start traits of Paracou

R/format.function.R

@@ -8,12 +8,12 @@
 ######
 ######
 ## FUNCTION TO PLOT MAP OF TREE
-fun.circles.plot <- function(plot.select,x,y,plot,D,inches){
+fun.circles.plot <- function(plot.select,x,y,plot,D,inches,...){
 x.t <- x[plot==plot.select]
 y.t <- y[plot==plot.select]
 D.t <- D[plot==plot.select]
 D.t[is.na(D.t)] <-  0
-symbols(x.t,y.t,circles=D.t ,main=plot.select,inches=inches)
+symbols(x.t,y.t,circles=D.t ,main=plot.select,inches=inches,...)
 }
 
 
@@ -246,6 +246,35 @@ save(list.temp,file=paste("./data/process/list",name.country,ecoregion,"Rdata",s
 }    
 
 
+#####################################
+#####################################
+### FUNCTION TO COMPUTE BA.SP.XY PER PLOT AND MERGE TOGETHER
+#### function to be apply per site
+fun.compute.BA.SP.XY.per.plot <- function(i,data.tree,Rlim,xy.name=c('x','y'),parallel=FALSE,rpuDist=FALSE){
+data.tree.s <- subset(data.tree,subset=data.tree[["plot"]] ==i)
+BA.SP.temp <- BA.SP.FUN.XY(obs.id=data.tree.s[['obs.id']],
+             xy.table=data.tree.s[,xy.name],
+             diam=data.tree.s[['D']],
+             sp=(data.tree.s[['sp']]),
+             Rlim=15,
+             parallel=FALSE,
+             rpuDist=FALSE)
+
+## replace NA per zero
+print('replacing NA per zero')
+BA.SP.temp[is.na(BA.SP.temp)] <- 0
+print('done')
+### rpud installation very cumbersome not needed ?
+### longer in parallel why ?
+if(sum(! rownames(BA.SP.temp)==data.tree.s[['obs.id']]) >0) stop('rows not in the good order')
+if(sum(!colnames(BA.SP.temp)==as.character((levels(data.tree.s[['sp']]))))>0) stop('colnames does mot match species name')
+
+### compute sum per row
+BATOT <- apply(BA.SP.temp,MARGIN=1,FUN=sum)
+data.res <- data.frame(obs.id=data.tree.s[['obs.id']],BA.SP.temp,BATOT=BATOT)
+return(data.res)
+}
+
 
 
 

merge.data.PARACOU.R

@@ -107,38 +107,12 @@ data.tree <- subset(data.paracou,select=c(vec.basic.var)) #,vec.abio.var.names
 
 ### species as factor because number
 data.tree[['sp']] <- factor(data.tree[['sp']])
+Rlim <- 15 # set size of neighborhood for competition index
 
+system.time(test <- fun.compute.BA.SP.XY.per.plot(1,data.tree=data.tree,Rlim=15,parallel=TRUE,rpuDist=FALSE))
 
-#### function to be apply per site
-fun.compute.BA.SP.XY.per.plot <- function(i,data.tree,Rlim,xy.name=c('x','y'),parallel=FALSE,rpuDist=FALSE){
-data.tree.s <- subset(data.tree,subset=data.tree[["plot"]] ==i)
-BA.SP.temp <- BA.SP.FUN.XY(obs.id=data.tree.s[['obs.id']],
-             xy.table=data.tree.s[,xy.name],
-             diam=data.tree.s[['D']],
-             sp=(data.tree.s[['sp']]),
-             Rlim=15,
-             parallel=FALSE,
-             rpuDist=FALSE)
-
-## replace NA per zero
-print('replacing NA per zero')
-BA.SP.temp[is.na(BA.SP.temp)] <- 0
-print('done')
-### rpud installation very cumbersome not needed ?
-### longer in parallel why ?
-if(sum(! rownames(BA.SP.temp)==data.tree.s[['obs.id']]) >0) stop('rows not in the good order')
-if(sum(!colnames(BA.SP.temp)==as.character((levels(data.tree.s[['sp']]))))>0) stop('colnames does mot match species name')
-
-### compute sum per row
-BATOT <- apply(BA.SP.temp,MARGIN=1,FUN=sum)
-data.res <- data.frame(obs.id=data.tree.s[['obs.id']],BA.SP.temp,BATOT=BATOT)
-return(data.res)
-}
-
-test <- fun.compute.BA.SP.XY.per.plot(1,data.tree=data.tree,Rlim=15)
-
-list.BA.SP.data <- mclapply(unique(data.tree[['plot']]),FUN=fun.compute.BA.SP.XY.per.plot,data.tree=data.tree,Rlim=15,mc.cores=4)
-data.BA.SP <- rbind.fill(list.BA.SP.data)
+list.BA.SP.data <- mclapply(unique(data.tree[['plot']]),FUN=fun.compute.BA.SP.XY.per.plot,data.tree=data.tree,Rlim=Rlim,mc.cores=4)
+data.BA.sp <- rbind.fill(list.BA.SP.data)
 dim(data.BA.SP)
 
