dat <- subset (dat, condNum != 4)
dat <- subset (dat, condNum != 5)
#dat <- subset (dat, sentNum == 1)
table(dat$block)
rm(list=ls()) #clear everything
# packages
requiredPackages <- c("dplyr", "ez", "ggplot2", "grid", "reshape2")
isPackageInstalled <- requiredPackages %in% rownames(installed.packages())
if (any(!isPackageInstalled)) {
install.packages(requiredPackages[!isPackageInstalled], repos = "http://cran.us.r-project.org")
}
lapply(requiredPackages, library, character.only = TRUE)
# read data
datDir <- "~/Documents/Work/Hagoort/Negation/Data"
files  <- list.files(datDir, pattern = "^hagoortNegation_preList_\\d+.txt$", full.names = TRUE)
dat    <- do.call(rbind, lapply(files, read.table, header = TRUE))
str(dat)
datDir <- "~/Documents/Work/Hagoort/Negation/Data"
files  <- list.files(datDir, pattern = "^hagoortNegation_preList_\\d+.txt$", full.names = TRUE)
dat    <- do.call(rbind, lapply(files, read.table, header = TRUE))
str(dat)
dat <- subset (dat, condNum != 4)
dat <- subset (dat, condNum != 5)
#dat <- subset (dat, sentNum == 1)
table(dat$block)
rm(list=ls()) #clear everything
# packages
requiredPackages <- c("dplyr", "ez", "ggplot2", "grid", "reshape2")
isPackageInstalled <- requiredPackages %in% rownames(installed.packages())
if (any(!isPackageInstalled)) {
install.packages(requiredPackages[!isPackageInstalled], repos = "http://cran.us.r-project.org")
}
lapply(requiredPackages, library, character.only = TRUE)
# read data
datDir <- "~/Documents/Work/Hagoort/Negation/Data"
files  <- list.files(datDir, pattern = "^hagoortNegation_preList_\\d+.txt$", full.names = TRUE)
dat    <- do.call(rbind, lapply(files, read.table, header = TRUE))
str(dat)
table(dat$block)
table(dat$trial)
table(dat$sentNum, dat$half, dat$vpNum, dat$condNum)
dat <- subset (dat, condNum != 4)
dat <- subset (dat, condNum != 5)
table(dat$sentNum, dat$half, dat$vpNum, dat$condNum)
table(dat$sentNum,dat$vpNum, dat$condNum, dat$affNegNum)
table(dat$vpNum, dat$condNum, dat$affNegNum)
table(dat$sentNum)
table(dat$sentNum, dat$block)
table(dat$sentNum, dat$affNegNum)
table(dat$sentNum, dat$affNegNum, dat$block)
table(dat$sentNum,dat$cond)
table(dat$sentNum, dat$affNegNum)
table(dat$vpNum, dat$condNum, dat$affNegNum)
table(dat$sentNum,dat$vpNum, dat$condNum, dat$affNegNum)
# hagoortNegation
# Sun Oct  4 17:52:44 2015
# R version 3.2.2
rm(list=ls()) #clear everything
# packages
requiredPackages <- c("dplyr", "ez", "ggplot2", "grid", "reshape2")
isPackageInstalled <- requiredPackages %in% rownames(installed.packages())
if (any(!isPackageInstalled)) {
install.packages(requiredPackages[!isPackageInstalled], repos = "http://cran.us.r-project.org")
}
lapply(requiredPackages, library, character.only = TRUE)
# read data
datDir <- "~/Documents/Work/Hagoort/Negation/Data"
files  <- list.files(datDir, pattern = "^hagoortNegation_preList_\\d+.txt$", full.names = TRUE)
