Introduction to R and data visualisation - Presciient

Introduction to R and data visualisation

What is R?

R is the most popular data mining and statistics package in the world, and it is free to use. It is also easy to use thanks to a range of intuitive graphical user interfaces for statistics, data mining, and interactive visualisation. It is used by a growing number of commercial and government organisations, and is also the tool of choice of elite data mining competition winners.

R is open source, flexible, and customisable. Over 2,500 R packages are available as extensions to the base environment, constituting one of the largest and most up-to-date collections of cutting edge Analytics tools in the world.

Course outline

This course is an introduction to the R programming language, beginning with the most basic operations of downloading and installing the environment. Participants will learn how to input and manipulate data and be instructed in all the aspects of procedural programming in R, allowing them to create their own R functions and customise code.

The course will also introduce R data structures, statistical operations, the creation of R graphics, and options for generating output from all of these to external files. It will also provide an overview of the use of packages in R, and an introduction to some of the most common data mining, interactive visualisation and integrated graphical user interface packages.

The course will be instructed by Presciient Director, Dr Eugene Dubossarsky, who has 20 years’ experience programming in R and its parent language, S.

Who should attend?

This is a practical course, suitable for existing and prospective data analysis practitioners in government and industry. Participants will be provided with a range of programmatic and user interface options for working with data in R.

The course is not academic, and does not assume any specialised knowledge beyond first year tertiary statistics. Its focus is developing a practical understanding of R the tool for business users.

Course outcomes

Attendees will, by the end of the course, have the basic skills, resources, guidance and confidence to immediately and self-sufficiently begin to use R in their work.

Course instructor

The course will be instructed by Presciient Director, Dr Eugene Dubossarsky. Eugene is a founder and Fellow of the Institute of Analytics Professionals of Australia (IAPA); Director, University of New South Wales School of Mathematics and Statistics Industry Advisory Board; and a recognised industry leader in Business Analytics. He has 20 years’ experience programming in R and its parent language, S.


The Introduction to R course provided clear and logical assistance to getting up and running with R. More than that, the real value was in providing guidance on the myriad of online resources and introducing me to a network of passionate and helpful R users. Eugene is a knowledgeable and approachable teacher. I wouldn’t hesitate in recommending the course. I feel that I am now fully on the road to applying R and using data to improve efficiency across my organisation.
—James Orton, Data and IT Manager, UNICEF Australia

I have been trying to convert my Stata programming skills to R, however, there have been many times where I just wanted to sit down with someone and have them explain the fundamentals of programming in R. Sure, a number of books and websites have helped me become familiar with R, however, I still didn’t feel ready to translate all of my familiar Stata commands to R (e.g. I am comfortable plotting graphics using ggplot2, however, revert back to Stata for data manipulation). I knew that a more effective way to learn and feel confident would be to sit down with someone and have them explain how they use R, how they clean data, how they plot graphics, etc. I knew that once I felt comfortable with cleaning my data in R, analysis would be less of an issue – I’m happy to research the specifics on my own.

Thank you, Eugene for advancing my R skills. I especially appreciate the time spent explaining the fundamentals of data manipulation – i.e. the code one needs to know before running any basic or sophisticated analysis. The pace of the workshop was perfect.
—Dr Chelsea Wise, Lecturer, Marketing, UTS Business School


The course assumes no tertiary level training in statistics. Attendees simply need to be familiar with working with structured, electronic data.

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