R is open source and free. It is flexible, powerful and intuitive and it is excellent for data visualisation. Thousands of developers in leading universities, corporate research labs and other institutions across the world add new packages or update the software daily, allowing its capabilities tend to exceed competing software.
This 5-day course will help build participants’ ability to work with data in R and undertake rigorous statistical analysis, including spatial analysis and linear regression, and create publication-standard graphs of the results. The end result will be more professional and easy to understand research..
This course is being held online via Zoom and run on Australian Eastern Standard Time (UTC +10)
(Canberra, Sydney, Melbourne time)
Please check your time zone.
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 work with data in R and undertake rigorous statistical analysis, including spatial analysis and linear regression, 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 three sessions per day:
- 10.00 am - 11.30 am - Session 1
- 12.00 pm - 1.30 pm - Session 2
- 2.30 pm - 4.00 pm - Session 3
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.
Dr Shaun Ratcliff is an applied data scientist, political scientist and survey researcher.
He is the Director of Data Science in the Public Affairs and Polling team at survey re-search firm YouGov. He was previously a lecturer of political science at the University of Sydney (2017-2022), where he coordinated the survey research program at the United States Studies Centre.
He manages complex, high profile projects for clients engaged in political activities, cam-paigns and related work. His work is largely focused on understanding how the public thinks about issues, organisations and events, and what influences their beliefs and ac-tions. He designs survey instruments and applies innovative data science methods to measure and track behaviours and attitudes and identify ways to engage with the pub-lic.
He has a PhD in political science from Monash University, and holds an affiliation with the University of Sydney, where he is a lecturer in data science in the Faculty of Engi-neering and holds an honorary appointment with the United States Studies Centre
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.
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.
Training in this course will be for 3 x 1.5 hour sessions daily, over ZOOM, for 5 days.
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.
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.
Really enjoyed the mix of instruction and self-directed activities.
The course is well managed and prepared by the instructor. It is the best course that I learned in the past two years.
I found the course very useful as I knew nothing about coding and R. So it was useful to start from the basics and learn from there.
Very happy with the Zoom experience, I think the features (chat, screen sharing) were really useful to facilitate the work.
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.
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.
Good balance of prepared exercises and working on own data
Added a new skill to my skill set.
[Shaun] made what could have been very dull, dry subject matter very accessible
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.
Comprehensive coverage, good application potential (graphing, transforming data, programming etc)
Instructor's bound course notes will be posted to you in advance.
1. BOOKING - ACSPRI does not accept ‘expressions of interest’ for course places, i.e. all bookings, are considered firm, and a cancellation fee is charged if you cancel your booking after the early-bird date.
2. DISCOUNT RATE – The discounted rate for ACSPRI members is available to all staff and students of member organisations. To be eligible for this rate:
The course fee must be paid by either the member organisation or by you. Where fees are paid by a non-member organisation the non-member rate applies.
You must either have a valid email address issued by the member organisation; or you must hold, or have a right to hold, a current staff or student identity card from the member organisation.
In addition, to be eligible for a full time student discount the participant must:
Hold, or have a right to hold, a current student identity card from the member organisation;
Be enrolled as a full-time student;
Make payment in full with your application, arrange electronic funds transfer (EFT), or contact ACSPRI to advise credit card details for payment, by the early-bird closing date;
Provide ACSPRI with contact details of your supervisor, so we can request them to confirm your eligibility for the full time student rate.
The early bird rate applies to all bookings paid in full by the early bird close date, otherwise you will be charged at the standard rate.
3. REFUNDS & CANCELLATIONS - Course fees are not refundable unless:
we cancel the course in which you have enrolled; or
you cancel your enrolment before the early-bird closing date.
A cancellation fee of $250 will be charged if you cancel within the period from the early-bird closing date of and one week prior to the commencement of the program. The full course fee will be charged if you cancel within 1 week of the beginning of your course.
4. PRE-REQUISITES - Course descriptions specify course pre-requisites. You must undertake to meet the pre-requisites of the course(s) in which you enrol. If in any doubt, you should contact ACSPRI prior to enrolling.
Delivery of this course is online - via Zoom.
Please ensure you have the following:
- Reliable Internet connection with at least 5Gb per day of data available (i.e. a 5 day course will use about 25Gb of data just on the Zoom application)
- A computer/laptop with the Zoom application installed (free)
- A webcam (built in to most laptops)
- A headset with a microphone (not required but ideal)
- A second monitor/screen if possible
Please also check the course page for specific software requirements (if any).