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NEWS.md
In summarytabl: Generate Summary Tables for Categorical, Ordinal, and Continuous Data
summarytabl 0.2.1
- Corrected package dependencies to resolve installation and compatibility issues. This update does not introduce new features or code changes.
summarytabl 0.2.0
Breaking Changes
- Functions prefixed with
cat_ and select_ no longer support columns of the haven_labelled type. Users must convert these columns to factors before using these functions. To convert haven_labelled columns to factors, consider using haven::as_factor() or labelled::unlabelled().
Minor improvements
New datasets
sdoh: A subset of the 2020 Social Determinants of Health Database.
Updates by function
cat_group_tbl()
Users can now specify how percentages are calculated in the output table using the margins argument. This provides greater flexibility in defining whether percentages are based on row totals, column totals, or overall totals.
nlsy_sub <-
nlsy |>
dplyr::mutate(
gender = ifelse(gender == 1, "male", "female"))
# Default: All
cat_group_tbl(data = nlsy_sub,
row_var = "gender",
col_var = "race",
pivot = "wider",
only = "percent")
# gender percent_race_Black percent_race_Hispanic `percent_race_Non-Black,Non-Hispanic`
# <chr> <dbl> <dbl> <dbl>
# 1 female 0.146 0.102 0.243
# 2 male 0.146 0.110 0.253
# Margins: Columnwise
cat_group_tbl(data = nlsy_sub,
row_var = "gender",
col_var = "race",
pivot = "wider",
margins = "columns",
only = "percent")
# gender percent_race_Black percent_race_Hispanic `percent_race_Non-Black,Non-Hispanic`
# <chr> <dbl> <dbl> <dbl>
# 1 female 0.5 0.483 0.490
# 2 male 0.5 0.517 0.510
# Margins: Rowwise
cat_group_tbl(data = nlsy_sub,
row_var = "gender",
col_var = "race",
pivot = "wider",
margins = "rows",
only = "percent")
# gender percent_race_Black percent_race_Hispanic `percent_race_Non-Black,Non-Hispanic`
# <chr> <dbl> <dbl> <dbl>
# 1 female 0.297 0.208 0.495
# 2 male 0.287 0.215 0.498
select_tbl()
select_tbl() now supports selecting variables either by stem or by full variable name. Both single and multiple values are accepted.
Set var_input = "stem" (default) when searching for variables using stems.
select_tbl(data = depressive, var_stem = "dep")
# # A tibble: 24 × 4
# variable values count percent
# <chr> <int> <int> <dbl>
# 1 dep_1 1 109 0.0678
# 2 dep_1 2 689 0.429
# 3 dep_1 3 809 0.503
# 4 dep_2 1 144 0.0896
# 5 dep_2 2 746 0.464
# 6 dep_2 3 717 0.446
Set var_input = "name" when searching for variables using their name.
select_tbl(data = depressive,
var_stem = c("dep_1", "dep_4", "dep_6"),
var_input = "name")
# # A tibble: 9 × 4
# variable values count percent
# <chr> <int> <int> <dbl>
# 1 dep_1 1 117 0.0714
# 2 dep_1 2 703 0.429
# 3 dep_1 3 818 0.499
# 4 dep_4 1 608 0.371
# 5 dep_4 2 854 0.521
# 6 dep_4 3 176 0.107
# 7 dep_6 1 398 0.243
# 8 dep_6 2 872 0.532
# 9 dep_6 3 368 0.225
Users are now required to use a named vector or list to indicate which values to exclude for each variable or for variables associated with a specific stem.
