Market_Basket_Analysis_Part1.R

# Importing libraries
library(data.table)           
library(readxl)               
library(tidyverse)
library(lubridate)
library(skimr)                
library(knitr)                
library(treemap)

# import raw data file and trim leading and trailing whitespaces
retail <- read_excel("Online Retail.xlsx", trim_ws = TRUE)

# First glance at the data
retail %>%  skim()


# CANCELLATIONS
# if the InvoiceNo starts with letter 'C', it indicates a cancellation
retail %>% 
  filter(grepl("C", retail$InvoiceNo)) %>% 
  summarise(Total = n())

# Cancellations are not needed for the analysis so they can be removed
retail  <- retail %>% 
  filter(!grepl("C", retail$InvoiceNo)) 

# CHECK: total row count - 532,621


# NEGATIVE QUANTITIES
# filtering by non positive Quantity, Description shows manually entered adjustments codes. 
retail %>% 
  filter(Quantity <= 0) %>% 
  group_by(Description, UnitPrice) %>% 
  summarise(count =n()) %>%
  arrange(desc(count)) %>% 
  ungroup()

# remove all rows with non-positive _Quantity_. 
{r}
retail  <- retail %>%
  filter(Quantity > 0)

# CHECK: total row count - 531,285

# NON-PRODUCT STOCKCODES 
# There are a handful of non-product related codes
stc <- c('AMAZONFEE', 'BANK CHARGES', 'C2', 'DCGSSBOY', 'DCGSSGIRL',
'DOT', 'gift_0001_', 'PADS', 'POST')

# Summary
retail %>%  
  filter(grepl(paste(stc, collapse="|"), StockCode))  %>% 
  group_by(StockCode, Description) %>% 
  summarise(count =n()) %>%
  arrange(desc(count)) %>% 
  ungroup()

# These can all be removed. 
retail <- filter(retail, !grepl(paste(stc, collapse="|"), StockCode))

# CHECK: total row count - 529,228

#DESCRIPTION
# Additional adjustment codes to remove
descr <- c( "check", "check?", "?", "??", "damaged", "found", 
"adjustment", "Amazon", "AMAZON", "amazon adjust", 
"Amazon Adjustment", "amazon sales", "Found", "FOUND",
"found box", "Found by jackie ", "Found in w/hse", "dotcom",
"dotcom adjust", "allocate stock for dotcom orders ta", "FBA",
"Dotcomgiftshop Gift Voucher £100.00", "on cargo order",
"wrongly sold (22719) barcode", "wrongly marked 23343",
"dotcomstock", "rcvd be air temp fix for dotcom sit", "Manual",
"John Lewis", "had been put aside", "for online retail orders",  
"taig adjust", "amazon", "incorrectly credited C550456 see 47",
"returned", "wrongly coded 20713", "came coded as 20713", 
"add stock to allocate online orders", "Adjust bad debt",
"alan hodge cant mamage this section", "website fixed",
"did  a credit  and did not tick ret", "michel oops",
"incorrectly credited C550456 see 47", "mailout", "test",
"Sale error",  "Lighthouse Trading zero invc incorr", "SAMPLES",
"Marked as 23343", "wrongly coded 23343","Adjustment", 
"rcvd be air temp fix for dotcom sit", "Had been put aside."
)

# Filtering out the unwanted entries.
retail <- retail %>% 
  filter(!Description %in% descr)

# CHECK: total row count - 528,732


# there are also some 600 NAs in _Description_. 
sum(is.na(retail$Description))

# given their small number (around 0.1% of total) I've opted to remove them.

retail <- retail %>% 
  filter(!is.na(Description))

# CHECK: total row count - 528,148


# CUSTOMER ID
# There is still a significant number of NAs in _CustomerID_. 
retail$CustomerID %>%  
  skim()


# there are almost 5 times as many Orders as there are Customers so I'm using `InvoiceNo` for orders 
sapply(retail[,c('InvoiceNo','CustomerID')], function(x) length(unique(x)))


# FINAL TOUCHES
# a couple of housekeeping tasks to sort out

retail <- retail %>%
  # Setting 'Description' and 'Country' as factors
  mutate(Description = as.factor(Description)) %>%
  mutate(Country = as.factor(Country)) %>% 
  # Changing 'InvoiceNo' type to numeric
  mutate(InvoiceNo = as.numeric(InvoiceNo)) %>% 
  # Extracting 'Date' and 'Time' from 'InvoiceDate'
  mutate(Date = as.Date(InvoiceDate)) %>% 
  mutate(Time = as.factor(format(InvoiceDate,"%H:%M:%S"))) 

glimpse(retail)


# Saving clensed data for analysis phase
saveRDS(retail, "retail.rds")


# EXPLORATORY DATA ANALYSIS

# What items do people buy more often?
retail %>% 
  group_by(Description) %>% 
  summarize(count = n()) %>% 
  top_n(10, wt = count) %>%
  arrange(desc(count)) %>% 
  ggplot(aes(x = reorder(Description, count), y = count))+
  geom_bar(stat = "identity", fill = "royalblue", colour = "blue") +
  labs(x = "", y = "Top 10 Best Sellers", title = "Most Ordered Products") +
  coord_flip() +
  theme_grey(base_size = 12)

# Top 10 most sold products represent around 3% of total items sold by the company
retail %>% 
  group_by(Description) %>% 
  summarize(count = n()) %>% 
  mutate(pct=(count/sum(count))*100) %>% 
  arrange(desc(pct)) %>% 
  ungroup() %>% 
  top_n(10, wt=pct)



# What time of day do people buy more often?
retail %>% 
  ggplot(aes(hour(hms(Time)))) + 
  geom_histogram(stat = "count",fill = "#E69F00", colour = "red") +
  labs(x = "Hour of Day", y = "") +
  theme_grey(base_size = 12)

# What day of the week do people buy more often?
retail %>% 
  ggplot(aes(wday(Date, 
                  week_start = getOption("lubridate.week.start", 1)))) + 
  geom_histogram(stat = "count" , fill = "forest green", colour = "dark green") +
  labs(x = "Day of Week", y = "") +
  scale_x_continuous(breaks = c(1,2,3,4,5,6,7),
                     labels = c("Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun")) +
  theme_grey(base_size = 14)

# How many items does each customer buy?
retail %>% 
  group_by(InvoiceNo) %>% 
  summarise(n = mean(Quantity)) %>%
  ggplot(aes(x=n)) +
  geom_histogram(bins = 100000, fill = "purple", colour = "black") + 
  coord_cartesian(xlim=c(0,100)) +
  scale_x_continuous(breaks=seq(0,100,10)) +
  labs(x = "Average Number of Items per Purchase", y = "") +
  theme_grey(base_size = 14)
 

# What is the average value per order?
retail %>% 
  mutate(Value = UnitPrice * Quantity) %>% 
  group_by(InvoiceNo) %>% 
  summarise(n = mean(Value)) %>%
  ggplot(aes(x=n)) +
  geom_histogram(bins = 200000, fill="firebrick3", colour = "sandybrown") + 
  coord_cartesian(xlim=c(0,100)) +
  scale_x_continuous(breaks=seq(0,100,10)) +
  labs(x = "Average Value per Purchase", y = "") + 
  theme_grey(base_size = 14)


# Which countries do they sell their goods to?
treemap(retail,
        index      = c("Country"),
        vSize      = "Quantity",
        title      = "",
        palette    = "Set2",
        border.col = "grey40")