Data Wrangling with dplyr - Part 1

Introduction to data wrangling with dplyr.

Introduction

According to a survey by CrowdFlower, data scientists spend most of their time cleaning and manipulating data rather than mining or modeling them for insights. As such, it becomes important to have tools that make data manipulation faster and easier. In today’s post, we introduce you to dplyr, a grammar of data manipulation.

Libraries, Code & Data

We will use the following libraries:

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

library(dplyr)
library(readr)

dplyr Verbs

dplyr provides a set of verbs that help us solve the most common data manipulation challenges while working with tabular data (dataframes, tibbles):

  • select: returns subset of columns
  • filter: returns a subset of rows
  • arrange: re-order or arrange rows according to single/multiple variables
  • mutate: create new columns from existing columns
  • summarise: reduce data to a single summary

Case Study

We will explore a dummy data set that resembles web logs of an online retail company. You can download the data from here or import it directly using read_csv() from the readr package. We will use dplyr to answer the following:

  • what is the average order value by device types?
  • what is the average number of pages visited by purchasers and non-purchasers?
  • what is the average time on site for purchasers vs non-purchasers?
  • what is the average number of pages visited by purchasers and non-purchasers using mobile?

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(), n_pages = col_double(), n_visit = col_double(), 
      duration = col_double(), order_value = col_double(), order_items = col_double()
    )
  )
## Warning in read_tokens_(data, tokenizer, col_specs, col_names, locale_, :
## length of NULL cannot be changed

## Warning in read_tokens_(data, tokenizer, col_specs, col_names, locale_, :
## length of NULL cannot be changed

## Warning in read_tokens_(data, tokenizer, col_specs, col_names, locale_, :
## length of NULL cannot be changed
ecom
## # A tibble: 1,000 x 8
##    referrer device n_visit n_pages duration purchase order_items
##    <fct>    <fct>    <dbl>   <dbl>    <dbl> <lgl>          <dbl>
##  1 google   laptop      10       1      693 FALSE              0
##  2 yahoo    tablet       9       1      459 FALSE              0
##  3 direct   laptop       0       1      996 FALSE              0
##  4 bing     tablet       3      18      468 TRUE               6
##  5 yahoo    mobile       9       1      955 FALSE              0
##  6 yahoo    laptop       5       5      135 FALSE              0
##  7 yahoo    mobile      10       1       75 FALSE              0
##  8 direct   mobile      10       1      908 FALSE              0
##  9 bing     mobile       3      19      209 FALSE              0
## 10 google   mobile       6       1      208 FALSE              0
## # ... with 990 more rows, and 1 more variable: order_value <dbl>
Data Dictionary

Below is the description of the data set:

  • referrer: referrer website/search engine
  • device: device used to visit the website
  • n_pages: number of pages visited
  • duration: time spent on the website (in seconds)
  • purchase: whether visitor purchased
  • order_value: order value of visitor (in dollars)
  • n_visit: number of visits

Average Order Value

AOV by Devices

ecom %>%
  filter(purchase) %>%
  select(device, order_value) %>%
  group_by(device) %>%
  summarise_all(funs(revenue = sum, orders = n())) %>%
  mutate(
    aov = revenue / orders
  ) %>%
  select(device, aov) %>%
  arrange(aov)
## # A tibble: 3 x 2
##   device   aov
##   <fct>  <dbl>
## 1 tablet 1426.
## 2 mobile 1431.
## 3 laptop 1824.

