merge.data.CANADA.R

### MERGE canada DATA
rm(list = ls())
source("./R/format.function.R")
source("./R/FUN.TRY.R")
library(reshape)

######################### READ DATA read individuals tree data
data.canada <- read.csv("./data/raw/DataCanada/Canada_Data2George_20130818.csv", 
    header = TRUE, stringsAsFactors = FALSE)
data.canada <- data.canada[which(!is.na(data.canada$Species)),]
colnames(data.canada)[2] <- "sp"

### read species names
species.clean <- read.csv("./data/raw/DataCanada/FIA_REF_SPECIES.csv", stringsAsFactors = FALSE)

###################################### MASSAGE TRAIT DATA HEIGHT DATA FOR TREE MISSING BRING US DATA FOR HEIGHT OVER
###################################### WHEN WE ANALYZE THAT DATASET LATER ON

########################################## FORMAT INDIVIDUAL TREE DATA

data.canada$G <- 10 * (data.canada$FinalDBH - data.canada$InitDBH)/data.canada$Interval  ## diameter growth in mm per year
data.canada$G[which(data.canada$InitDBH == 0 | data.canada$FinalDBH == -999)] <- NA
data.canada$year <- data.canada$Interval  ## number of year between measurement
data.canada$D <- data.canada[["InitDBH"]]
data.canada$D[data.canada$D == 0] <- NA ## diameter in cm
data.canada$dead <- as.numeric(data.canada$FinalDBH < 0)  ## dummy variable for dead tree 0 alive 1 dead
data.canada$plot <- (data.canada[["PLOT_ID"]]); data.canada[["PLOT_ID"]] <- NULL ## plot code
data.canada$htot <- rep(NA, nrow(data.canada))  ## height of tree in m - MISSING
data.canada$tree.id <- data.canada$PLOTTREE_I; data.canada$PLOTTREE_I <- NULL ## tree unique id
data.canada$sp.name <- NA
for (i in 1:length(unique(data.canada$sp))) {
    v <- species.clean$SPCD
    data.canada$sp.name[which(data.canada$sp == unique(data.canada$sp)[i])] <- species.clean$COMMON_NAME[which(v == 
        unique(data.canada$sp)[i])]
}
data.canada$weights <- 1/(pi*(0.5*data.canada$D/100)^2)


###################### ECOREGION merge greco to have no ecoregion with low number of observation
greco <- read.csv(file = "./data/raw/DataCanada/EcoregionCodes.csv", header = T, 
    sep = "\t")

table(data.canada$Ecocode)
## Some ecoregions still have small # of individuals, so either drop off for
## analysis later on or wait for Quebec data to come in library(RColorBrewer)
mycols <- brewer.pal(10, "Set3")

# ecoreg <- unclass(data.canada$eco_code)

# plot(data.canada[['CX']][order(ecoreg)],data.canada[['CY']][order(ecoreg)],pty='.',cex=.2,
# col = rep(mycols,as.vector(table(ecoreg))))

# legend('bottomright', col = mycols, legend = levels(data.canada$eco_code), pch
# = rep(19,length(levels(ecoreg))),cex=2) points(data.canada[['CX']][ecoreg ==
# 9],data.canada[['CY']][ecoreg == 9],pty='.',cex=.2, col = 'black') Highlight
# the region with 55 sites ## PA1219 looks to be similar to PA1209, merge them
# together data.canada$eco_codemerged <- combine_factor(data.canada$eco_code,
# c(1:8,6,9))

###################### PERCENT DEAD variable percent dead/cannot do with since dead variable is
###################### missing compute numer of dead per plot to remove plot with disturbance
perc.dead <- tapply(data.canada[["dead"]], INDEX = data.canada[["plot"]], FUN = function.perc.dead2)
data.canada <- merge(data.canada, data.frame(plot = names(perc.dead), perc.dead = perc.dead), 
    by = "plot", sort = FALSE)

########################################################### PLOT SELECTION FOR THE ANALYSIS Remove data with dead == 1
table(data.canada$dead)
data.canada <- data.canada[data.canada$dead == 0,]

vec.abio.var.names <- c("MAT", "MAP")