data-raw/convert_excel_to_rda.R
In gitrman/itns: Datasets from the book Introduction to the New Statistics
# Convert Excel data to .rda files
options(java.parameters = "-Xmx4096m") # to avoid memory errors when loading Excel worksheets
library(XLConnect)
# ESCI Ch3 - 16 datasets -----------------------------------------------------------------------------------------------
# PenLaptop studies
PenLap <- function(region1, region2, wb = 'data-raw/ESCI intro chapters 3-8 beta Sep 26 2015.xlsm', sheet = "Data two"){
Pen <- readWorksheet(loadWorkbook(wb), sheet = sheet, region = region1)
Laptop <- readWorksheet(loadWorkbook(wb), sheet = sheet, region = region2)
df <- data.frame(group = factor(rep(c("Pen", "Laptop"), times = c(nrow(Pen), nrow(Laptop)))),
transcription = unlist(c(Pen, Laptop))
)
rownames(df) <- NULL
df
}
pen_laptop1 <- PenLap('BJ8:BJ42', 'BM8:BM39')
pen_laptop2 <- PenLap('BR8:BR56', 'BU8:BU111')
# pen_laptop3 <- PenLap('BZ8:BZ62', 'CC8:CC63')
# devtools::use_data(pen_laptop1, pen_laptop2, pen_laptop3, overwrite = TRUE)
devtools::use_data(pen_laptop1, pen_laptop2, overwrite = TRUE)
# Thomason (Paired Groups, Correlation Chapters)
thomason1 <- readWorksheet(loadWorkbook('data-raw/ESCI intro chapters 3-8 beta Sep 26 2015.xlsm'), sheet = "Data paired", region = 'BH7:BI19')
thomason2 <- readWorksheet(loadWorkbook('data-raw/ESCI intro chapters 3-8 beta Sep 26 2015.xlsm'), sheet = "Data paired", region = 'BM7:BN23')
thomason3 <- readWorksheet(loadWorkbook('data-raw/ESCI intro chapters 3-8 beta Sep 26 2015.xlsm'), sheet = "Data paired", region = 'BR7:BS46')
cn <- c("pre", "post")
names(thomason1) <-(cn)
names(thomason2) <-(cn)
names(thomason3) <-(cn)
devtools::use_data(thomason1, thomason2, thomason3, overwrite = TRUE)
# Body Wellness Data (Correlation chapter)
female <- readWorksheet(loadWorkbook('data-raw/ESCI intro chapters 10-16 beta Jan 9 2016.xlsm'), sheet = "Scatterplots", region = 'BV6:BW65')
male <- readWorksheet(loadWorkbook('data-raw/ESCI intro chapters 10-16 beta Jan 9 2016.xlsm'), sheet = "Scatterplots", region = 'CA6:CB53')
body_well <- data.frame(
sex = c(rep("female", nrow(female)), rep("male", nrow(male))),
rbind(female, male)
)
colnames(body_well)[2:3] <- c("bodysat", "wellbeing")
devtools::use_data(body_well, overwrite = TRUE)
# Robust two-group datsets (Ch 16)
## NATSAL
rm(female, male)
female <- readWorksheet(loadWorkbook('data-raw/ESCI intro chapters 10-16 beta Jan 9 2016.xlsm'), sheet = "Robust two", region = 'BN9:BN52', header = F)
male <- readWorksheet(loadWorkbook('data-raw/ESCI intro chapters 10-16 beta Jan 9 2016.xlsm'), sheet = "Robust two", region = 'BK9:BK59', header = F)
natsal <- data.frame(
sex = c(rep("female", nrow(female)), rep("male", nrow(male))),
rbind(female, male)
)
colnames(natsal)[2] <- "partners"
## Dana
g1 <- readWorksheet(loadWorkbook('data-raw/ESCI intro chapters 10-16 beta Jan 9 2016.xlsm'), sheet = "Robust two", region = 'BS9:BS27', header = F)
g2 <- readWorksheet(loadWorkbook('data-raw/ESCI intro chapters 10-16 beta Jan 9 2016.xlsm'), sheet = "Robust two", region = 'BV9:BV27', header = F)
dana <- data.frame(
group = c(rep("group one", nrow(g1)), rep("group two", nrow(g2))),
rbind(g1, g2)
)
colnames(dana)[2] <- "contact"
devtools::use_data(dana, overwrite = TRUE)
# Exercise Data ------------------------------------------------------------------------------------------------------
rm(list = ls()) # delete all objects in the global environment
## Ch 3 - Descriptives
# College Survey 1
x <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch03-Excercises data - descriptives-gdc.xlsx'), sheet = "college_survey_1", region = 'A1:W244')
x <- x [, -c(3, 6, 8)] # drop variables that are not needed as they duplicate other variables
j <- which(sapply(x, class)=="character")
x[, j] <- plyr::colwise(as.factor)(x[ ,j])
colnames(x)[4] <- "School_Year" # fix typo in name
x$School_Year <- factor(x$School_Year, levels = c("First-year", "Sophomore", "Junior", "Senior", "Post-bac"), ordered = TRUE)
x$Student_Athlete_Code <- factor(x$Student_Athlete_Code, labels = c("No", "Yes"))
x$Raven_Score <- x$Raven_Score * 100
colnames(x) <- stringr::str_to_lower(names(x)) # convert all column names to lowercase
college_survey1 <-x
devtools::use_data(college_survey1, overwrite = TRUE)
# Religious Belief
rm(x,j)
x <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch03-Excercises data - descriptives-gdc.xlsx'), sheet = "religious_belief", region = 'A1:D201')
y <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch03-Excercises data - descriptives-gdc.xlsx'), sheet = "religious_belief", region = 'F1:I214')
z <- rbind(x, y)
z$Condition <- factor(z$Condition)
colnames(z) <- stringr::str_to_lower(names(z)) # convert all column names to lowercase
religious_belief <- z
devtools::use_data(religious_belief, overwrite = TRUE)
# College Survey 2
rm(X, y, z)
x <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch03-Excercises data - descriptives-gdc.xlsx'), sheet = "college_survey_2", region = 'A1:Q139')
x <- x [, -c(3, 7, 9)] # drop variables that are not needed as they duplicate other variables
j <- which(sapply(x, class)=="character")
x[, j] <- plyr::colwise(as.factor)(x[ ,j])
x$School_Year <- factor(x$School_Year, levels = c("First-year", "Sophomore", "Junior", "Senior", "Post-bac"), ordered = TRUE)
x$Emotion_Recognition <- x$Emotion_Recognition * 100
colnames(x) <- stringr::str_to_lower(names(x)) # convert all column names to lowercase
college_survey2 <-x
devtools::use_data(college_survey2, overwrite = TRUE)
## Ch5
# College data files are the same as for Ch3, but sorted differently
## Ch7 - Two Groups Data
# Anchor Estimate
rm(x, j)
x <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch07-Excercises data - two groups-gdcsmall.xlsx'), sheet = "anchor_estimate", region = 'A1:H91')
