Semantic Werks

Cameron Neylon is an advocate of open science (along with others like Michael Nielsen). Among other things, open science or Science 2.0 means keeping track of your mundane day-to-day activities (I like to call it “sciencing”) using some publicly accessible repository. This way other researchers (your competition?) can see what you are doing and comment/question/improve your work.  This also has benefits for the researcher herself, of course, in keeping track of what steps were followed. Nothing is worse than getting a good result and not being able to replicate it (“I swear it was there! I saw it!” “Yes of course you did, here, just take this pill..”). Actually there is something worse and that is losing data and not having a backup. Like me.

Recently I was working on an project – MSR/data mining of open source software – and saw one aspect of this that could be improved. I’m working with R, the open-source statistics toolkit to do data analysis. I’ve never used it before, so there’s a fair bit of reading the manual, copying/pasting of examples, and experimentation involved. This can be dangerous: am I selecting a result from my analysis just because it “looks right”, or because I really understand what the analysis is saying? I am also going to want to repeat what got the particular graph I end up with (e.g., change my numeric yearweek dates to R date objects).

Helpfully R has a command line history you can store in a ‘workspace‘. But one thing I would like to be able to do with this, and any command line environment, is to somehow identify the key commands. For instance, I modify an online example to plot a regression line for my data. I would like to somehow send that command to a “repeatable steps” repository to preserve a sense of useful workflow (not just history). The vanilla history typically has a lot of missteps, and going back through it can be a challenge. To make this tool even better, it should somehow parse out the dataset-specific pathnames and variable names. Then I would be left with something like:

> <data.frame.variable> <-  read.csv(file="<csv file name>",head=TRUE, sep=",")
>summary(data.frame.variable)
>data.frame.variable$<index column> <- sequence(nrow(data.frame.variable))
> <dates.variable> <- strptime(as.integer(data.frame.variable$dateweek.variable), "%Y%U")
> plot(dates.variable,data.frame.variable$<values.variable>

Which would allow me to insert my own variable definitions (a meta-language, I guess) and have that run automatically as a script in R.