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White Rose BBSRC DTP Training: An Introduction to Reproducible Analyses in R. 2021

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White Rose BBSRC DTP Training: An Introduction to Reproducible Analyses in R.

Training programme for first year bioscience PhD students at the White Rose Universities (York, Sheffield, Leeds).

Overview

Underlying all biological discoveries are data! The ability to generate reliable measures of biological phenomena through experimental design, modelling or simulation, and then analyse and communicate the results are essential skills for a biologist. This is has always been true but an explosion of large-scale, complex and noisy data has made the acquisition of data skills even more crucial. Such skills include being able to statistically analyse and visualise data generated by research from the ecological to the biomolecular. To critically evaluate inferences arising from these analyses and advancing the methodology is dependent on research findings being published with their data and analysis code. This is a characteristic known as “reproducibility” and it requires at least some coding. Coding makes everything you do with your raw data explicitly described, totally transparent and completely reproducible.

This programme comprises several modules lasting from 30 minutes to 2 hours. Topics have been chosen because they are: foundational, widely applicable and transferable conceptually.

Modules

1. Introduction and Principles of reproducibility
   Expected audience: Everyone

2. Introduction to R and working with data. Expected audience: Those without previous experience

3. RStudio Projects.
   Expected audience: Those without experience of RStudio projects

4. Tidying data and the tidyverse including the pipe.
   Expected audience: For those with previous experience of R but little of 'tidy data' and the tidyverse, such as having done "Introduction to R and working with data."

5. Advanced data import.
   Expected audience: For those with previous experience of R such as having done "Introduction to R and working with data" and "Tidying data and the tidyverse including the pipe."

6. R Markdown for Reproducible Reports.
   Expected audience: For those with previous experience of R such as having done "Introduction to R and working with data"

Learning outcomes

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