Superstore Analysis
By Abiodun Onadeji
2022-10-09
Superstore Analysis Project
#Loading Library
library(tidyverse)
library(janitor)
library(skimr)
library(lubridate)
library(readxl)
library(treemapify)Lets us import our excel file
#Importing and reading the data frame
superstore <- read_excel("Superstore.xls",
col_types = c("numeric", "text", "date",
"date", "text", "text", "text", "text",
"text", "text", "text", "numeric",
"text", "text", "text", "text", "text",
"numeric", "numeric", "numeric",
"numeric"))Lets View the first few rows of our data frame
#First six row of my data
head(superstore)## # A tibble: 6 × 21
## `Row ID` `Order ID` `Order Date` `Ship Date` Ship …¹ Custo…²
## <dbl> <chr> <dttm> <dttm> <chr> <chr>
## 1 1 CA-2016-1521… 2016-11-08 00:00:00 2016-11-11 00:00:00 Second… CG-125…
## 2 2 CA-2016-1521… 2016-11-08 00:00:00 2016-11-11 00:00:00 Second… CG-125…
## 3 3 CA-2016-1386… 2016-06-12 00:00:00 2016-06-16 00:00:00 Second… DV-130…
## 4 4 US-2015-1089… 2015-10-11 00:00:00 2015-10-18 00:00:00 Standa… SO-203…
## 5 5 US-2015-1089… 2015-10-11 00:00:00 2015-10-18 00:00:00 Standa… SO-203…
## 6 6 CA-2014-1158… 2014-06-09 00:00:00 2014-06-14 00:00:00 Standa… BH-117…
## # … with 15 more variables: `Customer Name` <chr>, Segment <chr>,
## # Country <chr>, City <chr>, State <chr>, `Postal Code` <dbl>, Region <chr>,
## # `Product ID` <chr>, Category <chr>, `Sub-Category` <chr>,
## # `Product Name` <chr>, Sales <dbl>, Quantity <dbl>, Discount <dbl>,
## # Profit <dbl>, and abbreviated variable names ¹`Ship Mode`, ²`Customer ID`EXPLORATORY DATA ANALYSIS
#The structure of the data
glimpse(superstore)## Rows: 9,994
## Columns: 21
## $ `Row ID` <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,…
## $ `Order ID` <chr> "CA-2016-152156", "CA-2016-152156", "CA-2016-138688", …
## $ `Order Date` <dttm> 2016-11-08, 2016-11-08, 2016-06-12, 2015-10-11, 2015-…
## $ `Ship Date` <dttm> 2016-11-11, 2016-11-11, 2016-06-16, 2015-10-18, 2015-…
## $ `Ship Mode` <chr> "Second Class", "Second Class", "Second Class", "Stand…
## $ `Customer ID` <chr> "CG-12520", "CG-12520", "DV-13045", "SO-20335", "SO-20…
## $ `Customer Name` <chr> "Claire Gute", "Claire Gute", "Darrin Van Huff", "Sean…
## $ Segment <chr> "Consumer", "Consumer", "Corporate", "Consumer", "Cons…
## $ Country <chr> "United States", "United States", "United States", "Un…
## $ City <chr> "Henderson", "Henderson", "Los Angeles", "Fort Lauderd…
## $ State <chr> "Kentucky", "Kentucky", "California", "Florida", "Flor…
## $ `Postal Code` <dbl> 42420, 42420, 90036, 33311, 33311, 90032, 90032, 90032…
## $ Region <chr> "South", "South", "West", "South", "South", "West", "W…
## $ `Product ID` <chr> "FUR-BO-10001798", "FUR-CH-10000454", "OFF-LA-10000240…
## $ Category <chr> "Furniture", "Furniture", "Office Supplies", "Furnitur…
## $ `Sub-Category` <chr> "Bookcases", "Chairs", "Labels", "Tables", "Storage", …
## $ `Product Name` <chr> "Bush Somerset Collection Bookcase", "Hon Deluxe Fabri…
## $ Sales <dbl> 261.9600, 731.9400, 14.6200, 957.5775, 22.3680, 48.860…
## $ Quantity <dbl> 2, 3, 2, 5, 2, 7, 4, 6, 3, 5, 9, 4, 3, 3, 5, 3, 6, 2, …
## $ Discount <dbl> 0.00, 0.00, 0.00, 0.45, 0.20, 0.00, 0.00, 0.20, 0.20, …
## $ Profit <dbl> 41.9136, 219.5820, 6.8714, -383.0310, 2.5164, 14.1694,…From the results shown above, the variable naming are not consistent, we need to clean it up
#Cleaning up the variable names
superstore <- clean_names(superstore)#Checking my variables or column name of the data frame after cleaning up
colnames(superstore)## [1] "row_id" "order_id" "order_date" "ship_date"
