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DataAnalysis.R
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# data analysis for Working Memory Sums task
library(plotrix)
# intialize grouping variables arrays
allConSubjAcc = c()
allConMedRT = c()
allConSumTenAcc = c()
allConSumNotTenAcc = c()
allConNumPers = c()
allConRTSumTen = c()
allConRTSumNotTen = c()
allCussSubjAcc = c()
allCussMedRT = c()
allCussSumTenAcc = c()
allCussSumNotTenAcc = c()
allCussNumPers = c()
allCussRTSumTen = c()
allCussRTSumNotTen = c()
###################### con group ######################################
baseFolder = "/Users/rorden/Documents/MATLAB/Working-Memory-Sums-master (4)/data/con"
fileList = list.files(baseFolder, pattern = ".csv", full.names = TRUE)
numberOfFiles = length(fileList)
for (i in 1:numberOfFiles) {
file = fileList[i]
data = read.csv(file)
subjAcc = round(mean(data$accuracy)*100, digits = 2)
sumIsTenTrials = round(mean(data$accuracy[data$trialType == 1])*100,digits=2)
sumNotTenTrials = round(mean(data$accuracy[data$trialType == 0])*100,digits=2)
averageRT = round(mean(data$RT[data$RT<999]), digits =2)
medianRT = round(median(data$RT[data$RT < 999]),digits=2)
numPers = length(data$RT[data$RT<0.1]) # perseverations are RT less than 100ms
RTSumTen = round(median(data$RT[data$trialType == 1 & data$RT < 999]),digits=2)
RTSumNotTen = round(median(data$RT[data$trialType == 0 & data$RT < 999]),digits=2)
allConSubjAcc = append(allConSubjAcc, subjAcc)
allConMedRT = append(allConMedRT, medianRT)
allConSumTenAcc = append(allConSumTenAcc, sumIsTenTrials)
allConSumNotTenAcc = append(allConSumNotTenAcc, sumNotTenTrials)
allConNumPers = append(allConNumPers, numPers)
allConRTSumTen = append(allConRTSumTen, RTSumTen)
allConRTSumNotTen = append(allConRTSumNotTen, RTSumNotTen)
}
dataForCard_zConWM = scale(allConSubjAcc)
###################### cuss group ######################################
baseFolder = "/Users/rorden/Documents/MATLAB/Working-Memory-Sums-master (4)/data/cuss"
fileList = list.files(baseFolder, pattern = ".csv", full.names = TRUE)
numberOfFiles = length(fileList)
for (i in 1:numberOfFiles) {
file = fileList[i]
data = read.csv(file)
subjAcc = round(mean(data$accuracy)*100, digits = 2)
sumIsTenTrials = round(mean(data$accuracy[data$trialType == 1])*100,digits=2)
sumNotTenTrials = round(mean(data$accuracy[data$trialType == 0])*100,digits=2)
averageRT = round(mean(data$RT[data$RT<999]), digits =2)
medianRT = round(median(data$RT[data$RT < 999]),digits=2)
numPers = length(data$RT[data$RT<0.1]) # perseverations are RT less than 100ms
RTSumTen = round(median(data$RT[data$trialType == 1 & data$RT < 999]),digits=2)
RTSumNotTen = round(median(data$RT[data$trialType == 0 & data$RT < 999]),digits=2)
allCussSubjAcc = append(allCussSubjAcc, subjAcc)
allCussMedRT = append(allCussMedRT, medianRT)
allCussSumTenAcc = append(allCussSumTenAcc, sumIsTenTrials)
allCussSumNotTenAcc = append(allCussSumNotTenAcc, sumNotTenTrials)
allCussNumPers = append(allCussNumPers, numPers)
allCussRTSumTen = append(allCussRTSumTen, RTSumTen)
allCussRTSumNotTen = append(allCussRTSumNotTen, RTSumNotTen)
}
dataForCard_zCussWM = scale(allCussSubjAcc)
t = t.test(allConSubjAcc, allCussSubjAcc, var.equal = TRUE)
t
t = t.test(allConMedRT, allCussMedRT, var.equal = TRUE)
t
t = t.test(allConSumTenAcc, allCussSumTenAcc, var.equal = TRUE)
t
t = t.test(allConSumNotTenAcc, allCussSumNotTenAcc, var.equal = TRUE)
t
t = t.test(allConNumPers, allCussNumPers, var.equal = TRUE)
t
t = t.test(allConRTSumTen, allCussRTSumTen, var.equal = TRUE)
t
t = t.test(allConRTSumNotTen, allCussRTSumNotTen, var.equal = TRUE)
t
t = t.test(allConRTSumTen, allConRTSumNotTen, paired = TRUE, var.equal = TRUE)
t
t = t.test(allCussRTSumTen, allCussRTSumNotTen, paired = TRUE, var.equal = TRUE)
t
myVarToPlotA = allConSumTenAcc
myVarToPlotB = allCussSumTenAcc
barCenters = barplot(c(mean(myVarToPlotA), mean(myVarToPlotB)),
main = "Subj Mean Acc Sum 10: Con vs. Cuss",
xlab = "Group",
ylim = c(0, 100))
# segments(barCenters, mean(myVarToPlotA) - std.error(myVarToPlotA) * 2, barCenters,
# mean(myVarToPlotB) + std.error(myVarToPlotB) * 2, lwd = 1.5)
# arrows(barCenters, mean(myVarToPlotA) - std.error(myVarToPlotA) * 2, barCenters,
# mean(myVarToPlotB) + std.error(myVarToPlotB) * 2, lwd = 1.5, angle = 90,
# code = 3, length = 0.05)