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<!DOCTYPE html>
<html lang="en">
<head>
<title>Best R Books: A Curated Reading List That Won't Waste Your Time</title>
<meta charset="utf-8">
<meta name="Description" content="The definitive ranked reading list, from R for Data Science to Advanced R and Statistical Rethinking. What each book does best, and who should skip it.">
<meta name="Keywords" content="best R books, R programming books, R for data science book, advanced R book, ggplot2 book, R book recommendations, free R books, statistical rethinking">
<meta name="Distribution" content="Global">
<meta name="Author" content="Selva Prabhakaran">
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n=this.dataset.tab;document.querySelectorAll('.sidebar-tab').forEach(function(x){x.classList.toggle('active',x.dataset.tab===n)});document.querySelectorAll('.sidebar-panel').forEach(function(p){p.classList.toggle('active',p.dataset.panel===n)});try{localStorage.setItem('rstat_sidebar_tab',n)}catch(e){}">Tools</button></div><div class="sidebar-panel active" data-panel="posts"><ul class="sidebar-menu list-unstyled"><li class="sidebar-section expanded"><div class="sidebar-section-header"><span class="sidebar-chevron">▸</span> Learn R<span class="section-meta" data-section-meta></span></div><ul class="sidebar-section-items list-unstyled"><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec0sub1" data-collapsed="false"><span class="subsec-chevron">▼</span> Getting Started</li><li data-subkey="sec0sub1"><a href="/Is-R-Worth-Learning-in-2026.html"><span class="progress-dot"></span>Is R Worth Learning?</a></li><li data-subkey="sec0sub1"><a href="/Install-R-and-RStudio-2026.html"><span class="progress-dot"></span>Install R & RStudio</a></li><li data-subkey="sec0sub1"><a href="/RStudio-IDE-Tour.html"><span class="progress-dot"></span>RStudio IDE Tour</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec0sub2" data-collapsed="false"><span class="subsec-chevron">▼</span> R Fundamentals</li><li data-subkey="sec0sub2"><a href="/R-Syntax-101.html"><span class="progress-dot"></span>R Syntax 101</a></li><li data-subkey="sec0sub2"><a href="/R-Data-Types.html"><span class="progress-dot"></span>R Data Types</a></li><li data-subkey="sec0sub2"><a href="/R-Vectors.html"><span class="progress-dot"></span>R Vectors</a></li><li data-subkey="sec0sub2"><a href="/R-Matrices.html"><span class="progress-dot"></span>R Matrices</a></li><li data-subkey="sec0sub2"><a href="/R-Factors.html"><span class="progress-dot"></span>R Factors</a></li><li data-subkey="sec0sub2"><a href="/R-Data-Frames.html"><span class="progress-dot"></span>R Data Frames</a></li><li data-subkey="sec0sub2"><a href="/R-Lists.html"><span class="progress-dot"></span>R Lists</a></li><li data-subkey="sec0sub2"><a href="/R-Control-Flow.html"><span class="progress-dot"></span>R Control Flow</a></li><li data-subkey="sec0sub2"><a href="/R-Special-Values.html"><span class="progress-dot"></span>R Special Values</a></li><li data-subkey="sec0sub2"><a href="/R-Type-Coercion.html"><span class="progress-dot"></span>R Type Coercion</a></li><li data-subkey="sec0sub2"><a href="/R-Functions.html"><span class="progress-dot"></span>Writing R Functions</a></li><li data-subkey="sec0sub2"><a href="/R-Beginner-Exercises-quiz.html"><span class="progress-dot"></span>R Fundamentals Quiz</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec0sub3" data-collapsed="false"><span class="subsec-chevron">▼</span> Working Effectively</li><li data-subkey="sec0sub3"><a href="/R-Subsetting.html"><span class="progress-dot"></span>R Subsetting</a></li><li data-subkey="sec0sub3"><a href="/Getting-Help-in-R.html"><span class="progress-dot"></span>Getting Help in R</a></li><li data-subkey="sec0sub3"><a href="/R-Project-Structure.html"><span class="progress-dot"></span>R Project Structure</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec0sub4" data-collapsed="false"><span class="subsec-chevron">▼</span> R Career & Resources</li><li data-subkey="sec0sub4"><a href="/R-vs-Python.html"><span class="progress-dot"></span>R vs Python</a></li><li data-subkey="sec0sub4"><a href="/How-to-Learn-R.html"><span class="progress-dot"></span>How to Learn R</a></li><li data-subkey="sec0sub4"><a href="/R-for-Excel-Users.html"><span class="progress-dot"></span>R for Excel Users</a></li><li data-subkey="sec0sub4"><a href="/R-Interview-Questions.html"><span class="progress-dot"></span>R Interview Questions</a></li><li data-subkey="sec0sub4"><a href="/R-Interview-Questions-quiz.html"><span class="progress-dot"></span>R Interview Readiness Quiz</a></li><li data-subkey="sec0sub4"><a href="/R-Cheat-Sheet.html"><span class="progress-dot"></span>R Cheat Sheet</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec0sub5" data-collapsed="false"><span class="subsec-chevron">▼</span> Professional R</li><li data-subkey="sec0sub5"><a href="/Data-Ethics-in-R.html"><span class="progress-dot"></span>Data Ethics</a></li><li data-subkey="sec0sub5"><a href="/Bias-in-Data-and-Models.html"><span class="progress-dot"></span>Bias in Data & Models</a></li><li data-subkey="sec0sub5"><a href="/Reproducibility-Crisis.html"><span class="progress-dot"></span>Reproducibility</a></li><li data-subkey="sec0sub5"><a href="/Data-Privacy-in-R.html"><span class="progress-dot"></span>Data Privacy</a></li><li data-subkey="sec0sub5"><a href="/Communicating-Uncertainty.html"><span class="progress-dot"></span>Communicating Uncertainty</a></li></ul></li><li class="sidebar-section"><div class="sidebar-section-header"><span class="sidebar-chevron">▸</span> Data Wrangling<span class="section-meta" data-section-meta></span></div><ul class="sidebar-section-items list-unstyled"><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec1sub1" data-collapsed="false"><span class="subsec-chevron">▼</span> Import & Setup</li><li data-subkey="sec1sub1"><a href="/Importing-Data-in-R.html"><span class="progress-dot"></span>Importing Data</a></li><li data-subkey="sec1sub1"><a href="/R-Pipe-Operator.html"><span class="progress-dot"></span>Pipe Operator</a></li><li data-subkey="sec1sub1"><a href="/Tidy-Data-in-R.html"><span class="progress-dot"></span>Tidy Data</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec1sub2" data-collapsed="false"><span class="subsec-chevron">▼</span> dplyr Essentials</li><li data-subkey="sec1sub2"><a href="/dplyr-filter-select.html"><span class="progress-dot"></span>dplyr filter & select</a></li><li data-subkey="sec1sub2"><a href="/dplyr-mutate-rename.html"><span class="progress-dot"></span>dplyr mutate & rename</a></li><li data-subkey="sec1sub2"><a href="/dplyr-group-by-summarise.html"><span class="progress-dot"></span>dplyr group_by & summarise</a></li><li data-subkey="sec1sub2"><a href="/dplyr-arrange-slice.html"><span class="progress-dot"></span>dplyr arrange & slice</a></li><li data-subkey="sec1sub2"><a href="/dplyr-across.html"><span class="progress-dot"></span>dplyr across()</a></li><li data-subkey="sec1sub2"><a href="/dplyr-case-when.html"><span class="progress-dot"></span>dplyr case_when()</a></li><li data-subkey="sec1sub2"><a href="/dplyr-Exercises-in-R-quiz.html"><span class="progress-dot"></span>dplyr Quiz</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec1sub3" data-collapsed="false"><span class="subsec-chevron">▼</span> Join & Reshape</li><li data-subkey="sec1sub3"><a href="/R-Joins.html"><span class="progress-dot"></span>R Joins</a></li><li data-subkey="sec1sub3"><a href="/pivot_longer-pivot_wider-Reshape-Data-in-R.html"><span class="progress-dot"></span>pivot_longer & pivot_wider</a></li><li data-subkey="sec1sub3"><a href="/tidyr-separate-unite-Split-Combine-Columns-in-R.html"><span class="progress-dot"></span>separate() & unite()</a></li><li data-subkey="sec1sub3"><a href="/tidyr-Exercises-in-R-quiz.html"><span class="progress-dot"></span>tidyr Quiz</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec1sub4" data-collapsed="false"><span class="subsec-chevron">▼</span> Clean & Quality</li><li data-subkey="sec1sub4"><a href="/Missing-Values-in-R-Detect-Count-Remove-Impute-NA.html"><span class="progress-dot"></span>Missing Values (NA)</a></li><li data-subkey="sec1sub4"><a