Online R Challenge

Rofficehours2

Using the R programming language can be a great tool in a variety of disciplines for data analysis and visualization. To help you get started, we’ve compiled several video tutorials, a list of resources and a space to chat about what you’re doing in R. Check out our page and join the conversation!

Getting started with R

Learn how to install R and RStudio in this tutorial with Elizabeth E. Esterly, a graduate student in computer science at the University of New Mexico.

Setting up your RStudio workspace

Learn your way around the RStudio environment and how to get it set up and ready to handle some serious data in this tutorial with Elizabeth E. Esterly, a graduate student in computer science at the University of New Mexico.

Focus on variables

Focus on the basics of assignment and variables in R in this mini-tutorial by Elizabeth E. Esterly, a graduate student in computer science at the University of New Mexico.

Focus on data import

Focus on the basics of data import in R in this mini-tutorial by Elizabeth E. Esterly, a graduate student in computer science at the University of New Mexico.

We have a workshop coming up!

Join us Dec. 4 for Tame Your Data With R Part 2! Register here. Please install R and RStudio on your computer ahead of time. Completing part one of the workshop is recommended, but not necessary.

We also offer weekly office hours.

Stop by the Advance at UNM space from 11 to 2 on Thursdays to have computer science grad student Elizabeth E. Esterly answer your questions.

 

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HELPFUL RESOURCES

Here are our top picks for you on using R:

Click here first for R language information and then here to download it.
 
And learn about data visualizations in R here.
 
See this document for the workshop materials from Workshop 1 on Oct. 23rd. 
Online R Challenge

Join our conversation

We’re here to chat about your work with data and R. Each week, we’ll ask a different question or address a new part of R. You can ask your questions too! UNM computer science graduate student Elizabeth E. Esterly will address your comments below.

 

Week of Nov. 13: What would you like to see in the tutorials section?

 

Week of Oct. 23: What got you interested in R?

 

Week of Oct. 9: What’s the story you want your data to tell?

 

Leave your thoughts in the comment section.

6 Comments

  1. Justin
    October 6, 2017 @ 9:14 am

    Hello, I was wondering R can work with very large .csv files, say in the multiple GB range, or if there are any tricks to handling them.

    Reply

    • advance
      October 8, 2017 @ 11:41 am

      Yes! R can work with large .csv files–up to about 10 GB, or the size of your computer’s memory (RAM), whichever is smaller. (For files larger than this, you’ll need to use specialized software like Hadoop.) But loading such a large dataset can be quite slow. To speed up the process, you can try using the fread function from the data.table package; more info can be found in the official R documentation here.
      I hope this helped!
      –E.

      Reply

  2. Sarah
    October 8, 2017 @ 1:11 pm

    I was wondering if R would be good for plotting or can it just do statistical analysis?

    Reply

    • advance
      October 9, 2017 @ 7:55 pm

      You can do some beautiful plots in R–check out the link we posted above on data visualization for an example. You can get the most out of R plotting by using the free ggplot2 library, which is part of a group of libraries called the Tidyverse. But R is strong for basic plotting with its built-in functions as well. For some hands-on instruction on how to get started with plotting in R, I’d encourage you to join our workshop series. Thanks for your question!
      –E.

      Reply

  3. Ally
    October 25, 2017 @ 11:26 am

    Hi,
    What’s the benefit of using R over other programming languages, such as Python?

    Reply

    • advance
      October 26, 2017 @ 10:46 am

      Hi Ally,
      Thanks for your question. Each language has its strengths and weaknesses, and each programmer has their own (often strongly-held!) opinions on what those are. Here’s my two cents: the main difference between Python and R is that Python is a programming language that came to be widely used by scientists of all kinds and, as such, acquired a lot of statistical and analytical tools as needed; R, on the other hand, was created by statisticians and scientists for analysis and developed richer programming capabilities as needed. If you need to create complicated custom data structures, Python may a better choice. If your data follows a traditional tabular structure, R may be a better choice. -E.

      Reply

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