This is a repository for:
R U ready 4 R? Introduction to Analyzing Educational Internet Data Using R
Friday, 03/06/2020, 1-5pm
In this workshop, we help members of the Students, Social Media & Schools research group at Florida State University learn how to use the R statistical software to analyze Internet data that is relevant to educational research. In particular, we focus on learning how to get started with R, how to analyze social media data, and how to begin one’s own analysis. This workshop promises to support participants to become more confident in their ability to engage in analyzing complex data sources collected from digital sources.
This workshop is for beginning and experienced scholars interested in using new statistical and computational research methods in their work. More specifically, the level of instruction will be suitable for researchers—from a wide variety of scholarly and professional backgrounds—looking for an introduction to R. There are no suggested prerequisites for attending, but we do require that participants bring a computer (Mac, Windows, or Linux are all suitable for working in R) to the workshop. To get the most from the workshop, install R and R Studio ahead of time (instructions below).
In this workshop, we help participants learn how to use the R programming language and statistical software to analyze Internet data that is relevant to educational research. R is becoming especially widely-used in educational research: At the 2019 American Educational Research Association Annual Meeting, for example, five professional development and training courses include the use of R. R is increasingly prioritized over other statistical software not only because it provides researchers with access to a wide range of powerful statistical techniques and tools at no cost and with considerable rigor but also because R is also useful for collecting research data, publishing results, and inviting reproducibility. As members of the AECT community increasingly engage in analyzing complex data sources collected from digital sources (Kimmons, Carpenter, Veletsianos, & Krutka, 2018; Romero-Hall, Kimmons, & Veletsianos, 2018; Rosenberg, Greenhalgh, Koehler, Hamilton, & Akcaolgu, 2016; Greehalgh, Staudt Willet, & Rosenberg, 2018; Veletsianos, Kimmons, Larsen, Dousay, & Lowenthal, 2018; Xing & Gao, 2018), R is especially relevant to those seeking to follow their example. Furthermore, the use of R—an open source software—is especially timely in light of conversations in the AECT community about the value of open scholarship.
Although R is becoming increasingly widely-used, R has a steep learning curve for beginners, calling for additional opportunities for professional learning for educational researchers. In this session, we provide support and guidance for using R for exploring Internet data—a task that R is particularly well suited for. We focus on the analysis of a dataset that is likely of interest to many AECT participants: social media data collected from educationally-relevant Twitter hashtags.
We will use a project-based learning approach to provide an overview of the use of R for educational research involving Internet data. By doing so, this session provides inspired professional learning related to learning how to use R to analyze the kinds of complex data sources that members of the AECT community increasingly seek to analyze. In doing so, this session will support participants to not only learn about R, how to set it up, and how to use it, but will also help participants to develop the confidence to access and analyze quantitative data.
The workshop objectives are as follows:
- Learn how to get started with R using open-source (and freely-available tools), including the R software and add-on packages.
- Learn how to use R to analyze research data: first, by running the commands from an already-completed research project, then by carrying out and document one’s own analysis using a dataset provided by the organizers.
- Learn several advanced uses of R, including: social network analysis, text analysis, and machine learning methods.
- Begin one’s own analysis of educational Internet data.
- Become more confident in one’s ability to access and analyze complex, quantitative sources of data.
- Understand how to learn more with respect to using R for research purposes.
If you have issues with any of the installations below (and don’t worry, they’re all very small and won’t take up much space on your computer) please contact me (staudtwi@msu.edu) and I can try to work with you to get it resolved before the workshop.
To download R:
- Visit this page to download R: https://cran.r-project.org/
- Find your operating system (Mac, Windows, or Linux)
- Download the 'latest release' on the page for your operating system and download and install the application
To download R Studio:
- Visit this page to download R studio: https://www.rstudio.com/products/rstudio/download/
- Find your operating system (Mac, Windows, or Linux)
- Download the 'latest release' on the page for your operating system and download and install the application
If you are having trouble downloading R or RStudion, don't worry, you're not alone. In fact, in the workshops we've run, we have seen enough new R users struggle just to get going that we now suggest using RStudio Cloud as an alternative.
This link will take you to the RStudio Cloud project for this workshop.
Once you have navigated to this webpage, log in using a Google or Github account. Then, create a permanent copy of the project in your own workspace (see the prompt at the top of the page guiding you to do this.
From here, you can write and run R code exactly as your would through RStudio on your computer. The downside is that opening and loading projects are slowed by Internet connection speeds. The upside is that you don't have to worry about R and RStudio downloads, and your computing power is running off of RStudio Cloud's servers, not your local machine. Once you start running advanced statistical models, computing power bgins to make a huge difference.
This link will take you to the slide deck for the workshop.
demo-doc.Rmd is an interactive R Markdown file that accompanies the presentation.
Much of the content of this workshop follows a workshop we facilitated at the AECT 2019 annual convention.
Thank you to Emily Bovee for co-developing the workshops this workshop is adapted from: https://github.com/jrosen48/MTSU-workshop and https://github.com/jrosen48/MSU-workshop-2019
Parts of the content for this workshop are also adapted from:
- The Data Science in Education Using R book by Emily A. Bovee, Ryan A. Estrellado, Jesse Mostipak, Joshua M. Rosenberg, and Isabella C. Velásquez to be published by Routledge in 2020
- An American Educational Research Association 2019 annual meeting workshop on Reproducible and transparent research with R by Daniel Anderson and Joshua Rosenberg
Finally, parts of the content for this workshop are inspired from content associated with the Data Science Specialization for UO COE (led by Daniel Anderson).