when do you want to set up new environments in R

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 bac开发者_JS百科k: how to inspect same name variable for different environments?

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.

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

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).

To answer your second question (that you've now deleted), use ls.str, or just access the object in the environment with $:

.myEnv <- new.env()
.myEnv$a <- 2
a <- 1
str(a)
ls.str(.myEnv, a)
str(.myEnv$a)