Welcome to the summarytabl package! This package makes
it easy to create simple tables for summarizing continuous, ordinal, and
categorical data. This document introduces you to some of
summarytabl’s most frequently used functions, along with
examples of how to apply them to your data frames.
To begin, load summarytabl
This package has three types of functions to help you summarize your data. Those for:
Functions for categorical data start with cat_, those
for multiple response data start with select_, and
functions for continuous data start with mean_.
To learn more about how these functions work, read the next few sections.
The cat_tbl() function can be used to generate a
frequency table for a categorical variable.
## # A tibble: 3 × 3
## race count percent
## <chr> <int> <dbl>
## 1 Black 868 0.292
## 2 Hispanic 631 0.212
## 3 Non-Black,Non-Hispanic 1477 0.496You can exclude certain values and eliminate missing values from the
data using the ignore and na.rm arguments,
respectively.
## # A tibble: 2 × 3
## race count percent
## <chr> <int> <dbl>
## 1 Hispanic 631 0.299
## 2 Non-Black,Non-Hispanic 1477 0.701Finally, you can choose what information to return using the
only argument.
# Default: counts and percentages
cat_group_tbl(data = nlsy,
row_var = "race",
col_var = "bthwht",
na.rm.row_var = TRUE)## # A tibble: 6 × 4
## race bthwht count percent
## <chr> <dbl> <int> <dbl>
## 1 Black 0 766 0.279
## 2 Hispanic 0 589 0.214
## 3 Non-Black,Non-Hispanic 0 1394 0.507
## 4 Black 1 102 0.449
## 5 Hispanic 1 42 0.185
## 6 Non-Black,Non-Hispanic 1 83 0.366## # A tibble: 2 × 2
## race count
## <chr> <int>
## 1 Hispanic 631
## 2 Non-Black,Non-Hispanic 1477# Percents only
cat_group_tbl(data = nlsy,
row_var = "race",
col_var = "bthwht",
na.rm.row_var = TRUE,
only = "percent")## # A tibble: 6 × 3
## race bthwht percent
## <chr> <dbl> <dbl>
## 1 Black 0 0.279
## 2 Hispanic 0 0.214
## 3 Non-Black,Non-Hispanic 0 0.507
## 4 Black 1 0.449
## 5 Hispanic 1 0.185
## 6 Non-Black,Non-Hispanic 1 0.366To create a grouped frequency table for two categorical variables,
use the cat_group_tbl() function.
## # A tibble: 4 × 4
## gender bthwht count percent
## <dbl> <dbl> <int> <dbl>
## 1 0 0 1340 0.487
## 2 1 0 1409 0.513
## 3 0 1 123 0.542
## 4 1 1 104 0.458Like cat_tbl(), you have the option to exclude certain
values and omit missing values (by row and/or column).
cat_group_tbl(data = nlsy,
row_var = "race",
col_var = "bthwht",
na.rm.row_var = TRUE,
ignore = c(race = "Non-Black,Non-Hispanic"),
pivot = "wider")## # A tibble: 2 × 5
## race count_bthwht_0 count_bthwht_1 percent_bthwht_0 percent_bthwht_1
## <chr> <int> <int> <dbl> <dbl>
## 1 Black 766 102 0.565 0.708
## 2 Hispanic 589 42 0.435 0.292If you want to ignore more than one value per row or column, provide them in a named list:
cat_group_tbl(data = nlsy,
row_var = "race",
col_var = "bthwht",
na.rm.row_var = TRUE,
ignore = list(race = c("Non-Black,Non-Hispanic", "Hispanic")),
pivot = "wider")## # A tibble: 1 × 5
## race count_bthwht_0 count_bthwht_1 percent_bthwht_0 percent_bthwht_1
## <chr> <int> <int> <dbl> <dbl>
## 1 Black 766 102 1 1Finally, you can choose what information to return using the
only argument.
