Pipe: Create a Pipe object that stores a value and allows command chaining with `$`.

Description

Create a Pipe object that stores a value and allows command chaining with $.

Usage

Pipe(value = NULL)

Arguments

value

value to pipe (default is NULL)

Value

Pipe object

Details

Pipe() function creates a Pipe object that provides object-like command chaining mechanism, which avoids using external operator and can be cleaner than operator-based pipline.

Pipe() creates a Pipe object that allows using $ to perform first-argument piping, call .() to evaluate an expression with . or symbol defined by lambda expression, for side effect, or simply extract an element from the stored value. $value or [] ends a pipeline and extracts its final value.

The functionality of Pipe object fully covers that of the pipe operator %>>% and provides more features. For example, Pipe object supports directly subsetting $value by [...], extracting element by [[...]], and assigning value by $item <-, [...] <-, and [[...]] <-.

A typical usage of Pipe object is to start with Pipe() and end with $value or [].

print() and str() are implemented for Pipe object. Use header = FALSE to suppress Pipe header message in printed results. Use options(Pipe.header = FASLE) to suppress it globally.

If the Pipe object is used in more than one pipelines, a recommended usage is to name the object specially so that it is easy to distinguish the Pipe object from the value it stores. For example, it can start with p.

Examples

Run this code

## Not run: 
# # Pipe as first-argument using $
# Pipe(rnorm(100))$mean()
# Pipe(rnorm(100))$plot(col="red")
# 
# # Extract the value from the Pipe object using []
# Pipe(rnorm(100))$c(4,5) []
# 
# # Pipe to an exrepssion with . or symbol defined in
# # lambda expression to represent the object
# Pipe(rnorm(100))$.(1 + .) []
# Pipe(rnorm(100))$.(x ~ 1 + x) []
# 
# # Pipe for side effect
# Pipe(rnorm(100))$
#   .(~ cat("number:",length(.),"\n"))$
#   summary()
# 
# Pipe(rnorm(100))$
#   .(~ x ~ cat("number:",length(x),"\n"))$
#   summary()
# 
# # Assignment
# Pipe(rnorm(100))$
#   .(~ x)$
#   mean()
# 
# Pipe(rnorm(100))$
#   .(~ x <- length(.))$
#   mean()
# 
# Pipe(rnorm(100))%
#   .(x <- c(min(.),max(.)))$
#   mean()
# 
# # Extract element with \code{.(name)}
# Pipe(mtcars)$lm(formula = mpg ~ cyl + wt)$.(coefficients)
# 
# # Command chaining
# Pipe(rnorm(100,mean=10))$
#   log()$
#   diff()$
#   plot(col="red")
# 
# Pipe(rnorm(100))$
#   density(kernel = "rect")$
#   plot(col = "blue")
# 
# # Store an continue piping
# pipe1 <- Pipe(rnorm(100,mean=10))$log()$diff()
# pipe1$plot(col="red")
# 
# # Subsetting, extracting, and assigning
# 
# p <- Pipe(list(a=1,b=2))
# p["a"]
# p[["a"]]
# p$a <- 2
# p["b"] <- NULL
# p[["a"]] <- 3
# p[length(.)] # . = p$value
# 
# # Data manipulation with dplyr
# library(dplyr)
# Pipe(mtcars)$
#   select(mpg,cyl,disp,hp)$
#   filter(mpg <= median(mpg))$
#   mutate(rmpg = mpg / max(mpg))$
#   group_by(cyl)$
#   do(data.frame(mean=mean(.$rmpg),median=median(.$rmpg))) []
# 
# # Graphics with ggvis
# library(ggvis)
# Pipe(mtcars)$
#   ggvis(~ mpg, ~ wt)$
#   layer_points()
# 
# # Data manipulation with rlist
# library(rlist)
# Pipe(list(1,2,3))$
#   list.map(. + 1)$
#   list.filter(. <= 5)$
#   list.sort(.) []
# 
# # Lazy evaluation
# p1 <- Pipe(mtcars)$
#   ggvis(~ mpg, ~ wt)
# p1$layer_points()
# p1$layer_bars()
# 
# # Stored Pipe
# f1 <- Pipe(rnorm(100))$plot
# f1(col="red")
# f1(col="green")
# ## End(Not run)