####################################################################################
#
# times
#
####################################################################################

library(plyr)
#source("CI-Functions.R")
source("CI-Functions-Bonferroni.R")

defaultpath <- dirname(rstudioapi::getActiveDocumentContext()$path)
setwd(defaultpath)
path <- 'plots/2_all_tasks/raw/' 

# Select phase to analyze
phase <- '_main'
#phase <- '_training'

file_data <- paste0("aggregated_data", phase,".csv")

if (exists ("mydata")) { rm(mydata) }
mydata <- read.table(file_data, header=T, sep=",")


#############################
# analysis of all questions #
#############################

# order for the transpose
elements <- mydata
elements <- elements [ order(elements$user, elements$vis), ]
#
myvars <- c("user", "vis", "mean_time")
elements <- elements [myvars]
#aggregating all cases per participant
statstable_time <- ddply(elements,
                    c("user","vis"),
                    summarise,
                    time=mean(mean_time)
)
elements <- statstable_time

#
elements <- reshape(elements, timevar="vis", idvar=c("user"), direction="wide")
colnames(elements) <- gsub("time.", "", colnames(elements))


# drop columns with N/A
elements <- na.omit(elements)


#########
# stats #
#########

data <- elements

techniqueA <- bootstrapMeanCI(data$storylines)
techniqueB <- bootstrapMeanCI(data$paohvis)

#
# Calculating means per condition
#
analysisData <- c()
analysisData$name <- c("PAOH","HSL")
analysisData$pointEstimate <- c(techniqueB[1], techniqueA[1])
analysisData$ci.max <- c(techniqueB[3], techniqueA[3])
analysisData$ci.min <- c(techniqueB[2], techniqueA[2])

datatoprint <- data.frame(factor(analysisData$name),analysisData$pointEstimate, analysisData$ci.min, analysisData$ci.max)
colnames(datatoprint) <- c("vis", "mean_time", "lowerBound_CI", "upperBound_CI ") #We use the name mean_time for the value of the mean even though it's not a time, it's just to parse the data for the plot

filename = paste0("time_means_task_all", phase)

write.table(datatoprint, paste0(path,"printed_",filename,".txt",seq=""), sep=",",row.names=FALSE)

#
# Calculating differences of means 
#

# CIs with adapted alpha value for multiple comparisons not needed here
diffBA = bootstrapMeanCI_corr(data$paohvis - data$storylines, 1)

analysisData <- c()
analysisData$name <- c("PAOH-HSL") 
analysisData$pointEstimate <- c(diffBA[1])
analysisData$ci.max <- c(diffBA[3])
analysisData$ci.min <- c(diffBA[2])
analysisData$level <- c(diffBA[4])
analysisData$ci_corr.max <- c(diffBA[6])
analysisData$ci_corr.min <- c(diffBA[5])

datatoprint <- data.frame(factor(analysisData$name), analysisData$pointEstimate, analysisData$ci.max, analysisData$ci.min, analysisData$level, analysisData$ci_corr.max, analysisData$ci_corr.min)
colnames(datatoprint) <- c("technique", "mean_time", "lowerBound_CI", "upperBound_CI", "corrected_CI", "lowerBound_CI_corr", "upperBound_CI_corr") #We use the name mean_time for the value of the mean even though it's not a time, it's just to parse the data for the plot

filename = paste0("time_diffs_task_all", phase)

write.table(datatoprint, paste0(path,"printed_",filename,".txt",seq=""), sep=",",row.names=FALSE)