library(ggplot2)
library(ggtext)
library(here)
library(dplyr)

here::here()

source("coefficients/agreement.CI.R")
source("coefficients/agreement.coefficients.R")

# Look at the number of revisions per referent (Action_code) and participant

fullLogs <- read.csv("logs/all_logs.csv", sep=",", stringsAsFactors=F)
revisedTrials <- fullLogs[fullLogs$Phase == "Revision", ]

revisedTrials$Action_code <- factor(revisedTrials$Action_code)
levels(revisedTrials$Action_code) <- list(VS1="SEL_SubC_CF", VS2="SEL_SubC_CM", VS3="SEL_SubC_CL", VS4="SEL_SubC_SL", VS5="SEL_SubC_SM", VS6="SEL_SubC_SR", VS7="SEL_SubC_G", VM1="MAN_M_SC", VM2="MAN_D_SubC", VM3="MAN_C_S", GS1="SEL_SupC_C", GS2="SEL_SupC_R", GS3="SEL_SupC_CS", GS4="SEL_SupC_CMC", GS5="SEL_SupC_CMNC", GS6="SEL_SupC_RS", GS7="SEL_SupC_RMC", GS8="SEL_SupC_RMNC", GS9="SEL_SupC_SVC", GS10="SEL_SupC_SVR", GM1="MAN_C_M", GM2="MAN_M_C", GM3="MAN_M_R", GM4="MAN_D_SupC", GM5="MAN_D_SupCC", GM6="MAN_D_SupCR", GM7="MAN_S_C", GM8="MAN_F_C")

countRevisionsReferents <- revisedTrials %>% count(Action_code)
summary(countRevisionsReferents)
countRevisionsReferents

countRevisionsParticipants <- revisedTrials %>% count(Participant)
summary(countRevisionsParticipants)
countRevisionsParticipants

# use modsOrSigns = "signs" for analyses regarding sign proposals, or modsOrSigns = "modalities" for analyses regarding modality proposals

#modsOrSigns = "signs"
modsOrSigns = "modalities"

data <- read.csv(paste("logs/",modsOrSigns,".csv",sep=""), sep=";", stringsAsFactors=F)

rightorder = c("VS1","VS2","VS3","VS4","VS5","VS6","VS7","VM1","VM2","VM3","GS1","GS2","GS3","GS4","GS5","GS6","GS7","GS8","GS9","GS10","GM1","GM2","GM3","GM4","GM5","GM6","GM7","GM8")

data$X <- factor(data$X, ordered = TRUE, levels = rightorder)
data <- data[order(data$X), ]
data$X <- as.character(data$X)

#full dataset
gestures <- data[, seq(2, ncol(data), by=1)]

names(gestures) <- paste0("P", 1:ncol(gestures))

# Agreement on Gestures
percent <- jack.CI.random.raters(gestures, percent.agreement)
kappa <- jack.CI.random.raters(gestures, fleiss.kappa)
alpha <- jack.CI.random.raters(gestures, krippen.alpha)

printCI("  Percent agreement", percent)
printCI("      Fleiss' Kappa", kappa)
printCI("Krippendorf's alpha", alpha)

# Chance Agreement and Bias

chance.gestures <- jack.CI.random.raters(gestures, chance.agreement)

printCI("Chance agreement (Gestures)", chance.gestures)

# Agreement over Individual Referents

refs <- list() # referents list
k <- list() # Kappa estimates
l <- list() # Lower bounds of 95% CI
u <- list() # Upper bounds of 95% CI

for(index in 1:nrow(gestures)){
  # Construct the jackknife CI for Fleiss' Kappa of each referent
  ci <- jack.CI.random.raters.fleiss.kappa.for.item(gestures, index)

  refs[index] <- data[index, 1]
  k[index] <- ci[1]
  l[index] <- ci[2]
  u[index] <- ci[3]
}

# Create a data frame with all the estimates
fleiss.df <- data.frame(Referent = as.character(refs), Kappa = as.double(k), Low = as.double(l), Upper = as.double(u))

