data update

DESCRIPTION

@@ -51,3 +51,4 @@ VignetteBuilder: knitr
 Roxygen: list(markdown = TRUE)
 Config/testthat/edition: 3
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+RoxygenNote: 7.3.3

NEWS.md

@@ -1,3 +1,11 @@
+# Developmental
+
+* Added new datasets: 
+  * `county_2023`, `EVs_region`, `generation_climate_action` added by [@npaterno](https://github.com/npaterno)
+  * `sdea` added by William Krenzer & Siobhán Griffin
+  * `Brazil_death_anxiety` added by Isaac Handley-Miner, Yuichi Shoda and Christian Unkelbach
+  * `skill_aquisition` added by Y. Andre Wang, Gareth Yu and Julia, G. Bottesini
+
 # openintro 2.5.1
 
 * Added new datasets:

R/data-Brazil_death_anxiety.R

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+#' Brazil death anxiety
+#'
+#' The dataset consists of responses from 200 individuals from Brazil.
+#' The main measure is the DAQ, a 15-item multidimensional scale that
+#' measures fears related to suffering, loneliness, the unknown, and
+#' personal extinction (see Variable Description). The scale measures
+#' range from 0 (“not at all”) to 8 (“very much”). In addition, the
+#' file contains a free-list measure of participants’ fears and worries,
+#' which may be coded, and several demographic variables.
+#'
+#' @format A data frame with 200 rows and 28 variables.
+#' \describe{
+#'   \item{participant}{Participant id}
+#'   \item{gender}{Gender of pariticipant.}
+#'   \item{year_born}{Year participant was born.}
+#'   \item{age_in_2015}{Age of participant when data was collected.}
+#'   \item{ethnicity}{Ethnicity	of participant.}
+#'   \item{marital_status}{Marital status of participant.}
+#'   \item{employment}{Employment status of participant.}
+#'   \item{occupation}{Occupation of participant.}
+#'   \item{fivefears_1}{The first entry in a free list of participants’ fears and worries.}
+#'   \item{fivefears_2}{The second entry in a free list of participants’ fears and worries.}
+#'   \item{fivefears_3}{The third entry in a free list of participants’ fears and worries.}
+#'   \item{fivefears_4}{The fourth entry in a free list of participants’ fears and worries.}
+#'   \item{fivefears_5}{The fifth entry in a free list of participants’ fears and worries.}
+#'   \item{worry_death}{Do you worry about dying?}
+#'   \item{incomplete_bucket_list}{Does it bother you that you may die before you have done everything you wanted to?}
+#'   \item{deathly_ill}{Do you worry that you may be very ill for a long time before you die?}
+#'   \item{concern_for_others}{Does it upset you to think that others may see you suffering when you die?}
+#'   \item{painful_death}{Do you worry that dying may be very painful?}
+#'   \item{loved_ones}{Do you worry that the persons most close to you won’t be with you when you are dying?}
+#'   \item{die_alone}{Do you worry that you may be alone when you are dying?}
+#'   \item{cognitive_function}{Does the thought bother you that you might lose control of your mind before death?}
+#'   \item{expenses_burden}{Do you worry that expenses connected with your dying will be a burden for other people?}
+#'   \item{as_you_wish}{Does it worry you that your instructions or will about your belongings may not be carried out after you die?}
+#'   \item{buried_alive}{Are you afraid that you may be buried before you are really dead?}
+#'   \item{left_behind}{Does the thought of leaving loved ones behind you when you die disturb you?}
+#'   \item{remembered}{Do you worry that those you care about may not remember you after your death?}
+#'   \item{permenant}{Does the thought worry you that with death you may be gone forever?}
+#'   \item{after_death}{Are you worried about not knowing what to expect after death?}
+#' }
+#' @source Jong, J., Halberstadt, J., Bluemke, M. et al. Death anxiety, exposure to death, mortuary preferences, and religiosity in five countries. Sci Data 6, 154 (2019). [Link to doi](https://doi.org/10.1038/s41597-019-0163-x)
+#'
+"Brazil_death_anxiety"

