Market

market <- data.frame(
  Bid = c(501,502,503,504,505,506),
  Cname = 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),
  CP = c(50,20000,800,60,18000,700),
  SP = c(70,25000,1000,80,22000,900),
  Discount = c(100,2000,150,50,500,300),
  Dtype = factor(c('home','store','home','store','home','home'))
)

market$Gross_revenue <- market$Quantity * market$SP
market$Total_cost <- market$Quantity * market$CP
market$Net_revenue <- market$Gross_revenue - market$Discount
market$Profit <- market$Net_revenue - market$Total_cost

subset(market, Profit >= 5000 & Membership == 'gold' & Dtype == 'home')

##  [1] Bid           Cname         Gender        Membership    Category     
##  [6] Quantity      CP            SP            Discount      Dtype        
## [11] Gross_revenue Total_cost    Net_revenue   Profit       
## <0 rows> (or 0-length row.names)

subset(market, Profit < 0)

##  [1] Bid           Cname         Gender        Membership    Category     
##  [6] Quantity      CP            SP            Discount      Dtype        
## [11] Gross_revenue Total_cost    Net_revenue   Profit       
## <0 rows> (or 0-length row.names)

subset(market, Quantity >= 2 & Discount > 1000 & Profit > 0)

##  [1] Bid           Cname         Gender        Membership    Category     
##  [6] Quantity      CP            SP            Discount      Dtype        
## [11] Gross_revenue Total_cost    Net_revenue   Profit       
## <0 rows> (or 0-length row.names)

subset(market, Net_revenue > 20000 | Membership == 'gold')

##   Bid  Cname Gender Membership    Category Quantity    CP    SP Discount Dtype
## 1 501   Aman   male       gold     Grocery       10    50    70      100  home
## 2 502   Riya female     silver Electronics        1 20000 25000     2000 store
## 3 503  Karan   male       gold    Clothing        3   800  1000      150  home
## 5 505  Rohit   male     silver Electronics        2 18000 22000      500  home
## 6 506 Simran female       gold    Clothing        5   700   900      300  home
##   Gross_revenue Total_cost Net_revenue Profit
## 1           700        500         600    100
## 2         25000      20000       23000   3000
## 3          3000       2400        2850    450
## 5         44000      36000       43500   7500
## 6          4500       3500        4200    700

market$PC <- ifelse(
  market$Profit >= 10000,
  'high profit',
  ifelse(market$Profit >= 0, 'moderate profit', 'loss')
)

market$Risk_flag <- ifelse(
  market$Discount > (0.2 * market$Gross_revenue) | market$Profit < 0,
  'risky',
  'safe'
)

aggregate(Profit ~ Membership + Category, data = market, sum)

##   Membership    Category Profit
## 1       gold    Clothing   1150
## 2     silver Electronics  10500
## 3       gold     Grocery    100
## 4       none     Grocery    110