Interactive Visualization with R for Social Scientists
Next course runs from [[date]]
Interactive Visualization with R for Social Scientists
Next course runs from [[date]]
Course overview
If you’re a social scientist looking to communicate your research in a new and engaging way, this course could be the right choice for you.
This course introduces you to techniques and tools for presenting data in visually attractive and interactive ways using the R programming language.
Data visualization is a powerful tool for any social scientist looking to disseminate and communicate their research findings. Data in a table or spreadsheet is valuable, but as human beings, we often find it challenging to see patterns or summarize tabular data. Visualizations allow you to do exactly that – create a visual representation of the data, giving you a chance to see and interpret complex data efficiently at a glance.
Course objectives
By the end of the course you will:
Be familiar with the workflows for producing visualizations
Be able to produce a range of visualizations from data
Understand the need for interactive visualizations and reports, and the associated workflows
Produce a range of visualizations relevant to the available data
Create a report that tells a story from data using appropriate interactive visualizations
For a bulk order of 5 or more learners on any of our courses, you can claim 50% discount. Contact us for more information.
Martin Hadley is currently a Research Technology Specialist at the University of Oxford specializing in data visualization. His background is in biophysics and statistical computing, completing his MPhys at University of Leeds. At University of Oxford, Martin is helping to launch a data visualization service for researchers and is experienced in teaching data science skills to social scientists.
Richard Traunmüller is currently a visiting Professor of Quantitative Methods at the University of Mannheim and on leave from his Junior Professorship in Empirical Democracy Research at Goethe University Frankfurt. Prior to coming to Frankfurt, he has held positions at the Universities of Konstanz, Berne, Mannheim, and Essex. Richard has taught semester long courses on data visualization at these universities and has been invited to teach statistical visualization at the German Institute of Global and Area Studies (GIGA) and the European University Institute (EUI) in Florence. In addition, he is a regular instructor for data visualization at the Essex Summer School in Social Science Data Analysis. His work has appeared in major social science journals such as Comparative Political Studies, European Journal of Political Research, and Political Analysis, amongst others.
The course is organized into a set of four interactive learning modules, and you should work through the modules sequentially.
The interactive learning modules contain a number of topic pages. Each topic page has a video to walk you through the concept and interactive text to reinforce what was covered in the video, quick questions and knowledge checks.
There are three additional types of activity in your course to facilitate deeper learning. These are presented in the relevant topic pages.
The vast majority of topics in the course are fundamentally practical. You are strongly encouraged to recreate and run the code as you work through them, and complete knowledge checks and activities.
You have 3 months' access to this course. During the first 4 weeks of your course you will receive learning support from Martin, your instructor. We recommend working through as many modules as possible in these initial 4 weeks so that you can make the most Martin’s expertise. He’ll be on hand to answer any questions, or help you if you get stuck.
After the learning support period, you’ll still have access to the course materials but you won’t receive assistance from the instructor. SAGE Campus will help you with any IT or platform issues you might have throughout the course.
Module 1 gives an overview of interactive visualization, and explains how to set up the various toolkits you’ll be using throughout the course
In this module, you’ll cover the following:
In this module, you’ll cover the following:
In this module, you’ll cover the following:
I found the Interactive Visualization with R for Social Scientists course to be a thorough, structured, and well-planned-out introduction to visualizing data in the R ecosystem. Martin, the course instructor, developed appropriate examples that spanned a range of social science data visualization contexts. His detailed explanations of various components in the R ecosystem would be helpful for beginners and novices alike. His pace and engaging manner made the course easy to follow in short bursts or drawn-out sessions. Overall, this is a fantastic introduction to data visualization concepts in social science research.
This course was first delivered by the Royal Statistical Society at the International conference in 2017. This course is an online course, which is an expanded version of that course.
The Royal Statistical Society is a charity which promotes statistics, data, and evidence for the public good; it is one of the world’s leading learned societies and the only UK professional body for all statisticians and data scientists. Its vision is ‘a world where data are at the heart of understanding and decision-making.’
Please see below answers to some of the most frequent questions we get about this course.
This course assumes a basic familiarity and comfort with the R language and RStudio. It's assumed that you know what the following things mean (and how to do them):
In addition, this course depends heavily on the pipe operator (%>%). Though the instructor will cover the basics of using %>% in code, it's well worthwhile reading http://r4ds.had.co.nz/pipes.html in preparation for the course.
Through the course you will be introduced to a wide-range of different packages for creating interactive charts, plots and network diagrams. Each of these packages has a slightly different approach to handling data. During the course you will develop an understanding of these approaches like accessing specific columns from "data frames". The exercises will provide you with the opportunity to test your understanding of these approaches but you should expect to mix these up a bit before you've mastered them.
Finally, the Shiny component of the course does cover the more conceptually complicated concepts of transferring data between "client" and "server" in order that you can create useful interactive web applications and visualizations of your research data. The course is deliberately designed to equip you with all of the basics to build Shiny apps and the exercises will slowly improve your understanding of how to craft Shiny apps that fit your needs.
It’s a good idea to install R Studio prior to starting this course as this will be used throughout the course (this should take about 10 minutes to do). There are a number of R libraries that are required but these will be discussed and can be installed via R Studio.
No, they are either open source or have community (free) versions.
A computer or laptop with the suggested software and a modern browser e.g. Internet Explorer 10+ or the latest versions of Chrome and Firefox.
While you can access the course on your mobile device, go through the content and answer questions, you will need a desktop or laptop computer to practice and complete the activities that require you to write and/or test code.
The course will be run over 4 weeks, during which you will have access to learning support provided by the course instructor. After the 4 weeks, you will still have access to the course materials for another 2 months, but you will not be able to receive learning support from the instructor, and if there is a course forum, you will not be able to ask any questions.
All of our courses offer a certificate of completion signed by your instructor. You will be able to download this certificate, from the Learning Platform, when you complete the course.
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