# R Programming Books

> Digital books on R programming, sports analytics, and data science.


## Posts

- [Fight Data Science in R: Proven Boxing Metrics & Models](https://rprogrammingbooks.com/fight-data-science-in-r-boxing/): Boxing analysis is no longer just about punch totals or “who looked busier.” Modern fight analysis is data science: repeatable pipelines, validated data, explainable models, and performance indicators that translate into strategy. This post shows how to build a professional fight data science workflow in R—from raw data to metrics, modeling, and tactical insights—using code you can adapt to your own datasets. You’ll get: a production-style project structure, data contracts, validation checks, feature engineering patterns, round-by-round models, fatigue and momentum signals, and high-signal visualizations for coaches and analysts. The goal is to help you move from “interesting charts” to decision-grade […]
- [Volleyball Analytics with R: The Complete Guide to Match Data, Sideout Efficiency, Serve Pressure, Heatmaps, and Predictive Models](https://rprogrammingbooks.com/r-volleyball-performance-analysis/): Volleyball Analytics Volleyball Analytics with R: A Practical, End-to-End Playbook Build a full volleyball analytics workflow in R: data collection, cleaning, scouting reports, skill KPIs, rotation/lineup analysis, sideout & transition, serve/receive, visualization, dashboards, and predictive modeling. Table of Contents Why Volleyball Analytics (and Why R) Volleyball Data Model: Events, Rally, Set, Match Data Sources: Manual Logs, Video Tags, DataVolley-Style Exports R Project Setup & Reproducibility Import & Clean Volleyball Event Data Core Volleyball KPIs (Serve, Pass, Attack, Block, Dig) Sideout, Break Point, Transition & Rally Phase Analytics Rotation, Lineup, Setter Distribution & Matchups Serve & Serve-Receive Analytics (Zones, Heatmaps, Pressure) […]
- [Rugby Analytics with R: Complete Guide to Performance Analysis in Rugby Union and League](https://rprogrammingbooks.com/rugby-performance-analysis-r/): Rugby is a sport defined by collisions, structure, and constant tactical adaptation. Unlike many other invasion sports, rugby alternates between highly structured moments—scrums, lineouts, restarts—and extended passages of chaotic open play. Each phase generates rich performance data: tackles, rucks, carries, kicks, meters gained, penalties conceded, turnovers, and spatial changes in territory. Despite this richness, rugby analytics has historically lagged behind other sports, especially in terms of open, reproducible analytical workflows. This gap presents a clear opportunity. R provides a complete environment for rugby performance analysis: data acquisition, cleaning, modeling, visualization, and automated reporting. For analysts, sports scientists, and coaches, R […]
- [How to Analyze Ball-by-Ball Cricket Data in R (cricketdata)](https://rprogrammingbooks.com/cricket-analytics-in-r-cricketdata/): Focus keyphrase: cricket analytics in R • Secondary: R cricket data analysis • Package: cricketdata Cricket analytics is no longer limited to season averages and simple leaderboards. With modern ball-by-ball datasets, we can quantify tempo, isolate phase-specific skills, evaluate matchups, and model outcomes under uncertainty. R is a strong environment for this work because it combines data wrangling, visualization, statistical modeling, and reproducible reporting in one place. What you’ll learn in this post: How cricket data is typically structured (match, innings, ball-by-ball) How to engineer metrics for batting and bowling that respect cricket context How to perform phase analysis (Powerplay […]
- [The Home Run Boom in Baseball — Visualized with R](https://rprogrammingbooks.com/the-home-run-boom-in-baseball-visualized-with-r/): From the dead-ball era to the launch-angle revolution Baseball has always evolved, but few trends tell a clearer story of transformation than the rise of the home run.A century ago, hitting even a handful of home runs in a season was an achievement. Today, it’s an expectation. Thanks to better training, analytics, and even changes in ball composition, home runs have skyrocketed — literally.Let’s explore how home runs per game have changed in Major League Baseball (MLB) from 1901 to today, and what that means for the modern game (and even for betting markets 👀). 📊 The Data Story We’ll […]
