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Data Wrangling with dplyr - Part 3

Introduction

In the previous post, we learnt to combine tables using dplyr. In this post, we will explore a set of helper functions in order to:

  • extract unique rows
  • rename columns
  • sample data
  • extract columns
  • slice rows
  • arrange rows
  • compare tables
  • extract/mutate data using predicate functions
  • count observations for different levels of a variable

Libraries, Code & Data

We will use the following packages:

The data sets can be downloaded from here and the codes from here.

library(dplyr)
library(readr)

Case Study

Let us look at a case study (e-commerce data) and see how we can use dplyr helper functions to answer questions we have about and to modify/transform the underlying data set.

Data

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

ecom
## # A tibble: 1,000 x 7
##    referrer device bouncers n_visit n_pages duration purchase
##    <fct>    <fct>  <lgl>      <dbl>   <dbl>    <dbl> <lgl>   
##  1 google   laptop TRUE          10       1      693 FALSE   
##  2 yahoo    tablet TRUE           9       1      459 FALSE   
##  3 direct   laptop TRUE           0       1      996 FALSE   
##  4 bing     tablet FALSE          3      18      468 TRUE    
##  5 yahoo    mobile TRUE           9       1      955 FALSE   
##  6 yahoo    laptop FALSE          5       5      135 FALSE   
##  7 yahoo    mobile TRUE          10       1       75 FALSE   
##  8 direct   mobile TRUE          10       1      908 FALSE   
##  9 bing     mobile FALSE          3      19      209 FALSE   
## 10 google   mobile TRUE           6       1      208 FALSE   
## # ... with 990 more rows

Data Dictionary

  • referrer: referrer website/search engine
  • device: device used to visit the website
  • bouncers: whether a visit bounced (exited from landing page)
  • duration: time spent on the website (in seconds)
  • purchase: whether visitor purchased
  • n_visit: number of visits
  • n_pages: number of pages visited/browsed

Data Sanitization

Let us ensure that the data is sanitized by checking the sources of traffic and devices used to visit the site. We will use distinct to examine the values in the referrer column



distinct(ecom, referrer)
## # A tibble: 5 x 1
##   referrer
##   <fct>   
## 1 google  
## 2 yahoo   
## 3 direct  
## 4 bing    
## 5 social

and the device column as well.

distinct(ecom, device)
## # A tibble: 3 x 1
##   device
##   <fct> 
## 1 laptop
## 2 tablet
## 3 mobile

Rename Columns

Columns can be renamed using rename().



rename(ecom, time_on_site = duration)
## # A tibble: 1,000 x 7
##    referrer device bouncers n_visit n_pages time_on_site purchase
##    <fct>    <fct>  <lgl>      <dbl>   <dbl>        <dbl> <lgl>   
##  1 google   laptop TRUE          10       1          693 FALSE   
##  2 yahoo    tablet TRUE           9       1          459 FALSE   
##  3 direct   laptop TRUE           0       1          996 FALSE   
##  4 bing     tablet FALSE          3      18          468 TRUE    
##  5 yahoo    mobile TRUE           9       1          955 FALSE   
##  6 yahoo    laptop FALSE          5       5          135 FALSE   
##  7 yahoo    mobile TRUE          10       1           75 FALSE   
##  8 direct   mobile TRUE          10       1          908 FALSE   
##  9 bing     mobile FALSE          3      19          209 FALSE   
## 10 google   mobile TRUE           6       1          208 FALSE   
## # ... with 990 more rows

Data Tabulation

Let us now look at the proportion or share of visits driven by different sources of traffic.



ecom %>%
  group_by(referrer) %>%
  tally()
## # A tibble: 5 x 2
##   referrer     n
##   <fct>    <int>
## 1 bing       194
## 2 direct     191
## 3 social     200
## 4 yahoo      207
## 5 google     208

We would also like to know the number of bouncers driven by the different sources of traffic.