j <- which(sapply(x, class)=="character")
x[, j] <- plyr::colwise(as.factor)(x[ ,j])
colnames(x) <- stringr::str_to_lower(names(x)) # convert all column names to lowercase
anchor_estimate <- x
devtools::use_data(anchor_estimate, overwrite = TRUE)
# Clean Moral
# Schnall 2014
rm(x)
x <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch07-Excercises data - two groups-gdcsmall.xlsx'), sheet = "clean_moral", region = 'A2:C42')
x$Condition <- factor(x$Condition)
colnames(x) <- stringr::str_to_lower(names(x)) # convert all column names to lowercase
# Johnson 2014
y <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch07-Excercises data - two groups-gdcsmall.xlsx'), sheet = "clean_moral", region = 'E2:G210')
y$Condition <- factor(y$Condition)
colnames(y) <- stringr::str_to_lower(names(y)) # convert all column names to lowercase
# Convert to .rda
clean_moral_schall <- x
clean_moral_johnson <- y
devtools::use_data(clean_moral_schall, clean_moral_johnson, overwrite = TRUE)
# math gender IAT
rm(x, y)
x <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch07-Excercises data - two groups-gdcsmall.xlsx'), sheet = "math_gender_IAT", region = 'A1:D89')
y <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch07-Excercises data - two groups-gdcsmall.xlsx'), sheet = "math_gender_IAT", region = 'F1:I156')
z <- rbind(x, y)
z$Location <- factor(z$Location)
z$Gender <- factor(z$Gender)
colnames(z) <- stringr::str_to_lower(names(z)) # convert all column names to lowercase
math_gender_iat <- z
devtools::use_data(math_gender_iat, overwrite = TRUE)
# Superstition Golf
rm(x, y, z)
x <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch07-Excercises data - two groups-gdcsmall.xlsx'), sheet = "superstition_golf Ex 6 7", region = 'A1:G112')
x[, 1:3] <- plyr::colwise(as.factor)(x[ , 1:3])
colnames(x) <- stringr::str_to_lower(names(x)) # convert all column names to lowercase
super_golf <- x
devtools::use_data(super_golf, overwrite = TRUE)
# Flag Priming
rm(x)
x <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch07-Excercises data - two groups-gdcsmall.xlsx'), sheet = "flag_priming", region = 'A1:E91')
x[, 2:4] <- plyr::colwise(factor)(x[ , 2:4])
colnames(x) <- stringr::str_to_lower(names(x)) # convert all column names to lowercase
flag_priming <- x
devtools::use_data(flag_priming, overwrite = TRUE)
# Ch 8 - Paired data -----------------------------------------------------------------------------
rm(list=ls(all=TRUE))
# Emotion Heartrate
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch08-Excercises data - paired-gdc.xlsx'), sheet = "emotion_heartrate", region = 'A1:E69')
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
df$first_emotion <- factor(df$first_emotion)
emotion_heartrate <- df
devtools::use_data(emotion_heartrate, overwrite = TRUE)
rm(df)
# Labels Flavor
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch08-Excercises data - paired-gdc.xlsx'), sheet = "labels_flavor", region = 'A1:F52')
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
df$suspicous <- factor(df$suspicous, levels = 0:1, labels = c("suspicious", "not suspicious"))
names(df)[6] <- "suspicious"
names(df)[1] <- "participant_id"
labels_flavor <- df
devtools::use_data(labels_flavor, overwrite = TRUE)
rm(df)
# Sensitization
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch08-Excercises data - paired-gdc.xlsx'), sheet = "sensitization Ex 5", region = 'A1:F13')
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
names(df)[1] <- "animal_id"
names(df)[3:6] <- c("trained_base", "trained_24h", "untrained_base", "untrained_24h") # rename as R colnames can't start with numbers
df$trained_side <- factor(df$trained_side)
sensitization <- df
devtools::use_data(sensitization, overwrite = TRUE)
rm(df)
# Learning Genes
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch08-Excercises data - paired-gdc.xlsx'), sheet = "learning_genes Ex 6", region = 'A1:G13')
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
df$tissue <- factor(df$tissue)
df$trained_side <- factor(df$trained_side)
learning_genes <- df
devtools::use_data(learning_genes, overwrite = TRUE)
rm(df)
# Habituation
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch08-Excercises data - paired-gdc.xlsx'), sheet = "habituation", region = 'A1:D15')
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
df$condition <- factor(df$condition)
habituation <- df
devtools::use_data(habituation, overwrite = TRUE)
rm(df)
# Ch 9 - Meta Analysis -----------------------------------------------------------------------------
# Anchor Adjust Meta-Analysis
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch09-Excercises data - meta-analysis-gdc.xlsx'), sheet = "anchor_adjust_meta-analysis", region = 'A1:I31')
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
df$location <- factor(df$location)
df$subset <- factor(df$subset)
df$country <- factor(df$country)
anchor_estimate_ma <- df
devtools::use_data(anchor_estimate_ma, overwrite = TRUE)
rm(df)
# Flag Priming
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch09-Excercises data - meta-analysis-gdc.xlsx'), sheet = "flag_priming_meta-analysis", region = 'A1:G26')
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
df$location <- factor(df$location)
flag_priming_ma <- df
devtools::use_data(flag_priming_ma, overwrite = TRUE)
rm(df)
# Math Gender IAT
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch09-Excercises data - meta-analysis-gdc.xlsx'), sheet = "math_gender_IAT_meta-analysis", region = 'A1:I31')
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
df$location <- factor(df$location)
df$subset <- factor(df$subset)
df$country <- factor(df$country)
math_gender_iat_ma <- df
devtools::use_data(math_gender_iat_ma, overwrite = TRUE)
rm(df)
