step_pca creates a specification of a recipe step that will convert numeric data into one or more principal components.

  role = "predictor",
  trained = FALSE,
  num_comp = 5,
  threshold = NA,
  options = list(),
  res = NULL,
  prefix = "PC",
  keep_original_cols = FALSE,
  skip = FALSE,
  id = rand_id("pca")

# S3 method for step_pca
tidy(x, type = "coef", ...)



A recipe object. The step will be added to the sequence of operations for this recipe.


One or more selector functions to choose which variables will be used to compute the components. See 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 principal component columns created by the original variables will be used as predictors in a model.


A logical to indicate if the quantities for preprocessing have been estimated.


The number of PCA components to retain as new predictors. If num_comp is greater than the number of columns or the number of possible components, a smaller value will be used.


A fraction of the total variance that should be covered by the components. For example, threshold = .75 means that step_pca should generate enough components to capture 75 percent of the variability in the variables. Note: using this argument will override and reset any value given to num_comp.


A list of options to the default method for stats::prcomp(). Argument defaults are set to retx = FALSE, center = FALSE, scale. = FALSE, and tol = NULL. Note that the argument x should not be passed here (or at all).


The stats::prcomp.default() object is stored here once this preprocessing step has be trained by prep.recipe().


A character string that will be the prefix to the resulting new variables. See notes below.


A logical to keep the original variables in the output. Defaults to FALSE.


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


For the tidy() method, either "coef" (for the variable loadings per component) or "variance" (how much variance does each component account for).


An updated version of recipe with the new step added to the sequence of existing steps (if any). For the tidy method, a tibble with columns terms (the selectors or variables selected), value (the loading), and component.


Principal component analysis (PCA) is a transformation of a group of variables that produces a new set of artificial features or components. These components are designed to capture the maximum amount of information (i.e. variance) in the original variables. Also, the components are statistically independent from one another. This means that they can be used to combat large inter-variables correlations in a data set.

It is advisable to standardize the variables prior to running PCA. Here, each variable will be centered and scaled prior to the PCA calculation. This can be changed using the options argument or by using step_center() and step_scale().

The argument num_comp controls the number of components that will be retained (the original variables that are used to derive the components are removed from the data). The new components will have names that begin with prefix and a sequence of numbers. The variable names are padded with zeros. For example, if num_comp < 10, their names will be PC1 - PC9. If num_comp = 101, the names would be PC001 - PC101.

Alternatively, threshold can be used to determine the number of components that are required to capture a specified fraction of the total variance in the variables.


Jolliffe, I. T. (2010). Principal Component Analysis. Springer.

See also


rec <- recipe( ~ ., data = USArrests) pca_trans <- rec %>% step_normalize(all_numeric()) %>% step_pca(all_numeric(), num_comp = 3) pca_estimates <- prep(pca_trans, training = USArrests) pca_data <- bake(pca_estimates, USArrests) rng <- extendrange(c(pca_data$PC1, pca_data$PC2)) plot(pca_data$PC1, pca_data$PC2, xlim = rng, ylim = rng)
with_thresh <- rec %>% step_normalize(all_numeric()) %>% step_pca(all_numeric(), threshold = .99) with_thresh <- prep(with_thresh, training = USArrests) bake(with_thresh, USArrests)
#> # A tibble: 50 x 4 #> PC1 PC2 PC3 PC4 #> <dbl> <dbl> <dbl> <dbl> #> 1 -0.976 1.12 -0.440 0.155 #> 2 -1.93 1.06 2.02 -0.434 #> 3 -1.75 -0.738 0.0542 -0.826 #> 4 0.140 1.11 0.113 -0.181 #> 5 -2.50 -1.53 0.593 -0.339 #> 6 -1.50 -0.978 1.08 0.00145 #> 7 1.34 -1.08 -0.637 -0.117 #> 8 -0.0472 -0.322 -0.711 -0.873 #> 9 -2.98 0.0388 -0.571 -0.0953 #> 10 -1.62 1.27 -0.339 1.07 #> # … with 40 more rows
tidy(pca_trans, number = 2)
#> # A tibble: 1 x 4 #> terms value component id #> <chr> <dbl> <chr> <chr> #> 1 all_numeric() NA NA pca_PMvd9
tidy(pca_estimates, number = 2)
#> # A tibble: 16 x 4 #> terms value component id #> <chr> <dbl> <chr> <chr> #> 1 Murder -0.536 PC1 pca_PMvd9 #> 2 Assault -0.583 PC1 pca_PMvd9 #> 3 UrbanPop -0.278 PC1 pca_PMvd9 #> 4 Rape -0.543 PC1 pca_PMvd9 #> 5 Murder 0.418 PC2 pca_PMvd9 #> 6 Assault 0.188 PC2 pca_PMvd9 #> 7 UrbanPop -0.873 PC2 pca_PMvd9 #> 8 Rape -0.167 PC2 pca_PMvd9 #> 9 Murder -0.341 PC3 pca_PMvd9 #> 10 Assault -0.268 PC3 pca_PMvd9 #> 11 UrbanPop -0.378 PC3 pca_PMvd9 #> 12 Rape 0.818 PC3 pca_PMvd9 #> 13 Murder 0.649 PC4 pca_PMvd9 #> 14 Assault -0.743 PC4 pca_PMvd9 #> 15 UrbanPop 0.134 PC4 pca_PMvd9 #> 16 Rape 0.0890 PC4 pca_PMvd9