merge script for US data

merge.data.US.R

+### MERGE us DATA
+### Edited by FH
+rm(list = ls()); source("./R/format.function.R"); library(reshape)
+
+#########################
+## READ DATA
+####################
+### read individuals tree data
+data.us <- read.csv("./data/raw/DataUS/FIA51_trees_w_supp.csv",header=TRUE,stringsAsFactors =FALSE)
+
+### read species names
+species.clean <- read.csv("./data/species.list/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
+#############
+
+## change unit and names of variables to be the same in all data for the tree 
+data.us$G <- 10*(data.us$FinalDbh-data.us$InitDbh)/data.us$Interval ## diameter growth in mm per year
+data.us$G[which(data.us$InitDbh == 0 | data.us$FinalDbh == -999)] <- NA
+data.us$year <- data.us$Interval ## number of year between measuremen
+data.us$D <- data.us[["InitDbh"]]; data.us$D[data.us$D == 0] <- NA ;## diameter in cm
+data.us$dead <- as.numeric(data.us$FinalDbh > 0) ## dummy variable for dead tree 0 alive 1 dead
+data.us$sp <- as.character(data.us[["Species"]]) ## species code
+data.us$plot <- (data.us[["PlotID"]]) ## plot code
+data.us$htot <- rep(NA,length(data.us[["Species"]])) ## height of tree in m - MISSING
+data.us$tree.id <- data.us$TreeID; ## tree unique id
+data.us$sp.name <- NA; 
+v <- species.clean$SPCD; for(i in 1:length(unique(data.us$sp))) { 
+	data.us$sp.name[which(data.us$sp == unique(data.us$sp)[i])] <- species.clean$COMMON_NAME[which(v == unique(data.us$sp)[i])] }
+	
+######################
+## ECOREGION
+###################
+## merge greco to have no ecoregion with low number of observation
+greco <- read.csv(file = "./data/raw/DataUS/EcoregionCodes.csv", header = T); colnames(greco)[1] <- "Ecocode" 
+
+table(data.us$Ecocode) 
+data.us <- merge(data.us, greco[,-4], by = "Ecocode"); data.us$DIVISION <- factor(data.us$DIVISION)
+## Some ecoregions still have small # of individuals, so create a variable which does division if # ind < 10000; else it reads Domain
+#
+data.us$eco_codemerged <- as.character(data.us$DIVISION);  tab.small.div <- table(data.us$eco_codemerged)
+sel.small.div <- which(table(data.us$eco_codemerged) < 10000)
+for(i in 1:length(sel.small.div)) { 
+	find.ind <- which(data.us$eco_codemerged == names(tab.small.div)[sel.small.div[i]]); print(length(find.ind))
+	data.us$eco_codemerged[find.ind] <- as.character(data.us$DOMAIN)[find.ind] 
+	}
+	
+data.us <- data.us[,-c(2,24,25)] ## Remove other ecocode related stuff to save space
+######################
+## PERCENT DEAD
+###################
+## variable percent dead/cannot do with since dead variable is missing
+## compute numer of dead per plot to remove plot with disturbance
+perc.dead <- tapply(data.us[["dead"]],INDEX=data.us[["plot"]],FUN=function.perc.dead)
+# ## VARIABLE TO SELECT PLOT WITH NOT BIG DISTURBANCE KEEP OFTHER VARIABLES IF AVAILABLE (disturbance record)
+data.us <- merge(data.us,data.frame(plot=names(perc.dead),perc.dead=perc.dead), by = "plot", sort=FALSE)
+
+###########################################################
+### PLOT SELECTION FOR THE ANALYSIS
+###################
+## Remove data with dead == 1
+table(data.us$dead)
+data.us <- data.us[data.us$dead == 1,]
+
+vec.abio.var.names <-  c("MAT","MAP")
+vec.basic.var <-  c("tree.id","sp","sp.name","plot","eco_codemerged","D","G","dead","year","htot","Lon","Lat","perc.dead")
+data.tree <- subset(data.us,select=c(vec.basic.var,vec.abio.var.names))
+
+##############################################
+## COMPUTE MATRIX OF COMPETITION INDEX WITH SUM OF BA PER SPECIES IN EACH PLOT in m^2/ha without the target species
+###########################
+data.BA.SP <- BA.SP.FUN(id.tree=as.vector(data.us[["tree.id"]]), diam=as.vector(data.us[["D"]]),
+	sp=as.vector(data.us[["sp"]]), id.plot=as.vector(data.us[["plot"]]),
+	weights=1/(10000*data.us[["PlotSize"]]), weight.full.plot=NA)
+
+## change NA and <0 data for 0
+data.BA.SP[is.na(data.BA.SP)] <- 0; data.BA.SP[,-1][data.BA.SP[,-1]<0] <- 0
+
+### CHECK IF sp and sp name for column are the same
+if(sum(!(names(data.BA.SP)[-1] %in% unique(data.us[["sp"]]))) >0) stop("competition index sp name not the same as in data.tree")
+
+#### compute BA tot for all competitors
+BATOT.COMPET <- apply(data.BA.SP[,-1],1,sum,na.rm=TRUE)
+data.BA.SP$BATOT.COMPET <- BATOT.COMPET; rm(BATOT.COMPET)
+### create data frame
+names(data.BA.SP) <- c("tree.id",names(data.BA.SP)[-1])
+data.BA.sp <- merge(data.frame(tree.id=data.us[["tree.id"]],ecocode=data.us[["eco_codemerged"]]),data.BA.SP,by="tree.id",sort=FALSE)
+## test
+if(sum(!data.BA.sp[["tree.id"]] == data.tree[["tree.id"]]) >0) stop("competition index not in the same order than data.tree")
+
+## save everything as a list
+list.us <- list(data.tree=data.tree,data.BA.SP=data.BA.sp,data.traits=data.traits)
+save(list.spain,file="./data/process/list.us.Rdata")
+