Foundations of R for Research - (2 days)

This 2-day workshop covers the foundations of using the free open-source R and RStudio programs to run their analysis. The course is a mix of lectures and hands-on exercises to introduce participants to the foundations for using R and RStudio.

Workshop - runs over 2 days

Dr Joanna Dipnall is a biostatistician with the School of Public Health and Preventative Medicine (SPHPM) at Monash University and Honorary Research Fellow with School of Medicine at Deakin University. She holds a B.Ec(Honours) from Monash University, and a PhD from the School of Medicine at Deakin University. She also lectures and tutors with the Department of Statistics, Data Science and Epidemiology at Swinburne University. Joanna has developed a novel Risk Index for Depression (RID) utilising SEM and machine learning techniques that brought together five key determinants of depression. She has been a teacher of Stata software for over 15 years, training across Australia and overseas and was a member of the Scientific Committee for the Oceania Stata Users Group Meeting in 2017.

About this course: 

This course is a step by step interactive introduction for participants with no experience with R and RStudio. Notes will be provided and a set of exercises, covering a variety of data sets, will be run together during the lecture and also individually at the end of the day. The course content is particularly suited for those involved in research in the business, education, social and health sciences.


Participants will first be introduced to the foundations of R such as libraries, matrix manipulation, objects, and data frames. Participants will use R Markdown in RStudio to generate reports, tables and graphics to Word, PDF and HTML files. Once the foundations have been covered a variety of data management skills will introduced (e.g. filtering data, merging data). The production of descriptive statistics tables and graphs will be covered so that participants can easily use RStudio to explore data and produce reports.


The last session on each day will finish with a set of exercises for participants to run on their own and practice what they have learned that day. Please note that due to the short 2-day structure, there will not be any time set aside for analysing participant’s own data.


Course syllabus: 

Day 1

  • Lecture 1: Foundations of R
  • Lecture 2: Foundation data management skills
  • Lecture 3: Producing basic statistics and tables
  • Lecture 4: Producing your first simple report
  • Exercises in R


Day 2: 

  • Lecture 1: Review of day 1
  • Lecture 2: Extending data management skills
  • Lecture 3: Basic R Graphics
  • Exercises in R


Course format: 

This workshop will take place in a classroom environment.

Participants are required to use their own laptop and have downloaded R and RStudio prior to the workshop

Recommended Background: 

This course assumes that participants have:

(1) A reasonable understanding of statistics to be able to comprehend the basic statistics such as mean, median and interquartile range.

(2) Some familiarity with a PC environment including keyboard skills and understanding of folder and file structures.

(3) Some experience in using Microsoft Word and Excel or their equivalent.

It does not assume prior experience with R, Stata, SAS, SPSS or any other specific statistical packages although any such experience would be helpful.

Recommended Texts: 

Course notes will be supplied.


No specific references are suggested but participants might find the following text useful:

  • “Discovering Statistics Using R” by Andy Field and Jeremy Miles.
  • “Data Analysis and Graphics Using R” by John Maindonald and W. John Braun.
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