This course is set up as Elective course for research master students from the Graduate School for Social and Behavioural Sciences of Utrecht University.FSW rema students can register for the course at STIP, other students need to contact the teacher about participating for this course on time, i.e. before July 15.

The goals of the course are:

- Working with R and RStudio
- Acquiring basic programming skills
- Performing exploratory data analysis (e.g. plots and tables)
- Working with the Linear Model
- Understanding the Generalized Linear Model
- Make presentations of statistical analysis using R Markdown

Relation goals and assessment:

These goals are tested in two assignments. The first one involves a statistical analysis in R during class.  In thist assignment students have to apply:

1. basic commands of the R-language (knowledge and understanding) 

2. manipulate data, make plots, and tables and perform statistical analysis with the Linear Model (applying):

3. interpret tables, plots and statistical output (judgment)

4. R- packages to expand their knowledge of R (learning skills) 

The second assignment involves a data analysis in groups, and a presentation of the results in class..The final grade is a weighed of the grades for these two assignments

 

R is statistical software environment that is currently the world's standard for data analysis and visualization. The main advantage of R is that it is extendable with countless packages that allow for any conceivable statistical procedure in a user-friendly way. RStudio is an IDE for R that includes extra functionality that make it even more easy to work with R, such as a sophisticated text editor and debugger, integrated help, additional tools for plotting and for making dynamic and interactive R Markdown documents, presentations and dashboards for sharing and publishing statistical analyses. This course offers an introduction to statistical analysis with R using RStudio. In the first part of the course students learn the basic data structures of R, how to create documents with R Markdown, and how to explore, manipulate and visualize data with the state-of-the-art R packages tidyverse and ggplot2. The second part of the course involves data analysis with the linear model and the generalized linear model.