# Power Performance
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch09-Excercises data - meta-analysis-gdc.xlsx'), sheet = "power_performance_meta-analysis", region = 'A1:O9')
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
df[, 1:7] <- plyr::colwise(as.factor)(df [, 1:7])
colnames(df)[13] <- "cohensd"
df <- df [, -12] # cull empty column
power_performance_ma <- df
devtools::use_data(power_performance_ma, overwrite = TRUE)
rm(df)
# Gambler Fallacy
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch09-Excercises data - meta-analysis-gdc.xlsx'), sheet = "gambler_fallacy_MA", region = 'A1:L13')
df$Study <- factor(df$Study)
df$Participant_Type <- factor(df$Participant_Type)
names(df) <- stringr::str_to_lower(names(df))
df <- df [, -6] # cull empty column
ma_gambler_fallacy <- df
devtools::use_data(ma_gambler_fallacy, overwrite = TRUE)
rm(df)
# Anchor Adjust Chicago
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch09-Excercises data - meta-analysis-gdc.xlsx'), sheet = "anchor_adjust_chicago_MA", region = 'A1:G37')
df$Location <- factor(df$Location)
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
ma_anchor_adjust_chicago <- df
devtools::use_data(ma_anchor_adjust_chicago, overwrite = TRUE)
rm(df)
# Anchor Adjust Everest
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch09-Excercises data - meta-analysis-gdc.xlsx'), sheet = "anchor_adjust_everest_MA", region = 'A1:G37')
df$Location <- factor(df$Location)
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
ma_anchor_adjust_everest <- df
devtools::use_data(ma_anchor_adjust_everest, overwrite = TRUE)
rm(df)
# Ch 11 - Correlation -----------------------------------------------------------------------------
# Exam Scores
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch11-Excercises data - correlation-gdc.xlsx'), sheet = "exam_scores", region = 'A1:C10')
colnames(df) <- c("id", "exam1", "exam_final")
exam_scores <- df
devtools::use_data(exam_scores, overwrite = TRUE)
rm(df)
# Sleep Beauty
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch11-Excercises data - correlation-gdc.xlsx'), sheet = "sleep_beauty", region = 'A1:D71')
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
sleep_beauty <- df[, 1:2] # only retain column 1 and 2
names(sleep_beauty)[1] <- "nightly_sleep_hours"
devtools::use_data(sleep_beauty, overwrite = TRUE)
rm(df)
# Campus Involvement
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch11-Excercises data - correlation-gdc.xlsx'), sheet = "campus_involvement", region = 'A1:F114')
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
df$gender <- factor(df$gender)
df$commuter <- factor(df$commuter, levels = 0:1, labels = c("resident", "commuter"))
campus_involvement <- df
devtools::use_data(campus_involvement, overwrite = TRUE)
rm(df)
# ITNS Ch11 Ex 7
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch11-Excercises data - correlation-gdc.xlsx'), sheet = "ITNS-Ch11-Ex7", region = 'A1:E146')
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
df$condition <- factor(df$condition)
colnames(df)[5] <- "temperature_rating"
ch11_ex7 <- df
devtools::use_data(ch11_ex7, overwrite = TRUE)
rm(df)
# Ch 12 - Regression -----------------------------------------------------------------------------
# Home Prices
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch12-Excercises data - regression-gdc.xlsx'), sheet = "home_prices", region = 'A1:G301')
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
names(df)[6] <- "size"
df$location <- factor(df$location)
df$status <- factor(df$status)
home_prices <- df
devtools::use_data(home_prices, overwrite = TRUE)
rm(df)
# Altruism Happiness
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch12-Excercises data - regression-gdc.xlsx'), sheet = "altruism_happiness", region = 'A1:H51')
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
df[, 1:2] <- plyr::colwise(as.factor)(df[, 1:2])
names(df)[6:8] <- c("well_being_2010_rounded", "kidney_rate_per1000", "wb_change")
df <- df[, -2] # drop abbreviation as not needed
altruism_happiness <- df
devtools::use_data(altruism_happiness, overwrite = TRUE)
rm(df)
# Ch 12 Ex 3
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch12-Excercises data - regression-gdc.xlsx'), sheet = "ITNS-Ch12-Ex3", region = 'A1:B211')
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
names(df)[2] <- "motor_skill"
ch12_ex3 <- df
devtools::use_data(ch12_ex3, overwrite = TRUE)
rm(df)
# Ch12 Ex4
# Exclude for now, unsure if this need to be included
# Home Prices Holdout
# Only include the actual data, not the regression workings
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch12-Excercises data - regression-gdc.xlsx'), sheet = "home_prices_holdout", region = 'A1:F11')
df <- df[, c(1, 2, 6)]
names(df) <- c("new_case", "size", "asking_price")
home_prices_holdout <- df
devtools::use_data(home_prices_holdout, overwrite = TRUE)
rm(df)
# Ch 13 Exercises - Extended Designs 1 -----------------------------------------------------------------------------
# Exclude summary statistics for now, use only raw data
# Organic Moral Replication
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch13-Exercises data - extended one.xlsx'), sheet = "organic_moral", region = 'A10:D48')
x <- na.omit(df[,1])
y <- na.omit(df[,2])
z <- na.omit(df[,3])
organic_moral <- data.frame(
food = factor(rep(c("organic", "control", "comfort"), c(length(x), length(y), length(z)))),
judge = c(x, y, z)
)
devtools::use_data(organic_moral, overwrite = TRUE)
rm(df, x, y, z)
# Inauthentic
x <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch13-Exercises data - extended one.xlsx'), sheet = "inauthentic", region = 'A2:D221')
y <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch13-Exercises data - extended one.xlsx'), sheet = "inauthentic", region = 'F2:I221')
