pierreqi/r_code_50k · Datasets at Hugging Face

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DrawNicerTree_PRP.R

# Draw a nicer Classification and regression decision trees with prp library(rattle) # Popular decision tree algorithm library(rpart) # Fancy tree plot library(rpart.plot) # Enhanced tree plot library(RColorBrewer) # Color selection for fancy tree plot library(party) # Alternative decision tree algorithm library(partykit) # Convert rpart object to binary tree library(caret) # Just a data source for this script but probably best packages in R # Get some data data(segmentationData) data <- segmentationData[, -c(1, 2)] # rpart decision tree form <- as.formula(Class ~ .) tree.1 <- rpart(form, data = data, control = rpart.control(minsplit = 20, cp =0)) plot(tree.1) text(tree.1) # Plot the tree with shorten variable names prp(tree.1, varlen = 3) # Interactively prue the tree new.tree.1 <- prp(tree.1, snip = TRUE)$obj prp(new.tree.1) #------------------------ tree.2 <- rpart(form, data) prp(tree.2) fancyRpartPlot(tree.2)

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# Chapter 7 Lab: Non-linear Modeling library(ISLR) attach(Wage) # Polynomial Regression and Step Functions fit=lm(wage~poly(age,4),data=Wage) coef(summary(fit)) fit2=lm(wage~poly(age,4,raw=T),data=Wage) coef(summary(fit2)) fit2a=lm(wage~age+I(age^2)+I(age^3)+I(age^4),data=Wage) coef(fit2a) fit2b=lm(wage~cbind(age,age^2,age^3,age^4),data=Wage) agelims=range(age) age.grid=seq(from=agelims[1],to=agelims[2]) preds=predict(fit,newdata=list(age=age.grid),se=TRUE) se.bands=cbind(preds$fit+2*preds$se.fit,preds$fit-2*preds$se.fit) par(mfrow=c(1,2),mar=c(4.5,4.5,1,1),oma=c(0,0,4,0)) plot(age,wage,xlim=agelims,cex=.5,col="darkgrey") title("Degree-4 Polynomial",outer=T) lines(age.grid,preds$fit,lwd=2,col="blue") matlines(age.grid,se.bands,lwd=1,col="blue",lty=3) preds2=predict(fit2,newdata=list(age=age.grid),se=TRUE) max(abs(preds$fit-preds2$fit)) fit.1=lm(wage~age,data=Wage) fit.2=lm(wage~poly(age,2),data=Wage) fit.3=lm(wage~poly(age,3),data=Wage) fit.4=lm(wage~poly(age,4),data=Wage) fit.5=lm(wage~poly(age,5),data=Wage) anova(fit.1,fit.2,fit.3,fit.4,fit.5) coef(summary(fit.5)) (-11.983)^2 fit.1=lm(wage~education+age,data=Wage) fit.2=lm(wage~education+poly(age,2),data=Wage) fit.3=lm(wage~education+poly(age,3),data=Wage) anova(fit.1,fit.2,fit.3) fit=glm(I(wage>250)~poly(age,4),data=Wage,family=binomial) preds=predict(fit,newdata=list(age=age.grid),se=T) pfit=exp(preds$fit)/(1+exp(preds$fit)) se.bands.logit = cbind(preds$fit+2*preds$se.fit, preds$fit-2*preds$se.fit) se.bands = exp(se.bands.logit)/(1+exp(se.bands.logit)) preds=predict(fit,newdata=list(age=age.grid),type="response",se=T) plot(age,I(wage>250),xlim=agelims,type="n",ylim=c(0,.2)) points(jitter(age), I((wage>250)/5),cex=.5,pch="|",col="darkgrey") lines(age.grid,pfit,lwd=2, col="blue") matlines(age.grid,se.bands,lwd=1,col="blue",lty=3) table(cut(age,4)) fit=lm(wage~cut(age,4),data=Wage) coef(summary(fit)) # Splines library(splines) fit=lm(wage~bs(age,knots=c(25,40,60)),data=Wage) pred=predict(fit,newdata=list(age=age.grid),se=T) plot(age,wage,col="gray") lines(age.grid,pred$fit,lwd=2) lines(age.grid,pred$fit+2*pred$se,lty="dashed") lines(age.grid,pred$fit-2*pred$se,lty="dashed") dim(bs(age,knots=c(25,40,60))) dim(bs(age,df=6)) attr(bs(age,df=6),"knots") fit2=lm(wage~ns(age,df=4),data=Wage) pred2=predict(fit2,newdata=list(age=age.grid),se=T) lines(age.grid, pred2$fit,col="red",lwd=2) plot(age,wage,xlim=agelims,cex=.5,col="darkgrey") title("Smoothing Spline") fit=smooth.spline(age,wage,df=16) fit2=smooth.spline(age,wage,cv=TRUE) fit2$df lines(fit,col="red",lwd=2) lines(fit2,col="blue",lwd=2) legend("topright",legend=c("16 DF","6.8 DF"),col=c("red","blue"),lty=1,lwd=2,cex=.8) plot(age,wage,xlim=agelims,cex=.5,col="darkgrey") title("Local Regression") fit=loess(wage~age,span=.2,data=Wage) fit2=loess(wage~age,span=.5,data=Wage) lines(age.grid,predict(fit,data.frame(age=age.grid)),col="red",lwd=2) lines(age.grid,predict(fit2,data.frame(age=age.grid)),col="blue",lwd=2) legend("topright",legend=c("Span=0.2","Span=0.5"),col=c("red","blue"),lty=1,lwd=2,cex=.8) # GAMs gam1=lm(wage~ns(year,4)+ns(age,5)+education,data=Wage) library(gam) gam.m3=gam(wage~s(year,4)+s(age,5)+education,data=Wage) par(mfrow=c(1,3)) plot(gam.m3, se=TRUE,col="blue") plot.gam(gam1, se=TRUE, col="red") gam.m1=gam(wage~s(age,5)+education,data=Wage) gam.m2=gam(wage~year+s(age,5)+education,data=Wage) anova(gam.m1,gam.m2,gam.m3,test="F") summary(gam.m3) preds=predict(gam.m2,newdata=Wage) gam.lo=gam(wage~s(year,df=4)+lo(age,span=0.7)+education,data=Wage) plot.gam(gam.lo, se=TRUE, col="green") gam.lo.i=gam(wage~lo(year,age,span=0.5)+education,data=Wage) library(akima) plot(gam.lo.i) gam.lr=gam(I(wage>250)~year+s(age,df=5)+education,family=binomial,data=Wage) par(mfrow=c(1,3)) plot(gam.lr,se=T,col="green") table(education,I(wage>250)) gam.lr.s=gam(I(wage>250)~year+s(age,df=5)+education,family=binomial,data=Wage,subset=(education!="1. < HS Grad")) plot(gam.lr.s,se=T,col="green")

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cibor.R

setwd("c:/users/kro/documents/github/ciborfixings/") library(xlsx) library(lubridate) library(stringr) library(dplyr) # ### Build sequence of dates to try # try_dates <- seq.Date(from = as.Date(ymd("2004-02-01")), to = as.Date(ymd("2014-11-05")), by = 1) # # ### Define the base url # base_url <- "http://www.finansraadet.dk/Historical%20Rates/cibor/" # # # # ####################################################### # ## Download files # ######################################################## # # ### Loop over try_dates and try to download a file # for(i in seq_along(try_dates)){ # # try_url_xlsx <- paste0(base_url, as.character(year(try_dates[i])), "/", as.character(try_dates[i]), ".xlsx") # try_url_xls <- paste0(base_url, as.character(year(try_dates[i])), "/", as.character(try_dates[i]), ".xls") # # try(download.file(url = try_url_xlsx, destfile = paste0("./data/", try_dates[i], ".xlsx"), mode = "wb"), silent = TRUE) # try(download.file(url = try_url_xls, destfile = paste0("./data/", try_dates[i], ".xls"), mode = "wb"), silent = TRUE) # # Sys.sleep(time = 0.5) # } # # ########################################################### # ## Remove files with file size == 0 # ############################################################ # # xl_file_names <- dir(path = "./data/") # # for(i in seq_along(xl_file_names)){ # # if(file.info(paste0("./data/", xl_file_names[i])$size) == 0){ # file.remove(paste0("./data/", xl_file_names[i])) # } # } # ########################################################### ## Read first column to figure out which rows to download ############################################################ ### only read up until 2011-05-07 as they move to a very old ### xls file format. ## minus 59 because of NAs xl_file_names <- dir(path = "./data/") xl_file_names <- xl_file_names[1:(1818-59)] ## Create list for data first_col <- vector(mode = "list", length = length(xl_file_names)) ## Bad names was used in the exploratory phase # bad_names <- c("danmarks nationalbank", "handelsafdelingen", "ciborfixing den", "stiller", "nordic operations", "nasdaq omx", "mail", # "ciborfixing", "fax", "to:", "market operations", "NA", "yours sincerely", # "This sheet containing rates from the CIBOR reporting banks shall be regarded as an integrated part of the control procedure", # "of CIBOR. The rates do not represent the final CIBOR rates and are not to be published by any CIBOR reporting bank, nor", # "by Finansraadet (Danish Bankers Association) or by anyone else before the official CIBOR rates are available. The official") ## These bank names are used going forward. good_names <- unique(tolower(c("Amtssparekassen Fyn", "Danske Bank", "Jyske Bank", "HSH Nordbank", "Nordea", "Nykredit Bank", "Spar Nord Bank", "Sydbank", "FIXING", "Fionia Bank", "ABN Amro Bank", "Barclays Capital", "Deutsche Bank", "Royal Bk of Scotland", "DANSKE BANK", "BARCLAYS", "Deutsche", "JYSKE BANK", "NORDEA", "NYKREDIT", "RBS FM", "SYDBANK"))) ## Read the first column of every file into a list object ## The column is being sequenced to single out the rows we want ## and matched based on the good_names ## I also do a bit of cleaning up like setting character to lower for(i in seq_along(xl_file_names)){ ## Print the file name being read print(xl_file_names[i]) ## Read file first_col[[i]] <- try(read.xlsx2(file = paste0("./data/", xl_file_names[i]),sheetIndex = 1, startRow = 1, colIndex = 1, endRow = 70, as.data.frame = TRUE, header = FALSE, colClasses = "character", stringsAsFactors = FALSE), silent = FALSE) ## Make sequence first_col[[i]]$row_number <- seq_along(first_col[[i]][,1]) ## Add file name first_col[[i]]$file_name <- xl_file_names[i] ## set characters to lower first_col[[i]]$X1 <- tolower(str_trim(first_col[[i]]$X1)) ## Test for good names test_good <- first_col[[i]]$X1 %in% good_names ## filter and keep only the good names first_col[[i]] <- first_col[[i]][test_good, ] ### Used in the exploratory phase # ## get length of each character string # strlen <- str_length(string = first_col[[i]][, 1]) # # ## remove rows where length > 0 or bigger than 50 or is na # first_col[[i]] <- first_col[[i]][strlen > 0,] # first_col[[i]] <- first_col[[i]][strlen < 50,] # first_col[[i]] <- first_col[[i]][!is.na(first_col[[i]]$X1),] # # ### test for bad names # test_bad <- tolower(first_col[[i]]$X1) %in% bad_names # ## remove bad names # first_col[[i]] <- first_col[[i]][ !test_bad, ] # # test_bad <- str_detect(first_col[[i]]$X1, "[[:digit:]]") # first_col[[i]] <- first_col[[i]][!test_bad,] } ## Take every list object and rowbind them all test_ob <- do.call("rbind", first_col) ## Group_by file_name and find the highest and lowest row number ## we can use for reading the the actual fixing and discard the rest test_ob <- test_ob %>% group_by(file_name) %>% summarise(min_row = min(row_number), max_row = max(row_number)) %>% ungroup ############################################################## ## Read the fixings ########################################################### ## create list object to save fixings into curve_values <- list() ## Loop over each row in test_ob and read the file name using these variables. for(i in seq_along(test_ob$file_name)){ ## Print out where you are print(test_ob$file_name[i]) ## Read the file curve_values[[i]] <- read.xlsx2(file = paste0("./data/", test_ob$file_name[i]), sheetIndex = 1, startRow = test_ob$min_row[i], endRow = test_ob$max_row[i], as.data.frame = TRUE, header = FALSE, stringsAsFactors = FALSE) ## Set first column tolower characters curve_values[[i]]$X1 <- tolower(curve_values[[i]]$X1) } ########################################################## ## clean and format the fixings ########################################################## ## Remove empty columns by looping through each reading and its columns. for(i in seq_along(curve_values)){ for(n in length(names(curve_values[[i]])):1){ if(is.character(curve_values[[i]][, n])){ test <- sum(str_length(string = curve_values[[i]][, n])) < 1 if(test){ curve_values[[i]] <- curve_values[[i]][, -n] } } } } ## Remove blank rows from the file for(i in seq_along(curve_values)){ test <- str_length(curve_values[[i]]$X1) > 0 curve_values[[i]] <- curve_values[[i]][test,] } ## Look at the length of each column and keep only ## the ones with length == 15 test_ob$col_len <- sapply(curve_values, FUN = function(x) length(names(x)),simplify = TRUE) ## Keep only file readings with col_len == 15 ## THis means that data from 2005-04-01 to 2011-04-01 ## is retained curve_values <- curve_values[test_ob$col_len == 15] test_ob <- test_ob[test_ob$col_len == 15, ] ## Set colnames for fixing period curve_col_names <- c("bank", "d7", "d14", "d30", "d60", "d90", "d120", "d150", "d180", "d210", "d240", "d270", "d300", "d330", "d360") ## Set colnames for(i in seq_along(curve_values)){ names(curve_values[[i]]) <- curve_col_names } ## set fixings as numeric instead of character for(i in seq_along(curve_values)){ for(n in seq_along(names(curve_values[[i]]))){ if(n == 1){ next } else { curve_values[[i]][,n] <- as.numeric(curve_values[[i]][,n]) } } } ## Set date for(i in seq_along(curve_values)){ curve_values[[i]]$date <- str_replace(string = test_ob$file_name[i], pattern = ".xls", replacement = "") curve_values[[i]]$date <- str_replace(string = curve_values[[i]]$date, pattern = "x", replacement = "") curve_values[[i]]$date <- ymd(curve_values[[i]]$date) } ## merge all the list objects curve_values <- do.call(rbind, curve_values) ## set the rows in the "correct" order curve_values <- curve_values %>% select(date, bank, d7:d360) ## write tidy data to the disk write.csv2(curve_values, file = "cibor_csv2.csv")

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test_that("Test Sys Details", { skip_on_cran() sys = get_sys_details(sys_info = FALSE, installed_packages = FALSE) expect_equal(length(sys), 13) expect_equal(is.na(sys$sys_info), TRUE) expect_equal(is.na(sys$installed_packages), TRUE) } )

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# # Anaquin - Sequin statistical analysis. Version 1.1.1. # # This R script was generated at %1%. # # %2% # library(Anaquin) data <- read.csv('%3%/%4%', row.names=1, sep='\t') title <- '%5%' xlab <- '%6%' ylab <- '%7%' # Expected input concentration (x-axis) input <- %8% # Measured expression (y-axis) measured <- %9% # Create Anaquin data for PlotLinear anaquin <- AnaquinData(analysis='PlotLinear', seqs=row.names(data), %10%=input, measured=measured) plotLinear(anaquin, title=title, xlab=xlab, ylab=ylab, showLOQ=%11%)

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library('adk') dyn.load('simulator1.so') source('simulator1n.r') mcell <- as.matrix(read.csv('data/day8slow.csv')) mcell2 <- as.matrix(read.csv('data/day8fast.csv')) ## Nsample = 10000 ## T = 8 ## d <- matrix(0, 101, 101) ## pvalue <- matrix(0, 101, 101) ## x <- seq(Nsample) ## i <- 1 ## for ( r2 in seq(0.4, 0.8, by=0.004) ){ ## j <- 1 ## for ( d2 in seq(0.2, 0.6, by=0.004) ){ ## x <- simulator1n(T, 0, r2, 0, d2, 0, 0, Nsample) ## dis <- adk.test(mcell, x) ## d[i, j] <- dis$adk[1,2] ## pvalue[i, j] <- dis$adk[2,2] ## cat(c(d[i, j], r2, d2, i, j),'\n') ## j <- j + 1 ## } ## i <- i + 1 ## } ## ## ----------------------------------- ## ## Plot the contour ## ## ----------------------------------- ## res <- which(d == max(d), arr.ind=T) ## r2max <- 0.4 + 0.004*res[1] ## d2max <- 0.2 + 0.004*res[2] ## par(bg = 'white') ## filled.contour(x = seq(0, 1, length.out=101), ## y = seq(0, 1, length.out=101), ## d, ## color=terrain.colors, ## plot.title = title(main = "Contour-plot of p-value under AD test (2D)", ## xlab = "proliferation rate", ## ylab = "death rate"), ## asp = 1, ## ## plot.axes={ axis(1); axis(2); points(r2max,d2max,pch=17) }, ## level=c(0, 0.0001, 0.01, 0.1, 0.2, 0.3, 0.5, 0.6, 0.65, 0.7)) ## mtext(paste("@", date()), side=1, line=4, adj=1.04, cex=.66) ## text(0, 0.9, adj = c(0,0), "The method of fitting parameters is based on") ## text(0, 0.85, adj = c(0,0), "Minimize the AD-distance (or maximize its p-Value).") ## text(0, 0.75, adj = c(0,0), "Sample A: experimental data (day 8 cell number).") ## text(0, 0.7, adj = c(0,0), "Sample B: simulated data from the population model.") ## text(0, 0.6, adj = c(0,0), "We do a grid-search on the whole parameter space.") ## text(0, 0.55, adj = c(0,0), "Each parameter set gives a sample B.") ## text(0, 0.5, adj = c(0,0), "The parameter set that minimize the distance") ## text(0, 0.45, adj = c(0,0), "(or maximize the p-value) between sample A-B") ## text(0, 0.4, adj = c(0,0), "is the winner.") ## arrows(x0=0.2, y0=0.4, x1=0.4, y1=0.25) ## dev.copy(pdf,'I_method.pdf') ## dev.off() ## ----------------------------------- ## Plot the fit ## ----------------------------------- Nsample = 50 T = 8 r1 = 0 r2 = 0.652 d1 = 0 d2 = 0.34 v = 0 w = 0 x <- simulator1n(T, r1, r2, d1, d2, v, w, Nsample) Fn <- ecdf(x) Fe <- ecdf(mcell) plot(Fn, xlim=c(0,300), main = "ECDF: simulation vs data", ylab = "Cumulative probability", xlab = "8-day Cell number ") lines(Fe, col='red') dis <- adk.test(mcell, x) cat(dis$adk[1,2]) ## good cell T = 8 r1 = 1.1 r2 = 0.652 d1 = 0.4 d2 = 0.34 v = 0.25 w = 0.4 y <- simulator1n(T, r1, r2, d1, d2, v, w, Nsample) Fn2 <- ecdf(y) Fe2 <- ecdf(mcell2) lines(Fn2, col='blue') lines(Fe2, col='green') dis2 <- adk.test(mcell2, y) cat(dis2$adk[1,2]) text(50, 0.6, adj = c(0, 0), "Model parameter estimated by minimize AD-distance.") text(50, 0.5, adj = c(0, 0), "Red: 8-day slow cell population") text(50, 0.45, adj = c(0, 0), "Black: simulated data using parameters from grid search") text(50, 0.4, adj = c(0, 0), "Two-parameter model: ( l2=0.652, d2=0.33 )") text(50, 0.35, adj = c(0, 0), "max p-value: 0.66") text(50, 0.25, adj = c(0, 0), "Green: 8-day fast cell population") text(50, 0.2, adj = c(0, 0), "Blue: simulated data using parameters from grid search") text(50, 0.15, adj = c(0, 0), "Four-parameter mixed model: ( l1=1.1, d1=0.4, v=0.25, w=0.4 )") text(50, 0.1, adj = c(0, 0), "max p-value: 0.52") mtext(paste("@", date()), side=1, line=4, adj=1.04, cex=.66) dev.copy(pdf,'II_result.pdf') dev.off()

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THEPROGRAMMINGCODE.r

# Make sure stripped and names are in same folder/working directory # as the code # Reading the files and making them nice stripped <- read.csv("stripped", header= F, comment.char = '#') #names <- read.delim("names", header= F, sep= "\n", quote= "", comment.char = '#') colnames(stripped) <- c("id", 1:109) # Can't set rownames, some rows are not unique apperantly # How often each number occurs for all entries only_sequences <- stripped[,-1] counts <- table(unlist(only_sequences)) #-1 cause not the name of each sequence ordered_all_counts <- counts[order(-counts)] saveRDS(ordered_all_counts, file = "results/all_counts") ordered.df <- as.data.frame(ordered_all_counts) # This will be used to find specific number for dashboard: counts["5"] # Nice Histogram: hist(counts, xlim = c(-20,100), ylim = c(0,500), breaks = 1e6) rowcount <- rowSums(stripped[, -1] == '1' ) count.1 <- apply(only_sequences, 1, function(x) length(which(x=="1"))) # Count per row # TODO: make sure the NA's don't give problems

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/R/halfcauchy.mle.R

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happylearning423/Rfast2

c4dd6b6c9d53ec28ad7caabb88c5e078864061fa

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refs/heads/master

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halfcauchy.mle.R

#[export] halfcauchy.mle <- function(x, tol = 1e-07) { n <- length(x) es <- 0.5 * (Rfast::nth(x, 3 * n/4) - Rfast::nth(x, n/4)) logs <- log(es) x2 <- x^2 down <- 1/(x2 + es^2) lik1 <- n * logs + sum( log(down) ) der <- n - 2 * es^2 * sum(down) der2 <- - 4 * es^4 * sum(down^2) logs <- logs - der/der2 es <- exp(logs) down <- 1/(x2 + es^2) lik2 <- n * logs + sum( log(down) ) i <- 2 while ( lik2 - lik1 > tol ) { i <- i + 1 lik1 <- lik2 der <- n - 2 * es^2 * sum(down) der2 <- - 4 * es^4 * sum(down^2) logs <- logs - der/der2 es <- exp(logs) down <- 1/(x2 + es^2) lik2 <- n * logs + sum( log(down) ) } list(iters = i, loglik = lik2 - n * log(2/pi), scale = es) } #[export] cauchy0.mle <- function(x, tol = 1e-07) { n <- length(x) es <- 0.5 * (Rfast::nth(x, 3 * n/4) - Rfast::nth(x, n/4)) logs <- log(es) x2 <- x^2 down <- 1/(x2 + es^2) lik1 <- n * logs + sum( log(down) ) der <- n - 2 * es^2 * sum(down) der2 <- - 4 * es^4 * sum(down^2) logs <- logs - der/der2 es <- exp(logs) down <- 1/(x2 + es^2) lik2 <- n * logs + sum( log(down) ) i <- 2 while ( lik2 - lik1 > tol ) { i <- i + 1 lik1 <- lik2 der <- n - 2 * es^2 * sum(down) der2 <- - 4 * es^4 * sum(down^2) logs <- logs - der/der2 es <- exp(logs) down <- 1/(x2 + es^2) lik2 <- n * logs + sum( log(down) ) } list(iters = i, loglik = lik2 - n * log(pi), scale = es) }

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/Initialization/Code/Initialize_Loop_and_Code_Fxns.R

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Initialize_Loop_and_Code_Fxns.R

# --------------------------------------------------- # # INITIALIZE BISTABILITY FORCING # # AND MEBM + CARBON CYCLE SCRIPTS # # --------------------------------------------------- # ## [1] INITIALIZE FORCING ## Function to initialize a list of parameters ## Each element is a vector of length = model.iters ## where the length depends on the number of unique ## model cases (calculated by this fxn) ## ## For example, to run two CO2 levels at two ice sheet wx coefficients ## we would get four total iterations. Other terms that don't vary ## would just have their value repeated four times in the list initialize.forcing <- function(co2.force = pCO2.vector, # atmos CO2 vector glwx.force = glwx.vector, # glacier weathering vector m.force = m, # vector of mass of carbon injected (or removed) t.start.force = t.exc.start, # time of volcanic anomaly start t.end.force = t.exc.end, # time of volcanic anomaly end SENSITIVITY.force = SENSITIVITY.VEC, # vector of sensitivity values duration.force = duration, # [years] how long model is run dt.force = dt, # [years] timestep CTRL.idx.force = CONTROL.idx, # which sensitivity.vec values are equal to control case value geog.force = geog.list # geography input files ){ # check whether geography is a list of many geogs, or a single geog if(is.null(nrow(geog.force))){ # if it doesn't have rows, then the vec is the list geog.vec <- names(geog.force) } else{ # if it does have rows, then the vec is just this file geog.vec <- LandFracFileName } # Get iters associated with time (three start and three end times are three simulations, not nine) length.time.force <- max(c(length(t.start.force), length(t.end.force), length(duration.force), length(dt.force))) # make a fake vec to expand the grid fake.vec <- rep(-9999, length.time.force) # expand grid (get every combination of relevant inputs) grid.expansion <- expand.grid(co2.force, glwx.force, m.force, SENSITIVITY.force, geog.vec, fake.vec) colnames(grid.expansion) <- c("co2.force", "glwx.force", "m.force", "SENSITIVITY.force", "geography.force", "x") # we put fake vec last because we know it will be repeated in row (rep(THIS, each=nrow(grid.expansion)/length(fake.vec))) # ... remove fake vec (was a placeholder) grid.expansion <- subset(grid.expansion, select=-x) # ... add the time force vecs grid.expansion$t.start.force <- rep(t.start.force, each = (nrow(grid.expansion)/length(t.start.force)) ) grid.expansion$t.end.force <- rep(t.end.force, each = (nrow(grid.expansion)/length(t.end.force)) ) grid.expansion$duration.force <- rep(duration.force, each = (nrow(grid.expansion)/length(duration.force)) ) grid.expansion$dt.force <- rep(dt.force, each = (nrow(grid.expansion)/length(dt.force)) ) # ... add control index if(length(CTRL.idx.force) < 2){ grid.expansion$CTRL.idx <- CTRL.idx.force[1] } else{ # equation relating index to value in sensitivity.vec sens.idx.fun <- approxfun(x=SENSITIVITY.force, y=c(1:length(SENSITIVITY.force))) sens.idx <- sens.idx.fun(grid.expansion$SENSITIVITY.force) grid.expansion$CTRL.idx <- CTRL.idx.force[sens.idx] } # order by control index so control iter is first grid.expansion <- as.data.table(grid.expansion) # for the 'order' fxn to work grid.out <- grid.expansion[order(-CTRL.idx)] # return the result return(grid.out) } # -------------------------------------------------------------------- # # -------------------------------------------------------------------- # # -------------------------------------------------------------------- # ## [2] INITIALIZE MEBM FILES ## terms are sourced (fxn does not need to be run to a variable) initialize.mebm <- function(sourcefile.Dir.in = sourcefile.Dir, # where are the files located MEBMmain.file.in = MEBMmain.file, # MEBM main solver ParSave.file.in = ParSave.file, # list of parameters to save for reproducibility CH2OCHOfxn.file.in = CH2OCHOfxn.file, # source CH2O-CHO functions MEBMsolver.file.in = MEBMsolver.file, # solver MEBMconst.file.in = MEBMconst.file, # physical constants MEBMode.file.in = MEBMode.file, # MEBM ODE to get mse MEBMhydro.file.in = MEBMhydro.file, # solve hydrologic fluxes MEBMmultistable_nav.file.in = MEBMmultistable_nav.file, # script to navigate temp boundary conditions and other useful fxns CH2OCHOwritelog.file.in = CH2OCHOwritelog.file # log file writer ){ # bring in the initialization scripts source(paste(sourcefile.Dir.in, MEBMmain.file.in, sep='/')) source(paste(sourcefile.Dir.in, ParSave.file.in, sep='/')) source(paste(sourcefile.Dir.in, CH2OCHOfxn.file.in, sep='/')) source(paste(sourcefile.Dir.in, MEBMsolver.file.in, sep='/')) source(paste(sourcefile.Dir.in, MEBMconst.file.in, sep='/')) source(paste(sourcefile.Dir.in, MEBMode.file.in, sep='/')) source(paste(sourcefile.Dir.in, MEBMhydro.file.in, sep='/')) source(paste(sourcefile.Dir.in, MEBMmultistable_nav.file.in, sep='/')) source(paste(sourcefile.Dir.in, CH2OCHOwritelog.file.in, sep='/')) } # -------------------------------------------------------------------- # # -------------------------------------------------------------------- # # -------------------------------------------------------------------- # ## [3] INITIALIZE CARBON CYCLE ## terms are sourced or just read right to the environment (fxn does not need to be run to a variable) initialize.c.cycle <- function(sourcefile.Dir.in = sourcefile.Ccycle){ # ------------------------------------------------------------------------- # # LOAD GEOCHEM INITIALIZATION FILES *************************************** # # ------------------------------------------------------------------------- # # CLiBeSO Files sw.file <<- "seawater values over time_interp.txt" # these go to global env. because they're read into the log file Berner.volc.file <<- "Berner Volcanism Interp.txt" # sens.file <<- "PaleozoicSensValues.txt" CO2.file <<- "InterpCO2_Aug2018.txt" # read in sw.chem <<- read.table(file = paste(sourcefile.Dir.in, sw.file, sep='/'), header = TRUE) Berner.volc <<- read.table(file = paste(sourcefile.Dir.in, Berner.volc.file, sep='/'), header = TRUE) # sens.parm <<- read.table(file = paste(sourcefile.Dir, sens.file, sep='/'), header = TRUE) interpCO2 <<- read.table(file = paste(sourcefile.Dir.in, CO2.file, sep='/'), header = TRUE) Ca.i <<- vector() Mg.i <<- vector() k.run <<- vector() clim.sens <<- Clim.Sens <<- vector() degassing <<- vector() # pCO2 <<- vector() # temp.i <<- vector() Age <<- vector() output <<- vector(mode='list',length=length(sw.chem$Age)) ## MODIFY BELOW TO INITIALIZE WITH PALEO CONDITIONS -- (Turned off for now) ## NOTE: some of these are not used in the calculations for certain scripts f=1 # set to run for one time step (use for-loop for Phanerozoic run instead) y=1 # time index in input data Age[f] <<- sw.chem$Age[y] Ca.i[f] <<- sw.chem$Ca[y] Mg.i[f] <<- sw.chem$Mg[y] degassing[f] <<- 1 # Berner.volc$Fvolc[y] } # -------------------------------------------------------------------- # # -------------------------------------------------------------------- # # -------------------------------------------------------------------- # ## [4] PRINT a summary of the simulation pre_run.summary <- function(df.force){ # COLLECT THE NUMBER OF UNIQUE VALUES FOR EACH CASE n.times.start <- length(unique(df.force$t.start.force)) n.times.end <- length(unique(df.force$t.end.force)) n.dur <- length(unique(df.force$duration.force)) n.time <- max(n.times.start, n.times.end, n.dur) n.geog <- length(unique(df.force$geography.force)) # n.sens.par <- length(unique(df.force$SENSITIVITY.force)) # we don't track sens par names here n.sens.val <- length(unique(df.force$SENSITIVITY.force)) n.m <- length(unique(df.force$m.force)) n.glwx <- length(unique(df.force$glwx.force)) n.co2 <- length(unique(df.force$co2.force)) force.values <- c("time grids" = n.time, "geographies" = n.geog, "param values" = n.sens.val, "init co2s" = n.co2, "carbon perturbations" = n.m, "glacial wx factors" = n.glwx) print.idx <- which(force.values > 1) # total sims n.sims <- nrow(df.force) n.summary <- paste(force.values[print.idx], names(force.values[print.idx]), collapse=' X ') # ------------------------------------- # # Print result cat(paste("=======================================================", paste("TOTAL SIMULATIONS TO RUN:", n.sims), "**", paste("One simulation for each:", n.summary), "=======================================================", sep='\n')) # ------------------------------------- # }

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/tests/testthat/test-disjoin.R

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liupfskygre/plyranges

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test-disjoin.R

context("disjoin ranges") test_that("matches IRanges/GRanges tests", { x <- IRanges() expect_identical(x, disjoin_ranges(x)) ir <- IRanges(c(1, 21, 10, 1, 15, 5, 20, 20), c(6, 20, 9, 3, 14, 11, 20, 19)) correct_ir <- IRanges(c(1, 4, 5, 7, 10, 20), c(3, 4, 6, 9, 11, 20)) test_ir <- disjoin_ranges(ir) expect_identical(test_ir, correct_ir) # check revmap test_ir <- ir %>% mutate(i = 1:n()) %>% disjoin_ranges(revmap = IRanges::IntegerList(i)) mcols(correct_ir)$revmap <- IRanges::IntegerList(c(1, 4), 1, c(1, 6), 6, 6, 7) expect_identical(test_ir, correct_ir) # -- granges gr <- GRanges(Rle(factor(c("chr1", "chr2", "chr1", "chr3")), c(1, 3, 2, 4)), IRanges(1:10, width=10:1, names=head(letters, 10)), Rle(c("-", "+", "*", "+", "-"), c(1, 2, 2, 3, 2)), score=1:10, GC=seq(1, 0, length=10), seqinfo=Seqinfo(paste("chr", 1:3, sep=""))) correct_gr <- GRanges(Rle(c("chr1", "chr2", "chr3"), c(3, 3, 4)), IRanges(start=c(6, 1, 5, 2, 3, 4, 7, 8, 9, 10), end=c(10, 10, 10, 2, 10, 10, 7, 10, 9, 10)), c("+", "-", "*", "+", "+", "*", "+", "+", "-", "-")) # matches directed expect_identical(disjoin_ranges_directed(gr), correct_gr) # this is the same as disjoin unstranded on correct_gr expect_identical(disjoin_ranges(gr), disjoin_ranges(correct_gr)) gr <- GRanges(Rle(c("chr1", "chr3"), c(2, 2)), IRanges(c(8, 6, 8, 6), c(11, 15, 11, 15), names=c("k", "l", "m", "n")), c("-", "-", "+", "*"), score=11:14, GC=c(.2, .3, .3, .1)) correct_gr <- GRanges(Rle(c("chr1", "chr3"), c(3, 2)), IRanges(c(6, 8, 12, 8, 6), c(7, 11, 15, 11, 15)), Rle(c("-", "+", "*"), c(3, 1, 1))) mcols(correct_gr)$revmap <- IRanges::IntegerList(2, 1:2, 2, 3, 4) expect_identical(gr %>% mutate(i = 1:n()) %>% disjoin_ranges_directed(revmap = IRanges::IntegerList(i)), correct_gr) # grouping works as expected grl <- GRangesList( GRanges(Rle(factor(c("chr1", "chr2", "chr1", "chr3")), c(1, 3, 2, 4)), IRanges(1:10, width=10:1), Rle(c("-", "+", "*", "+", "-"), c(1, 2, 2, 3, 2)), score=1:10, GC=seq(1, 0, length=10), seqinfo=Seqinfo(paste("chr", 1:3, sep=""))), GRanges("1", IRanges(1, 10), score=21, GC=.21), GRanges(), GRanges(Rle(c("chr1", "chr3"), c(2, 2)), IRanges(c(8, 6, 8, 6), c(11, 15, 11, 15)), strand(c("-", "-","+","*")), score=41:44, GC=c(.41, .42, .43, .44))) gr_by_group <- stack(grl, "name") %>% group_by(name) target <- stack(disjoin(grl, ignore.strand = TRUE), "name") current <- disjoin_ranges(gr_by_group) %>% mutate(name = Rle(name)) expect_identical(target, current) }) test_that("matches HelloRanges multinter", { oldwd <- getwd() setwd(system.file("unitTests", "data", "multiinter", package="HelloRanges")) bed_files <- list.files(pattern = ".bed$") correct_gr <- GRanges("chr1", IRanges(c(7, 9, 13, 16, 21, 23, 31, 33), c(8, 12, 15, 20, 22, 30, 32, 34)), i=IRanges::IntegerList(1, c(1,3), 1:3, 1:2, 2, 1:2, 2, 3)) gr_l <- S4Vectors::List(lapply(bed_files, function(x) { mutate(read_bed(x), grp = sub(".bed$", "", basename(x))) })) gr_by_group_r <- unlist(gr_l) %>% mutate(grp = factor(grp, levels = c("a", "b", "c"))) %>% group_by(grp) %>% reduce_ranges() test_gr <- gr_by_group_r %>% mutate(i = factor(as.integer(grp))) %>% disjoin_ranges(i = IRanges::IntegerList(i)) expect_identical(correct_gr, test_gr) # with names in place of integer mcols(correct_gr)$i <- as( extractList(factor(c("a", "b", "c")), mcols(correct_gr)$i), "SimpleFactorList" ) test_gr <- gr_by_group_r %>% disjoin_ranges(i = IRanges::FactorList(grp, compress = FALSE)) expect_identical(correct_gr, test_gr) setwd(oldwd) })

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/tests/testthat/test-seq_ttest.R

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cran/sprtt

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test-seq_ttest.R

#* @testing seq_ttest #* # MARTIN Skript ---------------------------------------------------------- .sprt.lr <- function(x, y, mu, d, type, alt){ ttest <- switch(type, one.sample = t.test(x, mu = mu), two.sample = t.test(x, y, mu = mu, var.equal = T), paired = t.test(x, y, mu = mu, paired = T)) # print(ttest) ncp <- ifelse(type == "two.sample", d/sqrt(1/length(x) + 1/length(y)), d * sqrt(length(x))) tval <- ifelse(alt == "less", -1 * as.vector(ttest$statistic), as.vector(ttest$statistic)) dof <- as.vector(ttest$parameter) # print(paste("ncp", ncp)) # print(paste("tval", tval)) # print(paste("dof", dof)) if(alt=="two.sided"){ lr <- df(tval^2, df1 = 1, df2 = dof, ncp = ncp^2)/df(tval^2, df1 = 1, df2 = dof) # l1 <- df(tval^2, df1 = 1, df2 = dof, ncp = ncp^2) # l2 <- df(tval^2, df1 = 1, df2 = dof) # # print(l1/l2) } else{ lr <- dt(tval, dof, ncp = ncp)/dt(tval, dof) } lr # print(lr) } .sprt.formula <- function(formula, data = NULL, mu = 0, d, alpha = 0.05, power = 0.95, alternative = "two.sided", paired = FALSE){ ##### CHECK INPUT match.arg(alternative, c("two.sided", "less", "greater")) if(!(alpha > 0 && alpha < 1)) stop("Invalid argument <alpha>: Probabilities must be in ]0;1[.") if(!(power > 0 && power < 1)) stop("Invalid argument <power>: Probabilities must be in ]0;1[.") if(d <= 0) stop("Invalid argument <d>: Must be greater than 0.") if(!is.numeric(mu)) stop("Invalid argument <mu>: Must be numeric.") if(!is.logical(paired)) stop("Invalid argument <paired>: Must be logical.") if ((length(formula) != 3L) || (length(formula[[3]]) != 1L)) stop("'formula' is incorrect. Please specify as 'x~y'.") temp <- model.frame(formula, data) x <- temp[,1] y <- temp[,2] whichNA <- is.na(x) | is.na(y) x <- x[!whichNA] y <- y[!whichNA] if(!is.numeric(x)) stop(paste("Dependent variable", names(temp)[1], "must be numeric.")) if(length(unique(y))!=2) stop(paste("Grouping factor", names(temp)[2], "must contain exactly two levels.")) if(paired){ if(!(table(y)[[1]]==table(y)[[2]])) stop("Unequal number of observations per group. Independent samples?") }else{ if(length(x)<3) stop("SPRT for two independent samples requires at least 3 observations.") } sd.check <- tapply(x, INDEX=y, FUN=sd) sd.check <- ifelse(is.na(sd.check), 0, sd.check) if(max(sd.check) == 0) stop("Can't perform SPRT on constant data.") ##### RETURN ARGUMENTS y <- as.factor(y) x.1 <- x[y == levels(y)[1]] x.2 <- x[y == levels(y)[2]] data.name <- paste(names(temp)[1], "by", names(temp)[2]) method <- ifelse(paired, "Paired SPRT t-test", "Two-Sample SPRT t-test") null.value <- mu attr(null.value, "names") <- "difference in means" if(paired){ estimate <- mean(x.1 - x.2) attr(estimate, "names") <- "mean of the differences" }else{ estimate <- c(mean(x.1), mean(x.2)) attr(estimate, "names") <- c("mean in group 1", "mean in group 2") } arg.list <- list(x = x.1, y = x.2, mu = mu, d = d, alpha = alpha, power = power, type = ifelse(paired, "paired", "two.sample"), alt = alternative) printarg.list <- list(estimate = estimate, null.value = null.value, alternative = alternative, effect.size = d, method = method, data.name = data.name) result <- do.call(.sprt.result, arg.list) output <- c(result, printarg.list) class(output) <- "sprt" return(output) } .sprt.default <- function(x, y = NULL, mu = 0, d, alpha = 0.05, power = 0.95, alternative = "two.sided", paired = FALSE){ ##### CHECK INPUT match.arg(alternative, c("two.sided", "less", "greater")) if(!(alpha > 0 && alpha < 1)) stop("Invalid argument <alpha>: Probabilities must be in ]0;1[.") if(!(power > 0 && power < 1)) stop("Invalid argument <power>: Probabilities must be in ]0;1[.") if(d<=0) stop("Invalid argument <d>: Must be greater than 0.") if(!is.numeric(mu)) stop("Invalid argument <mu>: Must be numeric.") if(!is.null(y)){ x.name <- deparse(substitute(x)) y.name <- deparse(substitute(y)) data.name <- paste(x.name, "and", y.name) if(!(is.atomic(x) && is.null(dim(x)))) warning(paste(x.name, "is not a vector. This might have caused problems."), call. = F) if(!(is.atomic(y) && is.null(dim(y)))) warning(paste(y.name, "is not a vector. This might have caused problems."), call. = F) if(is.factor(y)) stop("Is y a grouping factor? Use formula interface x ~ y.") if(!is.numeric(x)) stop(paste("Invalid argument:", x.name, "must be numeric.")) if(!is.numeric(y)) stop(paste("Invalid argument:", y.name, "must be numeric.")) if(!paired && (length(x) + length(y) < 3)) stop("SPRT for two independent samples requires at least 3 observations.") sd.check <- c(sd(x), sd(y)) sd.check <- ifelse(is.na(sd.check), 0, sd.check) if(!(max(sd.check) > 0)) stop("Can't perform SPRT on constant data.") if(!is.logical(paired)) stop("Invalid argument <paired>: Must be logical.") type <- ifelse(paired, "paired", "two.sample") method <- ifelse(paired, "Paired SPRT t-test", "Two-Sample SPRT t-test") null.value <- mu attr(null.value, "names") <- "difference in means" if(paired){ if(length(x) != length(y)) stop("Unequal number of observations per group. Independent samples?") whichNA <- is.na(x) | is.na(y) x <- x[!whichNA] y <- y[!whichNA] estimate <- mean(x - y) attr(estimate, "names") <- "mean of the differences" }else{ x <- x[!is.na(x)] y <- y[!is.na(y)] estimate <- c(mean(x), mean(y)) attr(estimate, "names") <- c(paste("mean of", x.name), paste("mean of", y.name)) } }else{ data.name <- deparse(substitute(x)) x <- x[!is.na(x)] if(!is.numeric(x)) stop(paste("Invalid argument:", data.name, "must be numeric.")) sd.check <- ifelse(is.na(sd(x)), 0, sd(x)) if(!(sd.check > 0)) stop("Can't perform SPRT on constant data.") type <- "one.sample" method <- "One-Sample SPRT t-test" null.value <- mu attr(null.value, "names") <- "mean" estimate <- mean(x) attr(estimate, "names") <- "mean of x" } ##### RETURN ARGUMENTS arg.list <- list(x = x, y = y, mu = mu, d = d, alpha = alpha, power = power, type = type, alt = alternative) printarg.list <- list(estimate = estimate, null.value = null.value, alternative = alternative, effect.size = d, method = method, data.name = data.name) result <- do.call(.sprt.result, arg.list) output <- c(result, printarg.list) class(output) <- "sprt" return(output) } .sprt.result <- function(x, y, mu, d, alpha, power, type, alt){ A <- power/alpha B <- (1 - power)/(1 - alpha) lr <- .sprt.lr(x, y, mu, d, type, alt) if(lr >= A){ decision <- "accept H1" }else if(lr <= B){ decision <- "accept H0" }else{ decision <- "continue sampling" } attr(lr, "names") <- "likelihood ratio" parameters <- c(alpha, power) attr(parameters, "names") <- c("Type I error", "Power") thresholds <- c(B, A) attr(thresholds, "names") <- c("lower", "upper") return(list(statistic = lr, decision = decision, parameters = parameters, thresholds = thresholds)) } print.sprt <- function(x){ cat(" ", x$method, "\n") cat("\ndata:", x$data.name, "\n") cat(names(x$statistic), " = ", round(x$statistic, digits = 5), ", decision = ", x$decision, sep="") cat("\nSPRT thresholds:\n") print(round(x$thresholds, digits = 5)) cat("alternative hypothesis: true", names(x$null.value), "is", ifelse(x$alternative=="two.sided", "not equal to", paste(x$alternative, "than")), x$null.value) cat("\neffect size: Cohen's d =", x$effect.size, "\n") print(x$parameters) cat("sample estimates:\n") print(round(x$estimate, digits = 5)) } sprt.t.test <- function(...) UseMethod(".sprt") .sprt.result <- function(x, y, mu, d, alpha, power, type, alt){ A <- power/alpha B <- (1 - power)/(1 - alpha) lr <- .sprt.lr(x, y, mu, d, type, alt) if(lr >= A){ decision <- "accept H1" }else if(lr <= B){ decision <- "accept H0" }else{ decision <- "continue sampling" } attr(lr, "names") <- "likelihood ratio" parameters <- c(alpha, power) attr(parameters, "names") <- c("Type I error", "Power") thresholds <- c(B, A) attr(thresholds, "names") <- c("lower", "upper") return(list(statistic = lr, decision = decision, parameters = parameters, thresholds = thresholds)) } print.sprt <- function(x){ cat(" ", x$method, "\n") cat("\ndata:", x$data.name, "\n") cat(names(x$statistic), " = ", round(x$statistic, digits = 5), ", decision = ", x$decision, sep="") cat("\nSPRT thresholds:\n") print(round(x$thresholds, digits = 5)) cat("alternative hypothesis: true", names(x$null.value), "is", ifelse(x$alternative=="two.sided", "not equal to", paste(x$alternative, "than")), x$null.value) cat("\neffect size: Cohen's d =", x$effect.size, "\n") print(x$parameters) cat("sample estimates:\n") print(round(x$estimate, digits = 5)) } sprt.t.test <- function(...) UseMethod(".sprt") # x <- rnorm(50) # sprt.t.test(x, d = 0.3) #----------------------------------------------------------------- context("seq_ttest: test main function") test_that("seq_ttest: comparison results with original script from m. schnuerch", { x <- rnorm(50) d <- 0.8 results_original <- sprt.t.test(x = x, d = d, power = 0.8) results_sprtt <- seq_ttest(x, d = d, power = 0.8) expect_true(results_sprtt@likelihood_ratio - results_original$statistic[[1]] < 1e-5) expect_equal(results_sprtt@decision, results_original$decision) x <- rnorm(20) y <- as.factor(c(rep(1,10), rep(2,10))) d <- 0.95 results_original <- sprt.t.test(x ~ y, d = d) results_sprtt <- seq_ttest(x ~ y, d = d) expect_true(results_sprtt@likelihood_ratio - results_original$statistic[[1]] < 1e-5) expect_equal(results_sprtt@decision, results_original$decision) # same data, but different input x_1 <- x[1:(length(x) * 0.5)] x_2 <- x[(length(x) * 0.5 + 1):length(x)] results_sprtt2 <- seq_ttest(x_1, x_2, d = d) expect_true(results_sprtt@likelihood_ratio_log - results_sprtt2@likelihood_ratio_log < 1e-5) expect_equal(results_sprtt@decision, results_sprtt2@decision) x <- rnorm(20) y <- as.factor(c(rep(1,10), rep(2,10))) d <- 0.95 results_numeric <- seq_ttest(x, d = d) results_formula <- seq_ttest(x ~ 1, d = d) expect_true(results_formula@likelihood_ratio - results_numeric@likelihood_ratio < 1e-5) expect_equal(results_sprtt@decision, results_original$decision) x_1 <- rnorm(5) x_2 <- rnorm(5) x <- c(x_1, x_2) y <- as.factor(c(rep(1,5),rep(2,5))) results_original <- sprt.t.test(x ~ y, d = d) results_sprtt <- seq_ttest(x ~ y, d = d) expect_true(results_sprtt@likelihood_ratio - results_original$statistic[[1]] < 1e-5) expect_equal(results_sprtt@decision, results_original$decision) x_1 <- rnorm(10) x_2 <- rnorm(10) x <- c(x_1, x_2) y <- as.factor(c(rep(1,10),rep(2,10))) results_original <- sprt.t.test(x_1, x_2, d = d) results_sprtt <- seq_ttest(x_1, x_2, d = d) expect_true(results_sprtt@likelihood_ratio - results_original$statistic[[1]] < 1e-5) expect_equal(results_sprtt@decision, results_original$decision) # same data, but different input results_sprtt <- seq_ttest(x_1, x_2, d = 0.5, paired = TRUE) results_sprtt2 <- seq_ttest(x ~ y, d = 0.5, paired = TRUE) expect_true(results_sprtt@likelihood_ratio_log - results_sprtt2@likelihood_ratio_log < 1e-5) expect_equal(results_sprtt@decision, results_sprtt2@decision) d <- 0.7 x <- rnorm(30) y <- rnorm(30) paired = TRUE t_test <- t.test(x, y, paired = paired) results_original <- sprt.t.test(x, y, d = d, paired = paired, alt = "two.sided") results_sprtt <- seq_ttest(x, y, d = d, paired = paired, alt = "two.sided") expect_true(results_sprtt@likelihood_ratio - results_original$statistic[[1]] < 1e-5) expect_equal(results_sprtt@decision, results_original$decision) results_original <- sprt.t.test(x, y, d = d, paired = paired, alt = "less") results_sprtt <- seq_ttest(x, y, d = d, paired = paired, alt = "less") expect_true(results_sprtt@likelihood_ratio - results_original$statistic[[1]] < 1e-5) expect_equal(results_sprtt@decision, results_original$decision) results_original <- sprt.t.test(x, y, d = d, paired = paired, alt = "greater") results_sprtt <- seq_ttest(x, y, d = d, paired = paired, alt = "greater") expect_true(results_sprtt@likelihood_ratio - results_original$statistic[[1]] < 1e-5) expect_equal(results_sprtt@decision, results_original$decision) }) # test_that("", { # # # })

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##Step1: Merges the training and the test sets to create one data set. #features trainData <- read.table("./train/X_train.txt") #dim(trainData) => 7352*561 testData <- read.table("./test/X_test.txt") #dim(testData) => 2947*561 Data <- rbind(trainData,testData) #dim(Data) => 10299*561 #activity labels trainLabel <- read.table("./train/y_train.txt") #dim(trainLabel) => 7352*1 testLabel <- read.table("./test/y_test.txt") #dim(testLabel) => 2947*1 Label <- rbind(trainLabel,testLabel) #dim(Label) => 10299*1 #subject trainSubject <- read.table("./train/subject_train.txt") #dim(trainSubject) => 7532*1 testSubject <- read.table("./test/subject_test.txt")#dim(testSubject) => 2947*1 Subject <- rbind(trainSubject,testSubject)#dim(Subject) => 10299*1 ##Step2: Extracts only the measurements on the mean and standard deviation for each measurement. features <- read.table("features.txt") #dim(features) => 561*2 (561rows features) meanstd <- grep("mean\$\$|std\$\$", features[, 2]) #find mean or std features Data <- Data[,meanstd] #dim(Data) => 10299*66, remove others(only choose mean and std features) names(Data) <- features[meanstd,2] #rename column names to features names(Data) <- gsub("\$\$",replacement="",names(Data)) #remove '(' and ')' names(Data) <- gsub("-",replacement="",names(Data)) #remove '-' ##Step3: Uses descriptive activity names to name the activities in the data set. activity <- read.table("activity_labels.txt") #6 activities Label[,1] <- activity[Label[,1],2] #rename (1,2...-> WALKING,WALKING_UPSTAIRS...) ##Step4: Appropriately labels the data set with descriptive activity names. names(Label) <- "activity" names(Subject) <- "subject" tidyData <- cbind(Subject,Label,Data) #merge 3 dataset #write.table(tidyData, "tidy_data.txt",row.name=FALSE) #creat a new file(first file) ##Step5: Creates a second, independent tidy data set with the average of each variable for each activity and each subject. subject_n <- split(tidyData$activity,tidyData$subject) #30 subjects subject_pre <- lapply(subject_n,length) #every subjects have how much datas(rows) upperb <- 1 #upper bound of split matrix lowerb <- 0 #lower bound of split matrix subjects <- length(table(Subject)) #30 subjects activities <- dim(activity)[1] #6 activities current <- matrix(NA, nrow=subjects*activities, ncol=dim(tidyData)[2]) #initial final dataset current <- as.data.frame(current) temp <- matrix(NA,nrow=activities,ncol=dim(tidyData)[2]-2) temp2 <- NULL current[,1] <- sort(rep(seq(1:subjects),activities)) #1 1 1 1 1 1 2 2 2 2 2 2 .... current[,2] <- rep(activity[,2],subjects) #WALKING,WALKING_UPSTAIRS... for(i in 1:subjects){ #30 subjects lowerb <- lowerb + as.numeric(subject_pre[i]) m <- tidyData[upperb:lowerb,] #split dataset into 6 subsets(subjects) for(k in 3:68){ #66 variables r <- tapply(m[,k],m$activity,mean) #mean temp[,k-2] <- r #bind 66 variables } temp2 <- rbind(temp2,temp) #bind 6 subjects upperb <- lowerb + 1 } for(i in 3:dim(tidyData)[2]){ #bind with subject and activity current[,i] <- temp2[,i-2] } colnames(current) <- names(tidyData) #rename column names write.table(current, "tidy_data_second.txt",row.name=FALSE) #creat a new file(second file)

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##### APUNTES DE CLASE 07/11/2019. Distancias ## LUCÍA SAIZ LAPIQUE library(readr) ## Cargamos los datos Dist <- read_delim("C:/Users/Luli/Desktop/TODO/GitHub/lulisaila-Master_Data_Science_CUNEF/Tecnicas de Agrupación y Segmentación/BBDD/EMDDistancias.csv", ";", escape_double = FALSE, trim_ws = TRUE) Dist # le quitamos la primera columna dist <- Dist[,-1] dist # convertimos la base de datos en una matriz particular: dist2 <- as.matrix(dist) dist2 # creo un objeto de ciudad con la primera columna que excluimos al principio ciudad <- Dist[,1] ciudad # transformamos matriz de distancias, donde tenia las distintas ciudades con sus # respectivas distancias, las situo en un mapa donde las tendre en funcion de su # distancia relativa # necesito saber cuales son las coordenadas en el grafico de las ciudades y posteriormente # le voy a pegar las etiquetas de su nombre a cada coordenada. d <- as.dist(as(dist, "matrix")) d # va cruzando Albacete con todas y pone todas las distancias. Todo lo que hay # en la diagonal de la matriz ppal. # El objeto d que hemos creado es ahora una matriz de distancias, no una cualquiera # obtengo los cuadrados de cada una de las distancias generando la matriz de # cuadrados. Todas las distancias euclideas (distancia "ordinaria" entre dos # puntos de un espacio eucl?deo, la cual se deduce a partir del teorema de # Pit?goras) trabajan con cuadrados dist_cuad <- dist2 * dist2 dist_cuad # matriz de doble centrado: matriz identidad (Mj) N * N, a la que se le resta # 1 / n (numero de variables) (en nuestro caso un septimo). Matriz de unos de # dimension n * n. # Objetivo: obtener la matriz "P" # Primero obtengo la matriz identidad 7x7: ident <- diag(7) # creo un vector con unos, repetidos 7 veces. Me crea un vector con fila de 7 unos que replico 7 veces uno <- rep(1, 7) Uno <- rep(uno, each = 7) # vector fila de 49 unos dim(Uno) = c(7, 7) # matriz 7x7 a partir del vector de unos Uno # Objetivo: J = ident - 1/n Uno # la divido en dos partes: jota_1 <- 1/7 * Uno jota <- ident - jota_1 jota # Aplicamos el doble centrado: # Producto matricial B_1 <- jota %*% dist_cuad %*% jota B <- -1/2 * B_1 round(B, 3) B # matriz cuadrada 7x7 # sacamos los autovectors y los autovalores de nuestra matriz AV <- eigen(B) AV AV$vec AV$values # no me interesan todos los autovalores y autovectores, solo los mayores dos # creamos objetos reducidos para la solucion de dos dimensiones AV_red <- AV$values[1:2] # le pedimos los dos mayores autovalores reducidos (los primeros) AV_red AVvect_red <- AV$vec[, 1:2] # pedidmos los dos mayores autovectores reducidos (los primeros) AVvect_red # Generamos la mtriz de coordenadas: voy a pasar de un espacio de 7x7 a uno de # dos dimensiones ?nicamente. Cada ciudad se refiere a otras seis, cada una # est? posicionada con respecto a otras seis. Quiero pasar esa informaciona un # espacio de dos dimensiones. # Necesito las coordenadas en el nuevos sistema de dos dimensiones. # Cuando paso a mas de tres es ijmposible representarlo en dos dimensiones # para obtener las coordenadas: necesito mi vector de autovalores reducido # (formado por lambda 1 y lambda 2) y la matriz E, formada por e1 y e2 (AVvect_red) # que voy a multiplicar por una matriz diagonal con lo que vamos a obtener la matriz # de coordenadas AV_red_mat <- sqrt(AV_red)*diag(2) # matriz diagonal con los autovalores reducidos de antes X <- AVvect_red %*% AV_red_mat # premultiplicar por los autovectores X # reultado: matriz de coordenadas X <- as.data.frame(X) # convertirlo en dataframe colnames(X) <- c("H", "V") # cambiar los nombres a coordenada horizontal y vertical X # data frame de posiciones donde Albacete esta posicion <- as.data.frame(cbind(ciudad, X)) posicion H <- posicion$H H V <- posicion$V V plot(H, V, main = "Posiciones relativas", xlab = "Dimension 1", ylab = "Dimension 2", col = "blue", pch = 19, cex = 1, lty = "solid", lwd = 2, frame.plot = FALSE) text(H, V, labels = ciudad, cex = 0.7, pos = 3) abline(h = c(0), v = (0), lty = 2, col = "blue") # nueva base de datos para hacerlo de otra forma corr <- read.csv("C:/Users/Luli/Desktop/TODO/GitHub/lulisaila-Master_Data_Science_CUNEF/Tecnicas de Agrupación y Segmentación/BBDD/EMDCorr.csv", header = TRUE, sep = ";") corr2 <- corr[, -1] corr2 var <- corr[, 1] str(var) str(corr2) d <- as.dist(as(corr2, "matrix")) d # libreria de escalado multidimensional # MDS clasico o analisis de coordenadas ppales ms <- cmdscale(dist(corr)) ms dist(ms) dim(ms) plot(ms[,1], ms[,2], xaxt = "n", yaxt = "n", xlab = "Dimension 1", ylab = "Dimension 2", type = "p", bty = "n", col = "blue", pch = 19, cex = 1, lty = "solid", lwd = 2, frame.plot = FALSE) axis(side = 1, at = seq(-1, 1.5, 0.5), lwd = 3) axis(side = 2, at = seq(-0.5, 0.5, 0.25), lwd = 3) text(ms[,1], ms[,2], labels = var, cex = 0.6, pos = 3) abline(h = c(0), v = (0), lty = 2, col = "blue")

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# making table data sets library(dplyr) library(tidyr) library(MorpheusData) #############benchmark 43 catalog <- read.table(text= "supplier_key part_id S1 P1 S1 P4 S2 P2 S2 P3 S3 P5 S4 P3 S4 P6 S5 P4 S5 P2 S6 P4 S7 P6 S8 P5 S8 P2 S9 P1 S10 P6 S11 P2 S11 P7", header=T) parts <- read.table(text= "part_id color P1 red P2 green P3 yellow P4 red P5 green P6 blue P7 green", header=T) suppliers <- read.table(text= "supplier_key sname S1 SN1 S2 SN2 S3 SN3 S4 SN4 S5 SN5 S6 SN6 S7 SN7 S8 SN8 S9 SN9 S10 SN10 S11 SN11", header=T) # write.csv(catalog, "sql/catalog.csv", row.names=FALSE) # catalog <- read.csv("sql/catalog.csv", check.names = FALSE) # fctr.cols <- sapply(catalog, is.factor) # int.cols <- sapply(catalog, is.integer) # catalog[, fctr.cols] <- sapply(catalog[, fctr.cols], as.character) # catalog[, int.cols] <- sapply(catalog[, int.cols], as.numeric) # save(catalog, file = "sql/catalog.rdata") # write.csv(suppliers, "sql/suppliers.csv", row.names=FALSE) # suppliers <- read.csv("sql/suppliers.csv", check.names = FALSE) # fctr.cols <- sapply(suppliers, is.factor) # int.cols <- sapply(suppliers, is.integer) # suppliers[, fctr.cols] <- sapply(suppliers[, fctr.cols], as.character) # suppliers[, int.cols] <- sapply(suppliers[, int.cols], as.numeric) # save(suppliers, file = "sql/suppliers.rdata") # write.csv(parts, "sql/parts.csv", row.names=FALSE) # parts <- read.csv("sql/parts.csv", check.names = FALSE) # fctr.cols <- sapply(parts, is.factor) # int.cols <- sapply(parts, is.integer) # parts[, fctr.cols] <- sapply(parts[, fctr.cols], as.character) # parts[, int.cols] <- sapply(parts[, int.cols], as.numeric) # save(parts, file = "sql/parts.rdata") # 5.2.10 # df1=inner_join(parts,catalog) %>% # inner_join(suppliers) %>% # filter (color == "green") %>% # group_by(color,sname) %>% # summarize(n = n()) %>% # ungroup() # df2=inner_join(parts,catalog) %>% # inner_join(suppliers) %>% # group_by(sname) %>% # summarize(n = n()) # inner_join(df1,df2) %>% # select(sname,n) input=inner_join(parts,catalog) %>% inner_join(suppliers) write.csv(input, "data-raw/s22_input1.csv", row.names=FALSE) s22_input1 <- read.csv("data-raw/s22_input1.csv", check.names = FALSE) fctr.cols <- sapply(s22_input1, is.factor) int.cols <- sapply(s22_input1, is.integer) s22_input1[, fctr.cols] <- sapply(s22_input1[, fctr.cols], as.character) s22_input1[, int.cols] <- sapply(s22_input1[, int.cols], as.numeric) save(s22_input1, file = "data/s22_input1.rdata") df1=input %>% filter(color != "green") %>% select(sname) df2=input %>% select(sname) df3=setdiff(df2,df1) df4=input %>% group_by(sname) %>% summarise(n = n()) output=inner_join(df4,df3) write.csv(output, "data-raw/s22_output1.csv", row.names=FALSE) s22_output1 <- read.csv("data-raw/s22_output1.csv", check.names = FALSE) fctr.cols <- sapply(s22_output1, is.factor) int.cols <- sapply(s22_output1, is.integer) s22_output1[, fctr.cols] <- sapply(s22_output1[, fctr.cols], as.character) s22_output1[, int.cols] <- sapply(s22_output1[, int.cols], as.numeric) save(s22_output1, file = "data/s22_output1.rdata") input=inner_join(parts,catalog) %>% inner_join(suppliers) df1=input %>% filter(color != "green") %>% select(sname) df2=input %>% select(sname) df3=setdiff(df2,df1) df4=input %>% group_by(sname) %>% summarise(n = n()) inner_join(df4,df3)

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/scrape_satr.R \name{scrape_satr} \alias{scrape_satr} \title{Scrape SATR scores from a student's transcript} \usage{ scrape_satr(.data) } \arguments{ \item{.data}{A character vector returned from the read_transcript function.} } \value{ A tibble of SATR test scores } \description{ Scrape SATR scores from a student's transcript } \examples{ \dontrun{ scrape_satr(dat) } }

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regtest-lmtree.R

suppressWarnings(RNGversion("3.5.2")) library("partykit") set.seed(29) n <- 1000 x <- runif(n) z <- runif(n) y <- rnorm(n, mean = x * c(-1, 1)[(z > 0.7) + 1], sd = 3) z_noise <- factor(sample(1:3, size = n, replace = TRUE)) d <- data.frame(y = y, x = x, z = z, z_noise = z_noise) fmla <- as.formula("y ~ x | z + z_noise") (m_mob <- mob(formula = fmla, data = d, fit = partykit:::lmfit)) (m_lm2 <- lmtree(formula = fmla, data = d)) mods <- nodeapply(m_lm2, ids = nodeids(m_lm2, terminal = TRUE), function(x) x$info$object) sum(sapply(mods, function(x) sum(x$residuals^2)))

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initPop.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/Initialization.R \name{initPop} \alias{initPop} \title{initPop} \usage{ initPop(dat, popSize = 30, genomes = NULL, fitfunc = "AIC", family = "gaussian") } \arguments{ \item{dat}{dataframe, data to operation(default sets dependent variable in first column and independent varialbes is other columns)} \item{popSize}{integer, population size(default=30)} \item{genomes}{list, composed of chromosomes and fitness value with lenght equal to population size(default=NULL)} \item{fitfunc}{method, model selection method(default is AIC)} \item{family}{family,for linear regression model the family should be a continuous probability density function (default is gaussian family)} } \description{ Initializes population }

