Help for package summarytabl

Type:

Package

Title:

Generate Summary Tables for Categorical, Ordinal, and Continuous Data

Version:

0.2.1

Maintainer:

Ama Nyame-Mensah <ama@anyamemensah.com>

URL:

https://anyamemensah.github.io/summarytabl/, https://github.com/anyamemensah/summarytabl

BugReports:

https://github.com/anyamemensah/summarytabl/issues

Description:

Provides functions for tabulating and summarizing categorical, multiple response, ordinal, and continuous variables in R data frames. Makes it easy to create clear, structured summary tables, so you spend less time wrangling data and more time interpreting it.

License:

MIT + file LICENSE

Encoding:

UTF-8

LazyData:

true

Imports:

cli, dplyr (≥ 1.1.4), purrr (≥ 1.1.0), rlang, stats, tibble, tidyr,

RoxygenNote:

7.3.3

Suggests:

knitr, rmarkdown, testthat (≥ 3.0.0)

VignetteBuilder:

knitr

Config/testthat/edition:

Depends:

R (≥ 4.1.0)

NeedsCompilation:

Packaged:

2025-11-06 10:14:57 UTC; AmaNM

Author:

Ama Nyame-Mensah [aut, cre]

Repository:

CRAN

Date/Publication:

2025-11-06 11:20:02 UTC

summarytabl: Generate Summary Tables for Categorical, Ordinal, and Continuous Data

Description

Author(s)

Maintainer: Ama Nyame-Mensah ama@anyamemensah.com

Summarize two categorical variables

Description

cat_group_tbl() summarizes nominal or categorical variables by a grouping variable, returning frequency counts and percentages.

Usage

cat_group_tbl(
  data,
  row_var,
  col_var,
  margins = "all",
  na.rm.row_var = FALSE,
  na.rm.col_var = FALSE,
  pivot = "longer",
  only = NULL,
  ignore = NULL
)

Arguments

data

A data frame.

row_var

A character string of the name of a variable in data containing categorical data. This is the primary categorical variable.

col_var

A character string of the name of a variable in data containing categorical data. This is the secondary categorical variable.

margins

A character string that determines how percentage values are calculated; whether they sum to one across rows, columns, or the entire table (i.e., all). Defaults to all, but can also be set to rows or columns.

na.rm.row_var

A logical value indicating whether missing values for row_var should be removed before calculations. Default is FALSE.

na.rm.col_var

A logical value indicating whether missing values for col_var should be removed before calculations. Default is FALSE.

pivot

A character string that determines the format of the table. By default, longer returns the data in the long format. To return the data in the wide format, specify wider.

only

A character string or vector of character strings of the types of summary data to return. Default is NULL, which returns both counts and percentages. To return only counts or percentages, use count or percent, respectively.

ignore

An optional named vector or list that defines values to exclude from row_var and col_var. If set to NULL (default), all values are retained. To exclude multiple values from row_var or col_var, provide them as a named list.

Value

A tibble showing the count and percentage of each category in row_var by each category in col_var.

Author(s)

Ama Nyame-Mensah

Examples

cat_group_tbl(data = nlsy,
              row_var = "gender",
              col_var = "bthwht",
              pivot = "wider",
              only = "count")

cat_group_tbl(data = nlsy,
              row_var = "birthord",
              col_var = "breastfed",
              pivot = "longer")

Summarize a categorical variable

Description

cat_tbl() summarizes nominal or categorical variables, returning frequency counts and percentages.

Usage

cat_tbl(data, var, na.rm = FALSE, only = NULL, ignore = NULL)

Arguments

data

A data frame.

var

A character string of the name of a variable in data containing categorical data.

na.rm

A logical value indicating whether missing values should be removed before calculations. Default is FALSE.

only

ignore

An optional vector that contains values to exclude from var. Default is NULL, which retains all values.

Value

A tibble showing the count and percentage of each category in var

Author(s)

Ama Nyame-Mensah

Examples

cat_tbl(data = nlsy, var = "gender")

cat_tbl(data = nlsy, var = "race", only = "count")

cat_tbl(data = nlsy,
        var = "race",
        ignore = "Hispanic",
        only = "percent",
        na.rm = TRUE)

Check a named vector

Description

This function checks whether named lists and vectors contain invalid values (like NULL or NA), have invalid names (such as missing or empty names), ensures the number of valid names matches the number of supplied values, and confirms that valid names from the object correspond to the provided names. If any of these checks fail, the function returns the default value.

Usage

check_named_vctr(x, names, default)

Arguments

x

A named vector.

names

A character vector or list of character vectors of length one specifying the names to be matched.

default

Default value to return

Value

Either the original object, x, or the default value.

