sdf_quantile

Compute (Approximate) Quantiles with a Spark DataFrame

Description

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

Usage

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

Arguments

Argument	Description
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.