Why R is better than Excel for teaching statistics

October 4, 2010

(This article was first published on Research tips » R, and kindly contributed to R-bloggers)

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:


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