DataCo SMART SUPPLY CHAIN FOR BIG DATA ANALYSIS

Supply Chain

1. Data’s Background

1.1 Brief

Hello Everyone ! hope you guys in well. In this LBB it will continuing the previous LBB about P4DS with simply analytics statistic, but for this LBB it will be focus on interactive plotting visualization of data-set as our necessary, it talks about a data set of Supply Chains used by the company DataCo Global was used for the analysis.

source dataset : https://www.kaggle.com/datasets/shashwatwork/dataco-smart-supply-chain-for-big-data-analysis?select=DataCoSupplyChainDataset.csv

1.2 Data’s POV

DataCo SMART SUPPLY CHAIN, one of the enormous logistic company moving in 3PL help many companies to fulfill their demands all world as DataCo’s Client.

Areas of important registered activities : Provisioning , Production , Sales , Commercial Distribution.It also allows the correlation of Structured Data with Unstructured Data for knowledge generation.

Types of Products : Clothing , Sports , and Electronic Supplies.

In this case, I will break down about sales progress every typed of products, how about customer satisfaction on progress shipping, and many more.

for the detail of information meaning of columns you all can read detail of description DataCo Supply Chain below :

df_detail <- read.csv('Dataset/DescriptionDataCoSupplyChain.csv')
knitr::kable(df_detail)

FIELDS	DESCRIPTION
Type	: Type of transaction made
Days for shipping (real)	: Actual shipping days of the purchased product
Days for shipment (scheduled)	: Days of scheduled delivery of the purchased product
Benefit per order	: Earnings per order placed
Sales per customer	: Total sales per customer made per customer
Delivery Status	: Delivery status of orders: Advance shipping , Late delivery , Shipping canceled , Shipping on time
Late_delivery_risk	: Categorical variable that indicates if sending is late (1), it is not late (0).
Category Id	: Product category code
Category Name	: Description of the product category
Customer City	: City where the customer made the purchase
Customer Country	: Country where the customer made the purchase
Customer Email	: Customer’s email
Customer Fname	: Customer name
Customer Id	: Customer ID
Customer Lname	: Customer lastname
Customer Password	: Masked customer key
Customer Segment	: Types of Customers: Consumer , Corporate , Home Office
Customer State	: State to which the store where the purchase is registered belongs
Customer Street	: Street to which the store where the purchase is registered belongs
Customer Zipcode	: Customer Zipcode
Department Id	: Department code of store
Department Name	: Department name of store
Latitude	: Latitude corresponding to location of store
Longitude	: Longitude corresponding to location of store
Market	: Market to where the order is delivered : Africa , Europe , LATAM , Pacific Asia , USCA
Order City	: Destination city of the order
Order Country	: Destination country of the order
Order Customer Id	: Customer order code
order date (DateOrders)	: Date on which the order is made
Order Id	: Order code
Order Item Cardprod Id	: Product code generated through the RFID reader
Order Item Discount	: Order item discount value
Order Item Discount Rate	: Order item discount percentage
Order Item Id	: Order item code
Order Item Product Price	: Price of products without discount
Order Item Profit Ratio	: Order Item Profit Ratio
Order Item Quantity	: Number of products per order
Sales	: Value in sales
Order Item Total	: Total amount per order
Order Profit Per Order	: Order Profit Per Order
Order Region	: Region of the world where the order is delivered : Southeast Asia ,South Asia ,Oceania ,Eastern Asia, West Asia , West of USA , US Center , West Africa, Central Africa ,North Africa ,Western Europe ,Northern , Caribbean , South America ,East Africa ,Southern Europe , East of USA ,Canada ,Southern Africa , Central Asia , Europe , Central America, Eastern Europe , South of USA
Order State	: State of the region where the order is delivered
Order Status	: Order Status : COMPLETE , PENDING , CLOSED , PENDING_PAYMENT ,CANCELED , PROCESSING ,SUSPECTED_FRAUD ,ON_HOLD ,PAYMENT_REVIEW
Product Card Id	: Product code
Product Category Id	: Product category code
Product Description	: Product Description
Product Image	: Link of visit and purchase of the product
Product Name	: Product Name
Product Price	: Product Price
Product Status	: Status of the product stock :If it is 1 not available , 0 the product is available
Shipping date (DateOrders)	: Exact date and time of shipment
Shipping Mode	: The following shipping modes are presented : Standard Class , First Class , Second Class , Same Day

2. Input Data

DataCo SMART SUPPLY CHAIN, one of the enormous logistic company moving in 3PL help many companies to fulfill their demands all world as DataCo’s Client.

Make sure our data placed in the same folder our R project data (Rdproj). in this case we will use first analyst in first file DataCoSupplyChainDataset.csv assign as df

# Data Input and Checking Data
df <- read.csv('Dataset/DataCoSupplyChainDataset.csv')

Input Data is Done

2.1 Data Inspection

In this sub section, I will see how does the data look like, we can see by this code

#Inspecting Data
head(df)

2.2 table structure data

2.2.1 Check df’s table structure data

#We can use this code to know specific of structure data
str(df)

