merge.data.SWISS.R

### MERGE Swiss DATA
### Edited by FH
rm(list = ls()); source("./R/format.function.R"); library(reshape); library(foreign)

#########################
## READ DATA
####################
### read individuals tree data
data.swiss1 <- read.csv("./data/raw/DataSwiss/LFI12.csv",header=TRUE,stringsAsFactors =FALSE)
data.swiss2 <- read.csv("./data/raw/DataSwiss/LFI23.csv",header=TRUE,stringsAsFactors =FALSE)
data.swiss3 <- read.csv("./data/raw/DataSwiss/LFI34.csv",header=TRUE,stringsAsFactors =FALSE)
data.swiss <- rbind(data.swiss1, data.swiss2, data.swiss3)
rm(data.swiss1, data.swiss2, data.swiss3)
data.swiss <- data.swiss[order(data.swiss$BANR),]; data.swiss <- data.swiss[,c(2:19)]
colnames(data.swiss) <- c("Inventid","siteid","treeid","x","y","spcode","sp.name","year","dbh1","dbh2","ba1","ba2","ba_diff","dbh_diff","repfactor1","repfactor2","ht1","ht2")

## Do not need to read in spp list as it is already available in data.swiss

######################################
## MASSAGE TRAIT DATA
############################
## Compute maximum height per species plus sd from observed height to add variables to the traits data base
## Because we have two heights, then take the max of the two heights and then bootstrap 
res.quant.boot <- t(sapply(levels(factor(data.swiss[["spcode"]])),FUN=f.quantile.boot,R=1000,x=log10(apply(data.swiss[,c("ht1","ht2")],1,max,na.rm=T)),fac=factor(data.swiss[["spcode"]])))

## create data base
data.max.height <- data.frame(code=rownames(res.quant.boot),Max.height.mean=res.quant.boot[,1],Max.height.sd=res.quant.boot[,2],Max.height.nobs=res.quant.boot[,3])
rm(res.quant.boot)
#write.csv(data.max.height,file="./data/process/data.max.height.swiss.csv")

##########################################
## FORMAT INDIVIDUAL TREE DATA
#############
## change unit and names of variables to be the same in all data for the tree 
data.swiss$G <- 10*(data.swiss$dbh_diff)/data.swiss$year ## diameter growth in mm per year - SOME EXTREMELY NEGATIVE HERE!
data.swiss$D <- data.swiss[["dbh1"]]; data.swiss$D[data.swiss$D == 0] <- NA ;## diameter in cm
data.swiss$dead <- rep(NA, length(data.swiss[["dbh1"]])) ## Mortality - MISSING
data.swiss$plot <- data.swiss$siteid ## plot code
data.swiss$htot <- data.swiss$ht1 ## height of tree in m 

######################
## ECOREGION
###################
## Ecoregion not available for swiss data 

######################
## 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.swiss[["dead"]],INDEX=data.swiss[["plot"]],FUN=function.perc.dead)
# ## VARIABLE TO SELECT PLOT WITH NOT BIG DISTURBANCE KEEP OFTHER VARIABLES IF AVAILABLE (disturbance record)
data.swiss <- merge(data.swiss,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.swiss$dead)
## Nothing to remove

data.climate <- read.dbf(file = "./data/raw/DataSwiss/LFI14_climate.dbf")
data.climate <- data.climate[,c(1,7,15:19)]
data.climate$MAP <- apply(data.climate[,4:7],1,sum)

data.swiss <- merge(data.swiss, data.frame(siteid = data.climate$CLNR, swb = data.climate$swb_100, MAT = data.climate$tave_68, MAP = data.climate$MAP), sort = F, all.x = T)
rm(data.climate)

##############################################
## COMPUTE MATRIX OF COMPETITION INDEX WITH SUM OF BA PER SPECIES IN EACH PLOT in m^2/ha without the target species