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magrittr: Simplifying R code with pipes

R is a functional language, which means that your code often contains a lot of ( parentheses ). And complex code often means nesting those parentheses together, which make code hard to read and understand. But there's a very handy R package — magrittr, by Stefan Milton Bache — which lets you transform nested function calls into a simple pipeline of operations that's easier to write and understand. Hadley Wickham's dplyr package benefits from the %>% pipeline operator provided by magrittr. Hadley showed at useR! 2014 an example of a data transformation operation using traditional R function calls: hourly_delay <- filter( summarise( group_by( filter( flights, !is.na(dep_delay) ), date, hour delay =mean(dep_delay), n> 10 )  Here's the same code, but rather than nesting one function call inside the next, data is passed from one function to the next using the %>% operator: hourly_delay <- flights %>% filter(!is.na(dep_delay)) %>% group_by(date, hour) %>% summarise( delay = mean(dep_delay), n = n() ) %>% filter(n > 10) You can read this version aloud to easily get a sense of what it does: the flights data frame is filtered (to remove missing values of the dep_delay variable), grouped by hours within days, the mean delay is calculated withn groups, and returns the mean delay for those hours with more than 10 flights. You can use the %>% operator with standard R functions — and even your own functions — too. The rules are simple: the object on the left hand side is passed as the first argument to the function on the right hand side. So:  my.data %>% my.function is the same as my.function(my.data) my.data %>% my.function(arg=value) is the same as my.function(my.data, arg=value) It's even possible to pass in data to something other than the first argument of the function  using a . (dot) operator to mark the place where the object goes — see the magrittr vignette for details. This new "pipelining" operation is a really useful addition to the R language, and R developers are starting to use it to make their code simpler to write and maintain. Hadley Wickham's newest R package, tidyr, makes it easy to clean up data sets for analysis by stringing together operations like "gather" and "spread" using the %>% operator. And speaking of pipelining, you may have been wondering where the name "magrittr" comes from. Here's the answer:  The only other question is: will Stefan be making this coffee mug available? magrittr vignette: Ceci n'est pas un pipe 

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More Stories By David Smith

David Smith is Vice President of Marketing and Community at Revolution Analytics. He has a long history with the R and statistics communities. After graduating with a degree in Statistics from the University of Adelaide, South Australia, he spent four years researching statistical methodology at Lancaster University in the United Kingdom, where he also developed a number of packages for the S-PLUS statistical modeling environment. He continued his association with S-PLUS at Insightful (now TIBCO Spotfire) overseeing the product management of S-PLUS and other statistical and data mining products.<

David smith is the co-author (with Bill Venables) of the popular tutorial manual, An Introduction to R, and one of the originating developers of the ESS: Emacs Speaks Statistics project. Today, he leads marketing for REvolution R, supports R communities worldwide, and is responsible for the Revolutions blog. Prior to joining Revolution Analytics, he served as vice president of product management at Zynchros, Inc. Follow him on twitter at @RevoDavid

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