#> 'data.frame':    180519 obs. of  53 variables:
#>  $ Type                         : chr  "DEBIT" "TRANSFER" "CASH" "DEBIT" ...
#>  $ Days.for.shipping..real.     : int  3 5 4 3 2 6 2 2 3 2 ...
#>  $ Days.for.shipment..scheduled.: int  4 4 4 4 4 4 1 1 2 1 ...
#>  $ Benefit.per.order            : num  91.2 -249.1 -247.8 22.9 134.2 ...
#>  $ Sales.per.customer           : num  315 311 310 305 298 ...
#>  $ Delivery.Status              : chr  "Advance shipping" "Late delivery" "Shipping on time" "Advance shipping" ...
#>  $ Late_delivery_risk           : int  0 1 0 0 0 0 1 1 1 1 ...
#>  $ Category.Id                  : int  73 73 73 73 73 73 73 73 73 73 ...
#>  $ Category.Name                : chr  "Sporting Goods" "Sporting Goods" "Sporting Goods" "Sporting Goods" ...
#>  $ Customer.City                : chr  "Caguas" "Caguas" "San Jose" "Los Angeles" ...
#>  $ Customer.Country             : chr  "Puerto Rico" "Puerto Rico" "EE. UU." "EE. UU." ...
#>  $ Customer.Email               : chr  "XXXXXXXXX" "XXXXXXXXX" "XXXXXXXXX" "XXXXXXXXX" ...
#>  $ Customer.Fname               : chr  "Cally" "Irene" "Gillian" "Tana" ...
#>  $ Customer.Id                  : int  20755 19492 19491 19490 19489 19488 19487 19486 19485 19484 ...
#>  $ Customer.Lname               : chr  "Holloway" "Luna" "Maldonado" "Tate" ...
#>  $ Customer.Password            : chr  "XXXXXXXXX" "XXXXXXXXX" "XXXXXXXXX" "XXXXXXXXX" ...
#>  $ Customer.Segment             : chr  "Consumer" "Consumer" "Consumer" "Home Office" ...
#>  $ Customer.State               : chr  "PR" "PR" "CA" "CA" ...
#>  $ Customer.Street              : chr  "5365 Noble Nectar Island" "2679 Rustic Loop" "8510 Round Bear Gate" "3200 Amber Bend" ...
#>  $ Customer.Zipcode             : int  725 725 95125 90027 725 14150 725 33162 725 94583 ...
#>  $ Department.Id                : int  2 2 2 2 2 2 2 2 2 2 ...
#>  $ Department.Name              : chr  "Fitness" "Fitness" "Fitness" "Fitness" ...
#>  $ Latitude                     : num  18.3 18.3 37.3 34.1 18.3 ...
#>  $ Longitude                    : num  -66 -66 -122 -118 -66 ...
#>  $ Market                       : chr  "Pacific Asia" "Pacific Asia" "Pacific Asia" "Pacific Asia" ...
#>  $ Order.City                   : chr  "Bekasi" "Bikaner" "Bikaner" "Townsville" ...
#>  $ Order.Country                : chr  "Indonesia" "India" "India" "Australia" ...
#>  $ Order.Customer.Id            : int  20755 19492 19491 19490 19489 19488 19487 19486 19485 19484 ...
#>  $ order.date..DateOrders.      : chr  "1/31/2018 22:56" "1/13/2018 12:27" "1/13/2018 12:06" "1/13/2018 11:45" ...
#>  $ Order.Id                     : int  77202 75939 75938 75937 75936 75935 75934 75933 75932 75931 ...
#>  $ Order.Item.Cardprod.Id       : int  1360 1360 1360 1360 1360 1360 1360 1360 1360 1360 ...
#>  $ Order.Item.Discount          : num  13.1 16.4 18 22.9 29.5 ...
#>  $ Order.Item.Discount.Rate     : num  0.04 0.05 0.06 0.07 0.09 ...
#>  $ Order.Item.Id                : int  180517 179254 179253 179252 179251 179250 179249 179248 179247 179246 ...
#>  $ Order.Item.Product.Price     : num  328 328 328 328 328 ...
#>  $ Order.Item.Profit.Ratio      : num  0.29 -0.8 -0.8 0.08 0.45 ...
#>  $ Order.Item.Quantity          : int  1 1 1 1 1 1 1 1 1 1 ...
#>  $ Sales                        : num  328 328 328 328 328 ...
#>  $ Order.Item.Total             : num  315 311 310 305 298 ...
#>  $ Order.Profit.Per.Order       : num  91.2 -249.1 -247.8 22.9 134.2 ...
#>  $ Order.Region                 : chr  "Southeast Asia" "South Asia" "South Asia" "Oceania" ...
#>  $ Order.State                  : chr  "Java Occidental" "Rajast\xe1n" "Rajast\xe1n" "Queensland" ...
#>  $ Order.Status                 : chr  "COMPLETE" "PENDING" "CLOSED" "COMPLETE" ...
#>  $ Order.Zipcode                : int  NA NA NA NA NA NA NA NA NA NA ...
#>  $ Product.Card.Id              : int  1360 1360 1360 1360 1360 1360 1360 1360 1360 1360 ...
#>  $ Product.Category.Id          : int  73 73 73 73 73 73 73 73 73 73 ...
#>  $ Product.Description          : logi  NA NA NA NA NA NA ...
#>  $ Product.Image                : chr  "http://images.acmesports.sports/Smart+watch " "http://images.acmesports.sports/Smart+watch " "http://images.acmesports.sports/Smart+watch " "http://images.acmesports.sports/Smart+watch " ...
#>  $ Product.Name                 : chr  "Smart watch " "Smart watch " "Smart watch " "Smart watch " ...
#>  $ Product.Price                : num  328 328 328 328 328 ...
#>  $ Product.Status               : int  0 0 0 0 0 0 0 0 0 0 ...
#>  $ shipping.date..DateOrders.   : chr  "2/3/2018 22:56" "1/18/2018 12:27" "1/17/2018 12:06" "1/16/2018 11:45" ...
#>  $ Shipping.Mode                : chr  "Standard Class" "Standard Class" "Standard Class" "Standard Class" ...

📌 From this result, we find some of data type not in the correct type. we need to convert it into correct type (data coertion), there’s several type data should be corrected

2.2.2 Type of Data

Here the list of data should be corrected

• Type chr -> fct
• Delivery.Status chr -> fct
• Category.Id int -> fct
• Category.Name chr -> fct
• Customer.City chr -> fct
• Customer.Country chr -> fct
• Department.Id int -> fct
• Department.Name chr -> fct
• Market chr -> fct
• Order.City chr -> fct
• Order.Country chr -> fct + USCII & UTC8
• Order.Customer.Id int -> fct
• order.date..DateOrders chr -> datetime (using lubridate)
• Order.Region chr -> fct
• Order.State chr -> fct
• Order.Status chr -> fct
• Product.Name chr -> fct
• shipping.date..DateOrders. chr -> datetime (using lubridate)
• Shipping.Mode chr -> fct

# explicit coercion
df[,c("Type", "Delivery.Status", "Category.Id","Category.Name","Customer.Segment","Customer.State","Customer.Street","Customer.Zipcode","Customer.City","Customer.Country","Department.Id","Department.Name","Market","Order.City","Order.Country","Customer.Id","Order.Region","Order.State","Order.Status","Product.Name","Shipping.Mode")]<-lapply(df[,c("Type","Delivery.Status","Category.Id","Category.Name","Customer.Segment","Customer.State","Customer.Street","Customer.Zipcode","Customer.City","Customer.Country","Department.Id","Department.Name","Market","Order.City","Order.Country","Customer.Id","Order.Region","Order.State","Order.Status","Product.Name","Shipping.Mode")],as.factor)

