#Calculating the iBS metric
#Step 1:load input rasters

#load species diversity output
Diversity<-raster('DiversityLucia.tif')
#load natural index output
NI<-raster('NaturalIndexLucia.tif')


#Step 2: Normalize both inputs

#normaize diversity to range 0 to 1
Diversity_norm<- rescale(Diversity, to = c(0, 1))
#normaize natural index to range 0 to 1
NI_norm<- rescale(NI, to = c(0, 1))


#sTEP 3: Multiply the normalized rasters to output the iBS
iBS_Lucia <- Diversity_norm * NI_norm


#export
writeRaster(iBS_Lucia, 'iBS_Lucia.tif')



#Extracting values for violin plots

#STEP 1: generate random points for the three categories
#in both study areas

#first specify an extent for three areas NP, AF, P
#kakamega
NP<-randomPoints(Nonforest_KK, n=150, ext=Nonforest_KK)
AF<-randomPoints(Agroforest_KK, n=150, Agroforest_KK) 
P<-randomPoints(forest_KK, n=150, ext=forest_KK)

#lucia
NP_lu<-randomPoints(Nonforest_lu, n=150, ext=Nonforest_lu)
P_lu<-randomPoints(forest_lu, n=150, ext=forest_lu)

#load the rasters(iBS ) that values will be extracted from
iBS_KK<-raster('iBS_KK.tif')
SB_KK<-raster('SB_KK.tif')
iBS_Lucia<-raster('iBS_Lucia.tif')
DF_FI<-raster('DF_FI.tif')

#STEP 2: using the random points extract values from rasters

#Kakamega
#iBS
NP_iBS_KK<-data.frame(raster::extract(iBS_KK, NP))
AF_iBS_KK<-data.frame(raster::extract(iBS_KK, AF))
P_iBS_KK<-data.frame(raster::extract(iBS_KK, P))

#SB
NP_SB_KK<-data.frame(raster::extract(SB_KK, NP))
AF_SB_KK<-data.frame(raster::extract(SB_KK, AF))
P_SB_KK<-data.frame(raster::extract(SB_KK, P))

#Lucia
#iBS
NP_iBS_lu<-data.frame(raster::extract(iBS_Lucia, NP_lu))
P_iBS_lu<-data.frame(raster::extract(iBS_Lucia, P_lu))

#DF
NP_DF_lu<-data.frame(raster::extract(DF_FI, NP_lu))
P_DF_lu<-data.frame(raster::extract(DF_FI, P_lu))

#STEP3: Combine dataframes

#combine the dataframes from iBS ratsers in both sites
Df1<-rbind(NP_iBS_KK,AF_iBS_KK,P_iBS_KK, NP_iBS_lu, P_iBS_lu)

#combine all dataframes in the order they sould appear
Df2<-rbind(NP_iBS_KK,AF_iBS_KK,P_iBS_KK, 
		NP_SB_KK, AF_SB_KK,  P_SB_KK
		NP_iBS_lu, P_iBS_lu,
		NP_DF_lu, P_DF_lu)

#STEP 4: Violinplotters
install.packages('violinplotters')
library(violinplotter)

violinplotter(RasterVals ~ Group, 
              data=Df1, 
              TITLE="", 
              XLAB="", 
              YLAB="", 
              VIOLIN_COLOURS=c(c("black", "black",
                                        "black", 
                                        "black","black")), 
                                        PLOT_BARS=TRUE, 
              ERROR_BAR_COLOURS=c("#636363","#1c9099","#de2d26"), 
              SHOW_SAMPLE_SIZE=FALSE, 
              SHOW_MEANS=TRUE, 
              CATEGORICAL=TRUE, 
              LOGX=FALSE, 
              LOGX_BASE=10, 
              MANN_WHITNEY=TRUE, 
              HSD=TRUE, 
              ALPHA=0.02, 
              REGRESS=FALSE)

violinplotter(RasterVals ~ Group, 
              data=Df2, 
              TITLE="", 
              XLAB="", 
              YLAB="", 
              VIOLIN_COLOURS=c(c("black", "black",
                                        "black", "orange", "orange","orange",
                                        "black","black","blue", "blue")), 
              PLOT_BARS=TRUE, 
              ERROR_BAR_COLOURS=c("#636363","#1c9099","#de2d26"), 
              SHOW_SAMPLE_SIZE=FALSE, 
              SHOW_MEANS=TRUE, 
              CATEGORICAL=TRUE, 
              LOGX=FALSE, 
              LOGX_BASE=10, 
              MANN_WHITNEY=TRUE, 
              HSD=TRUE, 
              ALPHA=0.02, 
              REGRESS=FALSE)