Previous usage: value 3 is not excluded from analysis
select_tbl(data = depressive,
var_stem = "dep",
ignore = 3)
# # A tibble: 24 × 4
# variable values count percent
# <chr> <int> <int> <dbl>
# 1 dep_1 1 109 0.0678
# 2 dep_1 2 689 0.429
# 3 dep_1 3 809 0.503
# 4 dep_2 1 144 0.0896
# 5 dep_2 2 746 0.464
# 6 dep_2 3 717 0.446
# 7 dep_3 1 1162 0.723
# 8 dep_3 2 392 0.244
# 9 dep_3 3 53 0.0330
# 10 dep_4 1 601 0.374
Updated usage: value 3 is successfully excluded from analysis
select_tbl(data = depressive,
var_stem = "dep",
ignore = c(dep = 3))
# # A tibble: 16 × 4
# variable values count percent
# <chr> <int> <int> <dbl>
# 1 dep_1 1 37 0.167
# 2 dep_1 2 185 0.833
# 3 dep_2 1 47 0.212
# 4 dep_2 2 175 0.788
# 5 dep_3 1 133 0.599
# 6 dep_3 2 89 0.401
# 7 dep_4 1 108 0.486
# 8 dep_4 2 114 0.514
select_group_tbl()
select_group_tbl() now supports selecting variables either by stem or by full variable name. Both single and multiple values are accepted.
Set var_input = "stem" (default) when searching for variables using stems.
select_group_tbl(data = depressive,
var_stem = "dep",
group = "sex")
# # A tibble: 48 × 5
# variable sex values count percent
# <chr> <int> <int> <int> <dbl>
# 1 dep_1 1 1 55 0.0342
# 2 dep_1 1 2 325 0.202
# 3 dep_1 1 3 440 0.274
# 4 dep_1 2 1 54 0.0336
# 5 dep_1 2 2 364 0.227
# 6 dep_1 2 3 369 0.230
# 7 dep_2 1 1 82 0.0510
Set var_input = "name" when searching for variables using their name.
select_group_tbl(data = depressive,
var_stem = c("dep_1", "dep_4", "dep_6"),
var_input = "name",
group = "sex")
# # A tibble: 18 × 5
# variable sex values count percent
# <chr> <int> <int> <int> <dbl>
# 1 dep_1 1 1 58 0.0354
# 2 dep_1 1 2 332 0.203
# 3 dep_1 1 3 443 0.270
# 4 dep_1 2 1 59 0.0360
# 5 dep_1 2 2 371 0.226
# 6 dep_1 2 3 375 0.229
# 7 dep_4 1 1 300 0.183
# 8 dep_4 1 2 436 0.266
# 9 dep_4 1 3 97 0.0592
# 10 dep_4 2 1 308 0.188
# 11 dep_4 2 2 418 0.255
# 12 dep_4 2 3 79 0.0482
Users can now specify how percentages are calculated in the output table using the margins argument. This provides greater flexibility in defining whether percentages are based on row totals, column totals, or overall variable totals.
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)
))
# Default: All
select_group_tbl(data = tas_recoded,
var_stem = "involved_",
group = "sex",
na_removal = "pairwise",
pivot = "wider",
only = "percent")
# variable values percent_sex_female percent_sex_male
# <chr> <chr> <dbl> <dbl>
# 1 involved_arts selected 0.0839 0.0740
# 2 involved_arts unselected 0.395 0.447
# Margins: Columnwise
select_group_tbl(data = tas_recoded,
var_stem = "involved_",
group = "sex",
na_removal = "pairwise",
pivot = "wider",
margins = "columns",
only = "percent")
# variable values percent_sex_female percent_sex_male
# <chr> <chr> <dbl> <dbl>
# 1 involved_arts selected 0.175 0.142
# 2 involved_arts unselected 0.825 0.858
# Margins: Rowwise
select_group_tbl(data = tas_recoded,
var_stem = "involved_",
group = "sex",
na_removal = "pairwise",
pivot = "wider",
margins = "rows",
only = "percent")
# variable values percent_sex_female percent_sex_male
# <chr> <chr> <dbl> <dbl>
# 1 involved_arts selected 0.531 0.469
# 2 involved_arts unselected 0.469 0.531
mean_tbl()
mean_tbl() now supports selecting variables either by stem or by full variable name. Both single and multiple values are accepted.