Author(s)

Ama Nyame-Mensah

Examples


# returns NULL
check_named_vctr(x = c(one = 1, two = 2, 3), 
                 names = c("one", "two", "three"),
                 default = NULL)
                 
# returns x
check_named_vctr(x = list(one = 1, two = 2, three = 3), 
                 names = list("one", "two", "three"),
                 default = NULL)  

# also returns x
check_named_vctr(x = c(baako = 1, mmienu = 2, mmiensa = 3), 
                 names = list("baako", "mmienu", "mmiensa"),
                 default = NULL)

Depressive Symptoms Data

Description

Subset of data from the National Longitudinal Survey of Youth (NLSY) 1979 Children and Young Adults. This dataset includes survey responses about feelings and behaviors linked to depressive symptoms in children and young adults. For more information about the National Longitudinal Survey of Youth, visit: https://www.nlsinfo.org/.

Usage

depressive

Format

A data frame with 11,551 rows and 12 columns:

cid: Child identification number)
race: race of child (1 = Hispanic, 2 = Black, 3 = Non-Black,Non-Hispanic)
sex: sex of child (1 = male, 2 = female)
yob: year of child's bith
dep_1: how often child feels sad and blue (1 = often, 2 = sometimes, 3 = hardly ever)
dep_2: how often child feels nervous, tense, or on edge (1 = often, 2 = sometimes, 3 = hardly ever)
dep_3: how often child feels happy (1 = often, 2 = sometimes, 3 = hardly ever)
dep_4: how often child feels bored (1 = often, 2 = sometimes, 3 = hardly ever)
dep_5: how often child feels lonely (1 = often, 2 = sometimes, 3 = hardly ever)
dep_6: how often child feels tired or worn out (1 = often, 2 = sometimes, 3 = hardly ever)
dep_7: how often child feels excited about something (1 = often, 2 = sometimes, 3 = hardly ever)
dep_8: how often child feels too busy to get everything (1 = often, 2 = sometimes, 3 = hardly ever)

Summarize multiple response variables by group or pattern

Description

mean_group_tbl() calculates summary statistics (i.e., mean, standard deviation, minimum, maximum, and count of non-missing values) for continuous (i.e., interval and ratio-level) variables, grouped either by another variable in your dataset or by a matched pattern in the variable names.

Usage

mean_group_tbl(
  data,
  var_stem,
  group,
  var_input = "stem",
  regex_stem = FALSE,
  ignore_stem_case = FALSE,
  group_type = "variable",
  group_name = NULL,
  regex_group = FALSE,
  ignore_group_case = FALSE,
  remove_group_non_alnum = TRUE,
  na_removal = "listwise",
  only = NULL,
  var_labels = NULL,
  ignore = NULL
)

Arguments

data

A data frame.

var_stem

A character vector with one or more elements, where each represents either a variable stem or the complete name of a variable present in data. A variable 'stem' refers to a common naming pattern shared among related variables, typically reflecting repeated measures of the same idea or a group of items assessing a single concept.

group

A character string representing a variable name or a pattern used to search for variables in data.

var_input

A character string specifying whether the values supplied to var_stem should be treated as variable stems (stem) or as complete variable names (name). By default, this is set to stem, so the function searches for variables that begin with each stem provided. Setting this argument to name directs the function to look for variables that exactly match the provided names.

regex_stem

A logical value indicating whether to use Perl-compatible regular expressions when searching for variable stems. Default is FALSE.

ignore_stem_case

A logical value indicating whether the search for columns matching the supplied var_stem is case-insensitive. Default is FALSE.

group_type

A character string that defines how the group argument should be interpreted. Should be one of pattern or variable. Defaults to variable, which searches for a matching variable name in data.

group_name

An optional character string used to rename the group column in the final table When group_type is set to variable, the column name defaults to the matched variable name from data. When set to pattern, the default column name is group.

regex_group

A logical value indicating whether to use Perl-compatible regular expressions when searching for group variables or matching variable name patterns. Default is FALSE.

ignore_group_case

A logical value specifying whether the search for a grouping variable (if group_type is variable) or for variables matching a pattern (if group_type is pattern) should be case-insensitive. Default is FALSE. Set to TRUE to ignore case.

remove_group_non_alnum

A logical value indicating whether to remove all non-alphanumeric characters (i.e., anything that is not a letter or number) from group. Default is TRUE.

na_removal

A character string that specifies the method for handling missing values: pairwise or listwise. Defaults to listwise.

only

var_labels

An optional named character vector or list used to assign custom labels to variable names. Each element must be named and correspond to a variable included in the returned table. If var_input is set to stem, and any element is either unnamed or refers to a variable not present in the table, all labels will be ignored and the table will be printed without them.

ignore

An optional named vector or list indicating values to exclude from variables matching specified stems (or names), and, if applicable, from a grouping variable in data. Defaults to NULL, indicating that all values are retained. To specify exclusions for variables identified by var_stem, use the corresponding stems or variable names as names in the vector or list. To exclude multiple values from these variables or a grouping variable, supply them as a named list.

