Prepare data for tab_plain.

An internal step of the build, exported before the pipeline had one. Every one of its jobs is now an argument of tab() — na_drop_all is filter = !is.na(...), and cleannames, other_if_less_than and other_level are formals of tab() itself — so calling it by hand prepares data for a function that would prepare it again. It will be made internal in 2.1.0.

Usage

tab_prepare(
  data,
  ...,
  na_drop_all,
  cleannames = NULL,
  other_if_less_than = 0,
  other_level = "Others",
  levels_collapse = NULL
)

Arguments

data

A dataframe.

...

Variables then to be passed in tab_plain.

na_drop_all

<tidy-select> Removes all observation with a NA in any of the chosen variables.

cleannames

Set to TRUE to clean levels names, by removing prefix numbers like "1-", and text in parentheses.

other_if_less_than

When set to a positive integer, levels with less count than it will be merged into an "Others" level.

other_level

The name of the "Other" level, as a character vector of length one.

levels_collapse

A named list, one element per variable, each a named list of character vectors: the levels to merge, named by the merged level's label (the shape forcats::fct_collapse takes). Applied before other_if_less_than. NULL merges nothing.

Value

A modified data.frame.

Examples

data <- dplyr::starwars |>
tab_prepare(sex, hair_color, gender, other_if_less_than = 5,
            na_drop_all = sex)
#> Warning: `tab_prepare()` was deprecated in tabxplor 2.0.0.
#> ℹ Its work is done by tab() itself: `na_drop_all` is `filter = !is.na(...)`,
#>   and `cleannames` / `other_if_less_than` / `other_level` are tab() arguments.
data
#> # A tibble: 83 × 14
#>    name     height  mass hair_color skin_color eye_color birth_year sex   gender
#>    <chr>     <int> <dbl> <fct>      <chr>      <chr>          <dbl> <fct> <fct> 
#>  1 Luke Sk…    172    77 Others     fair       blue            19   male  mascu…
#>  2 C-3PO       167    75 NA         gold       yellow         112   none  mascu…
#>  3 R2-D2        96    32 NA         white, bl… red             33   none  mascu…
#>  4 Darth V…    202   136 none       white      yellow          41.9 male  mascu…
#>  5 Leia Or…    150    49 brown      light      brown           19   fema… femin…
#>  6 Owen La…    178   120 Others     light      blue            52   male  mascu…
#>  7 Beru Wh…    165    75 brown      light      blue            47   fema… femin…
#>  8 R5-D4        97    32 NA         white, red red             NA   none  mascu…
#>  9 Biggs D…    183    84 black      light      brown           24   male  mascu…
#> 10 Obi-Wan…    182    77 Others     fair       blue-gray       57   male  mascu…
#> # ℹ 73 more rows
#> # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>,
#> #   vehicles <list>, starships <list>