1 Reproducible workflow
As part of a reproducible workflow, caching of function calls, code chunks, and other elements of a project is a critical component. The objective of a reproducible workflow is is likely that an entire work flow from raw data to publication, decision support, report writing, presentation building etc., could be built and be reproducible anywhere, on any computer, operating system, with any starting conditions, on demand. The reproducible::Cache function is built to work with any R function.
1.1 Differences with other approaches
Cache uses 2 key the archivist functions saveToLocalRepo and loadFromLocalRepo, but does not use archivist::cache. Similar to archivist::cache, there is some reliance on digest::digest to determine whether the arguments are identical in subsequent iterations. It also but does many things that make standard caching with digest::digest don’t work reliably between systems. For these, the function .robustDigest is introduced to make caching transferable between systems. This is relevant for file paths, environments, parallel clusters, functions (which are contained within an environment), and many others (e.g., see ?.robustDigest for methods). Cache also adds important elements like automated tagging and the option to retrieve disk-cached values via stashed objects in memory using memoise::memoise. This means that running Cache 1, 2, and 3 times on the same function will get progressively faster. This can be extremely useful for web apps built with, say shiny.
1.2 Function-level caching
Any function can be cached using: Cache(FUN = functionName, ...).
This will be a slight change to a function call, such as: projectRaster(raster, crs = crs(newRaster)) to Cache(projectRaster, raster, crs = crs(newRaster)).
This is particularly useful for expensive operations.
library(raster)## Loading required package: splibrary(reproducible)## Registered S3 method overwritten by 'R.oo':
## method from
## throw.default R.methodsS3tmpDir <- file.path(tempdir(), "reproducible_examples", "Cache")
checkPath(tmpDir, create = TRUE)## [1] "/tmp/RtmptuRuQo/reproducible_examples/Cache"ras <- raster(extent(0, 1000, 0, 1000), vals = 1:1e6, res = 1)
crs(ras) <- "+proj=lcc +lat_1=48 +lat_2=33 +lon_0=-100 +ellps=WGS84"
newCRS <- "+init=epsg:4326 +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"
# No Cache
system.time(map1 <- projectRaster(ras, crs = newCRS))## user system elapsed
## 2.629 0.235 2.925# With Cache -- a little slower the first time because saving to disk
system.time(map1 <- Cache(projectRaster, ras, crs = newCRS, cacheRepo = tmpDir,
notOlderThan = Sys.time()))## user system elapsed
## 3.612 0.207 4.025# vastly faster the second time
system.time(map2 <- Cache(projectRaster, ras, crs = newCRS, cacheRepo = tmpDir))## loading cached result from previous projectRaster call, adding to memoised copy## user system elapsed
## 0.306 0.012 0.319# even faster the third time
system.time(map3 <- Cache(projectRaster, ras, crs = newCRS, cacheRepo = tmpDir))## loading memoised result from previous projectRaster call.## user system elapsed
## 0.036 0.004 0.039all.equal(map1, map2) # TRUE## [1] TRUEall.equal(map1, map3) # TRUE## [1] TRUE1.3 Caching examples
1.3.1 Basic use
library(raster)
library(magrittr)##
## Attaching package: 'magrittr'## The following object is masked from 'package:raster':
##
## extracttry(clearCache(tmpDir, ask = FALSE), silent = TRUE) # just to make sure it is clear
ranNumsA <- Cache(rnorm, 10, 16, cacheRepo = tmpDir)