## [5] "ship_mode" "customer_id" "customer_name" "segment"
## [9] "country" "city" "state" "postal_code"
## [13] "region" "product_id" "category" "sub_category"
## [17] "product_name" "sales" "quantity" "discount"
## [21] "profit"The variable names are now consistence.
#Checking out the dimension of the data frame
dim(superstore)## [1] 9994 21There are 9994 Rows or Observation and 21 Columns or Variables, in the data frame.
#Checking for missing value in the data frame
sum(is.na(superstore))## [1] 0There are no missing value in the data frame
#checking out the data type of the variables
sapply(superstore, class)## $row_id
## [1] "numeric"
##
## $order_id
## [1] "character"
##
## $order_date
## [1] "POSIXct" "POSIXt"
##
## $ship_date
## [1] "POSIXct" "POSIXt"
##
## $ship_mode
## [1] "character"
##
## $customer_id
## [1] "character"
##
## $customer_name
## [1] "character"
##
## $segment
## [1] "character"
##
## $country
## [1] "character"
##
## $city
## [1] "character"
##
## $state
## [1] "character"
##
## $postal_code
## [1] "numeric"
##
## $region
## [1] "character"
##
## $product_id
## [1] "character"
##
## $category
## [1] "character"
##
## $sub_category
## [1] "character"
##
## $product_name
## [1] "character"
##
## $sales
## [1] "numeric"
##
## $quantity
## [1] "numeric"
##
## $discount
## [1] "numeric"
##
## $profit
## [1] "numeric"#Getting the summary of the variables
summary(superstore)## row_id order_id order_date
## Min. : 1 Length:9994 Min. :2014-01-03 00:00:00.00
## 1st Qu.:2499 Class :character 1st Qu.:2015-05-23 00:00:00.00
## Median :4998 Mode :character Median :2016-06-26 00:00:00.00
## Mean :4998 Mean :2016-04-30 00:07:12.25
## 3rd Qu.:7496 3rd Qu.:2017-05-14 00:00:00.00
## Max. :9994 Max. :2017-12-30 00:00:00.00
## ship_date ship_mode customer_id
## Min. :2014-01-07 00:00:00.00 Length:9994 Length:9994
## 1st Qu.:2015-05-27 00:00:00.00 Class :character Class :character
## Median :2016-06-29 00:00:00.00 Mode :character Mode :character
## Mean :2016-05-03 23:06:58.56
## 3rd Qu.:2017-05-18 00:00:00.00
## Max. :2018-01-05 00:00:00.00
## customer_name segment country city
## Length:9994 Length:9994 Length:9994 Length:9994
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## state postal_code region product_id
## Length:9994 Min. : 1040 Length:9994 Length:9994
## Class :character 1st Qu.:23223 Class :character Class :character
## Mode :character Median :56431 Mode :character Mode :character
## Mean :55190
## 3rd Qu.:90008
## Max. :99301
## category sub_category product_name sales
## Length:9994 Length:9994 Length:9994 Min. : 0.444
## Class :character Class :character Class :character 1st Qu.: 17.280
## Mode :character Mode :character Mode :character Median : 54.490
## Mean : 229.858
## 3rd Qu.: 209.940
## Max. :22638.480
## quantity discount profit
## Min. : 1.00 Min. :0.0000 Min. :-6599.978
## 1st Qu.: 2.00 1st Qu.:0.0000 1st Qu.: 1.729
## Median : 3.00 Median :0.2000 Median : 8.666
## Mean : 3.79 Mean :0.1562 Mean : 28.657
## 3rd Qu.: 5.00 3rd Qu.:0.2000 3rd Qu.: 29.364
## Max. :14.00 Max. :0.8000 Max. : 8399.976#### We would not need the row_id and the country variable in our analysis. Now, we would go ahead to remove row_id and the country Columns(Variable) since the data frame is for United State
#Dropping row_id and country column(variables)
superstore <- within(superstore, rm(row_id, country))#confirming the removal of row_id and country variable
superstore %>%
head(5)## # A tibble: 5 × 19