dat    <- do.call(rbind, lapply(files, read.table, header = TRUE))
str(dat)
datDir <- "~/Documents/Work/Hagoort/Negation/Data"
files  <- list.files(datDir, pattern = "^hagoortNegation_\\d+.txt$", full.names = TRUE)
dat    <- do.call(rbind, lapply(files, read.table, header = TRUE))
datDir <- "~/Documents/Work/Hagoort/Negation/Data"
files  <- list.files(datDir, pattern = "^hagoortNegation_\\d+.txt$", full.names = TRUE)
dat    <- do.call(rbind, lapply(files, read.table, header = TRUE))
rm(list=ls()) #clear everything
# packages
requiredPackages <- c("dplyr", "ez", "ggplot2", "grid", "reshape2")
isPackageInstalled <- requiredPackages %in% rownames(installed.packages())
if (any(!isPackageInstalled)) {
install.packages(requiredPackages[!isPackageInstalled], repos = "http://cran.us.r-project.org")
}
lapply(requiredPackages, library, character.only = TRUE)
# read data
datDir <- "~/Documents/Work/Hagoort/Negation/Data"
files  <- list.files(datDir, pattern = "^hagoortNegation_\\d+.txt$", full.names = TRUE)
dat    <- do.call(rbind, lapply(files, read.table, header = TRUE))
str(dat)
table(dat$block)
table(dat$trial)
table(dat$itemNum, dat$half)
table(dat$itemNum)
table(dat$sentNum)
View(dat)
View(dat)
View(dat)
View(dat)
table(dat$sentNum)
table(dat$affNegNum, dat$cond)
numRuns <- 10000
numQuestions <- 10
dat <- data.frame(diffGuess1 = numeric(numRuns), diffGuess2 = numeric(numRuns), diffMeanGuess = numeric(numRuns))
for (run in seq(numRuns)){
answer    <- floor(runif(numQuestions, 1, 101))
guess1    <- floor(runif(numQuestions, 1, 101))
guess2    <- floor(runif(numQuestions, 1, 101))
meanGuess <- (guess1+guess2)/2
dat$diffGuess1[run]    <- sum(abs(guess1 - answer))/numQuestions
dat$diffGuess2[run]    <- sum(abs(guess2 - answer))/numQuestions
dat$diffMeanGuess[run] <- sum(abs(meanGuess - answer))/numQuestions
}
colMeans(dat)
boxplot(dat)
numRuns <- 10000
numQuestions <- 10
dat <- data.frame(diffGuess1 = numeric(numRuns), diffGuess2 = numeric(numRuns), diffMeanGuess = numeric(numRuns))
for (run in seq(numRuns)){
answer    <- floor(runif(numQuestions, 1, 101))
guess1    <- floor(runif(numQuestions, 1, 101))
guess2    <- floor(runif(numQuestions, 1, 101))
meanGuess <- (guess1+guess2)/2
dat$diffGuess1[run]    <- sum(abs(guess1 - answer))/numQuestions
dat$diffGuess2[run]    <- sum(abs(guess2 - answer))/numQuestions
dat$diffMeanGuess[run] <- sum(abs(meanGuess - answer))/numQuestions
}
for (run in seq(numRuns)){
answer    <- floor(runif(numQuestions, 1, 101))
guess1    <- floor(runif(numQuestions, 1, 101))
guess2    <- floor(runif(numQuestions, 1, 101))
meanGuess <- (guess1+guess2)/2
dat$diffGuess1[run]    <- sum(abs(guess1 - answer))/numQuestions
dat$diffGuess2[run]    <- sum(abs(guess2 - answer))/numQuestions
dat$diffMeanGuess[run] <- sum(abs(meanGuess - answer))/numQuestions
}
colMeans(dat)
boxplot(dat)
