####################################### ####################################### ###### EXPLORE DATA SET BEFORE ANALYSIS rm(list = ls()) source("R/process.data/process-fun.R") source("R/process.data/test.tree.CWM-fun.R") source("R/utils/plot.R") filedir <- "output/processed" mat.perc <- read.csv(file=file.path(filedir, "all.sites.perc.traits.csv"), stringsAsFactors=FALSE) ### read all data library(data.table) system.time(data.all <- fread(file.path(filedir, "data.all.csv"), stringsAsFactors=FALSE)) if(dim(data.all)[1] != sum(mat.perc[['num.obs']])) stop('error not same dimension per ecoregion and total') ## plots fun.plot.hist.trait.per.set(data.all) to.pdf(fun.hist.var.set(data.all,var='BATOT',cex=0.6), filename="figs/test.processed/fig.BATOT.set.pdf") to.pdf(fun.hist.var.set(data.all,var='G',cex=0.6), filename="figs/test.processed/fig.G.set.pdf") to.pdf(fun.hist.var.set(data.all,var='BA.G',cex=0.6), filename="figs/test.processed/fig.BA.G.set.pdf") to.pdf(fun.hist.var.set(data.all,var='D',cex=0.6), filename="figs/test.processed/fig.D.set.pdf") to.dev(fun.plot.xy.set(data.all,var.x='BATOT',var.y='BA.G',cex=0.6),dev=png, filename="figs/test.processed/fig.xy.BATOT.BA.G.set.png") to.dev(fun.plot.xy.set(data.all,var.x='D',var.y='BA.G',cex=0.6),dev=png, filename="figs/test.processed/fig.xy.D.BA.G.set.png") to.dev(fun.plot.xy.set(data.all,var.x='D',var.y='G',cex=0.6,col=col.vec[data.all$set]),dev=png, filename="figs/test.processed/fig.xy.D.G.set.png") to.dev(fun.plot.xy.set(data.all,var.x='BATOT',var.y='G',cex=0.6),dev=png, filename="figs/test.processed/fig.xy.BATOT.G.set.png") ######## ### TODO #- look at BATOT ALONG MAP AND MAT (log scale) #- how to compute FD on plot with diferent size #- pattern of CWM #- pattern ED angio/conif ## remove outlier following Condit approach data.all[['G']][!(trim.positive.growth(data.all[['G']]) & trim.negative.growth(dbh1=data.all[['D']]*10, dbh2=data.all[['D']]*10 +data.all[['year']]*data.all[['G']]))] <- NA data.all[['BA.G']][!(trim.positive.growth(data.all[['G']]) & trim.negative.growth(data.all[['D']]*10,dbh2=data.all[['D']]*10 +data.all[['year']]*data.all[['G']]))] <- NA ### compute mean BATOT, number of species, traits and VAR OF TRAITS per cluster system.time(data.summarise <- fun.compute.all.var.cluster(data.all)) ### NEED TO CHECK WHY JAPAN REACH 300 of BATOT par(mfrow=c(1,2)) plot(data.summarise$MAP,data.summarise$BATOT, ,col=col.vec[data.summarise$set],cex=0.1) fun.boxplot.breaks((data.summarise$MAP),data.summarise$BATOT,Nclass=15,add=TRUE) legend("topright",legend=names(col.vec),col=col.vec,pch=1) plot(log(data.summarise$MAP),data.summarise$BATOT, ,col=col.vec[data.summarise$set],cex=0.1) fun.boxplot.breaks(log(data.summarise$MAP),data.summarise$BATOT,Nclass=15,add=TRUE) by(data.summarise,INDICES=data.summarise$set,function(data,col.vec) {x11();plot(data$MAP,data$BATOT, main=unique(data$set), col=col.vec[data$set])},col.vec) plot(data.summarise$MAP,data.summarise$sd.SLA, ,col=col.vec[data.summarise$set]) plot(data.summarise$MAP,data.summarise$mean.SLA, ,col=col.vec[data.summarise$set]) pch.vec <- 1:14 names(pch.vec) <- names(col.vec) plot(data.summarise$n_sp,data.summarise$sd.Wood.density, ,col=col.vec[data.summarise$set],pch=pch.vec[data.summarise$set]) legend("topright",legend=names(col.vec),col=col.vec,pch=pch.vec) # library(ggplot2) qplot(data=data.summarise,x=set,y=BATOT,geom="boxplot",las=3) ggplot(data.summarise[,], aes( x=n_sp, y=sd.Wood.density) ) +facet_wrap(~set)+ geom_point(size=1 ) + geom_density2d() ggplot(data.all[,], aes( x=BATOT, y=Wood.density.CWM.fill) ) +facet_wrap(~set)+ geom_point(size=1 ) + geom_density2d()