href="/Data-Quality-Checking-in-R.html"><span class="progress-dot"></span>Data Quality Checking</a></li><li data-subkey="sec1sub4"><a href="/janitor-Package-in-R.html"><span class="progress-dot"></span>janitor Package</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec1sub5" data-collapsed="false"><span class="subsec-chevron">▼</span> Strings & Dates</li><li data-subkey="sec1sub5"><a href="/stringr-in-R.html"><span class="progress-dot"></span>stringr</a></li><li data-subkey="sec1sub5"><a href="/R-Regex-stringr-Pattern-Matching.html"><span class="progress-dot"></span>Regex Patterns</a></li><li data-subkey="sec1sub5"><a href="/lubridate-in-R.html"><span class="progress-dot"></span>lubridate</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec1sub6" data-collapsed="false"><span class="subsec-chevron">▼</span> Scale & Connect</li><li data-subkey="sec1sub6"><a href="/DBI-in-R.html"><span class="progress-dot"></span>DBI & Databases</a></li><li data-subkey="sec1sub6"><a href="/DuckDB-in-R.html"><span class="progress-dot"></span>DuckDB & duckplyr</a></li><li data-subkey="sec1sub6"><a href="/Web-Scraping-in-R-with-rvest.html"><span class="progress-dot"></span>Web Scraping (rvest)</a></li><li data-subkey="sec1sub6"><a href="/REST-APIs-in-R-with-httr2.html"><span class="progress-dot"></span>REST APIs (httr2)</a></li></ul></li><li class="sidebar-section"><div class="sidebar-section-header"><span class="sidebar-chevron">▸</span> Visualization<span class="section-meta" data-section-meta></span></div><ul class="sidebar-section-items list-unstyled"><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec2sub1" data-collapsed="false"><span class="subsec-chevron">▼</span> ggplot2 Foundations</li><li data-subkey="sec2sub1"><a href="/ggplot2-Grammar-of-Graphics.html"><span class="progress-dot"></span>Grammar of Graphics</a></li><li data-subkey="sec2sub1"><a href="/ggplot2-Getting-Started.html"><span class="progress-dot"></span>ggplot2 Getting Started</a></li><li data-subkey="sec2sub1"><a href="/ggplot2-Aesthetics-aes-Map-Data.html"><span class="progress-dot"></span>ggplot2 Aesthetics (aes)</a></li><li data-subkey="sec2sub1"><a href="/ggplot2-Colours.html"><span class="progress-dot"></span>ggplot2 Colours</a></li><li data-subkey="sec2sub1"><a href="/ggplot2-Scales.html"><span class="progress-dot"></span>ggplot2 Scales</a></li><li data-subkey="sec2sub1"><a href="/ggplot2-Themes-in-R.html"><span class="progress-dot"></span>ggplot2 Themes</a></li><li data-subkey="sec2sub1"><a href="/ggplot2-Labels-and-Annotations.html"><span class="progress-dot"></span>Labels & Annotations</a></li><li data-subkey="sec2sub1"><a href="/ggplot2-Facets.html"><span class="progress-dot"></span>ggplot2 Facets</a></li><li data-subkey="sec2sub1"><a href="/ggplot2-Exercises-in-R-quiz.html"><span class="progress-dot"></span>ggplot2 Quiz</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec2sub2" data-collapsed="false"><span class="subsec-chevron">▼</span> Core Charts</li><li data-subkey="sec2sub2"><a href="/ggplot2-Scatter-Plots.html"><span class="progress-dot"></span>Scatter Plots</a></li><li data-subkey="sec2sub2"><a href="/ggplot2-Line-Charts.html"><span class="progress-dot"></span>Line Charts</a></li><li data-subkey="sec2sub2"><a href="/ggplot2-Bar-Charts.html"><span class="progress-dot"></span>Bar Charts</a></li><li data-subkey="sec2sub2"><a href="/ggplot2-Distribution-Charts.html"><span class="progress-dot"></span>Distribution Charts</a></li><li data-subkey="sec2sub2"><a href="/Error-Bars-in-R.html"><span class="progress-dot"></span>Error Bars</a></li><li data-subkey="sec2sub2"><a href="/geom_smooth-in-R.html"><span class="progress-dot"></span>geom_smooth()</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec2sub3" data-collapsed="false"><span class="subsec-chevron">▼</span> Distributions & Groups</li><li data-subkey="sec2sub3"><a href="/Violin-Plot-in-R.html"><span class="progress-dot"></span>Violin Plot</a></li><li data-subkey="sec2sub3"><a href="/Ridgeline-Plot-in-R.html"><span class="progress-dot"></span>Ridgeline Plot</a></li><li data-subkey="sec2sub3"><a href="/Lollipop-Chart-in-R.html"><span class="progress-dot"></span>Lollipop Chart</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec2sub4" data-collapsed="false"><span class="subsec-chevron">▼</span> Relationships</li><li data-subkey="sec2sub4"><a href="/Bubble-Chart-in-R.html"><span class="progress-dot"></span>Bubble Chart</a></li><li data-subkey="sec2sub4"><a href="/Heatmap-in-R.html"><span class="progress-dot"></span>Heatmap in R</a></li><li data-subkey="sec2sub4"><a href="/Correlation-Matrix-Plot-in-R.html"><span class="progress-dot"></span>Correlation Matrix</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec2sub5" data-collapsed="false"><span class="subsec-chevron">▼</span> Advanced Charts</li><li data-subkey="sec2sub5"><a href="/Pie-Donut-Chart-in-R.html"><span class="progress-dot"></span>Pie & Donut Chart</a></li><li data-subkey="sec2sub5"><a href="/Treemap-in-R.html"><span class="progress-dot"></span>Treemap</a></li><li data-subkey="sec2sub5"><a href="/Waffle-Chart-in-R.html"><span class="progress-dot"></span>Waffle Chart</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec2sub6" data-collapsed="false"><span class="subsec-chevron">▼</span> Exploratory Analysis</li><li data-subkey="sec2sub6"><a href="/Exploratory-Data-Analysis-in-R.html"><span class="progress-dot"></span>EDA (7-Step Framework)</a></li><li data-subkey="sec2sub6"><a href="/Univariate-EDA-in-R.html"><span class="progress-dot"></span>Univariate EDA</a></li><li data-subkey="sec2sub6"><a href="/Bivariate-EDA-in-R.html"><span class="progress-dot"></span>Bivariate EDA</a></li><li data-subkey="sec2sub6"><a href="/Descriptive-Statistics-in-R.html"><span class="progress-dot"></span>Descriptive Statistics</a></li><li data-subkey="sec2sub6"><a href="/Correlation-Analysis-in-R.html"><span class="progress-dot"></span>Correlation Analysis</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec2sub7" data-collapsed="false"><span class="subsec-chevron">▼</span> Interactive & Maps</li><li data-subkey="sec2sub7"><a href="/Combining-ggplot2-with-plotly.html"><span class="progress-dot"></span>ggplot2 + plotly Interactive</a></li><li data-subkey="sec2sub7"><a href="/Interactive-Maps-in-R-with-leaflet.html"><span class="progress-dot"></span>Leaflet Interactive Maps</a></li><li data-subkey="sec2sub7"><a href="/Spatial-Data-in-R-with-sf.html"><span class="progress-dot"></span>Spatial Data (sf)</a></li><li data-subkey="sec2sub7"><a href="/Choropleth-Maps-in-R.html"><span class="progress-dot"></span>Choropleth Maps (sf)</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec2sub8" data-collapsed="false"><span class="subsec-chevron">▼</span> Customization & Reference</li><li data-subkey="sec2sub8"><a href="/ggplot2-Legends-in-R.html"><span class="progress-dot"></span>ggplot2 Legends</a></li><li data-subkey="sec2sub8"><a href="/ggplot2-Secondary-Axis.html"><span class="progress-dot"></span>Secondary Axis</a></li><li data-subkey="sec2sub8"><a href="/ggplot2-Log-Scale.html"><span class="progress-dot"></span>Log Scale</a></li><li data-subkey="sec2sub8"><a href="/patchwork-Package.html"><span class="progress-dot"></span>patchwork (Combine Plots)</a></li><li data-subkey="sec2sub8"><a href="/Publication-Quality-Figures-in-R.html"><span class="progress-dot"></span>Publication-Ready Figures</a></li><li data-subkey="sec2sub8"><a href="/ggplot2-cheatsheet.html"><span class="progress-dot"></span>ggplot2 Quickref</a></li></ul></li><li class="sidebar-section"><div class="sidebar-section-header"><span class="sidebar-chevron">▸</span> Statistics<span class="section-meta" data-section-meta></span></div><ul class="sidebar-section-items list-unstyled"><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec3sub1" data-collapsed="false"><span class="subsec-chevron">▼</span> EDA & Data Quality</li><li data-subkey="sec3sub1"><a href="/Automated-EDA-in-R.html"><span class="progress-dot"></span>Automated EDA</a></li><li data-subkey="sec3sub1"><a href="/Missing-Data-Visualization-in-R-naniar.html"><span class="progress-dot"></span>Missing Data Viz (naniar)</a></li><li data-subkey="sec3sub1"><a