This was the topic of a recent conversation on the Australian and New Zealand R mailing list. Here is an edited list of some of the comments made.

• R is free.
• R is well-documented.
• R runs (really well) on *nix as well as Windows and Mac OS.
• R is open-source. Trust in the R software is evident by its support among distinguished statisticians. However, the R user need not rely on trust, as the source code for R is freely available for public scrutiny.
• R has a much broader range of statistical packages for doing specialist work.
• R has an enthusiastic user base who can offer helpful advice for free.
• R creates far better graphics than Excel.
• R has certain data structures such as data frames that can make analysis more straightforward than in Excel
• R is better for doing complex jobs
• R is a better educational tool as it uses standard statistical vocabulary rather than home-baked terminology.
• R is easier to learn, use, and script than Excel.
• R allows students easily to work with scripts, thus allowing the work to be reproducible.
• R is intended to lead students towards programming; Excel is designed to keep people away from programming and encourages them to rely on someone else doing their programming (and often their thinking) for them.
• Excel is known to be inaccurate whereas R is thoroughly tested. For a critique of Excel, see McCullough & Heiser (2008).
• The statistical package available in Excel is very limited in capability and should only be used by experienced applied statisticians who can work out when its output should be ignored.
• While R takes a while to learn, it provides a broad range of possible analyses and does not constrain users to a very limited set of methods (as is the case for Excel).

Further comments on this theme are available at the following sites:

• http://www.cs.uiowa.edu/~jcryer/JSMTalk2001.pdf
• http://www.daheiser.info/excel/frontpage.html
• http://www.practicalstats.com/xlsstats/excelstats.html
• http://www.burns-stat.com/pages/Tutor/spreadsheet_addiction.html
• http://en.wikibooks.org/wiki/Statistics/Numerical_Methods/Numerics_in_Excel