## order_id order_date ship_date ship_…¹ custo…² custo…³
## <chr> <dttm> <dttm> <chr> <chr> <chr>
## 1 CA-2016-152156 2016-11-08 00:00:00 2016-11-11 00:00:00 Second… CG-125… Claire…
## 2 CA-2016-152156 2016-11-08 00:00:00 2016-11-11 00:00:00 Second… CG-125… Claire…
## 3 CA-2016-138688 2016-06-12 00:00:00 2016-06-16 00:00:00 Second… DV-130… Darrin…
## 4 US-2015-108966 2015-10-11 00:00:00 2015-10-18 00:00:00 Standa… SO-203… Sean O…
## 5 US-2015-108966 2015-10-11 00:00:00 2015-10-18 00:00:00 Standa… SO-203… Sean O…
## # … with 13 more variables: segment <chr>, city <chr>, state <chr>,
## # postal_code <dbl>, region <chr>, product_id <chr>, category <chr>,
## # sub_category <chr>, product_name <chr>, sales <dbl>, quantity <dbl>,
## # discount <dbl>, profit <dbl>, and abbreviated variable names ¹ship_mode,
## # ²customer_id, ³customer_nameANALYSIS
Analysis of the superstore will be carried out based on the following;
Product Level Analysis
Customer Level Analysis
Region and Time Series Analysis
1. Product Level Analysis
#Let us look at the product category available
unique(superstore$category)## [1] "Furniture" "Office Supplies" "Technology"#Number of products in each category
superstore %>%
group_by(category) %>%
summarise(total=n()) %>%
arrange(desc(total))## # A tibble: 3 × 2
## category total
## <chr> <int>
## 1 Office Supplies 6026
## 2 Furniture 2121
## 3 Technology 1847#sub_category products are divided into
n_distinct(superstore$sub_category)## [1] 17#number of products in each sub-category
superstore %>%
group_by(sub_category) %>%
summarise(total=n()) %>%
arrange(desc(total))## # A tibble: 17 × 2
## sub_category total
## <chr> <int>
## 1 Binders 1523
## 2 Paper 1370
## 3 Furnishings 957
## 4 Phones 889
## 5 Storage 846
## 6 Art 796
## 7 Accessories 775
## 8 Chairs 617
## 9 Appliances 466
## 10 Labels 364
## 11 Tables 319
## 12 Envelopes 254
## 13 Bookcases 228
## 14 Fasteners 217
## 15 Supplies 190
## 16 Machines 115
## 17 Copiers 68#The distribution of subcategory
ggplot(superstore, aes(x= sub_category, y = category, fill=category)) +
geom_col(position = position_dodge(),show.legend = FALSE)+ coord_flip()+
theme_classic() + labs(title = "Category by Sub_Category",
subtitle ="Distribution of Subcategory")+
theme(plot.title = element_text(size=20, face="bold", hjust = 0.5))
From the visualization above, one can easily decide which Category & Sub-Category to lookout for when purchasing a product from the store
#Count by Category and Subcategory
ggplot(superstore, aes(x= sub_category, fill = category)) +
geom_bar() + theme_bw() + coord_flip() +
labs(title = "Count by Category and Sub_Category",
x = "Sub_Category", y = "Frequency")+
theme(plot.title = element_text(size=20, face="bold", hjust = 0.5))
The store has wide variety of Office Supplies especially in Binders and Paper
#Total Profit and Sale by Sub_Category
superstore %>%
group_by(sub_category) %>%
summarise( total_sales = sum(sales),total_profit =sum(profit)) %>%
pivot_longer(c("total_sales", "total_profit")) %>%
ggplot(aes(x=sub_category, y = value, fill = name)) +
geom_col(position = position_dodge()) + theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
theme(legend.title = element_blank()) +
scale_y_continuous(labels = scales::comma)+
labs(title = "Total Profit and Sales by Sub_Category") +
theme(plot.title = element_text(size=20, face="bold", hjust = 0.5))
Highest profit is earned in Copiers while the sales of Chairs and Phones is high when compared to other products. However, the profit on Tables and Bookcases is not significant Hence, these departments are in loss.
