Data Science: Statistical Programming with R

Description

Students learn to operate R, to perform data manipulation and visualisation, work with the (generalized) linear models, conduct simulation studies (e.g. bootstrap) and present results of data analyses in publication ready tables and figures. We will work with the RStudio environment and R Markdown. The focus will be on the tidyverse package.

R is a very popular and powerful platform for data manipulation, visualization, and analysis and has a number of advantages over other statistical software packages. A wide community of users contribute to R, resulting in broad coverage of statistical procedures, including many that are not available in any other statistical programme.

In this course we will cover the following topics:

• An introduction to the R environment (RStudio) and R Markdown (for reproducible data analysis)
• Data manipulation
• Summarizing data in tables
• Data visualisation
• Statistical analysis and inference
• Present results of statistical analyses in appropriate tables and figures
• Bootstrapping

No previous experience with R is required. For participants who already have some experience with R, we offer more challenging exercises.

Participants should bring their own laptop with both R and RStudio installed. The installation instructions will be provided to participants well in advance of the course start date.

Data Science specialisation
This course can be taken separately, but is also part of a series of 8 courses in the Summer School Data Science specialisation taught by UU’s department of Methodology & Statistics:

1. Data Science: Advanced Techniques for Handling Missing Data in analysis and prediction workflows (Course code S28, 24-27 March 2025)
2. Data Science: Programming with Python (Course code S17, 7-11 July 2025)
3. Data Science: Network Science (Course code S37, 7-11 July 2025)
4. Data Science: Statistical Programming with R (This course)
5. Data Science: Applied Text Mining (Course code S42, 14-18 July 2025)
6. Data Science: Machine Learning with Python (Course code S70, 21-25 July 2025)
7. Data Science: Data Analysis (Course code S31, 2026)
8. Data Science: Text Mining with R (Course code S41,2026)

Upon completing, within 5 years, 3 out of 8 courses in the Summer School Data Science specialisation (no more than one text mining course), students can obtain a certificate. 
Please see here for more information about the full specialization

Target audience

Applied researchers and (master) students who already use statistical software and would like to learn to use, or improve their usage of, the R environment. Understanding basic statistical theory such as t-tests, hypothesis testing, and regression is required. Participants from a variety of fields—including sociology, psychology, education, human development, marketing, business, biology, medicine, political science, and communication sciences—will benefit from this course.

If you are unsure whether this course is the right fit for you, feel free to reach out to us with your questions at ms.summerschool@uu.nl.  

For an overview of all our summer school courses offered by the Department of Methodology and Statistics please click here.

Aim of the course

The course teaches students the skills needed to understand how R works and how to use R for a variety of statistical analyses. The following skills and learning goals are covered in this course.

• Being able to work with the R environment (RStudio) and the online resources.
• Performing reproducible data analyses with Rmarkdown(Quarto) and RStudio.
• Mastering data manipulation (cleaning, transformation, recoding) with tidyverse. 
• Summarizing data in publication-ready tables.
• Creating high-quality plots with ggplot.
• Working with pipelines (tidyverse).
• Fitting and interpreting (generalized) linear models.
• Presenting results of statistical analyses in appropriate tables and figures.
• Being able to perform a bootstrap.

Study load

Five full days. A typical course day starts at 9.00 and ends at 16.30 with breaks for coffee & tea, lunch, and sodas.

You will receive a certificate upon course completion. Please be aware that this course does not include graded activities, and therefore we cannot provide a transcript of grades.

Tags

• analysing data
• data generation
• data manipulation
• Data Science
• graphics
• M&S
• multilevel analysis
• multivariate data analysis
• statistical inference
• statistical programming
• R
• linear models
• Generalized linear models
• Reporting statistical analyses