Caching openSenseMap Data for Reproducibility

Norwin Roosen


It may be useful to download data from openSenseMap only once. For reproducible results, the data should be saved to disk, and reloaded at a later point.

This avoids..

This vignette shows how to use this built in opensensmapr feature, and how to do it yourself in case you want to save to other data formats.

# this vignette requires:

Using the opensensmapr Caching Feature

All data retrieval functions of opensensmapr have a built in caching feature, which serializes an API response to disk. Subsequent identical requests will then return the serialized data instead of making another request.

To use this feature, just add a path to a directory to the cache parameter:

b = osem_boxes(grouptag = 'ifgi', cache = tempdir())

# the next identical request will hit the cache only!
b = osem_boxes(grouptag = 'ifgi', cache = tempdir())

# requests without the cache parameter will still be performed normally
b = osem_boxes(grouptag = 'ifgi')

Looking at the cache directory we can see one file for each request, which is identified through a hash of the request URL:

list.files(tempdir(), pattern = 'osemcache\\..*\\.rds')
## [1] "osemcache.17db5c57fc6fca4d836fa2cf30345ce8767cd61a.rds"

You can maintain multiple caches simultaneously which allows to only store data related to a script in the same directory:

cacheDir = getwd() # current working directory
b = osem_boxes(grouptag = 'ifgi', cache = cacheDir)

# the next identical request will hit the cache only!
b = osem_boxes(grouptag = 'ifgi', cache = cacheDir)

To get fresh results again, just call osem_clear_cache() for the respective cache:

osem_clear_cache()        # clears default cache
osem_clear_cache(getwd()) # clears a custom cache

Custom (De-) Serialization

If you want to roll your own serialization method to support custom data formats, here’s how:

# first get our example data:
measurements = osem_measurements('Windrichtung')

If you are paranoid and worry about .rds files not being decodable anymore in the (distant) future, you could serialize to a plain text format such as JSON. This of course comes at the cost of storage space and performance.

# serializing senseBoxes to JSON, and loading from file again:
write(jsonlite::serializeJSON(measurements), 'measurements.json')
measurements_from_file = jsonlite::unserializeJSON(readr::read_file('measurements.json'))
## [1] "osem_measurements" "tbl_df"            "tbl"              
## [4] "data.frame"

This method also persists the R object metadata (classes, attributes). If you were to use a serialization method that can’t persist object metadata, you could re-apply it with the following functions:

# note the toJSON call instead of serializeJSON
write(jsonlite::toJSON(measurements), 'measurements_bad.json')
measurements_without_attrs = jsonlite::fromJSON('measurements_bad.json')
## [1] "data.frame"
measurements_with_attrs = osem_as_measurements(measurements_without_attrs)
## [1] "osem_measurements" "tbl_df"            "tbl"              
## [4] "data.frame"

The same goes for boxes via osem_as_sensebox().