# Default: counts and percentages
cat_group_tbl(data = nlsy,
row_var = "race",
col_var = "bthwht",
na.rm.row_var = TRUE)## # A tibble: 6 × 4
## race bthwht count percent
## <chr> <dbl> <int> <dbl>
## 1 Black 0 766 0.279
## 2 Hispanic 0 589 0.214
## 3 Non-Black,Non-Hispanic 0 1394 0.507
## 4 Black 1 102 0.449
## 5 Hispanic 1 42 0.185
## 6 Non-Black,Non-Hispanic 1 83 0.366# Counts only
cat_group_tbl(data = nlsy,
row_var = "race",
col_var = "bthwht",
na.rm.row_var = TRUE,
only = "count")## # A tibble: 6 × 3
## race bthwht count
## <chr> <dbl> <int>
## 1 Black 0 766
## 2 Hispanic 0 589
## 3 Non-Black,Non-Hispanic 0 1394
## 4 Black 1 102
## 5 Hispanic 1 42
## 6 Non-Black,Non-Hispanic 1 83# Percents only
cat_group_tbl(data = nlsy,
row_var = "race",
col_var = "bthwht",
na.rm.row_var = TRUE,
only = "percent")## # A tibble: 6 × 3
## race bthwht percent
## <chr> <dbl> <dbl>
## 1 Black 0 0.279
## 2 Hispanic 0 0.214
## 3 Non-Black,Non-Hispanic 0 0.507
## 4 Black 1 0.449
## 5 Hispanic 1 0.185
## 6 Non-Black,Non-Hispanic 1 0.366With select_tbl(), you can produce a summary table for
multiple response variables with the same variable stem. A variable stem
is a common prefix found in related variable names, often corresponding
to similar survey items, that represents a shared concept before unique
identifiers.
For example, the depressive dataset contains eight
variables that share the same stem, with each one representing a
different item (such as a statement, question, or indicator) used to
measure depression:
## [1] "cid" "race" "sex" "yob" "dep_1" "dep_2" "dep_3" "dep_4" "dep_5"
## [10] "dep_6" "dep_7" "dep_8"With the select_tbl() function, you can summarize these
responses to see how many survey respondents chose each answer option
for every variable:
## # 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
## # ℹ 14 more rowsYou can also use the ignore and na_removal
arguments to exclude values from the data and specify how missing values
should be handled. By default, missing values are removed listwise, but
you can set na_removal to pairwise for
pairwise removal instead:
# Default listwise removal, value '3' removed from data
select_tbl(data = depressive,
var_stem = "dep",
ignore = 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
## 9 dep_5 1 61 0.275
## 10 dep_5 2 161 0.725
## 11 dep_6 1 86 0.387
## 12 dep_6 2 136 0.613
## 13 dep_7 1 126 0.568
## 14 dep_7 2 96 0.432
## 15 dep_8 1 81 0.365
## 16 dep_8 2 141 0.635# Pairwise removal, value '3' removed from data
select_tbl(data = depressive,
var_stem = "dep",
ignore = 3,
na_removal = "pairwise")## # A tibble: 16 × 4
## variable values count percent
## <chr> <int> <int> <dbl>
## 1 dep_1 1 120 0.145
## 2 dep_1 2 709 0.855
## 3 dep_2 1 151 0.165
## 4 dep_2 2 762 0.835
## 5 dep_3 1 1192 0.746
## 6 dep_3 2 406 0.254
## 7 dep_4 1 611 0.416
## 8 dep_4 2 856 0.584
## 9 dep_5 1 206 0.264
## 10 dep_5 2 574 0.736
## 11 dep_6 1 399 0.312
## 12 dep_6 2 879 0.688
## 13 dep_7 1 1046 0.674
## 14 dep_7 2 507 0.326
## 15 dep_8 1 323 0.287
## 16 dep_8 2 801 0.713Set the pivot argument to wider to reshape
the resulting table into a wide format. By default, the summary table is
presented in the long format.