- [Predicting NBA Games with R and hoopR (Win Probabilities + Heatmaps)](https://rprogrammingbooks.com/nba-predictions-in-r-hoopr/): If you love sports analytics, this post will show you how to predict NBA matchups using real ESPN data and the R ecosystem — specifically hoopR, tidyverse, and ggplot2. We’ll use a simple Poisson model to simulate thousands of possible scores, compute win probabilities, and visualize expected outcomes for any matchup — all inside R. 🏀 What you’ll build A fully reproducible NBA prediction pipeline that: ⚙️ Why this approach? 💻 Full R code You can copy and run this entire block in RStudio.Change the HOME_TEAM and AWAY_TEAM codes to simulate any matchup. library(tidyverse)library(hoopR)library(ggplot2) SEASON <- 2024HOME_TEAM <- “BOS”AWAY_TEAM <- […]
- [How to Analyze NBA Player Stats in R Using hoopR and ggplot2](https://rprogrammingbooks.com/nba-top-10-scorers-r-hoopr/): Ever wondered how to pull and visualize NBA player stats using R?With just a few lines of code and the {hoopR} package, you can load real NBA data directly from ESPN and create professional-looking visualizations in minutes. Here’s how to build a quick chart of the Top 10 NBA Scorers (2023–24) using tidyverse and hoopR. #Load libraries library(tidyverse)library(hoopR) #Load NBA player box scores nba_data <- hoopR::load_nba_player_box(seasons = 2024) #Calculate games played and average points top_scorers <- nba_data %>%filter(is.na(did_not_play) | did_not_play == FALSE | did_not_play == “FALSE”) %>%group_by(athlete_display_name, team_abbreviation) %>%summarise(games_played = n(),avg_points = mean(points, na.rm = TRUE),.groups = “drop”) %>%filter(games_played >= […]
- [200 R Programming Prompts & Code Snippets for Data Analysis and Modeling](https://rprogrammingbooks.com/200-r-programming-prompts-data-analysis/): R is a versatile programming language designed for statistical computing and graphics. It can act as a calculator, provide numerical and graphical summaries of data and handle a variety of specific analyses:contentReference[oaicite:0]{index=0}. Whether you’re exploring data, running statistical tests or building predictive models, R offers built-in functions and packages to streamline the workflow. The summary() function, for example, gives a quick overview of each variable’s distribution:contentReference[oaicite:1]{index=1}, and the lm() function fits linear models and returns coefficients and diagnostics:contentReference[oaicite:2]{index=2}. The following sections present 200 practical prompts and code snippets organized by topic, from basic data operations to advanced modeling and visualization. […]
- [From Data to Victory: Advanced Basketball Analytics with R](https://rprogrammingbooks.com/basketball-analytics-with-r-data/): A hands-on tutorial with reproducible R code for play-by-play analysis, shot charts, and data-driven decision making. This post accompanies and expands on the book listed here: Basketball Analytics with R — Product Page Table of Contents Setup & Data Ingestion Cleaning and Tidy Structures Lineups and On/Off Impact Shot Charts and Efficiency Maps Pace, Four Factors, and Possessions Win Probability and In-Game Leverage Scouting Reports with Reproducible R Markdown Where to Go Next 1) Setup & Data Ingestion We will use hoopR (part of the SportsDataverse) to access ESPN-based schedules, box scores, and play-by-play (pbp) data, along with tidyverse for […]
- [R vs SQL: Do You Still Need SQL if You’re a Data Scientist in R?](https://rprogrammingbooks.com/r-vs-sql-data-science/): In analytics teams, the question often arises: “If I already know R, do I still need SQL?” The short answer is: yes—most of the time. R and SQL solve different—yet complementary—problems. SQL is the lingua franca for querying and shaping data where it lives (databases, data warehouses), while R excels at modeling, visualization, and reproducible analysis. This guide explains when to use each, how they work together, and how to build a smooth R+SQL workflow. What each tool is best at SQL: Filtering, joining, aggregating at scale in the database; indexing and query planning; governance and access control. R: Statistics, […]