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/R/eedb_compare_behavior.r

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eedb_compare_behavior.r

load.behavior.struct = function(gameId, path=paste0(ee$struc.path,"/Behavior/",gameId), files = NULL) { restore.point("load.behavior.struct") # By default load all behaviors associated with the game if (is.null(files)) files = list.files(path) name = paste0("behavior_",gameId) # Load behaviors from all files objs = lapply(files, function(f) { file = paste0(path,"/",f) load.struct(name=name,file=file,typeName="behaviors", just.obj=TRUE) }) # combine behaviors from all files obj = do.call(c,objs) # generate a structure struc = obj.to.struct(obj,name=name) struc } eval.behaviors = function(bs, game.dt) { restore.point("eval.behaviors") N = NROW(game.dt) brows = which(is.subtype(bs$df$type,"behavior")) bnames = bs$df$name[brows] nb = length(brows) bok = rep(FALSE,nb) benv.li = replicate(nb,new.env(parent=.GlobalEnv), simplify=FALSE) names(bok) = names(benv.li) = bnames while(TRUE) { had.import = FALSE some.checked = FALSE i = 1 for (i in seq_along(benv.li)) { if (bok[i]) next obj = bs$obj.li[[ brows[i] ]] some.checked = TRUE if (is.subtype(bs$df$type[brows[i]],"intersectBehavior")) { ret = import.intersectBehavior(i=i,obj=obj,bs=bs,benv.li=benv.li,bok=bok) message("intersectBehavior ",bnames[i], ": ", ret ) bok[i] = ret had.import = had.import | ret # A simple behavior consisting of several actions } else { bsi = obj.to.struct(obj,bnames[i]) ret = compute.variables(gs=bsi,venv=game.dt,denv=benv.li[[i]],N=N) had.import = bok[i] = TRUE } } if ((!had.import) | (!some.checked)) break } bdt.li = lapply(benv.li,data.env.to.dt) bdt.li } #' Gets a list containing action compartors for a given variable #' for different conditions get.actionComparator = function(var.name, gs, comparator.type = "nicer") { restore.point("get.actionComparator") vrows = which(gs$df$name == var.name & is.subtype(gs$df$type,"variable")) comps = NULL vrow = vrows[1] for (vrow in vrows) { obj = gs$obj.li[[vrow]] cond <- get.conditions(vrow,gs) comp = obj[[comparator.type]] if (!has._if.children(comp)) { attr(comp,"cond") <- cond comps = c(comps,list(comp)) # The actionComparator is splitted by different _if conditions # add a separate actionComparator for each condition } else { upcomp = move._if.upwards(comp) i = 1 for (i in seq_along(upcomp)) { actcond = combine.conditions(cond,upcomp[[i]][[1]]) attributes(actcond) = NULL actcomp = upcomp[[i]][[2]] attr(actcomp,"cond") <- actcond comps = c(comps,list(actcomp)) } } } return(comps) } examples.get.actionComparator = function() { setwd("C:/libraries/ExpEconDB") init.ee("C:/libraries/ExpEconDB") gs = load.game.struct("LureOfAuthority") get.actionComparator("recommendation",gs,comparator.type="nicer") } get.actionComparator.points = function(val, comp, game.dt, N= NROW(game.dt)) { restore.point("get.actionComparator.points") if (!is.null(comp$points)) { comp.points = names(comp$points) comp.points = lapply(comp.points,function(p) rep(eval(parse(text=p),game.dt),length.out=N)) other.ind = which(sapply(comp$points, function(p) p == "_other")) if (length(other.ind)>0) { other.points = comp.points[[other.ind]] comp.points = comp.points[-other.ind] comp$points = comp$points[-other.ind] } else { other.points = NA } comp.val = lapply(comp$points,function(p) { return(eval(parse(text=p),game.dt)) }) points = rep(other.points, length.out = N) for (i in seq_along(comp.points)) { rows = which(is.true(val == comp.val[[i]])) points[rows] = comp.points[[i]][rows] } points[is.na(val)] = NA return(points) } else if (!is.null(comp$order)) { if (comp$order == "decreasing") return(-val) return(val) } } var.compare.to.behavior = function(var,bdt,game.dt,gs,comparator.type="nicer", N= NROW(game.dt)) { restore.point("var.compare.to.behavior") comps = get.actionComparator(var,gs,comparator.type=comparator.type) val = rep(NA,N) i = 1 for (i in seq_along(comps)) { comp = comps[[i]] gpoints = get.actionComparator.points(val = game.dt[[var]], comp=comp,game.dt = game.dt) bpoints = get.actionComparator.points(val = bdt[[var]], comp=comp,game.dt = game.dt) cond = attr(comp,"cond") if (!is.null(cond)) { rows = which(eval(parse(text=cond),game.dt) & !is.na(bpoints)) } else { rows = which(!is.na(bpoints)) } arows = rows[gpoints[rows]>bpoints[rows]] val[arows] = 1 arows = rows[gpoints[rows]<bpoints[rows]] val[arows] = -1 arows = rows[gpoints[rows]==bpoints[rows]] val[arows] = 0 } return(val) } examples.var.compare.to.behavior = function() { setwd("C:/libraries/ExpEconDB") init.ee("C:/libraries/ExpEconDB") gameId = "LureOfAuthority" bs = load.behavior.struct(gameId) game.dt = load.game.data(gameId) gs = load.game.struct(gameId) bdt.li = eval.behaviors(bs,game.dt=game.dt) var.compare.to.behavior("recommendation",bdt = bdt.li$informedRecommendPreferred,game.dt=game.dt,gs=gs) compare.to.behavior(bdt = bdt.li$informedRecommendPreferred,game.dt=game.dt,gs=gs) bc = compare.to.behaviors(bdt.li,game.dt=game.dt,gs=gs) tabulate.behavior.comparisons(bc) } #' Compare actual data with a specified behavior, e.g. w.r.t. niceness compare.to.behavior = function(bdt,game.dt,gs, comparator.type = "nicer", T=NROW(game.dt)) { restore.point("is.nicer.than.behavior") val = rep(NA,T) col = names(bdt)[1] for (col in names(bdt)) { col.val = var.compare.to.behavior(col,bdt = bdt,game.dt=game.dt,gs=gs, comparator.type=comparator.type) NaN.rows = is.true(val * col.val == -1 | is.nan(val)) One.rows = is.true(!NaN.rows & ((val + col.val >= 1) | ( is.na(val) & col.val==1))) Neg.rows = is.true(!NaN.rows & ((val + col.val <= -1) | ( is.na(val) & col.val==-1))) Zero.rows = is.true((val == 0 | is.na(val)) & col.val == 0) val[NaN.rows] = NaN val[One.rows] = 1 val[Neg.rows] = -1 val[Zero.rows] = 0 } val } #' Compare actual data with a specified behavior, e.g. w.r.t. niceness compare.to.behaviors = function(bdt.li,game.dt,gs, comparator.type = "nicer") { restore.point("is.nicer.than.behavior") lapply(bdt.li,compare.to.behavior, game.dt=game.dt,gs=gs,comparator.type=comparator.type) } # Which rows in data are consistent with a specified behavior? # returns NA for rows for which the conditions of the behavior are not satisfied matches.behavior = function(bdt,game.dt) { restore.point("matches.behavior") same = rep(TRUE,N) for (col in names(bdt)) { same = same & game.dt[[col]] == bdt[[col]] } same } # Which rows in data are consistent with a specified behavior? # returns NA for rows for which the conditions of the behavior are not satisfied matches.behaviors = function(bdt.li,game.dt) { li = lapply(bdt.li,matches.behavior, game.dt=game.dt) do.call(data.table,li) } import.intersectBehavior = function(i,obj,bs,benv.li,bok) { restore.point("import.intersectBehavior") subs = obj$behaviors if (!all(bok[subs])) return(FALSE) benv = benv.li[[i]] for (sub in subs) { sub.env = benv.li[[sub]] vars = objects(sub.env) for (v in vars) { if (!exists(v,benv)) { benv[[v]] = sub.env[[v]] } else { rows = !is.na(sub.env[[v]]) benv[[v]][rows] = sub.env[[v]][rows] } } } return(TRUE) } tabulate.behavior.comparisons = function(bc, comparator.type = "nicer") { li = lapply(bc,function(val) { list("bigger" = sum(is.true(val==1)), "equal" = sum(is.true(val==0)), "smaller" = sum(is.true(val==-1)),"not.comparable" = sum(is.nan(val)), "NA" = sum(is.na(val)) ) }) df = as.data.frame(rbindlist(li)) rownames(df) = names(bm) df$bigger.share = round(df$bigger / (df$bigger+df$equal+df$smaller+df$not.comparable),4) df$equal.share = round(df$equal / (df$bigger+df$equal+df$smaller+df$not.comparable),4) df$smaller.share = round(df$smaller / (df$bigger+df$equal+df$smaller+df$not.comparable),4) if (comparator.type == "nicer") { colnames(df) = c("nicer","as.nice","less.nice","not.comparable", "NA","nicer.share","as.nice.share","less.nice.share") } df } examples.tabulate.behavior.comparisons = function() { setwd("C:/libraries/ExpEconDB") init.ee("C:/libraries/ExpEconDB") gameId = "LureOfAuthority" bs = load.behavior.struct(gameId) game.dt = load.game.data(gameId) gs = load.game.struct(gameId) bdt.li = eval.behaviors(bs,game.dt=game.dt) bc = compare.to.behaviors(bdt.li,game.dt=game.dt,gs=gs) tabulate.behavior.comparisons(bc) } tabulate.behavior.matches = function(bm) { li = lapply(bm,function(val) { list(true = sum(is.true(val)), false = sum(is.false(val)), "NA" = sum(is.na(val)) ) }) df = as.data.frame(rbindlist(li)) rownames(df) = names(bm) df$true.share = round(df$true / (df$true+df$false),4) df$na.share = round(df[["NA"]] / (df$true+df$false+df[["NA"]]),4) df } examples.load.behavior.struct = function() { setwd("C:/libraries/ExpEconDB") init.ee("C:/libraries/ExpEconDB") gameId = "LureOfAuthority" bs = load.behavior.struct(gameId) bs bs$obj.li[[97]] dt.li = tableBehavior.comp.stat.dt(bs=bs,vars=c("searchP","searchA","util_1","util_2"),gameId=gameId) dt = dt.li$util_1 d = dt[key == "delegate",] d$better.del = d$val1 <= d$val2 setkeyv(d,c("key","var","behavior", "variant", "keyv1","keyv2")) d game.dt = load.game.data(gameId) bdt.li = eval.behaviors(bs,game.dt=game.dt) bm = matches.behaviors(bdt.li,game.dt) tabulate.behavior.matches(bm) dt = cbind(game.dt,be) } tableBehavior.comp.stat.dt = function(bs,vars, be = NULL, keyCols=NULL,gameId=NULL) { restore.point("tableBehavior.comp.stat") ret = combine.tableBehavior(bs=bs,vars=vars,be=be,keyCols=keyCols,gameId=gameId) be = ret$be; vars=ret$vars; keyCols=ret$keyCols; be.dt=ret$dt be.dt bk.cols = c("behavior",keyCols) # Simply remove multiple equilibria!!!!! # Need to correct!!! be.dt = be.dt[!duplicated(be.dt[,bk.cols,with=FALSE]),] bk.list = lapply(bk.cols,function(col) unique(be.dt[[col]])) names(bk.list) = bk.cols var = "util_1" key = "delegate" csgrid.li = lapply(vars, function(var) { grid.li = lapply(bk.cols, function(key) { myvar = var restore.point("jhfjsdhfj") var = myvar vals = bk.list[[key]] if (length(vals)<=1) return(NULL) # All possible value combinations vg = expand.grid(list(seq_along(vals),seq_along(vals))) vg = vg[vg[,1]<vg[,2],] vg[] = vals[as.matrix(vg)] names(vg) = c("keyv1","keyv2") vg other.cols = setdiff(bk.cols,key) grid.list = c(list(vg.ind = 1:NROW(vg)),bk.list[other.cols]) gr = as.data.table(expand.grid(grid.list)) i = 1 gr[[key]] = vg[gr$vg.ind,i] mgr = merge(gr,be.dt, all.y=FALSE, by = bk.cols) dt = mgr[,c(bk.cols,"vg.ind"),with=FALSE] dt$keyv1 = vg[mgr$vg.ind,i] dt$val1 = mgr[[var]] i = 2 gr[[key]] = vg[gr$vg.ind,i] mgr = merge(gr,be.dt, all.y=FALSE, by = bk.cols) dt$keyv2 = vg[mgr$vg.ind,i] dt$val2 = mgr[[var]] dt$key = key dt$var = var dt[,vg.ind:=NULL] dt[[key]] = vals[1] setcolorder(dt,c(bk.cols,c("var", "key", "keyv1","keyv2","val1","val2"))) dt }) rbindlist(grid.li) }) names(csgrid.li)= vars csgrid.li } # Do not deal with strategyMethod yet make.obsTableBehavior = function(gameId,vars, keyCols, meanMedian="mean",obsData=NULL, util=NULL,n=NULL) { restore.point("make.obsTableBehavior") # Load data if (is.null(obsData)) { obsData = load.game.data(gameId=gameId) } # Get number of players if (is.null(n)) { str = colnames(obsData) str = str[str.starts.with(str,"player_")] str = str.split(str,"_") iv = sapply(str,function(v) as.numeric(v[2])) n = max(iv) } vars = union(vars,paste0("util_",1:n)) # Get utility formula if (is.null(util)) { util = list("payoffUtil",n=n) } util.formula = do.call(util[[1]],util[-1]) # Add utility to observed data for (i in 1:n) { run.dt(obsData,paste0("util_",i," := ", util.formula[i])) } # Make obsData smaller obsData = obsData[,c(keyCols,vars),with=FALSE] # Aggregate data by keyCol fun = meanMedian[1] code = paste0("list(", paste0(vars,"=",fun,"(",vars,")", collapse=","), ")") data = run.dt(obsData,code,by=keyCols) # Generate name and shortName name = paste0("obs_",gameId,"_",paste0(util[[1]]),"_",paste0(sample(c(1:9,LETTERS),5),collapse="")) arg.names = setdiff(names(util[-1]),"n") if (length(arg.names)>0) { args = substring(as.character(unlist(util[arg.names])),1,4) shortName = paste0("obs_",meanMedian[1],"_",str.replace(util[[1]],"Util","")[[1]],"_",paste0(args,collapse="_")) } else { shortName = paste0("obs_",meanMedian[1],"_",str.replace(util[[1]],"Util","")[[1]]) } # Generate object values = list(sourceGameId=gameId,destGameId=gameId,shortName=shortName,numPlayers=n,numRows=NROW(data),keyCols=keyCols,meanMedian=meanMedian, actions=setdiff(vars,paste0("util_",1:n)), multiple=FALSE,obsData=obsData,data=data) obj = new.obj(typeName="obsTableBehaviors",name=name,values = values) obj } examples.make.obsTableBehavior = function() { gameId = "LureOfAUthority" vars = c("searchP","searchA","util_1","util_2") keyCols = c("variant","delegate") make.obsTableBehavior(gameId=gameId,vars=vars,keyCols=keyCols) } combine.tableBehavior = function(bs,vars,be=NULL,keyCols=NULL, add.obs=TRUE, gameId, meanMedian="mean") { restore.point("tableBehavior.comp.stat") # Find relavant behaviors manually if (is.null(be)) { rows = is.subtype(bs$df$type,"tableBehaviors") objs = bs$obj.li[rows] names(objs) = bs$df$name[rows] has.action = sapply(objs, function(obj) all(vars %in% names(obj$data))) objs = objs[has.action] be = names(objs) } else { objs = bs$obj.li[[1]][be] } # Find common keyCols if (is.null(keyCols)) { li = lapply(objs, function(obj) obj$keyCols) keyCols = intersect.vector.list(li) } if (add.obs) { obs.obj = make.obsTableBehavior(gameId=gameId,vars=vars,keyCols=keyCols) obs.objs = list(obs.obj) names(obs.objs) = get.name(obs.obj) objs = c(objs,obs.objs) } dt = rbindlist(lapply(objs, function(obj) { restore.point("hshfghsf") dt = obj$data[,c(keyCols,vars),with=FALSE] dt[,"behavior":=obj$shortName,with=FALSE] setcolorder(dt,c("behavior",keyCols,vars)) dt })) return(list(be=be,keyCols=keyCols,vars=vars,dt=dt)) }

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/R/nb.glm.dispersion.models.v1.R

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nb.glm.dispersion.models.v1.R

##' Specify a dispersion model. The parameters of the specified model ##' are to be estimated from the data using the function ##' \code{optim.disp.apl} or \code{optim.disp.pl}. ##' ##' This functions calls \code{disp.fun.<model>} to specify a ##' dispersion model (a list), using output from a call to ##' \code{disp.predictor.<predictor>} as argument list, where ##' \code{<model>} is \code{model} from the input in lower case (one ##' of "nb2", "nbp", "nbq", "nbs" or "step") and \code{<predictor>} is ##' \code{predictor} from the input (one of "pi", "mu", or "rs") ##' ##' @title (private) Specify a dispersion model ##' @param nb NB data, output from \code{\link{prepare.nb.data}} ##' @param x a matrix, design matrix (specifying the treatment structure). ##' @param model a string giving the name of the disperion model, ##' can be one of "NB2", "NBP", "NBQ", "NBS" or "step" (not case ##' sensitive). ##' @param predictor a string giving the name of the predictor to use ##' in the dispersion model, can be one of "pi" and "mu", or "rs". ##' \code{"pi"}, preliminarily estimated mean relative frequencies; \code{"mu"}, preliminarily estimated ##' mean frequencies; \code{"rs"}, row sums. ##' @param subset a list of logical, ##' @param ... additional parameter to \code{disp.fun.*} ##' @return a list, output from the call to the funtion \code{disp.fun.<model>}. make.disp = function(nb.data, x, model, predictor, subset=filter.mu.pre(nb.data, x), ...) { predictor.fun.name = paste("disp.predictor", predictor, sep="."); predictor = do.call(predictor.fun.name, list(nb.data=nb.data, x=x)); disp.fun.name = paste("disp.fun", tolower(model), sep="."); do.call(disp.fun.name, c(predictor, list(subset=subset), ...)); } ##' Specify a dispersion model where the parameters of the ##' model will be estimated separately for different groups ##' ##' @title Specify a dispersion model where the parameters of the ##' model will be estimated separately for different groups ##' @param disp.fun ##' @param grp.ids ##' @param predictor ##' @param subset ##' @param predictor.label ##' @param ... ##' @return a list, disp.by.group = function(disp.fun, grp.ids, predictor, subset, predictor.label="Predictor", ...) { ## grp.ids = rep(1:4, each=3); ## grp.ids = rep(1, 3); m = length(grp.ids); grps = split(1:m, grp.ids); n.grps = length(grps); res = lapply(grps, function(grp) do.call(disp.fun, list(predictor=predictor[,grp,drop=FALSE], subset=subset, predictor.label=predictor.label, ...))); ## This seems tedious par.init = res[[1]]$par.init; n.pars = length(par.init); par.ids = list(1:n.pars); par.lower = res[[1]]$par.lower; par.upper = res[[1]]$par.upper; funs = list(res[[1]]$fun); offset = res[[1]]$offset; if (n.grps > 1) { for (i in 2:n.grps) { par.init = c(par.init, res[[i]]$par.init); par.lower = c(par.lower, res[[i]]$par.lower); par.upper = c(par.upper, res[[i]]$par.upper); funs[[i]] = res[[i]]$fun offset = c(offset, res[[i]]$offset); par.ids[[i]] = n.pars * (i-1) + 1:length(res[[i]]$par.init); } } env = new.env(parent=baseenv()); assign("m", nrow(predictor), envir=env) assign("n", ncol(predictor), envir=env) assign("funs", funs, envir=env) assign("grps", grps, envir=env) assign("n.grps", n.grps, envir=env) assign("par.ids", par.ids, envir=env) fun=function(par){ x = matrix(0, m, n); for (i in 1:n.grps) { x[,grps[[i]]]=funs[[i]](par[par.ids[[i]]]); } x } environment(fun) = env; list(name=paste(res[[1]]$name, "by group"), fun=fun, par.init=par.init, par.lower=par.lower, par.upper=par.upper, subset=subset, predictor=predictor, offset=offset, predictor.label = predictor.label, grp.ids = grp.ids) } ##' @name Dispersion Models ##' ##' @title (private) Specify a NB2, NBP, NBS, NBS, or STEP dispersion model ##' ##' @details Specify a NBP dispersion model. The parameters of the specified ##' model are to be estimated from the data using the function ##' \code{optim.disp.apl} or \code{optim.disp.pl}. ##' ##' Under the NBP model, the log dispersion is modeled as a linear ##' function of specified predictor with a scalar offset, ##' ##' log(phi) = par[1] + par[2] * log(predictor/offset). ##' ##' Under this parameterization, par[1] is the dispersion value when ##' the value of predictor equals the offset. This function will ##' return a function (and related settings) to be estimated by either ##' \code{optim.disp.apl} or \code{optim.disp.pl}. The logical vector ##' \code{subset} specifieds which rows will be used when estimating ##' the paramters (\code{par}) of the dispersion model. ##' ##' Once estimated, the dispersion function will be applied to all ##' values of the \code{predictor} matrix. Care needs to be taken to ##' either avoid \code{NA/Inf} values when preparing the predictor ##' matrix or handle \code{NA/Inf} values afterwards (e.g., when ##' performing hypothesis tests). ##' ##' @rdname disp.fun ##' @param predictor a m-by-n matrix having the same dimensions as the NB counts, predictor of the dispersion. See Details. ##' @param subset a logical vector of length \eqn{m}, specifying the subset of rows to be used when estimating the dispersion model parameters. ##' @param offset a scalar offset. ##' @param predictor.label a string describing the predictor ##' @param par.init a numeric vector, initial values of par. ##' @param label a string character describing the predictor. ##' @param par.lower a numeric vector, lower bounds of the parameter values. ##' @param par.upper a numeric vector, upper bounds of the parameter values. ##' @return a list ##' \item{fun}{a function that takes a vector, \code{par}, as ##' input and outputs a matrix of dispersion values (same dimension as ##' counts)} ##' \item{par.init, par.lower, par.upper}{same as input} ##' \item{subset}{same as input} ##' \item{predictor, offset, predictor.lable}{same as input} disp.fun.nb2 = function(predictor, subset, offset=NULL, predictor.label="Predictor", par.init = -1 ) { ## 2014-04-21 env = new.env(parent=baseenv()); assign("d", dim(predictor), envir=env) fun=function(par){ array(exp(par), d); } environment(fun) = env; list(name="NB2", fun = fun, par.init = par.init, subset=subset, predictor=predictor, predictor.label=predictor.label) } ##' @rdname disp.fun disp.fun.nbp = function(predictor, subset, offset=median(predictor[subset,]), predictor.label="Predictor", par.init = c(log(0.1), 0), par.lower = c(log(1e-20), -1.1), par.upper = c(0, 0.1) ) { ## 2014-04-18 ## 2014-04-20 env = new.env(parent=baseenv()); assign("z", log(predictor/offset), envir=env) fun=function(par){ exp(par[1] + par[2] * z); } environment(fun) = env; list(name="NBP", fun = fun, par.init = par.init, par.lower = par.lower, par.upper = par.upper, subset=subset, predictor=predictor, offset=offset, predictor.label=predictor.label) } ##' @rdname disp.fun disp.fun.nbq = function(predictor, subset, offset=median(predictor[subset,]), predictor.label="Predictor", par.init = c(log(0.1), 0, 0), par.lower = c(log(1e-20), -1.0, -0.2), par.upper = c(0, 1.0, 0.2) ) { ## 2014-04-18 ## 2014-04-20 env = new.env(parent=baseenv()); assign("z", log(predictor/offset), envir=env) fun = function(par) { exp(par[1] + par[2] * z + par[3] * z^2); } environment(fun) = env; list(name="NBQ", fun = fun, par.init = par.init, par.lower = par.lower, par.upper = par.upper, subset=subset, predictor=predictor, offset=offset, predictor.label =predictor.label) } ##' @rdname disp.fun disp.fun.nbs = function(predictor, subset, offset=NULL, predictor.label="Predictor", df = 6, par.init = rep(-1, df) ) { ## 2014-04-18 z = as.vector(log(predictor)); ## Specify the boundary knots Boundary.knots = range(z[subset]); ## Boundary.knots = c(1e-4, quantile(z, 0.99)); ## Specify the knots ## It is not clear yet how to select the knots zs = sort(z[subset]); m = length(zs); l = quantile(z[subset], 0.05); r = quantile(z[subset], 0.95); ## if (m > 300) { ## l = max(l, zs[101]+0.01); ## r = min(r, zs[m-100]); ## } knots = seq(l, r, length=df-2); ## Specify the spline basis by the providing the knots ## s = ns(z, df = df); s = ns(z, knots=knots, Boundary.knots = Boundary.knots, intercept=TRUE); ## Specify the dispersion function as a function of par ## Create a minimal enviroment for the function env = new.env(parent=baseenv()); assign("s", s, envir=env) assign("d", dim(predictor), envir=env) fun = function(par) { phi = exp(s %*% par); dim(phi) = d; phi } environment(fun) = env; list(name="NBS", fun = fun, par.init = par.init, subset=subset, predictor=predictor, offset=offset, predictor.label =predictor.label) } ##' @rdname disp.fun disp.fun.step = function(predictor, subset, offset=NULL, predictor.label="Predictor", df = 6, knots = NULL, par.init = rep(-1, df) ) { ## 2014-04-21 z = log(predictor); if (is.null(knots)) { p = seq(0, 1, length=df+1)[-c(1, df+1)]; knots = quantile(z[subset], p); } else { knots = sort(knots); } ## Specify the starting and ending positions of each step l = c(min(z, knots)-0.01, knots); r = c(knots, max(z, knots)+0.01); ## Identify indices of z values belonging to each step ids = list(df); m = length(z); for (i in 1:df) { ids[[i]] = (1:m)[z>=l[i] & z< r[i]]; } d = dim(predictor); ## Specify the dispersion function as a function of par ## Create a minimal enviroment for the function env = new.env(parent=baseenv()); assign("df", df, envir=env) assign("ids", ids, envir=env) assign("d", d, envir=env) assign("knots", knots, envir=env) fun = function(par) { lphi = array(NA, d); for (i in 1:df) { lphi[ids[[i]]] = par[i]; } exp(lphi) } environment(fun) = env; ## Specify the lower and upper bounds for the components of par ## Not needed list(name="step", fun = fun, par.init = par.init, subset=subset, predictor=predictor, predictor.label = predictor.label) } ##' Create a logical vector specifyfing the subset of rows to be used when estimating the dispersion model ##' ##' @title Create a logical vector specifyfing the subset of rows to be used when estimating the dispersion model ##' @param nb ##' @param x ##' @param mu.lower ##' @param mu.upper ##' @return a logical vector specifyfing the subset of rows to be used when estimating the dispersion model filter.mu.pre = function(nb.data, x, mu.lower=1, mu.upper=Inf, phi.pre=0.1) { ## Fit NB regression models to the rows of the count matrix using a ## preliminary constant dispersion parameter, phi0. obj = irls.nb(nb.data$counts, nb.data$eff.lib.sizes, x, phi=phi.pre); ## Obtain preliminary estimates of the mean relative frequencies. mu.pre = obj$mu; ## Specify the subset of rows to be used when estimating the dispersion model subset = rowSums(is.na(mu.pre) | mu.pre<mu.lower | mu.pre>mu.upper)==0; } disp.predictor.pi = function(nb.data, x, phi.pre=0.1) { ## preliminary constant dispersion parameter, phi0. obj = irls.nb(nb.data$counts, nb.data$eff.lib.sizes, x, phi=phi.pre, beta0 = rep(NA, dim(x)[2])); ## Obtain preliminary estimates of the mean relative frequencies. mu.pre = obj$mu; pi.pre = t(t(mu.pre)/nb.data$eff.lib.sizes); ## Replace extremely small pi values ## 11-22-2013 eps = 1/sum(nb.data$eff.lib.sizes); pi.pre[pi.pre<eps] = eps; list(predictor = pi.pre, predictor.label = "pi.pre"); } ##' Dispersion precitor ##' @title Dispersion precitor ##' @param nb.data ##' @param x ##' @param phi.pre ##' @param mu.lower ##' @param mu.upper ##' @return a logical vector disp.predictor.mu = function(nb.data, x, phi.pre=0.1, mu.lower=1, mu.upper=Inf) { ## Fit NB regression models to the rows of the count matrix using a ## preliminary constant dispersion parameter, phi0. obj = irls.nb(nb.data$counts, nb.data$eff.lib.sizes, x, phi=phi.pre, beta0 = rep(NA, dim(x)[2])); ## Obtain preliminary estimates of the mean relative frequencies. mu.pre = obj$mu; ## Replace extremely small pi values ## 11-22-2013 eps = 1/ncol(nb.data$counts); mu.pre[mu.pre<eps] = eps; list(predictor = mu.pre, predictor.label = "mu.pre"); } disp.predictor.rs = function(nb.data, x, phi.pre=0.1, mu.lower=1, mu.upper=Inf) { rs = array(rowSums(nb.data$counts), dim(nb.data$counts)); ## Replace extremely small row sums eps = 1/ncol(nb.data$counts); rs[rs<eps] = eps; list(predictor = rs, predictor.label = "row sum"); }

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# # This is a Shiny web application. You can run the application by clicking # the 'Run App' button above. # # Find out more about building applications with Shiny here: # # http://shiny.rstudio.com/ # library(shiny) library(shinydashboard) library(caret) library(randomForest) classificationModel= readRDS("./classification_model.rds") print("model") print(classificationModel) regressionModel= readRDS("./regression_model.rds") ui <- dashboardPage( dashboardHeader(title = "Prediction Dashboard"), dashboardSidebar( sidebarMenu( h3("Prediction Types"), menuItem("Classification", tabName = "classification", icon = icon("dashboard")), menuItem("Regression", tabName = "regression", icon = icon("th")), h3("VLE Data"), numericInput("total_clicks", "Total Clicks", value = 100), numericInput("total_elements", "Total Elements", value = 100), sliderInput("active_days", "Active Days", min = 0, max = 50, value = 25), numericInput("average_daily_clicks", "Average Daily Clicks", value = 25), numericInput("average_elements", "Average Daily Elements", value = 25), h3("Assessment Data"), sliderInput("avgScore", "Average Score", min = 0, max = 100, value = 50), numericInput("delivered", "Deliverd Assessments", value = 5), numericInput("sumDelays", "Delayed Days", value = 0) ) ), dashboardBody( tabItems( # First tab content tabItem(tabName = "classification", fluidRow( box(title="Student Info", radioButtons("gender", "Gender", choices = list("Male" = "M", "Female" = "F"),selected = "M"), selectInput("region", "Region", choices = list("East Anglian Region"="East Anglian Region", "Yorkshire Region"="Yorkshire Region", "East Midlands Region"="East Midlands Region", "South East Region"="South East Region", "North Western Region"= "North Western Region", "Scotland"="Scotland", "South West Region"="South West Region", "West Midlands Region"="West Midlands Region", "Wales"="Wales", "Ireland"="Ireland", "South Region"="South Region", "London Region"="London Region", "North Region"="North Region"),selected = "London Region"), selectInput("highest_education", "Highest Level of Education", choices =list("A Level or Equivalent", "Lower Than A Level", "HE Qualification", "Post Graduate Qualification", "No Formal quals" ), selected="HE Qualification"), selectInput("imd_band", "IMD Band", choices =list("0-10%", "20-30%", "30-40%", "40-50%", "50-60%", "60-70%", "70-80%", "80-90%", "90-100%" ), selected="50-60%"), selectInput("age_band", "Age Band", choices =list("0-35", "35-55", "55<=" ), selected="0-35"), numericInput("num_of_prev_attempts", "Previous Attempts", value = 0), numericInput("studied_credits", "Studied Credits", value = 60), radioButtons("disability", "Disability", choices = list("Yes" = "Y", "No" = "N"),selected = "N") ), valueBoxOutput("classificationPrediction"), ), ), tabItem(tabName = "regression", fluidRow( valueBoxOutput("regressionPrediction"), ) ) ), # Second tab content ) ) server <- function(input, output) { output$classificationPrediction <- renderValueBox({ dataset=data.frame("total_clicks"=input$total_clicks, "total_elements"=input$total_elements, "active_days"= input$active_days, "average_daily_clicks"=input$average_daily_clicks, "average_elements" = input$average_elements, "avgScore" = input$avgScore, "delivered" =input$delivered, "sumDelays" = input$sumDelays, "gender"= input$gender, "region"= input$region, "highest_education"= input$highest_education, "imd_band"=input$imd_band, "age_band"=input$age_band, "num_of_prev_attempts"=input$num_of_prev_attempts, "studied_credits"=input$studied_credits, "disability"=input$disability, "final_result"=NA ) print(classificationModel) predictedValue=predict(classificationModel,dataset) print(predictedValue) valueBox( ifelse(predictedValue[1]=="Pass","Pass","Fail"),"Prediction", icon = icon(ifelse(predictedValue[1]=="Pass","check","exclamation")), color = ifelse(predictedValue[1]=="Pass","green","red") ) }) output$regressionPrediction <- renderValueBox({ datasetRegression=data.frame("total_clicks"=input$total_clicks, "total_elements"=input$total_elements, "active_days"= input$active_days, "average_daily_clicks"=input$average_daily_clicks, "average_elements" = input$average_elements, "avgScore" = input$avgScore, "delivered" =input$delivered, "sumDelays" = input$sumDelays, "score"=NA ) value=predict(regressionModel,datasetRegression) valueBox( format(value[1], digits=2, nsmall=2),"Final Grade", icon = icon(ifelse(value[1]>70,"check",ifelse(value[1]>50,"exclamation","times"))), color = ifelse(value[1]>70,"green",ifelse(value>50,"yellow","red")) ) }) } shinyApp(ui, server)

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## ----setup, include=FALSE----------------------------------------------------- library(knitr) opts_chunk$set(fig.align = "center", out.width = "70%", fig.width = 5, fig.height = 4, dev.args=list(pointsize=10), par = TRUE, # needed for setting hook collapse = TRUE, # collapse input & ouput code in chunks warning = FALSE) knit_hooks$set(par = function(before, options, envir) { if(before && options$fig.show != "none") par(mar=c(4.1,4.1,1.1,1.1), mgp=c(3,1,0), tcl=-0.5) }) # setupKnitr() # knit_hooks$set(rgl = hook_webgl) set.seed(1) # for exact reproducibility ## ----------------------------------------------------------------------------- library(msir) ## ----------------------------------------------------------------------------- n <- 200 p <- 5 b <- as.matrix(c(1,-1,rep(0,p-2))) x <- matrix(rnorm(n*p), nrow = n, ncol = p) y <- exp(0.5 * x%*%b) + 0.1*rnorm(n) MSIR <- msir(x, y) summary(MSIR) plot(MSIR, type = "evalues") plot(MSIR, type = "coefficients", which = 1) plot(MSIR, type = "2Dplot") ## ----------------------------------------------------------------------------- n <- 200 p <- 5 b <- as.matrix(c(1,-1,rep(0,p-2))) x <- matrix(rnorm(n*p), nrow = n, ncol = p) y <- (0.5 * x%*%b)^2 + 0.1*rnorm(n) MSIR <- msir(x, y) summary(MSIR) plot(MSIR, type = "evalues") plot(MSIR, type = "coefficients", which = 1) plot(MSIR, type = "2Dplot") ## ----------------------------------------------------------------------------- n <- 300 p <- 5 b1 <- c(1, 1, 1, rep(0, p-3)) b2 <- c(1,-1,-1, rep(0, p-3)) b <- cbind(b1,b2) x <- matrix(rnorm(n*p), nrow = n, ncol = p) y <- x %*% b1 + (x %*% b1)^3 + 4*(x %*% b2)^2 + rnorm(n) MSIR <- msir(x, y) summary(MSIR) plot(MSIR, type = "evalues") plot(MSIR, type = "coefficients", which = 1:2) plot(MSIR, which = 1:2) plot(MSIR, which = 1, type = "2Dplot", span = 0.7) plot(MSIR, which = 2, type = "2Dplot", span = 0.7) ## ---- eval=requireNamespace("rgl", quietly = TRUE), echo=1, fig.width = 4, fig.height = 4---- plot(MSIR, type = "spinplot") rgl::rglwidget(width=500, height=450) ## ---- eval=requireNamespace("rgl", quietly = TRUE), echo=1, fig.width = 4, fig.height = 4---- plot(MSIR, type = "spinplot", span = 0.75) rgl::rglwidget(width=500, height=450) ## ----------------------------------------------------------------------------- x1 <- rnorm(100) x2 <- rnorm(100) y <- 2*x1 + x2^2 + 0.5*rnorm(100) ## ---- eval=requireNamespace("rgl", quietly = TRUE), echo=1-------------------- spinplot(x1, y, x2) rgl::rglwidget(width=500, height=450) ## ---- eval=requireNamespace("rgl", quietly = TRUE), echo=1-------------------- spinplot(x1, y, x2, scaling="aaa") rgl::rglwidget(width=500, height=450) ## ---- eval=requireNamespace("rgl", quietly = TRUE), echo=1-------------------- spinplot(x1, y, x2, rem.lin.trend = "TRUE") rgl::rglwidget(width=500, height=450) ## ---- eval=requireNamespace("rgl", quietly = TRUE), echo=1-------------------- spinplot(x1, y, x2, fit.smooth = TRUE) rgl::rglwidget(width=500, height=450) ## ---- eval=requireNamespace("rgl", quietly = TRUE), echo=1-------------------- spinplot(x1, y, x2, fit.ols = TRUE) rgl::rglwidget(width=500, height=450) ## ----------------------------------------------------------------------------- x <- iris[,1:3] y <- iris[,5] ## ---- eval=requireNamespace("rgl", quietly = TRUE), echo=1-------------------- spinplot(x) rgl::rglwidget(width=500, height=450) ## ---- eval=requireNamespace("rgl", quietly = TRUE), echo=1-------------------- spinplot(x, markby = y) rgl::rglwidget(width=500, height=450) ## ---- eval=requireNamespace("rgl", quietly = TRUE), echo=1-------------------- spinplot(x, markby = y, pch = c(0,3,1), col.points = c("lightcyan", "yellow", "lightgreen"), background = "black") rgl::rglwidget(width=500, height=450) ## ---- echo=-1------------------------------------------------------------------------------------- options(width=100) sessionInfo()

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/log.R \name{FirstTime} \alias{FirstTime} \title{First Time} \usage{ FirstTime(time, tz) } \arguments{ \item{time}{Vector of timestamps.} \item{tz}{Vector of timezones.} } \value{ A POSIXct object. } \description{ Returns first timestamp with valid Git timezone (first timestamp from 2007 and beyond that is not UTC timezone). }

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Twitter_Scrape.R

getwd() setwd("/home/arpan/Documents/Ajax Intel/Sentiment_analysis") #-----SAVE THE APIs------ consumer_key<- c("x8GzVDZNm376fDEhhgUjeDdLc", "BaXijPbyZTBtmjKsXVDPwTPCc", "AKJsxNqX2D8uTo9orgjRirvWL", "Ch6e19OXkGNeUP8QBYUIr5WRH", "IOhPOwbSc5eBFq5uDVSBHf6Gb", "V4n64KR9jNd2UQtqsm6RlsZ3s", "FiU2YPcxQbCqV8eENDeqMTmcX", "NfdqgKlVZGK1WwcIdcaoaK39r") consumer_secret <- c("l2vc71gcyo2i213kXyLv4ZBNSjQO9hmburVwkyIrEdFQsg395C", "qwuO61IXqt7C3HuGs9USpVm5lsMF3HbHxhdViw8cUmCftzTI2i", "QOKk0ctHhbXNQ5QaipqofrZQzWM92mfkcoP60xe7HJzjSUCz6F", "OwsNjjFe5kNcZOgwBWYopcpuJEY5I30HnEWxXPs8HPzLzPo8AW", "tfLMqlZ1hfuvqcrbQjWnjYbUlw2fK60ZzXdzB5sxuSM1xJHlkG", "7f0j0xbkmY9Pu1sA6bjpMCVyj13RiZc5UxSGX5x5VnsunvFL4v", "MX8LleP8SoCuDWqWVGAT2KtTW1oxye2IqJq55evR716S5mRmuX", "OLccQscdKuE3F23aj5vE2h3jOBGZN2OCx2oT8vLhMeTunQkdhz") access_token<- c("843639734782119937-tdHQ7GTqMEl40Zd5MJgfgweN0uivqY2", "80344503-5mEqgYjpElSZ9euB0CsrrnIfgiy2AG9O5CtWEzlOE", "2617540074-5l6gGJhCP8iw9DS7sVD9qsFaUGfWGO9fqlHt5Wg", "842813040290553856-pdKA5MciQNPc7h68eckie9EXR2cOPGv", "47391724-Hs2WBnocdSmIEwnl4JLUuPGMkQMmwHT5L2l2dpF3w", "843506548412440576-YaJW4CfOrwnTSwHteWkwTC94OW7R0cz", "840746479413272576-s7EBAfxbT0idIhGZs27ONcCNpKqM1Zt", "526850301-TSWPjJ2A8oHZ5dBRoS5hXyMfeUF5CYhh19ykgJI0") access_secret <-c("1ZG2StMVZ5XuN0GG7OTQ6ZEPuCCO8wnbxnlAGo5xS58sG", "s0kTmeiautNWCOfkDPmkJ4gTsUafzoz6iNtIEijWW4mN8", "VVMfNIzgPEUmCk5QyIWr5A4ZSC2Lxy7CERoUtWs4jAe0l", "D5SrdfyhCLZaKIsRM4ZaJEnVRVqD9OE9NAhkus4at9Tl3", "S1lf38C5Ax9Q1ZHuVrvxDTjak7YNo1va8AoBEF9ZPd63B", "LepBEZIBMZwllWMRl8TjoTK3KBtJEYud4A76AQ1uIRs4x", "vFdBepLoljrHItEZ315bT9Lsqy2qYxfrCsdbYoP5PcTcW", "DCyejp7s8BFCjdP0TM4AkOwYzfvuG4HxAgR4L5AKRM7t7") #-----DOWNLOAD THE STOCKS FILE----- URL_prod_rev <- "https://raw.githubusercontent.com/datasets/s-and-p-500-companies/master/data/constituents-financials.csv" SP500 <- read.csv(URL_prod_rev, header=T, sep=",", stringsAsFactors=FALSE) # SP500_tickers <- paste("$",SP500$Symbol, sep = "") SP500_tickers <- paste(SP500_tickers,SP500$Name, sep = " ") # -----ACCESS THE TWITTER ACCOUNTS WITH API CALLS---- twitter_access <- function(consumer_key,consumer_secret,access_token,access_secret){ library(ROAuth) library(twitteR) setup_twitter_oauth(consumer_key ,consumer_secret, access_token, access_secret ) cred <- OAuthFactory$new(consumerKey = consumer_key, consumerSecret = consumer_secret, requestURL='https://api.twitter.com/oauth/request_token', accessURL='https://api.twitter.com/oauth/access_token', authURL='https://api.twitter.com/oauth/authorize') return(TRUE) } # ------TWITTER CRAWLER FUNCTION TO PULL DATA----------- twitter_crawler <- function(crawl_j){ tweets <<- list() crawl_begin <-((crawl_j-1)*63)+1 crawl_end <- crawl_j*63 for (i in SP500_tickers[crawl_begin:crawl_end]) { tweets[i] <<- list(searchTwitter(i,n=1 ,lang = "en", since = '2017-03-20')) } } twitter_df <- function(){ tweets<- unlist(tweets) #convert to data frame tweet_df <<- do.call("rbind", lapply(tweets,as.data.frame)) return(tweet_df) } final_df <- data.frame(names=NULL) for(i in 1:8){ twitter_access(consumer_key[i] ,consumer_secret[i], access_token[i], access_secret[i] ) twitter_crawler(i) twitter_df() final_df <- rbind(final_df, tweet_df) } ################################# Jian's Version ######################### URL_prod_rev <- "https://raw.githubusercontent.com/datasets/s-and-p-500-companies/master/data/constituents-financials.csv" SP500 <- read.csv(URL_prod_rev, header=T, sep=",", stringsAsFactors=FALSE) SP500_tickers <- paste("$",SP500$Symbol, sep = "") SP500_tickers <- paste(SP500_tickers,SP500$Name, sep = " ") ##################################################### First 63 Stock Tweets ################################ setupCrawler <- function(consumer_key,consumer_secret,access_token,access_secret) { library(ROAuth) library(twitteR) setup_twitter_oauth(consumer_key ,consumer_secret, access_token, access_secret ) } setupCrawler("x8GzVDZNm376fDEhhgUjeDdLc" ,"l2vc71gcyo2i213kXyLv4ZBNSjQO9hmburVwkyIrEdFQsg395C", "843639734782119937-tdHQ7GTqMEl40Zd5MJgfgweN0uivqY2", "1ZG2StMVZ5XuN0GG7OTQ6ZEPuCCO8wnbxnlAGo5xS58sG") tweets<- list() for (i in SP500_tickers[1:63]) { tweets[i] <- list(searchTwitter(i,n=3 ,lang = "en", since = '2017-03-22')) } tweets<- unlist(tweets) #convert to data frame df_stocks_1to63 <- do.call("rbind", lapply(tweets,as.data.frame)) ###################################################### 64 to 126 ############################################### setupCrawler <- function(consumer_key,consumer_secret,access_token,access_secret) { library(ROAuth) library(twitteR) setup_twitter_oauth(consumer_key ,consumer_secret, access_token, access_secret ) } setupCrawler("x8GzVDZNm376fDEhhgUjeDdLc" ,"l2vc71gcyo2i213kXyLv4ZBNSjQO9hmburVwkyIrEdFQsg395C", "843639734782119937-tdHQ7GTqMEl40Zd5MJgfgweN0uivqY2", "1ZG2StMVZ5XuN0GG7OTQ6ZEPuCCO8wnbxnlAGo5xS58sG") tweets<- list() for (i in SP500_tickers[1:63]) { tweets[i] <- list(searchTwitter(i,n=3 ,lang = "en", since = '2017-03-22')) } tweets<- unlist(tweets) #convert to data frame df_stocks_1to63 <- do.call("rbind", lapply(tweets,as.data.frame))

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/linear_SVM.R

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linear_SVM.R

#TR=dati di training #YTR=classe dati di training #TS=dati di test #YTS=classe dati di test #questo classificatore si trova nel package LibLineaR linear_SVM <- function(TR,YTR,TS,YTS){ #addestramento linear_model<-LiblineaR(data=TR,target=YTR,type=1,cost=1,bias=TRUE,verbose=FALSE) # #predizione sui dati di test test_prediction <- predict(linear_model,TS,decisionValues = TRUE) #matrice di confusione sui dati di test confusion_matrix<-table(predicted=test_prediction$predictions,observation=YTS) #nella matrice di confusione: #elemento [i,j] classe predetta i classe reale j accuracy <- round((confusion_matrix["0","0"]+confusion_matrix["1","1"])/ nrow(TS),4) TPR_0 <- round(((confusion_matrix["0","0"])/(confusion_matrix["0","0"]+confusion_matrix["1","0"])),4) TPR_1 <- round(((confusion_matrix["1","1"])/(confusion_matrix["1","1"]+confusion_matrix["0","1"])),4) kappa <- calculate_kappa(confusion_matrix); return(c(accuracy,TPR_0,TPR_1, kappa)) }

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/bin/sample.R

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sample.R

## --------------------------- ## Script name: sample.R ## Purpose of script: ## Author: Qiongzi Qiu ## Date Created: 2019-01-01 ## Copyright (c) Qiongzi Qiu, 2019 ## Email: 3110102477@zju.edu.cn ## --------------------------- ## Notes: blahblah ## --------------------------- setwd("work dictionary") library(ggplot2) library(reshape2) library(ggpubr) library(ComplexHeatmap) library(circlize) source("some package") options(stringsAsFactors = FALSE) ##### boxplot sample ##### infile_list = c('replace', 'replace', 'replace') for(infile in infile_list){ dat <- read.table(infile, header=T, sep='\t') mutation_type = strsplit(infile, '\\.')[[1]][2] dat['replace'] <- ifelse(dat$KO_non_pval_adj<0.05 & dat$KO_non_t_statistic>0 & !is.na(dat$KO_non_t_statistic), TRUE, FALSE) dat_melt = melt(dat[,c('replace', 'replace', 'replace', 'replace')], id.vars='replace') dat_melt[dat_melt=='replace'] <- gsub('_', ' ', mutation_type) dat_melt[dat_melt=='replace'] <- 'replace' my_comparisons = list(c('replace', 'replace'), c('replace', 'replace'), c('replace', 'replace')) boxplot_tmp = ggboxplot(dat_melt, x='variable', y='value', xlab=NULL, ylab='replace') + stat_compare_means(comparisons = my_comparisons, method.args=list(alternative='greater')) + facet_wrap( ~ replace, scales="fixed") outfile = paste0(mutation_type, '_replace.pdf') ggsave(outfile, boxplot_tmp) } ##### heatmap sample ##### infile_list = c('replace', 'replace', 'replace', 'replace') for(infile in infile_list){ dat = read.table(infile, header=T, sep='\t') dat[,'replace'] = dat[,'replace']/(dat[,'replace'] + dat[,'replace']) dat[is.na(dat[,'replace']),'replace'] = 'replace' for(type_tmp in c(TRUE, FALSE)){ if(type_tmp==TRUE){ dat_tmp = dat[dat$KO_non_up>0, ] outfile = gsub('replace', 'replace', infile) }else{ dat_tmp = dat[dat$KO_non_prop==1, ] outfile = gsub('replace', 'replace', infile) } dat_reshape = reshape(dat_tmp[,c('replace', 'replace', 'replace')], idvar='replace', timevar='replace', direction='wide') rownames(dat_reshape) = dat_reshape[,1] dat_reshape = t(dat_reshape[,-1]) count_row = as.numeric(apply(is.na(dat_reshape)==F, 1, sum)) count_col = as.numeric(apply(is.na(dat_reshape)==F, 2, sum)) dat_reshape = dat_reshape[count_row>0, count_col>0] dat_reshape = t(apply(dat_reshape, 1, as.numeric)) dat_reshape[dat_reshape==1]=2 dat_reshape[dat_reshape<1]=1 dat_reshape[is.na(dat_reshape)]=0 count_row = as.matrix(cbind(as.numeric(apply(dat_reshape==2, 1, sum)), as.numeric(apply(dat_reshape==1, 1, sum)))) count_col = as.matrix(cbind(as.numeric(apply(dat_reshape==2, 2, sum)), as.numeric(apply(dat_reshape==1, 2, sum)))) annotation_row = rowAnnotation(Count=row_anno_barplot(count_row, gp = gpar(fill = c("black", "grey")))) annotation_col = HeatmapAnnotation(Count = anno_barplot(count_col, gp = gpar(fill = c("black", "grey"), col = c(NA, NA)))) pdf(outfile, width=6, height=6) p = Heatmap(as.matrix(dat_reshape), col = c('white', 'grey', 'black'), show_column_names = F, na_col='white', row_order = order(rowSums(count_row), decreasing = T), column_order = order(rowSums(count_col), decreasing = T), top_annotation = annotation_col, right_annotation = annotation_row, heatmap_legend_param = list( title = "", at = c(2, 1, 0), border = "black", ncol = 3, labels = c("replace", "replace", "replace") )) draw(p, heatmap_legend_side = "bottom") dev.off() } }

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vectors06.R

# compare round() and signif() by binding rowwise into matrix rbind(round(z, digits = 2), signif(z, digits = 2))

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/HW10/HW10-9-health.R

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HW10-9-health.R

library(WDI) library(readr) library(dplyr) library(ggplot2) data = read_csv("~/Downloads/WDI_csv/WDIData.csv") indic = list("SP.ADO.TFRT", "SP.DYN.CBRT.IN", "SH.DTH.COMM.ZS", "SH.DTH.NCOM.ZS", "SP.REG.DTHS.ZS", "SP.POP.DPND.YG", "SH.STA.BRTC.ZS", "SH.DTH.INJR.ZS", "SP.REG.BRTH.ZS", "SP.DYN.CDRT.IN", "SP.DYN.TFRT.IN", "SH.IMM.IDPT", "SH.TBS.INCD", "SM.POP.TOTL", "SP.DYN.LE00.IN", "SH.STA.TRAF.P5", "SH.STA.ANVC.ZS", "SH.DYN.AIDS.ZS", "SN.ITK.DEFC.ZS", "SH.MED.BEDS.ZS") alls = NULL for (ind in 1:20){ ind =1 ind = ind + 1 str = indic[ind] data %>% filter(`Indicator Code` == str) ->gdp name = gdp[1,3] name = name[[1]] se= c("Country Code","1997", "1998", "1999", "2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009","2010", "2011","2012","2013","2014","2015", "2016") gdp = gdp[,se] gdp %>% filter(`Country Code` == "IRN") -> irn gdp %>% filter(`Country Code` != "IRN") -> rest restmean = colMeans(rest[,-1], na.rm = TRUE) irnmean = colMeans(irn[,-1], na.rm = TRUE) fac1 = cbind(irnmean, restmean) fac1 = as.data.frame(fac1) fac1 <- add_rownames(fac1, "year") fac1gg = data.frame(year = 0, value = 0 ,country =0, type = 0) s = 1 for (i in 1:(nrow(fac1))){ x = fac1[i,] fac1gg[s,] = c(x$year, x$irnmean, "iran", name) s = s+1 fac1gg[s,] = c(x$year, x$restmean, "rest", name) s = s + 1 } fac1gg %>% filter(value != "NaN") -> fac1gg alls = rbind(alls, fac1gg) mypath <- file.path("~/Desktop/Data Analysis/HW10/images/",paste("hmyplot_", ind, ".jpg", sep = "")) jpeg(file=mypath) ggplot(fac1gg, aes(x= year , y = value , color = country)) + geom_point() + ggtitle(name) + theme(axis.text.x = element_text(angle=-90, vjust=0.5)) dev.off() }

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/HIcalculation/functions/quant_delta_map_vTimeSlices.r

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quant_delta_map_vTimeSlices.r

#Function that applies bias correction using quantile delta mapping (QDM) for different time slices #Input: # - model_grid: Model name (string) # - fname_OBS: File name of heat stress indicator for reference dataset for bias correction (string) # - fname_MOD: File name of heat stress indicator for model dataset (string) # - dir_data: Folder where data are stored (string) # - folder_out: Output folder (string) # - var_name: Name of variable to be bias corrected (string) # - time_ref: Vector indicating reference period (2-element vector) # - time_app: Vector indicating application period (2-element vector) # - time_mod: Vector indicating the whole time period for which data is present in fname_MOD (2 element vector) # - DOYs: Number of days per year (string) # - mask_land: Flag that indicates if only the land gridponts or all gridpoints should be included (bool) quant_delta_map_vTimeSlices <- function(model_grid, fname_OBS, fname_MOD, dir_data, folder_out, var_name, time_ref, time_app, time_mod, DOYs=365, mask_land=TRUE) { #Load libraries .libPaths(c("/uio/kant/div-cicero-u1/clems/R/x86_64-conda_cos6-linux-gnu-library/3.5", .libPaths())) library(MBC) library(ncdf4) library(doParallel) library(feather) #Define file names f_name_YEARS_MOD <- paste0(dir_data, 'CMIP6/CMIP6_merged/', toString(time_mod[1]), '-', toString(time_mod[2]), '_dates_YEARS_', toString(DOYs), '.csv') f_name_MONTHS_MOD <- paste0(dir_data, 'CMIP6/CMIP6_merged/', toString(time_mod[1]), '-', toString(time_mod[2]), '_dates_MONTHS_', toString(DOYs), '.csv') f_name_YEARS_ERA <- paste0(dir_data, 'ERA5/ERA5_regrid/', toString(time_ref[1]), '-', toString(time_ref[2]), '_dates_YEARS_365.csv') f_name_MONTHS_ERA <- paste0(dir_data, 'ERA5/ERA5_regrid/', toString(time_ref[1]), '-', toString(time_ref[2]), '_dates_MONTHS_365.csv') fname_gridsel <- paste('/div/amoc/exhaustion/Heat_Health_Global/Data/Masks_Heights_Grids/Land_sea_masks/Land_without_Antarctica_', model_grid, '.nc', sep="") #Read years and months YR_MOD <- read.csv(file=f_name_YEARS_MOD, header=FALSE, sep=",") MN_MOD <- read.csv(file=f_name_MONTHS_MOD, header=FALSE, sep=",") YR_ERA <- read.csv(file=f_name_YEARS_ERA, header=FALSE, sep=",") MN_ERA <- read.csv(file=f_name_MONTHS_ERA, header=FALSE, sep=",") #Select data for reference and application periods sel_ref_OBS <- YR_ERA>=time_ref[1] & YR_ERA<=time_ref[2] sel_ref_MOD <- YR_MOD>=time_ref[1] & YR_MOD<=time_ref[2] sel_app_MOD <- YR_MOD>=time_app[1] & YR_MOD<=time_app[2] #Get months in reference and application periods mon_ref_ERA <- MN_ERA[sel_ref_OBS] mon_ref_MOD <- MN_MOD[sel_ref_MOD] mon_app_MOD <- MN_MOD[sel_app_MOD] #Create indices for selecting right time for NetCDF files sta_ref_OBS = c(1, 1, which(sel_ref_OBS)[1]) sta_ref_MOD = c(1, 1, which(sel_ref_MOD)[1]) sta_app_MOD = c(1, 1, which(sel_app_MOD)[1]) len_ref_OBS = c(-1, -1, sum(sel_ref_OBS)) len_ref_MOD = c(-1, -1, sum(sel_ref_MOD)) len_app_MOD = c(-1, -1, sum(sel_app_MOD)) #Open data sets nc_OBS <- nc_open(fname_OBS) nc_MOD <- nc_open(fname_MOD) #Read data data_OBS_ref <- ncvar_get(nc_OBS, var_name, start=sta_ref_OBS, count=len_ref_OBS) data_MOD_ref <- ncvar_get(nc_MOD, var_name, start=sta_ref_MOD, count=len_ref_MOD) data_MOD_app <- ncvar_get(nc_MOD, var_name, start=sta_app_MOD, count=len_app_MOD) #Close NetCDFs nc_close(nc_OBS) nc_close(nc_MOD) #Create indicex for all grid points, on which to carry out the analysis if (mask_land==TRUE) { #Read which grid cells to consider nc_gridsel <- nc_open(fname_gridsel) data_gridsel <- ncvar_get(nc_gridsel, "selection") vec_gridsel <- as.vector(data_gridsel) #Convert to vector nc_close(nc_gridsel) #Select land grid points ind = which(data_gridsel==1, arr.ind = T) N = dim(ind)[1] } else { #Select all grid points data_gridsel = matrix(data=TRUE, nrow=dim(data_MOD_app)[1], ncol=dim(data_MOD_app)[2]) ind = which(data_gridsel==TRUE, arr.ind = T) N = dim(ind)[1] } #Define number of cores for parallel computing registerDoParallel(cores=40) #Write time selection to file sel_time = sel_app_MOD path = paste0(folder_out, 'Time_selection.feather') write_feather(as.data.frame(sel_time), path) #Loop over all selected grid points foreach(n=1:N) %dopar% { # for (n in 1:N) { #Select data on grid point OBSref <- data_OBS_ref[ind[n,1], ind[n,2], ] MODref <- data_MOD_ref[ind[n,1], ind[n,2], ] MODapp <- data_MOD_app[ind[n,1], ind[n,2], ] #Create data frame for output QDM <- vector(,len_app_MOD[3]) * NA #Loop over all months for(i in 1:12) { #Select data selR_ERA <- mon_ref_ERA==(i%%12 + 12*(i%%12==0)) selR_MOD <- mon_ref_MOD==(i%%12 + 12*(i%%12==0)) selA_MOD <- mon_app_MOD==(i%%12 + 12*(i%%12==0)) #Select data for quantile mapping calibration OBS_R <- OBSref[selR_ERA] MOD_R <- MODref[selR_MOD] MOD_A <- MODapp[selA_MOD] #Check for NaNs out_corr <- rep(NA, length(MOD_A)) ch1 <- !is.nan(OBS_R) ch2 <- !is.nan(MOD_R) ch3 <- !is.nan(MOD_A) #Perform QDM if data is not NaN if (!(sum(ch1)==0 | sum(ch2)==0 | sum(ch3)==0)) { QDM_out <- QDM(OBS_R[ch1], MOD_R[ch2], MOD_A[ch3], ratio=FALSE, n.tau=50) #Consider NaNs in output out_corr[ch3] <- QDM_out$mhat.p QDM[selA_MOD] <- out_corr #Save in data frame } } #Write to file path = paste0(folder_out, 'Grid_', toString(ind[n,1]), '_', toString(ind[n,2]), '_data.feather') write_feather(as.data.frame(QDM), path) } #Overwrite big data sets to make them smaller (in case they would not be deleted from cache) data_OBS_ref <- 0 data_MOD_ref <- 0 data_MOD_app <- 0 }

