Due by 11:59 PM on Friday, December 7, 2018

The objectives of this class include “Be curious and confident with data,” “Feel comfortable with R,” and “Communicate the results of your analyses in accessible language.” To help you with this, you will write a code-through tutorial of some statistical, data scientific, or data-based principle or example.

One of the reasons R is so popular is because the R community is exceptionally generous and open and sharing.So are Python and other modern open source languages too.

The internet is full of tutorials and code-throughs where people explain how to do something interesting with R.

You will complete this assignment on your own, but you can get help from others (but you can’t all write about the same topic).


Here’s what you need to do:

  1. Decide what statistical or data scientific concept you want to demonstrate. Perhaps you want to show why sample size is important, or demonstrate how to calculate summary statistics for groups, or how to make nice plots, or how to run an interpret a regression model. This is entirely up to you!

  2. Write a tutorial that walks a reader through a demonstration of this concept. Your problem sets have been focused on a final product. In this assignment, here the focus is didactic. Show intermediate steps, explain why you do what you do, etc. The reader should learn something new from this tutorial.

    There’s no word count here, but there should be several steps and examples of the concept you’re demonstrating.

    You should write this in R Markdown, since that allows you to mix prose and code.

    Your demonstration must be reproducible. Either use built-in datasets like iris or mtcars, or point the reader to a CSV file from the internet and incorporate read_csv() into your code-through.

  3. Knit the final version of your tutorial as a PDF, Word file, or HTML file.

  4. Upload the knitted document to Learning Suite.

    If you’re feeling especially brave, post the HTML version of your document to some public website (Medium, a personal blog, etc.), tell me, and I’ll promote it in the #rstats community on Twitter. You’ll probably get super rich and famous if you do this.

I will grade this using a check system.


The code examples I include on each week’s class page are a good example of didactic tutorials. In addition, the R-Weekly e-mail newsletter includes dozens of these kinds of tutorials every week, and Mara Averick (chief tidyverse advocate at RStudio) regularly tweets out links to different posts as well. Here are some others examples to give you a jist of what you can do: Yours won’t be nearly as complicated as these, by the way. Nor do they need to be.