# Check again df's table structure data
# now in the column with the data type factor the frequency appears
str(df)

#> 'data.frame':    180519 obs. of  53 variables:
#>  $ Type                         : Factor w/ 4 levels "CASH","DEBIT",..: 2 4 1 2 3 4 2 4 1 1 ...
#>  $ Days.for.shipping..real.     : int  3 5 4 3 2 6 2 2 3 2 ...
#>  $ Days.for.shipment..scheduled.: int  4 4 4 4 4 4 1 1 2 1 ...
#>  $ Benefit.per.order            : num  91.2 -249.1 -247.8 22.9 134.2 ...
#>  $ Sales.per.customer           : num  315 311 310 305 298 ...
#>  $ Delivery.Status              : Factor w/ 4 levels "Advance shipping",..: 1 2 4 1 1 3 2 2 2 2 ...
#>  $ Late_delivery_risk           : int  0 1 0 0 0 0 1 1 1 1 ...
#>  $ Category.Id                  : Factor w/ 51 levels "2","3","4","5",..: 48 48 48 48 48 48 48 48 48 48 ...
#>  $ Category.Name                : Factor w/ 50 levels "Accessories",..: 41 41 41 41 41 41 41 41 41 41 ...
#>  $ Customer.City                : Factor w/ 563 levels "Aguadilla","Alameda",..: 67 67 453 286 67 506 67 319 67 458 ...
#>  $ Customer.Country             : Factor w/ 2 levels "EE. UU.","Puerto Rico": 2 2 1 1 2 1 2 1 2 1 ...
#>  $ Customer.Email               : chr  "XXXXXXXXX" "XXXXXXXXX" "XXXXXXXXX" "XXXXXXXXX" ...
#>  $ Customer.Fname               : chr  "Cally" "Irene" "Gillian" "Tana" ...
#>  $ Customer.Id                  : Factor w/ 20652 levels "1","2","3","4",..: 20650 19387 19386 19385 19384 19383 19382 19381 19380 19379 ...
#>  $ Customer.Lname               : chr  "Holloway" "Luna" "Maldonado" "Tate" ...
#>  $ Customer.Password            : chr  "XXXXXXXXX" "XXXXXXXXX" "XXXXXXXXX" "XXXXXXXXX" ...
#>  $ Customer.Segment             : Factor w/ 3 levels "Consumer","Corporate",..: 1 1 1 3 2 1 3 2 2 2 ...
#>  $ Customer.State               : Factor w/ 46 levels "91732","95758",..: 37 37 6 6 37 32 37 11 37 6 ...
#>  $ Customer.Street              : Factor w/ 7458 levels "1 Bright Manor",..: 3684 1401 6218 1804 6346 967 767 5489 910 6741 ...
#>  $ Customer.Zipcode             : Factor w/ 995 levels "603","612","674",..: 8 8 942 721 8 174 8 341 8 923 ...
#>  $ Department.Id                : Factor w/ 11 levels "2","3","4","5",..: 1 1 1 1 1 1 1 1 1 1 ...
#>  $ Department.Name              : Factor w/ 11 levels "Apparel","Book Shop",..: 5 5 5 5 5 5 5 5 5 5 ...
#>  $ Latitude                     : num  18.3 18.3 37.3 34.1 18.3 ...
#>  $ Longitude                    : num  -66 -66 -122 -118 -66 ...
#>  $ Market                       : Factor w/ 5 levels "Africa","Europe",..: 4 4 4 4 4 4 4 4 4 4 ...
#>  $ Order.City                   : Factor w/ 3597 levels "Zany\xe1n","S\xe3o Jos\xe9 dos Campos",..: 491 549 549 3190 3190 3172 1335 1335 1335 1335 ...
#>  $ Order.Country                : Factor w/ 164 levels "Camer\xfan","Ben\xedn",..: 90 89 89 41 41 41 57 57 57 57 ...
#>  $ Order.Customer.Id            : int  20755 19492 19491 19490 19489 19488 19487 19486 19485 19484 ...
#>  $ order.date..DateOrders.      : chr  "1/31/2018 22:56" "1/13/2018 12:27" "1/13/2018 12:06" "1/13/2018 11:45" ...
#>  $ Order.Id                     : int  77202 75939 75938 75937 75936 75935 75934 75933 75932 75931 ...
#>  $ Order.Item.Cardprod.Id       : int  1360 1360 1360 1360 1360 1360 1360 1360 1360 1360 ...
#>  $ Order.Item.Discount          : num  13.1 16.4 18 22.9 29.5 ...
#>  $ Order.Item.Discount.Rate     : num  0.04 0.05 0.06 0.07 0.09 ...
#>  $ Order.Item.Id                : int  180517 179254 179253 179252 179251 179250 179249 179248 179247 179246 ...
#>  $ Order.Item.Product.Price     : num  328 328 328 328 328 ...
#>  $ Order.Item.Profit.Ratio      : num  0.29 -0.8 -0.8 0.08 0.45 ...
#>  $ Order.Item.Quantity          : int  1 1 1 1 1 1 1 1 1 1 ...
#>  $ Sales                        : num  328 328 328 328 328 ...
#>  $ Order.Item.Total             : num  315 311 310 305 298 ...
#>  $ Order.Profit.Per.Order       : num  91.2 -249.1 -247.8 22.9 134.2 ...
#>  $ Order.Region                 : Factor w/ 23 levels "Canada","Caribbean",..: 16 14 14 12 12 12 8 8 8 8 ...
#>  $ Order.State                  : Factor w/ 1089 levels "B\xedo-B\xedo",..: 500 62 62 800 800 800 423 423 423 423 ...
#>  $ Order.Status                 : Factor w/ 9 levels "CANCELED","CLOSED",..: 3 6 2 3 7 1 3 8 2 2 ...
#>  $ Order.Zipcode                : int  NA NA NA NA NA NA NA NA NA NA ...
#>  $ Product.Card.Id              : int  1360 1360 1360 1360 1360 1360 1360 1360 1360 1360 ...
#>  $ Product.Category.Id          : int  73 73 73 73 73 73 73 73 73 73 ...
#>  $ Product.Description          : logi  NA NA NA NA NA NA ...
#>  $ Product.Image                : chr  "http://images.acmesports.sports/Smart+watch " "http://images.acmesports.sports/Smart+watch " "http://images.acmesports.sports/Smart+watch " "http://images.acmesports.sports/Smart+watch " ...
#>  $ Product.Name                 : Factor w/ 118 levels "adidas Brazuca 2014 Official Match Ball",..: 83 83 83 83 83 83 83 83 83 83 ...
#>  $ Product.Price                : num  328 328 328 328 328 ...
#>  $ Product.Status               : int  0 0 0 0 0 0 0 0 0 0 ...
#>  $ shipping.date..DateOrders.   : chr  "2/3/2018 22:56" "1/18/2018 12:27" "1/17/2018 12:06" "1/16/2018 11:45" ...
#>  $ Shipping.Mode                : Factor w/ 4 levels "First Class",..: 4 4 4 4 4 4 1 1 3 1 ...

