The Urban Big Data Centre at the University of Glasgow is pleased to announce the return of our SASNet data skills training programme for summer 2017.

The programme will run from 1st August - 6th September offering an opportunity for skills development in key areas of data analytics, methods, and data management. Please visit http://ubdc.ac.uk/outreach-plus-training/sasnet/summer-training-2017 to view the full selection of courses.

Please contact keith.maynard@glasgow.ac.uk with any enquiries about the training programme.

Principles of Visualising Data with R and ggplot

Course instructor: Dan Olner, Sheffield Methods Institute, University of Sheffield

Course duration: 1 day (Friday 11th August 2017, 10:00am – 4:30pm)

Course location: Jura teaching lab, Level 4 Annexe, Glasgow University Library

Audience: PhD / postdoc / public with interest in data

Fees: £35 - For UK registered students

£60 - For staff at UK academic institutions, Research Council UK funded researchers, UK public sector staff and staff at UK registered charity organisations
£100 - For all other participants

Pre-requisite knowledge: Basic descriptive statistics. Basics of Rstudio and R's data types covered in Introduction to R. A self-learning document can also be supplied in advance of the course on request.

Course summary:

This is a one-day workshop on visualising data with R and ggplot. R is an excellent tool for wrestling data into useable shapes for visualising, but it has a steep initial learning curve with a lot of pitfalls along the way. This workshop aims to help you hit the ground running as painlessly as possible, providing a basis for creating the visualisations you want.

You will be introduced to some of the standard ways of using R to tidy, re-arrange and format data, and then how to put this data into graphic form using ggplot. We will look at a typical data preparation and visualisation workflow, using Land Registry house price data as an example. The aim of the day is to make the underlying principles of tidy data and ggplot as clear as possible, so that you can make full use of its flexibility in deciding how to visualise your own data.

As well as learning the technical skills required to prepare data and visualise it, we will also look at the difference between visualisation for communication (e.g. in journal articles) and the vital role visualisation can play in deepening your own insight into data (including how programming choices differ for these two).

The course will assume you know the basics of Rstudio and R's data types. If you don't know these but still want to apply, a short self-learning document (3 to 4 hours) built especially to lead into this course can be provided.

Course content:

• Preparing "tidy data" ready for visualisation
• Using ggplot to visualise your tidy data
• Understanding the principles that underpin ggplot
• Thinking about the different roles of visualisation
• R programming tips and tricks

Payment and registration:

Lunch will be provided in the break room adjacent to the IT lab - please specify any dietary or access requirements when registering.

Payment by card preferred.

If you are unable to pay by debit or credit card we are able to accept payment by invoice. Please supply the following information by e-mail to keith.maynard@glasgow.ac.uk within 14 days of registering on Eventbrite in order to confirm your place on the course:

• Contact name for invoice
• Purchase Order number (note that the supplier is University of Glasgow)
• Contact e-mail address for payment correspondence
• Full name of organisation/department
• VAT registration (if applicable)
• Currency
• Address for Invoice to be sent and telephone number

To cancel your place and request a refund, please email keith.maynard@glasgow.ac.uk. Please note that a refund of course fees less 10% administrative charge can only be issued if you cancel your place in writing at least 7 days prior to the course after which time fees will be non-refundable. Substitutes can be made at no extra charge up until 5pm on the last working day before the course.

Related 2017 courses:

Introduction to R

Using R: Mapping Spatial Data

Getting started with Data Management

Introduction to Areal Data Modelling using R

Measuring Segregation and Scale in R