Inspect a dataset's codebook

Inspect the types, observed counts, distinct observed values, and variable labels of a dataset or selected columns.

Usage

codebook(.data, ...)

Arguments

.data

The input data (data frame, tibble, or data.table).

...

<tidy-select> or <data-masking> Columns to select or compute with select_or_transmute(). Existing columns, selection helpers, and named expressions can be mixed. Omit to include all columns.

Value

An ungrouped table with one row per selected variable, in selection order, and columns variable, type, n (observed values), and unique (distinct observed values). A label column is included when labels are available. Empty or entirely missing columns have zero counts; selecting no columns returns zero rows with variable, type, n, and unique columns. The input's data.frame, tibble, or data.table container and supported preview settings are preserved; other subclasses are not reconstructed.

Details

Counting

• Counts ignore existing groups. Data are ungrouped before selection, so grouping columns can be omitted. The input data remain unchanged.

• Missing values, including NaN, tagged missing values, and SPSS user-missing values, are excluded.

• List columns count distinct elements without flattening; NULL and empty elements are included. Matrix and nested data-frame columns count complete rows, excluding rows with missing cells.

Labels and types

• Variable labels come from the label attribute, not value labels. Unlabelled variables receive NA when a label column is present.

• Units are appended to type when available.

See also

ds(), variables(), browse()

Other inspection functions: browse(), misstable(), names, var_type(), variables()

Examples

# All variables
codebook(lifeexp)
#> # A tibble: 6 x 5
#>   variable  label                    type             n unique
#>   <chr>     <chr>                    <chr>        <int>  <int>
#> 1 region    Region                   double+label    68      3
#> 2 country   Country                  character       68     68
#> 3 popgrowth Avg. annual % growth     double          68     32
#> 4 lexp      Life expectancy at birth double          68     20
#> 5 gnppc     GNP per capita           double          67     64
#> 6 safewater Safe water               double          54     32

# Selected variables
codebook(lifeexp, 1:4)
#> # A tibble: 4 x 5
#>   variable  label                    type             n unique
#>   <chr>     <chr>                    <chr>        <int>  <int>
#> 1 region    Region                   double+label    68      3
#> 2 country   Country                  character       68     68
#> 3 popgrowth Avg. annual % growth     double          68     32
#> 4 lexp      Life expectancy at birth double          68     20

codebook(lifeexp, where(is.numeric))
#> # A tibble: 5 x 5
#>   variable  label                    type             n unique
#>   <chr>     <chr>                    <chr>        <int>  <int>
#> 1 region    Region                   double+label    68      3
#> 2 popgrowth Avg. annual % growth     double          68     32
#> 3 lexp      Life expectancy at birth double          68     20
#> 4 gnppc     GNP per capita           double          67     64
#> 5 safewater Safe water               double          54     32

# Inspect an existing column alongside a computed column
codebook(lifeexp, country, gnppc_thousands = gnppc / 1000)
#> # A tibble: 2 x 5
#>   variable        label          type          n unique
#>   <chr>           <chr>          <chr>     <int>  <int>
#> 1 country         Country        character    68     68
#> 2 gnppc_thousands GNP per capita double       67     64

starwars <- dplyr::starwars
codebook(starwars, films:starships)
#> # A tibble: 3 x 4
#>   variable  type      n unique
#>   <chr>     <chr> <int>  <int>
#> 1 films     list     87     24
#> 2 vehicles  list     87     11
#> 3 starships list     87     16

codebook(starwars, ends_with("color"))
#> # A tibble: 3 x 4
#>   variable   type          n unique
#>   <chr>      <chr>     <int>  <int>
#> 1 hair_color character    82     11
#> 2 skin_color character    87     31
#> 3 eye_color  character    87     15