# A tibble: 10,000 × 9
TransactionID CustomerID ProductID Quantity Date Region Category
<int> <int> <int> <int> <date> <chr> <chr>
1 1 225 205 10 2023-09-29 West Electronics
2 2 255 164 5 2023-04-14 North Books
3 3 561 274 4 2023-10-23 East Clothing
4 4 946 194 10 2023-09-25 North Books
5 5 554 286 2 2023-05-19 West Clothing
6 6 457 409 8 2023-11-04 West Books
7 7 651 424 9 2023-02-15 North Clothing
8 8 577 288 4 2023-05-13 East Clothing
9 9 354 202 7 2023-04-02 East Books
10 10 203 481 5 2023-10-28 West Books
# ℹ 9,990 more rows
# ℹ 2 more variables: PricePerUnit <dbl>, TotalPrice <dbl>Mini Project
# A tibble: 10,000 × 9
TransactionID CustomerID ProductID Quantity Date Region Category
<int> <int> <int> <int> <date> <chr> <chr>
1 1 225 205 10 2023-09-29 West Electronics
2 2 255 164 5 2023-04-14 North Books
3 3 561 274 4 2023-10-23 East Clothing
4 4 946 194 10 2023-09-25 North Books
5 5 554 286 2 2023-05-19 West Clothing
6 6 457 409 8 2023-11-04 West Books
7 7 651 424 9 2023-02-15 North Clothing
8 8 577 288 4 2023-05-13 East Clothing
9 9 354 202 7 2023-04-02 East Books
10 10 203 481 5 2023-10-28 West Books
# ℹ 9,990 more rows
# ℹ 2 more variables: PricePerUnit <dbl>, TotalPrice <dbl>$TransactionID
[1] FALSE
$CustomerID
[1] FALSE
$ProductID
[1] FALSE
$Quantity
[1] FALSE
$Date
[1] FALSE
$Region
[1] FALSE
$Category
[1] FALSE
$PricePerUnit
[1] FALSE
$TotalPrice
[1] FALSE# A tibble: 1 × 18
TransactionID_mean TransactionID_sd TransactionID_n_distinct CustomerID_mean
<dbl> <dbl> <int> <dbl>
1 5000. 2887. 10000 505.
# ℹ 14 more variables: CustomerID_sd <dbl>, CustomerID_n_distinct <int>,
# ProductID_mean <dbl>, ProductID_sd <dbl>, ProductID_n_distinct <int>,
# Quantity_mean <dbl>, Quantity_sd <dbl>, Quantity_n_distinct <int>,
# PricePerUnit_mean <dbl>, PricePerUnit_sd <dbl>,
# PricePerUnit_n_distinct <int>, TotalPrice_mean <dbl>, TotalPrice_sd <dbl>,
# TotalPrice_n_distinct <int># A tibble: 16 × 5
Region Category TotalQuantity TotalRevenue AvgPrice
<chr> <chr> <int> <dbl> <dbl>
1 East Books 3471 920655. 270.
2 East Clothing 3460 865629. 250.
3 East Electronics 3222 864570. 272.
4 East Home 4097 981225. 240.
5 North Books 3321 866966. 263.
6 North Clothing 3445 844426. 250.
7 North Electronics 3395 891862. 264.
8 North Home 3576 868925. 243.
9 South Books 3107 859358. 267.
10 South Clothing 3387 822032. 242.
11 South Electronics 3253 881414. 272.
12 South Home 3742 864230. 237.
13 West Books 3205 872384. 273.
14 West Clothing 3226 796732. 249.
15 West Electronics 3165 897541. 278.
16 West Home 3943 928295. 235.# A tibble: 16 × 5
Region Category TotalQuantity TotalRevenue AvgPrice
<chr> <chr> <int> <dbl> <dbl>
1 East Home 4097 981225. 240.
2 East Books 3471 920655. 270.
3 East Clothing 3460 865629. 250.
4 East Electronics 3222 864570. 272.
5 North Electronics 3395 891862. 264.
6 North Home 3576 868925. 243.
7 North Books 3321 866966. 263.
8 North Clothing 3445 844426. 250.
9 South Electronics 3253 881414. 272.
10 South Home 3742 864230. 237.
11 South Books 3107 859358. 267.
12 South Clothing 3387 822032. 242.
13 West Home 3943 928295. 235.
14 West Electronics 3165 897541. 278.
15 West Books 3205 872384. 273.
16 West Clothing 3226 796732. 249.
# A tibble: 35 × 5
CustomerID Category TotalQuantity MeanQuantity SDQuantity
<int> <chr> <int> <dbl> <dbl>
1 18 Electronics 44 14.4 8.99
2 33 Books 48 14.5 9.08
3 51 Home 58 16.1 10.1
4 63 Clothing 47 14.8 9.47
5 74 Clothing 44 14.8 9.47
6 90 Electronics 42 14.4 8.99
7 172 Books 44 14.5 9.08
8 184 Books 42 14.5 9.08
9 198 Books 50 14.5 9.08
10 211 Clothing 46 14.8 9.47
# ℹ 25 more rows# A tibble: 1,990 × 4
Region ProductID TotalQuantitySold Rank
<chr> <int> <int> <int>
1 East 337 88 1
2 East 250 79 2
3 East 391 74 3
4 East 285 73 4
5 East 445 71 5
6 East 436 70 6
7 East 280 69 7
8 East 406 64 8
9 East 217 62 9
10 East 361 61 10
# ℹ 1,980 more rows# A tibble: 40 × 4
Region ProductID TotalQuantitySold Rank
<chr> <int> <int> <int>
1 East 337 88 1
2 East 250 79 2
3 East 391 74 3
4 East 285 73 4
5 East 445 71 5
6 East 436 70 6
7 East 280 69 7
8 East 406 64 8
9 East 217 62 9
10 East 361 61 10
# ℹ 30 more rows