view scripts_R/PlotOutliersCountry.R @ 77:bde45ce0eeab branch-tests

plots and figures for results

source("MetadataPlots.R")

PlotCountryOutliers(df=read.csv("../data/results/global_outliers.csv",header=TRUE), output="../data/results/global_outliers.pdf")
PlotCountryOutliers(df=read.csv("../data/results/global_outliers_rhy.csv",header=TRUE), output="../data/results/global_outliers_rhy.pdf")
PlotCountryOutliers(df=read.csv("../data/results/global_outliers_mel.csv",header=TRUE), output="../data/results/global_outliers_mel.pdf")
PlotCountryOutliers(df=read.csv("../data/results/global_outliers_mfc.csv",header=TRUE), output="../data/results/global_outliers_mfc.pdf")
PlotCountryOutliers(df=read.csv("../data/results/global_outliers_chr.csv",header=TRUE), output="../data/results/global_outliers_chr.pdf")
PlotCountryOutliers(df=read.csv("../data/results/spatial_outliers.csv",header=TRUE), output="../data/results/spatial_outliers.pdf")

library(ape)
library(cluster) 

df = read.csv("../data/results/cluster_freq.csv")
data = df[,2:dim(df)[2]]
rownames(data) <- df$labels
distMahal = as.dist(apply(data, 1, function(i) mahalanobis(data, i, cov = cov(data),tol=1e-18)))
hc=hclust(distMahal, method="average")
mypal = c("#000000", "#9B0000", "#9B0000", "#9B0000", "#9B0000")
clus5 = cutree(hc, 5)
pdf('../data/results/hierarchical_cluster.pdf')
par(mar=c(1,1,1,1))
plot(as.phylo(hc),type="fan",tip.color=mypal[clus5], cex=.5, label.offset=.5)
dev.off()