# Learning Statistics with R: A tutorial for psychology students and other beginners

Danielle Navarro, University of New South Wales

Copyright Year: 2018

Publisher: Danielle Navarro

Language: English

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## Conditions of Use

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CC BY-SA

## Reviews

This text, version 0.6, clocks in at over 600 manuscript pages (to date no version has been typeset) -- but the length is worth it to gain great coverage. Navarro covers not only everything you could expect to learn in a two-course sequence of... read more

This text, version 0.6, clocks in at over 600 manuscript pages (to date no version has been typeset) -- but the length is worth it to gain great coverage. Navarro covers not only everything you could expect to learn in a two-course sequence of undergraduate behavioral science statistics -- descriptive statistics, probability, analysis of variance, regression, and a very welcome chapter on Bayesian approaches-- plus how to implement a lot of data description and analysis in R, including step-by. It is a effective and useful mashup of these two topics. It does not have an index or glossary. I did not miss them, as it’s easy to use the search function to find the first instance of any term within the text, and Navarro is very good about defining and contextualizing new terms clearly as she goes.

Version 0.6 appeared free of errors as far as I could see. Furthermore, it did a nonpartisan job of framing debates that are throwing out a lot of light at the moment (such as the debates between proponents of frequentist vs. Bayesian approaches). Navarro’s approach is exemplary: she carefully contextualizes the issues at stake, explains why she feels as she does, and provides useful resources to follow up in more detail.

The content of the book seems up-to-date; indeed, at the moment I write this, the book has emerged as a common recommendation on social media for those hoping to learn R, so it’s clear that it is broadly seen as relevant. Navarro has already implemented several revisions, showing that necessary updates are easy and straightforward to incorporate. The core information in the book (statistics) is nearly timeless and should not need constant updating.

Clarity is absolutely paramount when one is attempting to learn a new skill -- or to learn two new skills, R and statistics, as is envisioned here. Navarro is an extremely useful guide to this process: it’s as if she takes your hand and walks you through step by step, so that learning these new skills is quite painless. Version 0.6 features clear and carefully chosen examples, no doubt honed over the prior versions. As noted above, new terms are clearly defined (almost obviating the need for a glossary or index).

Navarro has thought carefully about when/where and how to introduce new concepts, and then is thoughtful in using them consistently. She includes summary sections at the end of each chapter that are more helpful than ‘typical’ summaries, and useful sample R code is provided where appropriate.

Where possible, the book is modular. For example, a reader who is reasonably competent in statistics but using the book to learn R would have no trouble using the Table of Contents and closing chapter summaries to jump right to a specific section (say on graphing) that captures what they need to learn. A reader who is trying to use the book to learn statistics would be best advised to go in order, as later topics build on earlier ones and trying to do an end-run around this organization is ill-advised, but this is not a flaw of the text per se; it is inherent in learning about the topic.

The book is organized carefully and intentionally. There is a ‘received organization’ to most texts that introduce behavioral statistics, building in terms of complexity (for example, covering t-tests before moving to analysis of variance). The book follows this in the relevant sections (most of its second half). There was more scope for choice and design in the book’s first half, which introduces all of the topics the reader will need to understand before diving into implementing statistical learning in R. Here, Navarro has done a fantastic job of making choices that are friendly to the reader.

The text is rendered as a PDF, and everything is laid out quite cleanly, with helpful clickable links in the Table of Contents to each section.

The text was very competently copy-edited (despite being still being in, as it were, beta-testing) and did not appear to contain any unintentional errors.

I found nothing culturally insensitive or offensive in the text. The author is not based in North America, which was occasionally lightly apparent, which I consider all to the good.

A valuable asset of this book is its congenial tone. Navarro is chatty and funny, sometime even a bit irreverent, and the reader benefits quite a bit from this well calibrated conversational tone.

## Table of Contents

I. Background

- Chapter 1: Why do we learn statistics?
- Chatper 2: A brief introduction to research design

II. An introduction to R

- Chapter 3: Getting started with R
- Chapter 4: Additional R concepts

III. Working with data

- Chapter 5: Descriptive statistics
- Chapter 6: Drawing graphs
- Chapter 7: Pragmatic matters
- Chapter 8: Basic programming

IV. Statistical theory

- Prelude
- Chapter 9: Introduction to probability
- Chapter 10: Estimating unknown quantities from a sample
- Chapter 11: Hypothesis testing

V. Statistical tools

- Chapter 12: Categorical data analysis
- Chapter 13: Comparing two means
- Chapter 14: Comparing several means (one-way ANOVA)
- Chapter 15: Linear regression
- Chapter 16: Factorial ANOVA

VI. Other topics

- Chapter 17: Bayesian statistics
- Chapter 18: Epilogue
- References

## About the Book

*Learning Statistics with R* covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software. The book discusses how to get started in R as well as giving an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book.

## About the Contributors

### Author

**Danielle Navarro, PhD** is a computational cognitive scientist at the University of New South Wales. Her research focuses on human concept learning, reasoning and decision making. She is also interested in language and cultural evolution, cognitive development, and statistical methods in the behavioural sciences