#The total number of products available in the store is
n_distinct(superstore$product_name)## [1] 1850#Distribution of Top 10 Product
superstore %>%
group_by(product_name) %>%
summarize(count_of_products=n()) %>%
arrange(desc(count_of_products)) %>%
slice_head(n=10) %>%
ggplot(superstore,
mapping = aes(x= product_name,
y= count_of_products))+
geom_segment(aes(x = reorder(product_name, count_of_products), xend = product_name,
y = 0, yend = count_of_products), size =1,)+
geom_point(size=10, color= "orange",
shape = 20, stroke = 2) + coord_flip() + theme_bw() +
labs(title = "Distribution of Top 10 Product",
x = NULL) +
theme(plot.title = element_text(size=15, face="bold", hjust = 0.5)) +
geom_text(aes(label= count_of_products),
color="black", size=4, vjust=0.5, fontface='bold')
#Count of Sub-Category by region
superstore %>%
ggplot(aes(x=sub_category, fill= region)) +
geom_bar(position = position_dodge(width = 0.7))+
theme_minimal()+ theme(axis.text.x = element_text(angle = 90))+
scale_y_continuous(expand = c(0,0)) +
labs(title = "Count of Sub-Category Product Sales by Region")+
theme(plot.title = element_text(size=15, face="bold",hjust = 0.5))
People residing in Western part of United State tends to order more from superstore.
—————————————————————-
To analyze further, we are going to calculate the cost and profit percentage variables
