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Not that sure --- R is such a clusterfuck that someone managed to write a succesful 150-page book about the different ways in which you can shoot yourself in the foot with it.

R is a terrible language with great libraries. I would heartily recommend R for any statistics job. Just don't try to write any programs in it.

Not that sure --- R is such a clusterfuck that someone managed to write a succesful 150-page book about the different ways in which you can shoot yourself in the foot with it.

A subset of a data.frame is a data.frame, unless you haven't set drop = FALSE, and "select" only one column.
By default "the result is coerced to the lowest possible dimension. The default is to drop if only one column is left, but not to drop if only one row is left."
When a result is reduced to 1 dimension its type changes, and R will throw an error if you use a data.frame method on the result.
I advise anyone using R seriously to read The R Inferno to learn to avoid the many non-obvious features of R

... this is all posturing and drama, but good on Prof. Norm Matloff for getting some attention. R is rather usefull, has quite a few extremely usefull features as a language, including some of the best list/indices handling I've seen anywhere. Excellent libraries for statistical work, but it also has quite a few the most downright abhorrent language decision I've seen anywhere ever, with the amazingly poor string handling (for a scripted language) topping that list ( http://www.burns-stat.com/page... )

Python, C, Mathematica and R all have different strengths for mathematical work / numerical calculations though, and using the best tool for the job is what it's about. As always, what the best tool actually is, is also rather subjective, as which tool will best solve a specific task is always dependent on your skill with the different tools. I do agree with professor though, even though there's quite abit of Python hype (python + scipy/matplotlib is amazing) R is not being replaced anytime soon. It's too good at what it's good at.

You're in luck! The R Inferno

Spreadsheets are dangerous when they become complex. Given that you mention programming language, that's a pretty good sign that the application is too complex to be done safely in a spreadsheet. See http://www.burns-stat.com/documents/tutorials/spreadsheet-addiction/ for more.

PLEASE ACTUALLY READ WHAT YOU LINK TO.
MODERATORS: LOOK AT WHAT YOU ARE CALLING INFORMATIVE.
YEP, I'M YELLING. DEALING WITH STUIPIDITY IS FRUSTRATING.

Excel and other spreadsheets suck at stats:

That is one camp of thought. There are others. Every package has it's limitations

* Burns, P. (2005). Spreadsheet Addiction.

Doesn't talk about never using statistics. Talks about misusing them by pressing them past their limits. "I know there are many spreadsheets in financial companies that take all night to compute. These are complicated and commonly fail. When such spreadsheets are replaced by code more suited to the task, it is not unusual for the computation time to be cut to a few minutes and the process much easier to understand."

* Cryer, J. (2001). Problems with using Microsoft Excel for StatisticsPDF.

Focuses on poor charting in the Excel 95 era. Title should be problems for using Excel for graphing. The article is a decade old. Excel has had several refreshes.

* Pottel, H. (n.d.). Statistical flaws in Excel. PDF

Another article about Excel 97 and 2000. Decade old software. Many flaws since addressed, and new flaws added. Clearly Excel bashing was popular around 2000.

* Practical Stats (n.d.), Is Microsoft Excel an Adequate Statistics Package?

This one suggests it's just fine for the submitter's purposes.

"Excel’s limitations, and its errors, make this a very questionable practice for scientific applications. For business applications where questions might be simpler and precision not as necessary, Excel may be just fine"

* Heiser, D. (2008). Errors, faults and fixes for Excel statistical functions and routines

For a more comprehensive and technical discussion, see the papers by Yu (2008); Yalta (2008); and McCullough & Heiser in Computational Statistics and Data Analysis 52(10).

Gets very technical, and I bet some of those remarks are valid, but if it's important you become aware of and work around the problem. If it's not, there is no problem. If you don't understand what you're asking Excel to calculate and why it might be wrong, it doesn't matter.

The more you go into this, the more it requires specialist training. The idea that just replacing one software package with flaws and features you don't understand with another geekier more difficult product with flaws and features you don't understand is ridiculous. As is moderation on slashdot. The comments are being moderated by monkeys practicing to type up Shakespeare..

Excel and other spreadsheets suck at stats:

* Burns, P. (2005). Spreadsheet Addiction.
* Cryer, J. (2001). Problems with using Microsoft Excel for StatisticsPDF.
* Pottel, H. (n.d.). Statistical flaws in Excel. PDF
* Practical Stats (n.d.), Is Microsoft Excel an Adequate Statistics Package?
* Heiser, D. (2008). Errors, faults and fixes for Excel statistical functions and routines

For a more comprehensive and technical discussion, see the papers by Yu (2008); Yalta (2008); and McCullough & Heiser in Computational Statistics and Data Analysis 52(10).

Please, please, please have a look at http://www.stat.uiowa.edu/~jcryer/JSMTalk2001.pdf and at http://www.burns-stat.com/pages/Tutor/spreadsheet_addiction.html "The hard way looks easy, the easy way looks hard."

It is no good idea to do statistics with Excel:

* Burns, P. (2005): Spreadsheet Addiction.

* Cryer, J. (2001): Problems with using Microsoft Excel for Statistics. (PDF)

* Pottel, H. (n.d.): Statistical flaws in Excel. (PDF)

* Practical Stats (n.d.): Is Microsoft Excel an Adequate Statistics Package?

* Heiser, D. (2008): Errors, faults and fixes for Excel statistical functions and routines

For a more comprehensive and technical discussion, see the papers by Yu (2008); Yalta (2008); and McCullough & Heiser in Computational Statistics and Data Analysis 52(10)

Excel is great for manipulating large sets of data.

Except if the data set if actually large. 1M rows barely makes for a trivial set of test data in my world.

It wouldn't be so bad if at least the answers Excel gave were right on the tiny sets it does support.

Spreadsheet addiction is a good intro to its many flaws; the issues outlined in "Poor statistics" alone are sufficient to render it worthless for the topic of discussion here.

Amen to that.

Don't do it!