#Applied Library Lubridate to change typical column date to correct form
library(lubridate)
df$order.date..DateOrders. <- mdy_hm(df$order.date..DateOrders.)
df$shipping.date..DateOrders. <- mdy_hm(df$shipping.date..DateOrders.)

str(df$order.date..DateOrders.)

#>  POSIXct[1:180519], format: "2018-01-31 22:56:00" "2018-01-13 12:27:00" "2018-01-13 12:06:00" ...

str(df$shipping.date..DateOrders.)

#>  POSIXct[1:180519], format: "2018-02-03 22:56:00" "2018-01-18 12:27:00" "2018-01-17 12:06:00" ...

#Change date time to EST Zone
df$order.date..DateOrders. <-  with_tz(df$order.date..DateOrders., tz="EST")
df$shipping.date..DateOrders. <- with_tz(df$shipping.date..DateOrders., tz="EST")
str(df$order.date..DateOrders.)

#>  POSIXct[1:180519], format: "2018-01-31 17:56:00" "2018-01-13 07:27:00" "2018-01-13 07:06:00" ...

str(df$shipping.date..DateOrders.)

#>  POSIXct[1:180519], format: "2018-02-03 17:56:00" "2018-01-18 07:27:00" "2018-01-17 07:06:00" ...

Name of Countries not in correct as usual, we need to change a correct form

#Due to our character names of countries is "non-ASCII" we are using iconv with id ASCII and UTF-8
df$Order.Country <- iconv(df$Order.Country,"ASCII","UTF-8") 

2.2.3 Correct coercion

str(df)

#> 'data.frame':    180519 obs. of  53 variables:
#>  $ Type                         : Factor w/ 4 levels "CASH","DEBIT",..: 2 4 1 2 3 4 2 4 1 1 ...
#>  $ Days.for.shipping..real.     : int  3 5 4 3 2 6 2 2 3 2 ...
#>  $ Days.for.shipment..scheduled.: int  4 4 4 4 4 4 1 1 2 1 ...
#>  $ Benefit.per.order            : num  91.2 -249.1 -247.8 22.9 134.2 ...
#>  $ Sales.per.customer           : num  315 311 310 305 298 ...
#>  $ Delivery.Status              : Factor w/ 4 levels "Advance shipping",..: 1 2 4 1 1 3 2 2 2 2 ...
#>  $ Late_delivery_risk           : int  0 1 0 0 0 0 1 1 1 1 ...
#>  $ Category.Id                  : Factor w/ 51 levels "2","3","4","5",..: 48 48 48 48 48 48 48 48 48 48 ...
#>  $ Category.Name                : Factor w/ 50 levels "Accessories",..: 41 41 41 41 41 41 41 41 41 41 ...
#>  $ Customer.City                : Factor w/ 563 levels "Aguadilla","Alameda",..: 67 67 453 286 67 506 67 319 67 458 ...
#>  $ Customer.Country             : Factor w/ 2 levels "EE. UU.","Puerto Rico": 2 2 1 1 2 1 2 1 2 1 ...
#>  $ Customer.Email               : chr  "XXXXXXXXX" "XXXXXXXXX" "XXXXXXXXX" "XXXXXXXXX" ...
#>  $ Customer.Fname               : chr  "Cally" "Irene" "Gillian" "Tana" ...
#>  $ Customer.Id                  : Factor w/ 20652 levels "1","2","3","4",..: 20650 19387 19386 19385 19384 19383 19382 19381 19380 19379 ...
#>  $ Customer.Lname               : chr  "Holloway" "Luna" "Maldonado" "Tate" ...
#>  $ Customer.Password            : chr  "XXXXXXXXX" "XXXXXXXXX" "XXXXXXXXX" "XXXXXXXXX" ...
#>  $ Customer.Segment             : Factor w/ 3 levels "Consumer","Corporate",..: 1 1 1 3 2 1 3 2 2 2 ...
#>  $ Customer.State               : Factor w/ 46 levels "91732","95758",..: 37 37 6 6 37 32 37 11 37 6 ...
#>  $ Customer.Street              : Factor w/ 7458 levels "1 Bright Manor",..: 3684 1401 6218 1804 6346 967 767 5489 910 6741 ...
#>  $ Customer.Zipcode             : Factor w/ 995 levels "603","612","674",..: 8 8 942 721 8 174 8 341 8 923 ...
#>  $ Department.Id                : Factor w/ 11 levels "2","3","4","5",..: 1 1 1 1 1 1 1 1 1 1 ...
#>  $ Department.Name              : Factor w/ 11 levels "Apparel","Book Shop",..: 5 5 5 5 5 5 5 5 5 5 ...
#>  $ Latitude                     : num  18.3 18.3 37.3 34.1 18.3 ...
#>  $ Longitude                    : num  -66 -66 -122 -118 -66 ...
#>  $ Market                       : Factor w/ 5 levels "Africa","Europe",..: 4 4 4 4 4 4 4 4 4 4 ...
#>  $ Order.City                   : Factor w/ 3597 levels "Zany\xe1n","S\xe3o Jos\xe9 dos Campos",..: 491 549 549 3190 3190 3172 1335 1335 1335 1335 ...
#>  $ Order.Country                : chr  "Indonesia" "India" "India" "Australia" ...
#>  $ Order.Customer.Id            : int  20755 19492 19491 19490 19489 19488 19487 19486 19485 19484 ...
#>  $ order.date..DateOrders.      : POSIXct, format: "2018-01-31 17:56:00" "2018-01-13 07:27:00" ...
#>  $ Order.Id                     : int  77202 75939 75938 75937 75936 75935 75934 75933 75932 75931 ...
#>  $ Order.Item.Cardprod.Id       : int  1360 1360 1360 1360 1360 1360 1360 1360 1360 1360 ...
#>  $ Order.Item.Discount          : num  13.1 16.4 18 22.9 29.5 ...
#>  $ Order.Item.Discount.Rate     : num  0.04 0.05 0.06 0.07 0.09 ...
#>  $ Order.Item.Id                : int  180517 179254 179253 179252 179251 179250 179249 179248 179247 179246 ...
#>  $ Order.Item.Product.Price     : num  328 328 328 328 328 ...
#>  $ Order.Item.Profit.Ratio      : num  0.29 -0.8 -0.8 0.08 0.45 ...
#>  $ Order.Item.Quantity          : int  1 1 1 1 1 1 1 1 1 1 ...
#>  $ Sales                        : num  328 328 328 328 328 ...
#>  $ Order.Item.Total             : num  315 311 310 305 298 ...
#>  $ Order.Profit.Per.Order       : num  91.2 -249.1 -247.8 22.9 134.2 ...
#>  $ Order.Region                 : Factor w/ 23 levels "Canada","Caribbean",..: 16 14 14 12 12 12 8 8 8 8 ...
#>  $ Order.State                  : Factor w/ 1089 levels "B\xedo-B\xedo",..: 500 62 62 800 800 800 423 423 423 423 ...
#>  $ Order.Status                 : Factor w/ 9 levels "CANCELED","CLOSED",..: 3 6 2 3 7 1 3 8 2 2 ...
#>  $ Order.Zipcode                : int  NA NA NA NA NA NA NA NA NA NA ...
#>  $ Product.Card.Id              : int  1360 1360 1360 1360 1360 1360 1360 1360 1360 1360 ...
#>  $ Product.Category.Id          : int  73 73 73 73 73 73 73 73 73 73 ...
#>  $ Product.Description          : logi  NA NA NA NA NA NA ...
#>  $ Product.Image                : chr  "http://images.acmesports.sports/Smart+watch " "http://images.acmesports.sports/Smart+watch " "http://images.acmesports.sports/Smart+watch " "http://images.acmesports.sports/Smart+watch " ...
#>  $ Product.Name                 : Factor w/ 118 levels "adidas Brazuca 2014 Official Match Ball",..: 83 83 83 83 83 83 83 83 83 83 ...
#>  $ Product.Price                : num  328 328 328 328 328 ...
#>  $ Product.Status               : int  0 0 0 0 0 0 0 0 0 0 ...
#>  $ shipping.date..DateOrders.   : POSIXct, format: "2018-02-03 17:56:00" "2018-01-18 07:27:00" ...
#>  $ Shipping.Mode                : Factor w/ 4 levels "First Class",..: 4 4 4 4 4 4 1 1 3 1 ...

