Agenda

• what are pipes?
• why use pipes?
• what are the different types of pipes?
• combining operations with pipes
• case studies

Introduction

R code contain a lot of parentheses in case of a sequence of multiple operations. When you are dealing with complex code, it results in nested function calls which are hard to read and maintain. The magrittr package by Stefan Milton Bache provides pipes enabling us to write R code that is readable.

Pipes allow us to clearly express a sequence of multiple operations by:

• structuring operations from left to right
• avoiding
  • nested function calls
  • intermediate steps
  • overwriting of original data
• minimizing creation of local variables

Pipes

If you are using tidyverse, magrittr will be automatically loaded. We will look at 3 different types of pipes:

• %>% : pipe a value forward into an expression or function call
• %<>%: result assigned to left hand side object instead of returning it
• %$% : expose names within left hand side objects to right hand side expressions

Libraries

library(magrittr)
library(readr)
library(purrr)
library(dplyr)
library(stringr)

Data

## # A tibble: 1,000 x 4
##    referrer n_pages duration purchase
##    <fct>      <dbl>    <dbl> <lgl>   
##  1 google         1      693 FALSE   
##  2 yahoo          1      459 FALSE   
##  3 direct         1      996 FALSE   
##  4 bing          18      468 TRUE    
##  5 yahoo          1      955 FALSE   
##  6 yahoo          5      135 FALSE   
##  7 yahoo          1       75 FALSE   
##  8 direct         1      908 FALSE   
##  9 bing          19      209 FALSE   
## 10 google         1      208 FALSE   
## # ... with 990 more rows

Data Dictionary

• referrer: referrer website/search engine
• n_pages: number of pages visited
• duration: time spent on the website (in seconds)
• purchase: whether visitor purchased

Sample Data

ecom_mini <- sample_n(ecom, size = 10)

head

head(ecom, 10)

## # A tibble: 10 x 4
##    referrer n_pages duration purchase
##    <fct>      <dbl>    <dbl> <lgl>   
##  1 google         1      693 FALSE   
##  2 yahoo          1      459 FALSE   
##  3 direct         1      996 FALSE   
##  4 bing          18      468 TRUE    
##  5 yahoo          1      955 FALSE   
##  6 yahoo          5      135 FALSE   
##  7 yahoo          1       75 FALSE   
##  8 direct         1      908 FALSE   
##  9 bing          19      209 FALSE   
## 10 google         1      208 FALSE

Using pipe

ecom %>% head(10)

## # A tibble: 10 x 4
##    referrer n_pages duration purchase
##    <fct>      <dbl>    <dbl> <lgl>   
##  1 google         1      693 FALSE   
##  2 yahoo          1      459 FALSE   
##  3 direct         1      996 FALSE   
##  4 bing          18      468 TRUE    
##  5 yahoo          1      955 FALSE   
##  6 yahoo          5      135 FALSE   
##  7 yahoo          1       75 FALSE   
##  8 direct         1      908 FALSE   
##  9 bing          19      209 FALSE   
## 10 google         1      208 FALSE

Square Root

y <- ecom_mini$n_pages
y <- sqrt(y)

# combine above steps
sqrt(ecom_mini$n_pages)

##  [1] 1.000000 1.000000 2.000000 1.000000 4.472136 1.000000 1.414214
##  [8] 1.000000 1.414214 1.000000

Square Root - Using pipe

# select n_pages variable and assign it to y
ecom_mini %$% 
  n_pages -> y

# compute square root of y and assign it to y 
y %<>% sqrt()

Square Root - Using Pipe

Square Root - Using pipe

ecom_mini %$% 
  n_pages %>% 
  sqrt() -> y

y

##  [1] 1.000000 1.000000 2.000000 1.000000 4.472136 1.000000 1.414214
##  [8] 1.000000 1.414214 1.000000

Correlation

Correlation

# without pipe
ecom1 <- subset(ecom, purchase)
cor(ecom1$n_pages, ecom1$duration)

## [1] 0.4290905

Correlation - Using pipe

# with pipe
ecom %>%
  subset(purchase) %$% 
  cor(n_pages, duration)

## [1] 0.4290905

# using filter from dplyr and pipe
ecom %>%
  filter(purchase) %$% 
  cor(n_pages, duration)

## [1] 0.4290905

Visualization

barplot(table(subset(ecom, purchase)$referrer))

Visualization