process data Canda NVS

R/format.data/NSW.R

@@ -44,6 +44,13 @@ data.nswtnd$Date.of.measure <- as.vector(sapply(data.nswtnd$Date.of.measure, fun
     unlist(strsplit(x, "/"))[3]
 }))  ## Extract the years only
 data.nsw <- rbind(data.nswbrc, data.nswbrt, data.nswbs1, data.nswbs2, data.nswtnd)
+
+
+######
+## NEED TO CREATE plot of 10x10m for all tree with either x y coordinates or subplot in BS2
+
+
+
 ###################################### MASSAGE TRAIT DATA
 data.trait <- read.csv("data/raw/NSW/NSW_traits.csv", header = TRUE, stringsAsFactors = FALSE)
 data.trait$sp <- data.trait[["Species.all"]]; data.trait[["Species.all"]] <- NULL ## There is not sp.code in data.nsw; using spp name as code
@@ -58,6 +65,8 @@ data.trait$Wood.density.mean <- data.trait[["WD_basic_kg.m3"]]/1000; data.trait[
 data.trait$Wood.density.sd <- NA
 data.trait$Max.height.mean <- data.trait$Log10_Hmax_m; data.trait$Log10_Hmax_m <- NULL
 data.trait$Max.height.sd <- NA
+
+
 ########################################## FORMAT INDIVIDUAL TREE DATA Each tree has at most 3 observations (from prelim
 ########################################## checks of the data)
 data.nsw$tree.id <- apply(data.nsw[, 1:2], 1, paste, collapse = "_")
@@ -89,7 +98,7 @@ data.nsw$year <- (data.nsw$year2 - data.nsw$year1)  ## number of year between me
 data.nsw$G <- 10 * (data.nsw$dbh2 - data.nsw$dbh1)/(data.nsw$year)  ## diameter growth in mm per year
 #head(data.nsw[order(data.nsw$G), ])
 data.nsw$D <- data.nsw[["dbh1"]] ## diameter in cm
-data.nsw$dead <- rep(NA, nrow(data.nsw))  ## dummy variable for dead tree 0 alive 1 dead - MISSING
+data.nsw$dead <- rep(NA, nrow(data.nsw))  ## dummy variable for dead tree 0 alive 1 dead - MISSING USE M
 data.nsw$sp <- as.numeric(factor(data.nsw[["species"]])); ## species code - use the spp name as code
 data.nsw$sp.name <- data.nsw[["species"]]; data.nsw$species <- NULL
 data.nsw$plot <- as.character(data.nsw[["Plot"]]); data.nsw$Plot <- NULL ## plot code

R/process.data/Canada.R

+#################
+################
+### PROCESS THE DATA FOR Canada
+
+rm(list = ls())
+source("process.fun.R")
+library(reshape)
+
+data.tree <- read.csv("../../output/formatted/Canada/tree.csv", stringsAsFactors = FALSE)
+data.tree <- subset(data.tree,subset=!is.na(data.tree[["D"]]))
+
+
+## change sp
+data.traits <- read.csv("../../output/formatted/Canada/traits.csv")
+
+data.traits$sp <- paste("sp",data.traits$sp,sep=".")
+data.tree$sp <- paste("sp",data.tree$sp,sep=".")
+
+
+
+
+# vector of ecoregion name
+ecoregion.unique <- unique(data.tree[["ecocode"]])
+
+#### lapply function
+
+
+system.time(lapply(ecoregion.unique, FUN = fun.data.per.ecoregion, data.tot = data.tree, 
+    weight.full.plot = NA, site.name = "Canada", data.TRAITS = data.traits)) 

R/process.data/NVS.R

+#################
+################
+### PROCESS THE DATA FOR NVS
+
+rm(list = ls())
+source("process.fun.R")
+library(reshape)
+
+data.tree <- read.csv("../../output/formatted/NVS/tree.csv", stringsAsFactors = FALSE)
+data.tree <- subset(data.tree,subset=!is.na(data.tree[["D"]]))
+
+
+## change sp
+data.traits <- read.csv("../../output/formatted/NVS/traits.csv")
+
+data.traits$sp <- paste("sp",data.traits$sp,sep=".")
+data.tree$sp <- paste("sp",data.tree$sp,sep=".")
+
+
+
+
+# vector of ecoregion name
+ecoregion.unique <- unique(data.tree[["ecocode"]])
+
+#### lapply function
+
+
+system.time(lapply(ecoregion.unique, FUN = fun.data.per.ecoregion, data.tot = data.tree, 
+    weight.full.plot = NA, site.name = "NVS", data.TRAITS = data.traits))