# 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. |