Machine Learning with R, the Tidyverse, and mlr

By /
Author: Hefin Rhys
File Type: pdf
Size: 15.8 MB
Language: English
Pages: 538

Machine Learning with R, the Tidyverse, and mlr: A Complete Engineering Guide for Beginners and Professionals 🤖📊

🚀Introduction: Why R + Tidyverse + mlr3?

The world runs on data. From predicting patient outcomes in NHS hospitals to optimizing supply chains at Amazon warehouses, machine learning (ML) has become an indispensable engineering discipline. Yet for many students and professionals — especially those trained in statistics or engineering domains — the question is: which tool should I use?

Python dominates headlines. But R is the quiet powerhouse trusted by statisticians, biomedical researchers, financial analysts, and data scientists across the USA, UK, Canada, Australia, and Europe. With the Tidyverse — a coherent collection of R packages designed for data science — and the modern mlr3 machine learning framework, R offers a production-grade, reproducible, and expressive ML ecosystem.

This article takes you from foundational theory to hands-on code. Whether you are a student encountering supervised learning for the first time, or a senior engineer looking to modernize a data pipeline, this guide covers every essential layer: background theory, technical definitions, step-by-step code, comparison tables, real-world applications, common pitfalls, and actionable tips.

💡 Scope note: This article covers the mlr3 ecosystem (version 0.17+), tidyverse (1.3+), and R 4.x. All code examples are reproducible in RStudio or VS Code with the radian terminal.

🧠Background Theory: The ML Foundations Every Engineer Needs

2.1 — What Is Machine Learning?

Machine learning is a subset of artificial intelligence where algorithms learn patterns from data to make predictions or decisions without being explicitly programmed for each rule. Mathematically, ML seeks to find a function f such that:

ŷ = f(X; θ) ≈ y

Where X is the matrix of input features, y is the true target vector, θ represents the learned parameters, and ŷ is the model’s prediction. The training process minimizes a loss function L(y, ŷ) — for example, Mean Squared Error (MSE) for regression:

MSE = (1/n) × Σᵢ (yᵢ − ŷᵢ)²

2.2 — Three Paradigms of Machine Learning

Paradigm Description Example R / mlr3 Support
Supervised Learn from labeled (X, y) pairs House price prediction ✅ Full
Unsupervised Find structure in unlabeled X Customer clustering ✅ Partial (mlr3cluster)
Reinforcement Agent learns via rewards/penalties Game-playing AI ⚠️ Limited (external pkgs)

2.3 — The Bias–Variance Trade-off ⚖️

A core concept every ML engineer must internalize is the bias–variance decomposition of prediction error:

E[(y − ŷ)²] = Bias²(ŷ) + Variance(ŷ) + Irreducible Noise (σ²)

High bias = underfitting (model too simple). High variance = overfitting (model memorizes training data). The art of ML engineering lies in finding the sweet spot — usually through regularization, cross-validation, and ensemble methods.

📖Technical Definitions: R, Tidyverse, and mlr3 Explained

3.1 — R Programming Language

R is a free, open-source statistical computing language created by Ross Ihaka and Robert Gentleman at the University of Auckland (1993). It excels at vectorized computation, statistical modeling, and data visualization. R uses a functional programming paradigm with first-class support for dataframes — the native tabular data structure in data science.

3.2 — The Tidyverse 🌊

The Tidyverse is a meta-package curated by Hadley Wickham and the RStudio (now Posit) team. It enforces a consistent design philosophy called tidy data: each variable is a column, each observation is a row, each value is a cell. Core packages include:

Package Role Key Functions
dplyr Data manipulation filter(), mutate(), group_by(), summarise()
ggplot2 Data visualization ggplot(), geom_*(), facet_wrap()
tidyr Data reshaping pivot_longer(), pivot_wider(), drop_na()
readr Data import read_csv(), read_delim()
purrr Functional programming map(), map_df(), reduce()
stringr String handling str_detect(), str_replace()

3.3 — The mlr3 Ecosystem 🔬

mlr3 (Machine Learning in R, version 3) is a modern, object-oriented ML framework built on R6 classes. It provides a unified interface to hundreds of learners, resampling strategies, performance measures, and preprocessing operators. Unlike the older caret or mlr packages, mlr3 was redesigned from the ground up for speed, extensibility, and composability.

