Capítulo14 Pipes

Fecha de la ultima revisión

## [1] "2024-11-07"
library(tidyverse)
library(magrittr) # El paquete de Pipe, se activa usando tidyverse

El tema proviene de los siguientes sitios.

English: https://r4ds.had.co.nz/pipes.html

Español: https://es.r4ds.hadley.nz/

14.1 Temas: El paquete “magrittr” y sus funciones

Los pipes que vamos a evaluar son los siguientes:

  • %>% “Pipe”

  • %!>% “Eager Pipe”

  • %$% “Exposition pipe”

  • %<>% “Assigment pipe”

  • %T>% “Tee Pipe”

  • %in% “In Pipe”

Función basica para calcular el precio básico de de diamantes por quilates

head(diamonds, n=3)
## # A tibble: 3 × 10
##   carat cut     color clarity depth table price     x     y     z
##   <dbl> <ord>   <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1  0.23 Ideal   E     SI2      61.5    55   326  3.95  3.98  2.43
## 2  0.21 Premium E     SI1      59.8    61   326  3.89  3.84  2.31
## 3  0.23 Good    E     VS1      56.9    65   327  4.05  4.07  2.31
diamonds$Precio_q = diamonds$price/diamonds$carat # crear una nueva variable

head(diamonds, n=3)
## # A tibble: 3 × 11
##   carat cut     color clarity depth table price     x     y     z Precio_q
##   <dbl> <ord>   <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>    <dbl>
## 1  0.23 Ideal   E     SI2      61.5    55   326  3.95  3.98  2.43    1417.
## 2  0.21 Premium E     SI1      59.8    61   326  3.89  3.84  2.31    1552.
## 3  0.23 Good    E     VS1      56.9    65   327  4.05  4.07  2.31    1422.
diamonds2 <- diamonds %>% 
  dplyr::mutate(price_per_carat = price / carat)

Calcular el promedio de precio por la calidad de los diamantes usando pipes

diamonds %>% 
  group_by(cut) %>% 
  summarize(Precio_color= mean(price))
## # A tibble: 5 × 2
##   cut       Precio_color
##   <ord>            <dbl>
## 1 Fair             4359.
## 2 Good             3929.
## 3 Very Good        3982.
## 4 Premium          4584.
## 5 Ideal            3458.

Calcular el promedio de precio por color de los diamantes usando pipes

14.2 %>% pipes

Un ejemplo básico del pipe

car_data <- 
  mtcars %>%
  subset(hp > 100) %>%
  aggregate(. ~ cyl, data = ., FUN = . %>% mean %>% round(2)) %>%
  transform(kpl = mpg %>% multiply_by(0.4251)) %>%
  print
##   cyl   mpg   disp     hp drat   wt  qsec   vs   am gear carb       kpl
## 1   4 25.90 108.05 111.00 3.94 2.15 17.75 1.00 1.00 4.50 2.00 11.010090
## 2   6 19.74 183.31 122.29 3.59 3.12 17.98 0.57 0.43 3.86 3.43  8.391474
## 3   8 15.10 353.10 209.21 3.23 4.00 16.77 0.00 0.14 3.29 3.50  6.419010

Un ejemplo sin usar el pipe para calcular la misma información

car_data <- 
  transform(aggregate(. ~ cyl, 
                      data = subset(mtcars, hp > 100), 
                      FUN = function(x) round(mean(x), 2)), 
                      kpl = mpg*0.4251)

