Reproducible research is vitally important for scientific studies, but what about for businesses?

A LOT of analysts still do things the manual way. That might involve using a combination of Microsoft Access, Excel and Word to produce their analysis.

Toolsets aside, it’s hard to easily update your analysis if you’ve found an error in your sql queries or made a calculation error in Excel. If you find a problem it usually means repeating multiple steps to correct your mistakes.

My preference for the past two to three years has been to use R to make my reports as accessible and repoducible as possible.

To that extent I’m sharing a template for how I set up all of my analyses. There is probably hundreds of ways that this could be improved, but it works the majority of time for me. In the future, I’d like to learn more about make, I think it could really improve this process. The way things currently are, each time the shell script is invoked, the analysis is rebuilt from scratch.

Let’s take a look at the setup. Each analysis includes the following items:

1. code: a folder to store code for processing data
2. images: a folder to store images produced for reporting
3. README.Rmd: This is a a R file used to produce the README.md file.
4. README.md This is a tl:dr of the analysis pdf file.
5. main.R: This is the main R file. It lists the steps for the analysis
6. run.sh: This is the bash file that will automatically run ‘main.R’ when invoked.

The main.R is the primary file that is used to run the analysis. I’ve included some example code that you can check out. It will call all of the other files in the code folder and run them. Lastly, it will knit all of the files together as a README.md file and a pdf file.

The run.sh is a bash file that will run the main.R file when invoked.

Within the code folder, the first file load.R should be used to load all of your raw data. If you’re working with CSV files, I recommend adding a folder called data and cleanData. Depending on your preference, you may want to use load.R to clean your data as well.

The next file, functions.R is normally used for exploratory data analysis and manipulation of data.

The last file, plots.R is normally used to create any plots for the analysis.

Since R stores all of the data in memory, you can break your analysis up into as many files as you want. If you do that, I recommend keeping track of which files are doing what. I normally do this with the README.md files within folders.

Feel free to fork this project on github and make changes for yourself. You could even use this in a private repo and then change the upstream to a new repo when you clone this from github.

If you want to checkout out the project, you can find it here on Github: Reproducible Research Template for R by Chip Oglesby