Applied R
Methods for Data Science pathway
Course At A Glance
- Level: Introductory to lower-intermediate
- Best for: Researchers who want a practical route into data analysis with R
- Environment: R and RStudio
- Main themes: Core syntax, objects, functions, loops, and tidyverse workflows
- Outcome: A usable foundation for reproducible data work in R rather than a surface-level introduction
Why This Course Matters
Applied R is built for researchers who want to move from ad hoc spreadsheet-style analysis toward clearer, more reproducible workflows. It introduces R as a practical research tool for organising data, writing reusable code, and working more systematically with analysis tasks.
The course is paced for beginners, but it still aims at real analytical confidence. By the end, learners should understand not just what commands to run, but how to structure work so that it is easier to repeat, explain, and improve.
What You Will Actually Do
Learners work through practical R tasks rather than abstract theory.
You will spend time:
- getting comfortable in RStudio
- creating and updating objects
- writing simple reusable functions
- using loops and control flow
- manipulating data with tidyverse tools
- building habits that make analysis easier to rerun and share
Who This Is For
This course is a strong fit if you:
- are new to R and want a clear entry point
- use or support research analysis and reporting
- want to move from manual data handling to more reproducible workflows
- are looking for a practical companion to the broader Methods for Data Science pathway
Basic familiarity with files and folders is helpful, but no previous R experience is required.
What You Will Build Confidence In
Starting Well
Use RStudio more comfortably and understand the shape of a good project workflow.
Thinking With Objects
Create, inspect, and manipulate values and data structures in ways that support analysis.
Writing Reusable Code
Move from one-off commands to small functions and repeatable patterns.
Working Tidy
Use tidyverse tools to filter, transform, summarise, and communicate data more clearly.
Course Journey
| Lesson | What you will practise | Why it matters |
|---|---|---|
| 01 RStudio Intro | RStudio layout, projects, and the basic workflow | Gives learners a practical starting environment rather than a blank screen |
| 02 R Variables | Objects, types, vectors, and basic manipulation | Builds the foundation for all later work in R |
| 03 R Functions | Writing and using functions for repeated tasks | Helps learners structure work more cleanly and avoid repetition |
| 04 For Loops | Iteration and control flow | Shows how to scale simple logic across repeated tasks |
| 05 The Tidyverse | Modern data wrangling with tidyverse tools | Connects R directly to everyday analytical workflows |
What You Will Leave With
By the end of the course, learners should be able to:
- work more comfortably inside RStudio
- create and manipulate basic R objects with less uncertainty
- write short functions for repeated tasks
- understand how loops and iteration work in practice
- perform common data wrangling tasks using tidyverse tools
- approach future R learning with a stronger conceptual base
What To Prepare
Before the course, learners should have:
- a laptop with current versions of R and RStudio
- access to the lesson data files
- enough familiarity with folders and files to locate and organise course materials
Preparation details are in Setup.
Course Materials
Learners
- Setup: installation and preparation guidance
- Reference: glossary and key concepts
- Discussion: prompts for reflection and group discussion
Instructors
- Instructor Notes: teaching emphasis and facilitation advice
- Additional Material: optional extensions and demonstrations
- Extra Exercises: extra practice material for consolidation