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############################################
############################################
## FUNCTION TO EXTRACT DECTED OUTLIER AND FORMAT TRY DATA
## Georges Kunstler 14/06/2013
########################################################
########################################################
########################################################
########################################################
###Build a function that extract the variables
##'Description of the function to extract data from original TRY data
##'
##' based on the data structure of extraction from TRY data base
##' @title fun.extract.try
##' @param ObservationID.t list of data identifier that we want to extract
##' @param data try data object
##' @param Non.Trait.Data list of names of non traits data that we want to extract
##' @param Trait.Data list of names of traits data that we want to extract
##' @return data.frame with one line per observation id with clumns with ObservationID Species Nontrait data for Traits: OrigValue OrigUnit StdValue
##' @author Kunstler
fun.extract.try <- function(ObservationID.t,data,Non.Trait.Data,Trait.Data){
data.temp <- data[data$ObservationID==ObservationID.t,]
## Non trait data
Vec.Non.Trait.Data <- rep(NA,length(Non.Trait.Data))
names(Vec.Non.Trait.Data) <- Non.Trait.Data
for (i in 1:length(Non.Trait.Data)){
if( sum(data.temp$DataName==Non.Trait.Data[i])==1){
Vec.Non.Trait.Data[i] <- data.temp[data.temp$DataName==Non.Trait.Data[i],"OrigValueStr"]
}
if(sum(data.temp$DataName==Non.Trait.Data[i])>1){
## if(sum(data.temp$DataName==Non.Trait.Data[i] & grepl("Mean",data.temp$ValueKindName,
## fixed=TRUE))!=1){ print("error in ValueKindName")}
Vec.Non.Trait.Data[i] <- data.temp[data.temp$DataName==Non.Trait.Data[i] ,
"OrigValueStr"][1]
}
}
## Trait data
Vec.Trait.Data.OrigValue <-Vec.Trait.Data.OrigUnit <- Vec.Trait.Data.StdValue <-
rep(NA,length(Trait.Data))
names(Vec.Trait.Data.OrigValue) <- paste("OrigValue",Trait.Data)
names(Vec.Trait.Data.OrigUnit) <- paste("OrigUnitName",Trait.Data)
names(Vec.Trait.Data.StdValue) <- paste("StdValue",Trait.Data)
for (i in 1:length(Trait.Data)){
if(sum(grepl(Trait.Data[i],data.temp$TraitName, fixed=TRUE))==1){
Vec.Trait.Data.OrigValue[i] <- data.temp[grepl(Trait.Data[i],data.temp$TraitName, fixed=TRUE),"OrigValue"]
Vec.Trait.Data.OrigUnit[i] <- data.temp[grepl(Trait.Data[i],data.temp$TraitName, fixed=TRUE),"OrigUnitStr"]
Vec.Trait.Data.StdValue[i] <- data.temp[grepl(Trait.Data[i],data.temp$TraitName, fixed=TRUE),"StdValue"]
}
if( sum(grepl(Trait.Data[i],data.temp$TraitName, fixed=TRUE))>1){
if(sum((data.temp$ValueKindName %in% c("Best estimate","Mean","Site specific mean") & !is.na(data.temp$ValueKindName)))==1){
Vec.Trait.Data.OrigValue[i] <- mean(data.temp[grepl(Trait.Data[i],data.temp$TraitName, fixed=TRUE)&
(data.temp$ValueKindName %in% c("Best estimate","Mean","Site specific mean") & !is.na(data.temp$ValueKindName)) ,"OrigValue"])
Vec.Trait.Data.OrigUnit[i] <- (data.temp[grepl(Trait.Data[i],data.temp$TraitName, fixed=TRUE) &
(data.temp$ValueKindName %in% c("Best estimate","Mean","Site specific mean") & !is.na(data.temp$ValueKindName)),"OrigUnitStr"])[1]
Vec.Trait.Data.StdValue[i] <- mean(data.temp[grepl(Trait.Data[i],data.temp$TraitName, fixed=TRUE) &
(data.temp$ValueKindName %in% c("Best estimate","Mean","Site specific mean") & !is.na(data.temp$ValueKindName)),"StdValue"])
}
if(sum(data.temp$ValueKindName %in% c("Best estimate","Mean","Site specific mean") )<1){
Vec.Trait.Data.OrigValue[i] <- mean(data.temp[grepl(Trait.Data[i],data.temp$TraitName, fixed=TRUE),"OrigValue"],na.rm=T)
Vec.Trait.Data.OrigUnit[i] <- (data.temp[grepl(Trait.Data[i],data.temp$TraitName, fixed=TRUE) ,"OrigUnitStr"])[1]
Vec.Trait.Data.StdValue[i] <- mean(data.temp[grepl(Trait.Data[i],data.temp$TraitName, fixed=TRUE) ,"StdValue"],na.rm=T)
}
}
}