- [R vs Julia: Speed, Statistics, and the Future of Data Science](https://rprogrammingbooks.com/r-vs-julia-data-science/): In the rapidly evolving world of data science and analytics, two languages stand out for statisticians and researchers: R and Julia. While R has been a trusted companion in statistical computing for decades, Julia has emerged as a modern language promising both speed and flexibility. This raises a crucial question: is Julia the future of analytics, or does R still hold the crown? In this article, we compare R and Julia across multiple dimensions—performance, usability, visualization, statistical modeling, community, and adoption. By the end, you’ll understand when to choose R, when Julia may be a better fit, and why many […]
- [R vs Python in 2025: A Complete Comparison for Data Science](https://rprogrammingbooks.com/r-vs-python-data-science/): In the world of data science, two programming languages dominate the conversation: R and Python. Both have powerful ecosystems, passionate communities, and proven success in real-world analytics. Yet the question persists: which language is better for data science? The truth is that both R and Python excel in different areas. Choosing the right tool often depends on your goals, your background, and the context of the project. In this article, we compare them across multiple dimensions—usability, visualization, machine learning, community, and industry adoption—to help you make an informed choice. 1. Learning Curve and Usability R was built specifically for statistical […]
- [How to Collect and Clean Sports Data in R (FBref, Sports-Reference, ESPN + rvest Tutorial)](https://rprogrammingbooks.com/collecting-cleaning-sports-data-r/): This hands-on tutorial walks through acquiring sports data for analysis in R—either by downloading CSVs from public sources or by using R-based APIs and web scraping. You’ll learn how to load raw data, deal with common “messiness” (types, missing values, inconsistent formats), and produce a tidy dataset that’s ready for analysis and visualization. What you’ll learn Where to find structured sports data (FBref, Sports-Reference, ESPN, and league sites). How to import CSVs and spreadsheets into R with reliable types. How to scrape public tables with rvest (ethically and robustly). How to standardize team/player names, dates, and scores. How to handle […]
- [Top R Packages for Sports Data | Essential Libraries for Analytics](https://rprogrammingbooks.com/top-r-packages-for-sports-data-essential-libraries-for-analytics/): An overview of essential R libraries for sports analytics with examples. Table of Contents tidyverse: Data Wrangling ggplot2: Data Visualization Shiny: Interactive Dashboards worldfootballR: Soccer Data nflfastR: NFL Play-by-Play Data Conclusion tidyverse: Data Wrangling The tidyverse is a collection of packages for data science. It includes dplyr, readr, and tidyr, making it a must for sports analytics workflows. library(tidyverse) # Example: filter a game log for one team games %>% filter(team == "Lakers") %>% summarise(avg_points = mean(points)) ggplot2: Data Visualization ggplot2 lets you create flexible and beautiful charts for sports data analysis. library(ggplot2) games %>% ggplot(aes(x = date, y = […]
- [How to Get Started in Sports Analytics with R | Beginner-Friendly Guide](https://rprogrammingbooks.com/how-started-sports-analytics-r/)
- [How to Transform Football Data with R: ggsoccer & worldfootballR Guide](https://rprogrammingbooks.com/r-soccer-analytics-worldfootballr-ggsoccer/): A practical, code-first walkthrough for fetching rich football data, building models, and producing clear visuals entirely in R. What You’ll Learn Everything below is copy-ready R. Adjust team/season names to your league of interest. Setup Get Data with worldfootballR worldfootballR provides friendly functions to pull leagues, teams, players, and match-level tables from FBref and Transfermarkt. League & Team URLs Team & Player Season Stats Match Results & Events Transfer & Valuation (Transfermarkt) Visuals with ggsoccer ggsoccer adds a soccer pitch as a ggplot2 layer so you can plot events cleanly. Basic Pitch Shot Map (toy example) Build a Minimal Expected […]
