Data Extraction
Finish the section with practice on abstract local furniture store selling data!
Tarefa
Swipe to start coding
You will work with the data below. This data is stored in the store variable.
| Item | Price | Sold |
|---|---|---|
| Sofa | 340 | 67 |
| Armchair | 150 | 81 |
| Dining table | 115 | 79 |
| Bookshelf | 160 | 42 |
| Kitchen Cabinet | 70 | 67 |
Your tasks are:
- Add a new column named
'Revenue'with the revenue of each item (multiply'Price'and'Sold'). To access columns, use the$sign. - Using the
sum()function, calculate the total number of items sold. Save initems_soldvariable and output its value. - Calculate total revenue using
sum()function. Save in thetotal_revenuevariable and output its value. - Find out the average price of the sold item - divide
total_revenuebyitems_sold.
Solução
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# Initial data frame
prices <- c(340, 150, 115, 160, 70)
items <- c('Sofa', 'Armchair', 'Dining table',
'Bookshelf', 'Kitchen Cabinet')
sold <- c(67, 81, 79, 42, 67)
store <- data.frame(items, prices, sold)
colnames(store) <- c("Item", "Price", "Sold")
# Add new column
store$Revenue <- store$Sold * store$Price
# Total number of items sold
items_sold <- sum(store$Sold)
items_sold
# Total revenue
total_revenue <- sum(store$Revenue)
total_revenue
# Average price of sold item
total_revenue/items_sold
Tudo estava claro?
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Seção 2. Capítulo 7
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# Initial data frame
prices <- c(340, 150, 115, 160, 70)
items <- c('Sofa', 'Armchair', 'Dining table',
'Bookshelf', 'Kitchen Cabinet')
sold <- c(67, 81, 79, 42, 67)
store <- data.frame(items, prices, sold)
colnames(store) <- c("Item", "Price", "Sold")
# Add new column
___ <- store$Sold * ___
# Total number of items sold
___ <- ___(store$___)
___
# Total revenue
___ <- sum(___)
___
# Average price of sold item
___/___
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