## # 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
## # ℹ 14 more rows## # A tibble: 8 × 7
## variable count_value_1 count_value_2 count_value_3 percent_value_1
## <chr> <int> <int> <int> <dbl>
## 1 dep_1 109 689 809 0.0678
## 2 dep_2 144 746 717 0.0896
## 3 dep_3 1162 392 53 0.723
## 4 dep_4 601 836 170 0.374
## 5 dep_5 194 556 857 0.121
## 6 dep_6 387 860 360 0.241
## 7 dep_7 1028 490 89 0.640
## 8 dep_8 317 784 506 0.197
## # ℹ 2 more variables: percent_value_2 <dbl>, percent_value_3 <dbl>You can use the var_labels argument to include variable
labels in your summary table to make the variable names easier to
interpret:
select_tbl(data = depressive,
var_stem = "dep",
pivot = "wider",
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"
)
)## # A tibble: 8 × 8
## variable variable_label count_value_1 count_value_2 count_value_3
## <chr> <chr> <int> <int> <int>
## 1 dep_1 how often child feels sad … 109 689 809
## 2 dep_2 how often child feels nerv… 144 746 717
## 3 dep_3 how often child feels happy 1162 392 53
## 4 dep_4 how often child feels bored 601 836 170
## 5 dep_5 how often child feels lone… 194 556 857
## 6 dep_6 how often child feels tire… 387 860 360
## 7 dep_7 how often child feels exci… 1028 490 89
## 8 dep_8 how often child feels too … 317 784 506
## # ℹ 3 more variables: percent_value_1 <dbl>, percent_value_2 <dbl>,
## # percent_value_3 <dbl>Finally, you can choose what information to return using the
only argument.
## # A tibble: 8 × 7
## variable count_value_1 count_value_2 count_value_3 percent_value_1
## <chr> <int> <int> <int> <dbl>
## 1 dep_1 109 689 809 0.0678
## 2 dep_2 144 746 717 0.0896
## 3 dep_3 1162 392 53 0.723
## 4 dep_4 601 836 170 0.374
## 5 dep_5 194 556 857 0.121
## 6 dep_6 387 860 360 0.241
## 7 dep_7 1028 490 89 0.640
## 8 dep_8 317 784 506 0.197
## # ℹ 2 more variables: percent_value_2 <dbl>, percent_value_3 <dbl>## # A tibble: 8 × 4
## variable count_value_1 count_value_2 count_value_3
## <chr> <int> <int> <int>
## 1 dep_1 109 689 809
## 2 dep_2 144 746 717
## 3 dep_3 1162 392 53
## 4 dep_4 601 836 170
## 5 dep_5 194 556 857
## 6 dep_6 387 860 360
## 7 dep_7 1028 490 89
## 8 dep_8 317 784 506## # A tibble: 8 × 4
## variable percent_value_1 percent_value_2 percent_value_3
## <chr> <dbl> <dbl> <dbl>
## 1 dep_1 0.0678 0.429 0.503
## 2 dep_2 0.0896 0.464 0.446
## 3 dep_3 0.723 0.244 0.0330
## 4 dep_4 0.374 0.520 0.106
## 5 dep_5 0.121 0.346 0.533
## 6 dep_6 0.241 0.535 0.224
## 7 dep_7 0.640 0.305 0.0554
## 8 dep_8 0.197 0.488 0.315With select_group_tbl(), you can create a summary table
for multiple response variables with the same variable stem, grouped
either by another variable in your dataset or by matching a pattern in
the variable names.
