R/data.R
In hneth/ds4psy: Data Science for Psychologists
## data.R | ds4psy
## hn | uni.kn | 2025 02 02
## Documentation of data sets included in /data.
# (01) Positive Psychology data: ----------
# (01a) posPsy_p_info: ------
#' Positive Psychology: Participant data
#'
#' \code{posPsy_p_info} is a dataset containing details of 295 participants.
#'
#' \describe{
#'
#' \item{id}{Participant ID.}
#'
#' \item{intervention}{Type of intervention:
#' 3 positive psychology interventions (PPIs), plus 1 control condition:
#' 1: "Using signature strengths",
#' 2: "Three good things",
#' 3: "Gratitude visit",
#' 4: "Recording early memories" (control condition).}
#'
#' \item{sex}{Sex: 1 = female, 2 = male.}
#'
#' \item{age}{Age (in years).}
#'
#' \item{educ}{Education level: Scale from 1: less than 12 years, to 5: postgraduate degree.}
#'
#' \item{income}{Income: Scale from 1: below average, to 3: above average.}
#'
#' }
#'
#' See codebook and references at \url{https://bookdown.org/hneth/ds4psy/B-1-datasets-pos.html}.
#'
#' @format A table with 295 cases (rows) and 6 variables (columns).
#'
#' @family datasets
#'
#' @source
#' \strong{Articles}
#'
#' \itemize{
#'
#' \item Woodworth, R. J., O’Brien-Malone, A., Diamond, M. R., & Schüz, B. (2017).
#' Web-based positive psychology interventions: A reexamination of effectiveness.
#' \emph{Journal of Clinical Psychology}, \emph{73}(3), 218--232.
#' doi: \code{10.1002/jclp.22328}
#'
#' \item Woodworth, R. J., O’Brien-Malone, A., Diamond, M. R. and Schüz, B. (2018).
#' Data from, ‘Web-based positive psychology interventions: A reexamination of effectiveness’.
#' \emph{Journal of Open Psychology Data}, \emph{6}(1).
#' doi: \code{10.5334/jopd.35}
#' }
#'
#' See \url{https://openpsychologydata.metajnl.com/articles/10.5334/jopd.35/} for details
#' and \doi{10.6084/m9.figshare.1577563.v1} for original dataset.
#'
#' Additional references at \url{https://bookdown.org/hneth/ds4psy/B-1-datasets-pos.html}.
"posPsy_p_info"
# (01b) posPsy_AHI_CESD: ------
#' Positive Psychology: AHI CESD data
#'
#' \code{posPsy_AHI_CESD} is a dataset containing answers to the 24 items of the
#' Authentic Happiness Inventory (AHI) and answers to the
#' 20 items of the Center for Epidemiological Studies Depression (CES-D) scale
#' (Radloff, 1977) for multiple (1 to 6) measurement occasions.
#'
#' \strong{Codebook}
#'
#' \itemize{
#'
#' \item 1. \strong{id}: Participant ID.
#'
#' \item 2. \strong{occasion}: Measurement occasion:
#' 0: Pretest (i.e., at enrolment),
#' 1: Posttest (i.e., 7 days after pretest),
#' 2: 1-week follow-up, (i.e., 14 days after pretest, 7 days after posttest),
#' 3: 1-month follow-up, (i.e., 38 days after pretest, 31 days after posttest),
#' 4: 3-month follow-up, (i.e., 98 days after pretest, 91 days after posttest),
#' 5: 6-month follow-up, (i.e., 189 days after pretest, 182 days after posttest).
#'
#' \item 3. \strong{elapsed.days}: Time since enrolment measured in fractional days.
#'
#' \item 4. \strong{intervention}: Type of intervention:
#' 3 positive psychology interventions (PPIs), plus 1 control condition:
#' 1: "Using signature strengths",
#' 2: "Three good things",
#' 3: "Gratitude visit",
#' 4: "Recording early memories" (control condition).
#'
#' \item 5.-28. (from \strong{ahi01} to \strong{ahi24}): Responses on 24 AHI items.
#'
#' \item 29.-48. (from \strong{cesd01} to \strong{cesd20}): Responses on 20 CES-D items.
#'
#' \item 49. \strong{ahiTotal}: Total AHI score.
#'
#' \item 50. \strong{cesdTotal}: Total CES-D score.
#'
#' }
#'
#' See codebook and references at \url{https://bookdown.org/hneth/ds4psy/B-1-datasets-pos.html}.
#'
#' @format A table with 992 cases (rows) and 50 variables (columns).
#'
#' @family datasets
#'
#' @seealso
#' \code{\link{posPsy_long}} for a corrected version of this file (in long format).
#'
#' @source
#' \strong{Articles}
#'
#' \itemize{
#'
#' \item Woodworth, R. J., O’Brien-Malone, A., Diamond, M. R., & Schüz, B. (2017).
#' Web-based positive psychology interventions: A reexamination of effectiveness.
#' \emph{Journal of Clinical Psychology}, \emph{73}(3), 218--232.
#' doi: \code{10.1002/jclp.22328}
#'
#' \item Woodworth, R. J., O’Brien-Malone, A., Diamond, M. R. and Schüz, B. (2018).
#' Data from, ‘Web-based positive psychology interventions: A reexamination of effectiveness’.
#' \emph{Journal of Open Psychology Data}, \emph{6}(1).
#' doi: \code{10.5334/jopd.35}
#' }
#'
#' See \url{https://openpsychologydata.metajnl.com/articles/10.5334/jopd.35/} for details
#' and \doi{10.6084/m9.figshare.1577563.v1} for original dataset.
#'
#' Additional references at \url{https://bookdown.org/hneth/ds4psy/B-1-datasets-pos.html}.
"posPsy_AHI_CESD"
# (01c) posPsy_long: ------
#' Positive Psychology: AHI CESD corrected data (in long format)
#'
#' \code{posPsy_long} is a dataset containing answers to the 24 items of the
#' Authentic Happiness Inventory (AHI) and answers to the
#' 20 items of the Center for Epidemiological Studies Depression (CES-D) scale
#' (see Radloff, 1977) for multiple (1 to 6) measurement occasions.
#'
#' This dataset is a corrected version of \code{\link{posPsy_AHI_CESD}}
#' and in long-format.
#'
#' @format A table with 990 cases (rows) and 50 variables (columns).
#'
#' @family datasets
#'
#' @seealso
#' \code{\link{posPsy_AHI_CESD}} for source of this file and codebook information;
#' \code{\link{posPsy_wide}} for a version of this file (in wide format).
#'
#' @source
#' \strong{Articles}
#'
#' \itemize{
#'
#' \item Woodworth, R. J., O’Brien-Malone, A., Diamond, M. R., & Schüz, B. (2017).
#' Web-based positive psychology interventions: A reexamination of effectiveness.
#' \emph{Journal of Clinical Psychology}, \emph{73}(3), 218--232.
#' doi: \code{10.1002/jclp.22328}
#'
#' \item Woodworth, R. J., O’Brien-Malone, A., Diamond, M. R. and Schüz, B. (2018).
#' Data from, ‘Web-based positive psychology interventions: A reexamination of effectiveness’.
#' \emph{Journal of Open Psychology Data}, \emph{6}(1).
#' doi: \code{10.5334/jopd.35}
#' }
#'
#' See \url{https://openpsychologydata.metajnl.com/articles/10.5334/jopd.35/} for details
#' and \doi{10.6084/m9.figshare.1577563.v1} for original dataset.
#'
#' Additional references at \url{https://bookdown.org/hneth/ds4psy/B-1-datasets-pos.html}.
"posPsy_long"
# (01d) posPsy_wide: ------
#' Positive Psychology: All corrected data (in wide format)
#'
#' \code{posPsy_wide} is a dataset containing answers to the 24 items of the
#' Authentic Happiness Inventory (AHI) and answers to the
#' 20 items of the Center for Epidemiological Studies Depression (CES-D) scale
#' (see Radloff, 1977) for multiple (1 to 6) measurement occasions.
#'
#' This dataset is based on \code{\link{posPsy_AHI_CESD}} and
#' \code{\link{posPsy_long}}, but is in wide format.
#'
#' @family datasets
#'
#' @seealso
#' \code{\link{posPsy_AHI_CESD}} for the source of this file,
#' \code{\link{posPsy_long}} for a version of this file (in long format).
#'
#' @source
#' \strong{Articles}
#'
#' \itemize{
#'
#' \item Woodworth, R. J., O’Brien-Malone, A., Diamond, M. R., & Schüz, B. (2017).
#' Web-based positive psychology interventions: A reexamination of effectiveness.
#' \emph{Journal of Clinical Psychology}, \emph{73}(3), 218--232.
#' doi: \code{10.1002/jclp.22328}
#'
#' \item Woodworth, R. J., O’Brien-Malone, A., Diamond, M. R. and Schüz, B. (2018).
#' Data from, ‘Web-based positive psychology interventions: A reexamination of effectiveness’.
#' \emph{Journal of Open Psychology Data}, \emph{6}(1).
#' doi: \code{10.5334/jopd.35}
#' }
#'
#' See \url{https://openpsychologydata.metajnl.com/articles/10.5334/jopd.35/} for details
#' and \doi{10.6084/m9.figshare.1577563.v1} for original dataset.
#'
#' Additional references at \url{https://bookdown.org/hneth/ds4psy/B-1-datasets-pos.html}.
"posPsy_wide"
# (02) False Positive Psychology data: ----------
# https://bookdown.org/hneth/ds4psy/B-2-datasets-false.html
#' Data: False Positive Psychology
#'
#' \code{falsePosPsy_all} is a dataset containing the data from 2 studies designed to
#' highlight problematic research practices within psychology.
#'
#' Simmons, Nelson and Simonsohn (2011) published a controversial article
#' with a necessarily false finding. By conducting simulations and 2 simple behavioral experiments,
#' the authors show that flexibility in data collection, analysis, and reporting
#' dramatically increases the rate of false-positive findings.
