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README.md

memoise

CRAN status R build status Codecov test coverage

The memoise package makes it easy to memoise R functions. Memoisation (http://en.wikipedia.org/wiki/Memoization) caches function calls so that if a previously seen set of inputs is seen, it can return the previously computed output.

Installation

Install from CRAN with

install.packages("memoise")

Usage

To memoise a function, use memoise():

library(memoise)
f <- function(x) {
  Sys.sleep(1)
  mean(x)
}
mf <- memoise(f)

system.time(mf(1:10))
#>    user  system elapsed 
#>   0.000   0.000   1.003
system.time(mf(1:10))
#>    user  system elapsed 
#>   0.031   0.001   0.032

You can clear mf’s cache with:

forget(mf)
#> [1] TRUE

And you can test whether a function is memoised with is.memoised().

Caches

By default, memoise uses an in-memory cache. But you can customise this with the cache arugment and another built-in cache:

  • cache_filesystem() allows caching using files on a local filesystem. 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")
    
    # Store in Dropbox
    dbc <- cache_filesystem("~/Dropbox/.rcache")
  • cache_s3() allows caching on Amazon S3 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>"
    )
    cache <- cache_s3("<unique bucket name>")
  • cache_gcs() saves the cache to Google Cloud Storage. It requires you to authenticate by downloading a JSON authentication file, and specifying a pre-made bucket:

    Sys.setenv(
      "GCS_AUTH_FILE" = "<google-service-json>",
      "GCS_DEFAULT_BUCKET" = "unique-bucket-name"
    )
    gcs <- cache_gcs()
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