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Commit d913c8b8 authored by Georges Kunstler's avatar Georges Kunstler
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new version of competition function to US Spain Swiss

parent aa116602
master BRIEFCOM ctfs package test.dplyr
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+105 -94
R/FUN.TRY.R
+ 15
− 7
View file @ d913c8b8
......@@ -89,7 +89,7 @@ fun.extract.try <- function(ObservationID.t, data, Non.Trait.Data, Trait.Data) {
names(TF.exp.data) <- "TF.exp.data"
res.temp <- data.frame(ObservationID = ObservationID.t, AccSpeciesName = unique(data.temp$AccSpeciesName),
t(Vec.Non.Trait.Data), TF.exp.data, t(Vec.Trait.Data.OrigValue), t(Vec.Trait.Data.OrigUnit),
t(Vec.Trait.Data.StdValue))
t(Vec.Trait.Data.StdValue), stringsAsFactors =FALSE)
return(res.temp)
}
......@@ -227,8 +227,12 @@ fun.turn.list.in.DF <- function(sp, res.list) {
data.genus <- t(sapply(sp, FUN = function(i, res.list) res.list[[i]]$genus, res.list = res.list))
data.nobs <- t(sapply(sp, FUN = function(i, res.list) res.list[[i]]$nobs, res.list = res.list))
## create data.frame withh all observation
extract.species.try <- data.frame(data.mean, data.sd, data.exp, data.genus, data.nobs)
names(extract.species.try) <- c(paste(c("Leaf.N", "Seed.mass", "SLA", "Wood.Density"), "mean", sep = "."), paste(c("Leaf.N", "Seed.mass", "SLA", "Wood.Density"), "sd", sep = "."), paste(c("Leaf.N", "Seed.mass", "SLA", "Wood.Density"), "exp", sep = "."), paste(c("Leaf.N", "Seed.mass", "SLA", "Wood.Density"), "genus", sep = "."), paste(c("Leaf.N", "Seed.mass", "SLA", "Wood.Density"), "nobs", sep = "."))
extract.species.try <- data.frame(data.mean, data.sd, data.exp, data.genus, data.nobs, stringsAsFactors =FALSE)
names(extract.species.try) <- c(paste(c("Leaf.N", "Seed.mass", "SLA", "Wood.Density"), "mean", sep = "."),
paste(c("Leaf.N", "Seed.mass", "SLA", "Wood.Density"), "sd", sep = "."),
paste(c("Leaf.N", "Seed.mass", "SLA", "Wood.Density"), "exp", sep = "."),
paste(c("Leaf.N", "Seed.mass", "SLA", "Wood.Density"), "genus", sep = "."),
paste(c("Leaf.N", "Seed.mass", "SLA", "Wood.Density"), "nobs", sep = "."))
return(extract.species.try)
}
......@@ -252,7 +256,11 @@ fun.extract.format.sp.traits.TRY <- function(sp, sp.syno.table, data) {
##### TRANSFORM LIST IN A TABLE
extract.species.try <- fun.turn.list.in.DF(sp, res.list)
##### TODO ADD A TEST OF GOOD EXTRACTION OF TRAITS
test.num <- sample(1:length(sp),1)
if( extract.species.try[test.num,"SLA.mean"] != fun.species.traits(sp[test.num], species.table = sp.syno.table,
traits = traits, data = data)$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")
......@@ -262,8 +270,8 @@ fun.extract.format.sp.traits.TRY <- function(sp, sp.syno.table, data) {
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])
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 = ".")
......@@ -272,7 +280,7 @@ fun.extract.format.sp.traits.TRY <- function(sp, sp.syno.table, data) {
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)
extract.species.try.2, stringsAsFactors =FALSE)
if (sum(!data.frame.TRY[["sp"]] == sp) > 0)
stop("Wrong order of species code")
return(data.frame.TRY)
......