Value

A tibble showing summary statistics for continuous variables, grouped either by a specified variable in the dataset or by matching patterns in variable names.

Author(s)

Ama Nyame-Mensah

Examples

sdoh_child_ages_region <- 
  dplyr::select(sdoh, c(REGION, ACS_PCT_AGE_0_4, ACS_PCT_AGE_5_9,
                        ACS_PCT_AGE_10_14, ACS_PCT_AGE_15_17))

mean_group_tbl(data = sdoh_child_ages_region,
               var_stem = "ACS_PCT_AGE",
               group = "REGION",
               group_name = "us_region",
               na_removal = "pairwise",
               var_labels = c(
                 ACS_PCT_AGE_0_4 = "% of population between ages 0-4",
                 ACS_PCT_AGE_5_9 = "% of population between ages 5-9",
                 ACS_PCT_AGE_10_14 = "% of population between ages 10-14",
                 ACS_PCT_AGE_15_17 = "% of population between ages 15-17"))

set.seed(0222)
grouped_data <-
  data.frame(
    symptoms.t1 = sample(c(0:10, -999), replace = TRUE, size = 50),
    symptoms.t2 = sample(c(NA, 0:10, -999), replace = TRUE, size = 50)
  )

mean_group_tbl(data = grouped_data,
               var_stem = "symptoms",
               group = ".t\\d",
               group_type = "pattern",
               na_removal = "listwise",
               ignore = c(symptoms = -999))

Summarize continuous variables

Description

mean_tbl() calculates summary statistics (i.e., mean, standard deviation, minimum, maximum, and count of non-missing values) for continuous (i.e., interval and ratio-level) variables.

Usage

mean_tbl(
  data,
  var_stem,
  var_input = "stem",
  regex_stem = FALSE,
  ignore_stem_case = FALSE,
  na_removal = "listwise",
  only = NULL,
  var_labels = NULL,
  ignore = NULL
)

Arguments

data

A data frame.

var_stem

var_input

regex_stem

A logical value indicating whether to use Perl-compatible regular expressions when searching for variable stems. Default is FALSE.

ignore_stem_case

A logical value indicating whether the search for columns matching the supplied var_stem is case-insensitive. Default is FALSE.

na_removal

A character string that specifies the method for handling missing values: pairwise or listwise. Defaults to listwise.

only

A character string or vector of character strings specifying which summary statistics to return. Defaults to NULL, which includes mean (mean), standard deviation (sd), minimum (min), maximum (max), and count of non-missing values (nobs).

var_labels

ignore

An optional named vector or list indicating values to exclude from variables matching specified stems (or names). Defaults to NULL, indicating that all values are retained. To specify exclusions for variables identified by var_stem, use the corresponding stems or variable names as names in the vector or list. To exclude multiple values from these variables, supply them as a named list.

Value

A tibble showing summary statistics for continuous variables.

Author(s)

Ama Nyame-Mensah

Examples

sdoh_child_ages <- 
  dplyr::select(sdoh, c(ACS_PCT_AGE_0_4, ACS_PCT_AGE_5_9,
                        ACS_PCT_AGE_10_14, ACS_PCT_AGE_15_17))

mean_tbl(data = sdoh_child_ages, var_stem = "ACS_PCT_AGE")

mean_tbl(data = sdoh_child_ages,
         var_stem = "ACS_PCT_AGE",
         na_removal = "pairwise",
         var_labels = c(
           ACS_PCT_AGE_0_4 = "% of population between ages 0-4",
           ACS_PCT_AGE_5_9 = "% of population between ages 5-9",
           ACS_PCT_AGE_10_14 = "% of population between ages 10-14",
           ACS_PCT_AGE_15_17 = "% of population between ages 15-17"))

National Longitudinal Survey of Youth (NLSY) Data

Description

These data are a subset from the National Longitudinal Survey of Youth (NLSY) 1979 Children and Young Adults.The data contains 2,976 observations and 10 variables.

For more information about the National Longitudinal Survey of Youth, visit https://www.nlsinfo.org/.