# All same
ranNumsB <- Cache(rnorm, 10, 16, cacheRepo = tmpDir) # recovers cached copy## loading cached result from previous rnorm call, adding to memoised copyranNumsC <- Cache(cacheRepo = tmpDir) %C% rnorm(10, 16) # recovers cached copy## loading memoised result from previous 'rnorm' pipe sequence call.ranNumsD <- Cache(quote(rnorm(n = 10, 16)), cacheRepo = tmpDir) # recovers cached copy## loading memoised result from previous rnorm call.# Any minor change makes it different
ranNumsE <- Cache(cacheRepo = tmpDir) %C% rnorm(10, 6) # different1.5 Example 2: using the “accessed” tag
ranNumsA <- Cache(rnorm, 4, cacheRepo = tmpDir, userTags = "objectName:a")
ranNumsB <- Cache(runif, 4, cacheRepo = tmpDir, userTags = "objectName:b")
# access it again, from Cache
ranNumsA <- Cache(rnorm, 4, cacheRepo = tmpDir, userTags = "objectName:a")## loading cached result from previous rnorm call, adding to memoised copywholeCache <- showCache(tmpDir)## Cache size:## Total (including Rasters): 504 bytes## Selected objects (not including Rasters): 504 bytes# keep only items accessed "recently" (i.e., only objectName:a)
onlyRecentlyAccessed <- showCache(tmpDir, userTags = max(wholeCache[tagKey == "accessed"]$tagValue))## Cache size:## Total (including Rasters): 504 bytes## Selected objects (not including Rasters): 504 bytes# inverse join with 2 data.tables ... using: a[!b]
# i.e., return all of wholeCache that was not recently accessed
toRemove <- unique(wholeCache[!onlyRecentlyAccessed], by = "artifact")$artifact
clearCache(tmpDir, toRemove, ask = FALSE) # remove ones not recently accessed## Cache size:## Total (including Rasters): 504 bytes## Selected objects (not including Rasters): 504 bytesshowCache(tmpDir) # still has more recently accessed## Cache size:## Total (including Rasters): 0 bytes## Selected objects (not including Rasters): 0 bytes## Empty data.table (0 rows and 3 cols): md5hash,name,createdDateclearCache(tmpDir, ask = FALSE)1.6 Example 3: using keepCache
ranNumsA <- Cache(rnorm, 4, cacheRepo = tmpDir, userTags = "objectName:a")
ranNumsB <- Cache(runif, 4, cacheRepo = tmpDir, userTags = "objectName:b")
# keep only those cached items from the last 24 hours
oneDay <- 60 * 60 * 24
keepCache(tmpDir, after = Sys.time() - oneDay, ask = FALSE)## Cache size:## Total (including Rasters): 504 bytes## Selected objects (not including Rasters): 504 bytes## artifact tagKey
## 1: 3262f9dc02b5fba2ad991c7219430e79 format
## 2: 3262f9dc02b5fba2ad991c7219430e79 name
## 3: 3262f9dc02b5fba2ad991c7219430e79 class
## 4: 3262f9dc02b5fba2ad991c7219430e79 date
## 5: 3262f9dc02b5fba2ad991c7219430e79 cacheId
## 6: 3262f9dc02b5fba2ad991c7219430e79 objectName
## 7: 3262f9dc02b5fba2ad991c7219430e79 function
## 8: 3262f9dc02b5fba2ad991c7219430e79 object.size
## 9: 3262f9dc02b5fba2ad991c7219430e79 accessed
## 10: 3262f9dc02b5fba2ad991c7219430e79 otherFunctions
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## 26: 39a699fbc97b792c2e3793781657578a cacheId
## 27: 39a699fbc97b792c2e3793781657578a objectName
## 28: 39a699fbc97b792c2e3793781657578a function
## 29: 39a699fbc97b792c2e3793781657578a object.size
## 30: 39a699fbc97b792c2e3793781657578a accessed
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## 41: 39a699fbc97b792c2e3793781657578a preDigest