href="/Outlier-Detection-in-R.html"><span class="progress-dot"></span>Outlier Detection</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec3sub2" data-collapsed="false"><span class="subsec-chevron">▼</span> Probability</li><li data-subkey="sec3sub2"><a href="/Sample-Spaces-Events-and-Probability-Axioms-in-R-With-Monte-Carlo-Proof.html"><span class="progress-dot"></span>Probability Axioms</a></li><li data-subkey="sec3sub2"><a href="/Conditional-Probability-in-R.html"><span class="progress-dot"></span>Conditional Probability</a></li><li data-subkey="sec3sub2"><a href="/Random-Variables-in-R.html"><span class="progress-dot"></span>Random Variables</a></li><li data-subkey="sec3sub2"><a href="/Binomial-and-Poisson-Distributions-in-R.html"><span class="progress-dot"></span>Binomial vs Poisson</a></li><li data-subkey="sec3sub2"><a href="/Normal-t-F-and-Chi-Squared-Distributions-in-R.html"><span class="progress-dot"></span>Normal, t, F, Chi-Squared</a></li><li data-subkey="sec3sub2"><a href="/Central-Limit-Theorem-in-R.html"><span class="progress-dot"></span>Central Limit Theorem</a></li><li data-subkey="sec3sub2"><a href="/Sampling-Distributions-in-R.html"><span class="progress-dot"></span>Sampling Distributions</a></li><li data-subkey="sec3sub2"><a href="/Law-of-Large-Numbers-vs-CLT-in-R.html"><span class="progress-dot"></span>LLN vs CLT</a></li><li data-subkey="sec3sub2"><a href="/What-Is-Probability-Simulation-First-Intuition-in-R-Before-the-Formulas.html"><span class="progress-dot"></span>Probability (Simulation-First)</a></li><li data-subkey="sec3sub2"><a href="/Expected-Value-and-Variance-in-R.html"><span class="progress-dot"></span>Expected Value and Variance</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec3sub3" data-collapsed="false"><span class="subsec-chevron">▼</span> Inference & Estimation</li><li data-subkey="sec3sub3"><a href="/Maximum-Likelihood-Estimation-in-R.html"><span class="progress-dot"></span>Maximum Likelihood Estimation</a></li><li data-subkey="sec3sub3"><a href="/Hypothesis-Testing-in-R.html"><span class="progress-dot"></span>Hypothesis Testing</a></li><li data-subkey="sec3sub3"><a href="/Sample-Size-Planning-in-R.html"><span class="progress-dot"></span>Sample Size Planning</a></li><li data-subkey="sec3sub3"><a href="/Which-Statistical-Test-in-R.html"><span class="progress-dot"></span>Choosing the Right Test</a></li><li data-subkey="sec3sub3"><a href="/Statistical-Tests-in-R.html"><span class="progress-dot"></span>Statistical Tests</a></li><li data-subkey="sec3sub3"><a href="/Measures-of-Association-in-R.html"><span class="progress-dot"></span>Measures of Association</a></li><li data-subkey="sec3sub3"><a href="/Point-Estimation-in-R.html"><span class="progress-dot"></span>Point Estimation</a></li><li data-subkey="sec3sub3"><a href="/Confidence-Intervals-in-R.html"><span class="progress-dot"></span>Confidence Intervals</a></li><li data-subkey="sec3sub3"><a href="/Type-I-and-Type-II-Errors-in-R.html"><span class="progress-dot"></span>Type I and II Errors</a></li><li data-subkey="sec3sub3"><a href="/Statistical-Power-Analysis-in-R.html"><span class="progress-dot"></span>Power Analysis</a></li><li data-subkey="sec3sub3"><a href="/Effect-Size-in-R.html"><span class="progress-dot"></span>Effect Size</a></li><li data-subkey="sec3sub3"><a href="/t-Tests-in-R.html"><span class="progress-dot"></span>t-Tests</a></li><li data-subkey="sec3sub3"><a href="/Proportion-Tests-in-R.html"><span class="progress-dot"></span>Proportion Tests</a></li><li data-subkey="sec3sub3"><a href="/Normality-and-Variance-Tests-in-R.html"><span class="progress-dot"></span>Normality & Variance Tests</a></li><li data-subkey="sec3sub3"><a href="/Chi-Square-Tests-in-R.html"><span class="progress-dot"></span>Chi-Square Tests</a></li><li data-subkey="sec3sub3"><a href="/Wilcoxon-Mann-Whitney-and-Kruskal-Wallis-in-R.html"><span class="progress-dot"></span>Wilcoxon, Mann-Whitney & Kruskal-Wallis</a></li><li data-subkey="sec3sub3"><a href="/Multiple-Comparisons-in-R.html"><span class="progress-dot"></span>Multiple Testing Correction</a></li><li data-subkey="sec3sub3"><a href="/Hypothesis-Testing-Exercises-in-R-quiz.html"><span class="progress-dot"></span>Hypothesis Testing Quiz</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec3sub4" data-collapsed="false"><span class="subsec-chevron">▼</span> Regression</li><li data-subkey="sec3sub4"><a href="/Linear-Regression.html"><span class="progress-dot"></span>Linear Regression</a></li><li data-subkey="sec3sub4"><a href="/Logistic-Regression-With-R.html"><span class="progress-dot"></span>Logistic Regression</a></li><li data-subkey="sec3sub4"><a href="/Variable-Selection-and-Importance-With-R.html"><span class="progress-dot"></span>Feature Selection</a></li><li data-subkey="sec3sub4"><a href="/Model-Selection-in-R.html"><span class="progress-dot"></span>Model Selection</a></li><li data-subkey="sec3sub4"><a href="/Missing-Value-Treatment-With-R.html"><span class="progress-dot"></span>Missing Value Treatment</a></li><li data-subkey="sec3sub4"><a href="/Outlier-Treatment-With-R.html"><span class="progress-dot"></span>Outlier Analysis</a></li><li data-subkey="sec3sub4"><a href="/adv-regression-models.html"><span class="progress-dot"></span>Advanced Regression Models</a></li><li data-subkey="sec3sub4"><a href="/Linear-Regression-Exercises-in-R-quiz.html"><span class="progress-dot"></span>Linear Regression Quiz</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec3sub5" data-collapsed="false"><span class="subsec-chevron">▼</span> Reporting</li><li data-subkey="sec3sub5"><a href="/Statistical-Consulting-in-R.html"><span class="progress-dot"></span>Statistical Consulting</a></li><li data-subkey="sec3sub5"><a href="/Statistical-Report-Writing-in-R.html"><span class="progress-dot"></span>Statistical Report Writing</a></li><li data-subkey="sec3sub5"><a href="/Bootstrap-Confidence-Intervals-in-R.html"><span class="progress-dot"></span>Bootstrap Confidence Intervals</a></li><li data-subkey="sec3sub5"><a href="/Reporting-Statistics-in-R.html"><span class="progress-dot"></span>Reporting Statistics</a></li><li data-subkey="sec3sub5"><a href="/Correlation-in-R.html"><span class="progress-dot"></span>Correlation (Pearson, Spearman, Kendall)</a></li><li data-subkey="sec3sub5"><a href="/Linear-Regression-Assumptions-in-R.html"><span class="progress-dot"></span>Linear Regression Assumptions</a></li><li data-subkey="sec3sub5"><a href="/Dummy-Variables-in-R.html"><span class="progress-dot"></span>Dummy Variables in R</a></li><li data-subkey="sec3sub5"><a href="/Interaction-Effects-in-R.html"><span class="progress-dot"></span>Interaction Effects</a></li><li data-subkey="sec3sub5"><a href="/Regression-Diagnostics-in-R.html"><span class="progress-dot"></span>Regression Diagnostics</a></li><li data-subkey="sec3sub5"><a href="/Logistic-Regression-in-R.html"><span class="progress-dot"></span>Logistic Regression (glm + ROC)</a></li><li data-subkey="sec3sub5"><a href="/Variable-Selection-in-R.html"><span class="progress-dot"></span>Variable Selection</a></li><li data-subkey="sec3sub5"><a href="/Poisson-Regression-in-R.html"><span class="progress-dot"></span>Poisson Regression</a></li><li data-subkey="sec3sub5"><a href="/Ridge-and-Lasso-Regression-in-R.html"><span class="progress-dot"></span>Ridge & Lasso Regression</a></li><li data-subkey="sec3sub5"><a href="/Polynomial-and-Spline-Regression-in-R.html"><span class="progress-dot"></span>Polynomial & Splines</a></li><li data-subkey="sec3sub5"><a href="/Regression-Tables-in-R.html"><span class="progress-dot"></span>Regression Tables (3 packages)</a></li><li data-subkey="sec3sub5"><a href="/One-Way-ANOVA-in-R.html"><span class="progress-dot"></span>One-Way ANOVA</a></li><li data-subkey="sec3sub5"><a href="/Post-Hoc-Tests-After-ANOVA.html"><span class="progress-dot"></span>Post-Hoc Tests After ANOVA</a></li><li data-subkey="sec3sub5"><a href="/Two-Way-ANOVA-in-R.html"><span class="progress-dot"></span>Two-Way ANOVA</a></li><li data-subkey="sec3sub5"><a href="/Repeated-Measures-ANOVA-in-R.html"><span class="progress-dot"></span>Repeated Measures ANOVA</a></li><li data-subkey="sec3sub5"><a href="/ANCOVA-in-R.html"><span