#'
#' \describe{
#' \item{study}{Study ID.}
#' \item{id}{Participant ID.}
#' \item{aged}{Days since participant was born (based on their self-reported birthday).}
#' \item{aged365}{Age in years.}
#' \item{female}{Is participant a woman? 1: yes, 2: no.}
#' \item{dad}{Father's age (in years).}
#' \item{mom}{Mother's age (in years).}
#' \item{potato}{Did the participant hear the song 'Hot Potato' by The Wiggles? 1: yes, 2: no.}
#' \item{when64}{Did the participant hear the song 'When I am 64' by The Beatles? 1: yes, 2: no.}
#' \item{kalimba}{Did the participant hear the song 'Kalimba' by Mr. Scrub? 1: yes, 2: no.}
#' \item{cond}{In which condition was the participant?
#' control: Subject heard the song 'Kalimba' by Mr. Scrub;
#' potato: Subject heard the song 'Hot Potato' by The Wiggles;
#' 64: Subject heard the song 'When I am 64' by The Beatles.}
#' \item{root}{Could participant report the square root of 100? 1: yes, 2: no.}
#' \item{bird}{Imagine a restaurant you really like offered a 30 percent discount for dining between 4pm and 6pm.
#' How likely would you be to take advantage of that offer?
#' Scale from 1: very unlikely, 7: very likely.}
#' \item{political}{In the political spectrum, where would you place yourself?
#' Scale: 1: very liberal, 2: liberal, 3: centrist, 4: conservative, 5: very conservative.}
#' \item{quarterback}{If you had to guess who was chosen the quarterback of the year in Canada last year,
#' which of the following four options would you choose?
#' 1: Dalton Bell, 2: Daryll Clark, 3: Jarious Jackson, 4: Frank Wilczynski.}
#' \item{olddays}{How often have you referred to some past part of your life as “the good old days”?
#' Scale: 11: never, 12: almost never, 13: sometimes, 14: often, 15: very often.}
#' \item{feelold}{How old do you feel?
#' Scale: 1: very young, 2: young, 3: neither young nor old, 4: old, 5: very old.}
#' \item{computer}{Computers are complicated machines.
#' Scale from 1: strongly disagree, to 5: strongly agree.}
#' \item{diner}{Imagine you were going to a diner for dinner tonight, how much do you think you would like the food?
#' Scale from 1: dislike extremely, to 9: like extremely.}
#' }
#'
#' See \url{https://bookdown.org/hneth/ds4psy/B-2-datasets-false.html} for codebook and more information.
#'
#'
#' @format A table with 78 cases (rows) and 19 variables (columns):
#'
#' @family datasets
#'
#' @source
#' \strong{Articles}
#'
#' \itemize{
#'
#' \item Simmons, J.P., Nelson, L.D., & Simonsohn, U. (2011).
#' False-positive psychology: Undisclosed flexibility in data collection and analysis
#' allows presenting anything as significant.
#' \emph{Psychological Science}, \emph{22}(11), 1359--1366.
#' doi: \code{10.1177/0956797611417632}
#'
#' \item Simmons, J.P., Nelson, L.D., & Simonsohn, U. (2014).
#' Data from paper "False-Positive Psychology:
#' Undisclosed Flexibility in Data Collection and Analysis
#' Allows Presenting Anything as Significant".
#' \emph{Journal of Open Psychology Data}, \emph{2}(1), e1.
#' doi: \code{10.5334/jopd.aa}
#' }
#'
#' See files at \url{https://openpsychologydata.metajnl.com/articles/10.5334/jopd.aa/} and
#' the archive at \url{https://zenodo.org/record/7664} for original dataset.
"falsePosPsy_all"
# (03) Transforming data / dplyr (Chapter 3): outliers ----------
# https://bookdown.org/hneth/ds4psy/3-6-transform-ex.html
#' Outlier data.
#'
#' \code{outliers} is a fictitious dataset containing the id, sex, and height
#' of 1000 non-existing, but otherwise normal people.
#'
#' \strong{Codebook}
#'
#' \describe{
#' \item{id}{Participant ID (as character code)}
#' \item{sex}{Gender (female vs. male)}
#' \item{height}{Height (in cm)}
#' }
#'
#' @format A table with 100 cases (rows) and 3 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data at \url{http://rpository.com/ds4psy/data/out.csv}.
"outliers"
# (03.14) pi data: --------
# https://bookdown.org/hneth/ds4psy/10-3-iter-essentials.html
# Orig. data source <http://www.geom.uiuc.edu/~huberty/math5337/groupe/digits.html>
# # pi_all <- readLines("./data/pi_100k.txt") # from local data file
# pi_data <- "http://rpository.com/ds4psy/data/pi_100k.txt" # URL of online data file
# pi_100k <- readLines(pi_data) # read from online source
#
# # Check:
# dim(pi_100k) # NULL !
#
# # Check number of missing values:
# sum(is.na(pi_100k)) # 0 missing values
#
# # Save to /data:
# usethis::use_data(pi_100k, overwrite = TRUE)
#' Data: 100k digits of pi.
#'
#' \code{pi_100k} is a dataset containing the first 100k digits of pi.
#'
#' @format A character of \code{nchar(pi_100k) = 100001}.
#'
#' @family datasets
#'
#' @source
#' See TXT data at \url{http://rpository.com/ds4psy/data/pi_100k.txt}.
#'
#' Original data at \url{http://www.geom.uiuc.edu/~huberty/math5337/groupe/digits.html}.
"pi_100k"
# (06) Importing data / readr (Chapter 6): ----------
# https://bookdown.org/hneth/ds4psy/6-3-import-essentials.html
# (06a) data_t1.csv: ----
# Note: Same as (6a) below.
# data_t1 <- readr::read_csv("http://rpository.com/ds4psy/data/data_t1.csv")
#
# # Check:
# dim(data_t1) # 20 observations (rows) x 4 variables (columns)
#
# # Check number of missing values:
# sum(is.na(data_t1)) # 3 missing values
#
# # Save to /data:
# usethis::use_data(data_t1, overwrite = TRUE)
#' Data table data_t1.
#'
#' \code{data_t1} is a fictitious dataset to practice importing and joining data
#' (from a CSV file).
#'
#' @format A table with 20 cases (rows) and 4 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data at \url{http://rpository.com/ds4psy/data/data_t1.csv}.
"data_t1"
# (06b) data_t1_de.csv: ----
# data_t1_de <- readr::read_csv2("http://rpository.com/ds4psy/data/data_t1_de.csv")
#
# # Check:
# dim(data_t1_de) # 20 observations (rows) x 4 variables (columns)
#
# # Check number of missing values:
# sum(is.na(data_t1_de)) # 3 missing values
#
# # Save to /data:
# usethis::use_data(data_t1_de, overwrite = TRUE)
#' Data import data_t1_de.
#'
#' \code{data_t1_de} is a fictitious dataset to practice importing data
#' (from a CSV file, de/European style).
#'
#' @format A table with 20 cases (rows) and 4 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data at \url{http://rpository.com/ds4psy/data/data_t1_de.csv}.
"data_t1_de"
# (06c) data_t1_tab.csv: ----
# data_t1_tab <- read_tsv("http://rpository.com/ds4psy/data/data_t1_tab.csv")
#
# # Check:
# dim(data_t1_tab) # 20 observations (rows) x 4 variables (columns)
#
# # Check number of missing values:
# sum(is.na(data_t1_tab)) # 3 missing values
#
# # Save to /data:
# usethis::use_data(data_t1_tab, overwrite = TRUE)
#' Data import data_t1_tab.
#'
#' \code{data_t1_tab} is a fictitious dataset to practice importing data
#' (from a TAB file).
#'
#' @format A table with 20 cases (rows) and 4 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See TAB-delimited data at \url{http://rpository.com/ds4psy/data/data_t1_tab.csv}.
"data_t1_tab"
# (06d) data_1.dat: ----
# my_file <- "http://rpository.com/ds4psy/data/data_1.dat"
#
# data_1 <- readr::read_delim(my_file, delim = ".",
# col_names = c("initials", "age", "tel", "pwd"),
# na = c("-77", "-99"))
#
# # Check:
# dim(data_1) # 100 observations (rows) x 4 variables (columns)
#
# # Check number of missing values:
# sum(is.na(data_1)) # 15 missing values
#
# # Save to /data:
# usethis::use_data(data_1, overwrite = TRUE)
#' Data import data_1.
#'
#' \code{data_1} is a fictitious dataset to practice importing data
#' (from a DELIMITED file).
#'
#' @format A table with 100 cases (rows) and 4 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See DELIMITED data at \url{http://rpository.com/ds4psy/data/data_1.dat}.
"data_1"
# (06e) data_2.dat: ----
# my_file_path <- "http://rpository.com/ds4psy/data/data_2.dat" # from online source
#
# # read_fwf:
# data_2 <- readr::read_fwf(my_file_path,
# fwf_cols(initials = c(1, 2),
# age = c(4, 5),
# tel = c(7, 10),
# pwd = c(12, 17)))
#
# # Check:
# dim(data_2) # 100 observations (rows) x 4 variables (columns)
#
# # Check number of missing values:
# sum(is.na(data_2)) # 0 missing values
#
# # Save to /data:
# usethis::use_data(data_2, overwrite = TRUE)
#' Data import data_2.
#'
#' \code{data_2} is a fictitious dataset to practice importing data
#' (from a FWF file).
#'
#' @format A table with 100 cases (rows) and 4 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See FWF data at \url{http://rpository.com/ds4psy/data/data_2.dat}.
"data_2"
# (07) Tidying data / tidyr (Chapter 7): ----------
# https://bookdown.org/hneth/ds4psy/7-3-tidy-essentials.html
# (07a) table6.csv: ------
# ## Load data (as comma-separated file):
# table6 <- readr::read_csv("http://rpository.com/ds4psy/data/table6.csv") # from online source
#
# # Check:
# dim(table6) # 6 observations (rows) x 2 variables (columns)
#
# # Check number of missing values:
# sum(is.na(table6)) # 0 missing values
#
# # Save to /data:
# usethis::use_data(table6, overwrite = TRUE)
#' Data: table6
#'
#' \code{table6} is a fictitious dataset to practice reshaping and tidying data.