# What's up? Emotion-specific activation of vertical space during language processing
# Experiment 3
# R version 3.2.2
# clear everything
rm(list = ls())
# packages
library("dplyr")
library("ez")
# read combined vp final datafile
dataDir <- "~/Desktop/Emo/Exp3/"
dat     <- read.table(file = paste0(dataDir, "exp3.txt"), header=TRUE, encoding="utf-8")
# create factors
dat$VP        <- factor(dat$VP)
dat$Wort      <- factor(dat$Wort)
dat$Valence   <- factor(dat$Valence)
dat$WordGroup <- factor(dat$WordGroup)
dat$RespDir   <- factor(dat$RespDir)
# code outliers + correct/error trials
dat$isOutlier  <- ifelse(dat$RT_Release < 100 | dat$RT_Release > mean(dat$RT_Release[dat$isError == 0]) + 3*sd(dat$RT_Release[dat$isError == 0]), 1, 0)
dat$isExcluded <- ifelse(dat$isError | dat$isOutlier, 1, 0)
# percentage of removed trials
perOutlier <- (sum(dat$isOutlier)/nrow(dat))*100
perError   <- (sum(dat$isError)/nrow(dat))*100
# aggregate data over trials
datAggVP <- dat %>%
group_by(VP, RespDir, Valence, WordGroup) %>%
summarize(nTotalVP     = n(),
rtVP         = mean(RT_Release[isExcluded == 0]),
nErrorVP     = sum(isError),
nOutlierVP   = sum(isOutlier),
perErrorVP   = (nErrorVP/nTotalVP)*100,
perOutlierVP = (nOutlierVP/nTotalVP)/100)
# aggregate data over trials
datAggItem <- dat %>%
group_by(Wort, RespDir, Valence, WordGroup) %>%
summarize(nTotalItem     = n(),
rtItem         = mean(RT_Release[isExcluded == 0]),
nErrorItem     = sum(isError),
nOutlierItem   = sum(isOutlier),
perErrorItem   = (nErrorItem/nTotalItem)*100,
perOutlierItem = (nOutlierItem/nTotalItem)/100)
# aggregate data over vps
datAgg <- datAggVP %>%
group_by(RespDir, Valence, WordGroup) %>%
summarize(nTotal   = n(),
Rt       = mean(rtVP),
sdRt     = sd(rtVP),
seRt     = sdRt/sqrt(nTotal),
perError = mean(perErrorVP),
sdError  = sd(perErrorVP),
seError  = sdError/sqrt(nTotal))
# ANOVA RT analysis
datAggVP       <- as.data.frame(datAggVP)
aovRT_F1       <- ezANOVA(datAggVP, dv = .(rtVP), wid = .(VP), within = .(RespDir, Valence, WordGroup), return_aov = TRUE)
aovRT_F1$means <- model.tables(aovRT_F1$aov, type = "mean")
aovRT_F1$ANOVA
aovRT_F1$means
datAggItem     <- as.data.frame(datAggItem)
aovRT_F2       <- ezANOVA(datAggItem, dv = .(rtItem), wid = .(Wort), within = .(RespDir), between = .(WordGroup, Valence), return_aov = TRUE)
aovRT_F2$means <- model.tables(aovRT_F2$aov, type = "mean")
aovRT_F2$ANOVA
aovRT_F2$means
# Location words only
datAggVPloc       <- datAggVP[datAggVP$WordGroup == "Location", ]
aovRTloc_F1       <- ezANOVA(datAggVPloc, dv = .(rtVP), wid = .(VP), within = .(RespDir, Valence ), return_aov = TRUE)
aovRTloc_F1$means <- model.tables(aovRTloc_F1$aov, type = "mean")
aovRTloc_F1$ANOVA
aovRTloc_F1$means
datAggItemLoc      <- datAggItem[datAggItem$WordGroup == "Location", ]
datAggItemLoc$Wort <- factor(datAggItemLoc$Wort)
aovRTloc_F2        <- ezANOVA(datAggItemLoc, dv = .(rtItem), wid = .(Wort), within = .(RespDir), between = .(Valence), return_aov = TRUE)
