![]() ![]() Vars_of_interest % we can read the data munging process from left to right, just like English. Without pipes we’d use: # complete base R way: This can be read as “Take the trees data set, then show only the trees with girth greater than 9, then select the height and volume of those trees, then compute summary statistics on those two variables”. When reading code, we can then read the pipe operator simply as: ‘then’. ![]() The standard pipe takes the object to its left, and passes it as the first argument in the function to the right. These pipes have a history of being introduced alongside the dplyr package, which together makes for some incredibly powerful, yet concise code (so powerful, during a technical job interview I was asked to stop using dplyr/pipes…). So, here we will do a short run through of the basic piping operator (%>%) for those new to the concept, and discuss some of other pipes that could be useful to experienced useRs. This readability quickly translates into more efficient code by writing less, and understanding more.įirst of all, pipes are infix functions, which call their arguments on either side, instead of the more common prefix functions which take arguments after the function is called. Straight from the highly recommended magrittr vignette, the purpose of pipes and the magrittr package itself is to “decrease development time and to improve readability and maintainability of code” - who wouldn’t like that?Īs mentioned above, pipes are a fantastic way to improve readability in your code, an attribute that has been written about many times. The pipes we will be discussing today are from the magrittr pacakge, which is where dplyr’s ‘standard’ pipe comes from (repo is here). you have undoubtedly came across the fantastic dplyr package and then by default, the the standard pipe. If you have been using R for data ‘plumbing’/wrangling etc. Data scientists and the Mario Brothers agree - pipes rock. ![]()
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