comparison scripts_R/Metadata_subsetBLSM.R @ 70:cc028157502a branch-tests

scripts R

+#df = read.csv("/Users/mariapanteli/Documents/2014-2015/Python/pythoncode/MergeBL-Smith/data/df_BLSM.csv",header=TRUE)
+#df = read.csv("data/df_subset_remove.csv",header=TRUE)
+#df = read.csv("/Users/mariapanteli/Documents/2014-2015/Python/pythoncode/CodeForBL/data/metadataBL_new.csv",header=TRUE)
+#df = read.csv("/Users/mariapanteli/Documents/2014-2015/Python/pythoncode/MergeBL-Smith/data/metadata_BLSM.csv",header=TRUE)
+#df = df[1:29182,] # BL data
+df = read.csv('data/df_and_clusters.csv', header=T)
+
+source("MetadataPlots.R")
+
+## for plos use arial
+#install.packages("extrafont")
+library(extrafont)
+font_import()
+loadfonts()
+Arial <- Type1Font(family="Arial", metrics=c("ArialMT.afm","arial-BoldMT.afm","Arial-ItalicMT.afm", "Arial-BoldItalicMT.afm"))
+postscriptFonts(Arial=Arial)
+par(family="Arial")
+
+#pdf(file="data/country_distribution_BL.pdf")
+pdf(file="data/country_distribution.pdf")
+PlotCountryCounts(df)
+dev.off()
+postscript(file="data/country_distribution.eps")
+PlotCountryCounts(df)
+dev.off()
+
+pdf(file="data/year_distribution.pdf", width=6, height=4)
+PlotYearDistribution(df)
+dev.off()
+postscript("data/year_distribution.eps", width=10)
+PlotYearDistribution(df)
+dev.off()
+#PlotBarChart(df, cat="Year", ordercat="REGION", mincount=10)
+
+#pdf(file="data/language_distribution_BL.pdf")
+levels(df$Language)[which(levels(df$Language)=="Southwestern Caribbean Creole English")]="SouthW Carib. Creole English"
+df$Language[which(df$Language=="Southwestern Caribbean Creole English")] = "SouthW Carib. Creole English"
+levels(df$Language)[which(levels(df$Language)=="Lesser Antillean Creole French")]="Lesser Antil. Creole French"
+df$Language[which(df$Language=="Lesser Antillean Creole French")] = "Lesser Antil. Creole French"
+df$REGION[which(df$Country=="French Guiana")] = "South America"
+pdf(file="data/language_distribution.pdf")
+PlotBarChart(df, cat="Language", ordercat="Region", mincount=10)
+dev.off()
+postscript("data/language_distribution.eps", width=8, height=10)
+PlotBarChart(df, cat="Language", ordercat="Region", mincount=10)
+dev.off()
+
+#language phylogeny
+df = read.csv('data/metadata_BLSM_language.csv', header=T)
+pdf(file="data/language_iso3_iso1.pdf")
+PlotBarChart(df, cat="Language_iso3", ordercat="Language_iso1", mincount=10)
+dev.off()
+
+# PlotCountryCounts(df)
+# PlotCountryCultureNcounts(df, mincount=20)
+# PlotCountryLanguageNcounts(df, mincount=20)
+# PlotYearDistribution(df)
+# PlotLanguageDistribution(df)
+# PlotCultureDistribution(df)
+# PlotNxNcounts(df, cat1="Country", cat2="Genre_Album", mincount=20)
+
+df = read.csv('data/df_and_clusters.csv', header=T)
+#PlotBarChart(df, cat="Clusters", ordercat="CountryLang", mincount=1,legend=F)
+df$REGION[which(df$Country=="French Guiana")] = "South America"
+g = ggplot(df,aes(df$Clusters, fill=df$REGION))+geom_bar()
+levels(df$REGION)[which(levels(df$REGION)=="South America")]="S. America"
+levels(df$REGION)[which(levels(df$REGION)=="North America")]="N. America"
+
+#library(rworldmap)
+#wrld = getMap()
+#regiondata<-wrld@data[,c("ADMIN","GEO3", "Stern")]
+#df<-merge(df,regiondata,by.x="Country",by.y="ADMIN",all.x=T)
+
+#cluster_labels_df = read.csv('data/clusters_top3_labels.csv')
+cluster_labels_df = read.csv('data/clusters_top3_countries.csv')
+cluster_labels = paste(cluster_labels_df[,1],cluster_labels_df[,2],cluster_labels_df[,3],sep="")
+#df$CountryLang = as.factor(paste(df$Country, df$Language, sep="-"))
+
+countrycounts = table(df$Clusters,df$Country)
+library(cluster)
+library(ape)
+library(gridExtra)
+library(ggdendro)
+library(dendextend)
+hc = hclust(dist(countrycounts), method="average")
+hc2=hc
+#hc2$labels = as.character(1:length(cluster_labels))
+hc2$labels = ""
+#dhc <- as.dendrogram(hc2)
+# library(dynamicTreeCut)
+# clusters <- cutreeDynamic(hc2, minClusterSize = k_clust,method = "tree")
+# clusters <- clusters[order.dendrogram(dhc)]
+# clusters_numbers <- unique(clusters) - (0 %in% clusters)
+# n_clusters <- length(clusters_numbers)
+# library(colorspace)