📌 all data is ready to managed.

3. Transaction DataCo Around the World

DataCo SMART SUPPLY CHAIN, is the one of the enormous logistic company moving in 3PL help many companies to fulfill their demands all world as DataCo’s Client.

3.1 Business Case 1

We want to know based on data-set, how many transaction has been handled by DataCo

# Using Dimension of Table
nrow(df)

#> [1] 180519

📈Insight :

• There is 180,519 transactions has been handled by DataCo in 2014-2018

3.2 Business Case 2

Summary table of df.

summary(df)

#>        Type       Days.for.shipping..real. Days.for.shipment..scheduled.
#>  CASH    :19616   Min.   :0.000            Min.   :0.000                
#>  DEBIT   :69295   1st Qu.:2.000            1st Qu.:2.000                
#>  PAYMENT :41725   Median :3.000            Median :4.000                
#>  TRANSFER:49883   Mean   :3.498            Mean   :2.932                
#>                   3rd Qu.:5.000            3rd Qu.:4.000                
#>                   Max.   :6.000            Max.   :4.000                
#>                                                                         
#>  Benefit.per.order  Sales.per.customer          Delivery.Status 
#>  Min.   :-4274.98   Min.   :   7.49    Advance shipping :41592  
#>  1st Qu.:    7.00   1st Qu.: 104.38    Late delivery    :98977  
#>  Median :   31.52   Median : 163.99    Shipping canceled: 7754  
#>  Mean   :   21.98   Mean   : 183.11    Shipping on time :32196  
#>  3rd Qu.:   64.80   3rd Qu.: 247.40                             
#>  Max.   :  911.80   Max.   :1939.99                             
#>                                                                 
#>  Late_delivery_risk  Category.Id                 Category.Name  
#>  Min.   :0.0000     17     :24551   Cleats              :24551  
#>  1st Qu.:0.0000     18     :22246   Men's Footwear      :22246  
#>  Median :1.0000     24     :21035   Women's Apparel     :21035  
#>  Mean   :0.5483     46     :19298   Indoor/Outdoor Games:19298  
#>  3rd Qu.:1.0000     45     :17325   Fishing             :17325  
#>  Max.   :1.0000     48     :15540   Water Sports        :15540  
#>                     (Other):60524   (Other)             :60524  
#>       Customer.City      Customer.Country  Customer.Email    
#>  Caguas      :66770   EE. UU.    :111146   Length:180519     
#>  Chicago     : 3885   Puerto Rico: 69373   Class :character  
#>  Los Angeles : 3417                        Mode  :character  
#>  Brooklyn    : 3412                                          
#>  New York    : 1816                                          
#>  Philadelphia: 1577                                          
#>  (Other)     :99642                                          
#>  Customer.Fname      Customer.Id     Customer.Lname     Customer.Password 
#>  Length:180519      5654   :    47   Length:180519      Length:180519     
#>  Class :character   5004   :    45   Class :character   Class :character  
#>  Mode  :character   10591  :    45   Mode  :character   Mode  :character  
#>                     3708   :    44                                        
#>                     5715   :    44                                        
#>                     9371   :    44                                        
#>                     (Other):180250                                        
#>     Customer.Segment Customer.State                  Customer.Street  
#>  Consumer   :93504   PR     :69373   9126 Wishing Expressway :   122  
#>  Corporate  :54789   CA     :29223   4388 Burning Goose Ridge:   117  
#>  Home Office:32226   NY     :11327   4720 Noble Hills Wynd   :   116  
#>                      TX     : 9103   2878 Hazy Wagon  Thicket:   113  
#>                      IL     : 7631   398 Emerald Grove       :   109  
#>                      FL     : 5456   3593 Blue Brook Acres   :   108  
#>                      (Other):48406   (Other)                 :179834  
#>  Customer.Zipcode Department.Id   Department.Name     Latitude     
#>  725    : 66770   7      :66861   Fan Shop:66861   Min.   :-33.94  
#>  921    :   337   4      :48998   Apparel :48998   1st Qu.: 18.27  
#>  23455  :   334   5      :33220   Golf    :33220   Median : 33.14  
#>  957    :   297   3      :14525   Footwear:14525   Mean   : 29.72  
#>  79109  :   292   6      : 9686   Outdoors: 9686   3rd Qu.: 39.28  
#>  (Other):112486   2      : 2479   Fitness : 2479   Max.   : 48.78  
#>  NA's   :     3   (Other): 4750   (Other) : 4750                   
#>    Longitude                Market              Order.City    
#>  Min.   :-158.03   Africa      :11614   Santo Domingo:  2211  
#>  1st Qu.: -98.45   Europe      :50252   New York City:  2202  
#>  Median : -76.85   LATAM       :51594   Los Angeles  :  1845  
#>  Mean   : -84.92   Pacific Asia:41260   Tegucigalpa  :  1783  
#>  3rd Qu.: -66.37   USCA        :25799   Managua      :  1682  
#>  Max.   : 115.26                        Mexico City  :  1484  
#>                                         (Other)      :169312  
#>  Order.Country      Order.Customer.Id order.date..DateOrders.         
#>  Length:180519      Min.   :    1     Min.   :2014-12-31 19:00:00.00  
#>  Class :character   1st Qu.: 3258     1st Qu.:2015-09-21 08:49:00.00  
#>  Mode  :character   Median : 6457     Median :2016-06-11 08:06:00.00  
#>                     Mean   : 6691     Mean   :2016-06-12 12:47:04.67  
#>                     3rd Qu.: 9779     3rd Qu.:2017-03-01 03:42:00.00  
