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    <p>I'm a statistician at a university in the UK. Generally people here use R for data analysis, it's fairly easy to learn if you know C/Perl. Its real power is in the way you can import and modify data interactively. It's very easy to take a number of say CSV (or Excel) files and merge them, create new columns based on others and then throw that into a GLM, GAM or some other model. Plotting is trivial too and doesn't require knowledge of a whole new language (like PGPLOT or GNUPLOT.) Of course, you also have the advantage of having a bunch of built-in features (from simple things like mean, standard deviation etc all the way to neural networks, splines and GL plotting.)</p> <p>Having said this, there are a couple of issues. With very large datasets R can become very slow (I've only really seen this with >50,000x30 datasets) and since it's interpreted you don't get the advantage of Fortran/C in this respect. But, you can (very easily) get R to call C and Fortran shared libraries (either from something like netlib or ones you've written yourself.) So, a usual workflow would be to:</p> <ul> <li>Work out what to do.</li> <li>Prototype the code in R.</li> <li>Run some preliminary analyses.</li> <li>Re-write the slow code into C or Fortran and call that from R.</li> </ul> <p>Which works very well for me.</p> <p>I'm one of the only people in my department (of >100 people) using version control (in my case using git with githuib.com.) This is rather worrying, but they just don't seem to be keen on trying it out and are content with passing zip files around (yuck.)</p> <p>My suggestion would be to continue using LabView for the acquisition (and perhaps trying to get your co-workers to agree on a toolset for acquisition and making is available for all) and then move to exporting the data into a CSV (or similar) and doing the analysis in R. There's really very little point in re-inventing the wheel in this respect.</p>
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    1. USDavid Lawrence Miller
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