Sales Transactions Dataset
sales.RdThis dataset contains sales transaction data, including order details, customer information, pricing, and revenue metrics.
Format
A data frame with multiple rows and 17 columns:
- order_key
integer. Unique identifier for each order.- line_number
integer. Line number within an order, representing individual items.- order_date
Date. Date when the order was placed.- delivery_date
Date. Date when the order was delivered.- customer_key
integer. Unique identifier for the customer.- store_key
integer. Unique identifier for the store where the transaction occurred.- product_key
integer. Unique identifier for the product.- quantity
numeric. Number of units sold in the transaction.- unit_price
numeric. Price per unit of the product in the original currency.- net_price
numeric. Final price per unit after discounts.- unit_cost
numeric. Cost per unit of the product.- currency_code
character. Currency code (e.g., "USD", "EUR").- exchange_rate
numeric. Exchange rate applied to the transaction currency.- gross_revenue
numeric. Total revenue before any deductions.- net_revenue
numeric. Revenue after deductions such as discounts and taxes.- cogs
numeric. Cost of goods sold (COGS).- margin
numeric. Profit margin calculated asnet_revenue - cogs.
Examples
data(sales)
head(sales)
#> # A tibble: 6 × 17
#> order_key line_number order_date delivery_date customer_key store_key
#> <dbl> <dbl> <date> <date> <dbl> <dbl>
#> 1 233000 0 2021-05-18 2021-05-18 1855811 585
#> 2 233100 0 2021-05-19 2021-05-19 1345436 550
#> 3 233100 1 2021-05-19 2021-05-19 1345436 550
#> 4 233100 2 2021-05-19 2021-05-19 1345436 550
#> 5 233200 0 2021-05-20 2021-05-20 926315 370
#> 6 233200 1 2021-05-20 2021-05-20 926315 370
#> # ℹ 11 more variables: product_key <dbl>, quantity <dbl>, unit_price <dbl>,
#> # net_price <dbl>, unit_cost <dbl>, currency_code <chr>, exchange_rate <dbl>,
#> # gross_revenue <dbl>, net_revenue <dbl>, cogs <dbl>, margin <dbl>
summary(sales)
#> order_key line_number order_date delivery_date
#> Min. :233000 Min. :0.000 Min. :2021-05-18 Min. :2021-05-18
#> 1st Qu.:268901 1st Qu.:0.000 1st Qu.:2022-05-12 1st Qu.:2022-05-12
#> Median :287603 Median :1.000 Median :2022-11-15 Median :2022-11-17
#> Mean :288707 Mean :1.173 Mean :2022-11-26 Mean :2022-11-27
#> 3rd Qu.:311000 3rd Qu.:2.000 3rd Qu.:2023-07-07 3rd Qu.:2023-07-08
#> Max. :339801 Max. :6.000 Max. :2024-04-20 Max. :2024-04-23
#> customer_key store_key product_key quantity
#> Min. : 1401 Min. : 10 Min. : 1 Min. : 1.000
#> 1st Qu.: 539021 1st Qu.: 450 1st Qu.: 504 1st Qu.: 1.000
#> Median :1224765 Median :999999 Median :1446 Median : 2.000
#> Mean :1122913 Mean :538154 Mean :1214 Mean : 3.147
#> 3rd Qu.:1669633 3rd Qu.:999999 3rd Qu.:1642 3rd Qu.: 4.000
#> Max. :2099336 Max. :999999 Max. :2517 Max. :10.000
#> unit_price net_price unit_cost currency_code
#> Min. : 0.95 Min. : 0.855 Min. : 0.48 Length:7794
#> 1st Qu.: 47.95 1st Qu.: 46.008 1st Qu.: 22.05 Class :character
#> Median : 208.50 Median : 197.800 Median : 86.91 Mode :character
#> Mean : 311.36 Mean : 292.896 Mean : 128.42
#> 3rd Qu.: 369.00 3rd Qu.: 342.015 3rd Qu.: 164.18
#> Max. :3748.50 Max. :3748.500 Max. :1241.95
#> exchange_rate gross_revenue net_revenue cogs
#> Min. :0.7056 Min. : 1.9 Min. : 1.71 Min. : 0.96
#> 1st Qu.:0.9461 1st Qu.: 115.8 1st Qu.: 107.74 1st Qu.: 52.00
#> Median :1.0000 Median : 409.3 Median : 387.00 Median : 183.88
#> Mean :1.0284 Mean : 997.1 Mean : 937.38 Mean : 411.63
#> 3rd Qu.:1.0000 3rd Qu.: 1136.2 3rd Qu.: 1064.14 3rd Qu.: 490.76
#> Max. :1.6080 Max. :29988.0 Max. :25789.68 Max. :9935.64
#> margin
#> Min. :7.213e-01
#> 1st Qu.:5.280e+01
#> Median :1.990e+02
#> Mean :5.258e+02
#> 3rd Qu.:5.613e+02
#> Max. :1.622e+04