Extract patterns from nominal data

step_dummy_extract() creates a specification of a recipe step that will convert nominal data (e.g. characters or factors) into one or more integer model terms for the extracted levels.

Usage

step_dummy_extract(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  sep = NULL,
  pattern = NULL,
  threshold = 0,
  other = "other",
  naming = dummy_extract_names,
  levels = NULL,
  sparse = "auto",
  keep_original_cols = FALSE,
  skip = FALSE,
  id = rand_id("dummy_extract")
)

Arguments

recipe: A recipe object. The step will be added to the sequence of operations for this recipe.
...: One or more selector functions to choose variables for this step. See selections() for more details.
role: Not used by this step since no new variables are created.
trained: A logical to indicate if the quantities for preprocessing have been estimated.
sep: Character string containing a regular expression to use for splitting. strsplit() is used to perform the split. sep takes priority if pattern is also specified.
pattern: Character string containing a regular expression used for extraction. gregexpr() and regmatches() are used to perform pattern extraction using perl = TRUE.
threshold: A numeric value between 0 and 1, or an integer greater or equal to one. If less than one, then factor levels with a rate of occurrence in the training set below threshold will be pooled to other. If greater or equal to one, then this value is treated as a frequency and factor levels that occur less than threshold times will be pooled to other.
other: A single character value for the other category, default to "other".
naming: A function that defines the naming convention for new dummy columns. See Details below.
levels: A list that contains the information needed to create dummy variables for each variable contained in terms. This is NULL until the step is trained by prep().
sparse: A single string. Should the columns produced be sparse vectors. Can take the values "yes", "no", and "auto". If sparse = "auto" then workflows can determine the best option. Defaults to "auto".
keep_original_cols: A logical to keep the original variables in the output. Defaults to FALSE.
skip: A logical. Should the step be skipped when the recipe is baked by bake()? While all operations are baked when prep() is run, some operations may not be able to be conducted on new data (e.g. processing the outcome variable(s)). Care should be taken when using skip = TRUE as it may affect the computations for subsequent operations.
id: A character string that is unique to this step to identify it.

Value

An updated version of recipe with the new step added to the sequence of any existing operations.

Details

step_dummy_extract() will create a set of integer dummy variables from a character variable by extracting individual strings by either splitting or extracting then counting those to create count variables.

Note that threshold works in a very specific way for this step. While it is possible for one label to be present multiple times in the same row, it will only be counted once when calculating the occurrences and frequencies.

This recipe step allows for flexible naming of the resulting variables. For an unordered factor named x, with levels "a" and "b", the default naming convention would be to create a new variable called x_b. The naming format can be changed using the naming argument; the function dummy_names() is the default.

Tidying

When you tidy() this step, a tibble is returned with columns terms, columns , and id:

terms: character, the selectors or variables selected
columns: character, names of resulting columns
id: character, id of this step

The return value is ordered according to the frequency of columns entries in the training data set.

Sparse data

This step produces sparse columns if sparse = "yes" is being set. The default value "auto" won't trigger production fo sparse columns if a recipe is prep()ed, but allows for a workflow to toggle to "yes" or "no" depending on whether the model supports sparse_data and if the model is is expected to run faster with the data.

The mechanism for determining how much sparsity is produced isn't perfect, and there will be times when you want to manually overwrite by setting sparse = "yes" or sparse = "no".

Case weights

This step performs an unsupervised operation that can utilize case weights. As a result, case weights are only used with frequency weights. For more information, see the documentation in case_weights and the examples on tidymodels.org.

Examples

data(tate_text, package = "modeldata")

dummies <- recipe(~ artist + medium, data = tate_text) |>
  step_dummy_extract(artist, medium, sep = ", ") |>
  prep()

dummy_data <- bake(dummies, new_data = NULL)

dummy_data |>
  select(starts_with("medium")) |>
  names() |>
  head()
#> [1] "medium_X1.person"                   
#> [2] "medium_X1.projection.and.1.monitor" 
#> [3] "medium_X100.digital.prints.on.paper"
#> [4] "medium_X100.works.on.paper"         
#> [5] "medium_X11.photographs"             
#> [6] "medium_X11.works.on.panel"          

# More detailed splitting
dummies_specific <- recipe(~medium, data = tate_text) |>
  step_dummy_extract(medium, sep = "(, )|( and )|( on )") |>
  prep()

dummy_data_specific <- bake(dummies_specific, new_data = NULL)

dummy_data_specific |>
  select(starts_with("medium")) |>
  names() |>
  head()
#> [1] "medium_X1.monitor"          "medium_X1.person"          
#> [3] "medium_X1.projection"       "medium_X10.light.boxes"    
#> [5] "medium_X10.tranformers"     "medium_X100.digital.prints"

tidy(dummies, number = 1)
#> # A tibble: 2,673 × 3
#>    terms  columns id                 
#>    <chr>  <chr>   <chr>              
#>  1 artist Thomas  dummy_extract_oEGyP
#>  2 artist Schütte dummy_extract_oEGyP
#>  3 artist John    dummy_extract_oEGyP
#>  4 artist Akram   dummy_extract_oEGyP
#>  5 artist Zaatari dummy_extract_oEGyP
#>  6 artist Joseph  dummy_extract_oEGyP
#>  7 artist Beuys   dummy_extract_oEGyP
#>  8 artist Richard dummy_extract_oEGyP
#>  9 artist Ferrari dummy_extract_oEGyP
#> 10 artist León    dummy_extract_oEGyP
#> # ℹ 2,663 more rows
tidy(dummies_specific, number = 1)
#> # A tibble: 1,216 × 3
#>    terms  columns              id                 
#>    <chr>  <chr>                <chr>              
#>  1 medium paper                dummy_extract_2Slih
#>  2 medium Etching              dummy_extract_2Slih
#>  3 medium Photograph           dummy_extract_2Slih
#>  4 medium colour               dummy_extract_2Slih
#>  5 medium gelatin silver print dummy_extract_2Slih
#>  6 medium Screenprint          dummy_extract_2Slih
#>  7 medium Lithograph           dummy_extract_2Slih
#>  8 medium on paper             dummy_extract_2Slih
#>  9 medium canvas               dummy_extract_2Slih
#> 10 medium aquatint             dummy_extract_2Slih
#> # ℹ 1,206 more rows

# pattern argument can be useful to extract harder patterns
color_examples <- tibble(
  colors = c(
    "['red', 'blue']",
    "['red', 'blue', 'white']",
    "['blue', 'blue', 'blue']"
  )
)

dummies_color <- recipe(~colors, data = color_examples) |>
  step_dummy_extract(colors, pattern = "(?<=')[^',]+(?=')") |>
  prep()

dummies_data_color <- dummies_color |>
  bake(new_data = NULL)

dummies_data_color
#> # A tibble: 3 × 4
#>   colors_blue colors_red colors_white colors_other
#>         <int>      <int>        <int>        <int>
#> 1           1          1            0            0
#> 2           1          1            1            0
#> 3           3          0            0            0