Learning R

Learning R

One of our rationales for using R this semester is the range of support resources available that have been developed by the user community. As you will hear many times in class, almost every problem you encounter in R, someone else has also encountered. A few tips:

1. Start with R’s internal help. in your console, you can search for documentation for a given command by prefacing it with a question mark (e.g. ?mean). You can also type help() to access general help in R.

2. Next, turn to the internet. You might start with R Documentation or Stack Overflow.

3. Get help from our class community. A big portion of our time together in class will be devoted to learning from each other - part of that includes learning to troubleshoot problems. We’ll devote some synchronous time in class to working through challenges, and will supplement that with asynchronous means of communication via our course Slack channel. Ask questions early and often - there’s no need to unduly suffer through setbacks (although as you solve these you will learn deeply how to address similar problems in the future).

Whenever I get discouraged and want to quit something, I remember the words of my then 3 year-old after she puked carrots all over the living room floor: "I'm gonna need more carrots."

— Jessica Valenti (@JessicaValenti) January 18, 2021

While not set up as a support resource, there are other websites that may help you to extend your capacity in R.

• Some of the initial tutorials we’ll use to learn R come from Data Carpentry, especially their series on R for Social Scientists and Introduction to R for Geospatial Data.
• The swirl package is designed to teach you the basics of R in R.
• Check out other useful tutorials available at r4ds, STHDA.com, and R Bloggers.
• A “classic” framing document for new learners of R is The R Inferno.
• Another important framing document is Keiran Healy’s The Plain Person’s Guide to Plain Text Social Science.
• Tidy Tuesday: podcast that walks through data analysis using a new dataset weekly in R. Also check out Julia Silge’s blog and video tutorials that perform more advanced analysis on Tidy Tuesday data.

Good reasons to not be a Data Scientist:

- It is a lot of work
- Literally nobody will know what you're talking about
- In the end, your computer will be your only real friend

— 🔥 Kareem Carr 🔥 (@kareem_carr) January 22, 2021