#'
#' This dataset is a further variant of the \code{table1} to \code{table5} datasets
#' of the \bold{tidyr} package.
#'
#' @format A table with 6 cases (rows) and 2 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data at \url{http://rpository.com/ds4psy/data/table6.csv}.
"table6"
# (07b) table7.csv: ------
# # Load data (as comma-separated file):
# table7 <- readr::read_csv("http://rpository.com/ds4psy/data/table7.csv") # from online source
#
# # Check:
# dim(table7) # 6 observations (rows) x 1 (horrendous) variable (column)
#
# # Check number of missing values:
# sum(is.na(table7)) # 0 missing values
#
# # Save to /data:
# usethis::use_data(table7, overwrite = TRUE)
#' Data: table7
#'
#' \code{table7} is a fictitious dataset to practice reshaping and tidying data.
#'
#' This dataset is a further variant of the \code{table1} to \code{table5} datasets
#' of the \bold{tidyr} package.
#'
#' @format A table with 6 cases (rows) and 1 (horrendous) variable (column).
#'
#' @family datasets
#'
#' @source
#' See CSV data at \url{http://rpository.com/ds4psy/data/table7.csv}.
"table7"
# (07c) table8.csv: ------
# # Load data (as comma-separated file):
# table8 <- readr::read_csv("http://rpository.com/ds4psy/data/table8.csv") # from online source
#
# # Check:
# dim(table8) # 3 observations (rows) x 5 variables (columns)
#
# # Check number of missing values:
# sum(is.na(table8)) # 0 missing values
#
# # Save to /data:
# usethis::use_data(table8, overwrite = TRUE)
#' Data: table8
#'
#' \code{table9} is a fictitious dataset to practice reshaping and tidying data.
#'
#' This dataset is a further variant of the \code{table1} to \code{table5} datasets
#' of the \bold{tidyr} package.
#'
#' @format A table with 3 cases (rows) and 5 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data at \url{http://rpository.com/ds4psy/data/table8.csv}.
"table8"
# (07c2) table9: The contingency table tidyr::table2 as a 3-dimensional array (xtabs) ------
# # Data from tidyr::table1 as a contingency table (with a dedicated "count" variable):
# ct <- tidyr::table2
#
# # Create 3-dimensional array (xtabs < table):
# table9 <- stats::xtabs(formula = count ~., data = ct)
# dim(table9) # 3 2 2
# str(table9)
# sum(table9) # 2940985206
#' Data table9.
#'
#' \code{table9} is a fictitious dataset to practice reshaping and tidying data.
#'
#' This dataset is a further variant of the \code{table1} to \code{table5} datasets
#' of the \bold{tidyr} package.
#'
#' @format A 3 x 2 x 2 array (of type "xtabs") with 2940985206 elements (frequency counts).
#'
#' @family datasets
#'
#' @source
#' Generated by using \code{stats::xtabs(formula = count ~., data = tidyr::table2)}.
"table9"
# (07d) exp_wide.csv: ------
# https://bookdown.org/hneth/ds4psy/7-5-tidy-ex.html
# exp_wide <- readr::read_csv("http://rpository.com/ds4psy/data/exp_wide.csv") # from online source
#
# # Check:
# dim(exp_wide) # 10 observations (rows) x 7 variables (columns)
#
# # Check number of missing values:
# sum(is.na(exp_wide)) # 0 missing values
#
# # Save to /data:
# usethis::use_data(exp_wide, overwrite = TRUE)
#' Data exp_wide.
#'
#' \code{exp_wide} is a fictitious dataset to practice tidying data
#' (here: converting from wide to long format).
#'
#' @format A table with 10 cases (rows) and 7 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data at \url{http://rpository.com/ds4psy/data/exp_wide.csv}.
"exp_wide"
# (07e) Chapter 7: Exercise 1: 'Four messes and one tidy table': ------
# https://bookdown.org/hneth/ds4psy/7-4-tidy-ex.html#tidy:ex01
# (07e1): t_1.csv: -----
#' Data: t_1
#'
#' \code{t_1} is a fictitious dataset to practice tidying data.
#'
#' @format A table with 8 cases (rows) and 9 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data at \url{http://rpository.com/ds4psy/data/t_1.csv}.
"t_1"
# (07e2): t_2.csv: -----
#' Data: t_2
#'
#' \code{t_2} is a fictitious dataset to practice tidying data.
#'
#' @format A table with 8 cases (rows) and 5 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data at \url{http://rpository.com/ds4psy/data/t_2.csv}.
"t_2"
# (07e3): t_3.csv: -----
#' Data: t_3
#'
#' \code{t_3} is a fictitious dataset to practice tidying data.
#'
#' @format A table with 16 cases (rows) and 6 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data at \url{http://rpository.com/ds4psy/data/t_3.csv}.
"t_3"
# (07e4): t_4.csv: -----
#' Data: t_4
#'
#' \code{t_4} is a fictitious dataset to practice tidying data.
#'
#' @format A table with 16 cases (rows) and 8 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data at \url{http://rpository.com/ds4psy/data/t_4.csv}.
"t_4"
# (08) Joining data / dplyr (Chapter 8): ----------
# https://bookdown.org/hneth/ds4psy/8-3-join-essentials.html
# (08a) data_t1.csv: ----
# Note: Same as (4a) above.
# data_t1 <- readr::read_csv("http://rpository.com/ds4psy/data/data_t1.csv")
#
# # Check:
# dim(data_t1) # 20 observations (rows) x 4 variables (columns)
#
# # Check number of missing values:
# sum(is.na(data_t1)) # 3 missing values
#
# # Save to /data:
# usethis::use_data(data_t1, overwrite = TRUE)
# See (4a) above.
# (08b) data_t2.csv: ----
# data_t2 <- readr::read_csv(file = "http://rpository.com/ds4psy/data/data_t2.csv")
#
# # Check:
# dim(data_t2) # 20 observations (rows) x 4 variables (columns)
#
# # Check number of missing values:
# sum(is.na(data_t2)) # 3 missing values
#
# # Save to /data:
# usethis::use_data(data_t2, overwrite = TRUE)
#' Data table data_t2.
#'
#' \code{data_t2} is a fictitious dataset to practice importing and joining data
#' (from a CSV file).
#'
#' @format A table with 20 cases (rows) and 4 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data at \url{http://rpository.com/ds4psy/data/data_t2.csv}.
"data_t2"
# Exercise 1:
# (08c) t3.csv: ----
# t3 <- readr::read_csv(file = "http://rpository.com/ds4psy/data/t3.csv")
#
# # Check:
# dim(t3) # 10 observations (rows) x 4 variables (columns)
#
# # Check number of missing values:
# sum(is.na(t3)) # 3 missing values
#
# # Save to /data:
# usethis::use_data(t3, overwrite = TRUE)
#' Data: t3
#'
#' \code{t3} is a fictitious dataset to practice importing and joining data
#' (from a CSV file).
#'
#' @format A table with 10 cases (rows) and 4 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data at \url{http://rpository.com/ds4psy/data/t3.csv}.
"t3"
# (08d) t4.csv: ----
# t4 <- readr::read_csv(file = "http://rpository.com/ds4psy/data/t4.csv")
#
# # Check:
# dim(t4) # 10 observations (rows) x 4 variables (columns)
#
# # Check number of missing values:
# sum(is.na(t4)) # 2 missing values
#
# # Save to /data:
# usethis::use_data(t4, overwrite = TRUE)
#' Data: t4
#'
#' \code{t4} is a fictitious dataset to practice importing and joining data
#' (from a CSV file).
#'
#' @format A table with 10 cases (rows) and 4 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data at \url{http://rpository.com/ds4psy/data/t4.csv}.
"t4"
# Exercise 3:
# (08e) data_t3.csv: ----
# data_t3 <- readr::read_csv(file = "http://rpository.com/ds4psy/data/data_t3.csv")
#
# # Check:
# dim(data_t3) # 20 observations (rows) x 4 variables (columns)
#
# # Check number of missing values:
# sum(is.na(data_t3)) # 3 missing values
#
# # Save to /data:
# usethis::use_data(data_t3, overwrite = TRUE)
#' Data table data_t3.
#'
#' \code{data_t3} is a fictitious dataset to practice importing and joining data
#' (from a CSV file).
#'
#' @format A table with 20 cases (rows) and 4 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data at \url{http://rpository.com/ds4psy/data/data_t3.csv}.
"data_t3"
# (08f) data_t4.csv: ----
# data_t4 <- readr::read_csv(file = "http://rpository.com/ds4psy/data/data_t4.csv")
#
# # Check:
# dim(data_t4) # 20 observations (rows) x 4 variables (columns)
#
# # Check number of missing values:
# sum(is.na(data_t4)) # 3 missing values
#
# # Save to /data:
# usethis::use_data(data_t4, overwrite = TRUE)
#' Data table data_t4.
#'
#' \code{data_t4} is a fictitious dataset to practice importing and joining data
#' (from a CSV file).
#'
#' @format A table with 20 cases (rows) and 4 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data at \url{http://rpository.com/ds4psy/data/data_t4.csv}.
"data_t4"
# (09) Text data (Chapter 9): --------
# (09a) countries: ----
# # Source: <https://www.gapminder.org/data/documentation/gd004/>
# file <- "GM_lifeExpectancy_by_country_v11.csv"
# path <- "./data-raw/raw_data_sources/_gapminder/"
# datapath <- paste0(path, file)
# datapath
#
# GM_life_expectancy <- readr::read_csv2(file = datapath)
# GM_life_expectancy
#
# countries <- GM_life_expectancy$country
# countries
#' Data: Names of countries
#'
#' \code{countries} is a dataset containing the names of
#' 197 countries (as a vector of text strings).
#'
#' @format A vector of type \code{character}
#' with \code{length(countries) = 197}.