- [Unlock Winning Secrets: Basketball Analytics with R and hoopR](https://rprogrammingbooks.com/basketball-analytics-with-r-and-hoopr/): Basketball has become one of the most data-driven sports in the world. From the NBA to NCAA programs, teams now rely heavily on advanced metrics to make smarter decisions on the court. With the rise of open data sources, it’s easier than ever for analysts, students, and even fans to explore the game using modern tools. One of the most exciting resources for R users is the hoopR package. This package provides direct access to NBA and NCAA data, including play-by-play logs, box scores, team stats, and even shot locations. Instead of scraping or manually collecting information, hoopR allows you […]
- [How to Install and Use worldfootballR in R (Step-by-Step Guide)](https://rprogrammingbooks.com/install-use-worldfootballr-r/): If you are interested in football data analytics, the worldfootballR package in R is one of the most powerful tools you can use. It allows you to collect and analyze soccer data from FBref, Transfermarkt, and Understat with just a few lines of code. In this step-by-step guide, we will show you how to install and use worldfootballR effectively. Step 1: Install R and RStudio Before using worldfootballR, make sure you have installed the latest versions of R and RStudio. You can download them here: Step 2: Install worldfootballR You can install worldfootballR from CRAN with a single command: If […]
- [worldfootballR: The Complete Guide for Soccer Data in R](https://rprogrammingbooks.com/worldfootballr-guide/): worldfootballR: The Complete Guide for Soccer Data in R worldfootballR is one of the most popular R packages for collecting and analyzing soccer data. Whether you are a data scientist, a football analyst, or just a fan who loves statistics, this package makes it simple to pull structured data from FBref, Transfermarkt, and Understat. With tidy outputs ready for analysis, you can focus on exploring tactics, player performance, and predictive models instead of manual scraping. What is worldfootballR? worldfootballR is an open-source package in R created to streamline the access of soccer data. It allows users to extract detailed statistics […]
- [Football Analytics with R: NFL Data Science using nflfastR and the nflverse](https://rprogrammingbooks.com/nfl-analytics-with-r-nflfastr-nflverse/): A hands-on, reproducible guide to loading NFL play-by-play, computing EPA/success, win probability, and fourth-down decisions — all with tidy R workflows. Why this guide NFL play-by-play (PBP) data enables powerful, interpretable metrics: Expected Points Added (EPA), success rate, win probability (WP), and fourth-down decision models. This tutorial shows a clean path to: Setup: packages & data We’ll use the nflverse ecosystem. Recent workflows favor nflreadr for loading PBP, while historical code may use nflfastR. We’ll show both where useful. Tip: If you hit a column name mismatch across seasons, standardize with janitor::clean_names(). Load play-by-play (multiple seasons) Pull two recent regular […]
- [Soccer Analytics with R: Using worldfootballR for Data-Driven Football Insights](https://rprogrammingbooks.com/soccer-analytics-with-r-worldfootballr/): A practical, reproducible walkthrough to pull open football data, build tidy datasets, and produce actionable xG-based insights and visuals — all in R with worldfootballR. Why this guide Football data is abundant, but turning it into clear, reproducible insights is the real edge. In this tutorial you will: You can adapt the same steps to other leagues and seasons with a couple of parameter changes. Setup: packages & data sources We’ll use the excellent worldfootballR package to access open football data sources (e.g. Understat, FBref). Understat provides shot-level events with expected goals (xG), perfect for quick, practical modeling. Note: Function […]
