R Makefile definitions updated for SAS, STATA, PSPP, python, perl


Simple rules added for several stats packages plus …

GNU make rules are now more complete for data analysis projects. Steps in the process may involve data manipulation using software other than R. Unfortunately, I have been unable to thoroughly test these rules because I don’t have access to all these packages on Windows, MacOSX and linux. However, all that will probably be needed is to set variables to set up options or paths to programs specifically for your setup. Also, please note that PSPP is included since the command line batch facility seems to have disappeared in the versions I have access to of SPSS. PSPP has a much more limited data processing and analysis steps available.

Rules are added for:

  • SAS
  • PSPP (SPSS on Windows now uses weird project system not compatible with simple command line batch)
  • python
  • perl

As part of any data analysis project, while I predominantly use R, some steps using other software may be required. To aid reproducibility, syntax can be placed under version control and any output regenerated using make and the pattern rules in r-rules.mk. Help may be obtained using make help or for the new rules make help-stats-others.

Unlike statistical packages, python and perl don’t have a target file with any particular specified file name extension. It can be set with the variables PY_OUT_EXT or PL_OUT_EXT which by default is txt.

In the Makefile we can include the following:

PYTHON = /usr/local/bin/python
PY_OUT_EXT = out

myFile.out: myFile.py simple.csv

At the command prompt:

make myFile.out

If you have myFile.sas, myFile.do, myFile.sps then you can produce output files for SAS, STATA and PSPP using the following commands:

make myFile.lst
make myFile.log
make myFile.list

Check out the file r-rules.mk from http://github.com/petebaker/r-makefile-definitions

R Makefile definitions updated

2016-06-23 at 16:23:57 (Version 0.2.9004)

  1. fixed Rscript –vanilla R CMD –vanilla bug for latexmk
  2. added variables for programs like cat, rm, pdfjam, latexmk to include PROG_OPTS to set options and LATEXMK_PRE which can be prepended to latex
  3. changed outputting R syntax from .Rmd and .Rnw files to produce -syntax.R to avoid dependency loops where a .tex file then .pdf might be produced instead of using rmarkdown

Latest version available at

github: R Makefile definitions

2016-06-19 at 23:27:34

  1. added in various rmarkdown outputs like ioslides, slidy, beamer, tufte, rtf, odt

2016-05-19 at 11:58:34

  1. modified beamer from .Rnw to be more generic
  2. added beamer example and preamble .Rnw files which can be used to produce presentation, handouts, notes, articles and handouts and multiple page per page handouts from a single .Rnw file
    • see make help-beamer

useR! 2015 Aalborg Tutorial

Efficient statistical consulting using R: Workflow for data analysis projects
Tutorial Tuesday, June 30 at 9:00-12:00


Please install the dryworkflow package from github using these commands in R:

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

You should also install GNU make and git. Git should be installed if you have RStudio installed. Make will be installed on linux systems. Windows users should install Rtools and for MACOSX  please install XCode. For more details please see https://github.com/petebaker/dryworkflow

dryworkflow package 0.1.9016 available on github

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.

Further details and installation instructions are available at https://github.com/petebaker/dryworkflow

Makefile definitions for R


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R related Makefile definitions

GNU Make is a commonly used tool as part of the process for managing software projects written in languages like C or python. For data analysis projects, it’s main strengths are that it allows the data analyst to repeat just those steps needed when data or R syntax is changed in addition to clearly outlining the steps required.


Data analysis can involve many steps including reading data; cleaning and transforming data; plotting data, statistical analysis and finally writing reports. While we can try and keep track of each step manually by using good documentation and being highly organised, it can prove to be more efficient to employ computer tools to augment these practices. One such approach is to use a tool like GNU Make. Such an approach does not obviate the need to be organised and document the work but it can certainly prove helpful, especially as a project grows in size. While it is far from perfect, make is widely used in software development and also proves to be useful for efficiently carrying out tasks in data analysis. Unfortunately, make does not provide standard rules for producing .Rout files from .R files, .pdf files from .Rnw files, .docx files from .Rmd files and so on. It is straight forward to define a pattern rule to output a .Rout file from a .R syntax file by including the following two lines in a Makefile

%.Rout: %.R
<TAB> R CMD BATCH --vanilla $<

which runs the command R CMD BATCH –vanilla to produce the output file. The left hand side of the colon (:) is the target which depends on the prerequisite file(s) to the right of the colon. Here, % is a wildcard. So, for any .R syntax file, say mySyntax.R, you can then use ‘make mySyntax.Rout’ to produce the .Rout output file noting that nothing happens if the target is newer than the prerequisite since it is already ‘up to date’. To actually use this rule in practice, we may have several prerequisite files like an R syntax file and several data files. In the Makefile we may specify the dependencies as

readData.Rout: readData.R data1.csv data2.csv oldData.RData

so we can run the syntax file by typing ‘make readData.Rout’ at the command prompt. If any of the files readData.R, data1.csv, data2.csv or oldData.RData have changed recently, and so are newer than the target file readData.Rout, then the predefined R batch command is run to get a new output file, otherwise readData.Rout is ‘up to date’. Similar rules can be set up for producing reports from markdown or sweave files. The file common.mk contains many such rules and can be included in a standard Makefile to facilitate a more efficient workflow. You can obtain common.mk at github https://github.com/petebaker/r-makefile-definitions

Using common.mk

  1. Download the file to a directory you commonly use to store functions and definitions. Ideally, this would something like:
    • ~/lib or C:\MyLibrary
  2. put the following line in yourMakefile
    • include ~/lib/common.mk where ~ will be expanded to be your HOME directory, or
    • include C:/MyLibrary/common.mk (in windows)

Example Makefiles

.PHONY : all
all: test.pdf test.html test.docx test2.pdf test-stitch.Rout test-stitch.pdf

## produce pdf, html, docx from test.Rmd
test.pdf: ${@:.pdf=.Rmd}
test.html: ${@:.html=.Rmd}
test.docx: ${@:.docx=.Rmd}

## produce pdf from test2.rmd
test2.pdf: ${@:.pdf=.rmd}

## use stitch to produce pdf via rmarkdown (exactly as in RStudio)
test-stitch.pdf: ${@:.pdf=.R}

## if you have common.mk in ~/lib directory comment line below 
## and uncomment the second line
include common.mk
##include ~/lib/common.mk

More usually, if there is a sequence of steps relying on a data file, say myData.csv  then your Makefile may look something like

.PHONY: all
all: report.pdf

## produce report from .Rmd once previous steps carried out
report.pdf: ${@:.pdf=.Rmd} summaryAndPlots.Rout

## summarise data
summaryAndPlots.Rout: ${@:.Rout=.R} read.Rout

## read data
read.Rout: ${@:.Rout=.R} myData.csv

include ~/lib/common.mk
Run this with the command ‘make’ at the command line or even better: set up RStudio or your editor to do this at the press of a button.


To use these makefile definitions 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.


Definitions in ‘common.mk’ have been developed and tested on linux and tested on windows. Some tweaking may be required to suit your set up or your preferred workflow. Peter Baker