library(groundhog)
set.groundhog.folder("C:\\Groundhog_R\\R4.2.3_2023-04-01")
GroundhogDay <- '2023-04-01'

groundhog.library("dplyr",GroundhogDay)
groundhog.library("ggplot2",GroundhogDay)
groundhog.library("ggrepel",GroundhogDay)
groundhog.library("tidyr",GroundhogDay)
groundhog.library("rstatix",GroundhogDay)
groundhog.library("ggpmisc",GroundhogDay)
groundhog.library("readr",GroundhogDay)
groundhog.library("lubridate",GroundhogDay)

setwd("D:\\Google Drive\\0_SpekboomResearchGroup2.0\\3_Experiments\\RegionalTransect21(10)_ParentPlantMaterialExperiment\\manuscript/")

read_csv("data_manuscript/chirps_data.csv")-> chirps
read_csv("data_manuscript/harvestdata_2023-08-29.csv")-> roots
read_csv("data_manuscript/parentplantleafmoisturedata_2023-08-29.csv")-> leaves

################################################################################
# Obtaining the rainfall for various months before the harvest date 
f <- function(n_months) {
  chirps%>%
    as_tibble()%>%
    mutate(date=ymd(date))%>%
    filter(date<=ymd("2021-10-13"))%>%
    filter(date>=ymd("2021-10-13")-months(n_months))%>%
    group_by(Site)%>%
    summarise(ppt_sum=sum(chirps))%>%
    return()
}
f(1)%>%rename(months_1=ppt_sum)%>%
  left_join(f(2)%>%rename(months_2=ppt_sum))%>%
  left_join(f(3)%>%rename(months_3=ppt_sum))%>%
  left_join(f(6)%>%rename(months_6=ppt_sum))%>%
  left_join(f(9)%>%rename(months_9=ppt_sum))%>%
  left_join(f(12)%>%rename(months_12=ppt_sum))%>%
  round(0)->
  chirps_summary
################################################################################
# Obtaining leaf moisture values for each site
leaves%>%
  group_by(Site,Individual)%>%
  summarise(LeafMoisture=mean(LeafMoisture))%>%
  group_by(Site)%>%
  summarise(LeafMoisture=mean(LeafMoisture))->
  leaves_summary
################################################################################
# Obtaining root values for each site
roots%>%
  filter(SamplingEvent%in%4:8)%>%
  group_by(Site,Individual,SamplingEvent)%>%
  summarise(RootMass=mean(RootMass,na.rm=T))%>%
  group_by(Site,SamplingEvent)%>%
  summarise(RootMass=mean(RootMass))%>%
  mutate(SamplingEvent=paste0("SE_",SamplingEvent))%>%
  pivot_wider(names_from = SamplingEvent,values_from = RootMass)->
  rootmass_summary
################################################################################
left_join(chirps_summary,leaves_summary)%>%
  left_join(rootmass_summary)->
  dat
################################################################################
dat%>%
  select(-SE_4,-SE_5,-SE_6,-SE_7,-SE_8)%>%
  pivot_longer(cols=2:7,names_to = "months_n",values_to = "sum_ppt")%>%
  mutate(months_n=factor(months_n,levels=c("months_1","months_2","months_3","months_6","months_9","months_12"),
                         labels=c("1 month","2 months","3 months","6 months","9 months","12 months")))%>%
  group_by(months_n)%>%
  cor_test(vars=LeafMoisture,vars2=sum_ppt)  


dat%>%
  pivot_longer(cols=2:7,names_to = "months_n",values_to = "sum_ppt")%>%
  mutate(months_n=factor(months_n,levels=c("months_1","months_2","months_3","months_6","months_9","months_12"),
                         labels=c("1 month","2 months","3 months","6 months","9 months","12 months")))%>%
  ggplot(aes(sum_ppt,LeafMoisture))+
  geom_smooth(method="lm")+
  geom_point(aes(size=SE_7,fill=SE_7),shape=21)+
  #geom_point(aes(size=SE_7),shape=1)+
  scale_fill_gradient2(low = "red",mid="orange",high="green",
                        )+
  geom_text_repel(aes(label=Site))+
  facet_wrap(~months_n,scales="free")+
  xlab("Sum of precipitation")+
  ylab("Mean leaf moisture (%; n=5)")+
  guides(size="none")+
  labs(fill="Root\nmass\n(g)")+
  # stat_poly_eq(aes(label =  sprintf("%s*\"; \"*%s",
  #                                   after_stat(rr.label),
  #                                   after_stat(p.value.label))),
  #              small.r=TRUE,
  #              small.p=TRUE)
  stat_poly_eq(use_label(c("F","R2","P"),sep="*\"; \"*"),small.r=TRUE,small.p=TRUE)
ggsave("FinalFiguresV2/Fig_S3.jpg",width=10,height=6)