#>                     Max.   :20757     Max.   :2018-01-31 18:38:00.00  
#>                                                                       
#>     Order.Id     Order.Item.Cardprod.Id Order.Item.Discount
#>  Min.   :    1   Min.   :  19.0         Min.   :  0.00     
#>  1st Qu.:18057   1st Qu.: 403.0         1st Qu.:  5.40     
#>  Median :36140   Median : 627.0         Median : 14.00     
#>  Mean   :36222   Mean   : 692.5         Mean   : 20.66     
#>  3rd Qu.:54144   3rd Qu.:1004.0         3rd Qu.: 29.99     
#>  Max.   :77204   Max.   :1363.0         Max.   :500.00     
#>                                                            
#>  Order.Item.Discount.Rate Order.Item.Id    Order.Item.Product.Price
#>  Min.   :0.0000           Min.   :     1   Min.   :   9.99         
#>  1st Qu.:0.0400           1st Qu.: 45131   1st Qu.:  50.00         
#>  Median :0.1000           Median : 90260   Median :  59.99         
#>  Mean   :0.1017           Mean   : 90260   Mean   : 141.23         
#>  3rd Qu.:0.1600           3rd Qu.:135390   3rd Qu.: 199.99         
#>  Max.   :0.2500           Max.   :180519   Max.   :1999.99         
#>                                                                    
#>  Order.Item.Profit.Ratio Order.Item.Quantity     Sales        
#>  Min.   :-2.7500         Min.   :1.000       Min.   :   9.99  
#>  1st Qu.: 0.0800         1st Qu.:1.000       1st Qu.: 119.98  
#>  Median : 0.2700         Median :1.000       Median : 199.92  
#>  Mean   : 0.1206         Mean   :2.128       Mean   : 203.77  
#>  3rd Qu.: 0.3600         3rd Qu.:3.000       3rd Qu.: 299.95  
#>  Max.   : 0.5000         Max.   :5.000       Max.   :1999.99  
#>                                                               
#>  Order.Item.Total  Order.Profit.Per.Order          Order.Region  
#>  Min.   :   7.49   Min.   :-4274.98       Central America:28341  
#>  1st Qu.: 104.38   1st Qu.:    7.00       Western Europe :27109  
#>  Median : 163.99   Median :   31.52       South America  :14935  
#>  Mean   : 183.11   Mean   :   21.98       Oceania        :10148  
#>  3rd Qu.: 247.40   3rd Qu.:   64.80       Northern Europe: 9792  
#>  Max.   :1939.99   Max.   :  911.80       Southeast Asia : 9539  
#>                                           (Other)        :80655  
#>                       Order.State              Order.Status   Order.Zipcode   
#>  Inglaterra                 :  6722   COMPLETE       :59491   Min.   : 1040   
#>  California                 :  4966   PENDING_PAYMENT:39832   1st Qu.:23464   
#>  Isla de Francia            :  4580   PROCESSING     :21902   Median :59405   
#>  Renania del Norte-Westfalia:  3303   PENDING        :20227   Mean   :55426   
#>  San Salvador               :  3055   CLOSED         :19616   3rd Qu.:90008   
#>  Nueva York                 :  2753   ON_HOLD        : 9804   Max.   :99301   
#>  (Other)                    :155140   (Other)        : 9647   NA's   :155679  
#>  Product.Card.Id  Product.Category.Id Product.Description Product.Image     
#>  Min.   :  19.0   Min.   : 2.00       Mode:logical        Length:180519     
#>  1st Qu.: 403.0   1st Qu.:18.00       NA's:180519         Class :character  
#>  Median : 627.0   Median :29.00                           Mode  :character  
#>  Mean   : 692.5   Mean   :31.85                                             
#>  3rd Qu.:1004.0   3rd Qu.:45.00                                             
#>  Max.   :1363.0   Max.   :76.00                                             
#>                                                                             
#>                                     Product.Name   Product.Price    
#>  Perfect Fitness Perfect Rip Deck         :24515   Min.   :   9.99  
#>  Nike Men's CJ Elite 2 TD Football Cleat  :22246   1st Qu.:  50.00  
#>  Nike Men's Dri-FIT Victory Golf Polo     :21035   Median :  59.99  
#>  O'Brien Men's Neoprene Life Vest         :19298   Mean   : 141.23  
#>  Field & Stream Sportsman 16 Gun Fire Safe:17325   3rd Qu.: 199.99  
#>  Pelican Sunstream 100 Kayak              :15500   Max.   :1999.99  
#>  (Other)                                  :60600                    
#>  Product.Status shipping.date..DateOrders.             Shipping.Mode   
#>  Min.   :0      Min.   :2015-01-02 19:00:00.0   First Class   : 27814  
#>  1st Qu.:0      1st Qu.:2015-09-25 01:59:00.0   Same Day      :  9737  
#>  Median :0      Median :2016-06-15 03:32:00.0   Second Class  : 35216  
#>  Mean   :0      Mean   :2016-06-16 00:45:23.2   Standard Class:107752  
#>  3rd Qu.:0      3rd Qu.:2017-03-04 16:29:00.0                          
#>  Max.   :0      Max.   :2018-02-06 17:14:00.0                          
#> 

Basic Thinking Way :

• DataCo wants to know about business performance in past 4 years (2014-2018)
• Data Analyst should know what’s behind the number of the summary table of dataset

For this reports, I’m trying to analyze 180,519 transactions in DataCo which distributed in 164 different countries in 5 markets and 23 Regions. the insight will be divided into 3 types :

📌 General Knowledge Insight of business process :

• Based on the summary, we can find the highest market in Latam, Europe, Pacific Asia, USCA, Africa, breakdown to the highest order state will be in Central America, Western Europe, South America, Oceania, Northern Europe, Southeast Asia.
• we can find that Estados Unidos(United States) has the most highest order destination handled by Data Co since 2014 - 2018 and followed by France, Mexico, Alemania, Australia, and Brazil.