#'
#' @family datasets
#'
#' @source
#' Data from \url{https://www.gapminder.org}:
#' Original data at \url{https://www.gapminder.org/data/documentation/gd004/}.
"countries"
# (09b) fruits: ----
# Source: <https://simple.wikipedia.org/wiki/List_of_fruits>
# fruits
# length(fruits) # 122
#' Data: Names of fruits
#'
#' \code{fruits} is a dataset containing the names of
#' 122 fruits (as a vector of text strings).
#'
#' Botanically, "fruits" are the seed-bearing structures
#' of flowering plants (angiosperms) formed from the ovary
#' after flowering.
#'
#' In common usage, "fruits" refer to the fleshy
#' seed-associated structures of a plant
#' that taste sweet or sour,
#' and are edible in their raw state.
#'
#' @format A vector of type \code{character}
#' with \code{length(fruits) = 122}.
#'
#' @family datasets
#'
#' @source
#' Data based on \url{https://simple.wikipedia.org/wiki/List_of_fruits}.
"fruits"
# (09c) flowery phrases: ----
#' Data: Flowery phrases
#'
#' \code{flowery} contains versions and variations
#' of Gertrude Stein's popular phrase
#' "A rose is a rose is a rose".
#'
#' The phrase stems from Gertrude Stein's poem "Sacred Emily"
#' (written in 1913 and published in 1922, in "Geography and Plays").
#' The verbatim line in the poem actually reads
#' "Rose is a rose is a rose is a rose".
#'
#' See \url{https://en.wikipedia.org/wiki/Rose_is_a_rose_is_a_rose_is_a_rose}
#' for additional variations and sources.
#'
#' @format A vector of type \code{character}
#' with \code{length(flowery) = 60}.
#'
#' @family datasets
#'
#' @source
#' Data based on \url{https://en.wikipedia.org/wiki/Rose_is_a_rose_is_a_rose_is_a_rose}.
"flowery"
# (09e) Bushisms: ----
#' Data: Bushisms
#'
#' \code{Bushisms} contains phrases spoken by
#' or attributed to U.S. president George W. Bush
#' (the 43rd president of the United States,
#' in office from January 2001 to January 2009).
#'
#' @format A vector of type \code{character}
#' with \code{length(Bushisms) = 22}.
#'
#' @family datasets
#'
#' @source
#' Data based on \url{https://en.wikipedia.org/wiki/Bushism}.
"Bushisms"
# (09e) Trumpisms: ----
#' Data: Trumpisms
#'
#' \code{Trumpisms} contains frequent words and characteristic phrases
#' by U.S. president Donald J. Trump (the 45th president of the United States,
#' in office from January 20, 2017, to January 20, 2021).
#'
#' @format A vector of type \code{character}
#' with \code{length(Trumpisms) = 168}
#' (on 2021-01-28).
#'
#' @family datasets
#'
#' @source
#' Data originally based on a collection of \emph{Donald Trump's 20 most frequently used words} on \url{https://www.yourdictionary.com}
#' and expanded by interviews, public speeches, and Twitter tweets from \code{https://twitter.com/realDonaldTrump}.
"Trumpisms"
# (10) Time data (Chapter 10): --------
# (10a) fame: ----
# Fame data (DOB and DOD of famous people):
# Chapter 10 (Time data), Exercise 3
# See Exercise 3 at https://bookdown.org/hneth/ds4psy/10-4-time-ex.html#time:ex03
# See file all_DATASETs.R for raw data (as tables).
#' Data: fame
#'
#' \code{fame} is a dataset to practice working with dates.
#'
#' \code{fame} contains the names, areas, dates of birth (DOB), and
#' --- if applicable --- the dates of death (DOD) of famous people.
#'
#' @format A table with 67 cases (rows) and 4 variables (columns).
#'
#' @family datasets
#'
#' @source
#' Student solutions to exercises, dates mostly from \url{https://www.wikipedia.org/}.
"fame"
# (10b) exp_num_dt data: ----
# Experimental numeracy and date-time (dt) data:
# File is a combination from 2 sources:
# A. numeracy data:
# See generating code chunk "data-create-numeracy-data" in ds4psy_book file "55_datasets.Rmd".
# numeracy <- readr::read_csv("../ds4psy/data-raw/numeracy.csv") # local csv file
# numeracy <- readr::read_csv("http://rpository.com/ds4psy/data/numeracy.csv") # online
# numeracy # 1000 x 12
# B. dt data:
# See generating code chunk "data-create-time-bday-data" in ds4psy_book file "55_datasets.Rmd".
# dt <- readr::read_csv("../ds4psy/data-raw/dt.csv") # from local file
# dt <- readr::read_csv("http://rpository.com/ds4psy/data/dt.csv") # online file
# dt # 1000 x 9
## Check:
# dim(exp_num_dt) # 1000 observations (rows) x 15 variables (columns)
# sum(is.na(exp_num_dt)) # 130 missing values
#
## 250202: Recode the gender variable into true binary variable:
# table(exp_num_dt$gender)
# exp_num_dt$gender[exp_num_dt$gender == "male"] <- "not female"
# table(exp_num_dt$gender)
#
## Store data:
# usethis::use_data(exp_num_dt, overwrite = TRUE)
#' Data from an experiment with numeracy and date-time variables
#'
#' \code{exp_num_dt} is a fictitious set of data describing
#' 1000 non-existing, but surprisingly friendly people.
#'
#' \strong{Codebook}
#' The data characterize 1000 individuals (rows) in 15 variables (columns):
#'
#' \itemize{
#'
#' \item 1. \strong{name}: Participant initials.
#'
#' \item 2. \strong{gender}: Self-identified gender (as a binary variable).
#'
#' \item 3. \strong{bday}: Day (within month) of DOB.
#'
#' \item 4. \strong{bmonth}: Month (within year) of DOB.
#'
#' \item 5. \strong{byear}: Year of DOB.
#'
#' \item 6. \strong{height}: Height (in cm).
#'
#' \item 7. \strong{blood_type}: Blood type.
#'
#' \item 8. \strong{bnt_1} to 11. \strong{bnt_4}:
#' Correct response to corresponding BNT question?
#' (1: correct, 0: incorrect).
#'
#' \item 12. \strong{g_iq} and 13. \strong{s_iq}:
#' Scores from two IQ tests (general vs. social).
#'
#' \item 14. \strong{t_1} and 15. \strong{t_2}:
#' Study start and end time.
#'
#' }
#'
#' \code{exp_num_dt} was generated for practice purposes.
#' It allows
#' (1) converting data tables from wider into longer format,
#' (2) dealing with date- and time-related variables, and
#' (3) computing, analyzing, and visualizing test scores (e.g., numeracy, IQ).
#'
#' The \code{gender} variable was converted into a binary variable
#' (i.e., using 2 categories "female" and "not female").
#'
#' @format A table with 1000 cases (rows) and 15 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data files at
#' \url{http://rpository.com/ds4psy/data/numeracy.csv} and
#' \url{http://rpository.com/ds4psy/data/dt.csv}.
"exp_num_dt"
# (10c) dt_10 data: 10 Danish bdays ----
## Sources:
# dt_10 <- readr::read_csv("./data-raw/dt_10.csv") # local file
# dt_10_o <- readr::read_csv("http://rpository.com/ds4psy/data/dt_10.csv") # online
# all.equal(dt_10, dt_10_o)
## Check:
# dim(dt_10) # 10 x 7
#' Data from 10 Danish people
#'
#' \code{dt_10} contains precise DOB information of
#' 10 non-existent, but definitely Danish people.
#'
#' @format A table with 10 cases (rows) and 7 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data file at
#' \url{http://rpository.com/ds4psy/data/dt_10.csv}.
"dt_10"
# (11) Function data (Chapter 11): --------
# none yet.
# (12) Iteration / loops (Chapter 12): --------
# https://bookdown.org/hneth/ds4psy/10-3-iter-essentials.html
# (12a) tb data: ------
# tb <- readr::read_csv2("http://rpository.com/ds4psy/data/tb.csv")
#
# # Check:
# dim(tb) # 100 cases x 5 variables
#
# # Check number of missing values:
# sum(is.na(tb)) # 0 missing values
#
# # Save to /data:
# usethis::use_data(tb, overwrite = TRUE)
#' Data table tb.
#'
#' \code{tb} is a fictitious dataset describing
#' 100 non-existing, but otherwise ordinary people.
#'
#' \strong{Codebook}
#'
#' The table contains 5 columns/variables:
#'
#' \itemize{
#'
#' \item 1. \strong{id}: Participant ID.
#'
#' \item 2. \strong{age}: Age (in years).
#'
#' \item 3. \strong{height}: Height (in cm).
#'
#' \item 4. \strong{shoesize}: Shoesize (EU standard).
#'
#' \item 5. \strong{IQ}: IQ score (according Raven's Regressive Tables).
#'
#' }
#'
#' \code{tb} was originally created to practice loops and iterations
#' (as a CSV file).
#'
#' @format A table with 100 cases (rows) and 5 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data file at \url{http://rpository.com/ds4psy/data/tb.csv}.
"tb"
## Check data: ------
## Check for "marked UTF-8 strings":
# tools:::.check_package_datasets(".")
## ToDo: ----------
# - Add date/time data (Chapter 10: Time, e.g., DOB, time of test, task start/end, etc.)
# - Combine 2 datasets (currently online):
# a. numeracy.csv (1000 x 12, see book chapter 55_datasets.Rmd),
# b. dt.csv (1000 x 9): date and time variables (see book chapter 10_times.Rmd)
# - Consider combining with dataset `outliers` (1000 x 3), BUT: different genders and height values and regularities
# - Collect ds4psy survey data
# - Find some book/text to analyze (Chapter 9: Text data).
# - Add text data (Chapter 9: Text; e.g., dinos, fruit, veggies, attention check response on "i read instructions", some eBook for sentinent analysis, ...)
# - Add more info to codebooks (see data_190807.R in archive)
## eof. ----------------------
hneth/ds4psy documentation built on Feb. 17, 2025, 6:05 p.m.
