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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