Usage

nlsy

Format

A tibble with 2,976 rows and 11 columns:

CID: Child identification number)
race: race of child (Hispanic, Black, Non-Black,Non-Hispanic)
gender: gender of child (1 = male, 0 = female)
birthord: birth order of child
magebirth: Age of mother at birth of child
bthwht: whether child was born low birth weight (1 = yes, 0 = no)
breastfed: whether child was breastfed (1 = yes, 0 = no)
medu: Highest grade completed by child’s mother
math: PIAT Math Standard Score
read: PIAT Reading Recognition Standard Score
hhnum: Number of household members in household

2020 Social Determinants of Health (SDOH) Data

Description

Subset of data from the 2020 Social Determinants of Health (SDOH) Database. For more information about the 2020 SDOH Database, visit: https://www.ahrq.gov/sdoh/index.html.

Usage

sdoh

Format

A tibble with 3,229 rows and 29 columns:

YEAR: SDOH file year
COUNTYFIPS: State-county FIPS Code (5-digit)
STATEFIPS: State FIPS Code (2-digit)
STATE: State name
COUNTY: County name
REGION: Census region name
TERRITORY: Territory indicator (1= U.S. Territory, 0= U.S. State or DC)
ACS_PCT_AGE_0_4: Percentage of population between ages 0-4
ACS_PCT_AGE_5_9: Percentage of population between ages 5-9
ACS_PCT_AGE_10_14: Percentage of population between ages 10-14
ACS_PCT_AGE_15_17: Percentage of population between ages 15-17
NOAAC_PRECIPITATION_JAN: Monthly (January) precipitation (Inches)
NOAAC_PRECIPITATION_FEB: Monthly (February) precipitation (Inches)
NOAAC_PRECIPITATION_MAR: Monthly (March) precipitation (Inches)
NOAAC_PRECIPITATION_APR: Monthly (April) precipitation (Inches)
NOAAC_PRECIPITATION_MAY: Monthly (May) precipitation (Inches)
NOAAC_PRECIPITATION_JUN: Monthly (June) precipitation (Inches)
NOAAC_PRECIPITATION_JUL: Monthly (July) precipitation (Inches)
NOAAC_PRECIPITATION_AUG: Monthly (August) precipitation (Inches)
NOAAC_PRECIPITATION_SEP: Monthly (September) precipitation (Inches)
NOAAC_PRECIPITATION_OCT: Monthly (October) precipitation (Inches)
NOAAC_PRECIPITATION_NOV: Monthly (November) precipitation (Inches)
NOAAC_PRECIPITATION_DEC: Monthly (December) precipitation (Inches)
HHC_PCT_HHA_NURSING: Percentage of home health agencies offering nursing care services
HHC_PCT_HHA_PHYS_THERAPY: Percentage of home health agencies offering physical therapy services
HHC_PCT_HHA_OCC_THERAPY: Percentage of home health agencies offering occupational therapy services
HHC_PCT_HHA_SPEECH: Percentage of home health agencies offering speech pathology services
HHC_PCT_HHA_MEDICAL: Percentage of home health agencies offering medical social services
HHC_PCT_HHA_AIDE: Percentage of home health agencies offering home health aide services

Summarize multiple response variables by group or pattern

Description

select_group_tbl() displays frequency counts and percentages for multiple response variables (e.g., a series of questions where participants answer "Yes" or "No" to each item) as well as ordinal variables (such as Likert or Likert-type items with responses ranging from "Strongly Disagree" to "Strongly Agree", where respondents select one response per statement, question, or item), grouped either by another variable in your dataset or by a matched pattern in the variable names.

Usage

select_group_tbl(
  data,
  var_stem,
  group,
  var_input = "stem",
  regex_stem = FALSE,
  ignore_stem_case = FALSE,
  group_type = "variable",
  group_name = NULL,
  margins = "all",
  regex_group = FALSE,
  ignore_group_case = FALSE,
  remove_group_non_alnum = TRUE,
  na_removal = "listwise",
  pivot = "longer",
  only = NULL,
  var_labels = NULL,
  ignore = NULL,
  force_pivot = FALSE
)

Arguments

data

A data frame.

var_stem

group

A character string representing a variable name or a pattern used to search for variables in data.

var_input

regex_stem

A logical value indicating whether to use Perl-compatible regular expressions when searching for variable stems. Default is FALSE.

ignore_stem_case

A logical value indicating whether the search for columns matching the supplied var_stem is case-insensitive. Default is FALSE.

