Advanced R for Postdocs - Data Management and Project Workflow

Target group

Postdoctoral researchers of all disciplines

Technical Requirements

You will need a laptop with the latest versions of R and RStudio installed as well as an internet connection to Eduroam (guest accounts are available). All other material will be provided during the course.

Content

Have you already used R in its basic functions but want to learn more about advanced functions for data management and project workflow? In this R course, you will be introduced to:

• RStudio’s integrated work environment: R projects, R notebooks, version control with Git and Github, packages for project workflow
• Advanced functions for data management: import/export, data cleaning, regular expressions (strings/character data), working with lubridate for date data and forcats for factor variables
• Working with dataframes: pivoting, grouping, joining, data preparation for ggplot2 and longitudinal studies, deduplication, iteration and looping
• Workflow: how to structure analysis plans with R and RStudio
• Documenting the analysis using RMarkdown, packages to produce formatted tables and graphs, serialized reports and introduction to dashboards

Please note that this course solely focuses on data management. We will not discuss statistical techniques. The course does not teach ggplot2 either. The course assumes a basic level of proficiency with basic data management functions. Knowing the tidyverse packages is a plus but not a must. The target audience are researchers who have used R for statistical analyses in the past but are keen to learn about ways to write better and cleaner scripts and more easily document their analysis.

Objectives

After this course, you will

• know how to manage projects in RStudio
• understand the benefits of version control and know how to integrate it into your workflow
• be able to use basic and advanced functions to construct a data cleaning pipeline for your data management
• be able to document your analysis in a clear and structured way using RMarkdown documents and functions

Methods

Trainer input; individual, partner, and small group work; exchange with other participants; group reflection