Set var_input = "stem" (default) when searching for variables using stems.
mean_tbl(data = sdoh, var_stem = "HHC_PCT")
# # A tibble: 6 × 6
# variable mean sd min max nobs
# <chr> <dbl> <dbl> <dbl> <dbl> <int>
# 1 HHC_PCT_HHA_NURSING 58.2 49.3 0 100 3227
# 2 HHC_PCT_HHA_PHYS_THERAPY 56.7 48.8 0 100 3227
# 3 HHC_PCT_HHA_OCC_THERAPY 52.4 48.3 0 100 3227
# 4 HHC_PCT_HHA_SPEECH 49.1 47.6 0 100 3227
# 5 HHC_PCT_HHA_MEDICAL 42.2 46.2 0 100 3227
# 6 HHC_PCT_HHA_AIDE 55.1 48.6 0 100 3227
Set var_input = "name" when searching for variables using their name.
mean_tbl(data = sdoh,
var_stem = c("HHC_PCT_HHA_NURSING", "HHC_PCT_HHA_AIDE"),
var_input = "name")
# # A tibble: 2 × 6
# variable mean sd min max nobs
# <chr> <dbl> <dbl> <dbl> <dbl> <int>
# 1 HHC_PCT_HHA_NURSING 58.2 49.3 0 100 3227
# 2 HHC_PCT_HHA_AIDE 55.1 48.6 0 100 3227
Users are now required to use a named vector or list to indicate which values to exclude for each variable or for variables associated with a specific stem.
Previous usage: value 0 is not excluded from analysis
mean_tbl(data = sdoh,
var_stem = "HHC_PCT",
ignore = 0)
# # A tibble: 6 × 6
# variable mean sd min max nobs
# <chr> <dbl> <dbl> <dbl> <dbl> <int>
# 1 HHC_PCT_HHA_NURSING 58.2 49.3 0 100 3227
# 2 HHC_PCT_HHA_PHYS_THERAPY 56.7 48.8 0 100 3227
# 3 HHC_PCT_HHA_OCC_THERAPY 52.4 48.3 0 100 3227
# 4 HHC_PCT_HHA_SPEECH 49.1 47.6 0 100 3227
# 5 HHC_PCT_HHA_MEDICAL 42.2 46.2 0 100 3227
# 6 HHC_PCT_HHA_AIDE 55.1 48.6 0 100 3227
Updated usage: value 0 is successfully excluded from analysis
mean_tbl(data = sdoh,
var_stem = "HHC_PCT",
ignore = c(HHC_PCT = 0))
# # A tibble: 6 × 6
# variable mean sd min max nobs
# <chr> <dbl> <dbl> <dbl> <dbl> <int>
# 1 HHC_PCT_HHA_NURSING 100 0 100 100 1454
# 2 HHC_PCT_HHA_PHYS_THERAPY 98.0 7.52 25 100 1454
# 3 HHC_PCT_HHA_OCC_THERAPY 94.9 12.7 25 100 1454
# 4 HHC_PCT_HHA_SPEECH 91.9 16.6 20 100 1454
# 5 HHC_PCT_HHA_MEDICAL 87.7 20.2 9.09 100 1454
# 6 HHC_PCT_HHA_AIDE 96.6 9.75 42.9 100 1454
mean_group_tbl()
mean_group_tbl() now supports selecting variables either by stem or by full variable name. Both single and multiple values are accepted.
Set var_input = "stem" (default) when searching for variables using stems.
