knitr::opts_chunk$set(warning = FALSE)
library(ggplot2)

1. The Data

The data has been downloaded and it is in the .csv file. We create a variable called store_data and extract the contents of the dataset in that variable.

store_data <- read.csv("SampleSuperstore.csv")

1.1 Checking for any missing data in the dataset:

We take the sum of the output of is.na(store_data). There can be 2 possible output’s: TRUE and FALSE, and we know that the value assigned to TRUE is 1 and that to FALSE is 0. So, taking a sum of the function will give us the number of missing values in the entire dataset.

sum(is.na(store_data))
## [1] 0

As we see that the output of the previous function is zero, so, there are no missing values in the entire dataset.

1.2 Looking at the data:

We look at the summary of the data, it will give us the dimenssions, columns and type of the store_data dataset.

summary(store_data)
##           Ship.Mode           Segment              Country    
##  First Class   :1538   Consumer   :5191   United States:9994  
##  Same Day      : 543   Corporate  :3020                       
##  Second Class  :1945   Home Office:1783                       
##  Standard Class:5968                                          
##                                                               
##                                                               
##                                                               
##             City               State       Postal.Code        Region    
##  New York City: 915   California  :2001   Min.   : 1040   Central:2323  
##  Los Angeles  : 747   New York    :1128   1st Qu.:23223   East   :2848  
##  Philadelphia : 537   Texas       : 985   Median :56431   South  :1620  
##  San Francisco: 510   Pennsylvania: 587   Mean   :55190   West   :3203  
##  Seattle      : 428   Washington  : 506   3rd Qu.:90008                 
##  Houston      : 377   Illinois    : 492   Max.   :99301                 
##  (Other)      :6480   (Other)     :4295                                 
##             Category         Sub.Category      Sales              Quantity    
##  Furniture      :2121   Binders    :1523   Min.   :    0.444   Min.   : 1.00  
##  Office Supplies:6026   Paper      :1370   1st Qu.:   17.280   1st Qu.: 2.00  
##  Technology     :1847   Furnishings: 957   Median :   54.490   Median : 3.00  
##                         Phones     : 889   Mean   :  229.858   Mean   : 3.79  
##                         Storage    : 846   3rd Qu.:  209.940   3rd Qu.: 5.00  
##                         Art        : 796   Max.   :22638.480   Max.   :14.00  
##                         (Other)    :3613                                      
##     Discount          Profit         
##  Min.   :0.0000   Min.   :-6599.978  
##  1st Qu.:0.0000   1st Qu.:    1.729  
##  Median :0.2000   Median :    8.666  
##  Mean   :0.1562   Mean   :   28.657  
##  3rd Qu.:0.2000   3rd Qu.:   29.364  
##  Max.   :0.8000   Max.   : 8399.976  
##

We look at the first 6 rows of the store_data datset now, to see how the data looks like.

head(store_data)
##        Ship.Mode   Segment       Country            City      State Postal.Code
## 1   Second Class  Consumer United States       Henderson   Kentucky       42420
## 2   Second Class  Consumer United States       Henderson   Kentucky       42420
## 3   Second Class Corporate United States     Los Angeles California       90036
## 4 Standard Class  Consumer United States Fort Lauderdale    Florida       33311
## 5 Standard Class  Consumer United States Fort Lauderdale    Florida       33311
## 6 Standard Class  Consumer United States     Los Angeles California       90032
##   Region        Category Sub.Category    Sales Quantity Discount    Profit
## 1  South       Furniture    Bookcases 261.9600        2     0.00   41.9136
## 2  South       Furniture       Chairs 731.9400        3     0.00  219.5820
## 3   West Office Supplies       Labels  14.6200        2     0.00    6.8714
## 4  South       Furniture       Tables 957.5775        5     0.45 -383.0310
## 5  South Office Supplies      Storage  22.3680        2     0.20    2.5164
## 6   West       Furniture  Furnishings  48.8600        7     0.00   14.1694

Now we check the dimenssions of the data set

dim(store_data)
## [1] 9994   13

We see that there are 9994 rows and 13 columns in the dataset.

2. Exploratory Analysis

Now it is the time we start exploring the dataset. One of the key things that we need to check is the profit that was gained out of each state. First we need to aggregate the dataset and store the result in a new variable named as profit_state, and check it’s dimenssions.

profit_state <- aggregate(Profit~State, data = store_data, sum)
dim(profit_state)
## [1] 49  2

Now we make the first plot and check the Profit earned in each state.

barplot(profit_state$Profit, names.arg = profit_state$State, xlab = "States", ylab = "Profit", main = "State VS Profit")

As we see, all the state names aren’t visible, so we will try to make a plot for profit in each region of the country.

region_profit <- aggregate(Profit~Region, data = store_data, sum)
barplot(region_profit$Profit, names.arg = region_profit$Region, xlab = "Region", ylab = "Profit", main = "Region VS Profit")

This shows that the maximum profit was earned in the west region, and hence, making an investment in that region can be fairly successful.