group_type

group_name

margins

A character string that determines how percentage values are calculated; whether they sum to one across rows, columns, or the entire variable (i.e., all). Defaults to all, but can also be set to rows or columns. Note: This argument only affects the final table when group_type is variable.

regex_group

A logical value indicating whether to use Perl-compatible regular expressions when searching for group variables or matching variable name patterns. Default is FALSE.

ignore_group_case

remove_group_non_alnum

A logical value indicating whether to remove all non-alphanumeric characters (i.e., anything that is not a letter or number) from group. Default is TRUE.

na_removal

A character string that specifies the method for handling missing values: pairwise or listwise. Defaults to listwise.

pivot

A character string that determines the format of the table. By default, longer returns the data in the long format. To return the data in the wide format, specify wider.

only

var_labels

ignore

force_pivot

A logical value that enables pivoting to the 'wider' format even when variables have inconsistent value sets. By default, this is set to FALSE to prevent reshaping errors when values differ across variables in the returned table. Set to TRUE to override this safeguard and pivot to the 'wider' format regardless of value inconsistencies.

Value

A tibble displaying the count and percentage for each category in a multi-response variable, grouped either by a specified variable in the dataset or by matching patterns in variable names.

Author(s)

Ama Nyame-Mensah

Examples

select_group_tbl(data = stem_social_psych,
                 var_stem = "belong_belong",
                 group = "\\d",
                 group_type = "pattern",
                 group_name = "wave",
                 na_removal = "pairwise",
                 pivot = "wider",
                 only = "count")

tas_recoded <-
  tas |>
  dplyr::mutate(sex = dplyr::case_when(
    sex == 1 ~ "female",
    sex == 2 ~ "male",
    TRUE ~ NA)) |>
  dplyr::mutate(dplyr::across(
    .cols = dplyr::starts_with("involved_"),
    .fns = ~ dplyr::case_when(
      .x == 1 ~ "selected",
      .x == 0 ~ "unselected",
      TRUE ~ NA)
  ))

select_group_tbl(data = tas_recoded,
                 var_stem = "involved_",
                 group = "sex",
                 group_type = "variable",
                 na_removal = "pairwise",
                 pivot = "wider")

depressive_recoded <-
  depressive |>
  dplyr::mutate(sex = dplyr::case_when(
    sex == 1 ~ "male",
    sex == 2 ~ "female",
    TRUE ~ NA)) |>
  dplyr::mutate(dplyr::across(
    .cols = dplyr::starts_with("dep_"),
    .fns = ~ dplyr::case_when(
      .x == 1 ~ "often",
      .x == 2 ~ "sometimes",
      .x == 3 ~ "hardly",
      TRUE ~ NA
    )
  ))

select_group_tbl(data = depressive_recoded,
                 var_stem = "dep",
                 group = "sex",
                 group_type = "variable",
                 na_removal = "listwise",
                 pivot = "wider",
                 only = "percent",
                 var_labels =
                   c("dep_1" = "how often child feels sad and blue",
                     "dep_2" = "how often child feels nervous, tense, or on edge",
                     "dep_3" = "how often child feels happy",
                     "dep_4" = "how often child feels bored",
                     "dep_5" = "how often child feels lonely",
                     "dep_6" = "how often child feels tired or worn out",
                     "dep_7" = "how often child feels excited about something",
                     "dep_8" = "how often child feels too busy to get everything"))

Summarize multiple response variables

Description

select_tbl() displays frequency counts and percentages for multiple response variables (e.g., a series of questions where participants answer "Yes" or "No" to each item) as well as ordinal variables (such as Likert or Likert-type items with responses ranging from "Strongly Disagree" to "Strongly Agree", where respondents select one response per statement, question, or item).

Usage

select_tbl(
  data,
  var_stem,
  var_input = "stem",
  regex_stem = FALSE,
  ignore_stem_case = FALSE,
  na_removal = "listwise",
  pivot = "longer",
  only = NULL,
  var_labels = NULL,
  ignore = NULL,
  force_pivot = FALSE
)

Arguments

data

A data frame.

var_stem

var_input

regex_stem

A logical value indicating whether to use Perl-compatible regular expressions when searching for variable stems. Default is FALSE.

ignore_stem_case

A logical value indicating whether the search for columns matching the supplied var_stem is case-insensitive. Default is FALSE.

na_removal

A character string that specifies the method for handling missing values: pairwise or listwise. Defaults to listwise.

pivot

A character string that determines the format of the table. By default, longer returns the data in the long format. To receive the data in the wide format, specify wider.

only

var_labels

ignore

force_pivot

Value

A tibble displaying the count and percentage for each category in a multi-response variable.

