Statistical workshops and short courses
We offer an introductory workshop for the statistical programming language R, an essential tool for anyone using statistics for research or study.
Kia ora,
We are excited to explore a new initiative and would love to hear your thoughts! Please take a few minutes to complete our brief feasibility survey. Your valuable feedback will help us make informed decisions and ensure the success of this project.
Thank you in advance for your time and insights!
SCC Team
This page will be updated regularly as we develop new short courses and workshops. We offer tailored short courses at your organisation on request, please make an enquiry at consulting@stat.auckland.ac.nz if you are interested in this.
Introduction to R
We offer an introductory workshop for the statistical programming language R, an essential tool for anyone using statistics for research or study.
The R statistical programming language is used daily by millions of users around the world and is currently one of the fastest growing programming languages in the world. R was developed in the Department of Statistics here at the University of Auckland by Ross Ihaka and Robert Gentleman, so you have come to the right people to learn R!
Our 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.
We mostly use 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! You will learn how to manipulate the provided raw data using dplyr and tidyr, visualise the cleaned dataset using ggplot2, and run some analyses including t-tests, ANOVA, and regression.
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 – over 90% of participants from our most recent workshop indicated they would recommend our course to future participants.
Bookings are open to anyone and the details for our next introductory R workshop can be found below.
Introductory R Workshop
Date: 2-3 July 2025 (Registrations are now open)
Time: 9am – 5pm
Location: City Campus, University of Auckland
Introductory R Workshop
Date: 28 – 29 October 2025 (No registrations are being accepted yet)
Time: 9am – 5pm
Location: City Campus, University of Auckland
Workshop costs
University of Auckland students and staff: $400 excl. GST (Paid by PRESS, research grant or other UOA a/c)
University of Auckland students and staff: $400 + GST (Paid by debit/credit card)
Non-University of Auckland attendees: $650 + GST
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.