sdf_pivot
From sparklyr v0.9.3
by Javier Luraschi
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 atbl_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.
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 {
# }
Community examples
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