R/format.function.R
+ 14
− 11
View file @ d913c8b8
......@@ -62,6 +62,7 @@ BA.fun <- function(diam,weights){
##' @return data frame with obs.id and one column per species with basal area of the species (without the target tree)
##' @author Kunstler
BA.SP.FUN <- function(obs.id,diam,sp,id.plot,weights,weight.full.plot){
print(length(obs.id))
require(data.table)
id.plot <- as.character(id.plot)
obs.id <- as.character(obs.id)
......@@ -194,6 +195,7 @@ return(BA.SP.FUN.XY(obs.id[census==census.id],xy.table[census==census.id,],diam[
### wrapping function to run BA.SP.FUN.XY per census
BA.SP.FUN.XY.census <- function(census,obs.id,xy.table,diam,sp,Rlim,parallel=FALSE,rpuDist=FALSE){
unique.census <- unique(census)
print(unique.census)
res.list <- lapply(unique.census,FUN=BA.SP.FUN.XY.l,obs.id,xy.table,diam,sp,Rlim,parallel,rpuDist)
res.mat <- rbind.fill(res.list )
res.mat <- res.mat[match(obs.id,rownames(res.mat)),]
......@@ -264,6 +266,7 @@ if(is.null(data.tot[['weights']])) stop("Please create a weights vector, even if
require(data.table)
data.tot <- data.table(data.tot)
data <- data.tot[ecocode==ecoregion,]
print(dim(data))
rm(data.tot)
data.BA.SP <- BA.SP.FUN.census(census=data[['census']],
obs.id=as.vector(data[['obs.id']]),
......@@ -296,17 +299,17 @@ setkeyv(data,"obs.id")
if(sum(!data.BA.sp[["obs.id"]] == data[["obs.id"]]) >0) stop("competition index not in the same order than data")
#####
## ADD TRY DATA OR TRAITS IF NEEDED
if(!is.na(data.TRY)){
sp.extract <- species.lookup[species.lookup[["sp"]] %in% unique(data[["sp"]]),]
data.traits <- fun.extract.format.sp.traits.TRY(sp=sp.extract[["sp"]],sp.syno.table=sp.extract,data.TRY)
### TO DO ADD OPTION TO INCLUE OTHER DATA on MAX HEIGHT
## save everything as a list
print(dim(data.traits))
list.temp <- list(data.tree=data,data.BA.SP=data.BA.sp,data.traits=data.traits)
save(list.temp,file=paste("./data/process/list",name.country,ecoregion,"Rdata",sep="."))
if(is.data.frame(data.TRY)){
sp.extract <- species.lookup[species.lookup[["sp"]] %in% unique(data[["sp"]]),]
data.traits <- fun.extract.format.sp.traits.TRY(sp=sp.extract[["sp"]],sp.syno.table=sp.extract,data.TRY)
### TO DO ADD OPTION TO INCLUE OTHER DATA on MAX HEIGHT
## save everything as a list
print(dim(data.traits))
list.temp <- list(data.tree=data,data.BA.SP=data.BA.sp,data.traits=data.traits)
save(list.temp,file=paste("./data/process/list",name.country,ecoregion,"Rdata",sep="."))
}else{
list.temp <- list(data.tree=data,data.BA.SP=data.BA.sp,data.traits=NA)
saveRDS(list.temp,file=paste("./data/process/list",name.country,ecoregion,"Rdata",sep="."))
list.temp <- list(data.tree=data,data.BA.SP=data.BA.sp,data.traits=NA)
saveRDS(list.temp,file=paste("./data/process/list",name.country,ecoregion,"Rdata",sep="."))
}
}
......@@ -336,7 +339,7 @@ if(sum(!colnames(BA.SP.temp)==as.character((levels(data.tree.s[['sp']]))))>0) st
### 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)
data.res <- data.frame(obs.id=data.tree.s[['obs.id']],BA.SP.temp,BATOT=BATOT, stringsAsFactors =FALSE)
return(data.res)
}
......
merge.data.CANADA.R
+ 1
− 2
View file @ d913c8b8
### MERGE canada DATA Edited by FH
rm(list = ls())
source("./R/format.function.R")
source("./R/FUN.TRY.R")
library(reshape)
######################### READ DATA read individuals tree data data.canada <-
######################### read.csv('./data/raw/DataCanada/Canada_Data2George_20130816.csv',header=TRUE,stringsAsFactors
######################### =FALSE)
data.canada <- read.csv("./data/raw/DataCanada/Canada_Data2George_20130818.csv",
header = TRUE, stringsAsFactors = FALSE)
data.canada <- data.canada[which(!is.na(data.canada$Species)), ]
......
