact <- CausalImpact(simple.data, pre.period, post.period) plot(impact) ggsave(sprintf('results/causal_impact/%s.png', country_code)) return(impact) } # measure.impact('Canada', c(1901, 1931, c('United States', 'Mexico'))) for (name in names(country.year.neighbors.list)) { print(name) measure.impact(name, country.year.neighbors.list[[name]]) }

aca1ea8745d9f7de8e0684b542d4448ea1ba6a6d

a3e56dccec4c41f256583f45959ee64d6d269f57

/man/wine27.Rd

3a00fe10109004762099940d9b5bd12271636487

[]

no_license

cran/MBCbook

76b189b1b24303fc49ae748f34807c5253507229

71cd7f2313a55239d5b1c6c707308c783481b200

refs/heads/master

2020-12-22T01:03:39.836908

2019-07-02T06:00:03

236,623,831

0

null

UTF-8

R

false

2,155

rd

wine27.Rd

\name{wine27} \alias{wine27} \docType{data} \title{ The (27-dimensional) Italian Wine data set } \description{ The (27-dimensional) Italian Wine data set is the result of a chemical analysis of 178 wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 27 constituents found in each of the three types of wines. } \usage{data("wine27")} \format{ A data frame with 178 observations on the following 29 variables. \describe{ \item{\code{Alcohol}}{a numeric vector} \item{\code{Sugar.free_extract}}{a numeric vector} \item{\code{Fixed_acidity}}{a numeric vector} \item{\code{Tartaric_acid}}{a numeric vector} \item{\code{Malic_acid}}{a numeric vector} \item{\code{Uronic_acids}}{a numeric vector} \item{\code{pH}}{a numeric vector} \item{\code{Ash}}{a numeric vector} \item{\code{Alcalinity_of_ash}}{a numeric vector} \item{\code{Potassium}}{a numeric vector} \item{\code{Calcium}}{a numeric vector} \item{\code{Magnesium}}{a numeric vector} \item{\code{Phosphate}}{a numeric vector} \item{\code{Chloride}}{a numeric vector} \item{\code{Total_phenols}}{a numeric vector} \item{\code{Flavanoids}}{a numeric vector} \item{\code{Nonflavanoid_phenols}}{a numeric vector} \item{\code{Proanthocyanins}}{a numeric vector} \item{\code{Color_Intensity}}{a numeric vector} \item{\code{Hue}}{a numeric vector} \item{\code{OD280.OD315_of_diluted_wines}}{a numeric vector} \item{\code{OD280.OD315_of_flavanoids}}{a numeric vector} \item{\code{Glycerol}}{a numeric vector} \item{\code{X2.3.butanediol}}{a numeric vector} \item{\code{Total_nitrogen}}{a numeric vector} \item{\code{Proline}}{a numeric vector} \item{\code{Methanol}}{a numeric vector} \item{\code{Type}}{a factor with levels \code{Barbera}, \code{Barolo}, \code{Grignolino}} \item{\code{Year}}{a numeric vector} } } \details{ This data set is an expended version of the popular one from the UCI machine learning repository (http://archive.ics.uci.edu/ml/datasets/Wine). } \examples{ data(wine27) } \keyword{datasets}

e0199fb03566d406dd131363af98798813d7b3dc

eac21a885ac41794e6ef37f6abdf075e85897ed1

/day24.R

f88ab3e1c2b200e960b4fce77542afe348a75950

[]

no_license

d-sci/Advent-of-Code-2017

6872104df76b9b008ef0b15238d6934f410443ca

73c8f0d0ca27e4fad2b85215df136882f27c2afc

refs/heads/master

2021-09-02T14:13:38.027871

2018-01-03T04:48:33

116,091,965

0

null

UTF-8

R

false

1,589

r

day24.R

#Day 24 setwd("C:/Users/David.simons/Documents/advent of code") library(data.table) chunks <- rbindlist(lapply(strsplit(readLines("day24.txt"), "/"), function(x) as.list(as.numeric(x)))) chunks[, id := 1:nrow(chunks)] #part 1 ---- #recursively find strengths of all maximally long bridges strongestBridge <- function(startWith, used) { possible <- chunks[(V1==startWith | V2==startWith) & !(id %in% used)] if (nrow(possible)==0) return(chunks[id %in% used, sum(V1) + sum(V2)]) #base case; we've gone as far as possible strengths <- sapply(seq(nrow(possible)), function(i){ val <- if (possible[i, V1]==startWith) possible[i, V2] else possible[i, V1] strongestBridge(val, c(used, possible[i,id])) }) return(max(strengths)) } #recursion is slow in R but I'm satisfied print(strongestBridge(0, NULL)) #part 2 ---- #same as part 1, but need to save both strength and length longestBridge <- function(startWith, used) { possible <- chunks[(V1==startWith | V2==startWith) & !(id %in% used)] if (nrow(possible)==0) return(list(len=length(used), strength=chunks[id %in% used, sum(V1) + sum(V2)])) #base case; we've gone as far as possible bridges <- rbindlist(lapply(seq(nrow(possible)), function(i){ val <- if (possible[i, V1]==startWith) possible[i, V2] else possible[i, V1] longestBridge(val, c(used, possible[i,id])) })) return(list(len=bridges[,max(len)], strength=bridges[len==max(len), max(strength)])) } #recursion is slow in R but I'm satisfied print(longestBridge(0, NULL)$strength)

279fa596811688e316c5f3eea1e04fd9dd821e41

282acf6c53cceeda154ea8a8f7bd87ef80d0672e

/analyze.r

fa0cf77e7371ad116e8645b43eb56c06b0e76f84

[]

no_license

casras111/thesis

0f7729e92cdf30e6c0fd4dbce016f0126efa7142

995a062a18527205af926e6d490f926d396a08b1

refs/heads/master

2021-03-24T13:28:17.109233

2018-03-01T11:16:45

63,358,019

0

null

UTF-8

R

false

9,208

r

analyze.r

#analyze previous runs library(ggplot2) library(quantmod) library(reshape2) library(gridExtra) library(moments) #for skewness function startDate <- "1996-1-1" midDate <- "2005-12-31" midDate_1 <- "2006-1-1" endDate <- "2015-12-31" if (dir.exists("C:/Users/Claudiu/Dropbox")) { droppath <- "C:/Users/Claudiu/Dropbox" #Dell laptop } else { droppath <- "D:/Claudiu/Dropbox" #Home PC } #historical use for bootstrap #load(file="C:/Users/Claudiu/Dropbox/Thesis/Docs/Data/StocksList26092016Daily_5stocks.Rdata") #load(file="C:/Users/Claudiu/Dropbox/Thesis/Docs/Data/StocksList21092016Bootstrap10000_5stocks.Rdata") #load(file="C:/Users/Claudiu/Dropbox/Thesis/Docs/Data/StocksList16122016Monthly48stocks.Rdata") load(file=file.path(droppath,"Thesis/DataWork/StocksList_1_100_CPT_AVAR.Rdata")) temp <- StocksList load(file=file.path(droppath,"Thesis/DataWork/StocksList_101_200_CPT_AVAR.Rdata")) StocksList <- c(temp,StocksList) temp <- StocksList load(file=file.path(droppath,"Thesis/DataWork/StocksList_201_400_CPT_AVAR.Rdata")) StocksList <- c(temp,StocksList) temp <- StocksList load(file=file.path(droppath,"Thesis/DataWork/StocksList_401_600_CPT_AVAR.Rdata")) StocksList <- c(temp,StocksList) temp <- StocksList load(file=file.path(droppath,"Thesis/DataWork/StocksList_601_778_CPT_AVAR.Rdata")) StocksList <- c(temp,StocksList) stocknames <- names(StocksList) N <- dim(StocksList[[1]])[1] #number of periods, assumed consistent for all data structures #constant defining how many months of history to use, 120 for 10y monthly n_window <- round(N/2) calc_start <- N-n_window+1 bootcols <- grep("Boot",colnames(StocksList[[1]])) #vector with names of stocks that have completed runs completeStocks <- stocknames[!sapply(lapply(StocksList,last),anyNA)] completeIndx <- (1:length(StocksList))[!sapply(lapply(StocksList,last),anyNA)] #incomplete runs StocksList <- StocksList[completeIndx] StocksList <- StocksList[-417] #temp fix for TDS stock with stock split on 16/5/2005 for (i in 1:length(StocksList)) { #sum of squares of the error for variance risk predict RMSE1 <- with(StocksList[[i]][calc_start:N],sqrt(mean(((Price-CAPMPrice)/Price)^2))) RMSE2 <- with(StocksList[[i]][calc_start:N],sqrt(mean(((Price-VarPrice)/Price)^2))) RMSE3 <- with(StocksList[[i]][calc_start:N],sqrt(mean(((Price-SVarPrice)/Price)^2))) RMSE4 <- with(StocksList[[i]][calc_start:N],sqrt(mean(((Price-VAR5pctPrice)/Price)^2))) RMSE5 <- with(StocksList[[i]][calc_start:N],sqrt(mean(((Price-CPTPrice)/Price)^2))) RMSE6 <- with(StocksList[[i]][calc_start:N],sqrt(mean(((Price-AVARPrice)/Price)^2))) Err1 <- with(StocksList[[i]][calc_start:N],mean(((Price-VarPrice)/Price))) Err2 <- with(StocksList[[i]][calc_start:N],mean(((Price-SVarPrice)/Price))) cat(sprintf("%-4s RMSE: CAPM %.4f, Variance %.4f, Semivariance %.4f, VAR5pct %.4f, CPT %.4f, AVAR %.4f \n", stocknames[i],RMSE1,RMSE2,RMSE3,RMSE4,RMSE5,RMSE6)) StocksList[[i]]$RMSE_Var <- RMSE2 StocksList[[i]]$RMSE_SVar <- RMSE3 StocksList[[i]]$RMSE_VAR5Pct <- RMSE4 StocksList[[i]]$RMSE_CPT <- RMSE5 StocksList[[i]]$RMSE_AVAR <- RMSE6 StocksList[[i]]$Err_Var <- Err1 StocksList[[i]]$Err_SVar <- Err2 StocksList[[i]]$skewavg <- mean(StocksList[[i]]$Skew,na.rm=T) StocksList[[i]]$LogReturn <- ROC(StocksList[[i]]$Price,type="continuous",na.pad=F) #skewness (cumulative rolling) for n_window back history StocksList[[i]]$LogSkew <- rollapply(StocksList[[i]]$LogReturn,FUN=skewness, width=n_window,na.rm=T) } #descriptive statistics for 2 periods, average for all stocks for all months mean_func <- function(x,a,b) {return(mean(coredata(x[paste0(a,"/",b)]$Return),na.rm=T))} sd_func <- function(x,a,b) {return(sd(coredata(x[paste0(a,"/",b)]$Return),na.rm=T))} skew_func <- function(x,a,b) {return(skewness(coredata(x[paste0(a,"/",b)]$Return),na.rm=T))} StocksStat1 <- rbind(mean(sapply(StocksList, mean_func,startDate,midDate)), mean(sapply(StocksList, sd_func, startDate,midDate)), mean(sapply(StocksList, skew_func, startDate,midDate))) StocksStat2 <- rbind(mean(sapply(StocksList, mean_func,midDate_1,endDate)), mean(sapply(StocksList, sd_func, midDate_1,endDate)), mean(sapply(StocksList, skew_func,midDate_1,endDate))) StocksStat <- cbind(StocksStat1,StocksStat2) row.names(StocksStat) <- c("Mean","Std Dev","Skewness") colnames(StocksStat) <- c("1995-2005","2006-2015") print(StocksStat) #descriptive statistics - histogram for monthly cross-section of stocks returns statret <- sapply(StocksList,function(x) {return(coredata(x$Return))}) stat_df1 <- data.frame(Dates=index(StocksList[[1]]), Mean=apply(statret,1,mean)) stat_df1 <- stat_df1[-1,] #remove first NA row ggplot(stat_df1,aes(Mean))+geom_histogram(binwidth=0.03) #histograms for std dev and skewness stat_df2 <- data.frame(StdDev=apply(statret,2,sd,na.rm=T), Skewness=apply(statret,2,skewness,na.rm=T)) ggplot(stat_df2,aes(StdDev))+geom_histogram(binwidth=0.02) ggplot(stat_df2,aes(Skewness))+geom_histogram(binwidth=0.3) #extract vector of RMSE % RMSE_Var <- sapply(lapply(StocksList,last),function(x) {return(x$RMSE_Var)}) RMSE_SVar <- sapply(lapply(StocksList,last),function(x) {return(x$RMSE_SVar)}) RMSE_VAR5Pct <- sapply(lapply(StocksList,last),function(x) {return(x$RMSE_VAR5Pct)}) RMSE_CPT <- sapply(lapply(StocksList,last),function(x) {return(x$RMSE_CPT)}) RMSE_AVAR <- sapply(lapply(StocksList,last),function(x) {return(x$RMSE_AVAR)}) Err_Var <- sapply(lapply(StocksList,last),function(x) {return(x$Err_Var)}) Err_SVar <- sapply(lapply(StocksList,last),function(x) {return(x$Err_SVar)}) #descriptive statistics in % RMSE_summary <- 100*rbind(summary(RMSE_Var),summary(RMSE_SVar),summary(RMSE_VAR5Pct), summary(RMSE_CPT),summary(RMSE_AVAR)) row.names(RMSE_summary)<-c("Variance","Semivariance","VAR","CPT","AVAR") RMSE_summary plot.df <- data.frame(stock=names(RMSE_Var),Variance=RMSE_Var,Semivariance=RMSE_SVar, VaR=RMSE_VAR5Pct,CPT=RMSE_CPT,AVAR=RMSE_AVAR) plot.df <- melt(plot.df,id="stock",value.name="RMSE", variable.name="Risk_Measure") ggplot(plot.df,aes(Risk_Measure,RMSE))+geom_boxplot() #boxplot(RMSE_Var,RMSE_SVar,RMSE_VAR5Pct,names=c("Variance","Semivariance","VAR")) skew_last <- sapply(lapply(StocksList,last),function(x) {return(x$Skew)}) skew_first <- sapply(lapply(StocksList,function(x) {return(first(last(x,120)))}), function(x) {return(x$Skew)}) skew_avg <- 0.5*(skew_last+skew_first) skew_rollavg <- sapply(lapply(StocksList,last),function(x) {return(x$skewavg)}) skew_log_last <- sapply(lapply(StocksList,last),function(x) {return(x$LogSkew)}) skew_log_first <- sapply(lapply(StocksList,function(x) {return(first(last(x,120)))}), function(x) {return(x$LogSkew)}) skew_log_avg <- 0.5*(skew_log_last+skew_log_first) skew_df <- as.data.frame(cbind(skew_last,skew_first,skew_avg,skew_rollavg,skew_log_avg, RMSE_Var,RMSE_SVar,RMSE_CPT,RMSE_AVAR,Err_Var,Err_SVar)) skew_df$Var_best <- (skew_df$RMSE_Var < skew_df$RMSE_SVar) #Labeling G1 group with lower variance RMSE and G2 for semivariance skew_df$Predictor_Group <- ifelse((skew_df$RMSE_Var < skew_df$RMSE_SVar), "G1","G2") ggplot(skew_df,aes(Predictor_Group,skew_avg))+geom_boxplot() ggplot(skew_df,aes(skew_avg))+geom_density(aes(colour=Predictor_Group)) # ggtitle("Stock returns skewness in optimal RMSE groups") summary(skew_df$skew_avg) summary(skew_df[skew_df$Predictor_Group=="G1",]$skew_avg) summary(skew_df[skew_df$Predictor_Group=="G2",]$skew_avg) table(skew_df$Predictor_Group) skew_df$positive_skew <- ifelse(skew_df$skew_avg > 0.2,"Large","Small") with(skew_df,table(Predictor_Group,positive_skew)) skew_df$beta <- sapply(lapply(StocksList,last),function(x) {return(x$Beta)}) table(skew_df$Var_best,skew_df$beta>1.5) reg1 <- lm((skew_df$RMSE_Var>skew_df$RMSE_SVar) ~ skew_df$skew_first) summary(reg1) reg2 <- lm((skew_df$RMSE_Var>skew_df$RMSE_SVar) ~ skew_df$skew_last) summary(reg2) reg3 <- lm((skew_df$RMSE_Var>skew_df$RMSE_SVar) ~ skew_df$skew_rollavg) summary(reg3) #regression with average of first and last entry in 10y skew as a predictor reg4 <- lm((skew_df$RMSE_Var>skew_df$RMSE_SVar) ~ skew_df$skew_avg) summary(reg4) reg5 <- lm((skew_df$RMSE_Var>skew_df$RMSE_SVar) ~ skew_df$skew_log_avg) summary(reg5) logreg <- glm(!skew_df$Var_best ~ skew_df$skew_avg,family=binomial(link="logit")) summary(logreg) ggplot(skew_df,aes(Var_best,skew_avg))+geom_boxplot()+ ggtitle("RMSE Variance < RMSE Semivariance as a function of skewness") # boxplot(skew_df$skew_avg~skew_df$Var_best) # title("RMSE Variance < RMSE Semivariance as a function of skewness") #if data contains bootstrap columns show last period stats if (!is.null(dim(bootcols))) { for (i in seq_along(stocknames)) { print(stocknames[i]) print(StocksList[[i]][N,bootcols]) } } save(StocksList,file="../DataWork/StocksList_after_Analyze.Rdata")

3597803ba44bb4c1d0a0be5d5b1885fd75226047

7be028e961329bd28e739e7004e1f42b68181d0d

/R/stacf.R

4a216b4d0a96003426c6eadcb53155ee32d3e498

[ "MIT" ]

permissive

fcheysson/starma

a6293d9ac1ced9743d1e456c444ba16db2c36263

2e08ba5e122dda721d387e4aead8bf3304140e8a

refs/heads/main

2023-06-14T21:52:21.318306

2021-07-12T09:14:36

385,189,347

2

0

null

UTF-8

R

false

1,116

r

stacf.R

# The 'stacf' function is defined as, per (Pfeifer & Stuart, 1980), the # following expression: # acf(l,0,s) = cov(l,0,s) / sqrt( cov(l,l,0) * cov(0,0,0) ) stacf <- function(data, wlist, tlag.max=NULL, plot=TRUE, use.ggplot=TRUE) { # If only the weights matrix of first order is specified if (is.matrix(wlist)) wlist <- list(diag(dim(wlist)[1]), wlist) # If no tlag.max is specified if (is.null(tlag.max)) tlag.max <- floor(10 * log10(nrow(data))) # Call C++ function for optimized computation if (is.data.frame(data)) out <- stacfCPP(as.matrix(data), wlist, tlag.max) else out <- stacfCPP(data, wlist, tlag.max) colnames(out) <- paste("slag", 0:(length(wlist) - 1)) rownames(out) <- paste("tlag", 1:tlag.max) # Plot stacf (and still returns the stacf matrix for efficient use) if (plot) { stplot(out, 2 / sqrt(nrow(data) * ncol(data)), match.call(), ggplot=use.ggplot) invisible(out) } else out } # A faire : # - Essayer de separer les fichiers stcov.cpp et stacf.cpp tout en gardant # la coherence du stacfCPP (qui utilise la fonction stcovCPP definie dans # stcov.cpp).

5610d56094120d95401a7ded30f4d49e9b01da23

1f2ed7e0778776371702499954ab1b11d3ad3a4c

/man/oly12.Rd

8557a9a4c26c879b0eb34223181d43bb7882a7f4

[]

no_license

cran/VGAMdata

1e3b653b5a9d4921535fb7d2e6d4191aa2d9201a

fbbb0beb0bf79fff712d1b994cf51de5cb3b176b

refs/heads/master

2023-04-07T05:39:02.437835

2023-01-11T19:20:02

17,694,035

0

null

UTF-8

R

false

2,481

rd

oly12.Rd

\name{oly12} \alias{oly12} \docType{data} \title{ 2012 Summer Olympics: Individuals Data } \description{ Individual data for the Summer 2012 Olympic Games. } \usage{data(oly12)} \format{ A data frame with 10384 observations on the following 14 variables. \describe{ \item{\code{Name}}{The individual competitor's name. } \item{\code{Country}}{Country. } \item{\code{Age}}{A numeric vector, age in years. } \item{\code{Height}}{A numeric vector, height in m. } \item{\code{Weight}}{A numeric vector, weight in kg. } \item{\code{Sex}}{A factor with levels \code{F} and \code{M}. } \item{\code{DOB}}{A Date, date of birth. } \item{\code{PlaceOB}}{Place of birth. } \item{\code{Gold}}{Numeric vector, number of such medals won. } \item{\code{Silver}}{ Similar to \code{Gold}. } \item{\code{Bronze}}{ Similar to \code{Gold}. } \item{\code{Total}}{A numeric vector, total number of medals. } \item{\code{Sport}}{A factor with levels \code{Archery}, \code{Athletics}, \code{Athletics}, \code{Triathlon}, \code{Badminton}, etc. } \item{\code{Event}}{The sporting event. } } } \details{ This data set represents a very small modification of a \code{.csv} spreadsheet from the source below. Height has been converted to meters, and date of birth is of a \code{"Date"} class (see \code{\link[base]{as.Date}}). A few non-ASCII characters have been replaced by some ASCII sequence (yet to be fixed up properly). % yettodo: above. Some competitors share the same name. Some errors in the data are likely to exist. %% ~~ If necessary, more details than the __description__ above ~~ } \source{ Downloaded from \code{http://www.guardian.co.uk/sport/series/london-2012-olympics-data} in 2013-03; more recently it has changed to \url{https://www.theguardian.com/sport/series/london-2012-olympics-data}. %% ~~ reference to a publication or URL from which the data were obtained ~~ } %\references{ %% ~~ possibly secondary sources and usages ~~ %} \examples{ data(oly12) mtab <- with(oly12, table(Country, Gold)) (mtab <- head(sort(mtab[, "1"] + 2 * mtab[, "2"], decreasing = TRUE), 10)) \dontrun{ barplot(mtab, col = "gold", cex.names = 0.8, names = abbreviate(names(mtab)), beside = TRUE, main = "2012 Summer Olympic Final Gold Medal Count", ylab = "Gold medal count", las = 1, sub = "Top 10 countries") } } \keyword{datasets}

067d9f0f5bba8f0aeff74a5a7b3c400e903c8b8b

88311cfdacc0ada10cfb6e05c35411d3965dc582

/solution/2-similarity/part2a/visualise.R

972975d94f25000a0a810d4926ff9fd2d81dc2c1

[]

no_license

g-eorge/CCPDS-02

7aab360f3c77c7c4a77bc047a7d18ee8c6dc1b95

6e3095395723f7d679349595d6ed8f098504a1b8

refs/heads/master

2021-05-27T10:18:52.830892

2014-07-01T01:47:09

19,005,113

0

null

UTF-8

R

false

6,579

r

visualise.R

#! /usr/bin/env Rscript # Dependencies # install.packages("ggplot2") # install.packages("reshape") # Load packages library(ggplot2) library(reshape) # The providers that are least like the others provider_ids <- c('50195', '390180', '50441') # Plot colours for the providers scale_colours <- c() scale_colours[[provider_ids[1]]] <- '#1AA794' scale_colours[[provider_ids[2]]] <- '#F5435D' scale_colours[[provider_ids[3]]] <- '#A532FF' # Plot shapes for the procedures scale_shapes <- c() scale_shapes[[provider_ids[1]]] <- 15 scale_shapes[[provider_ids[2]]] <- 16 scale_shapes[[provider_ids[3]]] <- 17 numcols = 392 # The number of columns the vectorizer produced - 1 cls <- c("character", rep("numeric",numcols)) # Read in the data file df <- read.csv("vector_providers.txt", header=F, stringsAsFactors=F, colClasses=cls, row.names=1, sep="\t", na.strings="NA") ## Plot number of procedures types each provider carries out (DRG, APC, Total) counts <- df[,1:2] # Subset the procedure type count columns colnames(counts) <- c("drg_count", "apc_count") counts$total_count <- counts$drg_count + counts$apc_count # Compute a total column # Use a Z scale for easier comparison scaled_counts <- data.frame(scale(counts, center=T, scale=T)) # Pick out the providers we are interested in compare_counts <- scaled_counts[provider_ids,] compare_counts$id <- provider_ids # Build the plot with a box plot for comparison p <- ggplot(data = melt(scaled_counts), aes(x=variable, y=value)) p + geom_boxplot(alpha=0.4, size=0.5, color="grey") + geom_point(aes(colour=provider_ids[1], shape=provider_ids[1]), data=melt(compare_counts[1,], id.vars='id')) + geom_point(aes(colour=provider_ids[2], shape=provider_ids[2]), data=melt(compare_counts[2,], id.vars='id')) + geom_point(aes(colour=provider_ids[3], shape=provider_ids[3]), data=melt(compare_counts[3,], id.vars='id')) + scale_colour_manual(name="Provider", values=scale_colours) + scale_shape_manual(name="Provider", values=scale_shapes) + xlab("procedure type counts") + ylab("z-score") # Output the plot to a file ggsave(file = "exploring/plots/procedure_type_counts.png", width = 11, height = 8, dpi = 300) ## Plot the number of services each provider carries out for each procedure counts <- df[,seq(3,ncol(df),3)] # Subset the service count column for each procedure # Use a Z scale for easier comparison scaled_counts <- data.frame(scale(counts, center=T, scale=T)) # Pick out the providers we are interested in compare_counts <- scaled_counts[provider_ids,] compare_counts$id <- provider_ids # Build the plot p <- ggplot(data = melt(scaled_counts), aes(x=variable, y=value)) p + geom_point(aes(colour=provider_ids[1], shape=provider_ids[1]), data=melt(compare_counts[1,], id.vars='id')) + geom_point(aes(colour=provider_ids[2], shape=provider_ids[2]), data=melt(compare_counts[2,], id.vars='id')) + geom_point(aes(colour=provider_ids[3], shape=provider_ids[3]), data=melt(compare_counts[3,], id.vars='id')) + scale_colour_manual(name="Provider", values=scale_colours) + scale_shape_manual(name="Provider", values=scale_shapes) + xlab("procedure service counts") + ylab("z-score") + theme(axis.text.x = element_blank()) # Output the plot to a file ggsave(file = "exploring/plots/service_counts.png", width = 11, height = 8, dpi = 300) ## Plot the charges for procedures each provider carries out counts <- df[,c(seq(4,ncol(df),3))] # Subset the charges columns for each procedure # Use a Z scale for easier comparison scaled_counts <- data.frame(scale(counts, center=T, scale=T)) # Pick out the providers we are interested in compare_counts <- scaled_counts[provider_ids,] compare_counts$id <- provider_ids # Build the plot p <- ggplot(data = melt(scaled_counts), aes(x=variable, y=value)) p + geom_point(aes(colour=provider_ids[1], shape=provider_ids[1]), data=melt(compare_counts[1,], id.vars='id')) + geom_point(aes(colour=provider_ids[2], shape=provider_ids[2]), data=melt(compare_counts[2,], id.vars='id')) + geom_point(aes(colour=provider_ids[3], shape=provider_ids[3]), data=melt(compare_counts[3,], id.vars='id')) + scale_colour_manual(name="Provider", values=scale_colours) + scale_shape_manual(name="Provider", values=scale_shapes) + xlab("procedure charges") + ylab("z-score") + theme(axis.text.x = element_blank()) # Output the plot to a file ggsave(file = "exploring/plots/charges.png", width = 11, height = 8, dpi = 300) ## Plot the payments for procedures each provider carries out counts <- df[,c(seq(5,ncol(df),3))] # Subset the payments columns for each procedure # Use a Z scale for easier comparison scaled_counts <- data.frame(scale(counts, center=T, scale=T)) # Pick out the providers we are interested in compare_counts <- scaled_counts[provider_ids,] compare_counts$id <- provider_ids # Build the plot p <- ggplot(data = melt(scaled_counts), aes(x=variable, y=value)) p + geom_point(aes(colour=provider_ids[1], shape=provider_ids[1]), data=melt(compare_counts[1,], id.vars='id')) + geom_point(aes(colour=provider_ids[2], shape=provider_ids[2]), data=melt(compare_counts[2,], id.vars='id')) + geom_point(aes(colour=provider_ids[3], shape=provider_ids[3]), data=melt(compare_counts[3,], id.vars='id')) + scale_colour_manual(name="Provider", values=scale_colours) + scale_shape_manual(name="Provider", values=scale_shapes) + xlab("procedure payments") + ylab("z-score") + theme(axis.text.x = element_blank()) # Output the plot to a file ggsave(file = "exploring/plots/payments.png", width = 11, height = 8, dpi = 300) ## Plot everything in one plot scaled_all <- data.frame(scale(df, center=T, scale=T)) # Pick out the providers we are interested in compare_counts <- scaled_all[provider_ids,] compare_counts$id <- provider_ids # Build the plot p <- ggplot(data = melt(scaled_all), aes(x=variable, y=value)) p + geom_point(color='#202020', size=1, alpha=0.2) + geom_point(aes(colour=provider_ids[1], shape=provider_ids[1]), data=melt(compare_counts[1,], id.vars='id')) + geom_point(aes(colour=provider_ids[2], shape=provider_ids[2]), data=melt(compare_counts[2,], id.vars='id')) + geom_point(aes(colour=provider_ids[3], shape=provider_ids[3]), data=melt(compare_counts[3,], id.vars='id')) + scale_colour_manual(name="Provider", values=scale_colours) + scale_shape_manual(name="Provider", values=scale_shapes) + xlab("procedure counts, service counts, charges and payments") + ylab("z-score") + theme(axis.text.x = element_blank()) # Output the plot to a file ggsave(file = "exploring/plots/all.png", width = 11, height = 8, dpi = 300)

a2efd9abac3869e0aa56c7d99237242abb717e80

93a3ca0d2105970d92aba8ae04a7638d6938101c

/man/gt_get_data.Rd

14cff02b92e659ccca5435f0ef7426c7c5d623b3

[ "MIT" ]

permissive

geysertimes/geysertimes-r-package

6482a9b72299497d0ad154ec0713e404adae0b63

933b2465337555092f06f7cd8b12413d47bfaa89

refs/heads/master

2022-06-18T04:48:53.824869

2022-06-12T02:16:07

169,121,861

2

4

null

2020-07-26T18:02:03

2019-02-04T17:53:23

R

UTF-8

R

false

1,827

rd

gt_get_data.Rd

\name{gt_get_data} \alias{gt_get_data} \title{ Download GeyserTimes Data } \description{ Downloads the data from geysertimes.org. Reads the data and creates a tibble object in `dest_folder`. } \usage{ gt_get_data(dest_folder = file.path(tempdir(), "geysertimes"), overwrite = FALSE, quiet = FALSE, version = lubridate::today()) } \arguments{ \item{dest_folder}{ the location where the binary tibble object should be written. The default is under the current R session's temp directory which will disappear when the session ends. } \item{overwrite}{ a logical value, if\code{FALSE}, the data will not be downloaded again if copy of the data, with \code{version}, already exists in \code{dest_folder}. } \item{quiet}{ a logical value, if \code{TRUE}, no messages are displayed. } \item{version}{ a character string giving the version of the data to download. This should a date in the form \code{yyyy-mm-dd}. Typically, only the version with today's date is available. } } \details{ The data is downloaded from the GeyserTimes archive web site \url{https://geysertimes.org/archive/} to the \code{tempdir()} directory. The data is then read with \code{readr::read_tsv} with appropriate column types. The resulting \code{tibble} object is then saved as an binary (\code{.rds}) in \code{dest_folder}. } \value{ a character string giving the full path to the directory where the GeyserTimes data was stored. } \author{ Stephen Kaluzny <spkaluzny@gmail.com>. } \note{ Users are encouraged to set \code{dest_folder} to \code{gt_path()} to save a persistent copy of the data. } \seealso{ gt_load_eruptions, gt_load_geysers. } \examples{ \donttest{ dpath0 <- gt_get_data() # data saved under tempdir() dpath1 <- gt_get_data(dest=gt_path()) # data saved under gt_path() gt_cleanup_data(gt_version()) } } \keyword{geysertimes}

932436766d396791fa5efa04775006eb3f7bc586

771706de90263db2375687df55677276af8dcb57

/Assignment 8.R

daca64437110a38ee96f3d49b2a45f02c8515cde

[]

no_license

dduwill/Product-Review

f65fcdf62bc7ecdb5f078a8e509e0093e90e6915

91db629ae04414ea6ecab3ca62e82c6c77a7bb2f

refs/heads/master

2020-04-15T04:37:35.378919

2016-11-15T22:02:57

73,781,543

0

null

UTF-8

R

false

4,255

r

Assignment 8.R

library(rjson) library(dplyr) require(magrittr) library(quanteda) library(stm) library(tm) library(NLP) library(openNLP) library(ggplot2) library(ggdendro) library(cluster) library(fpc) #read json file setwd("C:/Users/weiyi/Desktop/R/Assignment 8") path <- "Automotive_5.json" data <- fromJSON(sprintf("[%s]", paste(readLines(path),collapse=","))) review <-sapply(data, function(x) x[[5]]) #Generate DFM help(corpus) corpus <- corpus(review) corpus <- toLower(corpus, keepAcronyms = FALSE) cleancorpus <- tokenize(corpus, removeNumbers=TRUE, removePunct = TRUE, removeSeparators=TRUE, removeTwitter=FALSE, verbose=TRUE) dfm <- dfm(cleancorpus, toLower = TRUE, ignoredFeatures =stopwords("SMART"), verbose=TRUE, stem=TRUE) # Reviewing top features topfeatures(dfm, 50) # displays 50 features #Cleaning corpus stop_words <- stopwords("SMART") ## additional junk words showing up in the data stop_words <- c(stop_words, "just", "get", "will", "can", "also", "much","need") stop_words <- tolower(stop_words) cleancorpus <- gsub("'", "", cleancorpus) # remove apostrophes cleancorpus <- gsub("[[:punct:]]", " ", cleancorpus) # replace punctuation with space cleancorpus <- gsub("[[:cntrl:]]", " ", cleancorpus) # replace control characters with space cleancorpus <- gsub("^[[:space:]]+", "", cleancorpus) # remove whitespace at beginning of documents cleancorpus <- gsub("[[:space:]]+$", "", cleancorpus) # remove whitespace at end of documents cleancorpus <- gsub("[^a-zA-Z -]", " ", cleancorpus) # allows only letters cleancorpus <- tolower(cleancorpus) # force to lowercase ## get rid of blank docs cleancorpus <- cleancorpus[cleancorpus != ""] # tokenize on space and output as a list: doc.list <- strsplit(cleancorpus, "[[:space:]]+") # compute the table of terms: term.table <- table(unlist(doc.list)) term.table <- sort(term.table, decreasing = TRUE) # remove terms that are stop words or occur fewer than 5 times: del <- names(term.table) %in% stop_words | term.table < 5 term.table <- term.table[!del] term.table <- term.table[names(term.table) != ""] vocab <- names(term.table) # now put the documents into the format required by the lda package: get.terms <- function(x) { index <- match(x, vocab) index <- index[!is.na(index)] rbind(as.integer(index - 1), as.integer(rep(1, length(index)))) } documents <- lapply(doc.list, get.terms) # Compute some statistics related to the data set: D <- length(documents) # number of documents (1) W <- length(vocab) # number of terms in the vocab (8941L) doc.length <- sapply(documents, function(x) sum(x[2, ])) # number of tokens per document [46, 27, 106 ...] N <- sum(doc.length) # total number of tokens in the data (863558L) term.frequency <- as.integer(term.table) # MCMC and model tuning parameters: K <- 10 G <- 3000 alpha <- 0.02 eta <- 0.02 # Fit the model: library(lda) set.seed(357) t1 <- Sys.time() fit <- lda.collapsed.gibbs.sampler(documents = documents, K = K, vocab = vocab, num.iterations = G, alpha = alpha, eta = eta, initial = NULL, burnin = 0, compute.log.likelihood = TRUE) t2 <- Sys.time() ## display runtime t2 - t1 theta <- t(apply(fit$document_sums + alpha, 2, function(x) x/sum(x))) phi <- t(apply(t(fit$topics) + eta, 2, function(x) x/sum(x))) reviews.LDA <- list(phi = phi, theta = theta, doc.length = doc.length, vocab = vocab, term.frequency = term.frequency) library(LDAvis) library(servr) # create the JSON object to feed the visualization: json <- createJSON(phi = reviews.LDA$phi, theta = reviews.LDA$theta, doc.length = reviews.LDA$doc.length, vocab = reviews.LDA$vocab, term.frequency = reviews.LDA$term.frequency) serVis(json, out.dir = 'vis', open.browser = TRUE)

2ea23c5ba92494e9669eae718b8d556be44b30aa

3bb85139690fe4f6c4575f1ca12aac3cccc758ea

/cachematrix.R

b8e67e68e3304032fb146fe193b46b90900dbb8f

[]

no_license

IZLID-LSSO/ProgrammingAssignment2

ed3a9fa5c5fc5cf846d869ae832be172702e108c

428b985932e540fe4bc923a7e12f0f20ed91f53b

refs/heads/master

2020-05-30T18:52:26.790726

2019-06-03T01:53:33

189,909,119

0

null

2019-06-03T00:25:06

null

UTF-8

R

false

928

r

cachematrix.R

## Two functions that compute and cashe the inverse of a matrix. ## This function creates a "matrix" that can store (cache) its calculated inverse. makeCacheMatrix <- function(x = matrix()) { inv <- NULL set <- function(y) { x <<- y inv <<- NULL } get <- function() x setInverse <- function(solveMatrix) inv <<- solveMatrix getInverse <- function() inv list( set = set, get = get, setInverse = setInverse, getInverse = getInverse ) } ## This function computes the inverse of the above function"getInverse", additionally ##if the value is not computed, a message is displayed while the cached data is returned. cacheSolve <- function(x, ...) { ## Return a matrix that is the inverse of 'x' inv <- x$getInverse() if(!is.null (inv)) { message("getting cached data") return(inv) } data <- x$get() inv <- solve(data) x$setInverse(inv) inv } ## End of Assignment

f29c2dd28757f3762f9451c10963aa33c68c9867

6a0a368b7509afbc729304fc0073b0b940b43e8f

/cachematrix.R

2e08ccea84129d89fccbc8f0d461fb5a2529468b

[]

no_license

ong625/ProgrammingAssignment2

d90bc676b9425df7181f7292b92b219219349f1f

f53cc747c49ea766affa975dc7358cb0beb0fd86

refs/heads/master

2022-11-27T07:58:30.358493

2020-08-03T04:04:48

284,584,988

0

null

2020-08-03T02:31:45

2020-08-03T02:31:44

null

UTF-8

R

false

665

r

cachematrix.R

## The code helps to invert matrices number input makeCacheMatrix <- function(x = matrix()) { r <- NULL set <- function(y){ x <<- y r<<- NULL } get <- function()x setInverse <- function(inverse) r <<- inverse getInverse <- function() r list(set = set, get = get, setInverse = setInverse, getInverse = getInverse) } ## Get the mean from the cache tp calculate the final mean cacheSolve <- function(x, ...) { ## Return a matrix that is the inverse of 'x' r<- x$getInverse() if(!is.null(r)){ message("getting cached data") return(r) } mat <- x$get() r <- solve(mat,...) x$setInverse(r) j }

b9eec90cd116558e71c13bf32b0a83f7b42b91d6

df5885ac73301c7050b373d1d3d9f89991e9dbcc

/Figure-11.R

0f800a98b77a89475d90c65db172107c977d3287

[]

no_license

Fabbiologia/BluePaper-10_Supplementary_informations

2af3103ea9b6d61becbe7db03b4939d1263169ef

e52a5f985c5df74297c09e937683eab68d2c9d9f

refs/heads/master

2020-12-15T03:08:23.841603

2020-04-14T16:41:36

234,975,349

0

null

UTF-8

R

false

3,897

r

Figure-11.R

library(tidyverse) library(patchwork) library(RCurl) ### Data loading and wrangling ---- toplot <- read.csv(text = getURL('https://raw.githubusercontent.com/Fabbiologia/BluePaper-10_Supplementary_informations/master/data/HabitatProtectedDataset.csv')) %>% filter(Cat %in% c('Total','mpa_all', 'mpa_all_m', 'mpa_all_nt')) %>% pivot_wider(names_from = Cat, values_from = Pixel_count) %>% replace(is.na(.), 0) %>% mutate_at(vars(mpa_all:mpa_all_nt), list(~(./Total)*100)) %>% filter(Total > 0) %>% #This filter out absent habitats group_by(Habitat) %>% mutate(mean_all = mean(mpa_all), median_all = median(mpa_all), mean_m = mean(mpa_all_m), median_m = median(mpa_all_m), mean_nt = mean(mpa_all_nt), median_nt = median(mpa_all_nt)) %>% ungroup() %>% mutate(Habitat = factor(.$Habitat, labels=c("Cold Corals", "Coral Reefs", "Estuaries", "Kelp", "Mangroves", "Ridges", "Saltmarshes", "Seagrasses", "Seamounts and Guyots", "Shelf and Canyons", "Trenches", "Hydrothermal vents"))) toplot$Habitat <- factor(toplot$Habitat, levels = c( "Estuaries", "Mangroves", "Saltmarshes", "Seagrasses", "Coral Reefs", "Kelp", "Shelf and Canyons", "Cold Corals", "Seamounts and Guyots", "Trenches", "Hydrothermal vents", "Ridges" )) toplot ### Data plotting ------- # day/night colours night_colour <- c("aquamarine") day_colour <- c("darkblue") source("GeneratedGradientData.R") # generate data for a one-hour sunrise gradient sunrise_pd <- GenerateGradientData(start_hour = 0, stop_hour = 13, start_colour = night_colour, stop_colour = day_colour, x_resolution = 1000) p1 <- ggplot(toplot, aes(x = Habitat, y = mpa_all, col=as.integer(Habitat), group=Habitat)) + geom_rect(xmin=0, xmax=13, ymin=-Inf, ymax=Inf, fill=day_colour)+ # gradient backgrounds for sunrise and sunset geom_rect(data = sunrise_pd, mapping = aes(xmax = xmax, xmin = xmin, ymax = ymax, ymin = ymin), fill = sunrise_pd$grad_colours, inherit.aes = FALSE) + geom_jitter(size = 2, alpha = 0.5, width = 0.2, col="black", fill="white")+ geom_hline(yintercept = 30, col="white", size=0.9)+ geom_segment(aes(x = Habitat, xend = Habitat, y = mean_all, yend = median_all), size = 0.1, col="white")+ geom_point(aes(y= mean_all), size = 3, pch=21, fill="blue") + geom_point(aes(y= median_all), size = 3, pch=21, fill="red") + labs(x = NULL, y = "% area within MPA in the EEZ") + ylim(0,100)+ theme(legend.position = "none", panel.background = element_blank(), panel.grid = element_blank(), axis.text.x = element_text(angle=90)) p1 ### uncomment to save ------ ggsave('figs/Figure_11.pdf') ggsave('figs/Figure_11.tiff') ggsave('figs/Figure_11.png', dpi = 300) # END OF SCRIPT ------

5835f8915957577b0b75aaace7a25a178f36f0e5

2a1a58c97642e4b4e568a18ad76dc6fbf246a125

/R/impreciseImputation.R

25163c12c9f837cf61815b59972ca2bbc17f5204

[]

no_license

cran/impimp

1657934ffa51d9e8afd65d3e5f9d1c961fc863f0

976176f80b808f5b7c0e88830fd49c5c4fa7295f

refs/heads/master

2020-03-30T08:04:13.559483

2019-02-03T17:43:16

150,986,631

0

null

UTF-8

R

false

13,690

r

impreciseImputation.R

# Copyright (C) 2018 Paul Fink, Eva Endres # # This file is part of impimp. # # imptree 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 of the License, or # (at your option) any later version. # # imptree is distributed in the hope that it 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 imptree. If not, see <https://www.gnu.org/licenses/>. #' @title Imprecise Imputation for Statistical Matching #' #' @description Impute a data frame imprecisely #' #' @param recipient a data.frame acting as recipient; see details. #' @param donor a data.frame acting as donor; see details. #' @param method 1-character string of the desired imputation method. #' The following values are possible, see details for an explanantion: #' \code{"variable_wise"} (default), \code{"case_wise"} and #' \code{"domain"}. #' @param matchvars a character vector containing the variable names #' to be used as matching variables. If \code{NULL} (default) all #' variables, present in both \code{donor} and \code{recipient} are #' used as matching variables. #' @param vardomains a named list containing the possible values of #' all variable in \code{donor} that are not present in #' \code{recipient}.\cr #' If set to \code{NULL} (default) the list is generated by first #' coercing all those variables to type \code{\link[base]{factor}} #' and then storing their levels. #' #' @details #' As in the context of statistical matching the data.frames #' \code{recipient} and \code{donor} are assumed to contain an #' overlapping set of variables. #' #' The missing values in \code{recipient} are subsituted with #' observed values in \code{donor} for approaches based on donation #' classes and otherwise with the set of all possible values for #' the variable in question. #' #' For \code{method = "domain"} a missing value of a variable in #' \code{recipient} is imputed by the set of all possible values #' of that variable. #' #' The other methods are based on donation classes which are formed #' based on the matching variables whose names are provided by #' \code{matchvars}. They need to be present in both \code{recipient} #' and \code{donor}: #' For \code{method = "variable_wise"} a missing value of a variable #' in \code{recipient} is imputed by the set of all observed values #' of that variable in \code{donor}. #' For \code{method = "case_wise"} the variables only present in #' \code{donor} are represented as tuples. A missing tuple in #' \code{recipient} is then imputed by the set of all observed #' tuples in \code{donor}. #' #' @section Reserved characters: #' The variable names and observations in \code{recipient} and #' \code{donor} must not contain characters that are reserved for #' internal purpose. #' The actual characters that are internally used are stored in the #' options \code{options("impimp.obssep")} and #' \code{options("impimp.varssep")}. The former is used to separate #' the values of a set-valued observation, while the other is used #' for a concise tupel representation. #' #' @note #' This method does not require that all variables in \code{recipient} #' and \code{donor} are \code{\link[base]{factor}} variables, however, #' the imputation methods apply coercion to factor, so purely #' numerical variables will be treated as factors eventually. #' It does assume (and test for it) that there are no missing #' values present in the matching variables. #' #' @return #' The data.frame resulting in an imprecise imputation #' of \code{donor} into \code{recipient}. #' It is also of class \code{"impimp"} and stores the imputation #' method in its attribute \code{"impmethod"}, the names of the #' variables of the resulting object containing imputed values #' in the attribute \code{"imputedvarnames"}, as well as the #' list of (guessed) levels of each underlying variable in #' \code{"varlevels"}. #' #' @keywords robust datagen #' #' @seealso for the estimation of probabilities \code{\link{impest}} #' and \code{\link{impestcond}}; \code{\link{rbindimpimp}} for #' joining two \code{impimp} objects #' #' @references Endres, E., Fink, P. and Augustin, T. (2018), #' Imprecise Imputation: A Nonparametric Micro Approach Reflecting #' the Natural Uncertainty of Statistical Matching with Categorical #' Data, \emph{Department of Statistics (LMU Munich): Technical Reports}, #' No. 214. URL \url{https://epub.ub.uni-muenchen.de/42423/}. #' #' @examples #' A <- data.frame(x1 = c(1,0), x2 = c(0,0), #' y1 = c(1,0), y2 = c(2,2)) #' B <- data.frame(x1 = c(1,1,0), x2 = c(0,0,0), #' z1 = c(0,1,1), z2 = c(0,1,2)) #' impimp(A, B, method = "variable_wise") #' #' ## Specifically setting the possible levels of 'z1' #' impimp(A, B, method = "domain", vardomains = list(z1 = c(0:5))) #' #' @importFrom stats setNames #' @export impimp <- function(recipient, donor, method = c("variable_wise", "case_wise", "domain"), matchvars = NULL, vardomains = NULL) { # Check the environment varsep <- getOption("impimp.varsep", ",") obssep <- getOption("impimp.obssep", "|") if(varsep == obssep) { stop(gettextf("option values %s and %s need to be different characters", sQuote("impimp.varsep"), sQuote("impimp.obssep"), domain = "R-impimp")) } # temporarily set stringsAsFactors to FALSE # and reset it to old value after exiting oldsAF <- options(stringsAsFactors = FALSE) on.exit(options(oldsAF)) # function argument matching method <- match.arg(method) # extract common variables cnames <- intersect(names(donor), names(recipient)) # Test if there is an non-empty intersection in the names if(!length(cnames)) { stop(gettextf("%s and %s do not contain any variables present in both", sQuote("recipient"), sQuote("donor"), domain = "R-impimp")) } if(is.null(matchvars)) { matchvars <- cnames } else if(is.character(matchvars)){ if(any(nm <- (match(matchvars, cnames, nomatch = 0L) == 0L))) { stop(gettextf("%s contains variable(s) which are not present in both %s and %s: %s", sQuote("matchvars"), sQuote("donor"), sQuote("recipient"), paste(sapply(matchvars[nm], dQuote), collapse = ", "), domain = "R-impimp")) } } else { stop(gettextf("%s must be NULL or a character vector", sQuote("matchvars"), domain = "R-impimp")) } # Test if the matching variables do not contain NA lapply(matchvars, function(x) { if(anyNA(recipient[ ,x])) { stop(gettextf("missing values in variable %s in %s", sQuote(x), sQuote("recipient"), domain = "R-impimp")) } if(anyNA(donor[ ,x])) { stop(gettextf("missing values in variable %s in %s", sQuote(x), sQuote("donor"), domain = "R-impimp")) } }) rnames <- setdiff(names(recipient), names(donor)) dnames <- setdiff(names(donor), names(recipient)) allnames <- c(rnames, cnames, dnames) # check for special package-reserved characters in variable names if(length(grep(varsep, allnames, fixed = TRUE))) { stop(gettextf(c("some variable names contain the character %s, reserved for internal purpose.", "\nRename the variable(s) or change the internal character by setting the option %s"), c(sQuote(varsep), sQuote("impimp.varsep")), domain = "R-impimp")) } # Do nothing if there are no variables in donor that aren't in recipient if(!length(dnames)) { warning(gettextf(c("no variable present only in %s and not in %s; ", "returning %s unmodified"), c(sQuote("donor"), sQuote("recipient")), sQuote("recipient"), domain = "R-impimp")) return(recipient) } # Construct the possible values for the variables from the # (partially) supplied argument 'vardomains' if(!is.null(vardomains)){ # partially match available levels lvls <- vardomains[allnames] } else { # else generate a list of empty ones lvls <- vector(length = length(allnames), mode = "list") } # generate the potentially missing levels # by using the factor based approach lvls <- lapply(stats::setNames(nm = allnames), function(varname) { varlevels <- lvls[[varname]] # if variable is not present in one df, # then NULL is returned for that df gvarlevels <- gather_levels(c(as.character(recipient[[varname]]), as.character(donor[[varname]]))) if(is.null(varlevels)) { varlevels <- gvarlevels } else if(length(lvldiff <- setdiff(gvarlevels, varlevels))) { varlevels <- c(lvldiff, varlevels) } varlevels }) # check for special package-reserved characters in variable values lapply(names(lvls), function(x) { if(length(grep(varsep, lvls[[x]], fixed = TRUE))) { stop(gettextf(c("variable %s contains the character %s, reserved for internal purpose.", "\nChange the internal character by setting the option %s"), c(sQuote(x), sQuote("impimp.varsep")), sQuote(varsep), domain = "R-impimp")) } if(length(grep(obssep, lvls[[x]], fixed = TRUE))) { stop(gettextf(c("variable %s contains the character %s, reserved for internal purpose.", "\nChange the internal character by setting the option %s"), c(sQuote(x), sQuote("impimp.obssep")), sQuote(obssep), domain = "R-impimp")) } }) dlvls <- lvls[dnames] # impute the domain for every missing cell if(method == "domain") { # add columns with NA to the data impRecipient <- cbind(recipient, matrix(NA, ncol = length(dnames), nrow = nrow(recipient), dimnames = list(c(), dnames))) # impute all the levels impRecipient[, dnames] <- imputation_values(dlvls, dnames) } else { # impute cell-wise within donor classes # create new variable to index the x structure # This is for donation classes recipient$cfactor <- factor(apply(recipient[, matchvars], MARGIN = 1, FUN = paste, collapse =",")) donor$cfactor <- factor(apply(donor[, matchvars], MARGIN = 1, FUN = paste, collapse =",")) ## transform into tuple notation for method == case_wise if(method == "case_wise") { donor <- cbind(donor, collapse_variables(donor, dnames)) dlvls <- collapse_variables( do.call("expand.grid", dlvls), dnames) dnames <- names(dlvls) } # extract level combinations of common variables # which are present in recipient clvls <- levels(recipient$cfactor) # initialize the resulting data.frame with NAs # in the variable sto be imputed impRecipient <- cbind(recipient, matrix(NA, ncol = length(dnames), nrow = nrow(recipient), dimnames = list(c(), dnames))) for(clvl in clvls) { ## Generate the levels to impute donorclass_donor <- donor[donor$cfactor == clvl, ] if(NROW(donorclass_donor) == 0) { # empty donor class in donor, use collection # of levels of the variables ##### Shall we leave this a warning or a message instead? ##### We can also opt for a 'verbose' option warning(gettextf("No donor found for donation class: %s", sQuote(clvl), domain = "R-impimp")) donor_dlvls <- dlvls } else { # extract observed donor values donor_dlvls <- lapply(stats::setNames(nm = dnames), function(x) { gather_levels(donorclass_donor[,x]) }) } # impute observed donor values impRecipient[impRecipient$cfactor == clvl, dnames] <- imputation_values(donor_dlvls, dnames) } } impRecipient <- impRecipient[, c(rnames, cnames, dnames)] if(!length(grep(class(impRecipient), "impimp", fixed = TRUE))) { class(impRecipient) <- c("impimp", class(impRecipient)) } attr(impRecipient, "impmethod") <- c(method, attr(impRecipient, "impmethod")) attr(impRecipient, "varlevels") <- lvls attr(impRecipient, "imputedvarnames") <- dnames impRecipient } #' @rdname impimp #' @param x object of class 'impimp' #' @param ... further arguments passed down to #' \code{\link[base]{print.data.frame}} #' @export print.impimp <- function(x, ...) { cat(gettextf("result of imprecise imputation with method %s\n", sQuote(attr(x, "impmethod")), domain ="R-impimp")) NextMethod(x, ...) } #' @rdname impimp #' @param z object to test for class \code{"impimp"} #' @export is.impimp <- function(z) { inherits(x = z, what = "impimp") }

