Learning statistics with R: A tidy tutorial

This repository contains source code for a tidyverse-focused revision to Learning statistics with R. A preview of the book can be found at https://djnavarro.github.io/tidylsrbook (though the eventual home for the online version of the book will be https://learningstatisticswithr.com). The current plan for the book is roughly:

• Part 1: Working with data
• Part 2: Learning from data
• Part 3: Tools of the trade
• Appendices

Part 1. Working with data

Chapters in this section start from the presumption that a data set already exists, and the analysts goal is to organise it, tidy it, describe it and visualise it.

Part 2: Learning from data

Chapters in this section start from the presumption that the analyst has a more ambitious goal than describing data, and introduces tools for making sound inferences from data.

• Statistical learning: orthodox and Bayesian (t-test and chi-square as examples; cover bootstrapping, cross-validation, bias-variance etc here)
• Linear modelling in R (linear regression, ANOVA)
• [haven't decided what else yet]

Part 3: Tools of the trade

Chapters in this section discuss other methodological tools that are central to psychological research

• Reproducibility, documentation and version control [git, GitHub, OSF, reprex, RMarkdown]
• Measurement and the design of experiments [sneak some philosphy of science in here]
• Implementing behavioural experiments in R [hm. Shiny, interactive graphics devices, maybe get off my lazy arse and write wrappers for jsPsych]

Appendices

• Data structures in R
• Programming in R
• Probability theory