For example, we often want to summarize survey responses by demographic variables like gender, age, or race:
dep_recoded <-
depressive |>
dplyr::mutate(
race = dplyr::case_match(.x = race,
1 ~ "Hispanic",
2 ~ "Black",
3 ~ "Non-Black/Non-Hispanic",
.default = NA)
) |>
dplyr::mutate(
dplyr::across(
.cols = dplyr::starts_with("dep"),
.fns = ~ dplyr::case_when(.x == 1 ~ "often",
.x == 2 ~ "sometimes",
.x == 3 ~ "hardly ever")
)
)
# longer format
select_group_tbl(data = dep_recoded,
var_stem = "dep",
group = "race",
pivot = "longer")## # A tibble: 72 × 5
## variable race values count percent
## <chr> <chr> <chr> <int> <dbl>
## 1 dep_1 Black hardly ever 248 0.307
## 2 dep_1 Hispanic hardly ever 187 0.231
## 3 dep_1 Non-Black/Non-Hispanic hardly ever 374 0.462
## 4 dep_1 Black often 45 0.413
## 5 dep_1 Hispanic often 28 0.257
## 6 dep_1 Non-Black/Non-Hispanic often 36 0.330
## 7 dep_1 Black sometimes 194 0.282
## 8 dep_1 Hispanic sometimes 155 0.225
## 9 dep_1 Non-Black/Non-Hispanic sometimes 340 0.493
## 10 dep_2 Black hardly ever 234 0.326
## # ℹ 62 more rows# wider format
select_group_tbl(data = dep_recoded,
var_stem = "dep",
group = "race",
pivot = "wider")## # A tibble: 24 × 8
## variable race count_value_hardly e…¹ count_value_often count_value_sometimes
## <chr> <chr> <int> <int> <int>
## 1 dep_1 Black 248 45 194
## 2 dep_1 Hisp… 187 28 155
## 3 dep_1 Non-… 374 36 340
## 4 dep_2 Black 234 49 204
## 5 dep_2 Hisp… 171 36 163
## 6 dep_2 Non-… 312 59 379
## 7 dep_3 Black 19 330 138
## 8 dep_3 Hisp… 20 246 104
## 9 dep_3 Non-… 14 586 150
## 10 dep_4 Black 45 183 259
## # ℹ 14 more rows
## # ℹ abbreviated name: ¹`count_value_hardly ever`
## # ℹ 3 more variables: `percent_value_hardly ever` <dbl>,
## # percent_value_often <dbl>, percent_value_sometimes <dbl>As with cat_group_tbl(), you can specify which values to
exclude and how remove missing values. However, when specifying values
to exclude, use the var_stem argument you provide to
determine which values to exclude for variables sharing the same
stem.
# Default listwise removal: 'often' value removed from all
# dep_ variables, and 'Non-Black/Non-Hispanic' value removed
# from race variable
select_group_tbl(data = dep_recoded,
var_stem = "dep",
group = "race",
pivot = "longer",
ignore = c(dep = "often", race = "Non-Black/Non-Hispanic"))## # A tibble: 31 × 5
## variable race values count percent
## <chr> <chr> <chr> <int> <dbl>
## 1 dep_1 Black hardly ever 18 0.667
## 2 dep_1 Hispanic hardly ever 9 0.333
## 3 dep_1 Black sometimes 16 0.444
## 4 dep_1 Hispanic sometimes 20 0.556
## 5 dep_2 Black hardly ever 18 0.581
## 6 dep_2 Hispanic hardly ever 13 0.419
## 7 dep_2 Black sometimes 16 0.5
## 8 dep_2 Hispanic sometimes 16 0.5
## 9 dep_3 Hispanic hardly ever 8 1
## 10 dep_3 Black sometimes 34 0.618
## # ℹ 21 more rows# Pairwise removal: 'often' value removed from all
# dep_ variables, and 'Non-Black/Non-Hispanic' value removed
# from race variable
select_group_tbl(data = dep_recoded,
var_stem = "dep",
group = "race",
pivot = "longer",
ignore = c(dep = "often", race = "Non-Black/Non-Hispanic"),
na_removal = "pairwise")## # A tibble: 32 × 5
## variable race values count percent
## <chr> <chr> <chr> <int> <dbl>
## 1 dep_1 Black hardly ever 256 0.574
## 2 dep_1 Hispanic hardly ever 190 0.426
## 3 dep_1 Black sometimes 203 0.561
## 4 dep_1 Hispanic sometimes 159 0.439
## 5 dep_2 Black hardly ever 241 0.584
## 6 dep_2 Hispanic hardly ever 172 0.416
## 7 dep_2 Black sometimes 213 0.563
## 8 dep_2 Hispanic sometimes 165 0.437
## 9 dep_3 Black hardly ever 20 0.5
## 10 dep_3 Hispanic hardly ever 20 0.5
## # ℹ 22 more rowsUse a list if you want to exclude several values from the same
var_stem or group variable:
select_group_tbl(data = dep_recoded,
var_stem = "dep",
group = "race",
pivot = "longer",
ignore = list(race = c("Hispanic", "Non-Black/Non-Hispanic")))## # A tibble: 24 × 5
## variable race values count percent
## <chr> <chr> <chr> <int> <dbl>
## 1 dep_1 Black hardly ever 248 1
## 2 dep_1 Black often 45 1
## 3 dep_1 Black sometimes 194 1
## 4 dep_2 Black hardly ever 234 1
## 5 dep_2 Black often 49 1
## 6 dep_2 Black sometimes 204 1
## 7 dep_3 Black hardly ever 19 1
## 8 dep_3 Black often 330 1
## 9 dep_3 Black sometimes 138 1
## 10 dep_4 Black hardly ever 45 1
## # ℹ 14 more rowsAnother application of select_group_tbl is summarizing
responses based on a matching pattern, such as survey time points (e.g.,
waves). To use this feature, set group_type to
pattern and enter the pattern to search for in the
group argument.
