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. 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
• Bootstrapping
• A short introduction into Shiny apps

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 computer with both R and RStudio installed.

R can be installed (for free) from here

RStudio can be installed (for free) from here

The Open-Source Desktop license is fine.

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: Programming with Python  (Course code S17, 8-12 July 2024)
2. Data Science: Statistical Programming with R (Course code S24, 8-12 July 2024)
3. Data Science: Multiple Imputation in Practice (Course code S28, 8-11 July 2024)
4. Data Science: Data Analysis (Course code S31, 15-19 July 2024)
5. Data Science: Network Science (Course code S37, 15-19 July 2024)
6. Data Science: Applied Text Mining (Course code S42, 15-19 July 2024)
7. Data Science: Machine Learning with Python (Course code S70, 22-26 July 2024)
8. Data Science: Text Mining with R (Course code S41, 19-22 August 2024)

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 specialisation.

We also offer tailor-made M&S courses and in-house M&S training. If you want to check out the possibilities, please contact us at ms.summerschool@uu.nl.

S24_Day to day program_2024.pdf

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.

A maximum of 80 participants will be allowed in this course, and selection for the course will be done on a first-come-first-served basis.

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

For participants who are also interested in more advanced data analysis techniques (machine learning) are recommended to follow the subsequent course Data Science: Data Analysis (Course code S31, 15-19 July 2024).

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;
• Perform reproducible data analyses with R Markdown and RStudio;
• Master data manipulation (cleaning, transformation, recoding) with tidyverse;
• Summarizing data in publication-ready tables;
• Make high-quality plots with ggplot;
• Work with pipelines (tidyverse);
• Fitting and interpreting (generalized) linear models;
• Being able to perform a bootstrap;
• Learn the basics of a shiny app.

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