Author(s)

Ama Nyame-Mensah

Examples

select_tbl(data = tas,
           var_stem = "involved_",
           na_removal = "pairwise")

select_tbl(data = depressive,
           var_stem = "dep",
           na_removal = "listwise",
           pivot = "wider",
           only = "percent")

var_label_example <-
  c("dep_1" = "how often child feels sad and blue",
    "dep_2" = "how often child feels nervous, tense, or on edge",
    "dep_3" = "how often child feels happy",
    "dep_4" = "how often child feels bored",
    "dep_5" = "how often child feels lonely",
    "dep_6" = "how often child feels tired or worn out",
    "dep_7" = "how often child feels excited about something",
    "dep_8" = "how often child feels too busy to get everything")

select_tbl(data = depressive,
           var_stem = "dep",
           na_removal = "pairwise",
           pivot = "longer",
           var_labels = var_label_example)

select_tbl(data = depressive,
           var_stem = "dep",
           na_removal = "pairwise",
           pivot = "wider",
           only = "count",
           var_labels = var_label_example)

Social Psychological (Simulated) Data

Description

Simulated data capturing social psychological responses in a real-world college setting. This dataset represents college students' feelings, attitudes, and perceptions related to their experiences in STEM degree programs. It was designed to reflect key psychological factors that influence student engagement, motivation, and persistence in STEM fields.

Usage

social_psy_data

Format

A data.frame with 10,200 rows and 17 columns:

id: participant id number)
belong_1: I feel like I belong at this institution (1=Strongly Disagree, 2=Disagree,3=Neither agree nor disagree,4=Agree,5=Strongly Agree)
belong_2: I feel like part of the community (1=Strongly Disagree, 2=Disagree,3=Neither agree nor disagree,4=Agree,5=Strongly Agree)
belong_3: I feel valued by this institution (1=Strongly Disagree, 2=Disagree,3=Neither agree nor disagree,4=Agree,5=Strongly Agree)
identity_1: This institution is a big part of who I am (1=Strongly Disagree,2=Disagree,3=Neither agree nor disagree,4=Agree,5=Strongly Agree)
identity_2: I feel comfortable being myself in this setting (1=Strongly Disagree,2=Disagree,3=Neither agree nor disagree,4=Agree, 5=Strongly Agree)
identity_3: This institution is a big part of who I am (1=Strongly Disagree, 2=Disagree,3=Neither agree nor disagree,4=Agree,5=Strongly Agree)
identity_4: I care about doing well at this institution (1=Strongly Disagree, 2=Disagree,3=Neither agree nor disagree,4=Agree,5=Strongly Agree)
selfEfficacy_1: I am confident about A (1=Strongly Disagree,2=Disagree, 3=Neither agree nor disagree,4=Agree,5=Strongly Agree)
selfEfficacy_2: I am confident about B (1=Strongly Disagree,2=Disagree, 3=Neither agree nor disagree,4=Agree,5=Strongly Agree)
selfEfficacy_3: I am confident about C (1=Strongly Disagree,2=Disagree, 3=Neither agree nor disagree,4=Agree,5=Strongly Agree)
selfEfficacy_4: I am confident about D (1=Strongly Disagree,2=Disagree, 3=Neither agree nor disagree,4=Agree,5=Strongly Agree)
selfEfficacy_5: I am confident about E (1=Strongly Disagree,2=Disagree, 3=Neither agree nor disagree,4=Agree,5=Strongly Agree)
selfEfficacy_6: I am confident about F (1=Strongly Disagree,2=Disagree, 3=Neither agree nor disagree,4=Agree,5=Strongly Agree)
selfEfficacy_7: I am confident about G (1=Strongly Disagree,2=Disagree, 3=Neither agree nor disagree,4=Agree,5=Strongly Agree)
gender: Participant's gender identity (1=Woman,2=Man,3=Non-binary, 4=Self-identify,5=Transgender,6=Gender-queer/non-conforming)
citizen: Participant's citizenship status (1=U.S. citizen,2=Non-U.S. citizen with permanent residency,3=Non-U.S. citizen with temporary visa,4=Other)

STEM Social Psychological (Simulated) Data

Description

Simulated data designed to reflect social psychological responses among college students. These data were generated to model attitudes, perceptions, and experiences of students participating in a Science, Technology, Engineering, and Mathematics (STEM) intervention program. The dataset aims to represent real- world psychological factors relevant to STEM education contexts.