ecom %>%
  group_by(referrer, bouncers) %>%
  tally()
## # A tibble: 10 x 3
## # Groups:   referrer [?]
##    referrer bouncers     n
##    <fct>    <lgl>    <int>
##  1 bing     FALSE      104
##  2 bing     TRUE        90
##  3 direct   FALSE       98
##  4 direct   TRUE        93
##  5 social   FALSE       93
##  6 social   TRUE       107
##  7 yahoo    FALSE      110
##  8 yahoo    TRUE        97
##  9 google   FALSE      101
## 10 google   TRUE       107

Let us look at how many conversions happen across different devices.

ecom %>%
  group_by(device, purchase) %>%
  tally() %>%
  filter(purchase)
## # A tibble: 3 x 3
## # Groups:   device [3]
##   device purchase     n
##   <fct>  <lgl>    <int>
## 1 laptop TRUE        31
## 2 tablet TRUE        36
## 3 mobile TRUE        36

Another way to extract the above information is by using count

ecom %>%
  count(referrer, purchase) %>%
  filter(purchase)
## # A tibble: 5 x 3
##   referrer purchase     n
##   <fct>    <lgl>    <int>
## 1 bing     TRUE        17
## 2 direct   TRUE        25
## 3 social   TRUE        20
## 4 yahoo    TRUE        22
## 5 google   TRUE        19

Sampling Data

dplyr offers sampling functions which allow us to specify either the number or percentage of observations. sample_n() allows sampling a specific number of observations.



sample_n(ecom, 700)
## # A tibble: 700 x 7
##    referrer device bouncers n_visit n_pages duration purchase
##    <fct>    <fct>  <lgl>      <dbl>   <dbl>    <dbl> <lgl>   
##  1 direct   laptop TRUE          10       1      983 FALSE   
##  2 social   laptop FALSE          8       3       30 FALSE   
##  3 bing     laptop TRUE           0       1      973 FALSE   
##  4 yahoo    mobile FALSE          8       5      115 FALSE   
##  5 yahoo    mobile TRUE           1       1      257 FALSE   
##  6 bing     laptop FALSE         10      17      493 FALSE   
##  7 google   laptop TRUE           5       1      376 FALSE   
##  8 yahoo    mobile TRUE          10       1      594 FALSE   
##  9 social   laptop TRUE           7       1      403 FALSE   
## 10 social   mobile FALSE          1      10      170 FALSE   
## # ... with 690 more rows

We can combine the sampling functions with other dplyr functions as shown below where we sample observation after grouping them according to the source of traffic.

ecom %>%
  group_by(referrer) %>%
  sample_n(100)
## # A tibble: 500 x 7
## # Groups:   referrer [5]
##    referrer device bouncers n_visit n_pages duration purchase
##    <fct>    <fct>  <lgl>      <dbl>   <dbl>    <dbl> <lgl>   
##  1 bing     mobile TRUE           7       1      569 FALSE   
##  2 bing     tablet TRUE           8       1      577 FALSE   
##  3 bing     mobile FALSE          4       2       22 FALSE   
##  4 bing     tablet FALSE          1      18      180 FALSE   
##  5 bing     tablet TRUE           5       1      151 FALSE   
##  6 bing     mobile TRUE           1       1      905 FALSE   
##  7 bing     laptop TRUE           3       1      271 FALSE   
##  8 bing     tablet TRUE           7       1      489 FALSE   
##  9 bing     tablet FALSE          1      17      170 FALSE   
## 10 bing     laptop TRUE           7       1      362 FALSE   
## # ... with 490 more rows

sample_frac() allows a specific percentage of observations.