11144f365afc3f8a8107e52a7f64f02db70f8453

f6a5600cd0c8cad6699710049c4edff1aa1934e4

/code/prioritizr_frontier.r

ce0b63cf3e8fa6e7d20f4c720e48c5668f58db01

[ "MIT" ]

permissive

pinskylab/ClimateAndMSP

04f251d6cf2bc16f34265d6f38cc3b580e886af8

e9a9d693e045c8318d13f37ace33036407430c8a

refs/heads/master

2023-03-02T15:55:47.637873

2023-02-21T19:25:56

30,681,601

0

1

null

2020-08-19T14:25:31

2015-02-12T02:36:09

R

UTF-8

R

false

24,203

r

prioritizr_frontier.r

# Set up and run Prioritizr with zones to simulate CMSP # Fixed budget across a range of weight present vs. future to get an efficiency frontier # set up to source from within R 3.5.3: source('code/5.1_prioritizr.r') # May need to set R_MAX_VSIZE=60000000 or larger in .Renviron to avoid hitting memory limits (Sys.getenv('R_MAX_VSIZE') to query) ############# ## Parameters ############# # choose the rcps # will use both for first planning period # will use only the second for the second planning period rcps <- c(26, 85) # choose the climate models to use for future planning #bcc-csm1-1-m, bcc-csm1-1, CanESM2, CCSM4, CESM1-CAM5, CNRM-CM5, GFDL-CM3, GFDL-ESM2M, GFDL-ESM2G, GISS-E2-R, GISS-E2-H, IPSL-CM5A-LR, IPSL-CM5A-MR, MIROC-ESM, MPI-ESM-LR, NorESM1-ME gcminds <- c(1, 2, 3, 4, 8, 9, 10, 14) # from running sample(1:16, 8) # CMSP goals consgoal <- 0.1 # proportion of presences to capture in conservation energygoal <- 0.2 # proportion of NPV fishgoal <- 0.5 # proportion of biomass cost <- 0.01 # basic cost of including each planning unit in a given zone # oceans to read in oceans <- c('Atl', 'Pac') # choose region and name these runs myregs <- c('ebs', 'goa', 'bc', 'wc', 'gmex', 'seus', 'neus', 'maritime', 'newf') # which time periods to use in the multi-period planning # contemporary time period must be in first slot, second time period must be the future planningperiods <- c('2007-2020', '2081-2100') # how many budget levels to examine nbudget <- 2 minbudget <- 0.75 maxbudget <- 0.90 # how many weights to examine (linear scale) nweight <- 91 minweight <- 0 maxweight <- 100 # set output name outname <- paste0('temp/frontierall_', format(Sys.time(), "%Y-%m-%d_%H%M%S"), '.csv') # optimality gap, number of threads, and time limit for gurobi solver gap <- 0.01 nthread <- 2 timelimit <- 1200 # seconds ###################### # Functions ###################### require(data.table) library(prioritizr) # only runs in R 3.5.3 for now (Gurobi 8.1.1) ##################### ## Load data ##################### # loads presence/absence and biomass data if(!(length(rcps) %in% c(1,2))){ stop('rcp must be length 1 or 2') } for (i in 1:length(rcps)){ print(paste0('Loading rcp', rcps[i])) for(j in 1:length(oceans)){ for(k in 1:length(planningperiods)){ # do both RCPs for first planning period. Do only 2nd rcp for 2nd planning period. if(k == 1 | (k == 2 & i == 2)){ print(paste(oceans[j], planningperiods[k])) prestemp <- fread(cmd = paste0('gunzip -c temp/presmap_', oceans[j], '_rcp', rcps[i], '_', planningperiods[k], '.csv.gz'), drop = 1) biotemp <- fread(cmd = paste0('gunzip -c temp/biomassmap_', oceans[j], '_rcp', rcps[i], '_', planningperiods[k], '.csv.gz'), drop = 1) # calculate ensemble mean across training GCMs and remaining RCPs prestemp <- prestemp[model %in% c(1:16)[gcminds], .(poccur = mean(poccur)), by = c('latgrid', 'longrid', 'year_range', 'rcp', 'spp')] biotemp <- biotemp[model %in% c(1:16)[gcminds], .(biomass = mean(biomass)), by = c('latgrid', 'longrid', 'year_range', 'rcp', 'spp')] if(i == 1 & j == 1 & k == 1){ presmap <- prestemp biomassmap <- biotemp } else { presmap <- rbind(presmap, prestemp) biomassmap <- rbind(biomassmap, biotemp) } } } } } rm(prestemp, biotemp) # average across the rcps presmap <- presmap[, .(poccur = mean(poccur)), by = c('latgrid', 'longrid', 'year_range', 'spp')] biomassmap <- biomassmap[, .(biomass = mean(biomass)), by = c('latgrid', 'longrid', 'year_range', 'spp')] # poccur threshold: how high does the probability of occurrence in the projections need to be to consider the species "present"? # use the thresholds calculated during model fitting from Morley et al. 2018 PLOS ONE poccurthresh <- fread('https://raw.githubusercontent.com/pinskylab/project_velocity/master/output/modeldiag_Nov2017_fitallreg_2017.csv', drop = 1)[, .(sppocean, thresh.kappa)] # load NatCap calculations windnpv <- fread(cmd = 'gunzip -c output/wind_npv.csv.gz', drop = 1) wavenpv <- fread(cmd = 'gunzip -c output/wave_npv.csv.gz', drop = 1) setnames(windnpv, c('lat', 'lon', 'npv'), c('latgrid', 'longrid', 'wind_npv')) setnames(wavenpv, c('lat', 'lon', 'npv'), c('latgrid', 'longrid', 'wave_npv')) # definition of fishery species by region fisheryspps <- fread('output/fishery_spps.csv', drop = 1) # which spp to include in fishery goal in each region # region definitions regiongrid <- fread(cmd = 'gunzip -c output/region_grid.csv.gz', drop = 1) ################################ ## Set up data for any region ################################ # Fix lon in regiongrid to match presmap (-360 to 0) regiongrid[longrid > 0, longrid := longrid - 360] # Add region information to presmap setkey(presmap, latgrid, longrid) setkey(regiongrid, latgrid, longrid) presmap <- merge(presmap, regiongrid[, .(latgrid, longrid, region)], all.x = TRUE) # add region information if(presmap[is.na(region) & !duplicated(presmap[,.(latgrid, longrid)]), .N] != 0){ # 0 missing region: good! stop('presmap is missing >0 regions') } # presmap[is.na(region) & !duplicated(presmap[,.(latgrid, longrid)]), ] # presmap[is.na(region) & !duplicated(presmap[,.(latgrid, longrid)]), plot(longrid, latgrid)] # Add region information to biomassmap setkey(biomassmap, latgrid, longrid) setkey(regiongrid, latgrid, longrid) biomassmap <- merge(biomassmap, regiongrid[, .(latgrid, longrid, region)], all.x = TRUE) # add region information if(biomassmap[is.na(region) & !duplicated(biomassmap[,.(latgrid, longrid)]), .N] != 0){ # 0 missing region: good! stop('biomassmap is missing >0 regions') } # Add poccur threshold to presmap poccurthresh[, ocean := gsub('.*_', '', sppocean)] poccurthresh[, spp := gsub('_Atl|_Pac', '', sppocean)] presmapPac <- merge(presmap[region %in% c('ebs', 'goa', 'bc', 'wc'), ], poccurthresh[ocean == 'Pac', .(spp, thresh.kappa)], by = 'spp') # have to do Atl and Pac separately since some species are in both regions but use different models presmapAtl <- merge(presmap[region %in% c('gmex', 'seus', 'neus', 'maritime', 'newf'), ], poccurthresh[ocean == 'Atl', .(spp, thresh.kappa)], by = 'spp') if(nrow(presmap) == nrow(presmapPac) + nrow(presmapAtl)){ presmap <- rbind(presmapPac, presmapAtl) rm(presmapPac, presmapAtl) } else { stop('merge of poccurthesh and presmap did not work') } # Fix a species name # ALSO DORYTEUTHIS/LOLIGO PEALEII? presmap[spp == 'theragra chalcogramma', spp := 'gadus chalcogrammus'] # zones # id and names for each zone zones <- data.frame(id = 1:3, name = c('conservation', 'fishery', 'energy')) ############################ # Run prioritizr # Fixed budget ############################# for (i in 1:length(myregs)) { print(paste0('Starting region ', myregs[i])) print(Sys.time()) ############################### # Set up data for this region ############################### # pus # planning features are each 1/4 deg square pus <- presmap[region == myregs[i], c('latgrid', 'longrid')] pus <- pus[!duplicated(pus),] dim(pus) # 2195 (ebs), 795 (goa), (bc), (wc), 651 (gomex), (seus), (neus), (maritime), (newf) if(nrow(pus) == 0) stop('pus has length zero') pus <- pus[order(pus$latgrid, pus$longrid),] pus$id <- 1:nrow(pus) pus$dummycost <- rep(cost, nrow(pus)) # set the same cost in each planning unit. can add separate costs for each zone. ############################################ ## Run prioritizr on 2007-2020 and 2081-2100 ############################################ # spps # id and name for each species # fishery features entered separately from conservation features, even if same species # plan on ensemble mean of all climate models for the current time-period sppstokeep <- presmap[region == myregs[i] & year_range == planningperiods[1], .(poccur = mean(poccur)), by = c('latgrid', 'longrid', 'spp', 'thresh.kappa')] # average across models dim(sppstokeep) sppstokeep <- sppstokeep[poccur >= thresh.kappa, ] sppstokeep <- merge(sppstokeep, pus[, .(latgrid, longrid, id)], by = c('latgrid', 'longrid')) # add pu id (and trim to focal pus) setnames(sppstokeep, 'id', 'pu') dim(sppstokeep) ngrid <- sppstokeep[ , .(ngrid = length(unique(pu))), by = 'spp'] sppstokeep <- merge(sppstokeep, ngrid, by = 'spp') sppstokeep[ , summary(ngrid)] # nspps <- sppstokeep[ , .(nspp = length(unique(spp))), by = 'pu'] sppstokeep <- merge(sppstokeep, nspps, by = 'pu') sppstokeep[, summary(nspp)] # sppstokeep <- sppstokeep[ngrid >= (nrow(pus)*0.05),] # trim to species found at poccur > poccurthresh in at least 5% of grids sppstokeep[ , length(unique(spp))] spps <- data.table(id = 1:length(unique(sppstokeep$spp)), name = gsub(' |_', '', sort(unique(sppstokeep$spp))), spp = sort(unique(sppstokeep$spp))) # fill spaces in species names. # add fishery features spps <- rbind(spps, data.table(id = max(spps$id) + 1:fisheryspps[region == myregs[i], length(projname)], name = paste0(gsub(' |_', '', fisheryspps[region == myregs[i], projname]), '_fishery'), spp = fisheryspps[region == myregs[i], projname])) # add wind and wave energy feature spps <- rbind(spps, data.table(id = max(spps$id) + 1, name = c('energy'), spp = c(NA))) # add future species (2081-2100) sppinds <- !grepl('energy', spps$name) # don't include energy in each time period temp1 <- spps[sppinds,] spps$name[sppinds] <- paste0(spps$name[sppinds], gsub('-', '', planningperiods[1])) temp1$name <- paste0(temp1$name, gsub('-', '', planningperiods[2])) temp1$id = temp1$id + max(spps$id) # make sure the ids don't overlap spps <- rbind(spps, temp1) # puvsp # which features are in each planning unit # Format conservation data puvsppa <- presmap[region == myregs[i] & year_range == planningperiods[1], .(poccur = mean(poccur)), by = c('latgrid', 'longrid', 'spp', 'thresh.kappa')] # pres/abs data. dim(puvsppa) puvsppa[, amount := as.numeric(poccur >= thresh.kappa)] # use pres/abs as conservation amount. puvsppa[, summary(amount)] puvsppa[, sort(unique(amount))] puvsppa[, poccur := NULL] puvsppa[ , name := paste0(gsub(' |_', '', spp), gsub('-', '', planningperiods[1]))] # trim out spaces from species names and append time period # Format fishery data puvspbio <- biomassmap[region == myregs[i] & year_range == planningperiods[1] & spp %in% fisheryspps[region == myregs[i], projname], .(biomass = mean(biomass)), by = c('latgrid', 'longrid', 'spp')] # biomass data. dim(puvspbio) puvspbio[, length(unique(spp))] # should be 10 puvspbio[, amount := biomass] # use biomass as amount for fishery targets puvspbio[ , name := paste0(gsub(' |_', '', spp), '_fishery', gsub('-', '', planningperiods[1]))] # trim out spaces from species names, append fishery # Format wind and wave data puvenergy <- merge(windnpv, wavenpv, by = c('latgrid', 'longrid'), all = TRUE) head(puvenergy) dim(windnpv) dim(wavenpv) dim(puvenergy) puvenergy[wind_npv < 0 | is.na(wind_npv), wind_npv := 0] # set negative or NA NPV to 0 puvenergy[wave_npv < 0 | is.na(wave_npv), wave_npv := 0] puvenergy[, amount := (wind_npv + wave_npv)/10000] # scale down to pass presolve checks puvenergy[, name := 'energy'] # combine puvsp <- rbind(puvsppa[, .(name, latgrid, longrid, amount, zone = 1)], puvspbio[, .(name, latgrid, longrid, amount, zone = 2)], puvenergy[, .(name, latgrid, longrid, amount, zone = 3)]) # Add species ids nrow(puvsp) puvsp <- merge(puvsp, spps[, .(id, name)], by = 'name') # merge in species IDs and trim to focal species nrow(puvsp) setnames(puvsp, 'id', 'species') # Add planning units puvsp <- merge(puvsp, pus[, .(latgrid, longrid, id)], by = c('latgrid', 'longrid')) # add pu id (and trim to focal pus) nrow(puvsp) setnames(puvsp, 'id', 'pu') # Check fishery species for adequate biomass and scale up if needed # Makes sure that no fishery species are eliminated by the next section checking for amount < 1e6 fishtotals <- puvsp[grepl('fishery', name), .(total = sum(amount), name = unique(name)), by = 'species'] for(j in which(fishtotals[, total != 1])){ scalar <- 1/fishtotals[j, total] # scale up so sum would be 1 puvsp[species == fishtotals[j, species], amount := amount * scalar] } # Trim out values < 1e-6 (will throw error in prioritizr) # Use 5e-6 to leave some buffer puvsp[amount < 5e-6, amount := 0] # Sort and trim columns and rows setkey(puvsp, pu, species) # order by pu then species puvsp <- puvsp[amount > 0, ] # trim only to presences # checks on the historical data if(length(unique(puvsp$pu)) != nrow(pus)) stop(paste0('region: ', myregs[i], '. puvsp planning units do not match pus.')) # planning units for species + NatCap: 2195 (ebs), 661 (goa), 549 (neus), 1342 (newf) if(!all(unique(puvsp$species) %in% spps$id)) stop(paste0('region: ', myregs[i], '. Some puvsp features are not in spps.')) # features that are species + fishery + NatCap if(min(sort(unique(table(puvsp$species)))) < 1) stop(paste0('region: ', myregs[i], '. Some species are not in a planning unit (hist).')) # make sure all species show up in some planning units (shouldn't see any 0s) if(min(sort(unique(table(puvsp$pu))) < 1)) stop(paste0('region: ', myregs[i], '. Some planning units do not have a species (hist).')) # make sure all planning units have some species (shouldn't see any 0s) if(!all(sort(unique(table(puvsp$pu, puvsp$species))) %in% c(0,1))) stop(paste0('region: ', myregs[i], '. Some planning unit-species combinations appear more than once (hist).')) # should be all 0s and 1s if(puvsp[, max(amount) > 1e6]) stop(paste0('region:', myregs[i], '. Amount > 1e6 (hist).')) # add future data puvsppa2 <- presmap[region == myregs[i] & year_range == planningperiods[2], .(poccur = mean(poccur)), by = c('latgrid', 'longrid', 'spp', 'thresh.kappa')] # pres/abs data. trim to focal models dim(puvsppa2) puvsppa2[, amount := as.numeric(poccur >= thresh.kappa)] # use pres/abs as conservation amount. should this instead be left as poccur? puvsppa2[, summary(amount)] puvsppa2[, sort(unique(amount))] puvsppa2[ , name := gsub(' |_', '', spp)] # trim out spaces from species names and add future puvsppa2[!grepl('energy', name), name := paste0(name, gsub('-', '', planningperiods[2]))] # append time period # Format future fishery data puvspbio2 <- biomassmap[region == myregs[i] & year_range == planningperiods[2] & spp %in% fisheryspps[region == myregs[i], projname], .(biomass = mean(biomass)), by = c('latgrid', 'longrid', 'spp')] # biomass data dim(puvspbio2) puvspbio2[, length(unique(spp))] # should be 10 puvspbio2[, amount := biomass] # use biomass as amount for fishery targets. puvspbio2[ , name := paste0(gsub(' |_', '', spp), '_fishery')] # trim out spaces from species names puvspbio2[!grepl('energy', name), name := paste0(name, gsub('-', '', planningperiods[2]))] # append time period # combine future data puvsp2 <- rbind(puvsppa2[, .(name, latgrid, longrid, amount, zone = 1)], puvspbio2[, .(name, latgrid, longrid, amount, zone = 2)]) # Add species ids to future nrow(puvsp2) puvsp2 <- merge(puvsp2, spps[, .(id, name)], by = 'name') # merge in species IDs and trim to focal species nrow(puvsp2) setnames(puvsp2, 'id', 'species') # Add planning units to future puvsp2 <- merge(puvsp2, pus[, .(latgrid, longrid, id)], by = c('latgrid', 'longrid')) # add pu id (and trim to focal pus) nrow(puvsp2) setnames(puvsp2, 'id', 'pu') # Check fishery species for adequate biomass and scale up if needed # Makes sure that no fishery species are eliminated by the next section checking for amount < 1e6 fishtotals2 <- puvsp2[grepl('fishery', name), .(total = sum(amount), name = unique(name)), by = 'species'] for(j in which(fishtotals2[, total != 1])){ scalar <- 1/fishtotals2[j, total] # scale up so sum would be 1 puvsp2[species == fishtotals2[j, species], amount := amount * scalar] } # Add historical and future data puvsp <- rbind(puvsp, puvsp2) # Trim out values < 1e-6 (will throw error in prioritizr) # Use 5e-6 to leave some buffer puvsp[amount < 5e-6, amount := 0] # Sort and trim columns and rows setkey(puvsp, pu, species) # order by pu then species puvsp <- puvsp[amount > 0, ] # trim only to presences # checks if(length(unique(puvsp$pu)) != nrow(pus)) stop(paste0('region: ', myregs[i], '. puvsp planning units do not match pus.')) # planning units for species + NatCap if(!all(unique(puvsp$species) %in% spps$id)) stop(paste0('region: ', myregs[i], '. Some puvsp features are not in spps.')) # features that are species + fishery + NatCap if(min(sort(unique(table(puvsp$species)))) < 1) stop(paste0('region: ', myregs[i], '. Some species are not in a planning unit.')) # make sure all species show up in some planning units (shouldn't see any 0s) if(min(sort(unique(table(puvsp$pu))) < 1)) stop(paste0('region: ', myregs[i], '. Some planning units do not have a species.')) # make sure all planning units have some species (shouldn't see any 0s) if(!all(sort(unique(table(puvsp$pu, puvsp$species))) %in% c(0,1))) stop(paste0('region: ', myregs[i], '. Some planning unit-species combinations appear more than once.')) # should be all 0s and 1s if(puvsp[, max(amount) > 1e6]) stop(paste0('region:', myregs[i], '. Amount > 1e6.')) #zone target # set zone-specific targets: rows are features, columns are zones zonetarget <- matrix(0, nrow = nrow(spps), ncol = nrow(zones), dimnames = list(spps$name, zones$name)) zonetarget[!grepl('energy|fishery', rownames(zonetarget)), 'conservation'] <- consgoal # set conservation zone target zonetarget[grepl('fishery', rownames(zonetarget)), 'fishery'] <- fishgoal # set fishing zone target zonetarget[grepl('energy', rownames(zonetarget)), 'energy'] <- energygoal # set energy goal target # trim out species that aren't present nrow(spps) spps <- spps[name %in% puvsp$name,] nrow(spps) nrow(zonetarget) zonetarget <- zonetarget[rownames(zonetarget) %in% puvsp$name,] nrow(zonetarget) # basic checks (automated) if(!all(colSums(zonetarget) > 0)) stop(paste0('region:', myregs[i], '. Some zone targets are 0.')) # reasonable targets? if(nrow(zonetarget) != nrow(spps)) stop(paste0('region: ', myregs[i], '. Zonetargets do not match spps.')) if(!all(rownames(zonetarget) == spps$name)) stop(paste0('region: ', myregs[i], '. Zonetargets order does not match spps order.')) if(sum(!(puvsp$pu %in% pus$id)) > 0) stop(paste0('region: ', myregs[i], '. Some planning units not in pus.')) if(sum(!(puvsp$species %in% spps$id)) > 0) stop(paste0('region: ', myregs[i], '. Some species units not in spps.')) if(sum(!(pus$id %in% puvsp$pu)) > 0) stop(paste0('region: ', myregs[i], '. Some pus units not in puvsp.')) if(sum(!(spps$id %in% puvsp$species)) > 0) stop(paste0('region: ', myregs[i], '. Some species units not in puvsp.')) # First solve the min cost problem cat('\tSolving min cost\n') p1 <- problem(pus, spps, cost_column = c('dummycost', 'dummycost', 'dummycost'), rij = puvsp, zones = zones) %>% add_min_set_objective() %>% add_relative_targets(zonetarget) %>% add_binary_decisions() %>% add_gurobi_solver(gap = gap) if(presolve_check(p1)){ s1 <- solve(p1) } else { stop(paste0('region:', myregs[i], '. Failed presolve check (min set).')) } # Loop through a range of budgets and relative weights on future vs. present frontier <- expand.grid(budget = seq(minbudget, maxbudget, length.out = nbudget), presweight = seq(minweight, maxweight, length.out = nweight)) frontier$region <- myregs[i] frontier$status <- NA frontier$presgoals <- NA frontier$futgoals <- NA for(j in 1:nrow(frontier)){ print(paste(myregs[i], frontier$budget[j], frontier$presweight[j])) # Set up a budget as fraction of min cost budget <- frontier$budget[j]*cost*s1[, sum(solution_1_conservation) + sum(solution_1_fishery) + sum(solution_1_energy)] # or with 0.9*attr(s1, 'objective') # Set up feature weights # Anything less than 0.01 will favor not adding a planning unit over meeting a feature target prewght <- frontier$presweight[j] futwght <- maxweight + minweight - frontier$presweight[j] wghts <- zonetarget wghts[grepl(gsub('-', '', planningperiods[1]), rownames(wghts)), ] <- prewght # historical wghts[grepl(gsub('-', '', planningperiods[2]), rownames(wghts)), ] <- futwght # future wghts[grepl('energy', rownames(wghts)), ] <- 0 # no attempt to meet energy goal wghts[zonetarget == 0] <- 0 # set zeros back to zero # Now solve the max representation problem for a limited budget cat('\tSolving min budget\n') p2 <- problem(pus, spps, cost_column = c('dummycost', 'dummycost', 'dummycost'), rij = puvsp, zones = zones) %>% add_max_features_objective(budget) %>% add_relative_targets(zonetarget) %>% add_feature_weights(wghts) %>% add_binary_decisions() %>% add_gurobi_solver(gap = gap, threads = nthread, time_limit = timelimit) # 10 minute time limit if(presolve_check(p2)){ s2 <- solve(p2) } else { stop(paste0('region:', myregs[i], '. Failed presolve check (min budget).')) } # save status frontier$status[j] <- attr(s2, 'status') # calculate goals met r2 <- feature_representation(p2, s2[, 5:7]) r2 <- r2[(!grepl('fishery|energy', r2$feature) & r2$zone == 'conservation') | (grepl('fishery', r2$feature) & r2$zone == 'fishery') | (grepl('energy', r2$feature) & r2$zone == 'energy'), ] # trim to feature/zone combinations we care about if(nrow(r2) != nrow(zonetarget)) stop('r2 and zonetargets do not match') r2$goal <- NA r2$goal[r2$zone == 'conservation'] <- consgoal r2$goal[r2$zone == 'fishery'] <- fishgoal r2$goal[r2$zone == 'energy'] <- energygoal r2$met <- r2$relative_held >= r2$goal frontier$presgoals[j] <- sum(r2$met[grepl(gsub('-', '', planningperiods[1]), r2$feature)]) # contemporary period goals met frontier$futgoals[j] <- sum(r2$met[grepl(gsub('-', '', planningperiods[2]), r2$feature)]) # future } if(i == 1){ frontierall <- frontier } else { frontierall <- rbind(frontierall, frontier) } write.csv(frontierall, file = outname) } print(Sys.time()) ######################## # Make a simple plot ######################## require(data.table) require(ggplot2) frontierall <- fread(outname, drop = 1) setkey(frontierall, region, budget, presweight) frontierall[, region := factor(region, levels = c('ebs', 'goa', 'bc', 'wc', 'gmex', 'seus', 'neus', 'maritime', 'newf'))] # set order # how many not optimal? print(frontierall[, .(notopt = sum(status != 'OPTIMAL'), total = .N)]) print(frontierall[, .(notopt = sum(status != 'OPTIMAL'), total = .N), by = region]) pdf('temp_figures/prioritizr_frontiers.pdf', height = 6, width = 6) ggplot(frontierall, aes(x = presgoals, y = futgoals, group = budget, color = budget)) + geom_path(size = 0.4) + geom_point(size = 0.3) + facet_wrap(~ region, nrow = 3, scales = 'free') dev.off()

d66901ecaea6f9a42dd71b4c806736b2f82bb08b

48aea1547fb612b127d5b5def716d48398236159

/man/CIMseq.testing-package.Rd

0dc41e324b8ff6d8ece9a460967805947ff58e59

[]

no_license

jasonserviss/CIMseq.testing

6a1951a5d1cd53a22704df631138050bc4e057c6

7039f9b52fb9280bb811662aa19d4fe7f7bf8398

refs/heads/master

2021-03-30T17:46:24.443721

2020-01-27T09:55:25

76,064,214

0

null

UTF-8

R

false

true

485

rd

CIMseq.testing-package.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/CIMseq.testing-package.R \docType{package} \name{CIMseq.testing-package} \alias{CIMseq.testing-package} \alias{CIMseq.testing} \title{Testing and analysis of the CIMseq and method.} \description{ Description } \details{ \tabular{ll}{ Package: \tab CIMseq\cr Type: \tab Package\cr Version: \tab 1.0\cr Date: \tab 2016-02-28\cr License: \tab GPL-3\cr } } \author{ Author: Jason T. Serviss } \keyword{package}

ac1ee8fbff6fb6fbf1a09f64920a125501480ea5

0ecd29c40cbecd945f5d8e3d2b2d27e4070ef897

/rstan_installation_helper.R

94b689be7d9c14d2ee8b1056bb5c5d5b568fe582

[]

no_license

paul-buerkner/2019_DAGStat_Stan_Tutorial

195bc4e440feff7be10f93057614c252fe2cf7f7

41f778f9c1188bad80d0b58542aada541d275ca3

refs/heads/master

2020-04-29T16:39:14.856833

2019-03-19T11:11:15

176,269,018

8

0

null

UTF-8

R

false

1,144

r

rstan_installation_helper.R

# install rstan # Quite a few other packages will be installed as well if (!require("rstan")) { install.packages("rstan") } # The following explains how to install a C++ compiler # which is required for Stan # -------- FOR WINDOWS ------- # This requires using R from Rstudio 1.1 or higher! library(rstan) example("stan_model", run.dontrun = TRUE) # RStudio will ask if you want to install Rtools, # in which case you should say Yes and click through the installer # If this doesn't work, go to download and install Rtools 3.5 # manually from https://cran.r-project.org/bin/windows/Rtools/ # make sure to check the box to change the System PATH # -------- FOR MAC ---------- # Please install Xcode, which you can download from the App-Store for free. # Installing Xcode may take some time and you may restart your machine afterwards # Make sure that a C++ compiler is installed and can be called within R via system("clang++ -v") # If no warning occurs and a few lines of difficult to read system code # are printed out, the compiler should work correctly # ----------------------- # try to run a demo model example("stan_model")

e763167ec62779096a002d1abf989af1d5a54e5e

7384fa7a27f0fddda69766c4d351efabb494d799

/cachematrix.R

08c89e9658042fa59aee19a4eab6bf0e004341fc

[]

no_license

abumeezo/ProgrammingAssignment2

edfb04334f35c41afc956dfd5d5e1cb6c267b939

63e9aaeded7f04cc8bace8bf01cf13e09e643db7

refs/heads/master

2021-01-13T15:52:19.018228

2016-12-19T04:20:53

76,826,210

0

null

2016-12-19T03:51:38

null

UTF-8

R

false

1,120

r

cachematrix.R

##Functions to create a special "matrix" object with a cached inverse ##and to retrieve the cached inverse if already calculated from inside the object itself ##This function creates the "matrix" object with cached inverse ##Object has internal functions to establish and return itself and its inverse makeCacheMatrix <- function(x = matrix()) { inverseOfMatrix <- NULL set <- function(y) { x <<- y inverseOfMatrix <<- NULL } get <- function() x setInverse <- function(inverseM) inverseOfMatrix <<- inverseM getInverse <- function() inverseOfMatrix list(set = set, get = get, setInverse = setInverse, getInverse = getInverse) } ##This function extracts cached inverse from a "matrix" object or ##computes and sets the inverse if it was not cached then returns it cacheSolve <- function(x, ...) { ## Return a matrix that is the inverse of 'x' inverseOfMatrix <- x$getInverse() if(!is.null(inverseOfMatrix)) { message("getting cached data") return(inverseOfMatrix) } data <- x$get() inverseOfMatrix <- solve(data, ...) x$setInverse(inverseOfMatrix) inverseOfMatrix }

bca932738cd7110522cc3a5917a64f1837ffd015

890c942249dd887b82ca07eee97f68149ffd1f49

/R/degs.R

8b4e4e7fc2a894ffe64827aae81c51b497c52081

[ "MIT" ]

permissive

lefeverde/QSPpaper

3285c4829273120508610fef2ecdef3186dd26b7

eec8fbedd1fefd1ed88dadbc77dd385ad78f274f

refs/heads/master

2023-01-18T21:39:56.050930

2022-12-24T17:35:23

240,388,280

0

null

UTF-8

R

false

465

r

degs.R

#' Wrapper to create a fit object (see \code{\link[limma]{eBayes}}) using the contrast method #' #' @param v a voom object #' @param group_cont vector of contrasts #' @param mod model matrix #' #' @return fit object #' @export #' #' @examples make_cont_fit <- function(v, group_cont, mod){ m <- data.frame(mod) cont_mod <- makeContrasts(contrasts = group_cont, levels=m) fit <- lmFit(v, m) %>% contrasts.fit(., cont_mod) %>% eBayes(.) return(fit) }

3842f59e4582f10f535df6b39f20ac6e86903009

93defdbd4e3c597ec4b7f95b5cdaf649e7cbb21c

/man/dot-extract_base_schedule.Rd

237c896518dc3dc8d05d42878943ba8b815c5615

[]

no_license

meysubb/collegeballR

0e909cbda2ec96f386fd5385168a65790507aab3

1727a03dc3bf0377d65849586c0e44c9a089b591

refs/heads/master

2021-05-05T13:31:13.268673

2019-07-25T01:06:32

105,055,203

17

5

null

2019-04-20T23:26:48

2017-09-27T18:36:46

HTML

UTF-8

R

false

true

507

rd

dot-extract_base_schedule.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/extract_base_schedule.R \name{.extract_base_schedule} \alias{.extract_base_schedule} \title{Extract Raw Base schedule} \usage{ .extract_base_schedule(team_id, year, sport) } \arguments{ \item{team_id}{Team ID (form team_mapping)} \item{sprt}{Tradiational Sport Name} \item{yr}{Select year, (example: 2015-2016 season is 2016)} } \description{ Extracts the date, team's played and the results } \examples{ } \keyword{internal}

106b776f269767b3681b2d3ffc91a718ae45600c

3b2a2137476edc5fb5dad4c3f0f29fa83252db0f

/man/notin.Rd

1b11b4450d58a48ddd0a7cbb27d9e12455461958

[]

no_license

woodwards/octopus

32d8c64947d634fd8cf32b5abdf34fea478baef6

5be3adffe27bd0d8300ff6394a59587f86c4bd51

refs/heads/master

2020-09-25T09:24:18.651141

2020-01-06T20:48:57

225,973,852

1

0

null

UTF-8

R

false

true

346

rd

notin.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/utility.R \name{\%notin\%} \alias{\%notin\%} \title{Returns TRUE if x is not in y.} \usage{ x \%notin\% y } \arguments{ \item{x}{Anything.} \item{y}{Anything.} } \value{ Logical. } \description{ Returns TRUE if x is not in y. } \examples{ "a" \%notin\% c("b", "c") }

d038121a9e733c43eb036e15362a4ee823293615

76dbc1754d4fac81e75fc054858ba91f99b55b2d

/R/mortalityhazard-consthaz.R

78c3b9f0e7b177a3e4e2d9f308644f8668e20ab6

[]

no_license

dfeehan/mortfit

e51ac12507385bd9024e8109aa1a3eaea2895fb5

8dfd82e93fde1bf408dbe59eb004cc8694603f88

refs/heads/master

2021-01-18T00:00:39.351697

2020-11-08T16:23:12

18,040,328

2

1

null

UTF-8

R

false

753

r

mortalityhazard-consthaz.R

######################## # constant hazard object consthaz.haz.fn <- function(theta, z) { alpha <- exp(theta[1]) return(rep(alpha,length(z))) } ## these starting values have been updated based on preliminary analysis consthaz.haz <- new("mortalityHazard", name="Constant Hazard", num.param=1L, theta.default=c(-2.48), theta.range=list(c(-2.73, -2.29)), optim.default=list(method="BFGS", control=list(reltol=1e-10)), haz.fn=consthaz.haz.fn, haz.to.prob.fn=functional::Curry(haz.to.prob, haz.fn=consthaz.haz.fn))

b7f48bb3fa02594c8937ebfd82abd33ccec55b9d

2171709c5b23d8e5f7c2194d4c77b8d1d3c232f3

/man/Content.Rd

2acf178c31593e5c7d08ef59848501167ba451c6

[]

no_license

colearendt/connectapi

9472351abc6f24c5d3bb9acc41721754d13f52af

00a01aa74aee8df5fcc67cee14b80c5239168b52

refs/heads/master

2021-07-19T07:33:16.802365

2020-05-19T12:50:52

168,455,783

0

null

UTF-8

R

false

true

4,214

rd

Content.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/content.R \name{Content} \alias{Content} \title{Content} \description{ An R6 class that represents content } \seealso{ Other R6 classes: \code{\link{Bundle}}, \code{\link{RStudioConnect}}, \code{\link{Task}}, \code{\link{Vanity}} } \concept{R6 classes} \section{Public fields}{ \if{html}{\out{<div class="r6-fields">}} \describe{ \item{\code{connect}}{An R6 Connect object} \item{\code{content}}{The content details from RStudio Connect} } \if{html}{\out{</div>}} } \section{Methods}{ \subsection{Public methods}{ \itemize{ \item \href{#method-new}{\code{Content$new()}} \item \href{#method-get_connect}{\code{Content$get_connect()}} \item \href{#method-get_content}{\code{Content$get_content()}} \item \href{#method-get_content_remote}{\code{Content$get_content_remote()}} \item \href{#method-update}{\code{Content$update()}} \item \href{#method-get_dashboard_url}{\code{Content$get_dashboard_url()}} \item \href{#method-get_jobs}{\code{Content$get_jobs()}} \item \href{#method-get_job}{\code{Content$get_job()}} \item \href{#method-print}{\code{Content$print()}} \item \href{#method-clone}{\code{Content$clone()}} } } \if{html}{\out{<hr>}} \if{html}{\out{<a id="method-new"></a>}} \if{latex}{\out{\hypertarget{method-new}{}}} \subsection{Method \code{new()}}{ \subsection{Usage}{ \if{html}{\out{<div class="r">}}\preformatted{Content$new(connect, content)}\if{html}{\out{</div>}} } } \if{html}{\out{<hr>}} \if{html}{\out{<a id="method-get_connect"></a>}} \if{latex}{\out{\hypertarget{method-get_connect}{}}} \subsection{Method \code{get_connect()}}{ \subsection{Usage}{ \if{html}{\out{<div class="r">}}\preformatted{Content$get_connect()}\if{html}{\out{</div>}} } } \if{html}{\out{<hr>}} \if{html}{\out{<a id="method-get_content"></a>}} \if{latex}{\out{\hypertarget{method-get_content}{}}} \subsection{Method \code{get_content()}}{ \subsection{Usage}{ \if{html}{\out{<div class="r">}}\preformatted{Content$get_content()}\if{html}{\out{</div>}} } } \if{html}{\out{<hr>}} \if{html}{\out{<a id="method-get_content_remote"></a>}} \if{latex}{\out{\hypertarget{method-get_content_remote}{}}} \subsection{Method \code{get_content_remote()}}{ \subsection{Usage}{ \if{html}{\out{<div class="r">}}\preformatted{Content$get_content_remote()}\if{html}{\out{</div>}} } } \if{html}{\out{<hr>}} \if{html}{\out{<a id="method-update"></a>}} \if{latex}{\out{\hypertarget{method-update}{}}} \subsection{Method \code{update()}}{ \subsection{Usage}{ \if{html}{\out{<div class="r">}}\preformatted{Content$update(...)}\if{html}{\out{</div>}} } } \if{html}{\out{<hr>}} \if{html}{\out{<a id="method-get_dashboard_url"></a>}} \if{latex}{\out{\hypertarget{method-get_dashboard_url}{}}} \subsection{Method \code{get_dashboard_url()}}{ \subsection{Usage}{ \if{html}{\out{<div class="r">}}\preformatted{Content$get_dashboard_url(pane = "")}\if{html}{\out{</div>}} } } \if{html}{\out{<hr>}} \if{html}{\out{<a id="method-get_jobs"></a>}} \if{latex}{\out{\hypertarget{method-get_jobs}{}}} \subsection{Method \code{get_jobs()}}{ \subsection{Usage}{ \if{html}{\out{<div class="r">}}\preformatted{Content$get_jobs()}\if{html}{\out{</div>}} } } \if{html}{\out{<hr>}} \if{html}{\out{<a id="method-get_job"></a>}} \if{latex}{\out{\hypertarget{method-get_job}{}}} \subsection{Method \code{get_job()}}{ \subsection{Usage}{ \if{html}{\out{<div class="r">}}\preformatted{Content$get_job(key)}\if{html}{\out{</div>}} } } \if{html}{\out{<hr>}} \if{html}{\out{<a id="method-print"></a>}} \if{latex}{\out{\hypertarget{method-print}{}}} \subsection{Method \code{print()}}{ \subsection{Usage}{ \if{html}{\out{<div class="r">}}\preformatted{Content$print(...)}\if{html}{\out{</div>}} } } \if{html}{\out{<hr>}} \if{html}{\out{<a id="method-clone"></a>}} \if{latex}{\out{\hypertarget{method-clone}{}}} \subsection{Method \code{clone()}}{ The objects of this class are cloneable with this method. \subsection{Usage}{ \if{html}{\out{<div class="r">}}\preformatted{Content$clone(deep = FALSE)}\if{html}{\out{</div>}} } \subsection{Arguments}{ \if{html}{\out{<div class="arguments">}} \describe{ \item{\code{deep}}{Whether to make a deep clone.} } \if{html}{\out{</div>}} } } }

303967312cdd875937c88bf2ee59ce2095c505da

c7ecb5298854ca192e5613f81e74265bd53f9e96

/Project 2/Drop Out Loop-Spec.R

aaed1efb20844600dc54aa7d6bb26a6c12a52206

[]

