Percentages for a batch of questions at once

Runs calc_percentage() over several columns and stacks the results into one tidy table with a variable column identifying the source question and a shared answer column – handy for tabulating a whole block of questions (e.g. every demo_ variable) in a single call.

Usage

calc_percentage_batch(
  data = NULL,
  ...,
  by = NULL,
  sort = c("none", "desc", "asc"),
  digits = 0,
  na_rm = TRUE,
  drop = NULL,
  weights = NULL,
  clean_names = FALSE,
  prefix = NULL
)

Arguments

data

A data frame.

...

Columns to tabulate, using tidyselect (e.g. starts_with("demo_") or demo_gender, demo_edu).

by, sort, digits, na_rm, drop, weights

Passed to calc_percentage() (so a wpct column appears when weighting is active, and drop removes unwanted answers from every question).

clean_names

If TRUE, tidy each variable with clean_label() instead of reporting the raw column name.

prefix

Optional question prefix to strip from variable, e.g. "ratings_". Implies clean_names.

Value

A tibble with variable, answer, n, pct (plus any by columns).

Details

Each selected column is tabulated with calc_percentage() and the results are row-bound, with a variable column naming the source question and the answers collected into a shared character answer column (factor levels differ between questions, so they are coerced to character when stacked). This is the tidy long shape you want for faceted plots or a single export of a whole question block. To send each question to its own Excel tab instead, see export_xlsx().

variable is the raw column name by default. Set clean_names = TRUE (or name a prefix) to get the same tidied wording calc_percentage_multi() and ipm_model() produce, which is what lets a batch table join to a model table on the question name.

See also

calc_percentage(), clean_label(), export_xlsx().

Other summaries: calc_percentage(), calc_percentage_multi(), calc_summary(), clear_checkpoints(), crosstab(), crosstab_banner()

Examples

calc_percentage_batch(podracing_survey, demo_gender, demo_job)
#> # A tibble: 8 × 4
#>   variable    answer                  n   pct
#>   <chr>       <chr>               <int> <dbl>
#> 1 demo_gender Female                399    42
#> 2 demo_gender Male                  525    55
#> 3 demo_gender Non-binary             27     3
#> 4 demo_job    Employed full-time    447    46
#> 5 demo_job    Employed part-time    129    13
#> 6 demo_job    Not in labour force   113    12
#> 7 demo_job    Student               218    23
#> 8 demo_job    Unemployed             59     6

calc_percentage_batch(podracing_survey, starts_with("demo_"))
#> # A tibble: 85 × 4
#>    variable answer     n   pct
#>    <chr>    <chr>  <int> <dbl>
#>  1 demo_age 16       103    10
#>  2 demo_age 17        13     1
#>  3 demo_age 18        20     2
#>  4 demo_age 19        21     2
#>  5 demo_age 20        20     2
#>  6 demo_age 21        16     2
#>  7 demo_age 22        16     2
#>  8 demo_age 23        24     2
#>  9 demo_age 24        23     2
#> 10 demo_age 25        32     3
#> # ℹ 75 more rows

# tidy question names, with the block's prefix removed
calc_percentage_batch(podracing_survey, starts_with("ratings_"),
                      prefix = "ratings_")
#> # A tibble: 30 × 4
#>    variable   answer        n   pct
#>    <chr>      <chr>     <int> <dbl>
#>  1 atmosphere Bad         138    14
#>  2 atmosphere Good        301    30
#>  3 atmosphere Ok          251    25
#>  4 atmosphere Very bad     53     5
#>  5 atmosphere Very good   257    26
#>  6 commentary Bad         330    33
#>  7 commentary Good        154    15
#>  8 commentary Ok          323    32
#>  9 commentary Very bad    158    16
#> 10 commentary Very good    35     4
#> # ℹ 20 more rows