Full course description
Date: 6 - 7 July, 2026
Modality: In-person
Duration: 9am – 5pm
Price: UoA student/staff: $400.00 (Incl. GST) Enter this code: UOA400
Non UoA student/staff: NZD $600.00 (inc. GST)
The introductory R workshop is designed for people who have either little or no experience using R, and typically most participants have no prior experience with R (70% of the most recent workshop’s participants had never used R before the workshop).
The introductory R course is run over two full days, with 4 sessions per day. Each session consists of a lecture followed by a practical, hands-on session where you will work through a problem set and instructors are available to answer questions. All participants are provided with a USB drive containing all workshop materials that is yours to keep after the workshop.
The workshop has been designed to be practical, so that you can immediately start working on your own analyses with the tools you will learn. It is based on our experience with real-world problems our collaborators have commonly needed to solve.
Short course structure and topics covered:
What’s covered in the workshop?
- Introduction
- Getting familiar with R
- Using R Studio and loading projects/scripts
- Basic functions using RReading in Data Files (.csv, .xls/.xlsx)
- Introduction to R Objects. How R thinks (vectors, matrices, basic data formats)
Working with data(sets)
- Cleaning and subsetting
- Merging datasets and reformatting
- Grouping variables and summarising
R graphics
- Starting with plots in R (boxplots, histograms, bar graphs)
- Graphics in R with ggplot2 (customising plots)
Data analysis
Introduction to performing t-tests, chi-square tests, ANOVA, and general linear models
Short course benefits and learning outcomes:
Participants will learn the ever-popular set of R packages called the “tidyverse” in the workshop – if you have ever seen even a snippet of R code, you have probably seen at least one tidyverse package being used!
Participants will learn how to:
- Manipulate the provided raw data using dplyr and tidyr
- Visualise the cleaned dataset using ggplot2
- Perform analyses in R including t-tests, ANOVA, and regression.
Who should attend? Any entry requirements?
Our introductory R workshop is designed for participants who have either little or no experience using R, and typically most participants have no prior experience with R.
Previous participant reviews:
As a clinical researcher for more than 20 years with some basic statistical knowledge, I’m reasonably comfortable doing my own analysis for most things. I’ve mainly used SPSS previously and have progressed to writing script rather than relying on drag and drop functionality in this software. However, I found there were limitations, especially with the visual presentation of data.
For the last seven years, I’ve also have a role as a health target champion for the Ministry of health and latterly Clinical Lead for Acute Care. In these roles I provide analysis and insights into Emergency Department performance to Te Whatu Ora and the Ministry of Health. This often involved visual trend analysis using downloads in .csv format from the National Collections and I found it time consuming and clunky using Excel to produce graphs, due to the way data was organised.
I was keen to learn about R as I’d seen the output that others were able to produce using this program. I found the Introduction to R course was very helpful, although being completely honest I did struggle to keep up at times! Fortunately, the facilitators were knowledgeable and happy to answer any questions.
The handouts and availability of multiple web resources were also useful after the course to reinforce and extend the knowledge I gained.
Within weeks of completing the course I was asked to provide insights around interventions that had been trialled to support ED performance recently at several hospitals. Using R, I was easily able to create graphs of trends over time and layer over the intervention start points to demonstrate the impact/lack of impact of the interventions and display the data at national and individual hospital level.
This information is being used currently to help inform decisions on future investment in the acute care system.
Peter Jones MBChB, MSc (Oxon), PhD, FACEM Associate Professor of Emergency Medicine, School of Medicine, University of Auckland Director of Emergency Medicine Research, Auckland City Hospital Chair NZEM Network
Payment for short courses is normally by credit card. If you need to pay by invoice or require assistance, please email shortcourses@auckland.ac.nz. Please note the University does not issue individual invoices for amounts less than $500 New Zealand dollars.
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