####This code is for SIAR analysis of individual animals to compare their consumption 5 sourceS by season
##This file produces figure 4 in the paper
##There is also code to use a 2 isotope analysis for comparison
#install.package(siar)
setwd("C:/Stikine grizzly isotope paper/SIAR/seasonal_3 isotopes")
library(siar)
library(coda)
library(reshape2)
##graphics.off()

##Load 3 Isotope Data
##Note you can only run 30 samples at a time through SIARsolo
##Break the data into 4 groups
consumers_season_grp1<-read.table("Sectioned samples_CNS_Grp1.txt",header=TRUE)
consumers_season_grp2<-read.table("Sectioned samples_CNS_Grp2.txt",header=TRUE)
consumers_season_grp3<-read.table("Sectioned samples_CNS_Grp3.txt",header=TRUE)
consumers_season_grp4<-read.table("Sectioned samples_CNS_Grp4.txt",header=TRUE)
##Load 2 Isotope Data need to change working directory
consumers_season_grp1<-read.table("Sectioned samples_CN_Grp1.txt",header=TRUE)
consumers_season_grp2<-read.table("Sectioned samples_CN_Grp2.txt",header=TRUE)
consumers_season_grp3<-read.table("Sectioned samples_CN_Grp3.txt",header=TRUE)
consumers_season_grp4<-read.table("Sectioned samples_CN_Grp4.txt",header=TRUE)

##Load source and fractionation data for 3 isotopes
sources<-read.table("sources.txt",header=TRUE)
corrections<-read.csv("corrections.csv",header=TRUE)

##Load source and fractionation data for 2 isotopes
sources<-read.table("sources_CN.txt",header=TRUE)
corrections<-read.table("corrections_CN.txt",header=TRUE)

##Load field data for seasonal samples
seasonal<-read.csv("All sectioned samples.csv",header=TRUE)
##Load field data and use a name to denote we are only analyzing 2 isotopes
seasonal_CN<-read.csv("All sectioned samples_CNS.csv",header=TRUE) ##note need to change extension
    ## on line 142 for CN analysis

##run model
model1<-siarsolomcmcv4(consumers_season_grp1, sources, corrections,iterations=100000,burnin=10000)
model2<-siarsolomcmcv4(consumers_season_grp2, sources, corrections,iterations=100000,burnin=10000)
model3<-siarsolomcmcv4(consumers_season_grp3, sources, corrections,iterations=100000,burnin=10000)
model4<-siarsolomcmcv4(consumers_season_grp4, sources, corrections,iterations=100000,burnin=10000)

##create plots
siarplotdata(model1)
siarhistograms(model1)
siarproportionbygroupplot(model1)
siarproportionbysourceplot(model1,grp=1)
siarproportionbysourceplot(model1,grp=2)
siarproportionbysourceplot(model1,grp=3)
siarproportionbysourceplot(model1,grp=4)
siarproportionbysourceplot(model1,grp=5)


##view data
siarhdrs(model1)


#########################################################
#########################################################
###LOOP THROUGH MODELS 1-4, SUMMARIZE DATA FROM
###EACH MODEL AND BIND TOGETHER TO CREATE ONE LARGE DF
###THANKS TO CLAYTON LAMB FOR WRITING THIS CODE
#########################################################
#########################################################

##LIST ALL 4 MODELS
model_list <- c("model1", "model2", "model3", "model4")

##CREATE EMPTY DATA FRAME TO DEPOSIT FINAL SUMMARIZED DATA
mean_diet<-data.frame()


#########################################
##oUTER LOOP, LOOPS THROUGH MODELS 1-4
#########################################


for(j in 1:length(model_list)){

##GET MODEL
model_x <- get(model_list[j])

##EXTRACT THE OUTPUT FROM MODEL
out<-model_x$output

##IDENTIFY ALL THE sOURCE_GROUPS, SUCH AS sALMONG1,ETC, THIS IS
## WHAT WE'LL LOOP THROUGH NEXT TO SUMMARIZE THE DATA WITHIN
##EACH MODEL
Source_Group<-colnames(out)

##CREATE EMPTY DATAFRAME TO DEPOSIT SUMMARIZED DATA IN THE INTERM
b<-data.frame()



