Prerequisites:

• Basic statistical knowledge
• Basic proficiency in R programming
• Familiarity with data structures
• Some experience in statistical analysis using R is an advantage

Who should enroll:

• Students are interested in the data handling skills
• Students who want to create advanced visualizations and smart report of their data analysis
• R users eager to explore the capabilities of tidyverse and ggplot2 packages

Concept of the course:

This workshop is designed for individuals who already have a foundational understanding of statistical analysis in R and are eager to elevate their skills to the next level. During the workshop, we will focus on data manipulation using a pipe function and will create complex graphs with ggplot2 package. We will follow the standard flow of a descriptive statistical analysis and data visualization, and will learn how to produce professional Markdown and HTML reports for results presentation.

Key Topics:

• Tidyverse Essentials:
  • Understanding the philosophy behind the tidyverse and packages “dplyr”, “tidyr” and other.
  • Chaining operations with the %>% operator.
  • Mastering the art of clean, logical and tidy data structures.
• Introduction to ggplot2:
  • Crafting informative visualizations.
  • Customizing plots for publication-ready graphics.
  • Creating complex plots with facets and layers.
  • Utilizing themes and color palettes for consistent styling.
• Markdown and HTML Reports:
  • Creating dynamic and reproducible reports with R Markdown.
  • Converting R Markdown to HTML for sharing and presentation.

By the end of this course, participants will be equipped with the knowledge of how efficiently handle, clean, manipulate, visualize data and create the summary reports.

The workshop is application-oriented and based on the “learning-by-doing” concept. The basics of R or statistical methods and their purpose will not be discussed in the course. At the end of the course, we will dedicate time to your questions and your visualizations that you want to create or improve.

Other:

The program should be installed on participants’ own laptops:

• R: https://cran.uni-muenster.de/
• RStudio: https://rstudio.com/products/rstudio/download/
• MS Teams: https://www.microsoft.com/en/microsoft-teams/download-app

Trainers:

Dr. Katherine Ogurtsova (Katherine.Ogurtsova@hhu.de) is working in the Institute of Occupational and Social Medicine, in the working group of Environmental Epidemiology, at University Clinic in the Heinrich Heine University. Her primary qualification includes statistical methods in medicine, epidemiology, and public health with the main focus on R-programming and methodological issues.