class="progress-dot"></span>ANCOVA</a></li><li data-subkey="sec3sub5"><a href="/Experimental-Design-Principles-in-R.html"><span class="progress-dot"></span>Experimental Design in R</a></li><li data-subkey="sec3sub5"><a href="/Factorial-Experiments-in-R.html"><span class="progress-dot"></span>Factorial Designs (2^k)</a></li><li data-subkey="sec3sub5"><a href="/AB-Testing-in-R.html"><span class="progress-dot"></span>A/B Testing</a></li><li data-subkey="sec3sub5"><a href="/MANOVA-in-R.html"><span class="progress-dot"></span>MANOVA</a></li><li data-subkey="sec3sub5"><a href="/Mixed-ANOVA-in-R.html"><span class="progress-dot"></span>Mixed ANOVA</a></li><li data-subkey="sec3sub5"><a href="/Multivariate-Statistics-in-R.html"><span class="progress-dot"></span>Multivariate Distances & Hotelling's T²</a></li><li data-subkey="sec3sub5"><a href="/PCA-in-R.html"><span class="progress-dot"></span>PCA with prcomp()</a></li><li data-subkey="sec3sub5"><a href="/Interpreting-PCA-Results-in-R.html"><span class="progress-dot"></span>Interpreting PCA Output</a></li><li data-subkey="sec3sub5"><a href="/Exploratory-Factor-Analysis-in-R.html"><span class="progress-dot"></span>Exploratory Factor Analysis</a></li><li data-subkey="sec3sub5"><a href="/CFA-and-Structural-Equation-Modeling-in-R.html"><span class="progress-dot"></span>SEM and CFA (lavaan)</a></li><li data-subkey="sec3sub5"><a href="/Linear-Discriminant-Analysis-in-R.html"><span class="progress-dot"></span>LDA (Linear Discriminant Analysis)</a></li><li data-subkey="sec3sub5"><a href="/Cluster-Analysis-in-R.html"><span class="progress-dot"></span>Clustering (k-Means / HC / DBSCAN)</a></li><li data-subkey="sec3sub5"><a href="/Correspondence-Analysis-in-R.html"><span class="progress-dot"></span>Correspondence Analysis</a></li><li data-subkey="sec3sub5"><a href="/t-SNE-and-UMAP-in-R.html"><span class="progress-dot"></span>t-SNE and UMAP</a></li><li data-subkey="sec3sub5"><a href="/Simple-Linear-Regression-in-R.html"><span class="progress-dot"></span>Simple Linear Regression</a></li><li data-subkey="sec3sub5"><a href="/Multiple-Regression-in-R.html"><span class="progress-dot"></span>Multiple Regression</a></li><li data-subkey="sec3sub5"><a href="/Robust-Regression-in-R.html"><span class="progress-dot"></span>Robust Regression (rlm)</a></li><li data-subkey="sec3sub5"><a href="/factoextra-and-FactoMineR.html"><span class="progress-dot"></span>factoextra (PCA + Clusters)</a></li><li data-subkey="sec3sub5"><a href="/Categorical-Data-in-R.html"><span class="progress-dot"></span>Categorical Data (Tables & Mosaic)</a></li><li data-subkey="sec3sub5"><a href="/Chi-Square-Test-of-Independence-in-R.html"><span class="progress-dot"></span>Chi-Square Test of Independence</a></li><li data-subkey="sec3sub5"><a href="/Chi-Square-Goodness-of-Fit-Test-in-R.html"><span class="progress-dot"></span>Chi-Square Goodness-of-Fit</a></li><li data-subkey="sec3sub5"><a href="/Fishers-Exact-Test-in-R.html"><span class="progress-dot"></span>Fisher's Exact Test</a></li><li data-subkey="sec3sub5"><a href="/Odds-Ratios-and-Relative-Risk-in-R.html"><span class="progress-dot"></span>Odds Ratios & Relative Risk</a></li><li data-subkey="sec3sub5"><a href="/Logistic-Regression-in-R-2.html"><span class="progress-dot"></span>Logistic Regression (Diagnostics)</a></li><li data-subkey="sec3sub5"><a href="/Poisson-and-Negative-Binomial-Regression.html"><span class="progress-dot"></span>Poisson & Negative Binomial Regression</a></li><li data-subkey="sec3sub5"><a href="/Multinomial-and-Ordinal-Logistic-Regression-in-R.html"><span class="progress-dot"></span>Multinomial & Ordinal Logistic Regression</a></li><li data-subkey="sec3sub5"><a href="/When-to-Use-Nonparametric-Tests-in-R.html"><span class="progress-dot"></span>When to Use Nonparametric Tests</a></li><li data-subkey="sec3sub5"><a href="/Wilcoxon-Signed-Rank-Test-in-R.html"><span class="progress-dot"></span>Wilcoxon Signed-Rank Test</a></li><li data-subkey="sec3sub5"><a href="/Mann-Whitney-U-Test-in-R.html"><span class="progress-dot"></span>Mann-Whitney U Test</a></li><li data-subkey="sec3sub5"><a href="/Kruskal-Wallis-Test-in-R-2.html"><span class="progress-dot"></span>Kruskal-Wallis Test</a></li><li data-subkey="sec3sub5"><a href="/Friedman-Test-in-R.html"><span class="progress-dot"></span>Friedman Test</a></li><li data-subkey="sec3sub5"><a href="/Spearman-and-Kendall-Correlation-in-R.html"><span class="progress-dot"></span>Spearman & Kendall Correlation</a></li><li data-subkey="sec3sub5"><a href="/Bootstrap-in-R.html"><span class="progress-dot"></span>Bootstrap (boot package)</a></li><li data-subkey="sec3sub5"><a href="/Quantile-Regression-in-R-2.html"><span class="progress-dot"></span>Quantile Regression</a></li><li data-subkey="sec3sub5"><a href="/Matrix-Operations-in-R.html"><span class="progress-dot"></span>Matrix Operations in R</a></li><li data-subkey="sec3sub5"><a href="/Solving-Linear-Systems-in-R.html"><span class="progress-dot"></span>Solving Linear Systems in R</a></li><li data-subkey="sec3sub5"><a href="/Eigenvalues-and-Eigenvectors-in-R.html"><span class="progress-dot"></span>Eigenvalues & Eigenvectors in R</a></li><li data-subkey="sec3sub5"><a href="/Singular-Value-Decomposition-in-R.html"><span class="progress-dot"></span>Singular Value Decomposition in R</a></li><li data-subkey="sec3sub5"><a href="/Projections-and-the-Hat-Matrix-in-R.html"><span class="progress-dot"></span>Projections & the Hat Matrix</a></li><li data-subkey="sec3sub5"><a href="/QR-Decomposition-in-R.html"><span class="progress-dot"></span>QR Decomposition in R</a></li><li data-subkey="sec3sub5"><a href="/Quadratic-Forms-in-R.html"><span class="progress-dot"></span>Quadratic Forms</a></li><li data-subkey="sec3sub5"><a href="/Matrix-Derivatives-and-the-Hessian-in-R.html"><span class="progress-dot"></span>Matrix Derivatives & Hessian</a></li><li data-subkey="sec3sub5"><a href="/Exponential-Family-Distributions-in-R.html"><span class="progress-dot"></span>Exponential Family Distributions</a></li><li data-subkey="sec3sub5"><a href="/Sufficient-Statistics-in-R.html"><span class="progress-dot"></span>Sufficient Statistics</a></li><li data-subkey="sec3sub5"><a href="/Complete-and-Ancillary-Statistics-in-R.html"><span class="progress-dot"></span>Complete & Ancillary Statistics</a></li><li data-subkey="sec3sub5"><a href="/UMVUE-in-R-2.html"><span class="progress-dot"></span>UMVUE (Rao-Blackwell & Lehmann-Scheffé)</a></li><li data-subkey="sec3sub5"><a href="/Cramer-Rao-Lower-Bound-in-R-2.html"><span class="progress-dot"></span>Cramér-Rao Lower Bound</a></li><li data-subkey="sec3sub5"><a href="/Asymptotic-Theory-in-R-2.html"><span class="progress-dot"></span>Asymptotic Theory</a></li><li data-subkey="sec3sub5"><a href="/Neyman-Pearson-Lemma-in-R-2.html"><span class="progress-dot"></span>Neyman-Pearson Lemma</a></li><li data-subkey="sec3sub5"><a href="/Likelihood-Ratio-Tests-and-Pivotal-Methods.html"><span class="progress-dot"></span>Likelihood Ratio & Pivotal Methods</a></li><li data-subkey="sec3sub5"><a href="/Decision-Theory-in-R.html"><span class="progress-dot"></span>Decision Theory</a></li><li data-subkey="sec3sub5"><a href="/Asymptotic-Relative-Efficiency-in-R.html"><span class="progress-dot"></span>Asymptotic Relative Efficiency</a></li><li data-subkey="sec3sub5"><a href="/Bayes-Theorem-in-R.html"><span class="progress-dot"></span>Bayes' Theorem</a></li><li data-subkey="sec3sub5"><a href="/Bayesian-Statistics-in-R.html"><span class="progress-dot"></span>Bayesian Statistics</a></li><li data-subkey="sec3sub5"><a href="/Conjugate-Priors-in-R.html"><span class="progress-dot"></span>Conjugate Priors</a></li><li data-subkey="sec3sub5"><a href="/Grid-Approximation-in-R.html"><span class="progress-dot"></span>Grid Approximation</a></li><li data-subkey="sec3sub5"><a href="/MCMC-in-R.html"><span class="progress-dot"></span>MCMC in R</a></li><li data-subkey="sec3sub5"><a href="/Gibbs-Sampling-in-R.html"><span class="progress-dot"></span>Gibbs Sampling</a></li><li data-subkey="sec3sub5"><a href="/Hamiltonian-Monte-Carlo-in-R.html"><span class="progress-dot"></span>Hamiltonian Monte Carlo</a></li><li data-subkey="sec3sub5"><a href="/Stan-in-R.html"><span