Syntax

Before we start exploring the dplyr verbs, let us look at their syntax:

  • the first argument is always a data.frame or tibble
  • the subsequent arguments provide the information required for the verbs to take action
  • the name of columns in the data need not be surrounded by quotes

Filter Rows

In order to compute the AOV, we must first separate the purchasers from non-purchasers. We will do this by filtering the data related to purchasers using the filter() function. It allows us to filter rows that meet a specific criteria/condition. The first argument is the name of the data frame and the rest of the arguments are expressions for filtering the data. Let us look at a few examples:

The first example we will look at filters all visits from device mobile. As we had learnt in the previous section, the first argument is our data set ecom and the next argument is the condition for filtering rows.



filter(ecom, device == "mobile")
## # A tibble: 344 x 8
##    referrer device n_visit n_pages duration purchase order_items
##    <fct>    <fct>    <dbl>   <dbl>    <dbl> <lgl>          <dbl>
##  1 yahoo    mobile       9       1      955 FALSE              0
##  2 yahoo    mobile      10       1       75 FALSE              0
##  3 direct   mobile      10       1      908 FALSE              0
##  4 bing     mobile       3      19      209 FALSE              0
##  5 google   mobile       6       1      208 FALSE              0
##  6 direct   mobile       9      14      406 TRUE               3
##  7 yahoo    mobile       7       1       19 FALSE              7
##  8 google   mobile       5       1      147 FALSE              0
##  9 bing     mobile       0       7      196 FALSE              4
## 10 google   mobile      10       1      338 FALSE              0
## # ... with 334 more rows, and 1 more variable: order_value <dbl>

We can specify multiple filtering conditions as well. In the below example, we specify two filter conditions:

  • visit from device mobile
  • resulted in a purchase or conversion



filter(ecom, device == "mobile", purchase)
## # A tibble: 36 x 8
##    referrer device n_visit n_pages duration purchase order_items
##    <fct>    <fct>    <dbl>   <dbl>    <dbl> <lgl>          <dbl>
##  1 direct   mobile       9      14      406 TRUE               3
##  2 bing     mobile       4      20      440 TRUE               3
##  3 bing     mobile       3      18      288 TRUE               6
##  4 social   mobile      10      11      242 TRUE               4
##  5 yahoo    mobile       6      14      322 TRUE               3
##  6 google   mobile       1      18      252 TRUE               3
##  7 social   mobile       7      16      352 TRUE              10
##  8 direct   mobile       4      18      324 TRUE               3
##  9 social   mobile       1      20      520 TRUE               5
## 10 yahoo    mobile       0      13      351 TRUE              10
## # ... with 26 more rows, and 1 more variable: order_value <dbl>

Here is another example where we specify multiple conditions:

  • visit from device tablet
  • made a purchase
  • browsed less than 15 pages
filter(ecom, device == "tablet", purchase, n_pages < 15)
## # A tibble: 12 x 8
##    referrer device n_visit n_pages duration purchase order_items
##    <fct>    <fct>    <dbl>   <dbl>    <dbl> <lgl>          <dbl>
##  1 social   tablet       7      10      290 TRUE               9
##  2 yahoo    tablet       2      14      364 TRUE               6
##  3 google   tablet       7      12      324 TRUE               2
##  4 direct   tablet       3      12      324 TRUE              10
##  5 yahoo    tablet       0      13      390 TRUE               5
##  6 social   tablet       2      12      300 TRUE               2
##  7 direct   tablet       6      13      338 TRUE               5
##  8 yahoo    tablet       2      10      280 TRUE               4
##  9 social   tablet      10      10      290 TRUE               9
## 10 direct   tablet       3      10      260 TRUE               7
## 11 google   tablet       9      14      308 TRUE               7
## 12 social   tablet      10      11      330 TRUE               1
## # ... with 1 more variable: order_value <dbl>
Case Study

Let us apply what we have learnt to the case study. We want to filter all visits that resulted in a purchase.

filter(ecom, purchase)
## # A tibble: 103 x 8
##    referrer device n_visit n_pages duration purchase order_items
##    <fct>    <fct>    <dbl>   <dbl>    <dbl> <lgl>          <dbl>
##  1 bing     tablet       3      18      468 TRUE               6
##  2 direct   mobile       9      14      406 TRUE               3
##  3 bing     tablet       5      16      368 TRUE               6
##  4 social   tablet       7      10      290 TRUE               9
##  5 direct   tablet       2      19      342 TRUE               5
##  6 social   tablet       9      20      420 TRUE               7
##  7 bing     mobile       4      20      440 TRUE               3
##  8 yahoo    tablet       2      16      480 TRUE               9
##  9 bing     mobile       3      18      288 TRUE               6
## 10 yahoo    tablet       2      14      364 TRUE               6
## # ... with 93 more rows, and 1 more variable: order_value <dbl>

Select Columns

After filtering the data, we need to select relevent variables to compute the AOV. Remember, we do not need all the columns in the data to compute a required metric (in our case, AOV). The select() function allows us to select a subset of columns. The first argument is the name of the data frame and the subsequent arguments specify the columns by name or position.