#Calculating Cost
superstore <- superstore %>%
mutate(cost = sales - profit)#showing the first five rows of the new data frame
superstore %>%
head(5)## # A tibble: 5 × 20
## order_id order_date ship_date ship_…¹ custo…² custo…³
## <chr> <dttm> <dttm> <chr> <chr> <chr>
## 1 CA-2016-152156 2016-11-08 00:00:00 2016-11-11 00:00:00 Second… CG-125… Claire…
## 2 CA-2016-152156 2016-11-08 00:00:00 2016-11-11 00:00:00 Second… CG-125… Claire…
## 3 CA-2016-138688 2016-06-12 00:00:00 2016-06-16 00:00:00 Second… DV-130… Darrin…
## 4 US-2015-108966 2015-10-11 00:00:00 2015-10-18 00:00:00 Standa… SO-203… Sean O…
## 5 US-2015-108966 2015-10-11 00:00:00 2015-10-18 00:00:00 Standa… SO-203… Sean O…
## # … with 14 more variables: segment <chr>, city <chr>, state <chr>,
## # postal_code <dbl>, region <chr>, product_id <chr>, category <chr>,
## # sub_category <chr>, product_name <chr>, sales <dbl>, quantity <dbl>,
## # discount <dbl>, profit <dbl>, cost <dbl>, and abbreviated variable names
## # ¹ship_mode, ²customer_id, ³customer_name#Top 10 product by Cost
superstore %>%
select(product_name, cost) %>%
slice(1:10) %>%
arrange(desc(cost))## # A tibble: 10 × 2
## product_name cost
## <chr> <dbl>
## 1 Bretford CR4500 Series Slim Rectangular Table 1341.
## 2 Mitel 5320 IP Phone VoIP phone 816.
## 3 Hon Deluxe Fabric Upholstered Stacking Chairs, Rounded Back 512.
## 4 Bush Somerset Collection Bookcase 220.
## 5 Belkin F5C206VTEL 6 Outlet Surge 80.4
## 6 Eldon Expressions Wood and Plastic Desk Accessories, Cherry Wood 34.7
## 7 Eldon Fold 'N Roll Cart System 19.9
## 8 DXL Angle-View Binders with Locking Rings by Samsill 12.7
## 9 Self-Adhesive Address Labels for Typewriters by Universal 7.75
## 10 Newell 322 5.31#Also calculating percentage profit
superstore <- superstore %>%
mutate(profit_percent = (profit/cost *100))#Category and Products with 100% profits
superstore %>%
select(category, product_name, profit_percent) %>%
filter(profit_percent==100) %>%
arrange(category)## # A tibble: 140 × 3
## category product_name profi…¹
## <chr> <chr> <dbl>
## 1 Furniture "GE 48\" Fluorescent Tube, Cool White Energy Saver, … 100
## 2 Furniture "GE 48\" Fluorescent Tube, Cool White Energy Saver, … 100
## 3 Furniture "GE 48\" Fluorescent Tube, Cool White Energy Saver, … 100
## 4 Furniture "GE 48\" Fluorescent Tube, Cool White Energy Saver, … 100
## 5 Furniture "GE 48\" Fluorescent Tube, Cool White Energy Saver, … 100
## 6 Office Supplies "Prang Dustless Chalk Sticks" 100
## 7 Office Supplies "Adams Telephone Message Book w/Frequently-Called Nu… 100
## 8 Office Supplies "OIC Binder Clips" 100
## 9 Office Supplies "Southworth Structures Collection" 100
## 10 Office Supplies "Alphabetical Labels for Top Tab Filing" 100
## # … with 130 more rows, and abbreviated variable name ¹profit_percent140 products got 100% profits from the three category, However, office supplies have more products with 100% profit as further shown below
#Products by Category with 100 percent Profit
superstore %>%
select(category, product_name, profit_percent) %>%
filter(profit_percent==100) %>%
arrange(category) %>%
ggplot(aes(x= category, fill = "Red")) +
geom_bar(show.legend = FALSE)+ theme_bw() +
labs(title = "Count of Category with 100% profit")+
theme(plot.title = element_text(size=20, face="bold",hjust = 0.5))
In furtherance to the analysis, let’s look at the Customer Level Analysis
2. Customer Level Analysis
#Total Customer Count
n_distinct(superstore$customer_name)## [1] 793#Top Ten Customer who ordered frequently from the store
superstore %>%
group_by(customer_name) %>%
summarise(top_10_customer=n()) %>%
arrange(desc(top_10_customer)) %>%
slice_head(n=10)## # A tibble: 10 × 2
## customer_name top_10_customer
## <chr> <int>
## 1 William Brown 37
## 2 John Lee 34
## 3 Matt Abelman 34
## 4 Paul Prost 34
## 5 Chloris Kastensmidt 32
## 6 Edward Hooks 32
## 7 Jonathan Doherty 32
## 8 Seth Vernon 32
## 9 Arthur Prichep 31
## 10 Emily Phan 31#The most Common ship_mode and segment used by customers
superstore %>%
group_by(ship_mode, segment) %>%
summarise(total=n()) %>%
ggplot(aes(x=ship_mode, y = total, fill= segment)) +
geom_col(position=position_dodge()) + facet_wrap(~segment)+ theme_bw()+
theme(axis.text.x = element_text(angle = 45))+
labs(title = "Distribution by Segment and Ship Mode") +
theme(plot.title = element_text(size=20, face="bold", hjust = 0.5))+
geom_text(aes(ship_mode, label = total),vjust = -0.4)## `summarise()` has grouped output by 'ship_mode'. You can override using the
## `.groups` argument.
The distribution is highest in consumer segment. Moreso,ship mode for standard class were highest for all segment
#Top 20 Customers by Profit and by State
superstore %>%
mutate(summarise(superstore,
profit_2 = round(profit, digits = 0))) %>%
select(customer_name, state, profit_2) %>%
arrange(desc(profit_2)) %>%
slice(1:20) %>%
ggplot(aes(x= reorder(customer_name, profit_2),
y= profit_2, fill = reorder(state, -profit_2))) +
geom_col() + coord_flip() + theme_classic() +
labs(title = "Top 20 Customers by Profit and State",
x= "Customer Name", y = "Profit (Dollars)") +
theme(legend.title = element_blank()) +
theme(plot.title = element_text(size=20, face="bold", hjust = 0.5)) +
geom_text(aes(customer_name, label=profit_2),
size = 2.9,hjust = -0.01, fontface= "bold")
The most profitable customer is from Indiana followed by Washington. However, we could see that majority of profitable customers are from New York and Michigan State.
