pipeR-package: The pipeR package

In pipeR: Multi-Paradigm Pipeline Implementation

Description

pipeR implements various function chaining methods: %>>% operator, Pipe object, and pipeline function. Each represents a distinct pipeline model yet shares a common set of features designed to build easy-to-read/write/maintain pipelines. To learn more, please visit pipeR Tutorial.

Details

pipeR package defines a set of syntax tailored for unified, intuitive piping experience. The package is designed to help organize code as a streamline that is consistent with logic and intuition.

The following example shows how traditional code can be written in different function chaining styles.

Examples

# Traditional code:
plot(density(sample(mtcars$mpg, size = 10000, replace = TRUE),
 kernel = "gaussian"), col = "red", main="density of mpg (bootstrap)")

# Operator-based pipeline using %>>%:
mtcars$mpg %>>%
  sample(size = 10000, replace = TRUE) %>>%
  density(kernel = "gaussian") %>>%
  plot(col = "red", main = "density of mpg (bootstrap)")

# Object-based pipeline using Pipe():
Pipe(mtcars$mpg)$
  sample(size = 10000, replace = TRUE)$
  density(kernel = "gaussian")$
  plot(col = "red", main = "density of mpg (bootstrap)")

# Argument-based pipeline using pipeline():
pipeline(mtcars$mpg,
  sample(size = 10000, replace = TRUE),
  density(kernel = "gaussian"),
  plot(col = "red", main = "density of mpg (bootstrap)"))

# Expression-based pipeline using pipeline():
pipeline({
  mtcars$mpg
  sample(size = 10000, replace = TRUE)
  density(kernel = "gaussian")
  plot(col = "red", main = "density of mpg (bootstrap)")
})

pipeR documentation built on May 1, 2019, 7:13 p.m.