Data Analysis with R
Overview
The purpose of this course is to provide a practical introduction to the programming language R for researchers in any field. We are convinced that R is a powerful tool that can ultimately make your life easier by enabling efficient solutions to your data analytical problems. Also, R is completely free. Who doesn’t like that?
This course aims to facilitate the participant’s first steps in R and equip them with the tools and understanding to expand their technical know-how according to the needs of their specific research. It is neither a computer science nor a methodological course, but aimed at the practical needs of empirically working researchers.
You might think of R as an overly complicated status symbol for ambitious quantitative researchers. This is not quite true anymore as R has become much more user-friendly over the last years. Think of R is your friend and helper if you are interested in doing: insightful graphs, regression analysis, QCA, quantitative text analysis, web-scraping, data visualization, systematic hand-coding of documents, formal modelling, etc.
While this course is most likely to attract people with solid statistical training who usually use Stata or SPSS, it can easily be attended by a broader audience. All you really need to know is [I] the structure of a typical dataset in the social sciences (rows are observations, columns are variables) and [II] the logic of dependent and independent variables.
You will learn how to use R to handle your data, describe it, analyse it (regressions), and transform it into beautiful graphs. At the end, you will have an idea of what R can do, and the resources to teach yourself more where needed.
Learning outcomes
By the end of the course students you shall be confident and equipped with all the knowledge required to perform analytical activities in R. Specifically,
- Understand the fundamental syntax of R through readings, practice exercises, demonstrations, and writing R code.
- Apply critical programming language concepts such as data types, iteration, control structures, functions, and boolean operators by writing R programs and through examples
- Import a variety of data formats into R using RStudio
- Prepare or tidy datas for in preparation for analysis
- Query data using SQL and R
- Analyze a data set in R and present findings using the appropriate R packages
- Visualize data attributes using ggplot2 and other R packages.