R for Biologists

Date:

16 - 20 November

1- 5 pm each day

Venue:

online

Places:

24 for each workshop

You will be contacted by our finance team for full payment. Once payment is made, your place will be confirmed and full details sent by our training team.

Registration fee:

£250 - University of Edinburgh staff/students
£263 - Other university or registered charity staff/students
£325 - Industrial researchers
 

Information:

Contact our training team

 


 

The aim of this course is to introduce participants to the statistical computing language 'R' using examples and skills relevant to biological data science. This online workshop is taught by experienced Edinburgh Genomics’ trainers. By the end of the workshop, you will be comfortable with the basics of the R and R studio environments, learning about the rules of the language and how R works with different data types and structures. We then move on to using functions, introducing a selection of packages for biological data science. We then focus on data handling and tidying datasets using the 'tidyverse' family of packages. Finally we use mock RNA-seq data to learn how to visualise your data to generate publication ready plots using the package ggplot2.

 


 

Instructors

  • Nathan Medd (Training and Outreach Manager)
  • Donald Dunbar (Head of Bioinformatics)

Workshop format

The workshop consists of presentations and hands-on tutorials.

Who should attend

Graduates, postgraduates, and PIs, who are using, or planning to use, the statistical software R to manipulate and analyse NGS data in their research. This is an introductory level course: no prior experience of R is necessary before starting the workshop.


 

Covered topics

Day 1
    • Intro to R 
    • Rstudio, markdown, how to edit and run code chunks
    • R project management
    • R object, variable and data types (character, integer etc)
    • R resources: cheat sheets, help
Day 2
    • Vectors and factors
    • Data frames
    • Matrices and arrays
    • Lists
    • Functions
Day 3
    • Data importing (readxl, readr, read.table)
    • Data cleaning 
    • Apply functions 
Day 4
    • Intro to the 'Tidyverse'
    • dplyr, pipes (%>% and magrittr), tidyr, readr
    • Intro to ggplot (do a fair bit of the early stuff we do now)
Day 5
    • Plot galleries and decisions trees
    • ggplot (finish the ggplot bit and maybe add some more fancy plots)
    • Interactive plots with Plotly and Shiny