Getting Started with tidylearn • tidylearn

Introduction

tidylearn provides a unified tidyverse-compatible interface to R’s machine learning ecosystem. It wraps proven packages like glmnet, randomForest, xgboost, e1071, cluster, and dbscan - you get the reliability of established implementations with the convenience of a consistent, tidy API.

What tidylearn does:

Provides one consistent interface (tl_model()) to 20 ML algorithms
Returns tidy tibbles instead of varied output formats
Offers unified ggplot2-based visualization across all methods
Enables pipe-friendly workflows

What tidylearn is NOT:

A reimplementation of ML algorithms (uses established packages under the hood)
A replacement for the underlying packages (you can access the raw model via model$fit)

Installation

# From CRAN
install.packages("tidylearn")

# Development version
# devtools::install_github("ces0491/tidylearn") # nolint

library(tidylearn)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

The Unified Interface

The core of tidylearn is the tl_model() function, which dispatches to the appropriate underlying package based on the method you specify. The wrapped packages include stats, glmnet, randomForest, xgboost, gbm, e1071, nnet, rpart, cluster, and dbscan.

Supervised Learning

Classification

Logistic regression handles two-class problems, so we take a binary subset of iris here. For three or more classes use "tree", "forest", "svm" or "nn".

# versicolor and virginica overlap, so this is a real classification
# problem -- setosa is linearly separable from the other two, which makes
# logistic regression fail to converge
iris_binary <- iris %>%
  filter(Species %in% c("versicolor", "virginica")) %>%
  mutate(Species = droplevels(Species))

model_logistic <- tl_model(iris_binary, Species ~ ., method = "logistic")
print(model_logistic)
#> tidylearn Model
#> ===============
#> Paradigm: supervised 
#> Method: logistic 
#> Task: Classification 
#> Formula: Species ~ . 
#> 
#> Training observations: 100

Predictions come back as a tibble with a .pred column. What .pred contains depends on type: "class" gives the predicted label, "prob" gives one column per class.

# Predicted class labels
predictions <- predict(model_logistic, type = "class")
head(predictions)
#> # A tibble: 6 × 1
#>   .pred     
#>   <fct>     
#> 1 versicolor
#> 2 versicolor
#> 3 versicolor
#> 4 versicolor
#> 5 versicolor
#> 6 versicolor

# Class probabilities
head(predict(model_logistic, type = "prob"))
#> # A tibble: 6 × 2
#>   versicolor virginica
#>        <dbl>     <dbl>
#> 1      1.000 0.0000117
#> 2      1.000 0.0000486
#> 3      0.999 0.00120  
#> 4      1.000 0.0000422
#> 5      0.999 0.00141  
#> 6      1.000 0.000102

Note that the default type = "response" means different things across methods — probabilities for logistic regression, class labels for trees and forests. Ask for type = "class" explicitly when you want labels, or let tl_evaluate() handle it:

tl_evaluate(model_logistic, metrics = c("accuracy", "f1"))
#> # A tibble: 2 × 2
#>   metric   value
#>   <chr>    <dbl>
#> 1 accuracy  0.98
#> 2 f1        0.98

Regression

# Regression with linear model
model_linear <- tl_model(mtcars, mpg ~ wt + hp, method = "linear")
print(model_linear)
#> tidylearn Model
#> ===============
#> Paradigm: supervised 
#> Method: linear 
#> Task: Regression 
#> Formula: mpg ~ wt + hp 
#> 
#> Training observations: 32

# Predictions
predictions_reg <- predict(model_linear)
head(predictions_reg)
#> # A tibble: 6 × 1
#>   .pred
#>   <dbl>
#> 1  23.6
#> 2  22.6
#> 3  25.3
#> 4  21.3
#> 5  18.3
#> 6  20.5

Unsupervised Learning

Dimensionality Reduction

# Principal Component Analysis
model_pca <- tl_model(iris[, 1:4], method = "pca")
print(model_pca)
#> tidylearn Model
#> ===============
#> Paradigm: unsupervised 
#> Method: pca 
#> Technique: pca 
#> 
#> Training observations: 150

# Transform data
transformed <- predict(model_pca)
head(transformed)
#> # A tibble: 6 × 5
#>   .obs_id   PC1    PC2     PC3      PC4
#>   <chr>   <dbl>  <dbl>   <dbl>    <dbl>
#> 1 1       -2.26 -0.478  0.127   0.0241 
#> 2 2       -2.07  0.672  0.234   0.103  
#> 3 3       -2.36  0.341 -0.0441  0.0283 
#> 4 4       -2.29  0.595 -0.0910 -0.0657 
#> 5 5       -2.38 -0.645 -0.0157 -0.0358 
#> 6 6       -2.07 -1.48  -0.0269  0.00659

Clustering

# K-means clustering
model_kmeans <- tl_model(iris[, 1:4], method = "kmeans", k = 3)
print(model_kmeans)
#> tidylearn Model
#> ===============
#> Paradigm: unsupervised 
#> Method: kmeans 
#> Technique: kmeans 
#> 
#> Training observations: 150

# Get cluster assignments
clusters <- model_kmeans$fit$clusters
head(clusters)
#> # A tibble: 6 × 2
#>   .obs_id cluster
#>   <chr>     <int>
#> 1 1             1
#> 2 2             1
#> 3 3             1
#> 4 4             1
#> 5 5             1
#> 6 6             1