merge.data.SPAIN.R
+ 57
− 57
View file @ d913c8b8
### MERGE spain DATA Edited by FH
rm(list = ls())
source("./R/format.function.R")
source("./R/FUN.TRY.R")
library(reshape)
######################### READ DATA read individuals tree data data.spain <-
......@@ -18,22 +19,9 @@ res.quant.boot <- t(sapply(levels(factor(data.spain[["SP_code"]])), FUN = f.quan
## create data base
data.max.height <- data.frame(code = rownames(res.quant.boot), Max.height.mean = res.quant.boot[,
1], Max.height.sd = res.quant.boot[, 2], Max.height.nobs = res.quant.boot[, 3])
1], Max.height.sd = res.quant.boot[, 2], Max.height.nobs = res.quant.boot[, 3], stringsAsFactors =FALSE)
rm(res.quant.boot)
write.csv(data.max.height, file = "./data/process/data.max.height.spain.csv") # I was planning to save processed data in that folder
# ## merge TRY with max height merge.TRY <-
# merge(merge.TRY,data.max.height,by='code') rm(data.max.height) ## use mean sd
# of max tree height over all species merge.TRY$Max.height.sd.1 <-
# rep(mean(merge.TRY[['Max.height.sd']],na.rm=TRUE),length=nrow(merge.TRY)) ###
# keep only variables needed in traits data names.traits.data <-
# c('code','Latin_name','Leaf.N.mean','Seed.mass.mean','SLA.mean','Wood.Density.mean',
# 'Leaf.Lifespan.mean','Max.height.mean','Leaf.N.sd.1','Seed.mass.sd.1','SLA.sd.1',
# 'Wood.Density.sd.1', 'Leaf.Lifespan.sd.1','Max.height.sd.1') data.traits <-
# merge.TRY[,names.traits.data] names(data.traits) <-
# c('sp','Latin_name','Leaf.N.mean','Seed.mass.mean','SLA.mean','Wood.Density.mean',
# 'Leaf.Lifespan.mean','Max.height.mean','Leaf.N.sd','Seed.mass.sd','SLA.sd',
# 'Wood.Density.sd', 'Leaf.Lifespan.sd','Max.height.sd') ## rename to have
# standard variables name rm(merge.TRY,names.traits.data)
################################################################ FORMAT INDIVIDUAL TREE DATA
......@@ -42,11 +30,14 @@ data.spain$G <- data.spain[["adbh"]] ## diameter growth in mm per year
data.spain$year <- data.spain[["years"]] ## number of year between measurement - MISSING
data.spain$D <- data.spain[["dbh1"]]/10 ## diameter in mm convert to cm
data.spain$dead <- as.numeric(data.spain[["Life_status"]] == "dead") ## dummy variable for dead tree 0 alive 1 dead - MIGHT WANT TO CHANGE THIS TO BE BASED ON MORTALITY_CUT
data.spain$sp <- as.character(data.spain[["SP_code"]]) ## species code
data.spain$sp <- paste("sp",(data.spain[["SP_code"]]) ,sep=".") ## species code
data.spain$sp.name <- data.spain[["SP_name"]]
data.spain$plot <- (data.spain[["Plot_ID_SFI"]]) ## plot code
data.spain$htot <- data.spain[["ht1"]] ## height of tree in m
data.spain$tree.id <- data.spain$Tree_ID_SFI ## tree unique id
data.spain$obs.id <- as.character(1:nrow(data.spain)) ## obs uniquue id
data.spain$census <- rep(1,nrow(data.spain)) ## only one census in spain
data.spain$weights <- as.vector(1/(pi * (data.spain[["R1"]])^2)) ## weights in 1/m^2
#### change coordinates system of x y to be in lat long WGS84
library(sp)
library(dismo)
......@@ -80,14 +71,14 @@ table(data.spain$eco_code)
## There's an eco-region with no code, and one with < 1000 sites The former we
## could drop as they were on the border of Spain
### PLOT ECOREGION
library(RColorBrewer)
mycols <- brewer.pal(10, "Set3")
ecoreg <- unclass(data.spain$eco_code)
pdf("./figs/ecoregion.spain.pdf")
plot(data.spain[["CX"]][order(ecoreg)], data.spain[["CY"]][order(ecoreg)], pty = ".",
cex = 0.2, col = rep(mycols, as.vector(table(ecoreg))))
legend("topleft", col = mycols, legend = levels(data.spain$eco_code), pch = rep(19,
length(levels(ecoreg))), cex = 2)
points(data.spain[["CX"]][ecoreg == 9], data.spain[["CY"]][ecoreg == 9], pty = ".",
......@@ -97,56 +88,65 @@ points(data.spain[["CX"]][ecoreg == 1], data.spain[["CY"]][ecoreg == 1], pty = "
