Branch a dplyr pipeline based on a set of conditions

Branch a dplyr pipeline based on a set of conditions

Usage

switch_pipeline(.x, ...)

Arguments

.x

a dataframe

...

a list of formulae of the type predicate ~ purrr function using .x as the single parameter

Value

the result of applying purrr function to .x in the case where predicate evaluates to true. Both predicate and function can refer to the pipeline dataframe using .x

Examples

iris %>% switch_pipeline(
  is_col_present(.x, Species) ~ .x %>% dplyr::rename(new = Species)
) %>% dplyr::glimpse()
#> Rows: 150
#> Columns: 5
#> $ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.…
#> $ Sepal.Width  <dbl> 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.…
#> $ Petal.Length <dbl> 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.…
#> $ Petal.Width  <dbl> 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.…
#> $ new          <fct> setosa, setosa, setosa, setosa, setosa, setosa, setosa, s…