Learn the basics of programming and data analysis in R in just 2 days online at #KOMEX2027, without any prior knowledge!
What Is This Course About?
The 2-day online course introduces students to the basics of programming in R: importing data, performing the necessary data cleaning and transformation steps, creating subsets or merging datasets with other external sources of data. It also teaches some basic applied tools for data analysis and hypothesis testing, such as comparing groups and regressions. Lastly, the course offers a brief introduction into data visualization (with ggplot2) and into workflows generating reports that combine code and text (with RMarkdown).
Learning Goals
After this course you will know:
- How to use RStudio for writing basic code and generating tabular/visual outputs
- How to load, export, merge and subset datasets in R
- How to run simple statistical models in R to compare groups or to fit OLS regressions
- How to create simple visualizations such as scatterplots, boxplots and line charts
- How to use R Markdown to seamlessly weave code and text together in reports
Recommended Readings for the Course
- Kieran Healy: Data visualization. A practical introduction. Princeton University Press
- Robert I. Kabacoff: R in Action. Data analysis and graphics with R and Tidyverse. Manning.
- Hadley Wickham, Garrett Grolemund, Mine Cetinkaya–Rundel: R for Data Science. O′Reilly.
Assignments for the Course
Synchronous sessions will involve quick exercises (not graded), done both individually and in pairs.
Schedule
Day 1:
- 10:00 –– 11:30 Course
- 11:30 –– 11:45 Break
- 11:45 –– 12:45 Course
- 12:45 –– 14:00 Lunch break
- 14:00 –– 15:30 Course
- 18:00 –– 19:00 Office hour
Day 2:
- 10:00 –– 11:30 Course
- 11:30 –– 11:45 Break
- 11:45 –– 12:45 Course
- 12:45 –– 14:00 Lunch break
- 14:00 –– 15:30 Course
In addition, students should plan for sufficient time to do the assigned daily homework and read the assigned literature in advance.
Who Is Your Instructor?
Daniel Kovarek is a Postdoctoral Research Fellow at the European University Institute, Florence, Italy. He studies political behavior at the voter and the elite level, applying surveys, experimental and big data methods. Daniel has been teaching a wide variety of graduate-level courses on applied statistics, research design, data visualization and programming. You can reach him at @kovarek.bsky.social
None. The course assumes no prior knowledge of statistics, programming, or coding. Participants should have R and RStudio installed on their computer before the start of the course, using the links below.
https://cran.r-project.org/mirrors.html