no_license

tommsmit/R_Projects

14e89784956ed333c6e8c03c33738bd9c01aad82

bafc0c5a60b295b5d75b6668734a422ff7440ba3

refs/heads/master

2023-07-16T01:39:01.576903

2021-08-24T21:07:53

360,184,072

0

1

null

UTF-8

R

false

59,900

r

Drop Out Loop-Spec.R

### Drop out Loop 2: Special Programs ### library(rvest) library(tm) library(pdftools) library(stringr) library(dplyr) library(plyr) library(data.table) library(Hmisc) library(tictoc) library(tidyverse) require(XML) library(ggplot2) library(shiny) school_year<-(c("1998-99","1999-00","2000-01","2001-02","2002-03","2003-04","2004-05","2005-06","2006-07","2007-08","2008-09","2009-10","2010-11","2011-12","2012-13","2013-14","2014-15","2015-16","2016-17","2017-18","2018-19")) drop_spec<-data.frame(Groups=character(),Students=numeric(), Students_Percentage=numeric(), Dropouts=numeric(), Dropouts_Percentage=numeric(), Annual_Dropout_Rate=numeric(), School_Year=character()) total_drop_spec<-data.frame(Groups=character(),Students=numeric(), Students_Percentage=numeric(), Dropouts=numeric(), Dropouts_Percentage=numeric(), Annual_Dropout_Rate=numeric(), School_Year=character()) for (i in 1:length(school_year)){ if (i==1){ a<-paste0("https://tea.texas.gov/sites/default/files/DropComp_",school_year[i],".pdf") dropout<-pdf_text(a) p1<-strsplit(dropout, "\n") table<-data.frame(p1[[86]][c(29:32,41:44)]) rnums<-nrow(table) table$Main<-as.character(table[1:rnums,1]) table$Main<-trimws(table$Main, which="left") table$Main<-stripWhitespace(table$Main) table$Main<-gsub("(?:Bilingual or ESLa)","ESL", table$Main) table$Main<-gsub("(?:Bilingual or English as a second language)","ESL", table$Main) table$Main<-gsub("(?:Second Language)","ESL", table$Main) table$Main<-gsub("(?:CTEb)","Career-Technical", table$Main) table$Main<-gsub("(?:Gifted and talented)","GT", table$Main) table$Main<-gsub("(?:Gifted/Talented)","GT", table$Main) table$Main<-gsub("(?:Section 504)","504", table$Main) table$Main<-gsub("(?:Special education)","Spec-Ed", table$Main) table$Main<-gsub("(?:Special Education)","Spec-Ed", table$Main) table$Main<-gsub("(?:Title I)","Title-I", table$Main) table$Main<-gsub("(?:At Risk)","At-Risk", table$Main) table$Main<-gsub("(?:Limited English Proficient)","ELL", table$Main) table$Main<-gsub("(?:English learner)","ELL", table$Main) table$Main<-gsub("(?:Foster care)","Foster-Care", table$Main) table$Main<-gsub("(?:Overage/Not on Grade)","Overage", table$Main) table$Main<-gsub("(?:,)","", table$Main) split_var<-as.data.frame(ldply(strsplit(table$Main, split = " "))) drop_spec<-data.frame(Groups=character(),Students=numeric(), Students_Percentage=numeric(), Dropouts=numeric(), Dropouts_Percentage=numeric(), Annual_Dropout_Rate=numeric(), School_Year=character()) drop_spec<-drop_spec[1:rnums,] drop_spec$Main<-table$Main drop_spec$Groups<-split_var[,1] drop_spec$Students<-split_var[,2] drop_spec$Students_Percentage<-split_var[,3] drop_spec$Dropouts<-split_var[,4] drop_spec$Dropouts_Percentage<-split_var[,5] drop_spec$Annual_Dropout_Rate<-split_var[,6] drop_spec$School_Year<-school_year[i] drop_spec$Students<-as.numeric(drop_spec$Students) drop_spec$Students_Percentage<-as.numeric(drop_spec$Students_Percentage) drop_spec$Dropouts<-as.numeric(drop_spec$Dropouts) drop_spec$Dropouts_Percentage<-as.numeric(drop_spec$Dropouts_Percentage) drop_spec$Annual_Dropout_Rate<-as.numeric(drop_spec$Annual_Dropout_Rate) drop_spec<-select(drop_spec,-Main) drop_spec<-rbind(drop_spec,NA,NA,NA,NA,NA,NA) drop_spec<-drop_spec[c(1,9,2,10,3,4,5,11,6,12,13,14,7,15,8),] } else if (i==2){ a<-paste0("https://tea.texas.gov/sites/default/files/DropComp_",school_year[i],".pdf") dropout<-pdf_text(a) p1<-strsplit(dropout, "\n") table<-data.frame(p1[[97]][c(28:31,41:45)]) rnums<-nrow(table) table$Main<-as.character(table[1:rnums,1]) table$Main<-trimws(table$Main, which="left") table$Main<-stripWhitespace(table$Main) table$Main<-gsub("(?:Bilingual/English as a Second Language)","ESL", table$Main) table$Main<-gsub("(?:Second Language)","ESL", table$Main) table$Main<-gsub("(?:CTEb)","Career-Technical", table$Main) table$Main<-gsub("(?:Gifted and talented)","GT", table$Main) table$Main<-gsub("(?:Gifted/Talented)","GT", table$Main) table$Main<-gsub("(?:Section 504)","504", table$Main) table$Main<-gsub("(?:Special education)","Spec-Ed", table$Main) table$Main<-gsub("(?:Special Education)","Spec-Ed", table$Main) table$Main<-gsub("(?:Title I)","Title-I", table$Main) table$Main<-gsub("(?:At Risk)","At-Risk", table$Main) table$Main<-gsub("(?:Limited English Proficient)","ELL", table$Main) table$Main<-gsub("(?:English learner)","ELL", table$Main) table$Main<-gsub("(?:Foster care)","Foster-Care", table$Main) table$Main<-gsub("(?:Overage/Not on Grade)","Overage", table$Main) table$Main<-gsub("(?:,)","", table$Main) split_var<-as.data.frame(ldply(strsplit(table$Main, split = " "))) drop_spec<-data.frame(Groups=character(),Students=numeric(), Students_Percentage=numeric(), Dropouts=numeric(), Dropouts_Percentage=numeric(), Annual_Dropout_Rate=numeric(), School_Year=character()) drop_spec<-drop_spec[1:rnums,] drop_spec$Main<-table$Main drop_spec$Groups<-split_var[,1] drop_spec$Students<-split_var[,2] drop_spec$Students_Percentage<-split_var[,3] drop_spec$Dropouts<-split_var[,4] drop_spec$Dropouts_Percentage<-split_var[,5] drop_spec$Annual_Dropout_Rate<-split_var[,6] drop_spec$School_Year<-school_year[i] drop_spec$Students<-as.numeric(drop_spec$Students) drop_spec$Students_Percentage<-as.numeric(drop_spec$Students_Percentage) drop_spec$Dropouts<-as.numeric(drop_spec$Dropouts) drop_spec$Dropouts_Percentage<-as.numeric(drop_spec$Dropouts_Percentage) drop_spec$Annual_Dropout_Rate<-as.numeric(drop_spec$Annual_Dropout_Rate) drop_spec<-select(drop_spec,-Main) drop_spec<-rbind(drop_spec,NA,NA,NA,NA,NA,NA) drop_spec<-drop_spec[c(1,10,2,11,3,4,5,12,7,13,14,6,8,15,9),] } else if (i==3){ a<-paste0("https://tea.texas.gov/sites/default/files/DropComp_",school_year[i],".pdf") dropout<-pdf_text(a) p1<-strsplit(dropout, "\n") table1<-data.frame(p1[[111]][c(5,7:9)]) table2<-data.frame(p1[[112]][5:9]) rnums1<-nrow(table1) rnums2<-nrow(table2) table1$Main<-as.character(table1[1:rnums1,1]) table2$Main<-as.character(table2[1:rnums2,1]) table1$Main<-trimws(table1$Main, which="left") table2$Main<-trimws(table2$Main, which="left") table1$Main<-stripWhitespace(table1$Main) table2$Main<-stripWhitespace(table2$Main) table1$Main<-gsub("(?:Bilingual or English as a second language)","ESL", table1$Main) table1$Main<-gsub("(?:Gifted and talented)","GT", table1$Main) table1$Main<-gsub("(?:Special education)","Spec-Ed", table1$Main) table1$Main<-gsub("(?:Title I)","Title-I", table1$Main) table2$Main<-gsub("(?:At risk)","At-Risk", table2$Main) table2$Main<-gsub("(?:Limited English proficient)","ELL", table2$Main) table1$Main<-gsub("(?:,)","", table1$Main) table2$Main<-gsub("(?:,)","", table2$Main) split_var1<-as.data.frame(ldply(strsplit(table1$Main, split = " "))) split_var2<-as.data.frame(ldply(strsplit(table2$Main, split = " "))) split_var<-rbind(split_var1,split_var2) drop_spec<-data.frame(Groups=character(),Students=numeric(), Students_Percentage=numeric(), Dropouts=numeric(), Dropouts_Percentage=numeric(), Annual_Dropout_Rate=numeric(), School_Year=character()) drop_spec<-drop_spec[1:rnums,] drop_spec$Main<-table$Main drop_spec$Groups<-split_var[,1] drop_spec$Students<-split_var[,2] drop_spec$Students_Percentage<-split_var[,3] drop_spec$Dropouts<-split_var[,4] drop_spec$Dropouts_Percentage<-split_var[,5] drop_spec$Annual_Dropout_Rate<-split_var[,6] drop_spec$School_Year<-school_year[i] drop_spec$Students<-as.numeric(drop_spec$Students) drop_spec$Students_Percentage<-as.numeric(drop_spec$Students_Percentage) drop_spec$Dropouts<-as.numeric(drop_spec$Dropouts) drop_spec$Dropouts_Percentage<-as.numeric(drop_spec$Dropouts_Percentage) drop_spec$Annual_Dropout_Rate<-as.numeric(drop_spec$Annual_Dropout_Rate) drop_spec<-select(drop_spec,-Main) drop_spec<-rbind(drop_spec,NA,NA,NA,NA,NA,NA) drop_spec<-drop_spec[c(1,10,2,11,3,4,5,12,7,13,14,6,8,15,9),] } else if (i==4){ a<-paste0("https://tea.texas.gov/sites/default/files/DropComp_",school_year[i],".pdf") dropout<-pdf_text(a) p1<-strsplit(dropout, "\n") table1<-data.frame(p1[[121]][c(5,7:9)]) table2<-data.frame(p1[[122]][5:9]) rnums1<-nrow(table1) rnums2<-nrow(table2) table1$Main<-as.character(table1[1:rnums1,1]) table2$Main<-as.character(table2[1:rnums2,1]) table1$Main<-trimws(table1$Main, which="left") table2$Main<-trimws(table2$Main, which="left") table1$Main<-stripWhitespace(table1$Main) table2$Main<-stripWhitespace(table2$Main) table1$Main<-gsub("(?:Bilingual or English as a second language)","ESL", table1$Main) table1$Main<-gsub("(?:Gifted and talented)","GT", table1$Main) table1$Main<-gsub("(?:Special education)","Spec-Ed", table1$Main) table1$Main<-gsub("(?:Title I)","Title-I", table1$Main) table2$Main<-gsub("(?:At risk)","At-Risk", table2$Main) table2$Main<-gsub("(?:Limited English proficient)","ELL", table2$Main) table1$Main<-gsub("(?:,)","", table1$Main) table2$Main<-gsub("(?:,)","", table2$Main) split_var1<-as.data.frame(ldply(strsplit(table1$Main, split = " "))) split_var2<-as.data.frame(ldply(strsplit(table2$Main, split = " "))) split_var<-rbind(split_var1,split_var2) drop_spec<-data.frame(Groups=character(),Students=numeric(), Students_Percentage=numeric(), Dropouts=numeric(), Dropouts_Percentage=numeric(), Annual_Dropout_Rate=numeric(), School_Year=character()) drop_spec<-drop_spec[1:rnums,] drop_spec$Main<-table$Main drop_spec$Groups<-split_var[,1] drop_spec$Students<-split_var[,2] drop_spec$Students_Percentage<-split_var[,3] drop_spec$Dropouts<-split_var[,4] drop_spec$Dropouts_Percentage<-split_var[,5] drop_spec$Annual_Dropout_Rate<-split_var[,6] drop_spec$School_Year<-school_year[i] drop_spec$Students<-as.numeric(drop_spec$Students) drop_spec$Students_Percentage<-as.numeric(drop_spec$Students_Percentage) drop_spec$Dropouts<-as.numeric(drop_spec$Dropouts) drop_spec$Dropouts_Percentage<-as.numeric(drop_spec$Dropouts_Percentage) drop_spec$Annual_Dropout_Rate<-as.numeric(drop_spec$Annual_Dropout_Rate) drop_spec<-select(drop_spec,-Main) drop_spec<-rbind(drop_spec,NA,NA,NA,NA,NA,NA) drop_spec<-drop_spec[c(1,10,2,11,3,4,5,12,7,13,14,6,8,15,9),] } else if (i==5){ a<-paste0("https://tea.texas.gov/sites/default/files/DropComp_",school_year[i],".pdf") dropout<-pdf_text(a) p1<-strsplit(dropout, "\n") table1<-data.frame(p1[[138]][c(5,7:9)]) table2<-data.frame(p1[[139]][5:9]) rnums1<-nrow(table1) rnums2<-nrow(table2) table1$Main<-as.character(table1[1:rnums1,1]) table2$Main<-as.character(table2[1:rnums2,1]) table1$Main<-trimws(table1$Main, which="left") table2$Main<-trimws(table2$Main, which="left") table1$Main<-stripWhitespace(table1$Main) table2$Main<-stripWhitespace(table2$Main) table1$Main<-gsub("(?:Bilingual or English as a second language)","ESL", table1$Main) table1$Main<-gsub("(?:Gifted and talented)","GT", table1$Main) table1$Main<-gsub("(?:Special education)","Spec-Ed", table1$Main) table1$Main<-gsub("(?:Title I)","Title-I", table1$Main) table2$Main<-gsub("(?:At risk)","At-Risk", table2$Main) table2$Main<-gsub("(?:Limited English proficient)","ELL", table2$Main) table1$Main<-gsub("(?:,)","", table1$Main) table2$Main<-gsub("(?:,)","", table2$Main) split_var1<-as.data.frame(ldply(strsplit(table1$Main, split = " "))) split_var2<-as.data.frame(ldply(strsplit(table2$Main, split = " "))) split_var<-rbind(split_var1,split_var2) drop_spec<-data.frame(Groups=character(),Students=numeric(), Students_Percentage=numeric(), Dropouts=numeric(), Dropouts_Percentage=numeric(), Annual_Dropout_Rate=numeric(), School_Year=character()) drop_spec<-drop_spec[1:rnums,] drop_spec$Main<-table$Main drop_spec$Groups<-split_var[,1] drop_spec$Students<-split_var[,2] drop_spec$Students_Percentage<-split_var[,3] drop_spec$Dropouts<-split_var[,4] drop_spec$Dropouts_Percentage<-split_var[,5] drop_spec$Annual_Dropout_Rate<-split_var[,6] drop_spec$School_Year<-school_year[i] drop_spec$Students<-as.numeric(drop_spec$Students) drop_spec$Students_Percentage<-as.numeric(drop_spec$Students_Percentage) drop_spec$Dropouts<-as.numeric(drop_spec$Dropouts) drop_spec$Dropouts_Percentage<-as.numeric(drop_spec$Dropouts_Percentage) drop_spec$Annual_Dropout_Rate<-as.numeric(drop_spec$Annual_Dropout_Rate) drop_spec<-select(drop_spec,-Main) drop_spec<-rbind(drop_spec,NA,NA,NA,NA,NA,NA) drop_spec<-drop_spec[c(1,10,2,11,3,4,5,12,7,13,14,6,8,15,9),] } else if (i==6){ a<-paste0("https://tea.texas.gov/sites/default/files/DropComp_",school_year[i],".pdf") dropout<-pdf_text(a) p1<-strsplit(dropout, "\n") table1<-data.frame(p1[[146]][c(5,7:9)]) table2<-data.frame(p1[[147]][5:9]) rnums1<-nrow(table1) rnums2<-nrow(table2) table1$Main<-as.character(table1[1:rnums1,1]) table2$Main<-as.character(table2[1:rnums2,1]) table1$Main<-trimws(table1$Main, which="left") table2$Main<-trimws(table2$Main, which="left") table1$Main<-stripWhitespace(table1$Main) table2$Main<-stripWhitespace(table2$Main) table1$Main<-gsub("(?:Bilingual or English as a second language)","ESL", table1$Main) table1$Main<-gsub("(?:Gifted and talented)","GT", table1$Main) table1$Main<-gsub("(?:Special education)","Spec-Ed", table1$Main) table1$Main<-gsub("(?:Title I)","Title-I", table1$Main) table2$Main<-gsub("(?:At risk)","At-Risk", table2$Main) table2$Main<-gsub("(?:Limited English proficient)","ELL", table2$Main) table1$Main<-gsub("(?:,)","", table1$Main) table2$Main<-gsub("(?:,)","", table2$Main) split_var1<-as.data.frame(ldply(strsplit(table1$Main, split = " "))) split_var2<-as.data.frame(ldply(strsplit(table2$Main, split = " "))) split_var<-rbind(split_var1,split_var2) drop_spec<-data.frame(Groups=character(),Students=numeric(), Students_Percentage=numeric(), Dropouts=numeric(), Dropouts_Percentage=numeric(), Annual_Dropout_Rate=numeric(), School_Year=character()) drop_spec<-drop_spec[1:rnums,] drop_spec$Main<-table$Main drop_spec$Groups<-split_var[,1] drop_spec$Students<-split_var[,2] drop_spec$Students_Percentage<-split_var[,3] drop_spec$Dropouts<-split_var[,4] drop_spec$Dropouts_Percentage<-split_var[,5] drop_spec$Annual_Dropout_Rate<-split_var[,6] drop_spec$School_Year<-school_year[i] drop_spec$Students<-as.numeric(drop_spec$Students) drop_spec$Students_Percentage<-as.numeric(drop_spec$Students_Percentage) drop_spec$Dropouts<-as.numeric(drop_spec$Dropouts) drop_spec$Dropouts_Percentage<-as.numeric(drop_spec$Dropouts_Percentage) drop_spec$Annual_Dropout_Rate<-as.numeric(drop_spec$Annual_Dropout_Rate) drop_spec<-select(drop_spec,-Main) drop_spec<-rbind(drop_spec,NA,NA,NA,NA,NA,NA) drop_spec<-drop_spec[c(1,10,2,11,3,4,5,12,7,13,14,6,8,15,9),] } else if (i==7){ a<-paste0("https://tea.texas.gov/sites/default/files/DropComp_",school_year[i],".pdf") dropout<-pdf_text(a) p1<-strsplit(dropout, "\n") table1<-data.frame(p1[[154]][c(5,7:9)]) table2<-data.frame(p1[[155]][5:9]) rnums1<-nrow(table1) rnums2<-nrow(table2) table1$Main<-as.character(table1[1:rnums1,1]) table2$Main<-as.character(table2[1:rnums2,1]) table1$Main<-trimws(table1$Main, which="left") table2$Main<-trimws(table2$Main, which="left") table1$Main<-stripWhitespace(table1$Main) table2$Main<-stripWhitespace(table2$Main) table1$Main<-gsub("(?:Bilingual or English as a second language)","ESL", table1$Main) table1$Main<-gsub("(?:Gifted and talented)","GT", table1$Main) table1$Main<-gsub("(?:Special education)","Spec-Ed", table1$Main) table1$Main<-gsub("(?:Title I)","Title-I", table1$Main) table2$Main<-gsub("(?:At risk)","At-Risk", table2$Main) table2$Main<-gsub("(?:Limited English proficient)","ELL", table2$Main) table1$Main<-gsub("(?:,)","", table1$Main) table2$Main<-gsub("(?:,)","", table2$Main) split_var1<-as.data.frame(ldply(strsplit(table1$Main, split = " "))) split_var2<-as.data.frame(ldply(strsplit(table2$Main, split = " "))) split_var<-rbind(split_var1,split_var2) drop_spec<-data.frame(Groups=character(),Students=numeric(), Students_Percentage=numeric(), Dropouts=numeric(), Dropouts_Percentage=numeric(), Annual_Dropout_Rate=numeric(), School_Year=character()) drop_spec<-drop_spec[1:rnums,] drop_spec$Main<-table$Main drop_spec$Groups<-split_var[,1] drop_spec$Students<-split_var[,2] drop_spec$Students_Percentage<-split_var[,3] drop_spec$Dropouts<-split_var[,4] drop_spec$Dropouts_Percentage<-split_var[,5] drop_spec$Annual_Dropout_Rate<-split_var[,6] drop_spec$School_Year<-school_year[i] drop_spec$Students<-as.numeric(drop_spec$Students) drop_spec$Students_Percentage<-as.numeric(drop_spec$Students_Percentage) drop_spec$Dropouts<-as.numeric(drop_spec$Dropouts) drop_spec$Dropouts_Percentage<-as.numeric(drop_spec$Dropouts_Percentage) drop_spec$Annual_Dropout_Rate<-as.numeric(drop_spec$Annual_Dropout_Rate) drop_spec<-select(drop_spec,-Main) drop_spec<-rbind(drop_spec,NA,NA,NA,NA,NA,NA) drop_spec<-drop_spec[c(1,10,2,11,3,4,5,12,7,13,14,6,8,15,9),] } else if (i==8){ a<-paste0("https://tea.texas.gov/sites/default/files/DropComp_",school_year[i],".pdf") dropout<-pdf_text(a) p1<-strsplit(dropout, "\n") table<-data.frame(p1[[60]][c(6,8:10,22:26)]) rnums<-nrow(table) table$Main<-as.character(table[1:rnums,1]) table$Main<-trimws(table$Main, which="left") table$Main<-stripWhitespace(table$Main) table$Main<-gsub("(?:Bilingual or ESLa)","ESL", table$Main) table$Main<-gsub("(?:Bilingual or English as a second language)","ESL", table$Main) table$Main<-gsub("(?:Second Language)","ESL", table$Main) table$Main<-gsub("(?:CTEb)","Career-Technical", table$Main) table$Main<-gsub("(?:Gifted and talented)","GT", table$Main) table$Main<-gsub("(?:Gifted/Talented)","GT", table$Main) table$Main<-gsub("(?:Section 504)","504", table$Main) table$Main<-gsub("(?:Special education)","Spec-Ed", table$Main) table$Main<-gsub("(?:Special Education)","Spec-Ed", table$Main) table$Main<-gsub("(?:Title I)","Title-I", table$Main) table$Main<-gsub("(?:At-risk)","At-Risk", table$Main) table$Main<-gsub("(?:Limited English proficient)","ELL", table$Main) table$Main<-gsub("(?:English learner)","ELL", table$Main) table$Main<-gsub("(?:Foster care)","Foster-Care", table$Main) table$Main<-gsub("(?:Overage/Not on Grade)","Overage", table$Main) table$Main<-gsub("(?:,)","", table$Main) split_var<-as.data.frame(ldply(strsplit(table$Main, split = " "))) drop_spec<-data.frame(Groups=character(),Students=numeric(), Students_Percentage=numeric(), Dropouts=numeric(), Dropouts_Percentage=numeric(), Annual_Dropout_Rate=numeric(), School_Year=character()) drop_spec<-drop_spec[1:rnums,] drop_spec$Main<-table$Main drop_spec$Groups<-split_var[,1] drop_spec$Students<-split_var[,2] drop_spec$Students_Percentage<-split_var[,3] drop_spec$Dropouts<-split_var[,4] drop_spec$Dropouts_Percentage<-split_var[,5] drop_spec$Annual_Dropout_Rate<-split_var[,6] drop_spec$School_Year<-school_year[i] drop_spec$Students<-as.numeric(drop_spec$Students) drop_spec$Students_Percentage<-as.numeric(drop_spec$Students_Percentage) drop_spec$Dropouts<-as.numeric(drop_spec$Dropouts) drop_spec$Dropouts_Percentage<-as.numeric(drop_spec$Dropouts_Percentage) drop_spec$Annual_Dropout_Rate<-as.numeric(drop_spec$Annual_Dropout_Rate) drop_spec<-select(drop_spec,-Main) drop_spec<-rbind(drop_spec,NA,NA,NA,NA,NA,NA) drop_spec<-drop_spec[c(1,10,2,11,3,4,5,12,7,13,14,6,8,15,9),] } else if (i==9){ a<-paste0("https://tea.texas.gov/sites/default/files/DropComp_",school_year[i],".pdf") dropout<-pdf_text(a) p1<-strsplit(dropout, "\n") table<-data.frame(p1[[61]][c(21,23:25,37:41)]) rnums<-nrow(table) table$Main<-as.character(table[1:rnums,1]) table$Main<-trimws(table$Main, which="left") table$Main<-stripWhitespace(table$Main) table$Main<-gsub("(?:Bilingual or ESLa)","ESL", table$Main) table$Main<-gsub("(?:Bilingual or English as a second language)","ESL", table$Main) table$Main<-gsub("(?:Second Language)","ESL", table$Main) table$Main<-gsub("(?:CTEb)","Career-Technical", table$Main) table$Main<-gsub("(?:Gifted and talented)","GT", table$Main) table$Main<-gsub("(?:Gifted/Talented)","GT", table$Main) table$Main<-gsub("(?:Section 504)","504", table$Main) table$Main<-gsub("(?:Special education)","Spec-Ed", table$Main) table$Main<-gsub("(?:Special Education)","Spec-Ed", table$Main) table$Main<-gsub("(?:Title I)","Title-I", table$Main) table$Main<-gsub("(?:At-risk)","At-Risk", table$Main) table$Main<-gsub("(?:Limited English proficient)","ELL", table$Main) table$Main<-gsub("(?:English learner)","ELL", table$Main) table$Main<-gsub("(?:Foster care)","Foster-Care", table$Main) table$Main<-gsub("(?:Overage/Not on Grade)","Overage", table$Main) table$Main<-gsub("(?:,)","", table$Main) split_var<-as.data.frame(ldply(strsplit(table$Main, split = " "))) drop_spec<-data.frame(Groups=character(),Students=numeric(), Students_Percentage=numeric(), Dropouts=numeric(), Dropouts_Percentage=numeric(), Annual_Dropout_Rate=numeric(), School_Year=character()) drop_spec<-drop_spec[1:rnums,] drop_spec$Main<-table$Main drop_spec$Groups<-split_var[,1] drop_spec$Students<-split_var[,2] drop_spec$Students_Percentage<-split_var[,3] drop_spec$Dropouts<-split_var[,4] drop_spec$Dropouts_Percentage<-split_var[,5] drop_spec$Annual_Dropout_Rate<-split_var[,6] drop_spec$School_Year<-school_year[i] drop_spec$Students<-as.numeric(drop_spec$Students) drop_spec$Students_Percentage<-as.numeric(drop_spec$Students_Percentage) drop_spec$Dropouts<-as.numeric(drop_spec$Dropouts) drop_spec$Dropouts_Percentage<-as.numeric(drop_spec$Dropouts_Percentage) drop_spec$Annual_Dropout_Rate<-as.numeric(drop_spec$Annual_Dropout_Rate) drop_spec<-select(drop_spec,-Main) drop_spec<-rbind(drop_spec,NA,NA,NA,NA,NA,NA) drop_spec<-drop_spec[c(1,10,2,11,3,4,5,12,7,13,14,6,8,15,9),] } else if (i==10){ a<-paste0("https://tea.texas.gov/sites/default/files/DropComp_",school_year[i],".pdf") dropout<-pdf_text(a) p1<-strsplit(dropout, "\n") table<-data.frame(p1[[62]][c(17,19:21,35:39)]) rnums<-nrow(table) table$Main<-as.character(table[1:rnums,1]) table$Main<-trimws(table$Main, which="left") table$Main<-stripWhitespace(table$Main) table$Main<-gsub("(?:Bilingual or ESLa)","ESL", table$Main) table$Main<-gsub("(?:Bilingual or English as a second language)","ESL", table$Main) table$Main<-gsub("(?:Second Language)","ESL", table$Main) table$Main<-gsub("(?:CTEb)","Career-Technical", table$Main) table$Main<-gsub("(?:Gifted and talented)","GT", table$Main) table$Main<-gsub("(?:Gifted/Talented)","GT", table$Main) table$Main<-gsub("(?:Section 504)","504", table$Main) table$Main<-gsub("(?:Special education)","Spec-Ed", table$Main) table$Main<-gsub("(?:Special Education)","Spec-Ed", table$Main) table$Main<-gsub("(?:Title I)","Title-I", table$Main) table$Main<-gsub("(?:At-risk)","At-Risk", table$Main) table$Main<-gsub("(?:Limited English proficient)","ELL", table$Main) table$Main<-gsub("(?:English learner)","ELL", table$Main) table$Main<-gsub("(?:Foster care)","Foster-Care", table$Main) table$Main<-gsub("(?:Overage/Not on Grade)","Overage", table$Main) table$Main<-gsub("(?:,)","", table$Main) split_var<-as.data.frame(ldply(strsplit(table$Main, split = " "))) drop_spec<-data.frame(Groups=character(),Students=numeric(), Students_Percentage=numeric(), Dropouts=numeric(), Dropouts_Percentage=numeric(), Annual_Dropout_Rate=numeric(), School_Year=character()) drop_spec<-drop_spec[1:rnums,] drop_spec$Main<-table$Main drop_spec$Groups<-split_var[,1] drop_spec$Students<-split_var[,2] drop_spec$Students_Percentage<-split_var[,3] drop_spec$Dropouts<-split_var[,4] drop_spec$Dropouts_Percentage<-split_var[,5] drop_spec$Annual_Dropout_Rate<-split_var[,6] drop_spec$School_Year<-school_year[i] drop_spec$Students<-as.numeric(drop_spec$Students) drop_spec$Students_Percentage<-as.numeric(drop_spec$Students_Percentage) drop_spec$Dropouts<-as.numeric(drop_spec$Dropouts) drop_spec$Dropouts_Percentage<-as.numeric(drop_spec$Dropouts_Percentage) drop_spec$Annual_Dropout_Rate<-as.numeric(drop_spec$Annual_Dropout_Rate) drop_spec<-select(drop_spec,-Main) drop_spec<-rbind(drop_spec,NA,NA,NA,NA,NA,NA) drop_spec<-drop_spec[c(1,10,2,11,3,4,5,12,7,13,14,6,8,15,9),] } else if (i==11){ a<-paste0("https://tea.texas.gov/sites/default/files/DropComp_",school_year[i],".pdf") dropout<-pdf_text(a) p1<-strsplit(dropout, "\n") table1<-data.frame(p1[[67]][c(30,32:34)]) table2<-data.frame(p1[[68]][6:10]) rnums1<-nrow(table1) rnums2<-nrow(table2) table1$Main<-as.character(table1[1:rnums1,1]) table2$Main<-as.character(table2[1:rnums2,1]) table1$Main<-trimws(table1$Main, which="left") table2$Main<-trimws(table2$Main, which="left") table1$Main<-stripWhitespace(table1$Main) table2$Main<-stripWhitespace(table2$Main) table1$Main<-gsub("(?:Bilingual or English as a second language)","ESL", table1$Main) table1$Main<-gsub("(?:Gifted and talented)","GT", table1$Main) table1$Main<-gsub("(?:Special education)","Spec-Ed", table1$Main) table1$Main<-gsub("(?:Title I)","Title-I", table1$Main) table2$Main<-gsub("(?:At-risk)","At-Risk", table2$Main) table2$Main<-gsub("(?:Limited English proficient)","ELL", table2$Main) table1$Main<-gsub("(?:,)","", table1$Main) table2$Main<-gsub("(?:,)","", table2$Main) split_var1<-as.data.frame(ldply(strsplit(table1$Main, split = " "))) split_var2<-as.data.frame(ldply(strsplit(table2$Main, split = " "))) split_var<-rbind(split_var1,split_var2) drop_spec<-data.frame(Groups=character(),Students=numeric(), Students_Percentage=numeric(), Dropouts=numeric(), Dropouts_Percentage=numeric(), Annual_Dropout_Rate=numeric(), School_Year=character()) drop_spec<-drop_spec[1:rnums,] drop_spec$Main<-table$Main drop_spec$Groups<-split_var[,1] drop_spec$Students<-split_var[,2] drop_spec$Students_Percentage<-split_var[,3] drop_spec$Dropouts<-split_var[,4] drop_spec$Dropouts_Percentage<-split_var[,5] drop_spec$Annual_Dropout_Rate<-split_var[,6] drop_spec$School_Year<-school_year[i] drop_spec$Students<-as.numeric(drop_spec$Students) drop_spec$Students_Percentage<-as.numeric(drop_spec$Students_Percentage) drop_spec$Dropouts<-as.numeric(drop_spec$Dropouts) drop_spec$Dropouts_Percentage<-as.numeric(drop_spec$Dropouts_Percentage) drop_spec$Annual_Dropout_Rate<-as.numeric(drop_spec$Annual_Dropout_Rate) drop_spec<-select(drop_spec,-Main) drop_spec<-rbind(drop_spec,NA,NA,NA,NA,NA,NA) drop_spec<-drop_spec[c(1,10,2,11,3,4,5,12,7,13,14,6,8,15,9),] } else if (i==12){ a<-paste0("https://tea.texas.gov/sites/default/files/DropComp_",school_year[i],".pdf") dropout<-pdf_text(a) p1<-strsplit(dropout, "\n") table<-data.frame(p1[[75]][c(15,17:19,33:37)]) rnums<-nrow(table) table$Main<-as.character(table[1:rnums,1]) table$Main<-trimws(table$Main, which="left") table$Main<-stripWhitespace(table$Main) table$Main<-gsub("(?:Bilingual or ESLa)","ESL", table$Main) table$Main<-gsub("(?:Bilingual or English as a second language)","ESL", table$Main) table$Main<-gsub("(?:Second Language)","ESL", table$Main) table$Main<-gsub("(?:CTEb)","Career-Technical", table$Main) table$Main<-gsub("(?:Gifted and talented)","GT", table$Main) table$Main<-gsub("(?:Gifted/Talented)","GT", table$Main) table$Main<-gsub("(?:Section 504)","504", table$Main) table$Main<-gsub("(?:Special education)","Spec-Ed", table$Main) table$Main<-gsub("(?:Special Education)","Spec-Ed", table$Main) table$Main<-gsub("(?:Title I)","Title-I", table$Main) table$Main<-gsub("(?:At-risk)","At-Risk", table$Main) table$Main<-gsub("(?:Limited English proficient)","ELL", table$Main) table$Main<-gsub("(?:English learner)","ELL", table$Main) table$Main<-gsub("(?:Foster care)","Foster-Care", table$Main) table$Main<-gsub("(?:Overage/Not on Grade)","Overage", table$Main) table$Main<-gsub("(?:,)","", table$Main) split_var<-as.data.frame(ldply(strsplit(table$Main, split = " "))) drop_spec<-data.frame(Groups=character(),Students=numeric(), Students_Percentage=numeric(), Dropouts=numeric(), Dropouts_Percentage=numeric(), Annual_Dropout_Rate=numeric(), School_Year=character()) drop_spec<-drop_spec[1:rnums,] drop_spec$Main<-table$Main drop_spec$Groups<-split_var[,1] drop_spec$Students<-split_var[,2] drop_spec$Students_Percentage<-split_var[,3] drop_spec$Dropouts<-split_var[,4] drop_spec$Dropouts_Percentage<-split_var[,5] drop_spec$Annual_Dropout_Rate<-split_var[,6] drop_spec$School_Year<-school_year[i] drop_spec$Students<-as.numeric(drop_spec$Students) drop_spec$Students_Percentage<-as.numeric(drop_spec$Students_Percentage) drop_spec$Dropouts<-as.numeric(drop_spec$Dropouts) drop_spec$Dropouts_Percentage<-as.numeric(drop_spec$Dropouts_Percentage) drop_spec$Annual_Dropout_Rate<-as.numeric(drop_spec$Annual_Dropout_Rate) drop_spec<-select(drop_spec,-Main) drop_spec<-rbind(drop_spec,NA,NA,NA,NA,NA,NA) drop_spec<-drop_spec[c(1,10,2,11,3,4,5,12,7,13,14,6,8,15,9),] } else if (i==13){ a<-paste0("https://tea.texas.gov/sites/default/files/DropComp_",school_year[i],".pdf") dropout<-pdf_text(a) p1<-strsplit(dropout, "\n") table<-data.frame(p1[[76]][c(6,8:10,24:28)]) rnums<-nrow(table) table$Main<-as.character(table[1:rnums,1]) table$Main<-trimws(table$Main, which="left") table$Main<-stripWhitespace(table$Main) table$Main<-gsub("(?:Bilingual or ESLa)","ESL", table$Main) table$Main<-gsub("(?:Bilingual or English as a second language)","ESL", table$Main) table$Main<-gsub("(?:Second Language)","ESL", table$Main) table$Main<-gsub("(?:CTEb)","Career-Technical", table$Main) table$Main<-gsub("(?:Gifted and talented)","GT", table$Main) table$Main<-gsub("(?:Gifted/Talented)","GT", table$Main) table$Main<-gsub("(?:Section 504)","504", table$Main) table$Main<-gsub("(?:Special education)","Spec-Ed", table$Main) table$Main<-gsub("(?:Special Education)","Spec-Ed", table$Main) table$Main<-gsub("(?:Title I)","Title-I", table$Main) table$Main<-gsub("(?:At-risk)","At-Risk", table$Main) table$Main<-gsub("(?:Limited English proficient)","ELL", table$Main) table$Main<-gsub("(?:English learner)","ELL", table$Main) table$Main<-gsub("(?:Foster care)","Foster-Care", table$Main) table$Main<-gsub("(?:Overage/Not on Grade)","Overage", table$Main) table$Main<-gsub("(?:,)","", table$Main) split_var<-as.data.frame(ldply(strsplit(table$Main, split = " "))) drop_spec<-data.frame(Groups=character(),Students=numeric(), Students_Percentage=numeric(), Dropouts=numeric(), Dropouts_Percentage=numeric(), Annual_Dropout_Rate=numeric(), School_Year=character()) drop_spec<-drop_spec[1:rnums,] drop_spec$Main<-table$Main drop_spec$Groups<-split_var[,1] drop_spec$Students<-split_var[,2] drop_spec$Students_Percentage<-split_var[,3] drop_spec$Dropouts<-split_var[,4] drop_spec$Dropouts_Percentage<-split_var[,5] drop_spec$Annual_Dropout_Rate<-split_var[,6] drop_spec$School_Year<-school_year[i] drop_spec$Students<-as.numeric(drop_spec$Students) drop_spec$Students_Percentage<-as.numeric(drop_spec$Students_Percentage) drop_spec$Dropouts<-as.numeric(drop_spec$Dropouts) drop_spec$Dropouts_Percentage<-as.numeric(drop_spec$Dropouts_Percentage) drop_spec$Annual_Dropout_Rate<-as.numeric(drop_spec$Annual_Dropout_Rate) drop_spec<-select(drop_spec,-Main) drop_spec<-rbind(drop_spec,NA,NA,NA,NA,NA,NA) drop_spec<-drop_spec[c(1,10,2,11,3,4,5,12,7,13,14,6,8,15,9),] } else if (i==14){ a<-paste0("https://tea.texas.gov/sites/default/files/DropComp_",school_year[i],".pdf") dropout<-pdf_text(a) p1<-strsplit(dropout, "\n") table<-data.frame(p1[[76]][c(6:10,25:29)]) rnums<-nrow(table) table$Main<-as.character(table[1:rnums,1]) table$Main<-trimws(table$Main, which="left") table$Main<-stripWhitespace(table$Main) table$Main<-gsub("(?:Bilingual or ESLa)","ESL", table$Main) table$Main<-gsub("(?:Bilingual or English as a second language)","ESL", table$Main) table$Main<-gsub("(?:Second Language)","ESL", table$Main) table$Main<-gsub("(?:CTEb)","Career-Technical", table$Main) table$Main<-gsub("(?:Gifted and talented)","GT", table$Main) table$Main<-gsub("(?:Gifted/Talented)","GT", table$Main) table$Main<-gsub("(?:Section 504)","504", table$Main) table$Main<-gsub("(?:Special education)","Spec-Ed", table$Main) table$Main<-gsub("(?:Special Education)","Spec-Ed", table$Main) table$Main<-gsub("(?:Title I)","Title-I", table$Main) table$Main<-gsub("(?:At-risk)","At-Risk", table$Main) table$Main<-gsub("(?:Limited English proficient)","ELL", table$Main) table$Main<-gsub("(?:English learner)","ELL", table$Main) table$Main<-gsub("(?:English language learner)","ELL", table$Main) table$Main<-gsub("(?:Foster care)","Foster-Care", table$Main) table$Main<-gsub("(?:Overage/Not on Grade)","Overage", table$Main) table$Main<-gsub("(?:,)","", table$Main) split_var<-as.data.frame(ldply(strsplit(table$Main, split = " "))) drop_spec<-data.frame(Groups=character(),Students=numeric(), Students_Percentage=numeric(), Dropouts=numeric(), Dropouts_Percentage=numeric(), Annual_Dropout_Rate=numeric(), School_Year=character()) drop_spec<-drop_spec[1:rnums,] drop_spec$Main<-table$Main drop_spec$Groups<-split_var[,1] drop_spec$Students<-split_var[,2] drop_spec$Students_Percentage<-split_var[,3] drop_spec$Dropouts<-split_var[,4] drop_spec$Dropouts_Percentage<-split_var[,5] drop_spec$Annual_Dropout_Rate<-split_var[,6] drop_spec$School_Year<-school_year[i] drop_spec$Students<-as.numeric(drop_spec$Students) drop_spec$Students_Percentage<-as.numeric(drop_spec$Students_Percentage) drop_spec$Dropouts<-as.numeric(drop_spec$Dropouts) drop_spec$Dropouts_Percentage<-as.numeric(drop_spec$Dropouts_Percentage) drop_spec$Annual_Dropout_Rate<-as.numeric(drop_spec$Annual_Dropout_Rate) drop_spec<-select(drop_spec,-Main) drop_spec<-rbind(drop_spec,NA,NA,NA,NA,NA) drop_spec<-drop_spec[c(1,2,3,11,4,5,6,12,7,13,14,8,9,15,10),] } else if (i==15){ a<-paste0("https://tea.texas.gov/sites/default/files/DropComp_",school_year[i],".pdf") dropout<-pdf_text(a) p1<-strsplit(dropout, "\n") table<-data.frame(p1[[76]][c(15:19,34:38)]) rnums<-nrow(table) table$Main<-as.character(table[1:rnums,1]) table$Main<-trimws(table$Main, which="left") table$Main<-stripWhitespace(table$Main) table$Main<-gsub("(?:Bilingual or ESLa)","ESL", table$Main) table$Main<-gsub("(?:Bilingual or English as a second language)","ESL", table$Main) table$Main<-gsub("(?:Second Language)","ESL", table$Main) table$Main<-gsub("(?:CTEb)","Career-Technical", table$Main) table$Main<-gsub("(?:Gifted and talented)","GT", table$Main) table$Main<-gsub("(?:Gifted/Talented)","GT", table$Main) table$Main<-gsub("(?:Section 504)","504", table$Main) table$Main<-gsub("(?:Special education)","Spec-Ed", table$Main) table$Main<-gsub("(?:Special Education)","Spec-Ed", table$Main) table$Main<-gsub("(?:Title I)","Title-I", table$Main) table$Main<-gsub("(?:At-risk)","At-Risk", table$Main) table$Main<-gsub("(?:Limited English proficient)","ELL", table$Main) table$Main<-gsub("(?:English learner)","ELL", table$Main) table$Main<-gsub("(?:English language learner)","ELL", table$Main) table$Main<-gsub("(?:Foster care)","Foster-Care", table$Main) table$Main<-gsub("(?:Overage/Not on Grade)","Overage", table$Main) table$Main<-gsub("(?:,)","", table$Main) split_var<-as.data.frame(ldply(strsplit(table$Main, split = " "))) drop_spec<-data.frame(Groups=character(),Students=numeric(), Students_Percentage=numeric(), Dropouts=numeric(), Dropouts_Percentage=numeric(), Annual_Dropout_Rate=numeric(), School_Year=character()) drop_spec<-drop_spec[1:rnums,] drop_spec$Main<-table$Main drop_spec$Groups<-split_var[,1] drop_spec$Students<-split_var[,2] drop_spec$Students_Percentage<-split_var[,3] drop_spec$Dropouts<-split_var[,4] drop_spec$Dropouts_Percentage<-split_var[,5] drop_spec$Annual_Dropout_Rate<-split_var[,6] drop_spec$School_Year<-school_year[i] drop_spec$Students<-as.numeric(drop_spec$Students) drop_spec$Students_Percentage<-as.numeric(drop_spec$Students_Percentage) drop_spec$Dropouts<-as.numeric(drop_spec$Dropouts) drop_spec$Dropouts_Percentage<-as.numeric(drop_spec$Dropouts_Percentage) drop_spec$Annual_Dropout_Rate<-as.numeric(drop_spec$Annual_Dropout_Rate) drop_spec<-select(drop_spec,-Main) drop_spec<-rbind(drop_spec,NA,NA,NA,NA,NA) drop_spec<-drop_spec[c(1,2,3,11,4,5,6,12,7,13,14,8,9,15,10),] } else if (i==15){ a<-paste0("https://tea.texas.gov/sites/default/files/DropComp_",school_year[i],".pdf") dropout<-pdf_text(a) p1<-strsplit(dropout, "\n") table<-data.frame(p1[[76]][c(15:19,34:38)]) rnums<-nrow(table) table$Main<-as.character(table[1:rnums,1]) table$Main<-trimws(table$Main, which="left") table$Main<-stripWhitespace(table$Main) table$Main<-gsub("(?:Bilingual or ESLa)","ESL", table$Main) table$Main<-gsub("(?:Bilingual or English as a second language)","ESL", table$Main) table$Main<-gsub("(?:Second Language)","ESL", table$Main) table$Main<-gsub("(?:CTEb)","Career-Technical", table$Main) table$Main<-gsub("(?:Gifted and talented)","GT", table$Main) table$Main<-gsub("(?:Gifted/Talented)","GT", table$Main) table$Main<-gsub("(?:Section 504)","504", table$Main) table$Main<-gsub("(?:Special education)","Spec-Ed", table$Main) table$Main<-gsub("(?:Special Education)","Spec-Ed", table$Main) table$Main<-gsub("(?:Title I)","Title-I", table$Main) table$Main<-gsub("(?:At-risk)","At-Risk", table$Main) table$Main<-gsub("(?:Limited English proficient)","ELL", table$Main) table$Main<-gsub("(?:English learner)","ELL", table$Main) table$Main<-gsub("(?:English language learner)","ELL", table$Main) table$Main<-gsub("(?:Foster care)","Foster-Care", table$Main) table$Main<-gsub("(?:Overage/Not on Grade)","Overage", table$Main) table$Main<-gsub("(?:,)","", table$Main) split_var<-as.data.frame(ldply(strsplit(table$Main, split = " "))) drop_spec<-data.frame(Groups=character(),Students=numeric(), Students_Percentage=numeric(), Dropouts=numeric(), Dropouts_Percentage=numeric(), Annual_Dropout_Rate=numeric(), School_Year=character()) drop_spec<-drop_spec[1:rnums,] drop_spec$Main<-table$Main drop_spec$Groups<-split_var[,1] drop_spec$Students<-split_var[,2] drop_spec$Students_Percentage<-split_var[,3] drop_spec$Dropouts<-split_var[,4] drop_spec$Dropouts_Percentage<-split_var[,5] drop_spec$Annual_Dropout_Rate<-split_var[,6] drop_spec$School_Year<-school_year[i] drop_spec$Students<-as.numeric(drop_spec$Students) drop_spec$Students_Percentage<-as.numeric(drop_spec$Students_Percentage) drop_spec$Dropouts<-as.numeric(drop_spec$Dropouts) drop_spec$Dropouts_Percentage<-as.numeric(drop_spec$Dropouts_Percentage) drop_spec$Annual_Dropout_Rate<-as.numeric(drop_spec$Annual_Dropout_Rate) drop_spec<-select(drop_spec,-Main) drop_spec<-rbind(drop_spec,NA,NA,NA,NA,NA) drop_spec<-drop_spec[c(1,2,3,11,4,5,6,12,7,13,14,8,9,15,10),] } else if (i==16){ a<-paste0("https://tea.texas.gov/sites/default/files/DropComp_",school_year[i],".pdf") dropout<-pdf_text(a) p1<-strsplit(dropout, "\n") table<-data.frame(p1[[79]][c(20:24,40:43)]) rnums<-nrow(table) table$Main<-as.character(table[1:rnums,1]) table$Main<-trimws(table$Main, which="left") table$Main<-stripWhitespace(table$Main) table$Main<-gsub("(?:Bilingual or ESLa)","ESL", table$Main) table$Main<-gsub("(?:Bilingual or English as a second language)","ESL", table$Main) table$Main<-gsub("(?:Second Language)","ESL", table$Main) table$Main<-gsub("(?:CTEb)","Career-Technical", table$Main) table$Main<-gsub("(?:Gifted and talented)","GT", table$Main) table$Main<-gsub("(?:Gifted/Talented)","GT", table$Main) table$Main<-gsub("(?:Section 504)","504", table$Main) table$Main<-gsub("(?:Special education)","Spec-Ed", table$Main) table$Main<-gsub("(?:Special Education)","Spec-Ed", table$Main) table$Main<-gsub("(?:Title I)","Title-I", table$Main) table$Main<-gsub("(?:At-risk)","At-Risk", table$Main) table$Main<-gsub("(?:Limited English proficient)","ELL", table$Main) table$Main<-gsub("(?:English learner)","ELL", table$Main) table$Main<-gsub("(?:English language learner)","ELL", table$Main) table$Main<-gsub("(?:Foster care)","Foster-Care", table$Main) table$Main<-gsub("(?:Overage/Not on Grade)","Overage", table$Main) table$Main<-gsub("(?:,)","", table$Main) split_var<-as.data.frame(ldply(strsplit(table$Main, split = " "))) drop_spec<-data.frame(Groups=character(),Students=numeric(), Students_Percentage=numeric(), Dropouts=numeric(), Dropouts_Percentage=numeric(), Annual_Dropout_Rate=numeric(), School_Year=character()) drop_spec<-drop_spec[1:rnums,] drop_spec$Main<-table$Main drop_spec$Groups<-split_var[,1] drop_spec$Students<-split_var[,2] drop_spec$Students_Percentage<-split_var[,3] drop_spec$Dropouts<-split_var[,4] drop_spec$Dropouts_Percentage<-split_var[,5] drop_spec$Annual_Dropout_Rate<-split_var[,6] drop_spec$School_Year<-school_year[i] drop_spec$Students<-as.numeric(drop_spec$Students) drop_spec$Students_Percentage<-as.numeric(drop_spec$Students_Percentage) drop_spec$Dropouts<-as.numeric(drop_spec$Dropouts) drop_spec$Dropouts_Percentage<-as.numeric(drop_spec$Dropouts_Percentage) drop_spec$Annual_Dropout_Rate<-as.numeric(drop_spec$Annual_Dropout_Rate) drop_spec<-select(drop_spec,-Main) drop_spec<-rbind(drop_spec,NA,NA,NA,NA,NA) drop_spec<-drop_spec[c(1,2,3,11,4,5,6,12,7,13,14,8,9,15,10),] } else if (i==17){ b<-paste0("https://tea.texas.gov/sites/default/files/dropcomp_",school_year[i],".pdf") dropout<-pdf_text(b) p2<-strsplit(dropout, "\n") table<-data.frame(p2[[79]][c(19:23,38:42)]) rnums<-nrow(table) table$Main<-as.character(table[1:rnums,1]) table$Main<-trimws(table$Main, which="left") table$Main<-stripWhitespace(table$Main) table$Main<-gsub("(?:Bilingual or ESLa)","ESL", table$Main) table$Main<-gsub("(?:Bilingual or English as a second language)","ESL", table$Main) table$Main<-gsub("(?:Second Language)","ESL", table$Main) table$Main<-gsub("(?:CTEb)","Career-Technical", table$Main) table$Main<-gsub("(?:Gifted and talented)","GT", table$Main) table$Main<-gsub("(?:Gifted/Talented)","GT", table$Main) table$Main<-gsub("(?:Section 504)","504", table$Main) table$Main<-gsub("(?:Special education)","Spec-Ed", table$Main) table$Main<-gsub("(?:Special Education)","Spec-Ed", table$Main) table$Main<-gsub("(?:Title I)","Title-I", table$Main) table$Main<-gsub("(?:At-risk)","At-Risk", table$Main) table$Main<-gsub("(?:Limited English proficient)","ELL", table$Main) table$Main<-gsub("(?:English learner)","ELL", table$Main) table$Main<-gsub("(?:English language learner)","ELL", table$Main) table$Main<-gsub("(?:Foster care)","Foster-Care", table$Main) table$Main<-gsub("(?:Overage/Not on Grade)","Overage", table$Main) table$Main<-gsub("(?:,)","", table$Main) split_var<-as.data.frame(ldply(strsplit(table$Main, split = " "))) drop_spec<-data.frame(Groups=character(),Students=numeric(), Students_Percentage=numeric(), Dropouts=numeric(), Dropouts_Percentage=numeric(), Annual_Dropout_Rate=numeric(), School_Year=character()) drop_spec<-drop_spec[1:rnums,] drop_spec$Main<-table$Main drop_spec$Groups<-split_var[,1] drop_spec$Students<-split_var[,2] drop_spec$Students_Percentage<-split_var[,3] drop_spec$Dropouts<-split_var[,4] drop_spec$Dropouts_Percentage<-split_var[,5] drop_spec$Annual_Dropout_Rate<-split_var[,6] drop_spec$School_Year<-school_year[i] drop_spec$Students<-as.numeric(drop_spec$Students) drop_spec$Students_Percentage<-as.numeric(drop_spec$Students_Percentage) drop_spec$Dropouts<-as.numeric(drop_spec$Dropouts) drop_spec$Dropouts_Percentage<-as.numeric(drop_spec$Dropouts_Percentage) drop_spec$Annual_Dropout_Rate<-as.numeric(drop_spec$Annual_Dropout_Rate) drop_spec<-select(drop_spec,-Main) drop_spec<-rbind(drop_spec,NA,NA,NA,NA,NA) drop_spec<-drop_spec[c(1,2,3,11,4,5,6,12,7,13,14,8,9,15,10),] } else if (i==18){ b<-paste0("https://tea.texas.gov/sites/default/files/dropcomp_",school_year[i],".pdf") dropout<-pdf_text(b) p2<-strsplit(dropout, "\n") table<-data.frame(p2[[79]][c(20:24,38:43)]) rnums<-nrow(table) table$Main<-as.character(table[1:rnums,1]) table$Main<-trimws(table$Main, which="left") table$Main<-stripWhitespace(table$Main) table$Main<-gsub("(?:Bilingual or ESLa)","ESL", table$Main) table$Main<-gsub("(?:Bilingual or English as a second language)","ESL", table$Main) table$Main<-gsub("(?:Second Language)","ESL", table$Main) table$Main<-gsub("(?:CTEb)","Career-Technical", table$Main) table$Main<-gsub("(?:Gifted and talented)","GT", table$Main) table$Main<-gsub("(?:Gifted/Talented)","GT", table$Main) table$Main<-gsub("(?:Section 504)","504", table$Main) table$Main<-gsub("(?:Special education)","Spec-Ed", table$Main) table$Main<-gsub("(?:Special Education)","Spec-Ed", table$Main) table$Main<-gsub("(?:Title I)","Title-I", table$Main) table$Main<-gsub("(?:At-risk)","At-Risk", table$Main) table$Main<-gsub("(?:Limited English proficient)","ELL", table$Main) table$Main<-gsub("(?:English learner)","ELL", table$Main) table$Main<-gsub("(?:English language learner)","ELL", table$Main) table$Main<-gsub("(?:Foster care)","Foster-Care", table$Main) table$Main<-gsub("(?:Overage/Not on Grade)","Overage", table$Main) table$Main<-gsub("(?:,)","", table$Main) split_var<-as.data.frame(ldply(strsplit(table$Main, split = " "))) drop_spec<-data.frame(Groups=character(),Students=numeric(), Students_Percentage=numeric(), Dropouts=numeric(), Dropouts_Percentage=numeric(), Annual_Dropout_Rate=numeric(), School_Year=character()) drop_spec<-drop_spec[1:rnums,] drop_spec$Main<-table$Main drop_spec$Groups<-split_var[,1] drop_spec$Students<-split_var[,2] drop_spec$Students_Percentage<-split_var[,3] drop_spec$Dropouts<-split_var[,4] drop_spec$Dropouts_Percentage<-split_var[,5] drop_spec$Annual_Dropout_Rate<-split_var[,6] drop_spec$School_Year<-school_year[i] drop_spec$Students<-as.numeric(drop_spec$Students) drop_spec$Students_Percentage<-as.numeric(drop_spec$Students_Percentage) drop_spec$Dropouts<-as.numeric(drop_spec$Dropouts) drop_spec$Dropouts_Percentage<-as.numeric(drop_spec$Dropouts_Percentage) drop_spec$Annual_Dropout_Rate<-as.numeric(drop_spec$Annual_Dropout_Rate) drop_spec<-select(drop_spec,-Main) drop_spec<-rbind(drop_spec,NA,NA,NA,NA) drop_spec<-drop_spec[c(1,2,3,12,4,5,6,13,7,14,8,9,10,15,11),] } else if (i==19){ b<-paste0("https://tea.texas.gov/sites/default/files/dropcomp_",school_year[i],".pdf") dropout<-pdf_text(b) p2<-strsplit(dropout, "\n") table<-data.frame(p2[[84]][c(18:22,36:43)]) rnums<-nrow(table) table$Main<-as.character(table[1:rnums,1]) table$Main<-trimws(table$Main, which="left") table$Main<-stripWhitespace(table$Main) table$Main<-gsub("(?:Bilingual or ESLa)","ESL", table$Main) table$Main<-gsub("(?:Bilingual or English as a second language)","ESL", table$Main) table$Main<-gsub("(?:Second Language)","ESL", table$Main) table$Main<-gsub("(?:CTEb)","Career-Technical", table$Main) table$Main<-gsub("(?:Gifted and talented)","GT", table$Main) table$Main<-gsub("(?:Gifted/Talented)","GT", table$Main) table$Main<-gsub("(?:Section 504)","504", table$Main) table$Main<-gsub("(?:Special education)","Spec-Ed", table$Main) table$Main<-gsub("(?:Special Education)","Spec-Ed", table$Main) table$Main<-gsub("(?:Title I)","Title-I", table$Main) table$Main<-gsub("(?:At-risk)","At-Risk", table$Main) table$Main<-gsub("(?:Limited English proficient)","ELL", table$Main) table$Main<-gsub("(?:English learner)","ELL", table$Main) table$Main<-gsub("(?:English language learner)","ELL", table$Main) table$Main<-gsub("(?:Foster care)","Foster-Care", table$Main) table$Main<-gsub("(?:Overage/Not on Grade)","Overage", table$Main) table$Main<-gsub("(?:,)","", table$Main) split_var<-as.data.frame(ldply(strsplit(table$Main, split = " "))) drop_spec<-data.frame(Groups=character(),Students=numeric(), Students_Percentage=numeric(), Dropouts=numeric(), Dropouts_Percentage=numeric(), Annual_Dropout_Rate=numeric(), School_Year=character()) drop_spec<-drop_spec[1:rnums,] drop_spec$Main<-table$Main drop_spec$Groups<-split_var[,1] drop_spec$Students<-split_var[,2] drop_spec$Students_Percentage<-split_var[,3] drop_spec$Dropouts<-split_var[,4] drop_spec$Dropouts_Percentage<-split_var[,5] drop_spec$Annual_Dropout_Rate<-split_var[,6] drop_spec$School_Year<-school_year[i] drop_spec$Students<-as.numeric(drop_spec$Students) drop_spec$Students_Percentage<-as.numeric(drop_spec$Students_Percentage) drop_spec$Dropouts<-as.numeric(drop_spec$Dropouts) drop_spec$Dropouts_Percentage<-as.numeric(drop_spec$Dropouts_Percentage) drop_spec$Annual_Dropout_Rate<-as.numeric(drop_spec$Annual_Dropout_Rate) drop_spec<-select(drop_spec,-Main) drop_spec<-rbind(drop_spec,NA,NA) drop_spec<-drop_spec[c(1,2,3,14,4,5,6,15,7,8,9,10,11,12,13),] } else if (i==20){ b<-paste0("https://tea.texas.gov/sites/default/files/dropcomp_",school_year[i],".pdf") dropout<-pdf_text(b) p2<-strsplit(dropout, "\n") table<-data.frame(p2[[84]][c(17:21,35:43)]) rnums<-nrow(table) table$Main<-as.character(table[1:rnums,1]) table$Main<-trimws(table$Main, which="left") table$Main<-stripWhitespace(table$Main) table$Main<-gsub("(?:Bilingual or ESLa)","ESL", table$Main) table$Main<-gsub("(?:Bilingual or English as a second language)","ESL", table$Main) table$Main<-gsub("(?:Second Language)","ESL", table$Main) table$Main<-gsub("(?:CTEb)","Career-Technical", table$Main) table$Main<-gsub("(?:Gifted and talented)","GT", table$Main) table$Main<-gsub("(?:Gifted/Talented)","GT", table$Main) table$Main<-gsub("(?:Section 504)","504", table$Main) table$Main<-gsub("(?:Special education)","Spec-Ed", table$Main) table$Main<-gsub("(?:Special Education)","Spec-Ed", table$Main) table$Main<-gsub("(?:Title I)","Title-I", table$Main) table$Main<-gsub("(?:At-risk)","At-Risk", table$Main) table$Main<-gsub("(?:Limited English proficient)","ELL", table$Main) table$Main<-gsub("(?:English learner)","ELL", table$Main) table$Main<-gsub("(?:English language learner)","ELL", table$Main) table$Main<-gsub("(?:Foster care)","Foster-Care", table$Main) table$Main<-gsub("(?:Overage/Not on Grade)","Overage", table$Main) table$Main<-gsub("(?:,)","", table$Main) split_var<-as.data.frame(ldply(strsplit(table$Main, split = " "))) drop_spec<-data.frame(Groups=character(),Students=numeric(), Students_Percentage=numeric(), Dropouts=numeric(), Dropouts_Percentage=numeric(), Annual_Dropout_Rate=numeric(), School_Year=character()) drop_spec<-drop_spec[1:rnums,] drop_spec$Main<-table$Main drop_spec$Groups<-split_var[,1] drop_spec$Students<-split_var[,2] drop_spec$Students_Percentage<-split_var[,3] drop_spec$Dropouts<-split_var[,4] drop_spec$Dropouts_Percentage<-split_var[,5] drop_spec$Annual_Dropout_Rate<-split_var[,6] drop_spec$School_Year<-school_year[i] drop_spec$Students<-as.numeric(drop_spec$Students) drop_spec$Students_Percentage<-as.numeric(drop_spec$Students_Percentage) drop_spec$Dropouts<-as.numeric(drop_spec$Dropouts) drop_spec$Dropouts_Percentage<-as.numeric(drop_spec$Dropouts_Percentage) drop_spec$Annual_Dropout_Rate<-as.numeric(drop_spec$Annual_Dropout_Rate) drop_spec<-select(drop_spec,-Main) drop_spec<-rbind(drop_spec,NA) drop_spec<-drop_spec[c(1,2,3,15,4,5,6,7,8,9,10,11,12,13,14),] } else if (i==21){ b<-paste0("https://tea.texas.gov/sites/default/files/dropcomp_",school_year[i],".pdf") dropout<-pdf_text(b) p2<-strsplit(dropout, "\n") table<-data.frame(p2[[84]][c(17:22,36:44)]) rnums<-nrow(table) table$Main<-as.character(table[1:rnums,1]) table$Main<-trimws(table$Main, which="left") table$Main<-stripWhitespace(table$Main) table$Main<-gsub("(?:Bilingual or ESLa)","ESL", table$Main) table$Main<-gsub("(?:Bilingual or English as a second language)","ESL", table$Main) table$Main<-gsub("(?:Second Language)","ESL", table$Main) table$Main<-gsub("(?:CTEb)","Career-Technical", table$Main) table$Main<-gsub("(?:Gifted and talented)","GT", table$Main) table$Main<-gsub("(?:Gifted/Talented)","GT", table$Main) table$Main<-gsub("(?:Section 504)","504", table$Main) table$Main<-gsub("(?:Special education)","Spec-Ed", table$Main) table$Main<-gsub("(?:Special Education)","Spec-Ed", table$Main) table$Main<-gsub("(?:Title I)","Title-I", table$Main) table$Main<-gsub("(?:At-risk)","At-Risk", table$Main) table$Main<-gsub("(?:Limited English proficient)","ELL", table$Main) table$Main<-gsub("(?:English learner)","ELL", table$Main) table$Main<-gsub("(?:English language learner)","ELL", table$Main) table$Main<-gsub("(?:Foster care)","Foster-Care", table$Main) table$Main<-gsub("(?:Overage/Not on Grade)","Overage", table$Main) table$Main<-gsub("(?:,)","", table$Main) split_var<-as.data.frame(ldply(strsplit(table$Main, split = " "))) drop_spec<-data.frame(Groups=character(),Students=numeric(), Students_Percentage=numeric(), Dropouts=numeric(), Dropouts_Percentage=numeric(), Annual_Dropout_Rate=numeric(), School_Year=character()) drop_spec<-drop_spec[1:rnums,] drop_spec$Main<-table$Main drop_spec$Groups<-split_var[,1] drop_spec$Students<-split_var[,2] drop_spec$Students_Percentage<-split_var[,3] drop_spec$Dropouts<-split_var[,4] drop_spec$Dropouts_Percentage<-split_var[,5] drop_spec$Annual_Dropout_Rate<-split_var[,6] drop_spec$School_Year<-school_year[i] drop_spec$Students<-as.numeric(drop_spec$Students) drop_spec$Students_Percentage<-as.numeric(drop_spec$Students_Percentage) drop_spec$Dropouts<-as.numeric(drop_spec$Dropouts) drop_spec$Dropouts_Percentage<-as.numeric(drop_spec$Dropouts_Percentage) drop_spec$Annual_Dropout_Rate<-as.numeric(drop_spec$Annual_Dropout_Rate) drop_spec<-select(drop_spec,-Main) } total_drop_spec<-rbind(total_drop_spec,drop_spec) print(paste0("Finished Year: ", school_year[i])) } ################################ Data Analysis ################################ x1<-total_drop_spec[1:15,] x2<-total_drop_spec[16:30,] x3<-total_drop_spec[31:45,] x4<-total_drop_spec[46:60,] x5<-total_drop_spec[61:75,] x6<-total_drop_spec[76:90,] x7<-total_drop_spec[91:105,] x8<-total_drop_spec[106:120,] x9<-total_drop_spec[121:135,] x10<-total_drop_spec[136:150,] x11<-total_drop_spec[151:165,] x12<-total_drop_spec[166:180,] x13<-total_drop_spec[181:195,] x14<-total_drop_spec[196:210,] x15<-total_drop_spec[211:225,] x16<-total_drop_spec[226:240,] x17<-total_drop_spec[241:255,] x18<-total_drop_spec[256:270,] x19<-total_drop_spec[271:285,] x20<-total_drop_spec[286:300,] x21<-total_drop_spec[301:315,] total1<-cbind(x1,x2,x3,x4,x5,x6,x7,x8,x9,x10,x11,x12,x13,x14,x15,x16,x17,x18,x19,x20,x21) total1<-total1[,c(-8,-15,-22,-29,-36,-34,-41,-48,-55,-62,-69,-76,-83,-90,-97,-104,-111,-118,-125,-132,-139)] label<-c("ESL","CTE","GT","504","Sped","Title I","At Risk","Dyslexia","ELL","Foster","Homele","Immig","Mig","Military","Overg") #All Groups compared by Year barplot(total1$Dropouts_Percentage.20~label) total3<-arrange(total_drop_spec,Groups) #1998-2019 by Group tot_ESL<-filter(total_drop_spec,Groups=="ESL") tot_CTE<-filter(total_drop_spec,Groups=="Career-Technical") tot_GT<-filter(total_drop_spec,Groups=="GT") tot_504<-filter(total_drop_spec,Groups=="504") tot_Spec<-filter(total_drop_spec,Groups=="Spec-Ed") tot_TitleI<-filter(total_drop_spec,Groups=="Title-I") tot_AtRisk<-filter(total_drop_spec,Groups=="At-Risk") tot_Dys<-filter(total_drop_spec,Groups=="Dyslexia") tot_ELL<-filter(total_drop_spec,Groups=="ELL") tot_Fost<-filter(total_drop_spec,Groups=="Foster-Care") tot_Homeless<-filter(total_drop_spec,Groups=="Homeless") tot_Immig<-filter(total_drop_spec,Groups=="Immigrant") tot_Mig<-filter(total_drop_spec,Groups=="Migrant") tot_Military<-filter(total_drop_spec,Groups=="Military-connected") tot_Overage<-filter(total_drop_spec,Groups=="Overage") barplot(tot_ESL$Dropouts_Percentage~school_year) barplot(tot_CTE$Dropouts_Percentage~school_year) barplot(tot_GT$Dropouts_Percentage~school_year) barplot(tot_504$Dropouts_Percentage~school_year) barplot(tot_Spec$Dropouts_Percentage~school_year) barplot(tot_TitleI$Dropouts_Percentage~school_year) barplot(tot_AtRisk$Dropouts_Percentage~school_year) barplot(tot_Dys$Dropouts_Percentage~school_year) barplot(tot_ELL$Dropouts_Percentage~school_year) barplot(tot_Fost$Dropouts_Percentage~school_year) barplot(tot_Homeless$Dropouts_Percentage~school_year) barplot(tot_Immig$Dropouts_Percentage~school_year) barplot(tot_Mig$Dropouts_Percentage~school_year) barplot(tot_Military$Dropouts_Percentage~school_year) barplot(tot_Overage$Dropouts_Percentage~school_year) total3<-cbind(tot_ESL,tot_CTE,tot_GT,tot_504,tot_Spec,tot_TitleI,tot_AtRisk,tot_Dys,tot_ELL,tot_Fost,tot_Homeless,tot_Immig,tot_Mig,tot_Military,tot_Overage,check.names=T) total2<-filter(total_drop_spec,Groups!="Overage") ggplot(total_drop_spec, aes(x=School_Year, y =Dropouts_Percentage, label=Groups,col=Groups)) + geom_label() ggplot(total2, aes(x=School_Year, y =Dropouts_Percentage,col=Groups)) + geom_jitter() ggplot(total2, aes(x=School_Year, y =Dropouts_Percentage,col=Groups,label=Groups)) + geom_label()

9e62fa555b9ac5c6277e638e12d85dd8bad0a61a

40962c524801fb9738e3b450dbb8129bb54924e1

/DAY - 5/Class/LineChartColourful.R

da0eb2436fa8ea8450359f2aa1bd10f3c028ff39

[]

no_license

klmsathish/R_Programming

628febe334d5d388c3dc51560d53f223585a0843

93450028134d4a9834740922ff55737276f62961

refs/heads/master

2023-01-14T12:08:59.068741

2020-11-15T13:23:31

309,288,498

1

0

null

UTF-8

R

false

286

r

LineChartColourful.R

#Line plot marks <- c(7,12,28,3,41) age <- c(14,7,6,19,3) #Line chart only accepts numbers(no string) plot(marks,type = "o",col = "red", xlab = "marks", ylab = "Age", main = "Marks Vs Age") #Mutiple lines in a single chart used for comparison lines(age, type = "o", col = "blue")

598cfe56ca964a7de5cc71cd7ecf017c4b7f1dd1

abea0b5d000d7c01d390eeb615427bc0322aa30f

/src/modify_asos/R_asos_pred.R

fc954d48612c38621c769051b30885b6075037a0

[]

no_license

janmandel/firewx-evaluation

5e176d8762f34b4e88a9446f1d898b3698abc5e5

51ca3c4a1c63d8c6ba00e910a87f4c87c2c0ac53

refs/heads/master

2020-05-05T01:10:49.662013

2017-08-24T17:40:06

null

0

null

UTF-8

R

false

9,517

r

R_asos_pred.R

############# ASOS FORECAST DATA - EXTRACT LANDFIRE / FIX FORMATTING ############ ### Set needed packages library(geosphere) library(raster) library(rgdal) library(sp) library(data.table) library(plyr) ### Set Working Directory setwd("/home/wpage/Documents/ASOS") ### Read-in Observed lat/long and Landfire data asos.obs = read.csv("/home/wpage/Documents/Output/Misc/landfire_asos.csv") LandFire = asos.obs ### Read-in and fix FORECAST weather data / convert rows to columns / fix lat long / fix times ## Get forecast data for each month files = list.files("/home/wpage/Documents/Output/asos") for (i in 1:length(files)) { temp = data.table(read.csv(paste("/home/wpage/Documents/Output/asos/",files[i],sep=""),header=TRUE)) assign(files[i],temp) } ## Start for loop to work with each file (month) one at a time for (j in 1:length(files)) { ## Fix misc variables temp = get(files[j]) drop = c("30-36 hour acc fcst","6-12 hour acc fcst","2-8 hour acc fcst","3-9 hour acc fcst", "4-10 hour acc fcst","5-11 hour acc fcst","30 min fcst","90-96 hour acc fcst","150-156 hour acc fcst", "210-216 hour acc fcst","270-276 hour acc fcst","330-336 hour acc fcst") temp = subset(temp, !Forecast %in% drop) temp$Value = as.numeric(as.character(temp$Value)) temp$Long = as.numeric(as.character(temp$Long)) temp$Lat = as.numeric(as.character(temp$Lat)) Fct_Old = temp Fct_Old$X = NULL ## Match forecast lat/longs to station lat/long and add station id to Fct data ObsLoc1 = data.frame(unique(asos.obs[,c("station_id","lon","lat")])) ObsLoc = as.data.frame(cbind(ObsLoc1$lon,ObsLoc1$lat)) #get observed lat/longs FixLong = function(long) { #Fix forecast long out = -(360-long) return(out) } Fct_Old$Long = unlist(lapply(Fct_Old$Long,FixLong)) #Fix long data FctLoc = unique(data.frame(Fct_Old$Long,Fct_Old$Lat)) #Get fixed forecast lat/long data FctLoc = FctLoc[complete.cases(FctLoc),] # remove NAs Match = data.frame(Obs.lon=NA,Obs.lat=NA,Fct.lon=NA,Fct.lat=NA) #Find the match to each lat/long for (i in 1:length(ObsLoc[,2])) { #Create data frame that matches lat/longs distance = distGeo(ObsLoc[i,],FctLoc,a=6378137, f=1/298.257223563) MinDist = grep(min(distance),distance) Match[i,1] = ObsLoc[i,1] Match[i,2] = ObsLoc[i,2] Match[i,3] = FctLoc[MinDist,1] Match[i,4] = FctLoc[MinDist,2] } Fct.n = data.frame() #Change data to appropriate lat/long for (i in 1:length(Match[,3])) { try = subset(Fct_Old,Long == Match[i,3] & Lat == Match[i,4]) try$Long = Match[i,1] try$Lat = Match[i,2] try$station_id = ObsLoc1[i,1] Fct.n = rbind(try,Fct.n)} Fct_Old = Fct.n ## Work on precip extract = data.frame(subset(subset(Fct_Old, Variable == "APCP"),Forecast == "1-7 hour acc fcst" & Value > 0)) for (i in 1:length(extract$Date)) { extract$Date[i] = paste(substr(extract$Date[i],1,4),"-",substr(extract$Date[i],5,6), "-",substr(extract$Date[i],7,8)," ",substr(extract$Date[i],9,10),sep="") } extract$Date = strptime(extract$Date, "%Y-%m-%d %H",tz="UTC") precip = data.frame(datetime=as.POSIXlt("2014-01-02 05", format="%Y-%m-%d %H",tz="UTC"),lon=NA,lat=NA,precip=NA,stringsAsFactors=FALSE) precip.1 = data.frame(datetime=as.POSIXlt("2014-01-02 05", format="%Y-%m-%d %H",tz="UTC"),lon=NA,lat=NA,precip=NA,stringsAsFactors=FALSE) #Break into smaller pieces extract1 = extract[1:(length(extract$Date)/2),] extract2 = extract[length(extract$Date)/2:length(extract$Date),] for (i in 1:length(extract1$Date)) { pre = data.frame(datetime=as.POSIXlt("2014-01-02 05", format="%Y-%m-%d %H",tz="UTC"),lon=NA,lat=NA,precip=NA,stringsAsFactors=FALSE) pre[1,1] = extract1$Date[i] +3600 pre[2,1] = extract1$Date[i] +7200 pre[3,1] = extract1$Date[i] +10800 pre[4,1] = extract1$Date[i] +14400 pre[5,1] = extract1$Date[i] +18000 pre[6,1] = extract1$Date[i] +21600 pre$lon = extract1$Long[i] pre$lat = extract1$Lat[i] pre$precip = (extract1$Value[i]) / 6 precip = rbind(pre,precip) } for (i in 1:length(extract2$Date)) { pre = data.frame(datetime=as.POSIXlt("2014-01-02 05", format="%Y-%m-%d %H",tz="UTC"),lon=NA,lat=NA,precip=NA,stringsAsFactors=FALSE) pre[1,1] = extract2$Date[i] +3600 pre[2,1] = extract2$Date[i] +7200 pre[3,1] = extract2$Date[i] +10800 pre[4,1] = extract2$Date[i] +14400 pre[5,1] = extract2$Date[i] +18000 pre[6,1] = extract2$Date[i] +21600 pre$lon = extract2$Long[i] pre$lat = extract2$Lat[i] pre$precip = (extract2$Value[i]) / 6 precip.1 = rbind(pre,precip.1) } precip = rbind(precip.1,precip) ## Change rows to columns according to type Temp = subset(Fct_Old, Variable == "TMP" & Forecast == "1 hour fcst") RH = subset(Fct_Old, Variable == "RH" & Forecast == "1 hour fcst") Wind = subset(Fct_Old, Variable == "WIND" & Forecast == "1 hour fcst") WDir = subset(Fct_Old, Variable == "WDIR" & Forecast == "1 hour fcst") TCDC = subset(Fct_Old, Variable == "TCDC" & Forecast == "1 hour fcst") t1 = merge(Temp,RH,by = c('Date','station_id'),all=TRUE) t2 = merge(t1,Wind,by = c('Date','station_id'),all=TRUE) t21 = data.frame(t2$Date,t2$station_id,t2$Long,t2$Lat,t2$Value.x,t2$Value.y,t2$Value) colnames(t21) = c("Date", "station_id","Long","Lat","tempC","rh","windKn") t3 = merge(t21,WDir,by = c('Date','station_id'),all=TRUE) t4 = merge(t3,TCDC,by = c('Date','station_id'),all=TRUE) Fct = data.frame(t4$Date,t4$station_id,t4$Long,t4$Lat,t4$tempC,t4$rh,t4$windKn, t4$Value.x,t4$Value.y) colnames(Fct) = c("datetime","station_id","lon","lat","tempK","rh","windKn","wind_direction_deg", "cloud_cover_percent") ## Convert forecast Date to date/time / increase time by 1 hour (match obs/forecast) fixDate = function(datetime) { time = paste(substr(datetime,1,4),"-",substr(datetime,5,6),"-",substr(datetime,7,8)," ", substr(datetime,9,10),sep="") } Fct$datetime = unlist(lapply(Fct$datetime,fixDate)) #Fix datetime Fct$datetime = strptime(Fct$datetime, "%Y-%m-%d %H",tz="UTC") Fct$datetime = Fct$datetime +3600 # add 1 hour to forecast time to match observed time ## Convert Kelvin to celsius KtoC = function(tempK) { tempC = tempK - 273.15 return(tempC) } Fct$tempK = unlist(lapply(Fct$tempK,KtoC)) #Fix temp data colnames(Fct)[5] = "air_temp_c" ## Convert wind speed from knots to mps Fct$windKn = Fct$windKn * 0.514444 colnames(Fct)[7] = "wind_speed_mps" ## Merge precip data & convert precip from inches to mm Fct = merge(Fct,precip,by = c('datetime','lon','lat'),all.x=TRUE,all.y=TRUE) Fct = Fct[-c(1,2), ] Fct$precip[is.na(Fct$precip)] = 0 Fct$precip = Fct$precip * 25.4 colnames(Fct)[10] = "precip_mm" ## Add columns data_type, station_type Fct$data_type = "pred" Fct$station_type = "ndfd" ### Get LANDFIRE data attach to Forecast data noburn.fm = c(-9999,91,92,93,98,99) LandFire = subset(LandFire, !(FM40 %in% noburn.fm)) Fct = merge(Fct,LandFire,by=c('station_id'),all=TRUE) Fct = Fct[,-c(3,4)] colnames(Fct)[c(11,12)] = c("lon","lat") ### Convert 10 m wind to 20 ft; 10m wind in m/s, Canopy height needs to be in meters Wind10to20_mps = function(m10Wind,CanopyH,FuelMod) { z.m = ifelse(FuelMod ==101|FuelMod==102|FuelMod==103|FuelMod==104| FuelMod==105|FuelMod==106|FuelMod==107|FuelMod==108|FuelMod==109| FuelMod==121|FuelMod==122|FuelMod==123|FuelMod==124,0.01, ifelse(FuelMod==141|FuelMod==142|FuelMod==143|FuelMod==144|FuelMod==145| FuelMod==146|FuelMod==147|FuelMod==148|FuelMod==149,0.43,1)) d = 0.65*CanopyH u.star = (m10Wind*0.4) / log(((10+CanopyH) - d)/z.m) newWind = (u.star/0.4)*log(((6.1+CanopyH)-d)/z.m) return(newWind) } Fct$wind_speed20ft_mps = mapply(Wind10to20_mps,Fct$wind_speed_mps,Fct$CH_m,Fct$FM40) ### Convert 20ft wind to mid-flame wind (per Andrews 2012 and Finney 2004) FBD = c(0.4,1,2,2,1.5,1.5,3,4,5,0.9,1.5,1.8,2.1,1.0,1.0,2.4,3,6,2,6,3,4.4,0.6,1,1.3,.5, 1,.2,.2,.3,.4,.6,.3,.4,.3,.6,1,1,1.2,2.7) FM = c(101,102,103,104,105,106,107,108,109,121,122,123,124,141,142,143,144,145,146,147, 148,149,161,162,163,164,165,181,182,183,184,185,186,187,188,189,201,202,203,204) FBD_ft = data.frame(cbind(FM,FBD)) Wind20ft_Mid_mps = function(Wind20ft,FuelMod,CC,CanopyH) { FH = grep(FuelMod,FBD_ft$FM) FH_ft = FBD_ft$FBD[FH] un.WAF = 1.83 / log((20+0.36*FH_ft)/(0.13*FH_ft)) f = (CC/100)*(pi/12) sh.WAF = 0.555 / (sqrt(f*3.28*CanopyH) * log(20+(1.18*CanopyH)/(0.43*CanopyH))) WAF = ifelse(CC>5,sh.WAF,un.WAF) mid_wind = Wind20ft * WAF return(mid_wind) } Fct$wind_speedMid_mps = mapply(Wind20ft_Mid_mps,Fct$wind_speed20ft_mps,Fct$FM40,Fct$CC_percent,Fct$CH_m) ### Clean up output / remove any duplicates Fct$wind_speed_mps = NULL Fct = Fct[c("station_id","station_type","data_type","lon","lat","datetime", "air_temp_c","rh","wind_speed20ft_mps","wind_speedMid_mps","wind_direction_deg", "cloud_cover_percent","precip_mm","FM40","asp_deg","elev_m","slope_deg", "CBD_kgm3","CBH_m","CC_percent","CH_m")] ## Remove any duplicated rows n.df = data.frame() stn = unique(Fct$station_id) for (i in 1:length(stn)) { try = subset(Fct, station_id == stn[i]) t = subset(try,!duplicated(try$datetime)) n.df = rbind(t,n.df) } Fct = n.df write.csv(Fct,file=files[j]) #Fct$datetime = as.character(Fct$datetime) #out = rbind.fill(Fct,asos.obs) ### End for loop for all files assign(files[j],Fct) }

f19c197e31d2b6d9a9f323c8d5dd85aa3142d8e8

8bebde68b834700de79052db26f459dd8636fec7

/R/hvalir.R

48b98d0aecf25d68280e49eccd5a939f39ec8ac0

[]

no_license

vonStadarhraun/mar

da025e84d86bba2db0a46c1f6f1917d98878535f

8d56708739faf9cd6eed98309c8df9f5f769416d

refs/heads/master

2022-11-30T19:05:54.908761

2020-08-12T12:51:32

286,978,909

0

null

2020-08-12T10:00:47

2020-08-12T10:00:46

null

UTF-8

R

false

585

r

hvalir.R

#' Hvalir #' #' @param con Tenging við Oracle #' #' @name hvalir_hvalir #' #' @return SQL fyrirspurn #' #' @export #' hvalir_hvalir <- function(con) { tbl_mar(con, 'hvalir.hvalir_v') %>% dplyr::mutate(veiddur_breidd = to_number(replace(nvl(veiddur_breidd,0),',','.')), veiddur_lengd = to_number(replace(decode(veiddur_lengd,'-',NULL,veiddur_lengd),',','.'))) %>% dplyr::select_(.dots = colnames(tbl_mar(mar,"hvalir.hvalir_v"))) %>% dplyr::mutate(ar = to_char(dags_veidi,'yyyy'), er_fostur = ifelse(substr(radnumer,-1,0)=='F',1,0)) }

ef672cf98a55274e2a56efafc28a0ef2f6ab2a93

3b107075ed5cf4c005d62c6fd13d6c42bd3e96ef

/R/zTDGSpill.R

25511e00b1b3a84c2f756ce77ea689b80c68290c

[]

no_license

ryankinzer/pitph2

14f9a5a6683e2598b16639e98335ae6ca8d8e50c

b1edbe76a3866e07ead26e3c1a2233f1cadbf8a0

refs/heads/master

2020-03-27T09:26:38.893562

2018-10-09T20:11:11

146,341,990

0

null

UTF-8

R

false

2,473

r

zTDGSpill.R

#------------------------------------------------------------------------------ # The function estimates the amount of TDG generated from spill. The output # doesn't represent the amount of TGD being reported at monitoring sites. # Monitoring site TDG is calculated with the companion function zTDGMON(). # Both TDG functions were originally written by Nick Beer at Columbia Basin # Research (GasGen.R). The current version is altered to handle vectorized # inputs and to include the lookup data within the function, so we don't need # to call them from a .csv file. # # To vectorize I removed the "if" statements which demand a rowwise/for loop # proceedure, and instead combine inputs and parameters with to run across input vectors. # # Author and Source: Nick Beer # Modified by Ryan Kinzer #------------------------------------------------------------------------------ zTDGSpill <- function(project_code, flow, spill_prop){ df <- tibble(id = 1:length(project_code), project_code, flow, spill_prop) coef_df <- tibble(project_code = c("BON", "TDA", "JDA", "MCN", "PRD", "WAN", "RIS", "RRH", "WEL", "IHR", "LMN", "LGS", "LWG", "CHJ", "DWR"), EQN = c(62, 62, 62, 62, 30, 62, 62, 30, 62, 62, 30, 62, 62, 30, 30), D0 = c(16.16, 21.9, 11.04, 12.38, 34.9, 17.63, 21.6, 24.47, 20.56, 11.15, 22.12, 9.304, 7.007, 20.92, 36.65), D1 = c(0.02983, 0.02109, 0.05969, 0.04007, -16.23, 0.08495, 0.007694, -47.83, 0.05935, 0.1009, -11.4, 0.1675, 0.2261, -14.74, -40.22), D2 = c(0, 0, 0, 0, -0.002783, 0, 0, -0.2692, 0, 0, -0.03437, 0, 0, -0.01815, -0.3211)) Gspill <- inner_join(df, coef_df, by = 'project_code') %>% mutate(Gspill = ifelse(EQN == 62, D0 + D1*flow*spill_prop, D0 + D1*exp(D2*flow*spill_prop))) %>% arrange(id) %>% pull(Gspill) return(Gspill) # CAUTION: It is possible to ask for a flow that exceeds the powerhouse's hydraulic capacity. # These formulas will compute a gas level, but it will be impossible to attain in the field. # Extra flow above the hydraulic capacity SHOULD be converted into spill. # This is not trapped in these computations. For example, IHR powerhouse capacity is 106 KCFS. # A flow of 200 with spill fraction of 0.15 will never happen there. # TEST # Compute the expected TDG in the spill water. #print(zTDGSpill("MCN",spill=0.45, flow=100)) }

2d57f739aad3688e089189c6d8987c0d15c85dca

f7e93d31f57542cf25fa0894b4a69355f40469a0

/man/theme_timeline.Rd

60f994c67b9d6b160a2bd378edda2f8b6f8e2096

[ "MIT" ]

permissive

kamenbliznashki/noaaeq

92a239b7c4e6aa06c3d78fab1ff2b4ed88e92c45

ecdca2eb4810196e1e6076c5a5df901e2d41ab1e

refs/heads/master

2020-12-06T10:21:08.917068

2020-01-08T02:28:32

232,437,089

0

null

UTF-8

R

false

true

609

rd

theme_timeline.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/geom_timeline.R \name{theme_timeline} \alias{theme_timeline} \title{Custom theme for use with the earthquake timeline plots} \usage{ theme_timeline() } \description{ The theme properly formats the axes, background and gridlines. } \examples{ \dontrun{ df \%>\% ggplot() + geom_timeline_label(aes(x=DATE, group=COUNTRY, size=EQ_PRIMARY, color=DEATHS, label=LOCATION_NAME)) + scale_y_continuous(limits = c(0, 4)) + labs(y='', size='Richter scale value', color = '# deaths') + theme_timeline() } }

b198d7658f48c81c7ffeac61b925bc5f4d294e76

78b6410be67a167fde91abb6a039847a45ce46cc

/man/n.Rd

936a4db3d8837da4756b750a42198aaec5ac5bd8

[]

no_license

reyesem/IntroAnalysis

fea3283abc4bd995339acfc7e74f2193812317e2

54cf3930879303fb128faf81bd1710b385300d6c

refs/heads/master

2023-07-12T08:45:27.546965

2023-06-29T22:07:02

123,822,392

0

null

2022-08-15T14:34:13

2018-03-04T19:42:24

HTML

UTF-8

R

false

true

317

rd

n.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/variable_summaries.R \name{n} \alias{n} \title{Compute sample size.} \usage{ n(x) } \arguments{ \item{x}{any vector.} } \description{ This is just an alias for \code{length(x)}. } \examples{ summarize_variable(am ~ 1, data = mtcars, n) }

026481c9e465b343fb6f067a5c184a04f58f3b24

c46a6ff80331d7f47bc3c379b7b6f51644a3925b

/Chapter_07/customTests.R

5db1d26f0be81fbd374a2eb220993307b8955d55

[]

no_license

elmstedt/stats20_swirl

6bb215dc600decaf03ecf441cf0e28bdbd525536

6de97f3613f941c5c39a85b9df4f26fa3b62e766

refs/heads/master

2021-05-22T02:29:59.080370

2020-10-06T07:42:50

252,929,124

1

0

null

UTF-8

R

false

4,076

r

customTests.R

# Put custom tests in this file. # Uncommenting the following line of code will disable # auto-detection of new variables and thus prevent swirl from # executing every command twice, which can slow things down. # AUTO_DETECT_NEWVAR <- FALSE # However, this means that you should detect user-created # variables when appropriate. The answer test, creates_new_var() # can be used for for the purpose, but it also re-evaluates the # expression which the user entered, so care must be taken. # Get the swirl state start_timer <- function() { e <- get('e', parent.frame()) e$`__lesson_start_time` <- now() TRUE } stop_timer <- function() { e <- get('e', parent.frame()) if(deparse(e$expr) == "stopwatch()") { start_time <- e$`__lesson_start_time` stop_time <- now() print(as.period(interval(start_time, stop_time))) } TRUE } # Get the swirl state getState <- function(){ # Whenever swirl is running, its callback is at the top of its call stack. # Swirl's state, named e, is stored in the environment of the callback. environment(sys.function(1))$e } # Get the value which a user either entered directly or was computed # by the command he or she entered. getVal <- function(){ getState()$val } # Get the last expression which the user entered at the R console. getExpr <- function(){ getState()$expr } # Retrieve the log from swirl's state getLog <- function(){ getState()$log } submit_log <- function(...){ si <- as.data.frame(t(Sys.info())) e <- get("e", parent.frame()) form_link <- "https://docs.google.com/forms/d/e/1FAIpQLScJ2lYafz7lqnhnD9Z7Dw-PZLfhhC3IihZKWkURFGcMseYeGg/viewform?entry.1752962042" form_link2 <- "http://bit.ly/stats20_19f_swirl" if(!grepl("=$", form_link)){ form_link <- paste0(form_link, "=") } p <- function(x, p, f, l = length(x)){if(l < p){x <- c(x, rep(f, p - l))};x} temp <- tempfile() log_ <- getLog() nrow_ <- max(unlist(lapply(log_, length))) log_tbl <- data.frame(user = rep(log_$user, nrow_), course_name = rep(log_$course_name, nrow_), lesson_name = rep(log_$lesson_name, nrow_), question_number = p(log_$question_number, nrow_, NA), correct = p(log_$correct, nrow_, NA), attempt = p(log_$attempt, nrow_, NA), skipped = p(log_$skipped, nrow_, NA), datetime = p(as.POSIXct.numeric(log_$datetime, origin="1970-01-01"), nrow_, NA), stringsAsFactors = FALSE) # write.csv(log_tbl, file = temp, row.names = FALSE) suppressWarnings(write.table(si, file = temp, row.names = FALSE, col.names = TRUE, sep = ",")) # drop if not working suppressWarnings(write.table(log_tbl, file = temp, row.names = FALSE, col.names = TRUE, append = TRUE, sep = ",")) encoded_log <- base64encode(temp) logname <- paste0("logfile - ", log_$lesson_name, ".txt") fileConn<-file(logname) writeLines(encoded_log, fileConn) close(fileConn) if(e$val == "Yes"){ file.show(logname, title = "Lesson Log") browseURL(paste0(form_link, encoded_log)) cat(paste0("If the submission page does not appear or the lesson log is not completely filled, you SHOULD submit it yourself.\nYou may do so by copying the encoded log record located in:\n\n", logname, "\n\nand pasting its contents into the form at:\n\n", form_link2, "\n\n")) } else { file.show(logname, title = "Lesson Log") cat(paste0("You have chosen not to submit, unless this is for a good reason (e.g. you are just repeating lessons for practice) you SHOULD submit it yourself.\nYou may do so by copying the encoded log record located in:\n\n", logname, "\n\nand pasting its contents into the form at:\n\n", form_link2, "\n\n")) } }

