Readable Code with Pipes

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4 years ago

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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, Code & Data

We will use the following packages in this post:

You can find the data sets here and the codes here.

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

Data

ecom <- 
  read_csv('https://raw.githubusercontent.com/rsquaredacademy/datasets/master/web.csv',
    col_types = cols_only(
      referrer = col_factor(levels = c("bing", "direct", "social", "yahoo", "google")),
      n_pages = col_double(), duration = col_double(), purchase = col_logical()
    )
  )

ecom
## # 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

We will create a smaller data set from the above data to be used in some examples:

ecom_mini <- sample_n(ecom, size = 10)
ecom_mini
## # A tibble: 10 x 4
##    referrer n_pages duration purchase
##    <fct>      <dbl>    <dbl> <lgl>   
##  1 google         2       54 FALSE   
##  2 direct        15      330 FALSE   
##  3 yahoo          1       47 FALSE   
##  4 yahoo          1      668 FALSE   
##  5 google        10      180 FALSE   
##  6 social         2       56 FALSE   
##  7 direct         1      675 FALSE   
##  8 google         1      523 FALSE   
##  9 bing           1      999 FALSE   
## 10 bing           8      120 FALSE

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

First Example

Let us start with a simple example. You must be aware of head(). If not, do not worry. It returns the first few observations/rows of data. We can specify the number of observations it should return as well. Let us use it to view the first 10 rows of our data set.

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

Now let us do the same but with %>%.

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

Time to try a slightly more challenging example. We want the square root of n_pages column from the data set.

y <- sqrt(ecom_mini$n_pages)

Let us break down the above computation into small steps:

select/expose the n_pages column from ecom data
compute the square root
assign the first few observations to y

Let us reproduce y using pipes.

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

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

Another way to compute the square root of y is shown below.

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

Visualization

Let us look at a data visualization example. We will create a bar plot to visualize the frequency of different referrer types that drove purchasers to the website. Let us look at the steps involved in creating the bar plot:

extract rows where purchase is TRUE
select/expose referrer column
tabulate referrer data using table()
use the tabulated data to create bar plot using barplot()

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

Using pipe

ecom %>%
  subset(purchase) %>%
  extract('referrer') %>%
  table() %>%
  barplot()

Correlation

Correlation is a statistical measure that indicates the extent to which two or more variables fluctuate together. In R, correlation is computed using cor(). Let us look at the correlation between the number of pages browsed and time spent on the site for visitors who purchased some product. Below are the steps for computing correlation:

extract rows where purchase is TRUE
select/expose n_pages and duration columns
use cor() to compute the correlation

# without pipe
ecom1 <- subset(ecom, purchase)
cor(ecom1$n_pages, ecom1$duration)
## [1] 0.4290905
# with pipe
ecom %>%
  subset(purchase) %$% 
  cor(n_pages, duration)
## [1] 0.4290905
# with pipe
ecom %>%
  filter(purchase) %$% 
  cor(n_pages, duration)
## [1] 0.4290905

Regression

Let us look at a regression example. We regress time spent on the site on number of pages visited. Below are the steps involved in running the regression:

use duration and n_pages columns from ecom data
pass the above data to lm()
pass the output from lm() to summary()

summary(lm(duration ~ n_pages, data = ecom))
## 
## Call:
## lm(formula = duration ~ n_pages, data = ecom)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -386.45 -213.03  -38.93  179.31  602.55 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  404.803     11.323  35.750  < 2e-16 ***
## n_pages       -8.355      1.296  -6.449 1.76e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 263.3 on 998 degrees of freedom
## Multiple R-squared:   0.04,  Adjusted R-squared:  0.03904 
## F-statistic: 41.58 on 1 and 998 DF,  p-value: 1.756e-10

Using pipe

ecom %$%
  lm(duration ~ n_pages) %>%
  summary()
## 
## Call:
## lm(formula = duration ~ n_pages)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -386.45 -213.03  -38.93  179.31  602.55 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  404.803     11.323  35.750  < 2e-16 ***
## n_pages       -8.355      1.296  -6.449 1.76e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 263.3 on 998 degrees of freedom
## Multiple R-squared:   0.04,  Adjusted R-squared:  0.03904 
## F-statistic: 41.58 on 1 and 998 DF,  p-value: 1.756e-10

String Manipulation

We want to extract the first name (jovial) from the below email id and convert it to upper case. Below are the steps to achieve this:

split the email id using the pattern @ using str_split()
extract the first element from the resulting list using extract2()
extract the first element from the character vector using extract()
extract the first six characters using str_sub()
convert to upper case using str_to_upper()

email <- 'jovialcann@anymail.com'

# without pipe
str_to_upper(str_sub(str_split(email, '@')[[1]][1], start = 1, end = 6))
## [1] "JOVIAL"
# with pipe
email %>%
  str_split(pattern = '@') %>%
  extract2(1) %>%
  extract(1) %>%
  str_sub(start = 1, end = 6) %>%
  str_to_upper()
## [1] "JOVIAL"

Another method that uses map_chr() from the purrr package.

email %>%
  str_split(pattern = '@') %>%
  map_chr(1) %>%
  str_sub(start = 1, end = 6) %>%
  str_to_upper()
## [1] "JOVIAL"

Data Extraction

Let us turn our attention towards data extraction. magrittr provides alternatives to $, [ and [[.

extract()
extract2()
use_series()

Extract Column By Name

To extract a specific column using the column name, we mention the name of the column in single/double quotes within [ or [[. In case of $, we do not use quotes.

