Tidymodels Workflow

A minimal machine learning pipeline using R’s tidymodels framework

Below is a minimal (yet complete) example of a machine learning pipeline that use’s R’s tidymodels framework and the Palmer Penguins dataset.

Note that the goal here isn’t necessarily to fit the best model or demonstrate all of the features; rather it’s just to demonstrate a tidymodels workflow.

library(tidymodels)
library(tidyverse)

set.seed(0408)

# there's a package for this, but let's just grab the csv
penguins <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-07-28/penguins.csv")

# drop rows missing body mass
penguins_complete <- penguins |>
    filter(!is.na(body_mass_g))

# split the data into training and validation
penguins_split <- initial_split(penguins, prop = .8)

trn <- training(penguins_split)
val <- testing(penguins_split)

# define a recipe for preprocessing
penguins_rec <- recipe(body_mass_g ~ ., data = trn) |>
    step_impute_mode(all_nominal_predictors()) |>
    step_impute_mean(all_numeric_predictors()) |>
    step_normalize(all_numeric_predictors()) |>
    step_dummy(all_nominal_predictors())

# define a model specification
lm_spec <- linear_reg() |>
    set_engine("lm")

# define a workflow with our preprocessor and our model
wf <- workflow(penguins_rec, lm_spec)

# fit the workflow
wf_fit <- wf |>
    fit(data = trn)

# predict testing data
y_hat <- unlist(predict(wf_fit, new_data = val))

# estimate performance
eval_tbl <- tibble(
    truth = val$body_mass_g,
    estimate = y_hat
)

rmse(eval_tbl, truth, estimate)

# A tibble: 1 × 3
  .metric .estimator .estimate
  <chr>   <chr>          <dbl>
1 rmse    standard        315.