DRY (Don't Repeat Yourself) Workflow for more efficient data analysis using R
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README.md

README.md

dryworkflow

The information below is also available in package help.

Also see the blog site for details.

Installation

In R as Administrator, to install please use the following:

library(devtools)  # available on CRAN (or github)
devtools::install_github("petebaker/dryworkflow", dependencies = TRUE)

Prerequisites

To use Makefile definitions and version control using git, you need to install

Note that Windows users can install Rtools (available from CRAN) to get a working version of make and may also need to install pandoc and latex to produce pdf files if they haven't already. Miktex is recommended although texlive will also work well.

MACOSX users should install a recent version of Xcode CLT (Xcode command line tools) and Homebrew in order to install make and git. Unfortunately, I don't yet know much about Macs as my brand new (and first) MacBook Pro is still in for repairs. For some hints try http://www.moncefbelyamani.com/how-to-install-xcode-homebrew-git-rvm-ruby-on-mac/. Finally, to produce pdf reports MacTex https://tug.org/mactex/ is recommended.

In linux, if they aren't already installed, simply install these packages using the system package manager.

The easiest way to install git and pandoc on all platforms is to install RStudio. If you don't have a favourite programmer's editor that you already use for R then this is the best way to use R as well. Install RStudio from http://rstudio.org. Note that you may need to put the directory containing RStudio etc in the PATH.

You can check that make, git and pandoc are installed by typing

git --version
make --version
pandoc --version

Finally, check that latex is available with

pdflatex --version

Using the dryworkflow package

The dryworkflow package produces a project skeleton for data analysis including R syntax files, report and Makefiles. Given data files and documents, the skeleton is generated with initial directories, template log files, template R syntax for data checking and initial analysis, makefiles and a git repository is initialised.

Templates

R syntax templates for reading, cleaning, merging, summarising and analysing data and Rmarkdown and Sweave templates for reports. The function copyTemplates may be used to get copies of these templates which can then be modified for use when creating a project skeleton.

Make and definitions

Makefiles are generated. The file common.mk provides pattern rules to produce .Rout and .pdf files from R syntax files and .html, .pdf and .docx files from .Rmd R markdown and .Rnw files. The function copyCommonMk may be used to get a copy the common.mk file used by the installed version of the dryworkflow package. The latest version of common.mk can always be found at https://github.com/petebaker/r-makefile-definitions

.gitignore

A .gitignore file is created in the base project directory to indicate files not to be tracked by git. The function copyGitIgnore may be used to get a copy the .gitignore file used by the installed version of the dryworkflow package. The latest version of .gitignore can always be found at https://github.com/petebaker/r-gitignore

Project Options

Note that option parameters are either set as an argument to the function createProjectSkeleton or automatically via global options using getOption("dryworkflow"). Customised options may be set in .Rprofile using global options and these will be set automatically when dryworkflow is loaded.

Examples

setting global options or put these in .Rprofile

current.opts <- options()
options("dryworkflow" = list(git = list(user.name = "My Name", user.email = "myname@email.com")))
library(dryworkflow)
options("dryworkflow")

A project with all default settings

## File: setupProject.R
##
## copy .csv file and codebook from dryWorkflow package
## noting that normally you just place files in current directory
## and then run 'createProjectSkeleton'
file.copy(system.file('demoFiles', 'small2.csv', package='dryworkflow'),
          'small2.csv')
file.copy(system.file('demoFiles', 'small2_codebook.csv',
                      package='dryworkflow'), 'small2_codebook.csv')

## NB: In practice, always check directories, R syntax  etc
##     before using 'make'
createProjectSkeleton(dir.proj = "testProject2",
                      name.project = "Experiment 1",
                      dontmove = "setupProject.R")