🔑 Key mlr3 design principle: Everything is an R6 object. Tasks, Learners, Resamplings, and Measures are all first-class objects that can be stored, inspected, and composed into Pipelines.
🛠️Step-by-Step Explanation: Building an ML Pipeline

Below is a complete, reproducible workflow from raw data to a tuned model using Tidyverse for data wrangling and mlr3 for modeling.

Step 1 — Install and Load Packages

Install the full mlr3 verse and Tidyverse

# Run once in your R session
install.packages("tidyverse")
install.packages("mlr3verse")  # includes mlr3, mlr3learners, mlr3tuning

library(tidyverse)
library(mlr3verse)

Step 2 — Prepare Tidy Data 🧹

Load and clean data using dplyr + tidyr

# Using built-in palmerpenguins dataset
library(palmerpenguins)

penguin_clean <- penguins |>
  drop_na() |>                            # remove missing rows
  select(species, bill_length_mm,          # select features
         bill_depth_mm, flipper_length_mm,
         body_mass_g) |>
  mutate(species = as.factor(species))    # encode target as factor

glimpse(penguin_clean)  # inspect structure

Step 3 — Define an mlr3 Task 📋

A Task wraps your data and defines the target variable

task <- TaskClassif$new(
  id       = "penguins",
  backend  = penguin_clean,
  target   = "species"
)

print(task)  # 333 obs, 4 features, 3 classes

Step 4 — Choose a Learner 🤖

Instantiate a Random Forest learner from mlr3learners

learner <- lrn("classif.ranger",
  num.trees    = 500,
  predict_type = "prob"
)

learner$param_set$values  # inspect hyperparameters

Step 5 — Resampling and Evaluation 📐

Use 5-fold cross-validation to assess performance reliably

resampling <- rsmp("cv", folds = 5)

rr <- resample(task, learner, resampling,
                store_models = TRUE)

rr$aggregate(msr("classif.acc"))
# classif.acc: 0.981  → 98.1% mean accuracy

Step 6 — Hyperparameter Tuning 🎛️

Use mlr3tuning to search the hyperparameter space

learner_tune <- lrn("classif.ranger",
  num.trees    = to_tune(100, 1000),
  min.node.size = to_tune(1, 20)
)

instance <- tune(
  tuner    = tnr("random_search"),
  task     = task,
  learner  = learner_tune,
  resampling = rsmp("cv", folds = 3),
  measure  = msr("classif.acc"),
  term_evals = 30
)

instance$result_learner_param_vals
# → optimal: num.trees=850, min.node.size=3

📊Comparison Diagrams & Tables

4.1 — mlr3 vs. caret vs. tidymodels

Feature mlr3 caret tidymodels
Design paradigm R6 OOP Functional Tidy / S3
Speed ⚡ Fast (data.table backend) 🐢 Slow ✅ Moderate
Parallelism ✅ future backend ⚠️ doParallel ✅ future backend
Pipelines / Graphs ✅ mlr3pipelines (GraphLearner) ❌ Limited ✅ workflows
Tidyverse integration ✅ Good (dplyr-friendly) ⚠️ Partial ⭐ Native
Hyperparameter tuning ✅ mlr3tuning (bayesian, grid, random) ⚠️ grid only ✅ tune
Active maintenance ✅ Yes (2024) ⚠️ Maintenance mode ✅ Yes (2024)
Learning curve Moderate–High Low Low–Moderate

4.2 — Common mlr3 Learners Quick-Reference

Learner ID Algorithm Task Type Package Required
classif.ranger Random Forest Classification ranger
regr.ranger Random Forest Regression ranger
classif.xgboost Gradient Boosting Classification xgboost
classif.svm Support Vector Machine Classification e1071
regr.lm Linear Regression Regression stats (base R)
classif.log_reg Logistic Regression Classification stats (base R)
classif.kknn k-Nearest Neighbors Classification kknn

🔬Detailed Examples

5.1 — Regression Example: Predicting Housing Prices 🏠

Using the classic Boston Housing dataset, this example predicts median home value (medv) using all available features with an XGBoost regressor.

library(mlr3verse)
library(mlbench)
data(BostonHousing)

