**Comment of the Day: **: Testing Time for Spreadsheets: "I'm biased as a MathWorks employee...

...but you may want to look into MATLAB. It is really strong in the kinds of data analysis and plotting that econ students need to do. MATLAB has a pretty non-programmer friendly editor and model that helps new users.

Jeff Dutky: "By all means steer your students away from spreadsheets...

...and toward more traditional programming languages (both R and MATLAB count as traditional programming languages here) that can be better documented, tested, and verified than a spreadsheet can. Building spreadsheets is akin to programming in assembly language: too little abstraction to be comprehensible on anything but toy problems.

Jorgensen: "Spreadsheets are a wonderful tool for simple mathematical and financial analysis...

...I use spreadsheets regularly for small (<1,000 records) simple ad hoc flat file databases - and for that too they are a wonderful tool.

The problems seem to come when people who are not really mathematically sophisticated build models in a spreadsheet that then grow in an undisciplined fashion to something that is unmanageable. I think that anyone going into economics or business should know how to use a spreadsheet but they need to be told of its limits and the need to move to more sophisticated tools for larger, more complicated, models.

JP Morgan lost $2 Billion dollars in the "London Whale" incident because of a programming error in a spreadsheet that had grown too large and complex.

Phil Koop said: "It may be that you ought to have your students do problem sets in R instead of Excel...

...but Lisa Pollack's observation, fine as it is, has no bearing on the matter.

I develop software for a living. Software that does abstract statistical modeling, complex data transformation and presentation. I don't use R for this but I sure as heck don't use Excel. Nevertheless, I use Excel all the time for small side calculations as I work. Sometimes I might use a tool like R or Matlab to prototype a model. R can also be useful for vetting a model by duplicating a statistical algorithm cheaply. But often Excel is the best tool for model vetting. One reason for this is that it is often the best way to communicate an algorithm or clearly demonstrate one. This is only partly because of lingua franca; although systems implemented in spreadsheets are generally opaque, a spreadsheet is often the most transparent way to implement a single clearly defined algorithm.

So whether R or Excel is the best tool depends on the nature of the problems you set. Of course, if you goal is for your students to learn R, problems for which R is a poor tool may nevertheless be a good means to that end, just as a "hello world" program is a good way of learning certain structural aspects of a programming system, even though not the best way of printing the words "hello world." Reply August 12, 2015 at 02:42 PM

Best Practices: "One of the standard practices in a good software development life cycle...

...is unit testing. There are unit testing frameworks for both R and Excel. Students should be expected to use these to show that their solutions produce correct outputs for a standard set of inputs relevant to the problem regardless of the language used. See: https://en.wikipedia.org/wiki/Unit_testinghttp://c2.com/cgi/wiki?TestDrivenDevelopment. Unit testing framework for R: http://sourceforge.net/projects/runit/, or Excel: http://www.sourcecodeonline.com/code/vba_tic_tac_toe-3.html

A: "That anything of importance depends on an Excel spreadsheet calculation...

...is really scary. (And do you really want to share Excel spreadsheets with macros enabled? May be a new route for spreading disasters.)--

After programming in C++ I found MatLab easy to use (but it is commercial - requires you to pay). My daughter's Biology (college) class had students draw some graphs with R, so that must be easy enough.--

But I am surprised nobody mentioned Python https://www.python.org/

which comes with various scientific/plotting packages http://scipy.org/> and there is a nice (matLab-like) IDE called Spyder included e.g. in the 'Anaconda' distribution https://store.continuum.io/cshop/anaconda/. I had thought that students in quantitative majors nowadays all know Python. (I even remember Prof. DeLong mentioning it in some post.)

Derrida Derider: "I'd defend Excel...

...It's got quite good auditing, testing and documenting tools these days - BUT YOU HAVE TO USE THEM. Also it can handle much more complex and larger data than a few years ago. Sure if they go on to do more Econ or other quantitative subjects they'll need other tools, but it is an amazingly flexible and versatile thing. Plus for good or ill they will definitely encounter it in their future workplace. But do tell your students you'll require evidence they've structured, tested and documented their work.