class="progress-dot"></span>Stan</a></li><li data-subkey="sec3sub5"><a href="/brms-in-R.html"><span class="progress-dot"></span>brms</a></li><li data-subkey="sec3sub5"><a href="/Choosing-Priors-in-R.html"><span class="progress-dot"></span>Choosing Priors</a></li><li data-subkey="sec3sub5"><a href="/Prior-Predictive-Checks-in-R.html"><span class="progress-dot"></span>Prior Predictive Checks</a></li><li data-subkey="sec3sub5"><a href="/Compare-Bayesian-Models-in-R.html"><span class="progress-dot"></span>Compare Bayesian Models</a></li><li data-subkey="sec3sub5"><a href="/Posterior-Predictive-Checks-in-R.html"><span class="progress-dot"></span>Posterior Predictive Checks</a></li><li data-subkey="sec3sub5"><a href="/Bayesian-Linear-Regression-in-R.html"><span class="progress-dot"></span>Bayesian Linear Regression</a></li><li data-subkey="sec3sub5"><a href="/Bayesian-Logistic-Regression-in-R.html"><span class="progress-dot"></span>Bayesian Logistic Regression</a></li><li data-subkey="sec3sub5"><a href="/Bayesian-Hierarchical-Models-in-R.html"><span class="progress-dot"></span>Bayesian Hierarchical Models</a></li><li data-subkey="sec3sub5"><a href="/Multilevel-Models-in-R.html"><span class="progress-dot"></span>Multilevel Models</a></li><li data-subkey="sec3sub5"><a href="/Bayesian-ANOVA-in-R.html"><span class="progress-dot"></span>Bayesian ANOVA</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec3sub6" data-collapsed="false"><span class="subsec-chevron">▼</span> Machine Learning</li><li data-subkey="sec3sub6"><a href="/Machine-Learning-Exercises-in-R-quiz.html"><span class="progress-dot"></span>Machine Learning Quiz</a></li></ul></li><li class="sidebar-section"><div class="sidebar-section-header"><span class="sidebar-chevron">▸</span> Time Series<span class="section-meta" data-section-meta></span></div><ul class="sidebar-section-items list-unstyled"><li data-subkey="sec4sub0"><a href="/Time-Series-Analysis-With-R.html"><span class="progress-dot"></span>Time Series Analysis</a></li><li data-subkey="sec4sub0"><a href="/Time-Series-Forecasting-With-R.html"><span class="progress-dot"></span>Time Series Forecasting</a></li><li data-subkey="sec4sub0"><a href="/Time-Series-Forecasting-With-R-part2.html"><span class="progress-dot"></span>More Time Series Forecasting</a></li><li data-subkey="sec4sub0"><a href="/Time-Series-Exercises-in-R-quiz.html"><span class="progress-dot"></span>Time Series Quiz</a></li></ul></li><li class="sidebar-section"><div class="sidebar-section-header"><span class="sidebar-chevron">▸</span> Advanced R<span class="section-meta" data-section-meta></span></div><ul class="sidebar-section-items list-unstyled"><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec5sub1" data-collapsed="false"><span class="subsec-chevron">▼</span> Functional Programming</li><li data-subkey="sec5sub1"><a href="/Functional-Programming-in-R.html"><span class="progress-dot"></span>Functional Programming</a></li><li data-subkey="sec5sub1"><a href="/R-Functional-Programming-Exercises-quiz.html"><span class="progress-dot"></span>Functional Programming Quiz</a></li><li data-subkey="sec5sub1"><a href="/purrr-map-Variants.html"><span class="progress-dot"></span>purrr map() Variants</a></li><li data-subkey="sec5sub1"><a href="/R-Anonymous-Functions.html"><span class="progress-dot"></span>R Anonymous Functions</a></li><li data-subkey="sec5sub1"><a href="/R-Function-Factories.html"><span class="progress-dot"></span>R Function Factories</a></li><li data-subkey="sec5sub1"><a href="/R-Function-Operators.html"><span class="progress-dot"></span>R Function Operators</a></li><li data-subkey="sec5sub1"><a href="/Reduce-Filter-Map-in-R.html"><span class="progress-dot"></span>Reduce, Filter, Map</a></li><li data-subkey="sec5sub1"><a href="/Memoization-in-R.html"><span class="progress-dot"></span>Memoization in R</a></li><li data-subkey="sec5sub1"><a href="/Writing-Composable-R-Code.html"><span class="progress-dot"></span>Composable R Code</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec5sub2" data-collapsed="false"><span class="subsec-chevron">▼</span> OOP in R</li><li data-subkey="sec5sub2"><a href="/OOP-in-R.html"><span class="progress-dot"></span>OOP in R: S3/S4/R6</a></li><li data-subkey="sec5sub2"><a href="/S3-Classes-in-R.html"><span class="progress-dot"></span>S3 Classes</a></li><li data-subkey="sec5sub2"><a href="/S3-Method-Dispatch-in-R.html"><span class="progress-dot"></span>S3 Method Dispatch</a></li><li data-subkey="sec5sub2"><a href="/S4-Classes-in-R.html"><span class="progress-dot"></span>S4 Classes</a></li><li data-subkey="sec5sub2"><a href="/S4-Methods-in-R.html"><span class="progress-dot"></span>S4 Methods & Dispatch</a></li><li data-subkey="sec5sub2"><a href="/R6-Classes-in-R.html"><span class="progress-dot"></span>R6 Classes</a></li><li data-subkey="sec5sub2"><a href="/R6-Advanced.html"><span class="progress-dot"></span>R6 Advanced</a></li><li data-subkey="sec5sub2"><a href="/Operator-Overloading-in-R.html"><span class="progress-dot"></span>Operator Overloading</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec5sub3" data-collapsed="false"><span class="subsec-chevron">▼</span> How R Works</li><li data-subkey="sec5sub3"><a href="/R-Names-and-Values.html"><span class="progress-dot"></span>R Names & Values</a></li><li data-subkey="sec5sub3"><a href="/R-Assignment-Deep-Dive.html"><span class="progress-dot"></span>R Assignment Deep Dive</a></li><li data-subkey="sec5sub3"><a href="/R-Memory-lobstr.html"><span class="progress-dot"></span>R Memory & lobstr</a></li><li data-subkey="sec5sub3"><a href="/R-Environments.html"><span class="progress-dot"></span>R Environments</a></li><li data-subkey="sec5sub3"><a href="/R-Lexical-Scoping.html"><span class="progress-dot"></span>Lexical Scoping</a></li><li data-subkey="sec5sub3"><a href="/R-Closures.html"><span class="progress-dot"></span>R Closures</a></li><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec5sub4" data-collapsed="false"><span class="subsec-chevron">▼</span> Debugging & Performance</li><li data-subkey="sec5sub4"><a href="/R-Conditions-System.html"><span class="progress-dot"></span>Conditions System</a></li><li data-subkey="sec5sub4"><a href="/R-Debugging.html"><span class="progress-dot"></span>Debugging R Code</a></li><li data-subkey="sec5sub4"><a href="/R-Common-Errors.html"><span class="progress-dot"></span>50 Common R Errors</a></li><li data-subkey="sec5sub4"><a href="/Parallel-Computing-With-R.html"><span class="progress-dot"></span>Parallel Computing</a></li><li data-subkey="sec5sub4"><a href="/Strategies-To-Improve-And-Speedup-R-Code.html"><span class="progress-dot"></span>Speedup R Code</a></li><li data-subkey="sec5sub4"><a href="/Shiny-Exercises-in-R-quiz.html"><span class="progress-dot"></span>Shiny Quiz</a></li></ul></li><li class="sidebar-section"><div class="sidebar-section-header"><span class="sidebar-chevron">▸</span> Classic Tutorials<span class="section-meta" data-section-meta></span></div><ul class="sidebar-section-items list-unstyled"><li data-subkey="sec6sub0"><a href="/R-Tutorial.html"><span class="progress-dot"></span>R Tutorial (Classic)</a></li><li data-subkey="sec6sub0"><a href="/ggplot2-Tutorial-With-R.html"><span class="progress-dot"></span>ggplot2 Short Tutorial</a></li><li data-subkey="sec6sub0"><a href="/Complete-Ggplot2-Tutorial-Part1-With-R-Code.html"><span class="progress-dot"></span>ggplot2 Tutorial 1 - Intro</a></li><li data-subkey="sec6sub0"><a href="/Complete-Ggplot2-Tutorial-Part2-Customizing-Theme-With-R-Code.html"><span class="progress-dot"></span>ggplot2 Tutorial 2 - Theme</a></li><li data-subkey="sec6sub0"><a href="/Top50-Ggplot2-Visualizations-MasterList-R-Code.html"><span class="progress-dot"></span>ggplot2 Tutorial 3 - Masterlist</a></li><li data-subkey="sec6sub0"><a href="/Association-Mining-With-R.html"><span class="progress-dot"></span>Association Mining</a></li><li data-subkey="sec6sub0"><a href="/Multi-Dimensional-Scaling-With-R.html"><span class="progress-dot"></span>Multi Dimensional Scaling</a></li><li data-subkey="sec6sub0"><a href="/Optimization-With-R.html"><span class="progress-dot"></span>Optimization</a></li><li data-subkey="sec6sub0"><a