Now we will try to make a comparision of the data for Profit on each State based on Region

region_state_profit <- aggregate(Profit~Region+State, data = store_data, sum)
barplot(region_state_profit$Profit, names.arg = region_state_profit$State, col = region_state_profit$Region, main = "Profit of the States as per the Regions")
legend("topright", legend = unique(region_state_profit$Region), col = c("green", "blue", "red", "black"), pch =  19)

Making another graph just to demonstrate the comparision of Region, State and Profit

qplot(Region, Profit, data = region_state_profit, color = State, main = "Profit based on Region and States")

We now make 2 new variables that store the profit and loss in each state. The variables are named as profit and loss respectively.

loss <- subset(store_data, Profit <= 0)
profit <- subset(store_data, Profit > 0)

We now check for the dimenssions of both the variables, iprofit and loss.

dim(loss)
## [1] 1936   13

The dimenssion of loss came out to be 1936*13.

dim(profit)
## [1] 8058   13

The dimenssion of profit came out to be 8058*13.

Now we make the plot for just the profit data of each State

qplot(Profit, State, data = profit, alpha = 0.5, main = "Profit VS State")

So we see that higest single profit amount was seen in the State of Indiana. Now we go on to plot the the individual loss amount data as per each state.

qplot(Profit, State, data = loss, alpha = 0.5, main = "Loss VS State", xlab = "Loss")

We see that the highest individual loss was incurred in the State of Ohio. But this dosent mean that the highest total loss was incurred in the same state. For that we aggregate the profit and loss data as per the states.

profit_state <- aggregate(Profit~State+Region, data = profit, sum)
loss_state <- aggregate(Profit~State+Region, data = loss, sum)

Now we will make a plot for both total profit and total loss according to the states.

qplot(Profit, State, data = profit_state, main = "Total Profit VS State", color = Region)

qplot(Profit, State, data = loss_state, main = "Total Loss VS State", xlab = "Loss", color = Region)

So we see that the highest Profitable State is California followed by Ney York and the state with the higest loss is Texas.

Now we make a more informative plot for the Region and the Profit data

qplot(Profit, data = region_state_profit, fill = Region, main = "Profit VS Region")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

So we see that most of the profit for the Central region is focussed around Zero

Now we check the Profit based on the Category and Sub Category

profit_sub_cat <- aggregate(Profit ~ Category+Sub.Category, data = store_data, sum)
barplot(profit_sub_cat$Profit, names.arg = profit_sub_cat$Sub.Category, main = "Profit VS Sub Category", xlab = "Sub Category", ylab = "Total Profit")

qplot(Profit, Sub.Category, data = store_data, alpha = 0.5, color = Category, ylab = "Sub Category", main = "Profit VS Sub Category")

qplot(Profit, Sub.Category, data = profit_sub_cat, main = "Total Profit VS Sub Category", ylab = "Sub Ctaegory", xlab = "Total Profit", color = Category)

We see that Copier gives the higest profit and Tables give the highest discount.

Now we will make graphs related to the quantity of the product sold based on the Category, which will show us what are the products that we should focus on for manufacturing purposes.

First we will see how much Quantity is required in each Segment

seg_quan <- aggregate(Quantity~Segment, data = store_data, sum)
barplot(seg_quan$Quantity, names.arg =  seg_quan$Segment, xlab = "Segment", ylab = "Quantity sold", main = "Quantity VS Segment")

As we see the largest Quantity requirement is by the Consumer segment.

Now we focus on the category of the product and the Quantity of sale of each product.

quan_sub_cat <- aggregate(Quantity ~ Category+Sub.Category, data = store_data, sum)
qplot(Quantity, Sub.Category, data = quan_sub_cat, color = Category, main = "Quantity VS Sub Category", ylab = "Sub Category")

Now we check for the Quantity of each sub categpory as per the Segment

sub_seg_quan <- aggregate(Quantity ~ Sub.Category+Segment, data = store_data, sum)
qplot(Quantity, data = sub_seg_quan, fill = Sub.Category, facets = Segment~., main = "Quantity of the Segments")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Now we move on to checking the profit based on Category, Sub Category and Segment.
First we move on to the profit provided by each segment

seg_profit <- aggregate(Profit~Segment, data = store_data, sum)
barplot(seg_profit$Profit, names.arg = seg_profit$Segment, xlab = "Segments", ylab = "Total Profit", main = "Profit in Each Segment")