6ec41fb7d5a6a83ed3a7aae73ecfc3f1dae4526f

96a7892b0ba2eb4e26979911642d725ce0225fae

/HW2/HW2.R

8e30dc7bd1d47253b1e8439e3be44e00d2f77bcb

[]

no_license

sachinshindegit/R-Programming

868e2052bfed62a51e1155d71e2ec25228723ec0

bd55286e79be0e675bd72fdcfb765b88f3989ba5

refs/heads/master

2021-01-10T04:35:27.053033

2016-01-10T21:10:14

43,481,702

0

null

UTF-8

R

false

161

r

HW2.R

library(boot) set.seed(1) Y=rnorm(100) X=rnorm(100) Y=X-2*X^2+rnorm(100) plot(X,Y) set.seed(1) Data <- data.frame(X, Y) z <- glm(Y ~ X) cv.glm(Data, z)$delta[1]

96b76019a45035db539b7cfd23cde55311116efa

bebba2b371a41e0fae55e2b5853a2870f9e6814a

/archive/isotria_lifehistoryfigs.R

333a5c29873cf15f8592a5d148e86803bfb0f534

[]

no_license

AileneKane/isotria

fa8015a69e1e80c095d598625d762ddcb2700d2a

230ee3e8f63cc450ced49a0f6932c4558f3c0c02

refs/heads/master

2021-08-28T01:38:51.555826

2021-08-16T23:24:41

66,016,437

0

null

UTF-8

R

false

32,866

r

isotria_lifehistoryfigs.R

#Figures and individual life history traits estimated from posterior samples of multistate model for Isotria medioloides Alton NH population #Data provided by Bill Brumback #Coding by Ailene Ettinger with help frmo Andy Roly and Elizabeth Crone #this file has code for all figures in the manuscript, and for estimating life history traits #(lifepsan, proportion dormant, length of dormancy) #setwd("~/isotria") #at usgs #setwd("/Users/aileneettinger/git/isotria/analyses") rm(list=ls()) options(stringsAsFactors=FALSE) ###Figure 1 isoinds<-read.csv("Isotria_Stage3_2016.csv", header=T)# isoinds<-isoinds[-which(isoinds$UniqueID=="X-01-244"),]#individual has to be removed because of errors in its monitoring #head(isoinds) #Add column for emergent/nonemergent isoinds$Emerg<-NA isoinds[which(isoinds$TotNoStems>0),]$Emerg=1 isoinds[which(isoinds$TotNoStems==0),]$Emerg=0 ##Select out just groups X and Y for this analysis isoindsX=isoinds[isoinds$Group=="X",] isoindsY=isoinds[isoinds$Group=="Y",] isoindsXY=isoinds[isoinds$Group=="X"|isoinds$Group=="Y",] isoindsXY$UniqueID=factor(isoindsXY$UniqueID) #dim(isoindsXY) isoindsXY$Group=factor(isoindsXY$Group) ##get isotria data into format such that 1=veg, 2=rep and 3=not seen #to do this, Add column for reproductive (=arrested, flowering, or fruiting)/not rep isoindsXY$Repro<-NA isoindsXY[which(isoindsXY$NoFrStems>0|isoindsXY$NoFlStems>0|isoindsXY$NoArrStems>0),]$Repro=1 isoindsXY[which(isoindsXY$NoFrStems==0&isoindsXY$NoFlStems==0&isoindsXY$NoArrStems==0),]$Repro=0 isoindsX$Repro<-NA isoindsX[which(isoindsX$NoFrStems>0|isoindsX$NoFlStems>0|isoindsX$NoArrStems>0),]$Repro=1 isoindsX[which(isoindsX$NoFrStems==0&isoindsX$NoFlStems==0&isoindsX$NoArrStems==0&isoindsX$Stage!="D"),]$Repro=0 isoindsY$Repro<-NA isoindsY[which(isoindsY$NoFrStems>0|isoindsY$NoFlStems>0|isoindsY$NoArrStems>0),]$Repro=1 isoindsY[which(isoindsY$NoFrStems==0 & isoindsY$NoFlStems==0 & isoindsY$NoArrStems==0&isoindsY$Stage!="D"),]$Repro=0 quartz(height=6,width=10) par(mfrow=c(1,1),mar=c(1,5,1,.5), oma=c(5,.5,.5,.5)) pop_x<-tapply(isoindsX$UniqueID,list(isoindsX$Year,isoindsX$Repro),length)#how does pop change over time, by group? pop_y<-tapply(isoindsY$UniqueID,list(isoindsY$Year,isoindsY$Repro),length)#how does pop change over time, by group? pop_x[which(is.na(pop_x))]=0#replace NAs with 0s pop_y[which(is.na(pop_y))]=0#replace NAs with 0s plot(pop_x[,1]~rownames(pop_x),type="l",ylab="# Individuals Observed", xlab="Year", xaxt="n", xlim=c(1985,2025),ylim=c(0,60), bty="l", lty=3,col="black", lwd=2, cex.axis=1.3, cex.lab=1.5) lines(pop_x[,2]~rownames(pop_x), lty=1, lwd=2) lines(pop_y[,1]~rownames(pop_y), lty=3,col="darkgray", lwd=2) lines(pop_y[,2]~rownames(pop_y), lty=1, col="darkgray", lwd=2) text(2015, pop_x[31,1]+1,labels="Control Group, X",adj=0,cex=1.1) text(2015.2, pop_x[31,1]-1.5,labels="(Vegetative)",adj=0,cex=1.1) text(2015, pop_y[31,1],labels="Cleared Group, Y",adj=0,cex=1.1) text(2015.2, pop_y[31,1]-2.5,labels="(Vegetative)",adj=0,cex=1.1) text(2015, pop_x[31,2],labels="Control Group, X",adj=0,cex=1.1) text(2015.2, pop_x[31,2]-2.5,labels="(Reproductive)",adj=0,cex=1.1) text(2015,pop_y[31,2],labels="Cleared Group, Y",adj=0,cex=1.1) text(2015.2, pop_y[31,2]-2.5,labels="(Reproductive)",adj=0,cex=1.1) abline(v=1997,lty=2,col="gray", lwd=3) axis(side=1,at=rownames(pop_x), labels=TRUE, cex.axis=1.3) mtext("Year",side=1, adj=.35, cex=1.5, line=2.5) ### select out vital rates to calculate dwell times, etc library(popbio) mod.samples<-read.csv("msmod_samples_complex.csv", header=T) # vital rates for group X prior to clearing phiV_Xpre<-mod.samples[,which(colnames(mod.samples)=="phiA0.1.")]#if not read in, colname=psiA0[1] phiR_Xpre<-mod.samples[,which(colnames(mod.samples)=="phiB0.1.")]#if not read in, colname=psiB0[1] pdormV_Xpre<-1-mod.samples[,which(colnames(mod.samples)=="pA0.1.")]#if not read in, colname=pA0[1] pdormR_Xpre<-1-mod.samples[,which(colnames(mod.samples)=="pB0.1.")]#if not read in, colname=pB0[1] veg.rep_Xpre<- mod.samples[,which(colnames(mod.samples)=="psiA0.1.")]#if not read in, colname=phiA0[1] rep.veg_Xpre<-mod.samples[,which(colnames(mod.samples)=="psiB0.1.")]#if not read in, colname=phiB0[1] # vital rates for group Y prior to clearing phiV_Ypre<-mod.samples[,which(colnames(mod.samples)=="phiA0.2.")]#if not read in, colname=psiA0[2] phiR_Ypre<-mod.samples[,which(colnames(mod.samples)=="phiB0.2.")]#if not read in, colname=psiB0[2] pdormV_Ypre<-1-mod.samples[,which(colnames(mod.samples)=="pA0.2.")]#if not read in, colname=pA0[2] pdormR_Ypre<-1-mod.samples[,which(colnames(mod.samples)=="pB0.2.")]#if not read in, colname=pB0[2] veg.rep_Ypre<- mod.samples[,which(colnames(mod.samples)=="psiA0.2.")]#if not read in, colname=phiA0[2] rep.veg_Ypre<-mod.samples[,which(colnames(mod.samples)=="psiB0.2.")]#if not read in, colname=phiB0[2] # vital rates for group X after clearing phiV_Xpost<-mod.samples[,which(colnames(mod.samples)=="phiA1.1.")]#if not read in, colname=psiA1[1] phiR_Xpost<-mod.samples[,which(colnames(mod.samples)=="phiB1.1.")]#if not read in, colname=psiB1[1] pdormV_Xpost<-1-mod.samples[,which(colnames(mod.samples)=="pA1.1.")]#if not read in, colname=pA1[1] pdormR_Xpost<-1-mod.samples[,which(colnames(mod.samples)=="pB1.1.")]#if not read in, colname=pB1[1] veg.rep_Xpost<- mod.samples[,which(colnames(mod.samples)=="psiA1.1.")]#if not read in, colname=phiA1[1] rep.veg_Xpost<-mod.samples[,which(colnames(mod.samples)=="psiB1.1.")]#if not read in, colname=phiB1[1] # vital rates for group Y after clearing phiV_Ypost<-mod.samples[,which(colnames(mod.samples)=="phiA1.2.")]#if not read in, colname=psiA1[2] phiR_Ypost<-mod.samples[,which(colnames(mod.samples)=="phiB1.2.")]#if not read in, colname=psiB1[2] pdormV_Ypost<-1-mod.samples[,which(colnames(mod.samples)=="pA1.2.")]#if not read in, colname=pA1[2] pdormR_Ypost<-1-mod.samples[,which(colnames(mod.samples)=="pB1.2.")]#if not read in, colname=pB1[2] veg.rep_Ypost<- mod.samples[,which(colnames(mod.samples)=="psiA1.2.")]#if not read in, colname=phiA1[2] rep.veg_Ypost<-mod.samples[,which(colnames(mod.samples)=="psiB1.2.")]#if not read in, colname=phiB1[2] ###Porportion of plants dormant in each condition get.propdorm <- function(phiV,veg.rep,pdormV,phiR,rep.veg,pdormR) { prop.dorm= array() for (i in 1:length(phiV)){ tmx = c(phiV[i]*(1-veg.rep[i])*pdormV[i], phiR[i]*rep.veg[i]*pdormV[i], phiV[i]*(1-veg.rep[i])*pdormV[i], phiR[i]*rep.veg[i]*pdormV[i], phiV[i]*veg.rep[i]*pdormR[i], phiR[i]*(1-rep.veg[i])*pdormR[i], phiV[i]*veg.rep[i]*pdormR[i], phiR[i]*(1-rep.veg[i])*pdormR[i], phiV[i]*(1-veg.rep[i])*(1-pdormV[i]), phiR[i]*rep.veg[i]*(1-pdormV[i]), phiV[i]*(1-veg.rep[i])*(1-pdormV[i]), phiR[i]*rep.veg[i]*(1-pdormV[i]), phiV[i]*veg.rep[i]*(1-pdormR[i]), phiR[i]*(1-rep.veg[i])*(1-pdormR[i]), phiV[i]*veg.rep[i]*(1-pdormR[i]), phiR[i]*(1-rep.veg[i])*(1-pdormR[i])) tmx = matrix(tmx, nrow = 4, byrow = T) eigen.analysis(tmx)$stable.stage prop.dorm[i] = sum(eigen.analysis(tmx)$stable.stage[1:2]) } return(prop.dorm)# } #even though clearing does not change the probability of dormancy per se, it could change the expected proportion of dormant plants via changes in other vital rates. propdorm_Xpre<-get.propdorm(phiV_Xpre,veg.rep_Xpre,pdormV_Xpre,phiR_Xpre,rep.veg_Xpre,pdormR_Xpre) propdorm_Ypre<-get.propdorm(phiV_Ypre,veg.rep_Ypre,pdormV_Ypre,phiR_Ypre,rep.veg_Ypre,pdormR_Ypre) propdorm_Xpost<-get.propdorm(phiV_Xpost,veg.rep_Xpost,pdormV_Xpost,phiR_Xpost,rep.veg_Xpost,pdormR_Xpost) propdorm_Ypost<-get.propdorm(phiV_Ypost,veg.rep_Ypost,pdormV_Ypost,phiR_Ypost,rep.veg_Ypost,pdormR_Ypost) #windows(height=6,width=10) #quartz(height=6,width=10) #par(mfrow=c(2,2)) #hist(propdorm_Xpre, xlim=c(0,1)) #hist(propdorm_Ypre,xlim=c(0,1)) hist(propdorm_Xpost,xlim=c(0,1)) hist(propdorm_Ypost,xlim=c(0,1)) mean(propdorm_Xpre);sd(propdorm_Xpre)#0.257 (0.052 plants dormant in uncleared prior to clearing mean(propdorm_Ypre);sd(propdorm_Ypre)#0.219 (0.050)plants dormant in cleared prior to clearing mean(propdorm_Xpost);sd(propdorm_Xpost)#0.10 (0.06) plants dormant in uncleared post clearing mean(propdorm_Ypost);sd(propdorm_Ypost)#0.094 (0.069) plants dormant in cleared post clearing ####Now life expectancy: ##test: #phiV=phiV_Ypost #veg.rep=veg.rep_Ypost #pdormV=pdormV_Ypost #phiR=phiR_Ypost #rep.veg=rep.veg_Ypost #pdormR=pdormR_Ypost #to figure out effect of survival on lifepsand estimates, plug in mean values for everything then try changing phi: #phiV=0.9999 #veg.rep=0.47 #pdormV=0.30 #phiR=0.99 #rep.veg=0.02 #pdormR=0.024 #with theabove mean parameters, lifespan_med is 30. #if i change phiV to 0.98, lifepsan_med is 35 #to 0.99, liefepsan=40; change of phiV frmo .99 to .999 moves lifespan from 69 to 74 #both phiV and phiR changed to .99; lifespan goes up to 69; #with PhiV at .99 and when phiR changed frmo .99 to .999-.9933, med lifespan=Inf #with PhiV at .99 and when phiR .991, med lifespan=77 #with PhiV at .99 and when phiR .992, med lifespan=85 #with PhiV at .99 and when phiR .993, med lifespan=97 #with PhiV at .99 and when phiR .9931, med lifespan=98 #with PhiV at .99 and when phiR .9932, med lifespan=99 #with PhiV at .99 and when phiR .99325-8, med lifespan=100 get.lifespan<- function(phiV,veg.rep,pdormV,phiR,rep.veg,pdormR){ lifespan_med= array() #lifespan_95th= array() #lifespan_rep_med= array() #lifespan_rep_95th= array() #nyrs_fl= array() for (i in 1:length(phiV)){ tmx = c(phiV[i]*(1-veg.rep[i])*pdormV[i], phiR[i]*rep.veg[i]*pdormV[i], phiV[i]*(1-veg.rep[i])*pdormV[i], phiR[i]*rep.veg[i]*pdormV[i], phiV[i]*veg.rep[i]*pdormR[i], phiR[i]*(1-rep.veg[i])*pdormR[i], phiV[i]*veg.rep[i]*pdormR[i], phiR[i]*(1-rep.veg[i])*pdormR[i], phiV[i]*(1-veg.rep[i])*(1-pdormV[i]), phiR[i]*rep.veg[i]*(1-pdormV[i]), phiV[i]*(1-veg.rep[i])*(1-pdormV[i]), phiR[i]*rep.veg[i]*(1-pdormV[i]), phiV[i]*veg.rep[i]*(1-pdormR[i]), phiR[i]*(1-rep.veg[i])*(1-pdormR[i]), phiV[i]*veg.rep[i]*(1-pdormR[i]), phiR[i]*(1-rep.veg[i])*(1-pdormR[i])) tmx = matrix(tmx, nrow = 4, byrow = T) ##### one way to calculate life span - calculate the probability of still being alive i years into the future n0 = c(0,0,1000,0)#lifespan starting from vegetative nsum = array() flwrsum = array() for(j in 1:1800){ n1 = tmx%*%n0 nsum[j] = sum(n1) flwrsum[j] = n1[4] n0 = n1 }# lifespan_med[i]= min(which(nsum <900)) # this is actually the median survival time #nyrs_fl[i]=sum(flwrsum)/1000 # number of years flowering, over an average lifetime = 1.9 without clearing, 11.6 with } return (lifespan_med) } lifespan_Xpre<-get.lifespan(phiV_Xpre,veg.rep_Xpre,pdormV_Xpre,phiR_Xpre,rep.veg_Xpre,pdormR_Xpre) lifespan_Ypre<-get.lifespan(phiV_Ypre,veg.rep_Ypre,pdormV_Ypre,phiR_Ypre,rep.veg_Ypre,pdormR_Ypre) lifespan_Xpost<-get.lifespan(phiV_Xpost,veg.rep_Xpost,pdormV_Xpost,phiR_Xpost,rep.veg_Xpost,pdormR_Xpost) lifespan_Ypost<-get.lifespan(phiV_Ypost,veg.rep_Ypost,pdormV_Ypost,phiR_Ypost,rep.veg_Ypost,pdormR_Ypost) lifespan_Xpost2<-lifespan_Xpost[-(which(lifespan_Xpost=="Inf"))] lifespan_Ypost2<-lifespan_Ypost[-(which(lifespan_Ypost=="Inf"))] #Alternatively, life expectancy can be calculated as -1/ln(s) LEV_Xpre<--1/(log(phiV_Xpre))#median=5.5 LEV_Xpost<--1/(log(phiV_Xpost))#median=7.69 LEV_Ypre<--1/(log(phiV_Ypre))#median=6.31 LEV_Ypost<--1/(log(phiV_Ypost))#median=60 get.lifespan_flow<- function(phiV,veg.rep,pdormV,phiR,rep.veg,pdormR){ lifespan_med= array() #lifespan_95th= array() #lifespan_rep_med= array() #lifespan_rep_95th= array() #nyrs_fl= array() for (i in 1:length(phiV)){ tmx = c(phiV[i]*(1-veg.rep[i])*pdormV[i], phiR[i]*rep.veg[i]*pdormV[i], phiV[i]*(1-veg.rep[i])*pdormV[i], phiR[i]*rep.veg[i]*pdormV[i], phiV[i]*veg.rep[i]*pdormR[i], phiR[i]*(1-rep.veg[i])*pdormR[i], phiV[i]*veg.rep[i]*pdormR[i], phiR[i]*(1-rep.veg[i])*pdormR[i], phiV[i]*(1-veg.rep[i])*(1-pdormV[i]), phiR[i]*rep.veg[i]*(1-pdormV[i]), phiV[i]*(1-veg.rep[i])*(1-pdormV[i]), phiR[i]*rep.veg[i]*(1-pdormV[i]), phiV[i]*veg.rep[i]*(1-pdormR[i]), phiR[i]*(1-rep.veg[i])*(1-pdormR[i]), phiV[i]*veg.rep[i]*(1-pdormR[i]), phiR[i]*(1-rep.veg[i])*(1-pdormR[i])) tmx = matrix(tmx, nrow = 4, byrow = T) ##### one way to calculate life span - calculate the probability of still being alive i years into the future n0 = c(0,0,0,1000)#lifespan starting from vegetative nsum = array() flwrsum = array() for(j in 1:1800){ n1 = tmx%*%n0 nsum[j] = sum(n1) flwrsum[j] = n1[4] n0 = n1 }# lifespan_med[i]= min(which(nsum <900)) # #nyrs_fl[i]=sum(flwrsum)/1000 # number of years flowering, over an average lifetime = 1.9 without clearing, 11.6 with } return (lifespan_med) } lifespan_flow_Xpre<-get.lifespan_flow(phiV_Xpre,veg.rep_Xpre,pdormV_Xpre,phiR_Xpre,rep.veg_Xpre,pdormR_Xpre) lifespan_flow_Ypre<-get.lifespan_flow(phiV_Ypre,veg.rep_Ypre,pdormV_Ypre,phiR_Ypre,rep.veg_Ypre,pdormR_Ypre) lifespan_flow_Xpost<-get.lifespan_flow(phiV_Xpost,veg.rep_Xpost,pdormV_Xpost,phiR_Xpost,rep.veg_Xpost,pdormR_Xpost) lifespan_flow_Ypost<-get.lifespan_flow(phiV_Ypost,veg.rep_Ypost,pdormV_Ypost,phiR_Ypost,rep.veg_Ypost,pdormR_Ypost) lifespan_flow_Xpost2<-lifespan_flow_Xpost[-(which(lifespan_flow_Xpost=="Inf"))] lifespan_flow_Ypost2<-lifespan_flow_Ypost[-(which(lifespan_flow_Ypost=="Inf"))] ###Length of each bout of dormancy # even though clearing does not change the probability of dormancy per se, it could change the expected proportion of dormant plants via changes in other vital rates. get.lengthdorm <- function(phiV,veg.rep,pdormV,phiR,rep.veg,pdormR){ mydorm_all=matrix(data=NA,nrow=length(phiV),ncol=11,byrow=TRUE) mnlengthdor=array() for (i in 1:length(phiV)){ tmx.dorm = c(phiV[i]*(1-veg.rep[i])*pdormV[i], phiR[i]*rep.veg[i]*pdormV[i], phiV[i]*(1-veg.rep[i])*pdormV[i], phiR[i]*rep.veg[i]*pdormV[i], phiV[i]*veg.rep[i]*pdormR[i], phiR[i]*(1-rep.veg[i])*pdormR[i], phiV[i]*veg.rep[i]*pdormR[i], phiR[i]*(1-rep.veg[i])*pdormR[i], 0,0,0,0,0,0,0,0) tmx.dorm = matrix(tmx.dorm, nrow = 4, byrow = T) # length of dormancy starting from dormant veg n0 = c(1000,0,0,0) nsum = array() for(j in 1:100){ n1 = tmx.dorm%*%n0 nsum[j] = sum(n1) n0 = n1 } mydorm = c(1, nsum[1:10]/1000)/(1+sum(nsum)/1000) mydorm_all[i,]= mydorm numinds<-mydorm*1000 dormls<-array() for (k in 1:length(numinds)){ inddormls<-c(rep(k,times=numinds[k])) dormls<-c(dormls,inddormls) } mnlengthdor[i]<-mean(dormls, na.rm=T) } return(mnlengthdor)# } lengthdorm_Xpre<-get.lengthdorm(phiV_Xpre,veg.rep_Xpre,pdormV_Xpre,phiR_Xpre,rep.veg_Xpre,pdormR_Xpre) lengthdorm_Ypre<-get.lengthdorm(phiV_Ypre,veg.rep_Ypre,pdormV_Ypre,phiR_Ypre,rep.veg_Ypre,pdormR_Ypre) lengthdorm_Xpost<-get.lengthdorm(phiV_Xpost,veg.rep_Xpost,pdormV_Xpost,phiR_Xpost,rep.veg_Xpost,pdormR_Xpost) lengthdorm_Ypost<-get.lengthdorm(phiV_Ypost,veg.rep_Ypost,pdormV_Ypost,phiR_Ypost,rep.veg_Ypost,pdormR_Ypost) ###Now, calculate length of each bout of dormancy and proportion dormant plant, starting with reproductive plants get.lengthdorm_flow <- function(phiV,veg.rep,pdormV,phiR,rep.veg,pdormR){ mnlengthdor=array() mydorm_all=matrix(data=NA,nrow=length(phiV),ncol=11,byrow=TRUE) for (i in 1:length(phiV)){ tmx.dorm = c(phiV[i]*(1-veg.rep[i])*pdormV[i], phiR[i]*rep.veg[i]*pdormV[i], phiV[i]*(1-veg.rep[i])*pdormV[i], phiR[i]*rep.veg[i]*pdormV[i], phiV[i]*veg.rep[i]*pdormR[i], phiR[i]*(1-rep.veg[i])*pdormR[i], phiV[i]*veg.rep[i]*pdormR[i], phiR[i]*(1-rep.veg[i])*pdormR[i], 0,0,0,0,0,0,0,0) tmx.dorm = matrix(tmx.dorm, nrow = 4, byrow = T) # length of dormancy starting from dormant flowering n0_flow = c(0,1000,0,0) nsum_flow = array() for(j in 1:100){ n1_flow = tmx.dorm%*%n0_flow nsum_flow[j] = sum(n1_flow) n0_flow = n1_flow } mydorm_flow = c(1, nsum_flow[1:10]/1000)/(1+sum(nsum_flow)/1000) #prop_dorm1yr_flow[i]= mydorm_flow[1] mydorm_all[i,]= mydorm_flow numinds<-mydorm_flow*1000 dormls<-array() for (k in 1:length(numinds)){ inddormls<-c(rep(k,times=numinds[k])) dormls<-c(dormls,inddormls) } mnlengthdor[i]<-mean(dormls, na.rm=T) } return(mnlengthdor)# } lengthdorm_flow_Xpre<-get.lengthdorm_flow(phiV_Xpre,veg.rep_Xpre,pdormV_Xpre,phiR_Xpre,rep.veg_Xpre,pdormR_Xpre) lengthdorm_flow_Ypre<-get.lengthdorm_flow(phiV_Ypre,veg.rep_Ypre,pdormV_Ypre,phiR_Ypre,rep.veg_Ypre,pdormR_Ypre) lengthdorm_flow_Xpost<-get.lengthdorm_flow(phiV_Xpost,veg.rep_Xpost,pdormV_Xpost,phiR_Xpost,rep.veg_Xpost,pdormR_Xpost) lengthdorm_flow_Ypost<-get.lengthdorm_flow(phiV_Ypost,veg.rep_Ypost,pdormV_Ypost,phiR_Ypost,rep.veg_Ypost,pdormR_Ypost) ##Figures #2x2table for each vital rate with first column control, second column logged #if model not loaded, then use model sample files to get estinat ms3a<-read.csv("isotria2stagemodsum_complex.csv", header=T) rownames(ms3a)<-ms3a[,1] surv_veg<-as.data.frame(rbind(ms3a$mean[grep("phiA0",substr(rownames(ms3a),1,5))],ms3a$mean[grep("phiA1",substr(rownames(ms3a),1,5))])) surv_rep<-as.data.frame(rbind(ms3a$mean[grep("phiB0",substr(rownames(ms3a),1,5))],ms3a$mean[grep("phiB1",substr(rownames(ms3a),1,5))])) emer_veg<-as.data.frame(rbind(ms3a$mean[grep("pA0",substr(rownames(ms3a),1,3))],ms3a$mean[grep("pA1",substr(rownames(ms3a),1,3))])) emer_rep<-as.data.frame(rbind(ms3a$mean[grep("pB0",substr(rownames(ms3a),1,3))],ms3a$mean[grep("pB1",substr(rownames(ms3a),1,3))])) trans_vr<-as.data.frame(rbind(ms3a$mean[grep("psiA0",substr(rownames(ms3a),1,5))],ms3a$mean[grep("psiA1",substr(rownames(ms3a),1,5))])) trans_rv<-as.data.frame(rbind(ms3a$mean[grep("psiB0",substr(rownames(ms3a),1,5))],ms3a$mean[grep("psiB1",substr(rownames(ms3a),1,5))])) surv_veg_med<-as.data.frame(rbind(ms3a$X50.[grep("phiA0",substr(rownames(ms3a),1,5))],ms3a$X50.[grep("phiA1",substr(rownames(ms3a),1,5))])) surv_rep_med<-as.data.frame(rbind(ms3a$X50.[grep("phiB0",substr(rownames(ms3a),1,5))],ms3a$X50.[grep("phiB1",substr(rownames(ms3a),1,5))])) emer_veg_med<-as.data.frame(rbind(ms3a$X50.[grep("pA0",substr(rownames(ms3a),1,3))],ms3a$X50.[grep("pA1",substr(rownames(ms3a),1,3))])) emer_rep_med<-as.data.frame(rbind(ms3a$X50.[grep("pB0",substr(rownames(ms3a),1,3))],ms3a$X50.[grep("pB1",substr(rownames(ms3a),1,3))])) trans_vr_med<-as.data.frame(rbind(ms3a$X50.[grep("psiA0",substr(rownames(ms3a),1,5))],ms3a$X50.[grep("psiA1",substr(rownames(ms3a),1,5))])) trans_rv_med<-as.data.frame(rbind(ms3a$X50.[grep("psiB0",substr(rownames(ms3a),1,5))],ms3a$X50.[grep("psiB1",substr(rownames(ms3a),1,5))])) colnames(surv_veg)<-c("control","logged") colnames(surv_rep)<-c("control","logged") colnames(emer_veg)<-c("control","logged") colnames(emer_rep)<-c("control","logged") colnames(trans_vr)<-c("control","logged") colnames(trans_rv)<-c("control","logged") colnames(surv_veg_med)<-c("control","logged") colnames(surv_rep_med)<-c("control","logged") colnames(emer_veg_med)<-c("control","logged") colnames(emer_rep_med)<-c("control","logged") colnames(trans_vr_med)<-c("control","logged") colnames(trans_rv_med)<-c("control","logged") ##use code below if model not loaded: surv_veg_q2.5<-c(ms3a$X2.5.[grep("phiA0",substr(rownames(ms3a),1,5))],ms3a$X2.5.[grep("phiA1",substr(rownames(ms3a),1,5))]) surv_rep_q2.5<-c(ms3a$X2.5.[grep("phiB0",substr(rownames(ms3a),1,5))],ms3a$X2.5.[grep("phiB1",substr(rownames(ms3a),1,5))]) trans_vr_q2.5<-c(ms3a$X2.5.[grep("psiA0",substr(rownames(ms3a),1,5))],ms3a$X2.5.[grep("psiA1",substr(rownames(ms3a),1,5))]) trans_rv_q2.5<-c(ms3a$X2.5.[grep("psiB0",substr(rownames(ms3a),1,5))],ms3a$X2.5.[grep("psiB1",substr(rownames(ms3a),1,5))]) emer_veg_q2.5<-c(ms3a$X2.5.[grep("pA0",substr(rownames(ms3a),1,3))],ms3a$X2.5.[grep("pA1",substr(rownames(ms3a),1,3))]) emer_rep_q2.5<-c(ms3a$X2.5.[grep("pB0",substr(rownames(ms3a),1,3))],ms3a$X2.5.[grep("pB1",substr(rownames(ms3a),1,3))]) surv_veg_q97.5<-c(ms3a$X97.5.[grep("phiA0",substr(rownames(ms3a),1,5))],ms3a$X97.5.[grep("phiA1",substr(rownames(ms3a),1,5))]) surv_rep_q97.5<-c(ms3a$X97.5.[grep("phiB0",substr(rownames(ms3a),1,5))],ms3a$X97.5.[grep("phiB1",substr(rownames(ms3a),1,5))]) trans_vr_q97.5<-c(ms3a$X97.5.[grep("psiA0",substr(rownames(ms3a),1,5))],ms3a$X97.5.[grep("psiA1",substr(rownames(ms3a),1,5))]) trans_rv_q97.5<-c(ms3a$X97.5.[grep("psiB0",substr(rownames(ms3a),1,5))],ms3a$X97.5.[grep("psiB1",substr(rownames(ms3a),1,5))]) emer_veg_q97.5<-c(ms3a$X97.5.[grep("pA0",substr(rownames(ms3a),1,3))],ms3a$X97.5.[grep("pA1",substr(rownames(ms3a),1,3))]) emer_rep_q97.5<-c(ms3a$X97.5.[grep("pB0",substr(rownames(ms3a),1,3))],ms3a$X97.5.[grep("pB1",substr(rownames(ms3a),1,3))]) #Figure 3, of vital rates x<-c(1,2,1,2) #x<-c(1,2,1.05,2.05)#jittered xerror<-c(1,1,2,2) #xerror<-c(1,1.05,2,2.05)#jittered x2<-c(3,4,3,4) #x2<-c(3,4,3.05,4.05)#jittered x2error<-c(3,3,4,4) #x2error<-c(3,3.05,4,4.05)#jittered windows(height=6,width=7) quartz(height=6,width=7) par(mfrow=c(3,1),mar=c(.5,4.1,1,.5), oma=c(3,.6,.5,.5)) #survival plot(x,c(surv_veg$control,surv_veg$logged), pch=21, bg=c("black","black","white","white"), ylim=c(0,1), ylab="Survival", xaxt="n", cex=1.5, xlab="", xlim=c(0.75,4.25), cex.lab=1.5, las=1,, cex.axis=1.3) lines(x[1:2],c(surv_veg$control), lty=1) lines(x[3:4],c(surv_veg$logged), lty=3) abline(v=2.5,lty=1, lwd=2) abline(v=1.5,lty=2,col="gray", lwd=2) abline(v=3.5,lty=2,col="gray", lwd=2) arrows(xerror,surv_veg_q2.5,xerror,surv_veg_q97.5, code=0,angle=90, length=0.1) points(x,c(surv_veg$control,surv_veg$logged),pch=21, bg=c("black","black","white","white"), cex=1.5) arrows(x2error,surv_rep_q2.5,x2error,surv_rep_q97.5, code=0,angle=90, length=0.1) lines(x2[1:2],c(surv_rep$control), lty=1) lines(x2[3:4],c(surv_rep$logged), lty=3) points(x2,c(surv_rep$control,surv_rep$logged),pch=21, bg=c("black","black","white","white"), cex=1.5) axis(side=1,at=c(1.5,3.5),labels=c("Vegetative","Reproductive" ),line=-15, tick=F, cex.axis=1.2) legend("bottomright",legend=c("Control", "Cleared"),pch=21,pt.cex=1.5,pt.bg=c("black","white"), bty="n", cex=1.2) #cbind(rownames(ms3a[25:32,]),ms3a$mean[25:32],ms3a$X2.5[25:32],ms3a$X97.5.[25:32]) #Dormancy(=1-)Emergence plot(x,c(1-emer_veg$control,1-emer_veg$logged), pch=21, bg="black", ylim=c(0,1), ylab="Dormancy", xaxt="n", cex=1.5, xlab="", xlim=c(0.75,4.25), cex.lab=1.5, las=1, cex.axis=1.3) lines(x[1:2],c(1-emer_veg$control), lty=1) lines(x[3:4],c(1-emer_veg$logged), lty=3) abline(v=2.5,lty=1, lwd=2) abline(v=1.5,lty=2,col="gray", lwd=2) abline(v=3.5,lty=2,col="gray", lwd=2) arrows(xerror,1-emer_veg_q2.5,xerror,1-emer_veg_q97.5, code=0,angle=90, length=0.1) points(x,c(1-emer_veg$control,1-emer_veg$logged),pch=21, bg=c("black","black","white","white"), cex=1.5) arrows(x2error,1-emer_rep_q2.5,x2error,1-emer_rep_q97.5, code=0,angle=90, length=0.1) lines(x2[1:2],c(1-emer_rep$control), lty=1) lines(x2[3:4],c(1-emer_rep$logged), lty=3) points(x2,c(1-emer_rep$control,1-emer_rep$logged),pch=21, bg=c("black","black","white","white"), cex=1.5) cbind(rownames(ms3a[41:48,]),1-ms3a$mean[41:48],1-ms3a$X2.5[41:48],1-ms3a$X97.5.[41:48])#check error bars #transition plot(x,c(trans_vr$control,trans_vr$logged), pch=21, bg=c("black","black","white","white"), ylim=c(0,1), ylab="Transition", xaxt="n", cex=1.6, xlab="", xlim=c(0.75,4.25), cex.lab=1.5, las=1, cex.axis=1.3) lines(x[1:2],c(trans_vr$control), lty=1) lines(x[3:4],c(trans_vr$logged), lty=3) abline(v=2.5,lty=1, lwd=2) abline(v=1.5,lty=2,col="gray", lwd=2) abline(v=3.5,lty=2,col="gray", lwd=2) arrows(xerror,trans_vr_q2.5,xerror,trans_vr_q97.5, code=0,angle=90, length=0.1) points(x,c(trans_vr$control,trans_vr$logged),pch=21, bg=c("black","black","white","white"), cex=1.5) arrows(x2error,trans_rv_q2.5,x2error,trans_rv_q97.5, code=0,angle=90, length=0.1) lines(x2[1:2],c(trans_rv$control), lty=1) lines(x2[3:4],c(trans_rv$logged), lty=3) points(x2,c(trans_rv$control,trans_rv$logged),pch=21, bg=c("black","black","white","white"), cex=1.5) axis(side=1,at=c(x[1:2],x2[1:2]),labels=c("pre","post","pre","post"), cex.axis=1.3) axis(side=1,at=c(x[1:2],x2[1:2]),labels=c("(1982-1997)","(1998-2015)","(1982-1997)","(1998-2015)"), line=1.2,tick=F, cex.axis=1.3) #####Figure 4, length of dormancy, lifepsan, etc #x<-c(1,2,1,2) windows(height=6,width=7) quartz(height=6,width=7) par(mfrow=c(3,1),mar=c(.5,4.1,1,.5), oma=c(3,.6,.5,.5)) #lifespan plot(x,c(mean(lifespan_Xpre, na.rm=T),mean(lifespan_Xpost2, na.rm=T),mean(lifespan_Ypre, na.rm=T),mean(lifespan_Ypost2, na.rm=T)), pch=21, bg=c("black","black","white","white"), ylim=c(0,100), ylab="Lifespan (yrs)", xaxt="n", cex=1.5, xlab="", xlim=c(0.75,4.25), cex.lab=1.5, cex.axis=1.3, las=1) abline(v=2.5,lty=1, lwd=2) abline(v=1.5,lty=2,col="gray", lwd=2) abline(v=3.5,lty=2,col="gray", lwd=2) lines(x[1:2],c(mean(lifespan_Xpre, na.rm=T),mean(lifespan_Xpost, na.rm=T)), lty=1) lines(x[3:4],c(mean(lifespan_Ypre, na.rm=T),mean(lifespan_Ypost2, na.rm=T)), lty=3) arrows(xerror,c(mean(lifespan_Xpre, na.rm=T)-sd(lifespan_Xpre, na.rm=T),mean(lifespan_Ypre, na.rm=T)-sd(lifespan_Ypre, na.rm=T),mean(lifespan_Xpost2, na.rm=T)-sd(lifespan_Xpost2, na.rm=T),mean(lifespan_Ypost2, na.rm=T)-sd(lifespan_Ypost2, na.rm=T)),xerror,c(mean(lifespan_Xpre, na.rm=T)+sd(lifespan_Xpre, na.rm=T),mean(lifespan_Ypre, na.rm=T)+sd(lifespan_Ypre, na.rm=T),mean(lifespan_Xpost2, na.rm=T)+sd(lifespan_Xpost2, na.rm=T),mean(lifespan_Ypost2, na.rm=T)+sd(lifespan_Ypost2, na.rm=T)), code=0,angle=90, length=0.1) points(x,c(mean(lifespan_Xpre, na.rm=T),mean(lifespan_Xpost2, na.rm=T),mean(lifespan_Ypre, na.rm=T),mean(lifespan_Ypost2, na.rm=T)), pch=21, bg=c("black","black","white","white"), cex=1.5) lines(x2[1:2],c(mean(lifespan_flow_Xpre, na.rm=T),mean(lifespan_flow_Xpost2, na.rm=T)), lty=1) lines(x2[3:4],c(mean(lifespan_flow_Ypre, na.rm=T),mean(lifespan_flow_Ypost2, na.rm=T)), lty=3) arrows(x2error,c(mean(lifespan_flow_Xpre, na.rm=T)-sd(lifespan_flow_Xpre, na.rm=T),mean(lifespan_flow_Ypre, na.rm=T)-sd(lifespan_flow_Ypre, na.rm=T),mean(lifespan_flow_Xpost2, na.rm=T)-sd(lifespan_flow_Xpost2, na.rm=T),mean(lifespan_flow_Ypost2, na.rm=T)-sd(lifespan_flow_Ypost2, na.rm=T)),x2error,c(mean(lifespan_flow_Xpre, na.rm=T)+sd(lifespan_flow_Xpre, na.rm=T),mean(lifespan_flow_Ypre, na.rm=T)+sd(lifespan_flow_Ypre, na.rm=T),mean(lifespan_flow_Xpost2, na.rm=T)+sd(lifespan_flow_Xpost2, na.rm=T),mean(lifespan_flow_Ypost2, na.rm=T)+sd(lifespan_flow_Ypost2, na.rm=T)), code=0,angle=90, length=0.1) points(x2,c(mean(lifespan_flow_Xpre, na.rm=T),mean(lifespan_flow_Xpost2, na.rm=T),mean(lifespan_flow_Ypre, na.rm=T),mean(lifespan_flow_Ypost2, na.rm=T)), pch=21, bg=c("black","black","white","white"), cex=1.5) axis(side=1,at=c(1.5,3.5),labels=c("Vegetative","Reproductive" ),line=-15, tick=F, cex.axis=1.2) #Length of dormancy, starting from veg (black) or rep (white) plot(x,c(mean(lengthdorm_Xpre, na.rm=T),mean(lengthdorm_Xpost, na.rm=T),mean(lengthdorm_Ypre, na.rm=T),mean(lengthdorm_Ypost, na.rm=T)), pch=21, bg=c("black","black","white","white"), ylim=c(0,2), ylab="Dormancy length (yrs)", xaxt="n", cex=1.5, xlab="", xlim=c(0.75,4.25), cex.lab=1.2,cex.lab=1.5, cex.axis=1.3, las=1) abline(v=2.5,lty=1, lwd=2) abline(v=1.5,lty=2,col="gray", lwd=2) abline(v=3.5,lty=2,col="gray", lwd=2) lines(x[1:2],c(mean(lengthdorm_Xpre, na.rm=T),mean(lengthdorm_Xpost, na.rm=T)), lty=1) lines(x[3:4],c(mean(lengthdorm_Ypre, na.rm=T),mean(lengthdorm_Ypost, na.rm=T)), lty=3) arrows(xerror,c(mean(lengthdorm_Xpre, na.rm=T)-sd(lengthdorm_Xpre, na.rm=T),mean(lengthdorm_Ypre, na.rm=T)-sd(lengthdorm_Ypre, na.rm=T),mean(lengthdorm_Xpost, na.rm=T)-sd(lengthdorm_Xpost, na.rm=T),mean(lengthdorm_Ypost, na.rm=T)-sd(lengthdorm_Ypost, na.rm=T)),xerror,c(mean(lengthdorm_Xpre, na.rm=T)+sd(lengthdorm_Xpre, na.rm=T),mean(lengthdorm_Ypre, na.rm=T)+sd(lengthdorm_Ypre, na.rm=T),mean(lengthdorm_Xpost, na.rm=T)+sd(lengthdorm_Xpost, na.rm=T),mean(lengthdorm_Ypost, na.rm=T)+sd(lengthdorm_Ypost, na.rm=T)), code=0,angle=90, length=0.1) points(x,c(mean(lengthdorm_Xpre, na.rm=T),mean(lengthdorm_Xpost, na.rm=T),mean(lengthdorm_Ypre, na.rm=T),mean(lengthdorm_Ypost, na.rm=T)), pch=21, bg=c("black","black","white","white"), cex=1.5) arrows(x2error,c(mean(lengthdorm_flow_Xpre, na.rm=T)-sd(lengthdorm_flow_Xpre, na.rm=T),mean(lengthdorm_flow_Ypre, na.rm=T)-sd(lengthdorm_flow_Ypre, na.rm=T),mean(lengthdorm_flow_Xpost, na.rm=T)-sd(lengthdorm_flow_Xpost, na.rm=T),mean(lengthdorm_flow_Ypost, na.rm=T)-sd(lengthdorm_flow_Ypost, na.rm=T)),x2error,c(mean(lengthdorm_flow_Xpre, na.rm=T)+sd(lengthdorm_flow_Xpre, na.rm=T),mean(lengthdorm_flow_Ypre, na.rm=T)+sd(lengthdorm_flow_Ypre, na.rm=T),mean(lengthdorm_flow_Xpost, na.rm=T)+sd(lengthdorm_flow_Xpost, na.rm=T),mean(lengthdorm_flow_Ypost, na.rm=T)+sd(lengthdorm_flow_Ypost, na.rm=T)), code=0,angle=90, length=0.1) lines(x2[1:2],c(mean(lengthdorm_flow_Xpre, na.rm=T),mean(lengthdorm_flow_Xpost, na.rm=T)), lty=1) lines(x2[3:4],c(mean(lengthdorm_flow_Ypre, na.rm=T),mean(lengthdorm_flow_Ypost, na.rm=T)), lty=3) points(x2,c(mean(lengthdorm_flow_Xpre, na.rm=T),mean(lengthdorm_flow_Xpost, na.rm=T),mean(lengthdorm_flow_Ypre, na.rm=T),mean(lengthdorm_flow_Ypost, na.rm=T)), pch=21, bg=c("black","black","white","white"), cex=1.5) axis(side=1,at=c(x[1:2],x2[1:2]),labels=c("pre","post","pre","post"), cex.axis=1.3) axis(side=1,at=c(x[1:2],x2[1:2]),labels=c("(1982-1997)","(1998-2015)","(1982-1997)","(1998-2015)"), line=1.2,tick=F, cex.axis=1.3) #proportion of plants dormant plot(x,c(mean(propdorm_Xpre, na.rm=T),mean(propdorm_Xpost, na.rm=T),mean(propdorm_Ypre, na.rm=T),mean(propdorm_Ypost, na.rm=T)), pch=21, bg=c("black","black","white","white"), ylim=c(0,1), ylab="Proportion Dormant", xaxt="n", cex=1.5, xlab="", xlim=c(0.75,4.25), cex.lab=1.5, cex.axis=1.3, las=1) abline(v=2.5,lty=1, lwd=2) abline(v=1.5,lty=2,col="gray", lwd=2) lines(x[1:2],c(mean(propdorm_Xpre, na.rm=T),mean(propdorm_Xpost, na.rm=T)), lty=1) lines(x[3:4],c(mean(propdorm_Ypre, na.rm=T),mean(propdorm_Ypost, na.rm=T)), lty=3) arrows(xerror,c(mean(propdorm_Xpre, na.rm=T)-sd(propdorm_Xpre, na.rm=T),mean(propdorm_Ypre, na.rm=T)-sd(propdorm_Ypre, na.rm=T),mean(propdorm_Xpost, na.rm=T)-sd(propdorm_Xpost, na.rm=T),mean(propdorm_Ypost, na.rm=T)-sd(propdorm_Ypost, na.rm=T)),xerror,c(mean(propdorm_Xpre, na.rm=T)+sd(propdorm_Xpre, na.rm=T),mean(propdorm_Ypre, na.rm=T)+sd(propdorm_Ypre, na.rm=T),mean(propdorm_Xpost, na.rm=T)+sd(propdorm_Xpost, na.rm=T),mean(propdorm_Ypost, na.rm=T)+sd(propdorm_Ypost, na.rm=T)), code=0,angle=90, length=0.1) points(x,c(mean(propdorm_Xpre, na.rm=T),mean(propdorm_Xpost, na.rm=T),mean(propdorm_Ypre, na.rm=T),mean(propdorm_Ypost, na.rm=T)), pch=21, bg=c("black","black","white","white"), cex=1.5) axis(side=1,at=c(x[1:2]),labels=c("pre","post"), cex.axis=1.3) axis(side=1,at=c(x[1:2]),labels=c("(1982-1997)","(1998-2015)"), line=1.2,tick=F, cex.axis=1.3) ##Figuring out why error bars are so wide for lifespan Ypost inf.params<-cbind(phiV_Ypost[which(lifespan_Ypost=="Inf")],veg.rep_Ypost[which(lifespan_Ypost=="Inf")],pdormV_Ypost[which(lifespan_Ypost=="Inf")],phiR_Ypost[which(lifespan_Ypost=="Inf")],rep.veg_Ypost[which(lifespan_Ypost=="Inf")],pdormR_Ypost[which(lifespan_Ypost=="Inf")]) noninf.params<-cbind(phiV_Ypost[which(lifespan_Ypost!="Inf")],veg.rep_Ypost[which(lifespan_Ypost!="Inf")],pdormV_Ypost[which(lifespan_Ypost!="Inf")],phiR_Ypost[which(lifespan_Ypost!="Inf")],rep.veg_Ypost[which(lifespan_Ypost!="Inf")],pdormR_Ypost[which(lifespan_Ypost!="Inf")]) t.test(noninf.params[,6],inf.params[,6]) high.params<-cbind(phiV_Ypost[which(lifespan_Ypost>200)],veg.rep_Ypost[which(lifespan_Ypost>200)],pdormV_Ypost[which(lifespan_Ypost>200)],phiR_Ypost[which(lifespan_Ypost>200)],rep.veg_Ypost[which(lifespan_Ypost>200)],pdormR_Ypost[which(lifespan_Ypost>200)]) low.params<-cbind(phiV_Ypost[which(lifespan_Ypost<200)],veg.rep_Ypost[which(lifespan_Ypost<200)],pdormV_Ypost[which(lifespan_Ypost<200)],phiR_Ypost[which(lifespan_Ypost<200)],rep.veg_Ypost[which(lifespan_Ypost<200)],pdormR_Ypost[which(lifespan_Ypost<200)]) lowlow.params<-cbind(phiV_Ypost[which(lifespan_Ypost<10)],veg.rep_Ypost[which(lifespan_Ypost<10)],pdormV_Ypost[which(lifespan_Ypost<10)],phiR_Ypost[which(lifespan_Ypost<10)],rep.veg_Ypost[which(lifespan_Ypost<10)],pdormR_Ypost[which(lifespan_Ypost<10)]) t.test(lowlow.params[,5],low.params[,5]) #vital rates from high lifespan estimates (>200 years) have the following differences from low lifespan estimates: #1) higher phis for both reproductive and veg plants #2) lower transition from reproductive to vegetative rep.veg_Ypost #vital rates frmo low low lifespand estimates (<10 years) have the following #1) higher phis for both reproductive and veg plants #2) higher transition from reproductive to vegetative rep.veg_Ypost

d661b4879f506a74cc74b88c3aa9a78080aa1a36

19706720652dd327c738e5b4ac30859fa87130e9

/cleaner final.R

aee8c455f09dd780e4d7939c9360a6c4b5841c26

[ "Apache-2.0" ]

permissive

souravbose1991/toxic_element

bea9cbc03b714e5dffde20ed1331a305698a6ae1

abca8b6d1d88a925e411d0cc182cdd49966d08bf

refs/heads/master

2021-04-29T21:24:40.310464

2018-08-19T18:27:50

121,615,503

0

null

UTF-8

R

false

19,761

r

cleaner final.R

train <- fread("train.csv", key=c("id")) test <- fread("C:\\Users\\HP LAP\\Desktop\\Kaggle\\Data\\test\\test.csv", key=c("id")) stopwords.en <- fread("stopwords-en.txt") profane <- c("damn", "dyke", "fuck", "shit", "ahole", "amcik", "andskota", "anus", "arschloch", "arse", "ash0le", "ash0les", "asholes", "ass", "Ass Monkey", "Assface", "assh0le", "assh0lez" , "asshole", "assholes", "assholz", "assrammer", "asswipe", "ayir", "azzhole", "b00b", "b00bs", "b17ch", "b1tch", "bassterds", "bastard", "bastards", "bastardz", "basterds", "basterdz", "bi7ch", "Biatch", "bitch", "bitch", "bitches", "Blow Job", "blowjob", "boffing", "boiolas", "bollock", "boobs", "breasts", "buceta", "butt-pirate", "butthole", "buttwipe", "c0ck", "c0cks", "c0k", "cabron", "Carpet Muncher", "cawk", "cawks", "cazzo", "chink", "chraa", "chuj", "cipa", "clit", "Clit", "clits", "cnts", "cntz", "cock", "cock-head", "cock-sucker", "Cock", "cockhead", "cocks", "CockSucker", "crap", "cum", "cunt", "cunt", "cunts", "cuntz", "d4mn", "daygo", "dego", "dick", "dick", "dike", "dild0", "dild0s", "dildo", "dildos", "dilld0", "dilld0s", "dirsa", "dominatricks", "dominatrics", "dominatrix", "dupa", "dyke", "dziwka", "ejackulate", "ejakulate", "Ekrem", "Ekto", "enculer", "enema", "f u c k", "f u c k e r", "faen", "fag", "fag", "fag1t", "faget", "fagg1t", "faggit", "faggot", "fagit", "fags", "fagz", "faig", "faigs", "fanculo", "fanny", "fart", "fatass", "fcuk", "feces", "feg", "Felcher", "ficken", "fitt", "Flikker", "flipping the bird", "foreskin", "Fotze", "fuck", "fucker", "fuckin", "fucking", "fucks", "Fudge Packer", "fuk", "fuk", "Fukah", "Fuken", "fuker", "Fukin", "Fukk", "Fukkah", "Fukker", "Fukkin", "futkretzn", "fux0r", "g00k", "gay", "gayboy", "gaygirl", "gays", "gayz", "God-damned", "gook", "guiena", "h00r", "h0ar", "h0r", "h0re", "h4x0r", "hell", "hells", "helvete", "hoar", "hoer", "hoer", "honkey", "hoore", "hore", "Huevon", "hui", "injun", "jackoff", "jap", "japs", "jerk-off", "jisim", "jism", "jiss", "jizm", "jizz", "kanker", "kawk", "kike", "klootzak", "knob", "knobs", "knobz", "knulle", "kraut", "kuk", "kuksuger", "kunt", "kunts", "kuntz", "Kurac", "kurwa", "kusi", "kyrpa", "l3i+ch", "l3itch", "lesbian", "Lesbian", "lesbo", "Lezzian", "Lipshitz", "mamhoon", "masochist", "masokist", "massterbait", "masstrbait", "masstrbate", "masterbaiter", "masterbat", "masterbat3", "masterbate", "masterbates", "masturbat", "masturbate", "merd", "mibun", "mofo", "monkleigh", "Motha Fucker", "Motha Fuker", "Motha Fukkah", "Motha Fukker", "mother-fucker", "Mother Fucker", "Mother Fukah", "Mother Fuker", "Mother Fukker", "motherfucker", "mouliewop", "muie", "mulkku", "muschi", "Mutha Fucker", "Mutha Fukah", "Mutha Fuker", "Mutha Fukkah", "Mutha Fukker", "n1gr", "nastt", "nazi", "nazis", "nepesaurio", "nigga", "nigger", "nigger", "nigger;", "nigur;", "niiger;", "niigr;", "nutsack", "orafis", "orgasim;", "orgasm", "orgasum", "oriface", "orifice", "orifiss", "orospu", "p0rn", "packi", "packie", "packy", "paki", "pakie", "paky", "paska", "pecker", "peeenus", "peeenusss", "peenus", "peinus", "pen1s", "penas", "penis", "penis-breath", "penus", "penuus", "perse", "Phuc", "phuck", "Phuck", "Phuker", "Phukker", "picka", "pierdol", "pillu", "pimmel", "pimpis", "piss", "pizda", "polac", "polack", "polak", "poontsee", "poop", "porn", "pr0n", "pr1c", "pr1ck", "pr1k", "preteen", "pula", "pule", "pusse", "pussee", "pussy", "puto", "puuke", "puuker", "qahbeh", "queef", "queer", "queers", "queerz", "qweers", "qweerz", "qweir", "rautenberg", "rectum", "retard", "sadist", "scank", "schaffer", "scheiss", "schlampe", "schlong", "schmuck", "screw", "screwing", "scrotum", "semen", "sex", "sexy", "sh!t", "Sh!t", "sh!t", "sh1t", "sh1ter", "sh1ts", "sh1tter", "sh1tz", "sharmuta", "shemale", "shi+", "shipal", "shit", "shits", "shitter", "Shitty", "Shity", "shitz", "shiz", "Shyt", "Shyte", "Shytty", "skanck", "skank", "skankee", "skankey", "skanks", "Skanky", "skribz", "skurwysyn", "slut", "sluts", "Slutty", "slutz", "son-of-a-bitch", "sphencter", "spic", "spierdalaj", "splooge", "suka", "teets", "teez", "testical", "testicle", "testicle", "tit", "tits", "titt", "titt", "turd", "twat", "va1jina", "vag1na", "vagiina", "vagina", "vaj1na", "vajina", "vittu", "vulva", "w00se", "w0p", "wank", "wank", "wetback", "wh00r", "wh0re", "whoar", "whore", "wichser", "wop", "xrated", "xxx", "Lipshits", "Mother Fukkah", "zabourah", "Phuk", "Poonani", "puta", "recktum", "sharmute", "Shyty", "smut", "vullva", "yed") stowwords.custom <- c("put", "far", "bit", "well", "article", "articles", "edit", "edits", "page", "pages", "talk", "page", "editor", "ax", "edu", "subject", "lines", "like", "likes", "line", "uh", "oh", "also", "get", "just", "hi", "hello", "ok", "editing", "edited", "dont", "use", "need", "take", "wikipedia", "give", "say", "look", "one", "make", "come", "see", "said", "now", "wiki", "know", "talk", "read", "hey", "time", "still", "user", "day", "want", "tell", "edit", "even", "ain't", "wow", "image", "jpg", "copyright", "sentence", "wikiproject", "background color", "align", "px", "pixel", "org", "com", "en", "ip", "ip address", "http", "www", "html", "htm", "wikimedia", "https", "httpimg", "url", "urls", "utc", "uhm","username","wikipedia", "what", "which", "who", "whom", "this", "that", "these", "those", "was", "be", "been", "being", "have", "has", "had", "having", "do", "does", "did", "doing", "would", "should", "could", "ought", "isn't", "aren't", "wasn't", "weren't", "hasn't", "haven't", "hadn't", "doesn't", "don't", "didn't", "won't", "wouldn't", "shan't", "shouldn't", "can't", "cannot", "couldn't", "mustn't", "let's", "that's", "who's", "what's", "here's", "there's", "when's", "where's", "why's", "how's", "a", "an", "the", "and", "but", "if", "or", "because", "as", "until", "while", "of", "at", "by", "for", "with", "about", "against", "between", "into", "through", "during", "before", "after", "above", "below", "to", "from", "up", "down", "in", "out", "on", "off", "over", "under", "again", "further", "then", "once", "here", "there", "when", "where", "why", "how", "all", "any", "both", "each", "few", "more", "most", "other", "some", "such", "no", "nor", "not", "only", "own", "same", "so", "than", "too", "very","articl","ani") #train <- train %>% mutate(filter="train") test <- test %>% mutate(filter="test") #all_comments <- train %>% bind_rows(test) #all_comments <- train all_comments <- test #all_comments <- all_comments[1:100,] nrow(all_comments) #******************************Train*************************************** # Create some new features relative to use of punctuation, emotj, ... all_comments.features <- all_comments %>% select(id, comment_text) %>% mutate( length = str_length(comment_text), use_cap = str_count(comment_text, "[A-Z]"), cap_len = use_cap / length, use_cap3plus = str_count(comment_text, "\\b[A-Z]{3,}\\b"), cap_len3plus = use_cap3plus / length, use_lower = str_count(comment_text, "[a-z]"), low_len = use_lower / length, image_cnt = str_count(comment_text, "\\b[\\w|:]*\\.(jpg|png|svg|jpeg|tiff|gif|bmp)\\b"), link_cnt = str_count(comment_text, "((f|ht)tp(s?)://\\S+)|(http\\S+)|(xml\\S+)"), wikilink_cnt = str_count(comment_text, "Wikipedia:(\\w|[[:punct:]])+\\b"), graph_cnt = str_count(comment_text, "[^[:graph:]]"), email_cnt = str_count(comment_text, "\\b[A-Z0-9._%+-]+@[A-Z0-9.-]+\\.[A-Z]{2,}\\b"), fact_cnt = image_cnt + link_cnt + wikilink_cnt + graph_cnt + email_cnt, nicknames_cnt = str_count(comment_text, "@\\w+"), use_exl = str_count(comment_text, fixed("!")), use_space = str_count(comment_text, fixed(" ")), use_double_space = str_count(comment_text, fixed(" ")), use_quest = str_count(comment_text, fixed("?")), use_punt = str_count(comment_text, "[[:punct:]]"), use_digit = str_count(comment_text, "[[:digit:]]"), digit_len = use_digit / length, use_break = str_count(comment_text, fixed("\n")), use_invis = str_count(comment_text, fixed("\\p{C}")), use_word = str_count(comment_text, "\\w+"), word_len = use_word / length, use_symbol = str_count(comment_text, "&|@|#|\\$|%|\\*|\\^"), use_symbol2plus = str_count(comment_text, "[&|@|#|\\$|%|\\*|\\^]{2,}"), use_symbol3plus = str_count(comment_text, "[&|@|#|\\$|%|\\*|\\^]{3,}"), use_symbol = use_symbol/ length, use_char = str_count(comment_text, "\\W*\\b\\w\\b\\W*"), use_i = str_count(comment_text, "(\\bI\\b)|(\\bi\\b)"), i_len = use_i / length, char_len = use_char / length, symbol_len = use_symbol / length, use_emotj = str_count(comment_text, "((?::|;|=)(?:-)?(?:\\)|D|P))"), cap_emo = use_emotj / length, prop_emot = str_count(replace_emoticon(comment_text))/ length, prop_names = str_count(replace_names(comment_text))/ length, prop_emoj = str_count(replace_emoji(comment_text))/ length, prop_kern = str_count(replace_kern(comment_text))/ length, prop_abbv = str_count(replace_abbreviation(comment_text))/ length, prop_contra = str_count(replace_contraction(comment_text))/ length, prop_slang = str_count(replace_internet_slang(comment_text))/ length, word_cnt = str_count(comment_text, "\\w+"), word_avglen = length / word_cnt, shit_prop = str_count(replace_word_elongation(comment_text))/length, use_nonascii = str_count(comment_text, "[^[:ascii:]]"), avg_sent = ((sentiment_by(get_sentences(comment_text)))[[4]]), uniqueword = lengths(regmatches(uniqueWords(comment_text), gregexpr("\\w+", uniqueWords(comment_text)))), prop_unique = uniqueword/lengths(regmatches(comment_text, gregexpr("\\w+", comment_text))), n_fword = str_count(comment_text, paste(profane,collapse = '|')), prop_fword = n_fword/word_cnt ) %>% select(-id) %T>% glimpse() #count stopwords all_comments.features$propstopwords_cnt <- str_count(removeWords(all_comments.features$comment_text, stopwords("en"))) all_comments.features$propstopwords <- str_count(removeWords(all_comments.features$comment_text, stopwords("en")))/all_comments.features$length #Package conversions all_comments.features$comment_text <- iconv(all_comments.features$comment_text, to='ASCII//TRANSLIT') all_comments.features$comment_text <- replace_emoticon(all_comments.features$comment_text) all_comments.features$comment_text <- replace_emoji(all_comments.features$comment_text) all_comments.features$comment_text <- replace_kern(all_comments.features$comment_text) all_comments.features$comment_text <- replace_abbreviation(all_comments.features$comment_text) all_comments.features$comment_text <- replace_contraction(all_comments.features$comment_text) all_comments.features$comment_text <- replace_names(all_comments.features$comment_text) all_comments.features$comment_text <- replace_word_elongation(all_comments.features$comment_text) all_comments.features$comment_text <- replace_internet_slang(all_comments.features$comment_text) #POS Features posdat_count <- counts(pos(all_comments.features$comment_text,progress.bar = TRUE, parallel = TRUE, cores = detectCores())) if (length(posdat_count) == 2){ posdat_prop <- data.frame() posdat_prop[1,1] <- posdat_count[1,1] colnames(posdat_prop)[1] <- "pos_prp_wrd.cnt" colnames(posdat_prop)[2] <- paste0("pos_prp_", colnames(posdat_count)[2]) } else { posdat_prop <- proportions(pos(all_comments.features$clean_comment_text,progress.bar = FALSE, parallel = TRUE, cores = detectCores())) names(posdat_prop) = paste0("pos_prp_", names(posdat_prop)) } names(posdat_count) = paste0("pos_cnt_", names(posdat_count)) all_comments.features <- cbind(all_comments.features,posdat_count,posdat_prop) gc() head(all_comments.features) nrow(all_comments.features) #Run it for both train and test and then combine all_comments.features <- all_comments.features %>% mutate(filter="train") all_comments.features_test <- all_comments.features_test %>% mutate(filter="test") all_comments.features <- all_comments.features %>% bind_rows(all_comments.features_test) # Remove all special chars, clean text and trasform words all_comments.clean <- all_comments.features %$% str_to_lower(comment_text) %>% # clear link str_replace_all("(f|ht)tp(s?)://\\S+", " ") %>% str_replace_all("http\\S+", "") %>% str_replace_all("xml\\S+", "") %>% str_replace_all("\\b\\w*:*\\w*\\.(jpg|png|svg|jpeg|tiff|gif|bmp)\\b", "") %>% str_replace_all("((f|ht)tp(s?)://\\S+)|(http\\S+)|(xml\\S+)", "") %>% #str_replace_all("Wikipedia:(\\w|[[:punct:]])+\\b", "") %>% str_replace_all("\\b[A-Z0-9._%+-]+@[A-Z0-9.-]+\\.[A-Z]{2,}\\b", "") %>% str_replace_all("\n", "") %>% #str_replace_all("\\p{C}", "") %>% # multiple whitspace to one str_replace_all("\\s{2}", " ") %>% # transform short forms str_replace_all("what's", "what is ") %>% str_replace_all("\\'s", " is ") %>% str_replace_all("\\'ve", " have ") %>% str_replace_all("can't", "cannot ") %>% str_replace_all("n't", " not ") %>% str_replace_all("i'm", "i am ") %>% str_replace_all("\\'re", " are ") %>% str_replace_all("\\'d", " would ") %>% str_replace_all("\\'ll", " will ") %>% str_replace_all("\\'scuse", " excuse ") %>% str_replace_all("pleas", " please ") %>% str_replace_all("sourc", " source ") %>% str_replace_all("peopl", " people ") %>% str_replace_all("remov", " remove ") %>% # multiple whitspace to one str_replace_all("\\s{2}", " ") %>% # transform shittext str_replace_all("(a|e)w+\\b", "") %>% str_replace_all("(y)a+\\b", "") %>% str_replace_all("(w)w+\\b", "") %>% str_replace_all("((a+)|(h+))(a+)((h+)?)\\b", "") %>% str_replace_all("((lol)(o?))+\\b", "") %>% str_replace_all("n ig ger", " nigger ") %>% str_replace_all("s hit", " shit ") %>% str_replace_all("g ay", " gay ") %>% str_replace_all("f ag got", " faggot ") %>% str_replace_all("c ock", " cock ") %>% str_replace_all("cu nt", " cunt ") %>% str_replace_all("idi ot", " idiot ") %>% str_replace_all("f u c k", " fuck ") %>% str_replace_all("fu ck", " fuck ") %>% str_replace_all("f u ck", " fuck ") %>% str_replace_all("c u n t", " cunt ") %>% str_replace_all("s u c k", " suck ") %>% str_replace_all("c o c k", " cock ") %>% str_replace_all("g a y", " gay ") %>% str_replace_all("ga y", " gay ") %>% str_replace_all("i d i o t", " idiot ") %>% str_replace_all("cocksu cking", "cock sucking") %>% str_replace_all("du mbfu ck", "dumbfuck") %>% str_replace_all("cu nt", "cunt") %>% str_replace_all("(?<=\\b(fu|su|di|co|li))\\s(?=(ck)\\b)", "") %>% str_replace_all("(?<=\\w(ck))\\s(?=(ing)\\b)", "") %>% str_replace_all("(?<=\\b\\w)\\s(?=\\w\\b)", "") %>% str_replace_all("((lol)(o?))+", "") %>% str_replace_all("(?<=\\b(fu|su|di|co|li))\\s(?=(ck)\\b)", "") %>% str_replace_all("(?<=\\w(uc))\\s(?=(ing)\\b)", "") %>% str_replace_all("(?<=\\b(fu|su|di|co|li))\\s(?=(ck)\\w)", "") %>% str_replace_all("(?<=\\b(fu|su|di|co|li))\\s(?=(k)\\w)", "c") %>% str_replace_all(fixed("sh*t"), "shit") %>% str_replace_all(fixed("$h*t"), "shit") %>% str_replace_all(fixed("$#*!"), "shit") %>% str_replace_all(fixed("$h*!"), "shit") %>% str_replace_all(fixed("sh!t"), "shit") %>% str_replace_all(fixed("@ss"), "ass") %>% str_replace_all(fixed("@$$"), "ass") %>% str_replace_all(fixed("a$$"), "ass") %>% str_replace_all(fixed("f*ck"), "fuck") %>% str_replace_all(fixed("f*uck"), "fuck") %>% str_replace_all(fixed("f***"), "fuck") %>% str_replace_all(fixed("f**k"), "fuck") %>% str_replace_all(fixed("c0ck"), "cock") %>% str_replace_all(fixed("a55"), "ass") %>% str_replace_all(fixed("$h1t"), "shit") %>% str_replace_all(fixed("b!tch"), "bitch") %>% str_replace_all(fixed("bi+ch"), "bitch") %>% str_replace_all(fixed("l3itch"), "bitch") %>% str_replace_all(fixed("p*ssy"), "pussy") %>% str_replace_all(fixed("d*ck"), "dick") %>% str_replace_all(fixed("n*gga"), "nigga") %>% str_replace_all(fixed("f*cking"), "fucking") %>% str_replace_all(fixed("shhiiitttt"), "shit") %>% str_replace_all(fixed("c**t"), "cunt") %>% str_replace_all(fixed("a**hole"), "asshole") %>% str_replace_all(fixed("@$$hole"), "asshole") %>% str_replace_all(fixed("fu"), "fuck you") %>% str_replace_all(fixed("wtf"), "what the fuck") %>% str_replace_all(fixed("ymf"), "motherfuck") %>% str_replace_all(fixed("f*@king"), "fucking") %>% str_replace_all(fixed("$#!^"), "shit") %>% str_replace_all(fixed("m0+#3rf~ck3r"), "motherfuck") %>% str_replace_all(fixed("pi55"), "piss") %>% str_replace_all(fixed("c~nt"), "cunt") %>% str_replace_all(fixed("c0ck$~ck3r"), "cocksucker") %>% # clean nicknames str_replace_all("@\\w+", " ") %>% # clean digit str_replace_all("[[:digit:]]", " ") %>% # remove linebreaks str_replace_all("\n", " ") %>% # remove graphics #str_replace_all("[^[:graph:]]", " ") %>% str_replace_all("'s\\b", " ") %>% # remove punctuation (if remain...) str_replace_all("[[:punct:]]", " ") %>% str_replace_all("[^[:alnum:]]", " ") %>% # remove single char str_replace_all("\\W*\\b\\w\\b\\W*", " ") %>% # remove words with len < 2 str_replace_all("\\b\\w{1,2}\\b", " ") %>% # multiple whitspace to one str_replace_all("\\s{2}", " ") %>% str_replace_all("\\s+", " ") %>% itoken(tokenizer = tokenize_word_stems) max_words = 200000 glove = fread("glove840b300dtxt/glove.840B.300d.txt", data.table = FALSE) %>% rename(word=V1) %>% mutate(word=gsub("[[:punct:]]"," ", rm_white(word) )) word_embed = all_comments.clean %>% left_join(glove) J = ncol(word_embed) ndim = J-2 word_embed = word_embed [1:(max_words-1),3:J] %>% mutate_all(as.numeric) %>% mutate_all(round,6) %>% #fill na with 0 mutate_all(funs(replace(., is.na(.), 0))) colnames(word_embed) = paste0("V",1:ndim) word_embed = rbind(rep(0, ndim), word_embed) %>% as.matrix() word_embed = list(array(word_embed , c(max_words, ndim)))