aovRTloc_F2$means  <- model.tables(aovRTloc_F2$aov, type = "mean")
aovRTloc_F2$ANOVA
aovRTloc_F2$means
# Valence words only
datAggVPval       <- datAggVP[datAggVP$WordGroup == "Valence", ]
aovRTval_F1       <- ezANOVA(datAggVPval, dv = .(rtVP), wid = .(VP), within = .(RespDir, Valence ), return_aov = TRUE)
aovRTval_F1$means <- model.tables(aovRTval_F1$aov, type = "mean")
aovRTval_F1$ANOVA
aovRTval_F1$means
datAggItemVal      <- datAggItem[datAggItem$WordGroup == "Valence", ]
datAggItemVal$Wort <- factor(datAggItemVal$Wort)
aovRTval_F2        <- ezANOVA(datAggItemVal, dv = .(rtItem), wid = .(Wort), within = .(RespDir), between = .(Valence), return_aov = TRUE)
aovRTval_F2$means  <- model.tables(aovRTval_F2$aov, type = "mean")
aovRTval_F2$ANOVA
aovRTval_F2$means
# ANOVA Error analysis
aovErr_F1       <- ezANOVA(datAggVP, dv = .(perErrorVP), wid = .(VP), within = .(RespDir, Valence, WordGroup), return_aov = TRUE)
aovErr_F1$means <- model.tables(aovRT_F1$aov, type = "mean")
aovErr_F1$ANOVA
aovErr_F1$means
aovErr_F2       <- ezANOVA(datAggItem, dv = .(perErrorItem), wid = .(Wort), within = .(RespDir), between = .(WordGroup, Valence), return_aov = TRUE)
aovErr_F2$means <- model.tables(aovRT_F2$aov, type = "mean")
aovErr_F2$ANOVA
aovErr_F2$means
aovRTval_F1$ANOVA
aovRTval_F2$ANOVA
# What's up? Emotion-specific activation of vertical space during language processing
# Experiment 4
# R version 3.2.2
# clear everything
rm(list = ls())
# packages
library("dplyr")
library("ez")
# read combined vp final datafile
dataDir <- "~/Desktop/Emo/Exp4/"
dat     <- read.table(file = paste0(dataDir, "exp4.txt"), header=TRUE, encoding="utf-8")
# create factors
dat$VP        <- factor(dat$VP)
dat$Wort      <- factor(dat$Wort)
dat$Valence   <- factor(dat$Valence)
dat$WordGroup <- factor(dat$WordGroup)
dat$RespDir   <- factor(dat$RespDir)
# code outliers + correct/error trials
dat$isOutlier  <- ifelse(dat$RT_Release < 100 | dat$RT_Release > mean(dat$RT_Release[dat$isError == 0]) + 3*sd(dat$RT_Release[dat$isError == 0]), 1, 0)
dat$isExcluded <- ifelse(dat$isError | dat$isOutlier, 1, 0)
# percentage of removed trials
perOutlier <- (sum(dat$isOutlier)/nrow(dat))*100
perError   <- (sum(dat$isError)/nrow(dat))*100
# aggregate data over trials
datAggVP <- dat %>%
group_by(VP, RespDir, Valence, WordGroup) %>%
summarize(nTotalVP     = n(),
rtVP         = mean(RT_Release[isExcluded == 0]),
nErrorVP     = sum(isError),
nOutlierVP   = sum(isOutlier),
perErrorVP   = (nErrorVP/nTotalVP)*100,
perOutlierVP = (nOutlierVP/nTotalVP)/100)
# aggregate data over trials
datAggItem <- dat %>%
group_by(Wort, RespDir, Valence, WordGroup) %>%
summarize(nTotalItem     = n(),
rtItem         = mean(RT_Release[isExcluded == 0]),
nErrorItem     = sum(isError),
nOutlierItem   = sum(isOutlier),
perErrorItem   = (nErrorItem/nTotalItem)*100,
perOutlierItem = (nOutlierItem/nTotalItem)/100)
