Please note: This course will be taught online only. In person study is not available for this course.
Johannes Karreth is Associate Professor in the Department of Politics and International Relations at Ursinus College near Philadelphia, Pennsylvania. He earned his Ph.D. at the University of Colorado Boulder and was previously Assistant Professor at the University at Albany-State University of New York. His research studies the impact of international actors and processes on politics, ranging from the macro-level (interstate conflicts, civil wars, trade disputes) to the micro-level (public opinion on globalization and immigration). His main interests in political methodology are Bayesian approaches to multilevel structures, and data visualization. He is the author of Incentivizing Peace: How International Organizations Can Help Prevent Civil Wars in Member Countries (with Jaroslav Tir; Oxford University Press 2018). His research has appeared in the Journal of Politics, Journal of Conflict Resolution, Journal of Peace Research, International Interactions, Comparative Political Studies, and other journals.
The course will introduce participants to the open-source statistical software R. R is a highly versatile software environment suitable for introductory and advanced quantitative social science and data analysis. The course offers participants a near-complete foundation to use R in other courses at ESS.
Specifically, the course will explore the following topics:
· Introduction to the R language and software architecture
· Use of the tidyverse suite of R packages
· Incorporating R code and document production (R Markdown)
· Workflow, reproducibility, and version control in R
· Data import and data management, including working with ”messy” datasets
· Data visualization
· Basic functions
Upon successful completion of this course, participants will have acquired intermediate R skills aligned with most ESS courses that rely on R. The course is suitable for researchers at the beginning of their quantitative training as well as researchers with advanced background in quantitative social science wishing to acquire a new, free, open-source, and highly versatile set of tools. A workflow for reproducible data analysis is also a core element of the course.
This is an introductory, highly accessible course. No prior knowledge of statistical or quantitative methods, or R, or computer programming is required. Participants with experience in other tools (e.g. SPSS, Stata, or SAS) will find the course structure helpful to transfer their skillsets into R.
Representative Background Reading
Since this is an introductory course, participants are not required to complete any prior reading.
Wickham, Hadley and Garrett Grolemund. 2016. R for Data Science. Sebastopol, CA: O’Reilly. Note: this book is available at no cost as an e-book at https://r4ds.had.co.nz/index.html.