a5820d8a9bf966a5f97255e07733d4ccfc6b0d6d

7cd1f7f9555954476d9538c070e5a43ef93ce3d2

/man/parse_keyvals.Rd

d24551531900347e221d2ff7830714ef4b71fe67

[]

no_license

vsbuffalo/msr

f015447cc8815ea6ae9a24f5a451537edfa0b087

18fb0020ceb8c6e45b82dfd036dda7e03f64a163

refs/heads/master

2021-01-11T14:35:19.611932

2018-05-25T19:41:19

80,166,891

20

2

null

UTF-8

R

false

true

430

rd

parse_keyvals.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/parse_ms.r \name{parse_keyvals} \alias{parse_keyvals} \title{Parse MS's key/value pairs, e.g. segsites and positions returning a list of key/vals (where vals can be list too)} \usage{ parse_keyvals(x) } \description{ Parse MS's key/value pairs, e.g. segsites and positions returning a list of key/vals (where vals can be list too) } \keyword{internal}

59621a895e08d583a6c05de5f2b8c7b3b64f45b4

f44335c0bb9597c994c06611ef34b4c4fe9637c1

/R/listas.R

20397d379c01d21a25d0028fc57a6a93b87136af

[]

no_license

cran/INQC

7467d5c33cf59d1602fc6c82549f49613743d297

ddce985594be74cdf93b135c0febd4ef7cfb3c1e

refs/heads/master

2023-03-17T14:39:12.484132

2021-05-24T13:00:02

334,129,195

0

null

UTF-8

R

false

2,407

r

listas.R

listas<-function(country='all',name='allstations.txt'){ #NECESITO parametrizar listas. Usar esa parametrizacion par subset de downloads too. #' Creates listings for stations ('non-blended' case) linking STAID and SOUID #' @description This function takes all the elements and rbinds them into a single list to process #' @param country country for which the list is created. If 'all', no country filter. #' @param name output file name, do not touch, default is always good. #' @return data frame and the list file containing all stations for all elements, linking STAID and SOUID #' and metadata #' @examples #' #Set a temporal working directory: #' wd <- tempdir(); wd0 <- setwd(wd) #' #Extract the non-blended ECA&D station files from the example data folder #' #Only TX (maximum air temperature) and CC (cloud cover) variables are used in the example #' path2txlist<-system.file("extdata", "ECA_blend_source_tx.txt", package = "INQC") #' txlist<-readr::read_lines_raw(path2txlist) #' readr::write_lines(txlist,'ECA_blend_source_tx.txt') #' path2cclist<-system.file("extdata", "ECA_blend_source_cc.txt", package = "INQC") #' cclist<-readr::read_lines_raw(path2cclist) #' readr::write_lines(cclist,'ECA_blend_source_cc.txt') #' options("homefolder"='./') #' liston.nb<-listas(country='all',name='allstations.txt') #' #The created list file can be found in the directory: #' print(wd) #' #Return to user's working directory: #' setwd(wd0) #' @export #Get value of 'Global variable' 'homefolder' homefolder <- getOption("homefolder") variables<- c('TX','TN','TG','RR','HU','PP','SS','FG', 'FX', 'DD','SD', 'CC') ene<-length(variables) missing= -9999 ereseunnyu<-0 for(i in 1:ene){ list<-paste(homefolder,'ECA_blend_source_',tolower(variables[i]),'.txt',sep='') if(file.exists(list)){ ereseunnyu<-ereseunnyu+1 x<-utils::read.csv(list,header=FALSE,stringsAsFactors = FALSE,flush=TRUE,strip.white = TRUE) names(x)<-c('STAID','SOUID','SOUNAME','CN','LAT','LON','HGTH','ELEI','START','STOP','PARID','PARNAME') if(ereseunnyu==1){todas<-x}else{todas<-rbind(todas,x)} } } if(country!='all'){target<-which(todas$CN == country);todas<-todas[target,]} utils::write.csv(todas,paste(homefolder,name,sep='')) ## as consequence of the previous action return(todas) }

dfb109a560663c8d93fbb9c33b8952b1378225af

72d9009d19e92b721d5cc0e8f8045e1145921130

/sppmix/man/GetBDCompfit.Rd

152097f9e40cfb5b44abc45f41fba8d431dc3149

[]

no_license

akhikolla/TestedPackages-NoIssues

be46c49c0836b3f0cf60e247087089868adf7a62

eb8d498cc132def615c090941bc172e17fdce267

refs/heads/master

2023-03-01T09:10:17.227119

2021-01-25T19:44:44

332,027,727

1

0

null

UTF-8

R

false

true

2,275

rd

GetBDCompfit.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/postgen_ops.R \name{GetBDCompfit} \alias{GetBDCompfit} \title{Retrieve parts of a BDMCMC fit} \usage{ GetBDCompfit(BDfit, num_comp, burnin = floor(BDfit$L/10)) } \arguments{ \item{BDfit}{Object of class \code{damcmc_res}.} \item{num_comp}{Number of components requested. Only the posterior realizations that have this many components will be returned. The function fails if the BDMCMC chain never visited this number of components.} \item{burnin}{Number of initial realizations to discard. By default, it is 1/10 of the total number of iterations.} } \value{ A list containing the following: \item{BDgens}{Realizations corresponging to this many mixture components. This is a \code{damcmc_res} object (same as the result of a \code{\link{est_mix_damcmc}} call). All realizations for the requested number of components are returned, that is, burnin is not applied to this object.} \item{BDsurf}{For the requested \code{num_comp}, this is the Poisson intensity surface based on the corresponding posterior means (label switching might be present).} \item{BDnormmix}{For the requested \code{num_comp}, this is a \code{\link{normmix}} object containing the corresponding ps, mus and sigmas (label switching might be present).} } \description{ The function can be used to obtain the realizations and the corresponding surface of posterior means, for a specific number of components. Use \code{\link{GetPMEst}} if you want just the surface. For examples see \url{http://faculty.missouri.edu/~micheasa/sppmix/sppmix_all_examples.html #GetBDCompfit} } \examples{ \donttest{ fit <- est_mix_bdmcmc(pp = spatstat::redwood, m = 7) GetBDTable(fit) #retrieve all BDMCMC realizations corresponding to a mixture with 5 components BDfit5comp=GetBDCompfit(fit,5) plot(BDfit5comp$BDsurf,main="Mixture intensity surface with 5 components") #plot with the correct window plot(BDfit5comp$BDnormmix,xlim =BDfit5comp$BDsurf$window$xrange,ylim = BDfit5comp$BDsurf$window$yrange ) plot(BDfit5comp$BDgens)} } \seealso{ \code{\link{est_mix_bdmcmc}}, \code{\link{GetBDTable}}, \code{\link{plot.damcmc_res}}, \code{\link{plot.normmix}} } \author{ Sakis Micheas }

075a2ad8ae7a55d5a7ee9969a0d77f07609120b9

06d9afe4e9666407ff607b142649d4c6e944d674

/man/ezDesign.Rd

bb0a61f165e066500fbb5c5dc7723429155bbefd

[]

no_license

cran/ez

fe4ae993c2ed1042d6f84c64e368970c502a5bff

1d7a35d30f31b1671e7f6548b15864ddfe61c5ef

refs/heads/master

2021-07-10T23:03:03.489960

2016-11-02T18:17:31

17,695,925

1

0

null

UTF-8

R

false

2,248

rd

ezDesign.Rd

\name{ezDesign} \alias{ezDesign} \title{Plot the balance of data in an experimental design} \description{ This function provides easy visualization of the balance of data in a data set given a specified experimental design. This function is useful for identifying missing data and other issues (see examples). } \usage{ ezDesign( data , x , y , row = NULL , col = NULL , cell_border_size = 10 ) } \arguments{ \item{data}{ Data frame containing the data to be visualized. } \item{x}{ Name of the variable to plot on the x-axis. } \item{y}{ Name of the variable to plot on the y-axis. } \item{row}{ Name of a variable by which to split the data into facet rows. } \item{col}{ Name of a variable by which to split the data into facet columns. } \item{cell_border_size}{ Numeric value specifying the size of the border seperating cells (0 specifies no border) } } \details{ The function works by counting the number of rows in \code{data} in each cell of the design specified by the factorial combination of \code{x}, \code{y}, \code{row}, \code{col} variables. } \value{ A printable/modifiable ggplot2 object. } \author{ Michael A. Lawrence \email{mike.lwrnc@gmail.com}\cr Visit the \code{ez} development site at \url{http://github.com/mike-lawrence/ez}\cr for the bug/issue tracker and the link to the mailing list. } \seealso{ \code{\link{ezPrecis}} } \examples{ #Read in the ANT2 data (see ?ANT2). data(ANT2) head(ANT2) ezPrecis(ANT2) #toss NA trials ANT2 = ANT2[!is.na(ANT2$rt),] ezDesign( data = ANT2 , x = trial , y = subnum , row = block , col = group ) #subnum #7 is missing data from the last half of the experiment \dontrun{ ezDesign( data = ANT2 , x = flank , y = subnum , row = cue ) #again, subnum#7 has half the data as the rest #now look at error rates, which affect the number of RTs we can use ezDesign( data = ANT2[ANT2$error==0,] , x = flank , y = subnum , row = cue ) #again, subnum#7 stands out because they have half the data as the rest #also, subnum#14 has no data in any incongruent cells, suggesting that ##they made all errors in this condition #finally, subnum#12 has virtually no data, suggesting that they mistakenly ##swapped responses } }

257cddc3647acb198e381e68d9529afca110fa07

e2f3fee3cb8f1abdee08724f0fe8a89b5756cfbe

/COSTdata/man/FRS_ob_1999.Rd

b8723bdcffadb91d85ed0e31506de4222be3d5d9

[]

no_license

BackupTheBerlios/cost-project

1a88c928f4d99db583a95324b31d6a02d9bd20c9

4ab39d16c48f031ca46512545895cb17e5586139

refs/heads/master

2021-01-21T12:39:53.387734

2012-03-26T14:58:36

40,071,425

0

null

UTF-8

R

false

3,125

rd

FRS_ob_1999.Rd

\name{FRS_ob_1999} %\alias{FRS_ob_trips} \alias{FRS_ob_1999} \docType{data} \title{FRS observer data} \description{ FRS observer data in the COST data exchange format. \cr Consists of 53 demersal sampling trips from 1999. Discards length distributions are sampled by haul, the landed length distribution is sampled by trip, and the age given length distribution of a sub-sample of the discarded fraction is pooled by trip. } \usage{ data(FRS_ob_1999) } \format{ Formal class 'csData' [package "COSTcore"] objects with 6 slots \cr@desc: description \cr@tr: data.frame of 16 variables \cr@hh: data.frame of 29 variables \cr@sl: data.frame of 17 variables \cr@hl: data.frame of 16 variables \cr@ca: data.frame of 31 variables \cr see csData for details of the variables } \details{ The FRS observer sampling protocol is as follows: \cr From each haul during the trip the discarded fraction of the catch (consisting of unsorted fish of various species) is sub-sampled by the observer; two representative baskets of the discarded fish being obtained. The ratio of the discarded weight to sub-sampled weight is estimated by the observer and expressed in terms of "baskests". The length frequencies are taken from all cod, haddock, whiting and saithe in the sub-sample. The otoliths are collected from cod, haddock, whiting and saithe of each length class in the sub-sampled fraction until (usually) 5 otoliths are obtained for each length class for each species. Some length classes are not represented and some have fewer than 5 individuals. At the end of the trip the landed fraction of the catch, which will have been sorted into commercial size classes, is sub-sampled and the length frequencies recorded for cod, haddock, whiting and saithe. No otoliths are taken of the landed fraction. This observer data therefore consists of three components: \cr1. The length distribution for a sub-sample of the discarded fraction of the catch by haul. This is contained in the hl table where \kbd{\$catchCat} = "DIS", trips are identifiable by trip code \kbd{\$trpCode}, and the individual hauls by station number \kbd{\$staNum} \cr2. The length distribution of a sub-sample of the landed fraction pooled by trip. This is contained in the hl table where \kbd{\$catchCat} = "LAN", and the station number is \kbd{\$staNum = 999}. \cr3. The age given length distribution of a sub-sampled fraction of the discarded catch pooled by trip. This is contained in the ca table where \kbd{\$catchCat} = "DIS", and the station number is \kbd{\$staNum} = 999. Note that to obtain a catch weight \kbd{\$wt} and sub-sampled weight \kbd{\$subSampWt} by species in the sl table these values have been obtained retrospectively from the species' length frequency distribution using a standard weight length relationship, and the raising factor of number of discarded "baskets" to number sub-sampled baskets. } \section{Warning }{This is a test data set only and should not to be used or cited without prior permission.} \source{ FRS Marine Laboratory, Aberdeen, Scotland. } \examples{ data(FRS_ob_1999) } \keyword{datasets}

b43b2f4dcf308e25c52ed4a407ee709ad3aed10d

0e6d3ed19aa2ef50bf4e4bd164cb3383c106a84f

/GWAS/JIA/individual_level/jia_analysis.R

f149c4fd13a82aa4724fa2e0239e4807753193cf

[ "MIT" ]

permissive

ollyburren/basis_paper

4cdefd86a8811efb0bbeeae6975b804f6f7639a6

7393390c1b1f5b673049202d293994704cebeafb

refs/heads/master

2020-03-29T22:06:03.889635

2019-10-23T17:06:15

150,402,845

0

null

UTF-8

R

false

5,249

r

jia_analysis.R

library(cowplot) ## analyse jia projections DATA.DIR <- '/home/ob219/share/as_basis/GWAS/individual_data/individual_proj' all.files <- list.files(path=DATA.DIR,pattern="*.RDS",full.names=TRUE) BASIS_FILE <- '/home/ob219/share/as_basis/GWAS/support/ss_basis_gwas.RDS' VARIANCE_FILE <- '/home/ob219/share/as_basis/GWAS/support/ss_av_june.RDS' res <- lapply(all.files,function(f){ trait <- basename(f) %>% gsub("\\_projection\\.RDS","",.) dat <- readRDS(f) t.DT <- data.table(ind=rownames(dat),dat) melt.DT <- melt(t.DT,id.vars='ind') melt.DT[,trait:=trait] }) %>% rbindlist res <- res[!trait %in% c('jiaUnA','jiamissing','raj_cd14','raj_cd4'),] t.test(res[variable=='PC3' & trait %in% c('jiaERA','jiasys')]$value,res[variable=='PC3' & !trait %in% c('jiaERA','jiasys')]$value) t.test(res[variable=='PC3' & trait=='jiaERA']$value,res[variable=='PC3' & !trait %in% c('jiaERA','jiasys')]$value) t.test(res[variable=='PC3' & trait=='jiasys']$value,res[variable=='PC3' & !trait %in% c('jiaERA','jiasys')]$value) ## look at all of them traits <- res$trait %>% unique all.compare <- lapply(paste('PC',1:10,sep=''),function(PC){ message(PC) lapply(traits,function(tra){ lapply(traits,function(tra2){ tt <- t.test(res[variable==PC & trait==tra]$value,res[variable==PC & trait==tra2]$value) data.table(pc=PC,trait1=tra,trait2=tra2,p=tt$p.value,t.stat=tt$statistic) }) %>% rbindlist }) %>% rbindlist }) %>% rbindlist all.compare <- all.compare[trait1 != trait2,] ## get rid of reciprocal comparisons all.compare <- all.compare[which(!duplicated(abs(t.stat))),] all.compare[abs(t)] all.compare[,fdr:=p.adjust(p,method="fdr")] all.compare[fdr<0.05,] all.compare.rest <- lapply(paste('PC',1:10,sep=''),function(PC){ message(PC) lapply(traits,function(tra){ tt <- t.test(res[variable==PC & trait==tra]$value,res[variable==PC & trait!=tra]$value) data.table(pc=PC,trait1=tra,p=tt$p.value,t.stat=tt$statistic) }) %>% rbindlist }) %>% rbindlist all.compare.rest[,fdr:=p.adjust(p,method="bonferroni"),by=pc] all.compare.rest[fdr<0.05,] ## get mean and variance across trait and pc summ.DT <- res[,list(mean.load=mean(value),var.load=var(value)),by=c('trait','variable')] pc.emp <- readRDS(BASIS_FILE) basis.DT <- data.table(trait=rownames(pc.emp$x),pc.emp$x) tmp <- basis.DT[trait=='control',] %>% t ctrl.DT <- data.table(variable=rownames(tmp)[-1],control.loading=as.numeric(tmp[-1,1])) #var.DT <- readRDS(VARIANCE_FILE) summ.DT[,variable:=factor(variable,levels=paste0('PC',1:11))] summ.DT <- merge(summ.DT,ctrl.DT,by='variable') #summ.DT <- merge(summ.DT,var.DT,by.x='variable',by.y='pc') summ.DT[,Z:=(mean.load-control.loading)/sqrt()] summ.DT[,Z:=(mean.load-control.loading)/sqrt(mfactor)] summ.DT[,p.value:=pnorm(abs(Z),lower.tail=FALSE) * 2] #bb.DT.m[,p.adj:=p.adjust(p.value),by='variable'] summ.DT[,p.adj:=p.adjust(p.value),by='variable'] summ.DT[,variable:=factor(variable,levels=paste0('PC',1:11))] summ.DT[,short.trait:=substr(trait,1,15),] summ.DT[,Subtype:=gsub("^jia","",trait)] pd <- position_dodge(0.1) #pa <- ggplot(summ.DT[!trait %in% c('jiaUnA','jiamissing','raj_cd14','raj_cd4'),],aes(x=variable,y=mean.load-control.loading,group=Subtype,col=Subtype)) + geom_point(position=pd) + #geom_line(position=pd) + guides(size=FALSE) + xlab("Principal Component") + ylab(expression(Delta~"Control Loading")) + pa <- ggplot(summ.DT[!trait %in% c('jiaUnA','jiamissing','raj_cd14','raj_cd4'),],aes(x=variable,y=mean.load-control.loading,group=Subtype,col=Subtype)) + geom_point(size=2,position=pd) + geom_line(position=pd) + ylab(expression(Delta*"Control Loading")) + xlab("Principal Component") + geom_hline(yintercept=0,color="black") + background_grid(major = "xy", minor = "none") + theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) save_plot(pa,file="~/tmp/ind_jia_line.pdf",base_aspect=1.3) jia.sum <- readRDS("~/share/as_basis/GWAS/tmp/jia_plot.RDS") jia.sum[,Subtype:=gsub("jia\\_","",trait)] pb <- ggplot(jia.sum[!trait %in% c('jiaUnA','jiamissing'),],aes(x=variable,y=value-control.loading,group=Subtype,col=Subtype)) + geom_point(position=pd) + geom_line(position=pd) + guides(size=FALSE) + xlab("Principal Component") + ylab(expression(Delta~"Control Loading")) ## comparison between summary and individual data plot_grid(pa + ggtitle("Genotypes") + geom_hline(yintercept=0,color='black'),pb + ggtitle("Summary statistics") + geom_hline(yintercept=0,color='black'), nrow=2) dev.print(pdf,"~/tmp/gt_vs_summ.pdf") ## next do t.test of sys and era and the rest to see which PC's are important to discern between g1 <- c('jiaERA','jiasys') diff <- lapply(paste('PC',1:11,sep=''),function(y){ t.tmp <- t.test(res[variable==y & trait %in% g1,]$value,res[variable==y & !trait %in% g1,]$value) data.table(pc=y,p.value=t.tmp$p.value,tstat=t.tmp$statistic) }) %>% rbindlist() diff[,p.adj:=p.adjust(p.value)] ## other class is g1 <- c('jiaPsA','jiaEO') g2 <- c('jiaRFneg','jiaRFpos','jiaPO') diff2 <- lapply(paste('PC',1:11,sep=''),function(y){ t.tmp <- t.test(res[variable==y & trait %in% g1,]$value,res[variable==y & trait %in% g2,]$value) data.table(pc=y,p.value=t.tmp$p.value,tstat=t.tmp$statistic) }) %>% rbindlist() diff2[,p.adj:=p.adjust(p.value)]

9b6d331ec04ffa4c2f18ec4999de1c23bd1c5120

bdbb30b1fa9d20d16b37dfe43c8796e43d919934

/Application/ui.R

644b4c3e57605864ff63835f64f7ac45299fec31

[]

no_license

pogh/Course-2015.09-Developing-Data-Products

8f4abc75c4b55ef1523bd0e14e484deb1171c86c

27cc269c0286896e1abe8a3955f3d30973ef8524

refs/heads/master

2021-05-30T04:25:58.459676

2015-09-14T09:33:54

null

0

null

UTF-8

R

false

838

r

ui.R

require(markdown) shinyUI(fluidPage( navbarPage("Exploratory Linear Modelling on the ‘mtcars’ Dataset"), fluidRow( column(4, includeMarkdown("documentation1.md"), hr(), uiOutput("colNamesDropdown1"), uiOutput("colNamesDropdown2"), sliderInput("size", "Display Size:", min = 1, max = 10, value = 5), hr(), includeMarkdown("documentation2.md") ), column(7, plotOutput("plot"), hr(), actionButton("btnSubmit", "Show Linear Model Detail"), br(),br(), textOutput("modelSummaryText"), br(), verbatimTextOutput("modelSummaryPrint"), plotOutput("modelPlot") ) ) ))

1da759a17a1602f2b5465ce3b2c0a9df7d20e93d

3f63ed18371a3237d501badeef43e2fe6a41cd45

/vectorFieldsInR.R

66db7bd2bbcdb7966fdad915ec313c999c539a3a

[]

no_license

johnwithrowjr/R_Libraries

9afff950b412dc14aace92d39cf9819bf96e52e4

69cdd55ef8f2d2cb1f5e5a1e806d4379fac98a8d

refs/heads/master

2021-01-18T23:50:35.750753

2016-06-10T21:32:31

55,651,823

0

null

UTF-8

R

false

978

r

vectorFieldsInR.R

plotVectorDivergence <- function(z) { vectorField(atanc(z),mag(z),rep(1:dim(z)[2],dim(z)[1]),rep(1:dim(z)[1],each=dim(x)[2])) } createVectorDivergence <- function(rstX,rstY,betamax=-1) { matX <- as.matrix(rstX@data) dim(matX) <- rstX@grid@cells.dim n <- dim(matX) matY <- as.matrix(rstY@data) dim(matY) <- rstY@grid@cells.dim if (dim(matX) != dim(matY)) { stop("The two rasters must have identical dimensions") } matXx <- as.matrix(rep(0),n[1]*n[2])) dim(matXx) <- n matXy <- as.matrix(rep(0),n[1]*n[2])) dim(matXy) <- n z <- as.matrix(rep(complex(real=0,imaginary=0),n[1]*n[2])) dim(z) <- n for (i in 2:(n[1]-1)) { for (j in 2:(n[2]-1)) { matXx[i,j] <- matX[i,j+1] - matX[i,j-1] matXy[i,j] <- matX[i+1,j] - matX[i-1,j] for (p in 1:betamax) { for (q in 1:betamax) { z[i,j] <- z[i,j] + complex(real=matY[i,j]/(matXx[i,j]-p/q*matXy[i,j]),imaginary=matY[i,j]/(matXy[i,j]-q/p*matXx[i,j])) } } } } plotVectorDivergence(z) z }

656ed8b42aa10270230095d95b02dc45794fcc29

bdc863461d5b665914b5cc369c4d445283917f29

/Tests/Candidates/10^3/Data 10^3 Test/PLOT FPOP 10^3 MultiTest Candidates.r

71ee89d907cb41e93d689eff29f44535bdd04a74

[]

no_license

lpishchagina/FPOPdim2

706b5942f7a07a4de834c0f7964444c18f600fa1

353d96dc1450e4bac90c1b270a3f0312d01ee626

refs/heads/main

2023-04-01T17:52:52.647384

2021-04-13T21:16:16

346,318,274

0

null

UTF-8

R

false

10,465

r

PLOT FPOP 10^3 MultiTest Candidates.r

library(ggplot2) library(ggpubr) ################################################################################ fname <- "PLOT FPOP 10^3 MultiTest Candidates.png" f1name <- "PLOT FPOP1 10^3 MultiTest Candidates.png" f2name <- "PLOT FPOP2 10^3 MultiTest Candidates.png" f3name <- "PLOT FPOP3 10^3 MultiTest Candidates.png" ################################################################################ dim <- c(2:10) s = "10^3" size <-1000 Time <-c(1:size) dimension <- c("dim 2","dim 3","dim 4","dim 5","dim 6","dim 7","dim 8","dim 9","dim 10") ################################# # FPOP1 # ################################# F1C <- matrix(nrow = size, ncol = length(dim)+1) F1C[,1] <- Time for (i in 1:length(dim)){ ffname = paste("dim",dim[i],"FPOP1",s,"MultiTest Candidates.txt") f_data <- readLines(con = ffname, n = -1) f_data <- strsplit(f_data,split = ' ') f_data <- sapply(f_data, FUN = function(x) {as.double(unlist(x))}) F1C[, i+1] <-f_data } ################################# # FPOP2 # ################################# F2C <- matrix(nrow = size, ncol = length(dim)+1) F2C[,1] <- Time for (i in 1:length(dim)){ ffname = paste("dim",dim[i],"FPOP2",s,"MultiTest Candidates.txt") f_data <- readLines(con = ffname, n = -1) f_data <- strsplit(f_data,split = ' ') f_data <- sapply(f_data, FUN = function(x) {as.double(unlist(x))}) F2C[, i+1] <-f_data } ################################# # FPOP3 # ################################# F3C <- matrix(nrow = size, ncol = length(dim)+1) F3C[,1] <- Time for (i in 1:length(dim)){ ffname = paste("dim",dim[i],"FPOP3",s,"MultiTest Candidates.txt") f_data <- readLines(con = ffname, n = -1) f_data <- strsplit(f_data,split = ' ') f_data <- sapply(f_data, FUN = function(x) {as.double(unlist(x))}) F3C[, i+1] <-f_data } ################################################################################ F1 <- as.data.frame(F1C) F2 <- as.data.frame(F2C) F3 <- as.data.frame(F3C) PLOT_dim = list() PLOT_dim[[1]] <- ggplot(F1, aes(Time))+geom_line(aes(y = F1C[,10], color = "dim 10"), size = 1)+geom_line(aes(y = F1C[,9], color = "dim 9"), size = 1)+geom_line(aes(y = F1C[,8], color = "dim 8"), size = 1)+geom_line(aes(y = F1C[,7], color = "dim 7"), size = 1)+geom_line(aes(y = F1C[,6], color = "dim 6"), size = 1)+geom_line(aes(y = F1C[,5], color = "dim 5"), size = 1)+geom_line(aes(y = F1C[,4], color = "dim 4"), size = 1)+geom_line(aes(y = F1C[,3], color = "dim 3"), size = 1)+geom_line(aes(y = F1C[,2], color = "dim 2"), size = 2)+labs( x = "Time", y = "Number of candidates being considered", title ="FPOP1:Candidates")+theme(legend.position = c(0, 1),legend.justification = c(0, 1)) PLOT_dim[[2]] <- ggplot(F2, aes(Time))+geom_line(aes(y = F2C[,10], color = "dim 10"), size = 1)+geom_line(aes(y = F2C[,9], color = "dim 9"), size = 1)+geom_line(aes(y = F2C[,8], color = "dim 8"), size = 1)+geom_line(aes(y = F2C[,7], color = "dim 7"), size = 1)+geom_line(aes(y = F2C[,6], color = "dim 6"), size = 1)+geom_line(aes(y = F2C[,5], color = "dim 5"), size = 1)+geom_line(aes(y = F2C[,4], color = "dim 4"), size = 1)+geom_line(aes(y = F2C[,3], color = "dim 3"), size = 1)+geom_line(aes(y = F2C[,2], color = "dim 2"), size = 2)+labs( x = "Time", y = "Number of candidates being considered", title ="FPOP2:Candidates")+theme(legend.position = c(0, 1),legend.justification = c(0, 1)) PLOT_dim[[3]] <- ggplot(F3, aes(Time))+geom_line(aes(y = F3C[,10], color = "dim 10"), size = 1)+geom_line(aes(y = F3C[,9], color = "dim 9"), size = 1)+geom_line(aes(y = F3C[,8], color = "dim 8"), size = 1)+geom_line(aes(y = F3C[,7], color = "dim 7"), size = 1)+geom_line(aes(y = F3C[,6], color = "dim 6"), size = 1)+geom_line(aes(y = F3C[,5], color = "dim 5"), size = 1)+geom_line(aes(y = F3C[,4], color = "dim 4"), size = 1)+geom_line(aes(y = F3C[,3], color = "dim 3"), size = 1)+geom_line(aes(y = F3C[,2], color = "dim 2"), size = 2)+labs( x = "Time", y = "Number of candidates being considered", title ="FPOP3:Candidates")+theme(legend.position = c(0, 1),legend.justification = c(0, 1)) png(filename = fname, width = 1500, height = 1000) ggarrange(PLOT_dim[[1]],PLOT_dim[[2]],PLOT_dim[[3]],ncol = 1) dev.off() png(filename = f1name, width = 1500, height = 1000) ggarrange(PLOT_dim[[1]],ncol = 1) dev.off() png(filename = f2name, width = 1500, height = 1000) ggarrange(PLOT_dim[[2]],ncol = 1) dev.off() png(filename = f3name, width = 1500, height = 1000) ggarrange(PLOT_dim[[3]],ncol = 1) dev.off() ################################################################################ Fdim10 = data.frame(Time,F1C[,10],F2C[,10],F3C[,10]) Fdim9 = data.frame(Time,F1C[,9],F2C[,9],F3C[,9]) Fdim8 = data.frame(Time,F1C[,8],F2C[,8],F3C[,8]) Fdim7 = data.frame(Time,F1C[,7],F2C[,7],F3C[,7]) Fdim6 = data.frame(Time,F1C[,6],F2C[,6],F3C[,6]) Fdim5 = data.frame(Time,F1C[,5],F2C[,5],F3C[,5]) Fdim4 = data.frame(Time,F1C[,4],F2C[,4],F3C[,4]) Fdim3 = data.frame(Time,F1C[,3],F2C[,3],F3C[,3]) Fdim2 = data.frame(Time,F1C[,2],F2C[,2],F3C[,2]) PLOT_FPOP = list() PLOT_FPOP[[2]] <- ggplot(Fdim2, aes(Time))+geom_line(aes(y = F1C[,2], color = "FPOP1"), size = 1)+geom_line(aes(y = F2C[,2], color = "FPOP2"), size = 1)+geom_line(aes(y = F3C[,2], color = "FPOP3"), size = 1)+labs( x = "Time", y = "Number of candidates being considered", title ="Dimension 2: Candidates")+theme(legend.position = c(0, 1),legend.justification = c(0, 1)) PLOT_FPOP[[3]] <- ggplot(Fdim3, aes(Time))+geom_line(aes(y = F1C[,3], color = "FPOP1"), size = 1)+geom_line(aes(y = F2C[,3], color = "FPOP2"), size = 1)+geom_line(aes(y = F3C[,3], color = "FPOP3"), size = 1)+labs( x = "Time", y = "Number of candidates being considered", title ="Dimension 3: Candidates")+theme(legend.position = c(0, 1),legend.justification = c(0, 1)) PLOT_FPOP[[4]] <- ggplot(Fdim4, aes(Time))+geom_line(aes(y = F1C[,4], color = "FPOP1"), size = 1)+geom_line(aes(y = F2C[,4], color = "FPOP2"), size = 1)+geom_line(aes(y = F3C[,4], color = "FPOP3"), size = 1)+labs( x = "Time", y = "Number of candidates being considered", title ="Dimension 4: Candidates")+theme(legend.position = c(0, 1),legend.justification = c(0, 1)) PLOT_FPOP[[5]] <- ggplot(Fdim5, aes(Time))+geom_line(aes(y = F1C[,5], color = "FPOP1"), size = 1)+geom_line(aes(y = F2C[,5], color = "FPOP2"), size = 1)+geom_line(aes(y = F3C[,5], color = "FPOP3"), size = 1)+labs( x = "Time", y = "Number of candidates being considered", title ="Dimension 5: Candidates")+theme(legend.position = c(0, 1),legend.justification = c(0, 1)) PLOT_FPOP[[6]] <- ggplot(Fdim6, aes(Time))+geom_line(aes(y = F1C[,6], color = "FPOP1"), size = 1)+geom_line(aes(y = F2C[,6], color = "FPOP2"), size = 1)+geom_line(aes(y = F3C[,6], color = "FPOP3"), size = 1)+labs( x = "Time", y = "Number of candidates being considered", title ="Dimension 6: Candidates")+theme(legend.position = c(0, 1),legend.justification = c(0, 1)) PLOT_FPOP[[7]] <- ggplot(Fdim7, aes(Time))+geom_line(aes(y = F1C[,7], color = "FPOP1"), size = 1)+geom_line(aes(y = F2C[,7], color = "FPOP2"), size = 1)+geom_line(aes(y = F3C[,7], color = "FPOP3"), size = 1)+labs( x = "Time", y = "Number of candidates being considered", title ="Dimension 7: Candidates")+theme(legend.position = c(0, 1),legend.justification = c(0, 1)) PLOT_FPOP[[8]] <- ggplot(Fdim8, aes(Time))+geom_line(aes(y = F1C[,8], color = "FPOP1"), size = 1)+geom_line(aes(y = F2C[,8], color = "FPOP2"), size = 1)+geom_line(aes(y = F3C[,8], color = "FPOP3"), size = 1)+labs( x = "Time", y = "Number of candidates being considered", title ="Dimension 8: Candidates")+theme(legend.position = c(0, 1),legend.justification = c(0, 1)) PLOT_FPOP[[9]] <- ggplot(Fdim9, aes(Time))+geom_line(aes(y = F1C[,9], color = "FPOP1"), size = 1)+geom_line(aes(y = F2C[,9], color = "FPOP2"), size = 1)+geom_line(aes(y = F3C[,9], color = "FPOP3"), size = 1)+labs( x = "Time", y = "Number of candidates being considered", title ="Dimension 9: Candidates")+theme(legend.position = c(0, 1),legend.justification = c(0, 1)) PLOT_FPOP[[1]] <- ggplot(Fdim10, aes(Time))+geom_line(aes(y = F1C[,10], color = "FPOP1"), size = 1)+geom_line(aes(y = F2C[,10], color = "FPOP2"), size = 1)+geom_line(aes(y = F3C[,10], color = "FPOP3"), size = 1)+labs( x = "Time", y = "Number of candidates being considered", title ="Dimension 10: Candidates")+theme(legend.position = c(0, 1),legend.justification = c(0, 1)) png(filename = "dim 2 PLOT FPOP 10^3 MultiTest Candidates.png", width = 1500, height = 1000) ggarrange(PLOT_FPOP[[2]],ncol = 1) dev.off() png(filename = "dim 3 PLOT FPOP 10^3 MultiTest Candidates.png", width = 1500, height = 1000) ggarrange(PLOT_FPOP[[3]],ncol = 1) dev.off() png(filename = "dim 4 PLOT FPOP 10^3 MultiTest Candidates.png", width = 1500, height = 1000) ggarrange(PLOT_FPOP[[4]],ncol = 1) dev.off() png(filename = "dim 5 PLOT FPOP 10^3 MultiTest Candidates.png", width = 1500, height = 1000) ggarrange(PLOT_FPOP[[5]],ncol = 1) dev.off() png(filename = "dim 6 PLOT FPOP 10^3 MultiTest Candidates.png", width = 1500, height = 1000) ggarrange(PLOT_FPOP[[6]],ncol = 1) dev.off() png(filename = "dim 7 PLOT FPOP 10^3 MultiTest Candidates.png", width = 1500, height = 1000) ggarrange(PLOT_FPOP[[7]],ncol = 1) dev.off() png(filename = "dim 8 PLOT FPOP 10^3 MultiTest Candidates.png", width = 1500, height = 1000) ggarrange(PLOT_FPOP[[8]],ncol = 1) dev.off() png(filename = "dim 9 PLOT FPOP 10^3 MultiTest Candidates.png", width = 1500, height = 1000) ggarrange(PLOT_FPOP[[9]],ncol = 1) dev.off() png(filename = "dim 10 PLOT FPOP 10^3 MultiTest Candidates.png", width = 1500, height = 1000) ggarrange(PLOT_FPOP[[1]],ncol = 1) dev.off() png(filename = "dim 2-10 PLOT FPOP 10^3 MultiTest Candidates.png", width = 1500, height = 1000) ggarrange(PLOT_FPOP[[2]],PLOT_FPOP[[3]],PLOT_FPOP[[4]],PLOT_FPOP[[5]],PLOT_FPOP[[6]],PLOT_FPOP[[7]],PLOT_FPOP[[8]],PLOT_FPOP[[9]],PLOT_FPOP[[1]],ncol = 1) dev.off() png(filename = "dim 2,3,4 PLOT FPOP 10^3 MultiTest Candidates.png", width = 1500, height = 1000) ggarrange(PLOT_FPOP[[2]],PLOT_FPOP[[3]],PLOT_FPOP[[4]],ncol = 1) dev.off() png(filename = "dim 5,6,7 PLOT FPOP 10^3 MultiTest Candidates.png", width = 1500, height = 1000) ggarrange(PLOT_FPOP[[5]],PLOT_FPOP[[6]],PLOT_FPOP[[7]],ncol = 1) dev.off() png(filename = "dim 8,9,10 PLOT FPOP 10^3 MultiTest Candidates.png", width = 1500, height = 1000) ggarrange(PLOT_FPOP[[8]],PLOT_FPOP[[9]],PLOT_FPOP[[1]],ncol = 1) dev.off()

977c327793b62f70fec96111fe29503937b35a27

08747ab934d09afeb31876584d2b8ab96a524648

/Projet_final/ui.R

3cf91599b2d4cccb359647c00b47900f0cce70e7

[]

no_license

vneyret/Rapportfin

3900a3c99bbb628abe546bd10a9743c28b2eae9b

9b35cfbaca42528d63e923e27df05d0ae4148d79

refs/heads/master

2022-07-24T09:53:22.124311

2020-05-15T08:21:29

263,560,439

0

null

2020-05-13T14:59:35

2020-05-13T07:44:48

HTML

UTF-8

R

false

9,030

r

ui.R

library(shiny) library(shinythemes) ui <- tagList( fluidPage(theme = shinytheme("flatly")), navbarPage( # title=div(img(src="logo.png"), "ISARA Projet Shiny G1"), "ISARA Projet Shiny G1", #Onglet Informations tabPanel("Informations", fluidRow(column(width=2), column( h3(p("ANOVA à 1 un facteur",style="color:black;text-align:center")), width=8,style="background-color:#e0eee0;border-radius: 8px") ), br(), fluidRow(column(width=2, icon("arrow-alt-circle-right","fa-5x"),align="center"), column( p("Cette application a pour but de réaliser une ANOVA à 1 facteur sur tous types de données numérique. Un ANOVA est un test statistique qui permet de tester des données paramétriques en comparant la moyenne entre plusieurs modalités d'un facteur. Deux hypothèses de travail sont alors testées :",style="color:black;text-align:justify"), withMathJax(), p(strong("H0 : Les moyennes sont toutes égales entre elles. Le facteur n’a pas un effet significatif sur la variable"),style="color:black; text-align:justify; padding:20px;border:1px solid black;background-color:white"), p(strong("H1 : Au moins une des moyennes est différente des autres. Le facteur a un effet significatif sur la variable"),style="color:black; text-align:justify; padding:20px;border:1px solid black;background-color:white"), width=8,style="background-color:#e0eee0 ;border-radius: 8px") ), br(), p("A titre d'exemple, nous utilisons un jeu de données provenant d'essais agronomiques réalisés sur du petit épeautre. Les essais se sont déroulés sur ", strong("l'année 2019"), "et ont eu lieu sur ", strong("9 variétés différentes."), "L'expérimentation s'est déroulée dans le département de l'Aude, une partie dans la Piège, une partie dans le Minervois et une troisième partie à l'école d'ingénieur de Purpan.", style="text-align:justify;color:black;background-color:#e0eee0;padding:15px;border-radius:8px"), br(), fluidPage( img(src ="ptitepeautre.png", align = "center", height = 300 , width = 300 ), img(src ="Parcelles.png", align = "right", height = 300, width = 500) ) ), #Onglet Données tabPanel("Charger Tableau", # Titre titlePanel("Importez votre tableau"), # Mise en page : disposition de la barre latérale sidebarLayout( # Panneau pour afficher les entrées sidebarPanel( # Imput pour sélectionner un fichier fileInput("file1", "Choisissez un fichier .CSV", multiple = FALSE, accept = c("text/csv", "text/comma-separated-values,text/plain", ".csv")), # Insertion d'une ligne horizontale tags$hr(), # Input pour insérer un bouton pour voir si le fichier a des en-tête checkboxInput("header", "En-tête", TRUE), # Input pour choisir le type de séparateur radioButtons("sep", "Séparateurs", choices = c(Comma = ",", Semicolon = ";"), # Tab = "\t"), selected = ","), # Insertion d'une ligne horizontale tags$hr(), # Input pour choisir le nombre de lignes à afficher ), # Panneau pour afficher les sorties mainPanel( # Affichage du tableau tableOutput("contents") ) ) ), tabPanel("Interprétation", mainPanel( tabsetPanel( tabPanel("Description", br(), h5(p("Nous affichons la",strong ("moyenne"), "pour les différentes modalités du facteur :", style="text-align:justify;color:black;background-color:#e0ffff;padding:15px;border-radius:8px")), br(), tableOutput("mean"), h5(p("Nous affichons le",strong ("boxplot"), "pour les différentes modalités du facteur. Le boxplot permet de visualiser des mesures statistiques clés telles que la médiane, la moyenne et les quartiles.", style="text-align:justify;color:black;background-color:#e0ffff;padding:15px;border-radius:8px")), plotOutput("boxplot") ), tabPanel("Hypothèses", br(), h5(p("Pour le test de l'ANOVA, il est nécessaire de tester la normalité des résidus.", style="text-align:justify;color:black;background-color:#e0ffff;padding:15px;border-radius:8px")), plotOutput("plotsindep"), br(), h5(p("Dans un premier temps, on effectue le test de Shapiro. Si p-value > 0.05, alors les résidus sont normaux et nous devons donc alors tester comme deuxième hypothèse l'égalité des variances par le test de Bartlett.", style="text-align:justify;color:black;background-color:#e0ffff;padding:15px;border-radius:8px")), verbatimTextOutput("shapiro"), br(), h5(p("Pour le test de Bartlett, si p-value > 0.05 alors les variances sont égales.", style="text-align:justify;color:black;background-color:#e0ffff;padding:15px;border-radius:8px")), verbatimTextOutput("bartlett"), br(), fluidRow(column(width=2, icon("arrow-alt-circle-right","fa-5x"),align="center"), column(10, h4(p("Si toutes les hypothèses sont vérifiées, nous pouvons faire une ANOVA",style="color:white;background-color:#36648b;padding:15px;border-radius:8px;text-align:justify"))), withMathJax()), ), tabPanel("Anova", br(), h5(p("Suite à la validation des hypothèses, on test les hypothèses de départ H0 et H1.", style="text-align:justify;color:black;background-color:#e0ffff;padding:15px;border-radius:8px")), tableOutput("anov"), br(), h5(p("Si la p-value > 0.05, cela signifie que ce n'est pas significatif, H0 est accepté.", strong("Le facteur n'a pas d'effet significatif sur la variable : les moyennes sont toutes égales entre elles."), br(), br(), "Si la p-value < 0.05, cela signifie que cela est significatif, H0 est rejété et H1 est donc accepté.", strong("Le facteur a un effet significatif sur la varialbe : au moins une des moyennes est différente des autres."), style="text-align:justify;color:black;background-color:#e0ffff;padding:15px;border-radius:8px")), br(), fluidRow(column(width=2, icon("arrow-alt-circle-right","fa-5x"),align="center"), column(10, h4(p("Pour savoir où se situent les différences :"), h4("Test TuckeyHSD",style="color:white;background-color:#36648b;padding:15px;border-radius:8px;text-align:justify"))), withMathJax())), tabPanel("Résultats", br(), h5(p(".", style="text-align:justify;color:black;background-color:#e0ffff;padding:15px;border-radius:8px")), plotOutput("tuck"), br(), h5(p(".", style="text-align:justify;color:black;background-color:#e0ffff;padding:15px;border-radius:8px")), tableOutput("classe")) ) ) ) ) )

90f271db65fe132d760e43161b2a9afc6503719c

3a5f227074d2d903633cd893b57af9125b536aaf

/man/ei_ind.Rd

32f570b62116b363974db9109ee062e94937a883

[]

no_license

cran/ITNr

eefd3a398e7bca302b05782913f300dae1fb7f26

35a833cc39458b6cf5f6b491a4327cca2effa63a

refs/heads/master

2023-06-24T15:43:02.117877

2023-03-31T13:10:11

120,759,051

0

null

UTF-8

R

false

true

750

rd

ei_ind.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/individual_EI_function.R \name{ei_ind} \alias{ei_ind} \title{Individual/Node level E-I Index} \usage{ ei_ind(gs, attrname) } \arguments{ \item{gs}{igraph object} \item{attrname}{Attribute name} } \value{ Group level results dataframe } \description{ This function calculates the E-I Index (External-internal) at the individual/node level } \examples{ require(igraph) ##Create random network (igraph object) gs<-erdos.renyi.game(30,0.05,directed = TRUE) ##Add vertex names V(gs)$name<-1:vcount(gs) ## Add an attribute V(gs)$letters<- rep(LETTERS[1:5],6) ##Calculate the Individual E-I Results EI_IND_DATAFRAME<-ei_ind(gs,"letters") }

848d9a10277a31349aaf0a97ce408f5ccc03bd2c

aae143af482690863b42f76af555f2617fabfc39

/R/build_nhtsa_url.R

f4c86cb80fea4c99baf7c5a602ab2097fa666a82

[ "MIT" ]

permissive

burch-cm/vindecodr

ded9ef48f6d36a2a98d021684211a64a0185117b

68b4debc479a7709dbefe6159c73a22b7b7a231f

refs/heads/main

2023-01-13T08:03:59.566698

2020-11-23T20:45:05

314,322,534

0

1

null

UTF-8

R

false

2,246

r

build_nhtsa_url.R

#' Build a NHTSA URL #' #' @description #' #' A family of functions to build URLs for the National Highway Transportation #' Safety Administration (NHTSA) vehicle identification number (VIN) decoder API. #' #' The `build_nhtsa_url()` function returns a closure containing the appropriate #' endpoint and file format request to pass to the NHTSA VIN API. #' #' * `build_vin_url()` takes a single VIN in a character string and returns #' an appropriately-formatted url for a NHTSA API request via the #' /vehicles/DecodeVINValues/ endpoint. #' #' * `build_vin_batch_url()` takes up to 50 VINs in a character vector and #' returns appropriately-formatted url for a NHTSA API request via the #' /vehicles/DecodeVINBatchValues/ endpoint. #' #' @param endpoint a string containing the appropriate endpoint. Candidate #' endpoints can be found at https://vpic.nhtsa.dot.gov/api/ #' @param format the file format to return from the API, one of 'json', 'xml', #' or 'csv'. Defaults to 'json'. #' @param vin a string containing the VIN to query. #' @param ... additional arguments to passed on to derived builder functions #' @return #' * `build_nhtsa_url()` returns a function which will in turn build a url which #' points to the specified endpoint on the NHTSA API #' #' * `build_vin_url()` returns a url as a string, formatted to query the NHTSA #' `DecodeVinValues` endpoint and decode a single VIN. #' * `build_vin_batch_url()` returns a url as a string, formatted to query the NHTSA #' `DecodeVinBatch Values` endpoint and decode multiple VINs in one call. #' #' @export #' #' @examples #' vin_url_xml <- build_nhtsa_url("/vehicles/DecodeVINValues/", format = "xml") #' build_vin_url("3VWLL7AJ9BM053541") #' build_vin_batch_url(c("3VWLL7AJ9BM053541", "JH4KA3140KC015221")) build_nhtsa_url <- function(endpoint, format = "json", ...) { baseurl <- "https://vpic.nhtsa.dot.gov/api" function(vin, ...) { paste0(baseurl, endpoint, vin, "?format=", format, ...) } } #' @rdname build_nhtsa_url #' @export build_vin_url <- build_nhtsa_url(endpoint = "/vehicles/DecodeVINValues/") #' @rdname build_nhtsa_url #' @export build_vin_batch_url <- build_nhtsa_url(endpoint = "/vehicles/DecodeVINValuesBatch/")

ccfbb670d39ba8a8666244c163a4ffa80e79f4af

547e448dd1b38c8b8fd4e4edc9cdff799670d498

/Archived/05-01-16/X2 - Make Gephi Files.R

f9f3e2ad0d607a6fbe9b3a42a06be4c18473c99e

[]

no_license

BrianAronson/ADHD-Peer-Influence

c20bd69fd918a92091f79622f9180504148f1b16

d726d8fff6ef967972ed41c73e602e2ede394956

refs/heads/master

2020-12-05T00:03:56.800043

2020-01-05T23:43:19

231,944,033

0

null

UTF-8

R

false

3,339

r

X2 - Make Gephi Files.R

#Attempt igraph # plot(g2,layout=layout.fruchterman.reingold(g2),vertex.size=2, # vertex.label=NA, edge.arrow.size=.025,vertex.color=DifficultyAttention) #Edgelists #Rename edgelists Geph1.0<-get.edgelist(g0) Geph1.1<-get.edgelist(g1) Geph1.2<-get.edgelist(g2) Geph2.0<-get.edgelist(g10) Geph2.1<-get.edgelist(g11) Geph2.2<-get.edgelist(g12) #Reformat edgelists for Gephi GephiEdges1.0<-data.frame(Source=Geph1.0[,1], Target=Geph1.0[,2]) GephiEdges1.1<-data.frame(Source=Geph1.1[,1], Target=Geph1.1[,2]) GephiEdges1.2<-data.frame(Source=Geph1.2[,1], Target=Geph1.2[,2]) GephiEdges2.0<-data.frame(Source=Geph2.0[,1], Target=Geph2.0[,2]) GephiEdges2.1<-data.frame(Source=Geph2.1[,1], Target=Geph2.1[,2]) GephiEdges2.2<-data.frame(Source=Geph2.2[,1], Target=Geph2.2[,2]) #Write Edgelists to CSV write.csv(GephiEdges1.0, file="edges1.0.csv") write.csv(GephiEdges1.1, file="edges1.1.csv") write.csv(GephiEdges1.2, file="edges1.2.csv") write.csv(GephiEdges2.0, file="edges2.0.csv") write.csv(GephiEdges2.1, file="edges2.1.csv") write.csv(GephiEdges2.2, file="edges2.2.csv") #Nodes covariates #Create a list of unique nodes GephiNodes1.0<-data.frame(ID=rownames(ADHDFriends58M0),DifficultyAttention=ADHDFriends58M0$DifficultyAttention) GephiNodes1.1<-data.frame(ID=rownames(ADHDFriends58M1),DifficultyAttention=ADHDFriends58M1$DifficultyAttention) GephiNodes1.2<-data.frame(ID=rownames(ADHDFriends58M2),DifficultyAttention=ADHDFriends58M2$DifficultyAttention, ChangeAttention=ADHDFriends58M2$DifficultyAttention-ADHDFriends58M0$DifficultyAttention, BinaryDifficultyAttention=ifelse(ADHDFriends58M2$DifficultyAttention>1,1,0), BinaryChangeAttention=ifelse(ADHDFriends58M2$DifficultyAttention-ADHDFriends58M0$DifficultyAttention>0,1,0), W1BinaryDifficultyAttention=ifelse(ADHDFriends58M0$DifficultyAttention>2,1,0)) GephiNodes2.0<-data.frame(ID=rownames(ADHDFriends77M0),DifficultyAttention=ADHDFriends77M0$DifficultyAttention) GephiNodes2.1<-data.frame(ID=rownames(ADHDFriends77M1),DifficultyAttention=ADHDFriends77M1$DifficultyAttention) GephiNodes2.2<-data.frame(ID=rownames(ADHDFriends77M2),DifficultyAttention=ADHDFriends77M2$DifficultyAttention, ChangeAttention=ADHDFriends77M2$DifficultyAttention-ADHDFriends77M0$DifficultyAttention, BinaryDifficultyAttention=ifelse(ADHDFriends77M2$DifficultyAttention>1,1,0), BinaryChangeAttention=ifelse(ADHDFriends77M2$DifficultyAttention-ADHDFriends77M0$DifficultyAttention>0,1,0), W1BinaryDifficultyAttention=ifelse(ADHDFriends77M0$DifficultyAttention>2,1,0)) #Write nodes to csv write.csv(GephiNodes1.0, file="nodes1.0.csv") write.csv(GephiNodes1.1, file="nodes1.1.csv") write.csv(GephiNodes1.2, file="nodes1.2.csv") write.csv(GephiNodes2.0, file="nodes2.0.csv") write.csv(GephiNodes2.1, file="nodes2.1.csv") write.csv(GephiNodes2.2, file="nodes2.2.csv") #table(GephiNodes2.2$DifficultyAttention) #table(GephiNodes1.2$DifficultyAttention) #table(GephiNodes2.2$ChangeAttention) #table(GephiNodes1.2$ChangeAttention) #Magnifier affect mean(GephiNodes1.1$DifficultyAttention, na.rm=TRUE) mean(GephiNodes2.1$DifficultyAttention, na.rm=TRUE) mean(GephiNodes1.2$ChangeAttention, na.rm=TRUE) mean(GephiNodes2.2$ChangeAttention, na.rm=TRUE)

