Analytics R 1: Introduction to reproducible analyses in R 2020/21

When

Due to the ongoing pandemic lockdown, the course will be delivered on-line from 7th to 19th April 2021 during the following times and dates:

Wed 7 Apr Release set up instructions 

Expected audience: Those without previous experience

Fri 9 Apr 1400-1530 Office hours to assist with set up  

Expected audience: Those without previous experience

Mon 12 Apr 1100 – 1130 Introduction and Principles of reproducibility

Expected audience: Everyone

Tue 13 Apr 1000-1200 Introduction to R and working with data

Expected audience: Those without previous experience

Wed 14 Apr 1300-1330 RStudio Projects.

Expected audience: Everyone

Thu 15 Apr 1400-1530 Tidying data and the tidyverse including the pipe

Expected audience: For those with previous experience (workshops aimed at those without previous experience may be sufficient).

Fri 16 Apr 1000-1130 Advanced data import

Expected audience: For those with previous experience (workshops aimed at those without previous experience may be sufficient).

Mon 19 Apr 1300-1500 R Markdown for Reproducible Reports

Expected audience: Everyone

Where

This course will be delivered on-line.  Links and joining instructions will be provided by the course tutor – see below. 

Who it is for

This course is mandatory for White Rose BBSRC DTP, CASE and White Rose Network (WRN) students who are in Year 1 (2020 starters).  Plus, those students who were in Year 1 last year and did not complete all the essential modules of the course are required to complete these this year, in readiness for completing Analytics R2, also this year. These students have been alerted individually via email.

NOTE: Analytics R is a two part, mandatory training session for DTP students.  Analytics 1: Introduction to reproducible analyses in R will be held for first years.  When this cohort is in their second year, they will undertake Analytics 2: Advanced reproducible analyses in R.

Course content

An increase in the complexity and scale of biological data means biologists are increasingly required to develop the data skills needed to design reproducible workflows for the simulation, collection, organisation, processing, analysis and presentation of data. Developing such data skills requires at least some coding, also known as scripting. This makes your work (everything you do with your raw data) explicitly described, totally transparent and completely reproducible. However, learning to code can be a daunting prospect for many biologists! That’s where an Introduction to reproducible analyses in R comes in!

R is a free and open source language especially well-suited to data analysis and visualisation and has a relatively inclusive and newbie-friendly community. R caters to users who do not see themselves as programmers, but then allows them to slide gradually into programming.

Prerequisites

No previous coding experience will be assumed.  Those with previous R coding experience will have additional and more advanced exercises to work on.

Learning outcomes

After this workshop the successful learner will be able to:

  • Find their way around the RStudio windows
  • Create and plot data using the base package and ggplot
  • Explain the rationale for scripting analysis
  • Use the help pages
  • Know how to make additional packages available in an R session
  • Reproducibly import data in a variety of formats
  • Understand what is meant by the working directory, absolute and relative paths and be able to apply these concepts to data import
  • Summarise data in a single group or in multiple groups
  • Recognise tidy data format and carry out some typical data tidying tasks
  • Develop highly organised analyses including well-commented scripts that can be understood by future you and others
  • Use R Markdown to produce reproducible analyses, figures and reports

Tutor

The course will be delivered by Emma Rand of The University of York.

Refreshments

Not applicable for the on-line course.

Travel arrangements

Not applicable for the on-line course.

How to register

Please use the link below to register for this course:

Contact

If you have any queries or you feel you already have sufficient previous experience of a particular module, please discuss this directly with the tutor, Emma Rand, email: emma.rand@york.ac.uk

For general DTP queries, contact Catherine Liddle, White Rose BBSRC Doctoral Training Partnership Co-ordinator, e-mail: c.m.liddle@leeds.ac.uk