Introduction

The memoise package can be very handy for caching the results of slow calculations. In interactive work, the slowest calculations can be reading data, so that is demonstrated here. The microbenchmark package shows timing results.

Methods and results

Setup

First, load the package being tested, and also a benchmarking package.

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library(memoise)
library(microbenchmark)

Test conventional function

The demonstration will be for reading a CTD file.

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library(oce)

## Loading required package: methods
## Loading required package: mapproj
## Loading required package: maps
## Loading required package: ncdf4
## Loading required package: tiff

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microbenchmark(d <- read.oce("/data/arctic/beaufort/2012/d201211_0002.cnv"))

## Unit: milliseconds
##                                                          expr   min    lq
##  d <- read.oce("/data/arctic/beaufort/2012/d201211_0002.cnv") 160.4 162.5
##  median    uq   max neval
##   162.9 167.6 258.6   100

Memoise the function

Memoising read.oce() is simple

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r <- memoise(read.oce)

Measure the speed of memoised code

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microbenchmark(d <- r("/data/arctic/beaufort/2012/d201211_0002.cnv"))

## Unit: microseconds
##                                                   expr   min    lq median
##  d <- r("/data/arctic/beaufort/2012/d201211_0002.cnv") 47.47 48.61   49.5
##     uq    max neval
##  52.57 165199   100

Conclusions

In this example, the speedup was by a factor of about 3000.

The operation tested here is quick enough for interactive work, but this is a 1-dbar file, and the time would be increased to several seconds for raw CTD data, and increased to perhaps a half minute or so if a whole section of CTD profiles is to be read. Using memoise() would reduce that half minute to a hundredth of a second – easily converting an annoyingly slow operation to what feels like zero time in an interactive session.

Resources

• R code used in this blog item
• Jekyll source code for this blog item