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## artifact tagKey
## tagValue createdDate
## 1: rda 2019-07-08 19:59:55
## 2: 3262f9dc02b5fba2ad991c7219430e79 2019-07-08 19:59:55
## 3: numeric 2019-07-08 19:59:55
## 4: 2019-07-08 19:59:55 2019-07-08 19:59:55
## 5: f7bee22047b8d0c1 2019-07-08 19:59:55
## 6: a 2019-07-08 19:59:55
## 7: rnorm 2019-07-08 19:59:55
## 8: 1008 2019-07-08 19:59:55
## 9: 2019-07-08 19:59:55 2019-07-08 19:59:55
## 10: vweave_rmarkdown 2019-07-08 19:59:55
## 11: process_file 2019-07-08 19:59:55
## 12: process_group 2019-07-08 19:59:55
## 13: process_group.block 2019-07-08 19:59:55
## 14: call_block 2019-07-08 19:59:55
## 15: block_exec 2019-07-08 19:59:55
## 16: in_dir 2019-07-08 19:59:55
## 17: timing_fn 2019-07-08 19:59:55
## 18: handle 2019-07-08 19:59:55
## 19: withVisible 2019-07-08 19:59:55
## 20: n:7eef4eae85fd9229 2019-07-08 19:59:55
## 21: .FUN:4f604aa46882b368 2019-07-08 19:59:55
## 22: rda 2019-07-08 19:59:55
## 23: 39a699fbc97b792c2e3793781657578a 2019-07-08 19:59:55
## 24: numeric 2019-07-08 19:59:55
## 25: 2019-07-08 19:59:55 2019-07-08 19:59:55
## 26: 3aef38d1fc02aee5 2019-07-08 19:59:55
## 27: b 2019-07-08 19:59:55
## 28: runif 2019-07-08 19:59:55
## 29: 1008 2019-07-08 19:59:55
## 30: 2019-07-08 19:59:55 2019-07-08 19:59:55
## 31: vweave_rmarkdown 2019-07-08 19:59:55
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## 41: n:7eef4eae85fd9229 2019-07-08 19:59:55
## 42: .FUN:881ec847b7161f3c 2019-07-08 19:59:55
## tagValue createdDate# Keep all Cache items created with an rnorm() call
keepCache(tmpDir, userTags = "rnorm", ask = FALSE)## Cache size:## Total (including Rasters): 504 bytes## Selected objects (not including Rasters): 252 bytes## artifact tagKey
## 1: 3262f9dc02b5fba2ad991c7219430e79 format
## 2: 3262f9dc02b5fba2ad991c7219430e79 name
## 3: 3262f9dc02b5fba2ad991c7219430e79 class
## 4: 3262f9dc02b5fba2ad991c7219430e79 date
## 5: 3262f9dc02b5fba2ad991c7219430e79 cacheId
## 6: 3262f9dc02b5fba2ad991c7219430e79 objectName
## 7: 3262f9dc02b5fba2ad991c7219430e79 function
## 8: 3262f9dc02b5fba2ad991c7219430e79 object.size
## 9: 3262f9dc02b5fba2ad991c7219430e79 accessed
## 10: 3262f9dc02b5fba2ad991c7219430e79 otherFunctions
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## artifact tagKey
## tagValue createdDate
## 1: rda 2019-07-08 19:59:55
## 2: 3262f9dc02b5fba2ad991c7219430e79 2019-07-08 19:59:55
## 3: numeric 2019-07-08 19:59:55
## 4: 2019-07-08 19:59:55 2019-07-08 19:59:55
## 5: f7bee22047b8d0c1 2019-07-08 19:59:55
## 6: a 2019-07-08 19:59:55
## 7: rnorm 2019-07-08 19:59:55
## 8: 1008 2019-07-08 19:59:55
## 9: 2019-07-08 19:59:55 2019-07-08 19:59:55
## 10: vweave_rmarkdown 2019-07-08 19:59:55
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## 18: handle 2019-07-08 19:59:55
## 19: withVisible 2019-07-08 19:59:55
## 20: n:7eef4eae85fd9229 2019-07-08 19:59:55
## 21: .FUN:4f604aa46882b368 2019-07-08 19:59:55
## tagValue createdDate# Remove all Cache items that happened within a rnorm() call
clearCache(tmpDir, userTags = "rnorm", ask = FALSE)## Cache size:## Total (including Rasters): 252 bytes## Selected objects (not including Rasters): 252 bytesshowCache(tmpDir) ## empty## Cache size:## Total (including Rasters): 0 bytes## Selected objects (not including Rasters): 0 bytes## Empty data.table (0 rows and 3 cols): md5hash,name,createdDate1.7 Example 4: searching for multiple objects in the cache
