Subversion Repositories public iLand

## Example for DBH-Distribution barcharts

library(RSQLite)
library(ggplot2)
library(plyr)
library(reshape)  
library(scales)

cols.species <- c("abal"="#088A29",
                  "acca"="#F3F781",
                  "acpl"="#86B404", 
                  "acps"="#58FAD0", 
                  "algl"="#61210B", 
                  "alin"="#A4A4A4", 
                  "alvi"="#0B3B17", 
                  "bepe"="#2E64FE", 
                  "cabe"= "#F7BE81", 
                  "casa"="#A9F5A9", 
                  "coav"="#58D3F7",
                  "fasy"="#2EFE2E", 
                  "frex"="#FF0000", 
                  "lade"="#8A4B08", 
                  "piab"="#FFFF00", 
                  "pice"="#FF8000", 
                  "pini"="#610B21", 
                  "pisy"="#B18904", 
                  "poni"="#000000", 
                  "potr"="#610B5E", 
                  "psme"="#DF0174", 
                  "qupe"="#F6CED8", 
                  "qupu"="#FA5882", 
                  "quro"="#B40431", 
                  "rops"="#F781D8", 
                  "saca"="#F5ECCE", 
                  "soar"="#E6E0F8", 
                  "soau"="#B40404", 
                  "tico"="#9F81F7",
                  "tipl"="#8000FF", 
                  "ulgl"="#DF7401")

### custom function for creating the dbh-distribution charts
### the data is generated by the "Dynamic stand" output of iLand.
barchart_graphs <-function(data, label) {
  ## extract dbh classes
  count_ha.sp=data.frame(species=data$species, dbh10=data$if_dbh_10_1_0_sum/100, dbh20=data$if_dbh_10_and_dbh_20_1_0_sum/100, dbh30=data$if_dbh_20_and_dbh_30_1_0_sum/100, dbh40=data$if_dbh_30_and_dbh_40_1_0_sum/100, dbh50=data$if_dbh_40_and_dbh_50_1_0_sum/100, dbh60=data$if_dbh_50_and_dbh_60_1_0_sum/100, dbh70=data$if_dbh_60_and_dbh_70_1_0_sum/100, dbh80=data$if_dbh_70_and_dbh_80_1_0_sum/100, dbh90=data$if_dbh_80_and_dbh_90_1_0_sum/100, dbh100=data$if_dbh_90_and_dbh_100_1_0_sum/100, dbh100plus=data$if_dbh_100_1_0_sum/100)
  # reshape (melt)
  count_ha.sp.melted <- melt.data.frame(count_ha.sp, id.vars="species", na.rm=FALSE)
  # and calculate a total
  count_ha.total <- colSums(count_ha.sp[,2:12])
  # now the plotting:
  barplot(count_ha.total+1, log="y", main=paste("Stemnumbers", label), ylab="N (+1)")
  ## the ggplot
  ggplot(count_ha.sp.melted, aes(x=variable,y=value,group=variable,fill=species)) + 
    geom_bar(stat="identity")  + scale_fill_manual(values=cols.species)+ 
    labs(xlab="dbh classes", ylab="Stem number", title=paste("dbh distribution", label))
}

#### load data from the database ######
conn<-dbConnect("SQLite", dbname = paste("C:/Users/werner/Desktop/bare_soil_afi413_pnv_buffer.sqlite",sep=""))
dyn.out=dbReadTable(conn,"dynamicstand")
dbDisconnect(conn)

summary(dyn.out)



### create charts for several timesteps ########
#data <- dyn.out[dyn.out$year == 50,]

barchart_graphs(dyn.out[dyn.out$year == 50,], "50")
barchart_graphs(dyn.out[dyn.out$year == 100,], "100")
barchart_graphs(dyn.out[dyn.out$year == 500,], "500")
barchart_graphs(dyn.out[dyn.out$year == 1000,], "1000")