car_data
##   cyl   mpg   disp     hp drat   wt  qsec   vs   am gear carb       kpl
## 1   4 25.90 108.05 111.00 3.94 2.15 17.75 1.00 1.00 4.50 2.00 11.010090
## 2   6 19.74 183.31 122.29 3.59 3.12 17.98 0.57 0.43 3.86 3.43  8.391474
## 3   8 15.10 353.10 209.21 3.23 4.00 16.77 0.00 0.14 3.29 3.50  6.419010
car_data <- 
  mtcars %>%
  subset(hp > 100) %>%
  group_by(cyl) %>% 
  summarise(across(everything(), list(mean))) %>% 
  transform(kpl = mpg_1 %>% multiply_by(0.4251)) %>% 
  print
##   cyl    mpg_1   disp_1     hp_1   drat_1     wt_1   qsec_1      vs_1      am_1
## 1   4 25.90000 108.0500 111.0000 3.940000 2.146500 17.75000 1.0000000 1.0000000
## 2   6 19.74286 183.3143 122.2857 3.585714 3.117143 17.97714 0.5714286 0.4285714
## 3   8 15.10000 353.1000 209.2143 3.229286 3.999214 16.77214 0.0000000 0.1428571
##     gear_1   carb_1       kpl
## 1 4.500000 2.000000 11.010090
## 2 3.857143 3.428571  8.392689
## 3 3.285714 3.500000  6.419010

14.3 Alternativas a los pipes

  • Pasos intermedios
  • Sobre escribir el original

14.4 %$%

library(magrittr)

#iris %>%
#  subset(Sepal.Length > mean(Sepal.Length)) |> 
#  cor(Sepal.Length, Sepal.Width)


iris %>%
  subset(Sepal.Length > mean(Sepal.Length)) %$%
 cor(Sepal.Length, Sepal.Width)
## [1] 0.3361992
iris %>%
  subset(Sepal.Length > mean(Sepal.Length)) |> 
  summarize(corr= cor(Sepal.Length, Sepal.Width))
##        corr
## 1 0.3361992

14.4.1 Otro ejemplo de Pipe: %$%

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

#data.frame(z = rnorm(100)) |>  
#  ts.plot(z)

#Para hacerlo la misma grafico con Tidyverse
z1=data.frame(z = rnorm(100)) 
library(data.table)
z1=data.table(z1)
z1$order= c(1:100)
z1|> 
  ggplot(aes(order, z))+
  geom_line()

14.5 %<>%

Pipe an object forward into a function or call expression and update the lhs object with the resulting value.

lhs %<>% rhs

  • lhs : An object which serves both as the initial value and as target.

  • rhs : a function call using the magrittr semantics.

# Example 1
head(iris)
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa
head(iris$Sepal.Length %<>% sqrt)
## [1] 2.258318 2.213594 2.167948 2.144761 2.236068 2.323790
# Example 2
x <- rnorm(100)
head(x, n=10)
##  [1]  1.361612477 -0.189292059  0.462969745 -0.007385678  2.689108440
##  [6]  1.459546809 -1.653300235 -0.203078277  0.618497216  0.597135379
x %<>% 
  abs %>% 
  sort

head(x, 20)
##  [1] 0.007385678 0.029096523 0.030999873 0.035387591 0.040816992 0.054441440
##  [7] 0.069795203 0.081581732 0.087776321 0.110958305 0.119075263 0.120358116
## [13] 0.120542457 0.131861167 0.133266112 0.145019780 0.145731942 0.150293091
## [19] 0.155538095 0.164714712

14.6 %in%

  • Filter for values specific values within a variable
# Example 1
names(iris)
## [1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width"  "Species"
iris |> 
  filter(Species %in% c("setosa", "virginica")) |> 
  head()
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1     2.258318         3.5          1.4         0.2  setosa
## 2     2.213594         3.0          1.4         0.2  setosa
## 3     2.167948         3.2          1.3         0.2  setosa
## 4     2.144761         3.1          1.5         0.2  setosa
## 5     2.236068         3.6          1.4         0.2  setosa
## 6     2.323790         3.9          1.7         0.4  setosa

14.7 %T>%

The tee pipe, %T>%, is useful when a series of operations have a function that does not return any value.

https://stackoverflow.com/questions/61196304/magrittr-tee-pipe-t-equivalent

rnorm(200) %>%
matrix(ncol = 2) %T>%
plot %>%  # plot usually does not return anything. 
colSums

## [1] 1.395464 5.606604