# aggregate data over vps
datAgg <- datAggVP %>%
group_by(RespDir, Valence, WordGroup) %>%
summarize(nTotal   = n(),
Rt       = mean(rtVP),
sdRt     = sd(rtVP),
seRt     = sdRt/sqrt(nTotal),
perError = mean(perErrorVP),
sdError  = sd(perErrorVP),
seError  = sdError/sqrt(nTotal))
# ANOVA RT analysis
datAggVP       <- as.data.frame(datAggVP)
aovRT_F1       <- ezANOVA(datAggVP, dv = .(rtVP), wid = .(VP), within = .(RespDir, Valence, WordGroup), return_aov = TRUE)
aovRT_F1$means <- model.tables(aovRT_F1$aov, type = "mean")
aovRT_F1$ANOVA
aovRT_F1$means
datAggItem     <- as.data.frame(datAggItem)
aovRT_F2       <- ezANOVA(datAggItem, dv = .(rtItem), wid = .(Wort), within = .(RespDir), between = .(WordGroup, Valence), return_aov = TRUE)
aovRT_F2$means <- model.tables(aovRT_F2$aov, type = "mean")
aovRT_F2$ANOVA
aovRT_F2$means
# Posture words only
datAggVPpos       <- datAggVP[datAggVP$WordGroup == "Posture", ]
aovRTpos_F1       <- ezANOVA(datAggVPpos, dv = .(rtVP), wid = .(VP), within = .(RespDir, Valence ), return_aov = TRUE)
aovRTpos_F1$means <- model.tables(aovRTpos_F1$aov, type = "mean")
aovRTpos_F1$ANOVA
aovRTpos_F1$means
datAggItemPos      <- datAggItem[datAggItem$WordGroup == "Posture", ]
datAggItemPos$Wort <- factor(datAggItemPos$Wort)
aovRTpos_F2        <- ezANOVA(datAggItemPos, dv = .(rtItem), wid = .(Wort), within = .(RespDir), between = .(Valence), return_aov = TRUE)
aovRTpos_F2$means  <- model.tables(aovRTpos_F2$aov, type = "mean")
aovRTpos_F2$ANOVA
aovRTpos_F2$means
# Non-posture words only
datAggVPval       <- datAggVP[datAggVP$WordGroup == "Non-Posture", ]
aovRTpos_F1       <- ezANOVA(datAggVPval, dv = .(rtVP), wid = .(VP), within = .(RespDir, Valence ), return_aov = TRUE)
aovRTpos_F1$means <- model.tables(aovRTpos_F1$aov, type = "mean")
aovRTpos_F1$ANOVA
aovRTpos_F1$means
datAggItemVal      <- datAggItem[datAggItem$WordGroup == "Non-Posture", ]
datAggItemVal$Wort <- factor(datAggItemVal$Wort)
aovRTnpos_F2        <- ezANOVA(datAggItemVal, dv = .(rtItem), wid = .(Wort), within = .(RespDir), between = .(Valence), return_aov = TRUE)
aovRTnpos_F2$means  <- model.tables(aovRTnpos_F2$aov, type = "mean")
aovRTnpos_F2$ANOVA
aovRTnpos_F2$means
# ANOVA Error analysis
aovErr_F1       <- ezANOVA(datAggVP, dv = .(perErrorVP), wid = .(VP), within = .(RespDir, Valence, WordGroup), return_aov = TRUE)
aovErr_F1$means <- model.tables(aovRT_F1$aov, type = "mean")
aovErr_F1$ANOVA
aovErr_F1$means
aovErr_F2       <- ezANOVA(datAggItem, dv = .(perErrorItem), wid = .(Wort), within = .(RespDir), between = .(WordGroup, Valence), return_aov = TRUE)
aovErr_F2$means <- model.tables(aovRT_F2$aov, type = "mean")
aovErr_F2$ANOVA
aovErr_F2$means
# Posture words only
datAggVPpos        <- datAggVP[datAggVP$WordGroup == "Non-Posture", ]
aovErrPos_F1       <- ezANOVA(datAggVPpos, dv = .(rtVP), wid = .(VP), within = .(RespDir, Valence ), return_aov = TRUE)