href="/Information-Value-With-R.html"><span class="progress-dot"></span>InformationValue Package</a></li></ul></li><li class="sidebar-section"><div class="sidebar-section-header"><span class="sidebar-chevron">▸</span> Practice Exercises<span class="section-meta" data-section-meta></span></div><ul class="sidebar-section-items list-unstyled"><li class="sidebar-divider sidebar-subsection-toggle" data-subkey="sec7sub1" data-collapsed="false"><span class="subsec-chevron">▼</span> Mastery Quizzes (Certificate)</li><li data-subkey="sec7sub1"><a href="/R-Beginner-Exercises-quiz.html"><span class="progress-dot"></span>R Fundamentals Quiz</a></li><li data-subkey="sec7sub1"><a href="/dplyr-Exercises-in-R-quiz.html"><span class="progress-dot"></span>dplyr Quiz</a></li><li data-subkey="sec7sub1"><a href="/ggplot2-Exercises-in-R-quiz.html"><span class="progress-dot"></span>ggplot2 Quiz</a></li><li data-subkey="sec7sub1"><a href="/Hypothesis-Testing-Exercises-in-R-quiz.html"><span class="progress-dot"></span>Hypothesis Testing Quiz</a></li><li data-subkey="sec7sub1"><a href="/Linear-Regression-Exercises-in-R-quiz.html"><span class="progress-dot"></span>Linear Regression Quiz</a></li><li data-subkey="sec7sub1"><a href="/Machine-Learning-Exercises-in-R-quiz.html"><span class="progress-dot"></span>Machine Learning Quiz</a></li><li data-subkey="sec7sub1"><a href="/tidyr-Exercises-in-R-quiz.html"><span 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<h1>Best R Books: A Curated Reading List That Won't Waste Your Time</h1>
<p class="lead">Most R book lists are undifferentiated 20-book dumps. This one is ranked, opinionated, and tells you exactly when to skip a book, because nobody reads 20 books. Pick one entry point, finish it, then branch by goal.</p>
<div class="post-byline" style="color:#6b7280;font-size:14px;margin:2px 0 18px 0;line-height:1.5;">By <strong>Selva Prabhakaran</strong> · Published May 10, 2026 · Last updated May 10, 2026</div>
<div class="engagement-header" data-difficulty="Intermediate" data-time="15"></div>
<h2>Why does R have so many good free books?</h2>
<p>R has a book problem most languages envy: there are too many <em>good</em> books, most are free online, and several are written by the people who built the tools you will use every day. The risk is not finding a bad book, it is reading five overlapping ones and never getting past the basics. The fix is to pick one entry point, finish it, then specialise. This list walks that exact path.</p>
<p>The reason R's book ecosystem looks this way is historical. R grew out of academic statistics, where sharing the textbook <em>is</em> the culture. Hadley Wickham and the Posit team extended that culture to the tidyverse, publishing most of their books free online under the bookdown project while also selling print editions through CRC Press. You get the same text either way.</p>
<p>That abundance has a cost. Plenty of older R books are technically still in print but effectively abandoned, they reference dplyr 0.5, the <code>plyr</code> package, or base graphics idioms nobody writes anymore. The signal for "still current" is simple: check when the book was last updated and whether it uses the <a class="auto-link" href="R-Pipe-Operator-in-R.html" title="R Pipe Operator: %>% vs |> (Complete Guide)">native pipe</a> <code>|></code>, <code>across()</code>, and tidyr 1.0+ syntax. If it uses <code>mutate_if()</code> or <code>%>%</code> exclusively without mention of <code>|></code>, it is from a previous era.</p>
<div class="callout callout-insight"><div class="callout-label">Key Insight</div><div class="callout-body"><strong>One book per stage, then move on.</strong> Most beginners get stuck in a loop of buying new R books for problems they already know how to solve. The readers who become fluent finish <em>one</em> book per stage (beginner, specialist, expert) instead of half-reading five.</div></div>
<h2>Where should a complete beginner start?</h2>
<p>If you have never written a line of R, there is one honest answer: <strong>R for Data Science (2nd edition)</strong> by Hadley Wickham, Mine Çetinkaya-Rundel and Garrett Grolemund. It is free at <a href="https://r4ds.hadley.nz/">r4ds.hadley.nz</a>, it is updated for the native pipe, and it walks you from zero to a complete data analysis workflow using the tidyverse. No other beginner book gets you productive as fast.</p>
<p>The 2nd edition matters. The 1st edition uses the <a class="auto-link" href="R-Pipe-Operator.html" title="The Pipe Operator in R: %>% vs |> — Master the Most Important Concept">magrittr pipe</a> <code>%>%</code>, older dplyr idioms, and a few deprecated <code>tidyr</code> functions. If you pick up a used copy from 2017, you will have to translate code as you read it. Always link to <a href="https://r4ds.hadley.nz/">r4ds.hadley.nz</a>, not <a href="https://r4ds.had.co.nz/">r4ds.had.co.nz</a>.</p>
<div class="callout callout-tip"><div class="callout-label">Tip</div><div class="callout-body"><strong>Read the 2nd edition, not the 1st.</strong> The 1st edition predates the native pipe and dplyr 1.0; you will spend time translating deprecated idioms instead of learning. Bookmark r4ds.hadley.nz and ignore older copies.</div></div>
<p>R for Data Science has two common alternatives, and each makes sense for a specific reader. <strong>Hands-On Programming with R</strong> by Garrett Grolemund (free at <a href="https://rstudio-education.github.io/hopr/">rstudio-education.github.io/hopr</a>) teaches R as a programming language, you build a slot machine, simulate dice, and learn about environments and scoping. Pick this one if your goal is to understand R, not just use it for analysis. <strong>Learning Statistics with R</strong> by Danielle Navarro teaches R and introductory statistics together. Pick this one if you also need the stats foundation, <a class="auto-link" href="Statistical-Tests-in-R.html" title="Statistical Tests in R">hypothesis testing</a>, regression, <a class="auto-link" href="https://r-statistics.co/How-to-do-One-Way-ANOVA-in-R.html" title="One-Way ANOVA in R: Compare Group Means (With Examples)">ANOVA</a>, and you do not have it yet from a prior course.</p>
<p>Here is the short version in table form:</p>
<table class="table table-striped">
<thead>
<tr>
<th>Book</th>
<th>Best for</th>
<th>Skip if</th>
</tr>
</thead>
<tbody>
<tr>
<td>R for Data Science (2e)</td>
<td>Data analysis workflow, tidyverse from day one</td>
<td>You already know dplyr and ggplot2 fluently</td>
</tr>
<tr>
<td>Hands-On Programming with R</td>
<td>Understanding R as a programming language</td>
<td>You come from another language and want analysis-ready skills fast</td>
</tr>
<tr>
<td>Learning Statistics with R</td>
<td>Learning stats and R together from zero</td>
<td>You already have an introductory stats course</td>
</tr>
</tbody>
</table>
<p>Finish the one you picked before adding a second. This is the most common place readers stall, buying a second beginner book as a form of procrastination.</p>
<h2>Which book teaches modern data wrangling best?</h2>
<p>If "modern data wrangling" means tidyverse, dplyr, tidyr, <a class="auto-link" href="stringr-in-R.html" title="stringr in R: 15 Functions That Handle Every String Task You'll Actually Encounter">stringr</a>, <a class="auto-link" href="lubridate-in-R.html" title="lubridate in R: Parse Dates Once, Stop Fighting Time Zones Forever">lubridate</a>, the answer is still <strong>R for Data Science</strong>. Chapters 4–8 cover the core verbs (<code>filter</code>, <code>select</code>, <code>mutate</code>, <code>summarise</code>, <code>group_by</code>), pivoting, and joins with exactly the depth a working analyst needs. You do not need a second book for everyday wrangling.</p>
<p>Two companions are worth knowing about. <strong>Practical Data Science with R (2nd edition)</strong> by Nina Zumel and John Mount is the book to read after R4DS if your job involves messy business data, inconsistent column headers, mixed types, join-key mismatches, and the everyday reality of data that was never designed for analysis. It is stronger on real-world data cleaning than R4DS, which uses well-behaved example datasets.</p>