mean_group_tbl(data = sdoh,
var_stem = "HHC_PCT",
group = "REGION")
# # A tibble: 24 × 7
# variable REGION mean sd min max nobs
# <chr> <chr> <dbl> <dbl> <dbl> <dbl> <int>
# 1 HHC_PCT_HHA_NURSING Midwest 57.4 49.5 0 100 1055
# 2 HHC_PCT_HHA_NURSING Northeast 74.2 43.9 0 100 217
# 3 HHC_PCT_HHA_NURSING South 58.8 49.2 0 100 1422
# 4 HHC_PCT_HHA_NURSING West 56 49.7 0 100 450
# 5 HHC_PCT_HHA_PHYS_THERAPY Midwest 55.2 48.9 0 100 1055
# 6 HHC_PCT_HHA_PHYS_THERAPY Northeast 68.0 43.1 0 100 217
# 7 HHC_PCT_HHA_PHYS_THERAPY South 58.4 49.0 0 100 1422
# 8 HHC_PCT_HHA_PHYS_THERAPY West 54.5 49.0 0 100 450
# 9 HHC_PCT_HHA_OCC_THERAPY Midwest 52.9 48.7 0 100 1055
# 10 HHC_PCT_HHA_OCC_THERAPY Northeast 64.8 42.8 0 100 217
Set var_input = "name" when searching for variables using their name.
mean_group_tbl(data = sdoh,
var_stem = c("ACS_PCT_AGE_0_4", "HHC_PCT_HHA_MEDICAL"),
var_input = "name",
group = "REGION")
# # A tibble: 8 × 7
# variable REGION mean sd min max nobs
# <chr> <chr> <dbl> <dbl> <dbl> <dbl> <int>
# 1 ACS_PCT_AGE_0_4 Midwest 5.90 1.13 2.4 12.0 1055
# 2 ACS_PCT_AGE_0_4 Northeast 5.04 0.829 0.95 8.12 217
# 3 ACS_PCT_AGE_0_4 South 5.76 1.26 0.98 18.4 1422
# 4 ACS_PCT_AGE_0_4 West 5.80 1.67 0.23 13.8 449
# 5 HHC_PCT_HHA_MEDICAL Midwest 33.0 43.3 0 100 1055
# 6 HHC_PCT_HHA_MEDICAL Northeast 62.2 42.6 0 100 217
# 7 HHC_PCT_HHA_MEDICAL South 45.7 46.8 0 100 1422
# 8 HHC_PCT_HHA_MEDICAL West 46.1 47.6 0 100 449
summarytabl 0.1.0
- Developmental release of summarytabl
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summarytabl documentation built on Nov. 6, 2025, 5:07 p.m.
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summarytabl 0.2.1
- Corrected package dependencies to resolve installation and compatibility issues. This update does not introduce new features or code changes.
summarytabl 0.2.0
Breaking Changes
- Functions prefixed with
cat_andselect_no longer support columns of thehaven_labelledtype. Users must convert these columns to factors before using these functions. To converthaven_labelledcolumns to factors, consider usinghaven::as_factor()orlabelled::unlabelled().
Minor improvements
New datasets
sdoh: A subset of the 2020 Social Determinants of Health Database.
Updates by function
cat_group_tbl()
Users can now specify how percentages are calculated in the output table using the margins argument. This provides greater flexibility in defining whether percentages are based on row totals, column totals, or overall totals.
nlsy_sub <-
nlsy |>
dplyr::mutate(
gender = ifelse(gender == 1, "male", "female"))
# Default: All
cat_group_tbl(data = nlsy_sub,
row_var = "gender",
col_var = "race",
pivot = "wider",
only = "percent")
# gender percent_race_Black percent_race_Hispanic `percent_race_Non-Black,Non-Hispanic`
# <chr> <dbl> <dbl> <dbl>
# 1 female 0.146 0.102 0.243
# 2 male 0.146 0.110 0.253
# Margins: Columnwise
cat_group_tbl(data = nlsy_sub,
row_var = "gender",
col_var = "race",
pivot = "wider",
margins = "columns",
only = "percent")
# gender percent_race_Black percent_race_Hispanic `percent_race_Non-Black,Non-Hispanic`
# <chr> <dbl> <dbl> <dbl>
# 1 female 0.5 0.483 0.490
# 2 male 0.5 0.517 0.510
# Margins: Rowwise
cat_group_tbl(data = nlsy_sub,
row_var = "gender",
col_var = "race",
pivot = "wider",
margins = "rows",
only = "percent")
# gender percent_race_Black percent_race_Hispanic `percent_race_Non-Black,Non-Hispanic`
# <chr> <dbl> <dbl> <dbl>
# 1 female 0.297 0.208 0.495
# 2 male 0.287 0.215 0.498
select_tbl()
select_tbl() now supports selecting variables either by stem or by full variable name. Both single and multiple values are accepted.