cex = 0.5, col = "black")
## Highlight the 'rare' ecoregions PA1219 looks to be similar to PA1209, merge
## them together
dev.off()
## merge
data.spain$eco_codemerged <- combine_factor(data.spain$eco_code, c(1:8, 6, 9))
data.spain <- data.spain[-which(data.spain$eco_codemerged == ""), ]
###################### PERCENT DEAD variable percent dead/cannot do with since dead variable is
###################### missing compute numer of dead per plot to remove plot with disturbance
#####
## PLOT of MERGED ECOREGION
## mycols <- brewer.pal(9, "Set3")
## plot(data.spain[["CX"]][order(data.spain$eco_codemerged)], data.spain[["CY"]][order(data.spain$eco_codemerged)], pty = ".",
## cex = 0.2, col = rep(mycols, as.vector(table(data.spain$eco_codemerged))))
## legend("topleft", col = mycols, legend = levels(data.spain$eco_codemerged), pch = rep(19,
## length(levels(data.spain$eco_codemerged))), cex = 2)
###################### PERCENT DEAD
###################### compute numer of dead per plot to remove plot with disturbance
perc.dead <- tapply(data.spain[["dead"]], INDEX = data.spain[["plot"]], FUN = function.perc.dead)
table(data.spain$dead)
## VARIABLE TO SELECT PLOT WITH NOT BIG DISTURBANCE KEEP OFTHER VARIABLES IF
## AVAILABLE (disturbance record)
data.spain <- merge(data.spain, data.frame(plot = as.numeric(names(perc.dead)), perc.dead = perc.dead),
sort = FALSE, by = "plot")
data.spain <- merge(data.spain, data.frame(plot = as.numeric(names(perc.dead)), perc.dead = perc.dead, stringsAsFactors =FALSE),
sort = FALSE, by = "plot")
########################################################### PLOT SELECTION FOR THE ANALYSIS Remove data with mortality == 1 or 2
table(data.spain$Mortality_Cut)
data.spain <- subset(data.spain, subset = (data.spain[["Mortality_Cut"]] == 0 | data.spain[["Mortality_Cut"]] ==
""))
data.spain <- subset(data.spain,
subset = (data.spain[["Mortality_Cut"]] == 0 |
data.spain[["Mortality_Cut"]] == ""))
colnames(data.spain)[colnames(data.spain) %in% c("mat", "pp", "PET")] <- c("MAT",
"PP", "PET")
colnames(data.spain)[names(data.spain) == "eco_codemerged"] <- c("ecocode")
vec.abio.var.names <- c("MAT", "PP", "PET")
vec.basic.var <- c("tree.id", "sp", "sp.name", "plot", "ecocode", "D", "G", "dead",
"year", "htot", "Lon", "Lat", "perc.dead")
data.tree <- subset(data.spain, select = c(vec.basic.var, vec.abio.var.names))
save(data.spain, file = "./data/process/datspain.RData")
############################################## COMPUTE MATRIX OF COMPETITION INDEX WITH SUM OF BA PER SPECIES IN EACH PLOT in
############################################## m^2/ha without the target species
data.BA.SP <- BA.SP.FUN(id.tree = as.vector(data.spain[["tree.id"]]), diam = as.vector(data.spain[["D"]]),
sp = as.vector(data.spain[["sp"]]), id.plot = as.vector(data.spain[["plot"]]),
weights = as.vector(1/(pi * (data.spain[["R1"]])^2)), weight.full.plot = 1/(pi *
(25)^2))
## change NA and <0 data for 0
data.BA.SP[which(is.na(data.BA.SP), arr.ind = TRUE)] <- 0
data.BA.SP[, -1][which(data.BA.SP[, -1] < 0, arr.ind = TRUE)] <- 0
### CHECK IF sp and sp name for column are the same
if (sum(!(names(data.BA.SP)[-1] %in% unique(data.spain[["sp"]]))) > 0) stop("competition index sp name not the same as in data.tree")
#### compute BA tot for all competitors
BATOT.COMPET <- apply(data.BA.SP[, -1], MARGIN = 1, FUN = sum, na.rm = TRUE)
data.BA.SP$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
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")
vec.basic.var <- c("tree.id","obs.id", "sp", "sp.name", "plot", "ecocode", "D", "G", "dead",
"year", "htot", "Lon", "Lat", "perc.dead","census","weights")