# default userTags is "and" matching; for "or" matching use |
ranNumsA <- Cache(runif, 4, cacheRepo = tmpDir, userTags = "objectName:a")
ranNumsB <- Cache(rnorm, 4, cacheRepo = tmpDir, userTags = "objectName:b")
# show all objects (runif and rnorm in this case)
showCache(tmpDir)## Cache size:## Total (including Rasters): 504 bytes## Selected objects (not including Rasters): 504 bytes## artifact tagKey
## 1: 77f28ba78ebb7dc186a3e14ba41aeff7 format
## 2: 77f28ba78ebb7dc186a3e14ba41aeff7 name
## 3: 77f28ba78ebb7dc186a3e14ba41aeff7 class
## 4: 77f28ba78ebb7dc186a3e14ba41aeff7 date
## 5: 77f28ba78ebb7dc186a3e14ba41aeff7 cacheId
## 6: 77f28ba78ebb7dc186a3e14ba41aeff7 objectName
## 7: 77f28ba78ebb7dc186a3e14ba41aeff7 function
## 8: 77f28ba78ebb7dc186a3e14ba41aeff7 object.size
## 9: 77f28ba78ebb7dc186a3e14ba41aeff7 accessed
## 10: 77f28ba78ebb7dc186a3e14ba41aeff7 otherFunctions
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## 20: 77f28ba78ebb7dc186a3e14ba41aeff7 preDigest
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## 24: f16511288cc20c4dd9cc04339f0d72f6 class
## 25: f16511288cc20c4dd9cc04339f0d72f6 date
## 26: f16511288cc20c4dd9cc04339f0d72f6 cacheId
## 27: f16511288cc20c4dd9cc04339f0d72f6 objectName
## 28: f16511288cc20c4dd9cc04339f0d72f6 function
## 29: f16511288cc20c4dd9cc04339f0d72f6 object.size
## 30: f16511288cc20c4dd9cc04339f0d72f6 accessed
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## 41: f16511288cc20c4dd9cc04339f0d72f6 preDigest
## 42: f16511288cc20c4dd9cc04339f0d72f6 preDigest
## artifact tagKey
## tagValue createdDate
## 1: rda 2019-07-08 19:59:55
## 2: 77f28ba78ebb7dc186a3e14ba41aeff7 2019-07-08 19:59:55
## 3: numeric 2019-07-08 19:59:55
## 4: 2019-07-08 19:59:55 2019-07-08 19:59:55
## 5: f7bee22047b8d0c1 2019-07-08 19:59:55
## 6: b 2019-07-08 19:59:55
## 7: rnorm 2019-07-08 19:59:55
## 8: 1008 2019-07-08 19:59:55
## 9: 2019-07-08 19:59:55 2019-07-08 19:59:55
## 10: vweave_rmarkdown 2019-07-08 19:59:55
## 11: process_file 2019-07-08 19:59:55
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## 14: call_block 2019-07-08 19:59:55
## 15: block_exec 2019-07-08 19:59:55
## 16: in_dir 2019-07-08 19:59:55
## 17: timing_fn 2019-07-08 19:59:55
## 18: handle 2019-07-08 19:59:55
## 19: withVisible 2019-07-08 19:59:55
## 20: n:7eef4eae85fd9229 2019-07-08 19:59:55
## 21: .FUN:4f604aa46882b368 2019-07-08 19:59:55
## 22: rda 2019-07-08 19:59:55
## 23: f16511288cc20c4dd9cc04339f0d72f6 2019-07-08 19:59:55
## 24: numeric 2019-07-08 19:59:55
## 25: 2019-07-08 19:59:55 2019-07-08 19:59:55
## 26: 3aef38d1fc02aee5 2019-07-08 19:59:55
## 27: a 2019-07-08 19:59:55
## 28: runif 2019-07-08 19:59:55
## 29: 1008 2019-07-08 19:59:55
## 30: 2019-07-08 19:59:55 2019-07-08 19:59:55
## 31: vweave_rmarkdown 2019-07-08 19:59:55
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## 39: handle 2019-07-08 19:59:55
## 40: withVisible 2019-07-08 19:59:55
## 41: n:7eef4eae85fd9229 2019-07-08 19:59:55
## 42: .FUN:881ec847b7161f3c 2019-07-08 19:59:55
## tagValue createdDate# show objects that are both runif and rnorm
# (i.e., none in this case, because objecs are either or, not both)
showCache(tmpDir, userTags = c("runif", "rnorm")) ## empty## Cache size:## Total (including Rasters): 504 bytes## Selected objects (not including Rasters): 0 bytes## Empty data.table (0 rows and 4 cols): artifact,tagKey,tagValue,createdDate# show objects that are either runif or rnorm ("or" search)
