Sales Analysis
Rihad Variawa
3/28/2019
Reading in a csv file
Taken from the link
sales_data <- read.csv('sales_data_sample.csv')sales_data is a ‘data.frame’. It is the main way that R deals with tables of data. Click on the arrow next to sales_data in the Environment pane to see the data types of each column Click on sales_data in the Environment pane to see the table. You can also type View(sales_data) to do this.
Run summary to see a summary of the data.
summary(sales_data)## ORDERNUMBER QUANTITYORDERED PRICEEACH ORDERLINENUMBER
## Min. :10100 Min. : 6.00 Min. : 26.88 Min. : 1.000
## 1st Qu.:10180 1st Qu.:27.00 1st Qu.: 68.86 1st Qu.: 3.000
## Median :10262 Median :35.00 Median : 95.70 Median : 6.000
## Mean :10259 Mean :35.09 Mean : 83.66 Mean : 6.466
## 3rd Qu.:10334 3rd Qu.:43.00 3rd Qu.:100.00 3rd Qu.: 9.000
## Max. :10425 Max. :97.00 Max. :100.00 Max. :18.000
##
## SALES ORDERDATE STATUS
## Min. : 482.1 11/14/2003 0:00: 38 Cancelled : 60
## 1st Qu.: 2203.4 11/24/2004 0:00: 35 Disputed : 14
## Median : 3184.8 11/12/2003 0:00: 34 In Process: 41
## Mean : 3553.9 11/17/2004 0:00: 32 On Hold : 44
## 3rd Qu.: 4508.0 11/4/2004 0:00 : 29 Resolved : 47
## Max. :14082.8 10/16/2004 0:00: 28 Shipped :2617
## (Other) :2627
## QTR_ID MONTH_ID YEAR_ID PRODUCTLINE
## Min. :1.000 Min. : 1.000 Min. :2003 Classic Cars :967
## 1st Qu.:2.000 1st Qu.: 4.000 1st Qu.:2003 Motorcycles :331
## Median :3.000 Median : 8.000 Median :2004 Planes :306
## Mean :2.718 Mean : 7.092 Mean :2004 Ships :234
## 3rd Qu.:4.000 3rd Qu.:11.000 3rd Qu.:2004 Trains : 77
## Max. :4.000 Max. :12.000 Max. :2005 Trucks and Buses:301
## Vintage Cars :607
## MSRP PRODUCTCODE CUSTOMERNAME
## Min. : 33.0 S18_3232: 52 Euro Shopping Channel : 259
## 1st Qu.: 68.0 S10_1949: 28 Mini Gifts Distributors Ltd.: 180
## Median : 99.0 S10_4962: 28 Australian Collectors, Co. : 55
## Mean :100.7 S12_1666: 28 La Rochelle Gifts : 53
## 3rd Qu.:124.0 S18_1097: 28 AV Stores, Co. : 51
## Max. :214.0 S18_2432: 28 Land of Toys Inc. : 49
## (Other) :2631 (Other) :2176
## PHONE ADDRESSLINE1
## (91) 555 94 44: 259 C/ Moralzarzal, 86 : 259
## 4155551450 : 180 5677 Strong St. : 180
## 03 9520 4555 : 55 636 St Kilda Road : 55
## 40.67.8555 : 53 67, rue des Cinquante Otages: 53
## (171) 555-1555: 51 Fauntleroy Circus : 51
## 6175558555 : 51 897 Long Airport Avenue : 49
## (Other) :2174 (Other) :2176
## ADDRESSLINE2 CITY STATE POSTALCODE
## :2521 Madrid : 304 :1486 28034 : 259
## Level 3 : 55 San Rafael : 180 CA : 416 97562 : 205
## Suite 400: 48 NYC : 152 MA : 190 10022 : 152
## Level 15 : 46 Singapore : 79 NY : 178 94217 : 89
## Level 6 : 46 Paris : 70 NSW : 92 : 76
## 2nd Floor: 36 San Francisco: 62 Victoria: 78 50553 : 61
## (Other) : 71 (Other) :1976 (Other) : 383 (Other):1981
## COUNTRY TERRITORY CONTACTLASTNAME CONTACTFIRSTNAME
## USA :1004 APAC : 221 Freyre : 259 Diego : 259
## Spain : 342 EMEA :1407 Nelson : 204 Valarie: 257
## France : 314 Japan: 121 Young : 115 Julie : 117
## Australia: 185 NA's :1074 Frick : 91 Michael: 84
## UK : 144 Brown : 88 Sue : 84
## Italy : 113 Yu : 80 Juri : 60
## (Other) : 721 (Other):1986 (Other):1962
## DEALSIZE
## Large : 157
## Medium:1384
## Small :1282
##
##
##
## Or can run summary on individual columns.
summary(sales_data$PRICEEACH)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 26.88 68.86 95.70 83.66 100.00 100.00summary(sales_data$PRODUCTLINE)## Classic Cars Motorcycles Planes Ships