- [Sports Analytics with R: Multi-Sport Performance, Strategy & Data Science](https://rprogrammingbooks.com/sports-analytics-with-r/): Sports Analytics with R – Practical Multi-Sport Guide Sports Analytics with R: Multi-Sport Performance, Strategy & Data Science sports analytics with R helps analysts, coaches, and learners turn raw match data into clear, repeatable insights across football, basketball, tennis, golf, boxing, and baseball. This article outlines an end-to-end workflow—cleaning, visualization, modeling, and dashboards—and links to a concise ebook with real datasets and annotated code. sports analytics with R R sports programming multi-sport data analysis in R R programming for sports sports data science with R NFL analytics with R NBA analytics with R tennis analytics with R golf analytics with […]
- [Tennis Analytics with R: Player Performance, Match Strategy & Data Science](https://rprogrammingbooks.com/tennis-analytics-with-r/): Tennis Analytics with R – Practical Guide to Player Stats & Strategy Tennis Analytics with R: Player Performance, Match Strategy & Data Science tennis analytics with R turns raw match data into actionable insights. Learn how to clean and structure tennis datasets, visualize performance across surfaces, model outcomes, and build dashboards—then go deeper with a hands-on ebook packed with annotated code and real data. tennis analytics with R R tennis programming tennis data analysis in R R programming for tennis tennis performance analytics R tennis match analysis in R tennis strategy modeling R tennis dashboards with R Contents Why R […]
- [NBA Analytics with R: Player Performance, Team Strategy & Data Science](https://rprogrammingbooks.com/nba-analytics-with-r/): NBA Analytics with R – Practical Guide to Basketball Data & Strategy NBA Analytics with R: Player Performance, Team Strategy & Data Science NBA analytics with R helps analysts, coaches, and data-driven fans turn raw basketball data into clear, repeatable insights. This guide outlines the full workflow—loading, cleaning, modeling, visualization, and reporting—and links to a hands-on ebook with annotated code and real datasets. NBA analytics with R R basketball programming basketball data analysis in R R programming for basketball NBA data science with R Contents Why R for NBA analytics? Data pipeline: load & clean Advanced metrics in R (PER, […]
- [Golf Analytics with R: Performance, Strategy & Data-Driven Improvement](https://rprogrammingbooks.com/golf-analytics-with-r/): Golf Analytics with R: Performance, Strategy & Data-Driven Improvement Golf analytics with R helps golfers, coaches, and analysts turn real data into actionable improvements. This page explains the workflow—cleaning, visualization, modeling, and dashboards—and links to a complete, hands-on guide with annotated code and real datasets. golf analytics with R R golf programming golf data analysis in R R programming for golf golf performance analytics R Contents Why R for golf analytics? What you’ll learn End-to-end workflow Statistical models & forecasting Dashboards & reporting Get the complete guide Why R for golf analytics? R combines data cleaning, visualization, modeling, and reporting […]
- [Boxing Analytics with R – Data Science for Fight Performance](https://rprogrammingbooks.com/boxing-analytics-with-r/): Boxing Analytics with R – Data Science for Fight Performance Boxing Analytics with R: Data Science for Fight Performance and Strategy Boxing analytics with R helps fighters, coaches, and analysts transform raw fight data into actionable insights. From punch counts and timing to opponent behavior and strategy, this guide shows how to use R for performance optimization and data-driven fight preparation. boxing analytics with R R boxing programming fight performance analysis in R boxing data science with R R programming for boxing Contents Why R for boxing analytics? Fight data sources & structure Key performance metrics in R Visualizing fight […]
