analysis.R

#!/usr/bin/env Rscript
############################################# MERGE FRENCH DATA
path.formatted <- "output/formatted"
path.processed <- "output/processed"
library(plyr)

source("R/analysis/analysis-fun.R")
source("R/utils/plot.R")
source("R/utils/maps.R")
source("R/utils/climate.R")

## load all sites
data <- load_all_formatted_data(path.formatted)


get_unique_plot_coords <- function(set, data){
	i <- !duplicated(paste(data[[set]]$tree$Lon, data[[set]]$tree$Lat)) #find duplicates
	data.frame(set = set, Lon = data[[set]]$tree$Lon[i], Lat = data[[set]]$tree$Lat[i], ecocode = data[[set]]$tree$ecocode[i], stringsAsFactors=FALSE)
}

x <- rbind.fill(lapply(names(data), get_unique_plot_coords, data=data))
write.csv(x, file.path(path.processed, "all.sites.coords.csv"), row.names=FALSE)
x <- read.csv(file.path(path.processed, "all.sites.coords.csv"), stringsAsFactors = FALSE)


## GET climate with Mark V function
all.sites.coords <- read.csv(file.path(path.processed, "all.sites.coords.csv"), stringsAsFactors = FALSE)
clim.all <- GetClimate(lats=all.sites.coords$Lat,lons=all.sites.coords$Lon)
site.clim.all <- cbind(all.sites.coords,clim.all$MAT,clim.all$MAP)
write.csv(site.clim.all, file.path(path.processed, "all.sites.clim.csv"), row.names=FALSE)

## change ecocode of Japan to tropical to have big point

x[x$set=='Japan','ecocode'] <- 'tropical'
# Map for all sites
to.dev(world.map.all.sites(x, add.legend =TRUE),
  png,file=file.path("figs", "world_map_all.png"), height =500, width = 1000)


# make summary table
data.summary <- lapply(data, summarise.data)

tab.1 <- rbind.fill(lapply(data.summary,summary.table.1))

write.csv(tab.1, file.path(path.processed, "summary_tab.csv"), row.names = names(data.summary))