z <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch13-Exercises data - extended one.xlsx'), sheet = "inauthentic", region = 'K2:N221')
# IV, Cleaning, Alienation, Neutral
i <- 1:219
j <- 1
g1 <- data.frame(group = as.character("auth_notruth"), cleaning = x[i, j], neutral = y[i, j], alienation = z[i, j])
i <- 1:214
j <- 2
g2 <- data.frame(group = as.character("auth_gen"), cleaning = x[i, j], neutral = y[i, j], alienation = z[i, j])
i <- 1:199
j <- 3
g3 <- data.frame(group = as.character("inauth_notruth"), cleaning = x[i, j], neutral = y[i, j], alienation = z[i, j])
i <- 1:206
j <- 4
g4 <- data.frame(group = as.character("inauth_gen"), cleaning = x[i, j], neutral = y[i, j], alienation = z[i, j])
inauthentic <- rbind(g1, g2, g3, g4)
devtools::use_data(inauthentic, overwrite = TRUE)
rm(x, y, z, g1, g2, g3, g4, i, j)
# IQ Booster
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch13-Exercises data - extended one.xlsx'), sheet = "IQBoosters", region = 'A1:F11')
df <- reshape2:: melt(df, measure.vars = 1:6, variable.name = "drug", value.name = "iq")
levels(df$drug) <- c("Placebo", "DrugA", "DrugB", "DrugC", "DrugD", "DrugE")
iq_boost <- df
devtools::use_data(iq_boost, overwrite = TRUE)
rm(df)
# Ch 14 Exercises - Extended Designs 2 -----------------------------------------------------------------------------
# Videogame aggression
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch14-Exercises data - extended two IVs.xlsx'), sheet = "videogame_aggression", region = 'A1:D57')
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
library(tidyr)
library(dplyr)
df <- gather(df, key = "group", value = "aggression", 1:4, factor_key = TRUE)
df <- separate(df, group, into = c("violence", "difficulty"))
df$violence <- factor(df$violence)
df$difficulty <- factor(df$difficulty)
videogame_aggression <- df
devtools::use_data(videogame_aggression, overwrite = TRUE)
rm(df)
# Self-Explain Time
x <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch14-Exercises data - extended two IVs.xlsx'), sheet = "self-explain_time", region = 'A1:F18')
y <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch14-Exercises data - extended two IVs.xlsx'), sheet = "self-explain_time", region = 'H1:M22')
z <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch14-Exercises data - extended two IVs.xlsx'), sheet = "self-explain_time", region = 'O1:T32')
df <- rbind(x, z, y)
df <- df [, -6]
df <- gather(df, key = "time", value = "knowledge", 4:5, factor_key = TRUE)
levels(df$time) <- c("pre", "post")
cn <- c("id", "condition", "grade", "time", "knowledge")
names(df) <- cn
df[df$condition == "Add'l Practice", "condition"] <- "Additional Practice"
df$condition <- factor(df$condition)
self_explain_time <- df
devtools::use_data(self_explain_time, overwrite = TRUE)
rm(x, y, z, df)
# Blame1
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch14-Exercises data - extended two IVs.xlsx'), sheet = "blame1", region = 'A1:D106')
x <- na.omit(df[,1])
y <- na.omit(df[,2])
z <- na.omit(df[,3])
zz <-na.omit(df[,4])
df <- data.frame(condition = rep(c("black_low","black_high", "white_low", "White_high"), c(length(x), length(y), length(z), length(zz))),
blame = c(x, y, z, zz)
)
blame1 <- df
blame1$id <- 1:nrow(blame1) # add an identifier
blame1 <- separate(blame1, col = "condition", into = c("race", "ses"))
blame1$race <- factor(blame1$race)
blame1$ses <- factor(blame1$ses)
blame1 <- blame1[, c(4, 1:3)]
devtools::use_data(blame1, overwrite = TRUE)
rm(x, y, z, zz, df)
# Blame 2
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch14-Exercises data - extended two IVs.xlsx'), sheet = "blame2", region = 'A1:D130')
x <- na.omit(df[,1])
y <- na.omit(df[,2])
z <- na.omit(df[,3])
zz <-na.omit(df[,4])
df <- data.frame(condition = rep(c("black_low","black_high", "white_low", "White_high"), c(length(x), length(y), length(z), length(zz))),
blame = c(x, y, z, zz)
)
blame2 <- df
blame2 <- df
blame2$id <- 1:nrow(blame2) # add an identifier
blame2 <- separate(blame2, col = "condition", into = c("race", "ses"))
blame2$race <- stringr::str_to_lower(blame2$race)
blame2$race <- factor(blame2$race)
blame2$ses <- factor(blame2$ses)
blame2 <- blame2[, c(4, 1:3)]
devtools::use_data(blame2, overwrite = TRUE)
rm(x, y, z, zz, df)
devtools::use_data(blame2, overwrite = TRUE)
## Add Ch 6 Sleep Deprivation Data
# Source = https://www.statcrunch.com/app/index.php?dataid=1053539
stickgold <- data.frame(sleep = factor(rep("deprived", 11)),
improvement = c( -14.7,
-10.7,
-10.7,
2.2,
2.4,
4.5,
7.2,
9.6,
10,
21.3,
21.8)
)
devtools::use_data(stickgold, overwrite = TRUE)
## Add Ch 14 Rattan Data
options(java.parameters = "-Xmx4096m") # to avoid memory errors when loading Excel worksheets
library(XLConnect)
df <- readWorksheet(loadWorkbook('data-raw/ESCI intro chapters 10-16 beta Jan 9 2016.xlsm'), sheet = "Ind groups comparisons", region = 'AY8:BA26')
x <- na.omit(df[,1])
y <- na.omit(df[,2])
z <- na.omit(df[,3])
df <- data.frame(group = rep(c("comfort", "challng", "control"), c(length(x), length(y), length(z))),
motivation = c(x, y, z)
)
rattan <- df
rattan$group <- factor(rattan$group)
devtools::use_data(rattan, overwrite = TRUE)
rm(x, y, z, df)
#------ Add Religion Sharing and Study Strategies datasets
library(readr)
religion_sharing <- as.data.frame(read_csv("data-raw/religion_sharing/Religion_Sharing.csv"))
names(religion_sharing) <- c("statistic", "non_religious_parents", "christian_parents", "muslim_parents")
devtools::use_data(religion_sharing, overwrite = TRUE)
study_strategies <- as.data.frame(read_csv("data-raw/study_strategies/study_strategies.csv"))
names(study_strategies) <- c("dv", "statistic", "self_explain", "elaborative_interrogation", "repetition_control")
devtools::use_data(study_strategies, overwrite = TRUE)
gitrman/itns documentation built on May 17, 2019, 5:29 a.m.