aovErrPos_F1$means <- model.tables(aovRTpos_F1$aov, type = "mean")
aovErrPos_F1$ANOVA
aovErrPos_F1$means
datAggItemPos      <- datAggItem[datAggItem$WordGroup == "Non-Posture", ]
datAggItemPos$Wort <- factor(datAggItemPos$Wort)
aovRTnpos_F2       <- ezANOVA(datAggItemVal, dv = .(rtItem), wid = .(Wort), within = .(RespDir), between = .(Valence), return_aov = TRUE)
aovRTnpos_F2$means <- model.tables(aovRTnpos_F2$aov, type = "mean")
aovRTnpos_F2$ANOVA
aovRTnpos_F2$means
# Non-posture words only
datAggVPpos        <- datAggVP[datAggVP$WordGroup == "Non-Posture", ]
aovErrPos_F1       <- ezANOVA(datAggVPpos, dv = .(rtVP), wid = .(VP), within = .(RespDir, Valence ), return_aov = TRUE)
aovErrPos_F1$means <- model.tables(aovRTpos_F1$aov, type = "mean")
aovErrPos_F1$ANOVA
aovErrPos_F1$means
datAggItemPos      <- datAggItem[datAggItem$WordGroup == "Non-Posture", ]
datAggItemPos$Wort <- factor(datAggItemPos$Wort)
aovRTnpos_F2        <- ezANOVA(datAggItemPos, dv = .(rtItem), wid = .(Wort), within = .(RespDir), between = .(Valence), return_aov = TRUE)
aovRTnpos_F2$means  <- model.tables(aovRTnpos_F2$aov, type = "mean")
aovRTnpos_F2$ANOVA
aovRTnpos_F2$means
aovRTpos_F1$ANOVA
aovRT_F1$ANOVA
aovErr_F1$ANOVA
aovErr_F2$ANOVA
# "R version 3.2.4 (2016-03-10)"
# clear everything
rm(list = ls())
# packages
# install.packages("dplyr")
# install.packages("ez")
# install.packages("cowplot")
# install.packages("ggplot2")
# load packages
library("dplyr")
library("ez")
library("cowplot")
library("ggplot2")
# data dir
setwd("/Users/Carolin/Documents/Work/MS/5keys/JEP_LML_Data/exp1")
dat <- read.table("exp1data.txt", header=TRUE)
# remove practise trials
dat <- dat[dat$practice != 1,]
# "R version 3.2.4 (2016-03-10)"
# clear everything
rm(list = ls())
# packages
# install.packages("dplyr")
# install.packages("ez")
# install.packages("cowplot")
# install.packages("ggplot2")
# load packages
library("dplyr")
library("ez")
library("cowplot")
library("ggplot2")
# data dir
setwd("/Users/Carolin/Documents/Work/MS/5keys/JEP_LMC_Data/exp1")
dat <- read.table("exp1data.txt", header=TRUE)
# remove practise trials
dat <- dat[dat$practice != 1,]
# data structure
str(dat)
# change vp/word number to factor
dat$vpNum      <- factor(dat$vpNum)
dat$wordNumber <- factor(dat$wordNumber)
# vp info
table(dat$vpGender[!duplicated(dat$vpNum)])
mean(dat$vpAge[!duplicated(dat$vpNum)])
sd(dat$vpAge[!duplicated(dat$vpNum)])
table(dat$vpHandedness[!duplicated(dat$vpNum)])
hist(dat$respTime)
hist(dat$resleaseTime)
hist(dat$releaseTime)
hist(dat$respTime)
# "R version 3.2.4 (2016-03-10)"
# clear everything
rm(list = ls())
# packages
# install.packages("dplyr")
# install.packages("ez")
# install.packages("cowplot")
# install.packages("ggplot2")
# load packages
library("dplyr")
library("ez")
library("cowplot")
library("ggplot2")
# data dir
setwd("/Users/Carolin/Documents/Work/MS/5keys/JEP_LMC_Data/exp2")
