supermarket<-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)
)
supermarket$grossrev<-supermarket$quantity*supermarket$sellingprice
supermarket
## billid customername gender membership category quantity costprice
## 1 501 aman male gold grocery 10 50
## 2 502 riya female silver electronics 1 20000
## 3 503 karan male gold clothing 3 800
## 4 504 neha female none grocery 8 60
## 5 505 rohit male silver electronics 2 18000
## 6 506 simran female gold clothing 5 700
## sellingprice discount grossrev
## 1 70 100 700
## 2 25000 2000 25000
## 3 1000 150 3000
## 4 80 50 640
## 5 22000 500 44000
## 6 900 300 4500
supermarket$TotalCost<-supermarket$quantity*supermarket$costprice
supermarket
## 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 grossrev TotalCost
## 1 70 100 700 500
## 2 25000 2000 25000 20000
## 3 1000 150 3000 2400
## 4 80 50 640 480
## 5 22000 500 44000 36000
## 6 900 300 4500 3500
supermarket$NetRevenue<-supermarket$grossrev-supermarket$discount
supermarket
## billid customername gender membership category quantity costprice
## 1 501 aman male gold grocery 10 50
## 2 502 riya female silver electronics 1 20000
## 3 503 karan male gold clothing 3 800
## 4 504 neha female none grocery 8 60
## 5 505 rohit male silver electronics 2 18000
## 6 506 simran female gold clothing 5 700
## sellingprice discount grossrev TotalCost NetRevenue
## 1 70 100 700 500 600
## 2 25000 2000 25000 20000 23000
## 3 1000 150 3000 2400 2850
## 4 80 50 640 480 590
## 5 22000 500 44000 36000 43500
## 6 900 300 4500 3500 4200
supermarket$Profit<-supermarket$NetRevenue-supermarket$TotalCost
supermarket
## 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 grossrev TotalCost NetRevenue Profit
## 1 70 100 700 500 600 100
## 2 25000 2000 25000 20000 23000 3000
## 3 1000 150 3000 2400 2850 450
## 4 80 50 640 480 590 110
## 5 22000 500 44000 36000 43500 7500
## 6 900 300 4500 3500 4200 700
##Step 3: Multi-Condition Based Analysis
##1. Find Gold members who have Profit > 5000 and chose Home delivery.
supermarket$deliverymode <- ifelse(
supermarket$membership == "gold" & supermarket$Profit > 5000,
"home delivery",
"pick up"
)
supermarket
## billid customername gender membership category quantity costprice
## 1 501 aman male gold grocery 10 50
## 2 502 riya female silver electronics 1 20000
## 3 503 karan male gold clothing 3 800
## 4 504 neha female none grocery 8 60
## 5 505 rohit male silver electronics 2 18000
## 6 506 simran female gold clothing 5 700
## sellingprice discount grossrev TotalCost NetRevenue Profit deliverymode
## 1 70 100 700 500 600 100 pick up
## 2 25000 2000 25000 20000 23000 3000 pick up
## 3 1000 150 3000 2400 2850 450 pick up
## 4 80 50 640 480 590 110 pick up
## 5 22000 500 44000 36000 43500 7500 pick up
## 6 900 300 4500 3500 4200 700 pick up
##Find all loss-making transactions (Profit < 0).
supermarket$trans<-ifelse(supermarket$Profit<0,"loss","profit")
supermarket[which(supermarket$trans=="loss")]
## data frame with 0 columns and 6 rows
supermarket
## 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 grossrev TotalCost NetRevenue Profit deliverymode
## 1 70 100 700 500 600 100 pick up
## 2 25000 2000 25000 20000 23000 3000 pick up
## 3 1000 150 3000 2400 2850 450 pick up
## 4 80 50 640 480 590 110 pick up
## 5 22000 500 44000 36000 43500 7500 pick up
## 6 900 300 4500 3500 4200 700 pick up
## trans
## 1 profit
## 2 profit
## 3 profit
## 4 profit
## 5 profit
## 6 profit