## 967 331 306 234
## Trains Trucks and Buses Vintage Cars
## 77 301 607max(sales_data$SALES)## [1] 14082.8mean(sales_data$SALES)## [1] 3553.889min(sales_data$SALES)## [1] 482.13sd(sales_data$SALES)## [1] 1841.865We will now go through select(), arrange(), filter(), mutate(), group_by(), summarise()
- select() function select specific columns first argument is always the dataset, and each argument after is the fields you want
select(sales_data, QUANTITYORDERED, PRICEEACH)## QUANTITYORDERED PRICEEACH
## 1 30 95.70
## 2 34 81.35
## 3 41 94.74
## 4 45 83.26
## 5 49 100.00
## 6 36 96.66
## 7 29 86.13
## 8 48 100.00
## 9 22 98.57
## 10 41 100.00
## 11 37 100.00
## 12 23 100.00
## 13 28 100.00
## 14 34 100.00
## 15 45 92.83
## 16 36 100.00
## 17 23 100.00
## 18 41 100.00
## 19 46 94.74
## 20 42 100.00
## 21 41 100.00
## 22 20 72.55
## 23 21 34.91
## 24 42 76.36
## 25 24 100.00
## 26 66 100.00
## 27 26 100.00
## 28 29 100.00
## 29 38 100.00
## 30 37 100.00
## 31 45 100.00
## 32 21 100.00
## 33 34 100.00
## 34 23 100.00
## 35 42 100.00
## 36 47 100.00
## 37 35 100.00
## 38 29 100.00
## 39 34 100.00
## 40 32 100.00
## 41 21 100.00
## 42 34 100.00
## 43 37 100.00
## 44 47 100.00
## 45 48 100.00
## 46 40 100.00
## 47 26 100.00
## 48 30 100.00
## 49 32 100.00
## 50 41 100.00
## 51 36 100.00
## 52 24 100.00
## 53 23 100.00
## 54 50 100.00
## 55 39 99.91
## 56 29 96.34
## 57 27 100.00
## 58 37 100.00
## 59 37 100.00
## 60 27 100.00
## 61 42 100.00
## 62 38 96.34
## 63 24 100.00
## 64 23 100.00
## 65 47 100.00
## 66 22 100.00
## 67 44 100.00
## 68 40 100.00
## 69 22 100.00
## 70 47 100.00
## 71 39 96.34
## 72 34 100.00
## 73 45 100.00
## 74 20 100.00
## 75 40 68.92
## 76 26 51.15
## 77 39 100.00
## 78 50 44.51
## 79 45 100.00
## 80 45 100.00
## 81 27 100.00
## 82 46 100.00
## 83 31 100.00
## 84 33 100.00
## 85 22 100.00
## 86 20 100.00
## 87 41 100.00
## 88 45 100.00
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## 90 34 100.00
## 91 49 100.00
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## 93 43 100.00
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## 95 36 100.00
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## 97 29 100.00
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## 100 26 100.00
## 101 39 76.67
## 102 22 100.00
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## 105 66 100.00
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## 123 22 100.00
## 124 33 100.00
## 125 47 64.93
## 126 25 48.05
## 127 26 75.47
## 128 48 54.68
## 129 39 100.00
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## 131 32 100.00
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## 133 19 100.00
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## 150 30 100.00
## 151 38 100.00
## 152 40 100.00
## 153 46 61.99
## 154 26 100.00
## 155 27 100.00
## 156 43 100.00
## 157 35 65.63
## 158 37 100.00
## 159 37 46.90
## 160 27 100.00
## 161 38 100.00
## 162 33 100.00
## 163 42 100.00
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## 166 41 100.00
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## 169 21 100.00
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## 173 28 100.00
## 174 24 100.00
## 175 44 100.00
## 176 50 100.00
## 177 21 100.00
## 178 33 100.00
## 179 33 100.00
## 180 31 100.00
## 181 41 71.47
## 182 45 79.65
## 183 33 85.39
## 184 45 76.00
## 185 26 99.04
## 186 12 100.00
## 187 41 100.00
## 188 33 100.00
## 189 46 100.00
## 190 33 100.00
## 191 20 100.00
## 192 44 100.00
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## 203 31 100.00
## 204 38 100.00
## 205 42 64.00
## 206 33 57.22
## 207 48 52.36
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## 211 33 69.12