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# Convert Excel data to .rda files
options(java.parameters = "-Xmx4096m") # to avoid memory errors when loading Excel worksheets
library(XLConnect)
# ESCI Ch3 - 16 datasets -----------------------------------------------------------------------------------------------
# PenLaptop studies
PenLap <- function(region1, region2, wb = 'data-raw/ESCI intro chapters 3-8 beta Sep 26 2015.xlsm', sheet = "Data two"){
Pen <- readWorksheet(loadWorkbook(wb), sheet = sheet, region = region1)
Laptop <- readWorksheet(loadWorkbook(wb), sheet = sheet, region = region2)
df <- data.frame(group = factor(rep(c("Pen", "Laptop"), times = c(nrow(Pen), nrow(Laptop)))),
transcription = unlist(c(Pen, Laptop))
)
rownames(df) <- NULL
df
}
pen_laptop1 <- PenLap('BJ8:BJ42', 'BM8:BM39')
pen_laptop2 <- PenLap('BR8:BR56', 'BU8:BU111')
# pen_laptop3 <- PenLap('BZ8:BZ62', 'CC8:CC63')
# devtools::use_data(pen_laptop1, pen_laptop2, pen_laptop3, overwrite = TRUE)
devtools::use_data(pen_laptop1, pen_laptop2, overwrite = TRUE)
# Thomason (Paired Groups, Correlation Chapters)
thomason1 <- readWorksheet(loadWorkbook('data-raw/ESCI intro chapters 3-8 beta Sep 26 2015.xlsm'), sheet = "Data paired", region = 'BH7:BI19')
thomason2 <- readWorksheet(loadWorkbook('data-raw/ESCI intro chapters 3-8 beta Sep 26 2015.xlsm'), sheet = "Data paired", region = 'BM7:BN23')
thomason3 <- readWorksheet(loadWorkbook('data-raw/ESCI intro chapters 3-8 beta Sep 26 2015.xlsm'), sheet = "Data paired", region = 'BR7:BS46')
cn <- c("pre", "post")
names(thomason1) <-(cn)
names(thomason2) <-(cn)
names(thomason3) <-(cn)
devtools::use_data(thomason1, thomason2, thomason3, overwrite = TRUE)
# Body Wellness Data (Correlation chapter)
female <- readWorksheet(loadWorkbook('data-raw/ESCI intro chapters 10-16 beta Jan 9 2016.xlsm'), sheet = "Scatterplots", region = 'BV6:BW65')
male <- readWorksheet(loadWorkbook('data-raw/ESCI intro chapters 10-16 beta Jan 9 2016.xlsm'), sheet = "Scatterplots", region = 'CA6:CB53')
body_well <- data.frame(
sex = c(rep("female", nrow(female)), rep("male", nrow(male))),
rbind(female, male)
)
colnames(body_well)[2:3] <- c("bodysat", "wellbeing")
devtools::use_data(body_well, overwrite = TRUE)
# Robust two-group datsets (Ch 16)
## NATSAL
rm(female, male)
female <- readWorksheet(loadWorkbook('data-raw/ESCI intro chapters 10-16 beta Jan 9 2016.xlsm'), sheet = "Robust two", region = 'BN9:BN52', header = F)
male <- readWorksheet(loadWorkbook('data-raw/ESCI intro chapters 10-16 beta Jan 9 2016.xlsm'), sheet = "Robust two", region = 'BK9:BK59', header = F)
natsal <- data.frame(
sex = c(rep("female", nrow(female)), rep("male", nrow(male))),
rbind(female, male)
)
colnames(natsal)[2] <- "partners"
## Dana
g1 <- readWorksheet(loadWorkbook('data-raw/ESCI intro chapters 10-16 beta Jan 9 2016.xlsm'), sheet = "Robust two", region = 'BS9:BS27', header = F)
g2 <- readWorksheet(loadWorkbook('data-raw/ESCI intro chapters 10-16 beta Jan 9 2016.xlsm'), sheet = "Robust two", region = 'BV9:BV27', header = F)
dana <- data.frame(
group = c(rep("group one", nrow(g1)), rep("group two", nrow(g2))),
rbind(g1, g2)
)
colnames(dana)[2] <- "contact"
devtools::use_data(dana, overwrite = TRUE)
# Exercise Data ------------------------------------------------------------------------------------------------------
rm(list = ls()) # delete all objects in the global environment
## Ch 3 - Descriptives
# College Survey 1
x <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch03-Excercises data - descriptives-gdc.xlsx'), sheet = "college_survey_1", region = 'A1:W244')
x <- x [, -c(3, 6, 8)] # drop variables that are not needed as they duplicate other variables
j <- which(sapply(x, class)=="character")
x[, j] <- plyr::colwise(as.factor)(x[ ,j])
colnames(x)[4] <- "School_Year" # fix typo in name
x$School_Year <- factor(x$School_Year, levels = c("First-year", "Sophomore", "Junior", "Senior", "Post-bac"), ordered = TRUE)
x$Student_Athlete_Code <- factor(x$Student_Athlete_Code, labels = c("No", "Yes"))
x$Raven_Score <- x$Raven_Score * 100
colnames(x) <- stringr::str_to_lower(names(x)) # convert all column names to lowercase
college_survey1 <-x
devtools::use_data(college_survey1, overwrite = TRUE)
# Religious Belief
rm(x,j)
x <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch03-Excercises data - descriptives-gdc.xlsx'), sheet = "religious_belief", region = 'A1:D201')
y <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch03-Excercises data - descriptives-gdc.xlsx'), sheet = "religious_belief", region = 'F1:I214')
z <- rbind(x, y)
z$Condition <- factor(z$Condition)
colnames(z) <- stringr::str_to_lower(names(z)) # convert all column names to lowercase
religious_belief <- z
devtools::use_data(religious_belief, overwrite = TRUE)
# College Survey 2
rm(X, y, z)
x <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch03-Excercises data - descriptives-gdc.xlsx'), sheet = "college_survey_2", region = 'A1:Q139')
x <- x [, -c(3, 7, 9)] # drop variables that are not needed as they duplicate other variables
j <- which(sapply(x, class)=="character")
x[, j] <- plyr::colwise(as.factor)(x[ ,j])
x$School_Year <- factor(x$School_Year, levels = c("First-year", "Sophomore", "Junior", "Senior", "Post-bac"), ordered = TRUE)
x$Emotion_Recognition <- x$Emotion_Recognition * 100
colnames(x) <- stringr::str_to_lower(names(x)) # convert all column names to lowercase
college_survey2 <-x
devtools::use_data(college_survey2, overwrite = TRUE)
## Ch5
# College data files are the same as for Ch3, but sorted differently
## Ch7 - Two Groups Data
# Anchor Estimate
rm(x, j)
x <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch07-Excercises data - two groups-gdcsmall.xlsx'), sheet = "anchor_estimate", region = 'A1:H91')
j <- which(sapply(x, class)=="character")
