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merge.data.SPAIN.R 7.66 KiB
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### MERGE spain DATA
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### Edited by FH
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rm(list = ls()); source("./R/format.function.R"); library(reshape)
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#########################
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## READ DATA
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####################
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### read individuals tree data
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data.spain <- read.table('./data/raw/DataSpain/Tree_data_SFI.txt',header=TRUE,stringsAsFactors=FALSE,sep = "\t")
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######################################
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## MASSAGE TRAIT DATA
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############################
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## Compute maximum height per species plus sd from observed height to add variables to the traits data base
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## Because we have two heights, then take the max of the two heights and then bootstrap
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res.quant.boot <- t(sapply(levels(factor(data.spain[["SP_code"]])),FUN=f.quantile.boot,R=1000,x=log10(apply(data.spain[,c("ht1","ht2")],1,max,na.rm=T)),fac=factor(data.spain[["SP_code"]])))
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#max.heights <- read.csv("/media/fhui/Lexar/Career & Work/GKunstler_competition/data/raw/DataSpain/MaximumHeigth.csv", header = T)
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#
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# ## create data base
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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])
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rm(res.quant.boot)
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write.csv(data.max.height,file="./data/process/data.max.height.spain.csv") # I was planning to save processed data in that folder
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#
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# ## merge TRY with max height
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# merge.TRY <- merge(merge.TRY,data.max.height,by="code")
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# rm(data.max.height)
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# ## use mean sd of max tree height over all species
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# merge.TRY$Max.height.sd.1 <- rep(mean(merge.TRY[["Max.height.sd"]],na.rm=TRUE),length=nrow(merge.TRY))
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#
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# ### keep only variables needed in traits data
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# names.traits.data <- c("code","Latin_name","Leaf.N.mean","Seed.mass.mean","SLA.mean","Wood.Density.mean",
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#   "Leaf.Lifespan.mean","Max.height.mean","Leaf.N.sd.1","Seed.mass.sd.1","SLA.sd.1", "Wood.Density.sd.1",
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#   "Leaf.Lifespan.sd.1","Max.height.sd.1")
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#
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# data.traits <- merge.TRY[,names.traits.data]
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# names(data.traits) <- c("sp","Latin_name","Leaf.N.mean","Seed.mass.mean","SLA.mean","Wood.Density.mean",
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#   "Leaf.Lifespan.mean","Max.height.mean","Leaf.N.sd","Seed.mass.sd","SLA.sd", "Wood.Density.sd",
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#   "Leaf.Lifespan.sd","Max.height.sd") ## rename to have standard variables name
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# rm(merge.TRY,names.traits.data)
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################################################################
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## FORMAT INDIVIDUAL TREE DATA
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#############
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## change unit and names of variables to be the same in all data for the tree
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data.spain$G <- data.spain[["adbh"]] ## diameter growth in mm per year
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data.spain$year <- rep(NA,length(data.spain[["adbh"]])) ## number of year between measurement/missing!
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data.spain$D <- data.spain[["dbh1"]]/10 ## diameter in mm convert to cm
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data.spain$dead <- rep(NA,length(data.spain[["adbh"]])) ## dummy variable for dead tree 0 alive 1 dead/missing!
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data.spain$sp <- as.character(data.spain[["SP_code"]]) ## species code
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data.spain$plot <- (data.spain[["Plot_ID_SFI"]]) ## plot code
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data.spain$htot <- data.spain[["ht1"]]## height of tree in m / here I want to keep the actual height not the max
