Description
This hands-on, beginner level series covers the fundamentals of R, including data types, functions, importing, manipulating and analyzing data as well as program design, data management, and task automation. Participants are encouraged to help one another and to apply what they have learned to their own research problems. The goal is to teach the practical knowledge needed to start programming, debugging and using R in everyday tasks.
You do not need any previous knowledge of the tools that are presented, or previous programming experience, but intermediate level experience with a computer is highly recommended.
You do need to have a laptop with a Mac, Linux or Windows operating system (not a tablet, Chromebook, etc) on which you have administrative privileges, as you need to pre-load specific software packages.
Prerequisites
Participants should be comfortable using a computer and navigating files and folders. No prior programming experience is necessary.
Details
Learning Objectives
- Master R and RStudio fundamentals, workspace navigation, and environment management.
- Write clean, modular R code to automate repetitive scientific workflows and data processing tasks.
- Manage file structures and organize projects efficiently using RStudio projects and working directories.
- Manipulate, filter, clean, and summarize data frames using dplyr and tidyr.
- Write and use custom functions to make scripts more efficient, reusable, and readable.
- Implement control flow structures (such as loops and conditional statements) for advanced scripting and automation.
- Utilize vectorization techniques to write efficient, high-performance R code without explicit loops.
- Troubleshoot errors effectively and seek help using built-in documentation and debugging tools.
Who Should Enrol
This training is designed for students, researchers, and professionals from diverse backgrounds who seek to build a strong foundation in programming fundamentals, modular code writing, and task automation using R. The workshop does not cover statistical analysis, rather it focuses on data manipulation and reproducible workflows using RStudio.