In what cases should new.env be used to create a new environment?

Question

In the "What is the most useful R trick?" (here), I read that using environments give "pass-by-reference capabilities". Are there any limits and/or gotchas with this approach?

Also, in general what are the pros and cons of using created environments? This is something I've been confused about for quite some time, so any clarity or reference would be very helpful to me.

Thank you in advance.

Answer 1

Well, if you don't understand them, and the people you might someday have to read your code (including your future self) don't understand environments, then you shouldn't use them! They were designed to be used to encapsulate name spaces in packages and such. The fact that you can use them for pass-by-reference and hash tables doesn't necessarily mean you should. It's a trick. Generally, use of deep magic is not really advisable, even if it makes your code a little faster.

Answer 2

While I agree with Harlan's overall advice (i.e. don't use something unless you understand it), I would add:

Environments are a fundamental concept in R, and in my view, extremely useful (in other words: they're worth understanding!). Environments are very important to understand issues related to scope. Some basic things that you should understand in this context:

1. search(): will show you the workspace; environments are listed in order of priority. The main environment is .GlobalEnv, and can always be referenced as such.
2. ls(): will show you what's contained in an environment
3. attach/detach: creates a new environment for an object
4. get, assign, <<-, and <-: you should know the difference between these functions
5. with: one method for working with an environment without attaching it.

Another pointer: have a look at the proto package (used in ggplot), which uses environments to provide controlled inheritance.

Lastly, I would point out that environments are very similar to lists: they can both store any kind of object within them (see this question). But depending on your use case (e.g. do you want to deal with inheritance and priority), a list can be easier to work with. And you can always attach a list as an environment.

Edit: If you want to see an example of proto at work in ggplot, have a look that the structure of a ggplot object, which is essentially a list composed partially of environments:

> p <- qplot(1:10, 1:10)
> str(p)
List of 8
 $ data       :'data.frame':    0 obs. of  0 variables
 $ layers     :List of 1
  ..$ :proto object 
 .. .. $ legend     : logi NA 
 .. .. $ inherit.aes: logi TRUE 
...
> class(p$layers[[1]])
[1] "proto"       "environment"
> is.environment(p$layers[[1]])
[1] TRUE

Notice how it's constructed using proto and is containing many environments as a result. You can also plot the relationships in these objects using graph.proto.