mages' blog

One of my take aways from last week's EARL conference was that R is more and more growing out of its academic roots into the enterprise. And with that come some challenges, e.g. how do I ensure consistent and systematic access to a set of R packages in an organisation, in particular when one team is providing packages to others?

Two packages can help here: roxyPackage and miniCRAN.

I wrote about roxyPackage earlier on this blog. It allows me to create a local repository to distribute my package, while at the same time execute and control the build process from within R. But what about my package's dependencies? Here miniCRAN helps. miniCRAN is a new package by Andrie de Vries that enables me to find and download all package dependencies and store them in a local repository, e.g. the one used by roxyPackage.

For more details about roxyPackage and miniCRAN read the respective package vignettes.

Example

To create a local sub-CRAN repository for the two packages I maintain on CRAN and with all their dependencies I use:

library("miniCRAN")
my.pkgs <- c("googleVis", "ChainLadder")
pkgs <- pkgDep(my.pkgs, suggests = TRUE, enhances=FALSE)
makeRepo(pkgs = pkgs, path="/Users/Shared/myCRANRepos")

And to visualise the dependencies:

dg <- makeDepGraph(my.pkgs, includeBasePkgs=FALSE, 
                   suggests=TRUE, enhances=TRUE)
set.seed(1)
plot(dg, legendPosEdge = c(-1, 1), 
     legendPosVertex = c(1, 1), vertex.size=20)

What a surprise! In total I end up with 42 packages from CRAN and I didn't expect any connection between the ChainLadder and googleVis package.

Bonus tip

Don't miss out on Pat Burns's insightful talk about effective risk management from EARL. His thoughts reminded me of the great Karl Popper: Good tests kill flawed theories; we remain alive to guess again.

Session Info

R version 3.1.1 (2014-07-10)
Platform: x86_64-apple-darwin13.1.0 (64-bit)

locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8

attached base packages:
[1] stats graphics  grDevices utils  datasets  methods  
[7] base     

other attached packages:
[1] miniCRAN_0.1-0

loaded via a namespace (and not attached):
[1] httr_0.5 igraph_0.7.1  stringr_0.6.2 tools_3.1.1  
[5] XML_3.98-1.1

Documenting code can be a bit of a pain. Yet, the older (and wiser?) I get, the more I realise how important it is. When I was younger I said 'documentation is for people without talent'. Well, I am clearly loosing my talent, as I sometimes struggle to understand what I programmed years ago. Thus, anything that soothes the pain of writing and maintaining documentation must be good and should help me to better understand my 'old me' in the future.

Ideally I want my R code and documentation in as few files as possible. A good start to achieve this is using roxygen2, an R package which has been around for some time. It allows me to tie R code together with the documentation in the same file and helps considerably in maintaining R packages.

The roxyPackage by Meik Michalke goes a step further, building on roxygen2. Meik presented his package at the Cologne R user group meeting a few weeks ago and I was intrigued by it. As I said in my notes to the meeting, with roxyPackage I can create a package from a single file of R code and documentation.

Here is an example of one R file to build a package using roxyPackage. For my toy example I wrote a doughnut plot function in R, something which is clearly missing ;)


I took the basic pie chart function and amended it to plot another white disc in the middle. On top of the function code I wrote the help file documentation using the roxygen2 syntax.

John D. Cook gave a great talk about 'Why and how people use R'. The talk resonated with me and highlighted why R is such a great tool for end user computing. A topic which has become increasingly important in the European insurance industry.

John's main point on why people use R is that R gets the job done and I think he is spot on. Of course that's the trouble with R sometimes as well, or to quote Bo again:

"The best thing about R is that it was developed by statisticians.
"The worst thing about R is that it was developed by statisticians."
Bo Cowgill, Google