R/data-EVs_region.R

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+#' Electric vehicles
+#'
+#' Pew research results regarding consideration of buying electric vehicle.
+#'
+#' @format A data frame with 5085 observations on the following 2 variables.
+#' \describe{
+#'    \item{\code{region}}{a factor with levels \code{Urban, Suburban} and \code{Rural}}
+#'    \item{\code{consideration}}{a factor with levels \code{Not too or not at all likely}, \code{Very or somewhat likely} and \code{Do not expect to buy a vehicle}}
+#'  }
+#'
+#' @source Pew Research Center, Survey of U.S. adults conducted April 28-May 4, 2025. "Americans' Views on Energy at the Start of Trump's Second Term"
+"EVs_region"

R/data-county_2023.R

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+#' county_2023
+#'
+#' County level data from the US Census American Community Survey 2023 5-year estimates.
+#'
+#' @name county_2023
+#' @docType data
+#' @format A data frame with 3144 rows and 43 columns.
+#' \describe{
+#'    \item{\code{geoid}}{Unique numeric code used by the U.S. Census Bureau to identify specific geographic areas.}
+#'    \item{\code{county_state}}{Name of county and state.}
+#'    \item{\code{name}}{Name of county.}
+#'    \item{\code{state}}{Name of state.}
+#'    \item{\code{individual_income}}{Median individual income for the county, measure in 2018 USD.}
+#'    \item{\code{population_size}}{Number of people residing in the county.}
+#'    \item{\code{white}}{Percent of the population who identified as only white.}
+#'    \item{\code{african_american}}{Percent of the population who identified as only Black or African American.}
+#'    \item{\code{age}}{Median age of those who live in the county, measured in years.}
+#'    \item{\code{age_under_5}}{Percent of the population under the age of 5.}
+#'    \item{\code{age_85_plus}}{Percent of the population age 85 and older.}
+#'    \item{\code{age_18_plus}}{Percent of the population age 18 and older.}
+#'    \item{\code{age_65_plus}}{Percent of the population age 65 and older.}
+#'    \item{\code{travel_time}}{Mean travel time to work, measured in minutes.}
+#'    \item{\code{household_size}}{Mean household size.}
+#'    \item{\code{family_size}}{Mean family size.}
+#'    \item{\code{one_unit_structure}}{Percent of properties with a residential building that contains a single housing dwelling unit.}
+#'    \item{\code{two_unit_structure}}{Percent of properties with a residential building that contains two or more separate housing units (such as a duplex or apartments).}
+#'    \item{\code{mobile_and_other}}{Percent of properties with mobile homes and all other types of housing structures.}
+#'    \item{\code{individual_income_25_plus}}{Median individual income for those age 25 or older.}
+#'    \item{\code{hs_grad}}{Percent of the population over the age 25 or older with at least a high school diploma.}
+#'    \item{\code{college_grad}}{Percent of the population over the age 25 or older with at least a Bachelor's Degree.}
+#'    \item{\code{total_households}}{Total number of households in the county.}
+#'    \item{\code{spanish_speaking}}{Percent of households that speak Spanish.}
+#'    \item{\code{indo_european_language}}{Percent of households that speak an Indo-European language other than English.}
+#'    \item{\code{api_language}}{Percent of households that speak an Asian and Pacific Islander language.}
+#'    \item{\code{non_euro_api}}{Percent of households speaking an non-European or Asian and Pacific Islander language.}
+#'    \item{\code{limited_english}}{Percent of households that speak limited English.}
+#'    \item{\code{poverty}}{Percent of population living below the poverty line.}
+#'    \item{\code{poverty_18}}{Percent of population under the age of 18 living below the poverty line.}
+#'    \item{\code{poverty_65}}{Percent of the population over the age of 65 living below the povery line.}
+#'    \item{\code{household_income_mean}}{Mean household income, measured in 2018 USD.}
+#'    \item{\code{household_income_median}}{Median household income, measured in 2018 USD.}
+#'    \item{\code{per_capita_income}}{Mean income per capita, measured in 2018 USD.}
+#'    \item{\code{veterans}}{Percent of the civilian population age 18 or older who served in the military.}
+#'    \item{\code{unemployment_20_64}}{Unemployment rate for those age 20 to 64.}
+#'    \item{\code{uninsured}}{Percent of the civilian population who is uninsured.}
+#'    \item{\code{uninsured_6}}{Percent of population under the age of 6 who are uninsured.}
+#'    \item{\code{uninsured_19}}{Percent of the population under the age of 19 who are uninsured.}
+#'    \item{\code{uninsured_65}}{Percent of the population age 65 and older who are uninsured.}
+#'    \item{\code{computers}}{Percent of households that have a laptop or desktop computer.}
+#'    \item{\code{smartphones}}{Percent of households that have a smartphone.}
+#'    \item{\code{broadband}}{Percent of households with computers that have access to broadband internet.}
+#'   }
+#' @source \url{https://www.census.gov/programs-surveys/acs/data.html} collected using the `tidycensus` package.
+#'
+"county_2023"

