working on BCI not finished

R/FUN.TRY.R

@@ -288,3 +288,62 @@ fun.extract.format.sp.traits.TRY <- function(sp, sp.syno.table, data) {
 }
 
 
+
+fun.extract.format.sp.traits.TRY <- function(sp, sp.syno.table, data,name.match.sp="Latin_name_syn",name.match.traits="Latin_name") {
+    ## check syno data if not create a table with column syno repating the species
+    
+    ### test data sp and sp.syno.table match
+    if (sum(!(sp %in% sp.syno.table[["sp"]])) > 0) 
+        stop("not same species name in sp and sp.syno.table")
+    if (sum((sp.syno.table[[name.match.sp]] %in% data[[name.match.traits]])) == 
+        0) 
+        stop("not a single similar species name in sp and Traits data")
+    ## traits to extract
+    traits <- c("Leaf.N", "Seed.mass", 
+        "SLA", "Wood.Density")
+
+    ### NEED TO ADD TEST IF SD available in the data
+    if(sum(grepl("sd",names(data)))>0) SD.TF <- TRUE
+
+    ## check traits available
+traits.mean <- names(data)[(names(data) %in% paste(traits,"mean",sep="."))]
+if (SD.TF)     traits.sd <- names(data)[(names(data) %in% paste(traits,"sd",sep="."))]
+
+    #### TO CHANGE HERE !!!!
+    res.list <- lapply(sp, FUN = fun.species.traits, species.table = sp.syno.table, 
+        traits = traits, data = data)
+    names(res.list) <- sp
+    
+    ##### TRANSFORM LIST IN A TABLE
+    extract.species.try <- fun.turn.list.in.DF(sp, res.list)
+
+    ##### TODO ADD A TEST OF GOOD EXTRACTION OF TRAITS
+    test.num <- sample((1:length(sp))[!is.na(extract.species.try[["SLA.mean"]])],1)
+
+    if( extract.species.try[test.num,"SLA.mean"] !=  fun.species.traits(sp[test.num], species.table = sp.syno.table, 
+        traits = traits, data = data)$mean[grep("SLA",traits)]) stop('traits value not good for the  species in extraction from TRY')
+    ############### add mean sd of species or genus if we want to use that
+    sd.vec.sp <- readRDS(file = "./data/process/sd.vec.sp.rds")
+    sd.vec.genus <- readRDS(file = "./data/process/sd.vec.genus.rds")
+    
+    sd.names <- paste(c("Leaf.N", "Seed.mass", "SLA", "Wood.Density"), 
+        "sd", sep = ".")
+    genus.names <- paste(c("Leaf.N", "Seed.mass", "SLA", "Wood.Density"), 
+        "genus", sep = ".")
+    ### add columns
+    extract.species.try.2 <- data.frame(extract.species.try,
+                                        extract.species.try[,sd.names], stringsAsFactors =FALSE)
+    
+    ## update value
+    sd.names.1 <- paste(sd.names, 1, sep = ".")
+    for (i in 1:length(sd.names.1)) {
+        extract.species.try.2[[sd.names.1[i]]][!extract.species.try.2[[genus.names[i]]]] <- sd.vec.sp[i]
+        extract.species.try.2[[sd.names.1[i]]][extract.species.try.2[[genus.names[i]]]] <- sd.vec.genus[i]
+    }
+    data.frame.TRY <- data.frame(sp = sp, Latin_name = sp.syno.table[["Latin_name_syn"]], 
+        extract.species.try.2, stringsAsFactors =FALSE)
+    if (sum(!data.frame.TRY[["sp"]] == sp) > 0) 
+        stop("Wrong order of species code")
+    return(data.frame.TRY)
+}
+

merge.data.BCI.R

@@ -72,6 +72,8 @@ data.trait$HEIGHT_SEM <- data.trait$HEIGHT_N <- data.trait$HEIGHT_AVG <- NULL
 #    20:21)], by = "Latin", all.x = T)
 
 
+## NO NEED TO MERGE NEED TO DECIDE STANDARD STRUCTURE FOR TRAITS AS TRY !!
+
 ########################################## FORMAT INDIVIDUAL TREE DATA
 data.bci <- data.bci[order(data.bci[["TreeID"]]),]
 data.bci$Date1 <- as.Date(data.bci$Date1)
@@ -80,6 +82,9 @@ data.bci$Date2 <- as.Date(data.bci$Date2)
 # data.bci$yr2 <- format(strptime(data.bci$Date2, format = '%Y-%m-%d'),'%Y')
 data.bci$year <- as.numeric(difftime(data.bci$Date2, data.bci$Date1, units = "weeks")/52)  ## Not rounded 
 data.bci$obs.id <- apply(data.bci[,c("TreeID","Census")],1,paste,collapse="_") 
+data.bci$treeid <- data.bci$TreeID
+data.bci$x <- data.bci$gx
+data.bci$y <- data.bci$gy
 
 ## change unit and names of variables to be the same in all data for the tree
 data.bci$G <- 10 * (data.bci$DBH1 - data.bci$DBH1)/data.bci$year  ## diameter growth in mm per year - BASED ON UNROUNDED YEARS
@@ -87,7 +92,6 @@ data.bci$D <- data.bci[["DBH1"]]
 data.bci$plot <- data.bci[["Quadrat"]]  ## plot code?
 data.bci$htot <- NA
 data.bci$sp.name <- data.bci$Latin
-data.bci$weights <- 1/(pi*(0.5*data.bci$D/100)^2)
 ###################### ECOREGION bci has only 1 eco-region
 
 ###################### PERCENT DEAD variable percent dead/cannot do with since dead variable is
@@ -101,10 +105,10 @@ data.bci <- merge(data.bci, data.frame(plot = names(perc.dead), perc.dead = perc
 table(data.bci$dead)
 data.bci <- data.bci[data.bci$dead == 0,]
 
-vec.abio.var.names <- c("MAT", "MAP")  ## MISSING 
+## vec.abio.var.names <- NA  ## MISSING 
 vec.basic.var <- c("treeid", "sp", "sp.name", "plot", "D", "G", "dead", "year", "htot", 
     "x", "y", "perc.dead")
-data.tree <- subset(data.bci, select = c(vec.basic.var, vec.abio.var.names))
+data.tree <- subset(data.bci, select = c(vec.basic.var))
 
 ############################################## COMPUTE MATRIX OF COMPETITION INDEX WITH SUM OF BA PER SPECIES IN EACH PLOT in
 ############################################## m^2/ha without the target species