13. Library

Programming

A list of books to help with R programming. Note that R is based on the S language, which is why the S books are included here.

R Foundations

• Hands-on programming with R
• An introduction to R
• What they forgot to teach you about R
• Advanced R
• R packages
• Happy Git and GitHub for the useR
• The R language definition
• Extending R
• The R inferno

S Language

• S programming
• Modern applied statistics with S

ggplot2

• ggplot2: Elegant graphics for data analysis

R Markdown

• R Markdown: The definitive guide
• R Markdown cookbook
• bookdown: Authoring Books and Technical Documents with R Markdown

Shiny

• Mastering Shiny
• Engineering production-grade Shiny apps

JavaScript

• JavaScript for R

Other

• The missing semester of your CS education

Data Science and Statistics

A list of books covering various topics in data science and statistics.

General

• R for data science
• Modern data science with R
• Statistical thinking for the 21st century
• Introduction to modern statistics
• Tidy modelling with R
• Designing Experiments and Analyzing Data: A Model Comparison Perspective
• All of statistics
• All of nonparametric statistics
• Advanced data analysis from an elementary point of view
• Computer age statistical inference
• Statistical Inference as Severe Testing

Methodology

• The open handbook of experience sampling methodology

Communication

• Telling stories with data
• Teaching tech together

Data Visualization

• Fundamentals of data visualization
• R graphics cookbook

Mathematics

• A concise introduction to pure mathematics
• Linear algebra and its applications with R
• A mathematical primer for social statistics
• Math for programmers

Probability

• Introduction to probability for data science
• Probability and statistics with R
• Probability and statistics for data science

Simulation

• Introducing Monte Carlo methods with R
• Handbook of Markov Chain Monte Carlo

General and Generalized Linear Models

• Data Analysis Using Regression and Multilevel/Hierarchical Models
• Regression and other stories
• Regression modelling strategies
• Regression diagnostics
• An R companion to applied regression
• Handbook of Regression Modeling in People Analytics
• Richly parameterized linear models

Generalized Additive Models

• Generalized additive models: An introduction with R
• Semiparametric regression

Bayesian Inference

• Statistical rethinking
• Bayesian data analysis
• Doing Bayesian data analysis
• Doing Bayesian data analysis in brms and the tidyverse
• Stan documentation

Statistical Learning

• An introduction to statistical learning
• The elements of statistical learning
• Deep learning
• Applied predictive modelling
• Introduction to data mining

Latent Variable Analysis

• Measuring the mind
• Principles and practice of structural equation modelling
• Longitudinal Structural Equation Modelling: A comprehensive introduction
• Confirmatory factor analysis for applied research
• Finite mixture models

Cluster Analysis

• Handbook of cluster analysis

Survival Analysis

• Applied survival analysis using R
• Modelling survival data

Time Series Analysis

• Time Series: A Data Analysis Approach Using R
• Time Series Analysis and Its Applications: With R Examples

Graph Theory

• Handbook of graphs and networks in people analytics

Text analysis

• Text mining with R
• Supervised machine learning for text analysis with R

Geospatial Analysis

• Geocomputation with R

Meta-Analysis

• Doing Meta-Analysis in R