E is for Exposition Pipe
This article is originally published at http://www.deeplytrivial.com/For the letter E, I want to talk about a set of operators provided by tidyverse (specifically the magrittr package) that makes for much prettier, easier-to-read code: pipes. The main pipe %>% pushes the object to the left of it forward into functions on the right, so that instead of coding f(x), it would be x %>% f(). This lets you chain together multiple functions to apply to a single object.
One of these pipes is known as the exposition pipe, and it looks like this: %$%. For many base R functions, there is no data attribute (i.e., data = ...), and so when you specify one or more variables, you have to also specify the data frame (which would be formatted as data$variable). For instance, to run a correlation between two variables, you'd need to write the code as cor(data$variable1, data$variable2).
The exposition pipe exposes the columns of a data frame to the function, so you only need to specify the data frame once. This cleans up your code, while also allowing these functions to be embedded in a larger string of data wrangling and manipulation. Here's how I can use the exposition code in action (but first, I've created a new version of the dataset that has a single Rating variable, which we created previously with the coalesce function, so you'll want to download and use that file going forward):
library(tidyverse)
## -- Attaching packages ------------------------------------------- tidyverse 1.3.0 --
## <U+2713> ggplot2 3.2.1 <U+2713> purrr 0.3.3
## <U+2713> tibble 2.1.3 <U+2713> dplyr 0.8.3
## <U+2713> tidyr 1.0.0 <U+2713> stringr 1.4.0
## <U+2713> readr 1.3.1 <U+2713> forcats 0.4.0
## -- Conflicts ---------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(magrittr)
##
## Attaching package: 'magrittr'
## The following object is masked from 'package:purrr':
##
## set_names
## The following object is masked from 'package:tidyr':
##
## extract
reads2019 <- read_csv("~/Downloads/Blogging A to Z/SarasReads2019_allrated.csv", col_names = TRUE)
## Parsed with column specification:
## cols(
## Title = col_character(),
## Pages = col_double(),
## date_started = col_character(),
## date_read = col_character(),
## Book.ID = col_double(),
## Author = col_character(),
## AdditionalAuthors = col_character(),
## AverageRating = col_double(),
## OriginalPublicationYear = col_double(),
## read_time = col_double(),
## MyRating = col_double(),
## Gender = col_double(),
## Fiction = col_double(),
## Childrens = col_double(),
## Fantasy = col_double(),
## SciFi = col_double(),
## Mystery = col_double(),
## SelfHelp = col_double()
## )
reads2019 %$%
cor(MyRating, read_time)
## [1] -0.03762191
reads2019 %>%
mutate(DifRating = MyRating - AverageRating) %$%
cor(DifRating, read_time)
## [1] 0.007745212
Tomorrow, we'll talk about filters!
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This article is originally published at http://www.deeplytrivial.com/
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