Microcredential ekomex A Basic Introduction to R for Beginners

Want to learn a powerful statistical software R? If so, join our 2-day hands-on online course to learn how to use R for data management, visualization, and descriptive statistics!

What Is This Course About?
This 2-day virtual course introduces the open-source statistical software R for beginners. It is designed for students without prior experience of R/RStudio. It teaches the basics of R and RStudio, namely, how to perform basic descriptive statistics and create visualizations in R (e.g., histograms, scatterplots, etc.). The course will also cover basic data management with R.

As an alternative: Join our 2-day course for beginners to learn how to use the popular statistical software R for analyzing data!

Learning Goals
After this course you will know:

• Basics of R and RStudio, basic functionality
• How to write code in R for basic tasks
• How to manage basic data with R
• Simple descriptive statistics and visualizations of data with R

Assignments for the Course
Short in-class assignments and assignments for self-learning will be provided. They will require independent implementation of code in R based on what will be taught in class.

Schedule

• Day 1
  09.00-13.00 – Lecture with script & exercises (on Zoom)
• Day 2
  09.00-11.00 – Self-guided exercises (independent learning)
  11.00-13.00 – Small-group tutoring on Zoom (solutions to exercises & troubleshooting)
  Group 1: TA 1
  Group 2: TA 2
  Group 3: TA 3

Recommended Readings for the Course

• Oana, I.E., Schneider, C.Q. and E. Thomann. 2021. Qualitative Comparative Analysis (QCA) Using R: A Beginner’s Guide. Cambridge University Press—Online appendix, chapter “introduction to R”
• Cotton, R. 2013. Learning R: a step-by-step function guide to data analysis. O'Reilly Media
• Grolemund, G., and H. Wickham. 2017. R for Data Science. O’Reilly Media

Who Is Your Instructor?
Dr. Karina Shyrokykh is Associate Professor of International Relations at Stockholm University who specializes in the European Union’s promotion of human rights, democracy and climate norms’ externally. In her research, she uses both quantitative text and numeric analyses and machine learning techniques. Publications include “Climate change on Twitter: Implications for climate governance research”, “Short text classification with machine learning in the social sciences: The case of climate change on Twitter”, “European Development Co-operation via Technical Assistance: An Outside-in Perspective”, and “Human rights sanctions and the role of black knights: Evidence from the EU’s post-Soviet neighbours”. Karina Shyrokykh teaches quantitative research methods including quantitative text analysis (with R) at the BA, MA, and PhD levels.