Taxonomic clades feature generator — step

Extract clades from a lineage, as defined in the {yatah} package.

Usage

step_taxonomy(
  recipe,
  ...,
  role = "predictor",
  trained = FALSE,
  rank = NULL,
  res = NULL,
  keep_original_cols = FALSE,
  skip = FALSE,
  id = rand_id("taxonomy")
)

# S3 method for step_taxonomy
tidy(x, ...)

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: For model terms created by this step, what analysis role should they be assigned? By default, the new columns created by this step from the original variables will be used as predictors in a model.
trained: A logical to indicate if the quantities for preprocessing have been estimated.
rank: The desired ranks, a combinaison of "kingdom", "phylum", "class", "order", "family", "genus", "species", or "strain". See yatah::get_clade() for more details.
res: This parameter is only produced after the recipe has been trained.
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.
x: A step_taxonomy object.

Value

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

Author

Antoine Bichat

Examples

data("cheese_taxonomy")
rec <-
  cheese_taxonomy %>%
  select(asv, lineage) %>%
  recipe(~ .) %>%
  step_taxonomy(lineage, rank = c("order", "genus")) %>%
  prep()
rec
#> 
#> ── Recipe ──────────────────────────────────────────────────────────────────────
#> 
#> ── Inputs 
#> Number of variables by role
#> predictor: 2
#> 
#> ── Training information 
#> Training data contained 74 data points and no incomplete rows.
#> 
#> ── Operations 
#> • Taxonomy features from: lineage | Trained
tidy(rec, 1)
#> # A tibble: 2 × 3
#>   terms   rank  id            
#>   <chr>   <chr> <chr>         
#> 1 lineage order taxonomy_gF0fi
#> 2 lineage genus taxonomy_gF0fi
bake(rec, new_data = NULL)
#> # A tibble: 74 × 3
#>    asv    lineage_order     lineage_genus
#>    <fct>  <chr>             <chr>        
#>  1 asv_01 Dothideales       Aureobasidium
#>  2 asv_02 Eurotiales        Aspergillus  
#>  3 asv_03 Eurotiales        Penicillium  
#>  4 asv_04 Eurotiales        Penicillium  
#>  5 asv_05 Eurotiales        Penicillium  
#>  6 asv_06 Eurotiales        Penicillium  
#>  7 asv_07 Eurotiales        Penicillium  
#>  8 asv_08 Eurotiales        Penicillium  
#>  9 asv_09 Eurotiales        Penicillium  
#> 10 asv_10 Saccharomycetales Debaryomyces 
#> # ℹ 64 more rows