sdf_quantile: Compute (Approximate) Quantiles with a Spark DataFrame
In sparklyr: R Interface to Apache Spark

sdf_quantile

R Documentation

Compute (Approximate) Quantiles with a Spark DataFrame

Given a numeric column within a Spark DataFrame, compute approximate quantiles.

sdf_quantile(
  x,
  column,
  probabilities = c(0, 0.25, 0.5, 0.75, 1),
  relative.error = 1e-05,
  weight.column = NULL
)

`x`	A `spark_connection`, `ml_pipeline`, or a `tbl_spark`.
`column`	The column(s) for which quantiles should be computed. Multiple columns are only supported in Spark 2.0+.
`probabilities`	A numeric vector of probabilities, for which quantiles should be computed.
`relative.error`	The maximal possible difference between the actual percentile of a result and its expected percentile (e.g., if 'relative.error' is 0.01 and 'probabilities' is 0.95, then any value between the 94th and 96th percentile will be considered an acceptable approximation).
`weight.column`	If not NULL, then a generalized version of the Greenwald- Khanna algorithm will be run to compute weighted percentiles, with each sample from 'column' having a relative weight specified by the corresponding value in 'weight.column'. The weights can be considered as relative frequencies of sample data points.