📌 Negative Info of business process :

• Delivery Status said Late Delivery is the most highest score followed by Advance Shipping, Shipping on-time, shipping canceled. It will be discussed more in next topic below.
• Data Analyst seems suspicious about trend of sales with profit.

📌 Search for additional information in the data-set :

• We will breakdown Indonesia market progress sales, delivery status, product’s segment until customer segmentation.
• We will find more information from data-set to know well detail of late delivery.

4. General Knowledge Insight of business process

In this general knowledge, we want to focus every market country and their items

4.1 The Most Order Market , Region and Country

4.1.1 The Most Order Market

#Calculate Market for frequent
a <-as.data.frame(table(df$Market))

library(dplyr)
library(glue)
library(plotly)

fig <- plot_ly(a, labels = ~Var1, values = ~Freq, type = 'pie',
        textposition = 'inside',
        textinfo = 'label+percent',
        insidetextfont = list(color = '#FFFFFF'),
        hoverinfo = 'text',
        text = ~paste('Order', Freq),
        marker = list(colors = "",
                      line = list(color = '#FFFFFF', width = 1)),
                      #The 'pull' attribute can also be used to create space between the sectors
        showlegend = FALSE)
fig <- fig %>% layout(title = 'The Most Order Market',
         xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
         yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))

fig

📈Insight :

• From database df, we all see the most order market from LATAM in 28,6%, Europe in 27,8%, Pacific Asia in 22.9%, USCA 14.3$, Africa 6.43%.

4.1.2 Subplot The Most Order Region and Country

#Calculate Region for frequent (Big 5 Regions)
b <- as.data.frame(table(df$Order.Region))
b1 <- head(b[order(b$Freq, decreasing=T),],5)
b1 <- b1 %>% 
  mutate(label=glue("Regions : {Var1}
                    Count Order : {Freq}"))
p2 <- ggplot(data= b1 , mapping = aes(y=reorder(Var1,Freq),x=Freq, text=label)) +
  geom_col(aes(fill=Freq)) + scale_fill_gradient(low ='#90e0ef', high ='#415a77') +
  theme(legend.position = 'none' )
p2 <- ggplotly(p2,tooltip="text")

# Make it table as C contains frequency of country (Big 5 Country)
c <- as.data.frame(table(df$Order.Country))
c1<-head(c[order(c$Freq, decreasing=T),],n=5)
c1 <- c1 %>% 
  mutate(label=glue("Country : {Var1}
                    Count Order : {Freq}"))
p3 <- ggplot(data= c1 , mapping = aes(y=reorder(Var1,Freq),x=Freq, text=label)) +
  geom_col(aes(fill=Freq)) + scale_fill_gradient(low ='#90e0ef', high ='#415a77') +
  theme(legend.position = 'none' )
p3 <- ggplotly(p3,tooltip="text")

subplot(p2,p3, nrows=2, margin = 0.08) %>% 
  layout(annotations = list( 
    list(x=0.2 , y = 1.0, text = "Big 5 Regions",xref = "paper",  
    yref = "paper",  
    xanchor = "center",  
    yanchor = "bottom",showarrow = FALSE), 
    list(x = 0.2 , y = 0.45, text = "Big 5 Countries",xref = "paper",  
    yref = "paper",  
    xanchor = "center",  
    yanchor = "bottom",showarrow = FALSE)))

📈Insight :

• From bar chart aboove, we all see the most order big 5 Regions from Central America, Western Europe, South America, Oceania, Northern Europe.
• continuing, in big 5 Countries from United States (Eestado Unidos), France (Francia), Mexico (Mixico), Alemania, Australia

5. Negative Info of business process

In this Positive Info of business process, we want to focus to know well about :

5.1 Data Co’s Profit

We need to know first time of order and the last time, we can use this code

time1 <- sort(df$order.date..DateOrders.)
head(time1,n=1)

#> [1] "2014-12-31 19:00:00 EST"

tail(time1,n=1)

#> [1] "2018-01-31 18:38:00 EST"

📈Insight :

• From database df, we all see the database not full in range of every year, example in 2014, the first day of recording on transaction data co on 31 Dec 2014.

datacoprofit <- df %>% 
  mutate(year = year(order.date..DateOrders.)) %>% 
  mutate(month = month(order.date..DateOrders.)) %>% 
  mutate(month_year = my(paste(month,year, sep = " "))) %>% 
  group_by(month_year) %>% 
  summarise(total=sum(Order.Profit.Per.Order))
datacoprofit

datacoprofit <- datacoprofit %>%
  mutate(label = glue("Period : {month_year}
                      Total Profit per Order : {total}")) %>% 
  ggplot() +
  geom_line(mapping = aes(x=month_year,y = total)) +
  geom_jitter(aes(x=month_year,y = total, text = label))+
  labs(title= "Profit Data Co",y = "Total Profit per Order", x = "Year")

ggplotly(datacoprofit, tooltip="text")

📈Insight :

• Start from August 2017, Profit’s Data Co is going down month by month

5.2 Data Co’s Sales Value (Value in Sales)

datasalesvalue <- df %>% 
  mutate(year = year(order.date..DateOrders.)) %>% 
  mutate(month = month(order.date..DateOrders.)) %>% 
  mutate(month_year = my(paste(month,year, sep = " "))) %>% 
  group_by(month_year) %>% 
  summarise(total=sum(Sales))

datasalesvalue <- datasalesvalue %>% 
  mutate(label = glue("Period : {month_year}
                      Total Profit per Order : {total}")) %>% 
  ggplot() +
  geom_line(mapping = aes(x=month_year,y = total)) +
  geom_jitter(aes(x=month_year,y = total, text = label)) +
  labs(title= "Sales Value Data Co",y = "Total Sales Value", x = "Year")

ggplotly(datasalesvalue, tooltip="text")

📈Insight :

• Start from August 2017, Sales value’s Data Co is going down month by month

5.3 How about the delivery status ?

del_status <- as.data.frame(table(df$Delivery.Status))
del_status <- del_status[order(del_status$Freq, decreasing=T),]

c3 <- del_status %>% 
  mutate(label=glue("Delivery Status : {Var1}
                    Count Order : {Freq}"))
c2 <- ggplot(data= c3 , mapping = aes(y=reorder(Var1,Freq),x=Freq, text=label)) +
  geom_col(aes(fill=Freq)) + scale_fill_gradient(low ='#90e0ef', high ='#415a77') +
  labs(y="Delivery Status")+
  theme(legend.position = 'none')
ggplotly(c2,tooltip="text")

📈Insight :

• Delivery Status about Late Delivery is the most highest score followed by Advance Shipping, Shipping on-time, shipping canceled.