sample_frac(ecom, size = 0.7)
## # A tibble: 700 x 7
##    referrer device bouncers n_visit n_pages duration purchase
##    <fct>    <fct>  <lgl>      <dbl>   <dbl>    <dbl> <lgl>   
##  1 yahoo    laptop FALSE          7       6      150 FALSE   
##  2 yahoo    mobile TRUE           7       1      194 FALSE   
##  3 social   laptop TRUE           5       1      544 FALSE   
##  4 bing     tablet FALSE          8       6      180 FALSE   
##  5 social   tablet FALSE          9      16      432 FALSE   
##  6 social   tablet FALSE          5      20      220 FALSE   
##  7 yahoo    mobile TRUE           7       1      632 FALSE   
##  8 yahoo    mobile TRUE           9       1      123 FALSE   
##  9 yahoo    laptop TRUE           5       1      611 FALSE   
## 10 bing     tablet FALSE          4      15      405 FALSE   
## # ... with 690 more rows

Data Extraction

In the first post, we had observed that dplyr verbs always returned a tibble. What if you want to extract a specific column or a bunch of rows but not as a tibble?

Use pull to extract columns either by name or position. It will return a vector. In the below example, we extract the device column as a vector. I am using head in addition to limit the output printed.

Sample Data

ecom_mini <- sample_n(ecom, size = 10)



pull(ecom_mini, device)
##  [1] tablet tablet mobile tablet laptop laptop laptop tablet mobile laptop
## Levels: laptop tablet mobile

Let us extract the first column from ecom using column position instead of name.

pull(ecom_mini, 1) 
##  [1] google yahoo  social google direct social yahoo  direct yahoo  bing  
## Levels: bing direct social yahoo google

You can use - before the column position to indicate the position in reverse. The below example extracts data from the last column.

pull(ecom_mini, -1) 
##  [1] FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE

Let us now look at extracting rows using slice(). In the below example, we extract data starting from the 5th row and upto the 15th row.



slice(ecom, 5:15)
## # A tibble: 11 x 7
##    referrer device bouncers n_visit n_pages duration purchase
##    <fct>    <fct>  <lgl>      <dbl>   <dbl>    <dbl> <lgl>   
##  1 yahoo    mobile TRUE           9       1      955 FALSE   
##  2 yahoo    laptop FALSE          5       5      135 FALSE   
##  3 yahoo    mobile TRUE          10       1       75 FALSE   
##  4 direct   mobile TRUE          10       1      908 FALSE   
##  5 bing     mobile FALSE          3      19      209 FALSE   
##  6 google   mobile TRUE           6       1      208 FALSE   
##  7 direct   laptop TRUE           9       1      738 FALSE   
##  8 direct   tablet FALSE          6      12      132 FALSE   
##  9 direct   mobile FALSE          9      14      406 TRUE    
## 10 yahoo    tablet FALSE          5       8       80 FALSE   
## 11 yahoo    mobile FALSE          7       1       19 FALSE

Use n() inside slice() to extract the last row.

slice(ecom, n())
## # A tibble: 1 x 7
##   referrer device bouncers n_visit n_pages duration purchase
##   <fct>    <fct>  <lgl>      <dbl>   <dbl>    <dbl> <lgl>   
## 1 google   mobile TRUE           9       1      269 FALSE

Between

between() allows us to test if the values in a column lie between two specific values. In the below example, we check how many visits browsed pages between 5 and 15.

ecom_sample <- sample_n(ecom, 30)
  
ecom_sample %>%
  pull(n_pages) %>%
  between(5, 15) 
##  [1]  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
## [12] FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE
## [23] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE

Case When

case_when() is an alternative to if else. It allows us to lay down the conditions clearly and makes the code more readable. In the below example, we create a new column repeat_visit from n_visit (the number of previous visits).

ecom %>%
  mutate(
    repeat_visit = case_when(
      n_visit > 0 ~ TRUE,
      TRUE ~ FALSE
    )
  ) %>%
  select(n_visit, repeat_visit) 
## # A tibble: 1,000 x 2
##    n_visit repeat_visit
##      <dbl> <lgl>       
##  1      10 TRUE        
##  2       9 TRUE        
##  3       0 FALSE       
##  4       3 TRUE        
##  5       9 TRUE        
##  6       5 TRUE        
##  7      10 TRUE        
##  8      10 TRUE        
##  9       3 TRUE        
## 10       6 TRUE        
## # ... with 990 more rows