Untitled

RETAIL SUPERMARKET SALES ANALYSIS ACTIVITY

retail <- data.frame(
  BillID = c(501,502,503,504,505,506),
  CustomerName = c("Aman","Riya","Karan","Neha","Rohit","Simran"),
  Gender = c("Male","Female","Male","Female","Male","Female"),
  Membership = c("Gold","Silver","Gold","None","Silver","Gold"),
  Category = c("Grocery","Electronics","Clothing","Grocery","Electronics","Clothing"),
  Quantity = c(10,1,3,8,2,5),
  CostPrice = c(50,20000,800,60,18000,700),
  SellingPrice = c(70,25000,1000,80,22000,900),
  Discount = c(100,2000,150,50,500,300),
  DeliveryType = c("Home","Store","Home","Store","Home","Home"),
  stringsAsFactors = FALSE
)

print(retail)

##   BillID CustomerName Gender Membership    Category Quantity CostPrice
## 1    501         Aman   Male       Gold     Grocery       10        50
## 2    502         Riya Female     Silver Electronics        1     20000
## 3    503        Karan   Male       Gold    Clothing        3       800
## 4    504         Neha Female       None     Grocery        8        60
## 5    505        Rohit   Male     Silver Electronics        2     18000
## 6    506       Simran Female       Gold    Clothing        5       700
##   SellingPrice Discount DeliveryType
## 1           70      100         Home
## 2        25000     2000        Store
## 3         1000      150         Home
## 4           80       50        Store
## 5        22000      500         Home
## 6          900      300         Home

#step2

retail$grossrevenue<- retail$Quantity * retail$SellingPrice
retail$Totalcost <- retail$Quantity * retail$CostPrice
retail$Netrevenue <- retail$grossrevenue * retail$Discount
retail$profit <- retail$Netrevenue - retail$Totalcost

print(retail$profit)

## [1]    69500 49980000   447600    31520 21964000  1346500

#step 3

gm <- subset(retail, Membership=="Gold" & profit>5000 & DeliveryType=="Home")
print(gm)

##   BillID CustomerName Gender Membership Category Quantity CostPrice
## 1    501         Aman   Male       Gold  Grocery       10        50
## 3    503        Karan   Male       Gold Clothing        3       800
## 6    506       Simran Female       Gold Clothing        5       700
##   SellingPrice Discount DeliveryType grossrevenue Totalcost Netrevenue  profit
## 1           70      100         Home          700       500      70000   69500
## 3         1000      150         Home         3000      2400     450000  447600
## 6          900      300         Home         4500      3500    1350000 1346500

loss <- subset(retail, profit < 0)
print(loss)

##  [1] BillID       CustomerName Gender       Membership   Category    
##  [6] Quantity     CostPrice    SellingPrice Discount     DeliveryType
## [11] grossrevenue Totalcost    Netrevenue   profit      
## <0 rows> (or 0-length row.names)

elecit <- subset(retail, Category=="Electronics" & Quantity>=2 & Discount>1000 & profit>0)
print(elecit)

##  [1] BillID       CustomerName Gender       Membership   Category    
##  [6] Quantity     CostPrice    SellingPrice Discount     DeliveryType
## [11] grossrevenue Totalcost    Netrevenue   profit      
## <0 rows> (or 0-length row.names)

pre <- subset(retail, Netrevenue>20000 | Membership=="Gold")
print(pre)

##   BillID CustomerName Gender Membership    Category Quantity CostPrice
## 1    501         Aman   Male       Gold     Grocery       10        50
## 2    502         Riya Female     Silver Electronics        1     20000
## 3    503        Karan   Male       Gold    Clothing        3       800
## 4    504         Neha Female       None     Grocery        8        60
## 5    505        Rohit   Male     Silver Electronics        2     18000
## 6    506       Simran Female       Gold    Clothing        5       700
##   SellingPrice Discount DeliveryType grossrevenue Totalcost Netrevenue   profit
## 1           70      100         Home          700       500      70000    69500
## 2        25000     2000        Store        25000     20000   50000000 49980000
## 3         1000      150         Home         3000      2400     450000   447600
## 4           80       50        Store          640       480      32000    31520
## 5        22000      500         Home        44000     36000   22000000 21964000
## 6          900      300         Home         4500      3500    1350000  1346500

#step4