Set var_input = "stem" (default) when searching for variables using stems.
select_tbl(data = depressive, var_stem = "dep")
# # A tibble: 24 × 4
# variable values count percent
# <chr> <int> <int> <dbl>
# 1 dep_1 1 109 0.0678
# 2 dep_1 2 689 0.429
# 3 dep_1 3 809 0.503
# 4 dep_2 1 144 0.0896
# 5 dep_2 2 746 0.464
# 6 dep_2 3 717 0.446
Set var_input = "name" when searching for variables using their name.
select_tbl(data = depressive,
var_stem = c("dep_1", "dep_4", "dep_6"),
var_input = "name")
# # A tibble: 9 × 4
# variable values count percent
# <chr> <int> <int> <dbl>
# 1 dep_1 1 117 0.0714
# 2 dep_1 2 703 0.429
# 3 dep_1 3 818 0.499
# 4 dep_4 1 608 0.371
# 5 dep_4 2 854 0.521
# 6 dep_4 3 176 0.107
# 7 dep_6 1 398 0.243
# 8 dep_6 2 872 0.532
# 9 dep_6 3 368 0.225
Users are now required to use a named vector or list to indicate which values to exclude for each variable or for variables associated with a specific stem.
Previous usage: value 3 is not excluded from analysis
select_tbl(data = depressive,
var_stem = "dep",
ignore = 3)
# # A tibble: 24 × 4
# variable values count percent
# <chr> <int> <int> <dbl>
# 1 dep_1 1 109 0.0678
# 2 dep_1 2 689 0.429
# 3 dep_1 3 809 0.503
# 4 dep_2 1 144 0.0896
# 5 dep_2 2 746 0.464
# 6 dep_2 3 717 0.446
# 7 dep_3 1 1162 0.723
# 8 dep_3 2 392 0.244
# 9 dep_3 3 53 0.0330
# 10 dep_4 1 601 0.374
Updated usage: value 3 is successfully excluded from analysis
select_tbl(data = depressive,
var_stem = "dep",
ignore = c(dep = 3))
# # A tibble: 16 × 4
# variable values count percent
# <chr> <int> <int> <dbl>
# 1 dep_1 1 37 0.167
# 2 dep_1 2 185 0.833
# 3 dep_2 1 47 0.212
# 4 dep_2 2 175 0.788
# 5 dep_3 1 133 0.599
# 6 dep_3 2 89 0.401
# 7 dep_4 1 108 0.486
# 8 dep_4 2 114 0.514
select_group_tbl()
select_group_tbl() now supports selecting variables either by stem or by full variable name. Both single and multiple values are accepted.
Set var_input = "stem" (default) when searching for variables using stems.
select_group_tbl(data = depressive,
var_stem = "dep",
group = "sex")
# # A tibble: 48 × 5
# variable sex values count percent
# <chr> <int> <int> <int> <dbl>
# 1 dep_1 1 1 55 0.0342
# 2 dep_1 1 2 325 0.202
# 3 dep_1 1 3 440 0.274
# 4 dep_1 2 1 54 0.0336
# 5 dep_1 2 2 364 0.227
# 6 dep_1 2 3 369 0.230
# 7 dep_2 1 1 82 0.0510
Set var_input = "name" when searching for variables using their name.
