This course will offer a gentle introduction to R, especially (although not exclusively) aimed at researchers in the human sciences who are not particularly tech-savvy or who do not have any experience programming. We will deal with:
- an introduction to working with R and RStudio
- data structures and data importation in R
- basic programming functions which can be used to carry out common, repetitive tasks (e.g. loops, writing your own functions, etc.)
- basics of working with textual data in R (time permitting)
- data exploration and descriptive statistics (e.g. mean, medians, measures of dispersion, etc.)
- introduction to data visualization with the base package (e.g. barplots, scatterplots, histograms, etc.)
- basic parametric and non-parametric inferential statistics (e.g. tests of independence, correlation and linear regression, etc.)
This should provide you with a solid basis which you can use to then further read up on and specialize in the types of analyses you need for your own research. During the course, we will also discuss resources and strategies on how to independently find out more information on particular types of analysis which are not covered in this basic course.
Note that this course is not an introduction to statistics. It assumes that researchers already have a basic familiarity with intro-level statistical concepts and tests (e.g. correlation, t-tests, etc.), but it will teach researchers how to carry out, interpret, and visualize the results of such tests in R.
No previous experience with R is required, although researchers are welcome to take the course as a refresher.
Some basic knowledge of descriptive and very basic inferential statistics (e.g. correlation, t-tests, etc.) is recommended.