x[, j] <- plyr::colwise(as.factor)(x[ ,j])
colnames(x) <- stringr::str_to_lower(names(x)) # convert all column names to lowercase
anchor_estimate <- x
devtools::use_data(anchor_estimate, overwrite = TRUE)
# Clean Moral
# Schnall 2014
rm(x)
x <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch07-Excercises data - two groups-gdcsmall.xlsx'), sheet = "clean_moral", region = 'A2:C42')
x$Condition <- factor(x$Condition)
colnames(x) <- stringr::str_to_lower(names(x)) # convert all column names to lowercase
# Johnson 2014
y <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch07-Excercises data - two groups-gdcsmall.xlsx'), sheet = "clean_moral", region = 'E2:G210')
y$Condition <- factor(y$Condition)
colnames(y) <- stringr::str_to_lower(names(y)) # convert all column names to lowercase
# Convert to .rda
clean_moral_schall <- x
clean_moral_johnson <- y
devtools::use_data(clean_moral_schall, clean_moral_johnson, overwrite = TRUE)
# math gender IAT
rm(x, y)
x <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch07-Excercises data - two groups-gdcsmall.xlsx'), sheet = "math_gender_IAT", region = 'A1:D89')
y <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch07-Excercises data - two groups-gdcsmall.xlsx'), sheet = "math_gender_IAT", region = 'F1:I156')
z <- rbind(x, y)
z$Location <- factor(z$Location)
z$Gender <- factor(z$Gender)
colnames(z) <- stringr::str_to_lower(names(z)) # convert all column names to lowercase
math_gender_iat <- z
devtools::use_data(math_gender_iat, overwrite = TRUE)
# Superstition Golf
rm(x, y, z)
x <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch07-Excercises data - two groups-gdcsmall.xlsx'), sheet = "superstition_golf Ex 6 7", region = 'A1:G112')
x[, 1:3] <- plyr::colwise(as.factor)(x[ , 1:3])
colnames(x) <- stringr::str_to_lower(names(x)) # convert all column names to lowercase
super_golf <- x
devtools::use_data(super_golf, overwrite = TRUE)
# Flag Priming
rm(x)
x <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch07-Excercises data - two groups-gdcsmall.xlsx'), sheet = "flag_priming", region = 'A1:E91')
x[, 2:4] <- plyr::colwise(factor)(x[ , 2:4])
colnames(x) <- stringr::str_to_lower(names(x)) # convert all column names to lowercase
flag_priming <- x
devtools::use_data(flag_priming, overwrite = TRUE)
# Ch 8 - Paired data -----------------------------------------------------------------------------
rm(list=ls(all=TRUE))
# Emotion Heartrate
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch08-Excercises data - paired-gdc.xlsx'), sheet = "emotion_heartrate", region = 'A1:E69')
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
df$first_emotion <- factor(df$first_emotion)
emotion_heartrate <- df
devtools::use_data(emotion_heartrate, overwrite = TRUE)
rm(df)
# Labels Flavor
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch08-Excercises data - paired-gdc.xlsx'), sheet = "labels_flavor", region = 'A1:F52')
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
df$suspicous <- factor(df$suspicous, levels = 0:1, labels = c("suspicious", "not suspicious"))
names(df)[6] <- "suspicious"
names(df)[1] <- "participant_id"
labels_flavor <- df
devtools::use_data(labels_flavor, overwrite = TRUE)
rm(df)
# Sensitization
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch08-Excercises data - paired-gdc.xlsx'), sheet = "sensitization Ex 5", region = 'A1:F13')
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
names(df)[1] <- "animal_id"
names(df)[3:6] <- c("trained_base", "trained_24h", "untrained_base", "untrained_24h") # rename as R colnames can't start with numbers
df$trained_side <- factor(df$trained_side)
sensitization <- df
devtools::use_data(sensitization, overwrite = TRUE)
rm(df)
# Learning Genes
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch08-Excercises data - paired-gdc.xlsx'), sheet = "learning_genes Ex 6", region = 'A1:G13')
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
df$tissue <- factor(df$tissue)
df$trained_side <- factor(df$trained_side)
learning_genes <- df
devtools::use_data(learning_genes, overwrite = TRUE)
rm(df)
# Habituation
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch08-Excercises data - paired-gdc.xlsx'), sheet = "habituation", region = 'A1:D15')
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
df$condition <- factor(df$condition)
habituation <- df
devtools::use_data(habituation, overwrite = TRUE)
rm(df)
# Ch 9 - Meta Analysis -----------------------------------------------------------------------------
# Anchor Adjust Meta-Analysis
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch09-Excercises data - meta-analysis-gdc.xlsx'), sheet = "anchor_adjust_meta-analysis", region = 'A1:I31')
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
df$location <- factor(df$location)
df$subset <- factor(df$subset)
df$country <- factor(df$country)
anchor_estimate_ma <- df
devtools::use_data(anchor_estimate_ma, overwrite = TRUE)
rm(df)
# Flag Priming
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch09-Excercises data - meta-analysis-gdc.xlsx'), sheet = "flag_priming_meta-analysis", region = 'A1:G26')
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
df$location <- factor(df$location)
flag_priming_ma <- df
devtools::use_data(flag_priming_ma, overwrite = TRUE)
rm(df)
# Math Gender IAT
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch09-Excercises data - meta-analysis-gdc.xlsx'), sheet = "math_gender_IAT_meta-analysis", region = 'A1:I31')
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
df$location <- factor(df$location)
df$subset <- factor(df$subset)
df$country <- factor(df$country)
math_gender_iat_ma <- df
devtools::use_data(math_gender_iat_ma, overwrite = TRUE)
rm(df)