To select the device and duration columns, we specify the data set i.e. ecom followed by the name of the columns.



select(ecom, device, duration)
## # A tibble: 1,000 x 2
##    device duration
##    <fct>     <dbl>
##  1 laptop      693
##  2 tablet      459
##  3 laptop      996
##  4 tablet      468
##  5 mobile      955
##  6 laptop      135
##  7 mobile       75
##  8 mobile      908
##  9 mobile      209
## 10 mobile      208
## # ... with 990 more rows

We can select a set of columns using :. In the below example, we select all the columns starting from referrer up to order_items. Remember that we can use : only when the columns are adjacent to each other in the data set.



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

What if you want to select all columns except a few? Typing the name of many columns can be cumbersome and may also result in spelling errors. We may use : only if the columns are adjacent to each other but that may not always be the case. dplyr allows us to specify columns that need not be selected using -. In the below example, we select all columns except n_pages and duration. Notice the - before both of them.



select(ecom, -n_pages, -duration)
## # A tibble: 1,000 x 6
##    referrer device n_visit purchase order_items order_value
##    <fct>    <fct>    <dbl> <lgl>          <dbl>       <dbl>
##  1 google   laptop      10 FALSE              0           0
##  2 yahoo    tablet       9 FALSE              0           0
##  3 direct   laptop       0 FALSE              0           0
##  4 bing     tablet       3 TRUE               6         434
##  5 yahoo    mobile       9 FALSE              0           0
##  6 yahoo    laptop       5 FALSE              0           0
##  7 yahoo    mobile      10 FALSE              0           0
##  8 direct   mobile      10 FALSE              0           0
##  9 bing     mobile       3 FALSE              0           0
## 10 google   mobile       6 FALSE              0           0
## # ... with 990 more rows
Case Study

For our case study, we need to select the column order_value to calculate the AOV. We also need to select the device column as we are computing the AOV for each device type.

select(ecom, device, order_value)
## # A tibble: 1,000 x 2
##    device order_value
##    <fct>        <dbl>
##  1 laptop           0
##  2 tablet           0
##  3 laptop           0
##  4 tablet         434
##  5 mobile           0
##  6 laptop           0
##  7 mobile           0
##  8 mobile           0
##  9 mobile           0
## 10 mobile           0
## # ... with 990 more rows

But we want the above data only for purchasers. Let us combine filter() and select() functions to extract order_value and order_items only for those visis that resulted in a purchase.

# filter all visits that resulted in a purchase
ecom1 <- filter(ecom, purchase)

# select the relevant columns
ecom2 <- select(ecom1, device, order_value)

# view data
ecom2
## # A tibble: 103 x 2
##    device order_value
##    <fct>        <dbl>
##  1 tablet         434
##  2 mobile         651
##  3 tablet        1049
##  4 tablet        1304
##  5 tablet         622
##  6 tablet        1613
##  7 mobile         184
##  8 tablet         286
##  9 mobile         764
## 10 tablet        1667
## # ... with 93 more rows

Grouping Data

We need to compute the total order value and total order items for each device in order to compute their AOV. To achieve this, we need to group the selected order_value and order_items by device type. group_by() allows us to group or split data based on particular (discrete) variable. The first argument is the name of the data set and the second argument is the name of the column based on which the data will be split.

To split the data by referrer type, we use group_by and specify the data set i.e. ecom and the column based on which to split the data i.e. referrer.