3763359fbf3daa406bdd3441e9c0ae34689cf8e1

0affcfeafed053dab542218ea09a6a11716b27da

/LaLigaEconomics.R

bba9859f8340b6fcabf088df1b1fa104f52a6184

[]

no_license

trickytaco/laliga-economic-analysis

26d3bdcfd6db6f9db9bbb502653ea613bc7b786a

bf59198e6eea4c51b983eb721acc53cee16c465f

refs/heads/master

2021-01-10T01:59:43.791331

2016-04-04T01:54:01

55,268,664

0

null

UTF-8

R

false

33,938

r

LaLigaEconomics.R

#Library to read Excel files library(xlsx) #library(ggplot2) #Set the working directory setwd("D:\\Statistics\\LaLiga\\laliga-economic-analysis") #Read in the standings for each season from 2005-06 to 2014-15 standingsList <- list() sheetNames <- c("2005-06", "2006-07", "2007-08", "2008-09", "2009-10", "2010-11", "2011-12", "2012-13", "2013-14", "2014-15") for (i in 1:10) { standingsList[[i]] <- read.xlsx("La Liga Standings.xlsx", sheetName = sheetNames[i]) } #Read in the Points sheet and set the column names PointsDF <- read.xlsx("La Liga economics.xlsx", sheetName = "Points") names(PointsDF) <- c("Team_ID", "Team", "2005-06", "2006-07", "2007-08", "2008-09", "2009-10", "2010-11", "2011-12", "2012-13", "2013-14", "2014-15") #Remove some non-data rows PointsDF <- PointsDF[-1:-3,-1] #Calculate the maximum value of the entire data table for plotting purposes allMax <- 0 for (i in 2:11) { if (max(PointsDF[,i][complete.cases(PointsDF[,i])]) > allMax) { allMax <- max(PointsDF[,i][complete.cases(PointsDF[,i])]) } } #Plot the first row's data as a line plot(factor(names(PointsDF)[2:11]), PointsDF[2,2:11], type="l", ylim = c(0,allMax), xaxt = "n", xlab = "Season", ylab = "Points", main = "Points by Season") #Add some information to the bottom axis axis(1, 1:10, names(PointsDF)[2:11]) #Now add a line for every other team to the plot for (i in 2:36) { lines(1:10, PointsDF[i,2:11], col = i) } #Copy the device to a png device dev.copy(png, file = ".\\plots\\basic\\points.png", width = 1280, height = 720, units = "px") #Close the device to save the file dev.off() #Read in the transfer expense sheet and set the column names TransExpDF <- read.xlsx("La Liga economics.xlsx", sheetName = "Transfer expense") names(TransExpDF) <- c("Team_ID", "Team", "2005-06", "2006-07", "2007-08", "2008-09", "2009-10", "2010-11", "2011-12", "2012-13", "2013-14", "2014-15") #Remove some non-data rows TransExpDF <- TransExpDF[-37:-39,-1] #Calculate the maximum value of the entire data table for plotting purposes allMax <- 0 for (i in 2:11) { if (max(TransExpDF[,i][complete.cases(TransExpDF[,i])]) > allMax) { allMax <- max(TransExpDF[,i][complete.cases(TransExpDF[,i])]) } } #Plot the first row's data as a line plot(factor(names(TransExpDF)[2:11]), TransExpDF[2,2:11], type="l", ylim = c(0,allMax), xaxt = "n", xlab = "Season", ylab = "Transfer Expenditure", main = "Transfer Expenditure by Season") #Add some information to the bottom axis axis(1, 1:10, names(TransExpDF)[2:11]) #Now add a line for every other team to the plot for (i in 1:36) { lines(1:10, TransExpDF[i,2:11], col = i) } #Copy the device to a png device dev.copy(png, file = ".\\plots\\basic\\transfer_expense.png", width = 1280, height = 720, units = "px") #Close the device to save the file dev.off() #Read in the transfer average expense sheet and set the column names TransExpAvgDF <- read.xlsx("La Liga economics.xlsx", sheetName = "Transfer expense - Rolling avg") names(TransExpAvgDF) <- c("Team_ID", "Team", "2005-06", "2006-07", "2007-08", "2008-09", "2009-10", "2010-11", "2011-12", "2012-13", "2013-14", "2014-15") #Remove some non-data rows TransExpAvgDF <- TransExpAvgDF[-37:-39,-1] #Calculate the maximum value of the entire data table for plotting purposes allMax <- 0 for (i in 2:11) { if (max(TransExpAvgDF[,i][complete.cases(TransExpAvgDF[,i])]) > allMax) { allMax <- max(TransExpAvgDF[,i][complete.cases(TransExpAvgDF[,i])]) } } #Plot the first row's data as a line plot(factor(names(TransExpAvgDF)[2:11]), TransExpAvgDF[2,2:11], type="l", ylim = c(0,allMax), xaxt = "n", xlab = "Season", ylab = "Transfer Expenditure Average", main = "Transfer Expenditure Average by Season") #Add some information to the bottom axis axis(1, 1:10, names(TransExpAvgDF)[2:11]) #Now add a line for every other team to the plot for (i in 1:36) { lines(1:10, TransExpAvgDF[i,2:11], col = i) } #Copy the device to a png device dev.copy(png, file = ".\\plots\\basic\\transfer_expense_average.png", width = 1280, height = 720, units = "px") #Close the device to save the file dev.off() #Read in the net transfer expense sheet and set the column names NetTransDF <- read.xlsx("La Liga economics.xlsx", sheetName = "Net transfer spend") names(NetTransDF) <- c("Team_ID", "Team", "2005-06", "2006-07", "2007-08", "2008-09", "2009-10", "2010-11", "2011-12", "2012-13", "2013-14", "2014-15") #Remove some non-data rows NetTransDF <- NetTransDF[-37:-39,-1] #Calculate the maximum and minimum value of the entire data table for plotting #purposes allMax <- 0 allMin <- 0 for (i in 2:11) { if (max(NetTransDF[,i][complete.cases(NetTransDF[,i])]) > allMax) { allMax <- max(NetTransDF[,i][complete.cases(NetTransDF[,i])]) } if (min(NetTransDF[,i][complete.cases(NetTransDF[,i])]) < allMin) { allMin <- min(NetTransDF[,i][complete.cases(NetTransDF[,i])]) } } #Plot the first row's data as a line plot(factor(names(NetTransDF)[2:11]), NetTransDF[2,2:11], type="l", ylim = c(allMin,allMax), xaxt = "n", xlab = "Season", ylab = "Net Transfer Expenditure", main = "Net Transfer Expenditure by Season") #Add some information to the bottom axis axis(1, 1:10, names(NetTransDF)[2:11]) #Now add a line for every other team to the plot for (i in 1:36) { lines(1:10, NetTransDF[i,2:11], col = i) } #Copy the device to a png device dev.copy(png, file = ".\\plots\\basic\\net_transfer_spend.png", width = 1280, height = 720, units = "px") #Close the device to save the file dev.off() #Read in the net transfer expense average sheet and set the column names NetTransAvgDF <- read.xlsx("La Liga economics.xlsx", sheetName = "Net transfer spend - Rolling av") names(NetTransAvgDF) <- c("Team_ID", "Team", "2005-06", "2006-07", "2007-08", "2008-09", "2009-10", "2010-11", "2011-12", "2012-13", "2013-14", "2014-15") #Remove some non-data rows NetTransAvgDF <- NetTransAvgDF[-37:-39,-1] #Calculate the maximum and minimum value of the entire data table for plotting #purposes allMax <- 0 allMin <- 0 for (i in 2:11) { if (max(NetTransAvgDF[,i][complete.cases(NetTransAvgDF[,i])]) > allMax) { allMax <- max(NetTransAvgDF[,i][complete.cases(NetTransAvgDF[,i])]) } if (min(NetTransAvgDF[,i][complete.cases(NetTransAvgDF[,i])]) < allMin) { allMin <- min(NetTransAvgDF[,i][complete.cases(NetTransAvgDF[,i])]) } } #Plot the first row's data as a line plot(factor(names(NetTransAvgDF)[2:11]), NetTransAvgDF[2,2:11], type="l", ylim = c(allMin,allMax), xaxt = "n", xlab = "Season", ylab = "Net Transfer Expenditure Average", main = "Net Transfer Expenditure Average by Season") #Add some information to the bottom axis axis(1, 1:10, names(NetTransAvgDF)[2:11]) #Now add a line for every other team to the plot for (i in 1:36) { lines(1:10, NetTransAvgDF[i,2:11], col = i) } #Copy the device to a png device dev.copy(png, file = ".\\plots\\basic\\net_transfer_spend_average.png", width = 1280, height = 720, units = "px") #Close the device to save the file dev.off() #Read in the market value sheet and set the column names MarketValueDF <- read.xlsx("La Liga economics.xlsx", sheetName = "Market Value") names(MarketValueDF) <- c("Team_ID", "Team", "2005-06", "2006-07", "2007-08", "2008-09", "2009-10", "2010-11", "2011-12", "2012-13", "2013-14", "2014-15") #Remove some non-data rows MarketValueDF <- MarketValueDF[-37:-39,-1] #Calculate the maximum value of the entire data table for plotting purposes allMax <- 0 for (i in 2:11) { if (max(MarketValueDF[,i][complete.cases(MarketValueDF[,i])]) > allMax) { allMax <- max(MarketValueDF[,i][complete.cases(MarketValueDF[,i])]) } } #Plot the first row's data as a line plot(factor(names(MarketValueDF)[2:11]), MarketValueDF[2,2:11], type="l", ylim = c(0,allMax), xaxt = "n", xlab = "Season", ylab = "Market Value", main = "Market Value by Season") #Add some information to the bottom axis axis(1, 1:10, names(MarketValueDF)[2:11]) #Now add a line for every other team to the plot for (i in 1:36) { lines(1:10, MarketValueDF[i,2:11], col = i) } #Copy the device to a png device dev.copy(png, file = ".\\plots\\basic\\market_value.png", width = 1280, height = 720, units = "px") #Close the device to save the file dev.off() #Correlate TransExpAvgDF to PointsDF year-by-year for (i in 2:11) { #Convert PointsDF and TransExpAvgDF into one-dimensional vectors so that #the correlation can be calculated. The complete.cases part is so that #the operation excludes the NA values. PointsDFVector <- c(as.matrix(PointsDF[,i]))[complete.cases(c(as.matrix(PointsDF[,i])))] TransExpAvgDFVector <- c(as.matrix(TransExpAvgDF[,i]))[complete.cases(c(as.matrix(TransExpAvgDF[,i])))] #Now calculate the correlation between the two vectors corVal <- cor(PointsDFVector, TransExpAvgDFVector) #Print the results along with the year print(paste(names(PointsDF)[i], as.character(corVal))) } #Correlate TransExpAvgDF to PointsDF year-by-year (no Madrid or Barcelona) for (i in 2:11) { #Convert PointsDF and TransExpAvgDF into one-dimensional vectors so that #the correlation can be calculated. The complete.cases part is so that #the operation excludes the NA values. The -1:-2 bits cause the first two #rows to be excluded. These are the RMD/BAR rows. PointsDFVector <- c(as.matrix(PointsDF[-1:-2,i]))[complete.cases(c(as.matrix(PointsDF[-1:-2,i])))] TransExpAvgDFVector <- c(as.matrix(TransExpAvgDF[-1:-2,i]))[complete.cases(c(as.matrix(TransExpAvgDF[-1:-2,i])))] #Now calculate the correlation between the two vectors corVal <- cor(PointsDFVector, TransExpAvgDFVector) #Print the results along with the year print(paste(names(PointsDF)[i], as.character(corVal))) } #Correlate MarketValueDF to PointsDF year-by-year for (i in 2:11) { PointsDFVector <- c(as.matrix(PointsDF[,i]))[complete.cases(c(as.matrix(PointsDF[,i])))] MarketValueDFVector <- c(as.matrix(MarketValueDF[,i]))[complete.cases(c(as.matrix(MarketValueDF[,i])))] corVal <- cor(PointsDFVector, MarketValueDFVector) print(paste(names(PointsDF)[i], as.character(corVal))) } #Correlate MarketValueDF to PointsDF year-by-year (no Madrid or Barcelona) for (i in 2:11) { #Convert PointsDF and TransExpAvgDF into one-dimensional vectors so that #the correlation can be calculated. The complete.cases part is so that #the operation excludes the NA values. The -1:-2 bits cause the first two #rows to be excluded. These are the RMD/BAR rows. PointsDFVector <- c(as.matrix(PointsDF[-1:-2,i]))[complete.cases(c(as.matrix(PointsDF[-1:-2,i])))] MarketValueDFVector <- c(as.matrix(MarketValueDF[-1:-2,i]))[complete.cases(c(as.matrix(MarketValueDF[-1:-2,i])))] #Now calculate the correlation between the two vectors corVal <- cor(PointsDFVector, MarketValueDFVector) #Print the results along with the year print(paste(names(PointsDF)[i], as.character(corVal))) } #Calculate the maximum Market value for plotting purposes marketMax <- 0 for (i in 2:11) { if (max(MarketValueDF[,i][complete.cases(MarketValueDF[,i])]) > marketMax) { marketMax <- max(MarketValueDF[,i][complete.cases(MarketValueDF[,i])]) } } #Create a linear regression model of points earned vs. market value (all #clubs.) for (i in 2:11) { #Convert PointsDF and MarketValueDF into one-dimensional vectors so that #the regression can be calculated. The complete.cases part is so that #the operation excludes the NA values. PointsDFVector <- c(as.matrix(PointsDF[,i]))[complete.cases(c(as.matrix(PointsDF[,i])))] MarketValueDFVector <- c(as.matrix(MarketValueDF[,i]))[complete.cases(c(as.matrix(MarketValueDF[,i])))] #Create the linear model and print the details pts_MktValFit <- lm(PointsDFVector ~ MarketValueDFVector) print(names(PointsDF)[i]) print(summary(pts_MktValFit)) #Plot the data points and trend line if (summary(pts_MktValFit)$coefficients[2] >= 0) { signStr <- "+" } else { signStr <- "-" } regFormula <- paste(round(summary(pts_MktValFit)$coefficients[1], 4), " ", signStr, " ", round(summary(pts_MktValFit)$coefficients[2], 4), "x", sep="") plot(MarketValueDFVector, PointsDFVector, type="p", xlim=c(0, marketMax), ylim=c(0, 114), main=paste("Points vs. Market Value, ", names(PointsDF)[i], "\n", regFormula, ", R-Squared = ", round(summary(pts_MktValFit)$adj.r.squared, 5), sep=""), xlab = "Market Value", ylab = "Points", pch=19) abline(pts_MktValFit) #Copy the device to a png device dev.copy(png, file = paste(".\\plots\\pointsVS\\points_vs_market_value_", names(PointsDF)[i], ".png", sep=""), width = 1280, height = 720, units = "px") #Close the device to save the file dev.off() } #Calculate the maximum Market value for plotting purposes (no RMD or BAR) marketMaxNORMDBAR <- 0 for (i in 2:11) { if (max(MarketValueDF[-1:-2,i][complete.cases(MarketValueDF[-1:-2,i])]) > marketMaxNORMDBAR) { marketMaxNORMDBAR <- max(MarketValueDF[-1:-2,i][complete.cases(MarketValueDF[-1:-2,i])]) } } #Create a linear regression model of points earned vs. market value (no #RMD or BAR.) for (i in 2:11) { #Convert PointsDF and MarketValueDF into one-dimensional vectors so that #the regression can be calculated. The complete.cases part is so that #the operation excludes the NA values. PointsDFVector <- c(as.matrix(PointsDF[-1:-2,i]))[complete.cases(c(as.matrix(PointsDF[-1:-2,i])))] MarketValueDFVector <- c(as.matrix(MarketValueDF[-1:-2,i]))[complete.cases(c(as.matrix(MarketValueDF[-1:-2,i])))] #Create the linear model and print the details pts_MktValFit <- lm(PointsDFVector ~ MarketValueDFVector) print(names(PointsDF)[i]) print(summary(pts_MktValFit)) #Plot the data points and trend line if (summary(pts_MktValFit)$coefficients[2] >= 0) { signStr <- "+" } else { signStr <- "-" } regFormula <- paste(round(summary(pts_MktValFit)$coefficients[1], 4), " ", signStr, " ", round(summary(pts_MktValFit)$coefficients[2], 4), "x", sep="") plot(MarketValueDFVector, PointsDFVector, type="p", xlim=c(0, marketMaxNORMDBAR), ylim=c(0, 114), main=paste("Points vs. Market Value (No RMD/BAR), ", names(PointsDF)[i], "\n", regFormula, ", R-Squared = ", round(summary(pts_MktValFit)$adj.r.squared, 5), sep=""), xlab = "Market Value", ylab = "Points", pch=19) abline(pts_MktValFit) #Copy the device to a png device dev.copy(png, file = paste(".\\plots\\pointsVS\\points_vs_market_value_", names(PointsDF)[i], "_noRMDBAR.png", sep=""), width = 1280, height = 720, units = "px") #Close the device to save the file dev.off() } #Calculate the maximum transfer expense average value for plotting purposes transExpAvgMax <- 0 for (i in 2:11) { if (max(TransExpAvgDF[,i][complete.cases(TransExpAvgDF[,i])]) > transExpAvgMax) { transExpAvgMax <- max(TransExpAvgDF[,i][complete.cases(TransExpAvgDF[,i])]) } } #Create a linear regression model of points earned vs. transfer expense average #(all clubs.) for (i in 2:11) { #Convert PointsDF and TransExpAvgDF into one-dimensional vectors so that #the regression can be calculated. The complete.cases part is so that #the operation excludes the NA values. PointsDFVector <- c(as.matrix(PointsDF[,i]))[complete.cases(c(as.matrix(PointsDF[,i])))] TransExpAvgDFVector <- c(as.matrix(TransExpAvgDF[,i]))[complete.cases(c(as.matrix(TransExpAvgDF[,i])))] #Create the linear model and print the details pts_TExpAvgFit <- lm(PointsDFVector ~ TransExpAvgDFVector) print(names(PointsDF)[i]) print(summary(pts_TExpAvgFit)) #Plot the data points and trend line if (summary(pts_TExpAvgFit)$coefficients[2] >= 0) { signStr <- "+" } else { signStr <- "-" } regFormula <- paste(round(summary(pts_TExpAvgFit)$coefficients[1], 4), " ", signStr, " ", round(summary(pts_TExpAvgFit)$coefficients[2], 4), "x", sep="") plot(TransExpAvgDFVector, PointsDFVector, type="p", xlim=c(0, transExpAvgMax), ylim=c(0, 114), main=paste("Points vs. Transfer Expense Average, ", names(PointsDF)[i], "\n", regFormula, ", R-Squared = ", round(summary(pts_TExpAvgFit)$adj.r.squared, 5), sep=""), xlab = "Transfer Expense Average", ylab = "Points", pch=19) abline(pts_TExpAvgFit) #Copy the device to a png device dev.copy(png, file = paste(".\\plots\\pointsVS\\points_vs_transfer_expense_average_", names(PointsDF)[i], ".png", sep=""), width = 1280, height = 720, units = "px") #Close the device to save the file dev.off() } #Calculate the maximum transfer expense average value for plotting purposes (no RMD or BAR) transExpAvgMaxNORMDBAR <- 0 for (i in 2:11) { if (max(TransExpAvgDF[-1:-2,i][complete.cases(TransExpAvgDF[-1:-2,i])]) > transExpAvgMaxNORMDBAR) { transExpAvgMaxNORMDBAR <- max(TransExpAvgDF[-1:-2,i][complete.cases(TransExpAvgDF[-1:-2,i])]) } } #Create a linear regression model of points earned vs. transfer expense average (no #RMD or BAR.) for (i in 2:11) { #Convert PointsDF and MarketValueDF into one-dimensional vectors so that #the regression can be calculated. The complete.cases part is so that #the operation excludes the NA values. PointsDFVector <- c(as.matrix(PointsDF[-1:-2,i]))[complete.cases(c(as.matrix(PointsDF[-1:-2,i])))] TransExpAvgDFVector <- c(as.matrix(TransExpAvgDF[-1:-2,i]))[complete.cases(c(as.matrix(TransExpAvgDF[-1:-2,i])))] #Create the linear model and print the details pts_TExpAvgFit <- lm(PointsDFVector ~ TransExpAvgDFVector) print(names(PointsDF)[i]) print(summary(pts_TExpAvgFit)) #Plot the data points and trend line if (summary(pts_TExpAvgFit)$coefficients[2] >= 0) { signStr <- "+" } else { signStr <- "-" } regFormula <- paste(round(summary(pts_TExpAvgFit)$coefficients[1], 4), " ", signStr, " ", round(summary(pts_TExpAvgFit)$coefficients[2], 4), "x", sep="") plot(TransExpAvgDFVector, PointsDFVector, type="p", xlim=c(0, transExpAvgMaxNORMDBAR), ylim=c(0, 114), main=paste("Points vs. Transfer Expense Average (No RMD/BAR), ", names(PointsDF)[i], "\n", regFormula, ", R-Squared = ", round(summary(pts_TExpAvgFit)$adj.r.squared, 5), sep=""), xlab = "Transfer Expense Average", ylab = "Points", pch=19) abline(pts_TExpAvgFit) #Copy the device to a png device dev.copy(png, file = paste(".\\plots\\pointsVS\\points_vs_transfer_expense_average_", names(PointsDF)[i], "_noRMDBAR.png", sep=""), width = 1280, height = 720, units = "px") #Close the device to save the file dev.off() } #COMMENTED OUT BECAUSE THIS ISN'T REALLY A VALID CALCULATION # #Calculate the maximum goal differential value for plotting purposes # goalDiffMin <- 0 # goalDiffMax <- 0 # for (i in 1:10) { # if (max(standingsList[[i]]$GD) > goalDiffMax) { # goalDiffMax <- max(standingsList[[i]]$GD) # } # if (min(standingsList[[i]]$GD) < goalDiffMin) { # goalDiffMin <- min(standingsList[[i]]$GD) # } # } # #Create a linear regression model of goal differential vs. market value # #(all clubs.) # for (i in 1:10) { # #Extract each season in standingsList and perform the calculations # goalDiffVector <- standingsList[[i]]$GD # TransExpAvgDFVector <- c(as.matrix(TransExpAvgDF[,i+1]))[match(standingsList[[i]]$Team, PointsDF$Team)] # #Create the linear model and print the details # GD_TExpAvgFit <- lm(goalDiffVector ~ TransExpAvgDFVector) # print(names(PointsDF)[i+1]) # print(summary(GD_TExpAvgFit)) # # #Plot the data points and trend line # if (summary(GD_TExpAvgFit)$coefficients[2] >= 0) { # signStr <- "+" # } else { # signStr <- "-" # } # regFormula <- paste(round(summary(GD_TExpAvgFit)$coefficients[1], 4), " ", # signStr, " ", # round(summary(GD_TExpAvgFit)$coefficients[2], 4), # "x", sep="") # plot(TransExpAvgDFVector, goalDiffVector, type="p", # xlim=c(0, transExpAvgMax), ylim=c(goalDiffMin, goalDiffMax), # main=paste("Transfer Expense Average vs. Goal Differential, ", # names(PointsDF)[i+1], "\n", regFormula, # ", R-Squared = ", # round(summary(GD_TExpAvgFit)$adj.r.squared, 5), sep=""), # xlab = "Transfer Expense Average", ylab = "Goal Differential", pch=19) # abline(GD_TExpAvgFit) # # #Copy the device to a png device # dev.copy(png, file = paste(".\\plots\\goal_differential_vs_transfer_expense_average_", # names(PointsDF)[i+1], ".png", sep=""), # width = 1280, height = 720, units = "px") # # #Close the device to save the file # dev.off() # } #Calculate the maximum goals for value for plotting purposes goalsForMax <- 0 for (i in 1:10) { if (max(standingsList[[i]]$GD) > goalsForMax) { goalsForMax <- max(standingsList[[i]]$GD) } } #Create a linear regression model of goals for vs. transfer expense average #(all clubs.) for (i in 1:10) { #Extract each season in standingsList and perform the calculations goalsForVector <- standingsList[[i]]$OF TransExpAvgDFVector <- c(as.matrix(TransExpAvgDF[,i+1]))[match(standingsList[[i]]$Team, PointsDF$Team)] #Create the linear model and print the details GF_TExpAvgFit <- lm(goalsForVector ~ TransExpAvgDFVector) print(names(PointsDF)[i+1]) print(summary(GF_TExpAvgFit)) #Plot the data points and trend line if (summary(GF_TExpAvgFit)$coefficients[2] >= 0) { signStr <- "+" } else { signStr <- "-" } regFormula <- paste(round(summary(GF_TExpAvgFit)$coefficients[1], 4), " ", signStr, " ", round(summary(GF_TExpAvgFit)$coefficients[2], 4), "x", sep="") plot(TransExpAvgDFVector, goalsForVector, type="p", xlim=c(0, transExpAvgMax), ylim=c(0, goalsForMax), main=paste("Goals For vs. Transfer Expense Average, ", names(PointsDF)[i+1], "\n", regFormula, ", R-Squared = ", round(summary(GF_TExpAvgFit)$adj.r.squared, 5), sep=""), xlab = "Transfer Expense Average", ylab = "Goals For", pch=19) abline(GF_TExpAvgFit) #Copy the device to a png device dev.copy(png, file = paste(".\\plots\\goalsForVS\\goals_for_vs_transfer_expense_average_", names(PointsDF)[i+1], ".png", sep=""), width = 1280, height = 720, units = "px") #Close the device to save the file dev.off() } #Calculate the maximum goals for value for plotting purposes (no RMD or BAR) goalsForMaxNORMDBAR <- 0 for (i in 1:10) { if (max(standingsList[[i]]$OF[!(standingsList[[i]]$Team %in% c("Real Madrid", "Barcelona"))]) > goalsForMaxNORMDBAR) { goalsForMaxNORMDBAR <- max(standingsList[[i]]$OF[!(standingsList[[i]]$Team %in% c("Real Madrid", "Barcelona"))]) } } #Create a linear regression model of goals for vs. transfer expense average #(no RMD or BAR.) for (i in 1:10) { #Extract each season in standingsList and perform the calculations goalsForVector <- standingsList[[i]]$OF[!(standingsList[[i]]$Team %in% c("Real Madrid", "Barcelona"))] TransExpAvgDFVector <- c(as.matrix(TransExpAvgDF[,i+1]))[match(standingsList[[i]]$Team[!(standingsList[[i]]$Team %in% c("Real Madrid", "Barcelona"))], PointsDF$Team)] #Create the linear model and print the details GF_TExpAvgFit <- lm(goalsForVector ~ TransExpAvgDFVector) print(names(PointsDF)[i+1]) print(summary(GF_TExpAvgFit)) #Plot the data points and trend line if (summary(GF_TExpAvgFit)$coefficients[2] >= 0) { signStr <- "+" } else { signStr <- "-" } regFormula <- paste(round(summary(GF_TExpAvgFit)$coefficients[1], 4), " ", signStr, " ", round(summary(GF_TExpAvgFit)$coefficients[2], 4), "x", sep="") plot(TransExpAvgDFVector, goalsForVector, type="p", xlim=c(0, transExpAvgMaxNORMDBAR), ylim=c(0, goalsForMaxNORMDBAR), main=paste("Goals For vs. Transfer Expense Average (No RMD/BAR), ", names(PointsDF)[i+1], "\n", regFormula, ", R-Squared = ", round(summary(GF_TExpAvgFit)$adj.r.squared, 5), sep=""), xlab = "Transfer Expense Average", ylab = "Goals For", pch=19) abline(GF_TExpAvgFit) #Copy the device to a png device dev.copy(png, file = paste(".\\plots\\goalsForVS\\goals_for_vs_transfer_expense_average_", names(PointsDF)[i+1], "_noRMDBAR.png", sep=""), width = 1280, height = 720, units = "px") #Close the device to save the file dev.off() } #Create a linear regression model of goals for vs. market value #(all clubs.) for (i in 1:10) { #Extract each season in standingsList and perform the calculations goalsForVector <- standingsList[[i]]$OF MarketValueDFVector <- c(as.matrix(MarketValueDF[,i+1]))[match(standingsList[[i]]$Team, PointsDF$Team)] #Create the linear model and print the details GF_MktValFit <- lm(goalsForVector ~ MarketValueDFVector) print(names(PointsDF)[i+1]) print(summary(GF_MktValFit)) #Plot the data points and trend line if (summary(GF_MktValFit)$coefficients[2] >= 0) { signStr <- "+" } else { signStr <- "-" } regFormula <- paste(round(summary(GF_MktValFit)$coefficients[1], 4), " ", signStr, " ", round(summary(GF_MktValFit)$coefficients[2], 4), "x", sep="") plot(MarketValueDFVector, goalsForVector, type="p", xlim=c(0, marketMax), ylim=c(0, goalsForMax), main=paste("Goals For vs. Market Value, ", names(PointsDF)[i+1], "\n", regFormula, ", R-Squared = ", round(summary(GF_MktValFit)$adj.r.squared, 5), sep=""), xlab = "Market Value", ylab = "Goals For", pch=19) abline(GF_MktValFit) #Copy the device to a png device dev.copy(png, file = paste(".\\plots\\goalsForVS\\goals_for_vs_market_value_", names(PointsDF)[i+1], ".png", sep=""), width = 1280, height = 720, units = "px") #Close the device to save the file dev.off() } #Create a linear regression model of goals for vs. market value #(no RMD/BAR.) for (i in 1:10) { #Extract each season in standingsList and perform the calculations goalsForVector <- standingsList[[i]]$OF[!(standingsList[[i]]$Team %in% c("Real Madrid", "Barcelona"))] MarketValueDFVector <- c(as.matrix(MarketValueDF[,i+1]))[match(standingsList[[i]]$Team[!(standingsList[[i]]$Team %in% c("Real Madrid", "Barcelona"))], PointsDF$Team)] #Create the linear model and print the details GF_MktValFit <- lm(goalsForVector ~ MarketValueDFVector) print(names(PointsDF)[i+1]) print(summary(GF_MktValFit)) #Plot the data points and trend line if (summary(GF_MktValFit)$coefficients[2] >= 0) { signStr <- "+" } else { signStr <- "-" } regFormula <- paste(round(summary(GF_MktValFit)$coefficients[1], 4), " ", signStr, " ", round(summary(GF_MktValFit)$coefficients[2], 4), "x", sep="") plot(MarketValueDFVector, goalsForVector, type="p", xlim=c(0, marketMaxNORMDBAR), ylim=c(0, goalsForMaxNORMDBAR), main=paste("Goals For vs. Market Value (No RMD/BAR), ", names(PointsDF)[i+1], "\n", regFormula, ", R-Squared = ", round(summary(GF_MktValFit)$adj.r.squared, 5), sep=""), xlab = "Market Value", ylab = "Goals For", pch=19) abline(GF_MktValFit) #Copy the device to a png device dev.copy(png, file = paste(".\\plots\\goalsForVS\\goals_for_vs_market_value_", names(PointsDF)[i+1], "_noRMDBAR.png", sep=""), width = 1280, height = 720, units = "px") #Close the device to save the file dev.off() } #Calculate the maximum goals against value for plotting purposes goalsAgainstMax <- 0 for (i in 1:10) { if (max(standingsList[[i]]$GD) > goalsAgainstMax) { goalsAgainstMax <- max(standingsList[[i]]$GD) } } #Create a linear regression model of goals against vs. market value #(all clubs.) for (i in 1:10) { #Extract each season in standingsList and perform the calculations goalsAgainstVector <- standingsList[[i]]$OA MarketValueDFVector <- c(as.matrix(MarketValueDF[,i+1]))[match(standingsList[[i]]$Team, PointsDF$Team)] #Create the linear model and print the details GA_MktValFit <- lm(goalsAgainstVector ~ MarketValueDFVector) print(names(PointsDF)[i+1]) print(summary(GA_MktValFit)) #Plot the data points and trend line if (summary(GA_MktValFit)$coefficients[2] >= 0) { signStr <- "+" xCoef <- round(summary(GA_MktValFit)$coefficients[2], 4) } else { signStr <- "-" xCoef <- -1 * round(summary(GA_MktValFit)$coefficients[2], 4) } regFormula <- paste(round(summary(GA_MktValFit)$coefficients[1], 4), " ", signStr, " ", xCoef, "x", sep="") plot(MarketValueDFVector, goalsAgainstVector, type="p", xlim=c(0, marketMax), ylim=c(0, goalsAgainstMax), main=paste("Goals Against vs. Market Value, ", names(PointsDF)[i+1], "\n", regFormula, ", R-Squared = ", round(summary(GA_MktValFit)$adj.r.squared, 5), sep=""), xlab = "Market Value", ylab = "Goals For", pch=19) abline(GA_MktValFit) #Copy the device to a png device dev.copy(png, file = paste(".\\plots\\goalsAgainstVS\\goals_against_vs_market_value_", names(PointsDF)[i+1], ".png", sep=""), width = 1280, height = 720, units = "px") #Close the device to save the file dev.off() } #Calculate the maximum goals against value for plotting purposes (no RMD or BAR) goalsAgainstMaxNORMDBAR <- 0 for (i in 1:10) { if (max(standingsList[[i]]$OA[!(standingsList[[i]]$Team %in% c("Real Madrid", "Barcelona"))]) > goalsAgainstMaxNORMDBAR) { goalsAgainstMaxNORMDBAR <- max(standingsList[[i]]$OA[!(standingsList[[i]]$Team %in% c("Real Madrid", "Barcelona"))]) } } #Create a linear regression model of goals against vs. market value #(no RMD/BAR.) for (i in 1:10) { #Extract each season in standingsList and perform the calculations goalsAgainstVector <- standingsList[[i]]$OA[!(standingsList[[i]]$Team %in% c("Real Madrid", "Barcelona"))] MarketValueDFVector <- c(as.matrix(MarketValueDF[,i+1]))[match(standingsList[[i]]$Team[!(standingsList[[i]]$Team %in% c("Real Madrid", "Barcelona"))], PointsDF$Team)] #Create the linear model and print the details GA_MktValFit <- lm(goalsAgainstVector ~ MarketValueDFVector) print(names(PointsDF)[i+1]) print(summary(GA_MktValFit)) #Plot the data points and trend line if (summary(GA_MktValFit)$coefficients[2] >= 0) { signStr <- "+" xCoef <- round(summary(GA_MktValFit)$coefficients[2], 4) } else { signStr <- "-" xCoef <- -1 * round(summary(GA_MktValFit)$coefficients[2], 4) } regFormula <- paste(round(summary(GA_MktValFit)$coefficients[1], 4), " ", signStr, " ", xCoef, "x", sep="") plot(MarketValueDFVector, goalsAgainstVector, type="p", xlim=c(0, marketMaxNORMDBAR), ylim=c(0, goalsAgainstMaxNORMDBAR), main=paste("Goals Against vs. Market Value (no RMD/BAR), ", names(PointsDF)[i+1], "\n", regFormula, ", R-Squared = ", round(summary(GA_MktValFit)$adj.r.squared, 5), sep=""), xlab = "Market Value", ylab = "Goals For", pch=19) abline(GA_MktValFit) #Copy the device to a png device dev.copy(png, file = paste(".\\plots\\goalsAgainstVS\\goals_against_vs_market_value_", names(PointsDF)[i+1], "_noRMDBAR.png", sep=""), width = 1280, height = 720, units = "px") #Close the device to save the file dev.off() }

7e4c63d17f456748e5f7ca0fabd873c8f51571ec

9aafde089eb3d8bba05aec912e61fbd9fb84bd49

/codeml_files/newick_trees_processed/715_0/rinput.R

73ce2a48d58103a6d781e11f94fae244b103ca16

[]

no_license

DaniBoo/cyanobacteria_project

6a816bb0ccf285842b61bfd3612c176f5877a1fb

be08ff723284b0c38f9c758d3e250c664bbfbf3b

refs/heads/master

2021-01-25T05:28:00.686474

2013-03-23T15:09:39

null

0

null

UTF-8

R

false

133

r

rinput.R

library(ape) testtree <- read.tree("715_0.txt") unrooted_tr <- unroot(testtree) write.tree(unrooted_tr, file="715_0_unrooted.txt")

66c3e36d314b270a6e79a6e1a1026ac91eeb8b5a

2f475f7067440bc1f4561aec79ac0216bf27d36f

/iowa_case_ts/server.R

42ea4631d7a0b9be917f7df21c1ec7b7acb5c063

[]

no_license

mkim0903/RshinyApp

c1979d0e1c611167db3d504c5de3e3f592b65ed3

f959718525631b0812564baf752e8b8d754965a7

refs/heads/main

2023-07-12T18:20:21.835733

2021-08-25T21:39:07

381,122,241

0

2

null

UTF-8

R

false

1,193

r

server.R

cols <- c("#045a8d", "#cc4c02") iowa.case.ts = function(date.update, plot.type) { dfplot = slid::dfplot if (plot.type == 'counts'){ ts <- ggplot(dfplot, aes(Date, DailyCases, colour = Group) ) + ## Plot observed geom_line(colour = 'darkgray') + geom_point() + scale_color_manual(values = c("Observation" = cols[1], "Prediction" = cols[2])) + ## Change labs labs(title = 'Daily new infected cases and prediction for Iowa') + xlab('Date') + ylab('Daily new cases') }else if (plot.type == 'logcounts'){ ts <- ggplot(dfplot, aes(Date, logDailyCases, colour = Group)) + ## Plot observed geom_line(colour = 'darkgray') + geom_point() + scale_color_manual(values = c("Observation" = cols[1], "Prediction" = cols[2])) + ## Change labs labs(title = 'Logarithm of daily new infected cases and prediction for Iowa') + xlab('Date') + ylab('Log (Daily new cases)') } return(ts) } shinyServer(function(input, output) { output$iowa_case_ts <- renderPlotly({ ts <- iowa.case.ts(date.update = date.update, plot.type = input$plot_type) }) })

d2351b965f6ad3d032926ee5b2de98aba59cafea

4d0716e85ee73c0ba88a83b45f141652bbf7625d

/other/2.1/Assignment_2.1_HillZach.R

c272afb03e02355763378ed3cd69a2bdb67e3814

[]

no_license

midumass/DSC-520

dc83acec5077b80fd15f7f0e2b879c8d17cb27bd

fcd08ca23a583dc633a6556484b77c76b9b96b4e

refs/heads/master

2022-08-14T08:25:56.821159

2020-05-28T04:08:20

255,189,729

0

null

UTF-8

R

false

2,683

r

Assignment_2.1_HillZach.R

# Assignment: Assignment 2.1 DSC 520 # Name: Hill, Zach # Date: 24MAR2019 # 1. What are the observational units in this study? # # Units of Observation in this study are the grades of students in a proferssor's course. # # 2. Identify the variables mentioned in the narrative paragraph and determine # which are categorical and quantitative? # # Categorical variables include the section topics (sports vs. other areas), # while quantitative variables would include the students scores and the number of students # with each score # # 3. Create one variable to hold a subset of your data set that contains only # the Regular Section and one variable for the Sports Section. library(readr) my_csv <- read_csv('scores.csv') sports <- subset(my_csv, Section == 'Sports') sports regular <- subset(my_csv, Section == 'Regular') regular # 4. Use the Plot function to plot each Sections scores and the number of # students achieving that score. Use additional Plot Arguments to label the # graph and give each axis an appropriate label. Once you have produced your # Plots answer the following questions: plot(sports$Score, sports$Count, main = 'Sports Sections', xlab = 'Scores', ylab = 'Number of Students') plot(regular$Score, regular$Count, main = 'Regular Sections', xlab = 'Scores', ylab = 'Number of Students') # a. Comparing and contrasting the point distributions between the two section, # looking at both tendency and consistency: Can you say that one section tended # to score more points than the other? Justify and explain your answer. # More students in the regular sections scored higher. Both the mean and the # median scores of the regular sections were higher than the sports sections. mean(my_csv$Score) mean(sports$Score) mean(regular$Score) median(my_csv$Score) median(sports$Score) median(regular$Score) # b. Did every student in one section score more points than every student in # the other section? If not, explain what a statistical tendency means in this # context. # No, students scores were fairly well distributed across all sections. The # sports sections had a broader range of scores than the regular sections but # tended towards doing above the mean of the other students in the sections. # The regular students were more evenly spread out. # c. What could be one additional variable that was not mentioned in the # narrative that could be influencing the point distributions between the two # sections? # It might be important to allow the students to have chosen whether they were # enrolled in the sports section or the regular section. If a student finds # no interest in sports, they might be less likely to do well with sports.

8a9d0d6af6ec667d53b51d68fc093e8cec327a16

c49aa09f1f83ee8f8c9d1e716ae38381ed3fafca

/feature_selection/ex_9/roc9_1_4.R

1498421675e62e021a128c7f50f27de5d366dd45

[]

no_license

whtbowers/multiomics

de879d61f15aa718a18dc866b1e5ef3848e27c42

81dcedf2c491107005d184f93cb6318865d00e65

refs/heads/master

2020-04-11T03:25:40.635266

2018-09-24T08:51:06

null

0

null

UTF-8

R

false

2,084

r

roc9_1_4.R

setwd("/home/whb17/Documents/project3/project_files/feature_selection/ex_9/") #setwd("/project/home17/whb17/Documents/project3/project_files/preprocessing/ex_9/") library(pROC) library(ggplot2) set.seed(12) # To direct to the correct folder date <- "2018-08-07/" ex_dir <- "ex_9/" # Features selected in Kaforou 2013 sel.gene.kaforou.tb_od <- read.csv("../../data/kaforou_2013/gene_tb_od_kaforou_2013.csv", header=TRUE, row.names = 1) # Complete datasets #df.gene.all <- read.csv("../../data/ex_9/gene_train_body.csv", header=TRUE, row.names = 1) df.prot.all <- read.csv("../../data/ex_9/prot_train_body.csv", header=TRUE, row.names = 1) df.gp.all <- cbind(df.prot.all, df.gene.all) df.meta <- read.csv("../../data/ex_9/gp_train_meta.csv", header=TRUE, row.names = 1) df.meta$group <- as.character(df.meta$group) # Selected features for tb vs od sel.gene.tb_od <- read.csv("../../data/ex_9/feat_sel/gene_tb_od_BH_LFC_lasso_sig_factors.csv", header=TRUE, row.names = 1) sel.prot.tb_od <- read.csv("../../data/ex_9/feat_sel/prot_tb_od_BH_LFC_lasso_sig_factors.csv", header=TRUE, row.names = 1) sel.gp.tb_od <- rbind(sel.prot.tb_od, sel.gene.tb_od) # Reconstitute probe ids so kaforou stuff can be searched by id all.probe.ids <- c() for (i in 1:length(df.gene.all)){ id.parts <- strsplit(colnames(df.gene.all)[i], split="_") recon.probe.id <- paste(id.parts[[1]][1], "_",id.parts[[1]][2], sep = "") all.probe.ids <- c(all.probe.ids, recon.probe.id) } #Get upreg and downreg factors for mine my.upreg.factors <- c() my.downreg.factors <- c() for (i in 1:nrow(sel.gp.tb_od)){ if (sel.gp.tb_od$reg_dir[i] == "up"){ my.upreg.factors <- c(my.upreg.factors, as.character(sel.gp.tb_od$features[i])) #print(paste("UP:", sel.gp.tb_od$features[i])) } else { my.downreg.factors <- c(my.downreg.factors, as.character(sel.gp.tb_od$features[i])) #print(paste("DOWN:", sel.gp.tb_od$features[i])) } } df.upreg.my.tb_od <- df.gp.all[match(my.upreg.factors, colnames(df.gp.all))] df.downreg.my.tb_od <- df.gp.all[match(my.downreg.factors, colnames(df.gp.all))]

a4f5e28b82a274b7869ac62a84df804d542baa20

050b136eb6bb7c7d57c18ea894104acf890e3bb7

/src/prep-inputs-static.R

da5f205f0e388e4e70a3769739da500676274ee2

[ "LicenseRef-scancode-unknown-license-reference", "BSD-2-Clause", "BSD-3-Clause" ]

permissive

LBNL-UCB-STI/gem

fc5bf991a4e2c95368f68bd7478f1cde40891a01

3ce8dcac69fe504bfec62b9cec7d9af0c1f1178e

refs/heads/master

2023-04-24T19:04:33.083931

2021-02-04T22:29:06

157,785,909

5

0

null

UTF-8

R

false

1,765

r

prep-inputs-static.R

############################################################################################# # Grid-Integrated Electric Mobility Model (GEM) # # This has functions needed to prepare static inputs (those that will never vary in an # experiment). # # Argument: none # Returns: list containing data tables used to run the model (as named data.tables) ############################################################################################# prep.inputs.static <- function(){ cat(pp('Creating static inputs\n')) ##### STATIC SETS ##### regions <- c('ENC','ESC','MAT-NL','MAT-NY','MTN','NENG','PAC-CA','PAC-NL','SAT-FL','SAT-NL','WNC','WSC-TX','WSC-NL') rmob <- as.vector(sapply(regions,function(x){ pp(x,c('-RUR','-URB'))})) rmobtor <- data.table('r'=rep(regions,each=2),'rmob'=rmob) g <- generators$g gtor <- generators[,list(g,r)] hydro <- generators$g[generators$FuelType=='Hydro'] solar <- generators$g[generators$FuelType=='Solar'] wind <- generators$g[generators$FuelType=='Wind'] inputs.sets <- list(t=pp('t',sprintf('%04d',seq(1,length(days)*24))),rmob=rmob,r=regions,rmobtor=rmobtor,g=g,gtor=gtor,hydro=hydro,solar=solar,wind=wind) ##### STATIC PARAMETERS ##### dates <- date.info(days,year) hours.to.simulate <- unlist(lapply(days,function(day){ pp('t',sprintf('%04d',(day-1)*24+1:24))})) load[,t:=pp('t',sprintf('%04d',as.numeric(substr(as.character(t),2,nchar(as.character(t))))))] setkey(load,r,t) demandLoad <- load[load$t%in%hours.to.simulate,list(r,t,value=demandLoad)] demandLoad[,t:=NULL] demandLoad[,t:=inputs.sets$t,by='r'] demandLoad <- demandLoad[,list(r,t,value)] inputs.parameters <- list(demandLoad=demandLoad) inputs <- list(sets=inputs.sets,parameters=inputs.parameters) inputs }

e3e022e147158a2de9e733700afb2289f0276fa6

27d0436a8c9725ca98962d239571478de7727a2a

/man/extract_ffd.Rd

f4d6786fb0083d7adca38772a5744188c7cc857a

[ "MIT" ]

permissive

lee269/iapdashboardadmin

7d3d762c1956c88512c324d3aea15b3f6958e89c

43312e4012f871f62f3ada29f085ee65670293a9

refs/heads/master

2020-12-02T08:50:05.691888

2020-02-22T12:09:54

230,950,866

0

null

UTF-8

R

false

true

563

rd

extract_ffd.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/extract_ffd.R \name{extract_ffd} \alias{extract_ffd} \title{Extracts FFD level data at 4 digit from a comtrade bulk download zipfile} \usage{ extract_ffd(file) } \arguments{ \item{file}{zipfile to process} } \value{ A tibble containing trade data } \description{ \code{extract_ffd} is a low level function which works on a single file. Use \code{\link{merge_ffd}} to do the same operation on a number of zipfiles in a single folder } \examples{ \dontrun{extract_ffd("152-2016.zip")} }

35baf40e4e470c3b5aed6424e0f20996ec5807bf

ee1af63213eaf268bf38a51e52883e43ca811937

/hands-on-with-r/project01.R

de0909a328de5f6767dcfbf941ca516ee0d0bee0

[]

no_license

geocarvalho/r-bioinfo-ds

06ce4ae515981989274ade8f582988ea6fef6ffa

596daf835f2d8c64055e96906e6f3bda7fa3d42b

refs/heads/master

2023-05-11T14:45:41.841356

2023-04-28T08:02:47

92,194,815

0

null

UTF-8

R

false

693

r

project01.R

# Chapter 1: Objects and functions roll <- function(list=1:6, size=2) { sum(sample(x=list, size=size, replace=TRUE)) } # Chapter 2: Packages and help pages library(ggplot2) x <- c(-1, -0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8, 1) y <- x^3 qplot(x, y) #histogram x <- c(1, 2, 2, 2, 3, 3) qplot(x, binwidth=1) x2 <- c(1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 4) qplot(x2, binwidth=1) x3 <- c(0, 1, 1, 2, 2, 2, 3, 3, 4) qplot(x3, binwidth=1) rolls <- replicate(1000, roll()) qplot(rolls, binwidth=1) roll <- function(list=1:6, size=2) { sum(sample(x=list, size=size, replace=TRUE, prob=c(1/8, 1/8, 1/8, 1/8, 1/8, 3/8))) } rolls <- replicate(1000, roll()) qplot(rolls, binwidth = 1)

77d10504bcdf3ab695860ce83d8d4a9fbf7e8dfa

e37c3e8e0b32162ca7ed72fc78d3815a87ecbb2b

/pairwise_approach/Carrie/Code/RunRatings.R

cdecac2e9c77f34a47cbb91fdd516533f4160e7d

[ "MIT" ]

permissive

cfowle/elo_sailor

7169e15f1eebaf5c9287f4a960ba8202a393f290

b3d436e749c20ffcadc73c030f1d28508c73fa90

refs/heads/master

2020-06-04T10:41:46.569880

2019-08-01T12:04:29

191,988,433

1

2

MIT

2019-07-29T11:44:32

2019-06-14T18:23:28

Python

UTF-8

R

false

2,081

r

RunRatings.R

############################################################################### ### PROJECT: ELO SAILOR ### CREATED: 2019-06-24 ### MODIFIED: 2019-06-24 ### REVIEWED: NO ### SUMMARY: RUNS ratingsS ############################################################################### ##Import college test dataset college = read_csv("../Input/neisa_only.csv") college %<>% mutate(X1 = NULL, raceID = 0, competitorName = school_coded) %>% rename(competitorID = school_coded) %>% group_by(regatta_id, day, raceID, competitorID, competitorName) %>% summarise(score = min(score), place = min(place)) %>% ungroup() ##TODO: Import clean results dataset ##TODO: If using existing ratingss, import existing ratingss table ##If starting fresh, create existingRatings, pastRatings, competitors, and ##regattas tables results = college existingRatings = data.frame(competitorID = character(), regattaID = character(), day = numeric(), rating = numeric()) pastRatings = existingRatings competitors = data.frame(competitorID = character(), name = character()) ##Get list of regattas regattaTable = results %>% select(regatta_id, day) %>% distinct() %>% mutate(name = regatta_id) %>% rename(regattaID = regatta_id) %>% filter(!is.na(regattaID)) ##Run ratingss for(i in 1:nrow(regattaTable)){ regatta = regattaTable[i,] id = regatta$regattaID[[1]] regattaResults = results %>% filter(regatta_id == id) output = updateExistingRatings(existingRatings, competitors, pastRatings, regatta, regattaResults) existingRatings = output[["current"]] pastRatings = output[["past"]] } ##TODO: Export results ratings = data.frame() for(i in 1:nrow(existingRatings)) { competitor = existingRatings[i,] print(competitor) ratings %<>% bind_rows(data.frame(competitor$competitorID, competitor$rating)) }

8892e8eb0099dc16393fb1f55709beefed00f7e6

fef6ba95f4a6a98e26f7f9f81bc457c562e62364

/tests/testthat/test-checkItemExists.R

e3dbee9e8645468c0f2793a132f20146891e3b97

[ "LicenseRef-scancode-warranty-disclaimer" ]

no_license

USGS-R/hazardItems

48b6701b082cde4a51edd417a86aa963ed2f9383

dcf69e2df7d4b0db5054c8193bcc4aca4d41e859

refs/heads/main

2023-04-13T20:30:37.493049

2020-08-13T20:42:26

10,981,467

5

10

NOASSERTION

2023-04-07T23:06:59

2013-06-26T22:59:23

R

UTF-8

R

false

211

r

test-checkItemExists.R

context("testing checkItemExists") test_that ("check if item exists", { setBaseURL("prod") expect_false(checkItemExists("CHEX123")) # bad itemID expect_true(checkItemExists("CCGftiy")) # good itemID })

9812e60704c3e71b4cca65493ad23c41abaf1b0c

1e7d70ac2935728335327b6b8e7755f48c6cbbb3

/ui.R

798764f9a9b034a6ea14bd9aa46e6e1dcd4e93f7

[]

no_license

cpulec/chitest

09b4a2782d9a54d07c19e6db74d6423fc5af3212

2da95246506cec543b721121f3edd0dbd6c54d54

refs/heads/master

2021-01-18T17:10:45.917941

2014-02-28T04:01:16

null

0

null

UTF-8

R

false

4,716

r

ui.R

library(shiny) # library(shinyIncubator) # library(ggplot2) # library(ggmap) library(rCharts) library(doSNOW) library(foreach) # Define UI for miles per gallon application shinyUI(pageWithSidebar( ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## Application title ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ headerPanel("Chicago Crime Data Visualisation"), ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## Sidebar Panel ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ sidebarPanel( wellPanel( helpText(HTML("<b>READY?</b>")), HTML("Continue to scroll down and modify the settings. Come back and click this when you are ready to render new plots."), submitButton("Update Graphs and Tables") ), wellPanel( helpText(HTML("<b>BASIC SETTINGS</b>")), selectInput("crimetype", "Choose Crime Type:", choice = c("THEFT", "BATTERY", "BURGLARY","ROBBERY")), helpText("Examples: BATTERY, THEFT etc."), dateInput("startdate", "Start Date of Data Collection:", value = "2000-01-01", format = "mm-dd-yyyy", min = "2000-01-01", max = "2014-09-29"), dateInput("enddate", "End Date of Data Collection:", value = "2015-01-02", format = "mm-dd-yyyy", min = "startdate", max = "2014-09-30"), ##Need some validation that enddate is after start date helpText("Note: Enter info here if we want") ), wellPanel( helpText(HTML("<b>MAP SETTINGS</b>")), textInput("center", "Enter a Location to Center Map, such as city or zipcode:", "Chicago"), selectInput("facet", "Choose Facet Type:", choice = c("none","type", "month", "category")), selectInput("type", "Choose Google Map Type:", choice = c("roadmap", "satellite", "hybrid","terrain")), checkboxInput("res", "High Resolution?", FALSE), checkboxInput("bw", "Black & White?", FALSE), sliderInput("zoom", "Zoom Level (Recommended - 14):", min = 9, max = 20, step = 1, value = 12) ), wellPanel( helpText(HTML("<b>DENSITY PLOT SETTINGS</b>")), sliderInput("alpharanage", "Alpha Range:", min = 0, max = 1, step = 0.1, value = c(0.1, 0.4)), sliderInput("bins", "Number of Bins:", min = 5, max = 50, step = 5, value = 15), sliderInput("boundwidth", "Boundary Lines Width:", min = 0, max = 1, step = 0.1, value = 0.1), selectInput("boundcolour", "Boundary Lines Colour:", choice = c("grey95","black", "white", "red", "orange", "yellow", "green", "blue", "purple")), selectInput("low", "Fill Gradient (Low):", choice = c("yellow", "red", "orange", "green", "blue", "purple", "white", "black", "grey")), selectInput("high", "Fill Gradient (High):", choice = c("red", "orange", "yellow", "green", "blue", "purple", "white", "black", "grey")) ), wellPanel( helpText(HTML("<b>MISC. SETTINGS</b>")), checkboxInput("watermark", "Use 'Blenditbayes' Watermark?", TRUE), helpText("Note: automatically disabled when 'Facet' is used.") ), wellPanel( helpText(HTML("<b>ABOUT US</b>")), HTML('Rajiv Shah & Chris Pulec'), HTML('<br>'), HTML('Big Data Guys'), HTML('<br>'), HTML('<a href="http://www.rajivshah.com" target="_blank">About Rajiv</a>, ') ), wellPanel( helpText(HTML("<b>VERSION CONTROL</b>")), HTML('Version 0.1.2'), HTML('<br>'), HTML('Deployed on 04-Feb-2013') ), wellPanel( helpText(HTML("<b>CREDITS</b>")), HTML('<a href="https://blenditbayes.shinyapps.io/crimemap/" target=" blank">Crime Data Visualization</a>, ') ) ), ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## Main Panel ## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ mainPanel( tabsetPanel( tabPanel("Introduction", includeMarkdown("docs/introduction.md")), #tabPanel("LondonR Demo", includeMarkdown("docs/londonr.md")), #tabPanel("Sandbox (rCharts)", showOutput("myChart", "nvd3")), #tabPanel("Sandbox", includeMarkdown("docs/sandbox.md")), tabPanel("Data", dataTableOutput("datatable")), tabPanel("Crime Map", plotOutput("map")), tabPanel("Trends", plotOutput("trends1")), tabPanel("To Do", includeMarkdown("docs/To_do.md")), tabPanel("Changes", includeMarkdown("docs/changes.md")) ) ) ))

150be4e469061e64ffdb1fc96e05101c366297cd

a94b8d7428112111fb0ec7a2db31dcca2a929f7e

/Figures/FigureS3/FigS3F.R

ce2185c421eaa19d51df22c33246f8dc42f5813b

[]

no_license

edcurry/esc-se-regions

7152064aa3d34bdcc27c1a18fcbdc2ea199ad153

63c41f671a0e6b54003b0fdc48146dad082017e5

refs/heads/master

2020-07-24T01:34:44.984848

2020-02-28T12:10:36

207,763,046

0

null

2020-01-20T12:57:52

2019-09-11T08:33:25

R

UTF-8

R

false

1,991

r

FigS3F.R

#################################################################### # # Wout Megchelenbrink # Jan. 17, 2020 # SE engaged in closest or more distal promoter interactions ################################################################### rm(list=ls()) source("include/style.R") chic <- unique(fread("DATA/CHIC_promoter_SE_interactions_Joshi_Sahlen_629_with_RPKM_ser_above_1_and_gene_status_KNOWN.tab")[gene_status == "KNOWN"], by=c("se_id","gene_name")) se <- fread("DATA/Superenhancers.tsv") DT <- merge(chic, se, by="se_id") se.with.closest <- DT[gene_name == closest_expressed_gene & rpkm_ser >= 1, unique(se_id)] se.with.distal <- DT[gene_name != closest_expressed_gene & rpkm_ser >= 1, unique(se_id)] se.with.both <- intersect(se.with.closest, se.with.distal) se.not.engaged <- se[!se_id %in% chic$se_id,se_id] se.not.expressed <- setdiff(chic[is.na(rpkm_ser) | rpkm_ser < 1, unique(se_id)], chic[rpkm_ser >= 1, unique(se_id)]) se[!se_id %in% se.with.closest & !se_id %in% se.with.distal & !se_id %in% se.not.engaged & !se_id %in% se.not.expressed] length(se.with.closest) + length(se.with.distal) - length(se.with.both) + length(se.not.engaged) + length(se.not.expressed) ## Barplot of interaction categories DT <- data.table(category=c("Closest and distal", "Only closest", "Only distal", "Only non-expressed", "No interaction"), N=c(length(se.with.both), length(se.with.closest)-length(se.with.both), length(se.with.distal)-length(se.with.both), length(se.not.engaged), length(se.not.expressed))) DT[, category:=factor(category, levels = rev(c("Closest and distal", "Only closest", "Only distal", "Only non-expressed", "No interaction")))] ggplot(DT, aes(x=category, y=N, fill=category, label=N)) + geom_bar(stat = "identity") + coord_flip() + scale_fill_manual(values = bs.col.dark[5:1], guide="none") + theme_SE() + xlab("") + ylab("Interacting SE (#)") + geom_text(nudge_x = 0, nudge_y = 10) ggsave("IMG/FigS3F.pdf", width = 4.5, height = 2.5)