# Power Performance
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch09-Excercises data - meta-analysis-gdc.xlsx'), sheet = "power_performance_meta-analysis", region = 'A1:O9')
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
df[, 1:7] <- plyr::colwise(as.factor)(df [, 1:7])
colnames(df)[13] <- "cohensd"
df <- df [, -12] # cull empty column
power_performance_ma <- df
devtools::use_data(power_performance_ma, overwrite = TRUE)
rm(df)
# Gambler Fallacy
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch09-Excercises data - meta-analysis-gdc.xlsx'), sheet = "gambler_fallacy_MA", region = 'A1:L13')
df$Study <- factor(df$Study)
df$Participant_Type <- factor(df$Participant_Type)
names(df) <- stringr::str_to_lower(names(df))
df <- df [, -6] # cull empty column
ma_gambler_fallacy <- df
devtools::use_data(ma_gambler_fallacy, overwrite = TRUE)
rm(df)
# Anchor Adjust Chicago
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch09-Excercises data - meta-analysis-gdc.xlsx'), sheet = "anchor_adjust_chicago_MA", region = 'A1:G37')
df$Location <- factor(df$Location)
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
ma_anchor_adjust_chicago <- df
devtools::use_data(ma_anchor_adjust_chicago, overwrite = TRUE)
rm(df)
# Anchor Adjust Everest
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch09-Excercises data - meta-analysis-gdc.xlsx'), sheet = "anchor_adjust_everest_MA", region = 'A1:G37')
df$Location <- factor(df$Location)
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
ma_anchor_adjust_everest <- df
devtools::use_data(ma_anchor_adjust_everest, overwrite = TRUE)
rm(df)
# Ch 11 - Correlation -----------------------------------------------------------------------------
# Exam Scores
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch11-Excercises data - correlation-gdc.xlsx'), sheet = "exam_scores", region = 'A1:C10')
colnames(df) <- c("id", "exam1", "exam_final")
exam_scores <- df
devtools::use_data(exam_scores, overwrite = TRUE)
rm(df)
# Sleep Beauty
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch11-Excercises data - correlation-gdc.xlsx'), sheet = "sleep_beauty", region = 'A1:D71')
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
sleep_beauty <- df[, 1:2] # only retain column 1 and 2
names(sleep_beauty)[1] <- "nightly_sleep_hours"
devtools::use_data(sleep_beauty, overwrite = TRUE)
rm(df)
# Campus Involvement
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch11-Excercises data - correlation-gdc.xlsx'), sheet = "campus_involvement", region = 'A1:F114')
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
df$gender <- factor(df$gender)
df$commuter <- factor(df$commuter, levels = 0:1, labels = c("resident", "commuter"))
campus_involvement <- df
devtools::use_data(campus_involvement, overwrite = TRUE)
rm(df)
# ITNS Ch11 Ex 7
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch11-Excercises data - correlation-gdc.xlsx'), sheet = "ITNS-Ch11-Ex7", region = 'A1:E146')
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
df$condition <- factor(df$condition)
colnames(df)[5] <- "temperature_rating"
ch11_ex7 <- df
devtools::use_data(ch11_ex7, overwrite = TRUE)
rm(df)
# Ch 12 - Regression -----------------------------------------------------------------------------
# Home Prices
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch12-Excercises data - regression-gdc.xlsx'), sheet = "home_prices", region = 'A1:G301')
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
names(df)[6] <- "size"
df$location <- factor(df$location)
df$status <- factor(df$status)
home_prices <- df
devtools::use_data(home_prices, overwrite = TRUE)
rm(df)
# Altruism Happiness
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch12-Excercises data - regression-gdc.xlsx'), sheet = "altruism_happiness", region = 'A1:H51')
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
df[, 1:2] <- plyr::colwise(as.factor)(df[, 1:2])
names(df)[6:8] <- c("well_being_2010_rounded", "kidney_rate_per1000", "wb_change")
df <- df[, -2] # drop abbreviation as not needed
altruism_happiness <- df
devtools::use_data(altruism_happiness, overwrite = TRUE)
rm(df)
# Ch 12 Ex 3
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch12-Excercises data - regression-gdc.xlsx'), sheet = "ITNS-Ch12-Ex3", region = 'A1:B211')
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
names(df)[2] <- "motor_skill"
ch12_ex3 <- df
devtools::use_data(ch12_ex3, overwrite = TRUE)
rm(df)
# Ch12 Ex4
# Exclude for now, unsure if this need to be included
# Home Prices Holdout
# Only include the actual data, not the regression workings
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch12-Excercises data - regression-gdc.xlsx'), sheet = "home_prices_holdout", region = 'A1:F11')
df <- df[, c(1, 2, 6)]
names(df) <- c("new_case", "size", "asking_price")
home_prices_holdout <- df
devtools::use_data(home_prices_holdout, overwrite = TRUE)
rm(df)
# Ch 13 Exercises - Extended Designs 1 -----------------------------------------------------------------------------
# Exclude summary statistics for now, use only raw data
# Organic Moral Replication
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch13-Exercises data - extended one.xlsx'), sheet = "organic_moral", region = 'A10:D48')
x <- na.omit(df[,1])
y <- na.omit(df[,2])
z <- na.omit(df[,3])
organic_moral <- data.frame(
food = factor(rep(c("organic", "control", "comfort"), c(length(x), length(y), length(z)))),
judge = c(x, y, z)
)
devtools::use_data(organic_moral, overwrite = TRUE)
rm(df, x, y, z)
# Inauthentic
x <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch13-Exercises data - extended one.xlsx'), sheet = "inauthentic", region = 'A2:D221')
y <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch13-Exercises data - extended one.xlsx'), sheet = "inauthentic", region = 'F2:I221')