#########################################
##INNER LOOP, LOOPS THROUGH ALL SOURCE_GROUPS
##TO CREATE MEAN AND CONFIDENCE INTERVALS
#########################################

for(i in 1:length(Source_Group)){
  
  ##create mean
  mean<-mean(out[,i])
  
  ##calculate highest density intervals for error bars
  lower<-HPDinterval(mcmc(out[,i]))[1]
  upper<-HPDinterval(mcmc(out[,i]))[2]
  
  ##CREATE INTERM DATAFRAME WITH A SINGLE ROW OF DATA FOR EACH iTH RECORD
  a<-data.frame(Source_Group=Source_Group[[i]],
                mean=mean,
                lower95CI=lower,
                upper95CI=upper
  )
  

  ##BIND THE a DATAFRAME ONTO THE LARGER b DATAFRAME TO CREATE A FULL DATAFRAME FOR EACH MODEL
  
  b<-rbind(b,a)
}


##BIND THE b DATAFRAME FROM EACH MODEL CREATED ABOVE INTO A LARGER DATA FRAME
##SO ALL RECORDS ARE IN 1 DATAFRAME
mean_diet<-rbind(mean_diet,b)


}

##CREATE UNIQUE ROW ID TO MATCH TO THE ROWS THE DATA CAME FROM
##IN THE "SEASONAL" DATAFRAME
mean_diet$ID <- rep(1:120, each=8) ##this is 7 for 2 isotopes and 8 for 3 isotoeps

##RENAME SOURCES TO GET RID OF GROUPS
mean_diet$Source <- rep(c("Salmon", "Moose", "Goat", "Marmot", "Vegetation", "SD1", "SD2", "SD3"), times=120)
   ## there are only SD1 and 2 for 2 isotopes and SD1-3 for 3 isotoeps         , "SD3"
##subset out sd1-3
mean_diet <- subset(mean_diet, !Source %in% c("SD1", "SD2", "SD3"))

#remove Source_Group Column
mean_diet <- subset(mean_diet, select=-Source_Group )


###MELT DATA USING RESHAPE2 PACKAGE THEN CAST THE DATA TO CREATE
##DATA IN LONG FORMAT, WITH ID AS THE ROW AND VARIABLE+SOURCE AS THE COLUMNS
diet_melt <- melt(mean_diet, id.vars = c("ID", "Source"))
diet_cast <- dcast(diet_melt, ID~variable+Source )


##BIND THE WIDE-FORMAT DIET DATA ONTO SEASONAL USING COLUMN BIND (CBIND)
seasonal_diet <- cbind(seasonal, diet_cast)
##SAVE DATA TO EXTERNAL DIRECTORY
write.csv(seasonal_diet_CN, file = "C:/Stikine grizzly isotope paper/SIAR/seasonal_2 isotopes/stikineseasonaldiet.csv")

#################################################################
##GGPLOT 
##MAKE FIGURE 4
#################################################################
library(ggplot2)

##SALMON##
ggplot(seasonal_diet, aes(section.date.since.June1, mean_Salmon))+
  geom_rect(aes(xmin=45, xmax=75, ymin=0, ymax=Inf),
            fill="pink", alpha=0.05)+
  geom_rect(aes(xmin=75, xmax=Inf, ymin=0, ymax=Inf),
            fill="pink", alpha=0.5)+
  geom_errorbar(aes(ymin=lower95CI_Salmon, ymax=upper95CI_Salmon), width=1, colour="grey")+ 
  geom_point(aes(shape=Sex,colour=Coast),size=2)+
    scale_color_manual(values=c("forestgreen","red"),
                     labels=c("Interior", "Coast"))+
  xlim(0,152)+
  geom_line(aes(group=Individual, colour=Coast))+
  ylab("salmon in diet (%)")+
  xlab("Number of days after June 1")+
  
  theme(axis.line.x = element_line(color="black"),
        axis.line.y = element_line(color="black"))
  
 ggsave(filename = "salmon.pdf")

