Overview
In this course you’ll learn how to ingest data into R data frames from a variety of sources. Using libraries from the “tidyverse” you’ll clean and prep the data for analysis. Finally you’ll visualise the data using charts.
Before starting this course you should already be familiar with the basics of R programming.
What you’ll need
- Laptop (minimum 16 GB RAM is recommended)
- headphones and microphone
- Microsoft Office (Word, Excel, PowerPoint)
- Google Chrome or Microsoft Edge
- R version 4.4.3 or later
- RStudio (any version from 2025 or later)
- Course Learning Outcomes:
Learning outcomes
Attendees will be able to:
- ingest data into R data frames for further analysis
- scrub, transform and prepare the data for further analysis
- perform simple statistical analysis on the data they’ve ingested
- export the transformed data for further analysis in other tools.
- be able to draw a wide variety of charts and explain what the data shows
Course topics
- Warm up exercises - getting back into R and RStudio after a break
- Collection objects in R - vectors, factors and matrices
- An introduction to data frames
- The anatomy of a data frame
- Limitations of data frames (in-memory limits)
- What is an rdata file?
- Introduction to the TidyVerse
- Cleansing data with dplyr
- Grouping and summarising data
- Performing simple statistical analysis on data
- Functional programming techniques e.g. sapply(), and why loops are to be avoided
- Drawing charts with ggplot2
- Managing R packages
- Workshop: attendees will be given data to analyse, and will present their results to the class.