R Package Documentation
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## data.R | ds4psy
## hn | uni.kn | 2025 02 02
## Documentation of data sets included in /data.
# (01) Positive Psychology data: ----------
# (01a) posPsy_p_info: ------
#' Positive Psychology: Participant data
#'
#' \code{posPsy_p_info} is a dataset containing details of 295 participants.
#'
#' \describe{
#'
#' \item{id}{Participant ID.}
#'
#' \item{intervention}{Type of intervention:
#' 3 positive psychology interventions (PPIs), plus 1 control condition:
#' 1: "Using signature strengths",
#' 2: "Three good things",
#' 3: "Gratitude visit",
#' 4: "Recording early memories" (control condition).}
#'
#' \item{sex}{Sex: 1 = female, 2 = male.}
#'
#' \item{age}{Age (in years).}
#'
#' \item{educ}{Education level: Scale from 1: less than 12 years, to 5: postgraduate degree.}
#'
#' \item{income}{Income: Scale from 1: below average, to 3: above average.}
#'
#' }
#'
#' See codebook and references at \url{https://bookdown.org/hneth/ds4psy/B-1-datasets-pos.html}.
#'
#' @format A table with 295 cases (rows) and 6 variables (columns).
#'
#' @family datasets
#'
#' @source
#' \strong{Articles}
#'
#' \itemize{
#'
#' \item Woodworth, R. J., O’Brien-Malone, A., Diamond, M. R., & Schüz, B. (2017).
#' Web-based positive psychology interventions: A reexamination of effectiveness.
#' \emph{Journal of Clinical Psychology}, \emph{73}(3), 218--232.
#' doi: \code{10.1002/jclp.22328}
#'
#' \item Woodworth, R. J., O’Brien-Malone, A., Diamond, M. R. and Schüz, B. (2018).
#' Data from, ‘Web-based positive psychology interventions: A reexamination of effectiveness’.
#' \emph{Journal of Open Psychology Data}, \emph{6}(1).
#' doi: \code{10.5334/jopd.35}
#' }
#'
#' See \url{https://openpsychologydata.metajnl.com/articles/10.5334/jopd.35/} for details
#' and \doi{10.6084/m9.figshare.1577563.v1} for original dataset.
#'
#' Additional references at \url{https://bookdown.org/hneth/ds4psy/B-1-datasets-pos.html}.
"posPsy_p_info"
# (01b) posPsy_AHI_CESD: ------
#' Positive Psychology: AHI CESD data
#'
#' \code{posPsy_AHI_CESD} is a dataset containing answers to the 24 items of the
#' Authentic Happiness Inventory (AHI) and answers to the
#' 20 items of the Center for Epidemiological Studies Depression (CES-D) scale
#' (Radloff, 1977) for multiple (1 to 6) measurement occasions.
#'
#' \strong{Codebook}
#'
#' \itemize{
#'
#' \item 1. \strong{id}: Participant ID.
#'
#' \item 2. \strong{occasion}: Measurement occasion:
#' 0: Pretest (i.e., at enrolment),
#' 1: Posttest (i.e., 7 days after pretest),
#' 2: 1-week follow-up, (i.e., 14 days after pretest, 7 days after posttest),
#' 3: 1-month follow-up, (i.e., 38 days after pretest, 31 days after posttest),
#' 4: 3-month follow-up, (i.e., 98 days after pretest, 91 days after posttest),
#' 5: 6-month follow-up, (i.e., 189 days after pretest, 182 days after posttest).
#'
#' \item 3. \strong{elapsed.days}: Time since enrolment measured in fractional days.
#'
#' \item 4. \strong{intervention}: Type of intervention:
#' 3 positive psychology interventions (PPIs), plus 1 control condition:
#' 1: "Using signature strengths",
#' 2: "Three good things",
#' 3: "Gratitude visit",
#' 4: "Recording early memories" (control condition).
#'
#' \item 5.-28. (from \strong{ahi01} to \strong{ahi24}): Responses on 24 AHI items.
#'
#' \item 29.-48. (from \strong{cesd01} to \strong{cesd20}): Responses on 20 CES-D items.
#'
#' \item 49. \strong{ahiTotal}: Total AHI score.
#'
#' \item 50. \strong{cesdTotal}: Total CES-D score.
#'
#' }
#'
#' See codebook and references at \url{https://bookdown.org/hneth/ds4psy/B-1-datasets-pos.html}.
#'
#' @format A table with 992 cases (rows) and 50 variables (columns).
#'
#' @family datasets
#'
#' @seealso
#' \code{\link{posPsy_long}} for a corrected version of this file (in long format).
#'
#' @source
#' \strong{Articles}
#'
#' \itemize{
#'
#' \item Woodworth, R. J., O’Brien-Malone, A., Diamond, M. R., & Schüz, B. (2017).
#' Web-based positive psychology interventions: A reexamination of effectiveness.
#' \emph{Journal of Clinical Psychology}, \emph{73}(3), 218--232.
#' doi: \code{10.1002/jclp.22328}
#'
#' \item Woodworth, R. J., O’Brien-Malone, A., Diamond, M. R. and Schüz, B. (2018).
#' Data from, ‘Web-based positive psychology interventions: A reexamination of effectiveness’.
#' \emph{Journal of Open Psychology Data}, \emph{6}(1).
#' doi: \code{10.5334/jopd.35}
#' }
#'
#' See \url{https://openpsychologydata.metajnl.com/articles/10.5334/jopd.35/} for details
#' and \doi{10.6084/m9.figshare.1577563.v1} for original dataset.
#'
#' Additional references at \url{https://bookdown.org/hneth/ds4psy/B-1-datasets-pos.html}.
"posPsy_AHI_CESD"
# (01c) posPsy_long: ------
#' Positive Psychology: AHI CESD corrected data (in long format)
#'
#' \code{posPsy_long} is a dataset containing answers to the 24 items of the
#' Authentic Happiness Inventory (AHI) and answers to the
#' 20 items of the Center for Epidemiological Studies Depression (CES-D) scale
#' (see Radloff, 1977) for multiple (1 to 6) measurement occasions.
#'
#' This dataset is a corrected version of \code{\link{posPsy_AHI_CESD}}
#' and in long-format.
#'
#' @format A table with 990 cases (rows) and 50 variables (columns).
#'
#' @family datasets
#'
#' @seealso
#' \code{\link{posPsy_AHI_CESD}} for source of this file and codebook information;
#' \code{\link{posPsy_wide}} for a version of this file (in wide format).
#'
#' @source
#' \strong{Articles}
#'
#' \itemize{
#'
#' \item Woodworth, R. J., O’Brien-Malone, A., Diamond, M. R., & Schüz, B. (2017).
#' Web-based positive psychology interventions: A reexamination of effectiveness.
#' \emph{Journal of Clinical Psychology}, \emph{73}(3), 218--232.
#' doi: \code{10.1002/jclp.22328}
#'
#' \item Woodworth, R. J., O’Brien-Malone, A., Diamond, M. R. and Schüz, B. (2018).
#' Data from, ‘Web-based positive psychology interventions: A reexamination of effectiveness’.
#' \emph{Journal of Open Psychology Data}, \emph{6}(1).
#' doi: \code{10.5334/jopd.35}
#' }
#'
#' See \url{https://openpsychologydata.metajnl.com/articles/10.5334/jopd.35/} for details
#' and \doi{10.6084/m9.figshare.1577563.v1} for original dataset.
#'
#' Additional references at \url{https://bookdown.org/hneth/ds4psy/B-1-datasets-pos.html}.
"posPsy_long"
# (01d) posPsy_wide: ------
#' Positive Psychology: All corrected data (in wide format)
#'
#' \code{posPsy_wide} is a dataset containing answers to the 24 items of the
#' Authentic Happiness Inventory (AHI) and answers to the
#' 20 items of the Center for Epidemiological Studies Depression (CES-D) scale
#' (see Radloff, 1977) for multiple (1 to 6) measurement occasions.
#'
#' This dataset is based on \code{\link{posPsy_AHI_CESD}} and
#' \code{\link{posPsy_long}}, but is in wide format.
#'
#' @family datasets
#'
#' @seealso
#' \code{\link{posPsy_AHI_CESD}} for the source of this file,
#' \code{\link{posPsy_long}} for a version of this file (in long format).
#'
#' @source
#' \strong{Articles}
#'
#' \itemize{
#'
#' \item Woodworth, R. J., O’Brien-Malone, A., Diamond, M. R., & Schüz, B. (2017).
#' Web-based positive psychology interventions: A reexamination of effectiveness.
#' \emph{Journal of Clinical Psychology}, \emph{73}(3), 218--232.
#' doi: \code{10.1002/jclp.22328}
#'
#' \item Woodworth, R. J., O’Brien-Malone, A., Diamond, M. R. and Schüz, B. (2018).
#' Data from, ‘Web-based positive psychology interventions: A reexamination of effectiveness’.
#' \emph{Journal of Open Psychology Data}, \emph{6}(1).
#' doi: \code{10.5334/jopd.35}
#' }
#'
#' See \url{https://openpsychologydata.metajnl.com/articles/10.5334/jopd.35/} for details
#' and \doi{10.6084/m9.figshare.1577563.v1} for original dataset.
#'
#' Additional references at \url{https://bookdown.org/hneth/ds4psy/B-1-datasets-pos.html}.
"posPsy_wide"
# (02) False Positive Psychology data: ----------
# https://bookdown.org/hneth/ds4psy/B-2-datasets-false.html
#' Data: False Positive Psychology
#'
#' \code{falsePosPsy_all} is a dataset containing the data from 2 studies designed to
#' highlight problematic research practices within psychology.
#'
#' Simmons, Nelson and Simonsohn (2011) published a controversial article
#' with a necessarily false finding. By conducting simulations and 2 simple behavioral experiments,
#' the authors show that flexibility in data collection, analysis, and reporting
#' dramatically increases the rate of false-positive findings.