Usage

stem_social_psych

Format

A data.frame with 786 rows and 37 columns:

id: student id number)
belong_belongStem_w1: I feel like I belong in STEM (1=Strongly disagree, 2=Somewhat disagree,3=Neither disagree nor agree,4=Somewhat agree,5=Strongly agree)
belong_outsiderStem_w1: I feel like an outsider in STEM (1=Strongly disagree, 2=Somewhat disagree,3=Neither disagree nor agree,4=Somewhat agree,5=Strongly agree)
identity_identityStem_w1: STEM is a big part of who I am. (1=Strongly disagree, 2=Somewhat disagree,3=Neither disagree nor agree,4=Somewhat agree,5=Strongly agree)
belong_welcomedStem_w1: I feel welcomed in STEM workplaces (1=Strongly disagree, 2=Somewhat disagree,3=Neither disagree nor agree,4=Somewhat agree,5=Strongly agree)
identity_noCommonStem_w1: I do not have much in common with the other students in my STEM classes.(1=Strongly disagree,2=Somewhat disagree,3=Neither disagree nor agree, 4=Somewhat agree, 5=Strongly agree)
selfEfficacy_passStemCourses_w1: pass my STEM courses.(1=Strongly disagree, 2=Somewhat disagree,3=Neither disagree nor agree,4=Somewhat agree, 5=Strongly agree)
selfEfficacy_learnConcepts_w1: learn the foundations and concepts of scientific thinking. (1=Strongly disagree, 2=Somewhat disagree,3=Neither disagree nor agree, 4=Somewhat agree, 5=Strongly agree)
selfEfficacy_stemField_w1: do well in a stem-related field. (1=Strongly disagree, 2=Somewhat disagree,3=Neither disagree nor agree,4=Somewhat agree,5=Strongly agree)
selfEfficacy_learnScience_w1: quickly learn new science areas, systems, techniques or concepts on my own. (1=Strongly disagree, 2=Somewhat disagree,3=Neither disagree nor agree, 4=Somewhat agree, 5=Strongly agree)
selfEfficacy_contributeProject_w1: contribute to a science project. (1=Strongly disagree, 2=Somewhat disagree,3=Neither disagree nor agree,4=Somewhat agree, 5=Strongly agree)
selfEfficacy_commScience_w1: clearly communicate scientific problems and findings to varied audiences (1=Strongly disagree,2=Somewhat disagree, 3=Neither disagree nor agree, 4=Somewhat agree,5=Strongly agree)
selfEfficacy_scientist_w1: become a scientist. (1=Strongly disagree, 2=Somewhat disagree,3=Neither disagree nor agree,4=Somewhat agree,5=Strongly agree)
selfEfficacy_completeUG_w1: complete an undergraduate STEM degree. (1=Strongly disagree, 2=Somewhat disagree,3=Neither disagree nor agree,4=Somewhat agree, 5=Strongly agree)
selfEfficacy_admitGrad_w1: get admitted to a graduate STEM program. (1=Strongly disagree,2=Somewhat disagree,3=Neither disagree nor agree,4=Somewhat agree, 5=Strongly agree)
selfEfficacy_successGrad_w1: be successful in a graduate STEM program. (1=Strongly disagree,2=Somewhat disagree,3=Neither disagree nor agree,4=Somewhat agree, 5=Strongly agree)
belong_belongStem_w2: I feel like I belong in STEM (1=Strongly disagree, 2=Somewhat disagree, 3=Neither disagree nor agree,4=Somewhat agree,5=Strongly agree)
belong_outsiderStem_w2: I feel like an outsider in STEM. (1=Strongly disagree, 2=Somewhat disagree,3=Neither disagree nor agree,4=Somewhat agree,5=Strongly agree)
identity_identityStem_w2: STEM is a big part of who I am. (1=Strongly disagree, 2=Somewhat disagree,3=Neither disagree nor agree,4=Somewhat agree,5=Strongly agree)
belong_welcomedStem_w2: I feel welcomed in STEM workplaces. (1=Strongly disagree, 2=Somewhat disagree,3=Neither disagree nor agree,4=Somewhat agree,5=Strongly agree)
identity_noCommonStem_w2: I do not have much in common with the other students in my STEM classes.(1=Strongly disagree,2=Somewhat disagree,3=Neither disagree nor agree, 4=Somewhat agree, 5=Strongly agree)
selfEfficacy_passStemCourses_w2: pass my STEM courses. (1=Strongly disagree, 2=Somewhat disagree,3=Neither disagree nor agree,4=Somewhat agree, 5=Strongly agree)
selfEfficacy_learnConcepts_w2: learn the foundations and concepts of scientific thinking. (1=Strongly disagree, 2=Somewhat disagree,3=Neither disagree nor agree, 4=Somewhat agree, 5=Strongly agree)
selfEfficacy_stemField_w2: do well in a stem-related field. (1=Strongly disagree, 2=Somewhat disagree,3=Neither disagree nor agree,4=Somewhat agree,5=Strongly agree)
selfEfficacy_learnScience_w2: quickly learn new science areas, systems, techniques or concepts on my own. (1=Strongly disagree, 2=Somewhat disagree,3=Neither disagree nor agree, 4=Somewhat agree, 5=Strongly agree)
selfEfficacy_contributeProject_w2: contribute to a science project. (1=Strongly disagree, 2=Somewhat disagree,3=Neither disagree nor agree,4=Somewhat agree, 5=Strongly agree)
selfEfficacy_commScience_w2: clearly communicate scientific problems and findings to varied audiences (1=Strongly disagree,2=Somewhat disagree, 3=Neither disagree nor agree, 4=Somewhat agree,5=Strongly agree)
selfEfficacy_scientist_w2: become a scientist. (1=Strongly disagree, 2=Somewhat disagree,3=Neither disagree nor agree,4=Somewhat agree,5=Strongly agree)
selfEfficacy_completeUG_w2: complete an undergraduate STEM degree. (1=Strongly disagree, 2=Somewhat disagree,3=Neither disagree nor agree,4=Somewhat agree, 5=Strongly agree)
selfEfficacy_admitGrad_w2: get admitted to a graduate STEM program. (1=Strongly disagree,2=Somewhat disagree,3=Neither disagree nor agree,4=Somewhat agree, 5=Strongly agree)
selfEfficacy_successGrad_w2: be successful in a graduate STEM program. (1=Strongly disagree,2=Somewhat disagree,3=Neither disagree nor agree,4=Somewhat agree, 5=Strongly agree)
is_male: Participant's current sex (0=Not Male,1=Male)
has_disability: Whether participant has a disability (0=No, 1=Yes)
firstGen: Whether participant is a first generation college student (0=No, 1=Yes)
stemMajor: Whether participant is a STEM Major (0=No, 1=Yes)
expLearning: Whether student has participated in an experiential learning program, such as an internship, research, or leadership opportunity. (0=No, 1=Yes)
urm: Whether participant is Asian, Middle Eastern/Arab or White (0) vs. Black, Indigenous, Hispanic/Latino, or Mixed Race (1)

