rprogrammingbooks

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 […]

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.

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.

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()

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

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

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

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

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

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