#'
#' \describe{
#' \item{study}{Study ID.}
#' \item{id}{Participant ID.}
#' \item{aged}{Days since participant was born (based on their self-reported birthday).}
#' \item{aged365}{Age in years.}
#' \item{female}{Is participant a woman? 1: yes, 2: no.}
#' \item{dad}{Father's age (in years).}
#' \item{mom}{Mother's age (in years).}
#' \item{potato}{Did the participant hear the song 'Hot Potato' by The Wiggles? 1: yes, 2: no.}
#' \item{when64}{Did the participant hear the song 'When I am 64' by The Beatles? 1: yes, 2: no.}
#' \item{kalimba}{Did the participant hear the song 'Kalimba' by Mr. Scrub? 1: yes, 2: no.}
#' \item{cond}{In which condition was the participant?
#' control: Subject heard the song 'Kalimba' by Mr. Scrub;
#' potato: Subject heard the song 'Hot Potato' by The Wiggles;
#' 64: Subject heard the song 'When I am 64' by The Beatles.}
#' \item{root}{Could participant report the square root of 100? 1: yes, 2: no.}
#' \item{bird}{Imagine a restaurant you really like offered a 30 percent discount for dining between 4pm and 6pm.
#' How likely would you be to take advantage of that offer?
#' Scale from 1: very unlikely, 7: very likely.}
#' \item{political}{In the political spectrum, where would you place yourself?
#' Scale: 1: very liberal, 2: liberal, 3: centrist, 4: conservative, 5: very conservative.}
#' \item{quarterback}{If you had to guess who was chosen the quarterback of the year in Canada last year,
#' which of the following four options would you choose?
#' 1: Dalton Bell, 2: Daryll Clark, 3: Jarious Jackson, 4: Frank Wilczynski.}
#' \item{olddays}{How often have you referred to some past part of your life as “the good old days”?
#' Scale: 11: never, 12: almost never, 13: sometimes, 14: often, 15: very often.}
#' \item{feelold}{How old do you feel?
#' Scale: 1: very young, 2: young, 3: neither young nor old, 4: old, 5: very old.}
#' \item{computer}{Computers are complicated machines.
#' Scale from 1: strongly disagree, to 5: strongly agree.}
#' \item{diner}{Imagine you were going to a diner for dinner tonight, how much do you think you would like the food?
#' Scale from 1: dislike extremely, to 9: like extremely.}
#' }
#'
#' See \url{https://bookdown.org/hneth/ds4psy/B-2-datasets-false.html} for codebook and more information.
#'
#'
#' @format A table with 78 cases (rows) and 19 variables (columns):
#'
#' @family datasets
#'
#' @source
#' \strong{Articles}
#'
#' \itemize{
#'
#' \item Simmons, J.P., Nelson, L.D., & Simonsohn, U. (2011).
#' False-positive psychology: Undisclosed flexibility in data collection and analysis
#' allows presenting anything as significant.
#' \emph{Psychological Science}, \emph{22}(11), 1359--1366.
#' doi: \code{10.1177/0956797611417632}
#'
#' \item Simmons, J.P., Nelson, L.D., & Simonsohn, U. (2014).
#' Data from paper "False-Positive Psychology:
#' Undisclosed Flexibility in Data Collection and Analysis
#' Allows Presenting Anything as Significant".
#' \emph{Journal of Open Psychology Data}, \emph{2}(1), e1.
#' doi: \code{10.5334/jopd.aa}
#' }
#'
#' See files at \url{https://openpsychologydata.metajnl.com/articles/10.5334/jopd.aa/} and
#' the archive at \url{https://zenodo.org/record/7664} for original dataset.
"falsePosPsy_all"
# (03) Transforming data / dplyr (Chapter 3): outliers ----------
# https://bookdown.org/hneth/ds4psy/3-6-transform-ex.html
#' Outlier data.
#'
#' \code{outliers} is a fictitious dataset containing the id, sex, and height
#' of 1000 non-existing, but otherwise normal people.
#'
#' \strong{Codebook}
#'
#' \describe{
#' \item{id}{Participant ID (as character code)}
#' \item{sex}{Gender (female vs. male)}
#' \item{height}{Height (in cm)}
#' }
#'
#' @format A table with 100 cases (rows) and 3 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data at \url{http://rpository.com/ds4psy/data/out.csv}.
"outliers"
# (03.14) pi data: --------
# https://bookdown.org/hneth/ds4psy/10-3-iter-essentials.html
# Orig. data source <http://www.geom.uiuc.edu/~huberty/math5337/groupe/digits.html>
# # pi_all <- readLines("./data/pi_100k.txt") # from local data file
# pi_data <- "http://rpository.com/ds4psy/data/pi_100k.txt" # URL of online data file
# pi_100k <- readLines(pi_data) # read from online source
#
# # Check:
# dim(pi_100k) # NULL !
#
# # Check number of missing values:
# sum(is.na(pi_100k)) # 0 missing values
#
# # Save to /data:
# usethis::use_data(pi_100k, overwrite = TRUE)
#' Data: 100k digits of pi.
#'
#' \code{pi_100k} is a dataset containing the first 100k digits of pi.
#'
#' @format A character of \code{nchar(pi_100k) = 100001}.
#'
#' @family datasets
#'
#' @source
#' See TXT data at \url{http://rpository.com/ds4psy/data/pi_100k.txt}.
#'
#' Original data at \url{http://www.geom.uiuc.edu/~huberty/math5337/groupe/digits.html}.
"pi_100k"
# (06) Importing data / readr (Chapter 6): ----------
# https://bookdown.org/hneth/ds4psy/6-3-import-essentials.html
# (06a) data_t1.csv: ----
# Note: Same as (6a) below.
# data_t1 <- readr::read_csv("http://rpository.com/ds4psy/data/data_t1.csv")
#
# # Check:
# dim(data_t1) # 20 observations (rows) x 4 variables (columns)
#
# # Check number of missing values:
# sum(is.na(data_t1)) # 3 missing values
#
# # Save to /data:
# usethis::use_data(data_t1, overwrite = TRUE)
#' Data table data_t1.
#'
#' \code{data_t1} is a fictitious dataset to practice importing and joining data
#' (from a CSV file).
#'
#' @format A table with 20 cases (rows) and 4 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data at \url{http://rpository.com/ds4psy/data/data_t1.csv}.
"data_t1"
# (06b) data_t1_de.csv: ----
# data_t1_de <- readr::read_csv2("http://rpository.com/ds4psy/data/data_t1_de.csv")
#
# # Check:
# dim(data_t1_de) # 20 observations (rows) x 4 variables (columns)
#
# # Check number of missing values:
# sum(is.na(data_t1_de)) # 3 missing values
#
# # Save to /data:
# usethis::use_data(data_t1_de, overwrite = TRUE)
#' Data import data_t1_de.
#'
#' \code{data_t1_de} is a fictitious dataset to practice importing data
#' (from a CSV file, de/European style).
#'
#' @format A table with 20 cases (rows) and 4 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data at \url{http://rpository.com/ds4psy/data/data_t1_de.csv}.
"data_t1_de"
# (06c) data_t1_tab.csv: ----
# data_t1_tab <- read_tsv("http://rpository.com/ds4psy/data/data_t1_tab.csv")
#
# # Check:
# dim(data_t1_tab) # 20 observations (rows) x 4 variables (columns)
#
# # Check number of missing values:
# sum(is.na(data_t1_tab)) # 3 missing values
#
# # Save to /data:
# usethis::use_data(data_t1_tab, overwrite = TRUE)
#' Data import data_t1_tab.
#'
#' \code{data_t1_tab} is a fictitious dataset to practice importing data
#' (from a TAB file).
#'
#' @format A table with 20 cases (rows) and 4 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See TAB-delimited data at \url{http://rpository.com/ds4psy/data/data_t1_tab.csv}.
"data_t1_tab"
# (06d) data_1.dat: ----
# my_file <- "http://rpository.com/ds4psy/data/data_1.dat"
#
# data_1 <- readr::read_delim(my_file, delim = ".",
# col_names = c("initials", "age", "tel", "pwd"),
# na = c("-77", "-99"))
#
# # Check:
# dim(data_1) # 100 observations (rows) x 4 variables (columns)
#
# # Check number of missing values:
# sum(is.na(data_1)) # 15 missing values
#
# # Save to /data:
# usethis::use_data(data_1, overwrite = TRUE)
#' Data import data_1.
#'
#' \code{data_1} is a fictitious dataset to practice importing data
#' (from a DELIMITED file).
#'
#' @format A table with 100 cases (rows) and 4 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See DELIMITED data at \url{http://rpository.com/ds4psy/data/data_1.dat}.
"data_1"
# (06e) data_2.dat: ----
# my_file_path <- "http://rpository.com/ds4psy/data/data_2.dat" # from online source
#
# # read_fwf:
# data_2 <- readr::read_fwf(my_file_path,
# fwf_cols(initials = c(1, 2),
# age = c(4, 5),
# tel = c(7, 10),
# pwd = c(12, 17)))
#
# # Check:
# dim(data_2) # 100 observations (rows) x 4 variables (columns)
#
# # Check number of missing values:
# sum(is.na(data_2)) # 0 missing values
#
# # Save to /data:
# usethis::use_data(data_2, overwrite = TRUE)
#' Data import data_2.
#'
#' \code{data_2} is a fictitious dataset to practice importing data
#' (from a FWF file).
#'
#' @format A table with 100 cases (rows) and 4 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See FWF data at \url{http://rpository.com/ds4psy/data/data_2.dat}.
"data_2"
# (07) Tidying data / tidyr (Chapter 7): ----------
# https://bookdown.org/hneth/ds4psy/7-3-tidy-essentials.html
# (07a) table6.csv: ------
# ## Load data (as comma-separated file):
# table6 <- readr::read_csv("http://rpository.com/ds4psy/data/table6.csv") # from online source
#
# # Check:
# dim(table6) # 6 observations (rows) x 2 variables (columns)
#
# # Check number of missing values:
# sum(is.na(table6)) # 0 missing values
#
# # Save to /data:
# usethis::use_data(table6, overwrite = TRUE)
#' Data: table6
#'
#' \code{table6} is a fictitious dataset to practice reshaping and tidying data.
