• 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

You could just type into the command prompt…

• … but that doesn't help much with documentation

• … but that doesn't help much with automation

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

• 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.

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

install.packages("ggplot2")
install.packages("dplyr")

• This is just one way of installing a package, there is also a GUI approach in the Packages pane

• Demonstrate "good practice" for organizing data files and analysis documents (R Markdown)
• 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

• Open intro-template.Rmd

• Click on Knit HTML to compile the document

• Important features:
  • YAML on top
  • Code in chunks
  • R Markdown syntax
    • Human readable!
    • Limited, so not too time consuming to master
  • Self contained workspace

Great news!? You just received some more data, in bits and pieces of course:

• gapminder-7080.csv
• gapminder-90plus.csv

Add the data from 1970s and 1980s and 1990s plus, and recreate the same plots as before.

• 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 R Markdown and providing data sources, or just simply providing the generated HTML of just a summary of the analysis is needed

• 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.