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

In the second line in the previous output, you can observe Groups: referrer [5] . The data is split into 5 groups as the referrer variable has 5 distinct values. For our case study, we need to group the data by device type.

# split ecom2 by device type
ecom3 <- group_by(ecom2, device)
ecom3
## # A tibble: 103 x 2
## # Groups:   device [3]
##    device order_value
##    <fct>        <dbl>
##  1 tablet         434
##  2 mobile         651
##  3 tablet        1049
##  4 tablet        1304
##  5 tablet         622
##  6 tablet        1613
##  7 mobile         184
##  8 tablet         286
##  9 mobile         764
## 10 tablet        1667
## # ... with 93 more rows

Summarise Data

The next step is to compute the total order value and total order items for each device. i.e. we need to reduce the order value and order items data to a single summary. We can achieve this using summarise(). As usual, the first argument is the name of a data set and the subsequent arguments are functions that can summarise data. For example, we can use min, max, sum, mean etc.

Let us compute the average number of pages browsed by referrer type:

  • split data by referrer type
  • compute the average number of pages using mean



# split data by referrer type
step_1 <- group_by(ecom, referrer)

# compute average number of pages
step_2 <- summarise(step_1, mean(n_pages))
step_2
## # A tibble: 5 x 2
##   referrer `mean(n_pages)`
##   <fct>              <dbl>
## 1 bing                6.13
## 2 direct              6.38
## 3 social              5.42
## 4 yahoo               5.99
## 5 google              5.73

Now let us compute both the mean and the median.

# split data by referrer type
step_1 <- group_by(ecom, referrer)

# compute average number of pages
step_2 <- summarise(step_1, mean(n_pages), median(n_pages))
step_2
## # A tibble: 5 x 3
##   referrer `mean(n_pages)` `median(n_pages)`
##   <fct>              <dbl>             <dbl>
## 1 bing                6.13                 1
## 2 direct              6.38                 1
## 3 social              5.42                 1
## 4 yahoo               5.99                 2
## 5 google              5.73                 1

Another way to achieve the above result is to use the summarise_all() function. How does that work? It generates the specified summary for all the columns in the data set except for the column based on which the data has been grouped or split. So we need to ensure that the data does not have any irrelevant columns.

  • split data by referrer type
  • select order_value and order_items
  • compute the average number of pages by applying the mean function to all the columns
# select relevant columns
step_1 <- select(ecom, referrer, order_value)

# split data by referrer type
step_2 <- group_by(step_1, referrer)

# compute average number of pages
step_3 <- summarise_all(step_2, funs(mean))
step_3
## # A tibble: 5 x 2
##   referrer order_value
##   <fct>          <dbl>
## 1 bing            316.
## 2 direct          441.
## 3 social          380.
## 4 yahoo           470.
## 5 google          328.

Let us compute mean and median number of pages for each referre type using summarise_all.

# select relevant columns
step_1 <- select(ecom, referrer, order_value)

# split data by referrer type
step_2 <- group_by(step_1, referrer)

# compute mean and median number of pages
step_3 <- summarise_all(step_2, funs(mean, median))
step_3
## # A tibble: 5 x 3
##   referrer  mean median
##   <fct>    <dbl>  <dbl>
## 1 bing      316.      0
## 2 direct    441.      0
## 3 social    380.      0
## 4 yahoo     470.      0
## 5 google    328.      0
Case Study

So far, we have split the data based on the device type and we have selected 2 columns, order_value and order_items. We need the sum of order value and order items. What function can we use to obtain them? The sum() function will generate the sum of the values and hence we will use it inside the summarise() function. Remember, we need to provide a name to the summary being generated.

ecom4 <- summarise(ecom3, revenue = sum(order_value),
          orders = n())
ecom4
## # A tibble: 3 x 3
##   device revenue orders
##   <fct>    <dbl>  <int>
## 1 laptop   56531     31
## 2 tablet   51321     36
## 3 mobile   51504     36

There you go, we have the total order value and total order items for each device type. If we use summarise_all(), it will generate the summary for the selected columns based on the function specified. To specify the functions, we need to use another argument funs and it can take any number of valid functions.

ecom4 <- summarise_all(ecom3, funs(revenue = sum, orders = n()))
ecom4
## # A tibble: 3 x 3
##   device revenue orders
##   <fct>    <dbl>  <int>
## 1 laptop   56531     31
## 2 tablet   51321     36
## 3 mobile   51504     36

Create Columns

To create a new column, we will use mutate(). The first argument is the name of the data set and the subsequent arguments are expressions for creating new columns based out of existing columns.

