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If a function is called multiple times with the same input, you can often speed things up by keeping a cache of known answers that it can retrieve. This is called memoisation The memoise package provides a simple syntax

mf <- memoise(f)

to create mf(), a memoised wrapper around f(). You can clear mf's cache with


and you can test whether a function is memoised with

is.memoised(mf) # TRUE
is.memoised(f)  # FALSE



External Caches

memoise also supports external caching in addition to the default in-memory caches.


Use cache_s3() to cache objects using s3 storage. Requires you to specify a bucket using cache_name. When creating buckets, they must be unique among all s3 users when created.

Sys.setenv("AWS_ACCESS_KEY_ID" = "<access key>",
           "AWS_SECRET_ACCESS_KEY" = "<access secret>")

mrunif <- memoise(runif, cache = cache_s3("<unique bucket name>"))

mrunif(10) # First run, saves cache
mrunif(10) # Loads cache, results should be identical


cache_filesystem can be used for a file system cache. This is useful for preserving the cache between R sessions as well as sharing between systems when using a shared or synced files system such as Dropbox or Google Drive.

fc <- cache_filesystem("~/.cache")
mrunif <- memoise(runif, cache = fc)
mrunif(20) # Results stored in local file

dbc <- cache_filesystem("~/Dropbox/.rcache")
mrunif <- memoise(runif, cache = dbc)
mrunif(20) # Results stored in Dropbox .rcache folder which will be synced between computers.

gdc <- cache_filesystem("~/Google Drive/.rcache")
mrunif <- memoise(runif, cache = gdc)
mrunif(20) # Results stored in Google Drive .rcache folder which will be synced between computers.

hadley/memoise documentation built on Jan. 6, 2018, 1:43 a.m.