R/data-generation_climate_action.R

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+#' Poll on climate change activism
+#'
+#' A Pew Research poll published in May of 2021 looks at how Americans' attitudes about climate change differ by generation, party and other factors.
+#'
+#' @format A data frame with 13664 observations on the following 2 variables.
+#' \describe{
+#'    \item{\code{generation}}{a factor with levels \code{Gen Z, Millenial, Gen X} and \code{Boomer & older}}
+#'    \item{\code{response}}{a factor with levels \code{Took action} and \code{Did not take action}}
+#'  }
+#'
+#' @source Pew Research Center, American Trends Panel. Survey conducted April 20 - 29, 2021. "Gen Z, Millennials Stand Out for Climate Change Activism, Social Media Engagement With Issue"
+"generation_climate_action"

R/data-sdea.R

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+#' Spatial distancing, emotional arousal
+#'
+#' This dataset is from a published research paper examining the effects of
+#' spatial distancing on emotional arousal to reactivated memories. It contains
+#' data on gender, age, trait reappraisal and suppression, desirability, and
+#' self-reported distress before and after experimental sessions (across three
+#' days). Participants underwent an experimental session, where they viewed images
+#' from the IAPs. For day 2, participants were spilt into four groups:
+#' reactivation + regulation, no regulation, no reactivation, no regulation.
+#' The reactivation & regulation group viewed emotional objects cut from the
+#' pictures and were asked to regulate them. When they saw the word "far" they
+#' had to imagine the object was far away from them.
+#'
+#' @format A data frame with 175 rows and 14 variables.
+#' \describe{
+#'  \item{id}{Subject ID number.}
+#'  \item{gender}{M = Male; F = Female.}
+#'  \item{age}{Age of the participants.}
+#'  \item{condition}{1 = Reactivation + Regulation; 2= No Regulation; 3 = No Reactivation; 4 = Neither.}
+#'  \item{sds_17}{Social desirability scale.}
+#'  \item{erq_reappraisal}{motion Regulation Questionnaire - Reappraisal Subscale.}
+#'  \item{erq_suppression}{Emotion Regulation Questionnaire - Suppression Subscale.}
+#'  \item{day1_pre_suds}{Day 1 Subjective Units of Distress Scale Pre-Session.}
+#'  \item{day1_post_suds}{Day 1 Subjective Units of Distress Scale Post-Session.}
+#'  \item{day2_pre_suds}{Day 2 Subjective Units of Distress Scale Pre-Session.}
+#'  \item{day2_post_suds}{Day 2 Subjective Units of Distress Scale Post-Session.}
+#'  \item{day3_pre_suds}{Day 3 Subjective Units of Distress Scale Pre-Session.}
+#'  \item{day3_post_suds}{Day 3 Subjective Units of Distress Scale Post-Session.}
+#'  \item{race}{The self identified race of the participant.}
+#' }
+#'
+#' @source  Parikh, N., McGovern, B. & LaBar, K.S. Spatial distancing reduces emotional arousal to reactivated memories. Psychon Bull Rev 26, 1967–1973 (2019).[Link to doi](https://doi.org/10.3758/s13423-019-01648-z)
+#'
+#' 		Parikh, N., McGovern, B., & LaBar, K. S. (2023). Data from: Spatial distancing reduces emotional arousal to reactivated memories. Duke Research Data Repositor [Link to full dataset](y. https://doi.org/10.7924/r4z89f54)
+#'
+"sdea"

