11.4 Cache time-consuming code chunks
When a code chunk is time-consuming to run, you may consider caching it via the chunk option
cache = TRUE. When the cache is turned on, knitr will skip the execution of this code chunk if it has been executed before and nothing in the code chunk has changed since then. When you modify the code chunk (e.g., revise the code or the chunk options), the previous cache will be automatically invalidated, and knitr will cache the chunk again.
For a cached code chunk, its output and objects will be automatically loaded from the previous run, as if the chunk were executed again. Caching is often helpful when loading results is much faster than computing the results. However, there is no free lunch. Depending on your use case, you may need to learn more about how caching (especially cache invalidation) works, so you can take the full advantage of it without confusing yourself why sometimes knitr invalidates your cache too often and sometimes there is not enough invalidation.
The most appropriate use case of caching is to save and reload R objects that take too long to compute in a code chunk, and the code does not have any side-effects, such as changing global R options via
options() (such changes will not be cached). If a code chunk has side-effects, we recommend that you do not cache it.
As we briefly mentioned earlier, the cache depends on chunk options. If you change any chunk options (except the option
include), the cache will be invalidated. This feature can be used to solve a common problem. That is, when you read an external data file, you may want to invalidate the cache when the data file is updated. Simply using
cache = TRUE is not enough:
You have to let knitr know if the data file has been changed. One way to do it is to add another chunk option
cache.extra = file.mtime('my-precious.csv') or more rigorously,
cache.extra = tools::md5sum('my-precious.csv'). The former means if the modification time of the file has been changed, we need to invalidate the cache. The latter means if the content of the file has been modified, we update the cache. Note that
cache.extra is not a built-in knitr chunk option. You can use any other name for this option, as long as it does not conflict with built-in option names.
Similarly, you can associate the cache with other information such as the R version (
cache.extra = getRversion()), the date (
cache.extra = Sys.Date()), or your operating system (
cache.extra = Sys.info()[['sysname']]), so the cache can be properly invalidated when these conditions change.
We do not recommend that you set the chunk option
cache = TRUE globally in a document. Caching can be fairly tricky. Instead, we recommend that you enable caching only on individual code chunks that are surely time-consuming and do not have side-effects.
If you are not happy with knitr’s design for caching, you can certainly cache objects by yourself. Below is a quick example:
In this case, the only (and also simple) way to invalidate the cache is to delete the file
results.rds. If you like this simple caching mechanism, you may use the function
xfun::cache_rds() introduced in Section 14.9.