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r

silverYY.r

#high sel #seed=132 # 145 #mig=2500 #now make flexible with number of microsatellites #set.seed(2);#1 is good #set.seed(5);#worked #set.seed(seed); library(batch) parseCommandArgs() library(Rcpp) library(inline) require(RcppArmadillo) run8pops = ' using namespace Rcpp; using namespace sugar; RNGScope scope; // Variables (and constants) section NumericVector inputs(INPUTS); int SSBinitA; SSBinitA=inputs[0]; double pop1_prop; pop1_prop=inputs[1]; int Nyrs; Nyrs=inputs[2]; int nsamp; nsamp=inputs[3]; int ngen; ngen=inputs[4]; double fishmort; fishmort=inputs[6]; int samptype; samptype=inputs[7]; int nsims; nsims=inputs[8]; int N; N=inputs[9]; int Yrs; Yrs=inputs[10]; int mmt; mmt=inputs[11]; double dpow; dpow=inputs[12]; double rmate; rmate=inputs[13]; int ages; ages=inputs[14]; int popdyn;popdyn=inputs[15]; int nSNP; nSNP=inputs[16]; int nmig; nmig=inputs[17]; double nmig_prop; int SSBinitB; SSBinitB=inputs[18]; int nmig1;nmig1=nmig; NumericVector fishsel(ages); //this comes from the average selectivity at age 1963-2011 //HIGH FISHSEL//fishsel[0]=0;//I changed this because of age structure //fishsel[0]=0.2202403;changed from this //fishsel[1]=0.2376; //fishsel[2]=0.9080;fishsel[3]=1.0866; //fishsel[4]=1.0712;fishsel[5]=1.0268;fishsel[6]=0.9764;fishsel[7]=0.9764; //mean fishsel //fishsel[0]=0.2376; fishsel[0]=0;fishsel[1]=0.818;fishsel[2]=0.974;fishsel[3]=0.895;fishsel[4]=0.847;fishsel[5]=0.848;fishsel[6]=0.848; int npops; if (pop1_prop<1){npops=2;}else{npops=1;} int n_l; //proportion of fish in population 1 if (pop1_prop<0.5){n_l=1;} if (pop1_prop==0.5){n_l=5;} NumericVector N_til_init_a(ages); //numbers at age with 1 recruit NumericVector N_til_init_a40(ages); //numbers at age with 1 recruit and F40% NumericVector S_l0(2); //SSB in pop1 and 2 NumericVector R_l0A(Nyrs); //recruits in popA for each year NumericVector R_l0B(Nyrs); //recruits in popB for each year NumericVector R_l0A_hat(Nyrs); //recruitment with epsilon error term (same as for estimating numbers) NumericVector R_l0B_hat(Nyrs); // NumericVector Q_a(ages); //maturity index at age popA (Skagerrak) NumericVector Q_b(ages); //maturity index at age popB (North Sea) //North Sea maturity ICES Q_b[0]=0.01;Q_b[1]=0.05;Q_b[2]=0.23;Q_b[3]=0.62;Q_b[4]=0.86;Q_b[5]=1;Q_b[6]=1; //Skagerrak cod maturity (Knutsen 2011) Q_a[0]= 0.03226423;Q_a[1]= 0.14982172;Q_a[2]= 0.48225745;Q_a[3]= 0.83117813;Q_a[4]= 0.96299517;Q_a[5]= 0.99278248; Q_a[6]= 0.99862647; double psi=1.75e-5; //used in weight at age (von bertallanffy) double theta=2.8571; //used in weight at age double Linf = 197; //wt at age double K=0.1030; //wt at age NumericVector Length(ages); //length at age NumericVector Weight_a(ages); //wt at age NumericVector Weight_b(ages); //wt at age double meanFullF=0.8962449; double varFullF=0.0232579; NumericVector age(ages); age=SeqLen(ages)-1; int temp; double temp2; NumericVector N_init_a(ages); NumericVector N_init_a1(ages); NumericVector N_init_a2(ages); NumericVector N_init_a1_FIRST(ages); NumericVector N_init_a2_FIRST(ages); //N_init declared below is sum of pop1_size and pop2_size (integer) List genotypes(2); int pop1_size; int pop2_size; List outmat(250); double n_sampA; double n_sampB; NumericMatrix out1(nsims,Nyrs); NumericMatrix out2(nsims,Nyrs); NumericMatrix out3(nsims,Nyrs); NumericMatrix out4(nsims,Nyrs); NumericMatrix out5(nsims,Nyrs); NumericMatrix out6(nsims,Nyrs); NumericMatrix out7(nsims,Nyrs); NumericMatrix out8(nsims,Nyrs); NumericMatrix out9(nsims,Nyrs); NumericMatrix out10(nsims,Nyrs); NumericMatrix out11(nsims,Nyrs); NumericMatrix out12(nsims,Nyrs); NumericMatrix out13(nsims,Nyrs); NumericMatrix out14(nsims,Nyrs); NumericMatrix out15(nsims,Nyrs); NumericMatrix out16(nsims,Nyrs); NumericVector out17(nsims);//1 is true Fst, 2 is initial SBPRA, 3 is initial SBPRB NumericVector out_fst60(nsims); NumericMatrix numsmat(nsims,ages); NumericMatrix fst_submatALL(nsims,Nyrs); NumericMatrix sig_submatALL(nsims,Nyrs); NumericMatrix fst_submat(nsims,9); NumericMatrix sig_submat(nsims,9); NumericMatrix fst_submat60(nsims,9); NumericMatrix sig_submat60(nsims,9); NumericMatrix fst_submat9(nsims,9); NumericMatrix sig_submat9(nsims,9); NumericMatrix fst_submat99(nsims,9); NumericMatrix sig_submat99(nsims,9); NumericMatrix out21(nsims,Nyrs);//subsample SSB/initial SSB popA NumericMatrix out22(nsims,Nyrs);//subsample SSB/initial SSB popB NumericMatrix out23(nsims,Nyrs);//est SSB/SB40 popA NumericMatrix out24(nsims,Nyrs);//est SSB/SB40 popB NumericMatrix out25(nsims,ngen+Nyrs);//fst_vec NumericVector genvec(nsims); List pop1(nSNP+1); //here is where I split the data into pop1 and pop2 List pop2(nSNP+1); List popA(Nyrs+ages); List popB(Nyrs+ages); int pick1; int pick2; double prob; double dir; int mover1; int mover2; NumericVector mean_arichA(Nyrs); NumericVector mean_arichB(Nyrs); double SPR_init_a; double SPR_init_b; double SPR_init_F40_a; double SPR_init_F40_b; double S_lyINIT_a; double S_lyINIT_b; NumericVector TACA(Nyrs); NumericVector TACB(Nyrs); NumericVector TAC(Nyrs); NumericVector S_hat_syjA(Nyrs); NumericVector S_hat_syjB(Nyrs); NumericVector S_hat_syj(Nyrs); NumericVector SSB_hatA(Nyrs); NumericVector SSB_hatB(Nyrs); NumericVector SSB_hat(Nyrs); NumericVector SPRAtrue(Nyrs); NumericVector SPRBtrue(Nyrs); NumericVector FishAvec(Nyrs); //vector of optimal fishing mortality for popA (separated management) NumericVector FishBvec(Nyrs);//vector of optimal fishing mortality for popB (separated management) double CVa=0.6085044; double CVs=0.779; double M=0.34; double h=0.9; double CVr=0.75; NumericVector M2(ages); NumericVector Surv(ages); M2[0]=0.9;//M2[0]=1.038; M2[1]=0.698;M2[2]=0.490;M2[3]=0.233;M2[4]=0.2;M2[5]=0.2;M2[6]=0.2;//natural mortality from ICES report //Surv[0]=1;Surv[1]=0.43403;Surv[2]=0.18838;Surv[3]=0.08177;Surv[4]=0.03549;Surv[5]=0.01540;Surv[6]=0.00669; Surv[0]=0.43403;Surv[1]=0.18838;Surv[2]=0.08177;Surv[3]=0.03549;Surv[4]=0.01540;Surv[5]=0.00669;Surv[6]=0.002; NumericVector epsilonA(Nyrs); NumericVector epsilonB(Nyrs); NumericVector epsilon(Nyrs); epsilonA[0]=0; epsilonB[0]=0; epsilon[0]=0; int genrule; int genrule2; NumericVector N_hat_aA(ages); NumericVector N_hat_aB(ages); NumericVector N_hat_a(ages); double sigma_ja2A=log(((1/pop1_prop)*pow(CVa,2))+1); double sigma_ja2B=log(((1-(1/pop1_prop))*pow(CVa,2))+1); double sigma_ja2=log(pow(CVa,2)+1); double sigma_js2A=log(((1/pop1_prop)*pow(CVs,2))+1); double sigma_js2B=log(((1-(1/pop1_prop))*pow(CVs,2))+1); double sigma_js2=log(pow(CVs,2)+1); double sigma_jr2=log(pow(CVr,2)+1);//may want to check this double psiA; double psiB; double psi_star; double rho=0.9; double Fish=0; //set here so initially it is not a problem double S_lyA;//true SSB double S_lyB; double eta_jyA; double eta_jyB; double eta_jy; NumericVector temp8; NumericVector temp9; NumericVector temp10; NumericVector temp11; NumericVector temp12; NumericVector temp13; NumericVector temp14; NumericVector temp15; NumericVector temp16; NumericVector temp17; int temp18; int temp19; NumericVector Wt; double FishA; double FishB; double sepruleA; double sepruleB; double seprule; NumericVector Effort_tot; NumericVector d(10); //10 is right because it is for the 10 areas NumericVector Effort(10); NumericVector Effort_norm; NumericVector vec; NumericVector spawnersA(ages); NumericVector spawnersB(ages); NumericVector spawnersA_rounded(ages); NumericVector spawnersB_rounded(ages); NumericVector spawnersA_rounded_star(ages); NumericVector spawnersB_rounded_star(ages); double pick_spawner_mal; double pick_spawner_fem; NumericVector which_spawnersA; int some_integerA; int some_integerB; List tmpListA; List tmpListB; int pickM; int pickF; double pick_moverA; double pick_moverB; int mov_pick1; int mov_pick2; //NumericMatrix moverA2Brow1; NumericMatrix RecA; NumericMatrix RecB; NumericMatrix recA; NumericMatrix recB; double rand_doub; int samp_mom; int samp_dad; int rand_allele; List tmpList_recsA(2); List tmpList_recsB(2); NumericVector NeAvec(Nyrs); //Keep Ne from each year popA here NumericVector NeBvec(Nyrs); //Keep Ne from each year popB here //NumericVector FSH; //IntegerVector FSH_int; List tmpdoneA(2); List tmpdoneB(2); NumericMatrix tmpageA_fem; NumericMatrix tmpageA_mal; NumericMatrix tmpageB_fem; NumericMatrix tmpageB_mal; NumericVector all_richA; NumericVector all_richB; NumericVector arichA(Nyrs); NumericVector arichB(Nyrs); NumericMatrix allA(Nyrs+1,ages); NumericMatrix allB(Nyrs+1,ages); NumericMatrix allspawnA(Nyrs,ages); NumericMatrix allspawnB(Nyrs,ages); NumericVector N_aA(ages); NumericVector N_aB(ages); List spawn_malA(Nyrs); List spawn_femA(Nyrs); List spawn_malB(Nyrs); List spawn_femB(Nyrs); NumericMatrix tmpMatrixA_1; NumericMatrix tmpMatrixA_2; NumericMatrix tmpMatrixB_1; NumericMatrix tmpMatrixB_2; IntegerVector NspawnersA(Nyrs); IntegerVector NspawnersB(Nyrs); NumericMatrix spawn_malA_thisyr; //twice as many genotypes as spawners NumericMatrix spawn_femA_thisyr; NumericMatrix spawn_malB_thisyr; NumericMatrix spawn_femB_thisyr; NumericMatrix tmpMatrixA_1plus; NumericMatrix tmpMatrixA_2plus; NumericMatrix tmpMatrixB_1plus; NumericMatrix tmpMatrixB_2plus; NumericMatrix tmpMatrixA_1details; NumericMatrix tmpMatrixA_2details; NumericMatrix tmpMatrixB_1details; NumericMatrix tmpMatrixB_2details; List tmpList_plusgroupA(2); List tmpList_plusgroupB(2); List tmpList_detailsA(2); List tmpList_detailsB(2); IntegerVector mal_spawnersA; //after random mating **also make sure you get the right row IntegerVector fem_spawnersA; //after random mating IntegerVector mal_spawnersB; //after random mating **also make sure you get the right row IntegerVector fem_spawnersB; //after random mating IntegerVector spawner_listM; //list of ones to choose from IntegerVector spawner_listF; NumericVector N_init_a1_star(ages); NumericVector N_init_a2_star(ages); NumericVector NtotalA(Nyrs+ages-1); NumericVector NtotalB(Nyrs+ages-1); NumericVector SB40_vecA(Nyrs); NumericVector SB40_vecB(Nyrs); NumericMatrix allelesA; NumericMatrix allelesB; NumericMatrix arichAmat; NumericMatrix arichBmat; NumericMatrix richnessA; NumericMatrix richnessB; NumericVector fst_vec(ngen+Nyrs); NumericVector fst_vec2(ngen+Nyrs); NumericVector Wrights_fst_vec(ngen+Nyrs); NumericVector Wrights_fst_vec1(ngen+Nyrs); NumericVector Wrights_fst_vec2(ngen+Nyrs); NumericVector Wrights_simple(ngen+Nyrs); NumericVector Wrights_simple1(ngen+Nyrs); NumericVector Wrights_fst_vec_sub(ngen+Nyrs); NumericVector Wrights_fst_vec_sub1(ngen+Nyrs); NumericVector Wrights_simple_sub(ngen+Nyrs); NumericVector arichsampA_vec(ngen+Nyrs); NumericVector arichsampB_vec(ngen+Nyrs); NumericVector arichsampA_ten(nSNP); NumericVector arichsampB_ten(nSNP); NumericVector ninecomp(9); NumericVector diffsamp(10); NumericVector nmig_vec(Nyrs); NumericVector ps; NumericVector s2; NumericMatrix arichAmat_sub620; NumericMatrix arichBmat_sub620; NumericMatrix arichAmat_sub640; NumericMatrix arichBmat_sub640; NumericMatrix arichAmat_sub680; NumericMatrix arichBmat_sub680; //NumericVector arichAmat_sub; //NumericVector arichBmat_sub; NumericMatrix talmat2; NumericMatrix talmat_sub2; NumericVector NtotalA2; NumericVector count_sub(nSNP);//list of number of unique alleles at each locus if (pop1_prop==1){ninecomp=NumericVector::create(1,1,1,1,1,1,1,1,1); if(samptype==1){diffsamp=rep(nsamp,10);}else{ diffsamp=NumericVector::create(200,180,155,130,110,90,65,40,20,10);}} if (pop1_prop==0.2){ninecomp=NumericVector::create(1,2,2,2,2,2,2,2,2); if(samptype==1){diffsamp=rep(nsamp,10);}else{ diffsamp=NumericVector::create(324,177,118,88,71,59,50,44,39,29);}} if (pop1_prop==0.5){ninecomp=NumericVector::create(1,2,1,1,2,2,2,2,2); if(samptype==1){diffsamp=rep(nsamp,10);}else{ diffsamp=NumericVector::create(170,130,100,65,33,170,130,100,65,33);}} if (pop1_prop<=0.1){ninecomp=NumericVector::create(2,2,2,2,2,2,2,2,2); if(samptype==1){diffsamp=rep(nsamp,10);}else{ diffsamp=NumericVector::create(324,177,118,88,71,59,50,44,39,29);}} if (pop1_prop==0.01){ninecomp=NumericVector::create(2,2,2,2,2,2,2,2,2); if(samptype==1){diffsamp=rep(nsamp,10);}else{ diffsamp=NumericVector::create(324,177,118,88,71,59,50,44,39,29);}} if (pop1_prop==0.05){ninecomp=NumericVector::create(2,2,2,2,2,2,2,2,2); if(samptype==1){diffsamp=rep(nsamp,10);}else{ diffsamp=NumericVector::create(324,177,118,88,71,59,50,44,39,29);}} for (int zcounter=0;zcounter<nsims;zcounter ++){ //change back to 10 Rcout<<"zcounter"<<std::endl; Rf_PrintValue(wrap(zcounter)); List data(nSNP+1); NumericVector MUS(nSNP); NumericVector SSBA_vec(Nyrs); NumericVector SSBB_vec(Nyrs); int num_wrong_pops; int num_wrong_pops60; NumericVector which_wrong_pops; //vector of the split locations NumericMatrix R_l0_mat;//this stores R_l0 estimates for each wrong pop for 100 yers NumericMatrix epsilon_vecmat; NumericMatrix TAC_vec; NumericVector which_wrong_pops60; //vector of the split locations NumericMatrix R_l0_mat60;//this stores R_l0 estimates for each wrong pop for 100 yers NumericMatrix epsilon_vecmat60; NumericMatrix TAC_vec60; if (popdyn==1){ // sim script - this is the part that sets up alleles at each locus for (int m=0; m < nSNP; m++){ // this is for the 10 loci we simulate NumericVector alleles; double prob; double dir; double mu; int pick; NumericVector alleles1(2*N); NumericVector PICKS(2*N); mu=0.1*as<double>(rbeta(1,0.6,1));//here scale down by 1/100 so max is 0.01 OR not MUS[m]=mu; //this takes mu back to 0.01* the rbeta alleles=rep(200,2*N); //cool! this works in C++ for (int k=0; k < Yrs; k++){ //repeat mutation and random mating each year //here is the mutate part for (int i=0; i < 2*N; i++){ //repeat mutation for each allele prob=as<double>(runif(1,0,1)); dir=as<double>(runif(1,0,1)); if(prob<mu&dir<0.5){ alleles[i]=alleles[i]+1; }else if (prob<mu&dir>0.5){ alleles[i]=alleles[i]-1; } } //now the random mating part - first choose a random allele for (int j=0; j < 2*N; j++){ //repeat random mating 2N times prob=as<double>(runif(1,0,(2*N)-1)); pick=int(prob); if((prob+0.5)>=(int(prob)+1)){ pick=int(prob)+1; } //take that random allele and save it PICKS[j]=pick; alleles1[j]=alleles[pick]; } alleles=alleles1; }//end of Yrs data[m]=alleles1; }//end of m loop data[nSNP]=MUS; // end of simRcpp script }//end popdyn==1 // initial numbers section (if there was one recruit) N_til_init_a[0]=1; for (int i=1; i < (ages-1); i++){ //fill in N_til_init; N_til_init_a[i]=N_til_init_a[i-1]*exp(-M2[i-1]);} N_til_init_a[ages-1]=N_til_init_a[ages-2]*exp(-M2[ages-2])/(1-exp(-M2[ages-1])); //establish the SBPR with 1 recruit and F40% N_til_init_a40[0]=1; for (int i=1; i < (ages-1); i++){ //fill in N_til_init; N_til_init_a40[i]=N_til_init_a40[i-1]*exp(-(M2[i-1]+fishsel[i-1]*fishmort));} N_til_init_a40[ages-1]=N_til_init_a40[ages-2]*exp(-(M2[ages-2]+fishsel[ages-2]*fishmort))/(1-exp(-(M2[ages-1]+fishsel[ages-1]*fishmort))); //for (int i=1; i < (ages-1); i++){ //fill in N_til_init; //N_til_init_a40[i]=N_til_init_a40[i-1]*Surv[i-1];} //N_til_init_a40[ages-1]=N_til_init_a40[ages-2]*Surv[ages-2]+N_til_init_a40[ages-1]*Surv[ages-1]; // establish length, weight of males and females for (int i=0; i < ages; i++){ Length[i]=Linf*(1-exp(-K*(age[i]))); Weight_b[i]=psi*pow((Linf*(1-exp(-K*(age[i])))),theta); } //0.001 is for kg to mt conversion Weight_a[0]=0;Weight_a[1]=0.5508;Weight_a[2]=1.1016;Weight_a[3]=1.6524; Weight_a[4]=2.2032;Weight_a[5]=2.7540;Weight_a[6]=3.3048; // determine initial number of recruits and the SSB in each population (pops 1 and 2) S_l0[0]=SSBinitA; //S_l0 initial spawning stock biomass for pop1 S_l0[1]=SSBinitB; //S_l0 initial spawning stock biomass pop2 NumericVector tempvec1a = Weight_a*N_til_init_a*Q_a; NumericVector tempvec1b = Weight_b*N_til_init_a*Q_b; R_l0A[0]=2.0*S_l0[0]/std::accumulate(tempvec1a.begin(), tempvec1a.end(), 0.0); //initial number recruits pop1 NOTE: if only one pop, all are in popA R_l0B[0]=2.0*S_l0[1]/std::accumulate(tempvec1b.begin(), tempvec1b.end(), 0.0); //init recruits pop2 double R_l0A_init=R_l0A[0]; //record the first recruits for beginning of mgmt_counter loop double R_l0B_init=R_l0B[0]; //n_init_a1 is the number of fish in each age class //this loop will also round to an even number in each age class so you can split into males and females for (int i=0; i<ages; i++){ N_init_a1[i]=N_til_init_a[i]*R_l0A[0]; temp=int(N_init_a1[i]); if((N_init_a1[i]+0.5)>=(int(N_init_a1[i])+1)){ temp=int(N_init_a1[i])+1;} N_init_a1[i]=temp; if (temp%2!=0){ temp2=as<double>(runif(1,-1,1)); if(temp2>=0){N_init_a1[i]=N_init_a1[i]+1;} else{N_init_a1[i]=N_init_a1[i]-1;} } N_init_a2[i]=N_til_init_a[i]*R_l0B[0]; temp=int(N_init_a2[i]); if((N_init_a2[i]+0.5)>=(int(N_init_a2[i])+1)){ temp=int(N_init_a2[i])+1;} N_init_a2[i]=temp; if (temp%2!=0){ temp2=as<double>(runif(1,-1,1)); if(temp2>=0){N_init_a2[i]=N_init_a2[i]+1;} else{N_init_a2[i]=N_init_a2[i]-1;} } }//end this for loop N_init_a1_FIRST = clone(N_init_a1); N_init_a2_FIRST = clone(N_init_a2); N_init_a = N_init_a1+N_init_a2; NumericVector tempvec_a = Weight_a*N_init_a*Q_a; NumericVector tempvec_b = Weight_b*N_init_a*Q_b; S_lyINIT_a=0.5*std::accumulate(tempvec_a.begin(), tempvec_a.end(), 0.0);//Initial Spawning bimass S_lyINIT_b=0.5*std::accumulate(tempvec_b.begin(), tempvec_b.end(), 0.0);//Initial Spawning bimass SPR_init_a=S_lyINIT_a/N_init_a[0]; //initial SPBR SPR_init_b=S_lyINIT_b/N_init_a[0]; //initial SPBR tempvec_a = Weight_a*N_til_init_a40*Q_a; tempvec_b = Weight_b*N_til_init_a40*Q_b; SPR_init_F40_a=0.5*std::accumulate(tempvec_a.begin(), tempvec_a.end(), 0.0);//Initial Spawning bimass SPR_init_F40_b=0.5*std::accumulate(tempvec_b.begin(), tempvec_b.end(), 0.0);//Initial Spawning bimass NumericVector temp6=(N_init_a1*Weight_a*Q_a); double S_lyINITA=0.5*std::accumulate(temp6.begin(),temp6.end(),0.0); NumericVector temp7=(N_init_a2*Weight_b*Q_b); double S_lyINITB=0.5*std::accumulate(temp7.begin(),temp7.end(),0.0); double SPR_initA; //initial SBPR popA double SPR_initB; //initial SBPR popB pop1_size = std::accumulate(N_init_a1.begin(),N_init_a1.end(),0.0); pop2_size = std::accumulate(N_init_a2.begin(),N_init_a2.end(),0.0); int N_init=pop1_size+pop2_size; if (popdyn==1){ // evolve script - takes alleles for all individuals, splits it (or not in the case of one population) and then evolves population(s) for ngen generations NumericVector transferdata(N_init); //size of pop1 and pop2 (out of the original size) NumericVector alleles_pop1_before(2*pop1_size); NumericVector alleles_pop2_before(2*pop2_size); NumericVector alleles_pop1_after(2*pop1_size); //after random mating NumericVector alleles_pop2_after(2*pop2_size); //after random mating NumericVector PICKS_pop1(2*pop1_size); NumericVector PICKS_pop2(2*pop2_size); //here we extract elements of the data list (with 10 microsatellites) NumericVector selectA(2*pop1_size); NumericVector selectB(2*pop2_size); int pick3; for (int i=0; i < 2*pop1_size; i++){ //create a list of random numbers 0-1999 twice as big as pop1_size to randomly select alleles from sim prob=as<double>(runif(1,0,(2*N)-1)); //works, I checked it pick3=int(prob); if((prob+0.5)>=(int(prob)+1)){ pick3=int(prob)+1; } selectA[i]=pick3;} for (int i=0; i < 2*pop2_size; i++){ //create a list of random numbers 0-1999 twice as big as pop2_size to randomly select alleles from sim prob=as<double>(runif(1,0,(2*N)-1)); pick3=int(prob); if((prob+0.5)>=(int(prob)+1)){ pick3=int(prob)+1; } selectB[i]=pick3;} for (int i=0; i < nSNP; i++){ transferdata = data[i]; NumericVector tx1(2*pop1_size); NumericVector tx2(2*pop2_size); for (int j=0; j < 2*pop1_size; j++){ tx1[j]=transferdata[selectA[j]]; } for (int j=0; j < 2*pop2_size; j++){ tx2[j]=transferdata[selectB[j]]; } pop1[i]=tx1; pop2[i]=tx2; } for (int k=0; k < ngen; k++){ //for ngen generations for (int j=0; j<nSNP; j++){ //for all 10 microsatellites //mutate alleles alleles_pop1_before = pop1[j]; for (int i=0; i < 2*pop1_size; i++){ //pop1 mutation for each allele prob=as<double>(runif(1,0,1)); dir=as<double>(runif(1,0,1)); if(prob<(MUS[j])&dir<0.5){ alleles_pop1_before[i]=alleles_pop1_before[i]+1; }else if (prob<(MUS[j])&dir>0.5){ alleles_pop1_before[i]=alleles_pop1_before[i]-1; } } alleles_pop2_before = pop2[j]; for (int i=0; i < 2*pop2_size; i++){ //pop2 mutation for each allele prob=as<double>(runif(1,0,1)); dir=as<double>(runif(1,0,1)); if(prob<(MUS[j])&dir<0.5){ alleles_pop2_before[i]=alleles_pop2_before[i]+1; }else if (prob<(MUS[j])&dir>0.5){ alleles_pop2_before[i]=alleles_pop2_before[i]-1; } } //random mating for (int i=0; i < 2*pop1_size; i++){ //random mating for each allele prob=as<double>(runif(1,-0.5,(2*(rmate*pop1_size)-0.5))); pick1=int(prob); if((prob+0.5)>=(int(prob)+1)){ pick1=int(prob)+1; } PICKS_pop1[i]=pick1; //also seems to work, values from zero to (2*pop1_size)-1 alleles_pop1_after[i]=alleles_pop1_before[pick1]; } //PICKS_pop1 is a set of random alleles from 0 to (2*pop1_size)-1 who will reproduce for (int i=0; i < 2*pop2_size; i++){ //random mating for each allele prob=as<double>(runif(1,-0.5,(2*(rmate*pop2_size)-0.5))); //was 2*pop2_size-0.5 pick2=int(prob); if((prob+0.5)>=(int(prob)+1)){ pick2=int(prob)+1; } PICKS_pop2[i]=pick2; alleles_pop2_after[i]=alleles_pop2_before[pick2]; } //for each of j microsatellites, I have mutated them, and then selected some //to reproduce next year. They are now stored in alleles_pop1_after and alleles_pop2_after //migration removed for now in order to achieve high enough Fst values between populations //int nmigevol; //nmigevol=nmig; //if(nmigevol>0){ //for (int m=0; m<nmigevol; m++){// for each migrant //mover1=alleles_pop1_after(PICKS_pop1[m]); //alleles_pop1_after.erase(PICKS_pop1[m]); //mover2=alleles_pop2_after(PICKS_pop2[m]); //alleles_pop2_after.erase(PICKS_pop2[m]); //alleles_pop1_after.insert(PICKS_pop1[m],mover2); //alleles_pop2_after.insert(PICKS_pop2[m],mover1); //} // for each migrant //} //if nmig>0 NumericVector clone_alleles_pop1_after=clone(alleles_pop1_after); NumericVector clone_alleles_pop2_after=clone(alleles_pop2_after); pop1[j]=clone_alleles_pop1_after; //update the list of alleles pop2[j]=clone_alleles_pop2_after; } //for each of 10 microsatellites Rcout<<"k in evolve"<<std::endl; Rf_PrintValue(wrap(k)); genotypes[0]=pop1; genotypes[1]=pop2; //end of evolve //********************************************************************************************** //TRY FST CALCS HERE if (k%200==0){ allelesA = NumericMatrix((2*pop1_size),nSNP); //is it really 2*? yes allelesB = NumericMatrix((2*pop2_size),nSNP); //is it really 2*? yes arichAmat=NumericMatrix(2*pop1_size,nSNP); arichBmat=NumericMatrix(2*pop2_size,nSNP); richnessA=NumericMatrix(Nyrs,nSNP); //holds each year allelic richness for each microsat richnessB=NumericMatrix(Nyrs,nSNP); // now fill up allelesA with genotypes (one in each column) List temp_genA; List temp_genB; NumericVector temp3; //CHECK WHETERH THIS WORKS FOR 1 population temp_genA=genotypes[0]; for (int i=0; i<nSNP; i++){ temp3=temp_genA[i]; allelesA(_,i)=temp3; //each column of allelesA has all alleles for that usat } temp_genB=genotypes[1]; for (int i=0; i<nSNP; i++){ temp3=temp_genB[i]; allelesB(_,i)=temp3; } //make a matrix to hold genotypes for keeping track of allelic richness //first year popA and popB arichAmat=allelesA; //arichAmat is all alleles males and females from population first year arichBmat=allelesB; //use allelesA same thing I think //*********************************************************************************************** //Fst (for the entire population) if nsamp is bigger than pop size, will give warning //create a matrix with unique alleles for each microsatellite in each column NumericMatrix tallyBOTHmat(2*pop2_size+2*pop1_size,nSNP); int some_integerBOTH = 0; NumericMatrix arichBOTHmat(2*pop2_size+2*pop1_size,nSNP);//all alleles both pops year 1 for (int i=0;i<2*pop1_size;i++){ arichBOTHmat(i,_)=arichAmat(i,_); } for (int i=0;i<2*pop2_size;i++){ arichBOTHmat(i+2*pop1_size,_)=arichBmat(i,_); } //get unique alleles for both populations for (int rich = 0; rich<nSNP;rich++){ NumericMatrix::Column inputBOTH = arichBOTHmat(_,rich); NumericVector xBOTH = clone<NumericVector>(inputBOTH); NumericVector tally_allelesBOTH(2*pop2_size+2*pop1_size); //this is a vector that holds the unique alleles int tallycounter=1; //******* int nallsBOTH=0; typedef std::map<double,int> imap ; typedef imap::value_type pair ; imap index ; int n = xBOTH.size() ; double current, previous = xBOTH[0] ; index.insert( pair( previous, 0 ) ); imap::iterator it = index.begin() ; for( int i=1; i<n; i++){ current = xBOTH[i] ; if( current == previous ){ xBOTH[i] = current + ( ++(it->second) / 100.0 ) ; } else { it = index.find(current) ; if( it == index.end() ){ it = index.insert( current > previous ? it : index.begin(), pair( current, 0 ) ) ; } else { xBOTH[i] = current + ( ++(it->second) / 100.0 ) ; } previous = current ; } if (xBOTH[i]-inputBOTH[i]==0) {nallsBOTH=nallsBOTH+1; tally_allelesBOTH[tallycounter]=inputBOTH[i]; tallycounter=tallycounter+1; } } tally_allelesBOTH[0]=inputBOTH[0]; //***************************** tallyBOTHmat(_,rich)=tally_allelesBOTH; } //end of rich BOTH loop (over 10 microsats) List tallist(nSNP); //list of 10 that will hold allele frequencies for each of 10 microsatellites. //First column is the actual allele size, second is freq from popA, third is freq popB NumericVector count(nSNP);//list of number of unique alleles at each locus //now set up a table to get allele frequencies for popA and popB for (int rich=0;rich<nSNP;rich++){ NumericMatrix::Column inputA=arichAmat(_,rich); NumericMatrix::Column inputB=arichBmat(_,rich); NumericMatrix::Column tal=tallyBOTHmat(_,rich); for(int i=0;i<tal.size();i++){ //figure out how many unique alleles are at this microsat (from tal) if(tal[i]!=0){count[rich]=count[rich]+1;} } NumericVector Counter=clone(count); int counter=Counter[rich]; NumericVector taltrunc(counter); //will hold all unique alleles from this microsat (both pops) NumericMatrix tallyAB(counter,3); //matrix that has alleles, freq at popA, freq at popB NumericVector howmanyA(counter); //number of alleles for this population at each allele NumericVector howmanyB(counter); for(int i=0;i<counter;i++){ //counter is the number of unique alleles at a locus taltrunc[i]=tal[i]; int counterA=0; //a counter for number of unique alleles at each locus int counterB=0; for (int j=0;j<2*pop1_size;j++){ if (inputA[j]==taltrunc[i])//go through all alleles to see how many match this unique one {counterA=counterA+1;} } howmanyA[i]=counterA; for (int j=0;j<2*pop2_size;j++){ if (inputB[j]==taltrunc[i]) {counterB=counterB+1;} } howmanyB[i]=counterB; } //end of counter tallyAB(_,0)=taltrunc; tallyAB(_,1)=howmanyA/(2*pop1_size); tallyAB(_,2)=howmanyB/(2*pop2_size); tallist[rich]=tallyAB; }//end of rich //create talmat, which has unique alleles first column then freqs for popA and B NumericMatrix talmat(std::accumulate(count.begin(),count.end(),0.0),5); for (int i=0;i<nSNP;i++){ int talcount=0; NumericMatrix taltmp(count[i],3); taltmp=as<SEXP>(tallist[i]); for (int j=std::accumulate(count.begin(),count.begin()+i,0.0);j<std::accumulate(count.begin(),count.begin()+i+1,0.0);j++) { talmat(j,_)=taltmp(talcount,_); talcount=talcount+1; } } //aha! talmat is a matrix with all loci. Genious! //talmat is the raw material to find Fst!!! //return(wrap(talmat)); //GET FST this function finds Fst between 2 populations, multiple loci and multiple alleles. double n_sampA=pop1_size; double n_sampB=pop2_size; double n_bar=0.5*(n_sampA+n_sampB); //sample size - can change this. double r=2; double C=0; NumericVector p_bar(talmat.nrow()); NumericMatrix::Column sampmatA =talmat(_,1); NumericMatrix::Column sampmatB =talmat(_,2); s2=NumericVector(talmat.nrow()); NumericVector h_bar(talmat.nrow()); NumericVector ones(talmat.nrow()); ones=rep(1,talmat.nrow()); p_bar=(n_sampA*sampmatA+n_sampB*sampmatB)/(2*n_bar); //each entry is average sample frequency of an allele s2=pow((sampmatB-p_bar),2)+pow((sampmatA-p_bar),2);//sample variance of allele freqs over pops (for each allele) //calculate mean heterozygosity (He=1-sum(all allele frequencies squared)), aka remove the homozygotes h_bar=((n_sampA*2*sampmatA*(ones-sampmatA))+(n_sampB*2*sampmatB*(ones-sampmatB)))/(2*n_bar); //this takes the mean of 2 populations //here heterozygosity assumes HWE. double nc=((r*n_bar)-((pow(n_sampA,2)/(r*n_bar))+(pow(n_sampB,2)/(r*n_bar))))/(r-1); //same as n_bar NumericVector a=(n_bar/nc)*(s2-(1/(n_bar-1))*((p_bar*(1-p_bar))-((r-1)/r)*s2-(0.25*h_bar))); NumericVector dvec=((2*n_bar)/((2*n_bar)-1))*((p_bar*(1-p_bar))-((r-1)/r)*s2); NumericVector b=(n_bar/(n_bar-1))*((p_bar*(1-p_bar))-((r-1)/r)*s2-(h_bar*(2*n_bar-1)/(4*n_bar))); NumericVector c=h_bar/2; NumericVector aplusdvec=a+b+c; double fst=std::accumulate(a.begin(),a.end(),0.0)/(std::accumulate(aplusdvec.begin(),aplusdvec.end(),0.0)); //the better one fst_vec[k]=fst; }//second k loop }//if k%100 }//end popdyn==1 //END TRY FST CALCS //********************************************************************************************** //mgmt_counter //0=combined //1=separated //2=separated genetics test //3=combined then separated //4=tier 5 //5=combined then no fishing //6=combined then tier5 //now take note of whether there are any false boundaries when the true boundary is not found //here you are looking at the significant test results (mistakes) //I am only interested in these if you do not correctly identify true split //and only if there really are 2 populations //but will calculate them here for all cases and then filter later for (int mgmt_counter=0;mgmt_counter<mmt;mgmt_counter ++){ N_init_a1=N_init_a1_FIRST; N_init_a2=N_init_a2_FIRST; //genrule is the integer that tells you whether genetics test found the correct split site. for (int k=0; k < Nyrs; k++){ //make k to be 110 ******should be Nyrs but 1 for now //calculate age structure next year temp8=(N_init_a1*Weight_a*Q_a); S_lyA=0.5*std::accumulate(temp8.begin(),temp8.end(),0.0); //spawning biomass of popA temp9=(N_init_a2*Weight_b*Q_b); S_lyB=0.5*std::accumulate(temp9.begin(),temp9.end(),0.0); //spawning biomass of popB SSBA_vec[k]=S_lyA; SSBB_vec[k]=S_lyB; if(N_init_a1[0]==0){SPRAtrue[k]=0;}else{SPRAtrue[k]=S_lyA/N_init_a1[0];}//true spawning biomass per recruit in each year if(N_init_a2[0]==0){SPRBtrue[k]=0;}else{SPRBtrue[k]=S_lyB/N_init_a2[0];} if(k>=1){ eta_jyA=as<double>(rnorm(1,0,sqrt(sigma_ja2A))); eta_jyB=as<double>(rnorm(1,0,sqrt(sigma_ja2B))); eta_jy=as<double>(rnorm(1,0,sqrt(sigma_ja2))); epsilonA[k]=rho*epsilonA[k-1]+pow((1-pow(rho,2)),0.5)*eta_jyA; epsilonB[k]=rho*epsilonB[k-1]+pow((1-pow(rho,2)),0.5)*eta_jyB; epsilon[k]=rho*epsilon[k-1]+pow((1-pow(rho,2)),0.5)*eta_jy; } //end of k>=1 loop N_hat_aA = N_init_a1*exp(epsilonA[k]-sigma_ja2A/2); N_hat_aB = N_init_a2*exp(epsilonB[k]-sigma_ja2B/2); N_hat_a=(N_init_a1+N_init_a2)*exp(epsilon[k]-sigma_ja2/2); psiA=as<double>(rnorm(1,0,sqrt(sigma_js2A))); psiB=as<double>(rnorm(1,0,sqrt(sigma_js2B))); psi_star=as<double>(rnorm(1,0,sqrt(sigma_js2))); R_l0A_hat[0]=N_hat_aA[0];//same correction applied here as to total numbers R_l0B_hat[0]=N_hat_aB[0]; if(N_hat_aA[0]==0){S_hat_syjA[k]=0;}else{ temp10= Weight_a*N_hat_aA*Q_a; S_hat_syjA[k]=0.5*std::accumulate(temp10.begin(),temp10.end(),0.0)*exp(psiA-(sigma_js2A/2))/N_hat_aA[0];} //simulated estimate of spawning biomass per recruit SSB_hatA[k]= 0.5*std::accumulate(temp10.begin(),temp10.end(),0.0)*exp(psiA-(sigma_js2A/2)); if(N_hat_aB[0]==0){S_hat_syjB[k]=0;}else{ temp11 = Weight_b*N_hat_aB*Q_b; S_hat_syjB[k]=0.5*std::accumulate(temp11.begin(),temp11.end(),0.0)*exp(psiB-(sigma_js2B/2))/N_hat_aB[0];} SSB_hatB[k]= 0.5*std::accumulate(temp11.begin(),temp11.end(),0.0)*exp(psiB-(sigma_js2B/2)); if(N_hat_a[0]==0){S_hat_syj[k]=0;}else{ temp12 = Weight_a*N_hat_a*Q_a; S_hat_syj[k]=0.5*std::accumulate(temp12.begin(),temp12.end(),0.0)*exp(psi-(sigma_js2/2))/N_hat_a[0];} SSB_hat[k]= 0.5*std::accumulate(temp12.begin(),temp12.end(),0.0)*exp(psi-(sigma_js2/2)); SPR_initA=SPR_init_F40_a*(std::accumulate(R_l0A_hat.begin(),R_l0A_hat.begin()+k,0.0)/k);//maybe get rid of this and other bad measures using it SPR_initB=SPR_init_F40_b*(std::accumulate(R_l0B_hat.begin(),R_l0B_hat.begin()+k,0.0)/k); SB40_vecA[k]=SPR_init_F40_a*(std::accumulate(R_l0A_hat.begin(),R_l0A_hat.begin()+k,0.0)/k); SB40_vecB[k]=SPR_init_F40_b*(std::accumulate(R_l0B_hat.begin(),R_l0B_hat.begin()+k,0.0)/k); FishA=as<double>(rlnorm(1,-0.39,0.46));//this if FullF for B20% FishB=as<double>(rlnorm(1,-0.39,0.46));//this is FullF for B40% //if (k>=649){ //FishA=as<double>(rlnorm(1,-0.01,0.4));//this if FullF for B20% //} FishAvec[k]=FishA; FishBvec[k]=FishB; temp15=Weight_a*N_init_a1*((fishsel*FishA)/(fishsel*FishA+M2))*(1-exp(-(fishsel*FishA+M2))); TACA[k]=std::accumulate(temp15.begin(),temp15.end(),0.0); temp16=Weight_b*N_init_a2*((fishsel*FishB)/(fishsel*FishB+M2))*(1-exp(-(fishsel*FishB+M2))); TACB[k]=std::accumulate(temp16.begin(),temp16.end(),0.0); //get spawning fish numbers spawnersA = Q_a*N_init_a1*0.5; spawnersB = Q_b*N_init_a2*0.5; for (int i=0; i<ages; i++){ temp=int(spawnersA[i]); if((spawnersA[i]+0.5)>=(int(spawnersA[i])+1)){ temp=int(spawnersA[i])+1; } spawnersA_rounded[i]=temp; } for (int i=0; i<ages; i++){ temp=int(spawnersB[i]); if((spawnersB[i]+0.5)>=(int(spawnersB[i])+1)){ temp=int(spawnersB[i])+1; } spawnersB_rounded[i]=temp; } //make sure there are not more spawners than actual fish. for(int i=0;i<ages;i++){ if((N_init_a1[i]/2)<spawnersA_rounded[i]){ spawnersA_rounded[i]=N_init_a1[i]/2; } if((N_init_a2[i]/2)<spawnersB_rounded[i]){ spawnersB_rounded[i]=N_init_a2[i]/2; } } NspawnersA[k]=std::accumulate(spawnersA_rounded.begin(),spawnersA_rounded.end(),0.0); //males and females same number of spawners as in here NspawnersB[k]=std::accumulate(spawnersB_rounded.begin(),spawnersB_rounded.end(),0.0); //generate delta which is the variance for recruitment double sigma_jr2_tot=log(pow(sigma_jr2,2)+1); double sigma_rec=as<double>(rnorm(1,0,sqrt(sigma_jr2_tot))); //N_aA and N_aB are numbers next year*****could double check this part N_aA[0]=((4*h*R_l0A[0]*S_lyA)/(S_l0[0]*(1-h)+S_lyA*(5*h-1)))*exp(sigma_rec-(sigma_jr2/2)); //S_l0[0] are spawners in popA //BH spawner recruit relationship N_aB[0]=((4*h*R_l0B[0]*S_lyB)/(S_l0[1]*(1-h)+S_lyB*(5*h-1)))*exp(sigma_rec-(sigma_jr2/2)); //S_l0[1] are spawners in popB Rcout<<"first migration"<<std::endl; //HERE IS MIGRATION IN POP DYN MODEL //if (k%4==0){ //for (int i=0;i<nmig;i++){ //N_aA[0]=N_aA[0]+2; //} //}//end of if k%=4==0 if(NspawnersA[k]==0){N_aA[0]=0;} if(NspawnersB[k]==0){N_aB[0]=0;} for(int i=1; i<(ages-1); i++){ N_aA[i]=N_init_a1[i-1]*exp(-(fishsel[i-1]*FishA+M2[i-1])); //correct because you want the selectivity of the next youngest age N_aB[i]=N_init_a2[i-1]*exp(-(fishsel[i-1]*FishB+M2[i-1])); //and fishsel now goes from 21 to 1 //N_aA[i]=N_init_a1[i-1]*Surv[i-1]; //correct because you want the selectivity of the next youngest age //N_aB[i]=N_init_a2[i-1]*Surv[i-1]; //and fishsel now goes from 21 to 1 } N_aA[ages-1]=N_init_a1[ages-2]*exp(-(fishsel[ages-2]*FishA+M2[ages-2]))+N_init_a1[ages-1]*exp(-(fishsel[ages-1]*FishA+M2[ages-1])); //dont forget about plus group N_aB[ages-1]=N_init_a2[ages-2]*exp(-(fishsel[ages-2]*FishB+M2[ages-2]))+N_init_a2[ages-1]*exp(-(fishsel[ages-1]*FishB+M2[ages-1])); //N_aA[ages-1]=N_init_a1[ages-2]*Surv[ages-2]+N_init_a1[ages-1]*Surv[ages-1]; //dont forget about plus group //N_aB[ages-1]=N_init_a2[ages-2]*Surv[ages-2]+N_init_a2[ages-1]*Surv[ages-1]; //now round N_aA and N_aB to integers for (int i=0; i<ages; i++){ temp18=int(N_aA[i]); if((N_aA[i]+0.5)>=(int(N_aA[i])+1)){ temp18=int(N_aA[i])+1; } N_aA[i]=temp18; if (temp18%2!=0){ temp2=as<double>(runif(1,-1,1)); if(temp2>=0){N_aA[i]=N_aA[i]+1;} else{N_aA[i]=N_aA[i]-1; } } } for (int i=0; i<ages; i++){ temp19=int(N_aB[i]); if((N_aB[i]+0.5)>=(int(N_aB[i])+1)){ temp19=int(N_aB[i])+1;} N_aB[i]=temp19; if (temp19%2!=0){ temp2=as<double>(runif(1,-1,1)); if(temp2>=0){N_aB[i]=N_aB[i]+1;} else{N_aB[i]=N_aB[i]-1; } } } //how many individuals are there? R_l0A[k]=N_init_a1[0]; R_l0B[k]=N_init_a2[0]; R_l0A_hat[k]=N_hat_aA[0]; //incorporate error into recruitment observation R_l0B_hat[k]=N_hat_aB[0]; NtotalA[k] = std::accumulate(N_init_a1.begin(),N_init_a1.end(),0.0); NtotalB[k] = std::accumulate(N_init_a2.begin(),N_init_a2.end(),0.0); allA.row(k)=rev(N_init_a1); //each row is the number of each age during that year - reversed so it is 21-1 for next section allB.row(k)=rev(N_init_a2); allspawnA.row(k)=rev(spawnersA_rounded); //each row is the number of each age during that year - reversed so it is 21-1 for next section allspawnB.row(k)=rev(spawnersB_rounded); //put this at the end N_init_a1=clone(N_aA); N_init_a2=clone(N_aB); //create separate matrices for each of the mgmt cases. Can return for separated case (left out here). out3(zcounter,k)=R_l0A[k];//true recruitment out4(zcounter,k)=R_l0B[k]; out21(zcounter,k)=0.4*SSBA_vec[k]/SB40_vecA[k];//true SSB div by our GThompson est of B40 out22(zcounter,k)=0.4*SSBB_vec[k]/SB40_vecB[k]; out23(zcounter,k)=SSBA_vec[k];//just ssb out24(zcounter,k)=SSBB_vec[k]; } //end of k<Nyrs loop NtotalA2=clone(NtotalA); //try calculating Ne here work from 1 to <ages> here NumericVector x_age(ages); NumericVector S_xA(ages); NumericVector S_xB(ages); NumericVector bxA(ages); NumericVector bxB(ages); NumericVector lxA(ages); NumericVector lxB(ages); NumericVector b_primexA(ages); NumericVector b_primexB(ages); NumericVector bxlxA(ages); NumericVector bxlxB(ages); NumericVector BxA(ages); NumericVector BxB(ages); double L_popA; //generation length double L_popB; //generation length NumericVector L_popAvec(ages); NumericVector L_popBvec(ages); NumericVector L_popA_vec(Nyrs); NumericVector L_popB_vec(Nyrs); NumericVector VkA_vec(Nyrs); NumericVector VkB_vec(Nyrs); NumericVector k_barA(ages); NumericVector k_barB(ages); NumericVector VxA(ages); NumericVector VxB(ages); NumericVector DxA(ages); NumericVector DxB(ages); NumericVector k_barADxA(ages); NumericVector k_barBDxB(ages); NumericVector SSDIxA(ages); NumericVector SSDIxB(ages); NumericVector delxA(ages); NumericVector delxB(ages); NumericVector SSDGxA(ages); NumericVector SSDGxB(ages); NumericVector SSDxA(ages); NumericVector SSDxB(ages); NumericVector newnumsA(ages);//calculate new numbers based on initial nums and lx. NumericVector newnumsB(ages); double k_bardotA; double k_bardotB; double VkA; double VkB; for (int k=0;k<Nyrs;k++){ //put in Nyrs x_age=age+1; N_init_a1_star = rev(allA.row(k)); //0 yr olds to <ages> yr olds here N_init_a2_star = rev(allB.row(k)); spawnersA_rounded_star = rev(allspawnA.row(k)); spawnersB_rounded_star = rev(allspawnB.row(k)); FishA = FishAvec[k]; FishB = FishBvec[k]; //S_xA = 1-(fishsel*FishA+M); //S_xB = 1-(fishsel*FishB+M); S_xA=rep((1-meanFullF),ages); S_xA[(ages-1)]=0; S_xB=rep((1-meanFullF),ages); S_xB[(ages-1)]=0; for (int i=0;i<ages;i++){ //make sure they are positive (neg can happen when fishing gets too high) if (S_xA[i]<0){S_xA[i]=0;} if (S_xB[i]<0){S_xB[i]=0;} } lxA[0]=1; lxB[0]=1; for (int i=1;i<ages;i++){ lxA[i] = S_xA[i-1]*lxA[i-1]; lxB[i] = S_xB[i-1]*lxB[i-1]; } newnumsA=0.5*N_init_a1_star[0]*lxA; newnumsB=0.5*N_init_a2_star[0]*lxB; for(int i=0;i<ages;i++){ if (NspawnersA[k]*newnumsA[i]>0) {bxA[i] = spawnersA_rounded_star[i]*N_init_a1_star[0]/(2*NspawnersA[k]*newnumsA[i]);}//div by2 because want half the number of N1s here else{bxA[i]=0;} if (NspawnersB[k]*newnumsB[i]>0) {bxB[i] = spawnersB_rounded_star[i]*N_init_a2_star[0]/(2*NspawnersB[k]*newnumsB[i]);} else{bxB[i]=0;} } bxlxA=bxA*lxA; bxlxB=bxB*lxB; for(int i=0;i<ages;i++){ if(std::accumulate(bxlxA.begin(),bxlxA.end(),0.0)>0){ b_primexA[i] = 2*bxA[i]/std::accumulate(bxlxA.begin(),bxlxA.end(),0.0);} else{b_primexA[i] =0;} if(std::accumulate(bxlxB.begin(),bxlxB.end(),0.0)>0){ b_primexB[i] = 2*bxB[i]/std::accumulate(bxlxB.begin(),bxlxB.end(),0.0);} else{b_primexB[i]=0;} } BxA=b_primexA*newnumsA; BxB=b_primexB*newnumsB; L_popAvec = x_age*BxA; L_popBvec = x_age*BxB; if (N_init_a1_star[0]>0){ L_popA = std::accumulate(L_popAvec.begin(),L_popAvec.end(),0.0)/N_init_a1_star[0];} else {L_popA=0;} if (N_init_a2_star[0]>0){ L_popB = std::accumulate(L_popBvec.begin(),L_popBvec.end(),0.0)/N_init_a2_star[0];} else{L_popB=0;} L_popA_vec[k]=L_popA; L_popB_vec[k]=L_popB; k_barA[0]=0; k_barB[0]=0; for (int i=1;i<ages;i++){ k_barA[i] = b_primexA[i]+k_barA[i-1]; k_barB[i] = b_primexB[i]+k_barB[i-1]; } VxA=k_barA;//just for males or females (but they have the same value) assume poisson variance VxB=k_barB; for (int i=0;i<(ages-1);i++){ if (newnumsA[i]-newnumsA[i+1]>=0){DxA[i] =newnumsA[i]-newnumsA[i+1];} else{DxA[i]=0;} if (newnumsB[i]-newnumsB[i+1]>=0){DxB[i] =newnumsB[i]-newnumsB[i+1];} else{DxB[i]=0;} } k_barADxA=k_barA*DxA; k_barBDxB=k_barB*DxB; if (N_init_a1_star[0]>0){ k_bardotA = std::accumulate(k_barADxA.begin(),k_barADxA.end(),0.0)/N_init_a1_star[0];} else{k_bardotA=0;}//divide by number of newborns if (N_init_a2_star[0]>0){ k_bardotB = std::accumulate(k_barBDxB.begin(),k_barBDxB.end(),0.0)/N_init_a2_star[0];} else{k_bardotB=0;} delxA=k_barA-k_bardotA; delxB=k_barB-k_bardotB; SSDIxA=DxA*VxA; SSDIxB=DxB*VxB; SSDGxA=DxA*delxA*delxA; SSDGxB=DxB*delxB*delxB; SSDxA=SSDIxA+SSDGxA; SSDxB=SSDIxB+SSDGxB; double SSDtA = std::accumulate(SSDxA.begin(),SSDxA.end(),0.0); double SSDtB = std::accumulate(SSDxB.begin(),SSDxB.end(),0.0); if (N_init_a1_star[0]>0){VkA = SSDtA/N_init_a1_star[0];} //just for females or males but the values are the same else{VkA=0;} if (N_init_a2_star[0]>0){VkB = SSDtB/N_init_a2_star[0];} else{VkB=0;} //VkA and VkB are total variance VkA_vec[k]=VkA; VkB_vec[k]=VkB; double NeA = (4*N_init_a1_star[0]*L_popA)/(VkA+2); double NeB = (4*N_init_a2_star[0]*L_popB)/(VkB+2); //if(NtotalA[k]>1){ //NeAvec[k]=NeA/NtotalA[k];} //else{NeAvec[k]=0;} //if(NtotalB[k]>1){ //NeBvec[k]=NeB/NtotalB[k];} //else{NeBvec[k]=0;} //calculate vkdot from males and females in pop and also teh VkA an dVkB. //then find Ne. NeAvec[k]=NeA; NeBvec[k]=NeB; }//end of k loop for finding Ne. out7(zcounter,_) = TACA; out8(zcounter,_)= TACB; out9(zcounter,_)=FishAvec; out10(zcounter,_)=FishBvec; out14(zcounter,_)=TAC; out15(zcounter,_)=NtotalA; out16(zcounter,_)=NtotalB; if (popdyn==1){ //Set up lists which will hold genotypes for males, females each pop //List popA(Nyrs+ages); //List popB(Nyrs+ages); //popA and B lists of 131 matrices. First 21 will be length of N_init_a1_FIRST (age 21 to 1), the rest will be number of offspring for that year NumericMatrix matsliceA; //popA males NumericMatrix matsliceB; //popA females //popA set up alleles for each individual from allelesA for(int i=0;i<ages;i++){ matsliceA=NumericMatrix(N_init_a1_FIRST[i],nSNP); matsliceB=NumericMatrix(N_init_a1_FIRST[i],nSNP); for (int j=std::accumulate(N_init_a1_FIRST.begin(),N_init_a1_FIRST.begin()+i,0.0); j<std::accumulate(N_init_a1_FIRST.begin(),N_init_a1_FIRST.begin()+i+1,0.0);j++){ matsliceA(j-std::accumulate(N_init_a1_FIRST.begin(),N_init_a1_FIRST.begin()+i,0.0),_)=allelesA(j,_);} for (int j=std::accumulate(N_init_a1_FIRST.begin(),N_init_a1_FIRST.begin()+i,0.0)+pop1_size; j<std::accumulate(N_init_a1_FIRST.begin(),N_init_a1_FIRST.begin()+i+1,0.0)+pop1_size;j++){ matsliceB(j-std::accumulate(N_init_a1_FIRST.begin(),N_init_a1_FIRST.begin()+i,0.0)-pop1_size,_)=allelesA(j,_);} List temp5(2); temp5[0]=matsliceA;temp5[1]=matsliceB; popA[ages-1-i]=temp5; } //pop2 set up alleles for each individual from allelesB NumericMatrix matsliceC; //popB males NumericMatrix matsliceD; //popB females for(int i=0;i<ages;i++){ matsliceC=NumericMatrix(N_init_a2_FIRST[i],nSNP); matsliceD=NumericMatrix(N_init_a2_FIRST[i],nSNP); for (int j=std::accumulate(N_init_a2_FIRST.begin(),N_init_a2_FIRST.begin()+i,0.0); j<std::accumulate(N_init_a2_FIRST.begin(),N_init_a2_FIRST.begin()+i+1,0.0);j++){ matsliceC(j-std::accumulate(N_init_a2_FIRST.begin(),N_init_a2_FIRST.begin()+i,0.0),_)=allelesB(j,_);} for (int j=std::accumulate(N_init_a2_FIRST.begin(),N_init_a2_FIRST.begin()+i,0.0)+pop2_size; j<std::accumulate(N_init_a2_FIRST.begin(),N_init_a2_FIRST.begin()+i+1,0.0)+pop2_size;j++){ matsliceD(j-std::accumulate(N_init_a2_FIRST.begin(),N_init_a2_FIRST.begin()+i,0.0)-pop2_size,_)=allelesB(j,_);} List temp6(2); temp6[0]=matsliceC;temp6[1]=matsliceD; popB[ages-1-i]=temp6; } NumericVector popA_first21 = rev(N_init_a1_FIRST); NumericVector popB_first21 = rev(N_init_a2_FIRST); NumericVector newrecsA = allA.column(ages-1); NumericVector newrecsB = allB.column(ages-1); NumericVector dimspopA(Nyrs+ages); NumericVector dimspopB(Nyrs+ages); NumericVector pluscounterA_list(Nyrs,-2.0); NumericVector pluscounterB_list(Nyrs,-2.0); for(int i=0;i<ages;i++){ dimspopA[i]=popA_first21[i]; dimspopB[i]=popB_first21[i]; } for(int i=ages;i<(Nyrs+ages+1);i++){ dimspopA[i]=newrecsA[i-ages+1]; dimspopB[i]=newrecsB[i-ages+1]; } for(int k=0;k<Nyrs-1;k++){ //Nyrs //set up spawning groups (for males and females, popA)*************************************************** Rcout<<"k at 2079"<<std::endl; Rf_PrintValue(wrap(k)); spawn_malA_thisyr = NumericMatrix(2*NspawnersA[k],nSNP); //twice as many genotypes as spawners spawn_femA_thisyr = NumericMatrix(2*NspawnersA[k],nSNP); some_integerA=0; spawnersA_rounded_star = allspawnA.row(k); //from 21 yr olds to newborns NumericVector N_init_a1_star = allA.row(k); //also from 21 yr olds to newborns first row is N_init_a1_FIRST tmpMatrixA_1 = NumericMatrix(0,nSNP); tmpMatrixA_2 = NumericMatrix(0,nSNP); //fix plus group problems if ((spawnersA_rounded_star[0]>0)){ //only worry about them if there is a plus group IntegerVector vec_plusA(k+1); int plus_counterA = -1; for(int j=0;j<k+1;j++){ if(j==0){ vec_plusA[j]=dimspopA(k-j);} //vec_plus is a vector of the cumulative number of rows in popA from the current plus group to previous years, etc. else{ vec_plusA[j]=vec_plusA[j-1]+dimspopA(k-j);} if (vec_plusA[j]<2*spawnersA_rounded_star[0]) //plus_counterA tells you how far back you need to go {plus_counterA = j;} } pluscounterA_list[k]=plus_counterA; for (int j=k-(plus_counterA+1); j<k+1;j++){ tmpList_plusgroupA = popA[j]; //this is the actual plus group tmpMatrixA_1plus=as<SEXP>(tmpList_plusgroupA[0]); tmpMatrixA_2plus=as<SEXP>(tmpList_plusgroupA[1]); for (int l=0;l<tmpMatrixA_1plus.nrow();l++){ spawn_malA_thisyr(some_integerA,_)=tmpMatrixA_1plus(l,_); spawn_femA_thisyr(some_integerA,_)=tmpMatrixA_2plus(l,_); some_integerA = some_integerA+1; if(some_integerA>=2*spawnersA_rounded_star[0]){j=k+1;l=tmpMatrixA_1plus.nrow();} } } }//end of if spawnersA_rounded_star[0]>0 //now your spawn_femA_thisyr has plus group in it. //use some_integerA to fill in the rest of spawn_femA_thisyr for(int i=1;i<ages;i++){ if((spawnersA_rounded_star[i]>0)){ mal_spawnersA = IntegerVector(spawnersA_rounded_star[i]); //after random mating **also make sure you get the right row fem_spawnersA=IntegerVector(spawnersA_rounded_star[i]); //after random mating//may be 0 if spawnersA_rounded_star is 1 b/c of indexing (0 is 1) spawner_listM = seq_len(N_init_a1_star[i]/2); //list of ones to choose from spawner_listF = seq_len(N_init_a1_star[i]/2); //choosing from half the number of individuals because also using the subsequent row tmpMatrixA_1 = NumericMatrix(0,nSNP); tmpMatrixA_2 = NumericMatrix(0,nSNP); tmpListA = popA[i+k]; tmpMatrixA_1=as<SEXP>(tmpListA[0]); tmpMatrixA_2=as<SEXP>(tmpListA[1]); //if (tmpMatrixA_1.nrow()>(2*N_init_a1_star[i])){ for (int j=0; j < spawnersA_rounded_star[i]; j++){ //random mating for each allele pick_spawner_mal=as<double>(runif(1,-0.5,((N_init_a1_star[i]/2)-j-0.5)));//b/c choosing spawnersA_rounded_star[i] from all available and from half so can use subsequent row as well pickM=int(pick_spawner_mal); if((pick_spawner_mal+0.5)>=(int(pick_spawner_mal)+1)){ pickM=int(pick_spawner_mal)+1; } pick_spawner_fem=as<double>(runif(1,-0.5,((N_init_a1_star[i]/2)-j-0.5)));//b/c you are choosing spawnersA_rounded[i] from all those available pickF=int(pick_spawner_fem); if((pick_spawner_fem+0.5)>=(int(pick_spawner_fem)+1)){ pickF=int(pick_spawner_fem)+1; } mal_spawnersA[j]=spawner_listM[pickM]; //numeric(0) happens when you skip one because it is zero. fem_spawnersA[j]=spawner_listF[pickF]; spawn_malA_thisyr(some_integerA,_)=tmpMatrixA_1(2*spawner_listM[pickM]-2,_); //the minus two is to get the indices to work out spawn_malA_thisyr(some_integerA+1,_)=tmpMatrixA_1(2*spawner_listM[pickM]-1,_);//and the next one as well in the row spawn_femA_thisyr(some_integerA,_)=tmpMatrixA_2(2*spawner_listF[pickF]-2,_); spawn_femA_thisyr(some_integerA+1,_)=tmpMatrixA_2(2*spawner_listF[pickF]-1,_); spawner_listM.erase(pickM); //this erases the one at the location you specify spawner_listF.erase(pickF); some_integerA=some_integerA+2; } //end js for A //}//end if loop } //end if spawnersA_rounded_star[i]>0 } //end of i loop (done with finding spawners) //set up spawning groups (for males and females, popB)*************************************************** spawn_malB_thisyr = NumericMatrix(2*NspawnersB[k],nSNP); //twice as many genotypes as spawners spawn_femB_thisyr = NumericMatrix(2*NspawnersB[k],nSNP); some_integerB=0; spawnersB_rounded_star = allspawnB.row(k); //from 21 yr olds to newborns NumericVector N_init_a2_star = allB.row(k); //also from 21 yr olds to newborns first row is N_init_a1_FIRST tmpMatrixB_1 = NumericMatrix(0,nSNP); tmpMatrixB_2 = NumericMatrix(0,nSNP); if (spawnersB_rounded_star[0]>0){ //only worry about them if there is a plus group IntegerVector vec_plusB(k+1); int plus_counterB = -1; for(int j=0;j<k+1;j++){ if(j==0){ vec_plusB[j]=dimspopB(k-j);} //vec_plus is a vector of the cumulative number of rows in popA from the current plus group to previous years, etc. else{ vec_plusB[j]=vec_plusB[j-1]+dimspopB(k-j);} if (vec_plusB[j]<2*spawnersB_rounded_star[0]) //plus_counterB tells you how far back you need to go {plus_counterB = j;} } pluscounterB_list[k]=plus_counterB; //problem is that by default it is 0 so it does not help for (int j=k-(plus_counterB+1); j<k+1;j++){ tmpList_plusgroupB = popB[j]; //this is the actual plus group tmpMatrixB_1plus=as<SEXP>(tmpList_plusgroupB[0]); tmpMatrixB_2plus=as<SEXP>(tmpList_plusgroupB[1]); for (int l=0;l<tmpMatrixB_1plus.nrow();l++){ spawn_malB_thisyr(some_integerB,_)=tmpMatrixB_1plus(l,_); spawn_femB_thisyr(some_integerB,_)=tmpMatrixB_2plus(l,_); some_integerB = some_integerB+1; if(some_integerB>=2*spawnersB_rounded_star[0]){j=k+1;l=tmpMatrixB_1plus.nrow();} //>=because of C++ indexing } } }//end of if spawnersB_rounded_star[0]>0 //now your spawn_femB_thisyr has plus group in it. for(int i=1;i<ages;i++){ if((spawnersB_rounded_star[i]>0)){ mal_spawnersB = IntegerVector(spawnersB_rounded_star[i]); //after random mating **also make sure you get the right row fem_spawnersB = IntegerVector(spawnersB_rounded_star[i]); //after random mating spawner_listM = seq_len(N_init_a2_star[i]/2); //list of ones to choose from spawner_listF = seq_len(N_init_a2_star[i]/2); //choosing from half the number of individuals because also using the subsequent row tmpMatrixB_1 = NumericMatrix(0,nSNP); tmpMatrixB_2 = NumericMatrix(0,nSNP); tmpListB = popB[i+k]; tmpMatrixB_1=as<SEXP>(tmpListB[0]); tmpMatrixB_2=as<SEXP>(tmpListB[1]); for (int j=0; j < spawnersB_rounded_star[i]; j++){ //random mating for each allele pick_spawner_mal=as<double>(runif(1,-0.5,((N_init_a2_star[i]/2)-j-0.5)));//b/c choosing spawnersB_rounded_star[i] from all available and from half so can use subsequent row as well pickM=int(pick_spawner_mal); if((pick_spawner_mal+0.5)>=(int(pick_spawner_mal)+1)){ pickM=int(pick_spawner_mal)+1; } pick_spawner_fem=as<double>(runif(1,-0.5,((N_init_a2_star[i]/2)-j-0.5)));//b/c you are choosing spawnersA_rounded[i] from all those available pickF=int(pick_spawner_fem); if((pick_spawner_fem+0.5)>=(int(pick_spawner_fem)+1)){ pickF=int(pick_spawner_fem)+1; } mal_spawnersB[j]=spawner_listM[pickM]; //numeric(0) happens when you skip one because it is zero. fem_spawnersB[j]=spawner_listF[pickF]; spawn_malB_thisyr(some_integerB,_)=tmpMatrixB_1(2*spawner_listM[pickM]-2,_); //the minus two is to get the indices to work out spawn_malB_thisyr(some_integerB+1,_)=tmpMatrixB_1(2*spawner_listM[pickM]-1,_);//and the next one as well in the row spawn_femB_thisyr(some_integerB,_)=tmpMatrixB_2(2*spawner_listF[pickF]-2,_); spawn_femB_thisyr(some_integerB+1,_)=tmpMatrixB_2(2*spawner_listF[pickF]-1,_); spawner_listM.erase(pickM); //this erases the one at the location you specify spawner_listF.erase(pickF); some_integerB=some_integerB+2; } //end js for B } //end if spawnersB_rounded_star[i]>0 } //end of i loop (done with finding spawners) spawn_femA[k]=spawn_femA_thisyr; spawn_malA[k]=spawn_malA_thisyr; spawn_femB[k]=spawn_femB_thisyr; spawn_malB[k]=spawn_malB_thisyr; //now establish genotypes for the new recruits RecA = NumericMatrix (2*allA(k+1,ages-1),nSNP); RecB = NumericMatrix (2*allB(k+1,ages-1),nSNP); //recruits for popA for(int j=0;j<allA(k+1,ages-1);j++){ rand_doub=as<double>(runif(1,-0.5,(spawn_femA_thisyr.nrow()/2)-1)); samp_mom=int(rand_doub); if((rand_doub+0.5)>=(int(rand_doub)+1)){ samp_mom=int(rand_doub)+1; } rand_doub=as<double>(runif(1,-0.5,(spawn_malA_thisyr.nrow()/2)-1)); samp_dad=int(rand_doub); if((rand_doub+0.5)>=(int(rand_doub)+1)){ samp_dad=int(rand_doub)+1; } for(int i=0;i<nSNP;i++){ rand_doub=as<double>(runif(1,-0.5,1.5)); rand_allele=int(rand_doub); if((rand_doub+0.5)>=(int(rand_doub)+1)){ rand_allele=int(rand_doub)+1; } RecA(2*j,i)=spawn_malA_thisyr(samp_dad*2+rand_allele,i); RecA(2*j+1,i)=spawn_femA_thisyr(samp_mom*2+rand_allele,i); } //end of all 10 microsatellites } //end of j RecAs recruits popA //recruits for popB for(int j=0;j<allB(k+1,ages-1);j++){ rand_doub=as<double>(runif(1,-0.5,(spawn_femB_thisyr.nrow()/2)-1)); samp_mom=int(rand_doub); if((rand_doub+0.5)>=(int(rand_doub)+1)){ samp_mom=int(rand_doub)+1; } rand_doub=as<double>(runif(1,-0.5,(spawn_malB_thisyr.nrow()/2)-1)); samp_dad=int(rand_doub); if((rand_doub+0.5)>=(int(rand_doub)+1)){ samp_dad=int(rand_doub)+1; } for(int i=0;i<nSNP;i++){ rand_doub=as<double>(runif(1,-0.5,1.5)); rand_allele=int(rand_doub); if((rand_doub+0.5)>=(int(rand_doub)+1)){ rand_allele=int(rand_doub)+1; } RecB(2*j,i)=spawn_malB_thisyr(samp_dad*2+rand_allele,i); RecB(2*j+1,i)=spawn_femB_thisyr(samp_mom*2+rand_allele,i); } //end of all 10 microsatellites } //end of j RecBs recruits popB //here take the matrices RecA and RecB which are random offspring //and select a subset of the individuals and make more of them //this represents a subset of parents being successful at mating //there might be better ways, like reducing number of spawners recA = clone(RecA); recB = clone(RecB); //mutate new recruits double prob; double dir; for (int j=0;j<nSNP;j++){ for (int i=0; i < recA.nrow(); i++){ //recA mutation for each allele prob=as<double>(runif(1,0,1)); dir=as<double>(runif(1,0,1)); if(prob<0.01*MUS[j]&dir<0.5){ recA(i,j)=recA(i,j)+1; }else if (prob<MUS[j]&dir>0.5){ recA(i,j)=recA(i,j)-1; } } for (int i=0; i < recB.nrow(); i++){ //recB mutation for each allele prob=as<double>(runif(1,0,1)); dir=as<double>(runif(1,0,1)); if(prob<0.01*MUS[j]&dir<0.5){ recB(i,j)=recB(i,j)+1; }else if (prob<MUS[j]&dir>0.5){ recB(i,j)=recB(i,j)-1; } } } //MIGRATION if ((k>1)&(k%1==0)){ Rcout<<"second migration"<<std::endl; nmig=nmig1; NumericVector BtoAlist(nmig); NumericVector AfromBlist(nmig); int whichone; if (recA.nrow()<=recB.nrow()){whichone=1;} if (recB.nrow()<recA.nrow()){whichone=2;} if (whichone==1){//if there are fewer recruits in recA if ((2*nmig)>recA.nrow()|(2*nmig)>recB.nrow()){nmig=recA.nrow()/2;} } if (whichone==2){//if there are fewer recruits in recB if ((2*nmig)>recA.nrow()|(2*nmig)>recB.nrow()){nmig=recB.nrow()/2;} } Rcout<<"mig,recA,recB,whichone"<<std::endl; Rf_PrintValue(wrap(nmig)); Rf_PrintValue(wrap(recA.nrow())); Rf_PrintValue(wrap(recB.nrow())); Rf_PrintValue(wrap(whichone)); nmig_vec[k]=nmig; Rf_PrintValue(wrap(1619)); NumericMatrix clonerecB=clone(recB); NumericMatrix clonerecA=clone(recA); Rcout<<"clonerecB.nrow() and clonerecA.nrow()"<<std::endl; Rf_PrintValue(wrap(clonerecB.nrow())); Rf_PrintValue(wrap(clonerecA.nrow())); BtoAlist=SeqLen(clonerecB.nrow()/2)-1; AfromBlist=SeqLen(clonerecA.nrow()/2)-1; Rf_PrintValue(wrap(1626)); for (int i=0;i<nmig;i++){ double probfromB=as<double>(runif(1,0,(clonerecB.nrow()/2)-1-i)); //works, I checked it double probtoA=as<double>(runif(1,0,(clonerecA.nrow()/2)-1-i)); int pick_fromB=int(probfromB); if((probfromB+0.5)>=(int(probfromB)+1)){ pick_fromB=int(probfromB)+1; } int pick_toA=int(probtoA); if((probtoA+0.5)>=(int(probtoA)+1)){ pick_toA=int(probtoA)+1; } Rf_PrintValue(wrap(1641)); NumericMatrix::Row moverB2Arow1=clonerecB(2*(BtoAlist[pick_fromB]),_); //minus 1 is for indexing NumericMatrix::Row moverB2Arow2=clonerecB((2*(BtoAlist[pick_fromB]))+1,_); recA(2*(AfromBlist[pick_toA]),_)=moverB2Arow1; recA((2*(AfromBlist[pick_toA]))+1,_)=moverB2Arow2; BtoAlist.erase(pick_fromB); AfromBlist.erase(pick_toA); }//end of migration Rf_PrintValue(wrap(1652)); //for (int i=0;i<2*nmig;i++){ // NumericMatrix::Row moverB2Arow=clonerecB(i,_); //minus 1 is for indexing // recA(i,_)=moverB2Arow; //} }//end of if k >1 Rf_PrintValue(wrap(1662)); //save new recruits to popA and popB NumericMatrix recA_1(allA(k+1,ages-1),nSNP); NumericMatrix recA_2(allA(k+1,ages-1),nSNP); for(int i=0;i<allA(k+1,ages-1);i++){ recA_1(i,_)=recA(i,_); recA_2(i,_)=recA(i+allA(k+1,ages-1),_); } tmpList_recsA[0] = recA_1; tmpList_recsA[1] = recA_2; NumericMatrix recB_1(allB(k+1,ages-1),nSNP); NumericMatrix recB_2(allB(k+1,ages-1),nSNP); for(int i=0;i<allB(k+1,ages-1);i++){ recB_1(i,_)=recB(i,_); recB_2(i,_)=recB(i+allB(k+1,ages-1),_); } tmpList_recsB[0] = recB_1; tmpList_recsB[1] = recB_2; popA[k+ages]=clone(tmpList_recsA); popB[k+ages]=clone(tmpList_recsB); }// end k loop Rf_PrintValue(wrap(1686)); Rcout<<"Nyrs"<<std::endl; Rf_PrintValue(wrap(Nyrs)); Rcout<<"ages"<<std::endl; Rf_PrintValue(wrap(ages)); //get genetic diversity - put all genotypes in GenotypesA1, GenotypesA2, GenotypesB1, GenotypesB2 NumericVector cumpopsA(Nyrs+ages);//cumpops holds the cumulative number (for counting purposes) NumericVector cumpopsB(Nyrs+ages); cumpopsA[0]=0; cumpopsB[0]=0; Rf_PrintValue(wrap(1696)); Rcout<<"dimspopA"<<std::endl; Rf_PrintValue(wrap(dimspopA)); Rcout<<"dimspopB"<<std::endl; Rf_PrintValue(wrap(dimspopB)); for(int i=1;i<(Nyrs+ages);i++){ cumpopsA[i]=dimspopA[i-1]+cumpopsA[i-1]; cumpopsB[i]=dimspopB[i-1]+cumpopsB[i-1]; } Rcout<<"cumpopsA"<<std::endl; Rf_PrintValue(wrap(cumpopsA)); Rcout<<"cumpopsB"<<std::endl; Rf_PrintValue(wrap(cumpopsB)); Rf_PrintValue(wrap(1701)); Rcout<<"cumpopsA[Nyrs+ages-1], cumpopsB[Nyrs+ages-1]"<<std::endl; Rf_PrintValue(wrap(cumpopsA[Nyrs+ages-1])); Rf_PrintValue(wrap(cumpopsB[Nyrs+ages-1])); NumericMatrix GenotypesA1(cumpopsA[Nyrs+ages-1],nSNP);//matrix to hold everything (we want this but come back to it) NumericMatrix GenotypesA2(cumpopsA[Nyrs+ages-1],nSNP); NumericMatrix GenotypesB1(cumpopsB[Nyrs+ages-1],nSNP); NumericMatrix GenotypesB2(cumpopsB[Nyrs+ages-1],nSNP); NumericMatrix tmpMatA_1; NumericMatrix tmpMatA_2; NumericMatrix tmpMatB_1; NumericMatrix tmpMatB_2; List tmpLA(2); List tmpLB(2); some_integerA=0; some_integerB=0; Rf_PrintValue(wrap(1714)); //pluscounterB_list is Nyrs long (110) NumericVector poprowsA(Nyrs+ages-1); for (int i=0;i<(Nyrs+ages-1);i++){ List hmmA(2); hmmA=popA[i]; NumericMatrix hmm2A=hmmA[1]; poprowsA[i]=hmm2A.nrow();} GenotypesA1=NumericMatrix(std::accumulate(poprowsA.begin(),poprowsA.end(),0.0),nSNP); GenotypesA2=NumericMatrix(std::accumulate(poprowsA.begin(),poprowsA.end(),0.0),nSNP); for(int k=0; k<(Nyrs+ages-1);k++){ tmpLA = popA[k]; tmpMatA_1 = as<SEXP>(tmpLA[0]); tmpMatA_2 = as<SEXP>(tmpLA[1]); for(int i=0; i<tmpMatA_1.nrow();i++){ GenotypesA1(some_integerA,_)=tmpMatA_1(i,_); GenotypesA2(some_integerA,_)=tmpMatA_2(i,_); some_integerA=some_integerA+1; } } //genotypesB1<sum of all popB or <tmpMatB_ NumericVector poprowsB(Nyrs+ages-1); for (int i=0;i<(Nyrs+ages-1);i++){ List hmm(2); hmm=popB[i]; NumericMatrix hmm2=hmm[1]; poprowsB[i]=hmm2.nrow();} GenotypesB1=NumericMatrix(std::accumulate(poprowsB.begin(),poprowsB.end(),0.0),nSNP); GenotypesB2=NumericMatrix(std::accumulate(poprowsB.begin(),poprowsB.end(),0.0),nSNP); //if (genotypesB1.nrow()< for(int k=0; k<(Nyrs+ages-1);k++){ tmpLB = popB[k]; tmpMatB_1 = as<SEXP>(tmpLB[0]); tmpMatB_2 = as<SEXP>(tmpLB[1]); for(int i=0; i<tmpMatB_1.nrow();i++){ GenotypesB1(some_integerB,_)=tmpMatB_1(i,_); GenotypesB2(some_integerB,_)=tmpMatB_2(i,_); some_integerB=some_integerB+1; } Rcout<<"k in this loop goes to 117 but 117 is empty"<<std::endl; Rf_PrintValue(wrap(k)); } //get allelic richness for popA - the initial year all_richA=NumericVector(nSNP); //set up a vector to hold number of alleles for each microsat int rich_nA = arichAmat.nrow(); for (int rich = 0; rich<nSNP;rich++){ NumericMatrix::Column inputA = arichAmat(_,rich); NumericVector xA = clone<NumericVector>(inputA); int nallsA=0; typedef std::map<double,int> imap ; typedef imap::value_type pair ; imap index ; int n = xA.size() ; double current, previous = xA[0] ; index.insert( pair( previous, 0 ) ); imap::iterator it = index.begin() ; for( int i=1; i<n; i++){ current = xA[i] ; if( current == previous ){ xA[i] = current + ( ++(it->second) / 100.0 ) ; } else { it = index.find(current) ; if( it == index.end() ){ it = index.insert( current > previous ? it : index.begin(), pair( current, 0 ) ) ; } else { xA[i] = current + ( ++(it->second) / 100.0 ) ; } previous = current ; } if (xA[i]-inputA[i]==0){nallsA=nallsA+1;} } all_richA[rich] = nallsA+1; } //end of rich A loop (over 10 microsats) //end of rch code popA richnessA(0,_)=all_richA; mean_arichA[0]=std::accumulate(all_richA.begin(),all_richA.end(),0.0)/nSNP; //get allelic richness for popB - the initial year all_richB=NumericVector(nSNP); //set up a vector to hold number of alleles for each microsat int rich_nB = arichBmat.nrow(); for (int rich = 0; rich<nSNP;rich++){ NumericMatrix::Column inputB = arichBmat(_,rich); NumericVector xB = clone<NumericVector>(inputB); int nallsB=0; typedef std::map<double,int> imap ; typedef imap::value_type pair ; imap index ; int n = xB.size() ; double current, previous = xB[0] ; index.insert( pair( previous, 0 ) ); imap::iterator it = index.begin() ; for( int i=1; i<n; i++){ current = xB[i] ; if( current == previous ){ xB[i] = current + ( ++(it->second) / 100.0 ) ; } else { it = index.find(current) ; if( it == index.end() ){ it = index.insert( current > previous ? it : index.begin(), pair( current, 0 ) ) ; } else { xB[i] = current + ( ++(it->second) / 100.0 ) ; } previous = current ; } if (xB[i]-inputB[i]==0){nallsB=nallsB+1;} } all_richB[rich] = nallsB+1; } //end of rich B loop (over 10 microsats) //end of rch code popB richnessB(0,_)=all_richB; //each row is allelic richness at each microsat in that year mean_arichB[0]=std::accumulate(all_richB.begin(),all_richB.end(),0.0)/nSNP; //get allelic richness in other years popA for (int k = 1; k<Nyrs; k++){ //Nyrs some_integerA = 0; arichAmat = NumericMatrix(2*NtotalA[k],nSNP); N_init_a1_star = allA.row(k); int j_a; int j_b; for (int i=0;i<ages;i++){ if ((cumpopsA[k+i]+N_init_a1_star[i])<=cumpopsA[Nyrs+ages-1]){ j_a = cumpopsA[k+i]; j_b = cumpopsA[k+i]+N_init_a1_star[i];}else {j_a = cumpopsA[Nyrs+ages-1]-N_init_a1_star[i]; j_b = cumpopsA[Nyrs+ages-1];} for(int j=j_a;j<j_b;j++){ arichAmat(some_integerA,_)=GenotypesA1(j,_); arichAmat(some_integerA+NtotalA[k],_)=GenotypesA2(j,_); some_integerA=some_integerA+1; } } //get allelic richness for popA (all years except the first one) all_richA=NumericVector(nSNP); //set up a vector to hold number of alleles for each microsat int rich_nA = arichAmat.nrow(); for (int rich = 0; rich<nSNP;rich++){ NumericMatrix::Column inputA = arichAmat(_,rich); NumericVector xA = clone<NumericVector>(inputA); //already defined xA int nallsA=0; typedef std::map<double,int> imap ; typedef imap::value_type pair ; imap index ; int n = xA.size() ; double current, previous = xA[0] ; index.insert( pair( previous, 0 ) ); imap::iterator it = index.begin() ; for( int i=1; i<n; i++){ current = xA[i] ; if( current == previous ){ xA[i] = current + ( ++(it->second) / 100.0 ) ; } else { it = index.find(current) ; if( it == index.end() ){ it = index.insert( current > previous ? it : index.begin(), pair( current, 0 ) ) ; } else { xA[i] = current + ( ++(it->second) / 100.0 ) ; } previous = current ; } if (xA[i]-inputA[i]==0) {nallsA=nallsA+1; } } //***************************** all_richA[rich] = nallsA+1; } //end of rich A loop (over 10 microsats) //end of rch code popA richnessA(k,_)=all_richA; mean_arichA[k]=std::accumulate(all_richA.begin(),all_richA.end(),0.0)/nSNP; // }//end of k= 1 to Nyrs popA //get allelic richness in other years popB // for (int k = 1; k<Nyrs; k++){ //Nyrs some_integerB = 0; arichBmat = NumericMatrix(2*NtotalB[k],nSNP); N_init_a2_star = allB.row(k); //int j_a; int j_b; for (int i=0;i<ages;i++){ if ((cumpopsB[k+i]+N_init_a2_star[i])<=cumpopsB[Nyrs+ages-1]){ j_a = cumpopsB[k+i]; j_b = cumpopsB[k+i]+N_init_a2_star[i];}else {j_a = cumpopsB[Nyrs+ages-1]-N_init_a2_star[i]; j_b = cumpopsB[Nyrs+ages-1];} for(int j=j_a;j<j_b;j++){ arichBmat(some_integerB,_)=GenotypesB1(j,_); arichBmat(some_integerB+NtotalB[k],_)=GenotypesB2(j,_); some_integerB=some_integerB+1; } } Rf_PrintValue(wrap(1658)); //get allelic richness for popB all_richB=NumericVector(nSNP); //set up a vector to hold number of alleles for each microsat int rich_nB = arichBmat.nrow(); Rf_PrintValue(wrap(1665)); for (int rich = 0; rich<nSNP;rich++){ NumericMatrix::Column inputB = arichBmat(_,rich); NumericVector xB = clone<NumericVector>(inputB); int nallsB=0; typedef std::map<double,int> imap ; typedef imap::value_type pair ; imap index ; int n = xB.size() ; double current, previous = xB[0] ; index.insert( pair( previous, 0 ) ); imap::iterator it = index.begin() ; for( int i=1; i<n; i++){ current = xB[i] ; if( current == previous ){ xB[i] = current + ( ++(it->second) / 100.0 ) ; } else { it = index.find(current) ; if( it == index.end() ){ it = index.insert( current > previous ? it : index.begin(), pair( current, 0 ) ) ; } else { xB[i] = current + ( ++(it->second) / 100.0 ) ; } previous = current ; } if (xB[i]-inputB[i]==0) {nallsB=nallsB+1; } } all_richB[rich] = nallsB+1; } //end of rich B loop (over 10 microsats) //end of rch code popB richnessB(k,_)=all_richB; //each row is allelic richness at each microsat in that year mean_arichB[k]=std::accumulate(all_richB.begin(),all_richB.end(),0.0)/nSNP; //for (int j=0;j<nSNP;j++){ //for (int i=0;i<(arichAmat.nrow()/2);i++){ //if (arichAmat((2*i),j)==arichAmat(((2*i)+1),j)){hetsA[j]=hetsA[j]+1;} //}} //for (int j=0;j<nSNP;j++){ //for (int i=0;i<(arichBmat.nrow()/2);i++){ //if (arichBmat((2*i),j)==arichBmat(((2*i)+1),j)){hetsB[j]=hetsB[j]+1;} //}} //*********************************************************************** //here get new FST based on this years arichAmat and arichBmat //arichAmat and arichBmat get generated each of k years so they are already available. //Fst (for the entire population) if nsamp is bigger than pop size, will give warning //create a matrix with unique alleles for each microsatellite in each column if ((k%20==0)&(NtotalA[k]>0)&(NtotalB[k]>0)&(mgmt_counter==0)){ //do this for all years for combined mgmt //for all k NumericMatrix tallyBOTHmat(2*NtotalB[k]+2*NtotalA[k],nSNP); int some_integerBOTH = 0; NumericMatrix arichBOTHmat(2*NtotalB[k]+2*NtotalA[k],nSNP);//all alleles both pops year 1 for (int i=0;i<2*NtotalA[k];i++){ arichBOTHmat(i,_)=arichAmat(i,_); } for (int i=0;i<2*NtotalB[k];i++){ arichBOTHmat(i+2*NtotalA[k],_)=arichBmat(i,_); } //get unique alleles for both populations for (int rich = 0; rich<nSNP;rich++){ NumericMatrix::Column inputBOTH = arichBOTHmat(_,rich); NumericVector xBOTH = clone<NumericVector>(inputBOTH); NumericVector tally_allelesBOTH(2*NtotalB[k]+2*NtotalA[k]); //this is a vector that holds the unique alleles int tallycounter=1; //******* int nallsBOTH=0; typedef std::map<double,int> imap ; typedef imap::value_type pair ; imap index ; int n = xBOTH.size() ; double current, previous = xBOTH[0] ; index.insert( pair( previous, 0 ) ); imap::iterator it = index.begin() ; for( int i=1; i<n; i++){ current = xBOTH[i] ; if( current == previous ){ xBOTH[i] = current + ( ++(it->second) / 100.0 ) ; } else { it = index.find(current) ; if( it == index.end() ){ it = index.insert( current > previous ? it : index.begin(), pair( current, 0 ) ) ; } else { xBOTH[i] = current + ( ++(it->second) / 100.0 ) ; } previous = current ; } if (xBOTH[i]-inputBOTH[i]==0) {nallsBOTH=nallsBOTH+1; tally_allelesBOTH[tallycounter]=inputBOTH[i]; tallycounter=tallycounter+1; }} tally_allelesBOTH[0]=inputBOTH[0]; tallyBOTHmat(_,rich)=tally_allelesBOTH; } //end of rich BOTH loop (over 10 microsats) List tallist(nSNP); //list of 10 that will hold allele frequencies for each of 10 microsatellites. //First column is the actual allele size, second is freq from popA, third is freq popB NumericVector count(nSNP);//list of number of unique alleles at each locus //now set up a table to get allele frequencies for popA and popB for (int rich=0;rich<nSNP;rich++){ NumericMatrix::Column inputA=arichAmat(_,rich); NumericMatrix::Column inputB=arichBmat(_,rich); NumericMatrix::Column tal=tallyBOTHmat(_,rich); for(int i=0;i<tal.size();i++){ //figure out how many unique alleles are at this microsat (from tal) if(tal[i]!=0){count[rich]=count[rich]+1;} } NumericVector Counter=clone(count); int counter=Counter[rich]; NumericVector taltrunc(counter); //will hold all unique alleles from this microsat (both pops) NumericMatrix tallyAB(counter,3); //matrix that has alleles, freq at popA, freq at popB NumericVector howmanyA(counter); //number of alleles for this population at each allele NumericVector howmanyB(counter); for(int i=0;i<counter;i++){ //counter is the number of unique alleles at a locus taltrunc[i]=tal[i]; int counterA=0; //a counter for number of unique alleles at each locus int counterB=0; for (int j=0;j<2*NtotalA[k];j++){ if (inputA[j]==taltrunc[i])//go through all alleles to see how many match this unique one {counterA=counterA+1;} } howmanyA[i]=counterA; for (int j=0;j<2*NtotalB[k];j++){ if (inputB[j]==taltrunc[i]) {counterB=counterB+1;} } howmanyB[i]=counterB; } //end of counter tallyAB(_,0)=taltrunc; tallyAB(_,1)=howmanyA/(2*NtotalA[k]); tallyAB(_,2)=howmanyB/(2*NtotalB[k]); tallist[rich]=tallyAB; }//end of rich create talmat, which has unique alleles first column then freqs for popA and B NumericMatrix talmat(std::accumulate(count.begin(),count.end(),0.0),5); for (int i=0;i<nSNP;i++){ int talcount=0; NumericMatrix taltmp(count[i],3); taltmp=as<SEXP>(tallist[i]); for (int j=std::accumulate(count.begin(),count.begin()+i,0.0);j<std::accumulate(count.begin(),count.begin()+i+1,0.0);j++) { talmat(j,_)=taltmp(talcount,_); talcount=talcount+1; } } double hetsA; double hetsB; for (int i=0;i<nSNP;i++){ for (int j=std::accumulate(count.begin(),count.begin()+i,0.0);j<std::accumulate(count.begin(),count.begin()+i+1,0.0);j++) { hetsA=0;hetsB=0; for (int k=0;k<100;k++){ if ((arichAmat((2*k),i)!=arichAmat(((2*k)+1),i))&((arichAmat((2*k),i)==talmat(j,0))|(arichAmat(((2*k)+1),i)==talmat(j,0)))){hetsA=hetsA+1;} if ((arichBmat((2*k),i)!=arichBmat(((2*k)+1),i))&((arichBmat((2*k),i)==talmat(j,0))|(arichBmat(((2*k)+1),i)==talmat(j,0)))){hetsB=hetsB+1;} } talmat(j,3)=hetsA/pop1_size; talmat(j,4)=hetsB/pop2_size; }} talmat2=clone(talmat); //talmat is the raw material to find Fst!!! //GET FST this function finds Fst between 2 populations, multiple loci and multiple alleles. n_sampA=NtotalA[k]; n_sampB=NtotalB[k]; double n_bar=0.5*(n_sampA+n_sampB); //sample size - can change this. double r=2; NumericVector p_bar(talmat2.nrow()); NumericMatrix::Column sampmatA =talmat2(_,1); NumericMatrix::Column sampmatB =talmat2(_,2); NumericVector s2(talmat2.nrow()); NumericVector h_bar(talmat2.nrow()); NumericVector ones(talmat2.nrow()); ones=rep(1,talmat2.nrow()); p_bar=(n_sampA*sampmatA+n_sampB*sampmatB)/(2*n_bar); //each entry is average sample frequency of an allele s2=((n_sampB*(pow((sampmatB-p_bar),2)))+(n_sampA*pow((sampmatA-p_bar),2)))/n_bar;//sample variance of allele freqs over pops (for each allele) for (int i=0;i<talmat2.nrow();i++){ h_bar[i]=(talmat2(i,3)+talmat2(i,4))/2; } double nc=((r*n_bar)-((pow(n_sampA,2)/(r*n_bar))+(pow(n_sampB,2)/(r*n_bar))))/(r-1); //same as n_bar double C2=r*(1-(nc/n_bar)); Rcout<<"C2"<<std::endl; Rf_PrintValue(wrap(C2)); NumericVector a=(n_bar/nc)*(s2-((1/(n_bar-1))*((p_bar*(1-p_bar))-(((r-1)/r)*s2)-(0.25*h_bar)))); NumericVector dvec=((2*n_bar)/((2*n_bar)-1))*((p_bar*(1-p_bar))-(((r-1)/r)*s2)); NumericVector b=(n_bar/(n_bar-1))*((p_bar*(1-p_bar))-(((r-1)/r)*s2)-(h_bar*(2*n_bar-1)/(4*n_bar))); NumericVector c=h_bar/2; NumericVector aplusdvec=a+b+c;//I added b here NumericVector aplusdvec2=a+dvec; double fst=std::accumulate(a.begin(),a.end(),0.0)/(std::accumulate(aplusdvec.begin(),aplusdvec.end(),0.0)); //the better one double fst2=std::accumulate(a.begin(),a.end(),0.0)/(std::accumulate(aplusdvec2.begin(),aplusdvec2.end(),0.0)); //the better one ps=p_bar*(1-p_bar); NumericVector theta=(s2-((1/((2*n_bar)-1))*((p_bar*(1-p_bar))-s2)))/(((1-((2*n_bar*C2)/(((2*n_bar)-1)*r)))*(p_bar*(1-p_bar)))+(1+((2*n_bar*C2)/((2*n_bar-1)*r)))*(s2/r)); NumericVector theta1=(s2-((1/(n_bar-1))*((p_bar*(1-p_bar))-s2-(h_bar/4))))/((((1-((n_bar*C2)/((n_bar-1)*r)))*(p_bar*(1-p_bar)))+(((1+n_bar*C2)/((n_bar-1)*r))*(s2/r))+((C2/(r*(n_bar-1)))*(h_bar/4)))); out_fst60[0]=fst; fst_vec[ngen+k]=fst; fst_vec2[ngen+k]=fst2; Wrights_fst_vec[ngen+k]=std::accumulate(theta.begin(),theta.end(),0.0)/talmat2.nrow(); Wrights_fst_vec1[ngen+k]=std::accumulate(theta1.begin(),theta1.end(),0.0)/talmat2.nrow(); Wrights_simple[ngen+k]=std::accumulate(s2.begin(),s2.end(),0.0)/std::accumulate(ps.begin(),ps.end(),0.0); }//end of if statement Rcout<<"k is"<<std::endl; Rf_PrintValue(wrap(k)); //****GET FST FOR JUST A RANDOM SAMPLE OF 100 if((k==620|k==640|k==680|(k>599&k%10==0))&mgmt_counter==0){ //ends at 3106 n_sampA=nsamp;//just 100 generally n_sampB=nsamp;//and 100 IntegerVector order(2*n_sampA+2*n_sampB); int nsig=100; NumericVector fst_subvec(nsig); IntegerVector ORDER(4*nsamp); Rf_PrintValue(wrap(2186)); //randomly select 2*diffsamp without replacement from populationA IntegerVector randsampA = seq_len(2*NtotalA[k]); IntegerVector whichsampA(2*n_sampA); NumericMatrix arichAmat_sub(2*n_sampA,nSNP); NumericMatrix arichBmat_sub(2*n_sampB,nSNP); int probA; int pickA; Rf_PrintValue(wrap(2193)); if((NtotalA[k]>=n_sampA)&(NtotalB[k]>=n_sampB)){ for (int i=0; i < n_sampA; i++){ probA=as<double>(runif(1,-0.5,(NtotalA[k]-0.5)));//sample without replacement pickA=int(probA); if((probA+0.5)>=(int(probA)+1)){ pickA=int(probA)+1; } whichsampA[i]=randsampA[pickA]-1;//because of C++ special indexing //randsampA.erase(pickA); } for (int i=0;i<n_sampA;i++){ arichAmat_sub(i,_)=arichAmat(whichsampA[i],_); //arichAmat is all alleles males and females from population that year arichAmat_sub(i+n_sampA,_)=arichAmat(whichsampA[i]+1,_); } //arichAmat_sub is a random sample of 2*n_sampA arichBmat_sub=NumericMatrix(2*n_sampB,nSNP); //randomly select 2*diffsamp without replacement from populationB //if (ninecomp[nine]==2){ IntegerVector randsampB = seq_len(2*NtotalB[k]); IntegerVector whichsampB(2*n_sampB); int probB; int pickB; for (int i=0; i < n_sampB; i++){ probB=as<double>(runif(1,-0.5,(NtotalB[k]-0.5)));//each time there is one fewer to choose from pickB=int(probB); if((probB+0.5)>=(int(probB)+1)){ pickB=int(probB)+1; } whichsampB[i]=randsampB[pickB]-1; //randsampB.erase(pickB); } for (int i=0;i<n_sampB;i++){ arichBmat_sub(i,_)=arichBmat(whichsampB[i],_); arichBmat_sub(i+n_sampB,_)=arichBmat(whichsampB[i]+1,_); } //arichAmat_sub //} //end of if ninecomp[nine]==2; if (k==620){arichAmat_sub620=arichAmat_sub; arichBmat_sub620=arichBmat_sub;} if (k==640){arichAmat_sub640=arichAmat_sub; arichBmat_sub640=arichBmat_sub;} if (k==680){arichAmat_sub680=arichAmat_sub; arichBmat_sub680=arichBmat_sub;} //by here you should have arichAmat_sub and arichBmat_sub create a matrix with unique alleles for each microsatellite in each column NumericMatrix tallyBOTHmat_sub(2*n_sampA+2*n_sampB,nSNP); //this has everything subsampled, just 200+200 total NumericMatrix arichBOTHmat_sub(2*n_sampA+2*n_sampB,nSNP); //all alleles both pops year 1 NumericMatrix arichBOTHmat_subrand(2*n_sampA+2*n_sampB,nSNP); int some_integerBOTH_sub = 0; for (int i=0;i<2*n_sampA;i++){ //holds 200 genotypes from each population arichBOTHmat_sub(i,_)=arichAmat_sub(i,_);} for (int i=0;i<2*n_sampB;i++){ arichBOTHmat_sub(i+2*n_sampA,_)=arichBmat_sub(i,_); } NumericVector fstsig(1); //this is going to count how many times the initial sample is larger than permuted ones fstsig[0]=0; for (int sigfst=0;sigfst<nsig;sigfst++){//want 100 later ORDER=seq_len(2*n_sampA+2*n_sampB)-1; order=rep(0,2*n_sampA+2*n_sampB); if (sigfst==0) {order=seq_len(2*n_sampA+2*n_sampB)-1;} else{ int probZ; int pickZ; for (int i=0; i < 2*n_sampA+2*n_sampB; i++){ //random mating for each allele 4*nsamp probZ=as<double>(runif(1,-0.5,(2*n_sampA+2*n_sampB-0.5-i))); pickZ=int(probZ); if((probZ+0.5)>=(int(probZ)+1)){ pickZ=int(probZ)+1; } order[i]=ORDER[pickZ]; ORDER.erase(pickZ); } }//end of is this the first iteration of sigfst or not for(int i=0;i<2*n_sampA+2*n_sampB;i++){ //rearrange arichBOTHmat_sub for significance calculations arichBOTHmat_subrand(i,_)=arichBOTHmat_sub(order[i],_); } //divide up arichBOTHmat_subrand into 2 populations again for significance calculations first time these will be truly from pops A and B but later just jumbles of both NumericMatrix arichAmat_subrand(2*n_sampA,nSNP); NumericMatrix arichBmat_subrand(2*n_sampB,nSNP); for (int i=0;i<2*n_sampA;i++){ arichAmat_subrand(i,_)=arichBOTHmat_subrand(i,_);} for (int i=0;i<2*n_sampB;i++){ arichBmat_subrand(i,_)=arichBOTHmat_subrand(i+2*n_sampA,_); } //get unique alleles for both populations for (int rich = 0; rich<nSNP;rich++){ NumericMatrix::Column inputBOTH_sub = arichBOTHmat_subrand(_,rich); NumericVector xBOTH_sub = clone<NumericVector>(inputBOTH_sub); NumericVector tally_allelesBOTH_sub(2*n_sampA+2*n_sampB); //this is a vector that holds the unique alleles int tallycounter_sub=1; int nallsBOTH_sub=0; typedef std::map<double,int> imap ; typedef imap::value_type pair ; imap index ; int n = xBOTH_sub.size() ; double current, previous = xBOTH_sub[0] ; index.insert( pair( previous, 0 ) ); imap::iterator it = index.begin() ; for( int i=1; i<n; i++){ current = xBOTH_sub[i] ; if( current == previous ){ xBOTH_sub[i] = current + ( ++(it->second) / 100.0 ) ; } else { it = index.find(current) ; if( it == index.end() ){ it = index.insert( current > previous ? it : index.begin(), pair( current, 0 ) ) ; } else { xBOTH_sub[i] = current + ( ++(it->second) / 100.0 ) ; } previous = current ; } if (xBOTH_sub[i]-inputBOTH_sub[i]==0) {nallsBOTH_sub=nallsBOTH_sub+1; tally_allelesBOTH_sub[tallycounter_sub]=inputBOTH_sub[i]; tallycounter_sub=tallycounter_sub+1; } } tally_allelesBOTH_sub[0]=inputBOTH_sub[0]; tallyBOTHmat_sub(_,rich)=tally_allelesBOTH_sub; } //end of rich BOTH loop (over 10 microsats) List tallist_sub(nSNP); //list of 10 that will hold allele frequencies for each of 10 microsatellites. //First column is the actual allele size, second is freq from popA, third is freq popB //now set up a table to get allele frequencies for popA and popB for (int rich=0;rich<nSNP;rich++){ NumericMatrix::Column inputA_sub=arichAmat_subrand(_,rich); NumericMatrix::Column inputB_sub=arichBmat_subrand(_,rich); NumericMatrix::Column tal_sub=tallyBOTHmat_sub(_,rich); for(int i=0;i<tal_sub.size();i++){ //figure out how many unique alleles are at this microsat (from tal) if(tal_sub[i]!=0){count_sub[rich]=count_sub[rich]+1;} } NumericVector Counter_sub=clone(count_sub); int counter_sub=Counter_sub[rich]; NumericVector taltrunc_sub(counter_sub); //will hold all unique alleles from this microsat (both pops) NumericMatrix tallyAB_sub(counter_sub,3); //matrix that has alleles, freq at popA, freq at popB NumericVector howmanyA_sub(counter_sub); //number of alleles for this population at each allele NumericVector howmanyB_sub(counter_sub); for(int i=0;i<counter_sub;i++){ //counter is the number of unique alleles at a locus taltrunc_sub[i]=tal_sub[i]; int counterA_sub=0; //a counter for number of unique alleles at each locus int counterB_sub=0; for (int j=0;j<2*n_sampA;j++){ if (inputA_sub[j]==taltrunc_sub[i])//go through all alleles to see how many match this unique one {counterA_sub=counterA_sub+1;} } howmanyA_sub[i]=counterA_sub; for (int j=0;j<2*n_sampB;j++){ if (inputB_sub[j]==taltrunc_sub[i]) {counterB_sub=counterB_sub+1;} } howmanyB_sub[i]=counterB_sub; } //end of counter tallyAB_sub(_,0)=taltrunc_sub; tallyAB_sub(_,1)=howmanyA_sub/(2*n_sampA); tallyAB_sub(_,2)=howmanyB_sub/(2*n_sampB); arichsampA_ten[rich]=0; arichsampB_ten[rich]=0; for(int i=0;i<taltrunc_sub.size();i++){ if (howmanyA_sub[i]!=0){arichsampA_ten[rich]=arichsampA_ten[rich]+1;} if (howmanyB_sub[i]!=0){arichsampB_ten[rich]=arichsampB_ten[rich]+1;} } tallist_sub[rich]=tallyAB_sub; }//end of rich arichsampA_vec[ngen+k]=std::accumulate(arichsampA_ten.begin(),arichsampA_ten.end(),0.0)/nSNP; arichsampB_vec[ngen+k]=std::accumulate(arichsampB_ten.begin(),arichsampB_ten.end(),0.0)/nSNP; //create talmat, which has unique alleles first column then freqs for popA and B NumericMatrix talmat_sub(std::accumulate(count_sub.begin(),count_sub.end(),0.0),5); for (int i=0;i<nSNP;i++){ int talcount_sub=0; NumericMatrix taltmp_sub(count_sub[i],3); taltmp_sub=as<SEXP>(tallist_sub[i]); for (int j=std::accumulate(count_sub.begin(),count_sub.begin()+i,0.0);j<std::accumulate(count_sub.begin(),count_sub.begin()+i+1,0.0);j++) { talmat_sub(j,_)=taltmp_sub(talcount_sub,_); talcount_sub=talcount_sub+1; } } talmat_sub2=clone(talmat_sub); double hetsA_sub; double hetsB_sub; for (int i=0;i<nSNP;i++){ for (int j=std::accumulate(count_sub.begin(),count_sub.begin()+i,0.0);j<std::accumulate(count_sub.begin(),count_sub.begin()+i+1,0.0);j++) { hetsA_sub=0;hetsB_sub=0; for (int k=0;k<100;k++){ if ((arichAmat_sub((2*k),i)!=arichAmat_sub(((2*k)+1),i))&((arichAmat_sub((2*k),i)==talmat_sub2(j,0))|(arichAmat_sub(((2*k)+1),i)==talmat_sub2(j,0)))){hetsA_sub=hetsA_sub+1;} if ((arichBmat_sub((2*k),i)!=arichBmat_sub(((2*k)+1),i))&((arichBmat_sub((2*k),i)==talmat_sub2(j,0))|(arichBmat_sub(((2*k)+1),i)==talmat_sub2(j,0)))){hetsB_sub=hetsB_sub+1;} } talmat_sub2(j,3)=hetsA_sub/100; talmat_sub2(j,4)=hetsB_sub/100; }} //talmat: GET FST for SUBSAMPLE this function finds Fst between 2 populations, multiple loci and multiple alleles. double n_bar_sub=0.5*(n_sampA+n_sampB); //sample size - can change this. already declared at 1746 double r_sub=2; NumericVector p_bar_sub(talmat_sub2.nrow()); NumericMatrix::Column sampmatA_sub =talmat_sub2(_,1); NumericMatrix::Column sampmatB_sub =talmat_sub2(_,2); NumericVector s2_sub(talmat_sub2.nrow()); NumericVector h_bar_sub(talmat_sub2.nrow()); for (int i=0;i<talmat_sub2.nrow();i++){ h_bar_sub[i]=(talmat_sub2(i,3)+talmat_sub2(i,4))/2; } NumericVector ones_sub(talmat_sub2.nrow()); ones_sub=rep(1,talmat_sub2.nrow()); p_bar_sub=(n_sampA*sampmatA_sub+n_sampB*sampmatB_sub)/(2*n_bar_sub); //each entry is average sample frequency of an allele s2_sub=((n_sampB*(pow((sampmatB_sub-p_bar_sub),2)))+(n_sampA*pow((sampmatA_sub-p_bar_sub),2)))/n_bar_sub;//sample variance of allele freqs over pops (for each allele) double nc_sub=((r_sub*n_bar_sub)-((pow(n_sampA,2)/(r_sub*n_bar_sub))+(pow(n_sampB,2)/(r_sub*n_bar_sub))))/(r_sub-1); //same as n_bar NumericVector a_sub=(n_bar_sub/nc_sub)*(s2_sub-((1/(n_bar_sub-1))*((p_bar_sub*(1-p_bar_sub))-((0.5)*s2_sub)-(0.25*h_bar_sub)))); NumericVector dvec_sub=((2*n_bar_sub)/((2*n_bar_sub)-1))*((p_bar_sub*(1-p_bar_sub))-(1/2)*s2_sub); NumericVector b_sub=(n_bar_sub/(n_bar_sub-1))*((p_bar_sub*(1-p_bar_sub))-((1/2)*s2_sub)-(h_bar_sub*(2*n_bar_sub-1)/(4*n_bar_sub))); NumericVector c_sub=h_bar_sub/2; NumericVector aplusdvec_sub=a_sub+b_sub+c_sub; double fst_sub=std::accumulate(a_sub.begin(),a_sub.end(),0.0)/std::accumulate(aplusdvec_sub.begin(),aplusdvec_sub.end(),0.0); //the better one fst_subvec[sigfst]=fst_sub; //if (sigfst==0&k==620|k==640|k==680){ //double C2_sub=r_sub*(1-(nc_sub/n_bar_sub)); //NumericVector ps_sub=p_bar_sub*(1-p_bar_sub); //NumericVector theta_sub=(s2_sub-((1/((2*n_bar_sub)-1))*((p_bar_sub*(1-p_bar_sub))-s2_sub)))/(((1-((2*n_bar_sub*C2_sub)/(((2*n_bar_sub)-1)*r_sub)))*(p_bar_sub*(1-p_bar_sub)))+(1+((2*n_bar_sub*C2_sub)/((2*n_bar_sub-1)*r_sub)))*(s2_sub/r_sub)); //NumericVector theta1_sub=(s2_sub-(1/(n_bar_sub-1)*((p_bar_sub*(1-p_bar_sub))-s2_sub-(h_bar_sub/4))))/((((1-((n_bar_sub*C2_sub)/((n_bar_sub-1)*r_sub)))*(p_bar_sub*(1-p_bar_sub)))+(((1+n_bar_sub*C2_sub)/((n_bar_sub-1)*r_sub))*(s2_sub/r_sub))+((C2_sub/(r_sub*(n_bar_sub-1)))*(h_bar_sub/4)))); //Wrights_fst_vec_sub1[ngen+k]=std::accumulate(theta1_sub.begin(),theta1_sub.end(),0.0)/talmat_sub2.nrow(); //Wrights_fst_vec_sub[ngen+k]=std::accumulate(theta_sub.begin(),theta_sub.end(),0.0)/talmat_sub2.nrow(); //Wrights_simple_sub[ngen+k]=std::accumulate(s2_sub.begin(),s2_sub.end(),0.0)/std::accumulate(ps_sub.begin(),ps_sub.end(),0.0); //} if(sigfst>0){ if(fst_sub>=fst_subvec[0]){fstsig[0]=fstsig[0]+1;}//count how many times random value is greater than true } }//end of sigfst NumericVector outfstsig=clone(fstsig); sig_submatALL(zcounter,k)=0.01*outfstsig[0]; fst_submatALL(zcounter,k)=fst_subvec[0]; if (k==640&mgmt_counter==0) {sig_submat60(zcounter,0)=0.01*outfstsig[0];//this gives significance of each Fst value fst_submat60(zcounter,0)=fst_subvec[0];//this gives subsampled Fst } if (k==680&mgmt_counter==0) {sig_submat99(zcounter,0)=0.01*outfstsig[0];//this gives significance of each Fst value fst_submat99(zcounter,0)=fst_subvec[0];//this gives subsampled Fst } if (k==620&mgmt_counter==0) {sig_submat9(zcounter,0)=0.01*outfstsig[0]; fst_submat9(zcounter,0)=fst_subvec[0]; } //arichAmat_sub=clone(arichBmat_sub); //each time shift so that we do not double sample //n_sampA=n_sampB; //}//end of nine comparisons } //end of if nsamp>Ntotal something like that }//end of k==9 or 60 and NtotalA and NtotalB>0 }//end of k= 1 to Nyrs popB out1(zcounter,_)=mean_arichA; out2(zcounter,_)=mean_arichB; out25(zcounter,_)=fst_vec; } //end of mgmt_counter } //end of zcounter }//end if popdyn==1 //mgmt==0 (combined) outmat[0]=out1; //mean_arichA; outmat[1]=out2; //mean_arichB; outmat[2]=out3; //R_l0A outmat[3]=out4; //R_l0B outmat[4]=out5; //NmigA_vec outmat[5]=out6; //NmigB_vec outmat[6]=out7; //TACA outmat[7]=out8; //TACB outmat[8]=out9; //FishAvec (fishing mortality calculated for population A - either sep or comb) outmat[9]=out10; //FishBvec (fishing mortality calculated for population B - either sep or comb) outmat[13]=out14; //TAC total catch for both pops combined mgmt only? outmat[14]=out15;//NtotalA; outmat[15]=out16;//NtotalB; outmat[20]=out21; //holds SSB/initialSSB popA outmat[21]=out22; //holds SSB/initial SSB popB outmat[22]=out23; //holds estSSBA/SB40 popA outmat[23]=out24; //holds estSSBB/SB40 popA outmat[24]=out25;//fst_vec; outmat[25]=Wrights_fst_vec; outmat[26]=Wrights_fst_vec1; outmat[27]=fst_vec2; outmat[28]=Wrights_simple; outmat[29]=ps;; outmat[30]=Wrights_fst_vec_sub1; outmat[31]=Wrights_simple_sub; outmat[32]=s2; //outmat[3]=allelesA; //outmat[32]=allelesB; outmat[33]=N_init_a1; outmat[34]=N_init_a2; outmat[35]=allA; outmat[36]=allB; outmat[37]=NeAvec; outmat[38]=NeBvec; //outmat[39]=popA; outmat[40]=nmig_vec; outmat[208]=fst_submatALL; outmat[209]=sig_submatALL; outmat[210]=fst_submat9; //this is the subsampled Fst, 1st column is true outmat[211]=sig_submat9; //holds significances outmat[212]=fst_submat60; //this is the subsampled Fst, 1st column is true outmat[213]=sig_submat60; //holds significances outmat[214]=fst_submat99; //this is the subsampled Fst, 1st column is true outmat[215]=sig_submat99; //holds significances outmat[216]=arichAmat_sub620; outmat[217]=arichBmat_sub620; outmat[218]=arichAmat_sub640; outmat[219]=arichBmat_sub640; outmat[220]=arichAmat_sub680; outmat[221]=arichBmat_sub680; outmat[230]=inputs; return(outmat); ' run2pops = cxxfunction(signature(INPUTS="numeric"), body = run8pops,plugin = "RcppArmadillo") #SSBinit,pop1_prop,Nyrs,nsamp,ngen,nmig,fishmort,samptype,nsims,N,Yrs,mmt,dpow (power of distance),Rmat,ages, popdyn (1 for yes 0 for no),nSNP (# of markers) #out=run2pops(c(15000,0.5,700,100,1,.34,0.12,1,1,3000,1000,1,1.15,1,7,1,13,1100,1655000)) #save(out,file="silverYYfindbug.RData") #this is runer file part out=run2pops(c(15000,0.5,700,100,1,.34,0.12,1,1,3000,1000,1,1.15,1,7,1,13,mig,1655000,0)) save(out,file=paste("silverYY_",mig,"_",seed,".RData",sep=""))