dat <- read.table("exp2data.txt", header=TRUE)
# remove practise trials
dat <- dat[dat$practice != 1,]
# data structure
str(dat)
# change vp/word number to factor
dat$vpNum      <- factor(dat$vpNum)
dat$wordNumber <- factor(dat$wordNumber)
# vp info
table(dat$vpGender[!duplicated(dat$vpNum)])
mean(dat$vpAge[!duplicated(dat$vpNum)])
sd(dat$vpAge[!duplicated(dat$vpNum)])
table(dat$vpHandedness[!duplicated(dat$vpNum)])
hist(dat$releaseTime)
hist(dat$respTime)
# "R version 3.2.4 (2016-03-10)"
# clear everything
rm(list = ls())
# packages
# install.packages("dplyr")
# install.packages("ez")
# install.packages("cowplot")
# install.packages("ggplot2")
# load packages
library("dplyr")
library("ez")
library("cowplot")
library("ggplot2")
# data dir
setwd("/Users/Carolin/Documents/Work/MS/5keys/JEP_LMC_Data/exp3")
dat <- read.table("exp3data.txt", header=TRUE)
# remove practise trials
dat <- dat[dat$practice != 1,]
# data structure
str(dat)
# change vp/word number to factor
dat$vpNum      <- factor(dat$vpNum)
dat$wordNumber <- factor(dat$wordNumber)
# vp info
table(dat$gender[!duplicated(dat$vpNum)])
mean(dat$age[!duplicated(dat$vpNum)])
sd(dat$age[!duplicated(dat$vpNum)])
table(dat$handedness[!duplicated(dat$vpNum)])
# choose dependent variable
# dat$dv <- (dat$respPosExitTime*1000)  # time VP leaves centre start region (response initiation)
# dat$dv <- (dat$respPosEnterTime*1000) # time VP enters 1 of 4 response regions
dat$dv <- (dat$respPressTime*1000)      # time VP presses mouse button within response region
hist(dat$dv)
dat$dv <- dat$dv - 100
hist(dat$dv)
# "R version 3.2.4 (2016-03-10)"
# clear everything
rm(list = ls())
# packages
# install.packages("dplyr")
# install.packages("ez")
# install.packages("cowplot")
# install.packages("ggplot2")
# load packages
library("dplyr")
library("ez")
library("cowplot")
library("ggplot2")
# data dir
setwd("/Users/Carolin/Documents/Work/MS/5keys/JEP_LMC_Data/exp4")
dat <- read.table("exp4data.txt", header=TRUE)
# remove practise trials and filler trials
dat <- dat[dat$practice != 1,]
dat <- dat[dat$wordNumber <= 80, ]
# data structure
str(dat)
# change vp/word number to factor
dat$vpNum      <- factor(dat$vpNum)
dat$wordNumber <- factor(dat$wordNumber)
# remove VPs > 20% error
dat <- dat[dat$vpNum != 32, ]
dat <- dat[dat$vpNum != 33, ]
# vp info
table(dat$gender[!duplicated(dat$vpNum)])
mean(dat$age[!duplicated(dat$vpNum)])
sd(dat$age[!duplicated(dat$vpNum)])
table(dat$handedness[!duplicated(dat$vpNum)])
# dependent variable
# dat$dv <- dat$respPosExitTime         # time VP leaves centre start region (response initiation)
# dat$dv <- (dat$respPosEnterTime*1000) # time VP enters 1 of 4 response regions
dat$dv <- (dat$respPressTime*1000)      # time VP presses left mouse button within response region
# reaction time is to colour onset so need to subtract 100ms
dat$dv <- dat$dv - 100
hist(dat$dv)
hist(dat$dv, 100)