- [Baseball Analytics with R: Sabermetrics & Data Science Made Practical](https://rprogrammingbooks.com/baseball-analytics-with-r/): Baseball Analytics with R – Practical Guide to Sabermetrics Baseball Analytics with R: Sabermetrics & Data Science Made Practical Baseball analytics with R transforms raw MLB data into actionable insights for evaluating players, comparing teams, and improving game strategy. This guide covers the complete workflow — data cleaning, sabermetrics, visualization, modeling, and reporting — with hands-on code and real datasets. baseball analytics with R R baseball programming baseball sabermetrics in R R programming for baseball MLB data analysis in R Contents Why R for baseball analytics? Data sources & structure Core sabermetrics in R Visualization for trends & roles Predictive […]
- [Learning R for Beginners: Programming and Statistics Made Simple](https://rprogrammingbooks.com/learning-r-for-beginners/): Learning R for Beginners: Programming and Statistics Made Simple Learning R for beginners doesn’t have to be intimidating. With the right structure and examples, you can progress from writing your first line of code to performing statistical analysis and creating visualizations that tell a story. This article introduces the practical steps, then points you to a concise resource — Learning R: A Beginner’s Guide to Programming and Statistics. Step-by-step approach to learning R for beginners (placeholder image—replace with your own). Contents Why R for beginners? R programming basics Statistics in R for beginners Data visualization with R Practical exercises Get […]
- [Hockey Analysis with R: A Practical, Reproducible Workflow](https://rprogrammingbooks.com/hockey-analysis-with-r/): Hockey Analysis with R: A Practical, Reproducible Workflow Welcome! This guide shows how hockey analysis with R turns raw game data into clear, repeatable insights—covering setup, cleaning, visualization, modeling, and reporting with production-friendly practices. Contents Why choose R for hockey analytics? End-to-end workflow Project setup & reproducibility Data cleaning & feature engineering Exploratory visualization Predictive modeling Strategy simulation & scenarios Reporting & communication FAQs Get the practical guide Why choose R for hockey analytics? R gives you a complete toolkit for hockey analysis with R: data wrangling, visual exploration, robust modeling, and publication quality reports. Its ecosystem makes reproducible research […]
- [Revolutionizing Sports With R Programming: Unlock Powerful Insights From Hockey Data](https://rprogrammingbooks.com/revolutionizing-sports-with-r-programming-unlock-powerful-insights-from-hockey-data/): In the data-driven era of sports, R programming has emerged as one of the most powerful tools for turning raw athletic performance data into actionable insight. From grassroots coaching to elite professional teams, sports analytics with R is helping organizations understand the game at a deeper level — and hockey is a prime example of this evolution. Why R Programming Matters in Sports Analytics While Python and spreadsheets have their place, R stands out for its statistical modeling capabilities, elegant visualizations, and open-source sports packages. It’s built from the ground up for data exploration — making it perfect for sports […]

## Pages

- [R Spanish](https://rprogrammingbooks.com/r-spanish/): Sistema de Trading Algorítmico con R Sistema de Trading Algorítmico mediante la Plataforma R Resumen del proyecto Este trabajo desarrolla un sistema automático de trading utilizando el lenguaje de programación R, basándose en principios de análisis técnico. Se implementan tres estrategias de trading aplicadas al mercado de acciones. Los resultados obtenidos se comparan con la estrategia “Comprar y Mantener” (Buy&Hold) mediante análisis de sensibilidad y el Valor de Expectativa. Palabras clave: Sistema de trading automático, Estrategia de trading, Análisis de sensibilidad, Valor de Expectativa, Buy&Hold. Objetivos del trabajo Analizar posibilidades del software R para diseñar sistemas automáticos de trading. Estudiar […]
- [Contact](https://rprogrammingbooks.com/contact/): Contact Whether you have questions about our books, suggestions for future topics, or need support with your purchase, feel free to reach out. 📧 Email: rprogrammingbooks@gmail.com We typically respond within 24–48 hours. Thanks for your interest in R Programming Books!
