html_R

# Step 1: Create the Data Frame (Mixed Data Types)


retail_data <- data.frame(
  BillID = c(501, 502, 503, 504, 505, 506),
  CustomerName = c("Aman", "Riya", "Karan", "Neha", "Rohit", "Simran"),
  Gender = factor(c("Male", "Female", "Male", "Female", "Male", "Female")),
  Membership = factor(c("Gold", "Silver", "Gold", "None", "Silver", "Gold")),
  Category = factor(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
)

# Check structure
str(retail_data)

## 'data.frame':    6 obs. of  10 variables:
##  $ BillID      : num  501 502 503 504 505 506
##  $ CustomerName: chr  "Aman" "Riya" "Karan" "Neha" ...
##  $ Gender      : Factor w/ 2 levels "Female","Male": 2 1 2 1 2 1
##  $ Membership  : Factor w/ 3 levels "Gold","None",..: 1 3 1 2 3 1
##  $ Category    : Factor w/ 3 levels "Clothing","Electronics",..: 3 2 1 3 2 1
##  $ Quantity    : num  10 1 3 8 2 5
##  $ CostPrice   : num  50 20000 800 60 18000 700
##  $ SellingPrice: num  70 25000 1000 80 22000 900
##  $ Discount    : num  100 2000 150 50 500 300
##  $ DeliveryType: chr  "Home" "Store" "Home" "Store" ...

print(retail_data)

##   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

#  1. Add GrossRevenue = Quantity * SellingPrice.
retail_data$GrossRevenue <- retail_data$Quantity*retail_data$SellingPrice

# 2. Add TotalCost = Quantity * CostPrice.
retail_data$TotalCost <- retail_data$Quantity*retail_data$CostPrice

# 3. Add NetRevenue = GrossRevenue - Discount.
retail_data$NetRevenue <- retail_data$GrossRevenue-retail_data$Discount

# 4. Add Profit = NetRevenue - TotalCost
retail_data$Profit <- retail_data$NetRevenue-retail_data$TotalCost

# 5. Find Gold members who have Profit > 5000 and chose Home delivery.
ProGoldCard <- subset(retail_data,
                                Membership == "Gold" &
                                  Profit > 100 &
                                  DeliveryType == "Home")

ProGoldCard

##   BillID CustomerName Gender Membership Category Quantity CostPrice
## 3    503        Karan   Male       Gold Clothing        3       800
## 6    506       Simran Female       Gold Clothing        5       700
##   SellingPrice Discount DeliveryType GrossRevenue TotalCost NetRevenue Profit
## 3         1000      150         Home         3000      2400       2850    450
## 6          900      300         Home         4500      3500       4200    700

# 6. Find all loss-making transactions (Profit < 0)
loss_making<- subset(retail_data,
                     Profit<0)
loss_making

##  [1] BillID       CustomerName Gender       Membership   Category    
##  [6] Quantity     CostPrice    SellingPrice Discount     DeliveryType
## [11] GrossRevenue TotalCost    NetRevenue   Profit      
## <0 rows> (or 0-length row.names)