Project Mosaic, University of North Carolina at Charlotte, Fall 2017
R has become a vital tool for statisticians, data scientists and (now) computational social scientists.
In this ten part series, I’ll cover critical R skills for social scientists including data acquisition, visualizations, machine learning, social network and text analysis.
Each workshop is open to UNCC graduate students, staff and faculty. To register, please sign up for each workshop at this link.
Participants new to R should attend the first three workshops that cover essential R skills.
Participants should bring their own laptops (if you want to run the code) with R/R Studio installed. See instructions below for details on how to get R/R Studio on your laptop.
| Ryan Wesslen | rwesslen@uncc.edu | @ryanwesslen |
| Workshops Meetings | Thursdays | 1:30 pm–4:00 pm | Friday 155 |
| Consulting Hours | Wednesdays | 1:00 pm–4:00 pm | Hickory Hall 11A |
| Thursdays | 10:00 am –12:00 pm | Hickory Hall 11A |
For consulting appointments, please see this link.
Walk-ins (aka day of) appointments are possible during these times but please email me first to confirm.
Each session is divided into three sections:
An introduction to R (basic skills, dataframes, tidyverse)
Intermediate R skills (RMarkdown, Git, Visualizations, APIs)
Computational social science tools (social network, machine learning and text)
Workshop signups are on a individual basis (e.g., each workshop must be registered). Given that workshops build off skills from the first section, participants in the later workshops are strongly encouraged to review materials from at least the first three workshops.
The first session assumes no knowledge of R or programming skills.
Later sessions will assume knowledge of materials from the first three workshops as well as probability, basic statistics and linear regression.
This course will use R, which is a free and open-source programming language primarily used for statistics and data analysis.
We will also use RStudio, which is an easy-to-use interface GUI for R.
Easiest Approach: Install Locally
Simply go to both links and download the latest versions of R and R Studio locally.
Alternative: Docker
Alternatively, you can use Docker either locally (Mac or Windows) or using a temporary instance at play-with-docker.com.
I strongly recommend this tutorial by Sean Ross to introduce Docker.
Once you have docker installed, run “docker run -d -p 8787:8787 rocker/tidyverse”. After the image download is complete, go to http://localhost#8787 link to open the browser.
If you’re using “play-with-docker”, you can open the browser by clicking the “8787” button that will appear near your IP. After clicking the link, go back to your terminal and copy your token (see the terminal) and press ok.
In your browser session, use the username/pwd rstudio/rstudio to open R Studio.
For Python users more use to Jupyter notebooks, see this Docker Hub repository. There are other alternatives so don’t feel obligated to use this Docker Hub.
I’ve created a Slack group named #rstats. You can join here: https://rstats-uncc.slack.com. You can sign up for a free (but limited) account to join.
The goal of these channels is to create a forum for general R questions. Ideally, if I can enlist a few other power R users, this will enable faster replies and help than just from me.
I’m still learning Slack so if you’ve used it before and have recommendations or ideas, please let me know.
Science should be open, and this course builds up other open licensed material, so unless otherwise noted, all materials for this class are licensed under a Creative Commons Attribution 4.0 International License.
The design of this course is based on materials by Pablo Barberá. The layout for this website was designed by Jeffrey Arnold.
For advanced users, I strongly recommend Pablo’s Fall 2017 USC Measurement Models and Statistical Computing Course, Benjamin Soltoff’s U of Chicago Computing for the Social Sciences Course or Jenny Bryan’s U of British Columbia STAT 545 Course.
Pablo’s materials reference work from:
My materials reference work from:
The source for the materials of this course is on GitHub at wesslen/fall2017-rworkshops
If you have any feedback on the course or find any typos or errors in this website go to issues, click on the “New Issue” button to create a new issue, and add your suggestion or describe the problem.