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data.spain$tree.id <- paste(sapply(data.spain[,"Tree_ID_SFI"],substr,1,6),".",
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                            sapply(data.spain[,"Tree_ID_SFI"],substr,7,10),sep="") ## tree unique id
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#### change coordinates system of x y to be in lat long WGS84/don't know how to do this
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library(sp); library(dismo); library(rgdal);
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data.sp <-  data.spain[,c("Tree_ID_SFI","CX","CY")]
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coordinates(data.sp) <- c("CX", "CY") # define x y
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proj4string(data.sp) <- CRS("+init=epsg:23030")  # define projection system of our data ## EPSG CODE 23030 ED50 / UTM zone 30N
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summary(data.sp)
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detach(package:rgdal)
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data.sp2 <- spTransform(data.sp,CRS("+init=epsg:4326")) ## change projection in WGS84 lat lon
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data.spain$Lon <- coordinates(data.sp2)[,"CX"]
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data.spain$Lat <- coordinates(data.sp2)[,"CY"]
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## ## plot on world map
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## library(rworldmap)
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## newmap <- getMap(resolution = "coarse")  # different resolutions available
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## plot(newmap) ## points(data.sp2,cex=0.2,col="red") rm(data.sp,data.sp2) save(data.spain, file = "./data/process/datspain.RData") ############################ ## merge greco to have no ecoregion with low number of observation greco <- read.csv(file = "./data/raw/DataSpain/R_Ecoregion.csv", header = T) greco <- greco[,c("Plot_ID_SFI","BIOME","eco_code")] greco2 <- greco[!duplicated(greco$Plot),]; rm(greco) data.spain <- merge(data.spain, greco2, by = "Plot_ID_SFI") rm(greco2) table(data.spain$eco_code) ## There's an eco-region with no code, and one with 55 sites library(RColorBrewer); mycols <- brewer.pal(10,"Set3"); ecoreg <- unclass(data.spain$eco_code); plot(data.spain[["CX"]][order(ecoreg)],data.spain[["CY"]][order(ecoreg)],pty=".",cex=.2, col = rep(mycols,as.vector(table(ecoreg)))); legend("bottomright", col = mycols, legend = levels(data.spain$eco_code), pch = rep(19,length(levels(ecoreg))),cex=2) points(data.spain[["CX"]][ecoreg == 9],data.spain[["CY"]][ecoreg == 9],pty=".",cex=.2, col = "black"); ## Highlight the region with 55 sites ## PA1219 looks to be similar to PA1209; merge them together data.spain$eco_codemerged <- combine_factor(data.spain$eco_code, c(1:8,6,9)) ####################### ## variable percent dead/cannot do with since dead variable is missing # ###compute numer of dead per plot to remove plot with disturbance # perc.dead <- tapply(data.spain[["dead"]],INDEX=data.spain[["idp"]],FUN=function.perc.dead) # ## VARIABLE TO SELECT PLOT WITH NOT BIG DISTURBANCE KEEP OFTHER VARIABLES IF AVAILABLE (disturbance record) # data.spain <- merge(data.spain,data.frame(idp=as.numeric(names(perc.dead)),perc.dead=perc.dead),sort=FALSE) data.spain$perc.dead <- NA ########################################################### ### PLOT SELECTION FOR THE ANALYSIS ################### ## Remove data with mortality == 1 or 2 table(data.spain$Mortality_Cut) data.spain <- subset(data.spain,subset= (data.spain[["Mortality_Cut"]] == 0 | data.spain[["Mortality_Cut"]] == "")) colnames(data.spain)[colnames(data.spain) %in% c("mat","pp","PET")] <- c("MAT","PP","PET") colnames(data.spain)[names(data.spain) =="eco_codemerged" ] <- c("ecocode") vec.abio.var.names <- c("MAT","PP","PET") vec.basic.var <- c("tree.id","sp","sp.name","plot","ecocode","D","G","dead","year","htot","Lon","Lat","perc.dead") data.tree <- subset(data.spain,select=c(vec.basic.var,vec.abio.var.names)) save(data.spain, file = "./data/process/datspain.RData") ############################################## ## COMPUTE MATRIX OF COMPETITION INDEX WITH SUM OF BA PER SPECIES IN EACH PLOT in m^2/ha without the target species ########################### data.BA.SP <- BA.SP.FUN(id.tree=as.vector(data.spain[["tree.id"]]), diam=as.vector(data.spain[["D"]]), sp=as.vector(data.spain[["sp"]]), id.plot=as.vector(data.spain[["plot"]]), weights=as.vector(1/(pi*(data.spain[["R1"]])^2)), weight.full.plot=1/(pi*(25)^2)) ## change NA and <0 data for 0 data.BA.SP[which(is.na(data.BA.SP),arr.ind=TRUE)] <- 0 data.BA.SP[,-1][which(data.BA.SP[,-1]<0,arr.ind=TRUE)] <- 0 ### CHECK IF sp and sp name for column are the same if(sum(! (names(data.BA.SP)[-1] %in% unique(data.spain[["sp"]]))) >0) stop("competition index sp name not the same as in data.tree") #### compute BA tot for all competitors BATOT.COMPET <- apply(data.BA.SP[,-1],MARGIN=1,FUN=sum,na.rm=TRUE) data.BA.SP$BATOT.COMPET <- 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=dataIFN.spain[["tree.id"]],ecocode=dataIFN.spain[["ecocode"]]),data.BA.SP,by="tree.id",sort=FALSE) ## test
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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.spain <- list(data.tree=data.tree,data.BA.SP=data.BA.sp,data.traits=data.traits) save(list.spain,file="./data/process/list.spain.Rdata")