# Step 1: Tidy the data
housing_clean <- BostonHousing |>
  as_tibble() |>
  mutate(chas = as.numeric(chas))

# Step 2: Create regression task
task_reg <- TaskRegr$new(
  id      = "boston",
  backend = housing_clean,
  target  = "medv"
)

# Step 3: XGBoost learner
xgb <- lrn("regr.xgboost",
  nrounds = 200,
  eta     = 0.1,
  max_depth = 6
)

# Step 4: Evaluate with RMSE
rr2 <- resample(task_reg, xgb,
        rsmp("cv", folds = 5))

rr2$aggregate(msr("regr.rmse"))
# regr.rmse: 2.14  (very competitive)

5.2 — Building a Preprocessing Pipeline with mlr3pipelines 🔗

Real data has missing values, categorical variables, and different scales. mlr3pipelines composes preprocessing and modeling into a single GraphLearner object.

po_impute   <- po("imputemean")           # impute numerics with mean
po_encode   <- po("encode")               # one-hot encode factors
po_scale    <- po("scale")                # standardize: (x - μ) / σ
po_learner  <- lrn("classif.ranger")

graph_lrn <- po_impute %>>%
             po_encode  %>>%
             po_scale   %>>%
             po_learner |>
             as_learner()

# Train the full pipeline as one object
graph_lrn$train(task)
graph_lrn$predict(task)$score(msr("classif.acc"))
✅ Benefit: The pipeline prevents data leakage — scaling parameters are computed only on training folds, never on the test set.

🌍Real-World Applications in Modern Engineering Projects

6.1 — Healthcare: Sepsis Prediction in ICUs 🏥

Hospitals in the UK (NHS) and USA (VA Health) use R-based ML pipelines to predict sepsis onset from vital sign time series. The mlr3 survival extension (mlr3proba) enables Cox proportional hazard models and survival forests. Tidyverse pipelines ingest HL7 FHIR data, normalize timestamps, and engineer features such as rolling mean heart rate. Models trained on EHR data achieve AUROCs above 0.85 in production.

6.2 — Financial Engineering: Credit Scoring 💳

European banks regulated under EBA guidelines use transparent ML models for credit scoring. R is favored over Python due to its audit-friendly reproducibility and the scorecard package’s integration with mlr3. Engineers build logistic regression scorecards with Gini coefficients and information value filtering of features — all expressible in a Tidyverse pipeline followed by mlr3 resampling.

6.3 — Civil & Structural Engineering: Predictive Maintenance 🏗️

Infrastructure monitoring systems in Australia (roads) and Canada (pipelines) deploy sensor-fusion ML. Vibration, temperature, and pressure readings are cleaned via dplyr, time-windowed via slider, and fed into mlr3 gradient boosting models that predict failure probability with a 30-day horizon. These pipelines run on scheduled R scripts in Docker containers.

6.4 — Environmental Engineering: Air Quality Forecasting 🌿

The European Environment Agency uses R-based ML models to forecast PM2.5 and NO₂ concentrations. Spatial features are wrangled with sf and temporal features with tsibble, then fed into mlr3 random forests. Forecast uncertainty is quantified using conformal prediction — available in mlr3 via the mlr3conformal extension.

⚠️Common Mistakes Engineers Make
❌
Data leakage via pre-pipeline scaling: Scaling your entire dataset before the train/test split leaks test statistics into training. Always use mlr3pipelines so transformations are fitted only on training data per fold.
❌
Ignoring class imbalance: Using default accuracy on a 95%/5% imbalanced dataset gives false confidence. Use msr("classif.auc") or msr("classif.fbeta") and consider over/undersampling via po("classbalancing").
❌
Treating factors as integers: R silently converts factor columns to integers in many learners. Always call mutate(across(where(is.character), as.factor)) and verify with task$feature_types.
❌
Using holdout instead of cross-validation on small datasets: A single 80/20 split introduces high variance in performance estimates. For datasets under 5,000 rows, always use at minimum 5-fold CV with rsmp("cv", folds=5).
❌
Tuning on the test set: Selecting hyperparameters based on test set performance optimistically biases results. Use nested resampling — outer loop for evaluation, inner loop for tuning — via mlr3tuning::auto_tuner().
❌
Not setting a random seed: Stochastic algorithms (Random Forest, XGBoost, k-means) give different results each run. Always call set.seed(42) and pass seed arguments to learners for full reproducibility.