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/Traits/09_finalizing continuous and catagorical trait data.R

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refs/heads/master

2023-07-25T00:04:44.189065

2023-07-12T22:45:22

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09_finalizing continuous and catagorical trait data.R

################################################################################ ## finalizing continuous and categorical traits.R: Checking imputed continuous data and gathered categorical data. ## ## Authors: Kimberly Komatsu, Meghan Avolio, Kevin Wilcox ################################################################################ #### Set up working space #### # rm(list=ls()) clean up workspace #library(FD) library(PerformanceAnalytics) # library(ggforce) library(scales) library(tidyverse) setwd('C:\\Users\\kjkomatsu\\Dropbox (Smithsonian)\\working groups\\CoRRE\\CoRRE_database\\Data') #Kim's # setwd("C:\\Users\\wilco\\Dropbox\\shared working groups\\sDiv_sCoRRE_shared\\CoRRE data\\") # Kevin's laptop wd theme_set(theme_bw()) theme_update(axis.title.x=element_text(size=30, vjust=-0.35, margin=margin(t=15)), axis.text.x=element_text(size=26), axis.title.y=element_text(size=30, angle=90, vjust=0.5, margin=margin(r=15)), axis.text.y=element_text(size=26), plot.title = element_text(size=34, vjust=2), panel.grid.major=element_blank(), panel.grid.minor=element_blank(), legend.title=element_blank(), legend.text=element_text(size=30)) #### Categorical trait data #### categoricalTraits <- read.csv("CleanedData\\Traits\\complete categorical traits\\sCoRRE categorical trait data_12142022.csv") %>% dplyr::select(family, species_matched, leaf_type, leaf_compoundness, stem_support, growth_form, photosynthetic_pathway, lifespan, clonal, mycorrhizal_type, n_fixation, rhizobial, actinorhizal) %>% mutate(photosynthetic_pathway = replace(photosynthetic_pathway, grep("possible", photosynthetic_pathway), NA)) %>% mutate(clonal = replace(clonal, clonal=="uncertain", NA)) %>% mutate(mycorrhizal_type = replace(mycorrhizal_type, mycorrhizal_type=="uncertain", NA)) %>% mutate(lifespan = replace(lifespan, lifespan=="uncertain", NA)) %>% mutate(n_fixation_type=ifelse(rhizobial=='yes', 'rhizobial', ifelse(actinorhizal=='yes', 'actinorhizal', 'none'))) %>% filter(lifespan != "moss") %>% select(-family, -n_fixation, -rhizobial, -actinorhizal) # #### Testing out stream graphs #### # categoricalTraitsGather <- categoricalTraits %>% # # filter(clonal!='NA') %>% # group_by(growth_form, leaf_type, leaf_compoundness) %>% # summarise(value=length(species_matched)) %>% # ungroup() %>% # gather_set_data(c(1:3)) # # ggplot(categoricalTraitsGather, aes(x, id = id, split = y, value = value)) + # geom_parallel_sets(aes(fill = growth_form), alpha = 0.3, axis.width = 0.1) + # geom_parallel_sets_axes(axis.width = 0.1) + # geom_parallel_sets_labels(colour = 'white') #### Pie Charts for each categorical trait #### # leaf type leafType <- categoricalTraits %>% group_by(leaf_type) %>% count() %>% ungroup() %>% mutate(proportion = round((n/sum(n)), digits=3)) %>% arrange(proportion) %>% mutate(labels=scales::percent(proportion)) ggplot(leafType, aes(x="", y=proportion, fill=leaf_type)) + geom_col() + coord_polar(theta="y") + scale_fill_manual(values=c('#7DCBBB', '#FFFFA4', '#B0AAD1', '#F7695F', '#6EA1C9', '#FBA550', '#A5DA56', '#AD68AF')) # ggsave('C:\\Users\\kjkomatsu\\Dropbox (Smithsonian)\\working groups\\CoRRE\\sDiv\\sDiv_sCoRRE_shared\\DataPaper\\2023_sCoRRE_traits\\figures\\pie chart\\4_leaf_type.png', width=8, height=8, units='in', dpi=300, bg='white') # leaf compoundness leafCompoundness <- categoricalTraits %>% group_by(leaf_compoundness) %>% count() %>% ungroup() %>% mutate(proportion = round((n/sum(n)), digits=3)) %>% arrange(proportion) %>% mutate(labels=scales::percent(proportion)) ggplot(leafCompoundness, aes(x="", y=proportion, fill=leaf_compoundness)) + geom_col() + coord_polar(theta="y") + scale_fill_manual(values=c('#7DCBBB', '#FFFFA4', '#B0AAD1', '#F7695F', '#6EA1C9', '#FBA550', '#A5DA56', '#AD68AF')) # ggsave('C:\\Users\\kjkomatsu\\Dropbox (Smithsonian)\\working groups\\CoRRE\\sDiv\\sDiv_sCoRRE_shared\\DataPaper\\2023_sCoRRE_traits\\figures\\pie chart\\5_leaf_compoundness.png', width=8, height=8, units='in', dpi=300, bg='white') # stem support stemSupport <- categoricalTraits %>% group_by(stem_support) %>% count() %>% ungroup() %>% mutate(proportion = round((n/sum(n)), digits=3)) %>% arrange(proportion) %>% mutate(labels=scales::percent(proportion)) ggplot(stemSupport, aes(x="", y=proportion, fill=stem_support)) + geom_col() + coord_polar(theta="y") + scale_fill_manual(values=c('#7DCBBB', '#FFFFA4', '#B0AAD1', '#F7695F', '#6EA1C9', '#FBA550', '#A5DA56', '#AD68AF')) # ggsave('C:\\Users\\kjkomatsu\\Dropbox (Smithsonian)\\working groups\\CoRRE\\sDiv\\sDiv_sCoRRE_shared\\DataPaper\\2023_sCoRRE_traits\\figures\\pie chart\\6_stem_support.png', width=8, height=8, units='in', dpi=300, bg='white') # growth form growthForm <- categoricalTraits %>% group_by(growth_form) %>% count() %>% ungroup() %>% mutate(proportion = round((n/sum(n)), digits=3)) %>% arrange(proportion) %>% mutate(labels=scales::percent(proportion)) ggplot(growthForm, aes(x="", y=proportion, fill=growth_form)) + geom_col() + coord_polar(theta="y") + scale_fill_manual(values=c('#7DCBBB', '#FFFFA4', '#B0AAD1', '#F7695F', '#6EA1C9', '#FBA550', '#A5DA56', '#AD68AF')) # ggsave('C:\\Users\\kjkomatsu\\Dropbox (Smithsonian)\\working groups\\CoRRE\\sDiv\\sDiv_sCoRRE_shared\\DataPaper\\2023_sCoRRE_traits\\figures\\pie chart\\1_growth_form.png', width=8, height=8, units='in', dpi=300, bg='white') # photosynthetic pathway photosyntheticPathway <- categoricalTraits %>% group_by(photosynthetic_pathway) %>% count() %>% ungroup() %>% mutate(proportion = round((n/sum(n)), digits=3)) %>% arrange(proportion) %>% mutate(labels=scales::percent(proportion)) ggplot(photosyntheticPathway, aes(x="", y=proportion, fill=photosynthetic_pathway)) + geom_col() + coord_polar(theta="y") + scale_fill_manual(values=c('#7DCBBB', '#FFFFA4', '#B0AAD1', '#F7695F', '#6EA1C9', '#FBA550', '#A5DA56', '#AD68AF')) # ggsave('C:\\Users\\kjkomatsu\\Dropbox (Smithsonian)\\working groups\\CoRRE\\sDiv\\sDiv_sCoRRE_shared\\DataPaper\\2023_sCoRRE_traits\\figures\\pie chart\\7_photosynthetic_pathway.png', width=8, height=8, units='in', dpi=300, bg='white') # lifespan lifespan <- categoricalTraits %>% group_by(lifespan) %>% count() %>% ungroup() %>% mutate(proportion = round((n/sum(n)), digits=3)) %>% arrange(proportion) %>% mutate(labels=scales::percent(proportion)) ggplot(lifespan, aes(x="", y=proportion, fill=lifespan)) + geom_col() + coord_polar(theta="y") + scale_fill_manual(values=c('#7DCBBB', '#FFFFA4', '#B0AAD1', '#F7695F', '#6EA1C9', '#FBA550', '#A5DA56', '#AD68AF')) # ggsave('C:\\Users\\kjkomatsu\\Dropbox (Smithsonian)\\working groups\\CoRRE\\sDiv\\sDiv_sCoRRE_shared\\DataPaper\\2023_sCoRRE_traits\\figures\\pie chart\\2_lifespan.png', width=8, height=8, units='in', dpi=300, bg='white') # clonal clonal <- categoricalTraits %>% group_by(clonal) %>% count() %>% ungroup() %>% mutate(proportion = round((n/sum(n)), digits=3)) %>% arrange(proportion) %>% mutate(labels=scales::percent(proportion)) ggplot(clonal, aes(x="", y=proportion, fill=clonal)) + geom_col() + coord_polar(theta="y") + scale_fill_manual(values=c('#7DCBBB', '#FFFFA4', '#B0AAD1', '#F7695F', '#6EA1C9', '#FBA550', '#A5DA56', '#AD68AF')) # ggsave('C:\\Users\\kjkomatsu\\Dropbox (Smithsonian)\\working groups\\CoRRE\\sDiv\\sDiv_sCoRRE_shared\\DataPaper\\2023_sCoRRE_traits\\figures\\pie chart\\3_clonal.png', width=8, height=8, units='in', dpi=300, bg='white') # mycorrhizal type mycorrhizalType <- categoricalTraits %>% group_by(mycorrhizal_type) %>% count() %>% ungroup() %>% mutate(proportion = round((n/sum(n)), digits=3)) %>% arrange(proportion) %>% mutate(labels=scales::percent(proportion)) ggplot(mycorrhizalType, aes(x="", y=proportion, fill=mycorrhizal_type)) + geom_col() + coord_polar(theta="y") + scale_fill_manual(values=c('#7DCBBB', '#FFFFA4', '#B0AAD1', '#F7695F', '#6EA1C9', '#FBA550', '#A5DA56', '#AD68AF')) # ggsave('C:\\Users\\kjkomatsu\\Dropbox (Smithsonian)\\working groups\\CoRRE\\sDiv\\sDiv_sCoRRE_shared\\DataPaper\\2023_sCoRRE_traits\\figures\\pie chart\\8_mycorrhizal_type.png', width=8, height=8, units='in', dpi=300, bg='white') # n fixation type nFixationType <- categoricalTraits %>% group_by(n_fixation_type) %>% count() %>% ungroup() %>% mutate(proportion = round((n/sum(n)), digits=3)) %>% arrange(proportion) %>% mutate(labels=scales::percent(proportion)) ggplot(nFixationType, aes(x="", y=proportion, fill=n_fixation_type)) + geom_col() + coord_polar(theta="y") + scale_fill_manual(values=c('#7DCBBB', '#FFFFA4', '#B0AAD1', '#F7695F', '#6EA1C9', '#FBA550', '#A5DA56', '#AD68AF')) # ggsave('C:\\Users\\kjkomatsu\\Dropbox (Smithsonian)\\working groups\\CoRRE\\sDiv\\sDiv_sCoRRE_shared\\DataPaper\\2023_sCoRRE_traits\\figures\\pie chart\\9_n_fixation_type.png', width=8, height=8, units='in', dpi=300, bg='white') #### Continuous traits #### # Read species data to remove mosses mossKey <- read.csv("CleanedData\\Traits\\complete categorical traits\\sCoRRE categorical trait data_12142022.csv") %>% dplyr::select(species_matched, leaf_type) %>% mutate(moss = ifelse(leaf_type=="moss", "moss","non-moss")) %>% dplyr::select(-leaf_type) # Read in imputed trait data and bind on species information ## this is trait data without replacement (all imputed) imputedRaw <- read.csv("CleanedData\\Traits\\gap filled continuous traits\\20230623_final\\imputed_traits_mice.csv") %>% bind_cols(read.csv('OriginalData\\Traits\\raw traits for gap filling\\TRYAusBIEN_continuous_June2023.csv')[,c('DatabaseID', 'DatasetID', 'ObservationID', 'family', 'genus', 'species_matched')]) %>% left_join(mossKey) %>% mutate(moss2=ifelse(moss %in% c('non-moss', NA), 1, 0)) %>% #accounts for all GEx spp being non-moss filter(moss2==1) %>% dplyr::select(-moss, -moss2) #removes 20 observations from 5 species imputedLong <- imputedRaw %>% pivot_longer(names_to='trait', values_to='imputed_value', seed_dry_mass:SRL) # Read original trait data and join with imputed data originalRaw <- read.csv('OriginalData\\Traits\\raw traits for gap filling\\TRYAusBIEN_continuous_June2023.csv') %>% pivot_longer(names_to='trait', values_to='original_value', seed_dry_mass:SRL) %>% na.omit() # Join original trait data with imputed data. Only keep traits of interest. allContinuous <- imputedLong %>% left_join(originalRaw) %>% filter(trait %in% c('LDMC', 'leaf_area', 'leaf_dry_mass', 'leaf_N', 'plant_height_vegetative', 'seed_dry_mass', 'SLA', 'SRL')) allContinuousWide <- allContinuous %>% select(-original_value) %>% pivot_wider(names_from=trait, values_from=imputed_value) # Calculate averages for each species meanContinuous <- allContinuous %>% group_by(family, species_matched, trait) %>% summarize_at(.vars=c('imputed_value', 'original_value'), .funs=list(mean=mean, sd=sd), na.rm=T) %>% ungroup() speciesCount <- meanContinuous %>% select(family, species_matched) %>% unique() %>% group_by(family) %>% summarize(num_species=length(family)) %>% ungroup() #147 families sum(speciesCount$num_species) # Compare imputed to original continuous trait data ggplot(data=na.omit(meanContinuous), aes(x=original_value_mean, y=imputed_value_mean)) + geom_point() + geom_abline(slope=1) + facet_wrap(~trait, scales='free') + xlab('Mean Original Value') + ylab('Mean Imputed Value') + theme(strip.text.x = element_text(size = 12)) # ggsave('C:\\Users\\kjkomatsu\\Dropbox (Smithsonian)\\working groups\\CoRRE\\sDiv\\sDiv_sCoRRE_shared\\DataPaper\\2023_sCoRRE_traits\\figures\\Fig x_mean original v imputed_20230620.png', width=12, height=12, units='in', dpi=300, bg='white') # Only grasses -- 11620 species ggplot(data=na.omit(subset(allContinuous, family=='Poaceae')), aes(x=original_value, y=imputed_value)) + geom_point() + geom_abline(slope=1) + facet_wrap(~trait, scales='free') # Only asters -- 11480 ggplot(data=na.omit(subset(allContinuous, family=='Asteraceae')), aes(x=original_value, y=imputed_value)) + geom_point() + geom_abline(slope=1) + facet_wrap(~trait, scales='free') # Only legumes -- 4620 ggplot(data=na.omit(subset(allContinuous, family=='Fabaceae')), aes(x=original_value, y=imputed_value)) + geom_point() + geom_abline(slope=1) + facet_wrap(~trait, scales='free') # Compare raw imputed and original data ggplot(data=na.omit(allContinuous), aes(x=original_value, y=imputed_value)) + geom_point() + geom_abline(slope=1) + facet_wrap(~trait, scales='free') + xlab('Original Value') + ylab('Imputed Value') + theme(strip.text.x = element_text(size = 12)) # ggsave('C:\\Users\\kjkomatsu\\Dropbox (Smithsonian)\\working groups\\CoRRE\\sDiv\\sDiv_sCoRRE_shared\\DataPaper\\2023_sCoRRE_traits\\figures\\Fig x_pre cleaning original v imputed.png', width=12, height=12, units='in', dpi=300, bg='white') allTogether <- allContinuous %>% pivot_longer(imputed_value:original_value, names_to='data_type', values_to='trait_value') %>% # mutate(data_type=ifelse(data_type=='original_value', DatabaseID, data_type)) %>% na.omit() # Look at boxplots for each trait ggplot(data=allTogether, aes(x=data_type, y=trait_value)) + geom_boxplot() + facet_wrap(~trait, scales='free') #### Clean imputed continuous trait data #### # Checked to ensure no negative values (confirmed that there are none) # Things that look problematic but Kim thinks are real: leaf_area (some forbs with huge leaves), plant_height_vegetative (vines that have big heights like Vitus sp and virginia creeper), seed dry mass (consistently low numbers for some species that probably do have tiny seeds like orchids) meanSD <- allContinuous %>% group_by(trait) %>% summarize(across('imputed_value', .fns=list(mean=mean, sd=sd))) %>% ungroup() meanSDSpecies <- allContinuous %>% group_by(trait, species_matched) %>% summarize(across('imputed_value', .fns=list(species_mean=mean, species_sd=sd, species_length=length))) %>% ungroup() cleanContinuous <- allContinuous %>% #calculate z-scores (error risk) for continuous traits left_join(meanSD) %>% left_join(meanSDSpecies) %>% mutate(error_risk_overall=(imputed_value-imputed_value_mean)/imputed_value_sd) %>% mutate(error_risk_species=(imputed_value-imputed_value_species_mean)/imputed_value_species_sd) %>% filter(error_risk_overall<4) %>% #drops 10,677 observations (0.52% of data) filter(error_risk_species<4 & error_risk_species>(-4)) %>% #drops an additional 19,183 observations (0.94% of data), all of which were from species with at least 18 observations for the given trait value being dropped mutate(trait2=ifelse(trait=='leaf_area', 'Leaf Area (leaf, +petiole)', ifelse(trait=='SLA', 'Specific Leaf Area (+petiole)', ifelse(trait=='SRL', 'Specific Root Length (all root)', ifelse(trait=='leaf_N', 'Leaf N Content', ifelse(trait=='plant_height_vegetative', 'Plant Vegetative Height', ifelse(trait=='seed_dry_mass', 'Seed Dry Mass', ifelse(trait=='leaf_dry_mass', 'Leaf Dry Mass', ifelse(trait=='LDMC', 'Leaf Dry Matter Content', trait))))))))) cleanContinousWide <- cleanContinuous %>% pivot_longer(cols=c('original_value', 'imputed_value'), names_to='data_type', values_to='trait_value') %>% mutate(data_type2=ifelse(data_type=='original_value', DatabaseID, data_type)) %>% na.omit() #### Root mean square error #### cleanContinuousNRMSEtrait <- cleanContinuous %>% select(trait, original_value, imputed_value) %>% na.omit() %>% mutate(sq_diff=(imputed_value-original_value)^2) %>% group_by(trait) %>% summarise(sum=sum(sq_diff), n=length(trait), min=min(original_value), max=max(original_value), mean=mean(original_value)) %>% ungroup() %>% mutate(NRMSE=sqrt(sum/n)/mean) cleanContinuousNRMSE <- cleanContinuous %>% select(original_value, imputed_value) %>% na.omit() %>% mutate(sq_diff=(imputed_value-original_value)^2) %>% summarise(sum=sum(sq_diff), n=length(sq_diff), min=min(original_value), max=max(original_value), mean=mean(original_value)) %>% mutate(NRMSE=sqrt(sum/n)/(mean)) #### Boxplots for each trait #### cleanContinousWide$trait2 = factor(cleanContinousWide$trait2, levels=c('Leaf Area (leaf, +petiole)', 'Leaf Dry Mass', 'Leaf Dry Matter Content', 'Specific Leaf Area (+petiole)', 'Leaf N Content', 'Plant Vegetative Height', 'Specific Root Length (all root)', 'Seed Dry Mass')) ggplot(data=cleanContinousWide, aes(x=as.factor(data_type2), y=trait_value)) + geom_jitter(aes(color=data_type2)) + geom_boxplot(color='black', alpha=0) + facet_wrap(~trait2, scales='free_y', ncol=3, labeller=label_wrap_gen(width=25)) + scale_x_discrete(breaks=c("AusTraits", "BIEN", "CPTD2", "TIPleaf", "TRY", "imputed_value"), limits=c("AusTraits", "BIEN", "CPTD2", "TIPleaf", "TRY", "imputed_value"), labels=c("Au", "BN", "C2", "TP", "TY", "imp")) + scale_color_manual(values=c('#4E3686', '#5DA4D9', '#80D87F', 'darkgrey', '#FED23F', '#EE724C')) + theme_bw() + theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(), legend.position='none', strip.text.x = element_text(size = 20), axis.title.x=element_text(size=22, vjust=-0.35, margin=margin(t=15)), axis.text.x=element_text(size=22), axis.title.y=element_text(size=22, angle=90, vjust=0.5, margin=margin(r=15)), axis.text.y=element_text(size=22)) + xlab('Data Type') + ylab('Trait Value') + scale_y_continuous(trans='log10', labels=label_comma()) # ggsave('C:\\Users\\kjkomatsu\\Dropbox (Smithsonian)\\working groups\\CoRRE\\sDiv\\sDiv_sCoRRE_shared\\DataPaper\\2023_sCoRRE_traits\\figures\\Fig 4_boxplots of original and imputed_20230623_jitter.png', width=14, height=15, units='in', dpi=300, bg='white') #Look at boxplots for each trait -- means by species cleanContinuousWideBoxplot <- cleanContinousWide %>% group_by(DatabaseID, data_type2, species_matched, trait, trait2) %>% summarise(trait_value_mean=trait_value) %>% ungroup() ggplot(data=cleanContinuousWideBoxplot, aes(x=as.factor(data_type2), y=trait_value_mean)) + geom_jitter(aes(color=data_type2)) + geom_boxplot(color='black', alpha=0) + facet_wrap(~trait2, scales='free_y', ncol=3, labeller=label_wrap_gen(width=25)) + scale_x_discrete(breaks=c("AusTraits", "BIEN", "CPTD2", "TIPleaf", "TRY", "imputed_value"), limits=c("AusTraits", "BIEN", "CPTD2", "TIPleaf", "TRY", "imputed_value"), labels=c("Au", "BN", "C2", "TP", "TY", "imp")) + scale_color_manual(values=c('#4E3686', '#5DA4D9', '#80D87F', 'darkgrey', '#FED23F', '#EE724C')) + theme_bw() + theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(), legend.position='none', strip.text.x = element_text(size = 20), axis.title.x=element_text(size=22, vjust=-0.35, margin=margin(t=15)), axis.text.x=element_text(size=22), axis.title.y=element_text(size=22, angle=90, vjust=0.5, margin=margin(r=15)), axis.text.y=element_text(size=22)) + xlab('Data Type') + ylab('Trait Value') + scale_y_continuous(trans='log10', labels=label_comma()) # ggsave('C:\\Users\\kjkomatsu\\Dropbox (Smithsonian)\\working groups\\CoRRE\\sDiv\\sDiv_sCoRRE_shared\\DataPaper\\2023_sCoRRE_traits\\figures\\Fig 4_boxplots of original and imputed_20230623_jitter_means.png', width=14, height=15, units='in', dpi=300, bg='white') # Look at boxplots for each trait ggplot(data=subset(cleanContinousWide, species_matched %in% c('Helianthus maximiliani', 'Potentilla anserina', 'Clintonia borealis')), aes(x=species_matched, y=trait_value)) + geom_boxplot(aes(color=data_type)) + facet_wrap(~trait2, scales='free') ggplot(data=subset(cleanContinousWide, species_matched=='Andropogon gerardii'), aes(x=data_type, y=trait_value)) + geom_boxplot() + facet_wrap(~trait2, scales='free') cleanContinuous$trait2 = factor(cleanContinuous$trait2, levels=c('Leaf Area (leaf, +petiole)', 'Leaf Dry Mass', 'Leaf Dry Matter Content', 'Specific Leaf Area (+petiole)', 'Leaf N Content', 'Plant Vegetative Height', 'Specific Root Length (all root)', 'Seed Dry Mass')) #### Correlation statistics for each trait #### #leaf area with(subset(cleanContinuous, trait=='leaf_area'), hist(log10(original_value))) with(subset(cleanContinuous, trait=='leaf_area'), hist(log10(imputed_value))) with(subset(cleanContinuous, trait=='leaf_area'), cor.test(original_value, imputed_value,method = "pearson", use = "complete.obs")) # r 0.9710058 summary(leaf_area <- lm(log10(imputed_value)~log10(original_value), data=subset(cleanContinuous, trait=='leaf_area'&!is.na(original_value)))) confint(leaf_area) # slope: 0.9741485, SE: 0.0009489, CI: 0.00185984 # Adjusted R-squared: 0.9792 # F-statistic: 1.054e+06 on 1 and 22378 DF, p-value: < 2.2e-16 # 2.5 % 97.5 % # (Intercept) 0.06288035 0.07264136 # log10(original_value) 0.97228869 0.97600834 #leaf dry mass with(subset(cleanContinuous, trait=='leaf_dry_mass'), hist(log10(original_value))) with(subset(cleanContinuous, trait=='leaf_dry_mass'), hist(log10(imputed_value))) with(subset(cleanContinuous, trait=='leaf_dry_mass'), cor.test(original_value, imputed_value,method = "pearson", use = "complete.obs")) # r 0.9577364 summary(leaf_dry_mass <- lm(log10(imputed_value)~log10(original_value), data=subset(cleanContinuous, trait=='leaf_dry_mass'&!is.na(original_value)))) confint(leaf_dry_mass) # slope: 0.9700605 , SE: 0.0009431, CI: 0.001848476 # Adjusted R-squared: 0.9709 # F-statistic: 1.058e+06 on 1 and 31750 DF, p-value: < 2.2e-16 # 2.5 % 97.5 % # (Intercept) 0.02538688 0.03084578 # log10(original_value) 0.96821193 0.97190914 #LDMC with(subset(cleanContinuous, trait=='LDMC'), hist(log10(original_value))) with(subset(cleanContinuous, trait=='LDMC'), hist(log10(imputed_value))) with(subset(cleanContinuous, trait=='LDMC'), cor.test(original_value, imputed_value,method = "pearson", use = "complete.obs")) # r 0.9787819 summary(LDMC <- lm(log10(imputed_value)~log10(original_value), data=subset(cleanContinuous, trait=='LDMC'&!is.na(original_value)))) confint(LDMC) # slope: 0.9541052, SE: 0.0004651, CI: 0.000911596 # Adjusted R-squared: 0.961 # F-statistic: 1.551e+06 on 1 and 63003 DF, p-value: < 2.2e-16 # 2.5 % 97.5 % # (Intercept) -0.02772662 -0.0259034 # log10(original_value) 0.95260343 0.9556070 #SLA with(subset(cleanContinuous, trait=='SLA'), hist(log10(original_value))) with(subset(cleanContinuous, trait=='SLA'), hist(log10(imputed_value))) with(subset(cleanContinuous, trait=='SLA'), cor.test(original_value, imputed_value,method = "pearson", use = "complete.obs")) # r 0.955878 summary(SLA <- lm(log10(imputed_value)~log10(original_value), data=subset(cleanContinuous, trait=='SLA'&!is.na(original_value)))) confint(SLA) # slope: 0.912210 SE: 0.002140 CI: 0.0041944 # Adjusted R-squared: 0.92 # F-statistic: 3.077e+05 on 1 and 26771 DF, p-value: < 2.2e-16 # 2.5 % 97.5 % # (Intercept) 0.1100690 0.1184596 # log10(original_value) 0.9089866 0.9154328 #leaf N with(subset(cleanContinuous, trait=='leaf_N'), hist(log10(original_value))) with(subset(cleanContinuous, trait=='leaf_N'), hist(log10(imputed_value))) with(subset(cleanContinuous, trait=='leaf_N'), cor.test(original_value, imputed_value,method = "pearson", use = "complete.obs")) # r 0.9627765 summary(leaf_N <- lm(log10(imputed_value)~log10(original_value), data=subset(cleanContinuous, trait=='leaf_N'&!is.na(original_value)))) confint(leaf_N) # slope: 0.939524 SE: 0.001490 CI: 0.0029204 # Adjusted R-squared: 0.9377 # F-statistic: 3.976e+05 on 1 and 26392 DF, p-value: < 2.2e-16 # 2.5 % 97.5 % # (Intercept) 0.07658514 0.08444615 # log10(original_value) 0.93660334 0.94244461 #plant vegetative height with(subset(cleanContinuous, trait=='plant_height_vegetative'), hist(log10(original_value))) with(subset(cleanContinuous, trait=='plant_height_vegetative'), hist(log10(imputed_value))) with(subset(cleanContinuous, trait=='plant_height_vegetative'), cor.test(original_value, imputed_value,method = "pearson", use = "complete.obs")) # r 0.9471077 summary(plant_height_vegetative <- lm(log10(imputed_value)~log10(original_value), data=subset(cleanContinuous, trait=='plant_height_vegetative'&!is.na(original_value)))) confint(plant_height_vegetative) # slope: 0.9217863 SE: 0.0009958 CI: 0.001951768 # Adjusted R-squared: 0.9307 # F-statistic: 8.569e+05 on 1 and 63842 DF, p-value: < 2.2e-16 # 2.5 % 97.5 % # (Intercept) -0.04571394 -0.04256993 # log10(original_value) 0.91983456 0.92373803 #SRL with(subset(cleanContinuous, trait=='SRL'), hist(log10(original_value))) with(subset(cleanContinuous, trait=='SRL'), hist(log10(imputed_value))) with(subset(cleanContinuous, trait=='SRL'), cor.test(original_value, imputed_value,method = "pearson", use = "complete.obs")) # r 0.9002603 summary(SRL <- lm(log10(imputed_value)~log10(original_value), data=subset(cleanContinuous, trait=='SRL'&!is.na(original_value)))) confint(SRL) # slope: 0.916581 SE: 0.005051 CI: 0.00989996 # Adjusted R-squared: 0.9215 # F-statistic: 3.293e+04 on 1 and 2804 DF, p-value: < 2.2e-16 # 2.5 % 97.5 % # (Intercept) 0.2794818 0.3558467 # log10(original_value) 0.9066768 0.9264859 #seed dry mass with(subset(cleanContinuous, trait=='seed_dry_mass'), hist(log10(original_value))) with(subset(cleanContinuous, trait=='seed_dry_mass'), hist(log10(imputed_value))) with(subset(cleanContinuous, trait=='seed_dry_mass'), cor.test(original_value, imputed_value,method = "pearson", use = "complete.obs")) # r 0.9930513 summary(seed_dry_mass <- lm(log10(imputed_value)~log10(original_value), data=subset(cleanContinuous, trait=='seed_dry_mass'&!is.na(original_value)))) confint(seed_dry_mass) # slope: 0.9888753 SE: 0.0002790 CI: 0.00054684 # Adjusted R-squared: 0.9962 # F-statistic: 1.256e+07 on 1 and 47880 DF, p-value: < 2.2e-16 # 2.5 % 97.5 % # (Intercept) 0.001306287 0.002106772 # log10(original_value) 0.988328318 0.989422193 # Compare cleaned imputed and original data ggplot(data=na.omit(cleanContinuous), aes(x=original_value, y=imputed_value)) + geom_abline(slope=1, linewidth=2, color='black') + geom_point(color='darkgrey') + geom_smooth(linewidth=2, se=T, color='#e47d0099', method='lm') + facet_wrap(~trait2, scales='free', ncol=3, labeller=label_wrap_gen(width=25)) + xlab('Original Value') + ylab('Imputed Value') + scale_y_continuous(trans='log10', labels = label_comma()) + scale_x_continuous(trans='log10', labels = label_comma()) + theme(strip.text.x = element_text(size = 28), axis.title.x=element_text(size=32, vjust=-0.35, margin=margin(t=15)), axis.text.x=element_text(size=32), axis.title.y=element_text(size=32, angle=90, vjust=0.5, margin=margin(r=15)), axis.text.y=element_text(size=32)) # ggsave('C:\\Users\\kjkomatsu\\Dropbox (Smithsonian)\\working groups\\CoRRE\\sDiv\\sDiv_sCoRRE_shared\\DataPaper\\2023_sCoRRE_traits\\figures\\Fig 5_original v imputed_20230623.png', width=18, height=16, units='in', dpi=300, bg='white') # look up some values for species that we know and make sure they are right ggplot(data=subset(cleanContinuous, species_matched %in% c('Ruellia humilis', 'Andropogon gerardii', 'Parthenocissus quinquefolia')), aes(x=species_matched, y=imputed_value)) + geom_boxplot() + facet_wrap(~trait, scales='free') + scale_x_discrete(breaks=c("Andropogon gerardii", "Parthenocissus quinquefolia", "Ruellia humilis"), limits=c("Andropogon gerardii", "Parthenocissus quinquefolia", "Ruellia humilis"), labels=c("Ag", "Pq", "Rh")) ##### Mean values for each species ##### meanCleanContinuous <- cleanContinuous %>% group_by(family, genus, species_matched, trait) %>% summarize(trait_value=mean(imputed_value)) %>% ungroup() meanSD <- meanCleanContinuous %>% group_by(trait) %>% summarize(across('trait_value', .fns=list(mean=mean, sd=sd))) %>% ungroup() meanSDFamily <- meanCleanContinuous %>% group_by(trait, family) %>% summarize(across('trait_value', .fns=list(family_mean=mean, family_sd=sd, family_length=length))) %>% ungroup() meanSDGenus <- meanCleanContinuous %>% group_by(trait, genus) %>% summarize(across('trait_value', .fns=list(genus_mean=mean, genus_sd=sd, genus_length=length))) %>% ungroup() meanCleanContinuousErrorRisk <- meanCleanContinuous %>% left_join(meanSD) %>% left_join(meanSDFamily) %>% left_join(meanSDGenus) %>% mutate(error_risk_overall=(trait_value-trait_value_mean)/trait_value_sd, error_risk_family=ifelse(trait_value_family_length>2, (trait_value-trait_value_family_mean)/trait_value_family_sd, NA), error_risk_genus=ifelse(trait_value_genus_length>2, (trait_value-trait_value_genus_mean)/trait_value_genus_sd, NA)) %>% select(family, genus, species_matched, trait, trait_value, error_risk_overall, error_risk_family, error_risk_genus) %>% left_join(meanContinuous) %>% select(-imputed_value_mean, imputed_value_sd, original_value_sd) %>% mutate(trait2=ifelse(trait=='leaf_area', 'Leaf Area (leaf, +petiole)', ifelse(trait=='SLA', 'Specific Leaf Area (+petiole)', ifelse(trait=='SRL', 'Specific Root Length (all root)', ifelse(trait=='leaf_N', 'Leaf N Content', ifelse(trait=='plant_height_vegetative', 'Plant Vegetative Height', ifelse(trait=='seed_dry_mass', 'Seed Dry Mass', ifelse(trait=='leaf_dry_mass', 'Leaf Dry Mass', ifelse(trait=='LDMC', 'Leaf Dry Matter Content', trait))))))))) ggplot(data=meanCleanContinuousErrorRisk, aes(x=trait_value)) + geom_histogram() + facet_wrap(~trait2) ggplot(data=na.omit(meanCleanContinuousErrorRisk), aes(x=original_value_mean, y=trait_value)) + geom_point() + geom_abline(slope=1) + geom_smooth(se=T, color='darkorange') + facet_wrap(~trait2, scales='free') + theme(strip.text.x = element_text(size = 12)) + xlab('Mean Original Value') + ylab('Mean Imputed Value') # ggsave('C:\\Users\\kjkomatsu\\Dropbox (Smithsonian)\\working groups\\CoRRE\\sDiv\\sDiv_sCoRRE_shared\\DataPaper\\2023_sCoRRE_traits\\figures\\Fig 5_mean original v imputed_20230623.png', width=12, height=12, units='in', dpi=300, bg='white') meanCleanContinuousWide <- meanCleanContinuousErrorRisk %>% pivot_longer(cols=c('original_value_mean', 'trait_value')) sppnum <- meanCleanContinuousWide %>% select(species_matched, family) %>% unique() ggplot(data=na.omit(meanCleanContinuousWide), aes(x=name, y=value)) + geom_boxplot() + facet_wrap(~trait, scales='free') + theme(strip.text.x = element_text(size = 12)) + xlab('') + ylab('Trait Value') + scale_y_continuous(trans='log10') # ggsave('C:\\Users\\kjkomatsu\\Dropbox (Smithsonian)\\working groups\\CoRRE\\sDiv\\sDiv_sCoRRE_shared\\DataPaper\\2023_sCoRRE_traits\\figures\\Fig 6_mean original v imputed_20230608.png', width=12, height=12, units='in', dpi=300, bg='white') ##### Combine continuous and categorical traits ##### correSpecies <- read.csv("CompiledData\\Species_lists\\FullList_Nov2021.csv") %>% #species names are standardized left_join(read.csv("CompiledData\\Species_lists\\species_families_trees_2021.csv")) %>% filter(tree.non.tree != "tree") %>% #Remove trees separate(species_matched, into=c('genus', 'species', 'subspp'), sep=' ') %>% filter(species!='sp.') %>% unite(col='species_matched', genus:species, sep=' ', remove=T) %>% select(family, species_matched) %>% unique() # # Import GEx species names # GExSpecies <- read.csv('OriginalData\\Traits\\GEx_species_tree_complete.csv') %>% # select(family, species_matched) %>% # unique() # # sppNames <- rbind(correSpecies, GExSpecies) %>% # unique() %>% # mutate(drop=ifelse(species_matched=='Dianella longifolia'&family=='Xanthorrhoeaceae', 1, # ifelse(species_matched=='Lancea tibetica'&family=='Phrymaceae', 1, 0))) %>% # filter(drop==0) %>% # select(-drop) longCategorical <- categoricalTraits %>% pivot_longer(leaf_type:n_fixation_type, names_to="trait", values_to="trait_value") %>% mutate(error_risk_overall=NA, error_risk_family=NA, error_risk_genus=NA) %>% left_join(correSpecies) #### START HERE: need GEx categorical to bind onto continuous data #### traitsAll <- meanCleanContinuousErrorRisk %>% select(family, species_matched, trait, trait_value, error_risk_overall, error_risk_family, error_risk_genus) %>% rbind(longCategorical) # write.csv(traitsAll, 'CleanedData\\Traits\\CoRRE_allTraitData_June2023.csv', row.names=F) traitsWide <- traitsAll %>% select(-error_risk_overall, -error_risk_family, -error_risk_genus) %>% pivot_wider(names_from=trait, values_from=trait_value) # write.csv(traitsWide, 'CleanedData\\Traits\\CoRRE_allTraitData_wide_June2023.csv', row.names=F) # #### testing if imputation runs are different #### # imputedRaw0620 <- read.csv("CleanedData\\Traits\\gap filled continuous traits\\20230620\\imputed_traits_mice.csv") %>% # mutate(run='run_2') %>% # rowid_to_column(var="rowid") # # imputedRaw0608 <- read.csv("CleanedData\\Traits\\gap filled continuous traits\\20230608\\imputed_traits_mice.csv") %>% # mutate(run='run_1') %>% # rowid_to_column(var="rowid") # # compareImputed <- rbind(imputedRaw0608, imputedRaw0620) %>% # pivot_longer(cols=seed_dry_mass:SRL, names_to='trait', values_to='values') %>% # pivot_wider(names_from=run, values_from=values) %>% # left_join(originalRaw) # # ggplot(data=subset(compareImputed, trait=='SLA'), aes(x=run_1, y=run_2)) + # geom_point() + # geom_abline(slope=1) + # xlab('Run 1 Value') + ylab('Run 2 Value') # # ggplot(data=subset(compareImputed, trait=='seed_dry_mass'), aes(x=run_1, y=run_2)) + # geom_point() + # geom_abline(slope=1) + # xlab('Run 1 Value') + ylab('Run 2 Value') + # scale_x_continuous(trans='log10') + # scale_y_continuous(trans='log10') # # ggplot(data=subset(compareImputed, trait=='plant_height_vegetative'), aes(x=run_1, y=run_2)) + # geom_point() + # geom_abline(slope=1) + # xlab('Run 1 Value') + ylab('Run 2 Value') + # scale_x_continuous(trans='log10') + # scale_y_continuous(trans='log10') # # ggplot(data=subset(compareImputed, trait=='SRL'), aes(x=run_1, y=run_2)) + # geom_point() + # geom_abline(slope=1) + # xlab('Run 1 Value') + ylab('Run 2 Value') # # ggplot(data=subset(compareImputed, trait=='LDMC'), aes(x=run_1, y=run_2)) + # geom_point() + # geom_abline(slope=1) + # xlab('Run 1 Value') + ylab('Run 2 Value') # # ggplot(data=subset(compareImputed, trait=='leaf_area'), aes(x=run_1, y=run_2)) + # geom_point() + # geom_abline(slope=1) + # xlab('Run 1 Value') + ylab('Run 2 Value') + # scale_x_continuous(trans='log10') + # scale_y_continuous(trans='log10') # # ggplot(data=subset(compareImputed, trait=='leaf_N'), aes(x=run_1, y=run_2)) + # geom_point() + # geom_abline(slope=1) + # xlab('Run 1 Value') + ylab('Run 2 Value') # # ggplot(data=subset(compareImputed, trait=='leaf_dry_mass'), aes(x=run_1, y=run_2)) + # geom_point() + # geom_abline(slope=1) + # xlab('Run 1 Value') + ylab('Run 2 Value') # # # #only those with original data # imputedRawRun1 <- read.csv("CleanedData\\Traits\\gap filled continuous traits\\20230608\\imputed_traits_mice.csv") %>% # bind_cols(read.csv('OriginalData\\Traits\\raw traits for gap filling\\TRYAusBIEN_continuous_June2023.csv')[,c('DatabaseID', 'DatasetID', 'ObservationID', 'family', 'genus', 'species_matched')]) %>% # left_join(mossKey) %>% # filter(moss!="moss") %>% # dplyr::select(-moss) #removes 6 species observations # # imputedLongRun1 <- imputedRawRun1 %>% # pivot_longer(names_to='trait', values_to='imputed_value', seed_dry_mass:SRL) %>% # rename(imputed_value_run1=imputed_value) # # imputedRawRun2 <- read.csv("CleanedData\\Traits\\gap filled continuous traits\\20230620\\imputed_traits_mice.csv") %>% # bind_cols(read.csv('OriginalData\\Traits\\raw traits for gap filling\\TRYAusBIEN_continuous_June2023.csv')[,c('DatabaseID', 'DatasetID', 'ObservationID', 'family', 'genus', 'species_matched')]) %>% # left_join(mossKey) %>% # filter(moss!="moss") %>% # dplyr::select(-moss) #removes 6 species observations # # imputedLongRun2 <- imputedRawRun2 %>% # pivot_longer(names_to='trait', values_to='imputed_value', seed_dry_mass:SRL) %>% # rename(imputed_value_run2=imputed_value) # # # Read original trait data and join with imputed data # originalRaw <- read.csv('OriginalData\\Traits\\raw traits for gap filling\\TRYAusBIEN_continuous_June2023.csv') %>% # pivot_longer(names_to='trait', values_to='original_value', seed_dry_mass:SRL) %>% # na.omit() # # # # Join original trait data with imputed data. Only keep traits of interest. # allContinuousComparison <- imputedLongRun1 %>% # left_join(imputedLongRun2) %>% # left_join(originalRaw) %>% # filter(trait %in% c('LDMC', 'leaf_area', 'leaf_dry_mass', 'leaf_N', 'plant_height_vegetative', 'seed_dry_mass', 'SLA', 'SRL')) # # originalComparison <- allContinuousComparison %>% # na.omit() # # ggplot(data=subset(originalComparison, trait=='SLA'), aes(x=imputed_value_run1, y=imputed_value_run2)) + # geom_point() + # geom_abline(slope=1) + # xlab('Run 1 Value') + ylab('Run 2 Value') # # ggplot(data=subset(originalComparison, trait=='seed_dry_mass'), aes(x=imputed_value_run1, y=imputed_value_run2)) + # geom_point() + # geom_abline(slope=1) + # xlab('Run 1 Value') + ylab('Run 2 Value') + # scale_x_continuous(trans='log10') + # scale_y_continuous(trans='log10') # # ggplot(data=subset(originalComparison, trait=='plant_height_vegetative'), aes(x=imputed_value_run1, y=imputed_value_run2)) + # geom_point() + # geom_abline(slope=1) + # xlab('Run 1 Value') + ylab('Run 2 Value') + # scale_x_continuous(trans='log10') + # scale_y_continuous(trans='log10') # # ggplot(data=subset(originalComparison, trait=='SRL'), aes(x=imputed_value_run1, y=imputed_value_run2)) + # geom_point() + # geom_abline(slope=1) + # xlab('Run 1 Value') + ylab('Run 2 Value') # # ggplot(data=subset(originalComparison, trait=='LDMC'), aes(x=imputed_value_run1, y=imputed_value_run2)) + # geom_point() + # geom_abline(slope=1) + # xlab('Run 1 Value') + ylab('Run 2 Value') # # ggplot(data=subset(originalComparison, trait=='leaf_area'), aes(x=imputed_value_run1, y=imputed_value_run2)) + # geom_point() + # geom_abline(slope=1) + # xlab('Run 1 Value') + ylab('Run 2 Value') + # scale_x_continuous(trans='log10') + # scale_y_continuous(trans='log10') # # ggplot(data=subset(originalComparison, trait=='leaf_N'), aes(x=imputed_value_run1, y=imputed_value_run2)) + # geom_point() + # geom_abline(slope=1) + # xlab('Run 1 Value') + ylab('Run 2 Value') # # ggplot(data=subset(originalComparison, trait=='leaf_dry_mass'), aes(x=imputed_value_run1, y=imputed_value_run2)) + # geom_point() + # geom_abline(slope=1) + # xlab('Run 1 Value') + ylab('Run 2 Value')

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B5_calc_curvature_and_torsion.R

mycalc_3dCT <- function(x){ # calc diff d1_mat <- t(apply(cbind(x, x[c(2:nrow(x),1),]), 1, function(xx){ xx[4:6] - xx[1:3] })) d1_mat2 <- t(apply(cbind(d1_mat, d1_mat[c(2:nrow(d1_mat),1),]), 1, function(xx){ (xx[1:3] + xx[4:6])/2 })) d2_mat <- t(apply(cbind(d1_mat, d1_mat[c(2:nrow(d1_mat),1),]), 1, function(xx){ xx[4:6] - xx[1:3] })) d3_mat <- t(apply(cbind(d2_mat, d2_mat[c(2:nrow(d2_mat),1),]), 1, function(xx){ xx[4:6] - xx[1:3] })) d3_mat2 <- t(apply(cbind(d3_mat, d3_mat[c(2:nrow(d3_mat),1),]), 1, function(xx){ (xx[1:3] + xx[4:6])/2 })) # cbind each axis XX <- cbind(x[,1], d1_mat2[,1], d2_mat[,1], d3_mat2[,1]) YY <- cbind(x[,2], d1_mat2[,2], d2_mat[,2], d3_mat2[,2]) ZZ <- cbind(x[,3], d1_mat2[,3], d2_mat[,3], d3_mat2[,3]) # calculating each term term1 <- (ZZ[,3]*YY[,2] - YY[,3]*ZZ[,2]) term2 <- (XX[,3]*ZZ[,2] - ZZ[,3]*XX[,2]) term3 <- (YY[,3]*XX[,2] - XX[,3]*YY[,2]) term4 <- (XX[,2]^2 + YY[,2]^2 + ZZ[,2]^2) # curvature curvature <- sqrt( term1^2 + term2^2 + term3^2 ) / (term4^{(3/2)}) # torsion torsion <- {(XX[,4]*term1) + (YY[,4]*term2) + (ZZ[,4]*term3)}/{ term1^2 + term2^2 + term3^2 } # length tmp1 <- cbind(x, x[c(2:nrow(x),1),]) rownames(tmp1) <- c() c_length <- sum(apply(tmp1, 1, function(x){ dist(rbind(x[1:3], x[4:6])) })) # return return(list(length=c_length ,curvature = curvature, torsion = torsion)) }

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/datasets.R \docType{data} \name{Inflation} \alias{Inflation} \title{Inflation data} \format{ A data frame with 23 observations on the following 5 variables. \itemize{ \item{country}{ country where data were collected} \item{ques}{ questionnaire index of independence} \item{inf}{ annual inflation rate, 1980-1989 (percent)} \item{legal}{ legal index of independence} \item{dev}{ developed (1) or developing (2) nation} } } \source{ These data are available from OzDASL, the Australasian Data and Story Library (\url{https://dasl.datadescription.com/}). } \description{ The article developed four measures of central bank independence and explored their relation to inflation outcomes in developed and developing countries. This datafile deals with two of these measures in 23 nations. } \examples{ data(Inflation) } \references{ A. Cukierman, S.B. Webb, and B. Negapi, "Measuring the Independence of Central Banks and Its Effect on Policy Outcomes," World Bank Economic Review, Vol. 6 No. 3 (Sept 1992), 353-398. } \keyword{datasets}

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test_that("get_SDA_hydric works", { skip_if_offline() skip_on_cran() # by areasymbol expect_equal(nrow(get_SDA_hydric(areasymbols = c("CA077", "CA630"))), 313) # by mukey expect_equal(nrow(get_SDA_hydric(mukeys = c(461994, 461995))), 2) })

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#!/usr/bin/env Rscript # Created by Dongmei Chen # Writing netCDFfiles for daily bioclimatic variables (minimum temperatures) library(ncdf4) args <- commandArgs(trailingOnly=T) print(paste('args:', args)) print("Starting...") source("/gpfs/projects/gavingrp/dongmeic/climate-space/R/damian/getDailyStats.R") inpath <- "/gpfs/projects/gavingrp/dongmeic/beetle/output/tables/" setwd(inpath) start_year <- 1901; end_year <- 2016; years <- start_year:end_year; nt <- length(years) print("calculating the biocliamtic variables using daily data") ptm <- proc.time() dim1 <- 277910; dim2 <- nt i <- as.numeric(args[1]) print(paste('i:', i)) indata1 <- read.csv(paste0(inpath, "na10km_v2_climatic_values_",years[i],".csv")) indata2 <- read.csv(paste0(inpath, "na10km_v2_climatic_values_",years[i+1],".csv")) indata <- rbind(indata1, indata2) df <- data.frame(Ncs=integer(), Acs=integer(), min20=integer(), min22=integer(), min24=integer(), min26=integer(), min28=integer()) #df <- data.frame(min30=integer(), min32=integer(), min34=integer(), min36=integer(), min38=integer(), min40=integer()) for(j in 1:dim1){ df.j <- indata[j,] for(m in 1:23){ df.m <- rbind(df.j, indata[j+dim1*m,]) df.j <- df.m #print(m) } df[j,] <- get.daily.stats(years[i], df.m$tmx, df.m$tmp, df.m$tmn) print(j) } write.csv(df, paste0("min/bioclimatic_variables_daily_min_",years[i+1],"_2.csv"), row.names = FALSE) print(paste("got data from", years[i+1])) proc.time() - ptm print("all done!")