showCache(tmpDir, userTags = "runif|rnorm")## Cache size:## Total (including Rasters): 504 bytes## Selected objects (not including Rasters): 504 bytes## artifact tagKey
## 1: 77f28ba78ebb7dc186a3e14ba41aeff7 format
## 2: 77f28ba78ebb7dc186a3e14ba41aeff7 name
## 3: 77f28ba78ebb7dc186a3e14ba41aeff7 class
## 4: 77f28ba78ebb7dc186a3e14ba41aeff7 date
## 5: 77f28ba78ebb7dc186a3e14ba41aeff7 cacheId
## 6: 77f28ba78ebb7dc186a3e14ba41aeff7 objectName
## 7: 77f28ba78ebb7dc186a3e14ba41aeff7 function
## 8: 77f28ba78ebb7dc186a3e14ba41aeff7 object.size
## 9: 77f28ba78ebb7dc186a3e14ba41aeff7 accessed
## 10: 77f28ba78ebb7dc186a3e14ba41aeff7 otherFunctions
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## tagValue createdDate
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## 4: 2019-07-08 19:59:55 2019-07-08 19:59:55
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## 6: b 2019-07-08 19:59:55
## 7: rnorm 2019-07-08 19:59:55
## 8: 1008 2019-07-08 19:59:55
## 9: 2019-07-08 19:59:55 2019-07-08 19:59:55
## 10: vweave_rmarkdown 2019-07-08 19:59:55
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## 15: block_exec 2019-07-08 19:59:55
## 16: in_dir 2019-07-08 19:59:55
## 17: timing_fn 2019-07-08 19:59:55
## 18: handle 2019-07-08 19:59:55
## 19: withVisible 2019-07-08 19:59:55
## 20: n:7eef4eae85fd9229 2019-07-08 19:59:55
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## 25: 2019-07-08 19:59:55 2019-07-08 19:59:55
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## 27: a 2019-07-08 19:59:55
## 28: runif 2019-07-08 19:59:55
## 29: 1008 2019-07-08 19:59:55
## 30: 2019-07-08 19:59:55 2019-07-08 19:59:55
## 31: vweave_rmarkdown 2019-07-08 19:59:55
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## 35: call_block 2019-07-08 19:59:55
## 36: block_exec 2019-07-08 19:59:55
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## 40: withVisible 2019-07-08 19:59:55
## 41: n:7eef4eae85fd9229 2019-07-08 19:59:55
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## tagValue createdDate# keep only objects that are either runif or rnorm ("or" search)
keepCache(tmpDir, userTags = "runif|rnorm", ask = FALSE)## Cache size:## Total (including Rasters): 504 bytes## Selected objects (not including Rasters): 504 bytes## artifact tagKey
## 1: 77f28ba78ebb7dc186a3e14ba41aeff7 format
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## 4: 2019-07-08 19:59:55 2019-07-08 19:59:55
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## 6: b 2019-07-08 19:59:55
## 7: rnorm 2019-07-08 19:59:55
## 8: 1008 2019-07-08 19:59:55
## 9: 2019-07-08 19:59:55 2019-07-08 19:59:55
## 10: vweave_rmarkdown 2019-07-08 19:59:55
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## 25: 2019-07-08 19:59:55 2019-07-08 19:59:55
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## 27: a 2019-07-08 19:59:55
## 28: runif 2019-07-08 19:59:55
## 29: 1008 2019-07-08 19:59:55
## 30: 2019-07-08 19:59:55 2019-07-08 19:59:55
## 31: vweave_rmarkdown 2019-07-08 19:59:55
## 32: process_file 2019-07-08 19:59:55
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## tagValue createdDateclearCache(tmpDir, ask = FALSE)1.8 Example 5: using caching to speed up rerunning expensive computations
ras <- raster(extent(0, 5, 0, 5), res = 1,
vals = sample(1:5, replace = TRUE, size = 25),
crs = "+proj=lcc +lat_1=48 +lat_2=33 +lon_0=-100 +ellps=WGS84")