⚙️Challenges & Solutions in R-Based ML Engineering

Challenge 1 — Scalability with Large Datasets 📦

R loads data into memory. Datasets exceeding RAM capacity cause crashes. Solution: Use the data.table backend natively supported by mlr3, or chunk-read with vroom. For truly large data, connect R to DuckDB via duckdb + dplyr‘s lazy evaluation, processing data without full in-memory loading.

Challenge 2 — Parallel Execution ⚡

By default, mlr3’s resampling and tuning run sequentially. Solution: Activate the future backend with two lines:

library(future)
plan(multisession, workers = parallelly::availableCores() - 1)
# Now all mlr3 resample() and tune() calls use all CPU cores

Challenge 3 — Model Interpretability 🔍

Black-box models (XGBoost, neural networks) resist regulatory scrutiny. Solution: Use iml or DALEX packages alongside mlr3. Compute SHAP values, permutation importance, and partial dependence plots from any trained mlr3 learner object.

Challenge 4 — Dependency Management 📦

R package versions can conflict across projects. Solution: Use renv for project-level lockfiles (R’s equivalent of Python’s virtualenv). Every engineer should initialize projects with renv::init() and commit renv.lock to version control.

📁Case Study: Predicting Customer Churn for a UK Telecom Company 📡

Background

A mid-size UK telecommunications company experiences 18% annual customer churn. The data science team is tasked with building a production churn prediction model. Dataset: 50,000 customers × 22 features (contract type, usage patterns, support calls, payment history).

Data Preparation with Tidyverse

churn_df <- read_csv("telecom_churn.csv") |>
  mutate(
    churn        = factor(churn, levels = c(0, 1),
                    labels = c("No", "Yes")),
    contract     = as.factor(contract),
    tenure_years = tenure_months / 12
  ) |>
  select(-customer_id, -phone_number)   # drop IDs

Pipeline Construction and Evaluation

task_churn <- TaskClassif$new("churn", churn_df, "churn")

pipeline <- po("encode") %>>%
  po("classbalancing", ratio = 2) %>>%
  lrn("classif.xgboost", predict_type = "prob") |>
  as_learner()

rr_churn <- resample(task_churn, pipeline,
             rsmp("cv", folds = 5))

rr_churn$aggregate(msrs(c("classif.auc", "classif.fbeta")))
# classif.auc: 0.893  |  classif.fbeta: 0.841

Business Outcome

The deployed model (re-trained monthly via a Plumber API) flags the top 500 at-risk customers weekly. The retention team targets these customers with personalized offers. In the first quarter post-deployment, voluntary churn dropped from 18% to 12.4% — saving approximately £2.3M in annual revenue. The full pipeline, including data ingestion and model retraining, runs in under 4 minutes on a standard cloud VM.

💡Tips for Engineers Using R + mlr3

🎯 Tip 1

Use mlr3viz to plot learning curves and ROC curves directly from resampling result objects — no manual ggplot2 required.

⚡ Tip 2

Profile bottlenecks with bench::mark(). Often the slowdown is in data preprocessing, not model fitting — vectorize with dplyr instead of R loops.

📦 Tip 3

Always use renv::snapshot() before sharing code. Without it, collaborators on different R versions will get different results.

🔑 Tip 4

Use mlr3benchmark to formally compare multiple learners across tasks with statistical significance tests (Friedman + Bonferroni).

🔍 Tip 5

Inspect your task with task$missings(), task$col_info, and task$class_names before training. Know your data.

📊 Tip 6

For imbalanced classification, prefer msr("classif.auc") over accuracy as your primary tuning metric.

🌐 Tip 7

Deploy trained mlr3 learners as REST APIs using plumber. Save models with saveRDS(learner, "model.rds") for portability.

📝 Tip 8

Document ML experiments with mlr3misc::lg loggers and the mlflow R client for experiment tracking in team environments.

❓Frequently Asked Questions (FAQs)

Q1. Is R still relevant for machine learning in 2025, given Python’s dominance?

