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    <p>The premise of the question is (partly) incorrect. R works as pass-by-promise and there is repeated copying in the manner you outline only when further assignments and alterations to the dataframe are made as the promise is passed on. So the number of copies will not be N*size where N is the stack depth, but rather where N is the number of levels where assignments are made. You are correct, however, that environments can be useful. I see on following the link that you have already found the 'proto' package. There is also a relatively recent introduction of a "reference class" sometimes referred to as "R5" where R/S3 was the original class system of S3 that is copied in R and R4 would be the more recent class system that seems to mostly support the BioConductor package development.</p> <p>Here is a link to an example by Steve Lianoglou (in a thread discussing the merits of reference classes) of embedding an environment inside an S4 object to avoid the copying costs:</p> <p><a href="https://stat.ethz.ch/pipermail/r-help/2011-September/289987.html" rel="noreferrer">https://stat.ethz.ch/pipermail/r-help/2011-September/289987.html</a></p> <p>Matthew Dowle's 'data.table' package creates a new class of data object whose access semantics using the "[" are different than those of regular R data.frames, and which is really working as pass-by-reference. It has superior speed of access and processing. It also can fall back on dataframe semantics since in later years such objects now inherit the 'data.frame' class.</p> <p>You may also want to investigate <a href="http://timhesterberg.home.comcast.net/~timhesterberg/Rpackages/TwoPackages5.pdf" rel="noreferrer">Hesterberg's dataframe package</a>.</p>
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    1. CO+1 I'm happy for you to expand and improve this answer.
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    2. COAnd +1 for link to the dataframe package, which is now incorporated (or at least in philosophy) to R 2.15-1
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    3. CO@DWin, thanks for the info. Pass-by-promise makes sense as an optimization though it doesn't help my case where multiple steps operate on the same data frame to turn it from "raw" to processable. Usually the modifications are performed in separate steps of a processing pipeline but in some cases they are performed in the same call chain as abstraction boundaries with different invariants are crossed. I will look into the packages you recommend.
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