##MARMOT###
ggplot(seasonal_diet, aes(section.date.since.June1, mean_Marmot))+
  geom_rect(aes(xmin=45, xmax=75, ymin=0, ymax=Inf),
            fill="pink", alpha=0.05)+
  geom_rect(aes(xmin=75, xmax=Inf, ymin=0, ymax=Inf),
            fill="pink", alpha=0.5)+
  geom_errorbar(aes(ymin=lower95CI_Marmot, ymax=upper95CI_Marmot), width=1, colour="grey")+ 
  geom_point(aes(shape=Sex,colour=Coast),size=2)+
  scale_color_manual(values=c("forestgreen","red"),
                     labels=c("Interior", "Coast"))+
  xlim(0,152)+
  geom_line(aes(group=Individual, colour=Coast))+
  
  ylab("marmot in diet (%)")+
  xlab("Number of days after June 1")+
  theme_classic()+
  theme(axis.line.x = element_line(color="black"),
        axis.line.y = element_line(color="black"))

ggsave(filename = "marmot.pdf")

##GOAT###
ggplot(seasonal_diet, aes(section.date.since.June1, mean_Goat))+
  geom_rect(aes(xmin=45, xmax=75, ymin=0, ymax=Inf),
            fill="pink", alpha=0.05)+
  geom_rect(aes(xmin=75, xmax=Inf, ymin=0, ymax=Inf),
            fill="pink", alpha=0.5)+
  geom_errorbar(aes(ymin=lower95CI_Goat, ymax=upper95CI_Goat), width=1, colour="grey")+ 
  geom_point(aes(shape=Sex,colour=Coast),size=2)+
  scale_color_manual(values=c("forestgreen","red"),
                     labels=c("Interior", "Coast"))+
  xlim(0,152)+
  geom_line(aes(group=Individual, colour=Coast))+
  ylab("mountain goat in diet (%)")+
  xlab("Number of days after June 1")+
  theme_classic()+
  theme(axis.line.x = element_line(color="black"),
        axis.line.y = element_line(color="black"))

ggsave(filename = "goat.pdf")

##MOOSE###
ggplot(seasonal_diet, aes(section.date.since.June1, mean_Moose))+
  geom_rect(aes(xmin=45, xmax=75, ymin=0, ymax=Inf),
            fill="pink", alpha=0.05)+
  geom_rect(aes(xmin=75, xmax=Inf, ymin=0, ymax=Inf),
            fill="pink", alpha=0.5)+
  geom_errorbar(aes(ymin=lower95CI_Moose, ymax=upper95CI_Moose), width=1, colour="grey")+ 
  geom_point(aes(shape=Sex,colour=Coast),size=2)+
  scale_color_manual(values=c("forestgreen","red"),
                     labels=c("Interior", "Coast"))+
  xlim(0,152)+
  geom_line(aes(group=Individual, colour=Coast))+
  ylab("moose in diet (%)")+
  xlab("Number of days after June 1")+
  theme_classic()+
  theme(axis.line.x = element_line(color="black"),
        axis.line.y = element_line(color="black"))

ggsave(filename = "moose.pdf")

##vEGETATION###
ggplot(seasonal_diet, aes(section.date.since.June1, mean_Vegetation))+
  geom_rect(aes(xmin=45, xmax=75, ymin=0, ymax=Inf),
            fill="pink", alpha=0.05)+
  geom_rect(aes(xmin=75, xmax=Inf, ymin=0, ymax=Inf),
            fill="pink", alpha=0.5)+
  geom_errorbar(aes(ymin=lower95CI_Vegetation, ymax=upper95CI_Vegetation), width=1, colour="grey")+ 
  geom_point(aes(shape=Sex,colour=Coast),size=2)+
  ##geom_pointrange(aes(x=section.date.since.June1, y=mean_Vegetation, 
      ##ymin=lower95CI_Vegetation, ymax=upper95CI_Vegetation), size=1, color="blue", fill="white", shape=22)+
  ##geom_jitter(aes(shape=Sex,colour=Coast),size=2, width = 20)+
  scale_color_manual(values=c("forestgreen","red"),
                     labels=c("Interior", "Coast"))+
  xlim(0,152)+
  geom_line(aes(group=Individual, colour=Coast))+
  ylab("vegetation in diet (%)")+
  xlab("Number of days after June 1")+
  theme_classic()+
  theme(axis.line.x = element_line(color="black"),
        axis.line.y = element_line(color="black"))

ggsave(filename = "vegetation.pdf")