#TOP 10 CUSTOMER by Sale and by state
superstore %>%
mutate(summarise(superstore,
sales_2 = round(sales, digits = 0))) %>%
select(customer_name, state, sales_2) %>%
arrange(desc(sales_2)) %>%
slice(1:10) %>%
ggplot(aes(x=reorder(customer_name, -sales_2),
y= sales_2, fill= reorder(state, -sales_2)))+
geom_col() + theme_bw() +
theme(axis.text.x = element_text(angle = 45, hjust=1))+
labs(title = "Top 10 Customers by Sales and State",
x= "Customer Name", y = "Total Sales") +
theme(plot.title = element_text(size=20, face="bold", hjust = 0.5))+
theme(legend.title = element_blank()) +
geom_text(aes(label = sales_2),
vjust=0.1, alpha=1, size = 4, fontface= "bold")
3. Region and Time Series Analysis
#Sales by Region
superstore %>%
group_by(region) %>%
summarize(total_sales = round(sum(sales))) %>%
ggplot(aes(area = total_sales, fill = region,
label= paste0(region, "\n",prettyNum(total_sales, ",")))) +
geom_treemap() + geom_treemap_text(color= "white",
place="centre", fontface = "bold", size = 25) +
theme(legend.position = "none")+ labs(title = "Sales by Region") +
theme(plot.title = element_text(size=20, face="bold", hjust = 0.5))
The west generated the highest Sales by Region with the total sales of $725,458
#Profit by Region
superstore %>%
group_by(region)%>%
summarize(total_profit = round(sum(profit))) %>%
ggplot(aes(area = total_profit , fill = region,
label= paste0(region, "\n",prettyNum(total_profit, ",")))) +
geom_treemap() +
geom_treemap_text(color= "white",
place="centre", fontface = "bold", size = 25) +
theme(legend.position = "none") + labs(title = "Profit by Region") +
theme(plot.title = element_text(size=20, face="bold", hjust = 0.5))
The west generated the highest profit by region with the total profit of $108,418
———————————————————
We shall be calculating shipment duration as well, extracting year from the order_date from the data frame
#Calculating shipment duration
superstore <- superstore %>%
mutate(duration_to_ship =
as.Date(superstore$ship_date) - as.Date(superstore$order_date)) %>%
arrange(desc(duration_to_ship))#Highest shipment duration and ship mode by top 20 product
superstore %>%
select(product_id, ship_mode,duration_to_ship) %>%
arrange(desc(duration_to_ship)) %>%
slice(1:20)## # A tibble: 20 × 3
## product_id ship_mode duration_to_ship
## <chr> <chr> <drtn>
## 1 FUR-TA-10000577 Standard Class 7 days
## 2 OFF-ST-10000760 Standard Class 7 days
## 3 OFF-ST-10004186 Standard Class 7 days
## 4 TEC-AC-10001998 Standard Class 7 days
## 5 OFF-LA-10000134 Standard Class 7 days
## 6 OFF-AR-10000380 Standard Class 7 days
## 7 OFF-BI-10003981 Standard Class 7 days
## 8 OFF-BI-10003274 Standard Class 7 days
## 9 OFF-ST-10002974 Standard Class 7 days
## 10 OFF-ST-10001414 Standard Class 7 days
## 11 OFF-BI-10000343 Standard Class 7 days
## 12 OFF-PA-10002749 Standard Class 7 days
## 13 OFF-LA-10004544 Standard Class 7 days
## 14 FUR-BO-10004695 Standard Class 7 days
## 15 TEC-PH-10002844 Standard Class 7 days
## 16 TEC-MA-10000864 Standard Class 7 days
## 17 TEC-AC-10000109 Standard Class 7 days
## 18 OFF-AR-10001026 Standard Class 7 days
## 19 FUR-TA-10003748 Standard Class 7 days
## 20 OFF-ST-10002485 Standard Class 7 daysFrom the above table, it takes maximum 7 days or one week using the Standard Class Ship Mode to ship a product out from the store.
#Average shipping duration by ship mode
superstore %>%
group_by(ship_mode) %>%
summarise(avg_duration = round(mean(duration_to_ship))) %>%
ggplot(aes(x= ship_mode,
y= avg_duration, color = ship_mode ))+
geom_segment(aes(x = reorder(ship_mode, avg_duration),
xend = ship_mode, y = 0, yend = avg_duration), size =2,
show.legend = FALSE) +
geom_point( size=10, shape = 20, stroke = 2, show.legend = FALSE) + coord_flip() + theme_bw()+
labs(title = "Average shipping duration by ship mode",
x = "Ship Mode", y= "Average Duration (days)") +
theme(plot.title = element_text(size=20, face="bold", hjust = 0.5)) +
geom_text(aes(label= avg_duration),
color="black", size=4, vjust=0.5, fontface='bold')## Don't know how to automatically pick scale for object of type difftime. Defaulting to continuous.