For example, the stem_social_psych dataset includes a
set of variables responded to by students at two different time points
(“w1” and “w2”). You can summarize the responses for one of set of these
variables using the following approach:
select_group_tbl(data = stem_social_psych,
var_stem = "belong_belong",
group = "_w\\d",
group_type = "pattern",
pivot = "longer")## # A tibble: 10 × 5
## variable group values count percent
## <chr> <chr> <dbl> <int> <dbl>
## 1 belong_belongStem_w1 w1 1 5 0.0185
## 2 belong_belongStem_w1 w1 2 20 0.0741
## 3 belong_belongStem_w1 w1 3 59 0.219
## 4 belong_belongStem_w1 w1 4 107 0.396
## 5 belong_belongStem_w1 w1 5 79 0.293
## 6 belong_belongStem_w2 w2 1 11 0.0407
## 7 belong_belongStem_w2 w2 2 11 0.0407
## 8 belong_belongStem_w2 w2 3 44 0.163
## 9 belong_belongStem_w2 w2 4 113 0.419
## 10 belong_belongStem_w2 w2 5 91 0.337Use the group_name argument to assign a descriptive name
to the column containing the matched pattern values.
select_group_tbl(data = stem_social_psych,
var_stem = "belong_belong",
group = "_w\\d",
group_type = "pattern",
group_name = "wave",
pivot = "longer")## # A tibble: 10 × 5
## variable wave values count percent
## <chr> <chr> <dbl> <int> <dbl>
## 1 belong_belongStem_w1 w1 1 5 0.0185
## 2 belong_belongStem_w1 w1 2 20 0.0741
## 3 belong_belongStem_w1 w1 3 59 0.219
## 4 belong_belongStem_w1 w1 4 107 0.396
## 5 belong_belongStem_w1 w1 5 79 0.293
## 6 belong_belongStem_w2 w2 1 11 0.0407
## 7 belong_belongStem_w2 w2 2 11 0.0407
## 8 belong_belongStem_w2 w2 3 44 0.163
## 9 belong_belongStem_w2 w2 4 113 0.419
## 10 belong_belongStem_w2 w2 5 91 0.337You can use the var_labels argument to include variable
labels in your summary table to make the variable names easier to
interpret:
select_group_tbl(data = stem_social_psych,
var_stem = "belong_belong",
group = "_w\\d",
group_type = "pattern",
group_name = "wave",
pivot = "longer",
var_labels = c(
belong_belongStem_w1 = "I feel like I belong in STEM (wave 1)",
belong_belongStem_w2 = "I feel like I belong in STEM (wave 2)"
))## # A tibble: 10 × 6
## variable variable_label wave values count percent
## <chr> <chr> <chr> <dbl> <int> <dbl>
## 1 belong_belongStem_w1 I feel like I belong in STEM… w1 1 5 0.0185
## 2 belong_belongStem_w1 I feel like I belong in STEM… w1 2 20 0.0741
## 3 belong_belongStem_w1 I feel like I belong in STEM… w1 3 59 0.219
## 4 belong_belongStem_w1 I feel like I belong in STEM… w1 4 107 0.396
## 5 belong_belongStem_w1 I feel like I belong in STEM… w1 5 79 0.293
## 6 belong_belongStem_w2 I feel like I belong in STEM… w2 1 11 0.0407
## 7 belong_belongStem_w2 I feel like I belong in STEM… w2 2 11 0.0407
## 8 belong_belongStem_w2 I feel like I belong in STEM… w2 3 44 0.163
## 9 belong_belongStem_w2 I feel like I belong in STEM… w2 4 113 0.419
## 10 belong_belongStem_w2 I feel like I belong in STEM… w2 5 91 0.337Finally, you can choose what information to return using the
only argument.
