src/Temp_Mull/Sich151125Bodendaten_sort.R

In environmentalinformatics-marburg/inkili: Find out which plots are included in lidar coverage

#Varnames: 
#insct = insct abundance of insct in general
#S=site; 
#R = Round(~Durchgang); 
#T = Trap; 
#beet = beetle; 
#beet_abbr = beetle abreviation
#eco=ecology; 
#spl=samples
#O= Ordnung; UK=Unterklasse; K=Klasse; TO=Teilordnung; UO=Unterordnung; F=Familie; x=Sonderfall
#srt=sorted
#trans=transponiert
#clp=clip
#abbr=abbreviation
#agg=aggregate
#tmp=temporary variable
#anm=animals (includes beetles and other insct)
#fin=final
#rich=richnes
#tec= technische informationen
library(base)

inpath <- ("/media/aziegler/Volume/Bodendaten/data")
outpath <- ("/media/aziegler/Volume/Bodendaten/data/output") 
#Beispiel Nutzung: writeChar(prjNew,paste0(outPath,"/",layer,".prj"))
setwd(inpath)

###source function plt_out.R to get rid of all the plots that are not covered 
# by lidar (always execute before writing out .csv)
source("/media/aziegler/Volume/scripts/plt_out.R")

###read data
#Varnames: 
#abun = abundance; S=site; R = Round (?Durchgang); T = Trap; 
#beet=beetle; eco=ecology; spl=samples
insct_SRT <- read.csv ("SP7_PTabund_per_site_and_trap_and_rnd.csv", header=T, sep=";")
insct_SR <- read.table("SP7_PTabund_means_per_site_and_rnd.txt", header=T, sep=",")
insct_S <- read.table("SP7_PTabund_means_per_site.txt", header=T, sep=",")
beet_spl <- read.csv ("SP7_PTabund_beetles_samples.csv", header=T, sep=";")

beet_eco <- read.csv ("SP7_PTabund_beetles_ecology.csv", header=T, sep=";")

sys_name <- read.csv (paste0 (inpath, "/output/systematische_bezeichnungen.csv"), header=T, sep=",")

###compile data table from "abun_SR"
#sort abun_SR and delete unnecessary columns
insct_SR_srt <- insct_SR[, c(1,(ncol(insct_SR)-1), ncol(insct_SR), 3, (ncol(insct_SR)-3), 4:(ncol(insct_SR)-5))]

#change column date in format date
insct_SR_srt$date_coll <- strptime(insct_SR_srt$date_coll, format = "%Y-%m-%d")
insct_SR_srt$date_coll <- as.Date(insct_SR_srt$date_coll)
insct_SR_srt$plt_rnd <- paste0(insct_SR_srt$plotID, "_", insct_SR_srt$rnd)
insct_SR_srt <- cbind(insct_SR_srt[1:3], insct_SR_srt[ncol(insct_SR_srt)], insct_SR_srt[5:ncol(insct_SR_srt)-1])


##only execute once
# generate table with beetle abbreviations
# beet_spl$spec_name <- paste0(beet_spl[,2], "_", beet_spl[,3], "_", 
#                                    beet_spl[,4], "_", beet_spl[,5], "_",
#                                    beet_spl[,6], "_", beet_spl[,7], "_", 
#                                    beet_spl[,8], "_", beet_spl[,9])
# beet_abbr <- cbind(beet_spl[1], beet_spl[ncol(beet_spl)])
# rownames(beet_abbr) <- NULL
# write.csv(beet_abbr, file=paste0(outpath, "/", "beet_abbr.csv"))

###adjust "beet_spl" to be included in insct_SR
#continue editing table "beet_spl"
rownames(beet_spl) <- as.list(as.character(beet_spl$spec_short))
beet_spl$spec_short <- NULL
beet_spl$superfamily <- NULL
beet_spl$family <- NULL
beet_spl$subfamily <- NULL
beet_spl$genus <- NULL
beet_spl$subgenus <- NULL
beet_spl$species <- NULL
beet_spl$genus_work <- NULL
beet_spl$species_work <- NULL
beet_spl$spec_name <- NULL
beet_spl$total <- NULL

beet_trans <- t(beet_spl)
beet_trans <- as.data.frame(beet_trans)
beet_trans$total <- rowSums(beet_trans)