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/sampleSolutions/.file.dome.R

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## Sample solution >>> (file.dome) ## Read the csv file tmp <- read.csv("./data/table_S3.csv", header = FALSE, as.is = TRUE) ## The object "tmp" should appear in the Data section of the Environment tab ## in the top-right pane. It has a spreadsheet symbol next to it. Click that - ## or type View(rawDat), and study the object. You should find: ## - all columns are named V<something> ## - rows 1 to 6 do not contain data ## - there is not a single row that could be used for column names ## - str(tmp) shows: all columns are characters ## This all needs to be fixed: head(tmp, 10) # Use head() to inspect tmp <- tmp[-(1:6), ] # Remove unneeded header rows colnames(tmp) <- c("genes", # gene names "B.ctrl", # Cell types are taken from "B.LPS", # Figure 4 of Jaitin et al. "MF.ctrl", # .ctrl and .LPS refer to control "MF.LPS", # and LPS challenge "NK.ctrl", # The cell types are: "NK.LPS", # B: B-cell "Mo.ctrl", # MF: Macrophage "Mo.LPS", # NK: Natural killer cell "pDC.ctrl", # Mo: Monocyte "pDC.LPS", # pDC: plasmacytoid dendritic cell "DC1.ctrl", # DC1: dendritic cell subtype 1 "DC1.LPS", # DC2: dendritic cell subtype 2 "DC2.ctrl", # "DC2.LPS", # "cluster") # Gene assigned to cluster by authors head(tmp) rownames(tmp) <- 1:nrow(tmp) # fix rownames str(tmp) # next: fix the column types for (i in 2:ncol(tmp)) { # convert character columns to numeric tmp[,i] <- as.numeric(tmp[ ,i]) if (any(is.na(tmp[,i]))) { message(sprintf("Caution: NA in column %d", i)) # always validate! } } str(tmp) ## if everything is good ... LPSdat <- tmp # assign to meaningful name rm(tmp) # always a good idea to clean up ## <<< Sample solution # [END]

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match_times = function(sodi, times) { sodi_times = unique(sodi$Time) matched_times = sodi_times[approx(x = sodi_times, y = 1:length(sodi_times), xout = times, method = "constant", rule = 2)$y] return(matched_times) }

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## Downloading data data1 <- read.csv("./Data/household_power_consumption.txt", header=T, sep=';', na.strings="?", comment.char="") data1$Date <- as.Date(data1$Date, format="%d/%m/%Y") ## Subsetting for Feb 1 and Feb 2, 2007 ourdata <- subset(data1, subset=(Date >= "2007-02-01" & Date <= "2007-02-02")) ## Converting dates datetime <- paste(as.Date(ourdata$Date), ourdata$Time) ourdata$Datetime <- as.POSIXct(datetime) ## Plot 1 hist(ourdata$Global_active_power, main="Global Active Power", xlab="Global Active Power (kilowatts)", ylab="Frequency", col="Red") ## Saving to png file dev.copy(png, file="plot1.png", height=480, width=480) dev.off()

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# This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library 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 Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ############################################################################### # FUNCTION: DESCRIPTION: # pfolioReturn Returns portfolio returns # FUNCTION: DESCRIPTION: # sampleCOV Returns sample covariance risk # normalVaR Returns normal Value at Risk # modifiedVaR Returns modified Cornish Fisher VaR # sampleVaR Returns sammple VaR from historical quantiles # FUNCTION: DESCRIPTION: # budgetsSampleCOV Covariance risk contribution and budgets # budgetsNormalVAR Normal VaR risk contribution and budgets # budgetsModifiedVAR Modified VaR risk contribution and budgets # budgetsNormalES Normal ES (CVaR) risk contribution and budgets # budgetsModifiedES Modified ES (CVaR) risk contribution and budgets # UTILITIES: DESCRIPTION: # .M34.MM Internal fast computing of M3 and M4 # .run Returns execution time information # .Ipower Internal utility function to compute M3 # .derIpower Internal utility function to compute M4 # .myVaR ... to do # DEPRECATED: DESCRIPTION: # .covarRisk Computes covariance portfolio risk # .mcr Computes marginal contribution to covariance risk # .mcrBeta Computes beta, the rescaled mcr to covariance risk # .riskContributions Computes covariance risk contributions # .riskBudgets Computes covariance risk budgets ############################################################################### sampleCOV <- function(x) { # A function implemented by Diethelm Wuertz # Description: # Returns sample covariance risk # Arguments: # x - a 'timeSeries' object # FUNCTION: # Return Value: cov(x) } # ----------------------------------------------------------------------------- normalVaR <- function(x, alpha=0.05) { # A function implemented by Diethelm Wuertz # Description: # Returns normal Value at Risk # Arguments: # x - a 'timeSeries' object # FUNCTION: # Mean and Centered 2nd Moment: x.mean <- colMeans(x) x.centered <- t(t(x) - x.mean) m2 <- colMeans(x.centered^2) # Gaussian: q <- qnorm(alpha) # Return Value: x.mean + q * sqrt(m2) } # ----------------------------------------------------------------------------- modifiedVaR <- function(x, alpha=0.05) { # A function implemented by Diethelm Wuertz # Description: # Returns modified Cornish Fisher VaR # Arguments: # x - a 'timeSeries' object # Details: # Includes Code Borrowed from Peterson and Boudt, GPL # FUNCTION: # Mean and Centered Moments: x.mean <- colMeans(x) x.centered <- t(t(x) - x.mean) m2 <- colMeans(x.centered^2) m3 <- colMeans(x.centered^3) m4 <- colMeans(x.centered^4) skew <- m3 / sqrt(m2^3) kurt <- m4 / (m2*m2) - 3 # Cornish Fisher: z <- qnorm(alpha) q <- z + (z*z-1)*skew/6 + z*(z*z-3)*kurt/24 - z*(2*z*z-5)*skew*skew/36 # Return Value: x.mean + q * sqrt(m2) } # ----------------------------------------------------------------------------- sampleVaR <- function(x, alpha=0.05) { # A function implemented by Diethelm Wuertz # Description: # Returns sammple VaR from historical quantiles # Arguments: # x - a 'timeSeries' object # FUNCTION: # Return Value: colQuantiles(x, alpha) } # ----------------------------------------------------------------------------- budgetsSampleCOV <- function(x, weights, mu=NULL, Sigma=NULL) { # A function implemented by Diethelm Wuertz # Description: # Arguments: # x - a 'timeSeries' object # Details: # Includes Code Borrowed from Peterson and Boudt, GPL # Rmetrics Re-Implementation # FUNCTION: # Risk: if(is.null(mu)) mu <- colMeans(x) if(is.null(Sigma)) Sigma <- cov(x) risk <- sqrt( t(weights) %*% Sigma %*% weights )[[1]] attr(risk, "estimator") <- substitute(FUN) # Risk Contributions: m2.pfolio <- (t(weights) %*% Sigma %*% weights)[[1]] dm2.pfolio <- as.vector(Sigma %*% weights) contribution <- dm2.pfolio/sqrt(m2.pfolio) * weights names(contribution) <- colnames(x) # Risk Budgets: budgets <- contribution/risk names(budgets) <- colnames(x) attr(budgets, "control") <- sum(budgets) # Return Value: list(riskCOV=risk, contribution=contribution, budgets=budgets) } # ----------------------------------------------------------------------------- budgetsNormalVAR <- function(x, weights, alpha=0.05, mu=NULL, Sigma=NULL) { # A function implemented by Diethelm Wuertz # Description: # Arguments: # x - a 'timeSeries' object # Details: # Includes Code Borrowed from Peterson and Boudt, GPL # FUNCTION: # Risk: if(is.null(mu)) mu <- colMeans(x) if(is.null(Sigma)) Sigma <- cov(x) risk <- -(t(weights) %*% mu + qnorm(alpha) * sqrt( t(weights) %*% Sigma %*% weights))[[1]] attr(risk, "estimator") <- substitute(FUN) attr(risk, "alpha") <- alpha # Risk Contributions: m2.pfolio <- (t(weights) %*% Sigma %*% weights) dm2.pfolio <- as.vector(Sigma %*% weights) contribution <- - (mu + qnorm(alpha)* dm2.pfolio/sqrt(m2.pfolio)) * weights names(contribution) <- colnames(x) # Risk Budgets: budgets <- contribution/risk names(budgets) <- colnames(x) attr(budgets, "sumBudgets") <- sum(budgets) # Return Value: list(riskVAR=risk, contributionVAR=contribution, budgetsVAR=budgets) } # ----------------------------------------------------------------------------- budgetsModifiedVAR <- function(x, weights, alpha=0.05, mu=NULL, Sigma=NULL, M3=NULL, M4=NULL) { # A function implemented by Diethelm Wuertz # Description: # Arguments: # x - a 'timeSeries' object # Details: # Includes code borrowed from Peterson and Boudt, GPL # FUNCTION: # Compute Moments: if(is.null(mu)) mu <- colMeans(x) if(is.null(Sigma)) Sigma <- cov(x) if(is.null(M3) || is.null(M4)) { MM <- .M34.MM(x, mu=mu) M3 <- MM$M3 M4 <- MM$M4 } # Risk: z <- qnorm(alpha) location <- t(weights) %*% mu pm2 <- t(weights) %*% Sigma %*% weights dpm2 <- as.vector(2 * Sigma %*% weights) pm3 <- weights %*% M3 %*% (weights %x% weights) dpm3 <- as.vector(3 * M3 %*% (weights %x% weights)) pm4 <- t(weights) %*% M4 %*% (weights %x% weights %x% weights) dpm4 <- as.vector(4 * M4 %*% (weights %x% weights %x% weights)) skew <- (pm3/pm2^(3/2))[[1]] exkurt <- (pm4/pm2^(2) - 3)[[1]] derskew <- (2 * (pm2^(3/2)) * dpm3 - 3 * pm3 * sqrt(pm2) * dpm2) / (2 * pm2^3) derexkurt <- ((pm2) * dpm4 - 2 * pm4 * dpm2)/(pm2^3) h <- z + (1/6) * (z^2 - 1) * skew h <- h + (1/24) * (z^3 - 3 * z) * exkurt - (1/36) * (2 * z^3 - 5 * z) * skew^2 risk <- -(location + h * sqrt(pm2)) # Risk Contribution: derGausVaR <- -as.vector(mu) - qnorm(alpha) * (0.5 * as.vector(dpm2))/sqrt(pm2) derMVaR <- derGausVaR + (0.5 * dpm2/sqrt(pm2)) * (-(1/6) * (z^2 - 1) * skew - (1/24) * (z^3 - 3 * z) * exkurt + (1/36) * (2 * z^3 - 5 * z) * skew^2) derMVaR <- derMVaR + sqrt(pm2) * (-(1/6) * (z^2 - 1) * derskew - (1/24) * (z^3 - 3 * z) * derexkurt + (1/36) * (2 * z^3 - 5 * z) * 2 * skew * derskew) contribution <- as.vector(weights) * as.vector(derMVaR) names(contribution) <- colnames(x) # Risk Budgets: budgets <- contribution/risk names(budgets) <- colnames(x) budgets attr(budgets, "sum(contribution)-risk") <- sum(contribution)-risk attr(budgets, "sum(budgets)") <- sum(budgets) # Return Value: list(modifiedVAR=risk, contribution=contribution, budgets=budgets) } # ----------------------------------------------------------------------------- budgetsNormalES <- function(x, weights, alpha=0.05, mu=NULL, Sigma=NULL) { # A function implemented by Diethelm Wuertz # Description: # x - a 'timeSeries' object # Arguments: # Details: # Includes Code Borrowed from Peterson and Boudt, GPL # FUNCTION: # Risk: if(is.null(mu)) mu <- colMeans(x) if(is.null(Sigma)) Sigma <- cov(x) location <- t(weights) %*% mu pm2 <- t(weights) %*% Sigma %*% weights dpm2 <- as.vector(2 * Sigma %*% weights) risk <- -location + dnorm(qnorm(alpha)) * sqrt(pm2)/alpha attr(risk, "estimator") <- substitute(FUN) attr(risk, "alpha") <- alpha # Contribution: derES <- -mu + (1/alpha) * dnorm(qnorm(alpha)) * (0.5 * dpm2)/sqrt(pm2) contribution <- weights * derES names(contribution) <- colnames(x) # Budgets: budgets <- contribution/risk names(budgets) <- colnames(x) attr(budgets, "sumBudgets") <- sum(budgets) # Return Value: list(normalES=risk, contribution=contribution, budgets=budgets) } # ----------------------------------------------------------------------------- budgetsModifiedES <- function(x, weights, alpha=0.05, mu=NULL, Sigma=NULL, M3=NULL, M4=NULL) { # A function implemented by Diethelm Wuertz # Description: # Arguments: # x - a 'timeSeries' object # Details: # Includes code borrowed from Peterson and Boudt, GPL # FUNCTION: # Settings: if(is.null(mu)) mu <- colMeans(x) if(is.null(Sigma)) Sigma <- cov(x) if(is.null(M3) || is.null(M4)) { MM <- .M34.MM(x, mu=mu) M3 <- MM$M3 M4 <- MM$M4 } # Risk: z <- qnorm(alpha) location <- t(weights) %*% mu pm2 <- (t(weights) %*% Sigma %*% weights)[[1]] dpm2 <- as.vector(2 * Sigma %*% weights) pm3 <- (weights %*% M3 %*% (weights %x% weights))[[1]] dpm3 <- as.vector(3 * M3 %*% (weights %x% weights)) pm4 <- (t(weights) %*% M4 %*% (weights %x% weights %x% weights))[[1]] dpm4 <- as.vector(4 * M4 %*% (weights %x% weights %x% weights)) skew <- (pm3/pm2^(3/2))[[1]] exkurt <- (pm4/pm2^(2) - 3)[[1]] derskew <- (2 * (pm2^(3/2)) * dpm3 - 3 * pm3 * sqrt(pm2) * dpm2)/(2 * pm2^3) derexkurt <- ((pm2) * dpm4 - 2 * pm4 * dpm2)/(pm2^3) h <- z + (1/6) * (z^2 - 1) * skew h <- h + (1/24) * (z^3 - 3 * z) * exkurt - (1/36) * (2 * z^3 - 5 * z) * skew^2 derh <- (1/6) * (z^2 - 1) * derskew + (1/24) * (z^3 - 3 * z) * derexkurt - (1/18) * (2 * z^3 - 5 * z) * skew * derskew E <- dnorm(h) E <- E + (1/24) * (.Ipower(4, h) - 6 * .Ipower(2, h) + 3 * dnorm(h)) * exkurt E <- E + (1/6) * (.Ipower(3, h) - 3 * .Ipower(1, h)) * skew E <- E + (1/72) * (.Ipower(6, h) - 15 * .Ipower(4, h) + 45 * .Ipower(2, h) - 15 * dnorm(h)) * (skew^2) E <- E/alpha risk <- MES <- -location + sqrt(pm2) * E # Risk Contributions: derMES <- -mu + 0.5 * (dpm2/sqrt(pm2)) * E derE <- (1/24) * (.Ipower(4, h) - 6 * .Ipower(2, h) + 3 * dnorm(h)) * derexkurt derE <- derE + (1/6) * (.Ipower(3, h) - 3 * .Ipower(1, h)) * derskew derE <- derE + (1/36) * (.Ipower(6, h) - 15 * .Ipower(4, h) + 45 * .Ipower(2, h) - 15 * dnorm(h)) * skew * derskew X <- -h * dnorm(h) + (1/24) * (.derIpower(4, h) - 6 * .derIpower(2, h) - 3 * h * dnorm(h)) * exkurt X <- X + (1/6) * (.derIpower(3, h) - 3 * .derIpower(1, h)) * skew X <- X + (1/72) * (.derIpower(6, h) - 15 * .derIpower(4, h) + 45 * .derIpower(2, h) + 15 * h * dnorm(h)) * skew^2 derE <- derE + derh * X derE <- derE/alpha derMES <- derMES + sqrt(pm2) * derE contribution <- as.vector(weights) * as.vector(derMES) names(contribution) <- colnames(x) # Risk Budgets: budgets <- contribution/risk names(budgets) <- colnames(x) attr(budgets, "sumBudgets") <- sum(budgets) # Return Value: list(modifedES=risk, contribution=contribution, budgets=budgets) } ############################################################################### .M34.MM <- function (x, mu=NULL) { # A function implemented by Diethelm Wuertz # Description: # Arguments: # x - a 'timeSeries' object # Details: # Includes Code Borrowed from Peterson and Boudt, GPL # Fast Rmetrics Implementation: n <- ncol(x) m <- nrow(x) if(is.null(mu)) mu <- colMeans(x) M3 <- matrix(rep(0, n^3), nrow=n, ncol=n^2) M4 <- matrix(rep(0, n^4), nrow=n, ncol=n^3) centret <- series(x) - matrix(rep(mu, each=m), ncol=n) for (i in c(1:m)) { cent <- centret[i, ] tcent <- t(cent) M <- (cent %*% tcent) %x% tcent M3 <- M3 + M M4 <- M4 + M %x% tcent } # Return Value: list(M3=M3/m, M4=M4/m, mu=mu) } # ----------------------------------------------------------------------------- .run <- function(FUN, times=10, mult=100, ...) { # A function implemented by Diethelm Wuertz # Description: # Arguments: # FUNCTION: # Timing: fun <- match.fun(FUN) now <- Sys.time() for (i in 1:as.integer(times)) ans <- fun(...) done <- Sys.time() time <- mult * as.numeric(done - now) # Print Timing Results: cat("Timing:\n") print(c(sec=round(time,0), times=times, mult=mult, runs=times*mult)) cat("\nResults:\n\n") # Return Value: ans } # ----------------------------------------------------------------------------- .Ipower <- function (power, h) { # Description: # Arguments: # Details: # A function borrowed from PerformanceAnalytics, GPL fullprod <- 1 if ((power%%2) == 0) { pstar <- power/2 for (j in c(1:pstar)) fullprod <- fullprod * (2 * j) I <- fullprod * dnorm(h) for (i in c(1:pstar)) { prod <- 1 for (j in c(1:i)) prod <- prod * (2 * j) I <- I + (fullprod/prod) * (h^(2 * i)) * dnorm(h) } } else { pstar <- (power - 1)/2 for (j in c(0:pstar)) { fullprod = fullprod * ((2 * j) + 1) } I <- -fullprod * pnorm(h) for (i in c(0:pstar)) { prod = 1 for (j in c(0:i)) prod = prod * ((2 * j) + 1) I <- I + (fullprod/prod) * (h^((2 * i) + 1)) * dnorm(h) } } return(I) } # ----------------------------------------------------------------------------- .derIpower <- function (power, h) { # Description: # Arguments: # Details: # A function borrowed from PerformanceAnalytics, GPL fullprod <- 1 if ((power%%2) == 0) { pstar <- power/2 for (j in c(1:pstar)) fullprod = fullprod * (2 * j) I <- -fullprod * h * dnorm(h) for (i in c(1:pstar)) { prod = 1 for (j in c(1:i)) prod = prod * (2 * j) I <- I + (fullprod/prod) * (h^(2 * i - 1)) * (2 * i - h^2) * dnorm(h) } } else { pstar = (power - 1)/2 for (j in c(0:pstar)) fullprod <- fullprod * ((2 * j) + 1) I <- -fullprod * dnorm(h) for (i in c(0:pstar)) { prod = 1 for (j in c(0:i)) prod <- prod * ((2 * j) + 1) I <- I + (fullprod/prod) * (h^(2 * i) * (2 * i + 1 - h^2)) * dnorm(h) } } return(I) } # ----------------------------------------------------------------------------- .myVaR <- function(x, alpha=0.05, method=c("normal", "modified", "sample")) { # A function implemented by Diethelm Wuertz # todo ... # Funcion Selection: fun <- match.fun(paste(match.arg(method), "VaR", sep="")) # Return Value: fun(x, alpha) } ############################################################################### # DEPRECATED - DO NOT REMOVE - REQUIRED BY PACKAGE appRmetricsHandbook .covarRisk <- function(data, weights=NULL, FUN="cov", ...) { # A function implemented by Diethelm Wuertz # Description: # Computes covariance portfolio risk # Arguments: # data - a multivariate timeSeries object of financial returns # weights - numeric vector of portfolio weights # FUN - a covariance estimator, which returns a matrix of # covariance estimates, by default the sample covariance # ... - Optional arguments passed to the function FUN # Example: # covarRisk(data) # FUNCTION: # Covariance Risk: covFun <- match.fun(FUN) COV <- covFun(data) # Portfolio Weights: N <- ncol(COV) if (is.null(weights)) weights = rep(1/N, N) names(weights) <- colnames(COV) # Covariance Portfolio Risk: covarRisk <- sqrt( t(weights) %*% COV %*% weights )[[1, 1]] # Return Value: covarRisk } # ----------------------------------------------------------------------------- .mcr <- function(data, weights=NULL, FUN="cov", ...) { # A function implemented by Diethelm Wuertz # Description # Computes marginal contribution to covariance risk # Arguments: # data - a multivariate timeSeries object of financial returns # weights - numeric vector of portfolio weights # FUN - a covariance estimator, which returns a matrix of # covariance estimates, by default the sample covariance # ... - Optional arguments passed to the function FUN # Details: # The formula are implemented according to Goldberg et al., # see also R script assetsPfolio.R # References: # Lisa Goldberg et al., Extreme Risk Management, 2009 # Scherer and Martin, Introduction to modern portfolio Optimimization # Example: # data <- assetsSim(100, 6); mcr(data) # FUNCTION: # Covariance Risk: covFun <- match.fun(FUN) COV <- covFun(data) N <- ncol(data) if (is.null(weights)) weights <- rep(1/N, N) # Marginal Contribution to Risk mcr <- (COV %*% weights)[, 1] / .covarRisk(data, weights, FUN, ...) names(mcr) <- colnames(data) # Return Value: mcr } # ----------------------------------------------------------------------------- .mcrBeta <- function(data, weights=NULL, FUN="cov", ...) { # A function implemented by Diethelm Wuertz # Description: # Computes beta, the rescaled marginal contribution to covariance risk # Arguments: # data - a multivariate timeSeries object of financial returns # weights - numeric vector of portfolio weights # FUN - a covariance estimator, which returns a matrix of # covariance estimates, by default the sample covariance # ... - Optional arguments passed to the function FUN # Example: # .mcrBeta(data) # FUNCTION: # Portfolio Beta: beta <- .mcr(data, weights, FUN = FUN, ...) / .covarRisk(data, weights, FUN = FUN, ...) # Return Value: beta } # ----------------------------------------------------------------------------- .riskContributions <- function(data, weights=NULL, FUN="cov", ...) { # A function implemented by Diethelm Wuertz # Description: # Computes covariance risk contributions # Arguments: # data - a multivariate timeSeries object of financial returns # weights - numeric vector of portfolio weights # FUN - a covariance estimator, which returns a matrix of # covariance estimates, by default the sample covariance # ... - Optional arguments passed to the function FUN # Example: # .riskContributions(data) # FUNCTION: # Risk Contributions: if (is.null(weights)) { N <- ncol(data) weights <- rep(1/N, times = N) } riskContributions <- weights * .mcr(data, weights, FUN, ...) # Return Value: riskContributions } # ----------------------------------------------------------------------------- .riskBudgets <- function(data, weights=NULL, FUN="cov", ...) { # A function implemented by Diethelm Wuertz # Description: # Computes covariance risk budgets # Arguments: # data - a multivariate timeSeries object of financial returns # weights - numeric vector of portfolio weights # FUN - a covariance estimator, which returns a matrix of # covariance estimates, by default the sample covariance # ... - Optional arguments passed to the function FUN # Example: # data <- 100*LPP2005.RET[, 1:6]; .riskBudgets(data) # FUNCTION: # Risk Budgets: riskBudgets <- .riskContributions(data, weights, FUN, ...) / .covarRisk(data, weights, FUN, ...) # Return Value: riskBudgets } ###############################################################################

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/Project_bioinformatics/Bra.popGenePaper/pi and heter plot/heter plot.r

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### cal he and ho based on hardy output in vcftool getwd() setwd("C:/Users/qxs/Desktop/") setwd("E:/Dropbox/Xubuntu-Win7_UAproject/Brassica Project/Chenlu_Rotation/Results_by_qxs##") # replace / with \t in the document t<-read.table(file="hardy_5424noneWGT.hwe",header = TRUE); t<-as.data.frame(t) t<-t[((t$P_HET_DEFICIT<=0.05)|(t$P_HET_EXCESS<=0.05)),] nrow(t) hohe.no<-t[,4]/t[,7] t<-read.table(file="hardy_27919WGT.hwe",header = TRUE); t<-as.data.frame(t) t<-t[((t$P_HET_DEFICIT<=0.05)|(t$P_HET_EXCESS<=0.05)),] nrow(t) hohe.wgt<-t[,4]/t[,7] #head(t) #head(t[,4]) #hohe<-t[,4]/t[,7] hist((hohe.wgt),breaks=200,col='skyblue',border=F,ylim=c(0,60000)) mean(hohe.wgt) summary(hohe.wgt) hist(hohe.no,breaks=200,add=T,col='red',border=F,ylim=c(0,60000)) mean(hohe.no) summary(hohe.no) mean<-c(mean(hohe.no,na.rm=T), mean(hohe.wgt,na.rm=T)) mean #0.3079451 0.2973541 boxplot(hohe.wgt,hohe.no, col=c("purple","orange"), ylab="Ho/He", main="Box plot of Ho/He", outline=FALSE, xlab=c("WGT gene none WGT gene")) t.test(hohe.wgt,hohe.no) #t = -54.687, df = 34055, p-value < 2.2e-16 #t = -2.1451, df = 5377.2, p-value = 0.03199 # only sign set.seed(42) hist(hohe.no,xlim=c(0,2),ylim=c(0,5000),breaks=200,col='skyblue',border=F) hist(hohe.wgt,xlim=c(0,2),ylim=c(0,5000),breaks=200,add=T,col=scales::alpha('red',.5),border=F) # library(ggplot2) # df<-cbind(hohe.wgt,hohe.no) # ggplot()+ # geom_boxplot(data=hohe.wgt)+ # geom_boxplot(data=hohe.no) # # p <- ggplot(hohe.wgt,)) # # p + geom_boxplot() ## # By the central limit theorem, means of samples from a population with finite variance # approach a normal distribution regardless of the distribution of the population. # Rules of thumb say that the sample means are basically normally distributed as long as # the sample size is at least 20 or 30. For a t-test to be valid on a sample of smaller size, # the population distribution would have to be approximately normal. # # The t-test is invalid for small samples from non-normal distributions, # but it is valid for large samples from non-normal distributions.

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library(ICC) source("importclean.R") smallimport <- import.clean("fiji_newscored_cleaned_Oct19.csv")[[2]] ICC_fiji <- function(df, scores){ fiji_icc_df <- df ls <- list() for (i in 1:length(scores)){ results_vec <- c() fiji_icc_df$HHID_unique <- as.factor(fiji_icc_df$HHID_unique) scoreno <- scores[i] ICC_formula <- paste0("ICCest(HHID_unique, ",scoreno,", data = fiji_icc_df, alpha = 0.05)") results <- try(eval(parse(text=ICC_formula))) if (class(results) == "try-error"){ next } # results_vec[1] <- results[["ICC"]] # results_vec[2] <- results[["LowerCI"]] # results_vec[3] <- results[["UpperCI"]] # names(results_vec) <- c("point_est", "2.5_est", "97.5_est") results[[length(results) + 1]] <- scoreno ls[[i]] <- results } # Organise results into a dataframe ICC_df <- data.frame(matrix(nrow=length(ls), ncol=4)) for (i in 1:length(ls)){ if (class(ls[[i]]) == "list") { ICC_df[i, 1] <- ls[[i]][[8]] ICC_df[i, 2] <- ls[[i]][["ICC"]] ICC_df[i, 3] <- ls[[i]][["LowerCI"]] ICC_df[i, 4] <- ls[[i]][["UpperCI"]] } } colnames(ICC_df) <- c("Dimension", "ICC est", "LowerCI", "UpperCI") return(ICC_df) }

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/scripts/graphs_response_letter_flow.R

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CourtneyCampany/WTC3_c13

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source("scripts/calculateCin.R") palette(1:12) feb <- subset(deltaPaired, DateTime >= as.Date('2014-02-01') & DateTime <= ('2014-02-28')) plot(Air_in ~ DateTime, data = feb, col = as.factor(feb$chamber)) plot(Air_out ~ DateTime, data = feb) c03 <- subset(deltaPaired, chamber == 'C03') c04 <- subset(deltaPaired, chamber == 'C04' & Air_in >= 6) c01 <- subset(deltaPaired, chamber == 'C01') c06 <- subset(deltaPaired, chamber == 'C06') firstDay <- c('2013-10-01', '2013-12-01', '2014-01-01', '2014-02-01', '2014-03-01', '2014-04-01') lastDay <- c('2013-10-31', '2013-12-31', '2014-01-31', '2014-02-28', '2014-03-31', '2014-04-30') names <- c('Oct-13', 'Dec-13', 'Jan-14', 'Feb-14', 'Mar-14', 'Apr-14') windows(12, 8) par(mfrow=c(2, 3), mar = c(3, 5, 4, 1)) for (i in 1:3){ plot(Air_in ~ DateTime, data = subset(c01, DateTime >= as.Date(firstDay[i]) & DateTime <= as.Date(lastDay[i])), ylim = c(6, 7.55), type = 'l', col = 'blue', cex.lab = 1.3, xlab = '', ylab = expression(italic(f)~(std~L~s^-1)), main = names[i]) lines(Air_out ~ DateTime, data = subset(c01, DateTime >= as.Date(firstDay[i]) & DateTime <= as.Date(lastDay[i])), col = 'blue', lty = 2) lines(Air_in ~ DateTime, data = subset(c06, DateTime >= as.Date(firstDay[i]) & DateTime <= as.Date(lastDay[i])), col = 'red') lines(Air_out ~ DateTime, data = subset(c06, DateTime >= as.Date(firstDay[i]) & DateTime <= as.Date(lastDay[i])), col = 'red', lty = 2) } legend('bottomright', bty = 'n', legend = c('in', 'out', 'amb', 'warm'), lty = c(1, 2, 1, 1), col = c('black', 'black', 'blue', 'red')) windows(12, 8) par(mfrow=c(2, 3), mar = c(3, 5, 4, 1)) for (i in 1:length(firstDay)){ plot(CO2Injection ~ DateTime, data = subset(c03, DateTime >= as.Date(firstDay[i]) & DateTime <= as.Date(lastDay[i])), ylim = c(0, 0.16), type = 'l', col = 'blue', cex.lab = 1.3, xlab = '', ylab = expression(italic(C)[inj]~(mmol~s^-1)), main = names[i]) lines(CO2Injection ~ DateTime, data = subset(c04, DateTime >= as.Date(firstDay[i]) & DateTime <= as.Date(lastDay[i])), col = 'red') } legend('topright', bty = 'n', legend = c('in', 'out', 'amb', 'warm'), lty = c(1, 2, 1, 1), col = c('black', 'black', 'blue', 'red'))

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tab_itemscale.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/tab_itemscale.R \name{tab_itemscale} \alias{tab_itemscale} \alias{sjt.itemanalysis} \title{Summary of item analysis of an item scale as HTML table} \usage{ tab_itemscale( df, factor.groups = NULL, factor.groups.titles = "auto", scale = FALSE, min.valid.rowmean = 2, alternate.rows = TRUE, sort.column = NULL, show.shapiro = FALSE, show.kurtosis = FALSE, show.corr.matrix = TRUE, CSS = NULL, encoding = NULL, file = NULL, use.viewer = TRUE, remove.spaces = TRUE ) sjt.itemanalysis( df, factor.groups = NULL, factor.groups.titles = "auto", scale = FALSE, min.valid.rowmean = 2, alternate.rows = TRUE, sort.column = NULL, show.shapiro = FALSE, show.kurtosis = FALSE, show.corr.matrix = TRUE, CSS = NULL, encoding = NULL, file = NULL, use.viewer = TRUE, remove.spaces = TRUE ) } \arguments{ \item{df}{A data frame with items.} \item{factor.groups}{If not \code{NULL}, \code{df} will be splitted into sub-groups, where the item analysis is carried out for each of these groups. Must be a vector of same length as \code{ncol(df)}, where each item in this vector represents the group number of the related columns of \code{df}. If \code{factor.groups = "auto"}, a principal component analysis with Varimax rotation is performed, and the resulting groups for the components are used as group index. See 'Examples'.} \item{factor.groups.titles}{Titles for each factor group that will be used as table caption for each component-table. Must be a character vector of same length as \code{length(unique(factor.groups))}. Default is \code{"auto"}, which means that each table has a standard caption \emph{Component x}. Use \code{NULL} to suppress table captions.} \item{scale}{Logical, if \code{TRUE}, the data frame's vectors will be scaled when calculating the Cronbach's Alpha value (see \code{\link[performance]{item_reliability}}). Recommended, when the variables have different measures / scales.} \item{min.valid.rowmean}{Minimum amount of valid values to compute row means for index scores. Default is 2, i.e. the return values \code{index.scores} and \code{df.index.scores} are computed for those items that have at least \code{min.valid.rowmean} per case (observation, or technically, row). See \code{mean_n} for details.} \item{alternate.rows}{Logical, if \code{TRUE}, rows are printed in alternatig colors (white and light grey by default).} \item{sort.column}{Numeric vector, indicating the index of the column that should sorted. by default, the column is sorted in ascending order. Use negative index for descending order, for instance, \code{sort.column = -3} would sort the third column in descending order. Note that the first column with rownames is not counted.} \item{show.shapiro}{Logical, if \code{TRUE}, a Shapiro-Wilk normality test is computed for each item. See \code{\link{shapiro.test}} for details.} \item{show.kurtosis}{Logical, if \code{TRUE}, the kurtosis for each item will also be shown (see \code{\link[psych]{kurtosi}} and \code{\link[psych]{describe}} in the \code{psych}-package for more details.} \item{show.corr.matrix}{Logical, if \code{TRUE} (default), a correlation matrix of each component's index score is shown. Only applies if \code{factor.groups} is not \code{NULL} and \code{df} has more than one group. First, for each case (df's row), the sum of all variables (df's columns) is scaled (using the \code{\link{scale}}-function) and represents a "total score" for each component (a component is represented by each group of \code{factor.groups}). After that, each case (df's row) has a scales sum score for each component. Finally, a correlation of these "scale sum scores" is computed.} \item{CSS}{A \code{\link{list}} with user-defined style-sheet-definitions, according to the \href{https://www.w3.org/Style/CSS/}{official CSS syntax}. See 'Details' or \href{https://strengejacke.github.io/sjPlot/articles/table_css.html}{this package-vignette}.} \item{encoding}{Character vector, indicating the charset encoding used for variable and value labels. Default is \code{"UTF-8"}. For Windows Systems, \code{encoding = "Windows-1252"} might be necessary for proper display of special characters.} \item{file}{Destination file, if the output should be saved as file. If \code{NULL} (default), the output will be saved as temporary file and opened either in the IDE's viewer pane or the default web browser.} \item{use.viewer}{Logical, if \code{TRUE}, the HTML table is shown in the IDE's viewer pane. If \code{FALSE} or no viewer available, the HTML table is opened in a web browser.} \item{remove.spaces}{Logical, if \code{TRUE}, leading spaces are removed from all lines in the final string that contains the html-data. Use this, if you want to remove parantheses for html-tags. The html-source may look less pretty, but it may help when exporting html-tables to office tools.} } \value{ Invisibly returns \itemize{ \item \code{df.list}: List of data frames with the item analysis for each sub.group (or complete, if \code{factor.groups} was \code{NULL}) \item \code{index.scores}: A data frame with of standardized scale / index scores for each case (mean value of all scale items for each case) for each sub-group. \item \code{ideal.item.diff}: List of vectors that indicate the ideal item difficulty for each item in each sub-group. Item difficulty only differs when items have different levels. \item \code{cronbach.values}: List of Cronbach's Alpha values for the overall item scale for each sub-group. \item \code{knitr.list}: List of html-tables with inline-css for use with knitr for each table (sub-group) \item \code{knitr}: html-table of all complete output with inline-css for use with knitr \item \code{complete.page}: Complete html-output. } If \code{factor.groups = NULL}, each list contains only one elment, since just one table is printed for the complete scale indicated by \code{df}. If \code{factor.groups} is a vector of group-index-values, the lists contain elements for each sub-group. } \description{ This function performs an item analysis with certain statistics that are useful for scale or index development. The resulting tables are shown in the viewer pane resp. webbrowser or can be saved as file. Following statistics are computed for each item of a data frame: \itemize{ \item percentage of missing values \item mean value \item standard deviation \item skew \item item difficulty \item item discrimination \item Cronbach's Alpha if item was removed from scale \item mean (or average) inter-item-correlation } Optional, following statistics can be computed as well: \itemize{ \item kurstosis \item Shapiro-Wilk Normality Test } If \code{factor.groups} is not \code{NULL}, the data frame \code{df} will be splitted into groups, assuming that \code{factor.groups} indicate those columns of the data frame that belong to a certain factor (see return value of function \code{\link{tab_pca}} as example for retrieving factor groups for a scale and see examples for more details). } \note{ \itemize{ \item The \emph{Shapiro-Wilk Normality Test} (see column \code{W(p)}) tests if an item has a distribution that is significantly different from normal. \item \emph{Item difficulty} should range between 0.2 and 0.8. Ideal value is \code{p+(1-p)/2} (which mostly is between 0.5 and 0.8). \item For \emph{item discrimination}, acceptable values are 0.20 or higher; the closer to 1.00 the better. See \code{\link[performance]{item_reliability}} for more details. \item In case the total \emph{Cronbach's Alpha} value is below the acceptable cut-off of 0.7 (mostly if an index has few items), the \emph{mean inter-item-correlation} is an alternative measure to indicate acceptability. Satisfactory range lies between 0.2 and 0.4. See also \code{\link[performance]{item_intercor}}. } } \examples{ # Data from the EUROFAMCARE sample dataset library(sjmisc) library(sjlabelled) data(efc) # retrieve variable and value labels varlabs <- get_label(efc) # recveive first item of COPE-index scale start <- which(colnames(efc) == "c82cop1") # recveive last item of COPE-index scale end <- which(colnames(efc) == "c90cop9") # create data frame with COPE-index scale mydf <- data.frame(efc[, start:end]) colnames(mydf) <- varlabs[start:end] \dontrun{ if (interactive()) { tab_itemscale(mydf) # auto-detection of labels tab_itemscale(efc[, start:end]) # Compute PCA on Cope-Index, and perform a # item analysis for each extracted factor. indices <- tab_pca(mydf)$factor.index tab_itemscale(mydf, factor.groups = indices) # or, equivalent tab_itemscale(mydf, factor.groups = "auto") }} } \references{ \itemize{ \item Jorion N, Self B, James K, Schroeder L, DiBello L, Pellegrino J (2013) Classical Test Theory Analysis of the Dynamics Concept Inventory. (\href{https://www.academia.edu/4104752/Classical_Test_Theory_Analysis_of_the_Dynamics_Concept_Inventory}{web}) \item Briggs SR, Cheek JM (1986) The role of factor analysis in the development and evaluation of personality scales. Journal of Personality, 54(1), 106-148. doi: 10.1111/j.1467-6494.1986.tb00391.x \item McLean S et al. (2013) Stigmatizing attitudes and beliefs about bulimia nervosa: Gender, age, education and income variability in a community sample. International Journal of Eating Disorders. doi: 10.1002/eat.22227 \item Trochim WMK (2008) Types of Reliability. (\href{https://conjointly.com/kb/types-of-reliability/}{web}) } }

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/read_write_db_example2.R

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refs/heads/master

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read_write_db_example2.R

library(tidyverse) library(usethis) library(DBI) library(RPostgreSQL) library(tidyverse) library(geosphere) library(lubridate) library(sf) #setting enviroment variables #set postgres_db = 'trackingdata' usethis::edit_r_environ() #access variables at anytime but will be specific to the machine that set enviromental variable Sys.getenv('postgres_db') #' function to connect to DB - note we should have stored our database name with using #' usethis::edit_r_environ() initConnection <- function(db = Sys.getenv('postgres_db')){ RPostgreSQL::dbConnect(RPostgreSQL::PostgreSQL(), dbname = db, host = "localhost", user = "postgres") } #---------------------- # Setup schema #---------------------- con <- initConnection() # ensure time is character and we have a primary key DBI::dbSendQuery(con, 'CREATE TABLE IF NOT EXISTS positions2 (id SERIAL PRIMARY KEY, player_display_name VARCHAR(64), time VARCHAR(10), seconds NUMERIC, lat NUMERIC, lon NUMERIC, speed_m_s NUMERIC, heart_rate_bpm NUMERIC, instantaneous_acceleration_impulse NUMERIC, match_day VARCHAR(4), match_date VARCHAR(10), team VARCHAR, distance NUMERIC);') # add constraint so we don't accidently add duplicate records DBI::dbSendQuery(con, "ALTER TABLE positions2 ADD CONSTRAINT nodup2 UNIQUE (player_display_name, seconds, lat, lon, speed_m_s, heart_rate_bpm, instantaneous_acceleration_impulse, match_day, match_date, team);") # close db connection DBI::dbDisconnect(con) # --------------------- # Data Cleaning #--------------------- # use seq_along(x) instead of 1:length(x) as 1:length does not account for 0 # loop to read file paths team_paths <- list.files('Data', full.names = T) my_paths <- character() team_paths <- list.files('Data', full.names = T) for(i in seq_along(team_paths)){ first_sub <- list.files(team_paths[i], full.names = T) for(j in seq_along(first_sub)){ # only include csv files in case we have other file extensions second_sub <- list.files(first_sub[j], full.names = T, pattern = '.csv') my_paths <- c(my_paths, second_sub) } } my_paths con <- initConnection() # function to read in our data and write to database read_with_metadata <- function(path, con=con){ message('reading file ', path) # specify column types - set time column to character df <- read_csv(path, col_types= cols('c','c','d','d','d','d','d')) %>% janitor::clean_names() %>% arrange(time) # throw error if we don't have the right number of columns if(ncol(df) != 7) { stop(glue::glue('error in {path}\n expected 7 columns, got {ncol(df)}. Please check the file.')) } # get metadata from file path matchDay <- str_extract(path, '(MD|RHB)([-+0-9]{0,})') matchDate <- str_extract(path, '[0-9]{4}-[0-9]{2}-[0-9]{2}') team <- str_extract(path, '(?<=Team )([0-9]{1,})') clean_df <- df %>% mutate( match_day = matchDay, match_date = matchDate, team = team ) %>% # remove nas in lat/lng filter(!is.na(lat), !is.na(lon)) # calculate distance between consecutive positions distance <- clean_df %>% st_as_sf(coords = c('lon', 'lat'), crs = 4326, remove = FALSE) %>% as('Spatial') %>% geosphere::distGeo() clean_df$distance <- distance # calculate time since the first timestamp # differnt files have different formats so read data depending on format if(str_detect(clean_df$time[1], '^\\d{2}:\\d{2}.\\d{1,}$')){ time_col <- strptime(x = clean_df$time, format = "%M:%OS") } else{ # else time should be in format HH:MM:SS time_col <- strptime(x = clean_df$time, format = "%H:%M:%OS") } # time_col <- strptime(x = clean_df$time, format = "%H:%M:%S") # remove NA's # time_since_start <- as.numeric(time_col - min(time_col, na.rm = T))/60 # clean_df$time_since_start <- time_since_start clean_df$seconds <- as.numeric(round(seconds(time_col)- seconds(min(time_col, na.rm=TRUE)))) summarised_df <- clean_df %>% # these columns stay constant group_by(player_display_name, seconds, match_day, match_date, team) %>% # these columns need to be summarised by second summarise(time = last(time), lat = mean(lat), lon = mean(lon), speed_m_s = mean(speed_m_s), heart_rate_bpm = mean(heart_rate_bpm), instantaneous_acceleration_impulse = mean(instantaneous_acceleration_impulse), distance = sum(distance)) # write to temporary table #suppressWarnings(dbSendQuery(con, "CREATE TABLE IF NOT EXISTS temp_tbl (like positions including all);")) dbSendQuery(con, "CREATE TABLE IF NOT EXISTS temp_tbl2 (id SERIAL PRIMARY KEY, player_display_name VARCHAR(64), time VARCHAR(10), seconds NUMERIC, lat NUMERIC, lon NUMERIC, speed_m_s NUMERIC, heart_rate_bpm NUMERIC, instantaneous_acceleration_impulse NUMERIC, match_day VARCHAR(4), match_date VARCHAR(10), team VARCHAR, distance NUMERIC)") dbWriteTable(con, 'temp_tbl2', summarised_df, row.names = FALSE, append=TRUE) # Insert into db - only write to new table if not a duplicate my_sql <- glue::glue_sql(" INSERT INTO positions2 (player_display_name, time, seconds, lat, lon, speed_m_s, heart_rate_bpm, instantaneous_acceleration_impulse, match_day, match_date, team, distance) SELECT player_display_name, time, seconds, lat, lon, speed_m_s, heart_rate_bpm, instantaneous_acceleration_impulse, match_day, match_date, team, distance FROM temp_tbl2 ON CONFLICT DO NOTHING;",.con=con) # run insert into dbSendQuery(con, my_sql) dbSendQuery(con, 'DROP TABLE temp_tbl2;') } # need to manually fix the path: Data/Team 1/20190415. MD/2019-04-15-Sonra 02-RawDataExport.csv # read in all files using function to clean data from paths found in loop lapply(my_paths, function(x) read_with_metadata(x, con)) player_distance_per_game <- tbl(con, "positions") %>% filter(team == '2') %>% group_by(player_display_name, match_date) %>% summarise(distance_covered = sum(distance, na.rm=T)) %>% arrange(desc(distance_covered)) player_1 <- tbl(con, "positions") %>% filter(player_display_name == 'Sonra 05') team_1 <- tbl(con, "positions") %>% filter(team == '1') %>% group_by(player_display_name, match_date, match_day) %>% summarise(totaldist = sum(distance, na.rm = T)) print(team_1) per_second <- player_1 %>% collect() %>% mutate(dt = parse_date_time(paste0(match_date,' ',time),orders = '%Y-%m-%d %H:%M%S',tz = 'GMT')) %>% group_by(dt) %>% summarise(speed_m_s = mean(speed_m_s), distance = sum(distance)) player_1_sf <- tbl(con, "positions") %>% filter(player_display_name == 'Sonra 02') %>% collect() %>% mutate(dt = lubridate::parse_date_time(paste0(match_date,' ',time),orders = '%Y-%m-%d %H:%M%S',tz = 'GMT')) %>% st_as_sf(coords = c('lon', 'lat'), crs = 4326, remove=F) DBI::dbDisconnect(con) #---------------- # -- HEATMAP #--------------- library(leaflet) library(leaflet.extras) leaflet(player_1_sf) %>% addProviderTiles('Esri.WorldImagery') %>% addHeatmap(group="heat", lng=~lon, lat=~lat, max=.6, blur = 60) tbl(con, 'positions') %>% filter(team == '1', player_display_name=='Sonra 02', match_day == 'MD', match_date == '2019-04-15' ) %>% glimpse #debug distance problem # data <- tbl(con, 'positions') %>% # filter(team == '2', # player_display_name == 'STATSports 01', # match_day == 'MD', # match_date == '2019-10-10') %>% # collect() # # # data %>% # select(lat, lon, speed_m_s, distance, time) %>% # st_as_sf(coords = c('lon', 'lat'), crs = 4326, remove = FALSE) %>% # as('Spatial') %>% # geosphere::distGeo() # # # # # df <- read_csv('Data/Team 2/20191010 MD/2019-10-10-STATSports 01-RawDataExport.csv') # con <- initConnection() # # ensure time is character and we have a primary key # DBI::dbSendQuery(con, 'CREATE TABLE IF NOT EXISTS test (id SERIAL PRIMARY KEY, # player_display_name VARCHAR(64), time VARCHAR(10), time_since_start NUMERIC, # lat NUMERIC, lon NUMERIC, speed_m_s NUMERIC, heart_rate_bpm NUMERIC, instantaneous_acceleration_impulse NUMERIC, # match_day VARCHAR(4), match_date VARCHAR(10), team VARCHAR, distance NUMERIC);') # # add constraint so we don't accidently add duplicate records # DBI::dbSendQuery(con, "ALTER TABLE test ADD CONSTRAINT noduptest UNIQUE (player_display_name, time, lat, lon, speed_m_s, heart_rate_bpm, instantaneous_acceleration_impulse, match_day, match_date, team);") # path <- 'Data/Team 2/20191010 MD/2019-10-10-STATSports 01-RawDataExport.csv' # df <- read_csv(path, col_types= cols('c','c','d','d','d','d','d')) %>% # janitor::clean_names() %>% # arrange(time) # # get metadata from file path # matchDay <- str_extract(path, '(MD|RHB)([-+0-9]{0,})') # matchDate <- str_extract(path, '[0-9]{4}-[0-9]{2}-[0-9]{2}') # team <- str_extract(path, '(?<=Team )([0-9]{1,})') # clean_df <- df %>% mutate( # match_day = matchDay, # match_date = matchDate, # team = team ) %>% # # remove nas in lat/lng # filter(!is.na(lat), !is.na(lon)) # # calculate distance between consecutive positions # distance <- clean_df %>% # st_as_sf(coords = c('lon', 'lat'), crs = 4326, remove = FALSE) %>% # as('Spatial') %>% # geosphere::distGeo() # clean_df$distance <- distance # # calculate time since the first timestamp # # differnt files have different formats so read data depending on format # if(str_detect(clean_df$time[1], '^\\d{2}:\\d{2}.\\d{1,}$')){ # time_col <- strptime(x = clean_df$time, format = "%M:%OS") # } else{ # # else time should be in format HH:MM:SS # time_col <- strptime(x = clean_df$time, format = "%H:%M:%OS") # } # # time_col <- strptime(x = clean_df$time, format = "%H:%M:%S") # # remove NA's # time_since_start <- as.numeric(time_col - min(time_col, na.rm = T))/60 # clean_df$time_since_start <- time_since_start # clean_df %>% # filter(team == '2', # player_display_name == 'STATSports 01', # match_day == 'MD', # match_date == '2019-10-10') # dbSendQuery(con, "CREATE TABLE IF NOT EXISTS test_temp_tbl (id SERIAL PRIMARY KEY, # player_display_name VARCHAR(64), time VARCHAR(10), time_since_start NUMERIC, # lat NUMERIC, lon NUMERIC, speed_m_s NUMERIC, heart_rate_bpm NUMERIC, instantaneous_acceleration_impulse NUMERIC, # match_day VARCHAR(4), match_date VARCHAR(10), team VARCHAR, distance NUMERIC)") # dbWriteTable(con, 'test_temp_tbl', clean_df, row.names = FALSE, overwrite=TRUE) # my_sql <- glue::glue_sql(" # INSERT INTO test # (player_display_name, time, time_since_start, # lat, lon, speed_m_s, heart_rate_bpm, instantaneous_acceleration_impulse, # match_day, match_date, team, distance) # SELECT player_display_name, time, time_since_start, # lat, lon, speed_m_s, heart_rate_bpm, instantaneous_acceleration_impulse, # match_day, match_date, team, distance # FROM test_temp_tbl # ON CONFLICT # DO NOTHING;",.con=con) # dbSendQuery(con, my_sql) # tbl(con, 'test') %>% # filter(team == '2', # player_display_name == 'STATSports 01', # match_day == 'MD', # match_date == '2019-10-10') %>% # collect() # # tbl(con, 'test') %>% # collect # tbl(con, 'test_temp_tbl') %>% df <- tbl(pool,'positions2') %>% filter(team == '1', player_display_name == 'Sonra 02', match_day == 'MD', match_date == '2019-04-08') %>% collect() df %>% arrange(.,seconds) %>% ggplot() + geom_line(aes(x= seconds/60, y = speed_m_s*50), col = 'green', alpha = 0.5) + geom_line(aes(x= seconds/60, y = heart_rate_bpm), col = '#cb4b16') + scale_y_continuous(name="BPM", sec.axis = sec_axis(~./50, name = "speed")) scale_x_continuous(name = 'minutes since start')+ ggthemes::theme_solarized(light=FALSE) #heart rate and speed smoothed df %>% arrange(.,seconds) %>% ggplot() + geom_smooth(aes(x= seconds/60, y = speed_m_s*50), col = 'green', alpha = 0.5, method = 'loess', span=0.1) + geom_smooth(aes(x= seconds/60, y = heart_rate_bpm), col = '#cb4b16' , method = 'loess', span=0.1) + scale_y_continuous(name="BPM", sec.axis = sec_axis(~./50, name = "speed")) scale_x_continuous(name = 'minutes since start')+ ggthemes::theme_solarized(light=FALSE)

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/PSmodel.R

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hannahlepper/MScProject

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refs/heads/master

2021-01-01T16:22:03.052919

2017-09-07T16:03:13

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PSmodel.R

library(deSolve) PSmodel <- function (t, x, pars) { with(as.list(c(x,pars)),{ CDR_survey <- CDR_int(CDR, cov, k, sens) del <- approxfun(x=survey_times(survey_interval),y=rep(c(CDR, CDR_survey, CDR),3),method="linear",rule=2) births <- Mu*(U + Ls + Lf + I + N + C) + Mui*I + Mun*N P <- U + Ls + Lf + I + N + C # Equations#### # Betas bw <- (1-r) * b bc <- r * b # FOIs foi_basic <- bw * U * (I + (c*N)) foi_comm <- bc * U *Ic foi_exo_react <- bw * a * x * (I + (c*N)) * Ls foi_exo_react_comm <- bc * a * x * Ic * Ls foi_reinf <- bw * a * x * (I + (c*N)) * C foi_reinf_comm <- bc * a * x * Ic * C # Derivatives dU <- births - foi_basic - foi_comm - Mu*U dLs <- ((1-a)*(foi_basic + foi_comm)) - foi_exo_react - foi_exo_react_comm - (vs + Mu)*Ls dLf <- (a * (foi_basic + foi_comm)) - (vf + Mu)*Lf dI <- sg*(vs*Ls + vf*Lf + foi_exo_react + foi_exo_react_comm + foi_reinf + foi_reinf_comm + p*C) + theta*N - (del(t)* tau + Mu + Mui + nc)*I dN <- (1-sg)*(vs*Ls + vf*Lf + foi_exo_react + foi_exo_react_comm + foi_reinf + foi_reinf_comm + p*C) - (theta + del(t)* tau + Mu + Mun + nc)*N dC <- (nc + del(t)* tau)*(I + N) - foi_reinf - foi_reinf_comm - (p + Mu)*C # Outputs==== Inc <- vs*Ls + vf*Lf + foi_exo_react + foi_exo_react_comm + foi_reinf + foi_reinf_comm + p*C Inc_first <- vf*Lf Inc_react <- vs*Ls + foi_exo_react_comm + foi_exo_react Inc_relap <- p*C Inc_recent <- Inc_first + foi_exo_react + foi_exo_react_comm + foi_reinf + foi_reinf_comm case_notifications <- del(t) * (I + N) cases_removed <- del(t) * tau * (I + N) treatment_cov <- case_notifications / Inc TB_prev <- I+N Inf_prev <- Ls + Lf + I + N + C TB_Deaths <- Mui*I + Mun*N dur_I <- 1/(Mu + Mui + del(t)* tau + nc) dur_N <- 1/(theta + Mu + Mun + del(t)* tau + nc) dur_active_TB <- (I*dur_I + N*dur_N)/(I + N) dur_active_inf_TB <- (I*dur_I + c*N*dur_N)/(I + c*N) list( c(dU,dLs,dLf,dI,dN,dC), Total = P, foi_basic = foi_basic, foi_reinf_comm = foi_reinf_comm, foi_reinf = foi_reinf, foi_exo_react_comm = foi_exo_react_comm, foi_exo_react = foi_exo_react, foi_comm = foi_comm, Inc = Inc * 100000, Inc_first = Inc_first * 100000, Inc_react = Inc_react * 100000, Inc_relap = Inc_relap *100000, Inc_recent = Inc_recent * 100000, case_notifications = case_notifications * 100000, treatment_cov = treatment_cov, cases_removed = cases_removed * 100000, Prev = TB_prev * 100000, Inf_prev = Inf_prev * 100000, Mort = TB_Deaths * 100000, dur_active_TB = dur_active_TB, dur_active_inf_TB = dur_active_inf_TB ) }) } # Detection rate functions==== CDR_int <- function(CDR, cov, k, sens) {CDR + 50 * (cov * k * sens)} survey_times <- function(survey_interval) { cumsum(c(499.98, 0.02, 0.02, rep(c(survey_interval-0.04, 0.02,0.02), 2))) } # Initials==== # Init_inf <- 0.2 # Fraction of the pop initially infected # yinit <- c(U=1-Init_inf,Ls=0.99*Init_inf,Lf=0,I=0.01*Init_inf,N=0,C=0) # # # Parameters==== # pars_base <- c(b=22, p=0.01, a=0.11, vf=0.67, vs=0.0005, # sg=0.45, x=0.65, nc=0.2,theta=0.015, # Mu=0.06, Mui=0.3, Mun=0.21, CDR=0.7, # cov = 0, k = 0, tau=0.91, r=0.2, # c=0.22, Ic = 0.002, survey_interval=5) # # # Solve==== # sol_base <-ode(y=yinit,times=seq(0,600, by=0.02),func=PSmodel, # parms=fullparset[80,]) # #time step = 0.02 of a year # # sol_base_df <- as.data.frame(sol_base) # plot(sol_base_df$time, sol_base_df$cases_removed, type = "l") # plot(sol_base_df$time, sol_base_df$Prev, type = "l", c(499, 510), c(50,150))

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jhirani-moj/R-Learning

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'''Resources''' # R for Data Science https://r4ds.had.co.nz/ # R Cheatsheet collection https://www.rstudio.com/resources/cheatsheets/ '''Basics''' Alt + Shift + K shows keyboard shortcuts Cmd + Ctrl + Enter runs everything after the cursor Cmd + Shift + S runs the whole script install.packages("tidyverse") # Install a package library(tidyverse) # Load a package ?ggplot() # Get Help x <- 3 * 4 # Object Assignment function_name(arg1 = val1, arg2 = val2) # Call a function View(flights) # View a full dataset ?object # Get help print() # Prints to the console getwd() # Get working directory setwd("/path/to/my/CoolProject") # Set Working directory problems(x) # Show problem data in a tibble # Function syntaxå rescale01 <- function(x) { rng <- range(x, na.rm = TRUE) (x - rng[1]) / (rng[2] - rng[1]) } '''Data Manipulation''' # Filtering df <- filter(df, col1 == 1, col2 == 1) # Basic Filtering df <- filter(df, is.na(col1), col2 != 1) # NA syntax df <- filter(df, col1 %in% c(1,2,3)) # In syntax (c function combines values into a vector) # Sorting df <- arange(df, col1, col2, col3) # Sort rows based upon values df <- arange(df, desc(col1)) # Sort Descending # Columns colnames(df) # Select columns df <- select(df, col1, col2, col3) # Select named columns df <- select(df, col1:col10) # Select columns between df <- select(df, -(col1:col10)) # Select columns except df <- select(df, starts_with('col')) # Selct columns that start with ... df <- select(df, ends_with('1')) # Selct columns that end with ... df <- select(df, contains('col')) # Selct columns that contain ... df <- select(df, matches("(.)\\1")) # Selct using a regex df <- select(df, col1, col2, everything()) # Order columns with specified variables first # Rename columns df <- rename(df, col1 = col2) # Rename a columns # Create columns (mutate) mutate(df, new_col1 = col1 - col2, new_col2 = col1 + col2 ) # Create new columns + get rid of old ones transmute( col1 = col1 - col2, col2 = col3 + col4 ) # Groupby / summarise gp <- group_by(df, col1, col2, col3) summarise(gp, delay = mean(col4, na.rm = TRUE)) # Pipes flights %>% group_by(year, month, day) %>% summarise(mean = mean(dep_delay, na.rm = TRUE)) # Tibbles & Parsing as_tibble(df) # Coerce a df to a tibble # Create a tibble tb <- tibble( `:)` = "smile", ` ` = "space", `2000` = "number" ) # Create a tibble using a tribble tribble( ~x, ~y, ~z, #--|--|---- "a", 2, 3.6, "b", 1, 8.5 ) c("TRUE", "FALSE") # Convert values to a vector / list df <- class(as.data.frame(tb)) # Convert a tibble to a dataframe df <- read_csv("data/file.csv") # Read in a csv to a tibble str(parse_logical(c("TRUE", "FALSE", "NA"))) # Parse logical values str(parse_integer(c("1", "2", "3"))) # Parse integers str(parse_date(c("2010-01-01", "1979-10-14"))) # Parse dates parse_number("$100") # Parse currency etc. parse_datetime("2010-10-01T2010") # Parse datetime guess_parser("2010-10-01") # Get R to guess the data format # Syntax for ggplot: ggplot(data = <DATA>) + <GEOM_FUNCTION>(mapping = aes(<MAPPINGS>)) # Examples # Simple Bar chart ggplot(data = df) + geom_bar(mapping = aes(x = var1)) # Basic gg plot ggplot(data = mpg) + # Creates the plot geom_point(mapping = aes(x = displ, y = hwy, color = class)) # Adds x, y & color data # Can also take args size, alpha, shape # Manually setting params (outside aes function) ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy), color = "blue")

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library(shiny) # Define UI for application that draws a histogram shinyUI(fluidPage( #theme=shinytheme("flatly"), # Application title titlePanel("Log based stats - Exploratory Data Analysis Example"), # Sidebar with a slider input for number of bins sidebarLayout( sidebarPanel( selectInput("InputDate", "Date:", Available_days), selectInput("InputHour", "Hour:", Hours, selected = 0), # selectInput("InputMinute", # "Minutes:", # Minutes), submitButton("Apply") ), mainPanel( textOutput("testText"), plotOutput("distPlot"), plotOutput("distPlot2") ) ) ) )

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BowenNCSU/BowenNCSU.github.io

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dental_gls.R

############################################################################## # # Using gls() to fit the dental data with artificial missingness # and unstructured covariance matrix # ############################################################################## library(nlme) # read in the data dent1 <- matrix(scan("dental_dropout_R.dat"),ncol=5,byrow=TRUE) child <- dent1[,2] age <- dent1[,3] distance <- dent1[,4] gender <- factor(dent1[,5]) # create time variable for each individual as required for the CorSymm # correlation structure time <- rep(seq(1,4,1),max(child)) dental.un <- gls(distance ~ -1 + gender + age:gender,correlation=corSymm(form = ~ time | factor(child)), weights = varIdent(form = ~ 1 |age),method="ML",na.action=na.omit) # This is the SAS code using proc mixed to produce the same analysis # data dent2; infile 'dental_dropout_sas.dat'; # input obsno child age distance gender; # run; # proc mixed method=ml data=dent2; # class child; # model distance = gender gender*age / noint solution chisq ; # repeated / type=un subject=child r=2 rcorr=2; # run;

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varioplot.R

library(tidyverse) library(gstat) library(sp) library(ncf) library(raster) # ------------------------------------------------------------------------- data <- read_rds("Data/hmscdata.rds") data <- data %>% ungroup() %>% na.omit() %>% as.data.frame() d <- data %>% select(plot_id, totals_n_larvae, an_maculipennis, an_claviger, cx_pipiens, cs_annulata, x, y) plot(d$x, d$y) coordinates(d) = ~x+y # ------------------------------------------------------------------------- par(mfrow = c(2,2)) bubble(d, zcol = "an_maculipennis") bubble(d, zcol = "an_claviger") bubble(d, zcol = "cx_pipiens") bubble(d, zcol = "cs_annulata") par(mfrow = c(1,1)) V <- variogram(cx_pipiens ~ 1, data = d) plot(V) VM <- vgm(psill=0.015, model="Gau", nugget=0.001, range=1000) plot(V, model=VM) variogram() # ------------------------------------------------------------------------- foo <- data %>% dplyr::select(x, y, totals_n_larvae) rounded <- foo %>% round() counts <- rasterFromXYZ(rounded, res = ) plot(counts, "Abundance Distribution (All)") P.idw <- gstat::idw(Precip_in ~ 1, P, newdata=grd, idp=2.0)

