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This crash course covers all aspects of conducting quantitative research. You will learn how to import data, how to perform the necessary data cleaning and transformation steps, create subsets or merge your dataset with other external sources of data. Some of these steps might be a bit different depending on the type of data you intend to work with (numeric, character, categorical, date, etc.), but the course discusses these differences and shows examples of working with all of them. Knowing that some of you will come with some experience with other statistical software, we cover how to import and export data to proprietary (STATA, SPSS, etc.) formats.
The course does not cover statistical theory, but it will go through some applied tools for data analysis and hypothesis testing, such as comparing groups and regressions. Topics (and skills) of data visualisation form an inherent part of this course: relying on ggplot2, you will create scatterplots, illustrate temporal trends, present distributions in intuitive ways and express regression results concisely with coefficient plots.
A separate session is dedicated to using RMarkdown, an essential workflow allowing you to generate an entire research paper or report (as an HTML, PDF or Word file). Following a learning-by-doing approach, you will have a chance to solve coding exercises both in teams (in breakout rooms) and alone, and to test your newly acquired knowledge via assignments between sessions.
After successful completion of this course you will be able to import and export data to/from R, conduct simple statistical analyses using R, visualize data, presenting patterns effectively and consicely, and transform, recode, label, merge and subset data in R.
