#=====================================================#
# L-EFT Co 2015 Task Analysis: Accuracy Data          #
# CO November 2015 - March 2016 L-EFT 2.0             #
# RELATIONSHIP LEFT 2.0 & GEFT & EF's                 #
# DATE: 09/10/2017 - Hanne Huygelier                  #
#=====================================================#

#==================
#     LIBRARIES
#==================
library(ggplot2)
library(psych)
library(gridExtra)
library(tidyr)
library(lme4)
library(lmerTest)

#==================
#     FOLDERS
#==================
DataFolder = "C://Users//u0097836//Documents//EFT_Project//Data Manuscript 2//Study 3//Summaries//"
setwd(DataFolder)

#==================
#       DATA
#==================
load(paste(DataFolder, "CORSI.Rdata", sep = ""))
load(paste(DataFolder, "EFTs_CO2.Rdata", sep = ""))
load(paste(DataFolder, "RAVEN.Rdata", sep = ""))

# CHANGE COLUMN NAMES:
colnames(EFT)[1] <- "participant"
colnames(SumScore)[2:3] <- c("RavenScore", "RavenRT")

# Raven --> proportion correct:
SumScore$RavenScore <- SumScore$RavenScore / 12

# MAKE LONG DATAFORMAT:
EFTLong <- gather(data = EFT, key = EFTVersion, value = Accuracy, GroupEFT:LEFT, factor_key = TRUE)

# MERGE DATAFRAMES WITH WIDE FORMAT:
Summary <- merge(Span, EFT, by = "participant")
Summary <- merge(Summary, SumScore, by = "participant")

# MERGE DATAFRAMES WITH LONG FORMAT:
Summary2 <- merge(Span, EFTLong, by = "participant")
Summary2 <- merge(Summary2, SumScore, by = "participant")

# CLEAN UP:
remove(Span, SumScore, EFT)

#=====================
# PLOT DATA
#=====================
# GGPLOT SETTINGS:
theme_set(theme_classic(base_size = 35))

# INTERACTION OF EFT's and WORKING MEMORY:
pl <- ggplot(data = Summary2, aes(x = PCL, y = Accuracy, col = EFTVersion))
pl <- pl + geom_line(aes(group = participant), col = "black", linetype = "dashed", alpha = 0.25)
pl <- pl + geom_point(size = 2)
pl <- pl + geom_smooth(se = T, method = "lm", formula = y ~ x)
pl <- pl + theme(legend.position = "bottom")
pl <- pl + scale_y_continuous(breaks = seq(0, 1, 0.25), limits = c(0, 1))
pl <- pl + scale_x_continuous(breaks = seq(3, 7, 1), limits = c(3, 7))
pl <- pl + labs(x = "Memory span", colour = "EFT Version: ")
pl <- pl + scale_color_manual(values = c("#3399ff", "#00cc00"), 
                              breaks = c("GroupEFT", "LEFT"), 
                              labels = c("G-EFT", "L-EFT"))

pl

# INTERACTION OF EFT's and IQ:
pl1 <- ggplot(data = Summary2, aes(x = RavenScore, y = Accuracy, col = EFTVersion))
pl1 <- pl1 + geom_line(aes(group = participant), col = "black", linetype = "dashed", alpha = 0.25)
pl1 <- pl1 + geom_point(size = 2)
pl1 <- pl1 + geom_smooth(se = T, method = "lm", formula = y ~ x)
pl1 <- pl1 + theme(legend.position = "bottom")
pl1 <- pl1 + scale_y_continuous(breaks = seq(0, 1, 0.25), limits = c(0, 1))
pl1 <- pl1 + scale_x_continuous(breaks = seq(0, 1, 0.25), limits = c(0, 1))
pl1 <- pl1 + labs(x = "RAPM Accuracy", colour = "EFT Version: ")
pl1 <- pl1 + scale_color_manual(values = c("#3399ff", "#00cc00"), 
                                breaks = c("GroupEFT", "LEFT"), 
                                labels = c("G-EFT", "L-EFT"))
pl1

grid.arrange(pl, pl1, ncol = 2)
Plot <- arrangeGrob(pl, pl1, ncol = 2)

ggsave(Plot, file = "C:/Users/u0097836/Documents/EFT_Project/Write_Ups/LEFT_Paper2/Manuscript_TBD/Study3.png",
       dpi = 600, width = 16, height = 8)

#=====================
# REGRESSION ANALYSIS
#=====================
M1 <- lmer(Accuracy ~ PCL + RavenScore + EFTVersion * PCL + RavenScore * EFTVersion + (1|participant), data = Summary2)
summary(M1)