# A slow operation, like GIS operation
notCached <- suppressWarnings(
# project raster generates warnings when run non-interactively
projectRaster(ras, crs = crs(ras), res = 5, cacheRepo = tmpDir)
)
cached <- suppressWarnings(
# project raster generates warnings when run non-interactively
# using quote works also
Cache(projectRaster, ras, crs = crs(ras), res = 5, cacheRepo = tmpDir)
)
# second time is much faster
reRun <- suppressWarnings(
# project raster generates warnings when run non-interactively
Cache(projectRaster, ras, crs = crs(ras), res = 5, cacheRepo = tmpDir)
)## loading cached result from previous projectRaster call, adding to memoised copy# recovered cached version is same as non-cached version
all.equal(notCached, reRun) ## TRUE## [1] TRUE1.9 Nested Caching
Nested caching, which is when Caching of a function occurs inside an outer function, which is itself cached. This is a critical element to working within a reproducible work flow. It is not enough during development to cache flat code chunks, as there will be many levels of “slow” functions. Ideally, at all points in a development cycle, it should be possible to get to any line of code starting from the very initial steps, running through everything up to that point, in less that 1 second. If the workflow can be kept very fast like this, then there is a guarantee that it will work at any point.
##########################
## Nested Caching
# Make 2 functions
inner <- function(mean) {
d <- 1
Cache(rnorm, n = 3, mean = mean)
}
outer <- function(n) {
Cache(inner, 0.1, cacheRepo = tmpdir2)
}
# make 2 different cache paths
tmpdir1 <- file.path(tempdir(), "first")
tmpdir2 <- file.path(tempdir(), "second")
# Run the Cache ... notOlderThan propagates to all 3 Cache calls,
# but cacheRepo is tmpdir1 in top level Cache and all nested
# Cache calls, unless individually overridden ... here inner
# uses tmpdir2 repository
Cache(outer, n = 2, cacheRepo = tmpdir1, notOlderThan = Sys.time())## [1] -1.0620433 -0.8208001 -0.2239667
## attr(,".Cache")
## attr(,".Cache")$newCache
## [1] TRUE
##
## attr(,"tags")
## [1] "cacheId:a7af5367c13aba8f"
## attr(,"call")
## [1] ""showCache(tmpdir1) # 2 function calls## Cache size:## Total (including Rasters): 504 bytes## Selected objects (not including Rasters): 504 bytes## artifact tagKey
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## 1: rda 2019-07-08 19:59:56
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## 4: 2019-07-08 19:59:56 2019-07-08 19:59:56
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## 6: outer 2019-07-08 19:59:56
## 7: 1008 2019-07-08 19:59:56
## 8: 2019-07-08 19:59:56 2019-07-08 19:59:56
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## tagValue createdDateshowCache(tmpdir2) # 1 function call## Cache size:## Total (including Rasters): 252 bytes## Selected objects (not including Rasters): 252 bytes## artifact tagKey
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## 4: 2019-07-08 19:59:56 2019-07-08 19:59:56
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## 6: inner 2019-07-08 19:59:56
## 7: 1008 2019-07-08 19:59:56
## 8: 2019-07-08 19:59:56 2019-07-08 19:59:56
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## 10: process_file 2019-07-08 19:59:56
## 11: process_group 2019-07-08 19:59:56
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## 14: block_exec 2019-07-08 19:59:56