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dragontaoran/proj_two_phase_mexy

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p = 0.6 rho = 0.3 hn_set = c(30, 40, 60) nsieve = 20 NJOB = 100 n2_set = c(25) dir.create("results", showWarnings=FALSE) for (hn in hn_set) { for(n2 in n2_set) { prefix = paste0("n2_", n2, "_hn", hn) load(paste0("res/", prefix, "/1.RData")) tmp = results for (njob in 2:NJOB) { load(paste0("res/", prefix, "/", njob, ".RData")) tmp = rbind(tmp, results) } results = tmp print(dim(results)) save(list="results", file=paste0("results/", prefix, ".RData")) } }

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shape_files_to_RData.R

### saving shapefiles as RData for faster loading census_tracts <- readOGR("../data/shapefiles/2010 Census Tracts/geo_export_4d4ca7d0-0c46-467e-8dee-99c93361f914.shp",stringsAsFactors = FALSE) save(census_tracts,file = "../newerhoods/clean_data/census_tracts.RData") pumas <- readOGR("../data/shapefiles/Public Use Microdata Areas (PUMA)/geo_export_112df737-99d9-4599-8357-4c0b1e37faeb.shp") save(pumas,file="../newerhoods/clean_data/pumas.RData") ntas <- readOGR("../data/shapefiles/nynta_18d/nynta.shp") ntas <- spTransform(ntas,CRS("+proj=longlat +ellps=WGS84 +no_defs")) save(ntas,file="../newerhoods/clean_data/ntas.RData") cds <- readOGR("../data/shapefiles/nycd_18d/nycd.shp") cds <- spTransform(cds,CRS("+proj=longlat +ellps=WGS84 +no_defs")) save(cds,file="../newerhoods/clean_data/cds.RData") precincts <- readOGR("../data/shapefiles/Police Precincts/geo_export_e7d15636-2d89-486a-bd6d-41a7dfa67b3d.shp") precincts <- spTransform(precincts,CRS("+proj=longlat +ellps=WGS84 +no_defs")) save(precincts,file="../newerhoods/clean_data/precincts.RData") school_dists <- readOGR("../data/shapefiles/School Districts/geo_export_af10107a-e4ca-45b9-a03e-0717947ea03b.shp") school_dists <- spTransform(school_dists,CRS("+proj=longlat +ellps=WGS84 +no_defs")) save(school_dists,file="../newerhoods/clean_data/school_dists.RData")

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expmeasure.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/expmeasure.R \name{expmeasure} \alias{expmeasure} \title{Open Expmeasure software} \usage{ expmeasure() } \description{ \code{expmeasure} opens a shiny-based software that helps iGEMers to analyse their data in part characterization experiments. } \details{ This function opens opens a shiny-based software that provides a range of statistical tools which can be useful for part characterization in iGEM. Data used in this software should be database file formats with each column specifies a variable and each row for an observation. } \examples{ ##Once you execute the following function ## It will not stop until you close the window of "Expmeasure" GUI \dontrun{ expmeasure() } } \author{ Wu Hao-Ran <haoranwu@zju.edu.cn> }

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compare_kipu_and_matched_controls.R

setwd('/Users/juusu53/Documents/projects/kipupotilaat/') source('./code/helper_functions_for_r_analysis.R') library(psych) library(RColorBrewer) library(tidyverse) library(apaTables) library(rstatix) library(ggpubr) library(WRS2) library(raincloudplots) library(gghalves) # NB: fix old style plot titles, send both new and old subs <- read.csv('data/all_pain_patients_with_activations_19_10_2020.csv', na.strings = 'NaN') subs$batch <- 'patient' subs_control <- read.csv('data/matched_controls_with_activations_18_11_2020.csv', na.strings = 'NaN') subs_control$batch <- 'control' subs_fixed <- make_total_colouring_columns(subs) %>% rename_emotions() subs_control_fixed <- make_total_colouring_columns(subs_control) %>% rename_emotions() subs_all_big <- subs_fixed %>%bind_rows(subs_control_fixed) ## total pixels data_long <- subs_all_big %>% select(subid, sex, batch, sadness_pos_color:neutral_total) %>% select(-contains("pain")) %>% select(-contains("sensitivity")) %>% pivot_longer(sadness_pos_color:neutral_total, names_to = "emotion", values_to="prop_coloured") %>% separate(emotion, into=c("emotion", "type", NA)) %>% pivot_wider(names_from=type, values_from=prop_coloured) %>% mutate(emotion = factor(emotion), subid = factor(subid), batch = factor(batch, levels=c('patient', 'control'))) %>% rename(group = batch) outliers_total_pixels <- data_long %>% group_by(group, emotion) %>% identify_outliers(total) data_long %>% group_by(group, emotion) %>% shapiro_test(total) ggqqplot(data_long, "total", ggtheme = theme_bw()) + facet_grid(emotion ~ group) # activations and deactivations basic_anova <- lm(total ~ group * emotion, data = data_long) summary(basic_anova) apa.aov.table(basic_anova, filename = "Table1_APA.doc", table.number = 1) special_anova <- bwtrim(total ~ group * emotion, id=subid, data = data_long) special_anova summarized_total <- data_long %>% group_by(emotion, group) %>% summarise(coloured = mean(total, na.rm=T), sd = sd(total, na.rm=T), n = n(), na_nums= sum(is.na(total))) %>% mutate(se = sd/sqrt(n)) summary_for_reporting_1 <- data_long %>% group_by(group) %>% summarise(coloured = mean(total, na.rm=T), sd = sd(total, na.rm=T), n = n(), na_nums= sum(is.na(total))) summary_for_reporting_2 <- data_long %>% group_by(emotion) %>% summarise(coloured = mean(total, na.rm=T), sd = sd(total, na.rm=T), n = n(), na_nums= sum(is.na(total))) # positive activations basic_anova_pos <- aov(pos ~ group * emotion, data = data_long) summary(basic_anova_pos) summarized_pos <- data_long %>% group_by(emotion, group) %>% summarise(coloured = mean(pos, na.rm=T), sd = sd(pos, na.rm=T), n = n(), na_nums= sum(is.na(pos))) %>% mutate(se = sd/sqrt(n)) # negative (inactivations) basic_anova_neg <- aov(neg ~ group * emotion, data = data_long) summary(basic_anova_neg) summarized_neg <- data_long %>% group_by(emotion, group) %>% summarise(coloured = mean(neg, na.rm=T), sd = sd(neg, na.rm=T), n = n(), na_nums= sum(is.na(neg))) %>% mutate(se = sd/sqrt(n)) g <- ggplot(data = data_long, aes(y = total, x = emotion, fill = group, col=group)) + # geom_half_violin(data=data_long, aes(y=total, x=emotion, fill=group), # position = position_nudge(x = .2, y = 0), alpha = .8, side = "r") + geom_point(position = position_jitterdodge(jitter.width = .15, dodge.width = 0.6), size = .9, alpha = 0.8) + geom_boxplot(width=0.4, outlier.shape = NA, alpha = 0.5, position = position_dodge(width=0.6), notch=TRUE, col='black') + scale_x_discrete(limits=c('fear','happiness','sadness', 'anger','disgust','surprise','neutral')) + expand_limits(x = 5.25) + labs(y=' ', x='') + #coord_flip() + ggtitle("C Combined") + theme_classic() + theme(text = element_text(size=20), plot.margin = margin(1.5,0.1,0.1,0.1, "cm"), axis.text.x = element_text(angle = 45, hjust = 1)) #g g2 <- ggplot(data = data_long, aes(y = pos, x = emotion, fill = group, col=group)) + # geom_half_violin(data=data_long, aes(y=total, x=emotion, fill=group), # position = position_nudge(x = .2, y = 0), alpha = .8, side = "r") + geom_point(position = position_jitterdodge(jitter.width = .15, dodge.width = 0.6), size = .9, alpha = 0.8) + geom_boxplot(width=0.4, outlier.shape = NA, alpha = 0.5, position = position_dodge(width=0.6), notch=TRUE, col='black') + scale_x_discrete(limits=c('fear','happiness','sadness', 'anger','disgust','surprise','neutral')) + expand_limits(x = 5.25) + labs(y='Proportion of body area coloured', x='') + #coord_flip() + ggtitle("A Activations") + theme_classic() + theme(text = element_text(size=20), plot.margin = margin(1.5,0.1,0.1,0.1, "cm"), axis.text.x = element_text(angle = 45, hjust = 1)) g3 <- ggplot(data = data_long, aes(y = neg, x = emotion, fill = group, col=group)) + # geom_half_violin(data=data_long, aes(y=total, x=emotion, fill=group), # position = position_nudge(x = .2, y = 0), alpha = .8, side = "r") + geom_point(position = position_jitterdodge(jitter.width = .15, dodge.width = 0.6), size = .9, alpha = 0.8) + geom_boxplot(width=0.4, outlier.shape = NA, alpha = 0.5, position = position_dodge(width=0.6), notch=TRUE, col='black') + scale_x_discrete(limits=c('fear','happiness','sadness', 'anger','disgust','surprise','neutral')) + expand_limits(x = 5.25) + labs(y=' ', x='') + #coord_flip() + theme_classic() + ggtitle("B Deactivations") + theme(text = element_text(size=20), plot.margin = margin(1.5,0.1,0.1,0.1, "cm"), axis.text.x = element_text(angle = 45, hjust = 1)) ggarrange(g2, g3, g, #labels = c("A activations", "B deactivations", "C activations and deactivations"), font.label = c(size = 24), hjust = c(-0.2,-0.55,-0.45), vjust = 1.5, ncol = 3, nrow = 1, legend = 'right', common.legend = TRUE) %>% ggexport(filename = '/Users/juusu53/Documents/projects/kipupotilaat/figures/helsinki_manuscript_figs/n_colored_pixels_patients_and_controls_dotandbox.png', width = 1000, height = 400, pointsize = 30) ## old style PLOT pd <- position_dodge(0.4) pjd <- position_jitterdodge(jitter.width = .15, dodge.width = 0.4) p <- ggplot(data=summarized_total, aes(x=emotion, y=coloured, colour=group, group=group)) + #geom_jitter(data=data_long, aes(x=emotion, y=total, colour=group, group=group), alpha=0.3) + geom_point(data=data_long,aes(x=emotion, y=total, colour=group, group=group), position = pjd, size = .9, alpha = 0.8) + geom_errorbar(aes(ymin=coloured-se, ymax=coloured+se), color='black',width=.2, position=pd) + scale_x_discrete(limits=c('fear','happiness','sadness', 'anger','disgust','surprise','neutral'))+ geom_point(position=pd, size=2) + geom_line(position=pd, size=2) + theme_classic() + theme(text = element_text(size=20), axis.text.x = element_text(angle = 45, hjust = 1))+ coord_fixed(ratio=7)+ theme(plot.margin = margin(1,0.1,0.1,0.1, "cm")) + labs(color = "Group", x='', y='') p ## ## positive pixels p1 <- ggplot(data=summarized_pos, aes(x=emotion, y=coloured, colour=group, group=group)) + #geom_jitter(data=data_long, aes(x=emotion, y=pos, colour=group, group=group), alpha=0.3) + geom_point(data=data_long,aes(x=emotion, y=pos, colour=group, group=group), position = pjd, size = .9, alpha = 0.8) + geom_line(position=pd, size=2) + geom_errorbar(aes(ymin=coloured-se, ymax=coloured+se), color='black',width=.2, position=pd) + geom_point(position=pd, size=2) + scale_x_discrete(limits=c('fear','happiness','sadness', 'anger','disgust','surprise','neutral'))+ theme_classic() + theme(text = element_text(size=20), axis.text.x = element_text(angle = 45, hjust = 1), axis.title.y = element_blank())+ coord_fixed(ratio=7)+ theme(plot.margin = margin(1,0.1,0.1,0.1, "cm")) + labs(color = "Group", y='Proportion coloured', x='') p1 ## negative pixels p2 <- ggplot(data=summarized_neg, aes(x=emotion, y=coloured, colour=group, group=group)) + #geom_jitter(data=data_long, aes(x=emotion, y=neg, colour=group, group=group), alpha=0.3) + geom_point(data=data_long,aes(x=emotion, y=neg, colour=group, group=group), position = pjd, size = .9, alpha = 0.8) + geom_errorbar(aes(ymin=coloured-se, ymax=coloured+se), color='black',width=.2, position=pd) + geom_point(position=pd, size=2) + geom_line(position=pd, size=2) + scale_x_discrete(limits=c('fear','happiness','sadness', 'anger','disgust','surprise','neutral'))+ theme_classic() + theme(text = element_text(size=20), axis.text.x = element_text(angle = 45, hjust = 1), axis.title.y = element_blank()) + coord_fixed(ratio=7) + theme(plot.margin = margin(1,0.1,0.1,0.1, "cm")) + labs(color = "Group") p2 ggarrange(p1, p2, p, labels = c("A Activations", "B Deactivations", "C combined"), font.label = c(size = 20), hjust = c(-0.3,-0.5,-0.8), vjust = 1.1, ncol = 3, nrow = 1, common.legend = TRUE, legend='bottom') %>% ggexport(filename = '/Users/juusu53/Documents/projects/kipupotilaat/figures/n_colored_pixels_patients_and_controls.png', width = 1300, height = 500, pointsize = 30) ## old style as facet wrap pd <- position_dodge(0.4) pjd <- position_jitterdodge(jitter.width = .15, dodge.width = 0.4) data_extra_long <- data_long %>% pivot_longer(cols=c(pos, neg, total),names_to='type', values_to='Proportion colored') %>% mutate(type = factor(type, levels = c('pos', 'neg', 'total'), labels = c('A Activations', 'B Deactivations', 'C Combined activations\nand deactivations'))) summarized_neg <- summarized_neg %>% mutate(type= 'neg') summarized_pos <- summarized_pos %>% mutate(type='pos') summarized_total <- summarized_total %>% mutate(type='total') summary_all <- rbind(summarized_neg, summarized_pos, summarized_total) %>% mutate(type = factor(type, levels = c('pos', 'neg', 'total'), labels = c('A Activations', 'B Deactivations', 'C Combined activations\nand deactivations'))) ggplot(data=summary_all, aes(x=emotion, y=coloured, colour=group, group=group)) + geom_point(data=data_extra_long,aes(x=emotion, y=`Proportion colored`, colour=group, group=group), position = pjd, size = .9, alpha = 0.5) + geom_errorbar(aes(ymin=coloured-se, ymax=coloured+se), color='black',width=.2, position=pd) + geom_point(position=pd, size=2) + geom_line(position=pd, size=2) + scale_x_discrete(limits=c('fear','happiness','sadness', 'anger','disgust','surprise','neutral'))+ theme_classic() + facet_wrap(~type) + theme(text = element_text(size=20), axis.text.x = element_text(angle = 45, hjust = 1), legend.position='bottom', strip.background = element_blank(), strip.text = element_text(hjust = 0)) + coord_fixed(ratio=7) + theme(plot.margin = margin(1,0.1,0.1,0.1, "cm")) + labs(color = "Group", y = 'Proportion coloured') ggsave('/Users/juusu53/Documents/projects/kipupotilaat/figures/helsinki_manuscript_figs/n_colored_pixels_act_deact_combo.pdf', width=300, height = 150, units = 'mm', limitsize=FALSE) # ## # # t.test(subs$sadness_total, subs_control$sadness_total) # ns # t.test(subs$happiness_total, subs_control$happiness_total) # * # t.test(subs$anger_total, subs_control$anger_total) # ns # t.test(subs$surprise_total, subs_control$surprise_total) # * # t.test(subs$fear_total, subs_control$fear_total) # *** # t.test(subs$disgust_total, subs_control$disgust_total) # * # t.test(subs$neutral_total, subs_control$neutral_total) # ns # # t.test(subs$pain_0_pos_color, subs_control$pain_0_pos_color) # *** # t.test(subs$pain_1_pos_color, subs_control$pain_1_pos_color) # *** # # t.test(subs$sensitivity_0_pos_color, subs_control$sensitivity_0_pos_color) # ns # t.test(subs$sensitivity_1_pos_color, subs_control$sensitivity_1_pos_color) # *** # t.test(subs$sensitivity_2_pos_color, subs_control$sensitivity_2_pos_color) # ns # # # kipu vs crps # t.test(subs[subs$groups == 'CRPS', 'sadness_total'], subs[subs$groups != 'CRPS', 'sadness_total']) # ns # t.test(subs[subs$groups == 'CRPS', 'happiness_total'], subs[subs$groups != 'CRPS', 'happiness_total']) # ns # t.test(subs[subs$groups == 'CRPS', 'anger_total'], subs[subs$groups != 'CRPS', 'anger_total']) # ns # t.test(subs[subs$groups == 'CRPS', 'surprise_total'], subs[subs$groups != 'CRPS', 'surprise_total']) # ns # t.test(subs[subs$groups == 'CRPS', 'fear_total'], subs[subs$groups != 'CRPS', 'fear_total']) # ** # t.test(subs[subs$groups == 'CRPS', 'disgust_total'], subs[subs$groups != 'CRPS', 'disgust_total']) # ** # t.test(subs[subs$groups == 'CRPS', 'sadness_total'], subs[subs$groups != 'CRPS', 'sadness_total']) # ns # # t.test(subs[subs$groups == 'CRPS', 'sensitivity_0_pos_color'], subs[subs$groups != 'CRPS', 'sensitivity_0_pos_color']) # ns # t.test(subs[subs$groups == 'CRPS', 'sensitivity_1_pos_color'], subs[subs$groups != 'CRPS', 'sensitivity_1_pos_color']) # ns # t.test(subs[subs$groups == 'CRPS', 'sensitivity_2_pos_color'], subs[subs$groups != 'CRPS', 'sensitivity_2_pos_color']) # ns # # emotions <- c('sadness','happiness','anger','surprise','fear','disgust','neutral') # pains <- c('current_pain','chronic_pain') # sensitivity <- c('tactile_sensitivity', 'pain_sensitivity','hedonic_sensitivity')

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00_d_merge_storms.R

.libPaths("/data/hydro/R_libs35") .libPaths() library(data.table) library(lubridate) library(dplyr) options(digit=9) options(digits=9) # Time the processing of this batch of files start_time <- Sys.time() ################################################################ main_in <- "/data/hydro/users/Hossein/lagoon/00_model_level_storm/" out_dir <- "/data/hydro/users/Hossein/lagoon/01_storm_cumPrecip/storm/" if (dir.exists(out_dir) == F) {dir.create(path = out_dir, recursive = T)} ################################################################ model_names <- c("bcc-csm1-1", "CanESM2", "CSIRO-Mk3-6-0", "HadGEM2-CC365", "IPSL-CM5A-LR", "MIROC5", "NorESM1-M", "bcc-csm1-1-m", "CCSM4", "GFDL-ESM2G", "HadGEM2-ES365", "IPSL-CM5A-MR", "MIROC-ESM-CHEM", "BNU-ESM", "CNRM-CM5", "GFDL-ESM2M", "inmcm4", "IPSL-CM5B-LR", "MRI-CGCM3") emission <- c("historical", "rcp45", "rcp85") rcp45_data <- data.table() rcp85_data <- data.table() hist_data <- data.table() for (model in model_names){ current_45 <- readRDS(paste0(main_in, model, "/rcp45/", "storm.rds")) current_85 <- readRDS(paste0(main_in, model, "/rcp85/", "storm.rds")) current_hist <- readRDS(paste0(main_in, model, "/historical/", "storm.rds")) print (dim(current_45)) current_45$model <- gsub("-", "_", current_45$model) current_85$model <- gsub("-", "_", current_85$model) current_hist$model <- gsub("-", "_", current_hist$model) rcp45_data <- rbind(rcp45_data, current_45) rcp85_data <- rbind(rcp85_data, current_85) hist_data <- rbind(hist_data, current_hist) } hist_data <- unique(hist_data) rcp45_data$emission <- "RCP 4.5" rcp85_data$emission <- "RCP 8.5" hist_data_45 <- hist_data hist_data_85 <- hist_data hist_data_45$emission <- "RCP 4.5" hist_data_85$emission <- "RCP 8.5" ########### Read observed storm: # storm_observed <- readRDS(paste0(out_dir, "storm_observed.rds")) storm_observed_45 <- storm_observed storm_observed_85 <- storm_observed storm_observed_45$emission <- "RCP 4.5" storm_observed_85$emission <- "RCP 8.5" # ########### all_storms <- rbind(rcp45_data, rcp85_data, hist_data_45, hist_data_85, storm_observed_45, storm_observed_85) saveRDS(all_storms, paste0(out_dir, "all_storms.rds")) print ("Do not be surprised, it is fast indeed. No Error!") # saveRDS(rcp45_data, paste0(out_dir, "storm_RCP45.rds")) # saveRDS(rcp85_data, paste0(out_dir, "storm_RCP85.rds")) # saveRDS(hist_data, paste0(out_dir, "storm_modeled_hist.rds")) end_time <- Sys.time() print( end_time - start_time)

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setwd("C:/Users/Feng/Downloads/data science/exploratory data analysis/ExData_Plotting1") load("mydata.Rda") png("plot3.png", width=480, height=480) with(mydata, plot(time, Sub_metering_1, type="l", ylab="Energy sub metering", xlab="")) with(mydata, lines(time, Sub_metering_2, col="red")) with(mydata, lines(time, Sub_metering_3, col="blue")) legend("topright", c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), col=c("black","red","blue"), lty=1) dev.off()

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### ============================================================================ # Run a script ================================================================= ### ============================================================================ # Set the session information # These settings will be used to determine which processing options are # implemented in the script. It will also form part of the saved documentation, # so options should be adjusted here rather than in the script directly. rm(list = ls()) settings <- list( name = "Graham Stewart", # user who ran the script email = "grahamstewart12@gmail.com", # user's email contact site = "JLA", # three letter site code year = 2019, # four digit year date = lubridate::today(), # date script was run info = devtools::session_info() # R session info: R, OS, packages ) # Set the script name (see below for options) script <- "01_combine_eddypro" # Run the script source(file.path( "~/Desktop/DATA/Flux/tools/engine", "processing", paste0(script, ".R") )) # Available scripts: # 01_combine_eddypro # 02_correct_eddypro # 03_biomet_qc # 04_biomet_gapfill # 05_footprint # 06_flux_qc # 07_footprint_cover # 08_daily_summary

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print(iris) str(iris) summary(iris) my.iris <- iris head(my.iris) ls() #Objetos creados en memoria por nosotros rm(my.iris) #Borrar el objeto my.iris ls() my.iris <- iris head(my.iris) my.iris$Petal.Area <- my.iris$Petal.Length * my.iris$Petal.Width my.iris$Petal.Area <- NULL my.iris <-iris[order(iris$Sepal.Length),] # Creación de Vectores x <-1:10 y <-iris$Species y # Esta compuesto por las categorías(niveles ) de las especies del dataset Iris ls() 1:5 5:1 c(1:5,5:1) seq(1,5) rep(1:5,5) #Repetir una secuencia n veces rep(1:5,each = 5) #Repite cada elemento n veces #Exploración de Vectores plot(x) table(y) summary(y) head(x) tail(x) table(iris$Species) x <- x^2 x x[1:3] x[c(1,3)] x[x>25] x[3:1] x[-(1:2)] x x[-length(x)] #Remueve el elemento en la posición señalada w <-table(iris$Species) w["setosa"] w[c("setosa","virginica")] my.iris <- iris colnames(my.iris)[5]<-"Especie" colnames(my.iris) z<-table(iris$Species) names(z) names(z)[1] <- "Tipo 1" names(z) z <-1:10 z z[z<5]<-100 x<-1:10 sample(x,4) #Elegir 4 elementos aleatorios del vector x sample(x,50,replace=TRUE) #Funciones x<- 1:10 mean(x) max(x) median(x) sum(x) prod(x) suma_cuadrados <- function(x) sum(x*x) suma_cuadrados(x) media <- function(x){ longitud <-length(x) suma<-sum(x) suma/longitud } media(x) tapply(iris$Petal.Length, iris$Species, mean) # Creación de Listas is.list(iris) x <-list("a" = 2.5, "b" = TRUE, c= 1:3) x$a typeof(x) length(x) str(x) x <- list(2,TRUE,1:3) x x <- list(name = "John", age = 19, speaks = c("English", "French")) x$name x$age x$speaks x[c(1:2)] x[-2] x[c(T,F,F)] x[c("age","speaks")] x[["name"]] <-"Patricia";x x[["married"]] <- TRUE x[["age"]] <- NULL str(x) x$married <- NULL # Gráfico de datos hist(iris$Sepal.Width) hist(iris$Sepal.Width,main="Iris: Histograma de la anchura de los petalos", xlab ="anchura del sépalo", ylab = "frecuencia", col = "steelblue") barplot(table(iris$Species)) plot(iris$Petal.Length, iris$Petal.Width, main="Edgar Anderson's Iris Data") boxplot(iris$Sepal.Width ~ iris$Species, col = "gray", main = "Especies de iris\nsegún la anchura del sépalo")

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EntropicDegreeAttack <-function(g, Target = "Nodes", Number = 1){ #This strategy starts with the highest entropicdegree nodes and removes tham in alpha-numeric order. #As degree is a function of nodes it can only be performed on vertices #g: a network an igraph object #Target: Either nodes or edges #Number the total number of nodes/edges to remove if(Target == "Nodes"){ df <- as_data_frame(g, what = "vertices") %>% mutate(metric = EntropicDegree(g, Scale = TRUE)) %>% arrange(desc(metric)) Out <- df$name[1:Number] } else { stop("Only Nodes available for the Degree attack strategy") } return(Out) }

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supplier1<-0.40 supplier2<-0.60 defective_supplier1<-0.10 defective_supplier2<-0.05 cat("prob tire comes from supplier1 if it is defective", (supplier1*defective_supplier1)/((supplier1*defective_supplier1)+ supplier2*defective_supplier2))

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wordcloud.R

library(KoNLP) library(wordcloud) library(RColorBrewer) library(tm) library(arules) library(combinat) par(family="AppleGothic") file_loc <- '/Users/KangHyungWon/Documents/Source/project/datamining/data.csv' f <- read.csv(file_loc) ## TITLE TEXT MINING myCorpus <- Corpus(VectorSource(f$title)) inspect(myCorpus) myCorpus <- tm_map(myCorpus, content_transformer(tolower)) myCorpus <- tm_map(myCorpus, removeWords, stopwords('english')) myCorpus <- tm_map(myCorpus, removeWords, '문의드립니다') myCorpus <- tm_map(myCorpus, removeWords, '요청의') myCorpus <- tm_map(myCorpus, removeWords, '요청드립니다') myCorpus <- tm_map(myCorpus, removeWords, '못하는') myCorpus <- tm_map(myCorpus, removeWords, '관련하여') myCorpus <- tm_map(myCorpus, removeWords, '이슈입니다') myCorpus <- tm_map(myCorpus, removeWords, '발생하는') myCorpus <- tm_map(myCorpus, removeWords, '사용시') myCorpus <- tm_map(myCorpus, removeWords, '실행시') myCorpus <- tm_map(myCorpus, removeWords, '오류의') myCorpus <- tm_map(myCorpus, removeWords, '안됩니다') myCorpus <- tm_map(myCorpus, removeWords, 'error') myCorpus <- tm_map(myCorpus, removeWords, '이동시') myCorpus <- tm_map(myCorpus, removeWords, '확인요청') myCorpus <- tm_map(myCorpus, removeWords, '수정요청') myCorpus <- tm_map(myCorpus, removeWords, '의') myCorpus <- tm_map(myCorpus, removePunctuation) myCorpus <- tm_map(myCorpus, stripWhitespace) myCorpus <- tm_map(myCorpus, removeNumbers) myDTM = TermDocumentMatrix(myCorpus) m = as.matrix(myDTM) v = sort(rowSums(m), decreasing = TRUE) d = data.frame(word =names(v),freq=v) pal <- brewer.pal(5,"Set1") wordcloud(words = d$word, freq = d$freq, min.freq = 2,scale=c(5,.9),max.words=200, random.order=FALSE, rot.per=0.1,random.color=T,colors=pal) ## CONTENT TEXT MINING f$content contentCorpus = Corpus(VectorSource(f$content)) inspect(contentCorpus) contentCorpus <- tm_map(contentCorpus, content_transformer(tolower)) contentCorpus <- tm_map(contentCorpus, removeWords, stopwords('english')) contentCorpus <- tm_map(contentCorpus, removeWords, '문의드립니다') contentCorpus <- tm_map(contentCorpus, removeWords, '요청의') contentCorpus <- tm_map(contentCorpus, removeWords, '요청드립니다') contentCorpus <- tm_map(contentCorpus, removeWords, '못하는') contentCorpus <- tm_map(contentCorpus, removeWords, '관련하여') contentCorpus <- tm_map(contentCorpus, removeWords, '이슈입니다') contentCorpus <- tm_map(contentCorpus, removeWords, '발생하는') contentCorpus <- tm_map(contentCorpus, removeWords, '사용시') contentCorpus <- tm_map(contentCorpus, removeWords, '실행시') contentCorpus <- tm_map(contentCorpus, removeWords, '오류의') contentCorpus <- tm_map(contentCorpus, removeWords, '안됩니다') contentCorpus <- tm_map(contentCorpus, removeWords, 'error') contentCorpus <- tm_map(contentCorpus, removeWords, '이동시') contentCorpus <- tm_map(contentCorpus, removeWords, '확인요청') contentCorpus <- tm_map(contentCorpus, removeWords, '수정요청') contentCorpus <- tm_map(contentCorpus, removeWords, '의') contentCorpus <- tm_map(contentCorpus, removeWords, 'span') contentCorpus <- tm_map(contentCorpus, removePunctuation) contentCorpus <- tm_map(contentCorpus, stripWhitespace) contentCorpus <- tm_map(contentCorpus, removeNumbers) contentDTM = TermDocumentMatrix(contentCorpus) contentm = as.matrix(contentDTM) contentv = sort(rowSums(contentm), decreasing = TRUE) contentd = data.frame(word =names(contentv),freq=contentv) pal <- brewer.pal(5,"Set1") wordcloud(words = contentd$word, freq = contentd$freq, min.freq = 2,scale=c(5,.9),max.words=200, random.order=FALSE, rot.per=0.1,random.color=T,colors=pal) ## YEAR DATA EXTRACT yearCorpus <- Corpus(VectorSource(f$YMD)) yearCorpus <- tm_map(yearCorpus, stripWhitespace) yearCorpus <- tm_map(yearCorpus, removePunctuation) yearDTM = TermDocumentMatrix(yearCorpus) ym = as.matrix(yearDTM) ymv = sort(rowSums(ym), decreasing = TRUE) ymd = data.frame(word =names(ymv), freq=ymv) ymd <- ymd[do.call(order, ymd), ] newData = read.table('/Users/KangHyungWon/Documents/Source/project/datamining/data.txt') barplot(ymd$freq, names.arg = ymd$word, border = NA, ylim = c(0, 200), las=1, ylab = "frequency", xlab = "Month") titleList = list(f$title) f$title nouns <- sapply(titleList,extractNoun,USE.NAMES = F) nouns <- sapply(nouns,extractNoun,USE.NAMES = F) nouns undata <- unlist(nouns) typeof(f) typeof(newData) nouns <- Filter(function(x){nchar(x)>=2}, undata) nouns <- gsub('요청', '', nouns) nouns <- gsub('문의', '', nouns) nouns <- gsub('관련', '', nouns) nouns <- gsub('오류', '', nouns) nouns <- gsub('문제', '', nouns) nouns <- gsub('발생', '', nouns) nouns <- gsub('발급', '', nouns) nouns <- gsub('확인', '', nouns) nouns <- gsub('1.', '', nouns) nouns <- gsub('적용', '', nouns) nouns <- gsub('4.', '', nouns) nouns <- gsub('27', '', nouns) nouns <- gsub('2.', '', nouns) nouns <- gsub('8.', '', nouns) nouns <- gsub('방문.', '', nouns) nouns <- gsub('ios', 'iOS', nouns) nouns <- gsub('IOS', 'iOS', nouns) nouns <- gsub('Push', 'Push', nouns) nouns <- gsub('PUSH', 'Push', nouns) nouns <- gsub('push', 'Push', nouns) nouns <- gsub('푸시', 'Push', nouns) nouns <- gsub('33', '', nouns) nouns2 <- nouns names(nouns2) = paste("Tr", 1: length(nouns2), sep="") wordtran <- as(nouns2, "transactions") write(unlist(nouns), '/Users/KangHyungWon/Documents/data-table.txt') revised <- read.table('/Users/KangHyungWon/Documents/data-table.txt') wordcount <- table(revised) wordcount <- wordcount[do.call(order, wordcount), ] wordcount pal <- brewer.pal(5,"Set1") par(family='AppleGothic') wordcloud(names(wordcount), freq=wordcount, scale=c(5,.5),rot.per=0.15,min.freq=3,max.words=200,random.order=F,random.color=T,colors=pal)

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/scripts/fig3_tnk.R

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refs/heads/master

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fig3_tnk.R

#extract t/nk cells seu[,seu$celltype %in% c("T cell","NK cell")] ->seu_T_NK #remove doubelt because driving clustering seu_T_NK <- seu_T_NK[,seu_T_NK$doublet==FALSE] seu_T_NK$previous_cluster <- seu$seurat_clusters[seu$celltype %in% c("T cell","NK cell")] #remove unwanted genes seu_T_NK = seu_T_NK[!(grepl("^MTRNR",rownames(seu_T_NK)) | grepl("MT-",rownames(seu_T_NK)) | grepl("ACTB",rownames(seu_T_NK))| grepl("RPS",rownames(seu_T_NK)) |grepl("RPL",rownames(seu_T_NK)) ),] library(Seurat) library(SeuratData) library(SeuratWrappers) seu_T_NK <- RunFastMNN(object.list = SplitObject(seu_T_NK, split.by = "Sample"),features = 2000) seu_T_NK <- RunUMAP(seu_T_NK, reduction = "mnn", dims = 1:30,seed.use=22L) seu_T_NK <- FindNeighbors(seu_T_NK, reduction = "mnn", dims = 1:30 ,k.param = 10) seu_T_NK <- FindClusters(seu_T_NK) #seu_T_NK <- FindClusters(seu_T_NK,resolution = 0.4) library(SingleR) pred_cell_fine_tnk <- pred_cell_fine[rownames(pred_cell_fine) %in% colnames(seu_T_NK),] tab <- table(Assigned = pred_cell_fine_tnk$pruned.labels, Cluster = seu_T_NK$seurat_clusters) library(pheatmap) pdf("T_NK/seurat_pheatmap_cluster_labels_fine.pdf",height = 7,width=8) p <-pheatmap(log2(tab + 10), color = colorRampPalette(c("white", "blue"))(101)) print(p) dev.off() #Idents(seu_T_NK) <- seu_T_NK$RNA_snn_res.0.4 Idents(seu_T_NK) <- seu_T_NK$seurat_clusters FindAllMarkers(seu_T_NK, only.pos = TRUE) -> seu_nk_markers saveRDS(seu_T_NK,"T_NK/seu_T_NK.rds") saveRDS(seu_nk_markers,"T_NK/seu_nk_markers.rds") top10 <- seu_nk_markers %>% group_by(cluster) %>% top_n(n = 30, wt = -p_val_adj) %>% top_n(n = 15, wt = avg_logFC) top10[!duplicated(top10$gene),]->top10 for (i in c(3,8,16)) { seu_nk_markers$gene[seu_nk_markers$cluster==i] -> features if (length(features)>180){features<- features[1:180]} pdf(paste0("T_NK/individual/dotplot-",i,".pdf"),height =20,width = 7) p <- DotPlot(seu_T_NK,features = features,cols="Spectral",cluster.idents = TRUE) + coord_flip() print(p) dev.off() } for (i in unique(seu_T_NK$seurat_clusters)){ seu_nk_markers$gene[seu_nk_markers$cluster==i][1:12] -> features png(paste0("T_NK/individual/umap-",i,".png"),width = 10, height = 8,units="in",res=300) p<- FeaturePlot(seu_T_NK, features = features) print(p) dev.off() } Idents(seu_T_NK) <- seu_T_NK$seurat_clusters ### assign cell types seu_T_NK$celltype <- 1 seu_T_NK$celltype[seu_T_NK$seurat_clusters=="0"] <- "Naive CD4+ T" seu_T_NK$celltype[seu_T_NK$seurat_clusters=="1"] <- "Effector memory CD4+ T" seu_T_NK$celltype[seu_T_NK$seurat_clusters=="2"|seu_T_NK$seurat_clusters=="6"] <- "Effector CD8+ T" seu_T_NK$celltype[seu_T_NK$seurat_clusters=="3"] <- "Naive CD8+ T" seu_T_NK$celltype[seu_T_NK$seurat_clusters=="4"|seu_T_NK$seurat_clusters=="8"] <- "CD56dim CD16+ NK" seu_T_NK$celltype[seu_T_NK$seurat_clusters=="5"] <- "Central memory CD8+ T" seu_T_NK$celltype[seu_T_NK$seurat_clusters=="7"] <- "Effector memory CD4+ T" seu_T_NK$celltype[seu_T_NK$seurat_clusters=="9"] <- "γδT" seu_T_NK$celltype[seu_T_NK$seurat_clusters=="10"] <- "Effector CD8+ T" seu_T_NK$celltype[seu_T_NK$seurat_clusters=="11"] <- "Effector CD4+ T" seu_T_NK$celltype[seu_T_NK$seurat_clusters=="12"] <- "MAIT" seu_T_NK$celltype[seu_T_NK$seurat_clusters=="13"] <- "Central memory CD4+ T" seu_T_NK$celltype[seu_T_NK$seurat_clusters=="14"] <- "CD56bri CD16- NK" seu_T_NK$celltype[seu_T_NK$seurat_clusters=="15"] <- "Naive CD4+ T" seu_T_NK$celltype[seu_T_NK$seurat_clusters=="16"] <- "Proliferative T/NK" seu_T_NK$celltype[seu_T_NK$seurat_clusters=="17"&seu_T_NK$previous_cluster=="22"] <- "iNK" seu_T_NK$celltype[seu_T_NK$seurat_clusters=="17"&!(seu_T_NK$previous_cluster=="22")] <- "Th2-like" ### seu_T_NK@meta.data ->metadata tmp1 = metadata %>% group_by(patient,days) %>% summarise(cell_n=n()) tmp2 = metadata %>% group_by(patient,days,seurat_clusters) %>% summarise(cluster_n=n()) tmp2 = tmp2 %>% left_join(tmp1) tmp2$cluster_n = tmp2$cluster_n/tmp2$cell_n tmp2 = tmp2 %>% full_join(unique(metadata[,c("patient","condition")]),by=c("patient"="patient")) tmp2 = tmp2 %>% full_join(unique(metadata[,c("patient","days","Sample")])) Idents(seu_T_NK) <- seu_T_NK$celltype UMAPPlot(seu_T_NK,cols=c(celltype_color,"orange","pink")) ggsave("T_NK/celltype_umap.png") seu_T_NK$celltype <- factor(seu_T_NK$celltype,levels=c("Naive CD4+ T","Central memory CD4+ T","Effector memory CD4+ T","Effector CD4+ T", "Naive CD8+ T", "Central memory CD8+ T","Effector CD8+ T", "γδT","Proliferative T/NK","MAIT","CD56bri CD16- NK","CD56dim CD16+ NK", "iNK","Th2-like")) Idents(seu_T_NK) <- seu_T_NK$celltype UMAPPlot(seu_T_NK,cols=c(celltype_color[-10],"orange","pink")) +theme(axis.ticks = element_blank(),axis.text = element_blank()) ggsave("fig3/new/celltype_umap3.png",width=10,height=6.5,units="in",dpi=300) #assign stage seu_T_NK$condition[seu_T_NK$condition=="Health control"]="HC" seu_T_NK@meta.data ->metadata metadata$stage = ">20" metadata$stage[metadata$days<20] = "10-20" metadata$stage[metadata$days<10] = "<10" metadata$stage[metadata$condition=="Asymptomatic"]="Asymptomatic" metadata$stage[metadata$condition=="HC"]="HC" metadata$stage[metadata$condition=="Severe"]="Severe" metadata$stage = factor(metadata$stage,levels = c("HC","Asymptomatic","Severe","<10","10-20",">20")) #marker genes dotplots #heatmap t_nk tmp1 = metadata %>% group_by(patient,days) %>% summarise(cell_n=n()) tmp2 = metadata %>% group_by(patient,days,celltype) %>% summarise(cluster_n=n()) tmp2 = tmp2 %>% left_join(tmp1) tmp2$cluster_n = tmp2$cluster_n/tmp2$cell_n tmp2 = tmp2 %>% full_join(unique(metadata[,c("patient","condition","stage","sex","days")]),by=c("patient"="patient","days"="days")) tmp2 %>% dplyr::select(celltype,cluster_n) %>% distinct() %>% group_by(celltype) %>% summarise(n=sum(cluster_n)) -> celltype_per tmp2 = tmp2 %>% full_join(celltype_per,by="celltype") %>% mutate(final_n=cluster_n/n) tmp2$id <- paste0(tmp2$patient,"_",tmp2$days) ##use celltype_pct library(reshape2) dcast(tmp2 %>% dplyr::select(id,celltype, final_n),celltype ~ id,mean) -> tmp2_heatmap2 tmp2_heatmap2[tmp2_heatmap2=="NaN"] <-0 rownames(tmp2_heatmap2) <- tmp2_heatmap2[,1] tmp2_heatmap2[,-1] -> tmp2_heatmap2 tmp2 %>% dplyr::select(patient,days,sex,id,condition) %>% distinct() -> tmp_col tmp2_heatmap2[,tmp_col$id] -> tmp2_heatmap2 tmp2_heatmap2[tmp2_heatmap2>0.13] <- 0.13 tmp2_heatmap2[tmp2_heatmap2<0.01] <- 0.01 patient_col <- celltype_color[1:13] names(patient_col) <- unique(tmp_col$patient) library(RColorBrewer) sex_col <- c(brewer.pal(2,"Paired"))[1:2] names(sex_col) <- unique(tmp_col$sex) library(pheatmap) pheatmap(tmp2_heatmap2, annotation_col = data.frame( Patient=tmp_col$patient, Sex= tmp_col$sex, Days= tmp_col$days, Condition=tmp_col$condition, row.names = colnames(tmp2_heatmap2) ), annotation_colors = list(Patient=patient_col,Sex=sex_col,Condition=condition_color), max.labels = Inf, normalize = TRUE, show.labels = FALSE, fontsize = 12, show_colnames = FALSE, treeheight_row = 0, treeheight_col = 0, border_color=NA ,filename ="fig3/new/heatmap_celltypes_labels.pdf" ,height=5,width=13) pdf("fig3/heatmap_celltypes_labels3.pdf",height=8,width=8) print(p) dev.off() #stage percentage barplot tmp2 library(ggpubr) my_comparisons <- list( c("Asymptomatic", "HC"), c("<10", "HC"), c("Severe", "HC"), c("<10", "Severe"), c("Asymptomatic", "Severe"), c("<10", "Asymptomatic"),c("<10",">20"), c("<10","10-20"), c("10-20",">20"), c(">20","Asymptomatic"), c(">20","HC"), c(">20","Severe"), c("10-20","Asymptomatic"), c("10-20","HC"), c("10-20","Severe") ) ggplot(data=tmp2,aes(x=stage,cluster_n,fill=stage))+ geom_boxplot(outlier.size = 0.1)+ geom_jitter(size=0.1)+ scale_fill_manual(values = cond_palette)+ facet_wrap(~celltype,nrow=3,scales = "free_y")+ stat_compare_means(label = "p.signif",method="wilcox.test",comparisons=my_comparisons ,hide.ns=TRUE)+ theme_cowplot(font_size = 15)+ theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank(),panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black")) +labs(y="Percentage") ggsave("fig3/new/stage_allp_bar.pdf",width = 15,height = 12) #reduce my_comparisons <- list( c("Asymptomatic", "HC"), c("<10", "HC"), c("Severe", "HC"), c("<10", "Severe"), c("Asymptomatic", "Severe"), c("<10", "Asymptomatic"), c(">20","Asymptomatic"), c(">20","HC"), c(">20","Severe"), c("10-20","Asymptomatic"), c("10-20","HC"), c("10-20","Severe") ) ggplot(data=tmp2 %>% filter(!celltype %in% c("Naive CD4+ T","MAIT","Naive CD8+ T","Proliferative T")),aes(x=stage,cluster_n,fill=stage))+ geom_boxplot(outlier.size = 0.1)+ geom_jitter(size=0.1)+ scale_fill_manual(values = cond_palette)+ facet_wrap(~celltype,nrow=2,scales = "free_y")+ stat_compare_means(label = "p.signif",method="wilcox.test",comparisons=my_comparisons ,hide.ns=TRUE)+ theme_cowplot(font_size = 15)+ theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank(),panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_blank(), axis.line = element_line(colour = "black")) +labs(y="Percentage") ggsave("fig3/stage_somep_bar.pdf",width = 15,height = 5) ##dotplot Idents(seu_T_NK) <- seu_T_NK$celltype featurstnk <- c("CD3D","CD4","GPR183","CCR7","SELL","S100A4","CD8A","GZMA","GZMB","NKG7","LTB","TRDV2","MKI67","TRAV1-2","NCAM1","FCGR3A","KIT","PTGDR2") DotPlot(seu_T_NK,features=featurstnk,cols="Spectral") +theme(axis.text.x = element_text(hjust = 1,angle = 60)) ggsave("fig3/new/dotplot2.pdf",width = 8,height = 5) ##umap for those genes FeaturePlot(seu_T_NK,featurstnk,order = TRUE) ggsave("fig3/umaps_features.png",width=16,height=18) #correspondence between previous clusters and new clusters library(tidyverse) tmp <- seu_T_NK@meta.data tmp %>% dplyr::select(celltype) -> tmp tmp$cluster_cell <- seu$cell_type[match(rownames(tmp),colnames(seu))] tmp %>% group_by(celltype,cluster_cell) %>% summarise(n=n()) -> tmp2 tmp2 %>% full_join(tmp %>% group_by(celltype) %>% summarise(cn=n()),by=c("celltype"="celltype")) %>% mutate(per=n/cn) -> tmp3 library() reshape2::dcast(tmp3,celltype~cluster_cell,mean)-> tmp4 tmp4[tmp4=="NaN"] <-NA rownames(tmp4) <- tmp4[,1] tmp4[,-1] -> tmp4 #tmp4 <- t(scale(t(tmp4))) library(pheatmap) pheatmap(tmp4, #max.labels = Inf, #normalize = TRUE, #scale="column", color = colorRampPalette(colors = c("white","blue"))(100), fontsize = 10, na_col="white", cluster_rows = FALSE, cluster_cols = FALSE,filename ="fig3/new/celltypes_cor.pdf" ,width = 6,height =5)

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/Code/primary_abund_manyglm_predictive.R

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CHANGElab-AlbacoreTuna/traits-review

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primary_abund_manyglm_predictive.R

########## ########## # This code contains the a multivariate regression component of the analysis # presented in Green et al. (2020) # A review on the use of traits in ecological research ########## ########## # AUTHOR: Cole B. Brookson & Natasha A. Hardy # DATE OF CREATION: 2021-01-20 ########## ########## # set-up ======================================================================= library(tidyverse) library(vegan) library(mvabund) library(reshape2) library(here) primary_abundance = read_csv(here(paste0('./data/processed-data', '/primary_traits_dummy_abundance_models.csv'))) # Multivariate Regression ====================================================== ###### begin NOTE ############################################################## # Some below sections have been commented out for simpler use. Some of the # regressions take a good while to run, so we've saved the result as an RDS # object which can be easily read into the environment using the code provided. ###### end NOTE ################################################################ # split into 'sites' and 'species' just to put it into typical ecological # multivariate context primary_abundance_traits = data.frame(primary_abundance[,1:11]) primary_abundance_species = data.frame(primary_abundance[,12:ncol(primary_abundance)]) # need to make it an mvabund readable object primary_abundance_mv = mvabund(primary_abundance_species) # go ahead with negative binomial mv_pd_nb_primabun = manyglm(primary_abundance_mv ~ primary_abundance_traits$PredictiveCat, data= primary_abundance_traits, family = 'negative.binomial') plot(mv_pd_nb_primabun) qqnorm(residuals(mv_pd_nb_primabun)[which(residuals(mv_pd_nb_primabun)<10000)]) saveRDS(mv_pd_nb_primabun, here("./data/manyglm-intermediate/mv_pd_nb_primabun.rds")) # model output significance test # mv_pd_nb_primabun_an = anova.manyglm(mv_pd_nb_primabun) # saveRDS(mv_pd_nb_primabun_an, # here("./data/manyglm-intermediate/mv_pd_nb_primabun_anova.rds")) mv_pd_nb_primabun_an = readRDS(here("./data/manyglm-intermediate/mv_pd_nb_primabun_anova.rds")) # write a table for this write_csv(mv_pd_nb_primabun_an$table, here("./output-tables/mv_pd_nb_primabun_anova_table.csv")) # individual adjusted p-values for species/traits - get univariate p-values # mv_pd_nb_primabun_an_uni = anova.manyglm(mv_pd_nb_primabun,p.uni="adjusted") # saveRDS(mv_pd_nb_primabun_an_uni, # here("./data/manyglm-intermediate/mv_pd_nb_primabun_univs.rds")) mv_pd_nb_primabun_an_uni = readRDS(here("./data/manyglm-intermediate/mv_pd_nb_primabun_univs.rds")) # get the direction of effect for each species with the main effect pd_coef_primabun = coef(mv_pd_nb_primabun) #figure out what the top traits are - recall traits are our 'species' here mv_pd_nb_primabun_species = sort(mv_pd_nb_primabun_an$uni.test[2,], decreasing=T,index.return=T)[1:5] #sort and select top species/traits mv_pd_nb_primabun_species$ix[1:5] #the column #s of the top impacted spp/traits #Need > 50% deviance explainaed --> result = 25 traits explain > 50% deviance sum(mv_pd_nb_primabun_an$uni.test[2,mv_pd_nb_primabun_species$ix[1:5]])*100/ sum(mv_pd_nb_primabun_an$uni.test[2,]) #25 species = 81.25088% Deviance #3 species explained >85% deviance pd_top_primabun = data.frame(dimnames(primary_abundance_species)[[2]][ mv_pd_nb_primabun_species$ix[1:5]]) #df with the names of the top 20 traits pd_top_primabun = pd_top_primabun %>% dplyr::rename('traits' = names(pd_top_primabun)) # write table for amount of deviance explained write_csv(pd_top_primabun, here("./output-tables/primary_predictive_top5.csv")) # Look at Top Coefficients ===================================================== # Create df to combine coef values, also p-values from univ anovas & the top 20 pd_coef_prim = data.frame(t(pd_coef_primabun)) pd_coef_prim$traits = rownames(pd_coef_prim) #convert rownames to a column pd_coef_prim = pd_coef_prim %>% dplyr::rename('coef_intercept' = `X.Intercept.`, 'coef_pd_yes' = names(pd_coef_prim)[2]) pd_top_coeffs = merge(pd_top_primabun, pd_coef_prim, by.x = 'traits', by.y = 'traits') # need to join with test statistic values pd_an_test = as.data.frame(t( # first transpose coef_filter mv_pd_nb_primabun_an_uni$uni.test)) pd_an_test$traits = rownames(pd_an_test) #convert rownames to a column pd_an_test = pd_an_test %>% dplyr::rename('deviance_explained' = names(pd_an_test)[2]) pd_top_coeffs = merge(pd_top_coeffs, pd_an_test, by.x = 'traits', by.y = 'traits') pd_top_coeffs = pd_top_coeffs %>% select(-"(Intercept)") # need to join with p-values pd_an_pvalue = data.frame(t( # first transpose coef_filter mv_pd_nb_primabun_an_uni$uni.p)) pd_an_pvalue$traits = rownames(pd_an_pvalue) #convert rownames to a column pd_an_pvalue = pd_an_pvalue %>% select(-names(pd_an_pvalue)[1]) pd_an_pvalue = pd_an_pvalue%>% dplyr::rename('p_value' = names(pd_an_pvalue)[1]) pd_top_coeffs = merge(pd_top_coeffs, pd_an_pvalue, by.x = 'traits', by.y = 'traits') write_csv(pd_top_coeffs, here("./output-tables/primary_predictive_top_coefs.csv")) #See how many papers actually have those traits papers_with_top_3_pd = primary_abundance_species top_3_pd = pd_top_primabun$traits papers_with_top_3_pd = papers_with_top_3_pd[top_3_pd] rownames(papers_with_top_3_pd) = primary_abundance_traits$DOI papers_with_top_3_pd = papers_with_top_3_pd[rowSums(papers_with_top_3_pd[, -1])>0, ]

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you-leee/purchase-prediction

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customerClustering.R

library(NbClust) plotSoSByClusterNum <- function(data, max_clusters = 15) { wss <- (nrow(data) - 1) * sum(apply(data, 2, var)) for (i in 2:max_clusters) wss[i] <- sum(kmeans(data, centers=i)$withinss) plot(1:max_clusters, wss, type="b", xlab="Number of Clusters", ylab="Within groups sum of squares") } fitCluster <- function(data, cluster_num) { fit <- kmeans(data, cluster_num) print(aggregate(data, by = list(fit$cluster), FUN=mean)) fit }

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/R_exercise/SpherHam/gauleg.r

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gauleg.r

## source("~/R/KlimDyn/SpherHam/gauleg.r") ## ## NUMERICAL RECIPES IN C: THE ART OF SCIENTIFIC COMPUTING ## gauleg <- function(n) { ## Given the lower and upper limits of integration x1 and x2, and given n, this routine returns ## arrays x[1..n] and w[1..n] of length n, containing the abscissas and weights of the Gauss- ## Legendre n-point quadrature formula. EPS <- 3.e-14 # n - Number of quadrature points. (Input) x <- array(0,c(n)) w <- array(0,c(n)) # IWEIGH WT(X) Interval Name # 1 1 (-1,+1) Legendre # x1 = -1 # x2 = +1 # # m=(n+1)/2 # The roots are symmetric in the interval, so xm=0.5*(x2+x1) # we only have to find half of them. xl=0.5*(x2-x1) for ( i in 1:m ) { # Loop over the desired roots. z=cos(3.141592654*(i-0.25)/(n+0.5)) # Starting with the above approximation to the ith root, # we enter the main loop of # refinement by Newton’s method. repeat { p1=1.0 p2=0.0 for ( j in 1:n ) { # Loop up the recurrence relation to get the p3=p2 # Legendre polynomial evaluated at z. p2=p1 p1=((2.0*j-1.0)*z*p2-(j-1.0)*p3)/j } # p1 is now the desired Legendre polynomial. # We next compute pp, its derivative, # by a standard relation involving also p2, # the polynomial of one lower order. pp=n*(z*p1-p2)/(z*z-1.0) z1=z z=z1-p1/pp # Newton’s method. if ( abs(z-z1) <= EPS) break } x[i]=xm-xl*z # Scale the root to the desired interval, x[n+1-i]=xm+xl*z # and put in its symmetric counterpart. w[i]=2.0*xl/((1.0-z*z)*pp*pp) # Compute the weight w[n+1-i]=w[i] # and its symmetric counterpart. } return( list ( x=x , w=w ) ) }

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/the real deal.R

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brianpclare/Network_Analysis

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the real deal.R

library(arm) library(tidyverse) library(magrittr) library(data.table) # chunk_size <- 5782 chunk_size <- 500 data <- fread("all_edges_real.csv", colClasses = c("character", "character", "numeric")) names <- union(data$node_1, data$node_2) %>% unique() n <- length(names) num_rows <- n * (n - 1) / 2 # rm(data) betas <- matrix(0, nrow = n, ncol = 1) rownames(betas) <- names # betas[, 1] <- 0 pi_vec <- rep(0.5, chunk_size) make_preds <- function(sample, chunk_size){ x <- rep(0, chunk_size) for(i in 1:chunk_size){ bl <- ncol(betas) x[i] <- 1/(1 + exp(-(betas[rownames(betas) == .subset2(sample, 1)[i], bl] + betas[rownames(betas) == .subset2(sample, 2)[i], bl]))) } return(unlist(x)) } chunk_calc <- function(index, chunk_size, sample){ Z <- matrix(0, nrow = chunk_size, ncol = n) colnames(Z) <- names Y <- rep(0, chunk_size) for (i in 1:chunk_size) { Z[[i, .subset2(sample, 1)[i]]] <- 1 Z[[i, .subset2(sample, 2)[i]]] <- 1 Y[i] <- .subset2(sample, 3)[i] } pi_vec <- make_preds(sample, chunk_size) W <- (pi_vec) * (1 - pi_vec) ZYPI <- crossprod(Z, (Y - pi_vec)) ZTWZ <- crossprod(Z, (W*Z)) return(list(ZTWZ, ZYPI)) } do_an_iteration <- function(c_name, chunk_size){ print(Sys.time()) index <- 0 ztwz <- matrix(0, nrow = n, ncol = n) zypi <- rep(0, n) while(TRUE){ df <- data[(index + 1):(index + chunk_size), ] new_stuff <- chunk_calc(index, nrow(df), df) ztwz <- ztwz + new_stuff[[1]] zypi <- zypi + new_stuff[[2]] index <- index + chunk_size print(index) if(index >= num_rows){ # if(index >= 1){ break } } bl <- ncol(betas) C <- chol(ztwz) betas <<- betas %>% cbind(betas[, bl] + backsolve(C, backsolve(C, zypi, transpose = TRUE)) ) print(Sys.time()) } do_an_iteration("two", chunk_size) # do_an_iteration("three", chunk_size) # do_an_iteration("four", chunk_size) # # cns <- c("five", "six", "seven", "eight", "nine", "ten") # # for(j in cns){ # do_an_iteration(j, chunk_size) # } data$preds <- make_preds(data, chunk_size = 93096)

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/cy_distribution.R

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rtmag/TBlab

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cy_distribution.R

inter=read.table("~/Downloads/AML_001_run1_nanoSV-WGS-intersect.tsv",sep="\t",stringsAsFactors=F) nano = inter[,5] nano[nano=="E-Nov_Ins_bp"]="Novel Insertion" nano[nano=="S-Nov_Ins_bp"]="Novel Insertion" nano[nano=="Nov_Ins"]="Novel Insertion" nano[nano=="Inter-Ins(1)"]="Translocation" nano[nano=="InterTx"]="Translocation" nano[nano=="Intra-Ins"]="Translocation" nano[nano=="Intra-Ins(1)"]="Translocation" nano[nano=="E-Nov_Ins"]="TRASH" nano[nano=="Inter-Ins(2)"]="TRASH" nano[nano=="Intra-Ins(2)"]="TRASH" nano[nano=="S-Nov_Ins"]="TRASH" nano[nano=="Del"]="Deletion" nano[nano=="Inv"]="Inversion" nano[nano=="TDupl"]="Duplication" # ngs = inter[,10] ngs[ngs=="<DEL>"]="Deletion" ngs[ngs=="<DUP>"]="Duplication" ngs[ngs=="<INV>"]="Inversion" ngs[ngs=="<TRA>"]="Translocation" ########### colu=cbind(ngs,nano) colu_noTrash=colu[!(colu[,2]=="TRASH"),] nano_table=table(colu_noTrash[,2]) ngs_table=table(colu_noTrash[,1]) ngs_table=c(ngs_table[1:3],0,ngs_table[4]) names(ngs_table)=c("Deletion","Duplication","Inversion","Novel Insertion","Translocation") pdf("PLOT.R") par(mfrow=c(1,2)) barplot(ngs_table,border=NA,col="salmon",main="NGS",ylim=c(0,250)) barplot(nano_table,border=NA,col="darkturquoise",main="NANOPORE",ylim=c(0,250)) dev.off() ##################################################################################################### inter=read.table("~/Downloads/AML_001_run1_nanoSV-WGS-intersect.tsv",sep="\t",stringsAsFactors=F) nano = inter[,5] nano[nano=="E-Nov_Ins_bp"]="Novel Insertion" nano[nano=="S-Nov_Ins_bp"]="Novel Insertion" nano[nano=="Nov_Ins"]="Novel Insertion" nano[nano=="Inter-Ins(1)"]="Insertion" nano[nano=="InterTx"]="Tra/Ins" nano[nano=="Intra-Ins"]="Tra/Ins" nano[nano=="Intra-Ins(1)"]="Insertion" nano[nano=="E-Nov_Ins"]="TRASH" nano[nano=="Inter-Ins(2)"]="TRASH" nano[nano=="Intra-Ins(2)"]="TRASH" nano[nano=="S-Nov_Ins"]="TRASH" nano[nano=="Del"]="Deletion" nano[nano=="Inv"]="Inversion" nano[nano=="TDupl"]="Duplication" # ngs = inter[,10] ngs[ngs=="<DEL>"]="Deletion" ngs[ngs=="<DUP>"]="Duplication" ngs[ngs=="<INV>"]="Inversion" ngs[ngs=="<TRA>"]="Translocation" ########### colu=cbind(ngs,nano) colu_noTrash=colu[!(colu[,2]=="TRASH"),] nano_table=table(colu_noTrash[,2]) ngs_table=table(colu_noTrash[,1]) #pdf("PLOT.R") par(mfrow=c(1,2)) barplot(ngs_table,border=NA,col="salmon",main="NGS",ylim=c(0,250)) barplot(nano_table,border=NA,col="darkturquoise",main="NANOPORE",ylim=c(0,250)) dev.off()

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/challenges/2021-04-27-sum-arrays/solutions/rust/ericwburden/ericwburden.R

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ericwburden.R

#' # Sum Arrays #' #' Given an array of values, return the sum of the values. #' #' ## Business Rules/Errata #' #' - Input must be an array. #' - The array may be nested more than one level. #' - All values must be integers. #' - Solutions shall not use built in methods to flatten the array to #' one-dimension. #' #' ## Examples #' #' One dimension: #' ``` #' sum_of_array([1,2,3,4,5]) => 15 #' ``` #' #' Two dimensions: #' ``` #' sum_of_array([1,2,[1,2,3],4,5]) => 18 #' ``` #' #' n dimensions: #' #' ``` #' sum_of_array([1,[1,2,[3,4],5],[6,7]]) => 29 #' ``` library(testthat) sum_of_array <- function(input_vec) { if (length(input_vec) > 1) { total <- 0 for (i in input_vec) { total <- total + sum_of_array(i) } total } else { input_vec } } test_that("Handles flat lists", { expect_equal(sum_of_array(c(1, 2, 3, 4, 5)), 15) }) test_that("Handles 2D lists", { input <- c(1, 2, c(1, 2, 3), 4, 5) expect_equal(sum_of_array(input), 18) }) test_that("Handles nD lists", { input <- c(1, c(1, 2, c(3, 4), 5), c(6, 7)) expect_equal(sum_of_array(input), 29) }) test_that("An interesting case", { input <- c(c(1, 2), c(c(3, 4), 5), c(c(c(6, 7), 8), 9)) expect_equal(sum_of_array(input), 45) })

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/R/ComputeClustersByTrt.R

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daskelly/earlycross

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2023-02-27T00:37:10.949245

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ComputeClustersByTrt.R

#' Given a Seurat object and treatment variable, compare #' clusters between levels of the treatment #' #' @param obj Seurat object #' @param trt_var Treatment variable #' @param rep_var Replicate/Individual ID variable #' @param ci Whether to plot binomial confidence intervals (Jeffreys) #' @param group_by Name of grouping variable (default is Idents) #' #' @importFrom magrittr '%>%' #' @examples #' pbmc_small$trt <- sample(c('drug', 'control'), ncol(pbmc_small), replace=TRUE) #' pbmc_small$genotype <- sample(c('1', '2', '3'), ncol(pbmc_small), replace=TRUE) #' head(ComputeClustersByTrt(pbmc_small, trt, genotype)) #' #' @export ComputeClustersByTrt <- function(obj, trt_var, rep_var, group_by = NULL, ci = TRUE, ci_alpha = 0.05) { # For curly curly {{ syntax see # https://www.tidyverse.org/blog/2019/06/rlang-0-4-0/ meta <- obj@meta.data %>% as.data.frame() %>% tibble::rownames_to_column("cell") if (is.null(group_by)) { meta$ident <- Idents(obj) } else { meta$ident <- meta[[group_by]] } grp_dat <- dplyr::group_by(meta, { { trt_var } }, { { rep_var } }) %>% dplyr::mutate(N_tot = dplyr::n()) %>% dplyr::ungroup() %>% dplyr::group_by({ { trt_var } }, { { rep_var } }, ident, N_tot) grp_dat <- grp_dat %>% dplyr::summarize(N = dplyr::n(), .groups = 'keep') %>% dplyr::mutate(frac = N/N_tot) # Put binomial confidence intervals around the cell abundance estimates Use # Jeffreys interval -- posterior dist'n is Beta(x + 1/2, n – x + 1/2) grp_stats <- dplyr::mutate(grp_dat, cells_per_thousand = frac * 1000) %>% dplyr::mutate(trt_var = {{ trt_var }}) if (ci) { grp_stats <- dplyr::mutate(grp_stats, lower = qbeta(ci_alpha/2, N + 1/2, N_tot - N + 1/2), upper = qbeta(1 - ci_alpha/2, N + 1/2, N_tot - N + 1/2), lower_per_thousand = lower * 1000, upper_per_thousand = upper * 1000) } grp_stats }

