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Create indicator (dummy) variables

Source: R/as_dummy.R
as_dummy.Rd

Expand categorical variables into indicator columns:

  • as_dummy() (tab_gen()): Add one 0/1 column per observed category of each selected variable, like Stata's tabulate x, generate(x_). Place the new columns right after their source variable.

Usage

as_dummy(.data, ..., .names = c("value", "index"), .remove = FALSE)

tab_gen(.data, ..., .names = c("value", "index"), .remove = FALSE)

Arguments

.data

A data frame, tibble, or data.table.

...

<tidy-select> The categorical variables to expand.

.names

How to name the new columns: "value" (default) appends the category, as in sex_male; "index" numbers the categories, as in sex_1, like Stata's generate() option.

.remove

If TRUE, drop the source variables. Defaults to FALSE.

Value

.data with integer indicator columns, keeping its data.frame, tibble, data.table, grouped or sf container and the original rows. Each indicator is 1 for its category, 0 otherwise, and NA for missing source values. New columns have a "label" attribute such as "sex==male"; for labelled vectors, this uses the value label.

Details

Categories are the observed, non-missing values: factor levels in level order (unused levels are skipped), and other values in ascending order. For labelled vectors, .names = "value" uses the codes, such as region_1. Missing values include tagged and SPSS user-missing values. New names must not already exist in .data, and grouping keys cannot be removed. For model formulas, R's factors already expand to dummies; use as_dummy() when the indicator columns themselves are needed.

See also

Other variable generation functions: cut, panel, row_egen, std()

Examples

as_dummy(lifeexp, region)
#> # A tibble: 68 x 9
#>    region     region_1 region_2 region_3 country popgrowth  lexp gnppc safewater
#>    <dbl+lbl>     <int>    <int>    <int> <chr>       <dbl> <dbl> <dbl>     <dbl>
#>  1 1 [Europe~        1        0        0 Albania      -0.6    74   870        49
#>  2 1 [Europe~        1        0        0 Armenia      -1.2    70   570        83
#>  3 1 [Europe~        1        0        0 Austria       0.1    78 27810        97
#>  4 1 [Europe~        1        0        0 Azerba~       0.9    64   510        49
#>  5 1 [Europe~        1        0        0 Belarus      -0.5    68  1540        82
#>  6 1 [Europe~        1        0        0 Belgium       0.2    77 26530       100
#>  7 1 [Europe~        1        0        0 Bosnia~       1.8    74  1230        87
#>  8 1 [Europe~        1        0        0 Bulgar~      -0.7    71  1440        90
#>  9 1 [Europe~        1        0        0 Croatia      -0.1    72    NA        85
#> 10 1 [Europe~        1        0        0 Czechia      -0.1    75  6140        95
#> # i 58 more rows

as_dummy(lifeexp, region, .names = "index", .remove = TRUE)
#> # A tibble: 68 x 8
#>    region_1 region_2 region_3 country            popgrowth  lexp gnppc safewater
#>       <int>    <int>    <int> <chr>                  <dbl> <dbl> <dbl>     <dbl>
#>  1        1        0        0 Albania                 -0.6    74   870        49
#>  2        1        0        0 Armenia                 -1.2    70   570        83
#>  3        1        0        0 Austria                  0.1    78 27810        97
#>  4        1        0        0 Azerbaijan               0.9    64   510        49
#>  5        1        0        0 Belarus                 -0.5    68  1540        82
#>  6        1        0        0 Belgium                  0.2    77 26530       100
#>  7        1        0        0 Bosnia and Herzeg~       1.8    74  1230        87
#>  8        1        0        0 Bulgaria                -0.7    71  1440        90
#>  9        1        0        0 Croatia                 -0.1    72    NA        85
#> 10        1        0        0 Czechia                 -0.1    75  6140        95
#> # i 58 more rows