## 15: in_dir 2019-07-08 19:59:56
## 16: timing_fn 2019-07-08 19:59:56
## 17: handle 2019-07-08 19:59:56
## 18: withVisible 2019-07-08 19:59:56
## 19: mean:22413394efd9f6a3 2019-07-08 19:59:56
## 20: .FUN:87e2c30917a34d25 2019-07-08 19:59:56# userTags get appended
# all items have the outer tag propagate, plus inner ones only have inner ones
clearCache(tmpdir1, ask = FALSE)
outerTag <- "outerTag"
innerTag <- "innerTag"
inner <- function(mean) {
d <- 1
Cache(rnorm, n = 3, mean = mean, notOlderThan = Sys.time() - 1e5, userTags = innerTag)
}
outer <- function(n) {
Cache(inner, 0.1)
}
aa <- Cache(outer, n = 2, cacheRepo = tmpdir1, userTags = outerTag)
showCache(tmpdir1) # rnorm function has outerTag and innerTag, inner and outer only have outerTag## Cache size:## Total (including Rasters): 756 bytes## Selected objects (not including Rasters): 756 bytes## artifact tagKey
## 1: 1a07751dce71234cb2ea0bd9eb9d2300 format
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## 45: c4feab88b8bf77f95567b7e0f86e1756 class
## 46: c4feab88b8bf77f95567b7e0f86e1756 date
## 47: c4feab88b8bf77f95567b7e0f86e1756 cacheId
## 48: c4feab88b8bf77f95567b7e0f86e1756 innerTag
## 49: c4feab88b8bf77f95567b7e0f86e1756 outerTag
## 50: c4feab88b8bf77f95567b7e0f86e1756 function
## 51: c4feab88b8bf77f95567b7e0f86e1756 object.size
## 52: c4feab88b8bf77f95567b7e0f86e1756 accessed
## 53: c4feab88b8bf77f95567b7e0f86e1756 otherFunctions
## 54: c4feab88b8bf77f95567b7e0f86e1756 otherFunctions
## 55: c4feab88b8bf77f95567b7e0f86e1756 otherFunctions
## 56: c4feab88b8bf77f95567b7e0f86e1756 otherFunctions
## 57: c4feab88b8bf77f95567b7e0f86e1756 otherFunctions
## 58: c4feab88b8bf77f95567b7e0f86e1756 otherFunctions
## 59: c4feab88b8bf77f95567b7e0f86e1756 otherFunctions
## 60: c4feab88b8bf77f95567b7e0f86e1756 otherFunctions
## 61: c4feab88b8bf77f95567b7e0f86e1756 otherFunctions
## 62: c4feab88b8bf77f95567b7e0f86e1756 otherFunctions
## 63: c4feab88b8bf77f95567b7e0f86e1756 preDigest
## 64: c4feab88b8bf77f95567b7e0f86e1756 preDigest
## 65: c4feab88b8bf77f95567b7e0f86e1756 preDigest
## artifact tagKey
## tagValue createdDate
## 1: rda 2019-07-08 19:59:56
## 2: 1a07751dce71234cb2ea0bd9eb9d2300 2019-07-08 19:59:56
## 3: numeric 2019-07-08 19:59:56
## 4: 2019-07-08 19:59:56 2019-07-08 19:59:56
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## 8: 1008 2019-07-08 19:59:56
## 9: 2019-07-08 19:59:56 2019-07-08 19:59:56
## 10: vweave_rmarkdown 2019-07-08 19:59:56
## 11: process_file 2019-07-08 19:59:56
## 12: process_group 2019-07-08 19:59:56
## 13: process_group.block 2019-07-08 19:59:56
## 14: call_block 2019-07-08 19:59:56
## 15: block_exec 2019-07-08 19:59:56
## 16: in_dir 2019-07-08 19:59:56
## 17: timing_fn 2019-07-08 19:59:56
## 18: handle 2019-07-08 19:59:56
## 19: withVisible 2019-07-08 19:59:56
## 20: n:82dc709f2b91918a 2019-07-08 19:59:56
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## 29: 1008 2019-07-08 19:59:56
## 30: 2019-07-08 19:59:56 2019-07-08 19:59:56
## 31: vweave_rmarkdown 2019-07-08 19:59:56
## 32: process_file 2019-07-08 19:59:56
## 33: process_group 2019-07-08 19:59:56
## 34: process_group.block 2019-07-08 19:59:56
## 35: call_block 2019-07-08 19:59:56
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## 37: in_dir 2019-07-08 19:59:56
## 38: timing_fn 2019-07-08 19:59:56
## 39: handle 2019-07-08 19:59:56
## 40: withVisible 2019-07-08 19:59:56
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## 43: rda 2019-07-08 19:59:56