- [About  Author](https://rprogrammingbooks.com/about-author/): About the Author Laureano Gallardo is a passionate data scientist and R programmer with a strong academic foundation and real-world experience. Graduating with highest honors in Statistics, Laureano stood out for his analytical precision and deep curiosity about how data can drive insight and innovation. From the very beginning of his academic journey, R became more than just a programming language—it became a creative and technical playground where he built models, explored complex datasets, and crafted visualizations that told compelling stories. With a specialization in statistical modeling, data visualization, and sports analytics, Laureano now shares his expertise through clear, practical […]
- [Home](https://rprogrammingbooks.com/): R Programming Books | Laureano Gallardo Welcome to R Programming Books Unlock the power of R with our curated library of digital books. Whether you’re analyzing sports performance or mastering advanced statistical methods, our downloadable guides are designed to teach you through real-world use cases and clean, reproducible code. Start learning today, and grow your data science skills with clarity and purpose. Explore the Books 🏒 Sports Analytics with R Dive into the world of data-driven sports performance. Analyze player metrics, predict outcomes, and enhance strategy using real-world sports datasets in R. 📊 Statistical & Analytical Methods in R Master […]
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## Products

- [Volleyball Analytics with R: Spike Data, Team Performance & Visualization](https://rprogrammingbooks.com/product/volleyball-analytics-with-r/): Volleyball Analytics with R: Spike Data, Team Performance & Visualization is a practical, research-style guide to analyzing volleyball using R. It teaches how to collect and structure match data, compute key player and team metrics (attack efficiency, serve impact, sideout/breakpoint performance), and build professional visualizations such as court heatmaps and match trend charts—using modern R workflows and volleyball-specific tools
- [Mixed Martial Arts (UFC) Analytics with R: Fight Data and Predictive Modeling](https://rprogrammingbooks.com/product/ufc-mma-analytics-with-r-fight-data-predictive-modeling/): Mixed Martial Arts (UFC) Analytics with R is a practical, publication-ready guide to building a complete UFC analytics workflow in R—from pulling fight statistics with modern scraping tools to producing clear visualizations and predictive models. You’ll learn how to clean and structure real UFC data, explore striking and grappling patterns, and create matchup features that translate into win-probability forecasts.  Written for analysts, R users, and serious MMA fans, the book includes code-heavy chapters, reproducible pipelines, and real UFC case studies that show how data can explain styles, trends, and outcomes.
- [Rugby Analytics with R: Performance Analysis for Rugby Union & League](https://rprogrammingbooks.com/product/rugby-analytics-with-r-book/): A practical, in-depth guide to analyzing Rugby Union and League performance using R—built for analysts, coaches, and data professionals.
- [Cricket Analytics with R: Data Science for Batting & Bowling Strategy](https://rprogrammingbooks.com/product/cricket-analytics-with-r/):               A practical 25-page PDF guide to cricket analytics in R—batting and bowling metrics, visualizations, and data-driven insights for T20, ODI, Test, and IPL.                          
- [Racing with Data: Formula 1 and NASCAR Analytics with R](https://rprogrammingbooks.com/product/racing-with-data-formula-1-and-nascar-analytics-with-r/): A professional, code-heavy guide to motorsport analytics with R. Analyze F1 and NASCAR lap times, telemetry, pit strategy, clustering, simulations, and predictive models—complete with tidyverse workflows and Shiny dashboards.
- [Bayesian Sports Betting with R: Probability, Kelly  Criterion and Betting Strategies](https://rprogrammingbooks.com/product/bayesian-sports-betting-with-r/): Bayesian Sports Betting with R is a professional guide to probability, Bayesian modeling, and the Kelly criterion. Learn how to build data-driven betting strategies with R, manage risk, and apply advanced analytics across sports like soccer, basketball, football, tennis, and baseball.
- [Geospatial Analysis with R: Mapping and Spatial Statistics Made Easy](https://rprogrammingbooks.com/product/geospatial-analysis-with-r-mapping-and-spatial-statistics-made-easy/): Learn geospatial analysis in R from the ground up. This book teaches you how to work with vector data using the sf package (which implements the OGC Simple Features standard), handle raster data efficiently with terra, and create publication‑quality maps with ggplot2, tmap, and leaflet. It covers coordinate systems, data wrangling, spatial statistics (including Moran’s I tests for autocorrelation), kriging and spatio‑temporal modelling, with step‑by‑step examples and case studies.
