ProjectTemplate is an R package that makes it easy to create and run projects so that you can spend less time thinking about organization and more time analyzing data.

1. Reproducible Project Workflow in R
(with ProjectTemplate)
Caitlin Hudon, Sr. Data Analyst @ web.com

2. Why reproducible workflow matters

3. Things I care about (in structuring projects)
+ Easy to find things (reports, data, etc.)
+ Easy to share
+ Ability to update with new data
+ Reproducibility
+ Being able to quickly verify results
+ The sanity of my future self

4. Enter:

5. Technical Benefits of ProjectTemplate
+ Easy to create new projects
+ Organizes and standardizes your projects
+ Automatically loads data and R packages needed
+ Automatically runs data munging scripts

6. Non-technical benefits
+ Automates the thoughtless parts of your project (so you
can use your energy on the important stuff)
+ Easier for someone new to your project to understand
steps / data / preparation involved to execute it

7. Create a project
library(‘ProjectTemplate’)
create.project(‘new_project’)

8. New ProjectTemplate Project
Munge Folder
Data Folder
Config Folder → global.dcf file

9. Load a project
library(‘ProjectTemplate’)
setwd(‘~/projects/new_project’)
load.project()

10. Advice
+ Number files in the munge folder so they run in order
+ Avoid manually updating raw data files (do this in code!)
+ Edit the global.dcf file (in config folder) to add libraries you
use, load libraries automatically, and adjust “strings as
factors” settings
+ Lots of other ways to make analysis reproducible;
ProjectTemplate is one part of larger ecosystem

11. Cheat Sheet
ProjectTemplate home page
+ Don’t miss the tutorial!
Great discussion on best practices for managing analysis projects
The talk that got me interested in ProjectTemplate (thanks, Hilary Parker!)

12. Questions? Ideas?
caitlinmhudon@gmail.com
@beeonaposy