8743fd44f3d7479bb4990233972a5977904c7105

71821a5612e50fc8120afc8c5dc18019dadb9e84

/1BM17CS024_DSR Lab/Lab2/cbind.R

f78ae5e812f0c136cc145a94797762e3d217e457

[]

no_license

dikshajain228/Semester-7

825229cd63c4a047ac5dd5c3896a43b9835a791d

996def1ada173ac60d9fd0e4c9da4f954d2de4f0

refs/heads/master

2023-02-04T22:19:25.984283

2020-12-20T07:48:06

297,544,965

0

1

null

UTF-8

R

false

254

r

cbind.R

list.files() getwd() setwd("C:/Users/Dell/Documents/R") getwd() delim <- read.delim("perfume.csv", sep = ',') delim head(delim) colnames(delim) new <- delim temp<-data.frame(num=c(1:100)) temp new<-cbind(new,new_col=temp) new head(new)

d44a51ac738a2869acee95fb1d8940bfd1810f43

3aef5a679c390d1f2c7ecba35eca09864164c5a5

/data-raw/onc3.R

0f087bb2c1f2a326da1f93a057bae16f89da2c36

[]

no_license

jeff-m-sullivan/hesim

576edfd8c943c62315890528039366fe20cf7844

fa14d0257f0d6d4fc7d344594b2c4bf73417aaf3

refs/heads/master

2022-11-14T07:35:15.780960

2022-09-02T03:13:49

140,300,858

0

null

UTF-8

R

false

6,063

r

onc3.R

# Data for a 3-state (Stable, Progression, Death) oncology model rm(list = ls()) library("flexsurv") library("hesim") library("data.table") # Simulate multi-state dataset ------------------------------------------------- sim_onc3_data <- function(n = 2500, seed = NULL){ if (!is.null(seed)) set.seed(seed) # Data age_mu <- 60 data <- data.table( intercept = 1, strategy_id = 1, strategy_name = sample(c("SOC", "New 1", "New 2"), n, replace = TRUE, prob = c(1/3, 1/3, 1/3)), patient_id = 1:n, female = rbinom(n, 1, .5), age = rnorm(n, mean = age_mu, sd = 5.5) ) data[, `:=` (new1 = ifelse(strategy_name == "New 1", 1, 0), new2 = ifelse(strategy_name == "New 2", 1, 0))] attr(data, "id_vars") <- c("strategy_id", "patient_id") # Transition matrix tmat <- rbind( c(NA, 1, 2), c(NA, NA, 3), c(NA, NA, NA) ) trans_dt <- create_trans_dt(tmat) # Parameters for each transition get_scale <- function(shape, mean) { scale <- mean/(gamma(1 + 1/shape)) scale_ph <- scale^{-shape} return(scale_ph) } matrixv <- function(v) { x <- matrix(v); colnames(x) <- "intercept" return(x) } params_wei <- function(shape, mean, beta_new1 = log(1), beta_new2 = log(1), beta_age, beta_female){ log_shape <- matrixv(log(shape)) scale = get_scale(shape, mean) beta_intercept <- log(scale) - mean(data$age) * beta_age scale_coefs <- matrix(c(beta_intercept, beta_new1, beta_new2, beta_age, beta_female), ncol = 5) colnames(scale_coefs) <- c("intercept", "new1", "new2", "age", "female") params_surv(coefs = list(shape = log_shape, scale = scale_coefs), dist = "weibullPH") } mstate_params <- params_surv_list( # 1. S -> P params_wei(shape = 2, mean = 6.25, beta_new1 = log(.7), beta_new2 = log(.6), beta_female = log(1.4), beta_age = log(1.03)), # 2. S -> D params_wei(shape = 2.5, mean = 10, beta_new1 = log(.85), beta_new2 = log(.75), beta_female = log(1.2), beta_age = log(1.02)), # 3. P -> D params_wei(shape = 3.5, mean = 8, beta_new1 = log(1), beta_female = log(1.3), beta_age = log(1.02)) ) # Create multi-state model mstatemod <- create_IndivCtstmTrans(mstate_params, input_data = data, trans_mat = tmat, clock = "reset", start_age = data$age) # Simulate data ## Observed "latent" transitions sim <- mstatemod$sim_disease(max_age = 100) sim[, c("sample", "grp_id", "strategy_id") := NULL] sim <- cbind( data[match(sim$patient_id, data$patient_id)][, patient_id := NULL], sim ) sim[, ":=" (intercept = NULL, strategy_id = NULL, status = 1, added = 0)] ## Add all possible states for each transition ### Observed 1->2 add 1->3 sim_13 <- sim[from == 1 & to == 2] sim_13[, ":=" (to = 3, status = 0, final = 0, added = 1)] sim <- rbind(sim, sim_13) ### Observed 1->3 add 1->2 sim_12 <- sim[from == 1 & to == 3 & added == 0] sim_12[, ":=" (to = 2, status = 0, final = 0, added = 1)] sim <- rbind(sim, sim_12) ### Sort and clean sim <- merge(sim, trans_dt, by = c("from", "to")) # Add transition ID setorderv(sim, c("patient_id", "from", "to")) sim[, added := NULL] ## Add right censoring rc <- data.table(patient_id = 1:n, time = stats::rexp(n, rate = 1/15)) sim[, time_rc := rc[match(sim$patient_id, rc$patient_id)]$time] sim[, status := ifelse(time_stop < 15 & time_stop < time_rc, status, 0)] sim[, time_stop := pmin(time_stop, 15, time_rc)] sim <- sim[time_start <= pmin(time_rc, 15)] ## Final data cleaning sim[, strategy_id := fcase( strategy_name == "SOC", 1L, strategy_name == "New 1", 2L, strategy_name == "New 2", 3L )] sim[, strategy_name := factor(strategy_id, levels = c(1, 2, 3), labels = c("SOC", "New 1", "New 2"))] label_states <- function (x) { fcase( x == 1, "Stable", x == 2, "Progression", x == 3, "Death" ) } sim[, from := label_states(from)] sim[, to := label_states(to)] sim[, c("new1", "new2", "final", "time_rc") := NULL] # Return sim[, time := time_stop - time_start] return(sim[, ]) } onc3 <- sim_onc3_data(n = 3000, seed = 102) # Check that coefficient estimates are consistent with "truth" fit_weibull <- function(i) { flexsurvreg(Surv(time, status) ~ strategy_name + female + age, data = onc3, subset = (transition_id == i), dist = "weibullPH") } fit_weibull(1) fit_weibull(2) fit_weibull(3) # Panel data version ----------------------------------------------------------- onc3p <- copy(onc3) onc3p[, n := 1:.N, by = c("patient_id", "time_start")] onc3p[, c("transition_id", "time") := NULL] # Time 0 onc3p_t0 <- onc3p[time_start == 0 & n == 1] onc3p_t0[, c("time_stop", "n", "to", "status") := NULL] setnames(onc3p_t0, c("time_start", "from"), c("time", "state")) # Time > 0 onc3p[, mstatus := mean(status), by = c("patient_id", "time_start")] onc3p <- onc3p[status == 1 | (mstatus == 0 & n == 1)] onc3p[, state := ifelse(mstatus == 0, from, to)] onc3p[, c("time_start", "n", "from", "to", "status", "mstatus") := NULL] setnames(onc3p, "time_stop", "time") # Full panel onc3p <- rbind(onc3p_t0, onc3p) setorderv(onc3p, c("patient_id", "time")) onc3p[, state_id := factor( state, levels = c("Stable", "Progression", "Death"), labels = 1:3)] # Save ------------------------------------------------------------------------- save(onc3, file = "../data/onc3.rda", compress = "bzip2") save(onc3p, file = "../data/onc3p.rda", compress = "bzip2")

1cbc2384137ca30e8790dca42aca2d05566cdca0

9d28e9c8305feb5f585761e629ae7ac862a23265

/exercises/solution_07_09.R

3d0851e6993bc424c333d324b4de72c58d2213c7

[ "MIT", "CC-BY-4.0" ]

permissive

awconway/NUR1027-FALL-2019

1e94759346933311c8b56da079f132dfc3b0abcb

5dd4fea0a17ebea7fd4c1e58b79626e94418d7a5

refs/heads/master

2023-01-21T09:53:53.446575

2022-08-15T18:27:45

190,033,550

0

1

MIT

2023-01-11T20:46:07

2019-06-03T15:34:45

HTML

UTF-8

R

false

68

r

solution_07_09.R

SEM <- 0.258 measurements <- 3 round(SEM*1.96*sqrt(measurements), 2)

eae92fbc23053f30d0a5b1d1fb3ab44e87d05629

fe36c4fdae6bdc7f426631675ebd4b4eedc6be87

/man/load_table_lineage.Rd

bc053c98f21dea9efe3465c9476b8bb62b82d37f

[ "MIT" ]

permissive

nyuglobalties/blueprintr

13f5c40ff263fdf069b3a3785312fad3513e493c

56d1da3f03b86ba3533107fab1926315505f8f57

refs/heads/main

2023-08-11T10:15:08.603385

2023-07-28T20:40:29

230,140,058

1

2

NOASSERTION

2023-07-28T20:40:31

2019-12-25T18:33:30

R

UTF-8

R

false

true

691

rd

load_table_lineage.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/lineage-tables.R \name{load_table_lineage} \alias{load_table_lineage} \title{Read blueprints from folder and get lineage} \usage{ load_table_lineage( directory = here::here("blueprints"), recurse = FALSE, script = here::here("_targets.R") ) } \arguments{ \item{directory}{A folder containing blueprint scripts} \item{recurse}{Should this function recursively load blueprints?} \item{script}{Where the targets/drake project script file is located. Defaults to using targets.} } \value{ An igraph of the table lineage for the desired blueprints } \description{ Read blueprints from folder and get lineage }

539b0a1819efe5889eb847664a11002f3ac3e650

ffdea92d4315e4363dd4ae673a1a6adf82a761b5

/data/genthat_extracted_code/clttools/examples/normal.simu.plot.Rd.R

0c75052b36d8ec4ec1f3d141d4c28bbd7345ab62

[]

no_license

surayaaramli/typeRrh

d257ac8905c49123f4ccd4e377ee3dfc84d1636c

66e6996f31961bc8b9aafe1a6a6098327b66bf71

refs/heads/master

2023-05-05T04:05:31.617869

2019-04-25T22:10:06

null

0

null

UTF-8

R

false

220

r

normal.simu.plot.Rd.R

library(clttools) ### Name: normal.simu.plot ### Title: Histogram and Q-Q plot of simulated Normal distribution ### Aliases: normal.simu.plot ### ** Examples normal.simu.plot(n = 5, mean = 3, sd =2, times = 100)

81e22b3228fa1e5e093300fbd64cb0b112dc03ba

6cbb51fe996e65a51a8d9f2f35e3159721933f25

/inst/shiny/ui_09_2_seuratWorkflow.R

a21987a2716ffcd99297f0ef6e9b4ee3793d4de3

[ "MIT" ]

permissive

compbiomed/singleCellTK

927fb97e257ba89cddee9a90f9cb7cb375a5c6fb

990e89e7ccfbf663f23c793454f72fb8c6878a32

refs/heads/master

2023-08-11T09:17:41.232437

2023-07-26T20:43:47

68,756,293

144

89

NOASSERTION

2023-09-06T18:22:08

2016-09-20T21:50:24

R

UTF-8

R

false

26,282

r

ui_09_2_seuratWorkflow.R

# User Interface for Seurat Workflow --- shinyPanelSeurat <- fluidPage( tags$script("Shiny.addCustomMessageHandler('close_dropDownSeuratHM', function(x){ $('html').click(); });"), h1("Seurat"), h5(tags$a(href = paste0(docs.artPath, "cnsl_seurat_curated_workflow.html"), "(help)", target = "_blank")), inlineCSS(list(".panel-danger>.panel-heading" = "background-color:#dcdcdc; color:#000000", ".panel-primary>.panel-heading" = "background-color:#f5f5f5; color:#000000; border-color:#dddddd", ".panel-primary" = "border-color:#dddddd;", ".panel-primary>.panel-heading+.panel-collapse>.panel-body" = "border-color:#dddddd;")), conditionalPanel( condition = "false", selectInput( "activePanelSelectSeurat", label = "Active Panel:", choices = c("", "Normalize Data", "Scale Data", "Highly Variable Genes", "Dimensionality Reduction", "tSNE/UMAP", "Clustering", "Find Markers", "Heatmap Plot"), selected = "" ) ), bsCollapse(id = "SeuratUI", open = "Normalize Data", bsCollapsePanel("Normalize Data", fluidRow( column(4, panel(heading = "Options", selectizeInput( inputId = "seuratSelectNormalizationAssay", label = "Select input matrix:", choices = NULL, selected = NULL, multiple = FALSE, options = NULL), #uiOutput("seuratSelectNormalizationAssay"), selectInput(inputId = "normalization_method", label = "Select normalization method: ", choices = c("LogNormalize", "CLR", "RC")), textInput(inputId = "scale_factor", label = "Set scaling factor: ", value = "10000"), actionButton(inputId = "normalize_button", "Normalize") ) ) ), style = "primary" ), bsCollapsePanel("Highly Variable Genes", fluidRow( column(4, fluidRow( column(12, panel(heading = "Compute HVG", selectInput(inputId = "hvg_method", label = "Select HVG method: ", choices = c("vst", "mean.var.plot", "dispersion")), textInput(inputId = "hvg_no_features", label = "Select number of features to find: ", value = "2000"), actionButton(inputId = "find_hvg_button", "Find HVG") ) ) ), br(), fluidRow( column(12, panel(heading = "Display HVG", numericInput(inputId = "hvg_no_features_view", label = "Select number of features to display: ", value = 10, step = 1), verbatimTextOutput(outputId = "hvg_output", placeholder = TRUE) ) ) ) ), column(8, fluidRow( column(12, panel(heading = "Plot", plotlyOutput(outputId = "plot_hvg") ) ) ) ) ), style = "primary"), # bsCollapsePanel("Scale Data", # fluidRow( # column(4, # panel(heading = "Options", # #selectInput(inputId = "model.use", label = "Select model for scaling: ", choices = c("linear", "poisson", "negbinom")), # materialSwitch(inputId = "do.scale", label = "Scale data?", value = TRUE), # materialSwitch(inputId = "do.center", label = "Center data?", value = TRUE), # textInput(inputId = "scale.max", label = "Max value for scaled data: ", value = "10"), # actionButton(inputId = "scale_button", "Scale") # ) # ) # ), # style = "primary" # ), bsCollapsePanel("Dimensionality Reduction", tabsetPanel(type = "tabs", tabPanel("PCA", br(), fluidRow( column(4, fluidRow( column(12, panel(heading = "PCA", numericInput(inputId = "pca_no_components", label = "Select number of components to compute: ", value = 50), materialSwitch(inputId = "pca_compute_elbow", label = "Compute ElbowPlot?", value = TRUE), materialSwitch(inputId = "pca_compute_jackstraw", label = "Compute JackStrawPlot?", value = FALSE), materialSwitch(inputId = "pca_compute_heatmap", label = "Compute Heatmap?", value = TRUE), conditionalPanel( condition = 'input.pca_compute_heatmap == true', numericInput(inputId = "pca_compute_heatmap_nfeatures", label = "Set number of features for heatmap:", value = 30, step = 1), ), numericInput(inputId = "seed_PCA", label = "Seed value for reproducibility of result:", value = 42, step = 1), actionButton(inputId = "run_pca_button", "Run PCA") ), panel(heading = "Select No. of Components", htmlOutput(outputId = "pca_significant_pc_output", inline = FALSE), numericInput(inputId = "pca_significant_pc_counter", label = "Select number of components for downstream analysis: ", min = 1, max = 20, value = 10) ) ) ) ), column(8, fluidRow( column(12, hidden( tags$div(class = "seurat_pca_plots", tabsetPanel(id = "seuratPCAPlotTabset", type = "tabs" ) )) ) ) ) ) ), tabPanel("ICA", br(), fluidRow( column(4, fluidRow( column(12, panel(heading = "ICA", textInput(inputId = "ica_no_components", label = "Select number of components to compute: ", value = "20"), materialSwitch(inputId = "ica_compute_heatmap", label = "Compute Heatmap?", value = TRUE), conditionalPanel( condition = 'input.ica_compute_heatmap == true', numericInput(inputId = "ica_compute_heatmap_nfeatures", label = "Set number of features for heatmap:", value = 30, step = 1), ), numericInput(inputId = "seed_ICA", label = "Seed value for reproducibility of result:", value = 42, step = 1), actionButton(inputId = "run_ica_button", "Run ICA") ), panel(heading = "Select No. of Components", #h5("Number of components suggested by ElbowPlot: "), #verbatimTextOutput(outputId = "ica_significant_pc_output", placeholder = TRUE), numericInput(inputId = "ica_significant_ic_counter", label = "Select number of components for downstream analysis: ", min = 1, max = 20, value = 10) ) ) ) ), column(8, fluidRow( column(12, hidden( tags$div(class = "seurat_ica_plots", tabsetPanel(id="seuratICAPlotTabset", type = "tabs" )) ) ) ) ) ) ) ), style = "primary"), bsCollapsePanel("2D-Embedding", tabsetPanel(id = "tsneUmapTabsetSeurat", type = "tabs", tabPanel("UMAP", br(), fluidRow( column(4, fluidRow( column(12, panel(heading = "UMAP", selectInput(inputId = "reduction_umap_method", label = "Select reduction method: ", choices = c("pca", "ica")), #textInput(inputId = "reduction_umap_count", label = "Select number of reduction components: ", value = "20"), numericInput(inputId = "min_dist_umap", label = "Set min.dist:", value = 0.3), numericInput(inputId = "n_neighbors_umap", label = "Set n.neighbors:", value = 30, step = 1), numericInput(inputId = "spread_umap", label = "Set spread:", value = 1), numericInput(inputId = "seed_UMAP", label = "Seed value for reproducibility of result:", value = 42, step = 1), htmlOutput(outputId = "display_message_umap", inline = FALSE), actionButton(inputId = "run_umap_button", "Run UMAP") ) ) ) ), column(8, fluidRow( panel(heading = "Plot", column(12, plotlyOutput(outputId = "plot_umap") ) ) ) ) ) ), tabPanel("tSNE", br(), fluidRow( column(4, fluidRow( column(12, panel(heading = "tSNE", selectInput(inputId = "reduction_tsne_method", label = "Select reduction method: ", choices = c("pca", "ica")), #textInput(inputId = "reduction_tsne_count", label = "Select number of reduction components: ", value = "20"), numericInput(inputId = "perplexity_tsne", label = "Set perplexity:", value = 30), numericInput(inputId = "seed_TSNE", label = "Seed value for reproducibility of result:", value = 1, step = 1), htmlOutput(outputId = "display_message_tsne", inline = FALSE), actionButton(inputId = "run_tsne_button", "Run tSNE") ) ) ) ), column(8, fluidRow( panel(heading = "Plot", column(12, plotlyOutput(outputId = "plot_tsne") ) ) ) ) ) ) ), style = "primary"), bsCollapsePanel("Clustering", fluidRow( column(4, fluidRow( column(12, panel(heading = "Options", selectInput(inputId = "reduction_clustering_method", label = "Select reduction method: ", choices = c("pca", "ica")), #textInput(inputId = "reduction_clustering_count", label = "Select number of reduction components: ", value = "20"), selectInput(inputId = "algorithm.use", label = "Select clustering algorithm: ", choices = list("Original Louvain algorithm" = "louvain", "Louvain algorithm with multilevel refinement" = "multilevel", "SLM algorithm" = "SLM")), numericInput(inputId = "resolution_clustering", label = "Set resolution:", value = 0.8), materialSwitch(inputId = "group.singletons", label = "Group singletons?", value = TRUE), htmlOutput(outputId = "display_message_clustering", inline = FALSE), actionButton(inputId = "find_clusters_button", "Find Clusters") ) ) ) ), column(8, fluidRow( column(12, hidden( tags$div(class = "seurat_clustering_plots", tabsetPanel(id = "seuratClusteringPlotTabset", type = "tabs" )) ) ) ) ) ), style = "primary"), bsCollapsePanel("Find Markers", fluidRow( column(4, fluidRow( column(12, panel(heading = "Options", h6("Compute marker genes that are either differentially expressed or conserved between selected groups and visualize them from the selected plots on right panel."), radioButtons( inputId = "seuratFindMarkerType", label = "Select type of markers to identify:", choices = c( "markers between all groups" = "markerAll", "markers differentially expressed between two selected groups" = "markerDiffExp", "markers conserved between two selected groups" = "markerConserved" ) ), selectInput( inputId = "seuratFindMarkerSelectPhenotype", label = "Select biological phenotype:", choices = NULL ), conditionalPanel( condition = "input.seuratFindMarkerType == 'markerDiffExp' || input.seuratFindMarkerType == 'markerConserved'", selectInput( inputId = "seuratFindMarkerGroup1", label = "Select first group of interest:", choices = NULL ), selectInput( inputId = "seuratFindMarkerGroup2", label = "Select second group of interest:", choices = NULL ) ), selectInput( inputId = "seuratFindMarkerTest", label = "Select test:", choices = c("wilcox", "bimod", "t", "negbinom", "poisson", "LR", "DESeq2") ), materialSwitch( inputId = "seuratFindMarkerPosOnly", label = "Only return positive markers?", value = FALSE ), actionButton(inputId = "seuratFindMarkerRun", "Find Markers") ) ) ) ), column(8, fluidRow( column(12, hidden( tags$div( class = "seurat_findmarker_table", filterTableUI(id = "filterSeuratFindMarker") ) ), br(), hidden( tags$div(class = "seurat_findmarker_jointHeatmap", bsCollapse( bsCollapsePanel( title = "Heatmap Plot", fluidRow( column(12, align = "center", panel( numericInput("findMarkerHeatmapPlotFullNumeric", value = 10, max = 2000, min = 2, step = 1, label = "Select number of top genes from each cluster/group to visualize in the heatmap below based on highest average log fold change value:"), actionButton("findMarkerHeatmapPlotFullNumericRun", label = "Plot"), hr(), shinyjqui::jqui_resizable( plotOutput(outputId = "findMarkerHeatmapPlotFull", height = "500px") ) ) ) ) ) ) ) ), br(), hidden( tags$div(class = "seurat_findmarker_plots", panel(heading = "Marker Gene Plots", HTML("<center><h5><span style='color:red; font-weight:bold; text-align:center;'>Click on the rows of the table above to plot the selected marker genes below!</span></h5></br></center>"), tabsetPanel(id = "seuratFindMarkerPlotTabset", type = "tabs")) ) ) ) ) ) ), style = "primary") ), nonLinearWorkflowUI(id = "nlw-seurat") )

80c5dda1e99a993694f8b45ab60f4a84ed2e49d0

d2f0c07eeba563b88021010e450ac7a29779972b

/dataJoin.R

131c4d8f4a6894edb0c20b00b7c8b5b3a8b770e5

[]

no_license

lefpoem/R_log

dc60495063fe26a8fe8291708a98594a9ff27df2

74c9f927daa4c185655d365186b01df71061dc1e

refs/heads/main

2023-06-07T05:56:55.643987

2021-07-04T09:17:39

381,149,578

0

null

UTF-8

R

false

591

r

dataJoin.R

# data frame1 df1 = data.frame(SiteId=c(1:6),Site=c("Goolge","Baidu","Numpy","Zhihu","CSDN","Pokect")) # data frame2 df2 = data.frame(SiteId=c(2,4,6,7,8),Country=c('CN','USA','CN','USA','IN')) print(df1) print(df2) # inner Join取交集 df3= merge(x=df1,y=df2,by="SiteId") print("----NATURAL JOIN----") print(df3) # full Join取交集 df4 = merge(x=df1,y=df2,by="SiteId",all=TRUE) print("----FULL JOIN----") print(df4) df5 = merge(x=df1,y=df2,by="SiteId",all.x=TRUE) print("----LEFT JOIN----") print(df5) df6 = merge(x=df1,y=df2,by="SiteId",all.y=TRUE) print("----RIGHT JOIN----") print(df6)

e2560aeff0c3a26c563912f604f867531936378c

8c9ce99672ce84da4400238e6f8278c130210100

/Gene Info sorter_V4.R

05fb39369b2cfa65064481eccb7bea1f8bad63b4

[]

no_license

debabratadutta6/Sesame-transcriptome

183cb8120b6611fe01889907c72676a7fa299d70

36b7199a52fb21e3b2ee735d484267a37cfc2a39

refs/heads/main

2022-12-31T02:03:31.042727

2020-10-24T03:58:45

306,801,341

0

null

UTF-8

R

false

3,055

r

Gene Info sorter_V4.R

winDialog("ok", "Please select Stringtie Assembled Transcripts file") options(stringsAsFactors=FALSE) data1 <- choose.files(default = "", caption = "Select input datafile", multi = TRUE, filters = Filters, index = nrow(Filters)) data11 <- read.csv(data1, header=F, sep=" ") data11 <- data11[-1,] data11 <- data11[-1,] winDialog("ok", "Please select Stringtie Gene Count file") data2 <- choose.files(default = "", caption = "Select input datafile", multi = TRUE, filters = Filters, index = nrow(Filters)) data21 <- read.csv(data2, header=TRUE, sep="\t") winDialog("ok", "Please select Stringtie Transcript Count file") data3 <- choose.files(default = "", caption = "Select input datafile", multi = TRUE, filters = Filters, index = nrow(Filters)) data31 <- read.csv(data3, header=TRUE, sep="\t") gene <- data11$V2 gene <- gsub(";", "", gene) transcript <- data11$V4 transcript <- gsub(";", "", transcript) locus1 <- data11$V6 locus1 <- gsub(";", "", locus1) locus2 <- data11$V8 locus2 <- gsub(";", "", locus2) num1 <- as.numeric(locus1) num2 <- as.numeric(locus2) bind <- as.data.frame(cbind (gene, transcript, locus1, locus2, num1, num2)) x <- nrow(bind) bind$seqs <- 1:x bind$isna1 <- is.na(bind$num1) bind$isna2 <- is.na(bind$num2) bind2 <- as.data.frame(bind) sub1 <- subset(bind2, bind2$isna1==TRUE) sub2 <- subset(bind2, bind2$isna2==TRUE) cols1 <- c(1,2,3,7) sub12 <- sub1[,cols1] colnames(sub12) <- c("gene", "transcript", "locus", "seqs") cols2 <- c(1,2,4,7) sub22 <- sub2[,cols2] colnames(sub22) <- c("gene", "transcript", "locus", "seqs") finalish <- rbind(sub12, sub22) final <- finalish[order(finalish$seqs),] final <- final[,-4] dup <- duplicated(final) final$dup <- dup sub3 <- subset(final, final$dup==FALSE) ############################################################################# sub4 <- sub3 colnames(sub4)[1] <- c("gene_id") merge1 <- merge(data21, sub4, by.x="gene_id") merge2 <- merge1 merge2$gene_id <- merge2$locus new1 <- merge2[,1:2] #dup1 <- duplicated(new1) #new1$dup <- dup1 #sub1 <- subset(new1, new1$dup==FALSE) sub1 <- new1 rows <- nrow(sub1) sub1$row <- 1:rows sub1$gene_id <- paste(sub1$row,"_",sub1$gene_id, sep="") new11 <- sub1[,-c(3:4)] write.table(new11, "Adjusted Gene Counts.tabular", quote=F, sep="\t", row.names=F) ############################################################################# sub5 <- sub3 colnames(sub5)[2] <- c("transcript_id") merge3 <- merge(data31, sub5, by.x="transcript_id") merge4 <- merge3 merge4$transcript_id <- merge4$locus new2 <- merge4[,1:2] #dup2 <- duplicated(new2) #new2$dup <- dup2 #sub2 <- subset(new2, new2$dup==FALSE) sub2 <- new2 rows <- nrow(sub2) sub2$row <- 1:rows sub2$transcript_id <- paste(sub2$row,"_",sub2$transcript_id, sep="") new21 <- sub2[,-3] write.table(new21, "Adjusted Transcript Counts.tabular", quote=F, sep="\t", row.names=F)

5e84bcd5ad7e2aa57c3b32656ce54a7553c8f625

556d3d35f85264e5c5c27b5de5c158dc7d2500dc

/rankhospital.R

627ff676413d1243ba4df197315e0f86d7252f38

[]

no_license

Dcroix/Programming-Assign-Three

68ec8c66f34546c1dfc69fc8642f49ed9e93d930

c39034d0555fba57ee5a1f68646fe6bfccd08708

refs/heads/master

2020-05-15T22:03:22.886827

2019-04-21T09:59:39

182,516,902

0

null

UTF-8

R

false

1,042

r

rankhospital.R

#This function returns the best or worst performing hospital for the identified state and outcome rankhospital <- function(state, outcome, num = "best"){ data <- read.csv("outcome-of-care-measures.csv") states<- levels(data[,7])[data[,7]] state_flag <- FALSE for (i in 1:length(states)){ if (state == states[i]){ state_flag <- TRUE } } if (!state_flag){ stop("invalid state") } if(!((outcome == "heart attack")|(outcome == "heart failure") | (outcome == "pneumonia"))){ stop ("invalid outcome") } col <- if (outcome == "heart attack"){ 11 } else if (outcome == "heart failure") { 17 }else { 23 } data[,col]<- suppressWarnings(as.numeric(levels(data[,col])[data[,col]])) data[,2] <- as.character(data[,2]) statedata <- data[grep(state, data$State),] orderdata <- statedata[order(statedata[,col], statedata[,2], na.last = NA),] if(num == "best") { orderdata[1,2] } else if (num == "worst"){ orderdata[nrow(orderdata),2] } else { orderdata[num,2] } }

408cba7ba434ecd613de3acbf9317d3829dea857

4bf26f1905d2d51a85591a24b29740bef8472abe

/src/install.dependencies.R

2cc60d62ee70f3f37384aaaa54c862169506155d

[]

no_license

NEONScience/swift.aqua

c8fc9d4f508602b29f14d3c0ff57c4ac08a8560d

b7c19f09dbab6a28797bcee4de04ca0e124c4060

refs/heads/master

2022-10-25T10:19:51.846812

2020-06-15T20:59:20

272,516,953

0

null

UTF-8

R

false

375

r

install.dependencies.R

# Install Packages for intialization install.packages("fst") install.packages("shiny") install.packages("dplyr") install.packages("plotly") install.packages("ggplot2") install.packages("DT") install.packages("tidyr") install.packages("data.table") install.packages("shinycssloaders") install.packages("shinydashboard") install.packages("viridis") install.packages("stringr")

a4f69aefc17cb0244749c0520c58a2bcfbef1b1b

4017621a72dcf76a3a9b66905ea0374e7acd0517

/data_analysis_scripts/InteractiveViz.R

76fd2adfb85cb192eeddfec162b25df58841ee5d

[]

no_license

JackLich10/data_plus_basketball

c9fed4ace98a74504ecf7c320c655ac5eaaea9a8

40ad762ec54b6f761ee81c54417cbd6e2f098518

refs/heads/master

2023-03-18T11:00:57.002177

2021-03-12T01:46:57

196,605,447

1

0

null

UTF-8

R

false

6,745

r

InteractiveViz.R

# load packages library(tidyverse) library(broom) library(ggiraph) library(rvest) library(modelr) # load data Duke201415teamstats <- read_csv("data/Duke201415teamstats.csv") ShotChart <- read_csv("data/shot_chart_NN_SVM.csv") # change from wide to long long <- subset %>% dplyr::select(game_number, opponent, PPS, ePPS_NN, ePPS_SVM, ePPS, shot_making_NN, shot_making_SVM, shot_making) %>% gather(type, value, -game_number, -opponent, -shot_making_NN, -shot_making_SVM, -shot_making) subset <- subset %>% mutate(pt_diff = pts - opp_pts) # add ePPS and shot making ShotChart <- ShotChart %>% mutate(EPS_NN = value * NN_probability, EPS_SVM = value * SVM_probability) ePPS_NN <- ShotChart %>% dplyr::group_by(game) %>% dplyr::summarise(ePPS = mean(EPS_NN)) %>% dplyr::select(ePPS) %>% pull() ePPS_SVM <- ShotChart %>% dplyr::group_by(game) %>% dplyr::summarise(ePPS = mean(EPS_SVM)) %>% dplyr::select(ePPS) %>% pull() ePPS = (ePPS_NN + ePPS_SVM)/2 Duke201415teamstats <- Duke201415teamstats %>% mutate(PPS = (pts - ft) /fga) subset <- Duke201415teamstats %>% filter(game_number %in% c(1:7, 9, 11:13, 16, 18, 22, 23, 26, 27, 29, 30, 32:35)) %>% dplyr::mutate(shot_making_NN = PPS - ePPS_NN, shot_making_SVM = PPS - ePPS_SVM, shot_making = PPS - ePPS) subset <- subset %>% dplyr::mutate(ePPS_NN = ePPS_NN, ePPS_SVM = ePPS_SVM, ePPS = ePPS) ShotChart <- ShotChart %>% dplyr::mutate(ePPS_NN = NN_probability * value, ePPS_SVM = SVM_probability * value, ePPS = (ePPS_NN +ePPS_SVM)/2) # function to scrape gameIDs get_game_ids <- function(Year) { url <- paste("https://www.espn.com/mens-college-basketball/team/schedule/_/id/150/season/", Year, sep = "") y <- read_html(url) %>% html_nodes(".ml4 a") %>% html_attr("href") %>% substr(57, 65) return(y) } gameIDs201415 <- get_game_ids("2015") NCAATourn <- gameIDs201415[1:6] gameIDs201415 <- gameIDs201415[-c(1:6)] gameIDs201415[c(34:39)] <- NCAATourn gameIDs201415 <- gameIDs201415[c(1:7, 9, 11:13, 16, 18, 22, 23, 26, 27, 29, 30, 32:35)] long$gameID <- paste(gameIDs201415) subset$gameID <- paste(gameIDs201415) long$tooltip <- paste(long$type, ": ", round(long$value, 2), sep = "") long$onclick <- sprintf("window.open(\"%s%s\")", "https://www.espn.com/mens-college-basketball/game?gameId=", as.character(long$gameID)) game_by_game <- long %>% filter(type %in% c("ePPS", "PPS")) %>% ggplot(aes(x = reorder(opponent, game_number), y = value, group = type, color = type, tooltip = tooltip, onclick = onclick)) + geom_line() + geom_point_interactive(aes(data_id = value), size = 2) + geom_label(aes(x = reorder(opponent, game_number), y = 0.75, label = round(shot_making, 1), fill = shot_making), color = "black", size = 2.5, label.size = 0.1, label.r = unit(0.1, "lines"), label.padding = unit(0.1, "lines")) + geom_text(aes(x = 3, y = 0.8, label = "Shot-Making:"), size = 3, inherit.aes = F) + scale_fill_gradient2(low = "blue", mid = "white", high = "red", labels = c(low = "Worse: -0.3", mid = "Expected: 0", high = "Better: 0.3"), breaks = c(-0.2, 0, 0.2), limits = c(-0.3, 0.3)) + scale_color_manual(values = c("#001A57", "grey", "light blue", "grey")) + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + labs(title = "Duke's Game-by-Game Shooting", subtitle = "2014-15 Season", x = "Opponent", y = "Points Per Shot", color = "Type", fill = "Shot-Making") ggiraph(code = {print(game_by_game)}) subset$tooltip <- paste("vs. ", subset$opponent, ": ", subset$pts, " - ", subset$opp_pts, sep = "") subset$onclick <- sprintf("window.open(\"%s%s\")", "https://www.espn.com/mens-college-basketball/game?gameId=", as.character(subset$gameID)) model <- lm(pts ~ shot_making, data = subset) r2 <- paste("R-squared: ", round(100 * glance(model)$r.squared, 4), "%", sep = "") p_val <- paste("P-value: ", round(glance(model)$p.value, 10), sep = "") shot_making_pt_diff <- subset %>% ggplot(aes(x = shot_making, y = pt_diff, color = result, tooltip = tooltip, onclick = onclick)) + geom_point_interactive(aes(data_id = shot_making), size = 3) + geom_hline(yintercept = 0, linetype = "dashed") + geom_vline(xintercept = 0, linetype = "dashed") + geom_text(aes(x = -0.11, y = 50, label = "Worse Than Expected Shot-Making:"), size = 3.5, inherit.aes = F) + geom_text(aes(x = 0.11, y = 50, label = "Better Than Expected Shot-Making:"), size = 3.5, inherit.aes = F) + geom_text(aes(x = 0.25, y = 38, label = r2), size = 2.5, inherit.aes = F) + geom_text(aes(x = 0.25, y = 35, label = p_val), size = 2.5, inherit.aes = F) + scale_color_manual(values = c("red", "#001A57")) + geom_smooth(aes(x = shot_making, y = pt_diff), method = "lm", se = F, inherit.aes = F) + labs(title = "Better shot-making leads to larger margins of victory", subtitle = "Duke 2014-15 Season", x = "Shot-Making Index", y = "Point Differential", color = "Result") ggiraph(code = {print(shot_making_pt_diff)}, width_svg = 8, height_svg = 7) subset$tooltip_ast <- paste("vs. ", subset$opponent, ": ", subset$pts, " - ", subset$opp_pts, ", Asts: ", subset$ast, sep = "") model2 <- lm(shot_making ~ ast, data = subset) r22 <- paste("R-squared: ", round(100 * glance(model2)$r.squared, 4), "%", sep = "") p_val2 <- paste("P-value: ", round(glance(model2)$p.value, 10), sep = "") ast_shot_making <- subset %>% ggplot(aes(x = ast, y = shot_making, color = result, tooltip = tooltip_ast, onclick = onclick)) + geom_point_interactive(aes(data_id = shot_making), size = 3) + geom_hline(yintercept = 0, linetype = "dashed") + geom_vline(xintercept = mean(subset$ast), linetype = "dashed") + geom_text(aes(x = 25, y = -0.05, label = "Worse Than Expected Shot-Making:"), size = 3, inherit.aes = F) + geom_text(aes(x = 25, y = 0.05, label = "Better Than Expected Shot-Making:"), size = 3, inherit.aes = F) + geom_text(aes(x = mean(subset$ast) + 2, y = -0.2, label = paste("Mean: ", as.character(round(mean(subset$ast), 1)), " asts", sep = "")), size = 3, inherit.aes = F) + geom_text(aes(x = 26, y = 0.26, label = r22), size = 2.5, inherit.aes = F) + geom_text(aes(x = 26, y = 0.24, label = p_val2), size = 2.5, inherit.aes = F) + scale_color_manual(values = c("red", "#001A57")) + geom_smooth(aes(x = ast, y = shot_making), method = "lm", se = F, inherit.aes = F) + labs(title = "More assists lead to better shot-making", subtitle = "Duke 2014-15 Season", x = "Assists", y = "Shot-Making Index", color = "Result") ggiraph(code = {print(ast_shot_making)})

68591ec4c7d0250bc1583b41fed00290291b665d

d812db15a12cfce3666d69812fbbb0da4b070c14

/code/package-list.R

56d0bec79761df9c608d21ef9501abf32f7da3b9

[ "MIT" ]

permissive

jvpoulos/patt-c

97fba2cec409113747f246cec1cc36ee6cf21f5d

d471872f710210516c540f313437f8fa69a91e21

refs/heads/master

2021-07-07T15:02:19.941944

2020-07-31T03:27:53

156,440,652

1

0

null

UTF-8

R

false

411

r

package-list.R

packages <- c("ggplot2","ggpubr","gridExtra","reshape2","dplyr","MASS","gbm","rpart","foreach","doParallel","downloader","SAScii","RCurl", "foreign","plyr","downloader","digest","SuperLearner","class", "randomForest","glmnet","gam","e1071","gbm","xgboost","ROCR","reporttools") weights <- c("cluster","HMisc","weights") # install cluster -> HMisc -> weights install.packages(c(packages,weights))

b2c158d78aeadd38533f16f792cc15f78df908f0

0feedfcb9f76e63e15727486747d9693d4863e5a

/主代码/portfolio_characteristics_rkt (in one box).R

191eb09f1efec89177fd594e0d8f3fd34fd40375

[]

no_license

jaynewton/paper_6

be06bd623707d87e0446f25eed0851d86738f561

331ce16dd031e9e3506fc91ac00bb7769cc2095f

refs/heads/master

2020-04-02T02:33:06.048234

2018-11-11T11:24:59

153,915,405

0

null

UTF-8

R

false

3,731

r

portfolio_characteristics_rkt (in one box).R

################################# load("F:/我的论文/第五篇/RData/da_all_m.RData") load("F:/我的论文/第五篇/主代码/individual investor preference/RData/da_individual_m.RData") load("F:/我的论文/第五篇/RData/da_inst_m.RData") load("F:/我的论文/第五篇/主代码/individual investor preference/RData/da_price_m.RData") load("F:/我的论文/第五篇/主代码/individual investor preference/RData/da_ivol_6m.RData") load("F:/我的论文/第五篇/主代码/individual investor preference/RData/da_size_m.RData") load("F:/我的论文/第五篇/主代码/individual investor preference/RData/da_profit_m.RData") load("F:/我的论文/第五篇/主代码/individual investor preference/RData/da_bm_m.RData") load("F:/我的论文/第五篇/主代码/beta anomaly/monthly data in five years/RData/da_beta_5y.RData") #load("F:/我的论文/第五篇/主代码/beta anomaly/daily data in one year/RData/da_beta_y.RData") load("F:/我的论文/第五篇/主代码/individual investor preference/RData/da_turnover_m.RData") load("F:/我的论文/第五篇/主代码/individual investor preference/RData/da_dividend_m.RData") da_all_m <- da_all_m[,.(ym,SecCode,max_ret)] da_individual_m <- da_individual_m[,.(ym,SecCode,individual)] da_ivol_6m <- da_ivol_6m[,.(ym,SecCode,ivol)] da_profit_m <- na.omit(da_profit_m) da_m <- merge(da_all_m,da_individual_m,by=c("ym","SecCode")) da_m <- merge(da_m,da_inst_m,by=c("ym","SecCode")) da_m <- merge(da_m,da_price_m,by=c("ym","SecCode")) da_m <- merge(da_m,da_ivol_6m,by=c("ym","SecCode")) da_m <- merge(da_m,da_size_m,by=c("ym","SecCode")) da_m <- merge(da_m,da_profit_m,by=c("ym","SecCode")) da_m <- merge(da_m,da_bm_m,by=c("ym","SecCode")) da_m <- merge(da_m,da_beta_5y,by=c("ym","SecCode")) da_m <- merge(da_m,da_turnover_m,by=c("ym","SecCode")) da_m <- merge(da_m,da_dividend_m,by=c("ym","SecCode")) #### ym_index <- sort(unique(da_m$ym)) k <- 5 y <- 13 # number of porfolio characteristics variables ret_p <- matrix(NA,nrow=length(ym_index),ncol=k) # p denotes portfolio colnames(ret_p) <- paste0("p",1:k) vari_level <- array(NA,c(length(ym_index),k,y)) # vari_level denotes variable level for (i in 1:length(ym_index)) { da_sub <- da_m[ym==ym_index[i],] da_sub <- da_sub[order(max_ret),] n_mid <- floor(nrow(da_sub)/k) if ((nrow(da_sub)-n_mid*(k-2))%%2==0){ n_f <- (nrow(da_sub)-n_mid*(k-2))/2 # f denotes first, l denotes last n_l <- n_f } else { n_f <- (nrow(da_sub)-n_mid*(k-2)-1)/2 n_l <- n_f+1 } x <- seq(from=n_f,to=nrow(da_sub),by=n_mid)[1:(k-1)] x <- c(x,nrow(da_sub)) da_sub$group_n <- cut(1:nrow(da_sub), c(0,x),labels = 1:k) for (j in 1:k) { vari_level[i,j,1] <- da_sub[group_n==j,mean(max_ret)] vari_level[i,j,2] <- da_sub[group_n==j,mean(individual)] vari_level[i,j,3] <- da_sub[group_n==j,mean(inst)] vari_level[i,j,4] <- da_sub[group_n==j,mean(price)] vari_level[i,j,5] <- da_sub[group_n==j,mean(ivol)] vari_level[i,j,6] <- da_sub[group_n==j,mean(size)] vari_level[i,j,7] <- da_sub[group_n==j,mean(eps)] vari_level[i,j,8] <- da_sub[group_n==j,mean(roe)] vari_level[i,j,9] <- da_sub[group_n==j,mean(opps)] vari_level[i,j,10] <- da_sub[group_n==j,mean(BM)] vari_level[i,j,11] <- da_sub[group_n==j,mean(be)] vari_level[i,j,12] <- da_sub[group_n==j,mean(turnover)] vari_level[i,j,13] <- da_sub[group_n==j,mean(dividend)] } } vari_level_m <- matrix(NA,nrow=k,ncol=y) # m denotes mean for (j in 1:k) { for (p in 1:y) { vari_level_m[j,p] <- mean(vari_level[,j,p],na.rm=T) } } colnames(vari_level_m) <- c("max_ret","individual","inst","price","ivol","size","eps", "roe","opps","BM","be","turnover","dividend") vari_level_m

cbf6621a8586452b820d509a3b1c81b69c4007c0

99fb6ea41554f6ebe7fbd21f368c68e0980770d1

/executable/microbiome_statistics_and_functions.R

75115a89dd59c9b4e3a08b7beb7f418f0f5a4cf0

[]

no_license

GreathouseLab/Preg_Diet_microbiome

6b504335492f4edec4dd268271a5ca5ed6a66ffd

6e05d84a5952452db90443014dd19db798e58004

refs/heads/master

2020-06-26T15:21:45.116303

2020-01-07T02:15:55

199,672,022

0

null

UTF-8

R

false

29,724

r

microbiome_statistics_and_functions.R

# =================================================== # # =================================================== # # # Microbiome Analysis Functions # Jun Chen, PhD # # =================================================== # # =================================================== # # Created: 06/10/2018 # # Last Editted: 06/10/2018 # # By: R. Noah Padgett # # =================================================== # # =================================================== # # Copyright R. Noah Padgett, 2018 # # This script is not guaranteed to be free of bugs and/or errors. # # This script can be freely used and shared as long as the author and # copyright information in this header remain intact. # # # You can edit script files (for example, this file) # and either cut and paste lines from file into R command line # (in Windows you can use ctrl-R to do this) # or in the R command line type: # # source("main.R") # # You may need to use full path name in the filename, or alternatively in the R console # window change to the directory containing the file by using the command: # # setwd("<path of your directory>") # # Or this file can be sourced through a different file that needs # the functions that are listed in this file. # =================================================== # # =================================================== # # This file contains the following functions # # Function # subset_data() # is.na.null() # perform_differential_analysis_para() # perform_differential_analysis_para_single_FE() # - FE = Fixed Effects # # =================================================== # # =================================================== # # =================================================== # # subset_data() # =================================================== # # Inputs # data.obj = a dataframe # samIDs = a vector of IDs to subset to # Returns subsetted dataset subset_data <- function (data.obj, samIDs) { data.obj$meta.dat <- data.obj$meta.dat[samIDs, , drop=FALSE] if (!is.na.null(data.obj$otu.tab)) { data.obj$otu.tab <- data.obj$otu.tab[, samIDs, drop=FALSE] data.obj$otu.tab <- data.obj$otu.tab[rowSums(data.obj$otu.tab) != 0, , drop=FALSE] data.obj$otu.name <- data.obj$otu.name[rownames(data.obj$otu.tab), , drop=FALSE] if (!is.na.null(data.obj$otu.name.full)) { data.obj$otu.name.full <- data.obj$otu.name.full[rownames(data.obj$otu.tab), , drop=FALSE] } } if (!is.na.null(data.obj$abund.list)) { data.obj$abund.list <- lapply(data.obj$abund.list, function(x) { xx <- x[, samIDs, drop=FALSE] xx <- xx[rowSums(xx) != 0, , drop=FALSE] }) } if (!is.na.null(data.obj$size.factor)) { data.obj$size.factor <- data.obj$size.factor[samIDs] } if (!is.na.null(data.obj$ko.list)) { data.obj$ko.list <- lapply(data.obj$ko.list, function(x) { xx <- x[, samIDs, drop=FALSE] xx <- xx[rowSums(xx) != 0, , drop=FALSE] }) } if (!is.na.null(data.obj$cog.list)) { data.obj$cog.list <- lapply(data.obj$cog.list, function(x) { xx <- x[, samIDs, drop=FALSE] xx <- xx[rowSums(xx) != 0, , drop=FALSE] }) } data.obj } # =================================================== # # is.na.null() # =================================================== # # This function is called by other functions as a logical check # Do not mess with this function! is.na.null <- function (x) { if (is.null(x)) { return(TRUE) } else { if (is.na(x)[1]) { return(TRUE) } else { return(FALSE) } } } # =================================================== # # perform_differential_analysis_para() # =================================================== # # The following function performs the taxonomic differential analysis # this is a monster of a function that does a LOT with lots of options. # # An example call: # perform_differential_analysis_para(data.obj0, grp.name='Status.cat', # adj.name=c('Gender', 'Age'), RE=FALSE, method='NB', # taxa.levels=c('Genus', 'Species'), winsor=TRUE, # winsor.qt=0.97, norm='TSS', norm.level='Species', # intersect.no=4, prev=0.1, minp=0.002, medianp=NULL, # mt.method='raw', cutoff=0.05, ann=paste0(df, '.BMI.TSSNB')) # # Arguments/Inputs # data.obj Data for analysis # grp.name # adj.name # subject # RE Random Effects? - Logical # method # zerop.cutoff # ZINB # LRT # taxa.levels # winsor # winsor.qt # norm # norm.level # intersect.no # prev # minp # medianp # mt.method # cutoff # ann # ... perform_differential_analysis_para <- function (data.obj, grp.name, adj.name=NULL, subject=NULL, RE=FALSE, method='Adaptive0', zerop.cutoff=0.25, ZINB='ZINB1', LRT=FALSE, taxa.levels=c('Phylum', 'Order', 'Class', 'Family', 'Genus'), winsor=TRUE, winsor.qt=0.97, norm='GMPR', norm.level='Genus', intersect.no=4,prev=0.1, minp=0.002, medianp=NULL, mt.method='fdr', cutoff=0.15, ann='', ...) { # To be completed # subject holds the random effects formula if (!RE) { if (!(method %in% c('ZINB', 'B', 'QB', 'NB', 'OP', 'Adaptive0', 'Adaptive1', 'Adaptive2'))) stop('The speficied model is not supported!\n') perform_differential_analysis_para_single <- perform_differential_analysis_para_single_FE if (!is.null(subject)) warning('subject will not be used. Are you sure you want to run fixed effects model? ') } else { if (!(method %in% c('ZINB', 'B', 'B0', 'QB', 'NB', 'OP', 'Adaptive0', 'Adaptive1', 'Adaptive2'))) stop('The speficied model does not have random effects implementation!\n') if (ZINB != 'ZINB1') stop('Currently only ZINB1 is supported!\n') if (is.null(subject)) warning('subject is not supplied. Fixed effects model will be used instead!\n') perform_differential_analysis_para_single <- perform_differential_analysis_para_single_RE } df <- data.obj$meta.dat grp <- df[, grp.name] ind <- !is.na(grp) #data.obj <- subset_data(data.obj, ind) grp <- grp[ind] df <- df[ind, ] if ('Species' %in% taxa.levels & !('Species' %in% names(data.obj$abund.list))) { data.obj$abund.list[['Species']] <- data.obj$otu.tab rownames(data.obj$abund.list[['Species']]) <- paste0("OTU", rownames(data.obj$otu.tab), ":", data.obj$otu.name[, 'Phylum'], ";", data.obj$otu.name[, 'Genus']) } dep <- colSums(data.obj$otu.tab) diff.seq.p <- summary(aov(dep ~ grp))[[1]][1, 'Pr(>F)'] if (!is.na(diff.seq.p) & diff.seq.p <= 0.05) { cat("Signficant sequencing depth confounding!\n") cat("For parametric test with sequence depth adjustment, please be cautious about the results!\n") cat("There may be potential residual sequence depth confounding!\n") } pv.list <- qv.list <- fc.list <- fc.lc.list <- fc.uc.list <- met.list <- list() res.final <- NULL if (norm == 'Precalculated') { dep <- data.obj$size.factor } if (norm == 'GMPR') { dep <- GMPR(data.obj$abund.list[[norm.level]], intersect.no) } if (norm == 'TSS') { dep <- colSums(data.obj$abund.list[[norm.level]]) } ldep <- log(dep) for (LOI in taxa.levels) { cat(LOI, "\n") taxon.ct <- data.obj$abund.list[[LOI]] if (winsor == TRUE) { # Addressing the outlier (97% percent) or at least one outlier taxon.ct.p <- t(t(taxon.ct) / dep) taxon.ct.p <- apply(taxon.ct.p, 1, function(x) { cutoff <- quantile(x, winsor.qt) x[x >= cutoff] <- cutoff x } ) # column/row switch taxon.ct <- t(round(taxon.ct.p * dep)) } prop <- t(t(taxon.ct) / colSums(taxon.ct)) if (!is.null(minp)) { prop <- prop[rowMaxs(prop) > minp & rowSums(prop!=0) > prev*ncol(prop), , drop=FALSE] taxon.ct <- taxon.ct[rownames(prop), , drop=FALSE] } if (!is.null(medianp)) { nz.mean <- apply(prop, 1, function(x) median(x[x!=0])) prop <- prop[nz.mean > medianp & rowSums(prop!=0) > prev*ncol(prop), , drop=FALSE] taxon.ct <- taxon.ct[rownames(prop), , drop=FALSE] } pv.vec <- fc.vec <- fc.lc.vec <- fc.uc.vec <- met.vec <- conv.vec <- NULL obj <- NULL for (taxon in rownames(taxon.ct)) { cat('.') taxon.abund <- taxon.ct[taxon, ] ######## Logistic regression ############### if (method == 'B0') error <- try(obj <- perform_differential_analysis_para_single(taxon.abund, ldep, grp.name, adj.name, subject, df, method='B0', LRT, ...)) if (method == 'B') error <- try(obj <- perform_differential_analysis_para_single(taxon.abund, ldep, grp.name, adj.name, subject, df, method='B', LRT, ...)) if (method == 'QB') error <- try(obj <- perform_differential_analysis_para_single(taxon.abund, ldep, grp.name, adj.name, subject, df, method='QB', LRT, ...)) ######## Overdispersed Poisson regression ######### if (method == 'OP') error <- try(obj <- perform_differential_analysis_para_single(taxon.abund, ldep, grp.name, adj.name, subject, df, method='OP', LRT, ...)) ######## Negative binomial regression ######### if (method == 'NB') error <- try(obj <- perform_differential_analysis_para_single(taxon.abund, ldep, grp.name, adj.name, subject, df, method='NB', LRT, ...)) ######## Zeroinflated negbinomial regression 1 ######## if (method == 'ZINB') error <- try(obj <- perform_differential_analysis_para_single(taxon.abund, ldep, grp.name, adj.name, subject, df, method=ZINB, LRT, ...)) # Adpative 0 selects OP and QB based on the zero proportion (Not optimal) if (method == 'Adaptive0') { temp <- mean(as.numeric(taxon.abund != 0)) if (temp > zerop.cutoff) { error <- try(obj <- perform_differential_analysis_para_single(taxon.abund, ldep, grp.name, adj.name, subject, df, method='QB', LRT, ...)) } else { error <- try(obj <- perform_differential_analysis_para_single(taxon.abund, ldep, grp.name, adj.name, subject, df, method='OP', LRT, ...)) } } # Adpative 1 selects NB and ZIB based on AIC if (method == 'Adaptive1') { error1 <- try(obj1 <- perform_differential_analysis_para_single(taxon.abund, ldep, grp.name, adj.name, subject, df, method='NB', LRT, ...)) error2 <- try(obj2 <- perform_differential_analysis_para_single(taxon.abund, ldep, grp.name, adj.name, subject, df, method=ZINB, LRT, ...)) if (class(error1) != 'try-error' & class(error2) != 'try-error') { if (obj1$aic < obj2$aic) { obj <- obj1 } else { obj <- obj2 } error <- error1 } else { # pv == 0 indicates some problems in fitting if (class(error1) != 'try-error' & obj1$pv != 0) { obj <- obj1 error <- error1 } else { if (class(error2) != 'try-error' & obj1$pv != 0) { obj <- obj2 error <- error2 } else { error <- error2 } } } } # Adaptive 2 starts with NB model, if it fails, it switches ZINB if (method == 'Adaptive2') { error1 <- try(obj1 <- perform_differential_analysis_para_single(taxon.abund, ldep, grp.name, adj.name, subject, df, method='NB', LRT, ...)) if (class(error1) == 'try-error' | obj1$pv == 0) { error2 <- try(obj2 <- perform_differential_analysis_para_single(taxon.abund, ldep, grp.name, adj.name, subject, df, method=ZINB, LRT, ...)) if (class(error2) != 'try-error') { obj <- obj2 error <- error2 } else { error <- error2 } } else { error <- error1 obj <- obj1 } } # Random Effects model # ZINB, B, NB, Adpative1 is implemented based on glmmADMB # Set P value NA for those not makes sense if (class(error) == "try-error" | abs(obj$lfc) > 100) { obj$pv <- obj$lfc <- obj$lfc.lci <- obj$lfc.uci <- obj$method <- NA } pv.vec <- rbind(pv.vec, obj$pv) fc.vec <- rbind(fc.vec, obj$lfc) fc.lc.vec <- rbind(fc.lc.vec, obj$lfc.lci) fc.uc.vec <- rbind(fc.uc.vec, obj$lfc.uci) met.vec <- rbind(met.vec, obj$method) } cat('\n') qv.vec <- matrix(p.adjust(pv.vec[, 1], 'fdr'), ncol=1) rownames(pv.vec) <- rownames(qv.vec) <- rownames(fc.vec) <- rownames(fc.uc.vec) <- rownames(fc.lc.vec) <- rownames(met.vec) <- rownames(prop) colnames(pv.vec) <- 'Pvalue' colnames(qv.vec) <- 'Qvalue' colnames(met.vec) <- 'Method' pv.list[[LOI]] <- pv.vec qv.list[[LOI]] <- qv.vec fc.list[[LOI]] <- fc.vec fc.lc.list[[LOI]] <- fc.lc.vec fc.uc.list[[LOI]] <- fc.uc.vec met.list[[LOI]] <- met.vec res <- cbind(pv.vec, qv.vec, fc.vec, fc.lc.vec, fc.uc.vec, met.vec) rownames(res) <- rownames(prop) #write.csv(res, paste0("Taxa_DifferentialAbundanceAnalysis_", LOI, "_", ann, ".csv")) if (mt.method == 'fdr') { res.final <- rbind(res.final, res[as.numeric(res[, 'Qvalue']) <= cutoff, , drop=F]) } if (mt.method == 'raw') { res.final <- rbind(res.final, res[ as.numeric(res[, 'Pvalue']) <= cutoff, , drop=F]) } } if (!is.null(res.final)) { colnames(res.final) <- colnames(res) res.final <- res.final[rowSums(is.na(res.final)) == 0, , drop=F] #write.csv(res.final, paste0("Taxa_DifferentialAbundanceAnalysis_AllLevels_", mt.method, '_', cutoff, "_", ann, ".csv")) } return(list(pv.list=pv.list, qv.list=qv.list, fc.list=fc.list, fc.uc.list=fc.uc.list, fc.lc.list=fc.lc.list, met.list=met.list)) } # =================================================== # # perform_differential_analysis_para_FE() # =================================================== # # The following function performs the taxonomic differential analysis # this is a monster of a function that does a LOT with lots of options. # perform_differential_analysis_para_single_FE <- function (taxon.abund, ldep, grp.name, adj.name=NULL, subject=NULL, df, method='NB', LRT=FALSE) { # ldep: log depth (size factor) if (!is.null(adj.name)) { if (sum(grepl(grp.name, c(adj.name)))) { stop('grp.name could not be part of adj.name or subject, or there will be problem!\n') } } if (!is.null(subject)) { warnings('Fixed effects model will ignore the subject variable! Please use randome effects model!\n') } if (LRT & method == 'OP') warning('Overdispersed Poisson does not support LRT! Wald test used!\n') if (is.null(adj.name)) { grp.name.adj.name <- grp.name } else { grp.name.adj.name <- paste(grp.name, '+', adj.name) } if (method == 'NB') { m1.nb <- glm.nb(as.formula(paste('taxon.abund ~', grp.name.adj.name, '+ offset(ldep)')), data = df) if (LRT) { m0.nb <- update(m1.nb, as.formula(paste('. ~ . -', grp.name))) code <- list(m1.conv=m1.nb$converged, m1.bound=m1.nb$boundary, m0.conv=m0.nb$converged, m0.bound=m0.nb$boundary) pv.nb <- anova(m1.nb, m0.nb)['Pr(Chi)'][2, ] method <- paste(method, 'LRT') } else { code <- list(m1.conv=m1.nb$converged, m1.bound=m1.nb$boundary) pv.nb <- wald.test(b = coef(m1.nb), Sigma = vcov(m1.nb), Terms = grep(grp.name, names(coef(m1.nb))))$result$chi2['P'] method <- paste(method, 'Wald') } aic.nb <- summary(m1.nb)$aic coef.nb <- coef(m1.nb) fc.nb <- coef.nb[grep(grp.name, names(coef.nb))] ci.nb <- confint.default(m1.nb) obj <- ci.nb[grep(grp.name, rownames(ci.nb)), ] if (is.vector(obj)) { fc.lc.nb <- obj[1] fc.uc.nb <- obj[2] } else { fc.lc.nb <- obj[, 1] fc.uc.nb <- obj[, 2] names(fc.lc.nb) <- paste(names(fc.lc.nb), '2.5%') names(fc.uc.nb) <- paste(names(fc.uc.nb), '97.5%') } return(list(method=method, pv=pv.nb, lfc=fc.nb, lfc.lci=fc.lc.nb, lfc.uci=fc.uc.nb, aic=aic.nb, code=code)) } if (method == 'B') { taxon.abund2 <- as.numeric(taxon.abund != 0) m1.b <- glm(as.formula(paste('taxon.abund2 ~', grp.name.adj.name, '+ ldep')), data = df, family=binomial) if (LRT) { m0.b <- update(m1.b, as.formula(paste('. ~ . -', grp.name))) code <- list(m1.conv=m1.b$converged, m1.bound=m1.b$boundary, m0.conv=m0.b$converged, m0.bound=m0.b$boundary) pv.b <- pchisq(2 * (logLik(m1.b) - logLik(m0.b)), df = df.residual(m0.b) - df.residual(m1.b), lower.tail=FALSE) method <- paste(method, 'LRT') } else { code <- list(m1.conv=m1.b$converged, m1.bound=m1.b$boundary) pv.b <- wald.test(b = coef(m1.b), Sigma = vcov(m1.b), Terms = grep(grp.name, names(coef(m1.b))))$result$chi2['P'] method <- paste(method, 'Wald') } aic.b <- summary(m1.b)$aic coef.b <- coef(m1.b) fc.b <- coef.b[grep(grp.name, names(coef.b))] ci.b <- confint.default(m1.b) obj <- ci.b[grep(grp.name, rownames(ci.b)), ] if (is.vector(obj)) { fc.lc.b <- obj[1] fc.uc.b <- obj[2] } else { fc.lc.b <- obj[, 1] fc.uc.b <- obj[, 2] names(fc.lc.b) <- paste(names(fc.lc.b), '2.5%') names(fc.uc.b) <- paste(names(fc.uc.b), '97.5%') } return(list(method=method, pv=pv.b, lfc=fc.b, lfc.lci=fc.lc.b, lfc.uci=fc.uc.b, aic=aic.b, code=code)) } # Rev: 2016_09_13 add 'QB', No likelihood ratio test if (method == 'QB') { taxon.abund2 <- as.numeric(taxon.abund != 0) m1.b <- glm(as.formula(paste('taxon.abund2 ~', grp.name.adj.name, '+ ldep')), data = df, family=quasibinomial) code <- list(m1.conv=m1.b$converged, m1.bound=m1.b$boundary) pv.b <- wald.test(b = coef(m1.b), Sigma = vcov(m1.b), Terms = grep(grp.name, names(coef(m1.b))))$result$chi2['P'] method <- paste(method, 'Wald') aic.b <- summary(m1.b)$aic coef.b <- coef(m1.b) fc.b <- coef.b[grep(grp.name, names(coef.b))] ci.b <- confint.default(m1.b) obj <- ci.b[grep(grp.name, rownames(ci.b)), ] if (is.vector(obj)) { fc.lc.b <- obj[1] fc.uc.b <- obj[2] } else { fc.lc.b <- obj[, 1] fc.uc.b <- obj[, 2] names(fc.lc.b) <- paste(names(fc.lc.b), '2.5%') names(fc.uc.b) <- paste(names(fc.uc.b), '97.5%') } return(list(method=method, pv=pv.b, lfc=fc.b, lfc.lci=fc.lc.b, lfc.uci=fc.uc.b, aic=aic.b, code=code)) } if (method == 'OP') { # No LRT m1.op <- glm(as.formula(paste('taxon.abund ~', grp.name.adj.name)), offset=ldep, data = df, family=quasipoisson) code <- list(m1.conv=m1.op$converged, m1.bound=m1.op$boundary) # pv.op <- pchisq(2 * (logLik(m1.op) - logLik(m0.op)), df = df.residual(m0.op) - df.residual(m1.op), lower.tail=FALSE) # LRT not applicable coef.op <- coef(m1.op) pv.op <- wald.test(b = coef.op, Sigma = vcov(m1.op), Terms = grep(grp.name, names(coef.op)))$result$chi2['P'] method <- paste(method, 'Wald') fc.op <- coef.op[grep(grp.name, names(coef.op))] ci.op <- confint.default(m1.op) obj <- ci.op[grep(grp.name, rownames(ci.op)), ] if (is.vector(obj)) { fc.lc.op <- obj[1] fc.uc.op <- obj[2] } else { fc.lc.op <- obj[, 1] fc.uc.op <- obj[, 2] names(fc.lc.op) <- paste(names(fc.lc.op), '2.5%') names(fc.uc.op) <- paste(names(fc.uc.op), '97.5%') } return(list(method=method, pv=pv.op, lfc=fc.op, lfc.lci=fc.lc.op, lfc.uci=fc.uc.op, aic=NULL, code=code)) } if (method == 'ZINB0') { m1.zinb <- zeroinfl(as.formula(paste('taxon.abund ~', grp.name.adj.name, '+ offset(ldep)')), data = df, dist = "negbin", EM = TRUE) if (LRT) { if (is.null(adj.name)) { m0.zinb <- zeroinfl(as.formula(paste('taxon.abund ~ offset(ldep)')), data = df, dist = "negbin", EM = TRUE) } else { m0.zinb <- zeroinfl(as.formula(paste('taxon.abund ~', adj.name, '+ offset(ldep)')), data = df, dist = "negbin", EM = TRUE) } code <- list(m1.conv=m1.zinb$converged, m0.conv=m0.zinb$converged) # LRT pv.zinb <- pchisq(2 * (logLik(m1.zinb) - logLik(m0.zinb)), df = df.residual(m0.zinb) - df.residual(m1.zinb), lower.tail=FALSE) method <- paste(method, 'LRT') } else { code <- list(m1.conv=m1.zinb$converged) pv.zinb <- wald.test(b = coef(m1.zinb), Sigma = vcov(m1.zinb), Terms = grep(grp.name, names(coef(m1.zinb))))$result$chi2['P'] method <- paste(method, 'Wald') } aic.zinb <- -2 * logLik(m1.zinb) + 2 * (m1.zinb$n - m1.zinb$df.residual) coef.zinb <- coef(m1.zinb) fc.zinb <- coef.zinb[grep(grp.name, names(coef.zinb))] ci.zinb <- confint.default(m1.zinb) obj <- ci.zinb[grep(grp.name, rownames(ci.zinb)), ] if (is.vector(obj)) { fc.lc.zinb <- obj[1] fc.uc.zinb <- obj[2] } else { fc.lc.zinb <- obj[, 1] fc.uc.zinb <- obj[, 2] names(fc.lc.zinb) <- paste(names(fc.lc.zinb), '2.5%') names(fc.uc.zinb) <- paste(names(fc.uc.zinb), '97.5%') } return(list(method=method, pv=pv.zinb, lfc=fc.zinb, lfc.lci=fc.lc.zinb, lfc.uci=fc.uc.zinb, aic=aic.zinb, code=code)) } if (method == 'ZINB1') { m1.zinb <- zeroinfl(as.formula(paste('taxon.abund ~', grp.name.adj.name, '+ offset(ldep) | ldep')), data = df, dist = "negbin", EM = TRUE) if (LRT) { if (is.null(adj.name)) { m0.zinb <- zeroinfl(as.formula(paste('taxon.abund ~ offset(ldep) | ldep')), data = df, dist = "negbin", EM = TRUE) } else { m0.zinb <- zeroinfl(as.formula(paste('taxon.abund ~', adj.name, '+ offset(ldep) | ldep')), data = df, dist = "negbin", EM = TRUE) } code <- list(m1.conv=m1.zinb$converged, m0.conv=m0.zinb$converged) # LRT pv.zinb <- pchisq(2 * (logLik(m1.zinb) - logLik(m0.zinb)), df = df.residual(m0.zinb) - df.residual(m1.zinb), lower.tail=FALSE) method <- paste(method, 'LRT') } else { code <- list(m1.conv=m1.zinb$converged) pv.zinb <- wald.test(b = coef(m1.zinb), Sigma = vcov(m1.zinb), Terms = grep(grp.name, names(coef(m1.zinb))))$result$chi2['P'] method <- paste(method, 'Wald') } aic.zinb <- -2 * logLik(m1.zinb) + 2 * (m1.zinb$n - m1.zinb$df.residual) coef.zinb <- coef(m1.zinb) fc.zinb <- coef.zinb[grep(grp.name, names(coef.zinb))] ci.zinb <- confint.default(m1.zinb) obj <- ci.zinb[grep(grp.name, rownames(ci.zinb)), ] if (is.vector(obj)) { fc.lc.zinb <- obj[1] fc.uc.zinb <- obj[2] } else { fc.lc.zinb <- obj[, 1] fc.uc.zinb <- obj[, 2] names(fc.lc.zinb) <- paste(names(fc.lc.zinb), '2.5%') names(fc.uc.zinb) <- paste(names(fc.uc.zinb), '97.5%') } return(list(method=method, pv=pv.zinb, lfc=fc.zinb, lfc.lci=fc.lc.zinb, lfc.uci=fc.uc.zinb, aic=aic.zinb, code=code)) } if (method == 'ZINB2') { m2.zinb <- zeroinfl(as.formula(paste('taxon.abund ~', grp.name.adj.name, '+ offset(ldep) |', grp.name.adj.name, '+ ldep')), data = df, dist = "negbin", EM = TRUE) if (LRT) { if (is.null(adj.name)) { m0.zinb <- zeroinfl(as.formula(paste('taxon.abund ~ offset(ldep) | ldep')), data = df, dist = "negbin", EM = TRUE) } else { m0.zinb <- zeroinfl(as.formula(paste('taxon.abund ~', adj.name, '+ offset(ldep) |', adj.name, ' + ldep')), data = df, dist = "negbin", EM = TRUE) } code <- list(m1.conv=m2.zinb$converged, m0.conv=m0.zinb$converged) # LRT pv2.zinb <- pchisq(2 * (logLik(m2.zinb) - logLik(m0.zinb)), df = df.residual(m0.zinb) - df.residual(m2.zinb), lower.tail=FALSE) method <- paste(method, 'LRT') } else { code <- list(m2.conv=m2.zinb$converged) pv2.zinb <- wald.test(b = coef(m2.zinb), Sigma = vcov(m2.zinb), Terms = grep(grp.name, names(coef(m2.zinb))))$result$chi2['P'] method <- paste(method, 'Wald') } aic2.zinb <- -2 * logLik(m2.zinb) + 2 * (m2.zinb$n - m2.zinb$df.residual) coef.zinb <- coef(m2.zinb) fc2.zinb <- coef.zinb[grep(grp.name, names(coef.zinb))] ci.zinb <- confint.default(m2.zinb) obj <- ci.zinb[grep(grp.name, rownames(ci.zinb)), ] if (is.vector(obj)) { fc2.lc.zinb <- obj[1] fc2.uc.zinb <- obj[2] } else { fc2.lc.zinb <- obj[, 1] fc2.uc.zinb <- obj[, 2] names(fc2.lc.zinb) <- paste(names(fc2.lc.zinb), '2.5%') names(fc2.uc.zinb) <- paste(names(fc2.uc.zinb), '97.5%') } return(list(method=method, pv=pv2.zinb, lfc=fc2.zinb, lfc.lci=fc2.lc.zinb, lfc.uci=fc2.uc.zinb, aic=aic2.zinb, code=code)) } } # =================================================== # # abundance_list_create() # =================================================== # # this function is designed to help transform the raw data into the # form usable in the Jun Chen functions above. # mydata is an object of raw counts and a grouping variable # group.Var is a grouping variable. # # return a matrix of the observed counts in the groups # across all individuals. abundance_list_create <- function(mydata,Group.Var) { N <- ncol(mydata) - 1 output <- matrix(nrow=length(unique(Group.Var)), ncol = N) i <- 1 for(i in 1:N){ person.counts <- aggregate(mydata[,(1+i)], by=list(Group.Var), FUN=sum) output[,i] <- person.counts[,2] } rownames(output) <- person.counts[,1] colnames(output) <- colnames(mydata)[2:(N+1)] return(output) }

