Data Analysis Using R

Data Analysis Using R

Initial tasks:

1. Log into a PC (or have your laptop ready to use)
2. Check you can load RStudio
3. Open the course notes page at https://UoMResearchIT.github.io/r-tidyverse-intro/

• There is a link to this slideshow there

4. There is no sign-in sheet. Attendance is recorded via the feedback form http://bit.ly/2xP95Ef

Introduction

• Aims of the day
• Research IT
• Teaching methods

Housekeeping

• Fire exit
• Toilets
• Coffee breaks and lunch
• Course timing
• Feedback (form and verbal)

Aims of the day

• Introduction to R
• No prior knowledge of R assumed
• Focus on using R for analysis rather than programming
• Does not teach statistics
• The aim is to teach you enough know how to find out more

Research IT

Described on the IT Services website

Announcements given via the Research IT blog and newsletter (sign up via the blog page) and on Twitter @UoM_eResearch

• Training courses teach computing skills for research
• Advice and guidance about research software
• Access to specialist support and consultancy e.g. code reviews
• Access to HPC systems - where you can run R.
• Full list of services on offer

Get in touch via the support centre

Why learn R?

• (probably) the most popular language for data-analysis and statistics
• It’s free
• It’s extensible
  • Interactive apps Shiny, e.g. gapminder visualisation
  • Reproducible research with Knitr
  • Presentations
• Over 13,000 packages
  • This does mean there’s (often) more than one way to do a task

R user groups

R user groups are a great way of finding out more about R.

• University of Manchester R user group
  • Meets monthly
  • Email list for announcements, questions etc.
  • Has tea, coffee and biscuits
  • Separate beginners’ group
• ManchesterR
  • City-wide group (not affiliated with the university)
  • Meets quarterly
  • Typically 3-4 presentations; often a commercial focus.

Teaching methods

• The course is based on the Software Carpentry R for reproducible scientific research course
  • Abridged
  • Doesn’t cover version control, but we offer a separate course on this
  • Teaches the tidyverse; “an ecosystem of packages designed with common APIs and a shared philosophy”

Teaching methods

• The course is interactive
  • These are the only slides
• Getting help
  • The sticky notes
• Course notes
  • Try to type-along without the notes
  • All the code is in the notes (Extras menu); you can cut and paste this into RStudio.
  • The slides will remain online after the course.

Let’s get started

https://uomresearchit.github.io/r-tidyverse-intro/