data.spain <- subset(data.spain, select = c(vec.basic.var, vec.abio.var.names))
##############################################
##############################################
#################### GENERATE ONE OBJECT PER ECOREGION
# vector of ecoregion name
ecoregion.unique <- unique(data.spain[["ecocode"]])
sum(data.spain[["ecocode"]] == ecoregion.unique[1])
### read TRY data
TRY.DATA.FORMATED <- readRDS("./data/process/TRY.DATA.FORMATED.rds")
## create lookup table for spain
species.lookup <- data.frame(sp=data.spain[!duplicated(data.spain[["sp"]]),"sp"],
Latin_name=data.spain[!duplicated(data.spain[["sp"]]),"sp.name"],
Latin_name_syn=data.spain[!duplicated(data.spain[["sp"]]),"sp.name"],
stringsAsFactors =FALSE)
#### lapply function to generate data per ecoregion
system.time(lapply(ecoregion.unique[1:2], FUN = fun.data.per.ecoregion, data.tot = data.spain,
plot.name = "plot", weight.full.plot = NA, name.country = "Spain", data.TRY = TRY.DATA.FORMATED,
species.lookup = species.lookup))
merge.data.SWISS.R
+ 10
− 13
View file @ d913c8b8
......@@ -59,9 +59,9 @@ res.quant.boot <- t(sapply(levels(factor(data.swiss[["spcode"]])), FUN = f.quant
## 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])
1], Max.height.sd = res.quant.boot[, 2], Max.height.nobs = res.quant.boot[, 3], stringsAsFactors =FALSE)
rm(res.quant.boot)
# write.csv(data.max.height,file='./data/process/data.max.height.swiss.csv')
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
......@@ -83,7 +83,7 @@ data.swiss$ecocode <- rep("A", nrow(data.swiss))
perc.dead <- tapply(data.swiss[["dead"]], INDEX = data.swiss[["plot"]], FUN = function.perc.dead)
# ## VARIABLE TO SELECT PLOT WITH NOT BIG DISTURBANCE KEEP OTHER VARIABLES IF
# AVAILABLE (disturbance record)
data.swiss <- merge(data.swiss, data.frame(plot = names(perc.dead), perc.dead = perc.dead),
data.swiss <- merge(data.swiss, data.frame(plot = names(perc.dead), perc.dead = perc.dead, stringsAsFactors =FALSE),
by = "plot", sort = FALSE)
########################################################### PLOT SELECTION FOR THE ANALYSIS Remove data with dead == 1
......@@ -94,15 +94,12 @@ data.climate <- data.climate[, c(1, 7, 15:19)]
data.climate$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)
MAT = data.climate$tave_68, MAP = data.climate$MAP, stringsAsFactors =FALSE), 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
data.BA.SP <- BA.SP.FUN.census(census=data.swiss[["census"]],obs.id=data.swiss[["obs.id"]],diam=data.swiss[["D"]],sp=data.swiss[["sp"]],id.plot=data.swiss[["plot"]],weights=data.swiss[["weights"]],weight.full.plot=NA)
## check if good order
sum(! (data.BA.SP[,"obs.id"]) == data.swiss[["obs.id"]])
##############################################
##############################################
#################### GENERATE ONE OBJECT PER ECOREGION
# vector of ecoregion name
ecoregion.unique <- unique(data.swiss[["ecocode"]])
......@@ -114,11 +111,11 @@ TRY.DATA.FORMATED <- readRDS("./data/process/TRY.DATA.FORMATED.rds")
## create lookup table for swiss
species.lookup <- data.frame(sp=data.swiss[!duplicated(data.swiss[["sp"]]),"sp"],
Latin_name=data.swiss[!duplicated(data.swiss[["sp"]]),"sp.name"],
Latin_name_syn=data.swiss[!duplicated(data.swiss[["sp"]]),"sp.name"])
Latin_name_syn=data.swiss[!duplicated(data.swiss[["sp"]]),"sp.name"], stringsAsFactors =FALSE)
#### lapply function
(lapply(ecoregion.unique, FUN = fun.data.per.ecoregion, data.tot = data.swiss,
#### lapply function to generate data for each ecoregion
system.time(lapply(ecoregion.unique, FUN = fun.data.per.ecoregion, data.tot = data.swiss,
plot.name = "plot", weight.full.plot = NA, name.country = "Swiss", data.TRY = TRY.DATA.FORMATED,
species.lookup = species.lookup))
......