+# cols <- rainbow_hcl(n_clusters)
+# dhc <- hc2 %>% as.dendrogram %>%
+#   set("branches_k_color", k=k_clust) %>% branches_attr_by_clusters(clusters, values = cols)
+k_clust = 5
+dhc <- hc2 %>% as.dendrogram %>%
+  set("branches_k_color", k=k_clust) %>% set("branches_lwd", 0.7) %>% 
+  set("labels_cex", 0.6) %>% set("labels_colors", k=k_clust) %>%
+  set("leaves_pch", 19) %>% set("leaves_cex", 0.5) 
+#ddata <- dendro_data(dhc, type = "rectangle")
+ddata <- as.ggdend(dhc)
+p <- ggplot(ddata)+coord_flip()
+#p <- ggplot(segment(ddata)) + 
+#  geom_segment(aes(x = x, y = y, xend = xend, yend = yend, colour=ddata$segments$col)) + 
+#  coord_flip() + theme_dendro() + theme(legend.position="none") +
+#  geom_text(aes(x = x, y = y, label = label, angle = -90, hjust = 0.5, vjust=1.3, colour=ddata$labels$col), data= label(ddata))
+
+#dend <- hc2 %>% as.dendrogram %>%
+#  set("branches_k_color", k = 5) %>% set("branches_lwd", 0.7) %>%
+#  set("labels_cex", 0.6) %>% set("labels_colors", k = 5) %>%
+#  set("leaves_pch", 19) %>% set("leaves_cex", 0.5) 
+#ggd1 <- as.ggdend(dend)
+#pp <- ggplot(ggd1, horiz = TRUE)
+
+library(stringr)
+for (i in 1:length(cluster_labels)){
+  cl = cluster_labels[i]
+  cl = str_replace_all(cl, "[(']", "")
+  cl = str_replace_all(cl, "[|]", "-")
+  cl = str_replace_all(cl, ", ", " (")
+  cl = str_replace_all(cl, "[)]", "), ")
+  cl = str_replace_all(cl, "nan", "NA")
+  #cl = paste(cl, "cluster",i)
+  cluster_labels[i] = cl
+}
+#cluster_idx = paste("cluster",1:length(cluster_labels))
+#df$Clusters = as.factor(df$Clusters)
+df$Clusters = factor(x=df$Clusters,levels=hc$labels[hc$order])
+cluster_labels = cluster_labels[hc$order]
+#g = ggplot(df,aes(as.factor(df$Clusters), fill=df$CountryLang))+geom_bar()
+#g = ggplot(df,aes(Clusters, fill=REGION))+geom_bar()+facet_grid(~REGION,space="free",scales="free")#,scales="free")
+g = ggplot(df,aes(as.factor(df$Clusters), fill=df$Region))+geom_bar()
+#g = ggplot(df,aes(as.factor(df$Clusters), fill=df$REGION))+geom_bar()
+g = g+scale_x_discrete(labels=cluster_labels)
+#g = g+scale_y_continuous(position="right")
+#g = g+scale_fill_brewer(palette="Paired")#+scale_fill_grey()
+g = g+scale_fill_brewer(palette="Paired")#+scale_fill_grey()
+#g = g+labs(y="Counts", x="Top 3 country-language tags in each cluster")+coord_flip()+theme_bw()#+guides(fill="none")
+g = g+labs(y="Counts", x="Clusters")+coord_flip()+theme_bw()#+guides(fill="none")
+#g = g+labs(y="Counts", x="Clusters")+coord_flip()+theme_bw()#+guides(fill="none")
+#g = g+guides(fill = guide_legend(title = "Region"))+theme(legend.position=c(.9,.8),legend.margin = unit(0, "cm"),legend.key.size = unit(0.3, "cm"),legend.title = element_text(size=10),legend.text = element_text(size=10))
+#g = g+guides(fill = guide_legend(title = "Region"))+theme(legend.position="left",legend.margin = unit(0, "cm"),legend.key.size = unit(0.3, "cm"),legend.title = element_text(size=9),legend.text = element_text(size=9))
+g = g+guides(fill = guide_legend(title = "Region"))+theme(legend.position="top",legend.title = element_text(size=9),legend.text = element_text(size=9))
+g = g+theme(panel.border = element_rect(colour = "white"),strip.background=element_rect(fill="white"),strip.text.x = element_blank())
+#g = g+theme(axis.text.y = element_text(colour = ddata$labels$col))
+ggsave('data/clusters_top3.pdf',plot=g)
+ggsave('data/clusters_top3.eps',plot=g)
+
+#g_legend<-function(a.gplot){
+#  tmp <- ggplot_gtable(ggplot_build(a.gplot))
+#  leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
+#  legend <- tmp$grobs[[leg]]
+#  return(legend)}
+#mylegend<-g_legend(g)
+
+#pdf(file="data/clusters_top3_hclust.pdf", width=12, height=5)
+#grid.arrange(arrangeGrob(g + theme(legend.position="none"),p + theme(legend.position="none"),nrow=1, widths=c(4,1)),mylegend, nrow=2,heights=c(10, 1))
+#dev.off()
+
+#grid.arrange(arrangeGrob(g,p,nrow=1, ncol=2))
+#ggsave('data/clusters_top3_hclust.pdf',plot=g_comb)
+#g=g+annotate(x=20, y=1:18, label=cluster_idx)+geom_text(aes(x=20,y=1:18,label=cluster_idx))
+#+guides(fill = guide_legend(title = "Region"))
+#grid.draw(cbind(ggplotGrob(g), ggplotGrob(pp), size = "last"))