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chilleochilleo
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added some java source files and completed smooth winds page
Added the source files used to compile the DGEN and SmoothWinds jar files. Added source files used to conduct experiments for both. Added a new page 'Smooth Winds' for building accurate local genealogies. Documentation soon to follow.
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‎R/r_makeFigsSmoothWinds.R

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library(ggpubr)
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library(Hmisc)
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library(gdata)
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figsPath = "/Users/leo/rice/tmp/"
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paperPath = "/Users/leo/rice/res/papers/RECOMB2016/svn/ISBRA19-Leo/draft1/figs/"
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paperPath = "/Users/leo/rice/res/papers/RECOMB2016/svn/WABI19-Leo/lipics-v2019-authors/figs/"
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myPlot <- ggpar(myPlot, x.text.angle = 90) #angles x labels
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ggsave(filename=paste(paperPath,"fig_mosq6tax_time.pdf",sep=""), plot=myPlot, width=8,height=8)
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ggsave(filename=paste(paperPath,"fig_mosq_6tax_time.pdf",sep=""), plot=myPlot, width=8,height=8)
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###
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# Fig - primates 11tax accuracy
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###
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fileToRead = paste(figsPath,"final_primates_11tax_20x/FinalResults/r_all",sep="")
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values = read.csv(fileToRead, header=TRUE)
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#do a lil renaming
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#values[1:21,]
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values [,1] = gsub("-CNTRD", "", values[,1])
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values [,1] = gsub("LessMem", "", values[,1])
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#sort em by mean
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means = aggregate(values[, 2], list(values$Method), mean)
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meansOrdered = means[order(means$x),]
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meansOrderedNames = meansOrdered[,1]
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list(meansOrderedNames)
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meanOrderedNames = fct_inorder(rev(meansOrderedNames)) #rev does reverse order (so currently best is on top, without rev its on bottom)
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#meanOrderedNames <- fct_inorder(meanOrderedNames, ordered = TRUE)
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meanOrderedNames <- factor(meanOrderedNames, fct_inorder(meanOrderedNames))
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#meanOrderedNames
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values$Method <- factor(values$Method, meanOrderedNames)
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#values$Method #WORKS?
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#command line with saving?
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myPlot <- ggerrorplot(values, x = "Method", y = "Accuracy", xlab = "Method Used", ylab = "Symmetric Difference",
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desc_stat = "mean_sd", color = "black",
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add = "jitter", add.params = list(color = "darkgray"),font.x = c(24, "bold", "black"),font.y = c(24, "bold", "black"),font.tickslab = c(18,"bold", "black")
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)
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myPlot <- ggpar(myPlot, x.text.angle = 90) #angles x labels
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# THIS IS AN OPTION ALSO
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myPlot <- myPlot + coord_flip()
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ggsave(filename=paste(paperPath,"fig_primates_11tax_accuracy.pdf",sep=""), plot=myPlot, width=12,height=8)
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###
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# Fig - generate ALL ACCURACY FIGS (since the naming conventions are easy and they are all the same)
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###
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myFunctionMakePlot <- function(inputFile,outputFile) {
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fileToRead = paste(figsPath,inputFile,sep="")
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values = read.csv(fileToRead, header=TRUE)
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#do a lil renaming
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values [,1] = gsub("-CNTRD", "", values[,1])
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values [,1] = gsub("LessMem", "", values[,1])
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#sort em by mean
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means = aggregate(values[, 2], list(values$Method), mean)
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meansOrdered = means[order(means$x),]
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meansOrderedNames = meansOrdered[,1]
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meanOrderedNames = fct_inorder(rev(meansOrderedNames)) #rev does reverse order (so currently best is on top, without rev its on bottom)
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#meanOrderedNames <- factor(meanOrderedNames, fct_inorder(meanOrderedNames))
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values$Method <- factor(values$Method, meanOrderedNames)
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#values$Method #WORKS?
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#command line with saving?