 ### TEST DATA FORMAT
@@ -148,9 +122,18 @@ if(sum(!colnames(BA.SP.temp)==as.character((levels(data.tree.s[['sp']]))))>0) st
 if(sum(!data.BA.SP[["obs.id"]] == data.tree[["obs.id"]]) >0) stop("competition index not in the same order than data.tree")
 
 ## REMOVE TREE IN BUFFER ZONE BUFFER ZONE
+not.in.buffer.zone <- (data.tree[['x']]<(250-Rlim) &
+data.tree[['x']]>(0+Rlim) &
+data.tree[['y']]<(250-Rlim) &
+data.tree[['y']]>(0+Rlim))
+
+# remove subset
+data.tree <- subset(data.tree,subset=not.in.buffer.zone)
+data.BA.sp <- subset(data.BA.sp,subset=not.in.buffer.zone)
+
 ## plot each plot
 pdf("./figs/plots.tree.pdf")
-lapply(unique(data.tree[["plot"]]),FUN=fun.circles.plot,data.tree[['x']],data.tree[['y']],data.tree[["plot"]],data.tree[["D"]],inches=0.2)
+lapply(unique(data.tree[["plot"]]),FUN=fun.circles.plot,data.tree[['x']],data.tree[['y']],data.tree[["plot"]],data.tree[["D"]],inches=0.2,xlim=c(0,250),ylim=c(0,250))
 dev.off()
 
 
@@ -162,16 +145,30 @@ dev.off()
 
 ### read species names
 species.clean <- read.csv("./data/raw/DataParacou/20130717_paracou_taxonomie.csv",stringsAsFactors=FALSE, header = T, sep = ";")
+species.clean$sp <- species.clean[["idTaxon"]]
+
+## select species in paracou
+species.paracou <- data.frame(sp=species.clean[as.character(species.clean[["sp"]]) %in% as.vector(na.exclude(unique(data.tree[["sp"]]))),c("sp")],
+           Latin_name=apply((species.clean[as.character(species.clean[["sp"]]) %in% as.vector(na.exclude(unique(data.tree[["sp"]])))
+                                           ,c("Genre","Espece")]),MARGIN=1,FUN=paste,collapse=" "))
+
+count.sp.paracou <- data.frame(sp=names(table(data.tree[["sp"]])),n.indiv=as.vector(table(data.tree[["sp"]])))
+species.paracou <- merge(species.paracou,count.sp.paracou,by="sp")
+
+tapply(species.paracou[["n.indiv"]],INDEX=species.paracou[["Latin_name"]],FUN=sum)
+
+length(grep("Indet",species.paracou[["Latin_name"]]))/nrow(species.paracou)
 
 ### need to read the different traits data based and merge .....
+bridge <- read.csv("./data/raw/DataParacou/BridgeDATA.g.csv",stringsAsFactors=FALSE, header = T, sep = ";")
+bridge$Latin_name <- paste(bridge[["Genus"]],bridge[["species"]],sep=" ")
 
+dataWD <- read.csv("./data/raw/DataParacou/WD-Species-Paracou-Ervan_GV.csv",stringsAsFactors=FALSE, header = T,sep=" ")
+seed.traits <- read.csv("./data/raw/DataParacou/Autour de Paracou - Releves par trait et taxon.txt",stringsAsFactors=FALSE, header = T, sep = "\t")
 
 ## SPECIES CODE COME FROM idTaxon in paracou_taxonomie and taxonid in paracou_1984_2012 to match the traits data we need to use the "Genus species"
 ## we better work not work with vernacular because this doesn't match necesseraly the Genus species taxonomie
 
-species.clean$sp <- species.clean[["idTaxon"]]
-## data.paracou <- merge(data.paracou, as.data.frame(species.clean[!duplicated(species.clean[["sp"]]),c("Genre","Espece","sp")]), by = "sp", sort = FALSE)
-
 
 
 ## save everything as a list