Given some raw JSON from an aggs query in Elasticsearch, parse the aggregations into a data.table.
chomp_aggs(aggs_json = NULL)
| aggs_json | A character vector. If its length is greater than 1, its elements will be pasted
together. This can contain a JSON returned from an |
|---|
A data.table representation of the result or NULL if the aggregation result is empty.
# A sample raw result from an aggs query combining date_histogram and extended_stats: result <- '{"aggregations":{"dateTime":{"buckets":[{"key_as_string":"2016-12-01T00:00:00.000Z", "key":1480550400000,"doc_count":123,"num_potatoes":{"count":120,"min":0,"max":40,"avg":15, "sum":1800,"sum_of_squares":28000,"variance":225,"std_deviation":15,"std_deviation_bounds":{ "upper":26,"lower":13}}},{"key_as_string":"2017-01-01T00:00:00.000Z","key":1483228800000, "doc_count":134,"num_potatoes":{"count":131,"min":0,"max":39,"avg":16,"sum":2096, "sum_of_squares":34000,"variance":225,"std_deviation":15,"std_deviation_bounds":{"upper":26, "lower":13}}}]}}}' # Parse into a data.table aggDT <- chomp_aggs(aggs_json = result) print(aggDT)#> dateTime num_potatoes.count num_potatoes.min #> 1: 2016-12-01T00:00:00.000Z 120 0 #> 2: 2017-01-01T00:00:00.000Z 131 0 #> num_potatoes.max num_potatoes.avg num_potatoes.sum #> 1: 40 15 1800 #> 2: 39 16 2096 #> num_potatoes.sum_of_squares num_potatoes.variance num_potatoes.std_deviation #> 1: 28000 225 15 #> 2: 34000 225 15 #> num_potatoes.std_deviation_bounds.upper #> 1: 26 #> 2: 26 #> num_potatoes.std_deviation_bounds.lower doc_count #> 1: 13 123 #> 2: 13 134