ac157c65e4ffd20028c82d2554858ea9b09b726b

233711a9c97ed63ac7fccbdbc896890b01784d03

/PrevisaoMacro/ipca-sarima.R

18eb55ea9bc09e3efd1b0d6aa2e7664b50130c4a

[]

no_license

econoquant/EconoQuantCode

d092f3efa226c0a7b5bfd6d8620f2cfb303c4d37

35d0fba8fd2d8afae65815da4283be0a8c88502b

refs/heads/master

2020-12-02T18:01:13.243372

2017-07-12T13:26:32

96,462,158

1

0

null

UTF-8

R

false

1,783

r

ipca-sarima.R

################### Carregar dados ####################################### ipca <- read.csv('ipca.csv',header = T, sep = ';', dec = ',') ipca <- ts(ipca[,2], start = c(1980,1), freq = 12) ##################### Selecionar subamostra ############################### library(changepoint) library(ggfortify) autoplot(cpt.meanvar(ipca), main='Variação mensal do IPCA (%)')+ scale_x_date(date_breaks = '1 year',date_labels = "%b %y") ipca <- window(ipca, c(1995,1), freq =12) ipca <- window(ipca, c(2004,1), freq =12) ipca <- window(ipca, c(2007,2), freq =12) train <- window(ipca, end = end(ipca) - c(1,0)) test <- window(ipca, start = end(train) + c(0,1)) ##################### SARIMA e analise residuos ########################### library(forecast) fit_sarima <- auto.arima(train, seasonal = T) summary(fit_sarima) ggtsdisplay(residuals(fit_sarima)) ggAcf(residuals(fit_sarima), main = "Autocorrelação resíduos") # Ljung Box: H0 resíduos sao iid Box.test(residuals(fit_sarima), lag=24, fitdf=length(coef(fit_sarima)), type="Ljung") ############### Avaliando a previsao ##################################### # 12 passoas a frente (dinamica) fcast.fit_sarima <- forecast(fit_sarima, h=length(test))$mean accuracy(fcast.fit_sarima, test) # 1 passoa a frente (estatica) fit <- Arima(test, model = fit_sarima) accuracy(fit) ############### Previsão ############################################### onestep <- fitted(fit) # valores previsao um passo a frente plot(forecast(fit_sarima, h=12),xlab='', ylab='(% a.m.)', bty='l', main='IPCA Mensal') lines(test, col='black', lwd=2) lines(onestep, col='red', lwd=2) legend('topleft', col=c('blue','red'), lty=c(1,1), lwd=c(2,2), legend=c('12 meses', '1 mês'))

715324680f72139831abc888b1aa7f1a66577d1a

d6c9f897714cea47c9b74547dd268462efd971b6

/Classifier/src/Classifier.R

8cf4592e0a246aa71e5bf929d106feb69fa8dd9d

[]

no_license

SilambarasanM/Data-Preparation-and-Analysis

dc883ba9d556f81150dd1dda6758b9fef3063991

dba14b6e07cd31319d95bc70226283d1f6dd3b7a

refs/heads/master

2021-01-18T21:25:16.695758

2016-05-16T08:42:11

52,257,666

1

0

null

UTF-8

R

false

7,531

r

Classifier.R

#Make sure system is connected to internet while running this code to install libraries #Install and load the required R libraries if (!require("plyr")) { install.packages("plyr", dependencies = TRUE) library(plyr) } if (!require("ggmap")) { install.packages("ggmap", dependencies = TRUE) library(ggmap) } if (!require("gplots")) { install.packages("gplots", dependencies = TRUE) library(gplots) } if (!require("rgdal")) { install.packages("rgdal", dependencies = TRUE) library(rgdal) } if (!require("clusterSim")) { install.packages("clusterSim", dependencies = TRUE) library(rgdal) } #Loading Source Files path = "C:\\Users\\admin\\Documents\\R Scripts\\Classifier" setwd(path) source("getGroceryCount.R") source("getExpectancy.R") source("getCrimeCount.R") source("getHousingData.R") source("getHardshipIndex.R") grocery_count<-getGroceryCount("GroceryStores_2013.csv", path) life_expectancy<-getExpectancy("Life_Expectancy_2000.csv", path) crime_count<-getCrimeCount("Crimes_2015.csv", path) housing_data<-getHousingData("Affordable_Rental_Housing_Developments.csv", path) hardship_index<-getHardshipIndex("SocioEconomicData_2008_2012.csv", path) #Loading Community Areas Information setwd("C:\\Users\\admin\\Documents\\R Scripts\\Classifier\\Community_Areas") area_data<-read.csv("community_areas.csv", header=TRUE, stringsAsFactor=FALSE) area_data<-area_data[,c("AREA_NUMBE","COMMUNITY")] colnames(area_data)<-c("Community.Area","Community.Name") #Filling Missing values community<-unique(area_data$Community.Area) for(i in 1: length(community)) { if(!community[i] %in% grocery_count$Community.Area) { gcount <- data.frame(Community.Area = community[i], Grocery.Count = 0) grocery_count <- rbind(grocery_count, gcount) } if(!community[i] %in% housing_data$Community.Area) { hd <- data.frame(Community.Area = community[i], Housing.Units = 0) housing_data <- rbind(housing_data, hd) } } grocery_count<-grocery_count[ order(grocery_count$Community.Area), ] housing_data<-housing_data[ order(housing_data$Community.Area), ] #Loading Community Areas Map Info sfn <- readOGR(".","community_areas", stringsAsFactors=FALSE) sfn<-spTransform(sfn, CRS("+proj=longlat +datum=WGS84")) ids<-sapply(slot(sfn, "polygons"), function(x) slot(x, "ID")) can<-sfn$area_numbe lookup<-do.call(rbind, Map(data.frame, Community.Area=can, id=ids)) lookup$Community.Area<-as.numeric(levels(lookup$Community.Area))[lookup$Community.Area] lookup$id<-as.numeric(levels(lookup$id))[lookup$id] lookup<-lookup[order(lookup$Community.Area),] sfn<-fortify(sfn) #Merging all data into one data frame - Profiling each Community Area community_profile<- data.frame(grocery_count$Community.Area, grocery_count$Grocery.Count, life_expectancy$Life.Expectancy, crime_count$Crime.Count, housing_data$Housing.Units, hardship_index$Hardship.Index) colnames(community_profile)<-c("Community.Area", "Grocery.Count", "Life.Expectancy", "Crime.Count", "Housing.Units", "Hardship.Index") normalized_data<-community_profile[,-1] normalized_data<-data.Normalization(normalized_data, type="n10", normalization="column") data_matrix<-data.matrix(normalized_data) data_matrix <- t(data_matrix) data_matrix <- data_matrix * 100 barplot(data_matrix, main="Features of Community Areas", xlab="Community Areas", ylab="Percentage", col=rainbow(5), legend = rownames(data_matrix)) #Assigning correct Polygon IDs to the Community Areas grocery_count$id<-"0" life_expectancy$id<-"0" crime_count$id<-"0" housing_data$id<-"0" hardship_index$id<-"0" community_profile$id<-"0" for (i in 1: length(community)){ grocery_count$id[i] <- lookup$id[i] life_expectancy$id[i] <- lookup$id[i] crime_count$id[i] <- lookup$id[i] housing_data$id[i] <- lookup$id[i] hardship_index$id[i] <- lookup$id[i] community_profile$id[i] <- lookup$id[i] } #Merging Community areas to corresponding Community Map info grocery_map_data<-merge(sfn, grocery_count, by=c("id")) expectancy_map_data<-merge(sfn, life_expectancy, by=c("id")) crime_map_data<-merge(sfn, crime_count, by=c("id")) housing_map_data<-merge(sfn, housing_data, by=c("id")) hardship_map_data<-merge(sfn, hardship_index, by=c("id")) #Loading Chicago Map chicago <- get_map(location = 'chicago', zoom = 'auto', maptype="roadmap") ggmap(chicago) + geom_polygon(aes(x = long, y = lat, group=id, fill=Grocery.Count),data = grocery_map_data, color ="black",alpha = .7, size = .2) + labs(title="Grocery Store Density by Community Areas (2013)") + scale_fill_gradient(high = "#56B1F7", low = "white") dev.new() ggmap(chicago) + geom_polygon(aes(x = long, y = lat, group=id, fill=Life.Expectancy),data = expectancy_map_data, color ="black",alpha = .7, size = .2) + labs(title="Life Expectancy by Community Areas (2010)") + scale_fill_gradient(high = "#56B1F7", low = "#132B43") dev.new() ggmap(chicago) + geom_polygon(aes(x = long, y = lat, group=id, fill=Crime.Count),data = crime_map_data, color ="black",alpha = .7, size = .2) + labs(title="Crime Incidents by Community Areas (2015)") + scale_fill_gradient(high = "#132B43", low = "#56B1F7") dev.new() ggmap(chicago) + geom_polygon(aes(x = long, y = lat, group=id, fill=Housing.Units),data = housing_map_data, color ="black",alpha = .7, size = .2) + labs(title="Affordable Housing Developments by Community Areas (2013)") + scale_fill_gradient(high = "#132B43", low = "#56B1F7") dev.new() ggmap(chicago) + geom_polygon(aes(x = long, y = lat, group=id, fill=Hardship.Index),data = hardship_map_data, color ="black",alpha = .7, size = .2) + labs(title="Hardship Index by Community Areas (2008-2012)") + scale_fill_gradient(high = "#132B43", low = "#56B1F7") #Classification Rules community_profile$class<-0 for (i in 1:nrow(community_profile)){ if (community_profile$Hardship.Index[i] < 25){ if((community_profile$Life.Expectancy[i] > 77.6) | ((community_profile$Grocery.Count[i] > 10) | (community_profile$Crime.Count[i] <= 1315))){ community_profile$class[i] = 1 } } if (community_profile$class[i] == 0){ if ((community_profile$Hardship.Index[i] <=50) & ((community_profile$Housing.Units[i] >350) | (community_profile$Crime.Count[i] > 3383) | (community_profile$Life.Expectancy[i] > 77.6))){ community_profile$class[i] = 2 } } if (community_profile$class[i] == 0){ if ((community_profile$Hardship.Index[i] <=50) & (community_profile$Crime.Count[i] <=3383)){ community_profile$class[i] = 3 } else{ if ((community_profile$Hardship.Index[i] <=75) & ((community_profile$Housing.Units[i] >350) | (community_profile$Crime.Count[i] > 3383) | (community_profile$Life.Expectancy[i] > 77.6))){ community_profile$class[i] = 3 } } } if (community_profile$class[i] == 0){ if ((community_profile$Hardship.Index[i] <=75) & (community_profile$Crime.Count[i] <=3383)){ community_profile$class[i] = 4 } else{ if((community_profile$Hardship.Index[i] > 75) & ((community_profile$Housing.Units[i] >350) | (community_profile$Crime.Count[i] > 3383) | (community_profile$Life.Expectancy[i] > 77.6))){ community_profile$class[i] = 4 } } } if (community_profile$class[i] == 0) community_profile$class[i] = 5 } #Plotting the Classifications on the Chicago Map community_profile<-merge(sfn, community_profile, by=c("id")) dev.new() ggmap(chicago) + geom_polygon(aes(x = long, y = lat, group=id, fill=class),data = community_profile, color ="black",alpha = .7, size = .2) + labs(title="Community Areas Classification") + scale_fill_gradient(high = "#132B43", low = "#56B1F7")

83d5723d47e4820dbb5d33bd01b7242c30a37b72

ca3fbf9bcf0349b35471e75344e95b5c92cdc2be

/plot1.R

56ae2d49388688c0349c65883868d023be8a9d1e

[]

no_license

AntoninPrunet/ExData_Plotting1

198642bdf789a278dd10129bb9f8cccf63c9fbe3

3f41d5a229f6a4a766f344017809fe8df22869b7

refs/heads/master

2022-05-22T00:28:47.652991

2020-04-28T16:17:01

259,387,287

0

null

2020-04-27T16:21:23

2020-04-27T16:21:22

null

UTF-8

R

false

657

r

plot1.R

if (!file.exists("household_power_consumption.txt")) { download.file("https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip", destfile = "Household Power Consumption") unzip("Household Power Consumption") } library(data.table) library(lubridate) library(dplyr) x<-fread("household_power_consumption.txt") x$Date<-dmy(x$Date) x<-filter(x,Date=="2007-02-01"|Date=="2007-02-02") x$Global_active_power<-as.numeric(x$Global_active_power) png("plot1.png",width=480, height=480) hist(x$Global_active_power,main="Global Active Power", col="red",xlab="Global Active Power (kilowatts)", ylim=c(0,1200)) dev.off()

0367e75f3c900e317a3aab1cc017b7f84281c9c2

23a0e63a84671fd7304cfae5cb8002e34d310a14

/references_cleane.R

0d10bc9112b8a1bc15c2efed835cff18b843cc25

[]

no_license

rxdavim/bibtex-cleaneR

35d1a91c7028ad4fc514e227bd89dd38d52dece2

1f3ecd91e433f2b70168f06574500c77cd3b387f

refs/heads/main

2023-08-10T00:54:35.740059

2021-09-06T09:40:24

null

0

null

UTF-8

R

false

9,749

r

references_cleane.R

library(fulltext) library(bib2df) library(bibtex) library(RefManageR) # library(readtext) library(doi2bib) library(dplyr) library(curl) library(stringr) library(RecordLinkage) library(rcrossref) library(aRxiv) library(foreach) library(doParallel) library(doSNOW) getAbstractFromDOI <- function(doi){ cat("\nGetting abstract for DOI: ", doi) tryCatch({ abstr <- cr_abstract(doi =doi) return(abstr) }, error = function(e){ cat("\nError getting abstract for doi: ", paste0(e)) return(NULL) }, warning = function(e){ cat("\nWarning getting abstract for doi: ", paste0(e)) return(NULL) }) return(NULL) } getCitationFromDOI <- function(doi, style="acm", locale="en-US"){ cat("\nGetting citation for DOI: ", doi, "\tStyle: ", style, "\tLocale: ", locale) tryCatch({ tmp_file <- tempfile(fileext = ".bib") cr_cn(dois = doi, format = "bibtex", style=style, locale="en-US") %>% write(file = tmp_file, append = FALSE) new_citation <- ReadBib(tmp_file, check=FALSE) return(new_citation) }, error = function(e){ cat("\nError getting citation for doi: ", paste0(e)) return(NULL) }, warning = function(e){ cat("\nWarning getting citation for doi: ", paste0(e)) return(NULL) }) return(NULL) } getTitleSimilarity <- function(old_title, new_title, sim_threshold=0.8){ sim <- -1 tryCatch({ clean_old <- tolower(gsub("[^0-9A-Za-z ]","" ,old_title, ignore.case = TRUE)) clean_new <- tolower(gsub("[^0-9A-Za-z ]","" ,new_title, ignore.case = TRUE)) sim <- levenshteinSim(clean_new, clean_old) if(sim >= sim_threshold){ cat("\n- Same title: YES! (Similarity: ", sim, " >= ", sim_threshold, "\n") return(TRUE) } else if(grepl(clean_new, clean_old, fixed = FALSE)){ cat("\n- Same title: YES! (New title included fully in old title)\n") return(TRUE) } }, error = function(e){ cat("\n- Same title: ERROR, ", paste0(e), "\n") return(FALSE) }) cat("\n- Same title: NO! (Similarity: ", sim, " >= ", sim_threshold, "\n") return(FALSE) } getReferenceListData <- function(bibentry){ list_data <- list() if(class(bibentry)[1] != "list"){ bibentry <- unclass(bibentry)[[1]] } fields <- names(bibentry) for(field in fields){ field_txt <- tolower(paste0(field)) data <- paste0(bibentry[[field]], collapse =" and ") data <- str_replace(data, "%2F", "/") list_data[[field]] <- data } list_data[["bibtype"]] <- attr(bibentry, "bibtype") list_data[["bibkey"]] <- attr(bibentry, "key") return(list_data) } getReferenceString <- function(bibentry, bibtype, bibkey){ if(tolower(class(bibentry)[1] == "bibentry")){ bibentry <- unclass(bibentry)[[1]] } fields <- names(bibentry) attr_data <- "" fields <- fields[fields != "r_updated"] fields <- c(fields, "r_updated") for(field in fields){ field_txt <- tolower(paste0(field)) data <- bibentry[[field]] if(!is.na(data) && str_length(data) > 0){ data <- paste0(bibentry[[field]], collapse =" and ") data <- str_replace(data, "%2F", "/") tabs_n <- 2 if(str_length(field_txt) <= 3){ tabs_n <- tabs_n+1 } if(str_length(field_txt) >= 9){ tabs_n <- tabs_n - 1 } attr_data <- paste0(attr_data, " ",field_txt,paste0(rep("\t", tabs_n), collapse=""), "= {",data, "},\n") } } ref_str <- paste0("@", bibtype, "{", bibkey, ",\n", attr_data, "}") return(ref_str) } mergeReferencesClass <- function(old_bib, new_bib, upd_bibkey=FALSE, upd_title=FALSE, upd_authors=TRUE, verbose=FALSE){ fields <- unique(RefManageR::fields(new_bib)[[1]]) # fields <- unique(c(RefManageR::fields(old_bib)[[1]], RefManageR::fields(new_bib)[[1]])) old_bib_unclassed <- unclass(old_bib)[[1]] new_bib <- unclass(new_bib)[[1]] for(field in fields){ if(field == "title" && !upd_title || field == "author" && !upd_authors){ cat("\n--Not Updating ", field) next } if(verbose){ cat("Updating field", field, "\tOld: ", paste0(old_bib_unclassed[[field]]), "\tNew: ", paste0(new_bib[[field]]), "\n") } if(!is.null(new_bib[[field]]) && !is.na(new_bib[[field]])){ old_bib_unclassed[[field]] <- str_replace_all(new_bib[[field]], "[{|}]", "") } } if(upd_bibkey){ cat("\n--Updating bibkey: ", attr(old_bib_unclassed, "key"), "=>", attr(new_bib, "key") ) attr(old_bib_unclassed, "key") <- attr(new_bib, "key") } return(old_bib_unclassed) } mergeReferencesDF <- function(old_bib, new_bib, upd_bibkey=FALSE, upd_title=FALSE, upd_authors=TRUE, verbose=TRUE){ for(field in colnames(new_bib)){ tryCatch({ if(field == "bibkey" && !upd_bibkey || field == "title" && !upd_title || field == "author" && !upd_authors){ cat("\n--Not Updating ", field) next } if(!is.null(new_bib[[field]]) && !is.na(new_bib[[field]])){ replacement <- str_replace_all(new_bib[[field]], "[{|}]", "") replacement <- str_replace_all(new_bib[[field]], "\textsinglequote", "\'") if(verbose){ cat("\nUpdating field", field, "\t\'", paste0(old_bib[[field]]), "\' => \'", paste0(replacement), "\'") } old_bib[[field]] <- replacement } else{ cat("\nNOT updating field (new is empty)", field, "\t\'", paste0(old_bib[[field]]), "\' => \'", paste0(replacement), "\'") } }, error = function(e){ cat("\n--Error updating DF ", field, ": ", paste0(e)) }) } return(old_bib) } cleanDoiUrl <- function(doi=NULL, url=NULL){ if(!is.null(doi)) doi_url <- doi else doi_url <- url doi_url <- str_replace_all(doi_url,"[{|}]", "") if(!is.null(doi_url) && length(doi_url) > 0){ if(grepl("doi", doi_url)){ last_slash_idx <- str_locate_all(doi_url,"/")[[1]][3] doi_url <- substr(doi_url, last_slash_idx+1, str_length(doi_url)) return(doi_url) } } if(!is.null(url)) stop("The URL is not a doi") return(doi_url) } updateBibEntry <- function(bib_data, index, out_file, style="acm", upd_bibkey=FALSE, upd_title=FALSE, upd_author=TRUE, upd_abstract=FALSE, is_cluster=FALSE, wd=NA){ if(is_cluster){ if(!is.na(wd)){ setwd(wd) } source("references_cleane.R") } bib_entry <- bib_data[index] bib_key <- bib_entry$bibkey new_entry <- NULL cat("\n----------- Exporting", index, "/", nrow(bib_data), ". ", bib_key, ": ", bib_entry$title, "-----------") field <- "doi" doi <- cleanDoiUrl(doi=bib_entry$doi) title <- str_replace_all(bib_entry$title, "[{|}]", "") if(is.na(doi) || is.null(doi) || length(doi) <= 0 ){ tryCatch({ field <- "url" doi <- cleanDoiUrl(url=bib_entry$url) }, error = function(e){ doi <- NULL url <- NULL }) } if(is.na(doi) || is.null(doi) || length(doi) <= 0 ){ if(is.null(title) || length(title) <= 0 ){ cat(paste0("\nNo DOI and no TITLE found, skipping\n")) } else{ cat(paste0("\nNo DOI FOUND, looking for it on CrossRef and ARXIV, query: ", title, "\n")) resCR <- cr_works(query = title, format = "text", limit=10) # https://docs.ropensci.org/rcrossref/reference/cr_works.html # resPlos <- ft_search(query = title, from="plos") resArxiv <- arxiv_search(query = noquote(paste0('ti:\"', title, '\"')), limit=10) dois <- list(c(resArxiv$doi, resCR$data$doi)) dois <- lapply(dois, function(z){ z[!is.na(z) & z != ""]})[[1]] cat("ARXIV DOIS: ", length(resArxiv$doi), "CR DOIS: ", length(resCR$data$doi), "Total: ", length(dois)) similarity_threshold <- 0.8 for(j in 1:length(dois)){ doi <- dois[[j]] cat("\nGetting data for DOI ", j, " of ", length(dois), ":\t", doi, "\n") ref <- getCitationFromDOI(doi, style) if(!is.null(ref)){ cat("\n- Current Title: ", bib_entry$title,"\n- New Title: ", ref$title) same_title <- getTitleSimilarity(bib_entry$title, ref$title, similarity_threshold) if(same_title){ cat("\nGetting new Reference") new_entry <- as.data.frame(getReferenceListData(ref)) break } } } } } else{ cat("\nFOUND DOI in field", field, ", looking for data") new_entry <- getCitationFromDOI(doi, style) } updated <- FALSE if(!is.null(new_entry) && length(new_entry) > 0){ tryCatch({ # bib_entry <- mergeReferencesClass(bib_entry, new_entry, upd_bibkey, upd_title, upd_author, verbose=FALSE) bib_entry <- mergeReferencesDF(bib_entry, new_entry, upd_bibkey, upd_title, upd_author, verbose=TRUE) if(upd_abstract){ tryCatch({ new_abstr <- getAbstractFromDOI(bib_entry$doi) if(!is.null(new_abstr)){ bib_entry[["abstract"]] <- new_abstr cat("\nAbstract updated") } }, error = function(e) { cat("\nError updating abstract: ", paste0(e)) }) } bib_entry[["r_updated"]] <- "YES" cat("\nUpdating OLD Reference") updated <- TRUE }, error = function(e) { cat("\nError merging references: ", paste0(e)) updated <- FALSE }) } if(!updated){ cat("\nNOT UPDATING") tryCatch({ bib_entry$r_updated <- "NO" }, error = function(e){ cat("\nError updating entry: ", paste0(e)) }) } # entry_str <- getReferenceString(bib_entry, tolower(attr(bibentry, "bibtype")), tolower(attr(bibentry, "key"))) # write(entry_str, file = out_file, append = TRUE) cat("\n-----------------------------------------\n") # entry_data <- getReferenceListData(bib_entry) ret_list <- list(bib_key=bib_entry) # names(ret_list) <- bib_key return(ret_list) }

567d4b5ef97a5c606892441a1e5de3ac20375808

ffdea92d4315e4363dd4ae673a1a6adf82a761b5

/data/genthat_extracted_code/tm/examples/writeCorpus.Rd.R

c54b336c27648d6585feae6e0bb24591d5a38b0b

[]

no_license

surayaaramli/typeRrh

d257ac8905c49123f4ccd4e377ee3dfc84d1636c

66e6996f31961bc8b9aafe1a6a6098327b66bf71

refs/heads/master

2023-05-05T04:05:31.617869

2019-04-25T22:10:06

null

0

null

UTF-8

R

false

265

r

writeCorpus.Rd.R

library(tm) ### Name: writeCorpus ### Title: Write a Corpus to Disk ### Aliases: writeCorpus ### ** Examples data("crude") ## Not run: ##D writeCorpus(crude, path = ".", ##D filenames = paste(seq_along(crude), ".txt", sep = "")) ## End(Not run)

07cdcb9c9abf05a7ad8f8010d18724bdfd077ad2

c46a6ff80331d7f47bc3c379b7b6f51644a3925b

/Chapter_04/scripts/f4.R

d9da9fd4c29f524c47df0532a8073ea26f51981d

[]

no_license

elmstedt/stats20_swirl

6bb215dc600decaf03ecf441cf0e28bdbd525536

6de97f3613f941c5c39a85b9df4f26fa3b62e766

refs/heads/master

2021-05-22T02:29:59.080370

2020-10-06T07:42:50

252,929,124

1

0

null

UTF-8

R

false

143

r

f4.R

f4 <- function(x) { # put the code below into an appropriate while loop. if (x %% 2 == 0) { x <- x / 2 } else { x <- 3 * x + 1 } x }

c74b103e292211a342f40f191b32404db43df2a6

663cb73a0c47ab0fc81bc0efab7d1bbce8b369b1

/ParseHTML.R

bb3c6c280661b8e7ad440e78229522d340391658

[]

no_license

wangguansong/nlxj-profiles

f48920eeee6b3fb5bf637207857cf67162ad5cdd

15661e472b259668c19ffbd4097ef3b3996e3669

refs/heads/master

2021-01-10T01:35:22.337574

2015-11-24T23:06:47

46,497,747

0

null

UTF-8

R

false

2,665

r

ParseHTML.R

######################################################################## ######################################################################## ParseHTML <- function(filename) { # Parse the HTML file, find the line contain the informations. # Return a list of strings??? corresponding to the <p> tags, which # contains informations of many people # The title of page is saved as the top of the list. # The link to the image is stripped from <img> tag. con <- file(filename, open="r", encoding="utf-8") line <- readLines(con,n=1) while (length(line)!= 0) { idx <- grep('<title>', line) if (length(idx) > 0) { title <- gsub(".*<title>(.*)</title>", "\\1", line) } idx <- grep('<div class=\"rich_media_content[ ]*\" id=\"js_content\">', line) line <- readLines(con, n=1) if (length(idx) == 0) next else break } close(con) # Split the line by "-----" rawinfo <- as.list(strsplit(line, "-{5,}")[[1]]) # Drop the last one in the list (ad of the platform) rawinfo[[length(rawinfo)]] <- NULL for (i in 1:length(rawinfo)) { # Split the line by "</p>" tags rawinfo[[i]] <- strsplit(rawinfo[[i]], "</p>")[[1]] # Delete "<p ...>" tags rawinfo[[i]] <- gsub("<p[^<]*?>", "", rawinfo[[i]]) # Replace "<br ...>" tags with spaces rawinfo[[i]] <- gsub("<br[^<]* />", " ", rawinfo[[i]]) # Strip the urls of the image from "<img ...>" tags rawinfo[[i]] <- gsub("<img.*src=\"(http://[^ ]*)\"[^<]*>", "\\1", rawinfo[[i]]) # Delete "<em ...>" and "</em>" tags rawinfo[[i]] <- gsub("<em[^<]*>", "", rawinfo[[i]]) rawinfo[[i]] <- gsub("</em>", "", rawinfo[[i]]) # Delete "<span ...>" and "</span>" tags rawinfo[[i]] <- gsub("<span[^<]*>", "", rawinfo[[i]]) rawinfo[[i]] <- gsub("</span>", "", rawinfo[[i]]) # Delete empty strings rawinfo[[i]] <- rawinfo[[i]][!rawinfo[[i]]==""] rawinfo[[i]] <- c(title, rawinfo[[i]]) # Delete   rawinfo[[i]] <- gsub(" ", "", rawinfo[[i]]) rawinfo[[i]] <- gsub("&", "&", rawinfo[[i]]) # Delete spaces in beginning and end rawinfo[[i]] <- gsub("^ +", "", rawinfo[[i]]) rawinfo[[i]] <- gsub(" +$", "", rawinfo[[i]]) } return(rawinfo) } ######################################################################## # Scan the HTML files, parse the individuals into a list htmllist <- list.files("html/", pattern="nlxj[0-9]+.html$", full.names=T) infolist <- list() for (i in 1:length(htmllist)) { print(htmllist[i]) rawinfo <- ParseHTML(htmllist[i]) infolist <- c(infolist, rawinfo) } remove(i, rawinfo)

24c88bcc998524b842a51396d7f075e9e4b745d5

071cd8492b051065de257750d5b16cd47409c996

/locus_discovery_config_files/BFP_AD_config.R

d771af6c5d8defa501a6a0a4127132f27c9dbd9b

[]

no_license

wpbone06/AD_and_Cardiometabolic_Trait_Bivariate_Scans

28734236a73b0c069241ccfebfe46648fbf6d93f

3dd4e32d8ddd23e8d55b64299ed31e59de915c90

refs/heads/master

2021-04-08T19:03:33.886867

2020-03-24T19:40:01

248,802,625

1

0

null

UTF-8

R

false

1,007

r

BFP_AD_config.R

trait1 = "AD" trait2 = "BFP" trait1GWASStr = c("Alzheimer") trait2GWASStr = c("body fat percentage","body fat %", "Body Fat Percentage","Body Fat") expPath="/project/voight_datasets/GWAS/01_alzD/AD_sumstats_Jansenetal.txt" outPath="/project/voight_GWAS/wbone/bivariate_scan_project/BodyFatPer_CHD_bivarscan/BFP_CHD_input_data/BFP_chr_pos_from_CHD.txt" trait1_BPcol = "BP" trait2_BPcol = "BP" trait1_CHRcol = "CHR" trait2_CHRcol = "CHR" trait1_Pcol = "P" trait2_Pcol = "P.value" exp_dat = read_exposure_data("/project/voight_datasets/GWAS/01_alzD/AD_sumstats_Jansenetal.txt",sep="\t",snp_col="SNP",effect_allele_col="A1",other_allele_col="A2",eaf_col="MAF",se_col="SE",pval_col=trait1_Pcol,beta_col="BETA") out_dat = read_outcome_data("/project/voight_GWAS/wbone/bivariate_scan_project/BodyFatPer_CHD_bivarscan/BFP_CHD_input_data/BFP_chr_pos_from_CHD.txt",sep="\t",snp_col="SNPID",effect_allele_col="Allele1",other_allele_col="Allele2",eaf_col="Freq1",se_col="StdErr",pval_col=trait2_Pcol,beta_col="Effect")

73c75396ffdcd5cda4920e453749f47e44cbe7fc

7967712d2e16907605f7d0acde096950eae5c3d4

/components/functions.R

b98f8550617006082ffbec3c08319e69da3094e2

[ "MIT" ]

permissive

pablo-vivas/ProbabilityDistributionsViewer

8efdff6f7347ee940f8efa202beec8b5ba298153

ba24761a890e7d4f06f180316ca088d8210c4940

refs/heads/master

2020-08-07T02:17:51.092184

2018-07-24T12:31:36

null

0

null

UTF-8

R

false

4,797

r

functions.R

library(shiny) library(shinydashboard) library(htmltools) # Variables ---- boxcolor <- "blue" mean.icon <- icon("star", lib = "glyphicon") variance.icon <- icon("resize-horizontal", lib = "glyphicon") # Functions ---- ## ui ---- ### Custom selectInput selectLanguageInput <- function(inputId, choices, selected = NULL, selectize = TRUE, width = NULL) { selected <- shiny::restoreInput(id = inputId, default = selected) choices <- shiny:::choicesWithNames(choices) if (is.null(selected)) { selected <- shiny:::firstChoice(choices) } else { selected <- as.character(selected) } selectTag <- htmltools::tags$select( id = inputId, shiny:::selectOptions(choices, selected) ) res <- div( class = "form-group shiny-input-container", style = paste0("width: ", htmltools::validateCssUnit(width), ";"), NULL, # For selectizeIt function. div(selectTag) ) shiny:::selectizeIt(inputId, res, NULL, nonempty = TRUE) } ### Panel for Distributions distPanel <- function(name, en) { if (missing(en)) { wiki <- paste0("http://ja.wikipedia.org/wiki/", name) } else { wiki <- paste0("http://en.wikipedia.org/wiki/", en) } box( width = 5, status = "primary", title = name, "参考 : ", a( target = "_blank", href = wiki, "Wikipedia", img(src = "img/external.png") ) ) } distBox <- function(name, wiki, i18n) { box( width = 5, status = "primary", title = i18n()$t(name), paste0(i18n()$t("Reference"), " : "), a( target = "_blank", href = i18n()$t(wiki), "Wikipedia", img(src = "img/external.png") ) ) } ### Formula Box formulaBox <- function(f_str, c_or_d, i18n) { if (c_or_d == "c") { f_title <- "Probability density function (PDF)" } else { f_title <- "Probability mass function" } f_text <- paste0("$$", f_str, "$$") box( width = 7, status = "primary", title = i18n()$t(f_title), helpText(f_text) ) } ### Sliders createSlider <- function(name, label, min, max, value, step = 1L) { sliderInput( inputId = name, label = label, min = min, max = max, value = value, step = step ) } ### Parameters Box createParamBox <- function(ns, c_or_d, rangeArgs, paramArgs = NULL, p_or_c = NULL, i18n = NULL) { # Selector choices <- c("p", "c") if (c_or_d == "c") { pdf <- i18n()$t("Probability density function (PDF)") } else { pdf <- i18n()$t("Probability mass function (PMF)") } cdf <- i18n()$t("Cumulative distribution function (CDF)") names(choices) <- c(pdf, cdf) pcButton <- radioButtons(ns("p_or_c"), "", choices, p_or_c) # Range Slider rangeArgs$name <- ns("range") rangeArgs$label <- i18n()$t("Range") rangeSlider <- do.call(createSlider, rangeArgs) # Parameter Sliders if (is.null(paramArgs)) { paramSliders <- NULL } else { paramSliders <- lapply(paramArgs, function(x) { x$name <- ns(x$name) label_name <- x$label_name label_symbol <- paste0("\$", x$label_symbol, "\$") if (is.na(label_name) || label_name == "") { x$label <- label_symbol } else { label_name <- i18n()$t(label_name) x$label <- paste(label_name, label_symbol) } # Remove "label_name" and "label_symbol" x <- x[!(names(x) %in% c("label_name", "label_symbol"))] do.call(createSlider, x) }) } # Box paramBox <- do.call( box, list( width = 5, title = i18n()$t("Parameters"), status = "primary", solidHeader = TRUE, withMathJax(), pcButton, rangeSlider, paramSliders ) ) return(paramBox) } ### Dynamic Value Box valueBoxRow <- function(ns, width = 6L) { fluidRow( valueBoxOutput(ns("meanBox"), width = width), valueBoxOutput(ns("varianceBox"), width = width) ) } valueBoxRowWide <- function(ns) { valueBoxRow(ns, width = 12L) } ## server ---- createFormula <- function(f_str, value) { paste0("\$", f_str, "\\!=\\!", value, "\$") } createBox <- function(f_str, value, param = "Mean", i18n = NULL) { if (param == "Variance") { icon <- variance.icon } else { icon <- mean.icon } param_str <- i18n()$t(param) if (is.null(f_str) | is.null(value)) { formula <- i18n()$t("Undefined") } else { value <- round(value, digits = 3) formula <- createFormula(f_str, value) formula <- withMathJax(formula) } box <- valueBox( formula, param_str, icon = icon, color = boxcolor ) return(box) } meanBox <- function(f_str, value, i18n) { f <- createBox(f_str, value, param = "Mean", i18n) return(f) } varianceBox <- function(f_str, value, i18n) { f <- createBox(f_str, value, param = "Variance", i18n) return(f) }

6a367ae7c8e2e213fbbec7b85acd42ce9d25e96a

5d9470e54c69e914800f770ff3ca95b72e0d02b0

/R/ziaq.R

b981d1d0b3aefd66e171db06132ee3f08391474d

[]

no_license

gefeizhang/ZIAQ

fffbd9da58490e455c58d65c2bf0bf49fadf6703

017da9ab92fac73faf4ae4e50934270893d296c1

refs/heads/master

2020-07-04T07:35:38.288467

2020-02-20T18:38:50

202,207,402

4

0

null

UTF-8

R

false

2,573

r

ziaq.R

#' Zero-inflation adjusted quantile regression for single cell RNA sequencing data #' #' This function fits the zero-inflation adjusted quantile regression model for the #' differential expression analysis in single cell RNA sequencing data #' @param Y_matrix a matrix for expression values with row representing indiviudal genes #' and column representing cells #' @param colDat a dataframe including the individual cell information #' @param formula a formula with the predictors included in \code{colDat}. The default #' is ~condition. #' @param group the variable name in \code{colDat} for the factor used in group comparsion. The default #' is condition. #' @param probs the quantile levels for the quantile regressions. #' The default is \code{c(0.25, 0.5, 0.75)} #' @param log_i TRUE or FALSE indicate whether to apply log transformation. #' The default is TRUE. #' @param parallel TRUE or FALSE indicate whether to apply parallel computing. #' The default is TRUE. #' @param no.core The number of cores used in parallel computing. The default #' is all available cores \code{detectCores()} #' @return \item{pvalue}{The p-values of all genes for testing the signficance #' of the specified \code{group} variable.} #' \item{res}{The full results from function \code{ziaq_fit} for all genes} #' @keywords ziaq_fit #' @export #' @import quantreg #' @import metap #' @import parallel #' @import stats #' #' @examples #' #Use simuluated data #'ymatrix = matrix(round(100* runif(100*150)), ncol = 100) #'rownames(ymatrix) = paste0('gene', 1:150) #' #'colDat = data.frame(condition = rep(c(1, 0), e = 50)) #' #'res = ziaq(ymatrix, colDat, formula = ~ condition, #' group = 'condition', probs = c(0.25, 0.5, 0.75), #' log_i = TRUE, parallel = FALSE, no.core = 1) #' #'print(res$pvalue) ziaq <-function (Y_matrix, colDat, formula = ~ condition, group = 'condition', probs = c(0.25, 0.5, 0.75), log_i = TRUE, parallel = FALSE, no.core = detectCores() ) { #require(parallel) if(parallel ){ cl <- makeCluster(getOption("cl.cores", no.core)) res = parApply(cl = cl, Y_matrix, 1, ziaq_fit, colDat = colDat, formula = formula, group = group, probs =probs,log_i = log_i ) }else{ res = apply(Y_matrix, 1, ziaq_fit, colDat = colDat, formula = formula, group = group, probs =probs,log_i = log_i ) } pval = sapply(res, function(x) return(x$pvalue)) return(list(pvalue = pval, full_results = res)) }

b0a38d25523a09e2481c70e89072d7e2ef84b452

8de7c88fd3ce03591c538694b3361f6b6c7fbf61

/R/transformPhylo.sim.R

21322e57e625cbe770b000a1a52c691cdae0c592

[]

no_license

ghthomas/motmot

b093742a4ed264076ca41bbc4fddf29d3cc00a93

c24372f5d5efbfbee6196c5459d0def31d547e54

refs/heads/master

2021-01-01T17:57:38.949773

2018-07-30T10:12:35

10,839,257

3

0

null

UTF-8

R

false

3,521

r

transformPhylo.sim.R

transformPhylo.sim <- function(phy, n=1, x=NULL, model=NULL, kappa=NULL, lambda=NULL, delta=NULL, alpha=NULL, psi=NULL, nodeIDs=NULL, rateType=NULL, cladeRates=NULL, branchRates=NULL, rate=NULL, group.means=NULL) { switch(model, "bm" = { transformPhy <- phy phyMat <- VCV.array(transformPhy) attr(phyMat, "class") <- "matrix" ydum <- as.matrix(t(rmvnorm(n, sigma = phyMat))) rownames(ydum) <- rownames(phyMat) }, "kappa" = { transformPhy <- transformPhylo(phy=phy, model="kappa", kappa=kappa) phyMat <- VCV.array(transformPhy) attr(phyMat, "class") <- "matrix" ydum <- as.matrix(t(rmvnorm(n, sigma = phyMat))) rownames(ydum) <- rownames(phyMat) }, "lambda" = { transformPhy <- transformPhylo(phy=phy, model="lambda", lambda=lambda) phyMat <- VCV.array(transformPhy) attr(phyMat, "class") <- "matrix" ydum <- as.matrix(t(rmvnorm(n, sigma = phyMat))) rownames(ydum) <- rownames(phyMat) }, "delta" = { transformPhy <- transformPhylo(phy=phy, model="delta", delta=delta) phyMat <- VCV.array(transformPhy) attr(phyMat, "class") <- "matrix" ydum <- as.matrix(t(rmvnorm(n, sigma = phyMat))) rownames(ydum) <- rownames(phyMat) }, "free" = { transformPhy <- transformPhylo(phy=phy, model="free", branchRates=branchRates) phyMat <- VCV.array(transformPhy) attr(phyMat, "class") <- "matrix" ydum <- as.matrix(t(rmvnorm(n, sigma = phyMat))) rownames(ydum) <- rownames(phyMat) }, "clade" = { transformPhy <- transformPhylo(phy=phy, model="clade", nodeIDs=nodeIDs, cladeRates=cladeRates, rateType=rateType) phyMat <- VCV.array(transformPhy) attr(phyMat, "class") <- "matrix" ydum <- as.matrix(t(rmvnorm(n, sigma = phyMat))) rownames(ydum) <- rownames(phyMat) }, "OU" = { transformPhy <- transformPhylo(phy=phy, model="OU", alpha=alpha) phyMat <- VCV.array(transformPhy) attr(phyMat, "class") <- "matrix" ydum <- as.matrix(t(rmvnorm(n, sigma = phyMat))) rownames(ydum) <- rownames(phyMat) }, "psi" = { transformPhy <- transformPhylo(phy=phy, model="psi", psi=psi) phyMat <- VCV.array(transformPhy) attr(phyMat, "class") <- "matrix" ydum <- as.matrix(t(rmvnorm(n, sigma = phyMat))) rownames(ydum) <- rownames(phyMat) }, "mixedRate" = { x <- as.matrix(x) dat <- data.frame(x=x, y=rep(0, length(x[,1]))) ntip <- Ntip(phy) rateData <- as.rateData(y="y", x="x", rateMatrix = NULL, phy=phy, data=dat) V <- transformRateMatrix(rateData, rate=rate) # expect.sd <- sqrt(mean(V[upper.tri(V)])) # expect.sd <- sqrt((1/ntip * sum(diag(V))) - ((1/ntip^2)*t(matrix(rep(1,ntip))) %*% V %*% matrix(rep(1,ntip)))) if (is.null(group.means)) {ydum <- as.matrix(t(rmvnorm(n, sigma = (V) ))) rownames(ydum) <- rownames(V)} else { x.means <- unique(rateData$x) n.means <- length(x.means) samp.means <- rep(NA, length(rateData$x)) ydum <- vector(mode="list", length=length(group.means)) for (i in 1:n.means) { samp.means[which(rateData$x == (i-1))] <- rep(0+(expect.sd*group.means[i]), length(which(rateData$x == (i-1)))) } ydum <- as.matrix(t(rmvnorm(n, mean=samp.means, sigma = (V) ))) rownames(ydum) <- rownames(V) } } ) return(ydum) }

8a0036224b05274ea9e07024c73ec1308b2abb25

ef10085faba12cbca8e6ef55e3575031dd82da71

/app.R

c9930c6aa221793647d6816a6b9d2d138417cd76

[]

no_license

RforOperations2018/project2-clarissp

eb3e75bb4c0c88ac90da665b46888f8f590ecb5b

7ee468dd088ae6f802ff3671432df0e1a09d7a14

refs/heads/master

2020-04-01T05:39:59.319277

2018-10-21T18:36:15

152,913,980

0

null

UTF-8

R

false

12,174

r

app.R

#Project 2 require(shiny) require(rgdal) require(leaflet) require(leaflet.extras) require(dplyr) require(readxl) require(stringr) require(shinydashboard) require(reshape2) require(dplyr) require(ggplot2) require(plotly) require(shinythemes) require(RSocrata) require(httr) require(jsonlite) #Shapefile for County Boundaries pacounty <- readOGR("PA_Counties_clip.shp") #Didn't end up using the subset of the counties because I couldn't get the over function to only display markers in my subsetted counties #Subsetting counties to Southwest counties #swcounty <- c("Armstrong", "Allegheny", "Beaver", "Cambria", "Fayette", "Greene", "Indiana", "Somerset", "Washington", "Westmoreland") #pa_swcounty <- pacounty[pacounty$NAME %in% swcounty,] #Transofrming projection of counties to match the following two layers proj4string(pa_swcounty) <- CRS("+proj=longlat +datum=NAD83 +no_defs +ellps=GRS80 +towgs84=0,0,0") pa_swcounty <- spTransform(pa_swcounty, CRS=CRS("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0")) #API for the Permit data permits <- readOGR("http://data-padep-1.opendata.arcgis.com/datasets/cea4b401782a4178b139c6b5c6a929f2_48.geojson") permitcounty <- c("Armstrong", "Beaver", "Cambria", "Greene", "Indiana", "Somerset", "Washington", "Westmoreland") sw_permits <- permits[permits$COUNTY %in% permitcounty,] #CSV for Permit data permitdata <- read.csv("Active_Underground_Permit_Boundaries.csv") sw_permitdata <- filter(permitdata, COUNTY == "Armstrong" | COUNTY == "Beaver" | COUNTY == "Cambria" | COUNTY == "Greene" | COUNTY == "Indiana" | COUNTY == "Somerset" | COUNTY == "Washington" | COUNTY == "Westmoreland") #API for Environmental Good Samaritan Act points surfacemine <- readOGR("http://data-padep-1.opendata.arcgis.com/datasets/67ed627a525548d5900c1b6964b8e619_25.geojson") #Another attempt to get the map API reactive function to work #getEsri <- function(url) { # Make Call #g <- GET(URLencode(url)) #c <- content(g) #readOGR(c) #} #Creating county column for Environmental Good Samaritan Act points (goodact) surfacemine$county <- over(surfacemine, pa_swcounty, fn = NULL) #Header of the shiny dashboard header <- dashboardHeader(title = "Permits in PA") #Sidebar of the shiny dashboard sidebar <- dashboardSidebar( sidebarMenu( id = "tabs", #Pages in the sidebar menuItem("Graphs", icon = icon("pie-chart"), tabName = "activepermit"), menuItem("Dataset", icon = icon("database"),tabName = "permittable"), menuItem("Map", icon= icon("map-o"), tabName = "permit"), #Select input for Type of Permits selectInput("type", "Permit Type(s):", choices = sort(unique(sw_permitdata$TYPE)), multiple = TRUE, selected = c("Room and Pillar") ), #Select input for Counties of Permits selectInput("counties", "Select a County:", choices = sort(unique(sw_permitdata$COUNTY)), multiple = TRUE, selected = c("Cambria","Somerset")), #Select input for Status of Permits selectInput("operator", "Operator(s) of Permit:", choices = sort(unique(sw_permitdata$OPERATOR)), multiple = TRUE, selected = "Rosebud Mining"), #Reset button for filters actionButton("reset", "Reset Filters", icon = icon("refresh")) ) ) #Body of the shiny dashboard body <- dashboardBody( tabItems( #Content for graphs page tabItem("activepermit", fluidPage( box(tabPanel("Bar Plot", plotlyOutput("permitbar")), width = 12), box(tabPanel("Pie Chart", plotlyOutput("permitpie")), width = 12) ) ), #Contents for dataset page tabItem("permittable", fluidPage( inputPanel( downloadButton("downloadData", "Download Active Permit Data") ), box(title = "Abandoned Mine Land Dataset", DT::dataTableOutput("permittable"), width = 12) ) ), #Content for map page tabItem("permit", fluidRow( box( selectInput("facility", "Type of Facility for Markers:", choices = sort(unique(surfacemine$PRIMARY_FACILITY_KIND)), multiple = TRUE, selected = "GROWING GREENER") ), box(title = "Active Permits in Southwest PA", leafletOutput("permitmap"), width = 12) )) ) ) ui <- dashboardPage(header, sidebar, body, skin = "black") #Defines server logic server <- function(input, output, session = session){ #Reactive function for permit types permitInput <- reactive({ if(length(input$type) > 0 ){ sw_permitdata <- subset(sw_permitdata, TYPE %in% input$type) } if(length(input$counties) > 0 ){ sw_permitdata <- subset(sw_permitdata, COUNTY %in% input$counties) } if(length(input$operator) > 0 ){ sw_permitdata <- subset(sw_permitdata, OPERATOR %in% input$operator) } return(sw_permitdata) }) #Icons for the markers icons <- awesomeIconList( makeAwesomeIcon(icon = "leaf", library = "fa", markerColor = "green") ) #Map for permits and reclamation sites output$permitmap <- renderLeaflet({ #facilitymarker <- facilityInput() leaflet() %>% addPolygons(data = pacounty, weight = 2, color = "black") %>% addPolygons(data = permits, weight = 1.5, color = "red") %>% #Data for the markers should be facilitymarker however I wasn't able to get the reactive function to work so I changed the data source so that at least you can see my map in the dashboard addAwesomeMarkers(data = surfacemine, icon = ~icons, popup = ~SITE_NAME) %>% addProviderTiles("Esri.WorldGrayCanvas", group = "Gray Canvas", options = providerTileOptions(noWrap = TRUE)) %>% addProviderTiles("CartoDB.DarkMatterNoLabels", group = "Dark Matter", options = providerTileOptions(noWrap = TRUE)) %>% # This basemap doesn't really make sense since your county lines are black! addProviderTiles("Esri.WorldTopoMap", group = "Topography", options = providerTileOptions(noWrap = TRUE)) %>% addLayersControl( baseGroups = c("Gray Canvas", "Dark Matter", "Topography"), options = layersControlOptions(collapsed = TRUE) ) }) #Pie chart for active permit data output$permitpie <- renderPlotly({ permit <- permitInput() plot_ly(data = permit, labels = permit$COUNTY, type = 'pie', textposition = 'inside', textinfo = 'label+percent', insidetextfont = list(color = '#FFFFFF'), hoverinfo = 'label+percent', showlegend = TRUE) }) #Bar plot for Active Permits output$permitbar <- renderPlotly({ permit <- permitInput() ggplot(data = permit, mapping = aes(x = COUNTY, fill = STATUS)) + geom_bar(stat = "count") + labs(title = "Active Underground Permits in Pennsylvania", x= "County", y= "Count of Permits", fill = "Status" ) + scale_fill_brewer(palette = "Pastel1") + theme_bw() + theme(plot.title = element_text(face = "bold", family = "American Typewriter"), axis.title.x = element_text( family = "American Typewriter" ), axis.text.x = element_text( family = "American Typewriter", angle = 45, vjust = 0.5 ), axis.title.y = element_text( family = "American Typewriter" ), axis.text.y = element_text( family = "American Typewriter" ), legend.position = "bottom", legend.box = "horizontal" ) }) #Data table for permit table output$permittable <- DT::renderDataTable({ subset(permitInput(), select = c("MINE", "OPERATOR", "TYPE", "STATUS", "COAL_SEAM", "COUNTY")) }) #Download button for the data table output$downloadData <- downloadHandler( filename = function() { paste("sw_permitdata", Sys.Date(), ".csv", sep="") }, content = function(file) { write.csv(permitInput(), file) } ) #Allows for the reset button to work observeEvent(input$reset, { updateSelectInput(session, "type", selected = c("Room and Pillar")) updateSelectInput(session, "counties", selected = c("Cambria","Somerset")) updateSelectInput(session, "coal", selected = c("Pittsburgh")) showNotification("You have successfully reset the filters", type = "message") }) #Attempt at reactive function for the map. All the commented out urls are my attempts to fix it. I just left them there so that you can see that I tried a bunch of different things facilityInput <- reactive({ filter <- ifelse(length(input$facility) > 0, paste0("%20IN%20(%27", paste(input$facility, collapse = "%27AND%27"),"%27"),"") #url <- URLencode(paste0('http://www.depgis.state.pa.us/arcgis/rest/services/emappa/eMapPA_External_Extraction/MapServer/25/query?where=PRIMARY_FACILITY_KIND', gsub(" ", "+", input$facility), "&outFields=*&returnGeometry=true&returnTrueCurves=false&maxAllowableOffset=&geometryPrecision=&outSR=&returnIdsOnly=false&returnCountOnly=false&orderByFields=&groupByFieldsForStatistics=&outStatistics=&returnZ=false&returnM=false&gdbVersion=&returnDistinctValues=false&resultOffset=&resultRecordCount=&queryByDistance=&returnExtentsOnly=false&datumTransformation=&parameterValues=&rangeValues=&f=json")) #marker <- getEsri(url) %>% #spTransform("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0") #return(marker) url <- paste0('http://www.depgis.state.pa.us/arcgis/rest/services/emappa/eMapPA_External_Extraction/MapServer/25/query?where=PRIMARY_FACILITY_KIND',filter, '&outFields=*&returnGeometry=true&returnTrueCurves=false&maxAllowableOffset=&geometryPrecision=&outSR=&returnIdsOnly=false&returnCountOnly=false&orderByFields=&groupByFieldsForStatistics=&outStatistics=&returnZ=false&returnM=false&gdbVersion=&returnDistinctValues=false&resultOffset=&resultRecordCount=&queryByDistance=&returnExtentsOnly=false&datumTransformation=&parameterValues=&rangeValues=&f=geojson') #url <- paste0('http://www.depgis.state.pa.us/arcgis/rest/services/emappa/eMapPA_External_Extraction/MapServer/25/query?where=1%', filter,'&outFields=*&returnGeometry=true&returnTrueCurves=false&maxAllowableOffset=&geometryPrecision=&outSR=&returnIdsOnly=false&returnCountOnly=false&orderByFields=&groupByFieldsForStatistics=&outStatistics=&returnZ=false&returnM=false&gdbVersion=&returnDistinctValues=false&resultOffset=&resultRecordCount=&queryByDistance=&returnExtentsOnly=false&datumTransformation=&parameterValues=&rangeValues=&f=geojson') print(url) facilityfilter <- readOGR(url) return(facilityfilter) }) } #http://www.depgis.state.pa.us/arcgis/rest/services/emappa/eMapPA_External_Extraction/MapServer/25/query?where=1%3D1&text=&objectIds=&time=&geometry=%7B%22xmin%22%3A-10315563.459876563%2C%22ymin%22%3A4644636.53163017%2C%22xmax%22%3A-6971902.094570702%2C%22ymax%22%3A5378432.0031676665%2C%22spatialReference%22%3A%7B%22wkid%22%3A102100%7D%7D&geometryType=esriGeometryEnvelope&inSR=&spatialRel=esriSpatialRelIntersects&relationParam=&outFields=*&returnGeometry=true&returnTrueCurves=false&maxAllowableOffset=&geometryPrecision=&outSR=&returnIdsOnly=false&returnCountOnly=false&orderByFields=&groupByFieldsForStatistics=&outStatistics=&returnZ=false&returnM=false&gdbVersion=&returnDistinctValues=false&resultOffset=&resultRecordCount=&queryByDistance=&returnExtentsOnly=false&datumTransformation=&parameterValues=&rangeValues=&f=geojson #Runs the application shinyApp(ui = ui, server = server)