Data Analysis in R: Online

This course is intended for applied data analysts, including academics and postgraduate students, policy specialists and others. It will examine questions dealt with in public policy, the social sciences and industry, using real data. This includes surveys, and economics and public health data. The unit will help build participants’ ability to undertake rigorous statistical analysis, including means, confidence intervals and linear regression in R, and create publication-standard graphs of the results. The end result will be more professional and easy to understand research. It provides the foundational skills needed for the ACSPRI course Advanced Statistical Analysis using R.


This course will be run over 5 days in two sessions per day:


10.00am - 12.00pm - Session 1

2.00pm - 4.00pm - Session 2


Exercises will be provided, including additional problems and datasets for extra practice outside of scheduled sessions, and one-on-one consultations can be scheduled by appointment the following week. 

Level 2 - runs over 5 days

Dr Shaun Ratcliff is a quantitative political scientist working at the United States Studies Centre at the University of Sydney. His research focuses on using traditional and novel data sources and methods to study public opinion and party behaviour in the US, Australia and comparative democracies. He is particularly interested in examining the policy preferences and behaviour of political actors, and the role of parties as interest aggregators, and how these influence public policy outcomes.

He teaches voter behaviour and public opinion, and the use of quantitative research methods to solve problems in the social sciences.

Prior to working at the University of Sydney, Shaun taught politics, political psychology and methodology in the social sciences at Monash University and the University of Melbourne.

He is an advocate for the use of quantitative research methods to better understand politics and society, and is a member of the executive committee of the Australian Society for Quantitative Political Science.

Shaun received a PhD in political science from Monash University, and he has a background working in politics and government relations, and has consulted for political campaigns.

About this course: 

We are in the middle of a data revolution. A new laptop computer can run processes impossible for a supercomputer a few generations ago. The internet makes data collection and distribution easier and cheaper than ever, with terabytes of information on consumer behaviour, public transport use, crime statistics and election results sourced from across the world now available almost anywhere in minutes or seconds.


These advances in modern computing allow us begin to answer important questions about the world, including what drives regional health issues, why certain choices were made by voters during elections, and whether individuals convicted of serious crimes are likely to reoffend.


Taught by a quantitative political scientists from the University of Sydney, this is a problem-based course for subject matter experts who want to use R to take their quantitative analysis to the next level. By the end of the week you will be able to better conduct descriptive analysis and regression in R, and will be able to create impressive looking data visualisations.


R is open source and free. It is flexible, powerful and intuitive and it is excellent for data visualisation. As it is open source, R has thousands of developers in leading universities, corporate research labs and other institutions across the world. This means its capabilities tend to exceed competing software, with new packages added or updated daily. This is particularly the case for data visualisation, in which R tends to lead the pack. As there is no licence, you can take it with you wherever you go. No matter where you work, you don't have to change software packages when you change employers. Consequently, R has becoming increasingly popular for academic research, economics analysis and public policy development. This trend is only likely to continue.


Being skilled in R will help build your personal capabilities and employment opportunities by making you a more flexible worker capable of undertaking analysis many other researchers and analysts cannot.

No prior experience with R, or any sophisticated quantitative methods are required for this course. Participants should be computer literate and use data in their occupations (or study, if they are a student) and understand some of the basics of statistics. Some basic knowledge with regression is helpful, as is the ability to do simple coding and programming.


If you are unsure whether this is for you, please contact Shaun for more information. He can talk you through the course and the kinds of things you will cover.

Course syllabus: 

Day 1

Operating in the R environment

The first day of the course will explore how to operate in the R environment. We will load and re-code data in R, and calculate descriptive statistics. We will visualise data so you can better understand its structure. We will cover graphing your results in a way that looks professional, so you and your audience can better understand important patterns in your data.


Day 2

Understanding your data

On the second day of the course, we will look at engaging in more complex descriptive analyses using survey data to understand smoking behaviour amongst Australian adolescents, and earnings in the United States. In the afternoon we will discuss the effects and significance of confounding factors when studying human behaviour, and how we can use linear regression to answer some of the questions in which we are interested. We will look at why controlling for potentially confounding variables such as education, income, gender and birthplace are important when answering social science questions. This will be followed by how can confounding factors also provide us with a greater substantive understanding of the research questions we are trying to answer.


Day 3


Spatial Data

Much of human behaviour can be understood (at least in part) as a function of geography. This includes election outcomes (often decided based on discrete geographic contests), crime and public health. In this lecture, we will discuss the importance of geographic data for understanding social phenomena. For instance, showing different ways of visualising data (graphs vs maps) that indicates that sometimes presenting and studying the geographic patterns can increase our understanding of different phenomena and behaviours.


On the third day of this course we will walk you through using spatial data to understand important social phenomena, including why some parts of the United States suffer from higher mortality rates from drugs and alcohol than others. We will then use these data to make interactive maps of displaying variations in the poverty rate, prescriptions of opiates and drug and alcohol mortality rate across US counties.


We will finish by studying geographical variation in Australian election results.


Day 4

More on regression

On day four of this course we will revise the concepts of linear regression. We will then spend more time learning how to fit linear regressions in R. We will look at working with a more difficult dataset, and building more complex models. We will then spend the afternoon revising what we have learnt so far this week. In the afternoon, we will build and present models designed to identify what best predicts quality of life. 


Day 5

Bringing it all together

We will finish the week by working on one of three problems and datasets. You will design and undertake a study using the methods we have covered during the week to answer your chosen question. 


Course format: 

Training in this course will be for 4 hours per day (2 morning, 2 afternoon) over ZOOM. 

You will need your own computer preloaded with R and an internet connection.

We will be in contact prior to the course to ensure you have the software you'll need.

Data and course notes will be provided.

Recommended Background: 

This is a course for subject matter experts who want to use more quantitative analysis in their work. By the end of the week you will be able to better conduct basic descriptive analysis and regression in R, and will be able to create impressive looking visualisations.

Participant feedback: 

The learning curve was steep, but the facilitator was excellent, the group was great, and the training worked well over Zoom. Thanks to Adam and Shaun for assisting me with computer issues outside of class time. (Online Winter 2020)

The break out rooms to work in a team was a great idea. We learnt how to write simple R code to begin with. We were all at the same level so could support each other. (Online Winter 2020)


Good balance of prepared exercises and working on own data (Summer 2020)


Added a new skill to my skill set. (Winter 2019)


[Shaun] made what could have been very dull, dry subject matter very accessible (Summer 2018)


It was a great introduction to R. I now feel confident starting to use R at work. The course was excellent at differentiating for the different levels. I felt challenged but not out of my depth. (Winter 2017)


Comprehensive coverage, good application potential (graphing, transforming data, programming etc) (Winter 2017)

Program where course next likely to be offered: 
Online Summer Program 2021

Instructor's bound course notes will be posted to you in advance.