# sdf_pivot

0th

Percentile

##### Pivot a Spark DataFrame

Construct a pivot table over a Spark Dataframe, using a syntax similar to that from reshape2::dcast.

##### Usage
sdf_pivot(x, formula, fun.aggregate = "count")
##### Arguments
x

A spark_connection, ml_pipeline, or a tbl_spark.

formula

A two-sided R formula of the form x_1 + x_2 + ... ~ y_1. The left-hand side of the formula indicates which variables are used for grouping, and the right-hand side indicates which variable is used for pivoting. Currently, only a single pivot column is supported.

fun.aggregate

How should the grouped dataset be aggregated? Can be a length-one character vector, giving the name of a Spark aggregation function to be called; a named R list mapping column names to an aggregation method, or an R function that is invoked on the grouped dataset.

• sdf_pivot
##### Examples
# NOT RUN {
library(sparklyr)
library(dplyr)

sc <- spark_connect(master = "local")
iris_tbl <- sdf_copy_to(sc, iris, name = "iris_tbl", overwrite = TRUE)

# aggregating by mean
iris_tbl %>%
mutate(Petal_Width = ifelse(Petal_Width > 1.5, "High", "Low" )) %>%
sdf_pivot(Petal_Width ~ Species,
fun.aggregate = list(Petal_Length = "mean"))

# aggregating all observations in a list
iris_tbl %>%
mutate(Petal_Width = ifelse(Petal_Width > 1.5, "High", "Low" )) %>%
sdf_pivot(Petal_Width ~ Species,
fun.aggregate = list(Petal_Length = "collect_list"))
# }
# NOT RUN {
# }