# Default: counts and percentages
select_group_tbl(data = stem_social_psych,
var_stem = "belong_belong",
group = "_w\\d",
group_type = "pattern",
group_name = "wave",
pivot = "longer",
only = "count")## # A tibble: 10 × 4
## variable wave values count
## <chr> <chr> <dbl> <int>
## 1 belong_belongStem_w1 w1 1 5
## 2 belong_belongStem_w1 w1 2 20
## 3 belong_belongStem_w1 w1 3 59
## 4 belong_belongStem_w1 w1 4 107
## 5 belong_belongStem_w1 w1 5 79
## 6 belong_belongStem_w2 w2 1 11
## 7 belong_belongStem_w2 w2 2 11
## 8 belong_belongStem_w2 w2 3 44
## 9 belong_belongStem_w2 w2 4 113
## 10 belong_belongStem_w2 w2 5 91# Counts only
select_group_tbl(data = stem_social_psych,
var_stem = "belong_belong",
group = "_w\\d",
group_type = "pattern",
group_name = "wave",
pivot = "longer",
only = "count")## # A tibble: 10 × 4
## variable wave values count
## <chr> <chr> <dbl> <int>
## 1 belong_belongStem_w1 w1 1 5
## 2 belong_belongStem_w1 w1 2 20
## 3 belong_belongStem_w1 w1 3 59
## 4 belong_belongStem_w1 w1 4 107
## 5 belong_belongStem_w1 w1 5 79
## 6 belong_belongStem_w2 w2 1 11
## 7 belong_belongStem_w2 w2 2 11
## 8 belong_belongStem_w2 w2 3 44
## 9 belong_belongStem_w2 w2 4 113
## 10 belong_belongStem_w2 w2 5 91# Percents only
select_group_tbl(data = stem_social_psych,
var_stem = "belong_belong",
group = "_w\\d",
group_type = "pattern",
group_name = "wave",
pivot = "longer",
only = "percent")## # A tibble: 10 × 4
## variable wave values percent
## <chr> <chr> <dbl> <dbl>
## 1 belong_belongStem_w1 w1 1 0.0185
## 2 belong_belongStem_w1 w1 2 0.0741
## 3 belong_belongStem_w1 w1 3 0.219
## 4 belong_belongStem_w1 w1 4 0.396
## 5 belong_belongStem_w1 w1 5 0.293
## 6 belong_belongStem_w2 w2 1 0.0407
## 7 belong_belongStem_w2 w2 2 0.0407
## 8 belong_belongStem_w2 w2 3 0.163
## 9 belong_belongStem_w2 w2 4 0.419
## 10 belong_belongStem_w2 w2 5 0.337With the mean_tbl() function, you can summarize a group
of continuous variables that share the same variable stem. The resulting
table provides descriptive statistics for each variable, including the
mean, standard deviation, minimum, maximum, and the number of
non-missing values.
## # A tibble: 3 × 6
## variable mean sd min max nobs
## <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 belong_1 3.83 1.10 1 5 10105
## 2 belong_2 2.52 1.22 1 5 10105
## 3 belong_3 3.77 0.995 1 5 10105Like the other functions in this package, you can use the
ignore argument to specify which values to exclude from all
variables associated with the provided variable stem.