merge.data.US.R
+ 5
− 4
View file @ d913c8b8
......@@ -41,7 +41,8 @@ data.us$htot <- rep(NA, length(data.us[["Species"]])) ## height of tree in m -
data.us$tree.id <- as.character(data.us$TreeID)
## tree unique id
data.us$sp.name <- NA
## census is missing use as only one census and check with mark
data.us$census <- rep(1,nrow(data.us))
### add plot weights for computation of competition index (in 1/m^2)
data.us$weights <- 1/(10000 * data.us[["PlotSize"]])
......@@ -68,7 +69,7 @@ for (i in 1:length(sel.small.div)) {
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),
data.us <- merge(data.us, data.frame(plot = names(perc.dead), perc.dead = perc.dead, stringsAsFactors = FALSE),
by = "plot", sort = FALSE)
......@@ -86,7 +87,7 @@ data.us[["sp"]] <- paste("sp", data.us[["sp"]], sep = ".")
## variables to keep
vec.abio.var.names <- c("MAT", "MAP")
vec.basic.var <- c("tree.id", "sp", "plot", "subplot", "ecocode", "D", "G", "dead",
"year", "htot", "Lon", "Lat", "perc.dead", "weights")
"year", "htot", "Lon", "Lat", "perc.dead", "weights","census")
data.tree <- subset(data.us, select = c(vec.basic.var, vec.abio.var.names))
rm(data.us)
## creat row unique id
......@@ -103,6 +104,6 @@ ecoregion.unique <- unique(data.tree[["ecocode"]])
#### lapply function
system.time(lapply(ecoregion.unique, FUN = fun.data.per.ecoregion, data.tot = data.tree,
system.time(lapply(ecoregion.unique[3], FUN = fun.data.per.ecoregion, data.tot = data.tree,
plot.name = "subplot", weight.full.plot = NA, name.country = "US", data.TRY = TRY.DATA.FORMATED,
species.lookup = species.clean))
ms/data.format.md
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......@@ -14,6 +14,7 @@ For the analysis we need for each ecoregion country (or big tropical plot) a lis
* First element is a data.frame for individual tree data with columns
- $obs.id4 a unique identifier of observqtion (if multiple observation for a same tree)
- $tree.id$ a unique identifier of each tree
- $sp$ the species code
- $plot$ the plot code
......@@ -22,10 +23,12 @@ For the analysis we need for each ecoregion country (or big tropical plot) a lis
- $G$ the diameter growth rate in mm / yr.
- $dead$ a dummy variable 0 alive 1 dead
- $year$ the number of year for the growth measurement
- $census$ the name of the year of the census 1
- $htot$ the height of the individual (m) for the data base for which it is availble to compute max height per species
- $Lon$ Longitude of the plot in WGS84
- $Lat$ Latitude of teh plots in WGS84
- $perc.dead$ the percentage of dead computed on each plot to exlude plot with perturbation (equal 1 for plot with known perturbation)
- $weights$ the weigths of teh tree to have an estimation of basal area per m^2 (so 1/(m^2))
- then the potential abiotic variables that we can use in the analysis
* Second element is a data.frame competition index with columns
......
ms/table.data.progress.ods
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