Let us add a new column avg_page_time i.e. time on site divided by number of pages visited.

# select duration and n_pages from ecom
mutate_1 <- select(ecom, n_pages, duration)
mutate(mutate_1, avg_page_time = duration / n_pages)
## # A tibble: 1,000 x 3
##    n_pages duration avg_page_time
##      <dbl>    <dbl>         <dbl>
##  1       1      693           693
##  2       1      459           459
##  3       1      996           996
##  4      18      468            26
##  5       1      955           955
##  6       5      135            27
##  7       1       75            75
##  8       1      908           908
##  9      19      209            11
## 10       1      208           208
## # ... with 990 more rows

We can create new columns based on other columns created using mutate. Let us create another column sqrt_avg_page_time i.e. square root of the average time on page using avg_page_time.

mutate(mutate_1,
       avg_page_time = duration / n_pages,
       sqrt_avg_page_time = sqrt(avg_page_time))
## # A tibble: 1,000 x 4
##    n_pages duration avg_page_time sqrt_avg_page_time
##      <dbl>    <dbl>         <dbl>              <dbl>
##  1       1      693           693              26.3 
##  2       1      459           459              21.4 
##  3       1      996           996              31.6 
##  4      18      468            26               5.10
##  5       1      955           955              30.9 
##  6       5      135            27               5.20
##  7       1       75            75               8.66
##  8       1      908           908              30.1 
##  9      19      209            11               3.32
## 10       1      208           208              14.4 
## # ... with 990 more rows
Case Study

Back to our case study, from the last step we have the total order value and total order items for each device category and can compute the AOV. We will create a new column to store AOV.



ecom5 <- mutate(ecom4, aov = revenue / orders)
ecom5
## # A tibble: 3 x 4
##   device revenue orders   aov
##   <fct>    <dbl>  <int> <dbl>
## 1 laptop   56531     31 1824.
## 2 tablet   51321     36 1426.
## 3 mobile   51504     36 1431.

Select Columns

The last step is to select the relevant columns. We will select the device type and the corresponding aov while getting rid of other columns. Use select() to extract the relevant columns.

ecom6 <- select(ecom5, device, aov)
ecom6
## # A tibble: 3 x 2
##   device   aov
##   <fct>  <dbl>
## 1 laptop 1824.
## 2 tablet 1426.
## 3 mobile 1431.

Arrange Data

Arranging data in ascending or descending order is one of the most common tasks in data manipulation. We can use arrange to arrange data by different columns. Let us say we want to arrange data by the number of pages browsed.



arrange(ecom, n_pages)
## # A tibble: 1,000 x 8
##    referrer device n_visit n_pages duration purchase order_items
##    <fct>    <fct>    <dbl>   <dbl>    <dbl> <lgl>          <dbl>
##  1 google   laptop      10       1      693 FALSE              0
##  2 yahoo    tablet       9       1      459 FALSE              0
##  3 direct   laptop       0       1      996 FALSE              0
##  4 yahoo    mobile       9       1      955 FALSE              0
##  5 yahoo    mobile      10       1       75 FALSE              0
##  6 direct   mobile      10       1      908 FALSE              0
##  7 google   mobile       6       1      208 FALSE              0
##  8 direct   laptop       9       1      738 FALSE              0
##  9 yahoo    mobile       7       1       19 FALSE              7
## 10 bing     laptop       1       1      995 FALSE              0
## # ... with 990 more rows, and 1 more variable: order_value <dbl>