#'
#' This dataset is a further variant of the \code{table1} to \code{table5} datasets
#' of the \bold{tidyr} package.
#'
#' @format A table with 6 cases (rows) and 2 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data at \url{http://rpository.com/ds4psy/data/table6.csv}.
"table6"
# (07b) table7.csv: ------
# # Load data (as comma-separated file):
# table7 <- readr::read_csv("http://rpository.com/ds4psy/data/table7.csv") # from online source
#
# # Check:
# dim(table7) # 6 observations (rows) x 1 (horrendous) variable (column)
#
# # Check number of missing values:
# sum(is.na(table7)) # 0 missing values
#
# # Save to /data:
# usethis::use_data(table7, overwrite = TRUE)
#' Data: table7
#'
#' \code{table7} is a fictitious dataset to practice reshaping and tidying data.
#'
#' This dataset is a further variant of the \code{table1} to \code{table5} datasets
#' of the \bold{tidyr} package.
#'
#' @format A table with 6 cases (rows) and 1 (horrendous) variable (column).
#'
#' @family datasets
#'
#' @source
#' See CSV data at \url{http://rpository.com/ds4psy/data/table7.csv}.
"table7"
# (07c) table8.csv: ------
# # Load data (as comma-separated file):
# table8 <- readr::read_csv("http://rpository.com/ds4psy/data/table8.csv") # from online source
#
# # Check:
# dim(table8) # 3 observations (rows) x 5 variables (columns)
#
# # Check number of missing values:
# sum(is.na(table8)) # 0 missing values
#
# # Save to /data:
# usethis::use_data(table8, overwrite = TRUE)
#' Data: table8
#'
#' \code{table9} is a fictitious dataset to practice reshaping and tidying data.
#'
#' This dataset is a further variant of the \code{table1} to \code{table5} datasets
#' of the \bold{tidyr} package.
#'
#' @format A table with 3 cases (rows) and 5 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data at \url{http://rpository.com/ds4psy/data/table8.csv}.
"table8"
# (07c2) table9: The contingency table tidyr::table2 as a 3-dimensional array (xtabs) ------
# # Data from tidyr::table1 as a contingency table (with a dedicated "count" variable):
# ct <- tidyr::table2
#
# # Create 3-dimensional array (xtabs < table):
# table9 <- stats::xtabs(formula = count ~., data = ct)
# dim(table9) # 3 2 2
# str(table9)
# sum(table9) # 2940985206
#' Data table9.
#'
#' \code{table9} is a fictitious dataset to practice reshaping and tidying data.
#'
#' This dataset is a further variant of the \code{table1} to \code{table5} datasets
#' of the \bold{tidyr} package.
#'
#' @format A 3 x 2 x 2 array (of type "xtabs") with 2940985206 elements (frequency counts).
#'
#' @family datasets
#'
#' @source
#' Generated by using \code{stats::xtabs(formula = count ~., data = tidyr::table2)}.
"table9"
# (07d) exp_wide.csv: ------
# https://bookdown.org/hneth/ds4psy/7-5-tidy-ex.html
# exp_wide <- readr::read_csv("http://rpository.com/ds4psy/data/exp_wide.csv") # from online source
#
# # Check:
# dim(exp_wide) # 10 observations (rows) x 7 variables (columns)
#
# # Check number of missing values:
# sum(is.na(exp_wide)) # 0 missing values
#
# # Save to /data:
# usethis::use_data(exp_wide, overwrite = TRUE)
#' Data exp_wide.
#'
#' \code{exp_wide} is a fictitious dataset to practice tidying data
#' (here: converting from wide to long format).
#'
#' @format A table with 10 cases (rows) and 7 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data at \url{http://rpository.com/ds4psy/data/exp_wide.csv}.
"exp_wide"
# (07e) Chapter 7: Exercise 1: 'Four messes and one tidy table': ------
# https://bookdown.org/hneth/ds4psy/7-4-tidy-ex.html#tidy:ex01
# (07e1): t_1.csv: -----
#' Data: t_1
#'
#' \code{t_1} is a fictitious dataset to practice tidying data.
#'
#' @format A table with 8 cases (rows) and 9 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data at \url{http://rpository.com/ds4psy/data/t_1.csv}.
"t_1"
# (07e2): t_2.csv: -----
#' Data: t_2
#'
#' \code{t_2} is a fictitious dataset to practice tidying data.
#'
#' @format A table with 8 cases (rows) and 5 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data at \url{http://rpository.com/ds4psy/data/t_2.csv}.
"t_2"
# (07e3): t_3.csv: -----
#' Data: t_3
#'
#' \code{t_3} is a fictitious dataset to practice tidying data.
#'
#' @format A table with 16 cases (rows) and 6 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data at \url{http://rpository.com/ds4psy/data/t_3.csv}.
"t_3"
# (07e4): t_4.csv: -----
#' Data: t_4
#'
#' \code{t_4} is a fictitious dataset to practice tidying data.
#'
#' @format A table with 16 cases (rows) and 8 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data at \url{http://rpository.com/ds4psy/data/t_4.csv}.
"t_4"
# (08) Joining data / dplyr (Chapter 8): ----------
# https://bookdown.org/hneth/ds4psy/8-3-join-essentials.html
# (08a) data_t1.csv: ----
# Note: Same as (4a) above.
# data_t1 <- readr::read_csv("http://rpository.com/ds4psy/data/data_t1.csv")
#
# # Check:
# dim(data_t1) # 20 observations (rows) x 4 variables (columns)
#
# # Check number of missing values:
# sum(is.na(data_t1)) # 3 missing values
#
# # Save to /data:
# usethis::use_data(data_t1, overwrite = TRUE)
# See (4a) above.
# (08b) data_t2.csv: ----
# data_t2 <- readr::read_csv(file = "http://rpository.com/ds4psy/data/data_t2.csv")
#
# # Check:
# dim(data_t2) # 20 observations (rows) x 4 variables (columns)
#
# # Check number of missing values:
# sum(is.na(data_t2)) # 3 missing values
#
# # Save to /data:
# usethis::use_data(data_t2, overwrite = TRUE)
#' Data table data_t2.
#'
#' \code{data_t2} is a fictitious dataset to practice importing and joining data
#' (from a CSV file).
#'
#' @format A table with 20 cases (rows) and 4 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data at \url{http://rpository.com/ds4psy/data/data_t2.csv}.
"data_t2"
# Exercise 1:
# (08c) t3.csv: ----
# t3 <- readr::read_csv(file = "http://rpository.com/ds4psy/data/t3.csv")
#
# # Check:
# dim(t3) # 10 observations (rows) x 4 variables (columns)
#
# # Check number of missing values:
# sum(is.na(t3)) # 3 missing values
#
# # Save to /data:
# usethis::use_data(t3, overwrite = TRUE)
#' Data: t3
#'
#' \code{t3} is a fictitious dataset to practice importing and joining data
#' (from a CSV file).
#'
#' @format A table with 10 cases (rows) and 4 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data at \url{http://rpository.com/ds4psy/data/t3.csv}.
"t3"
# (08d) t4.csv: ----
# t4 <- readr::read_csv(file = "http://rpository.com/ds4psy/data/t4.csv")
#
# # Check:
# dim(t4) # 10 observations (rows) x 4 variables (columns)
#
# # Check number of missing values:
# sum(is.na(t4)) # 2 missing values
#
# # Save to /data:
# usethis::use_data(t4, overwrite = TRUE)
#' Data: t4
#'
#' \code{t4} is a fictitious dataset to practice importing and joining data
#' (from a CSV file).
#'
#' @format A table with 10 cases (rows) and 4 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data at \url{http://rpository.com/ds4psy/data/t4.csv}.
"t4"
# Exercise 3:
# (08e) data_t3.csv: ----
# data_t3 <- readr::read_csv(file = "http://rpository.com/ds4psy/data/data_t3.csv")
#
# # Check:
# dim(data_t3) # 20 observations (rows) x 4 variables (columns)
#
# # Check number of missing values:
# sum(is.na(data_t3)) # 3 missing values
#
# # Save to /data:
# usethis::use_data(data_t3, overwrite = TRUE)
#' Data table data_t3.
#'
#' \code{data_t3} is a fictitious dataset to practice importing and joining data
#' (from a CSV file).
#'
#' @format A table with 20 cases (rows) and 4 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data at \url{http://rpository.com/ds4psy/data/data_t3.csv}.
"data_t3"
# (08f) data_t4.csv: ----
# data_t4 <- readr::read_csv(file = "http://rpository.com/ds4psy/data/data_t4.csv")
#
# # Check:
# dim(data_t4) # 20 observations (rows) x 4 variables (columns)
#
# # Check number of missing values:
# sum(is.na(data_t4)) # 3 missing values
#
# # Save to /data:
# usethis::use_data(data_t4, overwrite = TRUE)
#' Data table data_t4.
#'
#' \code{data_t4} is a fictitious dataset to practice importing and joining data
#' (from a CSV file).
#'
#' @format A table with 20 cases (rows) and 4 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data at \url{http://rpository.com/ds4psy/data/data_t4.csv}.
"data_t4"
# (09) Text data (Chapter 9): --------
# (09a) countries: ----
# # Source: <https://www.gapminder.org/data/documentation/gd004/>
# file <- "GM_lifeExpectancy_by_country_v11.csv"
# path <- "./data-raw/raw_data_sources/_gapminder/"
# datapath <- paste0(path, file)
# datapath
#
# GM_life_expectancy <- readr::read_csv2(file = datapath)
# GM_life_expectancy
#
# countries <- GM_life_expectancy$country
# countries
#' Data: Names of countries
#'
#' \code{countries} is a dataset containing the names of
#' 197 countries (as a vector of text strings).
#'
#' @format A vector of type \code{character}
#' with \code{length(countries) = 197}.