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/R/read_data_hooks.R

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jokergoo/epik

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refs/heads/master

2021-01-12T05:25:21.598897

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read_data_hooks.R

# == title # Read methylation dataset # # == param # -... please ignore, see 'details' section. # -RESET remove all hooks # -READ.ONLY please ignore # -LOCAL please ignore # # == detail # Methylation dataset from whole genome bisulfite sequencing is always huge and it does not # make sense to read them all into the memory. Normally, the methylation dataset is stored # by chromosome and this hook function can be set to read methylation data in a per-chromosome # manner. In the package, there are many functions use it internally to read methylation datasets. # # Generally, for methylation dataset, there are methylation rate (ranging from 0 to 1), CpG coverage and genomic positions # for CpG sites. Sometimes there is also smoothed methylation rate. All these datasets can be set # by defining a proper ``methylation_hooks$get_by_chr``. The value for ``methylation_hooks$get_by_chr`` # is a function with only one argument which is the chromosome name. This function defines how to # read methylation dataset for a single chromosome. The function must return a list which contains # following mandatory elements: # # -gr a `GenomicRanges::GRanges` object which contains genomic positions for CpG sites. Positions should be sorted. # -meth a matrix which contains methylation rate. This will be the main methylation dataset the epik # package uses, so it should be smoothed methylation rate if the CpG coverage is not high. # Note, this matrix must have column names which is sample names and will be used to match # other datasets (e.g. RNASeq) # -cov a matrix which contains CpG coverage. # # It can also contain some optional elements and they are not needed for the core analysis: # # -raw a matrix which contains unsmoothed methylation rate (or the original methylation rate calculatd # as the fraction of methylated CpG in a CpG site) # # Note each row in above datasets should correspond to the same CpG site. # # In following example code, assume the methylation data has been processed by bsseq package and saved as # ``path/bsseq_$chr.rds``, then the definition of ``methylation_hooks$get_by_chr`` is: # # methylation_hooks$get_by_chr = function(chr) { # obj = readRDS(paste0("path/bsseq_", chr, ".rds")) # lt = list(gr = granges(obj), # raw = getMeth(obj, type = "raw"), # cov = getCoverage(obj, type = "Cov"), # meth = getMeth(obj, type = "smooth") # return(lt) # } # # After ``methylation_hooks$get_by_chr`` is properly set, the "current chromosome" for the methylation dataset # can be set by ``methylation_hooks$set_chr(chr)`` where ``chr`` is the chromosome name you want to go. # After validating the dataset, following variables can be used directly: # # - ``methylation_hooks$gr`` # - ``methylation_hooks$meth`` # - ``methylation_hooks$sample_id`` # - ``methylation_hooks$cov`` # - ``methylation_hooks$raw`` if available # # ``methylation_hooks$set_chr(chr)`` tries to reload the data only when the current chromosome changes. # # == value # Hook functions # # == author # Zuguang Gu <z.gu@dkfz.de> # methylation_hooks = function(..., RESET = FALSE, READ.ONLY = NULL, LOCAL = FALSE) {} METH_OBJ = new.env() assign("meth", NULL, envir = METH_OBJ) assign("raw", NULL, envir = METH_OBJ) assign("cov", NULL, envir = METH_OBJ) assign("gr", NULL, envir = METH_OBJ) assign("chr", NULL, envir = METH_OBJ) assign("sample_id", NULL, envir = METH_OBJ) methylation_hooks = setGlobalOptions( get_by_chr = list(.value = NULL, .class = "function"), meth = list(.value = function() METH_OBJ$meth, .private = TRUE, .visible = FALSE), raw = list(.value = function() METH_OBJ$raw, .private = TRUE, .visible = FALSE), cov = list(.value = function() METH_OBJ$cov, .private = TRUE, .visible = FALSE), gr = list(.value = function() METH_OBJ$gr, .private = TRUE, .visible = FALSE), sample_id = list(.value = function() METH_OBJ$sample_id, .class = "character", .private = TRUE, .visible = FALSE), set_chr = list(.value = NULL, .class = "function", .private = TRUE, .visible = FALSE) ) methylation_hooks$set_chr = function(chr, verbose = TRUE) { previous_chr = METH_OBJ$chr if(!is.null(previous_chr)) { if(previous_chr == chr) { if(verbose) message(qq("[@{chr}] @{chr} is already set.")) return(invisible(NULL)) } } obj = methylation_hooks$get_by_chr(chr) # validate obj if(is.null(obj$meth)) { stop("The list which is returned by `methylation_hooks$get_by_chr` should contain `meth`.") } if(is.null(obj$gr)) { stop("The list which is returned by `methylation_hooks$get_by_chr` should contain `gr`.") } if(is.null(obj$raw)) { cat("The list which is returned by `methylation_hooks$get_by_chr` has no `raw`.") } if(is.null(obj$cov)) { cat("The list which is returned by `methylation_hooks$get_by_chr` has no `cov`.") } sample_id = colnames(obj$meth) if(is.null(sample_id)) { stop("The methylation matrix which is represented as `meth` must have column names which represents as sample ids.") } if(inherits(obj$meth, "data.frame")) { obj$meth = as.matrix(obj$meth) } if(!inherits(obj$meth, "matrix")) { stop("`meth` should be a matrix.") } if(!inherits(obj$gr, "GRanges")) { stop("`gr` should be a GRanges object.") } if(is.unsorted(start(obj$gr))) { stop("`gr` should be sorted.") } if(!is.null(obj$raw)) { if(inherits(obj$raw, "data.frame")) { obj$raw = as.matrix(obj$raw) } if(!inherits(obj$raw, "matrix")) { stop("`raw` should be a matrix.") } } if(!is.null(obj$cov)) { if(inherits(obj$cov, "data.frame")) { obj$cov = as.matrix(obj$cov) } if(!inherits(obj$cov, "matrix")) { stop("`cov` should be a matrix.") } } if(length(obj$gr) != nrow(obj$meth)) { stop("Number of rows in `meth` should be the same as the length of `gr`.") } if(!is.null(obj$raw)) { if(length(obj$gr) != nrow(obj$raw)) { stop("Number of rows in `raw` should be the same as the length of `gr`.") } if(!identical(colnames(obj$meth), colnames(obj$raw))) { stop("Column names of `raw` should be identical to the column names of `meth`.") } } if(!is.null(obj$cov)) { if(length(obj$gr) != nrow(obj$cov)) { stop("Number of rows in `cov` should be the same as the length of `gr`.") } if(!identical(colnames(obj$meth), colnames(obj$cov))) { stop("Column names of `cov` should be identical to the column names of `meth`.") } } assign("meth", obj$meth, envir = METH_OBJ) assign("raw", obj$raw, envir = METH_OBJ) assign("cov", obj$cov, envir = METH_OBJ) assign("gr", obj$gr, envir = METH_OBJ) assign("chr", chr, envir = METH_OBJ) assign("sample_id", sample_id, envir = METH_OBJ) if(verbose) { message(qq("Following methylation datasets have been set for @{chr}:")) message(qq("- `methylation_hooks$gr`: a GRanges object which contains positions of CpG sites.")) message(qq("- `methylation_hooks$meth`: methylation matrix")) if(!is.null(obj$raw)) message(qq("- `methylation_hooks$raw`: raw methylation matrix (unsmoothed)")) if(!is.null(obj$cov)) message(qq("- `methylation_hooks$cov`: CpG coverage matrix")) message(qq("There are @{length(obj$gr)} CpG sites, @{length(sample_id)} samples.")) } return(invisible(NULL)) } class(methylation_hooks) = c("methylation_hooks", "GlobalOptionsFun") # == title # Print the methylation_hooks object # # == param # -x a `methylation_hooks` objects # -... additional arguments # # == value # No value is returned # # == author # Zuguang Gu <z.gu@dkfz.de> # print.methylation_hooks = function(x, ...) { if(is.null(methylation_hooks$get_by_chr)) { str = "Please set `methylation_hooks$get_by_chr` to import methylation dataset. The value is a function with only one argument which is the chromosome name. The returned value should be a list which contains:\n" qqcat(str, strwrap = TRUE) qqcat("- `gr`: a GRanges object which contains positions of CpG sites.\n") qqcat("- `meth`: methylation matrix (mainly used in the package)\n") qqcat("- `raw`: raw methylation matrix (unsmoothed), optional.\n") qqcat("- `cov`: CpG coverage matrix.\n") } else { if(is.null(METH_OBJ$chr)) { qqcat("`methylation_hooks$get_by_chr` has been set. Use `methylation_hooks$set_chr() to set a chromosome.\n") } else { qqcat("`methylation_hooks$get_by_chr` has been set. Current chromosome is @{METH_OBJ$chr}\n") } } } strwrap2 = function(x) { paste0(strwrap(x), collapse = "\n") } # == title # Read ChIP-Seq dataset # # == param # -... please ignore, see 'details' section. # -RESET remove all hooks # -READ.ONLY please ignore # -LOCAL please ignore # # == details # Unlike methylation dataset which is always stored as matrix, ChIP-Seq dataset is stored # as a list of peak regions that each one corresponds to peaks in one sample. In many cases, # there are ChIP-Seq datasets for multiple histone marks that each mark does not include all # samples sequenced in e.g. whole genome bisulfite sequencing or RNA-Seq, thus, to import # such type of flexible data format, users need to define following hook functions: # # -sample_id This self-defined function returns a list of sample IDs given the name of a histone mark. # -peak This function should return a `GenomicRanges::GRanges` object which are peaks for a given # histone mark in a given sample. The `GenomicRanges::GRanges` object should better have a meta column named "density" # which is the density of the histone modification signals. (**Note when you want to take the histone # modification signals as quatitative analysis, please make sure they are properly normalized between samples**) # -chromHMM This hook is optional. If chromatin segmentation by chromHMM is avaialble, this hook # can be defined as a function which accepts sample ID as argument and returns # a `GenomicRanges::GRanges` object. The `GenomicRanges::GRanges` object should have a meta column named # "states" which is the chromatin states inferred by chromHMM. # # The ``chipseq_hooks$peak()`` must have two arguments ``mark`` and ``sid`` which are the name of the histone mark # and the sample id. There can also be more arguments such as chromosomes. # # As an example, let's assume the peak files are stored in a format of ``path/$sample_id/$mark.bed``, then we can define # hooks functions as: # # # here `qq` is from GetoptLong package which allows simple variable interpolation # chipseq_hooks$sample_id = function(mark) { # peak_files = scan(pipe(qq("ls path/*/@{mark}.bed")), what = "character") # sample_id = gsub("^path/(.*?)/.*$", "\\1", peak_files) # return(sample_id) # } # # # here ... is important that the epik package will pass more arguments to it # chipseq_hooks$peak = function(mark, sid, ...) { # peak_file = qq("path/@{sid}/@{mark}.bed") # df = read.table(peak_file, sep = "\t", stringsAsFactors = FALSE) # GRanges(seqnames = df[[1]], ranges = IRanges(df[[2]], df[[3]]), density = df[[5]]) # } # # Normally ``chipseq_hooks$peak()`` are not directly used, it is usually used by `get_peak_list` to read peaks from all samples as a list. # You can also add more arguments when defining ``chipseq_hooks$peak()`` that these arguments can be passed from `get_peak_list` as well. # For example, you can add chromosome name as the third argument that you do not need to read the full dataset at a time: # # # to make it simple, let's assume it only allows one single chromosome # chipseq_hooks$peak = function(mark, sid, chr) { # peak_file = qq("path/@{sid}/@{mark}.bed") # df = read.table(pipe(qq("awk '$1==\"@{chr}\"' @{peak_file}")), sep = "\t", stringsAsFactors = FALSE) # GRanges(seqnames = df[[1]], ranges = IRanges(df[[2]], df[[3]]), density = df[[5]]) # } # # then you can call `get_peak_list` as: # # get_peak_list(mark, chr = "chr1") # # The ``chipseq_hooks$chromHMM()`` must have one argument ``sid`` which is the sample id, also there can be more arguments such as chromosomes. # The usage for the additional argumetns are same as ``chipseq_hooks$peak()``. # # == value # Hook functions # # == seealso # `get_peak_list`, `get_chromHMM_list` # # == author # Zuguang Gu <z.gu@dkfz.de> # chipseq_hooks = function(..., RESET = FALSE, READ.ONLY = NULL, LOCAL = FALSE) {} chipseq_hooks = setGlobalOptions( sample_id = list(.value = function(mark) stop("you need to define `sample_id` hook"), .class = "function", .validate = function(f) length(as.list(f)) == 2, .failed_msg = strwrap2("The function should only have one argument which is the name of the histone mark.")), peak = list(.value = function(mark, sid, ...) stop("you need to define `peak` hook"), .class = "function", .validate = function(f) length(as.list(f)) >= 4, .failed_msg = strwrap2("The function should have more than two arguments which are the name of the histone mark, sample id and other stuff. If you only use the first two, simply add `...` as the third argument.")), chromHMM = list(.value = NULL, .class = "function", .validate = function(f) length(as.list(f)) >= 3, .failed_msg = strwrap2("The function should have more than one arguments which are the sample id and other stuff. If you only use the first one, simply add `...` as the second argument.")) ) # == title # Get a list of peak regions for a given histone mark # # == param # -mark name of the histone mark # -sample_id a vector of sample IDs. If not defined, it is the total samples that are available for this histone mark. # -... more arguments pass to `chipseq_hooks`$peak(). # # == details # It works after `chipseq_hooks` is set. # # == value # A list of `GenomicRanges::GRanges` objects. # # If you e.g. set "chr" as the third argument when defining `chipseq_hooks`$peak(), "chr" can also be passed here through ``...``. # # == author # Zuguang Gu <z.gu@dkfz.de> # get_peak_list = function(mark, sample_id = chipseq_hooks$sample_id(mark), ...) { peak_list = lapply(sample_id, function(sid) { oe = try(gr <- chipseq_hooks$peak(mark, sid, ...)) if(inherits(oe, "try-error")) { return(NULL) } else { return(gr) } }) names(peak_list) = sample_id peak_list[!sapply(peak_list, is.null)] } # == title # Get a list of chromatin segmentation regions # # == param # -sample_id a vector of sample IDs. # -merge if the sample IDs specified are from a same subgroup and user wants to merge them as consensus states # -window window # -min minimal overlap # -... more arguments pass to `chipseq_hooks`$chromHMM(). # # == details # It works after `chipseq_hooks` is set. # # == value # A list of `GenomicRanges::GRanges` objects. # # If you e.g. set "chr" as the third argument when defining `chipseq_hooks`$peak(), "chr" can also be passed here through ``...``. # # == author # Zuguang Gu <z.gu@dkfz.de> # get_chromHMM_list = function(sample_id, merge = FALSE, window = NULL, min = 0.5, ...) { lt = lapply(sample_id, function(sid) { oe = try(gr <- chipseq_hooks$chromHMM(sid, ...)) if(inherits(oe, "try-error")) { return(NULL) } else { return(gr) } }) names(lt) = sample_id lt = lt[!sapply(lt, is.null)] if(merge) { gr_list_1 = lt if(is.null(window)) { window = numbers::mGCD(c(sapply(gr_list_1, function(gr) numbers::mGCD(unique(width(gr)))))) message(qq("window is set to @{window}")) if(window == 1) { message("when converting bed files to GRanges objects, be careful with the 0-based and 1-based coordinates.") } } # if(is.null(all_states)) { all_states = unique(c(unlist(lapply(gr_list_1, function(gr) {unique(as.character(mcols(gr)[, 1]))})))) all_states = sort(all_states) message(qq("@{length(all_states)} states in total")) # } message("extracting states") m1 = as.data.frame(lapply(gr_list_1, function(gr) { k = round(width(gr)/window) s = as.character(mcols(gr)[, 1]) as.integer(factor(rep(s, times = k), levels = all_states)) })) m1 = as.matrix(m1) gr = makeWindows(gr_list_1[[1]], w = window) message("counting for each state") t1 = rowTabulates(m1) l = rowMaxs(t1) >= floor(min*length(lt)) t1 = t1[l, , drop = FALSE] gr = gr[l] message("determine states") states1 = rowWhichMax(t1) mcols(gr) = NULL gr$states = all_states[states1] return(gr) } else { return(lt) } }

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clean_query.R

#' Clean query #' #' Function to clean a query and return it without an api prefix #' #' @param data a \code{list} containing a valid query for the table, *e.g.* #' \code{list(field = value)} #' @param endpoint endpoint #' clean_query <- function(data, endpoint) { data_tmp <- lapply(data, function(x) { if (!is.character(x)) { output <- x } else if (grepl(paste0(endpoint, ".*([0-9]+$|[0-9]+/$)"), x)) { output <- basename(x) } else { output <- x } output }) data_tmp }

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/TVS credit/TVSCredit.R

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sumanpal94/Competitions

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TVSCredit.R

library(readr) GIM_Dataset <- read_csv("~/Downloads/5dbabd3a3e7e8_GIM_Dataset/GIM_Dataset.csv") names(GIM_Dataset) GIM_Dataset1 <- GIM_Dataset[,-1] # GIM_Dataset1 <- fastDummies::dummy_cols(GIM_Dataset[,-1]) # colnames(GIM_Dataset1) <- gsub(" ","_",colnames(GIM_Dataset1)) # colnames(GIM_Dataset1) <- gsub("/","_",colnames(GIM_Dataset1)) # colnames(GIM_Dataset1) <- gsub("-","_",colnames(GIM_Dataset1)) labels=as.matrix(GIM_Dataset1['V27']) df_train=GIM_Dataset1[-grep('V27',colnames(GIM_Dataset1))] #df_train=df_train[,c(importance_matrix$Feature[1:30])] X <- df_train y <- labels dtrain <- xgb.DMatrix(data.matrix(X), label = labels) cv <- xgb.cv(data = dtrain, nrounds = 30, nthread = 4, nfold = 10, metrics = list("rmse","auc"), max_depth = 40, eta = 0.05, objective = "binary:logistic") print(cv) print(cv, verbose=TRUE) xgb <- xgboost(data = data.matrix(X), label = y, eta = 0.5, max_depth = 30, nround=50, colsample_bytree = 0.5, seed = 1, eval_metric = "logloss", objective = "binary:logistic", nthread = 3 ) y_pred <- predict(xgb,data.matrix(X)) y_pred pred <- prediction(y_pred , y) perf <- performance(pred,"tpr","fpr") plot(perf,colorize=TRUE) y_pred[y_pred>=.5]=1 y_pred[y_pred<.5]=0 table(y,y_pred) AUC(y_pred,y) names <- dimnames(X)[[2]] importance_matrix <- xgb.importance(names, model = xgb) xgb.plot.importance(importance_matrix[1:30]) xgb.plot.tree(feature_names = names, model = xgb, n_first_tree = 2)

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/examples/05do_GCluster07.R

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05do_GCluster07.R

rm(list=ls()); library(GCluster); dat <- read.table("example_data/Rtsne_1.txt", header=T, row.names=1); out.s <- mstGCluster(dat=dat, k=0); out <- out.s[,c(2,3,5,6)]; out <- as.matrix(out); out2 <- GCluster(dat=dat, wt=4, k=10); clu.i <- out2$membership; out.c <- data.frame(dat, cluster=clu.i); write.table(out.c, "example_data/Rtsne_1_clu.txt", quote=F, sep="\t"); clu.n <- length(unique(clu.i)); cols <- rainbow(clu.n, alpha=0.5); pdf("05do_GCluster07.pdf", 6, 6); plot(dat, type="n", main="", xlab="", ylab=""); points(dat, pch=19, col=cols[clu.i], cex=1.2); segments(as.numeric(out[,1]), as.numeric(out[,2]), as.numeric(out[,3]), as.numeric(out[,4]), col=cols[9], lwd=2); dev.off();

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/Source Code/Analysis/Setting Up Movement Analysis.R

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hsiadd/Turtle-Backpack

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Setting Up Movement Analysis.R

#### Framework for beginning movement analysis ## This just allows you to generate a dataframe with information on turtle movement as collected through the GPS logger ## This data can then be used for analysis in any way ## Libraries library(tidyverse) library(sf) library(data.table) ## Set up the dataframe, including filtering out outliers setwd("C:/Users/luke/Dropbox/Black Rock/Data/GPS Backpacks") file_names <- dir("C:/Users/luke/Dropbox/Black Rock/Data/GPS Backpacks") GPSBase <- do.call("rbind", lapply(file_names, read.csv)) GPSBase <- GPSBase %>% filter(Lat > 40) %>% filter(Lat < 42) %>% filter(Lon < -73) %>% filter(Lon > -75) GPSBase <- filter(GPSBase, Lon != 0) GPSBase <- mutate(GPSBase, tottime = (Month - 6) * 30 * 24 * 60 + Day * 24 * 60 + Hour * 60 + Minute) GPSBase <- GPSBase %>% mutate(run = rleidv(GPSBase, cols = seq_along(c("Lon", "Lat")))) %>% distinct(run, .keep_all = TRUE) GPSBase <- mutate(GPSBase, vhf = as.character(vhf)) GPSBase <- mutate(GPSBase, tothours = (Month - 6) * 30 * 24 + Day * 24 + Hour + Minute / 60) GPSPoints <- st_as_sf(GPSBase, coords = c("Lon", "Lat"), crs = "+proj=longlat +datum=WGS84", remove = TRUE) GPSPoints <- st_transform(GPSPoints, crs = "+proj=utm +zone=18 +ellps=WGS84 +datum=WGS84 +units=m +no_defs") pythag <- function(a, b){ dist <- sqrt(a^2 + b^2) return(dist) } GPSPointsCoords <- st_coordinates(GPSPoints) GPSBase <- GPSBase %>% mutate(mN = GPSPointsCoords[,2]) %>% mutate(mE = GPSPointsCoords[,1]) GPSBase <- GPSBase %>% group_by(vhf) %>% arrange(tothours) %>% mutate(mdiff = pythag(mN - lag(mN), mE - lag(mE))) %>% mutate(m2diff = pythag(lead(mN) - lag(mN), lead(mE) - lag(mE))) GPSBase <- GPSBase %>% group_by(vhf) %>% arrange(tothours) %>% mutate(hourdiff = tothours - lag(tothours, default = first(tothours))) %>% mutate(mdiff = ifelse(hourdiff > 6, NA, mdiff)) GPSBase <- GPSBase %>% group_by(vhf) %>% arrange(tothours) %>% mutate(hour2diff = tothours - lead(tothours, default = first(tothours))) %>% mutate(m2diff = ifelse(hour2diff < -6, NA, m2diff)) GPSBase <- GPSBase %>% mutate(outlier = ifelse(mdiff > 150 & mdiff / m2diff > 2.5| mdiff > 100 & mdiff / m2diff > 3 | mdiff > 50 & mdiff /m2diff > 3.5 | mdiff > 150 & hourdiff < 1.5 & is.na(m2diff) == TRUE, "yes", "no")) GPSBase <- GPSBase %>% filter(outlier == "no" | is.na(outlier) == TRUE) ## Calculate movement speed for intervals (meters per hour) #### This is already filtering out significant jumps in time (>6 hours) due to the conditions when creating mdiff GPSBase <- mutate(GPSBase, mph = mdiff / hourdiff) ## Determine if turtle is moving at a given point (defined as movement over 25 meters) GPSBase <- mutate(GPSBase, moving = ifelse(mph > 25, 1, 0)) GPSBase <- mutate(GPSBase, moving = ifelse(is.na(mph) == TRUE, NA, moving)) ## Calculate the time of day at the middle of the interval for each samplin point #### This can be used to explore daily activity patterns GPSBase <- GPSBase %>% group_by(vhf) %>% arrange(tothours) %>% mutate(inthourav = ifelse(Day - lag(Day, default = first(Day)) == 0, (Hour + lag(Hour, default = first(Hour))) / 2, (Hour + 24 + lag(Hour, default = first(Hour))) / 2)) %>% mutate(inthourav = ifelse(inthourav > 24, inthourav - 24, inthourav)) ## Add a variable to indicate nocturnal movement ### This could optimized using a dataframe containing daily sunset/sunrise for more long term projects ### ^Shouldn't be too hard, just the dataframes based on the date then mutate by comparing the two variables GPSBase <- mutate(GPSBase, period = ifelse(inthourav >= 21 | inthourav <= 6, "night", "day")) ## Create a new dataframe with information on turtle "trips" (periods of sustained movement) #### This is essentially done by using the alternating movement variable to create unique trip ids #### Trips are then summarized by total movement, time, and differences in start and end position #### Critical here is the difference between distance (total movement between points throughout trip) and displacement (start location to end location) #### Split into two dataframes, one representing movement periods and one representing rest periods GPSBase <- GPSBase %>% ungroup() %>% arrange(vhf, tothours) %>% mutate(tripnum = rleidv(moving)) GPSBase <- GPSBase %>% group_by(tripnum) %>% mutate(trippoints = sum(tripnum) / max(tripnum)) %>% mutate(triptime = sum(hourdiff)) %>% mutate(tripdistance = sum(mdiff)) GPSBase <- GPSBase %>% group_by(tripnum) %>% mutate(final_mN = last(mN)) %>% mutate(final_mE = last(mE)) %>% mutate(first_mN = first(mN)) %>% mutate(first_mE = first(mE)) %>% mutate(withindisplace = ((((final_mN - first_mN) ** 2 + (final_mE - first_mE) ** 2) ** .5))) GPSTrips <- GPSBase %>% dplyr::select(vhf, moving, tripnum, triptime, trippoints,tripdistance, final_mN, final_mE, withindisplace) %>% distinct() GPSTrips <- GPSTrips %>% group_by(vhf) %>% mutate(tripdisplace = ((((final_mN - lag(final_mN, default = first(final_mN))) ** 2 + (final_mE - lag(final_mE, default = first(final_mE))) ** 2) ** .5))) %>% mutate(tripdisplace = ifelse(tripdisplace > 0, tripdisplace, withindisplace)) GPSTrips <- mutate(GPSTrips, triplinearity = tripdisplace / tripdistance) GPSMoving <- filter(GPSTrips, moving == 1) GPSRest <- filter(GPSTrips, moving == 0)

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library(shiny) fig.width = 400 fig.height = 400 shinyUI( fluidPage( fluidRow(column(3, br(), br(), aligh = "left", div( checkboxGroupInput("predcheckbox", label = "Predictors", choices = c("Age" = "age", "Education" = "education", "Pol. Interest" = "polinterest", "News site use" = "newssite"), inline = FALSE ) ) ), column(9, align = "center", plotOutput("mainplot", width = fig.width, height = fig.height ) ) ) ) )

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/classroom_objects.R \name{CourseWork} \alias{CourseWork} \title{CourseWork Object} \usage{ CourseWork(courseId = NULL, id = NULL, title = NULL, description = NULL, materials = NULL, state = NULL, alternateLink = NULL, creationTime = NULL, updateTime = NULL, dueDate = NULL, dueTime = NULL, maxPoints = NULL, workType = NULL, associatedWithDeveloper = NULL, submissionModificationMode = NULL, assignment = NULL, multipleChoiceQuestion = NULL) } \arguments{ \item{courseId}{Identifier of the course} \item{id}{Classroom-assigned identifier of this course work, unique per course} \item{title}{Title of this course work} \item{description}{Optional description of this course work} \item{materials}{Additional materials} \item{state}{Status of this course work} \item{alternateLink}{Absolute link to this course work in the Classroom web UI} \item{creationTime}{Timestamp when this course work was created} \item{updateTime}{Timestamp of the most recent change to this course work} \item{dueDate}{Optional date, in UTC, that submissions for this this course work are due} \item{dueTime}{Optional time of day, in UTC, that submissions for this this course work are due} \item{maxPoints}{Maximum grade for this course work} \item{workType}{Type of this course work} \item{associatedWithDeveloper}{Whether this course work item is associated with the Developer Console project making the request} \item{submissionModificationMode}{Setting to determine when students are allowed to modify submissions} \item{assignment}{Assignment details} \item{multipleChoiceQuestion}{Multiple choice question details} } \value{ CourseWork object } \description{ CourseWork Object } \details{ Autogenerated via \code{\link[googleAuthR]{gar_create_api_objects}} Course work created by a teacher for students of the course. }

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iamstu12/donkey_games

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# USER INTERFACE --------------------------------------------------------------- ui <- dashboardPage( # Theme ---- skin = "blue", # Main title ---- dashboardHeader(title = "Video Games", titleWidth = 250), # Sidebar ---- dashboardSidebar( width = 250, sidebarMenu( menuItem("Games History", tabName = "history", icon = icon("scroll")), menuItem("Game Genres", tabName = "genres", icon = icon("gamepad")), menuItem("Regional Sales", tabName = "regional", icon = icon("globe")), menuItem("Console Sales", tabName = "console", icon = icon("gamepad")) ) ), # Main body and tabs ---- dashboardBody( tabItems( # <----------------------------------------------------------------- tab 1 tabItem(tabName = "history", h2("Games History"), fluidRow( # <----------------------------------------- fluid row 1 box(title = "Controls", # <----------- drop down status = "primary", solidHeader = TRUE, width = 6, height = NULL, selectInput("decade", "Choose your decade:", choices = unique(decade$decade) ) ) ), # <----------------------------------------- closes fluid row 1 fluidRow( # <----------------------------------------- fluid row 2 box(title = "Plot", # <--------------- plot status = "primary", solidHeader = TRUE, width = 12, height = NULL, plotOutput("plot1") ) ), # <------------------------------------------ close fluid row 2 fluidRow( # <----------------------------------------- fluid row 3 box(title = "Table", # <-------------- table status = "primary", solidHeader = TRUE, width = 12, height = NULL, DT::dataTableOutput("table1") ) ) # <-------------------------------------------------- closes fluid row 3 ), # <------------------------------------------------------- closes tab 1 # <----------------------------------------------------------------- tab 2 tabItem(tabName = "genres", h2("Game Genres"), fluidRow( # <----------------------------------------- fluid row 1 box(title = "Controls", # <----------- drop down status = "primary", solidHeader = TRUE, width = 6, height = NULL, selectInput("genre", "Choose your genre:", choices = unique(genre$genre), ) ), ), # <----------------------------------------- closes fluid row 1 fluidRow( # <----------------------------------------- fluid row 2 box(title = "Plot", # <--------------- plot status = "primary", solidHeader = TRUE, width = 12, height = NULL, plotOutput("plot2") ) ), # <------------------------------------------ close fluid row 2 fluidRow( # <----------------------------------------- fluid row 3 box(title = "Table", # <-------------- table status = "primary", solidHeader = TRUE, width = 12, height = NULL, DT::dataTableOutput("table2") ) ) # <------------------------------------------ closes fluid row 3 ), # <------------------------------------------------------- closes tab 2 # <----------------------------------------------------------------- tab 3 tabItem(tabName = "regional", h2("Regional Sales"), fluidRow( # <----------------------------------------- fluid row 1 box(title = "Controls", # <----------- drop down status = "primary", solidHeader = TRUE, width = 6, height = NULL, selectInput("region", "Choose your region:", choices = unique(regional$region), ) ), ), # <----------------------------------------- closes fluid row 1 fluidRow( # <----------------------------------------- fluid row 2 box(title = "Plot", # <--------------- plot status = "primary", solidHeader = TRUE, width = 12, height = NULL, plotOutput("plot3") ) ), # <------------------------------------------ close fluid row 2 fluidRow( # <----------------------------------------- fluid row 3 box(title = "Table", # <-------------- table status = "primary", solidHeader = TRUE, width = 12, height = NULL, DT::dataTableOutput("table3") ) ) # <------------------------------------------ closes fluid row 3 ), # <------------------------------------------------------- closes tab 3 # <----------------------------------------------------------------- tab 4 tabItem(tabName = "console", h2("Top Games"), fluidRow( # <----------------------------------------- fluid row 1 box(title = "Controls", # <----------- drop down status = "primary", solidHeader = TRUE, width = 3, height = NULL, selectInput("console", "Choose your console:", choices = unique(console$console), ) ) ), # <----------------------------------------- closes fluid row 1 fluidRow( # <----------------------------------------- fluid row 2 box(title = "Plot", # <--------------- plot status = "primary", solidHeader = TRUE, width = 12, height = NULL, plotOutput("plot4") ) ), # <----------------------------------------- closes fluid row 2 fluidRow( # <----------------------------------------- fluid row 3 box(title = "Table", # <-------------- table status = "primary", solidHeader = TRUE, width = 12, height = NULL, DT::dataTableOutput("table4") ) ), # <------------------------------------------ close fluid row 3 ) # <-------------------------------------------------------- closes tab 4 ) # <------------------------------------------------------ closes tab items ) # <--------------------------------------------------- closes dashboard body ) # <----------------------------------------------------- closes dashboard page # SERVER ----------------------------------------------------------------------- server <- function(input, output) { # Plot 1 ---- output$plot1 <- renderPlot({ decade %>% ggplot(aes(x = reorder(genre, global_sales_millions), y = global_sales_millions)) + geom_col(alpha = 0.8, colour = "white", fill = "#cc9900", data = decade[decade$decade == input$decade,]) + coord_flip() + theme_light() + labs(title = "Top Performing Games Genres", subtitle = "", x = "Genre", y = "Global Unit Sales (millions)") }) # Table 1 ---- output$table1 <- DT::renderDataTable({ games %>% mutate(year_of_release = as.numeric(year_of_release)) %>% filter(year_of_release != "Unknown") %>% mutate(decade = floor(year_of_release / 10) * 10) %>% filter(decade != 2020) %>% group_by(decade, genre, name) %>% summarise(global_sales_millions = sum(global_sales)) %>% arrange(desc(global_sales_millions)) %>% filter(decade == input$decade) }) # Plot 2 ---- output$plot2 <- renderPlot({ genre %>% ggplot(aes(x = decade, y = decade_sales)) + geom_area(alpha = 0.5, fill = "#666633", data = genre[genre$genre == input$genre,]) + geom_line(colour = "#cc9900", size = 1.2, data = genre[genre$genre == input$genre,]) + geom_point(colour = "#666633", data = genre[genre$genre == input$genre,]) + labs(title = "", x = "Decade", y = "Global Unit Sales (millions)") + theme_light() }) # Table 2 ---- output$table2 <- DT::renderDataTable({ games %>% group_by(name, genre) %>% summarise(total_global_sales = sum(global_sales)) %>% arrange(desc(total_global_sales)) %>% filter(genre == input$genre) }) # Plot 3 ---- output$plot3 <- renderPlot({ regional %>% ggplot(aes(x = reorder(genre, sales_millions), y = sales_millions)) + geom_col(alpha = 0.8, colour = "white", fill = "#cc9900", data = regional[regional$region == input$region,]) + coord_flip() + theme_light() + labs(title = "", subtitle = "", x = "Genre", y = "Sales (millions)") }) # Table 3 ---- output$table3 <- DT::renderDataTable({ regional_2 %>% group_by(name, region) %>% summarise(total_sales = sum(sales)) %>% arrange(desc(total_sales)) %>% filter(region == input$region) }) # Plot 4 ---- output$plot4 <- renderPlot({ console2 %>% filter(console == input$console) %>% ggplot(aes(x = reorder(genre, sales_millions), y = sales_millions)) + geom_col(alpha = 0.8, colour = "white", fill = "#cc9900") + coord_flip() + theme_light() + labs(title = "", subtitle = "", x = "Genre", y = "Sales (millions)") }) # Table 4 ---- output$table4 <- DT::renderDataTable({ console %>% group_by(name, console) %>% summarise(total_sales = sum(sales)) %>% arrange(desc(total_sales)) %>% filter(console == input$console) }) } # APP FUNCTION ----------------------------------------------------------------- shinyApp(ui, server)

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/man/filt3dimArr.Rd

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cran/wrMisc

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filt3dimArr.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/filt3dimArr.R \name{filt3dimArr} \alias{filt3dimArr} \title{Filter three-dimensional array of numeric data} \usage{ filt3dimArr( x, filtVal, filtTy = ">", filtCrit = NULL, displCrit = NULL, silent = FALSE, debug = FALSE, callFrom = NULL ) } \arguments{ \item{x}{array (3-dim) of numeric data} \item{filtVal}{(numeric, length=1) for testing inferior/superor/equal condition} \item{filtTy}{(character, length=1) which type of testing to perform (may be 'eq','inf','infeq','sup','supeq', '>', '<', '>=', '<=', '==')} \item{filtCrit}{(character, length=1) which column-name consider when filtering filter with 'filtVal' and 'filtTy'} \item{displCrit}{(character) column-name(s) to display} \item{silent}{(logical) suppress messages} \item{debug}{(logical) additional messages for debugging} \item{callFrom}{(character) allow easier tracking of messages produced} } \value{ This function returns a list of filtered matrixes (by 3rd dim) } \description{ Filtering of matrix or (3-dim) array \code{x} : filter column according to \code{filtCrit} (eg 'inf') and threshold \code{filtVal} } \details{ and extract/display all col matching 'displCrit'. } \examples{ arr1 <- array(11:34, dim=c(4,3,2), dimnames=list(c(LETTERS[1:4]), paste("col",1:3,sep=""), c("ch1","ch2"))) filt3dimArr(arr1,displCrit=c("col1","col2"),filtCrit="col2",filtVal=7) } \seealso{ \code{\link{filterList}}; \code{\link{filterLiColDeList}}; }

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alnever/ExData_Plotting1

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refs/heads/master

2021-01-15T18:45:01.318808

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## Plot 3 by Al.Neverov library(dplyr) ## Download and unzip datafile if (!file.exists("household_power_consumption.txt")) { if (!file.exists("exdata_data_household_power_consumption.zip")) { fileUrl = "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2Fhousehold_power_consumption.zip" download.file(fileUrl, "exdata_data_household_power_consumption.zip") } unzip("exdata_data_household_power_consumption.zip") } ## Read datafile and filter it data <- tbl_df(read.table("household_power_consumption.txt", sep=";", header = TRUE, na.strings=c("NA","N/A","null","?"), stringsAsFactors = FALSE, colClasses = c("character", "character", rep("numeric", times = 7)))) %>% mutate(aDateTime = paste(Date, Time, sep = " "), Date = as.Date(Date,"%d/%m/%Y"), pDateTime = as.POSIXct(aDateTime, format = "%d/%m/%Y %H:%M:%S") ) %>% filter(as.POSIXlt(Date)$year + 1900 == 2007 & as.POSIXlt(Date)$mon + 1 == 2 & as.POSIXlt(Date)$mday %in% c(1,2)) ## Set parameters for device (screen) par(mfrow = c(1,1)) ## Draw the graphic for Sub metering 1 with(data, plot(pDateTime, Sub_metering_1, col = "black", type = "l", xlab = "", main = "", ylab = "Energy sub metering" )) ## Add lines for Sub metering 2 with(data, lines(pDateTime, Sub_metering_2, col = "red", type = "l", xlab = "", main = "", ylab = "Energy sub metering" )) ## Add lines for Sub metering 3 with(data, lines(pDateTime, Sub_metering_3, col = "blue", type = "l", xlab = "", main = "", ylab = "Energy sub metering" )) ## Add legend legend("topright", col = c("black", "red", "blue"), legend = c("Sub_metering_1", "Sub_metering_2", "Sub_metering_3"), lty = 1, lwd = 1 ) ## Copy the graph into the png file dev.copy(png, "plot3.png") dev.off()

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ckmah/FAT

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functional-analysis.R

library(cummeRbund) library(gage) library(pathview) library(preprocessCore) library(clusterProfiler) keggAnalysis <- function(fpkmData, cuff, kegg.gs, ref) { ids <- rownames(fpkmData) geneSet <- getGenes(cuff, ids) geneIDs <- featureNames(geneSet) # select genes that map and are not duplicates isUniqueMap <- !is.na(geneIDs[, 2]) & !duplicated(geneIDs[, 2]) mapped <- fpkmData[isUniqueMap,] # get entrez ids rownames(mapped) <- geneIDs[isUniqueMap, 2] # reference and sample column indices fpkmData.ref <- ref # fpkmData.samp <- 2 # convert from gene ID to entrez id entrezIDs <- pathview::id2eg(rownames(mapped), category = "symbol") entrezIDs.sel <- !is.na(entrezIDs[, 2]) # select converted genes mapped <- mapped[entrezIDs.sel,] rownames(mapped) <- entrezIDs[entrezIDs.sel, 2] return(list("mapped" = mapped, "kegg.gage" = gage(mapped, kegg.gs, sam.dir = FALSE))) # gs=unique(unlist(kegg.gs[rownames(keggs.p$greater)[1:3]])) # cnts.d = cnts.norm[, samp.idx] - rowMeans(cnts.norm[, ref.idx]) # sel <- cnts.kegg.p$greater[, "q.val"] < 0.1 & !is.na(cnts.kegg.p$greater[, "q.val"]) # path.ids <- rownames(cnts.kegg.p$greater)[sel] # sel.l <- cnts.kegg.p$less[, "q.val"] < 0.1 & !is.na(cnts.kegg.p$less[, "q.val"]) # path.ids.l <- rownames(cnts.kegg.p$less)[sel.l] # path.ids2 <- substr(c(path.ids, path.ids.l), 1, 8) # library(pathview) # pv.out.list <- sapply(path.ids2, function(pid) # pathview( # gene.data = cnts.d, # pathway.id = pid, # species = "hsa" # )) # } # # # extract gene names # gnames = cuff.res$gene # # # select genes present in cuffdiff output # sel = gnames != "-" # gnames = as.character(gnames[sel]) # cuff.fc = cuff.fc[sel] # names(cuff.fc) = gnames # # # convert to entrez gene id # gnames.eg = pathview::id2eg(gnames, category = "symbol") # # # filter for genes with > 0 fold change # sel2 = gnames > "" # cuff.fc = cuff.fc[sel2] # # names(cuff.fc) = gnames[sel2] # range(cuff.fc) # # # max of 10 fold change # cuff.fc[cuff.fc > 10] = 10 # cuff.fc[cuff.fc < -10] = -10 # exp.fc = cuff.fc # out.suffix = "cuff" #

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ElliotMeador84/misc_plots

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r

Sample_variance.R

library(tidyverse) library(gganimate) library(glue) vary_pop <- function(x) { mean((mean(x) - x) ^ 2) } k <- rnorm(1000000, mean = 500, sd = 50) # # df_5 <- map_df(1:10000, function(x) { tibble(value = sample(k, 5, T)) %>% mutate(sample = paste0('df.5_', x), size = 'Sample of 5') }) # # df_10 <- map_df(1:10000, function(x) { tibble(value = sample(k, 10, T)) %>% mutate(sample = paste0('df.10_', x), size = 'Sample of 10') }) # # df_25 <- map_df(1:10000, function(x) { tibble(value = sample(k, 25, T)) %>% mutate(sample = paste0('df.25_', x), size = 'Sample of 25') }) # # df_50 <- map_df(1:10000, function(x) { tibble(value = sample(k, 50, T)) %>% mutate(sample = paste0('df.50_', x), size = 'Sample of 50') }) df <- bind_rows(df_5, df_10, df_25, df_50) # df_var <- df %>% group_by(sample) %>% mutate('Sample variance' = var(value), 'Population variance' = vary_pop(value)) %>% select(-value) %>% distinct() %>% gather(calculation, value, -sample, -size) %>% ungroup() # size_levs <- df_var %>% count(size, sort = T) %>% pull(size) size.levels <- size_levs[c(3, 1, 2, 4)] df_var_gg <- df_var %>% mutate( calculation = fct_relevel(calculation, 'Sample variance', 'Population variance'), size = fct_relevel(size, size.levels) ) %>% filter(between(value, 0, 6000)) %>% group_by(size, calculation) %>% mutate(diverge = value - median(value)) %>% arrange(diverge) # save(df_var, file = '~/df_var.RData') # save(k, file = '~/k.RData') # # load('~/df_var.RData') # load('~/k.RData') df_median <- df_var_gg %>% group_by(size, calculation) %>% summarise(median = median(value), diff = round(vary_pop(k), 0) - round(median, 0)) %>% ungroup() %>% mutate(true.vary = ifelse( calculation == 'Population variance', 'True\npopulation\nvariance', NA)) p <- df_var_gg %>% ggplot(aes(value)) + geom_histogram(bins = 100, fill = Spectral[5], show.legend = F) + geom_vline(xintercept = vary_pop(k), color = 'grey', size = 1.25) + geom_vline( data = df_median, aes(xintercept = median), linetype = 4, size = 1.25, color = Spectral[11] ) + geom_text( data = df_median, aes(label = paste('Difference of\n', diff), x = median - 500), y = 475, size = 3) + geom_text( data = df_median, aes(label = true.vary), x = 2855, y = 50, size = 3 ) + geom_text( data = df_median, aes(label = size), x = 3500, y = 350, size = 3.25 )+ scale_x_continuous(labels = scales::comma) + scale_fill_manual(values = YlGnBu_n(100)) + facet_wrap(. ~ calculation , scales = 'free_x', ncol = 1) + theme_minimal()+ theme(panel.grid.minor = element_blank(), plot.caption = element_text(hjust = 0))+ labs() variance_p <- p + labs(title = expression(paste('Comparing sample variance ' [frac(Sigma(X - mu)^2, N - 1)], ' with population variance ' [frac(Sigma(X - mu)^2, N)])), subtitle = expression(paste('The difference between '[N-1], ' and '[N], ' in stastical analysis.')), caption = 'I created a random normal vector of length 1,000,000 with a mean of 500 and standard deviation\nof 50.\nThen I took 10,000 random samples of size 5, 25 and 50.\nLastly, I calculated the population variance and sample variance for each to see the difference that\ndividing by N and N-1 makes.\nThe difference between the formulas for sample and population variance is small, but it makes a\nBIG difference in accuracy.\nu/lane_dog') animated_plot <- variance_p + transition_states( size, transition_length = 1, state_length = 1, wrap = T ) p_gganimate <- gganimate::animate(animated_plot) anim_save(p_gganimate, filename = 'p_gganimate.gif')

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/tests/testthat/testSuite002-states-testState8.3.R

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2021-01-19T07:06:41.410215

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testSuite002-states-testState8.3.R

context("Test Suite 2 (States) --> State 8.3") library(RSelenium) library(testthat) source("functions.R", local=TRUE) drivers <- getRemDrivers("Test Suite 2 (States) --> State 8.3") rD <- drivers$rDr remDr <- drivers$remDr openRemDriver(remDr) tryCatch({ test_that("can connect to app", { connectToApp(remDr) }) test_that("app is ready within 30 seconds", { waitForAppReady(remDr) }) test_that("can navigate to state 8.3", { navigateToState(remDr, "8.3") }) test_that("SISample file upload buttons are displaying correctly", { expect_true(isDisplayed(remDr, pages$state8.3$selectors$si_sample_data_upload_label)) expect_equal(getText(remDr, pages$state8.3$selectors$si_sample_data_upload_label), "Choose a serial interval data file to upload") expect_true(isDisplayed(remDr, pages$state8.3$selectors$si_sample_data_upload_browse)) expect_true(isDisplayed(remDr, pages$state8.3$selectors$si_sample_header_button)) }) test_that("n2 input is displayed correctly", { expect_true(isDisplayed(remDr, pages$state8.3$selectors$n2_label)) expect_equal(getText(remDr, pages$state8.3$selectors$n2_label), paste("Choose n2, the posterior sample size to be drawn for", "R for each SI distribution sampled")) expect_true(isDisplayed(remDr, pages$state8.3$selectors$n2_input)) }) test_that("seed input is displayed correctly", { expect_true(isDisplayed(remDr, pages$state8.3$selectors$seed_label)) expect_equal(getText(remDr, pages$state8.3$selectors$seed_label), paste("Set a seed to be used by EpiEstim. A random seed will", "be chosen if this is left blank")) expect_true(isDisplayed(remDr, pages$state8.3$selectors$seed_input)) }) test_that("relevant control buttons are displayed", { expect_false(isDisplayed(remDr, pages$common$selectors$stop_button)) expect_true(isDisplayed(remDr, pages$common$selectors$prev_button)) expect_true(isEnabled(remDr, pages$common$selectors$prev_button)) expect_false(isDisplayed(remDr, pages$common$selectors$next_button)) expect_true(isDisplayed(remDr, pages$common$selectors$go_button)) expect_true(isEnabled(remDr, pages$common$selectors$go_button)) }) test_that("no errors are displaying", { expect_false(isDisplayed(remDr, pages$common$selectors$error_message)) expect_equal(getText(remDr, pages$common$selectors$error_message), "") }) }, error = function(e) { closeRemDrivers(remDr, rD) stop(e) }) closeRemDrivers(remDr, rD)

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/visualization.R

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refs/heads/main

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visualization.R

library(readr) library(dplyr) food <- read_csv("Food_Supply_Quantity_kg_Data.csv") #Categorize food types food$animal <- rowSums(food[,c("Animal fats", "Animal Products", "Aquatic Products, Other", "Eggs", "Fish, Seafood", "Meat", "Milk - Excluding Butter", "Miscellaneous", "Offals")], na.rm=TRUE) food$plant <- rowSums(food[,c("Alcoholic Beverages", "Cereals - Excluding Beer", "Fruits - Excluding Wine", "Oilcrops", "Pulses", "Spices", "Starchy Roots", "Stimulants", "Sugar & Sweeteners", "Sugar Crops", "Treenuts", "Vegetable Oils", "Vegetables", "Vegetal Products")], na.rm=TRUE) food$total <- rowSums(food[,c("animal", "plant")], na.rm=TRUE) #Remove null rows food<-food[!(food$Deaths==0),] food<-food[!is.na(food$Deaths),] #Plot pdf("visualization.pdf") plot(food$plant, food$Deaths, pch = 19, frame = TRUE, col = "blue", main = "Mortality rate from Covid-19 vs plant-based product consumption rate", xlab = "Plant-based product consumption rate (%)", ylab = "Mortality rate from Covid-19 (%)") abline(lm(food$Deaths ~ food$plant), col = "red") dev.off()

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/man/draw_null_bar.Rd

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ABMI/GEMINI

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refs/heads/g_temp

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2020-02-13T08:53:40

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draw_null_bar.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/Draw_function.R \name{draw_null_bar} \alias{draw_null_bar} \title{Draw bar plot with null ratio about CDM data} \usage{ draw_null_bar(dbName, rds, rdsConcept) } \arguments{ \item{dbName}{CDM database name} \item{rds}{loaded CDM data} \item{rdsConcept}{rds} } \value{ Return two bar plot. } \description{ \code(draw_null_bar) Draw bar plot with null ratio } \details{ \code(draw_null_bar) Draw bar plot with null ratio } \examples{ \dontrun{ draw_null_bar(dbName = 'MYCDM', rds = loaded_rds, rdsConcept = 'persontbl_location') } }

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/man/EllipticalSymmetry.Rd

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refs/heads/master

2023-05-25T00:55:19.097550

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EllipticalSymmetry.Rd

\name{EllipticalSymmetry} \alias{EllipticalSymmetry} \title{Generating function for EllipticalSymmetry-class} \description{Generates an object of class \code{"EllipticalSymmetry"}.} \usage{EllipticalSymmetry(SymmCenter = 0)} \arguments{ \item{SymmCenter}{ numeric: center of symmetry } } %\details{} \value{Object of class \code{"EllipticalSymmetry"}} %\references{} \author{Matthias Kohl \email{Matthias.Kohl@stamats.de}} %\note{} \seealso{\code{\link{EllipticalSymmetry-class}}, \code{\link{DistributionSymmetry-class}}} \examples{ EllipticalSymmetry() ## The function is currently defined as function(SymmCenter = 0){ new("EllipticalSymmetry", SymmCenter = SymmCenter) } } \concept{elliptical symmetry} \concept{symmetry} \keyword{distribution}

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/Runway_and_HVP_NFF_ECN_Date_Table.r

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Waylan49/Productivity_and_Quality_Data_Analysis

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Runway_and_HVP_NFF_ECN_Date_Table.r

setwd("C:/Users/Weilun_Chiu/Desktop/QWE/Qspeak") test1<-read_xlsx("Qspeak_Report.xlsx", sheet = 1, na = "") ### Be aware of "NA" problem temp<-test1 %>% select(AssemblyNo, Fault.Code.1) sum_record<-data.frame(table(temp$AssemblyNo)) sum_record<-sum_record %>% arrange(desc(Freq)) sum_qty<-sum(sum_record$Freq) nff<-temp %>% filter(is.na(Fault.Code.1)) nff_record<-data.frame(table(nff$AssemblyNo)) %>% arrange(desc(Freq)) names(nff_record)<-c("Assy", "Qty.") nffpart<-as.character(nff_record$Assy) len1<-length(nffpart) total_qty<-rep(0, len1) for(i in 1:len1){ ind<-which(sum_record$Var1==nffpart[i]) total_qty[i]<-sum_record$Freq[ind] } nff_record<-cbind(nff_record, total_qty) nff_record$percent<-round(nff_record$Qty./nff_record$total_qty, 3) nff_qty<-sum(nff_record$Qty.) nff_rate<-as.data.frame(round(t(data.frame(NFF=nff_qty, Sum=sum_qty, Percent=nff_qty/sum_qty)),4)) nff_top5<-nff_record[1:5, 1:2] others<-c("Others",nff_qty-sum(nff_top5$Qty.)) Total_NFF<-c("Total NFF", nff_qty) nff_top5$Assy<-as.character(nff_top5$Assy) nff_top5_1<-rbind(nff_top5, others, Total_NFF) nff_record<-nff_record %>% arrange(desc(percent)) %>% select(Assy, total_qty, Qty., percent) top5_nff_percent<-nff_record[1:5, ] nff_record1<-nff_record %>% arrange(desc(Qty.)) top5_nff_qty_percent<-nff_record1[1:5, ] top20_nff_percent<-nff_record[1:20, ] NFF_Final<-list(NFF_Rate=nff_rate, NFF_TOP5=nff_top5_1, TOP5_NFF_Percent=top5_nff_percent, TOP5_NFF_Qty_Percent=top5_nff_qty_percent, TOP20_NFF_Percent=top20_nff_percent, Raw.NFF=nff_record) write_xlsx(NFF_Final, "NFF_Data.xlsx") ecn<-temp %>% filter(Fault.Code.1 =="101") ecn_record<-data.frame(table(ecn$AssemblyNo)) %>% arrange(desc(Freq)) names(ecn_record)<-c("Assy", "Qty.") ecnpart<-as.character(ecn_record$Assy) len2<-length(ecnpart) total_qty1<-rep(0, len1) for(i in 1:len2){ ind<-which(sum_record$Var1==ecnpart[i]) total_qty1[i]<-sum_record$Freq[ind] } ecn_record<-cbind(ecn_record, total_qty1) ecn_record$percent<-round(ecn_record$Qty./ecn_record$total_qty, 3) ecn_record<-ecn_record %>% select(Assy, total_qty1, Qty., percent) ecn_qty<-sum(ecn_record$Qty.) ecn_rate<-as.data.frame(round(t(data.frame(ECN=ecn_qty, Sum=sum_qty, Percent=ecn_qty/sum_qty)),4)) ecn_top5<-ecn_record[1:5, c(1,3)] others<-c("Others",ecn_qty-sum(ecn_top5$Qty.)) Total_ECN<-c("Total ECN", ecn_qty) ecn_top5$Assy<-as.character(ecn_top5$Assy) ecn_top5_1<-rbind(ecn_top5, others, Total_ECN) ecn_record<-ecn_record %>% arrange(desc(percent)) top5_ecn_percent<-ecn_record[1:5, ] ecn_record1<-ecn_record %>% arrange(desc(Qty.)) top5_ecn_qty_percent<-ecn_record1[1:5, ] top20_ecn_percent<-ecn_record[1:20, ] ECN_Final<-list(ECN_Rate=ecn_rate, ECN_TOP5=ecn_top5_1, TOP5_ECN_Percent=top5_ecn_percent, TOP5_ECN_Qty_Percent=top5_ecn_qty_percent, TOP20_ECN_Percent=top20_ecn_percent, Raw.ECN=ecn_record) write_xlsx(ECN_Final, "ECN_Data.xlsx") #############################HVP Section temp<-QHVP %>% select(SupplierID, AssemblyNo, NFF) temp1<-mutate(temp, Type=ifelse(SupplierID %in% c("AMD", "INTEL", "INTEl", "IMTEL", "CAVIUM", "AMD"), "CPU", "Memory")) mem<-temp1 %>% filter(Type=="Memory") mem1<-mem[, c(2,3)] cpu<-temp1 %>% filter(Type=="CPU") cpu1<-cpu[, c(2,3)] #########################################################Memory mem_total<-data.frame(table(mem1$AssemblyNo)) MEM_TOTAL_QTY<-sum(mem_total$Freq) mem_nff<-mem1 %>% filter(NFF=="Y") mem_nff1<-data.frame(table(mem_nff$AssemblyNo)) mem_nff_part<-as.character(mem_nff1$Var1) len1<-length(mem_nff_part) total_qty<-rep(0, len1) for(i in 1:len1){ ind<-which(mem_total$Var1==mem_nff_part[i]) total_qty[i]<-mem_total$Freq[ind] } mem_nff_2<-cbind(mem_nff1, total_qty) mem_nff_3<-mem_nff_2 %>% select(Var1, total_qty, Freq) names(mem_nff_3)<-c("Assy", "Total_Qty", "NFF") mem_nff_3$Percent<-round(mem_nff_3$NFF/mem_nff_3$Total_Qty, 4) MEM_NFF_QTY<-sum(mem_nff_3$NFF) MEM_NFF_PERCENT<-round(MEM_NFF_QTY/MEM_TOTAL_QTY, 4) mem_nff_rate<-as.data.frame(t(data.frame(NFF=MEM_NFF_QTY, Sum=MEM_TOTAL_QTY, Percent=MEM_NFF_PERCENT))) mem_nff_top5<-mem_nff_3 %>% arrange(desc(NFF)) %>% select(Assy, NFF) mem_nff_top5<-mem_nff_top5[1:5,] Others<-c("Others", MEM_NFF_QTY-sum(mem_nff_top5$NFF)) Total<-c("Total", MEM_NFF_QTY) mem_nff_top5$Assy<-as.character(mem_nff_top5$Assy) mem_nff_top5<-rbind(mem_nff_top5, Others, Total) mem_nff_3<-mem_nff_3 %>% arrange(desc(NFF)) %>% arrange(desc(Percent)) mem_top5_nff_percent<-mem_nff_3[1:5,] mem_nff_4<-mem_nff_3 %>% arrange(desc(NFF)) mem_top5_nff_qty<-mem_nff_4[1:5, ] MEM_NFF_FINAL<-list(MEM_NFF_RATE=mem_nff_rate, MEM_NFF_TOP5=mem_nff_top5, MEM_TOP5_NFF_PERCENT=mem_top5_nff_percent, MEM_TOP5_NFF_QTY=mem_top5_nff_qty, Raw.MEM.NFF=mem_nff_3) write_xlsx(MEM_NFF_FINAL, "MEM_NFF_Data.xlsx") #########################################################CPU cpu_total<-data.frame(table(cpu1$AssemblyNo)) CPU_TOTAL_QTY<-sum(cpu_total$Freq) cpu_nff<-cpu1 %>% filter(NFF=="Y") cpu_nff1<-data.frame(table(cpu_nff$AssemblyNo)) cpu_nff_part<-as.character(cpu_nff1$Var1) len1<-length(cpu_nff_part) total_qty1<-rep(0, len1) for(i in 1:len1){ ind<-which(cpu_total$Var1==cpu_nff_part[i]) total_qty1[i]<-cpu_total$Freq[ind] } cpu_nff_2<-cbind(cpu_nff1, total_qty1) cpu_nff_3<-cpu_nff_2 %>% select(Var1, total_qty1, Freq) names(cpu_nff_3)<-c("Assy", "Total_Qty", "NFF") cpu_nff_3$Percent<-round(cpu_nff_3$NFF/cpu_nff_3$Total_Qty, 4) CPU_NFF_QTY<-sum(cpu_nff_3$NFF) CPU_NFF_PERCENT<-round(CPU_NFF_QTY/CPU_TOTAL_QTY, 4) cpu_nff_rate<-as.data.frame(t(data.frame(NFF=CPU_NFF_QTY, Sum=CPU_TOTAL_QTY, Percent=CPU_NFF_PERCENT))) cpu_nff_top5<-cpu_nff_3 %>% arrange(desc(NFF)) %>% select(Assy, NFF) cpu_nff_top5<-cpu_nff_top5[1:5,] Others<-c("Others", CPU_NFF_QTY-sum(cpu_nff_top5$NFF)) Total<-c("Total", CPU_NFF_QTY) cpu_nff_top5$Assy<-as.character(cpu_nff_top5$Assy) cpu_nff_top5<-rbind(cpu_nff_top5, Others, Total) cpu_nff_3<-cpu_nff_3 %>% arrange(desc(NFF)) %>% arrange(desc(Percent)) cpu_top5_nff_percent<-cpu_nff_3[1:5,] cpu_nff_4<-cpu_nff_3 %>% arrange(desc(NFF)) cpu_top5_nff_qty<-cpu_nff_4[1:5, ] CPU_NFF_FINAL<-list(CPU_NFF_RATE=cpu_nff_rate, CPU_NFF_TOP5=cpu_nff_top5, CPU_TOP5_NFF_PERCENT=cpu_top5_nff_percent, CPU_TOP5_NFF_QTY=cpu_top5_nff_qty, Raw.CPU.NFF=cpu_nff_3) write_xlsx(CPU_NFF_FINAL, "CPU_NFF_Data.xlsx")

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2023-07-22T15:18:46.363446

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anc.ML.R

## lightweight version of ace(...,method="ML") for continuous traits ## also allows missing data in x, in which case missing data are also estimated ## written by Liam J. Revell 2011, 2013, 2014, 2015 anc.ML<-function(tree,x,maxit=2000,model=c("BM","OU","EB"),...){ if(!inherits(tree,"phylo")) stop("tree should be an object of class \"phylo\".") if(model[1]=="BM") obj<-anc.BM(tree,x,maxit,...) else if(model[1]=="OU") obj<-anc.OU(tree,x,maxit,...) else if(model[1]=="EB") obj<-anc.EB(tree,x,maxit,...) else stop(paste("Do not recognize method",model)) obj } ## internal to estimate ancestral states under a BM model ## written by Liam J. Revell 2011, 2013, 2014, 2016, 2018 anc.BM<-function(tree,x,maxit,...){ if(hasArg(trace)) trace<-list(...)$trace else trace<-FALSE if(hasArg(vars)) vars<-list(...)$vars else vars<-FALSE if(hasArg(CI)) CI<-list(...)$CI else CI<-FALSE if(hasArg(se)) se<-list(...)$se else se<-setNames(rep(0,length(x)),names(x)) SE<-setNames(rep(0,Ntip(tree)),tree$tip.label) SE[names(se)]<-se E<-diag(SE) colnames(E)<-rownames(E)<-names(SE) if(hasArg(tol)) tol<-list(...)$tol else tol<-10*.Machine$double.eps ## check to see if any tips are missing data xx<-setdiff(tree$tip.label,names(x)) ## function returns the log-likelihood likelihood<-function(par,C,invC,detC,xvals,msp,trace,E=0){ sig2<-par[1] a<-par[2] y<-par[1:(tree$Nnode-1)+2] xvals<-c(xvals,setNames(par[1:length(msp)+tree$Nnode+1],msp)) xvals<-xvals[rownames(C)[1:length(tree$tip.label)]] z<-c(xvals,y)-a if(trace) cat(paste(round(sig2,6)," --- ",sep="")) if(sum(E)>0){ C<-sig2*C C[rownames(E),colnames(E)]<-C[rownames(E),colnames(E)]+E invC<-solve(C) detC<-determinant(C,logarithm=TRUE)$modulus[1] } logLik<-(-z%*%invC%*%z/(2*sig2)-nrow(C)*log(2*pi)/2-nrow(C)*log(sig2)/2- detC/2)[1,1] if(trace) cat(paste(round(logLik,6),"\n")) -logLik } ## compute C C<-vcvPhylo(tree) invC<-solve(C) detC<-determinant(C,logarithm=TRUE)$modulus[1] ## assign starting values zz<-fastAnc(tree,c(x,setNames(rep(mean(x),length(xx)),xx))) y<-zz[2:length(zz)] a<-zz[1] bb<-c(c(x,setNames(rep(mean(x),length(xx)),xx))[tree$tip.label],y) sig2<-((bb-a)%*%invC%*%(bb-a)/nrow(C))[1,1] fit<-optim(c(sig2,a,y,rep(mean(x),length(xx))),fn=likelihood,C=C,invC=invC, detC=detC,xvals=x,msp=xx,trace=trace,E=E,method="L-BFGS-B", lower=c(tol,rep(-Inf,tree$Nnode+length(xx))), control=list(maxit=maxit)) if(vars||CI){ H<-hessian(likelihood,fit$par,C=C,invC=invC,detC=detC, xvals=x,msp=xx,trace=trace,E=E) vcv<-solve(H) } states<-fit$par[1:tree$Nnode+1] names(states)<-c(length(tree$tip)+1,rownames(C)[(length(tree$tip)+1):nrow(C)]) obj<-list(sig2=fit$par[1],ace=states,logLik=-fit$value,counts=fit$counts, convergence=fit$convergence,message=fit$message,model="BM") if(vars) obj$var<-setNames(diag(vcv)[1:tree$Nnode+1], c(length(tree$tip)+1,rownames(C)[(length(tree$tip)+1):nrow(C)])) if(CI){ obj$CI95<-cbind(obj$ace-1.96*sqrt(diag(vcv)[1:tree$Nnode+1]), obj$ace+1.96*sqrt(diag(vcv)[1:tree$Nnode+1])) rownames(obj$CI95)<-c(length(tree$tip)+1, rownames(C)[(length(tree$tip)+1):nrow(C)]) } if(length(xx)>0){ obj$missing.x<-setNames(fit$par[1:length(xx)+tree$Nnode+1],xx) if(vars) obj$missing.var<-setNames(diag(vcv)[1:length(xx)+ tree$Nnode+1],xx) if(CI){ obj$missing.CI95<-cbind(obj$missing.x- 1.96*sqrt(diag(vcv)[1:length(xx)+tree$Nnode+1]), obj$missing.x+1.96*sqrt(diag(vcv)[1:length(xx)+tree$Nnode+1])) rownames(obj$missing.CI95)<-xx } } class(obj)<-"anc.ML" obj } ## internal to estimate ancestral states under an OU model (this may not work) ## written by Liam J. Revell 2014 anc.OU<-function(tree,x,maxit=2000,...){ ## check to see if any tips are missing data xx<-setdiff(tree$tip.label,names(x)) if(length(xx)>0) stop("Some tips of the tree do not have data. Try model=\"BM\".") if(hasArg(tol)) tol<-list(...)$tol else tol<-1e-8 if(hasArg(trace)) trace<-list(...)$trace else trace<-FALSE if(hasArg(a.init)) a.init<-list(...)$a.init else a.init<-2*log(2)/max(nodeHeights(tree)) likOU<-function(par,tree,x,trace){ sig2<-par[1] alpha<-par[2] a0<-par[3] a<-par[1:(tree$Nnode-1)+3] logLik<-logMNORM(c(x,a),rep(a0,Ntip(tree)+tree$Nnode-1),sig2*vcvPhylo(tree,model="OU",alpha=alpha)) if(trace) print(c(sig2,alpha,logLik)) -logLik } x<-x[tree$tip.label] pp<-rep(NA,tree$Nnode+2) pp[1:tree$Nnode+2]<-fastAnc(tree,x) pp[1]<-phyl.vcv(as.matrix(c(x,pp[2:tree$Nnode+2])),vcvPhylo(tree),lambda=1)$R[1,1] pp[2]<-a.init ## arbitrarily fit<-optim(pp,likOU,tree=tree,x=x,trace=trace,method="L-BFGS-B", lower=c(tol,tol,rep(-Inf,tree$Nnode)),upper=rep(Inf,length(pp)), control=list(maxit=maxit)) obj<-list(sig2=fit$par[1],alpha=fit$par[2], ace=setNames(fit$par[1:tree$Nnode+2],1:tree$Nnode+length(tree$tip.label)), logLik=-fit$value,counts=fit$counts,convergence=fit$convergence, message=fit$message,model="OU") class(obj)<-"anc.ML" obj } ## EB is the Early-burst model (Harmon et al. 2010) and also called the ACDC model ## (accelerating-decelerating; Blomberg et al. 2003). Set by the a rate parameter, EB fits a model where ## the rate of evolution increases or decreases exponentially through time, under the model ## r[t] = r[0] * exp(a * t), where r[0] is the initial rate, a is the rate change parameter, and t is ## time. The maximum bound is set to -0.000001, representing a decelerating rate of evolution. The minimum ## bound is set to log(10^-5)/depth of the tree. ## internal to estimate ancestral states under an EB model ## written by Liam J. Revell 2017 anc.EB<-function(tree,x,maxit=2000,...){ ## check to see if any tips are missing data xx<-setdiff(tree$tip.label,names(x)) if(length(xx)>0) stop("Some tips of the tree do not have data. Try model=\"BM\".") if(hasArg(tol)) tol<-list(...)$tol else tol<-1e-8 if(hasArg(trace)) trace<-list(...)$trace else trace<-FALSE if(hasArg(vars)) vars<-list(...)$vars else vars<-FALSE if(hasArg(CI)) CI<-list(...)$CI else CI<-FALSE if(hasArg(r.init)){ r.init<-list(...)$r.init obj<-phyl.vcv(as.matrix(x[tree$tip.label]), vcv(ebTree(tree,r.init)),1) s2.init<-obj$R[1,1] a0.init<-obj$alpha[1,1] } else { ## optimize r.init lik<-function(p,tree,x) logLik<--logMNORM(x,rep(p[3],Ntip(tree)), p[1]*vcvPhylo(tree,model="EB",r=p[2],anc.nodes=F)) obj<-phyl.vcv(as.matrix(x[tree$tip.label]),vcv(tree),1) fit.init<-optim(c(obj$R[1,1],0,obj$alpha[1,1]), lik,tree=tree,x=x,method="L-BFGS-B",lower=c(tol,-Inf,-Inf), upper=rep(Inf,3)) r.init<-fit.init$par[2] s2.init<-fit.init$par[1] a0.init<-fit.init$par[3] } likEB<-function(par,tree,x,trace){ sig2<-par[1] r<-par[2] obj<-fastAnc(ebTree(tree,r),x) a0<-obj[1] a<-obj[2:length(obj)] logLik<-logMNORM(c(x,a),rep(a0,Ntip(tree)+tree$Nnode-1), sig2*vcvPhylo(tree,model="EB",r=r)) if(trace) print(c(sig2,r,logLik)) -logLik } x<-x[tree$tip.label] pp<-rep(NA,2) pp[1]<-s2.init pp[2]<-r.init fit<-optim(pp,likEB,tree=tree,x=x,trace=trace,method="L-BFGS-B", lower=c(tol,-Inf),upper=rep(Inf,2),control=list(maxit=maxit)) obj<-list(sig2=fit$par[1],r=fit$par[2], ace=unclass(fastAnc(ebTree(tree,fit$par[2]),x)), logLik=-fit$value,counts=fit$counts,convergence=fit$convergence, message=fit$message,model="EB") if(vars||CI){ likEB.hessian<-function(par,tree,y){ sig2<-par[1] r<-par[2] a<-par[3:length(par)] logLik<-logMNORM(c(y,a[2:length(a)]),rep(a[1],Ntip(tree)+tree$Nnode-1), sig2*vcvPhylo(tree,model="EB",r=r)) -logLik } H<-hessian(likEB.hessian,c(fit$par,obj$ace),tree=tree,y=x) vcv<-solve(H) if(vars) obj$var<-setNames(diag(vcv)[1:tree$Nnode+1],1:tree$Nnode+Ntip(tree)) if(CI){ obj$CI95<-cbind(obj$ace-1.96*sqrt(diag(vcv)[1:tree$Nnode+2]), obj$ace+1.96*sqrt(diag(vcv)[1:tree$Nnode+2])) rownames(obj$CI95)<-1:tree$Nnode+Ntip(tree) } } class(obj)<-"anc.ML" obj } logMNORM<-function(x,x0,vcv) -t(x-x0)%*%solve(vcv)%*%(x-x0)/2-length(x)*log(2*pi)/2-determinant(vcv,logarithm=TRUE)$modulus[1]/2 ## print method for "anc.ML" ## written by Liam J. Revell 2015, 2016 print.anc.ML<-function(x,digits=6,printlen=NULL,...){ cat(paste("Ancestral character estimates using anc.ML under a(n)", x$model,"model:\n")) Nnode<-length(x$ace) if(is.null(printlen)||printlen>=Nnode) print(round(x$ace,digits)) else printDotDot(x$ace,digits,printlen) if(!is.null(x$var)){ cat("\nVariances on ancestral states:\n") if(is.null(printlen)||printlen>=Nnode) print(round(x$var,digits)) else printDotDot(x$var,digits,printlen) } if(!is.null(x$CI95)){ cat("\nLower & upper 95% CIs:\n") colnames(x$CI95)<-c("lower","upper") if(is.null(printlen)||printlen>=Nnode) print(round(x$CI95,digits)) else printDotDot(x$CI95,digits,printlen) } cat("\nFitted model parameters & likelihood:\n") if(x$model=="BM"){ obj<-data.frame(round(x$sig2,digits),round(x$logLik,digits)) colnames(obj)<-c("sig2","log-likelihood") rownames(obj)<-"" print(obj) } else if(x$model=="OU"){ obj<-data.frame(round(x$sig2,digits),round(x$alpha,digits), round(x$logLik,digits)) colnames(obj)<-c("sigma^2","alpha","logLik") rownames(obj)<-"" print(obj) } else if(x$model=="EB"){ obj<-data.frame(round(x$sig2,digits),round(x$r,digits), round(x$logLik,digits)) colnames(obj)<-c("sigma^2","r","logLik") rownames(obj)<-"" print(obj) } if(x$convergence==0) cat("\nR thinks it has found the ML solution.\n\n") else cat("\nOptimization may not have converged.\n\n") } ## S3 logLik method for "anc.ML" object class logLik.anc.ML<-function(object,...){ lik<-object$logLik if(object$model=="BM") attr(lik,"df")<-length(object$ace)+1 else if(object$model=="EB") attr(lik,"df")<-length(object$ace)+2 else if(object$model=="OU") attr(lik,"df")<-length(object$ace)+2 lik }

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/man/calEquation.Rd

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cardiomoon/predict3d

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refs/heads/master

2023-04-13T18:47:48.816345

2023-04-11T11:01:31

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calEquation.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/add_lines.R \name{calEquation} \alias{calEquation} \title{calculated slope and intercept from object of class lm} \usage{ calEquation( fit, mode = 1, pred = NULL, modx = NULL, modx.values = NULL, label = NULL, maxylev = 6, digits = 2 ) } \arguments{ \item{fit}{An object of class lm} \item{mode}{A numeric} \item{pred}{name of predictor variable} \item{modx}{name of modifier variable} \item{modx.values}{Numeric. Values of modifier variable} \item{label}{A character string} \item{maxylev}{maximum length of unique value of variable to be treated as a categorial variable} \item{digits}{Integer indicating the number of decimal places} } \description{ calculated slope and intercept from object of class lm } \examples{ fit=lm(mpg~wt*hp+carb,data=mtcars) calEquation(fit) calEquation(fit,pred="hp") }

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/shiny_project.r

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Ferrah-hichem/shiny_dashboard

