A simple way to memoise function results to improve performance by eliminating unnecessary computation or data retrieval activities.
Functions can be memoized with a simple call to memo.
> # a simple example function
> simple.function <- function (value) {
+ print("Executing!")
+ value
+ }
> # call memo function to memoise a function
> simple.function.memo <- memo(simple.function)
> # or like this
> simple.function %<>% memo()
> # or like this
> simple.function2 <- (function (value) value) %>% memo()
Calling a memo is exactly like calling a normal function, in fact it is a normal function! The memo has all the same arguments and defaults as the origional function so it can be used in legacy code without the need for any risky refactoring.
> # the first time we call the memo the function will execute > simple.function(10) [1] "Executing!" [1] 10 > # if we call the function again with the same parameter values then > # the cached value will be returned > simple.function(10) [1] 10 > # calling the memo with a different set of parameter values will > # cause the function to execute > simple.function(20) [1] "Executing!" [1] 20
Memoing a function can significantly improve the performance of a system by limiting how often expensive call are made. Functions that return a NULL value can be memoed by using the allow.null argument.
> # consider a slow function which is memoised, note that we have used the allow.null argument > # so that NULL is cached when returned from a function, the default is FALSE > slow.function <- (function (value) Sys.sleep(value)) %>% memo(allow.null = TRUE) > # the first time we call the slow function it takes some time > system.time(slow.function(3)) user system elapsed 0.00 0.00 3.01 > # subsequent calls make use of the cache and are much faster > system.time(slow.function(3)) user system elapsed 0.01 0.00 0.02
Install from CRAN:
install.packages("memofunc")
Install latest release:
devtools::install_github("rwetherall/memofunc")
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