- [Healthcare Analytics with R: Predictive Modeling for Medical Data – A Comprehensive Guide for Clinicians and Data Scientists](https://rprogrammingbooks.com/product/healthcare-analytics-r-predictive-modeling-medical-data/): Harness the power of R for modern healthcare data science. This in‑depth guide teaches you how to clean and model clinical records, sensor data and genomic profiles; perform survival analysis and causal inference; build machine‑learning models with tidymodels; and estimate epidemic dynamics. Featuring case studies, privacy guidance and examples using de‑identified datasets like MIMIC, it’s essential reading for clinicians and data scientists seeking to transform medical data into actionable insights.
- [Sports Prediction and Simulation with R: Monte Carlo, Elo Ratings, and Forecasting](https://rprogrammingbooks.com/product/sports-prediction-simulation-r/): A practical guide with R packages like PlayerRatings, NFLSimulatoR, and tidyverse for sports analytics and predictive modeling
- [Basketball Analytics with R: From hoopR Data to Winning Insights](https://rprogrammingbooks.com/product/basketball-analytics-with-r-from-hoopr-data-to-winning-insights/): Unlock the power of R programming for basketball analytics. With step-by-step code examples and a focus on the hoopR package, this 29-page guide (plus a 12-page bonus supplement) shows you how to work with NBA and NCAA data to generate practical, actionable insights.
- [Pickleball Analytics with R: Data-Driven Coaching & Visualization Guide](https://rprogrammingbooks.com/product/pickleball-analytics-with-r-book/): Professional guide to pickleball analytics with R (and some Python). Uses real data and includes complete code for coaches and analysts.    
- [Time Series Analysis with R: A Practical Guide to Modeling and Forecasting](https://rprogrammingbooks.com/product/time-series-analysis-in-r-book/):               Learn to prepare, model, and forecast time-series data in R with an end-to-end workflow: ARIMA/ETS/LSTM, STL decomposition, evaluation, and applied cases.                
- [Bayesian Sports Analytics with R: Predictive Modeling for Betting & Performance](https://rprogrammingbooks.com/product/bayesian-sports-analytics-r-predictive-modeling-betting-performance/):               Beginner’s guide to Bayesian sports analytics in R—use brms/rstanarm/footBayes to model games, evaluate players, predict outcomes, and inform betting.                
- [Football Analytics with R: NFL Data Science using nflfastR and nflverse](https://rprogrammingbooks.com/product/football-analytics-r-nflfastr-nflverse/): Learn how to work with NFL play-by-play data in R, calculate EPA, win probability, build models, and visualize plays. A practical guide for beginners in football analytics.
- [Mastering Football Data with worldfootballR: Complete Guide to Soccer Analytics in R](https://rprogrammingbooks.com/product/mastering-football-data-worldfootballr/): A 53-page practical guide to football analytics with worldfootballR in R. Learn to collect, analyze, and visualize soccer data from FBref, Transfermarkt, and Understat.
- [Mastering Sports Analytics with R: Insights from Soccer](https://rprogrammingbooks.com/product/mastering-sports-analytics-with-r-soccer/): Mastering Sports Analytics with R: Insights from Soccer is a practical 90-page guide that teaches you how to collect, analyze, and visualize soccer data using R, advanced statistics, and machine learning. Perfect for analysts, students, and coaches looking to apply data-driven insights to the world’s most popular sport.
- [Learning R: A Beginner’s Guide to Programming and Statistics](https://rprogrammingbooks.com/product/learning-r-a-beginners-guide-to-programming-and-statistics/): A practical, 75-page introduction to R programming and statistical analysis for beginners. Includes code examples, visualizations, and real datasets to help you build real-world data skills.    
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