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shiny_project.r

library(shiny) library(Hmisc) library(UsingR) library(reshape2) library(ggpubr) library(shinydashboard) library(DT) library(plyr) library(plotly) library(reader) library("ElemStatLearn") library("class") library("plotrix") library(CatEncoders) library(MLmetrics) library(ROSE) library(smotefamily) library(ggplot2) library(GGally) ui <- dashboardPage( dashboardHeader( title="FERRAH & GHARBI" ), dashboardSidebar( #chargement du fichier fileInput(inputId = "file1", label = "Veuillez choisir votre fichier CSV", accept = c("text/plain", ".csv") ), sidebarMenu( #Donnees quantitatives discrÃ¨tes: menuItem("Accueil", tabName = "accueill", icon = icon("home")), menuItem("Le dataset", tabName = "readData", icon = icon("table")), menuItem("Analyse des Donnees", tabName="datanalys", icon=icon("chart-line"), menuSubItem("Univariee", tabName = "quant", icon=icon("dice-one")), menuSubItem("Bivariee", tabName = "qual", icon=icon("dice-two")), menuSubItem("Qualitative VS Quantitative", tabName = "vs", icon=icon("chart-pie")), menuSubItem("Qualitative VS Qualitative", tabName = "vs2", icon=icon("chart-pie"))), menuItem("Churn", tabName="churn", icon=icon("recycle"), menuSubItem("Churn Variable", tabName = "ChurnIn", icon=icon("dice-one")), menuSubItem("Churn Vs Qualitatives", tabName = "ChurnQual", icon=icon("dice-one")), menuSubItem("Churn Vs Quantitatives", tabName = "ChurnQuant", icon=icon("dice-one")) ), menuItem("Modeles d'apprentissages", tabName = "app", icon=icon("recycle")), menuItem("Balances des Donnees", tabName="databalance", icon=icon("recycle")) ) ), dashboardBody( tags$head(tags$style(HTML(' .content-wrapper { background-color: #fcfffff5; } .main-sidebar{ background-color: #595f5e !important; } .navbar { background-color: #595f5e !important; } .col-sm-8{ width :100% !important; } .sidebar-toggle:hover{ background-color: #595f5e !important; } .logo{ background-color: #595f5e !important; } ' ))), tabItems( tabItem(tabName = "readData", h3("Les données du dataset IRIS",align = "center"), #tableOutput(outputId = "contents") DT::dataTableOutput("contents") ), tabItem(tabName = "analydata", tabItem(tabName = "Something"), ), tabItem(tabName = "stat", h3("Statistiques ",align = "center"), tableOutput(outputId = "statistique") ), tabItem(tabName = "qual", mainPanel( fluidRow( column(6,uiOutput('quantlistbi1'), #varSelectInput("choix", "Le choix de la variable", data(), multiple = FALSE), #selectInput("choix", "Le choix de la variable", #tableOutput(outputId = "input_list"),selected = 1), ), column(6,uiOutput('quantlistbi2'), #selectInput("choixx", "Le choix de la variable", # tableOutput(outputId = "input_list"),selected = 1), ) ), tabsetPanel( tabPanel("Nuage de points", fluidRow( h3("Nuage de point avec la regression linéaire", align="center"), column(6, plotOutput("nuagePoints")), column(6, textOutput("correlation")) ) ), tabPanel("Histogrammes dos à dos", fluidRow( column(8, offset = 1, plotOutput("histbackback")) ), style="padding-left: 150px; margin-top: 10px; padding-right: 350px;" ), tabPanel("Nuage de points et Histogrammes", fluidRow( column(8, offset = 1, plotOutput("nuagePointshist")) ), style="padding-left: 150px; margin-top: 10px; padding-right: 350px;" ), tabPanel("Caractéristiques", tableOutput("caract")), tabPanel("Boîtes parallèles", fluidRow( column(6, plotOutput("boxplotBasic")), column(6, plotOutput("boxplotGgplot")) ), fluidRow( column(4, offset = 4, textOutput("cor")) ) ) ) )), tabItem(tabName = "quant", tabsetPanel( tabPanel("Variables Quantitatives", mainPanel(uiOutput('quantlist'), #selectInput("radio", "Le choix de la variable", # tableOutput(outputId = "input_quant"),selected = 1), tabsetPanel( tabPanel("Le SUMMARY", fluidRow( h3("Le summary de la variable choisie", align = "center"), # Affichage d'un summary verbatimTextOutput(outputId = "summary") ) ), tabPanel("Graphes + boxplot", fluidRow( column(4, # Zone d'affichage du diagramme en bÃÂÃÂ¢tons des effectifs plotOutput(outputId = "effectifsDiag")), column(4, # Zone d'affichage du diagramme en bÃÂÃÂ¢tons des effectifs cumulÃÂÃÂ©s plotOutput(outputId = "effectifsCumDiag")), column(4, # Zone d'affichage de la boÃÂÃÂ®te ÃÂÃÂ moustaches plotOutput(outputId = "boiteMoustaches")) ) ), tabPanel("Histogrammes et courbes ", h3("Visualisation des graphes ", align = "center"), fluidRow( column(4, h5("Histogramme des effectifs", align = "center"), plotOutput(outputId = "effectifsHist")), column(4, h5("Histogramme des densitÃ©s de frequences", align = "center"), plotOutput(outputId = "effectifsHistFreqDens")), column(4, h5("Courbe cumulative", align ="center"), plotOutput(outputId = "effectifsCumCurve")) ), ) ) ) ), tabPanel("Variables Qualitatives", fluidRow(uiOutput('qualist'), tabsetPanel( tabPanel("Histogramme des effectifs",fluidPage(column(6, h5("Histogramme des effectifs", align = "center"), tableOutput(outputId = "statqq")), column(6, h5("Courbe cumulative", align ="center"), plotOutput(outputId = "effectifsDiagq")))), tabPanel("Diagrammes",fluidPage(column(6, h5("Diagramme en colonnes", align = "center"), plotOutput("colonnes")), column(6, h5("Diagramme en secteurs", align ="center"), plotOutput("secteurs")))) ), )) ), ), tabItem(tabName = "vs", h3("Quantitative VS Qualitative ",align = "center"), mainPanel( fluidRow( column(6,uiOutput('quantlistvs'), #varSelectInput("choix", "Le choix de la variable", data(), multiple = FALSE), #selectInput("choix", "Le choix de la variable", #tableOutput(outputId = "input_list"),selected = 1), ), column(6,uiOutput('qualistvs'), #selectInput("choixx", "Le choix de la variable", # tableOutput(outputId = "input_list"),selected = 1), ) ), tabsetPanel( tabPanel("Diag. Barres (1 var.)", fluidRow( column(6, plotOutput("barplotUni")), column(6, plotOutput("barplotOrderedUni")) ) ), tabPanel("Diag. Barres (2 var.)", fluidRow( column(6, plotOutput("barplotBi")), column(6, plotOutput("barplotDodgeBi")) ) ) ) , style = "font-size: 75%")), tabItem(tabName = "vs2", h3("Qualitative VS Qualitative ",align = "center"), mainPanel( fluidRow( column(6,uiOutput('qualistvs1'), #varSelectInput("choix", "Le choix de la variable", data(), multiple = FALSE), #selectInput("choix", "Le choix de la variable", #tableOutput(outputId = "input_list"),selected = 1), ), column(6,uiOutput('qualistvs2'), #selectInput("choixx", "Le choix de la variable", # tableOutput(outputId = "input_list"),selected = 1), ) ), tabsetPanel( tabPanel("Diag. Profils", fluidRow( column(6, plotOutput("barplotProfils")), column(6, tableOutput("contingency")) ) ), tabPanel("Indices", fluidRow( column(6, offset = 2, tableOutput("force")) ) ) ) , style = "font-size: 75%")), tabItem(tabName = "accueill", h3(" A propos du tp ",align = "center"), br(), br(), br(), strong("Problèmatique", style="font-size :15pt"), br(), br(), p("Le travail présenté dans cette application, rentre dans le cadre d’un Projet du module Data Science 2 pour les étudiants MLDS de l’université de Paris pour l’année universitaire 2020/2021. Le travail consiste de faire une analyse uni-variée, bi-variée, le traitement des donnees, la comparaison des methodes de classification et le churn sur l’ensemble du dataset choisi . L’application interagit d’une manière réactive à la demande de l’utilisateur. L’utilisateur à la possibilité de l’explorer. ", style=" font-size :13pt"), br(), br(), strong("Données utilisées :", style="font-size :15pt"), p("Le Data-set utilisé dans ce travail est un data-set classique intitulé « Bank additional » qui regroupe 41189 lignes d’observations de données des clients de la banque qui sont identifiées par 21 variables (9 quantitatives et 12 qualitative). ", style=" font-size :13pt"), br(), br(), br(), br(), br(), br(), br(), h3("Réalisé Par : FERRAH Hichem & GHARBI Mohamed",align = "center") ), #-------------------- CHURN ------------- tabItem(tabName = "ChurnIn", uiOutput('churnchoice'), plotlyOutput(outputId = "churnIntro1")), tabItem(tabName = "ChurnQual", uiOutput('churnqual'),plotlyOutput(outputId = "churnqualplot"), ), tabItem(tabName = "ChurnQuant", uiOutput('churnquant'), tabsetPanel( tabPanel("Pie Charts", sliderInput('sliderquant', "Ajuster la distribution pour ameliorer le Rendu ", 1, 5, 3,1),plotlyOutput(outputId = "churnquantplot") ), tabPanel("Bar plots", plotlyOutput(outputId = "churnquantplot2")) , tabPanel("pairwise Scatter", uiOutput('pairwise1'),uiOutput('pairwise2'), plotOutput(outputId = "churnquantplot3") ) ) ), #---------------------- Apprentissage Unbalanced ------------ tabItem(tabName = "app", mainPanel( tabsetPanel( tabPanel("KNN", fluidRow( h3("Apprentissage Supervise knn", align="center"), column(6, plotOutput("plot1")) ) ), tabPanel("LR", fluidRow( h3("Apprentissage Supervise LR", align="center"), column(8, offset = 1, plotOutput("plot2"), style="width: 850px !important") ) )))), #---------------------- data Balancing --------------- tabItem(tabName = "databalance", mainPanel( tabsetPanel( tabPanel("Equilibre", fluidRow( column(12,uiOutput("varbalance"),uiOutput("sampling"),uiOutput("go"),plotlyOutput("compare") ) ) ), tabPanel("KNN", fluidRow( h3("Apprentissage Supervise knn", align="center"), column(6, plotOutput("plot4")) ) ), tabPanel("LR", fluidRow( h3("Apprentissage Supervise LR", align="center"), column(8, offset = 1, plotOutput("plot5"), style="width: 850px !important") ) )))) ) ) ) server <- function(input, output) { options(shiny.maxRequestSize=200*1024^2) data <- reactive({ # Initialement, class(input$file1) = NULL # AprÃÂÃÂ¨s chargement, class(input$file1) = data.frame # avec les colonnes 'size', 'type', and 'datapath' columns. inFile <- input$file1 if (is.null(inFile)) return(NULL) delim<-get.delim(inFile$datapath, n = 10, comment = "#", skip = 0, delims = c("\t", "\t| +", " ", ";", ","), large = 10, one.byte = TRUE) print(delim) read.csv(inFile$datapath, header = TRUE, sep=delim) # data <- readLines(con <- file) # records <- sapply(data, strsplit, split=separators) # dataFrame <- data.frame(t(sapply(records,c))) # rownames(dataFrame) <- 1: nrow(dataFrame) # return(as.data.frame(dataFrame,stringsAsFactors = FALSE)) }) #data <- read.csv(url("https://raw.githubusercontent.com/Ferrah-hichem/datasets/master/bank-full.csv"), header = TRUE, sep=';') #input_list <- reactive({)}) # output$input_list <- renderTable(colnames(data)) # # input_list <<- colnames(data) # # input_quant <<- reactive({colnames(data)[!grepl('factor|logical|character',sapply(data,class))]}) # # input_qual <<- reactive({colnames(data)[grepl('factor|logical|character',sapply(data,class))]}) # output$input_quant <- renderTable(colnames(data)[!grepl('factor|logical|character',sapply(data,class))]) # output$input_qual <- renderTable(colnames(data)[grepl('factor|logical|character',sapply(data,class))]) # quant<- reactive({ names(data())[!grepl('factor|logical|character',sapply(data(),class))] }) output$list = renderUI({ selectInput('choix', 'Le choix de la variable', names(data())) }) output$list2 = renderUI({ selectInput('choix', 'Le choix de la variable', names(data())) }) output$quantlist = renderUI({ selectInput('radio', 'Le choix de la variable', names(data())[!grepl('factor|logical|character',sapply(data(),class))]) }) output$quantlistbi1 = renderUI({ selectInput('quantlistbi1', 'Le choix de la variable', names(data())[!grepl('factor|logical|character',sapply(data(),class))]) }) output$quantlistbi2 = renderUI({ selectInput('quantlistbi2', 'Le choix de la variable', names(data())[!grepl('factor|logical|character',sapply(data(),class))]) }) output$quantlistvs = renderUI({ selectInput('quantlistvs', 'Le choix de la variable', names(data())[!grepl('factor|logical|character',sapply(data(),class))]) }) output$qualist = renderUI({ selectInput('choixx', 'Le choix de la variable', names(data())[grepl('factor|logical|character',sapply(data(),class))]) }) output$qualistvs = renderUI({ selectInput('qualistvs', 'Le choix de la variable', names(data())[grepl('factor|logical|character',sapply(data(),class))]) }) output$qualistvs1 = renderUI({ selectInput('qualistvs1', 'Le choix de la variable', names(data())[grepl('factor|logical|character',sapply(data(),class))]) }) output$qualistvs2 = renderUI({ selectInput('qualistvs2', 'Le choix de la variable', names(data())[grepl('factor|logical|character',sapply(data(),class))]) }) output$churnchoice = renderUI({ selectInput('churnchoice', 'Le choix de la variable', names(data())) }) output$churnqual = renderUI({ selectInput('churnqual', 'Choisissez une Variable pour la comparer avec la variable Churn', names(data())[grepl('factor|logical|character',sapply(data(),class))]) }) output$churnquant = renderUI({ selectInput('churnquant', 'Choisissez une Variable pour la comparer avec la variable Churn', names(data())[!grepl('factor|logical|character',sapply(data(),class))]) }) output$varbalance = renderUI({ selectInput("varbalance", 'Choisissez une Variable Binaire "Desiquilibre" ', names(data())) }) output$pairwise1 = renderUI({ selectInput('pairwise1', 'Choisissez une 2eme Variable', names(data())[!grepl('factor|logical|character',sapply(data(),class))]) }) output$pairwise2 = renderUI({ selectInput('pairwise2', 'Choisissez une 3eme Variable', names(data())[!grepl('factor|logical|character',sapply(data(),class))]) }) output$go =renderUI({ validate( need(nrow(unique(data()[input$varbalance])) == 2, "... En attente de la variable ...") ) actionButton("go",label="valider",style="margin-left:51%; margin-top:-20%;") }) output$sampling = renderUI({ validate( need(nrow(unique(data()[input$varbalance])) == 2, "Cette Methode marche uniquement avec les variables binaires, veuillez choisir une autre") ) selectInput("sampling","Choisissez La methode du Data balancing",c("Random Oversampling","Random UnderSampling","Both")) }) tabStats <- reactive({ dt = data() dt2 =dt[,input$radio] # Calculer les effectifs et les effectifs cumulÃÂ©s table.tmp <- as.data.frame(table(dt2)) table.tmp <- cbind(table.tmp, cumsum(table.tmp[[2]])) # Calculer les frÃÂ©quences et les frÃÂ©quences cumulÃÂ©s table.tmp <- cbind(table.tmp, table.tmp[[2]]/nrow(data)*100, table.tmp[[3]]/nrow(data)*100) # Ajouter des noms de colonnes colnames(table.tmp) <- c(dt[1,input$radio], "Effectifs", "Effectifs Cum.", "Frequences", "Frequences Cum.") # Renvoyer le tableau statistique return(table.tmp) }) tabStat <- reactive({ dt = data() dt2 =dt[,input$choixx] # Calculer les effectifs et les effectifs cumulÃÂ©s table.tmp <- as.data.frame(table(dt2)) table.tmp <- cbind(table.tmp, cumsum(table.tmp[[2]])) # Calculer les frÃÂ©quences et les frÃÂ©quences cumulÃÂ©s table.tmp <- cbind(table.tmp, table.tmp[[2]]/nrow(data())*100, table.tmp[[3]]/nrow(data())*100) # Ajouter des noms de colonnes colnames(table.tmp) <- c(input$choixx, "Effectifs", "Effectifs Cum.", "Frequences", "Frequences Cum.") # Renvoyer le tableau statistique print(dim(table.tmp[,1])) return(table.tmp) }) # Commande pour le calcul du summary output$contents <- DT::renderDataTable({ #data() DT::datatable( data(), filter = 'top', extensions = c('Buttons'), options = list(scrollY = 650, scrollX = 500, deferRender = TRUE, scroller = TRUE, # paging = TRUE, # pageLength = 25, buttons = list('excel', list(extend = 'colvis', targets = 0, visible = FALSE)), dom = 'lBfrtip', fixedColumns = TRUE), rownames = FALSE) }) # Boîtes parallèles # ---- output$boxplotBasic <- renderPlot({ d <- data() d.stack <- melt(d, measure.vars = quant()) # Boxplot basique d.stack$value <- as.numeric(d.stack$value) boxplot(d.stack$value ~ d.stack$variable , col="grey", xlab = "Modalités", ylab = "Mesures") }) output$boxplotGgplot <- renderPlot({ d <- data() d.stack <- melt(d, measure.vars = quant()) d.stack$value <- as.numeric(d.stack$value) # Boxplot élaborée qplot(x = d.stack[,2], y = d.stack[,1], xlab = "Modalités", ylab = "Mesures", geom=c("boxplot", "jitter"), fill=d.stack[,2]) + theme(legend.title=element_blank()) }) output$histbackback <- renderPlot({ options(digits=1) x.var = input$quantlistbi1 ; y.var = input$quantlistbi2; dt = data() dt2 =dt[,input$quantlistbi1] dt2 = as.numeric(dt2) dt = data() dt3 =dt[,input$quantlistbi2] dt3 = as.numeric(dt3) histbackback(x = dt2, y = dt3, xlab = c(x.var, y.var), main = paste(x.var, "and", y.var), las = 2) }) output$nuagePoints <- renderPlot({ # Simple nuage de point options(scipen=999) x.var = input$quantlistbi1 ; y.var = input$quantlistbi2; plot(x = data()[, x.var], y = data()[, y.var], col = "blue", las = 2, cex.axis = 0.7, main = paste(y.var, "en fonction de", x.var), xlab = x.var, ylab = y.var, cex.lab = 1.2 ) # Droite de régression linéaire (y~x) abline(lm(data()[, y.var]~data()[, x.var]), col="red", lwd = 2) options(scipen=0) }) output$correlation <- renderText({ dt = data() dt2 =dt[,input$quantlistbi1] dt2 = as.numeric(dt2) dt = data() dt3 =dt[,input$quantlistbi2] dt3 = as.numeric(dt3) #x.var = input$choix ; y.var = input$choixx; coeff.tmp <- cov(dt2, dt3)/(sqrt(var(dt2)*var(dt3))) paste("Coefficient de corrélation linéaire =", round(coeff.tmp,digits = 2)) }) output$summary <- renderPrint({ dt = data() dt2 =dt[,input$radio] #print(dt2) t(summary.default(as.numeric(as.character(dt2)))) t(summary(dt2))}) output$statistique <- renderTable({ tabStats() }) output$statqq <- renderTable({ tabStat() }) # Nuage de points et histogrammes # ---- output$nuagePointshist <- renderPlot({ options(digits=1) dt = data() dt2 =dt[,input$quantlistbi1] dt2 = as.numeric(dt2) dt = data() dt3 =dt[,input$quantlistbi2] dt3 = as.numeric(dt3) EF = dt2; CA = dt3; scatter.with.hist( EF, CA) }) output$caract <- renderTable({ # Définition des colonnes choisies var.names <- quant() # Initialisation de la table caract.df <- data.frame() # Pour chaque colonne, calcul de min, max, mean et ecart-type for(strCol in var.names){ caract.vect <- c(min(data()[, strCol]), max(data()[,strCol]), mean(var(data()[,strCol])), sqrt(var(data()[,strCol]))) caract.df <- rbind.data.frame(caract.df, caract.vect) } # Définition des row/colnames rownames(caract.df) <- var.names colnames(caract.df) <- c("Minimum", "Maximum", "Moyenne", "Ecart-type") # Renvoyer la table caract.df }, rownames = TRUE, digits = 0) # Commande pour l'affichage du plot des effectifs output$effectifsDiag <- renderPlot({ dt = data() plot(table(data.frame(dt[,input$radio])), col ="blue", xlab =dt[1,input$radio], ylab ="Effectifs", main ="Distribution des effectifs") }) output$effectifsDiagq <- renderPlot({ dt = data() plot(table(data.frame(dt[,input$choixx])), col ="blue", xlab =dt[,input$choixx], ylab ="Effectifs", main ="Distribution des effectifs") }) effectifs <- reactive({ dt = data() return(table(dt[,input$choixx])) }) # Diagramme en colonnes output$colonnes <- renderPlot({ barplot(effectifs(), main = " ", ylab="Effectifs", las = 2, names.arg = substr(names(effectifs()), 1, 4)) }) # Diagramme en secteurs output$secteurs <- renderPlot({ pie(effectifs(), labels = substr(names(effectifs()), 1, 4), main = " ", col=c()) }) # Commande pour l'affichage du plot des frÃÂ©quences cumulÃÂ©es output$effectifsCumDiag <- renderPlot({ dt= table(data()[,input$radio]) dt= cumsum(dt)/nrow(data())*100 plot(ecdf(as.numeric(as.character(dt))), col ="blue", xlab = "La variable" , ylab ="Frequences cumulees", main ="Frequences cumulees ") }) # output$effectifsCumDiag <- renderPlot({ # # plot(ecdf(as.numeric(as.character(data.frame(tabStats()[,5])))), # # col ="blue", xlab = "La variable" , ylab ="Frequences cumulees", # # main ="Frequences cumulees ") # dt = data() # dt2= table(data.frame(dt[,input$radio]))/nrow(dt)*100 # # plot(dt2, # col ="blue", xlab = "La variable" , ylab ="Frequences cumulees", # main ="Frequences cumulees ") # }) # Commande pour l'affichage de la boÃÂ®te ÃÂ moustaches output$boiteMoustaches <- renderPlot({ # BoÃÂ®te ÃÂ moustaches dt = data() boxplot( data.frame(as.numeric(as.character(dt[,input$radio]))), col = grey(0.8), main = " ", ylab = "", las = 1) # Affichage complÃÂ©mentaires en Y des diffÃÂ©rents ÃÂ¢ges rug(data()[,input$radio], side = 2) }) # RÃ©cupÃ©ration des valeurs fecondite fecondite <- reactive({ if(!"Sepal.Length" %in% colnames(data())) return(NULL) data()$Sepal.Length }) # Histogrammes # ---- output$effectifsHist <- renderPlot({ dt = data() # Histogramme des effectifs hist(as.numeric(as.character(dt[,input$radio])) , freq = TRUE, cex.axis = 1.5, cex.main = 1.5, main = "Histogramme", col = "blue", xlab = dt[1,input$radio], ylab = "Effectifs", las = 1, right = FALSE, cex.lab = 1.5) }) output$effectifsCumCurve <- renderPlot({ dt = data() # RÃ©cupÃ©ration des infos Ã partir de l'histogramme tmp.hist <- hist(as.numeric(as.character(dt[,input$radio])) , plot = FALSE, right = FALSE) # Courbe cumulative (effectifs) plot(x = tmp.hist$breaks[-1], y = cumsum(tmp.hist$counts), xlab = dt[1,input$radio], ylab = "Effectifs cumulés", cex.axis = 1.5, cex.lab = 1.5, main = "Courbe cumulative ", type = "o", col = "blue", lwd = 2, cex.main = 1.5) }) output$effectifsHistFreqDens <- renderPlot({ dt = data() # Histogramme des densitÃ©s de frÃ©quences hist( as.numeric(as.character(dt[,input$radio])), freq = FALSE, cex.axis = 1.5, cex.main = 1.5, main = "Histogramme de la variable", col = "green", xlab = dt[1,input$radio] , ylab = "Densité de fréquences", las = 1, right = FALSE, cex.lab = 1.5) }) # Force de la liaison entre 'especes' et 'couleur' # ---- output$force <- renderTable({ force.df <- as.data.frame(matrix(NA, nrow = 3, ncol = 1)) rownames(force.df) = c("X2", "Phi2", "Cramer") # La table de contingence des profils observés #tab = with(data(), table(input$quantlistvs, input$qualistvs)) var1<-input$qualistvs1 var2<-input$qualistvs2 data<-cbind(data()[var1],data()[var2]) data<-as.data.frame(data) colnames(data)<-c("var1","var2") tab =with(data,table(var1,var2)) print(tab) # La table de contigence s'il y a indépendence tab.indep = tab n = sum(tab) tab.rowSum = apply(tab, 2, sum) tab.colSum = apply(tab, 1, sum) print(tab.colSum) print(tab.rowSum) print(c) for(i in c(1:length(tab.colSum))){ for(j in c(1:length(tab.rowSum))){ tab.indep[i,j] = tab.colSum[i]*tab.rowSum[j]/n } } # Calcul du X² force.df[1,1] = sum((tab-tab.indep)^2/tab.indep) # Calcul du Phi² force.df[2,1] = force.df[1,1]/n # Calcul du Cramer force.df[3,1] = sqrt(force.df[2,1]/(min(nrow(tab), ncol(tab))-1)) force.df }, rownames=TRUE, colnames=FALSE) # Unidimensionnel output$barplotUni <- renderPlot({ # Diagramme en barres de la variable 'Level' avec ggplot # list<-as.list(data()[,input$quantlistvs]) # print(list) # ggplot(data(), aes(x = paste(data))) + geom_bar(stat="identity") var<-sym(input$quantlistvs) ggplot(data(),aes(x = !!var) )+ geom_bar() }) output$barplotProfils <- renderPlot({ # Diagramme de profils entre les variables 'V2' et 'V6' var1<-sym(input$qualistvs1) var2<-sym(input$qualistvs2) ggplot(data(), aes(x = !!var2, fill = !!var1)) + geom_bar(position = "fill") }) # Bidimensionnel output$barplotBi <- renderPlot({ # Diagramme en barres entre les variables 'V2' et 'V6' var1<-sym(input$quantlistvs) var2<-sym(input$qualistvs) ggplot(data(), aes(x = !!var1, fill = !!var2)) + geom_bar() }) # output$barplotProfils <- renderPlot({ # var1<-sym(input$quantlistvs) # var2<-sym(input$qualistvs) # ggplot(data(), aes(x = !!var2, fill = !!var1)) + geom_bar(position = "fill") # }) # # # Table de contingence entre 'Sex' et 'Level' # ---- output$contingency <- renderTable({ var1<-input$qualistvs1 var2<-input$qualistvs2 data<-cbind(data()[var1],data()[var2]) data<-as.data.frame(data) colnames(data)<-c("var1","var2") tab = with(data,table(var1, var2)) # print(tab) # tab2 = with(data(), table(y, default)) # print(tab2) # print(tab) round(tab/sum(tab), 3) tab print(tab) }) #------------------ CHURN PART : --------------- churnvar <- reactive({ validate( need(nrow(unique(data()[input$churnchoice])) == 2, "Cette Variable ne peut pas etre une Varibale Churn Veuillez choisir une autre (Variable Binaire).") ) return(data()[input$churnchoice]) }) output$churnIntro1 <- renderPlotly({ var<-as.data.frame(churnvar()) # # pie(var$freq, labels = unique(data()$y), main = "Churn Variable", col=c("#ffa600","#bc5090")) # bp<- ggplot(df, aes(x="", y=value, fill=group))+ # geom_bar(width = 1, stat = "identity") + coord_polar("y", start=0) freq<- count(var) print(freq) freq<- cbind(freq,round(freq$freq/ nrow(var)*100,2)) colnames(freq) <- c("value","occu","percent") print(freq) # Compute the cumulative percentages (top of each rectangle) freq$ymax <- cumsum(freq$percent) # Compute the bottom of each rectangle freq$ymin <- c(0, head(freq$ymax, n=-1)) # Compute label position freq$labelPosition <- (freq$ymax + freq$ymin) / 2 # Compute a good label freq$label <- paste0(freq$percent,"%") print(freq) # Make the plot plot<-plot_ly(freq, labels = ~value, values = ~percent, type = 'pie', text="", textinfo = "",hoverinfo="text",hovertext=paste(freq$value," ",freq$occu)) %>% layout(title = paste("Variable Churn"), xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE), yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE), showlegend = TRUE) return(plot) }) slidervalues<- reactive({ values<-data()[input$churnchoice] max<-max(values)*0.5 min<-min(values)*0.05 }) output$churnqualplot <- renderPlotly({ print(nrow(count(names(data())[grepl('factor|logical|character',sapply(data(),class))]))) validate( need(nrow(unique(data()[input$churnchoice])) == 2, "Veuillez Choisir une variable Churn dans l'onglet 'Churn Variable' "), need(nrow(count(names(data())[grepl('factor|logical|character',sapply(data(),class))])) != 0, "Aucune Variable qualitative Detecte") ) filtervar1 <- data() filtervar2 <- data() var <- input$churnchoice values<-unique(data()[var]) print(values) print(values[1,1]) print(values[2,1]) valeurchurn1 <- values[1,1] valeurchurn2 <- values[2,1] filtervar1 <- data()[data()[var] == valeurchurn1, input$churnqual] filtervar2 <- data()[data()[var] == valeurchurn2, input$churnqual] var1<-as.data.frame(filtervar1) freq<- count(var1) print(freq) freq<- cbind(freq,round(freq$freq/ nrow(var1)*100,2)) colnames(freq) <- c("value","occu","percent") print(freq) # Compute the cumulative percentages (top of each rectangle) freq$ymax <- cumsum(freq$percent) # Compute the bottom of each rectangle freq$ymin <- c(0, head(freq$ymax, n=-1)) # Compute label position freq$labelPosition <- (freq$ymax + freq$ymin) / 2 # Compute a good label freq$label <- paste0(freq$percent,"%") print(freq) # Make the plot var2<-as.data.frame(filtervar2) freq2<- count(var2) print("freq2") print(freq2) freq2<- cbind(freq2,round(freq2$freq/ nrow(var2)*100,2)) colnames(freq2) <- c("value","occu","percent") print(freq2) # Compute the cumulative percentages (top of each rectangle) freq2$ymax <- cumsum(freq2$percent) # Compute the bottom of each rectangle freq2$ymin <- c(0, head(freq2$ymax, n=-1)) # Compute label position freq2$labelPosition <- (freq2$ymax + freq2$ymin) / 2 # Compute a good label freq2$label <- paste0(freq2$percent,"%") print(freq2) # Make the plot print(values[1,1]) print(values[2,1]) plot <- plot_ly(labels = ~value, values = ~percent,textinfo = 'value+percent') %>% add_pie(data = freq, name = values[1,1], domain = list(row = 0, column = 0))%>% add_pie(data = freq2, name = values[2,1], domain = list(row = 0, column = 1))%>% layout(title = paste("Distribution de",input$churnqual,"par rapport a ",input$churnchoice), showlegend = T, grid=list(rows=1, columns=2), xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE), yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE), annotations = list(x = c(0, .5), y = c(0, 0),text = c(paste("Distribution \n % Churn = ", values[1,1]),paste("Distribution \n % Churn = ", values[2,1])), xref = "papper", yref = "papper", showarrow = F )) return(plot) }) output$plot1 <- renderPlot({ df<- data.frame(lapply(data(), function(x) { if(!is.numeric(x)) as.numeric(factor(x)) else x })) df$y <- ifelse(df$y==1,0,1) samples <- sample(1:nrow(df), 0.7*nrow(df)) trainclass <- df[samples, ]$y testclass <- df[-samples, ]$y train = df[samples, 1:(ncol(df)-1)] test = df[-samples, 1:(ncol(df)-1)] kmax <- 10 err_valid <- rep(NA,kmax) for (k in 1:kmax) { print(k) samples <- sample(1:nrow(df), 0.7*nrow(df)) pred <- knn(train,test,trainclass,k=18) err_valid[k] <- F1_Score(y_pred=pred,y_true=testclass) } #plot(x=seq(1,kmax,by=1),y=err_test,type="o",col="blue") boxplot(err_valid) }) output$plot2 <- renderPlot({ df <- data() df$y <- ifelse(df$y=="no",0,1) kmax <- 10 err_valid <- rep(NA,kmax) for (k in 1:kmax) { samples <- sample(1:nrow(df), 0.7*nrow(df)) test_y <- df[-samples, ncol(df)] test_X <- df[-samples, 1:(ncol(df)-1)] print(k) train <- df[samples, ] model <- glm(y ~ .,data = train) pred <- predict(model,test_X,type="response") pred <- ifelse(pred>0.5,1,0) err_valid[k] <- F1_Score(y_pred=pred,y_true=test_y) } boxplot(err_valid) }) output$churnquantplot <- renderPlotly({ print(nrow(count(names(data())[!grepl('factor|logical|character',sapply(data(),class))]))) validate( need(nrow(unique(data()[input$churnchoice])) == 2, "Veuillez Choisir une variable Churn dans l'onglet 'Churn Variable' "), need(nrow(count(names(data())[!grepl('factor|logical|character',sapply(data(),class))])) != 0, "Aucune Variable qualitative Detecte") ) filtervar1 <- data() filtervar2 <- data() var <- input$churnchoice values<-unique(data()[var]) print(values) print(values[1,1]) print(values[2,1]) valeurchurn1 <- values[1,1] valeurchurn2 <- values[2,1] filtervar1 <- data()[data()[var] == valeurchurn1, input$churnquant] filtervar2 <- data()[data()[var] == valeurchurn2, input$churnquant] maxvar1<-max(filtervar1) maxvar2<-max(filtervar2) minvar1<-min(filtervar1) minvar2<-min(filtervar2) stepvar1<-abs((1/(2**(input$sliderquant-1)))*maxvar1) stepvar2<-abs((1/(2**(input$sliderquant-1)))*maxvar2) length<-2**(input$sliderquant) print(input$sliderquant) print(stepvar1) print(stepvar2) filtervar1<-cut(filtervar1, seq(minvar1,maxvar1,length.out = length)) filtervar2<-cut(filtervar2, seq(minvar2,maxvar2,length.out = length)) var1<-as.data.frame(filtervar1) freq<- count(var1) print(freq) freq<- cbind(freq,round(freq$freq/ nrow(var1)*100,2)) colnames(freq) <- c("value","occu","percent") print(freq) # Compute the cumulative percentages (top of each rectangle) freq$ymax <- cumsum(freq$percent) # Compute the bottom of each rectangle freq$ymin <- c(0, head(freq$ymax, n=-1)) # Compute label position freq$labelPosition <- (freq$ymax + freq$ymin) / 2 # Compute a good label freq$label <- paste0(freq$percent,"%") print(freq) # Make the plot var2<-as.data.frame(filtervar2) freq2<- count(var2) print("freq2") print(freq2) freq2<- cbind(freq2,round(freq2$freq/ nrow(var2)*100,2)) colnames(freq2) <- c("value","occu","percent") print(freq2) # Compute the cumulative percentages (top of each rectangle) freq2$ymax <- cumsum(freq2$percent) # Compute the bottom of each rectangle freq2$ymin <- c(0, head(freq2$ymax, n=-1)) # Compute label position freq2$labelPosition <- (freq2$ymax + freq2$ymin) / 2 # Compute a good label freq2$label <- paste0(freq2$percent,"%") print(freq2) # Make the plot print(values[1,1]) print(values[2,1]) plot <- plot_ly(labels = ~value, values = ~percent,textinfo = 'value+percent') %>% add_pie(data = freq, name = values[1,1], domain = list(row = 0, column = 0))%>% add_pie(data = freq2, name = values[2,1], domain = list(row = 0, column = 1))%>% layout(title = paste("Distribution de",input$churnquant,"par rapport a ",input$churnchoice), showlegend = T, grid=list(rows=1, columns=2), xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE), yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE), annotations = list(x = c(0, .5), y = c(0, 0),text = c(paste("Distribution \n % Churn = ", values[1,1]),paste("Distribution \n % Churn = ", values[2,1])), xref = "papper", yref = "papper", showarrow = F )) return(plot) }) output$churnquantplot2 <- renderPlotly({ print(nrow(count(names(data())[!grepl('factor|logical|character',sapply(data(),class))]))) validate( need(nrow(unique(data()[input$churnchoice])) == 2, "Veuillez Choisir une variable Churn dans l'onglet 'Churn Variable' "), need(nrow(count(names(data())[!grepl('factor|logical|character',sapply(data(),class))])) != 0, "Aucune Variable qualitative Detecte") ) filtervar1 <- data() filtervar2 <- data() var <- input$churnchoice values<-unique(data()[var]) print(values) print(values[1,1]) print(values[2,1]) valeurchurn1 <- values[1,1] valeurchurn2 <- values[2,1] filtervar1 <- data()[data()[var] == valeurchurn1, input$churnquant] filtervar2 <- data()[data()[var] == valeurchurn2, input$churnquant] var1<-as.data.frame(filtervar1) freq<- count(var1) print(freq) freq<- cbind(freq,round(freq$freq/ nrow(var1)*100,2)) colnames(freq) <- c("value","occu","percent") print(freq) # Compute the cumulative percentages (top of each rectangle) freq$ymax <- cumsum(freq$percent) # Compute the bottom of each rectangle freq$ymin <- c(0, head(freq$ymax, n=-1)) # Compute label position freq$labelPosition <- (freq$ymax + freq$ymin) / 2 # Compute a good label freq$label <- paste0(freq$percent,"%") print(freq) # Make the plot var2<-as.data.frame(filtervar2) freq2<- count(var2) print("freq2") print(freq2) freq2<- cbind(freq2,round(freq2$freq/ nrow(var2)*100,2)) colnames(freq2) <- c("value","occu","percent") print(freq2) # Compute the cumulative percentages (top of each rectangle) freq2$ymax <- cumsum(freq2$percent) # Compute the bottom of each rectangle freq2$ymin <- c(0, head(freq2$ymax, n=-1)) # Compute label position freq2$labelPosition <- (freq2$ymax + freq2$ymin) / 2 # Compute a good label freq2$label <- paste0(freq2$percent,"%") print(freq2) # Make the plot print(values[1,1]) print(values[2,1]) tab<-cbind(freq$value,freq$occu,freq2$occu) tab<- as.data.frame(tab) colnames(tab)<-c("value","occu1","occu2") colnames(tab) fig <- plot_ly(tab, x = ~value, y = ~occu1, type = 'bar', name = valeurchurn1) fig <- fig %>% add_trace(y = ~occu2, name = valeurchurn2) fig <- fig %>% layout(yaxis = list(title = 'Count'), barmode = 'group') return(fig) }) output$churnquantplot3 <- renderPlot({ print(nrow(count(names(data())[!grepl('factor|logical|character',sapply(data(),class))]))) validate( need(nrow(unique(data()[input$churnchoice])) == 2, "Veuillez Choisir une variable Churn dans l'onglet 'Churn Variable' "), need(nrow(count(names(data())[!grepl('factor|logical|character',sapply(data(),class))])) != 0, "Aucune Variable qualitative Detecte") ) tab<-cbind(data()[input$churnchoice],data()[input$churnquant],data()[input$pairwise1],data()[input$pairwise2]) tab<-as.data.frame(tab) colnames(tab)<-c("var0","var1","var2","var3") colnames(tab) print(tab) my_cols <- c("#000FF", "#FF0000") print(tab[,2:4]) fig <- ggpairs(tab, columns = 2:4,olumnLabels = c(input$churnquant,input$pairwise1,input$pairwise2),ggplot2::aes(colour=var0)) return(fig) }) #------------------ Balanced Data ----------------- samplevalue<- reactive({ validate( need(nrow(unique(data()[input$varbalance])) == 2, "... En attente de la variable ...") ) method<-input$sampling choice<-input$varbalance df<- data.frame(lapply(data(), function(x) { if(!is.numeric(x)) as.numeric(factor(x)) else x })) df[,input$varbalance] <- ifelse(df[,input$varbalance]==1,0,1) samples <- sample(1:nrow(df), 0.7*nrow(df)) train = df[samples, 1:(ncol(df))] data<-train values<-unique(data[choice]) print(values) print(values[1,1]) print(values[2,1]) valeurchoice1 <- values[1,1] valeurchoice2 <- values[2,1] print(valeurchoice1) print("----------------------------------") print(method) var1<-as.data.frame(data[data[choice] == valeurchoice1,choice]) var2<-as.data.frame(data[data[choice] == valeurchoice2,choice]) print(count(var1)) if(method=="Random Oversampling"){ n_samp<-max(count(var1)$freq,count(var2)$freq) print("n_samp") print(n_samp) return(n_samp) } if(method=="Random UnderSampling"){ n_samp<-min(count(var1)$freq,count(var2)$freq) print("n_samp") print(n_samp) return(n_samp) } if(method=="Both"){ n_samp<-max(count(var1)$freq,count(var2)$freq) print("n_samp") print(n_samp) return(n_samp) } # if(method=="Smote"){ # n_samp<-max(count(var1)$freq,count(var2)$freq) # n_samp<-cbind(n_samp,min(count(var1)$freq,count(var2)$freq)) # print(method) # return(n_samp) # } }) balanceddata<- eventReactive(input$go,{ print("worked!") df<- data.frame(lapply(data(), function(x) { if(!is.numeric(x)) as.numeric(factor(x)) else x })) df[,input$varbalance] <- ifelse(df[,input$varbalance]==1,0,1) samples <- sample(1:nrow(df), 0.7*nrow(df)) train = df[samples, 1:(ncol(df))] data<-train print(data) method<-input$sampling choice<-input$varbalance n_samp<-samplevalue()*2 f<-paste(choice," ~ ",paste(names(data()[names(data())!=choice]),collapse=" + ")) print(paste("f = ",f)) print(paste("f as formula = ",f)) f<-as.formula(f) curr_frame <<- sys.nframe() if(method=="Random Oversampling"){ over_res<-ovun.sample(get("f", sys.frame(curr_frame)), data=get("data", sys.frame(curr_frame)), method="over", N=get("n_samp", sys.frame(curr_frame)), seed=2021)$data print(over_res) return(over_res) } if(method=="Random UnderSampling"){ under_res<-ovun.sample(get("f", sys.frame(curr_frame)), data=get("data", sys.frame(curr_frame)), method="under", N=get("n_samp", sys.frame(curr_frame)), seed=2021)$data print(under_res) return(under_res) } if(method=="Both"){ both_res<-ovun.sample(get("f", sys.frame(curr_frame)), data=get("data", sys.frame(curr_frame)), method="both", N=get("n_samp", sys.frame(curr_frame)),p=0.5, seed=2021)$data print(both_res) return(both_res) } # if(method=="Smote"){ # n1<-n_samp[0] # n0<-n_samp[1] # r0<-0.6 # ntimes<-(( 1 - r0) / r0 ) * ( n0 / n1 ) - 1 # data[input$varbalance]<-factor(data[input$varbalance]) # print(names(data[names(data)!=input$varbalance])) # smote_res<-SMOTE(X = data[names(data)!=input$varbalance], target = data[input$varbalance], K = 5, dup_size = ntimes)$data # print(smote_res) # return(smote_res)} }) output$compare<-renderPlotly({ print("I'm here -1 ") print("I'm here 0 ") var <- input$varbalance filtervar1 <- data()[var] filtervar2 <- balanceddata()[var] print("I'm here 2 ") var1<-as.data.frame(filtervar1) freq<- count(var1) print(freq) freq<- cbind(freq,round(freq$freq/ nrow(var1)*100,2)) colnames(freq) <- c("value","occu","percent") print(freq) print("I'm here 3 ") # Compute the cumulative percentages (top of each rectangle) freq$ymax <- cumsum(freq$percent) # Compute the bottom of each rectangle freq$ymin <- c(0, head(freq$ymax, n=-1)) # Compute label position freq$labelPosition <- (freq$ymax + freq$ymin) / 2 # Compute a good label freq$label <- paste0(freq$percent,"%") print(freq) # Make the plot var2<-as.data.frame(filtervar2) freq2<- count(var2) print("freq2") print(freq2) freq2<- cbind(freq2,round(freq2$freq/ nrow(var2)*100,2)) colnames(freq2) <- c("value","occu","percent") print(freq2) # Compute the cumulative percentages (top of each rectangle) freq2$ymax <- cumsum(freq2$percent) # Compute the bottom of each rectangle freq2$ymin <- c(0, head(freq2$ymax, n=-1)) # Compute label position freq2$labelPosition <- (freq2$ymax + freq2$ymin) / 2 # Compute a good label freq2$label <- paste0(freq2$percent,"%") print(freq2) # Make the plot plot <- plot_ly(labels = ~value, values = ~percent,textinfo = 'occu',hovertext=~occu) %>% add_pie(data = freq, name = 'Avant', domain = list(row = 0, column = 0))%>% add_pie(data = freq2, name = 'Apres', domain = list(row = 0, column = 1))%>% layout(title = paste("Comparaisons des donnees avant et apres le sampling "), showlegend = T, grid=list(rows=1, columns=2), xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE), yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE), annotations = list(x = c(0, .5), y = c(0, 0),text = c("Avant","Apres"), xref = "papper", yref = "papper", showarrow = F )) return(plot) }) output$balancedplot <- renderPlotly({ plot_ly(data = balanceddata(), x = balanceddata()[,1], y = balanceddata()[,3],color = balanceddata()[input$varbalance]) }) # KNN ET LR APRES L'EQUILIBRE output$plot4 <- renderPlot({ df<- data.frame(lapply(balanceddata(), function(x) { if(!is.numeric(x)) as.numeric(factor(x)) else x })) print(df) df$y <- ifelse(df$y==1,0,1) samples <- sample(1:nrow(df), 0.7*nrow(df)) trainclass <- df[samples, ]$y testclass <- df[-samples, ]$y train = df[samples, 1:(ncol(df)-1)] test = df[-samples, 1:(ncol(df)-1)] kmax <- 10 err_valid <- rep(NA,kmax) for (k in 1:kmax) { print(k) samples <- sample(1:nrow(df), 0.7*nrow(df)) pred <- knn(train,test,trainclass,k=18) err_valid[k] <- F1_Score(y_pred=pred,y_true=testclass) } #plot(x=seq(1,kmax,by=1),y=err_test,type="o",col="blue") boxplot(err_valid) }) output$plot5 <- renderPlot({ df <- data() df$y <- ifelse(df$y=="no",0,1) kmax <- 10 err_valid <- rep(NA,kmax) for (k in 1:kmax) { samples <- sample(1:nrow(df), 0.7*nrow(df)) test_y <- df[-samples, ncol(df)] test_X <- df[-samples, 1:(ncol(df)-1)] print(k) train <- df[samples, ] model <- glm(y ~ .,data = train) pred <- predict(model,test_X,type="response") pred <- ifelse(pred>0.5,1,0) err_valid[k] <- F1_Score(y_pred=pred,y_true=test_y) } boxplot(err_valid) }) } shinyApp(ui, server)

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## Reading the data of household power consumption HPCData <- read.table("household_power_consumption.txt", header = TRUE, sep = ";", na.strings = "?") ##提取2007年2月1日与2007年2月2日的数据，存入子集data4 HPCData$Time <- paste(HPCData$Date, HPCData$Time) HPCData$Date <- as.Date(HPCData$Date, "%d/%m/%Y") HPCData$Time <- strptime(HPCData$Time, "%d/%m/%Y %H:%M:%S") day1 <- which(HPCData$Date == "2007-02-01") day2 <- which(HPCData$Date == "2007-02-02") data4 <- HPCData[c(day1, day2), ] ##绘图 png(filename = "plot4.png") par(mfcol = c(2,2)) ##第一副图 plot(data4[,c(2,3)], type = "l", xlab = "", ylab = "Global Active Power(kilowatts)") ##第二副图 plot(data4[, c(2,7)], type = "l", xlab = "", ylab = "Energy sub metering") points(data4[, c(2,8)], type = "l", col = "red") points(data4[, c(2,9)], type = "l", col = "blue") legend("topright", bty= "n", legend = c("Sub_metering_1","Sub_metering_2","Sub_metering_3"), pch = c("_","_","_"), col = c("black","red","blue")) ##第三幅图 plot(data4[,c(2,5)], type = "l", xlab = "datetime", ylab = "Voltage") ##第四幅图 plot(data4[,c(2,4)], type = "l", xlab = "datetime", ylab = "Global_reactive_power") dev.off()

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heatMatrix.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/plotMatrix.R \name{heatMatrix} \alias{heatMatrix} \title{Draw a heatmap of a given ScoreMatrix object} \usage{ heatMatrix(mat, grid = FALSE, col = NULL, xcoords = NULL, group = NULL, group.col = NULL, order = FALSE, user.order = FALSE, winsorize = c(0, 100), clustfun = NULL, main = "", legend.name = NULL, cex.legend = 1, xlab = NULL, cex.main = 1, cex.lab = 1, cex.axis = 1, newpage = TRUE) } \arguments{ \item{mat}{a \code{ScoreMatrix} object} \item{grid}{if TRUE, grid graphics will be used. if FALSE, base graphics will be used on the top level, so users can use par(mfrow) or par(mfcol) prior to calling the function. Default:FALSE} \item{col}{a vector of colors, such as the ones created by heat.colors(10). If NULL (which is default), jet color scheme (common in matlab plots) will be used.} \item{xcoords}{a vector of numbers showing relative positions of the bases or windows. It must match the number of columns in the \code{ScoreMatrix}. Alternatively, it could be a numeric vector of two elements. Such as c(0,100) showing the relative start and end coordinates of the first and last column of the \code{ScoreMatrix} object.} \item{group}{a list of vectors of row numbers or a factor. This grouping is used for rowside colors of the heatmap. If it is a list, each element of the list must be a vector of row numbers. Names of the elements of the list will be used as names of groups. If \code{group} is a factor , it's length must match the number of rows of the matrix, and factor levels will be used as the names of the groups in the plot.} \item{group.col}{a vector of color names to be used at the rowside colors if \code{group} argument is given or \code{clustfun} function is given.} \item{order}{Logical indicating if the rows should be ordered or not (Default:FALSE). If \code{order=TRUE} the matrix will be ordered with rowSums(mat) values in descending order. If \code{group} argument is provided, first the groups will be ordered in descending order of sums of rows then, everything within the clusters will be ordered by sums of rows. If \code{clustfun} is given then rows within clusters will be order in descending order of sums of rows.} \item{user.order}{a numerical vector indicating the order of groups/clusters (it works only when \code{group} or \code{clustfun} argument is given).} \item{winsorize}{Numeric vector of two, defaults to c(0,100). This vector determines the upper and lower percentile values to limit the extreme values. For example, c(0,99) will limit the values to only 99th percentile, everything above the 99 percentile will be equalized to the value of 99th percentile.This is useful for visualization of matrices that have outliers.} \item{clustfun}{a function for clustering rows of \code{mat} that returns a vector of integers indicating the cluster to which each point is allocated (a vector of cluster membership), e.g. k-means algorithm with 3 centers: function(x) kmeans(x, centers=3)$cluster. By default FALSE.} \item{main}{a character string for the plot title} \item{legend.name}{a character label plotted next to the legend} \item{cex.legend}{A numerical value giving the amount by which legend axis marks should be magnified relative to the default} \item{xlab}{label a character string for x-axis of the heatmap} \item{cex.main}{A numerical value giving the amount by which plot title should be magnified} \item{cex.lab}{A numerical value giving the amount by which axis labels (including 'legend.name') should be magnified relative to the default.} \item{cex.axis}{A numerical value giving the amount by which axis marks should be magnified relative to the default} \item{newpage}{logical indicating if \code{grid.newpage()} function should be invoked if \code{grid=TRUE}.} } \value{ returns clustering result invisibly, if clustfun is definied } \description{ The function makes a heatmap out of given \code{ScoreMatrix} object. If desired it can use clustering using given clustering function (e.g. k-means) and plot cluster color codes as a sidebar. In addition, user can define groups of rows using 'group' argument. } \examples{ data(cage) data(promoters) scores1=ScoreMatrix(target=cage,windows=promoters,strand.aware=TRUE, weight.col="tpm") set.seed(1000) \donttest{ heatMatrix(mat=scores1,legend.name="tpm",winsorize=c(0,99),xlab="region around TSS", xcoords=-1000:1000, cex.legend=0.8,main="CAGE clusters on promoters",cex.lab=1, cex.axis=0.9,grid=FALSE) ## examples using clustering functions ## k-means cl1 <- function(x) kmeans(x, centers=3)$cluster set.seed(1000) heatMatrix(mat=scores1,legend.name="tpm",winsorize=c(0,99),xlab="region around TSS", xcoords=-1000:1000,clustfun=cl1, cex.legend=0.8,main="CAGE clusters on promoters",cex.lab=1, cex.axis=0.9,grid=FALSE, user.order=c(1,3,2)) ## hierarchical clustering cl2 <- function(x) cutree(hclust(dist(x), method="complete"), k=3) set.seed(1000) heatMatrix(mat=scores1,legend.name="tpm",winsorize=c(0,99),xlab="region around TSS", xcoords=-1000:1000,clustfun=cl2, cex.legend=0.8,main="CAGE clusters on promoters",cex.lab=1, cex.axis=0.9,grid=FALSE) } }

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/man/wassermann.Rd

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green-striped-gecko/PopGenReport

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2023-07-10T17:01:23.072377

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wassermann.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/wassermann.r \name{wassermann} \alias{wassermann} \title{Partial Mantel tests on costdistance matrices} \usage{ wassermann(gen.mat, cost.mats, eucl.mat = NULL, plot = TRUE, nperm = 999) } \arguments{ \item{gen.mat}{pairwise genetic distance matrix} \item{cost.mats}{pairwise cost distance matrix} \item{eucl.mat}{pairwise Eukclidean distance matrix} \item{plot}{switch for control plots of the partial mantel test} \item{nperm}{number of permutations for the partial mantel test} } \value{ A table with the results of the partial mantel test. Using plot=TRUE results in diagnostic plots for the partial mantel tests. } \description{ This function implements the Causal modelling approach as suggested by Wassermann et al. 2010 and Cushman et al. 2010. It tests for the effect of landscape features using a cost distance matrix on the genetic structure of subpopulation/individuals. } \details{ see \code{\link{landgenreport}} } \examples{ \donttest{ library(raster) fric.raster <- readRDS(system.file("extdata","fric.raster.rdata", package="PopGenReport")) glc <- genleastcost(landgen, fric.raster, "D", NN=8) wassermann(eucl.mat = glc$eucl.mat, cost.mats = glc$cost.mats, gen.mat = glc$gen.mat) } } \references{ Wassermann, T.N., Cushman, S. A., Schwartz, M. K. and Wallin, D. O. (2010). Spatial scaling and multi-model inference in landscape genetics: Martes americana in northern Idaho. Landscape Ecology, 25(10), 1601-1612. } \seealso{ \code{\link{popgenreport}}, \code{\link{genleastcost}}, \code{\link{landgenreport}}, \code{\link{lgrMMRR}} } \author{ Bernd Gruber (bernd.gruber@canberra.edu.au) }

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akhikolla/updatedatatype-list2

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1612968539-test.R

testlist <- list(x1 = c(NaN, NaN, 1.390658140702e-309, -5.48143621863725e+303, 7.29057588494813e-304, 0, 7.78776273814619e-308, 1.11897728190755e+87, 2.77478592360575e+180, 2.77448002212291e+180, 2.77448001762435e+180, 2.77448001762435e+180, 2.77448001762435e+180, 2.77448001456822e+180, 5.50814852228496e-310, 2.77448001456315e+180, 2.77448001762435e+180, 2.06930173233488e+26, 2.7744800176243e+180, 2.74814147082125e+180, 8.73508392609645e-201, -1.51460052722868e+304, 3.01181841091939e-304, NaN, 4.056010690358e-312, 0, NaN, NaN, -9.64741376890647e+206, 2.77124415362576e+180, NaN, 6.76597883938242e-251, 2.77447923392651e+180, 2.83962624393768e+238, 2.8396262443943e+238, 2.8396262443943e+238 ), x2 = numeric(0), y1 = c(2.85279195360568e+180, 1.20826074561973e-178, 7.29112200597562e-304, 7.2911220195564e-304, 4.11214344899755e-317, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), y2 = numeric(0)) result <- do.call(palm:::euc_distances,testlist) str(result)

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/man/reduce_lr_cb.Rd

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reduce_lr_cb.Rd

% Generated by roxygen2: do not edit by hand % Please edit documentation in R/callbacks.R \name{reduce_lr_cb} \alias{reduce_lr_cb} \title{learning rate callback} \usage{ reduce_lr_cb(patience, cooldown, lr.plateau.factor, monitor = "val_acc") } \arguments{ \item{patience}{Number of epochs waiting for decrease in loss before reducing learning rate.} \item{cooldown}{Number of epochs without changing learning rate.} \item{lr.plateau.factor}{Factor of decreasing learning rate when plateau is reached.} } \description{ learning rate callback }

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/R/alcohol.R

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wli168/cchsflow

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alcohol.R

#' @title Number of drinks consumed in the past week #' #' @description \strong{NOTE:} this is not a function. #' #' This is a continuous variable derived by Statistics Canada that quantifies #' the amount of alcohol that is consumed in a week. This is calculated by #' adding the number of drinks consumed each day in the past week. #' Respondents of each CCHS cycle are asked how much alcohol they have #' consumed each day in the past week (ie. how much alcohol did you consume on #' Sunday, how much did you consume on Monday etc.). Each day is considered an #' individual variable and ALWDWKY takes the sum of all daily variables. #' #' @details This variable is present in every CCHS cycle used in cchsflow, and #' how it was derived remains consistent. #' #' @param ALWDWKY cchsflow variable name for number of drinks consumed in the #' past week #' #' @examples #' library(cchsflow) #' ?ALWDWKY #' #' @export ALWDWKY <- function(ALWDWKY) { # this is for documentation purposes only } #' @title Average daily alcohol consumption #' #' @description \strong{NOTE:} this is not a function. #' #' This is a continuous variable derived by Statistics Canada that quantifies #' the mean daily consumption of alcohol. This takes the value of ALWDWKY and #' divides it by 7. #' #' @details This variable is present in every CCHS cycle used in cchsflow, and #' how it was derived remains consistent. #' #' @param ALWDDLY cchsflow variable name for average daily alcohol consumption #' #' @examples #' library(cchsflow) #' ?ALWDDLY #' #' @export ALWDDLY <- function(ALWDDLY) { # this is for documentation purposes only } #' @title Type of drinker #' #' @description \strong{NOTE:} this is not a function. #' #' This is a categorical variable derived by Statistics Canada that uses #' various intermediate alcohol variables to categorize individuals into 4 #' distinct groups: #' #' \enumerate{ #' \item Regular Drinker #' \item Occasional Drinker #' \item Former Drinker #' \item Never Drinker #' } #' #' @details This variable is used in CCHS cycles from 2001 to 2007. How it was #' derived remained consistent during these years. #' #' Starting in 2007, Statistics Canada created a derived variable that looked #' at drinking type in the last 12 months. This new derived variable did not #' distinguish between former and never drinkers. If your research requires you #' to differentiate between former and never drinkers, we recommend using #' earlier cycles of the CCHS. #' #' @param ALCDTYP cchsflow variable name for type of drinker #' #' @examples #' library(cchsflow) #' ?ALCDTYP #' #' @export ALCDTYP <- function(ALCDTYP) { # this is for documentation purposes only } #' @title Type of drinker (12 months) #' #' @description \strong{NOTE:} this is not a function. #' #' This is a categorical variable derived by Statistics Canada that uses #' various intermediate alcohol variables to categorize individuals into 3 #' distinct groups: #' #' \enumerate{ #' \item Regular Drinker #' \item Occasional Drinker #' \item No drink in the last 12 months. #' } #' #' @details This variable was introduced in the 2007-2008 cycle of the CCHS, and #' became the sole derived variable that categorized people into various #' drinker types from 2009 onwards. Unlike ALCDTYP, this variable does not #' distinguish between former and never drinkers. #' #' @param ALCDTTM cchsflow variable name for type of drinker (12 months) #' #' @examples #' library(cchsflow) #' ?ALCDTTM #' #' @export ALCDTTM <- function(ALCDTTM) { # this is for documentation purposes only } #' @title Binge drinking #' #' @description This function creates a derived categorical variable that #' flags for binge drinking based on the number drinks consumed on a single #' day. #' #' @details In health research, binge drinking is defined as having an excess #' amount of alcohol in a single day. For males, this is defined as having five #' or more drinks; and for females it is four or more drinks. In the CCHS, #' respondents are asked to count the number of drinks they had during each #' day of the last week. #' #' @param DHH_SEX sex of respondent (1 - male, 2 - female) #' #' @param ALW_1 Drinks in the last week (1 - yes, 2 - no) #' #' @param ALW_2A1 Number of drinks on Sunday #' #' @param ALW_2A2 Number of drinks on Monday #' #' @param ALW_2A3 Number of drinks on Tuesday #' #' @param ALW_2A4 Number of drinks on Wednesday #' #' @param ALW_2A5 Number of drinks on Thursday #' #' @param ALW_2A6 Number of drinks on Friday #' #' @param ALW_2A7 Number of drinks on Saturday #' #' @return Categorical variable (binge_drinker) with two categories: #' #' \enumerate{ #' \item 1 - binge drinker #' \item 2 - non-binge drinker #' } #' #' @examples #' #' # Using binge_drinker_fun() to create binge_drinker values across CCHS cycles #' # binge_drinker_fun() is specified in variable_details.csv along with the #' # CCHS variables and cycles included. #' #' # To transform binge_drinker, use rec_with_table() for each CCHS cycle #' # and specify binge_drinker, along with the various alcohol and sex #' # variables. Then by using bind_rows() you can combine binge_drinker #' # across cycles. #' #' library(cchsflow) #' binge2001 <- rec_with_table( #' cchs2001_p, c( #' "ALW_1", "DHH_SEX", "ALW_2A1", "ALW_2A2", "ALW_2A3", "ALW_2A4", #' "ALW_2A5", "ALW_2A6", "ALW_2A7", "binge_drinker" #' ) #' ) #' #' head(binge2001) #' #' binge2009_2010 <- rec_with_table( #' cchs2009_2010_p, c( #' "ALW_1", "DHH_SEX", "ALW_2A1", "ALW_2A2", "ALW_2A3", "ALW_2A4", #' "ALW_2A5", "ALW_2A6", "ALW_2A7", "binge_drinker" #' ) #' ) #' #' tail(binge2009_2010) #' #' combined_binge <- bind_rows(binge2001, binge2009_2010) #' #' head(combined_binge) #' #' tail(combined_binge) #' #' # Using binge_drinker_fun() to generate binge_drinker with user-inputted #' # values. #' # #' # Let's say you are a male, and you had drinks in the last week. Let's say #' # you had 3 drinks on Sunday, 1 drink on #' # Monday, 6 drinks on Tuesday, 0 drinks on Wednesday, 3 drinks on Thurday, #' # 8 drinks on Friday, and 2 drinks on Saturday. Using binge_drinker_fun(), #' # we can check if you would be classified as a drinker. #' #' binge <- binge_drinker_fun(DHH_SEX = 1, ALW_1 = 1, ALW_2A1 = 3, ALW_2A2 = 1, #' ALW_2A3 = 6, ALW_2A4 = 0, ALW_2A5 = 3, #' ALW_2A6 = 8, ALW_2A7 = 2) #' #' print(binge) #' @export binge_drinker_fun <- function(DHH_SEX, ALW_1, ALW_2A1, ALW_2A2, ALW_2A3, ALW_2A4, ALW_2A5, ALW_2A6, ALW_2A7) { # If respondents had alcohol in the last week if_else2(ALW_1 == 1, # Males with at least one day with 5 or more drinks if_else2((DHH_SEX == 1 & (ALW_2A1 >= 5 | ALW_2A2 >= 5 | ALW_2A3 >=5 | ALW_2A4 >= 5 | ALW_2A5 >= 5 | ALW_2A6 >= 5 | ALW_2A7 >= 5)), 1, # Males with no days with 5 or more drinks if_else2((DHH_SEX == 1 & (ALW_2A1 %in% (0:4) & ALW_2A2 %in% (0:4) & ALW_2A3 %in% (0:4) & ALW_2A4 %in% (0:4) & ALW_2A5 %in% (0:4) & ALW_2A6 %in% (0:4) & ALW_2A7 %in% (0:4))), 2, # Females with at least one day with 4 or more drinks if_else2((DHH_SEX == 2 & (ALW_2A1 >= 4 | ALW_2A2 >= 4 | ALW_2A3 >= 4 | ALW_2A4 >= 4 | ALW_2A5 >= 4 | ALW_2A6 >= 4 | ALW_2A7 >= 4)), 1, # Females with no days with 4 or more drinks if_else2((DHH_SEX == 2 & (ALW_2A1 %in% (0:3) & ALW_2A2 %in% (0:3) & ALW_2A3 %in% (0:3) & ALW_2A4 %in% (0:3) & ALW_2A5 %in% (0:3) & ALW_2A6 %in% (0:3) & ALW_2A7 %in% (0:3))), 2, "NA(b)")))), # Respondents who didn't indicate they had alcohol in the last week "NA(a)") }

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cran/season

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ciPhase.R

# ciPhase.R # Confidence interval for circular phase #' Mean and Confidence Interval for Circular Phase #' #' Calculates the mean and confidence interval for the phase based on a chain #' of MCMC samples. #' #' The estimates of the phase are rotated to have a centre of \eqn{\pi}, the #' point on the circumference of a unit radius circle that is furthest from #' zero. The mean and confidence interval are calculated on the rotated values, #' then the estimates are rotated back. #' #' @param theta chain of Markov chain Monte Carlo (MCMC) samples of the phase. #' @param alpha the confidence level (default = 0.05 for a 95\% confidence #' interval). #' @return \item{mean}{the estimated mean phase.} \item{lower}{the estimated #' lower limit of the confidence interval.} \item{upper}{the estimated upper #' limit of the confidence interval.} #' @author Adrian Barnett \email{a.barnett@qut.edu.au} #' @references Fisher, N. (1993) \emph{Statistical Analysis of Circular Data}. #' Cambridge University Press. Page 36. #' #' Barnett, A.G., Dobson, A.J. (2010) \emph{Analysing Seasonal Health Data}. #' Springer. #' @examples #' \donttest{ #' theta = rnorm(n=2000, mean=0, sd=pi/50) # 2000 normal samples, centred on zero #' hist(theta, breaks=seq(-pi/8, pi/8, pi/30)) #' ciPhase(theta) #' } #' #' @export ciPhase ciPhase<-function(theta,alpha=0.05){ thetac<-seq(0,2*pi,pi/100) # proposed centres m<-length(thetac) d.theta<-vector(length=m,mode='numeric') for (i in 1:m){ d.theta[i]<-pi-mean(abs(pi-abs(theta-thetac[i]))) # Fisher page 36 } centre<-thetac[d.theta==min(d.theta)] if(length(centre)>1){centre<-centre[1]} # plot(thetac,d.theta) # rotate data to be centred on pi # only rotate if centre is in top-half of the circle ideal<-theta diff<-0 if (centre<pi/2|centre>3*pi/2){ diff<-pi-centre diffneg<- (-2*pi)+diff ideal<-theta+diff*(theta<pi)+diffneg*(theta>pi) } toret<-list() toret$mean<-mean(ideal)-diff toret$lower<-as.numeric(quantile(ideal,prob=alpha/2)-diff) toret$upper<-as.numeric(quantile(ideal,prob=1-(alpha/2))-diff) # cat('Mean',mean,'Lower',lower,'Upper',upper,'\n') return(toret) } # example # theta<-rnorm(n=2000,mean=0,sd=pi/50) # Normal, centred on zero # cis<-ciPhase(theta) # hist(theta,breaks=seq(-pi/8,pi/8,pi/30))

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/codes/howard.R

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sophielee1/NLP_byline_filter

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howard.R

### howard's code here # ## ## end of howard's code

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install.packages("devtools") install.packages("htmltools") install.packages("httpuv") install.packages("mime", dependencies=T) install.packages("xtable", dependencies=T) install.packages("magrittr", dependencies=T) install.packages("crosstalk", dependencies=T) install.packages("jsonlite", dependencies=T) install.packages("yaml", dependencies=T) install.packages("shiny", dependencies=T) # explorar mais install.packages("leaflet", dependencies=T) install.packages("Rcpp", dependencies=T) install.packages("ggplot2",dependencies = T) install.packages("ggmap",dependencies = T) install.packages("rgdal",dependencies = T) library(rgdal) library(ggplot2) library(maps) library(ggmap) library(magrittr) library(leaflet) library(jsonlite) shape = register_google(key = "")

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/man/secure-app.Rd

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pvictor/shinymanager

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% Generated by roxygen2: do not edit by hand % Please edit documentation in R/secure-app.R \name{secure-app} \alias{secure-app} \alias{secure_app} \alias{secure_server} \title{Secure a Shiny application and manage authentication} \usage{ secure_app(ui, ..., enable_admin = FALSE, head_auth = NULL) secure_server(check_credentials, session = shiny::getDefaultReactiveDomain()) } \arguments{ \item{ui}{UI of the application.} \item{...}{Arguments passed to \code{\link{auth_ui}}.} \item{enable_admin}{Enable or not access to admin mode, note that admin mode is only available when using SQLite backend for credentials.} \item{head_auth}{Tag or list of tags to use in the \code{<head>} of the authentication page (for custom CSS for example).} \item{check_credentials}{Function passed to \code{\link{auth_server}}.} \item{session}{Shiny session.} } \description{ Secure a Shiny application and manage authentication } \examples{ if (interactive()) { # define some credentials credentials <- data.frame( user = c("shiny", "shinymanager"), password = c("azerty", "12345"), stringsAsFactors = FALSE ) library(shiny) library(shinymanager) ui <- fluidPage( tags$h2("My secure application"), verbatimTextOutput("auth_output") ) # Wrap your UI with secure_app ui <- secure_app(ui) server <- function(input, output, session) { # call the server part # check_credentials returns a function to authenticate users res_auth <- secure_server( check_credentials = check_credentials(credentials) ) output$auth_output <- renderPrint({ reactiveValuesToList(res_auth) }) # your classic server logic } shinyApp(ui, server) } }

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akhikolla/updatedatatype-list3

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d1505cabc5bea8badb599bf1ed44efad5306636c

refs/heads/master

2023-03-25T09:44:15.112369

2021-03-20T15:57:10

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1612738732-test.R

testlist <- list(Rext = numeric(0), Rs = numeric(0), Z = numeric(0), alpha = numeric(0), atmp = numeric(0), relh = numeric(0), temp = c(7.06327792665088e-304, NaN, 5.43230922486616e-312, -6.90308500254685e+305, 9.07075240201063e-97, 9.70418706716128e-101, 1.67818560720548e-307, 9.70418706716122e-101, 9.70418706716128e-101, 9.70418706720286e-101, 9.70418706716128e-101, NaN, NaN, 1.33240877681e-105, 2.61035155593226e+180, 2.14838935209055e+174, 9.54424427230557e-307, 2.07507571253324e-322, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), u = numeric(0)) result <- do.call(meteor:::E_Penman,testlist) str(result)

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/cleanup.R

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no_license

nkannan97/-Soccer-Optimization

3c6e0a701ac72f5fe3ec7a8a3ed9559f6bc376c8

2348b18eec9d71bfb08e5f90e50399fdd92eff6c

refs/heads/master

2020-04-11T01:38:58.732942

2018-12-12T02:31:19

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cleanup.R

# read in the dataset t <- read.csv('/home/adithya/Desktop/SPRING2017/CS524/Project/data/CompleteDataset.csv', stringsAsFactors = FALSE) # get columns related to player attributes stats <- t[, which(colnames(t) == "Acceleration"):ncol(t)] stats <- stats[, -which(colnames(stats) == "Preferred.Positions")] # replace missing data with 0 (most likely goal keepers without forward playing statistics) stats[is.na(stats)] <- 0 # look for positions with +/- in their attributes and add/subtract those number stats2 <- apply(stats, 1, function(x) { temp = unlist(x) pos = grep("+", temp, fixed = T) if(any(pos)) { d = strsplit(temp[pos], "+", fixed = T) for(i in 1:length(d)) { temp[pos[i]] <- sum(as.integer(d[[i]])) } } pos = grep("-", temp, fixed = T) if(any(pos)) { d = strsplit(temp[pos], "-", fixed = T) for(i in 1:length(d)) { temp[pos[i]] <- as.integer(d[[i]][1]) - as.integer(d[[i]][2]) } } x <- temp }) # transpose and convert back to dataframe stats2 <- t(stats2) stats2 <- as.data.frame(stats2) # attach back to t dataframe t[, colnames(stats2)] <- stats2 t <- t[-which(t[,"X"] == "13771"),] # remove T. Wilson because he doesn't have a card on FUT # change players without a club to "No Club/Non FIFA" t[which(t[,"Club"] == ""),"Club"] <- "No Club/Non FIFA" t <- t[,-1] # change the values into numbers values <- t[,"Value"] values <- sapply(values, function(x) { substring(x, 2, nchar(x)) }) values <- unname(values) for(i in 1:length(values)) { if(substring(values[i], nchar(values[i])) == "M") { println(as.double(substring(values[i], 1, nchar(values[i])-1))) } else if (substring(values[i], nchar(values[i])) == "K") { } } values <- unlist(values) # write to file write.csv(t, file = '../data/modifiedCompleteDataset.csv')

35fd3b05c2baebc43cc600effe03bddff2369fa4

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/src/plot_data.R

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no_license

TinaQian2017/Reproduction_of_Projects

ac9146eab4470af9b3f80e576fd832ebb69a825e

a2f73946d0ec21bb893a5ad49223a87fb3e76a90

refs/heads/master

2021-04-27T04:38:03.233555

2018-02-23T06:35:24

122,582,551

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plot_data.R

# DSCI 522 milestone2 # Author: Yue (Tina) Qian # Date: 2017-12-13 # Function: the script generates a plot of data points for better understanding of the data # Read data data<-read.csv("results/simulated_data/summarized_data.csv",header = T) # visualize the data and save the plot library(ggplot2) g<-ggplot(data = data, aes(x=species,y=sepal_length))+ geom_boxplot()+ labs(x="Species",y="Sepal Length",title="Influence of Species on the Sepal Length")+ geom_jitter() ggsave(filename = "results/final_results/plot_data.png",plot = g)