Data Wrangling with dplyr

dplyr_verbs.R

# install
install.packages('dplyr')
install.packages('readr')

# library
library(dplyr)
library(readr)

# read data
ecom <- readr::read_csv('https://raw.githubusercontent.com/rsquaredacademy/datasets/master/web.csv')

# average order value
ecom %>%
  filter(purchase == 'true') %>%
  select(device, order_value, order_items) %>%
  group_by(device) %>%
  summarise_all(funs(sum)) %>%
  mutate(
    aov = order_value / order_items
  ) %>%
  select(device, aov)

# visits from mobile
filter(ecom, device == "mobile")

# visits from mobile that converted
filter(ecom, device == "mobile", purchase == "true")

# visits from mobile that visited more than 5 pages
filter(ecom, device == "mobile", n_pages > 5)

# for our case study, we want all visits that converted.
filter(ecom, purchase == "true")

# select device and purchase columns 
select(ecom, device, purchase)

# select all columns from device to purchase
select(ecom, device:purchase)

# select all columns excluding id and country
select(ecom, -id, -country)

# for our case study, we need to select the columns order value 
# and order items to calculate the AOV, we also need to select 
# the device column as we are computing the AOV for different devices.
select(ecom, device, order_value, order_items)

# select ecom, device, order_value and order_items for purchasers
ecom1 <- filter(ecom, purchase == "true")
ecom2 <- select(ecom1, device, order_value, order_items)
ecom2

# group data by referrer type
group_by(ecom, referrer)

# for our case study, we need to group the data by device type.
ecom3 <- group_by(ecom2, device)
ecom3

# total order value and order items
ecom4 <- summarise(ecom3, total_value = sum(order_value),
          total_items = sum(order_items))
ecom4

# compute aov
ecom5 <- mutate(ecom4, aov = order_value / order_items)
ecom5

# select relevant columns
ecom6 <- select(ecom5, device, aov)
ecom6

# full code
ecom1 <- filter(ecom, purchase == "true")
ecom2 <- select(ecom1, device, order_value, order_items)
ecom3 <- group_by(ecom2, device)
ecom4 <- summarise_all(ecom3, funs(sum))
ecom5 <- mutate(ecom4, aov = order_value / order_items)
ecom6 <- select(ecom5, device, aov)
ecom6

# full code using pipe
ecom %>%
  filter(purchase == 'true') %>%
  select(device, order_value, order_items) %>%
  group_by(device) %>%
  summarise_all(funs(sum)) %>%
  mutate(
    aov = order_value / order_items
  ) %>%
  select(device, aov)