Chapter 2 covers basic concepts and shows how to use R to compute descriptivestatistics and create nice-looking plots.Chapter 3 is concerned with how to import and handle data in R, and how to performroutine statistical analyses.Chapter 4 covers exploratory data analysis using statistical graphics, as well as un-supervised learning techniques like principal components analysis and clustering. Italso contains an introduction to R Markdown, a powerful markup language that canbe used e.g. to create reports.Chapter 5 describes how to deal with messy data - including filtering, rearrangingand merging datasets - and different data types.Chapter 6 deals with programming in R, and covers concepts such as iteration, conditional statements and functions.Chapters 4-6 can be read in any order.Chapter 7 is concerned with classical statistical topics like estimation, confidenceintervals, hypothesis tests, and sample size computations. Frequentist methods arepresented alongside Bayesian methods utilising weakly informative priors. It alsocovers simulation and important topics in computational statistics, such as the boot-strap and permutation tests.Chapter 8 deals with various regression models, including linear, generalised linearand mixed models. Survival models and methods for analysing different kinds ofcensored data are also included, along with methods for creating matched samples.Chapter 9 covers predictive modelling, including regularised regression, machinelearning techniques, and an introduction to forecasting using time series models.Much focus is given to cross-validation and ways to evaluate the performance ofpredictive models.Chapter 10 gives an overview of more advanced topics, including parallel computing,matrix computations, and integration with other…