when do you want to set up new environments in R

Question

per the discussion of R programming styles, I saw someone once said he puts all his custom function into a new environment and attach it. I also remember R environment maybe used as a hash table. Is this good style? When do you want to put your data/functions into a new enviroment? Or just use the.GlobalEnv whatever?

EDIT put my second part of question back: how to inspect same name variable for different environments?

Answer 1

Martin Mächler suggests that this is the one time you might want to consider attach(), although he suggested it in the context of attaching a .Rdata file to the search path but your Q is essentially the same thing.

The advantage is that you don't clutter the global environment with functions, that might get overwritten accidentally etc. Whilst I wouldn't go so far as to call this bad style, you might be better off sticking your custom functions into your own personal R package. Yes, this will incur a bit of overhead of setting up the package structure and providing some documentation to allow the package to be installed, but in the longer term this is a better solution. With tools like roxygen this process is getting easier to boot.

Personally, I haven't found a need for fiddling with environments in 10+ years of using R; well documented scripts that load, process and analyse data, cleaning up after themselves all the while have served me well thus far.

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Another suggestion for the second part of your question (now deleted) is to use with() (following on from @Joshua's example):

> .myEnv <- new.env()
> .myEnv$a <- 2
> a <- 1
> str(a)
 num 1
> ls.str(.myEnv, a)
a :  num 2
> str(.myEnv$a)
 num 2
> with(.myEnv, a)
[1] 2
> a
[1] 1

Answer 2

If your ecosystem of data and code has grown large enough that you are considering isolating it in an environment, you are better off creating a package. A package gives you much more support for:

• Managing a project that is growing large and complex by separating code and data into files so there is less to dig through at one time.

• A package makes it dead simple to hand off your work to someone else so they can use your code and data.

• A package provides additional support for documentation and reporting.

Setting up a package for R is so easy, just call package.skeleton(), that every project I work on gets its code and data stored in a package.

The only time I use environments is when I need to isolate a run of some code, usually a script written by someone else, so that it's side effects and variable names don't get crossed with mine. I do this by evalq(source('someScript.R', local=TRUE), SomeEnvironment).