# Compare with actual species
table(clusters$cluster, iris$Species)
#>    
#>     setosa versicolor virginica
#>   1     50          0         0
#>   2      0         48        14
#>   3      0          2        36

Data Preprocessing

tl_prepare_data() handles imputation, scaling and encoding in one call, and records what it did so the same transformation can be replayed on new data:

# Prepare data with multiple preprocessing steps
processed <- tl_prepare_data(
  iris,
  Species ~ .,
  impute_method = "mean",
  scale_method = "standardize",
  encode_categorical = FALSE
)
#> Scaling numeric features using method: standardize

# Check preprocessing steps applied
names(processed$preprocessing_steps)
#> [1] "scaling"

# Use processed data for modeling
model_processed <- tl_model(processed$data, Species ~ ., method = "forest")

Train-Test Splitting

# Simple random split
split <- tl_split(iris, prop = 0.7, seed = 123)

# Train model (three species, so a multiclass-capable method)
model_train <- tl_model(split$train, Species ~ ., method = "forest")

# Test predictions
predictions_test <- predict(model_train, new_data = split$test)
head(predictions_test)
#> # A tibble: 6 × 1
#>   .pred 
#>   <fct> 
#> 1 setosa
#> 2 setosa
#> 3 setosa
#> 4 setosa
#> 5 setosa
#> 6 setosa

# Stratified split (maintains class proportions)
split_strat <- tl_split(iris, prop = 0.7, stratify = "Species", seed = 123)

# Check proportions are maintained
prop.table(table(split_strat$train$Species))
#> 
#>     setosa versicolor  virginica 
#>  0.3333333  0.3333333  0.3333333
prop.table(table(split_strat$test$Species))
#> 
#>     setosa versicolor  virginica 
#>  0.3333333  0.3333333  0.3333333
prop.table(table(iris$Species))
#> 
#>     setosa versicolor  virginica 
#>  0.3333333  0.3333333  0.3333333

Wrapped Packages

tidylearn provides a unified interface to these established R packages:

Supervised Methods

Method	Underlying Package	Function Called
`"linear"`	stats	`lm()`
`"polynomial"`	stats	`lm()` with `poly()`
`"logistic"`	stats	`glm(..., family = binomial)`
`"ridge"`, `"lasso"`, `"elastic_net"`	glmnet	`glmnet()`
`"tree"`	rpart	`rpart()`
`"forest"`	randomForest	`randomForest()`
`"boost"`	gbm	`gbm()`
`"xgboost"`	xgboost	`xgb.train()`
`"svm"`	e1071	`svm()`
`"nn"`	nnet	`nnet()`
`"deep"`	keras	`keras_model_sequential()`

Unsupervised Methods

Method	Underlying Package	Function Called
`"pca"`	stats	`prcomp()`
`"mds"`	stats, MASS, smacof	`cmdscale()`, `isoMDS()`, etc.
`"kmeans"`	stats	`kmeans()`
`"pam"`	cluster	`pam()`
`"clara"`	cluster	`clara()`
`"hclust"`	stats	`hclust()`
`"dbscan"`	dbscan	`dbscan()`

Accessing the Underlying Model

You always have access to the raw model from the underlying package via $fit:

# Example: Access the raw randomForest object
model_forest <- tl_model(iris, Species ~ ., method = "forest")
class(model_forest$fit)  # This is the randomForest object
#> [1] "randomForest.formula" "randomForest"

# Use package-specific functions if needed
# randomForest::varImpPlot(model_forest$fit) # nolint

Next Steps

vignette("data-ingestion") — reading from files, databases and cloud sources
vignette("supervised-learning") — classification and regression in depth, and how to replay preprocessing on a test set
vignette("unsupervised-learning") — clustering, ordination, and choosing the number of clusters
vignette("market-basket") — association rules
vignette("tuning-and-pipelines") — hyperparameter search, and bundling a workflow you can save
vignette("automl") — searching across methods under a time budget
vignette("diagnostics") — assumptions, influence and model comparison
vignette("reporting") — plots and formatted gt tables
vignette("integration-workflows") — combining supervised and unsupervised steps
vignette("compute-backends") — when a fit is too slow or too large for this machine: GPU routing, cost estimates, and the cloud safety model

Summary

tidylearn is a wrapper package that provides:

Unified Interface: One function (tl_model()) that dispatches to proven packages like glmnet, randomForest, xgboost, e1071, and others
Transparency: Access raw model objects via model$fit for package-specific functionality
Tidy Output: All results are tibbles for easy manipulation with dplyr and ggplot2
Consistent Visualization: Unified ggplot2-based plots regardless of model type

The underlying algorithms are unchanged - tidylearn simply makes them easier to use together.

# Quick example combining everything
data_split <- tl_split(iris, prop = 0.7, stratify = "Species", seed = 42)

# Random forests are scale-invariant, so no scaling is needed here. When a
# method does need scaled inputs, the same transformation has to be applied
# to the test set -- see the Supervised Learning vignette.
model_final <- tl_model(data_split$train, Species ~ ., method = "forest")
test_preds <- predict(model_final, new_data = data_split$test)

accuracy <- mean(test_preds$.pred == data_split$test$Species)
cat("Test accuracy:", round(accuracy * 100, 1), "%\n")
#> Test accuracy: 93.3 %