1 Las pipes (%>%)
Facilitan la comprensión del código. El objetivo de la tubería es escribir código de una manera que sea más fácil de leer y comprender. Son muy poderosas para crear expresiones y secuencias de control que combinan múltiples operaciones sin la necesidad de crear variables intermedias.
library(tidyverse)
library(magrittr)# foo_foo <- little_bunny()# hop()
# scoop()
# bop()con variables intermedias
# foo_foo_1 <- hop(foo_foo, through = forest)
# foo_foo_2 <- scoop(foo_foo_1, up = field_mice)
# foo_foo_3 <- bop(foo_foo_2, on = head)pryr::object_size entrega el tamano
# dd <- ggplot2::diamonds
# dd1 <- dd %>%
# dplyr::mutate(price_per_carat = price / carat)
#
# dd$carat[1] <- NA
# pryr::object_size(dd)
# pryr::object_size(dd1)
# pryr::object_size(dd,dd1)1.1 Sobreescribimos la variable original
# foo_foo <- hop(foo_foo, through = forest)
# foo_foo <- scoop(foo_foo, up = field_mice)
# foo_foo <- bop(foo_foo, on = head)Componer funciones
# bop(
# scoop(
# hop(
# foo_foo,
# through = forest
# ),
# up = field_mice),
# on = head
# )usando pipes
# foo_foo %>%
# hop(through = forest) %>%
# scoop(up = field_mide) %>%
# bop(on = head)funciones
# my_own_pipe <- function(.){
# . <- hop(., through = forest)
# . <- scoop(., through = field_mice)
# . <- bop(., on = head)
# return(.)
# }1.2 assign, get y load
# assign("x",3)
# "x" %>% assign(6)# env <- enviroment()
# "x" %>% assign(6, envir = env)
# x2 tryCatch, try, supressMessages, supressWarnings
# tryCatch(stop("!"),
# error = function(e) "error")# stop("!") %>%
# tryCatch (error = function(e) "error")Otra herramientras de Magritr
rnorm(1000) %>%
matrix(ncol = 2) %T>%
plot() %>%
str()
## num [1:500, 1:2] 1.154 -0.185 -0.331 0.803 -0.458 ...mtcars %$% cor(disp, mpg)## [1] -0.8475514Boomerang: %<>%
mtcars %<>% filter(cyl == 6)