Reproducibility Toolkit

Overview

Teaching: 30 min
Exercises: 10 min

Questions

• What tools will we be using?

• How can we use these tools to improve reproducibility?

Objectives

• Learn to use R, RStudio and RMarkdown.

Our reproducibility toolkit

R + RStudio

Why R?

• Programming language for data analysis:
  • Free!
  • Open source
  • Widely used and supported across all disciplines
  • Can be used on Windows, Mac OS X, or Linux!

Why not language X?

• There are a number of other great programming tools out there that can also be used to improve the reproducibility of your analysis

• The key is to use some type of language that will allow you to automate and document your analysis

• Once you master one language you’ll probably find it easier to learn another

Once in R

You could just type into the command prompt…

• … but that doesn’t help much with documentation.
• … but that doesn’t help much with automation.

A better solution

• With RStudio you can combine your programming and your documentation.
• RStudio gives you a single environment to combine your documentation and your analysis - It runs on top of R - Gives you a bunch of really cool features that we’ll explore throughout the workshop.

R Packages

Packages are the fundamental units of reproducible R code. They include reusable R functions, the documentation that describes how to use them, and (often) sample data. (From: http://r-pkgs.had.co.nz)

• We will use the ggplot2 package for plots and dplyr for data wrangling in this session. Both packages come as part of the tidyverse suite of packages.

• If you have not yet done so, install this package by running the following in the Console:

install.packages("tidyverse")

Demo

Goals of the demo:

• Demonstrate “good practice” for organizing data files and analysis documents (RMarkdown)
• How to read data from a file
• How to manipulate the data, and document it in a reproducible way
• How easy it would be to revert any changes if need be
• How to subset data
• How to make simple plots in ggplot
• NOT about understanding all the R commands, but rather getting the big picture of how using R in this way facilitates reproducible analyses

R Markdown demo

Open intro-template.Rmd

Click on Knit HTML to compile the document

Important features:

• YAML on top
• Code in chunks
• RMarkdown syntax
  • Human readable!
  • Limited, so not too time consuming to master
• Self contained workspace

Extending the analysis

Great news!? We just received some more data, in bits and pieces of course: gapminder-7080.csv

gapminder-90plus.csv

Let’s walk through generation of new plots for the 1970s and 1980s and 1990s plus (these new analyses are already in the intro-tutorial.Rmd document).

Note that all code required to accomplish these tasks is also in the template. You do not need to come up with the R code, knit the document to combine the datasets and you’ll see that the code required for recreating the plots is the same as above. That’s the beauty of RMarkdown!

Take aways

• The analysis is self-documenting
• It’s easy to extend or refine analyses by copying and modifying code blocks
• The results of the analysis can be disseminated by sending RMarkdown and providing data sources, or just simply providing the generated HTML of just a summary of the analysis is needed

Reproducibility checklist

• Serves as a tool to help you think about the reproducibility of your data analysis.
• Many of the questions can be thought of as having a yes/no answer.
• A better approach would be to see the questions as being open ended with the real question being, “What can I do to improve the status of my project on this bullet point?”
• With that in mind, you’ll never get 100% of the bullets right for your project, but you’ll always be improving.

Key Points

• R, RStudio and RMarkdown allow for powerful reproducible research.