If we want to arrange the data in descending order, we can use desc(). Let us arrange the data in descending order.

arrange(ecom , desc(n_pages))
## # A tibble: 1,000 x 8
##    referrer device n_visit n_pages duration purchase order_items
##    <fct>    <fct>    <dbl>   <dbl>    <dbl> <lgl>          <dbl>
##  1 social   tablet       9      20      420 TRUE               7
##  2 bing     mobile       4      20      440 TRUE               3
##  3 yahoo    tablet       0      20      200 FALSE              0
##  4 direct   tablet       6      20      580 TRUE               5
##  5 social   mobile       1      20      520 TRUE               5
##  6 google   mobile       8      20      300 TRUE               7
##  7 social   laptop       4      20      200 FALSE              0
##  8 yahoo    mobile       3      20      480 FALSE              0
##  9 social   laptop      10      20      280 TRUE               1
## 10 yahoo    mobile       2      20      240 FALSE              0
## # ... with 990 more rows, and 1 more variable: order_value <dbl>

Data can be arranged by multiple variables as well. Let us arrange data first by number of visits and then by number of pages in a descending order.

arrange(ecom, n_visit, desc(n_pages))
## # A tibble: 1,000 x 8
##    referrer device n_visit n_pages duration purchase order_items
##    <fct>    <fct>    <dbl>   <dbl>    <dbl> <lgl>          <dbl>
##  1 yahoo    tablet       0      20      200 FALSE              0
##  2 google   laptop       0      19      418 TRUE               2
##  3 bing     laptop       0      18      180 FALSE              0
##  4 yahoo    laptop       0      18      522 TRUE               8
##  5 direct   tablet       0      18      252 FALSE              0
##  6 social   laptop       0      17      204 FALSE              0
##  7 bing     laptop       0      17      272 TRUE               9
##  8 bing     mobile       0      16      272 FALSE              0
##  9 yahoo    mobile       0      15      255 FALSE              0
## 10 direct   laptop       0      15      255 FALSE              0
## # ... with 990 more rows, and 1 more variable: order_value <dbl>
Case Study

If you observe ecom6, the aov column is arranged in descending order.

arrange(ecom6, aov)
## # A tibble: 3 x 2
##   device   aov
##   <fct>  <dbl>
## 1 tablet 1426.
## 2 mobile 1431.
## 3 laptop 1824.

AOV by Devices

Let us combine all the code from the above steps:

ecom1 <- filter(ecom, purchase)
ecom2 <- select(ecom1, device, order_value)
ecom3 <- group_by(ecom2, device)
ecom4 <- summarise_all(ecom3, funs(revenue = sum, orders = n()))
ecom5 <- mutate(ecom4, aov = revenue / orders)
ecom6 <- select(ecom5, device, aov)
ecom7 <- arrange(ecom6, aov)
ecom7
## # A tibble: 3 x 2
##   device   aov
##   <fct>  <dbl>
## 1 tablet 1426.
## 2 mobile 1431.
## 3 laptop 1824.

If you observe, at each step we create a new variable(data frame) and then use it as an input in the next step i.e. the output from one step becomes the input for the next. Can we achieve the final outcome i.e. ecom7 without creating the intermediate data (ecom1 - ecom6)? Yes, we can. We will use the %>% operator to chain the steps and get rid of the intermediate data.

ecom %>%
  filter(purchase) %>%
  select(device, order_value) %>%
  group_by(device) %>%
  summarise_all(funs(revenue = sum, orders = n())) %>%
  mutate(
    aov = revenue / orders
  ) %>%
  select(device, aov) %>%
  arrange(aov)
## # A tibble: 3 x 2
##   device   aov
##   <fct>  <dbl>
## 1 tablet 1426.
## 2 mobile 1431.
## 3 laptop 1824.

Below we map the description of each step to dplyr verbs.


Your Turn

  • what is the average number of pages visited by purchasers and non-purchasers?
  • what is the average time on site for purchasers vs non-purchasers?
  • what is the average number of pages visited by purchasers and non-purchasers using mobile?