#'
#' @family datasets
#'
#' @source
#' Data from \url{https://www.gapminder.org}:
#' Original data at \url{https://www.gapminder.org/data/documentation/gd004/}.
"countries"
# (09b) fruits: ----
# Source: <https://simple.wikipedia.org/wiki/List_of_fruits>
# fruits
# length(fruits) # 122
#' Data: Names of fruits
#'
#' \code{fruits} is a dataset containing the names of
#' 122 fruits (as a vector of text strings).
#'
#' Botanically, "fruits" are the seed-bearing structures
#' of flowering plants (angiosperms) formed from the ovary
#' after flowering.
#'
#' In common usage, "fruits" refer to the fleshy
#' seed-associated structures of a plant
#' that taste sweet or sour,
#' and are edible in their raw state.
#'
#' @format A vector of type \code{character}
#' with \code{length(fruits) = 122}.
#'
#' @family datasets
#'
#' @source
#' Data based on \url{https://simple.wikipedia.org/wiki/List_of_fruits}.
"fruits"
# (09c) flowery phrases: ----
#' Data: Flowery phrases
#'
#' \code{flowery} contains versions and variations
#' of Gertrude Stein's popular phrase
#' "A rose is a rose is a rose".
#'
#' The phrase stems from Gertrude Stein's poem "Sacred Emily"
#' (written in 1913 and published in 1922, in "Geography and Plays").
#' The verbatim line in the poem actually reads
#' "Rose is a rose is a rose is a rose".
#'
#' See \url{https://en.wikipedia.org/wiki/Rose_is_a_rose_is_a_rose_is_a_rose}
#' for additional variations and sources.
#'
#' @format A vector of type \code{character}
#' with \code{length(flowery) = 60}.
#'
#' @family datasets
#'
#' @source
#' Data based on \url{https://en.wikipedia.org/wiki/Rose_is_a_rose_is_a_rose_is_a_rose}.
"flowery"
# (09e) Bushisms: ----
#' Data: Bushisms
#'
#' \code{Bushisms} contains phrases spoken by
#' or attributed to U.S. president George W. Bush
#' (the 43rd president of the United States,
#' in office from January 2001 to January 2009).
#'
#' @format A vector of type \code{character}
#' with \code{length(Bushisms) = 22}.
#'
#' @family datasets
#'
#' @source
#' Data based on \url{https://en.wikipedia.org/wiki/Bushism}.
"Bushisms"
# (09e) Trumpisms: ----
#' Data: Trumpisms
#'
#' \code{Trumpisms} contains frequent words and characteristic phrases
#' by U.S. president Donald J. Trump (the 45th president of the United States,
#' in office from January 20, 2017, to January 20, 2021).
#'
#' @format A vector of type \code{character}
#' with \code{length(Trumpisms) = 168}
#' (on 2021-01-28).
#'
#' @family datasets
#'
#' @source
#' Data originally based on a collection of \emph{Donald Trump's 20 most frequently used words} on \url{https://www.yourdictionary.com}
#' and expanded by interviews, public speeches, and Twitter tweets from \code{https://twitter.com/realDonaldTrump}.
"Trumpisms"
# (10) Time data (Chapter 10): --------
# (10a) fame: ----
# Fame data (DOB and DOD of famous people):
# Chapter 10 (Time data), Exercise 3
# See Exercise 3 at https://bookdown.org/hneth/ds4psy/10-4-time-ex.html#time:ex03
# See file all_DATASETs.R for raw data (as tables).
#' Data: fame
#'
#' \code{fame} is a dataset to practice working with dates.
#'
#' \code{fame} contains the names, areas, dates of birth (DOB), and
#' --- if applicable --- the dates of death (DOD) of famous people.
#'
#' @format A table with 67 cases (rows) and 4 variables (columns).
#'
#' @family datasets
#'
#' @source
#' Student solutions to exercises, dates mostly from \url{https://www.wikipedia.org/}.
"fame"
# (10b) exp_num_dt data: ----
# Experimental numeracy and date-time (dt) data:
# File is a combination from 2 sources:
# A. numeracy data:
# See generating code chunk "data-create-numeracy-data" in ds4psy_book file "55_datasets.Rmd".
# numeracy <- readr::read_csv("../ds4psy/data-raw/numeracy.csv") # local csv file
# numeracy <- readr::read_csv("http://rpository.com/ds4psy/data/numeracy.csv") # online
# numeracy # 1000 x 12
# B. dt data:
# See generating code chunk "data-create-time-bday-data" in ds4psy_book file "55_datasets.Rmd".
# dt <- readr::read_csv("../ds4psy/data-raw/dt.csv") # from local file
# dt <- readr::read_csv("http://rpository.com/ds4psy/data/dt.csv") # online file
# dt # 1000 x 9
## Check:
# dim(exp_num_dt) # 1000 observations (rows) x 15 variables (columns)
# sum(is.na(exp_num_dt)) # 130 missing values
#
## 250202: Recode the gender variable into true binary variable:
# table(exp_num_dt$gender)
# exp_num_dt$gender[exp_num_dt$gender == "male"] <- "not female"
# table(exp_num_dt$gender)
#
## Store data:
# usethis::use_data(exp_num_dt, overwrite = TRUE)
#' Data from an experiment with numeracy and date-time variables
#'
#' \code{exp_num_dt} is a fictitious set of data describing
#' 1000 non-existing, but surprisingly friendly people.
#'
#' \strong{Codebook}
#' The data characterize 1000 individuals (rows) in 15 variables (columns):
#'
#' \itemize{
#'
#' \item 1. \strong{name}: Participant initials.
#'
#' \item 2. \strong{gender}: Self-identified gender (as a binary variable).
#'
#' \item 3. \strong{bday}: Day (within month) of DOB.
#'
#' \item 4. \strong{bmonth}: Month (within year) of DOB.
#'
#' \item 5. \strong{byear}: Year of DOB.
#'
#' \item 6. \strong{height}: Height (in cm).
#'
#' \item 7. \strong{blood_type}: Blood type.
#'
#' \item 8. \strong{bnt_1} to 11. \strong{bnt_4}:
#' Correct response to corresponding BNT question?
#' (1: correct, 0: incorrect).
#'
#' \item 12. \strong{g_iq} and 13. \strong{s_iq}:
#' Scores from two IQ tests (general vs. social).
#'
#' \item 14. \strong{t_1} and 15. \strong{t_2}:
#' Study start and end time.
#'
#' }
#'
#' \code{exp_num_dt} was generated for practice purposes.
#' It allows
#' (1) converting data tables from wider into longer format,
#' (2) dealing with date- and time-related variables, and
#' (3) computing, analyzing, and visualizing test scores (e.g., numeracy, IQ).
#'
#' The \code{gender} variable was converted into a binary variable
#' (i.e., using 2 categories "female" and "not female").
#'
#' @format A table with 1000 cases (rows) and 15 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data files at
#' \url{http://rpository.com/ds4psy/data/numeracy.csv} and
#' \url{http://rpository.com/ds4psy/data/dt.csv}.
"exp_num_dt"
# (10c) dt_10 data: 10 Danish bdays ----
## Sources:
# dt_10 <- readr::read_csv("./data-raw/dt_10.csv") # local file
# dt_10_o <- readr::read_csv("http://rpository.com/ds4psy/data/dt_10.csv") # online
# all.equal(dt_10, dt_10_o)
## Check:
# dim(dt_10) # 10 x 7
#' Data from 10 Danish people
#'
#' \code{dt_10} contains precise DOB information of
#' 10 non-existent, but definitely Danish people.
#'
#' @format A table with 10 cases (rows) and 7 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data file at
#' \url{http://rpository.com/ds4psy/data/dt_10.csv}.
"dt_10"
# (11) Function data (Chapter 11): --------
# none yet.
# (12) Iteration / loops (Chapter 12): --------
# https://bookdown.org/hneth/ds4psy/10-3-iter-essentials.html
# (12a) tb data: ------
# tb <- readr::read_csv2("http://rpository.com/ds4psy/data/tb.csv")
#
# # Check:
# dim(tb) # 100 cases x 5 variables
#
# # Check number of missing values:
# sum(is.na(tb)) # 0 missing values
#
# # Save to /data:
# usethis::use_data(tb, overwrite = TRUE)
#' Data table tb.
#'
#' \code{tb} is a fictitious dataset describing
#' 100 non-existing, but otherwise ordinary people.
#'
#' \strong{Codebook}
#'
#' The table contains 5 columns/variables:
#'
#' \itemize{
#'
#' \item 1. \strong{id}: Participant ID.
#'
#' \item 2. \strong{age}: Age (in years).
#'
#' \item 3. \strong{height}: Height (in cm).
#'
#' \item 4. \strong{shoesize}: Shoesize (EU standard).
#'
#' \item 5. \strong{IQ}: IQ score (according Raven's Regressive Tables).
#'
#' }
#'
#' \code{tb} was originally created to practice loops and iterations
#' (as a CSV file).
#'
#' @format A table with 100 cases (rows) and 5 variables (columns).
#'
#' @family datasets
#'
#' @source
#' See CSV data file at \url{http://rpository.com/ds4psy/data/tb.csv}.
"tb"
## Check data: ------
## Check for "marked UTF-8 strings":
# tools:::.check_package_datasets(".")
## ToDo: ----------
# - Add date/time data (Chapter 10: Time, e.g., DOB, time of test, task start/end, etc.)
# - Combine 2 datasets (currently online):
# a. numeracy.csv (1000 x 12, see book chapter 55_datasets.Rmd),
# b. dt.csv (1000 x 9): date and time variables (see book chapter 10_times.Rmd)
# - Consider combining with dataset `outliers` (1000 x 3), BUT: different genders and height values and regularities
# - Collect ds4psy survey data
# - Find some book/text to analyze (Chapter 9: Text data).
# - Add text data (Chapter 9: Text; e.g., dinos, fruit, veggies, attention check response on "i read instructions", some eBook for sentinent analysis, ...)
# - Add more info to codebooks (see data_190807.R in archive)
## eof. ----------------------
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