## # A tibble: 3 × 6
## variable mean sd min max nobs
## <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 belong_1 3.31 0.827 1 4 5752
## 2 belong_2 2.85 0.970 1 4 5752
## 3 belong_3 3.38 0.754 1 4 5752You can also specify how missing values are removed:
## # A tibble: 3 × 6
## variable mean sd min max nobs
## <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 belong_1 3.31 0.827 1 4 5752
## 2 belong_2 2.85 0.970 1 4 5752
## 3 belong_3 3.38 0.754 1 4 5752# Pairwise removal
mean_tbl(data = social_psy_data,
var_stem = "belong",
na_removal = "pairwise",
ignore = 5)## # A tibble: 3 × 6
## variable mean sd min max nobs
## <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 belong_1 3.27 0.898 1 4 6855
## 2 belong_2 2.37 1.10 1 4 9589
## 3 belong_3 3.36 0.808 1 4 7633Including variable labels in your summary table can help make the variable names easier to interpret.
mean_tbl(data = social_psy_data,
var_stem = "belong",
na_removal = "pairwise",
var_labels = c(
belong_1 = "I feel like I belong at this institution",
belong_2 = "I feel like part of the community",
belong_3 = "I feel valued by this institution")
)## # A tibble: 3 × 7
## variable variable_label mean sd min max nobs
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 belong_1 I feel like I belong at this instituti… 3.83 1.10 1 5 10138
## 2 belong_2 I feel like part of the community 2.52 1.22 1 5 10145
## 3 belong_3 I feel valued by this institution 3.77 0.995 1 5 10142Finally, you can choose what information to return using the
only argument.
# Default: all summary statistics returned
# (mean, sd, min, max, nobs)
mean_tbl(data = social_psy_data,
var_stem = "belong",
na_removal = "pairwise")## # A tibble: 3 × 6
## variable mean sd min max nobs
## <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 belong_1 3.83 1.10 1 5 10138
## 2 belong_2 2.52 1.22 1 5 10145
## 3 belong_3 3.77 0.995 1 5 10142# Means and non-missing observations returned
mean_tbl(data = social_psy_data,
var_stem = "belong",
na_removal = "pairwise",
only = c("mean", "nobs"))## # A tibble: 3 × 3
## variable mean nobs
## <chr> <dbl> <int>
## 1 belong_1 3.83 10138
## 2 belong_2 2.52 10145
## 3 belong_3 3.77 10142# Means and standard deviations returned
mean_tbl(data = social_psy_data,
var_stem = "belong",
na_removal = "pairwise",
only = c("mean", "sd"))## # A tibble: 3 × 3
## variable mean sd
## <chr> <dbl> <dbl>
## 1 belong_1 3.83 1.10
## 2 belong_2 2.52 1.22
## 3 belong_3 3.77 0.995With the mean_group_tbl() function, you can produce a
summary table for a series of continuous variables sharing the same
variable stem, grouped by another variable in your dataset or by
matching a pattern in variable names.
For example, we often want to present summary statistics for responses by demographic variables like gender, age, or race:
mean_group_tbl(data = stem_social_psych,
var_stem = "belong_belong",
group = "urm",
group_type = "variable")## # A tibble: 4 × 7
## variable urm mean sd min max nobs
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 belong_belongStem_w1 0 3.86 0.966 1 5 188
## 2 belong_belongStem_w1 1 3.89 1.03 1 5 80
## 3 belong_belongStem_w2 0 3.95 0.993 1 5 188
## 4 belong_belongStem_w2 1 3.99 1.07 1 5 80As with select_group_tbl(), you can specify which values
to exclude and whether to omit missing values. Be sure to use the
var_stem argument you provide to determine which values to
exclude for variables sharing the same stem.