R/data-skill_acquisition.R

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+#' Skill Acquisition
+#'
+#' This data set contains 1,035 observations of 18 variables from Experiment 1
+#' of Kardas and O’Brien (2018). In this experiment, the authors randomly assigned
+#' participants to one of six between-subject conditions in a 3 (type of exposure:
+#' watch, read, think) × 2 (amount of exposure: low, high) design. The key
+#' dependent variable is perceived ability, operationalized as how much
+#' participants believed they could pull off a “tablecloth trick” (removing the
+#' tablecloth from a table without upsetting the dishes on the table) based on
+#' the type of training they received.
+#'
+#' @format A data frame with 1035 rows and 18 variables.
+#' \describe{
+#'  \item{p_number}{participant ID}
+#'  \item{heard_before}{Whether the participant knows what “the tablecloth trick” was before the beginning of the study with levels 1 = Yes and 2 = No. All participants completed the study but participants who indicated they did not know were excluded from the analysis.}
+#'  \item{type_of_info}{Experimental condition for type of exposure with levels 1 = Watching, 2 = Reading and 3 = Control.}
+#'  \item{amt_of_info}{Experimental condition for amount of exposure with levels 1 = Low Exposure and 2 = High Exposure.}
+#'  \item{perceived_ability}{Dependent variable; “You jump in and give the trick ONE SHOT yourself. What do you feel are the chances that you’d successfully pull it off?” on a 1 to 7 scale where 1 = I feel there’s no chance at all I’d succeed on this attempt, 7 = I feel I’d definitely succeed without a doubt on this attempt.}
+#'  \item{manipulation_check}{Manipulation check for amount of information; “About how much of this ‘more information’ do you feel you were given?” on a 1 to 7 scale where 1 = very little/went by quickly, 7 = a lot/displayed for a while.}
+#'  \item{technical_difficulties}{Whether participants had any technical difficulties with levels 0 = No and 1 = Yes.}
+#'  \item{technical_difficulties_description}{Free text describing the technical difficulty.}
+#'  \item{gender}{Participant’s gender with levels 1 = Male, 2 = Female and 3 = Other.}
+#'  \item{ethnic_1}{Participant identified as Black with levels 0 = No, 1 = Yes.}
+#'  \item{ethnic_2}{Participant identified as Hispanic with levels 0 = No, 1 = Yes.}
+#'  \item{ethnic_3}{Participant identified as Asian with levels 0 = No, 1 = Yes.}
+#'  \item{ethnic_4}{Participant identified as American Indian with levels 0 = No, 1 = Yes.}
+#'  \item{ethnic_5}{Participant identified as White with levels 0 = No, 1 = Yes.}
+#'  \item{ethnic_6 }{Participant identified as Other with levels 0 = No, 1 = Yes.}
+#'  \item{age}{Participant’s age in years.}
+#'  \item{tried_before}{Whether participants had ever previously attempted a tablecloth trick in their everyday lives with levels 1 = Yes and 2 = No.}
+#'  \item{fail_ac}{Whether a participant failed an attention check with levels 0 = did not fail any attention checks and 1 = failed one or more attention checks.}
+#' }
+#'
+#' @source Kardas, M., & O’Brien, E. (2018). Easier seen than done: Merely watching others perform can foster an illusion of skill acquisition. Psychological Science, 29(4), 521-536. [Link to doi](https://doi.org/10.1177/0956797617740646) [Link to full dataset](https://osf.io/u3byh/files/bfj8n)
+#'
+"skill_acquisition"

data-raw/Brazil_death_anxiety/Brazil_death_anxiety-dataprep.R

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+# load packages ----------------------------------------------------------------
+
+library(tidyverse)
+library(janitor)
+
+# load data --------------------------------------------------------------------
+
+Brazil_death_anxiety <- read_csv("data-raw/Brazil_death_anxiety/Brazil_death_anxiety.csv")
+
+# cleaning: clean names --------------------------------------------------------
+
+Brazil_death_anxiety <- Brazil_death_anxiety |>
+  clean_names() |>
+  select(-country) |>
+  rename(employment = employment_fulltime)
+
+# save -------------------------------------------------------------------------
+
+usethis::use_data(Brazil_death_anxiety, overwrite = TRUE)