I kept adding resources until things got too unwieldy and the Course Resources page was becoming too large 😁. So I decided to split things into two pages. The Course Resources page lists materials directly related to and used/mentioned in the course. This page lists a lot of other resources that are not heavily featured in the course, but that might be useful and interesting. Everything listed here is broadly related to the course topic, i.e. the resources focus on Data Science/Stats/R Coding/GitHub/etc. For even more materials, see the links to various lists by others at the end of this document.

Most materials described below are (should be) freely available online. For better or for worse, a lot of the resources I list below are dynamic and ever changing. That means occasionally links might not work, sites go offline, chapters in online books get re-arranged, etc. If any link does not work and you can’t access the materials for some reason, let me know so I can update this document.

I placed them into categories according to main topic, but there is a lot of overlap. Many R coding resources focus on data analysis, and most data science resources I list focus on R.

I am familiar with some, but not all of these resources. Sometimes I just took a quick glimpse to decide if it was worth including them here. If you find particular resources especially helpful or unhelpful (both listed and not listed), I’d love to receive feedback.

General Data Science

  • Cloud Based Data Science - a nice online course covering many of the topics we cover at a somewhat more basic level. You can decide what to pay for it, including getting it for free. That course used to be called Chromebook Data Science and seems to be now updated and rebranded as Cloud Based Data Science. It is done by Jeff Leek and his team. You’ll run into Jeff multiple times throught this course.
  • “Data Science Specialization” on Coursera. One of the first comprehensive online offerings. Coursera has gotten more restrictive over the years, but I think you can still get each course for free.
  • Stat 545 is the name of Jenny Bryan’s previous course on Data Wrangling and exploratory analysis. She has since turned this into a stand-alone website/book/course/resource. Covers a bit similar topics to the R4DS book, but with a different emphasis and from a more comprehensive and advanced perspective.
  • Advanced data analysis for the social sciences
  • Advanced Data Science version 1 and version 2
  • Data science for economists
  • STOR 390 - Introduction to data science
  • Kaggle (owned by Google) is a website that hosts data analysis competitions. Everyone can participate and compete for - sometimes rather large - prizes. The website also has a lot of good datasets and code, as well as other resources related to data analysis. Definitely worth checking out.
  • I used to recommend and use Datacamp, an online platform that has interactive courses teaching R and Data Analysis (and other topics). Unfortunately, the company dealt rather poorly with a case of sexual harassment. They also became much less academic-friendly, their student discount is much less nice than it used to be, and apparently they recently sued R Studio (a company I think highly of). I’m not sure what the current status is on both their company culture and their academic/student-friendliness, but I have basically moved on. Too much other good stuff available to bother further.
  • Exploratory Data Analysis - materials for an online course teaching exploratory data analysis using R, taught by John Paul Helveston.
  • The journal PeerJ has a collection of articles on the topic of Practical Data Science for Stats. A lot of the papers in that collection use R.
  • Roger Peng and Hillary Parker have a Stats and Data Science related podcast called Not so standard deviations.
  • A few individuals, most notably Roger Peng, Brian Caffo and Jeff Leek have books on Leanpub related to R and data science. Most of the books have a minimum price of zero and are worth looking at. If you feel any of these Leanpub books are worth paying for, go ahead and do so. But I am fairly sure those authors do not rely on the book royalties for their living 😄, so if you can’t or don’t want to pay, getting them for free is ok. As a side note, Leanpub uses Markdown, which means if you write a report in (R)Markdown and want to turn it into a (self)-published book, it is rather easy to do with Leanpub. That’s how those individuals made their books, as spin-offs from their RMarkdown course materials.
  • ModernDive - Statistical Inference via Data Science - another good recent book covering data analysis with R.
  • Introduction to Modern Statistics is a free online textbook teaching statistics using R in a modern framework.
  • Reproducible Medical Research with R - free online book showing how to use R to do basic analysis.
  • Data Science for the Biomedical Sciences - another free online textbook. Part of a workshop, but can also be used for self-learning.
  • Elements of Statistical Learning - is a somewhat advanced book on statistical/machine learning. Not useful as introduction, but a potentially good reference.
  • Jesse Mostipak, aka Kiersi streams data science sessions on Twitch.
  • Nick Wan is another date science Twitch streamer.
  • David Robinson has videos of screencasts showing him digging into datasets from TidyTuesday and other sources.
  • Andrew Heiss has a lot of good materials related to R and data analysis on his website.

Data Sources and Wrangling

Data Visualization

  • Data Visualization - comprehensive materials for an online course on data visualization in R, taught by Andrew Heiss.
  • A great free book which discusses the principles of good data visualization is Fundamentals of Data Visualization. The book is not R specific (and doesn’t show R code, but all figures are made in R). * Data Visualization - A practical introduction is a fairly complete free online draft of a book by the same name. It provides a general introduction to making good graphs, and the R code for the figures is shown.
  • Flowing Data is a website with a lot of cool information on how to make great data visualizations. Some content is free, other parts are not.
  • The Esquisse R package lets you quickly make ggplots in an interactive manner. Very good to get started on some exploratory plots. You can take the ggplot code you generated and tweak further.
  • Graphics Principles is a website that gives general tips for effective visual communication. Examples using R are also provided.