# Default listwise removal
mean_group_tbl(data = stem_social_psych,
var_stem = "belong_belong",
group = "urm",
ignore = c(belong_belong = 5, urm = 0)
)## # A tibble: 2 × 7
## variable urm mean sd min max nobs
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 belong_belongStem_w1 1 3.30 0.916 1 4 46
## 2 belong_belongStem_w2 1 3.37 0.974 1 4 46# Pairwise removal
mean_group_tbl(data = stem_social_psych,
var_stem = "belong_belong",
group = "urm",
na_removal = "pairwise",
ignore = c(belong_belong = 5, urm = 0)
)## # A tibble: 2 × 7
## variable urm mean sd min max nobs
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 belong_belongStem_w1 1 3.38 0.887 1 4 103
## 2 belong_belongStem_w2 1 3.44 0.925 1 4 54Use a list if you want to exclude several values from the same
var_stem or group variable:
mean_group_tbl(data = stem_social_psych,
var_stem = "belong_belong",
group = "urm",
ignore = list(belong_belong = c(4,5), urm = 0)
)## # A tibble: 2 × 7
## variable urm mean sd min max nobs
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
## 1 belong_belongStem_w1 1 2.23 0.832 1 3 13
## 2 belong_belongStem_w2 1 2.15 0.987 1 3 13Another application of mean_group_tbl is summarizing
responses based on a matching pattern, such as survey time points (e.g.,
waves). To use this feature, set group_type to
pattern and enter the pattern to search for in the
group argument.
mean_group_tbl(data = stem_social_psych,
var_stem = "belong_belong",
group = "_w\\d",
group_type = "pattern")## # A tibble: 2 × 7
## variable group mean sd min max nobs
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 belong_belongStem_w1 w1 3.87 0.980 1 5 270
## 2 belong_belongStem_w2 w2 3.97 1.02 1 5 270Use the group_name argument to give a descriptive label
to the column with matched patterns or grouping variable values, and the
var_labels argument to add labels to the variables in the
summary table.
mean_group_tbl(data = stem_social_psych,
var_stem = "belong_belong",
group = "_w\\d",
group_type = "pattern",
group_name = "wave",
var_labels = c(
belong_belongStem_w1 = "I feel like I belong in computing",
belong_belongStem_w2 = "I feel like I belong in computing")
)## # A tibble: 2 × 8
## variable variable_label wave mean sd min max nobs
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 belong_belongStem_w1 I feel like I belong… w1 3.87 0.980 1 5 270
## 2 belong_belongStem_w2 I feel like I belong… w2 3.97 1.02 1 5 270Finally, you can choose what information to return using the
only argument.
# Default: all summary statistics returned
# (mean, sd, min, max, nobs)
mean_group_tbl(data = stem_social_psych,
var_stem = "belong_belong",
group = "_w\\d",
group_type = "pattern",
group_name = "wave",
var_labels = c(
belong_belongStem_w1 = "I feel like I belong in computing",
belong_belongStem_w2 = "I feel like I belong in computing")
)## # A tibble: 2 × 8
## variable variable_label wave mean sd min max nobs
## <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <int>
## 1 belong_belongStem_w1 I feel like I belong… w1 3.87 0.980 1 5 270
## 2 belong_belongStem_w2 I feel like I belong… w2 3.97 1.02 1 5 270# Means and non-missing observations only
mean_group_tbl(data = stem_social_psych,
var_stem = "belong_belong",
group = "_w\\d",
group_type = "pattern",
group_name = "wave",
var_labels = c(
belong_belongStem_w1 = "I feel like I belong in computing",
belong_belongStem_w2 = "I feel like I belong in computing"),
only = c("mean", "nobs")
)## # A tibble: 2 × 5
## variable variable_label wave mean nobs
## <chr> <chr> <chr> <dbl> <int>
## 1 belong_belongStem_w1 I feel like I belong in computing w1 3.87 270
## 2 belong_belongStem_w2 I feel like I belong in computing w2 3.97 270# Means and standard deviations only
mean_group_tbl(data = stem_social_psych,
var_stem = "belong_belong",
group = "_w\\d",
group_type = "pattern",
group_name = "wave",
var_labels = c(
belong_belongStem_w1 = "I feel like I belong in computing",
belong_belongStem_w2 = "I feel like I belong in computing"),
only = c("mean", "sd")
)## # A tibble: 2 × 5
## variable variable_label wave mean sd
## <chr> <chr> <chr> <dbl> <dbl>
## 1 belong_belongStem_w1 I feel like I belong in computing w1 3.87 0.980
## 2 belong_belongStem_w2 I feel like I belong in computing w2 3.97 1.02