sdf_rnorm: Generate random samples from the standard normal distribution

In sparklyr: R Interface to Apache Spark

Generate random samples from the standard normal distribution

Description

Generator method for creating a single-column Spark dataframes comprised of i.i.d. samples from the standard normal distribution.

Usage

sdf_rnorm(
  sc,
  n,
  mean = 0,
  sd = 1,
  num_partitions = NULL,
  seed = NULL,
  output_col = "x"
)

Arguments

sc	A Spark connection.
n	Sample Size (default: 1000).
mean	The mean value of the normal distribution.
sd	The standard deviation of the normal distribution.
num_partitions	Number of partitions in the resulting Spark dataframe (default: default parallelism of the Spark cluster).
seed	Random seed (default: a random long integer).
output_col	Name of the output column containing sample values (default: "x").

See Also

Other Spark statistical routines: sdf_rbeta(), sdf_rbinom(), sdf_rcauchy(), sdf_rchisq(), sdf_rexp(), sdf_rgamma(), sdf_rgeom(), sdf_rhyper(), sdf_rlnorm(), sdf_rpois(), sdf_rt(), sdf_runif(), sdf_rweibull()

sparklyr documentation built on May 29, 2024, 2:58 a.m.