UBDC Training 2017: Introduction to R (additional date)
- Thursday 12th May 2017
- 10:00 - 16:30 BST
- Jura Teaching Lab, Level 4 Annexe, University of Glasgow Library, Hillhead St, Glasgow G12 8QE get directions
The Urban Big Data Centre is running a further date of Introduction to R from our SASNet data skills training programme on Thursday 12th October 2017
Visit http://ubdc.ac.uk/outreach-plus-training/sasnet/summer-training-2017 to view the full selection of courses that were run over the summer.
We intend to run further courses in the coming months so please contact firstname.lastname@example.org if you would like to be added to the mailing list or with any other enquiries about the training programme.
Introduction to R
Course instructor: Katarzyna Sila-Nowicka, UBDC, University of Glasgow
Course duration: 1 day (Thursday 12th October 2017, 10:00am – 4:30pm)
Course location: Jura teaching lab, Level 4 Annexe, Glasgow University Library
Audience: Social scientists, students, practitioners
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
Payment methods information is available on the Eventbrite page for this course.
Pre-requisite knowledge: Basic statistical knowledge is necessary but no prior programming experience is required
As a powerful open-source tool for statistical analysis, R has been increasingly utilised in higher education, scientific research and practice. The purpose of this course is to introduce basic knowledge of R, such as loading data into R, managing various data types and sizes, performing basic statistical techniques and visualising data. By the end of the course, you will be able to manage your data in R, calculate descriptive statistics and visualize your results with plots. The training aims to provide analysts with the foundational skills needed to use R for statistical analysis, which can be applied in academic research or practical work.
- Load data from a variety of file formats
- Organise data into various data structures
- Manage data into various data structures
- Calculate descriptive statistics
- Visualise data
Object reference not set to an instance of an object.