# Sort the referents by their agreement values
fleiss.df <- fleiss.df[with(fleiss.df, order(-Kappa)),]

referents <- fleiss.df$Referent
offset=0.5
a=0.01

colorLabels <- "black"
yOffset <- 0.9

plot <- ggplot(fleiss.df, aes(x = Referent, y = Kappa)) +
  geom_hline(yintercept = 0, color = "red", size = .2) +
  geom_point(size = 1.8, color = "#0000aa", shape = 1) +
  scale_x_discrete(name ="Referent", limits = rightorder) +
  scale_y_continuous(breaks =seq(-1, 1, by=0.2),limits = c(-1,1)) +
  theme_bw() +
  geom_errorbar(aes(ymax = Upper, ymin = Low), width=0.01, color = "#0000aa") +
  theme(axis.text.x = element_text(angle=48, vjust=1, hjust=1)) +
  theme(panel.border = element_blank(),
        panel.grid.major.x = element_blank(),
        panel.grid.minor = element_blank(),
        panel.grid.major = element_line(colour="#000000", size=0.03),
        text = element_text(size = 20)
        ) +
  geom_vline(xintercept = 7.5, color = colorLabels, linetype="dotted", size = 1.5) +
  geom_vline(xintercept = 10.5, color = colorLabels, linetype="dotted", size = 1.5) +
  geom_vline(xintercept = 20.5, color = colorLabels, linetype="dotted", size = 1.5) +
  geom_richtext(x = 3.75, y = -yOffset, angle=90, label = "Value Selections", colour = colorLabels, size = 6, hjust = 0) +
  geom_richtext(x = 9, y = -yOffset, angle=90, label = "Value Manipulations", colour = colorLabels, size = 6, hjust = 0) +
  geom_richtext(x = 15.5, y = -yOffset, angle=90, label = "Grid Selections", colour = colorLabels, size = 6, hjust = 0) +
  geom_richtext(x = 24.5, y = -yOffset, angle=90, label = "Grid Manipulations", colour = colorLabels, size = 6, hjust = 0)

print(plot)

results = c()
for(i in 1:28){
  results=c(results,fleiss.df$Kappa[which(fleiss.df$Referent==rightorder[i])])
}
write.csv(data.frame(results,row.names = rightorder),paste("results_",modsOrSigns,".csv",sep=""))

print("Fleiss' Kappa overall -- descriptive statistics:")
summary(results)
print(paste("standard deviation:", sd(results)))

############################
### pairwise comparisons ###
############################

indices = seq(1, 28, by=1)

# comparisons between individual referents

  fleiss.pe <- fleiss.chance.agreement.raw.noinference(gestures)
  fleiss.pooled <- function(ratings) {generic.kappa(ratings, fleiss.pe)}


  for (index in indices){
    propsReferent1 <- gestures[indices[index],]
    for (ind in indices){
      if(ind > index) {
        propsReferent2 <- gestures[indices[ind],]
        diff.Kappa <- jack.CI.diff.random.raters(propsReferent1, propsReferent2, fleiss.pooled)
        if(!(diff.Kappa[2] < 0 && diff.Kappa[3] > 0) && diff.Kappa[1]>0.1) {
          print(paste("Comparing ", rightorder[index], " with ", rightorder[ind], ":"))
          printCI("Difference in Fleiss' Kappa (pooled pe)", diff.Kappa)
        }
      }
    }
  }


  # comparisons between groups

  propsReferentVS <- gestures[indices[1:7],]
  propsReferentVM <- gestures[indices[8:10],]
  propsReferentGS <- gestures[indices[11:20],]
  propsReferentGM <- gestures[indices[21:28],]

  diff.Kappa <- jack.CI.diff.random.raters(propsReferentVS, propsReferentVM, fleiss.pooled)
  print("Comparing VS and VW")
  printCI("Difference in Fleiss' Kappa (pooled pe)", diff.Kappa)
  diff.Kappa <- jack.CI.diff.random.raters(propsReferentVS, propsReferentGS, fleiss.pooled)
  print("Comparing VS and GS")
  printCI("Difference in Fleiss' Kappa (pooled pe)", diff.Kappa)
  diff.Kappa <- jack.CI.diff.random.raters(propsReferentVS, propsReferentGM, fleiss.pooled)
  print("Comparing VS and GM")
  printCI("Difference in Fleiss' Kappa (pooled pe)", diff.Kappa)

  diff.Kappa <- jack.CI.diff.random.raters(propsReferentVM, propsReferentGS, fleiss.pooled)
  print("Comparing VM and GS")
  printCI("Difference in Fleiss' Kappa (pooled pe)", diff.Kappa)
  diff.Kappa <- jack.CI.diff.random.raters(propsReferentVM, propsReferentGM, fleiss.pooled)
  print("Comparing VM and GM")
  printCI("Difference in Fleiss' Kappa (pooled pe)", diff.Kappa)

  diff.Kappa <- jack.CI.diff.random.raters(propsReferentGS, propsReferentGM, fleiss.pooled)
  print("Comparing GS and GM")
  printCI("Difference in Fleiss' Kappa (pooled pe)", diff.Kappa)