Efficient data analysis and reporting: DRY workflows in R

When analysing data for different projects, do you often find yourself repeating the same steps? Typically, these steps follow a familiar pattern of reading, cleaning, summarising, plotting and analysing data then producing a report. To aid reproducibility, naive examples using Rmarkdown are often presented. However, I routinely employ a modular approach combining GNU Make, R, Rmarkdown and/or Sweave files tracked under git. This system helps to implement a don't repeat yourself (DRY) approach and scales up well as projects become more complex.To aid automation, I have developed generic R, Rmarkdown, STATA, SAS and other pattern rules for GNU Make as well as R packages to generate a project skeleton consisting of initial directories, Makefiles, R syntax files for basic data cleaning and summaries; move data files and documents to standard directories; use codebook information to specify factors and check data; and finally initialise and add these to a local git repository. Comparisons will be made with alternate approaches such as ProjectTemplate and drake.GNU Make pattern rules and R software are available at github.com/pet....