5.4 How about the order status ?

order_status <- as.data.frame(table(df$Order.Status))
order_status <- order_status[order(order_status$Freq, decreasing=T),]

c4 <- order_status %>% 
  mutate(label=glue("Status Payment : {Var1}
                    Count Order : {Freq}"))
c5 <- ggplot(data= c4 , mapping = aes(y=reorder(Var1,Freq),x=Freq, text=label)) +
  geom_col(aes(fill=Freq)) + scale_fill_gradient(low ='#90e0ef', high ='#415a77') +
  labs(y="Status Payment")+
  theme(legend.position = 'none')
ggplotly(c5,tooltip="text")

📈Insight :

• Order Status about Complete is the most highest score followed by Pending Payment, Processing,Pending.

6. How About Indonesia Order ?

6.1 How many Cities has been ordered to Indonesia ?

#Make a new dataset of Indonesia
ind_city <- df %>% 
  group_by(Order.City, Order.Country) %>% 
  filter(Order.Country == "Indonesia") %>% 
  summarise(total = sum(Order.Item.Quantity)) %>%
  mutate(label = glue ( "Order City = {Order.City}
                        Total Order = {total}"
    
  )) %>%
  arrange(-total)
ind_city

ind_city <- ind_city %>% 
  ggplot()+
  geom_col(mapping=aes(y=reorder(Order.City,total),x=total, fill=total, text = label))+
  scale_fill_gradient(low ='#90e0ef', high ='#415a77') +
  labs(y="Order City", x="Total Order")+
  theme(legend.position = 'none')
ggplotly(ind_city, tooltip="text")

Insight :

• There’s 42 Cities in Indonesia has been ordering in Data’co, and the highest order item in Jakarta (Yakarta)

6.2 What’s the product’s name has been ordered to Indonesia ?

ind_prod <- df %>% 
  group_by(Product.Name, Category.Name) %>% 
  summarise(total = sum(Order.Item.Quantity)) %>% 
  arrange(- total)
ind_prod

📈Insight :

• Indonesia’s market is dominated by Cleats, women’s apparel, Indoor/Outdoor Games, etc
• The highest product’s name is Perfect Fitness Perfect Rip Deck,Nike Men’s Dri-FIT Victory Golf Polo, O’Brien Men’s Neoprene Life Vest, etc

6.3 What’s the enermous shipping mode according to value sales and customer segment to Indonesia ?

ind <- df %>% 
  group_by(Order.City, Order.Country) %>% 
  filter(Order.Country == "Indonesia")
ind

plot(xtabs(ind$Sales ~ Customer.Segment + Shipping.Mode , ind))

📈Insight :

• Segment Consumer was choosing standard class as an option, among them consumer is the highest of them

6.4 Manager Logistic want to know how many product and its type of payment have been delaying status in Indonesia ?

ind1 <- df %>% 
  group_by(Order.Country, Type) %>% 
  filter(Order.Country == "Indonesia") %>% 
  filter(Customer.Segment =="Consumer") %>% 
  filter(Type == "DEBIT") %>% 
  filter(Delivery.Status == "Late delivery") %>% 
  count() %>% 
  rename(total = n) %>% 
  summarise(sum = (total))
ind1

plot(xtabs(ind$Sales ~ Customer.Segment + Shipping.Mode + Type , ind))

📈Insight :

• Late delivery was dominated by Consumer who is choosing standard class with payment of Debit with total transaction 468

6.5 How about progress delivery status in every quarter of each year in Indonesia?

datasetship <- df %>%
  mutate(year = year(order.date..DateOrders.)) %>% 
  mutate(quarter = quarter(order.date..DateOrders.)) %>% 
  mutate(year_quarter = as.factor(paste(year, "Q", quarter))) %>% 
  group_by(Shipping.Mode,year_quarter, Delivery.Status,Order.Country) %>% 
  count(Shipping.Mode) %>% 
  rename(total = n) %>% 
  filter(Order.Country == "Indonesia") %>% 
  filter(Delivery.Status == "Late delivery") %>% 
  mutate(label=glue("Shipping Mode : {Shipping.Mode}
                     Total : {total}")) %>% 
  ggplot() +
    geom_line(aes(x = year_quarter, y = total, color = Shipping.Mode, group = Delivery.Status)) +
    geom_point(aes(x = year_quarter, y = total, color = Shipping.Mode, group = Delivery.Status))+
    scale_fill_brewer(palette = "Set3") +
    theme(legend.position = "none") 

ggplotly(datasetship)

📈Insight :

• Exclude of type payment, every progress in quarter to next quarter of the year, it seems get better, but it should double check again because of the performance getting better or the quantity of order from Indonesia getting lower ?

6.6 Progress Order Country Indonesia, getting worse ?

ind2 <- df %>%
  mutate(year = year(order.date..DateOrders.)) %>% 
  mutate(quarter = quarter(order.date..DateOrders.)) %>% 
  mutate(year_quarter = as.factor(paste(year, "Q", quarter))) %>%
  group_by(year_quarter, Order.Country) %>% 
  filter(Order.Country == "Indonesia") %>% 
  mutate(total=sum(Order.Item.Quantity)) %>% 
  count() %>% 
  rename(total = n) %>% 
  mutate(label=glue("Year Quarter : {year_quarter}
                     Total Order : {total}")) %>% 
    ggplot() +
    geom_col(aes(x = year_quarter, y = total)) +
    theme(legend.position = "none") 

ggplotly(ind2)

📈Insight :

• Growth order from Indonesia getting lower from 2017 Q4 to 2018 Q1 , we need to know well whats tha pain point from Indonesia.

7. Conclusion

• DataCo’s has total 180,519 transactions, the highest market in Latam, Europe, Pacific Asia, USCA, Africa, breakdown to the highest order state will be in Central America, Western Europe, South America, Oceania, Northern Europe, Southeast Asia.
• we can find that Estados Unidos(United States) has the most highest order destination handled by Data Co since 2014 - 2018 and followed by France, Mexico, Alemania, Australia, and Brazil.
• Indonesia was in 11st Rank highest order by Dataco’S
• Indonesia’s market is dominated by Cleats, women’s apparel, Indoor/Outdoor Games, etc
• The highest product’s name in Indonesia is Perfect Fitness Perfect Rip Deck,Nike Men’s Dri-FIT Victory Golf Polo, O’Brien Men’s Neoprene Life Vest, etc
• Segment Consumer was choosing standard class as an option, among them consumer is the highest of them but Late delivery was dominated by Consumer who is choosing standard class with payment of Debit with total transaction 468