#create table with plot information from the rownames of "beet_trans"
beet_info <- as.data.frame(cbind(rownames(beet_trans)))
colnames(beet_info)[1] <- "orig_name"
beet_info$plotID <- substr(beet_info$orig_name, 1, 4)
beet_info$date_coll <- substr(beet_info$orig_name, 6, 15)
beet_info$date_coll <- strptime(beet_info$date_coll, format = "%Y.%m.%d")
beet_info$date_coll <- as.Date(beet_info$date_coll)
beet_info$trap <- substr(beet_info$orig_name, 18, 20)

# join "beet_trans" with "beet_info"
beet_srt <- cbind(beet_info[2:4], beet_trans[ncol(beet_trans)], beet_trans[1:(ncol(beet_trans)-1)])
rownames(beet_srt) <- NULL

### aggregate beet_srt by plot and date(=round)
beet_SR_srt <- as.data.frame(aggregate(beet_srt[5], by = list(beet_srt$plotID, beet_srt$date_coll), FUN="mean"))
for (i in (6:ncol(beet_srt))){
  tmp_mean <- aggregate(beet_srt[i], by = list(beet_srt$plotID, beet_srt$date_coll), FUN="mean")
  beet_SR_srt <- cbind(beet_SR_srt, tmp_mean[3])
}
colnames(beet_SR_srt)[1] <- "plotID"
colnames(beet_SR_srt)[2] <- "date_coll"
beet_SR_srt$rnd <- paste0("jun", tolower(format(beet_SR_srt$date_coll, "%Y")))
beet_SR_srt$plt_rnd <- paste0(beet_SR_srt$plotID, "_", beet_SR_srt$rnd)
beet_SR_srt <- cbind(beet_SR_srt[1:2], beet_SR_srt[ncol(beet_SR_srt)-1], 
                     beet_SR_srt[ncol(beet_SR_srt)], beet_SR_srt[3:ncol(beet_SR_srt)-2])
colnames(beet_SR_srt)[1] <- "plotID_beet"
colnames(beet_SR_srt)[2] <- "date_coll_beet"
colnames(beet_SR_srt)[3] <- "rnd_beet"

###merge both aggregated Tables insct_SR_srt und beet_SR_srt
all_SR <- merge(insct_SR_srt, beet_SR_srt, by = "plt_rnd", all=T)
colnames(all_SR)[2:3] <- c("plotID", "date_coll")

all_SR <- plt_out(all_SR) #################################################################################ACHTUNG mehr Einträge als LIDAR
write.csv(all_SR, file=paste0(outpath, "/", "anm_SR.csv"), row.names=F)

###create one table only for insects without the beetle species
insct_SR_fin <- all_SR[, 1:38]
colnames(insct_SR_fin)[1:6] <- c("plot_rnd", "plotID", "date_coll", "rnd", "elevation", "total")

### create new collum with species richness for insct
insct_SR_spec <- insct_SR_fin[,7:ncol(insct_SR_fin)]
insct_SR_spec$rich_insct <- rowSums(insct_SR_spec>0)
insct_SR_fin <- cbind(insct_SR_fin, insct_SR_spec$rich_insct)
colnames(insct_SR_fin)[ncol(insct_SR_fin)] <- c("rich_insct")
insct_SR_fin <- plt_out(insct_SR_fin)#######################################################################dito

###analog to the prior: 
###create one table without the insect species
beet_SR_fin <- all_SR[, c(1:5, 39:ncol(all_SR))]
colnames(beet_SR_fin)[c(1:5, 9, 10)] <- c("plot_rnd", "plotID", "date_coll", 
                                         "rnd", "elevation", "plotID_beet", "date_coll_beet")

###create new collum with species richness for beetles
beet_SR_spec <- beet_SR_fin[, 12:ncol(beet_SR_fin)]
beet_SR_spec$rich_beet <- rowSums(beet_SR_spec>0)
beet_SR_fin <- cbind(beet_SR_fin, beet_SR_spec$rich_beet)
colnames(beet_SR_fin)[ncol(beet_SR_fin)] <- c("rich_beet")
beet_SR_fin <- plt_out(beet_SR_fin)####################################################################dito

###write out table with species richness for insct
write.csv(insct_SR_fin, file=paste0(outpath, "/", "insct_SR_fin.csv"), row.names=F)
write.csv(beet_SR_fin, file=paste0(outpath, "/", "beet_SR_fin.csv"), row.names=F)

###create one table with only technical/abiotic information
tec_info <- all_SR[,c(1:5)]
tec_info <- plt_out(tec_info)##################################################################dito
write.csv(tec_info, file=paste0(outpath, "/", "tec_info.csv"),row.names=F)