Perform PCA

This is a simple function that will perform PCA analysis on a passed recipe.

Usage

pca_your_recipe(.recipe_object, .data, .threshold = 0.75, .top_n = 5)

Arguments

.recipe_object

The recipe object you want to pass.

.data

The full data set that is used in the original recipe object passed into .recipe_object in order to obtain the baked data of the transform.

.threshold

A number between 0 and 1. A fraction of the total variance that should be covered by the components.

.top_n

How many variables loadings should be returned per PC

Value

A list object with several components.

Details

This is a simple wrapper around some recipes functions to perform a PCA on a given recipe. This function will output a list and return it invisible. All of the components of the analysis will be returned in a list as their own object that can be selected individually. A scree plot is also included. The items that get returned are:

1. pca_transform - This is the pca recipe.

2. variable_loadings

3. variable_variance

4. pca_estimates

5. pca_juiced_estimates

6. pca_baked_data

7. pca_variance_df

8. pca_rotattion_df

9. pca_variance_scree_plt

10. pca_loadings_plt

11. pca_loadings_plotly

12. pca_top_n_loadings_plt

13. pca_top_n_plotly

See also

https://recipes.tidymodels.org/reference/step_pca.html

Other Data Recipes: hai_data_impute(), hai_data_poly(), hai_data_scale(), hai_data_transform(), hai_data_trig()

Author

Steven P. Sanderson II, MPH

Examples

suppressPackageStartupMessages(library(timetk))
suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(purrr))
suppressPackageStartupMessages(library(healthyR.data))
suppressPackageStartupMessages(library(rsample))
suppressPackageStartupMessages(library(recipes))
suppressPackageStartupMessages(library(ggplot2))
suppressPackageStartupMessages(library(plotly))

data_tbl <- healthyR_data %>%
  select(visit_end_date_time) %>%
  summarise_by_time(
    .date_var = visit_end_date_time,
    .by       = "month",
    value     = n()
  ) %>%
  set_names("date_col", "value") %>%
  filter_by_time(
    .date_var = date_col,
    .start_date = "2013",
    .end_date = "2020"
  ) %>%
  mutate(date_col = as.Date(date_col))

splits <- initial_split(data = data_tbl, prop = 0.8)

rec_obj <- recipe(value ~ ., training(splits)) %>%
  step_timeseries_signature(date_col) %>%
  step_rm(matches("(iso$)|(xts$)|(hour)|(min)|(sec)|(am.pm)"))

output_list <- pca_your_recipe(rec_obj, .data = data_tbl)
#> Warning: !  The following columns have zero variance so scaling cannot be used:
#>   date_col_day, date_col_mday, and date_col_mday7.
#> ℹ Consider using ?step_zv (`?recipes::step_zv()`) to remove those columns
#>   before normalizing.
output_list$pca_variance_scree_plt

output_list$pca_loadings_plt

output_list$pca_top_n_loadings_plt