<p><strong>R Cookbook (2nd edition)</strong> by JD Long and Paul Teetor is indexed by problem, not by concept. You do not <em>read</em> it, you look things up. When you need "how do I read a fixed-width file" or "how do I compute a rolling mean by group," the cookbook points you at the three-line answer in seconds. Keep it as a reference, not a tutorial.</p>
<div class="callout callout-note"><div class="callout-label">Note</div><div class="callout-body"><strong>Prefer books published 2022 or later.</strong> dplyr 1.1 superseded the <code>_at</code>, <code>_if</code>, and <code>_all</code> variants with <code>across()</code>, and tidyr reworked <code>pivot_longer()</code> and <code>pivot_wider()</code>. Older books still teach <code>mutate_at()</code> and <code>gather()</code>/<code>spread()</code>. The code still runs, but nobody writes it that way anymore, and you will have to unlearn it later.</div></div>
<h2>What's the best book for R visualisation?</h2>
<p>R's visualisation story is dominated by ggplot2, and there are two books you should actually know about. The first is <strong>ggplot2: Elegant Graphics for Data Analysis (3rd edition)</strong> by Hadley Wickham, Danielle Navarro and Thomas Lin Pedersen, free at <a href="https://ggplot2-book.org/">ggplot2-book.org</a>. It teaches the <em>grammar</em> of graphics: layers, scales, facets, coordinates, themes. Read it once, slowly, and ggplot2 stops feeling like memorising incantations.</p>
<p>The second is <strong>R Graphics Cookbook (2nd edition)</strong> by Winston Chang, free at <a href="https://r-graphics.org/">r-graphics.org</a>. It is organised as 150+ recipes: "How do I add <a class="auto-link" href="Communicating-Uncertainty.html" title="Communicating Uncertainty: Don't Mislead Your Audience with Data Viz">error bars</a>?", "How do I make a grouped bar chart?", "How do I flip a legend?". You do not read it cover to cover, you open it when a visual problem lands on your desk.</p>
<div class="callout callout-tip"><div class="callout-label">Tip</div><div class="callout-body"><strong>The Cookbook is the one you'll keep open. The grammar book is the one you read once.</strong> Most working analysts need both: the grammar book for the mental model (you only need it once) and the cookbook for the daily recipes (you need it forever).</div></div>
<p>If you want to go beyond "what button do I press" into <em>why</em> a chart communicates well, <strong>Fundamentals of Data Visualization</strong> by Claus Wilke (free at <a href="https://clauswilke.com/dataviz/">clauswilke.com/dataviz</a>) is the best design-principles book in the R orbit. It is not strictly an R book, the examples happen to be in R, but it teaches chart selection, colour choice and proportional encoding better than anything else on this list.</p>
<h2>Which books will make you an expert R programmer?</h2>
<p>There is exactly one book that separates "I use R" from "I understand R," and it is <strong>Advanced R (2nd edition)</strong> by Hadley Wickham, free at <a href="https://adv-r.hadley.nz/">adv-r.hadley.nz</a>. It covers functions, environments, <a class="auto-link" href="R-Promise-Objects.html" title="R Promise Objects: Lazy Evaluation & Force() Explained">lazy evaluation</a>, R's three object-oriented systems (S3, S4, R6), and metaprogramming. Every concept that makes R look weird from outside, unquoted column names in dplyr, the funny scoping rules, formulas as first-class values, is explained from first principles.</p>
<p>Pair it with <strong>R Packages (2nd edition)</strong> by Hadley Wickham and Jenny Bryan, free at <a href="https://r-pkgs.org/">r-pkgs.org</a>. Once you understand R, you will want to organise your code into packages. This book walks you through the modern package workflow: <code>usethis</code>, <code>devtools</code>, <code>testthat</code>, <code>roxygen2</code>, <code>pkgdown</code>, and GitHub Actions for CI. It pairs with Advanced R the way a lab pairs with a lecture.</p>
<div class="callout callout-warning"><div class="callout-label">Warning</div><div class="callout-body"><strong>Advanced R is not a first R book.</strong> Attempting it before six months of hands-on R will frustrate you, the chapter on environments assumes you have already been confused by scoping in practice. Finish R for Data Science and write real code first, then come back.</div></div>
<p>A third option in this tier is <strong>The Art of R Programming</strong> by Norman Matloff. It takes a software-engineering angle rather than Wickham's mathematical-linguistic one, and it covers things Advanced R skips: debugging workflows, calling C from R, performance tuning on a lower level. It is older, but the fundamentals have not changed. Read it if Advanced R's metaprogramming chapters feel too abstract and you want something closer to the metal.</p>
<h2>Which books cover statistics and machine learning in R?</h2>
<p>The free textbook the entire statistical-learning field standardised on is <strong>An Introduction to Statistical Learning with R (2nd edition)</strong> by James, Witten, Hastie and Tibshirani, free at <a href="https://www.statlearning.com/">statlearning.com</a>. It covers linear and <a class="auto-link" href="Logistic-Regression-With-R.html" title="Logistic Regression with R">logistic regression</a>, resampling, tree methods, <a class="auto-link" href="Support-Vector-Machines-With-R.html" title="Support Vector Machines with R">SVM</a>, <a class="auto-link" href="Clustering-with-R.html" title="Clustering with R">clustering</a>, and (in the 2nd edition) deep learning and survival analysis, with R labs that make every method runnable. If you want <em>one</em> ML book with R, this is it.</p>
<p>For a more applied, "how do I actually put this into production" angle, <strong>Hands-On Machine Learning with R</strong> by Bradley Boehmke and Brandon Greenwell (free at <a href="https://bradleyboehmke.github.io/HOML/">bradleyboehmke.github.io/HOML</a>) is the sibling book. It walks through realistic ML pipelines, feature engineering, resampling, hyperparameter tuning, model interpretation, using <code>caret</code> and the tidymodels ecosystem.</p>
<p><strong>Tidy Modeling with R</strong> by Max Kuhn and Julia Silge (free at <a href="https://www.tmwr.org/">tmwr.org</a>) is the official tidymodels book and the one to read if you want your ML code to look like modern tidyverse. It replaces <code>caret</code> (which Kuhn himself wrote) with <code>parsnip</code>, <code>recipes</code>, <code>workflows</code> and <code>rsample</code>. If you are starting ML in R today, start with this book.</p>
<p>And then there is <strong>Statistical Rethinking (2nd edition)</strong> by Richard McElreath. It is a <a class="auto-link" href="Bayesian-Statistics-in-R.html" title="Bayesian Statistics in R: Build Genuine Intuition Before Opening Stan or brms">Bayesian statistics</a> book that uses R and Stan, and it is, by wide agreement, the best-written statistics textbook of the last decade. McElreath teaches causal reasoning, <a class="auto-link" href="Choosing-Priors-in-R.html" title="Choosing Priors in R: Why Your Bayesian Result Depends on This One Decision">prior choice</a> and posterior interpretation with clarity you rarely see in statistics writing. The printed book uses the <code>rethinking</code> package; Solomon Kurz maintains a free tidyverse-and-<code>brms</code> port at <a href="https://bookdown.org/content/4857/">bookdown.org/content/4857</a> if you prefer that.</p>
<div class="callout callout-insight"><div class="callout-label">Key Insight</div><div class="callout-body"><strong>ISLR teaches you <em>what</em> to do. Statistical Rethinking teaches you <em>why</em>.</strong> Most ML books show methods; <em>Rethinking</em> shows the reasoning that decides whether the method is appropriate at all. Read ISLR for competence, then <em>Rethinking</em> for judgement.</div></div>
<h2>What about specialised domains, time series, text, Bayesian, geospatial?</h2>
<p>Four specialised books are each the best in their niche, and all of them are free.</p>
