Applied R
Methods for Data Science pathway
Course At A Glance
- Level: Introductory to lower-intermediate
- Programme placement: The intermediate pathway label describes the course’s position in the programme; session 1 starts from the basics and requires no previous R experience
- Best for: Researchers who want a practical route into data analysis with R
- Environment: R and RStudio
- Structure: Eight lessons of 100 minutes each; two per half-day workshop
- Main themes: Foundations, data inspection, defensive programming, iteration, wrangling, visualisation, notebooks and dashboards
- 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
- recording code, outputs and decisions in a Quarto notebook
- building a small, checked Shiny dashboard
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 | What you keep |
|---|---|---|
| 01 R and RStudio Foundations | Projects, scripts, calculations, vectors, help and debugging | A saved and checked starting workflow |
| 02 Importing and Understanding Research Data | Structures, types, imports, identifiers, units and missingness | An inspectable input and selection |
| 03 Functions, Testing and Defensive Programming | Arguments, scope, contracts, failures and known-value checks | Reusable and tested calculations |
| 04 Iteration and Repeatable Workflows | Loops, vectorisation, conditions, lists and guarded exports | A repeatable operation with recorded outcomes |
| 05 Cleaning, Wrangling and Joining Data | Selection, summaries, joins, missingness, reshaping and file exchange | A checked table and reusable analysis |
| 06 Exploratory Analysis and Visualisation | Distributions, relationships, groups, scales and source checks | Figures with qualified interpretations |
| 07 Digital Research Notebooks | Quarto, saved execution, parameters, inline values and handover | A reproducible computational research record |
| 08 Interactive Research Dashboards | Shiny inputs, reactivity, validation, downloads and peer review | A checked local interactive app |
Each lesson has 60 minutes teaching, six five-minute activities and a separate ten-minute break. Room responses and debriefs are inside activity time. Eight lessons total 13 hours 20 minutes, including breaks, and can be delivered as four half-day workshops. Established filenames remain available; the former variables page is now the active second lesson.
Lessons 5–8 use one downloadable research project from checked data through figures, a Quarto notebook and a Shiny app. The inherited selection and plotting banks remain optional practice outside core timing.
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
- keep a checked notebook/app project and a record of its sources, assumptions and limitations
What To Prepare
Before the course, learners should have:
- a laptop with compatible versions of R and RStudio and the lesson packages available
- 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
- Additional Material: a checked Gapminder practice workflow and further learning
- Reference: glossary and key concepts
- Discussion: prompts for reflection and group discussion
Instructors
- Instructor Notes: teaching emphasis and facilitation advice
- Additional Material: instructor extensions and optional demonstrations
- Extra Exercises: extra practice material for consolidation
Community and Course Information
For the wider analytical pathway, see Methods for Data Science. Essential Digital Skills supports file organisation and research workflows; Applied Python offers a complementary programming route. Bring a focused follow-up question to Digital Skills Clinics.