sasMap: static code analysis for SAS

March 15, 2017
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(This article was first published on Mango Solutions » R Blog, and kindly contributed to R-bloggers)

Ava Yang

You may drop your weapons, this is not going to be about SAS vs R. If you work with a large amount of SAS legacy code, sasMap, an R package with a Shiny app, is for you. It evolved from our experience in migrating SAS to R, see Mark Sellor’s post about production R at ONS for an example.

Disclaimer: there’s no such thing as SAS-to-R auto translator, yet. sasMap is a map that can keep you on track by making it easy to look for a path through a wild land.

Overview


Often multiple macros are nested to construct main SAS analyses. User macros are held in sub-folders and are called in top level scripts. sasMap calculates summary statistics of SAS scripts and helps to understand macro and script dependency. The key functionalities of the package are:

  • Extract summary statistics such as procs and data steps
  • Draw a barplot of proc calls
  • Visualize static and interactive network of script dependency

And to accomplish this the package provides the following functions:

  • parseSASscript Parse a SAS script
  • parseSASfolder Parse a SAS folder
  • listProcs List frequency of various proc calls
  • drawProcs Draw frequency of various proc calls in a bar plot
  • plotSASmap Draw script dependency in static plot
  • plotSASmapJS Draw script dependency in interactive way

Demo


The package includes some dummy SAS code in the \examples\SAScode\Macros folder. The folder contains one high level script MainAnalysis.SAS and a subfolder called Macros where the user’s macros live. The main assumption is that each macro corresponds to a script of the same name. Some macros are called but don’t have a named script. For example, %summary in Util2.SAS, is not displayed in the static network representation, whereas it belongs to internal macros group in the interactive network graph.

The summary statistics include measures such as number of lines (nLines), Procs, number of data step (Data_step), macro calls (Macro_call) and macro defined (Macro_define).

# Install sasMap from github
devtools::install_github("MangoTheCat/sasMap")

# Load library
library(sasMap)

# Navigate to target directory
sasDir

# Parse SAS folder
kable(parseSASfolder(sasDir))

table1

# Draw frequency of proc calls
drawProcs(sasDir)

unnamed-chunk-1-1

# Draw network of SAS scripts. A pdf file can be created by specifying the file name.
net <- renderNetwork(sasDir)
# plotSASmap(net, width=10, height=10, pdffile='sasMap.pdf') 
plotSASmap(net, width=10, height=10)

unnamed-chunk-2-1

## Alternatively, draw it interactively (not run here)
plotSASmapJS(sasDir)

Put them together


The sasMap package is accompanied by a shiny app which you can run by executing the following line of code:

library(shiny)
runApp(system.file('shiny', package='sasMap'))

Once the “I want to specify a local directory (Warning: It only works when running the shiny app from a local machine).” box is ticked, exposed is a “Choose directory” button which makes it straightforward to direct to your SAS folder (thanks to the shinyFiles package). You can also view a demo version of the app here. For demo’s purpose, the deployed version has the dummy SAS code hard-coded.

Conclusion


At Mango we have benefited greatly from this way of working with SAS code (see this blogpost for more information). If you want to know more about the sasMap package or about SAS to R migration feel free to contact us by phone (+44 (0)1249 705 450) or mail ([email protected]). The code for this post is available on github as is the code for the package.
 

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