# base 
ecom_mini['n_pages']
## # A tibble: 10 x 1
##    n_pages
##      <dbl>
##  1       2
##  2      15
##  3       1
##  4       1
##  5      10
##  6       2
##  7       1
##  8       1
##  9       1
## 10       8
# magrittr
extract(ecom_mini, 'n_pages') 
## # A tibble: 10 x 1
##    n_pages
##      <dbl>
##  1       2
##  2      15
##  3       1
##  4       1
##  5      10
##  6       2
##  7       1
##  8       1
##  9       1
## 10       8

Extract Column By Position

We can extract columns using their index position. Keep in mind that index position starts from 1 in R. In the below example, we show how to extract n_pages column but instead of using the column name, we use the column position.

# base 
ecom_mini[2]
## # A tibble: 10 x 1
##    n_pages
##      <dbl>
##  1       2
##  2      15
##  3       1
##  4       1
##  5      10
##  6       2
##  7       1
##  8       1
##  9       1
## 10       8
# magrittr
extract(ecom_mini, 2) 
## # A tibble: 10 x 1
##    n_pages
##      <dbl>
##  1       2
##  2      15
##  3       1
##  4       1
##  5      10
##  6       2
##  7       1
##  8       1
##  9       1
## 10       8

Extract Column (as vector)

One important differentiator between [ and [[ is that [[ will return a atomic vector and not a data.frame. $ will also return a atomic vector. In magrittr, we can use use_series() in place of $.

# base 
ecom_mini$n_pages
##  [1]  2 15  1  1 10  2  1  1  1  8
# magrittr
use_series(ecom_mini, 'n_pages') 
##  [1]  2 15  1  1 10  2  1  1  1  8

Extract List Element By Name

Let us convert ecom_mini into a list using as.list() as shown below:

ecom_list <- as.list(ecom_mini)

To extract elements of a list, we can use extract2(). It is an alternative for [[.

# base 
ecom_list[['n_pages']]
##  [1]  2 15  1  1 10  2  1  1  1  8
# magrittr
extract2(ecom_list, 'n_pages')
##  [1]  2 15  1  1 10  2  1  1  1  8

Extract List Element By Position

# base 
ecom_list[[1]]
##  [1] google direct yahoo  yahoo  google social direct google bing   bing  
## Levels: bing direct social yahoo google
# magrittr
extract2(ecom_list, 1)
##  [1] google direct yahoo  yahoo  google social direct google bing   bing  
## Levels: bing direct social yahoo google

Extract List Element

We can extract the elements of a list using use_series() as well.

# base 
ecom_list$n_pages
##  [1]  2 15  1  1 10  2  1  1  1  8
# magrittr
use_series(ecom_list, n_pages)
##  [1]  2 15  1  1 10  2  1  1  1  8

Arithmetic Operations

magrittr offer alternatives for arithemtic operations as well. We will look at a few examples below.

add()
subtract()
multiply_by()
multiply_by_matrix()
divide_by()
divide_by_int()
mod()
raise_to_power()

Addition

1:10 + 1
##  [1]  2  3  4  5  6  7  8  9 10 11
add(1:10, 1)
##  [1]  2  3  4  5  6  7  8  9 10 11
`+`(1:10, 1)
##  [1]  2  3  4  5  6  7  8  9 10 11

Multiplication

1:10 * 3
##  [1]  3  6  9 12 15 18 21 24 27 30
multiply_by(1:10, 3)
##  [1]  3  6  9 12 15 18 21 24 27 30
`*`(1:10, 3)
##  [1]  3  6  9 12 15 18 21 24 27 30

Division

1:10 / 2
##  [1] 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0
divide_by(1:10, 2)
##  [1] 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0
`/`(1:10, 2)
##  [1] 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0

Power

1:10 ^ 2
##   [1]   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17
##  [18]  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34
##  [35]  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51
##  [52]  52  53  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68
##  [69]  69  70  71  72  73  74  75  76  77  78  79  80  81  82  83  84  85
##  [86]  86  87  88  89  90  91  92  93  94  95  96  97  98  99 100
raise_to_power(1:10, 2)
##  [1]   1   4   9  16  25  36  49  64  81 100
`^`(1:10, 2)
##  [1]   1   4   9  16  25  36  49  64  81 100

Logical Operators

There are alternatives for logical operators as well. We will look at a few examples below.

and()
or()
equals()
not()
is_greater_than()
is_weakly_greater_than()
is_less_than()
is_weakly_less_than()

Greater Than

1:10 > 5
##  [1] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
is_greater_than(1:10, 5)
##  [1] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE
`>`(1:10, 5)
##  [1] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE

Weakly Greater Than

1:10 >= 5
##  [1] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
is_weakly_greater_than(1:10, 5)
##  [1] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
`>=`(1:10, 5)
##  [1] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE

References

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