From the above, same day shipping mode, ship the product on the day it was ordered for, however, it will take an average of 5 days by the standard class ship mode
#Extracting year from the order date
superstore <- superstore %>%
mutate(order_year = year(order_date))#Profit and sales by year
superstore %>%
group_by(order_year) %>%
summarize(total_sales = round(sum(sales)),
total_profit= round(sum(profit))) %>%
pivot_longer(c("total_sales", "total_profit")) %>%
ggplot(aes(x= order_year, y= value, fill = name))+
geom_col(position = "dodge")+
scale_y_continuous(labels=scales::comma) + theme_bw()+
geom_text(aes(order_year, label = value), vjust =0.001,
size = 3, fontface= "bold",
position = position_dodge(width = 1))+
labs(title = "Profit and Sale by Year",
x= "Year", y = "Value (dollars)") +
theme(plot.title = element_text(size=20, face="bold", hjust = 0.5))+
theme(legend.title = element_blank())
The above shows that sales and profit increased for each year resulting in high Profit margin in year 2017
#profit and sales by year for each region
superstore %>%
group_by(order_year,region) %>%
summarize(Total_Sales = round(sum(sales)),
Total_Profit= round(sum(profit))) %>%
pivot_longer(c("Total_Sales", "Total_Profit")) %>%
ggplot(aes(x= order_year, y= value, fill = name))+
geom_col(position = position_dodge(width = 1)) + facet_wrap(~region)+
theme_bw()+
geom_text(aes(order_year, label = value), vjust =0.001,
size = 2.5, fontface= "bold",
position = position_dodge(width = 1))+
labs(title = "Sales and Profit by Year for each Region",
x= "Year", y = "Value (dollars)")+
theme(plot.title = element_text(size=18, face="bold", hjust = 0.5))+
theme(legend.title = element_blank())## `summarise()` has grouped output by 'order_year'. You can override using the
## `.groups` argument.
The above shows that the west Region have the highest total profit and sales across the year from 2014 to 2017. However, in the year 2015, the east has more profit and sales more than any other region including the west.
#Sales by Category for each year
superstore %>%
group_by(category, order_year) %>%
summarize(total_sales=sum(sales)) %>%
ggplot(aes(x=category, y= total_sales, fill = category)) +
geom_col() + facet_grid (~order_year) + theme_bw()+
scale_y_continuous(labels=scales::comma) +
theme(axis.text.x = element_blank()) +
labs(title = "Sales by Category for each Year",
x= NULL, y = "Sales (dollars)")+
theme(plot.title = element_text(size=20, face="bold", hjust = 0.5))## `summarise()` has grouped output by 'category'. You can override using the
## `.groups` argument.
From the above, Technological products have the highest sales in year 2014, 2016 and 2017. However, more of furniture products were sold in the year 2015.
#Profit Trend by Category for each Year
superstore %>%
group_by(category, order_year) %>%
summarize(total_profit=sum(profit)) %>%
ggplot(aes(x= order_year, y=total_profit, color = category)) +
geom_point(size = 5, alpha=0.5) + geom_line(size=1) +
theme_bw() +
labs(title = "Profit Trend by Year for each Category",
x= "Year", y = "Profit (dollars)")+
theme(plot.title = element_text(size=20, face="bold", hjust = 0.5))## `summarise()` has grouped output by 'category'. You can override using the
## `.groups` argument.
There is consistence increase in profit in office supplies and technological products from Year 2014 to 2017. However, there is a huge drop in profit in year 2015 and 2017 from the sales of furniture products.
#Sales, Profit and Discount Relationship
superstore %>%
ggplot(aes(x=sales, y=profit, color=discount))+
geom_point(size=3) + geom_rug() + theme_bw() +
labs(title="Sales,Profit and Discount Relationship")+
theme(plot.title=element_text(size=20, face="bold", hjust=0.5))