z <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch13-Exercises data - extended one.xlsx'), sheet = "inauthentic", region = 'K2:N221')
# IV, Cleaning, Alienation, Neutral
i <- 1:219
j <- 1
g1 <- data.frame(group = as.character("auth_notruth"), cleaning = x[i, j], neutral = y[i, j], alienation = z[i, j])
i <- 1:214
j <- 2
g2 <- data.frame(group = as.character("auth_gen"), cleaning = x[i, j], neutral = y[i, j], alienation = z[i, j])
i <- 1:199
j <- 3
g3 <- data.frame(group = as.character("inauth_notruth"), cleaning = x[i, j], neutral = y[i, j], alienation = z[i, j])
i <- 1:206
j <- 4
g4 <- data.frame(group = as.character("inauth_gen"), cleaning = x[i, j], neutral = y[i, j], alienation = z[i, j])
inauthentic <- rbind(g1, g2, g3, g4)
devtools::use_data(inauthentic, overwrite = TRUE)
rm(x, y, z, g1, g2, g3, g4, i, j)
# IQ Booster
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch13-Exercises data - extended one.xlsx'), sheet = "IQBoosters", region = 'A1:F11')
df <- reshape2:: melt(df, measure.vars = 1:6, variable.name = "drug", value.name = "iq")
levels(df$drug) <- c("Placebo", "DrugA", "DrugB", "DrugC", "DrugD", "DrugE")
iq_boost <- df
devtools::use_data(iq_boost, overwrite = TRUE)
rm(df)
# Ch 14 Exercises - Extended Designs 2 -----------------------------------------------------------------------------
# Videogame aggression
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch14-Exercises data - extended two IVs.xlsx'), sheet = "videogame_aggression", region = 'A1:D57')
colnames(df) <- stringr::str_to_lower(names(df)) # convert colnames to lower case
library(tidyr)
library(dplyr)
df <- gather(df, key = "group", value = "aggression", 1:4, factor_key = TRUE)
df <- separate(df, group, into = c("violence", "difficulty"))
df$violence <- factor(df$violence)
df$difficulty <- factor(df$difficulty)
videogame_aggression <- df
devtools::use_data(videogame_aggression, overwrite = TRUE)
rm(df)
# Self-Explain Time
x <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch14-Exercises data - extended two IVs.xlsx'), sheet = "self-explain_time", region = 'A1:F18')
y <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch14-Exercises data - extended two IVs.xlsx'), sheet = "self-explain_time", region = 'H1:M22')
z <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch14-Exercises data - extended two IVs.xlsx'), sheet = "self-explain_time", region = 'O1:T32')
df <- rbind(x, z, y)
df <- df [, -6]
df <- gather(df, key = "time", value = "knowledge", 4:5, factor_key = TRUE)
levels(df$time) <- c("pre", "post")
cn <- c("id", "condition", "grade", "time", "knowledge")
names(df) <- cn
df[df$condition == "Add'l Practice", "condition"] <- "Additional Practice"
df$condition <- factor(df$condition)
self_explain_time <- df
devtools::use_data(self_explain_time, overwrite = TRUE)
rm(x, y, z, df)
# Blame1
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch14-Exercises data - extended two IVs.xlsx'), sheet = "blame1", region = 'A1:D106')
x <- na.omit(df[,1])
y <- na.omit(df[,2])
z <- na.omit(df[,3])
zz <-na.omit(df[,4])
df <- data.frame(condition = rep(c("black_low","black_high", "white_low", "White_high"), c(length(x), length(y), length(z), length(zz))),
blame = c(x, y, z, zz)
)
blame1 <- df
blame1$id <- 1:nrow(blame1) # add an identifier
blame1 <- separate(blame1, col = "condition", into = c("race", "ses"))
blame1$race <- factor(blame1$race)
blame1$ses <- factor(blame1$ses)
blame1 <- blame1[, c(4, 1:3)]
devtools::use_data(blame1, overwrite = TRUE)
rm(x, y, z, zz, df)
# Blame 2
df <- readWorksheet(loadWorkbook('data-raw/ITNS-Ch14-Exercises data - extended two IVs.xlsx'), sheet = "blame2", region = 'A1:D130')
x <- na.omit(df[,1])
y <- na.omit(df[,2])
z <- na.omit(df[,3])
zz <-na.omit(df[,4])
df <- data.frame(condition = rep(c("black_low","black_high", "white_low", "White_high"), c(length(x), length(y), length(z), length(zz))),
blame = c(x, y, z, zz)
)
blame2 <- df
blame2 <- df
blame2$id <- 1:nrow(blame2) # add an identifier
blame2 <- separate(blame2, col = "condition", into = c("race", "ses"))
blame2$race <- stringr::str_to_lower(blame2$race)
blame2$race <- factor(blame2$race)
blame2$ses <- factor(blame2$ses)
blame2 <- blame2[, c(4, 1:3)]
devtools::use_data(blame2, overwrite = TRUE)
rm(x, y, z, zz, df)
devtools::use_data(blame2, overwrite = TRUE)
## Add Ch 6 Sleep Deprivation Data
# Source = https://www.statcrunch.com/app/index.php?dataid=1053539
stickgold <- data.frame(sleep = factor(rep("deprived", 11)),
improvement = c( -14.7,
-10.7,
-10.7,
2.2,
2.4,
4.5,
7.2,
9.6,
10,
21.3,
21.8)
)
devtools::use_data(stickgold, overwrite = TRUE)
## Add Ch 14 Rattan Data
options(java.parameters = "-Xmx4096m") # to avoid memory errors when loading Excel worksheets
library(XLConnect)
df <- readWorksheet(loadWorkbook('data-raw/ESCI intro chapters 10-16 beta Jan 9 2016.xlsm'), sheet = "Ind groups comparisons", region = 'AY8:BA26')
x <- na.omit(df[,1])
y <- na.omit(df[,2])
z <- na.omit(df[,3])
df <- data.frame(group = rep(c("comfort", "challng", "control"), c(length(x), length(y), length(z))),
motivation = c(x, y, z)
)
rattan <- df
rattan$group <- factor(rattan$group)
devtools::use_data(rattan, overwrite = TRUE)
rm(x, y, z, df)
#------ Add Religion Sharing and Study Strategies datasets
library(readr)
religion_sharing <- as.data.frame(read_csv("data-raw/religion_sharing/Religion_Sharing.csv"))
names(religion_sharing) <- c("statistic", "non_religious_parents", "christian_parents", "muslim_parents")
devtools::use_data(religion_sharing, overwrite = TRUE)
study_strategies <- as.data.frame(read_csv("data-raw/study_strategies/study_strategies.csv"))
names(study_strategies) <- c("dv", "statistic", "self_explain", "elaborative_interrogation", "repetition_control")
devtools::use_data(study_strategies, overwrite = TRUE)
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