Bibliography

Further reading

Below is a list of some highly recommended books, that either partially overlap with the content in this book or serve as a natural next step after you finish reading this book. All of these are available for free online.

  • The R Cookbook (https://rc2e.com/) by Long & Teetor (2019) contains tons of examples of how to perform common tasks in R.
  • R for Data Science (https://r4ds.had.co.nz/) by Wickham & Grolemund (2017) is similar in scope to Chapters 2-6 of this book, but with less focus on statistics and greater focus on tidyverse functions.
  • Advanced R (http://adv-r.had.co.nz/) by Wickham (2019) deals with advanced R topics, delving further into object-oriented programming, functions, and increasing the performance of your code.
  • R Packages (https://r-pkgs.org/) by Wickham and Bryan describes how to create your own R packages.
  • ggplot2: Elegant Graphics for Data Analysis (https://ggplot2-book.org/) by Wickham, Navarro & Lin Pedersen is an in-depth treatise of ggplot2.
  • Fundamentals of Data Visualization (https://clauswilke.com/dataviz/) by Wilke (2019) is a software-agnostic text on data visualisation, with tons of useful advice.
  • R Markdown: the definitive guide (https://bookdown.org/yihui/rmarkdown/) by Xie et al. (2018) describes how to use R Markdown for reports, presentations, dashboards, and more.
  • An Introduction to Statistical Learning with Applications in R (https://www.statlearning.com/) by James et al. (2013) provides an introduction to methods for regression and classification, with examples in R (but not using caret).
  • Hands-On Machine Learning with R (https://bradleyboehmke.github.io/HOML/) by Boehmke & Greenwell (2019) covers a large number of machine learning methods.
  • Forecasting: principles and practice (https://otexts.com/fpp2/) by Hyndman & Athanasopoulos, G. (2018) deals with forecasting and time series models in R.
  • Deep Learning with R (https://livebook.manning.com/book/deep-learning-with-r/) by Chollet & Allaire (2018) delves into neural networks and deep learning, including computer vision and generative models.

Online resources

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