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myPlot <- ggerrorplot(values, x = "Method", y = "Accuracy", xlab = "Method Used", ylab = "Symmetric Difference",
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desc_stat = "mean_sd", color = "black",
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add = "jitter", add.params = list(color = "darkgray"),font.x = c(24, "bold", "black"),font.y = c(24, "bold", "black"),font.tickslab = c(18,"bold", "black")
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)
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myPlot <- ggpar(myPlot, x.text.angle = 90) #angles x labels
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# THIS IS AN OPTION ALSO (flips it)
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myPlot <- myPlot + coord_flip()
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ggsave(filename=paste(paperPath,outputFile,sep=""), plot=myPlot, width=12,height=8)
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}
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myFunctionMakePlot("final_butterfly_8tax_20x/FinalResults/r_all","fig_butterfly_8tax_accuracy.pdf")
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myFunctionMakePlot("final_butterfly_12tax_20x/FinalResults/r_all","fig_butterfly_12tax_accuracy.pdf")
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myFunctionMakePlot("final_bird_8tax_20x/FinalResults/r_all","fig_bird_8tax_accuracy.pdf")
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myFunctionMakePlot("final_bird_12tax_20x/FinalResults/r_all","fig_bird_12tax_accuracy.pdf")
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myFunctionMakePlot("final_mosq_12tax_20x/FinalResults/r_all","fig_mosq_12tax_accuracy.pdf")
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myFunctionMakePlot("final_mosq_18tax_20x/FinalResults/r_all","fig_mosq_18tax_accuracy.pdf")
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myFunctionMakePlot("final_bird_4tax_20x/FinalResults/r_all","fig_bird_4tax_accuracy.pdf")
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myFunctionMakePlot("final_butterfly_4tax_20x/FinalResults/r_all","fig_butterfly_4tax_accuracy.pdf")
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myFunctionMakePlot("final_mosq_6tax_20x/FinalResults/r_all","fig_mosq_6tax_accuracy.pdf")
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myFunctionMakePlot("final_primates_11tax_20x/FinalResults/r_all","fig_primates_11tax_accuracy.pdf")
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myFunctionMakePlot("final_humans_4tax_20x/FinalResults/r_all","fig_humans_4tax_accuracy.pdf")
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###
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# GENERATE ALL TIMING FIGS
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###
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myFunctionMakeTimingPlot <- function(inputFile,outputFile) {
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fileToRead = paste(figsPath,inputFile,sep="")
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values = read.csv(fileToRead, header=TRUE)
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#command line with saving?
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myPlot <- ggerrorplot(values, x = "Method", y = "Time", xlab = "Method Used", ylab = "Time (s)",
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desc_stat = "mean_sd", color = "black",
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add = "jitter", add.params = list(color = "darkgray"),font.x = c(24, "bold", "black"),font.y = c(24, "bold", "black"),font.tickslab = c(14,"bold", "black")
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)
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myPlot <- ggpar(myPlot, x.text.angle = 90) #angles x labels
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ggsave(filename=paste(paperPath,outputFile,sep=""), plot=myPlot, width=8,height=8)
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}
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myFunctionMakeTimingPlot("final_mosq_6tax_20x/FinalTimings/r_all","fig_mosq_6tax_time.pdf")
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myFunctionMakeTimingPlot("final_mosq_12tax_20x/FinalTimings/r_all","fig_mosq_12tax_time.pdf")
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myFunctionMakeTimingPlot("final_mosq_18tax_20x/FinalTimings/r_all","fig_mosq_18tax_time.pdf")
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myFunctionMakeTimingPlot("final_bird_4tax_20x/FinalTimings/r_all","fig_bird_4tax_time.pdf")
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Outcome = c(controls, cases))
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#One option is simply to show the data points, which you can do like this:
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dat %>% ggplot(aes(x, Outcome)) +
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geom_jitter(width = 0.05)
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dat %>% ggplot(aes(x, Outcome)) +
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geom_jitter(width = 0.05)
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myPlot <- ggplot(dat,aes(x, Outcome)) +
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geom_jitter(width = 0.05)
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myPlot + theme(axis.text.x = element_text(angle = 90, hjust = 1))
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myPlot <- ggplot(dat,aes(x, Outcome)) +
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geom_jitter(width = 0.05)
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myPlot + theme(axis.text.x = element_text(angle = 90, hjust = 1))
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#maybe i just go with this
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myPlot + coord_flip()
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#maybe i just go with this
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myPlot + coord_flip()
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####
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# TESTING ORDER BY MEANS OF CATEGORIES
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###
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zz <- "chrom start
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chr2 39482
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chr1 203918
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chr1 198282
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chrX 7839028
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chr17 3874"
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Data <- read.table(text=zz, header = TRUE)
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#library(Hmisc)
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#library(gdata)
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Data$chrom <- reorder.factor(Data$chrom , levels = c("chr1","chr2", "chr17", "chrX"))
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Data[order(Data$chrom), ]
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#### MAKING A FUNCTION
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mysummary <- function(x,y) {
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cat(x)
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cat(y)
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}
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