sdf_partition {sparklyr} | R Documentation

## Partition a Spark Dataframe

### Description

Partition a Spark DataFrame into multiple groups. This routine is useful for splitting a DataFrame into, for example, training and test datasets.

### Usage

    sdf_partition(x, ..., weights = NULL, seed = sample(.Machine$integer.max,
      1))

### Arguments

`x` | An object coercable to a Spark DataFrame.
`...` | Named parameters, mapping table names to weights. The weights will be normalized such that they sum to 1.
`weights` | An alternate mechanism for supplying weights – when specified, this takes precedence over the `...` arguments.
`seed` | Random seed to use for randomly partitioning the dataset. Set this if you want your partitioning to be reproducible on repeated runs.

### Details

The sampling weights define the probability that a particular observation will be assigned to a particular partition, not the resulting size of the partition. This implies that partitioning a DataFrame with, for example,

`sdf_partition(x, training = 0.5, test = 0.5)`

is not guaranteed to produce `training` and `test` partitions of equal size.

### Value

An R `list` of `tbl_spark`s.

### Transforming Spark DataFrames

The family of functions prefixed with `sdf_` generally access the Scala Spark DataFrame API directly, as opposed to the `dplyr` interface which uses Spark SQL. These functions will 'force' any pending SQL in a `dplyr` pipeline, such that the resulting `tbl_spark` object returned will no longer have the attached 'lazy' SQL operations. Note that the underlying Spark DataFrame does execute its operations lazily, so that even though the pending set of operations (currently) are not exposed at the R level, these operations will only be executed when you explicitly `collect()` the table.

### See Also

Other Spark data frames: `sdf_copy_to`, `sdf_predict`, `sdf_register`, `sdf_sample`, `sdf_sort`

### Examples

    ## Not run:
    # randomly partition data into a 'training' and 'test'
    # dataset, with 60% of the observations assigned to the
    # 'training' dataset, and 40% assigned to the 'test' dataset
    data(diamonds, package = "ggplot2")
    diamonds_tbl <- copy_to(sc, diamonds, "diamonds")
    partitions <- diamonds_tbl %>%
      sdf_partition(training = 0.6, test = 0.4)
    print(partitions)

    # alternate way of specifying weights
    weights <- c(training = 0.6, test = 0.4)
    diamonds_tbl %>% sdf_partition(weights = weights)

    ## End(Not run)

[Package sparklyr version 0.6.3 Index]