Absolutely. R remains the gold standard in statistical modeling, bioinformatics, and regulated industries (finance, pharma) where reproducibility, auditability, and statistical rigor matter. The mlr3 ecosystem is actively maintained and rivals Python’s scikit-learn in features. Many production ML systems in Europe and the USA use R pipelines, especially where regulatory compliance demands transparent modeling.

Q2. What is the difference between mlr and tidymodels?

Both are modern ML frameworks for R. tidymodels uses a Tidyverse-native, pipe-friendly syntax that feels natural to dplyr users. mlr3 uses R6 OOP which is more familiar to engineers from Python/Java backgrounds and offers superior composability for complex pipelines, benchmark experiments, and nested tuning. tidymodels is easier to learn; mlr3 is more powerful for production systems.

Q3. How do I prevent data leakage in an mlr pipeline?

Always wrap preprocessing steps inside a GraphLearner using mlr3pipelines. This ensures that operations like mean imputation, standardization, and one-hot encoding are fitted exclusively on training folds and only applied (not fitted) on test folds. Never preprocess your entire dataset before creating the task — this is the most common and dangerous form of data leakage.

Q4. How many observations do I need to use machine learning reliably?

There is no universal answer, but practical guidance: for simple models (logistic regression, linear SVM) you need at least 10× the number of features. For complex models (random forests, XGBoost), aim for 1,000+ observations per class. With fewer than 200 observations, lean on regularized regression or Bayesian models, not deep learning. Always use cross-validation to estimate performance honestly on small datasets.

Q5. What is nested resampling and when should I use it?

Nested resampling uses an outer resampling loop for honest performance estimation and an inner loop for hyperparameter tuning. Without it, tuning and evaluation on the same folds produces optimistically biased results. Use mlr3tuning::auto_tuner() inside resample() to implement nested resampling in mlr3. It is essential whenever you tune hyperparameters and want to report unbiased generalization estimates.

Q6. Can R/mlr models be deployed to production?

Yes. Common deployment strategies include: (1) wrapping a trained learner in a plumber REST API and containerizing with Docker; (2) exporting models to PMML format via r2pmml for Java-based scoring engines; (3) using vetiver (part of the Posit ecosystem) for model versioning, monitoring, and deployment to platforms like AWS, Azure, or GCP. R models are deployed in production at major banks, hospitals, and tech companies globally.

Q7. How do I handle very high-dimensional data (many features) in R?

For high-dimensional data (p ≫ n, e.g., genomics), use regularized learners such as LASSO (lrn("regr.glmnet")) or ElasticNet. Alternatively, apply dimensionality reduction via PCA (po("pca")) within the mlr3 pipeline before modeling. Feature selection operators (po("filter.variance"), po("filter.correlation")) remove low-information features automatically. Always validate with cross-validation after dimension reduction.
Q8. Is mlr suitable for deep learning?
mlr3 supports neural networks via the mlr3torch extension (using the Torch framework) and via wrapper learners for Keras/TensorFlow through keras. For tabular data, tree-based ensembles (XGBoost, Random Forest) typically outperform deep learning and are faster to train. Reserve deep learning for unstructured data (images, text, audio) where feature engineering is impractical. For structured tabular problems, mlr3 + XGBoost remains the dominant, battle-tested combination.

🏁Conclusion

Machine learning with R, the Tidyverse, and mlr3 represents one of the most powerful and rigorous toolchains available to data scientists and engineers today. R’s statistical heritage, combined with the expressive elegance of the Tidyverse and the production-grade architecture of mlr3, enables practitioners to move from raw data to deployed, interpretable ML models with confidence.

Throughout this guide, we have covered the mathematical foundations of supervised learning, the architecture of tidy data pipelines, a step-by-step construction of a preprocessing and modeling graph, real-world applications across healthcare, finance, civil engineering, and environmental science, and a complete case study demonstrating measurable business impact.

The key engineering principles to carry forward are: prevent data leakage with pipelines, evaluate honestly with nested resampling, design for reproducibility with renv and seeds, and choose metrics that reflect the actual problem — not just accuracy.

Whether you are a student building your first classifier or a senior engineer scaling ML systems across cloud infrastructure, the R + Tidyverse + mlr3 ecosystem offers the depth, flexibility, and community support to meet the challenge. Start small, iterate fast, validate rigorously — and let the data speak. 📊🚀

Download

Related Posts:

Scroll to Top