Pitfalls and best practices in data analysis

Researcher degrees of freedom (p-hacking)

  • The concept of Researcher degrees of freedom, which is related to Data Dredging and p-hacking are important ideas to keep in mind when doing a data analysis. Note that this issue is often cast in the language of p-values since those are still (unfortunately) the most common approach to statistical analyses. But the concept applies even if one doesn’t use p-values.
  • You can find a fun hands-on exploration of the potential problem of researcher degrees of freedom in this 538 visualization and another choose-your-own adventure story here. * For further discussions of this general problem, see e.g. this article from 538 (which goes with the hands-on example just mentioned) or this article by Gelman and Loken, with a closely related article here.
  • This paper provides a nice and easy to follow illustration how researcher degrees of freedom, combined with incomplete reporting, can lead to apparently nonsensical results. The study is a (fake) psychology study, but everything applies in general and it is easy to follow.
  • Not surprisingly, xkcd has also covered the topic of p-hacking.

Reproducible research

  • This study provides a nice glimpse at the problems that still exist when trying to reproduce/replicate prior studies by re-running the code.
  • R Workflow is an online book describing how to do reproducible research using the R ecosystem and the still fairly new Quarto framework.
  • For more Quarto, the Awesome Quarto repository has a nice curated list of links to resources.

General Statistical Analysis

  • Common statistical tests are linear models is a website that illustrates how many standard statistical tests are equivalent to certain types of linear models. Very useful if you are bewildered by the zoo of statistical tests and wonder how they are related to regression models.
  • Library of Statistical Techniques is a collection of short explanations and code covering a range of different statistical topics. More general data analysis topics, e.g. wranging and visualization, are also covered.
  • Common statistical myths and how to push back - this is a collection of links to references that address/refute common statistical myths (i.e., things that are wrong but that are commonly done/said/written in the scientific literature anyway.)

Bayesian Analysis

While we don’t cover Bayesian methods in this course, I personally find them very useful and compelling. Here are some resources that could be worth checking out if you want to learn some Bayesian statistics/data analysis.

  • Statistical Rethinking by Richard McElreath. My favorite stats book (Bayesian or otherwise). It starts slow but goes pretty far. The book is not free (but worth the price), but there are resources on the website which are free.
  • Bayes Rules by Johnson, Ott and Dogucu. Very hands-on introduction to Bayesian statistics. The online version is free.

Causal Analysis

Unfortunately, as part of this course, we cannot cover the broad and important topic of causal analysis. However, it is a topic worth learning. If you are interested, here are a few basic references that can get you started. Most of the ones listed are fairly non-technical and thus beginner-friendly.

Machine Learning

  • Machine Learning University (MLU) is an educational offering from Amazon with several nice tutorials covering important ML-related topics. It also includes very basic statistical concepts such as linear/logistic regression.

R coding

  • R Studio primers are a great collection of lessons covering the basics of R coding and data analysis. I highly recommend them.
  • R Studio education is a fairly new website that I expect will contain an increasing collection to all kinds of useful teaching resources related to R and Data Science. Check their Learn section for links to resources.
  • Swirl is a package that teaches R inside R. Especially complete beginner students have found it to be a nice start since it provides very encouraging feedback. The downside is that all code writing happens interactively in the R console, which is not the way one writes real code. It’s still worth checking out if you want to get some more direct, hands-on R practice. Unfortunately, the package seems dormant and hasn’t been updated in a few years (but what’s in there probably still works?)
  • Ready for R - materials for a basic introductory online R course taught by Ted Laderas.
  • Modern R with the tidyverse - online book that provides a very nice introduction to important concepts of R coding with a focus on data analysis.
  • Intro to Programming for Analytics - materials for an online course teaching intro to programming with R, taught by John Paul Helveston.
  • Efficient R programming contains a lot of good tips and tricks towards writing better code.
  • R for Epidemiology - an introduction to R with a focus on tasks that are often used in Epidemiology/Public Health.
  • Tidy Modeling with R are the beginnings of a hopefully great and comprehensive book that describes analysis/modeling using the tidyverse set of packages.
  • Learning statistics with R - I’ve not read/used it, but heard from others who like it.
  • What They Forgot to Teach You About R is the beginning of an online book which covers some topics rarely found elsewhere. As of this writing, the book is fairly incomplete, but still worth checking out. Especially the first several chapters and the debugging R code sections are worth learning/reading.
  • The Introverse R package is providing more novice-friendly help files for important tidyverse functions. If you struggle with the default help file for a function, check out this package.


  • The Software Carpentry has a great introductory course that walks you through the basics of Git (and GitHub) step-by-step. This is useful if you want to know what exactly is going on, even if you mainly use a graphical interface for your Git/GitHub work. The whole course materials are online.

Lists and other sources

  • Big Book of R - a website listing and summarizing several hundred books, many free, related to R and Data Science. If you are looking for a resource on a specific topic, this is a good place to check.
  • By now, there are hundreds of books on R and Data Science available online. Many of these books are written in bookdown, a version of R Markdown. You will learn all about it in this course. It is worth checking out the main bookdown website as well as the archive list and scrolling through the list of books. Some of the books you can find there are very good. Of course, there is also a good bit of “noise”.
  • Another recent list of good R and Data Science resources can be found here.
  • Teach Data Science - a blog with short, informative posts on various aspects related to data science using R.
  • Machine Learning - an online reference (almost book) which nicely explains some of the basics of machine learning.
  • RStudio has a collection of materials for data science.
  • R Studio cheatsheets are 1 page reference documents that quickly let you see how you use specific R packages or do certain tasks. A very useful resource, definitely check them out.
  • A meta-cheatsheet - this is a cheat-sheet showing you links to different R packages and their cheat-sheets for specific tasks. A nice overview document, developed by the folks at business-science.io.
  • I created lists related to R and Data Analysis (as well as other topics). You can find all resource lists here. (These lists are works in progress, and some are better/more useful than others. Feel free to send me links/resources to include).