At a glance
Frequency
Twice a year
Duration
Four mornings
Study mode
In person
Fees
$275-$750 info below
What you will learn
This workshop covers the tools needed to efficiently work with data using R, particularly focusing on importing, rearranging, describing and visualising data.
This workshop has a focus on reproducible research, which means making sure that all of the steps in analysing your data are recorded and could be run again automatically: by you, if you discover an error in your data file or a step in your data processing; by a colleague, to do a similar analysis on their own data; or by someone else to verify your results.
Course topics
- The basics of R and RStudio.
- Using R Markdown to tie together your R code, output and analytical decisions.
- The benefits of a reproducible approach to data analysis.
- Concepts relating to types of data and how to best organise the data you collect.
- Importing data from commonly used file formats including Excel and CSV.
- Practical data-cleaning tasks to get your original data ready for analysis.
- Methods for summarising and describing data.
- Producing high-quality graphics with the ‘ggplot’ package.
- Presenting results from statistical analyses in tables and graphs.
The workshop focuses on specific aspects of the R statistical package, methods for reproducible research and ways to effectively work with data arising from real-world research. The Statistical Consulting Centre also offers a general, introductory statistics course, Statistics for Research Workers using R, which focuses on statistical concepts and methods. These courses are designed to have relatively little overlap and may be taken in either order.
Software
Presenters
Cameron Patrick
Cameron Patrick is a consultant for the Statistical Consulting Centre in the School of Mathematics and Statistics. He also supports University staff and graduate researchers.
Dr Sandy Clarke-Errey
Dr Sandy Clarke-Errey has been a consultant with the Statistical Consulting Centre since 2004, assisting university students and staff with R for a wide range of applications.
Who should take this course?
The workshop is suitable for researchers wanting efficient and effective strategies for working with quantitative data in a reproducible manner. This course is about what happens before and after traditional statistical analysis: getting your data in a form ready for analysis and presenting the results of statistical analyses. While designed for those who have not used R before, it may also be of interest to participants with some familiarity with R but not R Markdown or the ‘tidyverse’ family of packages.
What people are saying
"Very useful and I wish I had had the opportunity at the start of my PhD. I tried a few ways of learning R early on but this was more complete and organised and provides a good launching pad to continue."
"Useful in understanding how to operate R to perform analysis which is helpful in applying the concepts taught in my research."
"Well done, Sandy and Cam! Thanks so much for your hard work and good humour. It's dry stuff, but you managed to make it both interesting and entertaining."
"Clearly very knowledgeable and enthusiastic presenters, well-prepared material, professional and responsive presentation. Excellent course."
More information
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Course cost
$750
Course cost for University of Melbourne staff
$550
Course cost for University of Melbourne postgraduate students
$275
- The fee includes a set of notes from the lecture slides.
- All prices include GST.
- GST does not apply if paying through the Melbourne University finance system.
- $30 cancellation fee applies.
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This is a four-morning workshop.
- Each day will commence at 9.00 am and finish at approximately 12.30 pm.
- The sessions will mix lecture presentations with practical work; tutorial help will be readily available.
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The statistical package R will be used in the workshop, along with companion software including RStudio, R Markdown, ggplot2 and the ‘tidyverse’ collection of packages. Participants are welcome to use R and RStudio on their personal computer (Windows, Mac or Linux) during the course, but a PC will be available in the Wilson computer lab for use. Installation instructions will be sent out via email prior to the course commencing.
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No prior experience with R is necessary.
Most of this workshop assumes little statistical knowledge. However, some statistical concepts will be employed and may be introduced with less detail or rigour than a statistics-focused course. Background knowledge equivalent to an introductory statistics course will be beneficial but not necessary.
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