### EXPERIMENTAL DATA TYPE
TF.exp.data <- sum(grepl("Growth & measurement conditions - experimental tre",data.temp$NonTraitCategories, fixed=TRUE) )>0
names(TF.exp.data) <- 'TF.exp.data'
res.temp <- data.frame("ObservationID"=ObservationID.t,"AccSpeciesName"=unique(data.temp$AccSpeciesName) ,t(Vec.Non.Trait.Data),TF.exp.data,
t(Vec.Trait.Data.OrigValue),t(Vec.Trait.Data.OrigUnit),t(Vec.Trait.Data.StdValue))
return(res.temp)
}
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##' Detection of univar outlier based on method of Kattge et al. 2011
##'
##'
##' @title
##' @param x.na
##' @param log
##' @return TRUE FALSE vector to identify outlier TRUE : outlier
##' @author Kunstler
fun.out.TF2 <- function(x.na,log=TRUE){
x <- x.na[!is.na(x.na)]
x.num <- (1:length(x.na))[!is.na(x.na)]
TF.vec <- rep(FALSE,length(x.na))
if(log){
fit.dist <- fitdistr(log10(na.omit(x) ),'normal')
high.bound <- fit.dist$estimate["mean"]+2*(fit.dist$estimate["sd"]+fit.dist$sd["sd"])
low.bound <- fit.dist$estimate["mean"]-2*(fit.dist$estimate["sd"]+fit.dist$sd["sd"])
TF.vec[x.num[log10(x)>high.bound | log10(x)<low.bound]] <- TRUE
}else{
fit.dist <- fitdistr((na.omit(x) ),'normal')
high.bound <- fit.dist$estimate["mean"]+2*(fit.dist$estimate["sd"]+fit.dist$sd["sd"])
low.bound <- fit.dist$estimate["mean"]-2*(fit.dist$estimate["sd"]+fit.dist$sd["sd"])
TF.vec[x.num[(x)>high.bound | (x)<low.bound]] <- TRUE
}
return((TF.vec))
}
###################################
###################################
####### extract mean sd per species or genus
####### add species synonyme
fun.species.traits <- function(species,species.table,col.sp="Latin_name",col.sp.syno="Latin_name_syn",traits,data){
vec.mean <- vec.sd <- vec.nobs <- rep(NA,length(traits))
vec.exp <- vec.genus <- rep(FALSE,length(traits))
names(vec.mean) <- names(vec.sd) <- names(vec.exp) <- names(vec.genus) <- names(vec.nobs)<- traits
species.syno <- species.table[species.table[[col.sp]]==species,col.sp.syno]
#browser()
for(i in traits){
if(sum((data$AccSpeciesName %in% species.syno) & !is.na(data[[i]]))>0){ ## if data for this species or syno
if(sum((data$AccSpeciesName %in% species.syno) & (!is.na(data[[i]])) & (!data[["TF.exp.data"]]))>0){## if data with out experiments
x <- data[[i]][data$AccSpeciesName %in% species.syno & (!is.na(data[[i]])) &
(!data[["TF.exp.data"]])]
if(length(x)>50){## if more than 50 obs remove outlier
outlier <- fun.out.TF2(x.na=x,log=TRUE)
vec.mean[[i]] <- mean(log10(x[!outlier]))
vec.sd[[i]] <- sd(log10(x[!outlier]))
vec.nobs[[i]] <- length(x[!outlier])
}else{
vec.mean[[i]] <- mean(log10(x))
vec.sd[[i]] <- sd(log10(x))
vec.nobs[[i]] <- length(x)}
}else{### include experimental data
x <- data[[i]][data$AccSpeciesName %in% species.syno & (!is.na(data[[i]])) ]
if(length(x)>50){
outlier <- fun.out.TF2(x.na=x,log=TRUE)
vec.mean[[i]] <- mean(log10(x[!outlier]))
vec.sd[[i]] <- sd(log10(x[!outlier]))
vec.exp[[i]] <- TRUE
vec.nobs[[i]] <- length(x[!outlier])
}else{
vec.mean[[i]] <- mean(log10(x))
vec.sd[[i]] <- sd(log10(x))
vec.exp[[i]] <- TRUE
vec.nobs[[i]] <- length(x)}
}
}else{### compte data at genus level if no data for the species
genus <- sub(" .*","",species)
if(sum(grepl(genus,data$AccSpeciesName) & (!is.na(data[[i]])))>0){
x <- data[[i]][grepl(genus,data$AccSpeciesName,fixed=TRUE ) & (!is.na(data[[i]])) ]
if(length(x)>50){
outlier <- fun.out.TF2(x.na=x,log=TRUE)
vec.mean[[i]] <- mean(log10(x[!outlier]))
vec.sd[[i]] <- sd(log10(x[!outlier]))
vec.genus[[i]] <- TRUE
vec.nobs[[i]] <- length(x[!outlier])
}else{
vec.mean[[i]] <- mean(log10(x))
vec.sd[[i]] <- sd(log10(x))
vec.genus[[i]] <- TRUE
vec.nobs[[i]] <- length(x)}
}
}
}
return(list(mean=vec.mean,sd=vec.sd,exp=vec.exp,genus=vec.genus,nobs=vec.nobs))
}
#######################
### FUNCTIONS TO Manipulate species names
fun.get.genus <- function(x) gsub(paste(" ",gsub("^([a-zA-Z]* )","",x),sep=""),"",x,fixed=TRUE)
trim.trailing <- function (x) sub("\\s+$", "", x)