## 44: c4feab88b8bf77f95567b7e0f86e1756 2019-07-08 19:59:56
## 45: numeric 2019-07-08 19:59:56
## 46: 2019-07-08 19:59:56 2019-07-08 19:59:56
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## 50: rnorm 2019-07-08 19:59:56
## 51: 1008 2019-07-08 19:59:56
## 52: 2019-07-08 19:59:56 2019-07-08 19:59:56
## 53: vweave_rmarkdown 2019-07-08 19:59:56
## 54: process_file 2019-07-08 19:59:56
## 55: process_group 2019-07-08 19:59:56
## 56: process_group.block 2019-07-08 19:59:56
## 57: call_block 2019-07-08 19:59:56
## 58: block_exec 2019-07-08 19:59:56
## 59: in_dir 2019-07-08 19:59:56
## 60: timing_fn 2019-07-08 19:59:56
## 61: handle 2019-07-08 19:59:56
## 62: withVisible 2019-07-08 19:59:56
## 63: n:7f12988bd88a0fb8 2019-07-08 19:59:56
## 64: mean:22413394efd9f6a3 2019-07-08 19:59:56
## 65: .FUN:4f604aa46882b368 2019-07-08 19:59:56
## tagValue createdDate1.10 cacheId
Sometimes, it is not absolutely desirable to maintain the work flow intact because changes that are irrelevant to the analysis, such as changing messages sent to a user, may be changed, without a desire to rerun functions. The cacheId argument is for this. Once a piece of code is run, then the cacheId can be manually extracted (it is reported at the end of a Cache call) and manually placed in the code, passed in as, say, cacheId = "ad184ce64541972b50afd8e7b75f821b".
### cacheId
set.seed(1)
Cache(rnorm, 1, cacheRepo = tmpdir1)## [1] -0.6264538
## attr(,".Cache")
## attr(,".Cache")$newCache
## [1] TRUE
##
## attr(,"tags")
## [1] "cacheId:7072c305d8c69df0"
## attr(,"call")
## [1] ""# manually look at output attribute which shows cacheId: ad184ce64541972b50afd8e7b75f821b
Cache(rnorm, 1, cacheRepo = tmpdir1, cacheId = "ad184ce64541972b50afd8e7b75f821b") # same value## cacheId is not same as calculated hash. Manually searching for cacheId:ad184ce64541972b50afd8e7b75f821b## [1] 0.1836433
## attr(,".Cache")
## attr(,".Cache")$newCache
## [1] TRUE
##
## attr(,"tags")
## [1] "cacheId:ad184ce64541972b50afd8e7b75f821b"
## attr(,"call")
## [1] ""# override even with different inputs:
Cache(rnorm, 2, cacheRepo = tmpdir1, cacheId = "ad184ce64541972b50afd8e7b75f821b")## cacheId is not same as calculated hash. Manually searching for cacheId:ad184ce64541972b50afd8e7b75f821b## loading cached result from previous rnorm call, adding to memoised copy## [1] 0.1836433
## attr(,".Cache")
## attr(,".Cache")$newCache
## [1] FALSE
##
## attr(,"tags")
## [1] "cacheId:ad184ce64541972b50afd8e7b75f821b"
## attr(,"call")
## [1] ""## cleanup
unlink(c("filename.rda", "filename1.rda"))1.11 Working with the Cache manually
Since the cache is simply an archivist repository, all archivist functions will work as is. In addition, there are several helpers in the reproducible package, including showCache, keepCache and clearCache that may be useful. Also, one can access cached items manually (rather than simply rerunning the same Cache function again).
if (requireNamespace("archivist")) {
# get the RasterLayer that was produced with the gaussMap function:
mapHash <- unique(showCache(tmpDir, userTags = "projectRaster")$artifact)
map <- archivist::loadFromLocalRepo(md5hash = mapHash[1], repoDir = tmpDir, value = TRUE)
plot(map)
}## Cache size:## Total (including Rasters): 3.3 Kb## Selected objects (not including Rasters): 3.3 Kb
## cleanup
unlink(dirname(tmpDir), recursive = TRUE)