Why R is better than Excel for teaching statistics
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: