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https://github.com/smbache/magrittr

Improve the readability of R code with the pipe
https://github.com/smbache/magrittr

pipe r

Last synced: 2 months ago
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Improve the readability of R code with the pipe

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README 

        
---

output: github_document

---



```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/"

)

library(magrittr)

```



# magrittr 



[![CRAN status](https://www.r-pkg.org/badges/version/magrittr)](https://cran.r-project.org/package=magrittr)

[![Codecov test coverage](https://codecov.io/gh/tidyverse/magrittr/branch/main/graph/badge.svg)](https://app.codecov.io/gh/tidyverse/magrittr?branch=main)

[![R-CMD-check](https://github.com/smbache/magrittr/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/smbache/magrittr/actions/workflows/R-CMD-check.yaml)



## Overview



The magrittr package offers a set of operators which make your code more

readable by:



* structuring sequences of data operations left-to-right (as opposed to 

  from the inside and out),

* avoiding nested function calls, 

* minimizing the need for local variables and function definitions, and

* making it easy to add steps anywhere in the sequence of operations.



The operators pipe their left-hand side values forward into expressions that

appear on the right-hand side, i.e. one can replace `f(x)` with 

`x %>% f()`, where `%>%` is the (main) pipe-operator. When coupling 

several function calls with the pipe-operator, the benefit will become

more apparent. Consider this pseudo example:



```{r, eval = FALSE}

the_data <-

  read.csv('/path/to/data/file.csv') %>%

  subset(variable_a > x) %>%

  transform(variable_c = variable_a/variable_b) %>%

  head(100)

```



Four operations are performed to arrive at the desired data set, and they 

are written in a natural order: the same as the order of execution. Also, no 

temporary variables are needed. If yet another operation is required, it is 

straightforward to add to the sequence of operations wherever it may be needed.



If you are new to magrittr, the best place to start is the 

[pipes chapter](https://r4ds.had.co.nz/pipes.html) in R for data science.



## Installation



```{r, eval = FALSE}

# The easiest way to get magrittr is to install the whole tidyverse:

install.packages("tidyverse")



# Alternatively, install just magrittr:

install.packages("magrittr")



# Or the development version from GitHub:

# install.packages("devtools")

pak::pak("tidyverse/magrittr")

```



## Usage



### Basic piping

  

* `x %>% f` is equivalent to `f(x)`

* `x %>% f(y)` is equivalent to `f(x, y)`

* `x %>% f %>% g %>% h` is equivalent to `h(g(f(x)))`



Here, "equivalent" is not technically exact: evaluation is non-standard,

and the left-hand side is evaluated before passed on to the right-hand side

expression. However, in most cases this has no practical implication.



### The argument placeholder



* `x %>% f(y, .)` is equivalent to `f(y, x)`

* `x %>% f(y, z = .)` is equivalent to `f(y, z = x)`

 

### Re-using the placeholder for attributes



It is straightforward to use the placeholder several times

in a right-hand side expression. However, when the placeholder

only appears in a nested expressions magrittr will still apply

the first-argument rule. The reason is that in most cases this

results more clean code. 



`x %>% f(y = nrow(.), z = ncol(.))` is equivalent to 

   `f(x, y = nrow(x), z = ncol(x))`



The behavior can be

overruled by enclosing the right-hand side in braces:



`x %>% {f(y = nrow(.), z = ncol(.))}` is equivalent to 

   `f(y = nrow(x), z = ncol(x))`



### Building (unary) functions



Any pipeline starting with the `.` will return a function which can later

be used to apply the pipeline to values. Building functions in magrittr 

is therefore similar to building other values.



```{r}

f <- . %>% cos %>% sin 

# is equivalent to 

f <- function(.) sin(cos(.)) 

```



### Pipe with exposition of variables



Many functions accept a data argument, e.g. `lm` and `aggregate`, which

is very useful in a pipeline where data is first processed and then passed

into such a function. There are also functions that do not have a data 

argument, for which it is useful to expose the variables in the data.

This is done with the `%$%` operator:



```{r exposition}

iris %>%

  subset(Sepal.Length > mean(Sepal.Length)) %$%

  cor(Sepal.Length, Sepal.Width)



data.frame(z = rnorm(100)) %$%

  ts.plot(z)

```



## Code of Conduct



Please note that the magrittr project is released with a [Contributor Code of Conduct](https://magrittr.tidyverse.org/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.