Now we move on to findind the Profit as per the Category and Segment

qplot(Profit, Segment, data = store_data, color = Category, alpha = 0.5)

seg_cat_profit <- aggregate(Profit~Segment+Category, data = store_data, sum)
qplot(Profit, data = seg_cat_profit, fill = Category, facets = Segment~., main = "Profit according to Segments")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(store_data, aes(log10(Profit), Segment, color = Category)) + geom_boxplot() + labs(title = "Profit VS Segment", x = "log of Profit")

Checking the Shipping Mode with the Profit can be a key aspect of the Explorotory Analysis.

ship_profit <- aggregate(Profit~Ship.Mode, data = store_data, sum)
barplot(ship_profit$Profit, names.arg = ship_profit$Ship.Mode, xlab = "Shipping Mode", ylab = "Total Profit", main = "Profit VS Shipping Mode")

We now look at the explore the Shipping Mode data with Profit based on the Category and Region.

ggplot(data = store_data, aes(Ship.Mode, Profit, color = Category)) + geom_point(aes(alpha = 0.5)) + facet_wrap(~Region, nrow = 4, ncol = 1) + labs(title = "Shipping Mode and Profit according to the Category as per Region", x = "Shipping Mode")

We now move on to checking the Profit based on the Category, Segment and Region, and we do that by making a new variable, namely profit_cat_seg_reg.

profit_cat_seg_reg <- aggregate(Profit ~ Region+Category+Segment, data = store_data, sum)
ggplot(profit_cat_seg_reg, aes(x = Region, y = Profit, color = Category)) + geom_point() + facet_wrap(~Segment, nrow = 3, ncol = 1) + labs(title = "Profit per Region as per Category and Segment")

We see that one of the highest profits in each section are:

Consumer
  1. West - Higest profit by Technology followed by Office Supplies and Furniture
  2. South - Higest profit by Technology followed by Office Supplies and Furniture
  3. East - Higest profit by Technology followed by Office Supplies and Furniture
  4. North - Higest profit by Technology followed by Office Supplies and Furniture
Corporate
  1. West - Higest profit by Office Supplies followed by Technology and Furniture
  2. South - Higest profit by Office Supplies followed by Technology and Furniture
  3. East - Higest profit by Office Supplies followed by Technology and Furniture
  4. North - Higest profit by Technology followed by Furniture and Office Supplies
Home Office
  1. West - Higest profit by Office Supplies followed by Technology and Furniture
  2. South - Higest profit by Office Supplies followed by Furniture and Technology
  3. East - Higest profit by Technology followed by Office Supplies and Furniture
  4. North - Higest profit by Office Supplies followed by Technology and Furniture

3. Summary

Few key points that we notice based on the Profit:

  1. Profit is maximum in the Western Region of the country.
  2. The maximum profits in all the States comes from the State of California(West) followed by New York(East)
  3. The maximum profit was earned by selling the copiers.
  4. The Quantity of product bought was most by the Consumers segment followed by Corporate and Home Offices segments
  5. The products sold in the:
  1. Furnishing Category: Maximum was Furnishings, Minimum was Bookcases
  2. Office Supplies: Maximum was Binders, Minimum was Tables
  3. Technology: Maximum was Phones, Minimum was Copiers
  1. For all the Segments, Technology is the most profitable category, followed by Office Supplies and Furniture.
  2. We see that the Standard Shipping is the most profitable Shipping Mode in all the Categories. But it is important to note that Data provided for making an analysis of the Shipping Mode depence on Profit is quite insufficient, because the Shipping mode depends on a lo*t of other external factors.

Few key points that we notice based on the Loss:

  1. Profit is minimum in the Central Region of the country.
  2. The states of Texas(Central), Pensylvania(East) and Ohio(East) incurred the highest amount of loss.
  3. Highest Losses are incurred in selling the Tables(Furniture), Bookcases(Furniture) and Supplies(Office Supplies) respectively.
  4. We see that the Quantity of Copiers(Technology) sold is the least, but still it was able to give the highest profit. This is a key point, and the copany should advertise the Copier more and produce it in ample amount, so that they can increase their profit.
  5. We see that the profit for by the Furniture Category is least in all the Segments, so, we can use this analysis to make the Furniture only when ordered and as Furniture is a depleting comodity, it is advisable not to store it for a long time. The company can hence invest the money saved into making new Technology, so that more profit can be made out of the same investment.