select_group_tbl(data = depressive,
var_stem = c("dep_1", "dep_4", "dep_6"),
var_input = "name",
group = "sex")
# # A tibble: 18 × 5
# variable sex values count percent
# <chr> <int> <int> <int> <dbl>
# 1 dep_1 1 1 58 0.0354
# 2 dep_1 1 2 332 0.203
# 3 dep_1 1 3 443 0.270
# 4 dep_1 2 1 59 0.0360
# 5 dep_1 2 2 371 0.226
# 6 dep_1 2 3 375 0.229
# 7 dep_4 1 1 300 0.183
# 8 dep_4 1 2 436 0.266
# 9 dep_4 1 3 97 0.0592
# 10 dep_4 2 1 308 0.188
# 11 dep_4 2 2 418 0.255
# 12 dep_4 2 3 79 0.0482
Users can now specify how percentages are calculated in the output table using the margins argument. This provides greater flexibility in defining whether percentages are based on row totals, column totals, or overall variable totals.
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)
))
# Default: All
select_group_tbl(data = tas_recoded,
var_stem = "involved_",
group = "sex",
na_removal = "pairwise",
pivot = "wider",
only = "percent")
# variable values percent_sex_female percent_sex_male
# <chr> <chr> <dbl> <dbl>
# 1 involved_arts selected 0.0839 0.0740
# 2 involved_arts unselected 0.395 0.447
# Margins: Columnwise
select_group_tbl(data = tas_recoded,
var_stem = "involved_",
group = "sex",
na_removal = "pairwise",
pivot = "wider",
margins = "columns",
only = "percent")
# variable values percent_sex_female percent_sex_male
# <chr> <chr> <dbl> <dbl>
# 1 involved_arts selected 0.175 0.142
# 2 involved_arts unselected 0.825 0.858
# Margins: Rowwise
select_group_tbl(data = tas_recoded,
var_stem = "involved_",
group = "sex",
na_removal = "pairwise",
pivot = "wider",
margins = "rows",
only = "percent")
# variable values percent_sex_female percent_sex_male
# <chr> <chr> <dbl> <dbl>
# 1 involved_arts selected 0.531 0.469
# 2 involved_arts unselected 0.469 0.531
mean_tbl()
mean_tbl() now supports selecting variables either by stem or by full variable name. Both single and multiple values are accepted.
Set var_input = "stem" (default) when searching for variables using stems.
mean_tbl(data = sdoh, var_stem = "HHC_PCT")
# # A tibble: 6 × 6
# variable mean sd min max nobs
# <chr> <dbl> <dbl> <dbl> <dbl> <int>
# 1 HHC_PCT_HHA_NURSING 58.2 49.3 0 100 3227
# 2 HHC_PCT_HHA_PHYS_THERAPY 56.7 48.8 0 100 3227
# 3 HHC_PCT_HHA_OCC_THERAPY 52.4 48.3 0 100 3227
# 4 HHC_PCT_HHA_SPEECH 49.1 47.6 0 100 3227
# 5 HHC_PCT_HHA_MEDICAL 42.2 46.2 0 100 3227
# 6 HHC_PCT_HHA_AIDE 55.1 48.6 0 100 3227
Set var_input = "name" when searching for variables using their name.
mean_tbl(data = sdoh,
var_stem = c("HHC_PCT_HHA_NURSING", "HHC_PCT_HHA_AIDE"),
var_input = "name")
# # A tibble: 2 × 6
# variable mean sd min max nobs
# <chr> <dbl> <dbl> <dbl> <dbl> <int>
# 1 HHC_PCT_HHA_NURSING 58.2 49.3 0 100 3227
# 2 HHC_PCT_HHA_AIDE 55.1 48.6 0 100 3227
Users are now required to use a named vector or list to indicate which values to exclude for each variable or for variables associated with a specific stem.
