Preface

Welcome to Production Data Science with R — a hands-on guide for data scientists who want to move beyond notebooks and build systems that run in production environments.

Who This Book Is For

This book is designed for:

Data Scientists transitioning from exploratory analysis to production systems
ML Engineers working in R-centric organizations
Analytics Engineers building automated data pipelines
Tech Leads who need to evaluate production-ready R architectures

You should have intermediate R programming skills and basic familiarity with statistics and machine learning concepts.

What You Will Learn

By the end of this book, you will be able to:

Build end-to-end machine learning pipelines using tidymodels and xgboost
Create interactive dashboards with shiny and plotly
Implement marketing attribution models using Markov chains
Develop demand forecasting ensembles combining Prophet and XGBoost
Perform customer segmentation with RFM analysis and K-Means clustering
Deploy real-time fraud detection systems with Redis and isolation forests
Orchestrate automated ETL pipelines using targets
Design robust A/B testing frameworks with sequential testing and Bayesian methods

How to Use This Book

Each chapter follows a consistent structure:

Business Context: Why this project matters
Architecture Overview: System design and data flow
Implementation: Production-grade code with detailed explanations
Evaluation: Metrics, validation, and monitoring
Deployment: How to put it into production
Exercises: Hands-on challenges to deepen your understanding

Note: All code in this book is production-tested and follows R community best practices. Code blocks marked with r are executable R code. Some database connections and API endpoints use placeholder configurations — replace them with your actual credentials.

Software Requirements

You will need R version 4.2 or higher. The primary packages used throughout this book include:

install.packages(c(
  "tidyverse", "tidymodels", "xgboost", "shiny", "shinydashboard",
  "plotly", "DT", "prophet", "modeltime", "timetk", "lubridate",
  "cluster", "factoextra", "DALEX", "vip", "plumber", "targets",
  "tarchetypes", "blastula", "pwr", "infer", "broom", "bayesAB",
  "gsDesign", "ChannelAttribution", "igraph", "solitude", "redis",
  "jsonlite", "DBI", "RPostgres", "pool", "bigrquery", "logger"
))

About the Code

The code in this book represents real-world production patterns. We prioritize:

Reproducibility: Every analysis is wrapped in functions and version-controlled
Scalability: Database operations use lazy evaluation with dbplyr
Observability: Logging, health checks, and monitoring are built in
Safety: Error handling, connection pooling, and graceful degradation

Let’s build something that lasts.

This book was built with bookdown.

Production Data Science with R

Eight End-to-End Projects with Production-Grade Implementation