Reposted from the American Statistical Association Facebook group. Note that I disagree with what follows below, but as a publisher, I am supposed to be neutral. So I won't make any comments.
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: