[R] Persistent state in a function?

[Boris Steipe]

All -
Thanks, this has been a real eye-opener.

Here's my variation based on what I've learned so far. It's based on Bert's earlier function-returning-a-closure example. I hope I got the terminology right.

# ========================================================

makeCache <- function(){   # returns a "closure",
	                   # i.e. a function
	                   # plus its private, lexically
	                   # scoped environment

   myCache <- numeric()  # a variable that we want to persist;
                         # makeCache() creates the 
                         # environment that holds myCache and
                         # the function useCache() that uses myCache

   useCache <- function(x){
 	  myCache <<- c(myCache, x)  # appends a value to myCache
 	                             # <<- does _not_ assign to the
 	                             # global environment, but searches
 	                             # through the parent environments
 	                             # and assigns to the global environment
 	                             # only if no match was found along
 	                             # the way.
 	  print(myCache)
   }
   
   return(useCache)     # return the function plus its environment
}

# ======= creating instances of the closure and using them

cacheThis <- makeCache() # cacheThis is the closure that was created
                         # by makeCache

cacheThis(17)  # 17
cacheThis(13)  # 17 13
cacheThis(11)  # 17 13 11


cacheThat <- makeCache() # create another closure

cacheThat(1)  # 1
cacheThat(2)  # 1 2
cacheThat(3)  # 1 2 3
cacheThat(5)  # 1 2 3 5

# ======= accessing the private variables

# The caches for cacheThis() and cacheThat() are not visible
# from the (default) global environment:
ls()  # [1] "cacheThat" "cacheThis" "makeCache" 

# To access them from the global environment, use
# ls(), exists(), get() and assign(), with their environment
# argument:
 
ls.str(envir = environment(cacheThis))

ls.str(envir = environment(cacheThat))

exists("myCache", envir = environment(cacheThat))
exists("noSuchThing", envir = environment(cacheThat))

# The following won't work - save() needs a name as symbol or string:
save(get("myCache", envir = environment(cacheThis)), file="myCache.Rdata")

# do this instead:
tmp <- get("myCache", envir = environment(cacheThis))
save(tmp, file="myCache.Rdata")
rm(tmp)

# add a number we don't want...
cacheThis(6) # 17 13 11 6

# restore cache from saved version
load("myCache.Rdata") # this recreates "tmp"
assign("myCache", tmp, envir = environment(cacheThis))

# cache another prime ...
cacheThis(7) # 17 13 11 7

# etc.

# ========================================================

I don't yet understand the pros and cons of using local() instead of a generating function. From my current understanding, local() should end up doing the same thing - I think that's why Martin calls one a "variant" of the other. But I'll play some more with this later today. Is there a Preferred Way?

memoise has some nice ideas - such as creating a hash from the arguments passed into a function to see if the cached results need to be recomputed. In my use case, I'd like to have more explicit access to the cached results to be able to store, reload and otherwise manipulate them.

I haven't looked at R6 yet.

Cheers,
Boris












On Mar 23, 2016, at 5:58 PM, Martin Morgan <martin.morgan at roswellpark.org> wrote:

> Use a local environment to as a place to store state. Update with <<- and resolve symbol references through lexical scope E.g.,
> 
>    persist <- local({
>        last <- NULL                # initialize
>        function(value) {
>            if (!missing(value))
>                last <<- value      # update with <<-
>            last                    # use
>        }
>    })
> 
> and in action
> 
> > persist("foo")
> [1] "foo"
> > persist()
> [1] "foo"
> > persist("bar")
> [1] "bar"
> > persist()
> [1] "bar"
> 
> A variant is to use a 'factory' function
> 
>    factory <- function(init) {
>        stopifnot(!missing(init))
>        last <- init
>        function(value) {
>            if (!missing(value))
>                last <<- value
>            last
>        }
>    }
> 
> and
> 
> > p1 = factory("foo")
> > p2 = factory("bar")
> > c(p1(), p2())
> [1] "foo" "bar"
> > c(p1(), p2("foo"))
> [1] "foo" "foo"
> > c(p1(), p2())
> [1] "foo" "foo"
> 
> The 'bank account' exercise in section 10.7 of RShowDoc("R-intro") illustrates this.
> 
> Martin
> 
> On 03/19/2016 12:45 PM, Boris Steipe wrote:
>> Dear all -
>> 
>> I need to have a function maintain a persistent lookup table of results for an expensive calculation, a named vector or hash. I know that I can just keep the table in the global environment. One problem with this approach is that the function should be able to delete/recalculate the table and I don't like side-effects in the global environment. This table really should be private. What I don't know is:
>>  -A- how can I keep the table in an environment that is private to the function but persistent for the session?
>>  -B- how can I store and reload such table?
>>  -C- most importantly: is that the right strategy to initialize and maintain state in a function in the first place?
>> 
>> 
>> For illustration ...
>> 
>> -----------------------------------
>> 
>> myDist <- function(a, b) {
>>     # retrieve or calculate distances
>>     if (!exists("Vals")) {
>>         Vals <<- numeric() # the lookup table for distance values
>>                            # here, created in the global env.
>>     }
>>     key <- sprintf("X%d.%d", a, b)
>>     thisDist <- Vals[key]
>>     if (is.na(thisDist)) {          # Hasn't been calculated yet ...
>>         cat("Calculating ... ")
>>         thisDist <- sqrt(a^2 + b^2) # calculate with some expensive function ...
>>         Vals[key] <<- thisDist      # store in global table
>>     }
>>     return(thisDist)
>> }
>> 
>> 
>> # run this
>> set.seed(112358)
>> 
>> for (i in 1:10) {
>>     x <- sample(1:3, 2)
>>     print(sprintf("d(%d, %d) = %f", x[1], x[2], myDist(x[1], x[2])))
>> }
>> 
>> 
>> Thanks!
>> Boris
>> 
>> ______________________________________________
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>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>> 
> 
> 
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