Previous usage: value 0 is not excluded from analysis
mean_tbl(data = sdoh,
var_stem = "HHC_PCT",
ignore = 0)
# # A tibble: 6 × 6
# variable mean sd min max nobs
# <chr> <dbl> <dbl> <dbl> <dbl> <int>
# 1 HHC_PCT_HHA_NURSING 58.2 49.3 0 100 3227
# 2 HHC_PCT_HHA_PHYS_THERAPY 56.7 48.8 0 100 3227
# 3 HHC_PCT_HHA_OCC_THERAPY 52.4 48.3 0 100 3227
# 4 HHC_PCT_HHA_SPEECH 49.1 47.6 0 100 3227
# 5 HHC_PCT_HHA_MEDICAL 42.2 46.2 0 100 3227
# 6 HHC_PCT_HHA_AIDE 55.1 48.6 0 100 3227
Updated usage: value 0 is successfully excluded from analysis
mean_tbl(data = sdoh,
var_stem = "HHC_PCT",
ignore = c(HHC_PCT = 0))
# # A tibble: 6 × 6
# variable mean sd min max nobs
# <chr> <dbl> <dbl> <dbl> <dbl> <int>
# 1 HHC_PCT_HHA_NURSING 100 0 100 100 1454
# 2 HHC_PCT_HHA_PHYS_THERAPY 98.0 7.52 25 100 1454
# 3 HHC_PCT_HHA_OCC_THERAPY 94.9 12.7 25 100 1454
# 4 HHC_PCT_HHA_SPEECH 91.9 16.6 20 100 1454
# 5 HHC_PCT_HHA_MEDICAL 87.7 20.2 9.09 100 1454
# 6 HHC_PCT_HHA_AIDE 96.6 9.75 42.9 100 1454
mean_group_tbl()
mean_group_tbl() now supports selecting variables either by stem or by full variable name. Both single and multiple values are accepted.
Set var_input = "stem" (default) when searching for variables using stems.
mean_group_tbl(data = sdoh,
var_stem = "HHC_PCT",
group = "REGION")
# # A tibble: 24 × 7
# variable REGION mean sd min max nobs
# <chr> <chr> <dbl> <dbl> <dbl> <dbl> <int>
# 1 HHC_PCT_HHA_NURSING Midwest 57.4 49.5 0 100 1055
# 2 HHC_PCT_HHA_NURSING Northeast 74.2 43.9 0 100 217
# 3 HHC_PCT_HHA_NURSING South 58.8 49.2 0 100 1422
# 4 HHC_PCT_HHA_NURSING West 56 49.7 0 100 450
# 5 HHC_PCT_HHA_PHYS_THERAPY Midwest 55.2 48.9 0 100 1055
# 6 HHC_PCT_HHA_PHYS_THERAPY Northeast 68.0 43.1 0 100 217
# 7 HHC_PCT_HHA_PHYS_THERAPY South 58.4 49.0 0 100 1422
# 8 HHC_PCT_HHA_PHYS_THERAPY West 54.5 49.0 0 100 450
# 9 HHC_PCT_HHA_OCC_THERAPY Midwest 52.9 48.7 0 100 1055
# 10 HHC_PCT_HHA_OCC_THERAPY Northeast 64.8 42.8 0 100 217
Set var_input = "name" when searching for variables using their name.
mean_group_tbl(data = sdoh,
var_stem = c("ACS_PCT_AGE_0_4", "HHC_PCT_HHA_MEDICAL"),
var_input = "name",
group = "REGION")
# # A tibble: 8 × 7
# variable REGION mean sd min max nobs
# <chr> <chr> <dbl> <dbl> <dbl> <dbl> <int>
# 1 ACS_PCT_AGE_0_4 Midwest 5.90 1.13 2.4 12.0 1055
# 2 ACS_PCT_AGE_0_4 Northeast 5.04 0.829 0.95 8.12 217
# 3 ACS_PCT_AGE_0_4 South 5.76 1.26 0.98 18.4 1422
# 4 ACS_PCT_AGE_0_4 West 5.80 1.67 0.23 13.8 449
# 5 HHC_PCT_HHA_MEDICAL Midwest 33.0 43.3 0 100 1055
# 6 HHC_PCT_HHA_MEDICAL Northeast 62.2 42.6 0 100 217
# 7 HHC_PCT_HHA_MEDICAL South 45.7 46.8 0 100 1422
# 8 HHC_PCT_HHA_MEDICAL West 46.1 47.6 0 100 449
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