Panel Study of Income Dynamics (PSID) Transition into Adulthood Supplement (TAS) Data

Description

Subset of data from the Panel Study of Income Dynamics (PSID) Transition into Adulthood Supplement. This dataset includes information from young adults about how they spend their free time, including participation in organized activities such as clubs, sports or athletic teams, social-action groups, and other structured extracurricular engagements. For more information about the Panel Study of Income Dynamics, visit: https://psidonline.isr.umich.edu/GettingStarted.aspx.

Usage

tas

Format

A tibble with 2,526 rows and 8 columns:

pid: personal identification number)
sex: sex of individual (1 = female, 2 = male)
involved_arts: whether the individual participated in any organized activities related to art, music, or the theater in the last 12 months (1 = yes, 0 = no)
involved_sports: whether the individual was a member of any athletic or sports teams in the last 12 months (1 = yes, 0 = no)
involved_schoolClubs: whether the individual was involved with any high school or college clubs or student government in the last 12 months (1 = yes, 0 = no)
involved_election: whether the individual voted in the national election in November 2016 that was held to elect the President (1 = yes, 0 = no)
involved_socialActionGrps: whether the individual was involved in any political groups, solidarity or ethnic-support groups or social-action groups in the last 12 months (1 = yes, 0 = no)
involved_volunteer: whether the individual was involved in any unpaid volunteer or community service work in the last 12 months (1 = yes, 0 = no)

summarytabl: Generate Summary Tables for Categorical, Ordinal, and Continuous Data

Description

Author(s)

See Also

Summarize two categorical variables

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Summarize a categorical variable

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Check a named vector

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Depressive Symptoms Data

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Summarize multiple response variables by group or pattern

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Summarize continuous variables

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National Longitudinal Survey of Youth (NLSY) Data

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2020 Social Determinants of Health (SDOH) Data

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Summarize multiple response variables by group or pattern

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Value

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Summarize multiple response variables

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Author(s)

Examples

Social Psychological (Simulated) Data

Description

Usage

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STEM Social Psychological (Simulated) Data

Description

Usage

Format

Panel Study of Income Dynamics (PSID) Transition into Adulthood Supplement (TAS) Data

Description

Usage

Format