<p><strong><a class="auto-link" href="Time-Series-Forecasting-With-R.html" title="Time Series Forecasting with R">Forecasting</a>: Principles and Practice (3rd edition)</strong> by Rob Hyndman and George Athanasopoulos, free at <a href="https://otexts.com/fpp3/">otexts.com/fpp3</a>, is the time-series book. It uses the modern <code>fable</code> / <code>tsibble</code> stack, covers exponential smoothing, ARIMA and dynamic regression, and is written by the maintainer of the <code>forecast</code> and <code>fable</code> packages. If you work on time-series problems, this is the only book you need.</p>
<p><strong>Text Mining with R</strong> by Julia Silge and David Robinson, free at <a href="https://www.tidytextmining.com/">tidytextmining.com</a>, is the NLP-adjacent book. It introduces the <code>tidytext</code> workflow (one-token-per-row) and walks through sentiment analysis, TF-IDF, topic modelling, and the <code>gutenbergr</code> workflow. For classical text analytics in R, it is the default.</p>
<p><strong>Bayesian Data Analysis (3rd edition)</strong> by Gelman, Carlin, Stern, Dunson, Vehtari and Rubin is the heavyweight Bayesian reference, free at <a href="http://www.stat.columbia.edu/~gelman/book/">stat.columbia.edu/~gelman/book</a>. It is denser than Statistical Rethinking and more of a reference than a teaching book. Read <em>Rethinking</em> first, then BDA3 when you need depth.</p>
<p><strong>Geocomputation with R</strong> by Robin Lovelace, Jakub Nowosad and Jannes Muenchow, free at <a href="https://r.geocompx.org/">r.geocompx.org</a>, is the spatial-data book. It covers <code>sf</code>, <code>terra</code>, raster operations, map-making with <code>tmap</code>, and the modern spatial ecosystem. If you work with geographic data, this is the entry point.</p>
<h2>How do I pick the right book for <em>my</em> goal?</h2>
<p>After the beginner stage, the single most common mistake is to keep reading "general R" books instead of branching by what you actually want to build. The diagram below shows four reading paths, each starting from the same entry point.</p>
<p><img src="screenshots/Best-R-Books-reading-path.webp" alt="R book reading paths, four goals, one starting book" class="img-responsive img-zoomable" loading="lazy" width="1734" height="1282" /></p>
<p><em>Figure 1: Four reading paths, one entry point, where to go after finishing R for Data Science.</em></p>
<p>The rule of thumb is one book per stage. Finish <em>R for Data Science</em>, then pick exactly one branch: the data-analyst path (Graphics Cookbook + R Cookbook), the statistician path (ISLR + Statistical Rethinking), the R-developer path (Advanced R + R Packages), or the ML-engineer path (Hands-On ML + Tidy Modeling). Resist the urge to read two branches at once, you will make slower progress on both than you would on one.</p>
<p>The path depends on what you already know and what your work actually requires. A PhD student in biostatistics should probably read ISLR before anything else, because their job is models. A frontend-turned-data-person at a startup should probably go straight to Tidy Modeling with R, because their job is pipelines. A research scientist building a package to share with colleagues should pair Advanced R with R Packages from the start, because their job is code reuse.</p>
<div class="callout callout-tip"><div class="callout-label">Tip</div><div class="callout-body"><strong>When to pay, when to read free.</strong> Almost every book above is legitimately free online, and the free version is identical to the print edition. Buy print only if you read better on paper, not out of a sense that "real" books are paid. The authors get royalties on the print edition either way, and the online version is not a pirated copy; it is the official one.</div></div>
<h2>Summary</h2>
<table class="table table-striped">
<thead>
<tr>
<th>Goal</th>
<th>Start here</th>
<th>Then read</th>
</tr>
</thead>
<tbody>
<tr>
<td>Complete beginner</td>
<td>R for Data Science (2e)</td>
<td>Pick one branch below</td>
</tr>
<tr>
<td>Data analyst (business)</td>
<td>R for Data Science (2e)</td>
<td>R Graphics Cookbook + R Cookbook</td>
</tr>
<tr>
<td>Statistician / researcher</td>
<td>R for Data Science (2e)</td>
<td>ISLR → Statistical Rethinking</td>
</tr>
<tr>
<td>R developer / package author</td>
<td>R for Data Science (2e)</td>
<td>Advanced R + R Packages</td>
</tr>
<tr>
<td>ML engineer</td>
<td>R for Data Science (2e)</td>
<td>Tidy Modeling with R + Hands-On ML with R</td>
</tr>
<tr>
<td>Visualisation specialist</td>
<td>R for Data Science (2e)</td>
<td>ggplot2 book + Fundamentals of Data Visualization</td>
</tr>
<tr>
<td>Time-series forecaster</td>
<td>R for Data Science (2e)</td>
<td>Forecasting: Principles and Practice (3e)</td>
</tr>
<tr>
<td>Text / NLP analyst</td>
<td>R for Data Science (2e)</td>
<td>Text Mining with R</td>
</tr>
<tr>
<td>Spatial / GIS analyst</td>
<td>R for Data Science (2e)</td>
<td>Geocomputation with R</td>
</tr>
<tr>
<td>Bayesian modeller</td>
<td>R for Data Science (2e)</td>
<td>Statistical Rethinking → BDA3</td>
</tr>
</tbody>
</table>
<p>The meta-lesson of this list: R's free-book ecosystem is so good that the question is never "can I afford the book", it is "can I finish the book." Pick one per stage. Finish it. Then branch.</p>
<h2>References</h2>
<ol>
<li>Wickham, H., Çetinkaya-Rundel, M., Grolemund, G., <em>R for Data Science (2nd edition)</em>, O'Reilly (2023). <a href="https://r4ds.hadley.nz/">Free online</a></li>
<li>Wickham, H., <em>Advanced R (2nd edition)</em>, CRC Press (2019). <a href="https://adv-r.hadley.nz/">Free online</a></li>
<li>Wickham, H., Bryan, J., <em>R Packages (2nd edition)</em>, O'Reilly (2023). <a href="https://r-pkgs.org/">Free online</a></li>
<li>Wickham, H., Navarro, D., Pedersen, T. L., <em>ggplot2: Elegant Graphics for Data Analysis (3rd edition)</em>. <a href="https://ggplot2-book.org/">Free online</a></li>
<li>Chang, W., <em>R Graphics Cookbook (2nd edition)</em>, O'Reilly (2018). <a href="https://r-graphics.org/">Free online</a></li>
<li>James, G., Witten, D., Hastie, T., Tibshirani, R., <em>An Introduction to Statistical Learning with R (2nd edition)</em>, Springer (2021). <a href="https://www.statlearning.com/">Free online</a></li>
<li>Boehmke, B., Greenwell, B., <em>Hands-On Machine Learning with R</em>, CRC Press (2019). <a href="https://bradleyboehmke.github.io/HOML/">Free online</a></li>
<li>Kuhn, M., Silge, J., <em>Tidy Modeling with R</em>, O'Reilly (2022). <a href="https://www.tmwr.org/">Free online</a></li>
<li>McElreath, R., <em>Statistical Rethinking (2nd edition)</em>, CRC Press (2020). <a href="https://xcelab.net/rm/statistical-rethinking/">Book site</a></li>
<li>Hyndman, R. J., Athanasopoulos, G., <em>Forecasting: Principles and Practice (3rd edition)</em>, OTexts (2021). <a href="https://otexts.com/fpp3/">Free online</a></li>
<li>Silge, J., Robinson, D., <em>Text Mining with R</em>, O'Reilly (2017). <a href="https://www.tidytextmining.com/">Free online</a></li>
<li>Grolemund, G., <em>Hands-On Programming with R</em>, O'Reilly (2014). <a href="https://rstudio-education.github.io/hopr/">Free online</a></li>
<li>Wilke, C. O., <em>Fundamentals of Data Visualization</em>, O'Reilly (2019). <a href="https://clauswilke.com/dataviz/">Free online</a></li>
<li>Lovelace, R., Nowosad, J., Muenchow, J., <em>Geocomputation with R (2nd edition)</em>, CRC Press (2025). <a href="https://r.geocompx.org/">Free online</a></li>
<li>Gelman, A. et al., <em>Bayesian Data Analysis (3rd edition)</em>, CRC Press (2013). <a href="http://www.stat.columbia.edu/~gelman/book/">Free online</a></li>
</ol>
<h2>Continue Learning</h2>
<ul>
<li><a href="Is-R-Worth-Learning-in-2026.html">Is R Worth Learning in 2026?</a>, the honest case for and against investing your reading time in R.</li>
<li><a href="How-to-Learn-R.html">Learn R in 12 Months: A Week-by-Week Roadmap</a>, the structured study plan that pairs with this reading list.</li>
<li><a href="R-vs-Python-for-Data-Science.html">R vs Python for Data Science</a>, if you are still choosing which language to invest your book-time in.</li>
</ul>
<aside class="continue-reading-block" data-continue-block><div class="cr-eyebrow">Continue Reading</div><a class="cr-link" data-continue-link href="#"></a></aside>
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