step_lda creates a specification of a recipe step that will return the lda dimension estimates of a text variable.

  role = "predictor",
  trained = FALSE,
  columns = NULL,
  lda_models = NULL,
  num_topics = 10,
  prefix = "lda",
  skip = FALSE,
  id = rand_id("lda")

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



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 step_lda, this indicates the variables to be encoded into a tokenlist. See recipes::selections() for more details. For the tidy method, these are not currently used.


For model terms created by this step, what analysis role should they be assigned?. By default, the function assumes that the new columns created by the original variables will be used as predictors in a model.


A logical to indicate if the recipe has been baked.


A list of tibble results that define the encoding. This is NULL until the step is trained by recipes::prep.recipe().


A WarpLDA model object from the text2vec package. If left to NULL, the default, will it train its model based on the training data. Look at the examples for how to fit a WarpLDA model.


integer desired number of latent topics.


A prefix for generated column names, default to "lda".


A logical. Should the step be skipped when the recipe is baked by recipes::bake.recipe()? While all operations are baked when recipes::prep.recipe() 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.


A character string that is unique to this step to identify it


A step_lda object.



An updated version of recipe with the new step added to the sequence of existing steps (if any).

See also

Other character to numeric steps: step_sequence_onehot(), step_textfeature()


if (requireNamespace("text2vec", quietly = TRUE)) { # \donttest{ library(recipes) library(modeldata) data(okc_text) okc_rec <- recipe(~ ., data = okc_text) %>% step_lda(essay0) okc_obj <- okc_rec %>% prep() juice(okc_obj) %>% slice(1:2) tidy(okc_rec, number = 1) tidy(okc_obj, number = 1) # Changing the number of topics. recipe(~ ., data = okc_text) %>% step_lda(essay0, essay1, num_topics = 20) %>% prep() %>% juice() %>% slice(1:2) # Supplying A pre-trained LDA model trained using text2vec library(text2vec) tokens <- word_tokenizer(tolower(okc_text$essay5)) it <- itoken(tokens, ids = seq_along(okc_text$essay5)) v <- create_vocabulary(it) dtm <- create_dtm(it, vocab_vectorizer(v)) lda_model <- LDA$new(n_topics = 15) recipe(~ ., data = okc_text) %>% step_lda(essay0, essay1, lda_models = lda_model) %>% prep() %>% juice() %>% slice(1:2) # } }
#> # A tibble: 2 x 38 #> essay2 essay3 essay4 essay5 essay6 essay7 essay8 essay9 lda_essay0_w1 #> <fct> <fct> <fct> <fct> <fct> <fct> <fct> <fct> <dbl> #> 1 "writ… "that… "musi… "frie… "roma… "usua… "i ha… "you'… 0.160 #> 2 "pick… "i lo… "non-… "dese… "ever… "maki… "this… "you'… 0.09 #> # … with 29 more variables: lda_essay0_w2 <dbl>, lda_essay0_w3 <dbl>, #> # lda_essay0_w4 <dbl>, lda_essay0_w5 <dbl>, lda_essay0_w6 <dbl>, #> # lda_essay0_w7 <dbl>, lda_essay0_w8 <dbl>, lda_essay0_w9 <dbl>, #> # lda_essay0_w10 <dbl>, lda_essay0_w11 <dbl>, lda_essay0_w12 <dbl>, #> # lda_essay0_w13 <dbl>, lda_essay0_w14 <dbl>, lda_essay0_w15 <dbl>, #> # lda_essay1_w1 <dbl>, lda_essay1_w2 <dbl>, lda_essay1_w3 <dbl>, #> # lda_essay1_w4 <dbl>, lda_essay1_w5 <dbl>, lda_essay1_w6 <dbl>, #> # lda_essay1_w7 <dbl>, lda_essay1_w8 <dbl>, lda_essay1_w9 <dbl>, #> # lda_essay1_w10 <dbl>, lda_essay1_w11 <dbl>, lda_essay1_w12 <dbl>, #> # lda_essay1_w13 <dbl>, lda_essay1_w14 <dbl>, lda_essay1_w15 <dbl>