## 212 36 100.00
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## 214 21 100.00
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## 231 21 100.00
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## 233 33 99.21
## 234 29 100.00
## 235 49 100.00
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## 237 20 100.00
## 238 39 63.20
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## 244 49 100.00
## 245 38 100.00
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## 255 43 100.00
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## 257 24 100.00
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## 259 30 100.00
## 260 24 100.00
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## 262 22 100.00
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## 264 44 100.00
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## 308 29 100.00
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## 310 35 100.00
## 311 46 83.63
## 312 44 95.93
## 313 34 96.73
## 314 35 100.00
## 315 25 72.38
## 316 10 100.00
## 317 29 100.00
## 318 39 100.00
## 319 42 100.00
## 320 46 100.00
## 321 49 100.00
## 322 27 100.00
## 323 50 100.00
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## 326 20 100.00
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## 331 24 100.00
## 332 45 100.00
## 333 20 100.00
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## 335 24 100.00
## 336 49 63.38
## 337 26 100.00
## 338 49 62.09
## 339 34 100.00
## 340 34 95.35
## 341 33 100.00
## 342 22 100.00
## 343 39 89.38
## 344 32 63.84
## 345 24 75.01
## 346 21 63.84
## 347 24 73.42
## 348 36 63.84
## 349 20 81.40
## 350 30 64.64
## 351 44 82.99
## 352 28 92.57
## 353 37 77.41
## 354 20 74.21
## 355 25 90.17
## 356 35 76.61
## 357 38 83.79
## 358 41 69.43
## 359 22 76.61
## 360 49 81.40
## 361 38 73.42
## 362 33 100.00
## 363 36 93.56
## 364 34 81.62
## 365 24 67.83
## 366 36 70.26
## 367 34 90.17
## 368 41 100.00
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## 370 24 100.00
## 371 21 100.00
## 372 24 100.00
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## 374 26 100.00
## 375 37 100.00
## 376 49 100.00
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## 378 48 100.00
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## 381 46 100.00
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## 383 41 100.00
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## 385 38 100.00
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## 390 42 64.16
## 391 49 35.71
## 392 32 66.58
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## 469 40 100.00
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## 471 30 87.06
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## 476 39 100.00
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## 478 32 100.00
## 479 31 100.00
## 480 50 100.00
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## 482 43 100.00
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## 484 28 100.00
## 485 36 100.00
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## 487 25 100.00
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## 490 50 100.00
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## 494 43 86.30
## 495 46 95.13
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## 497 50 50.18
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## 501 51 95.55
## 502 41 50.14
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## 504 42 54.99
## 505 49 43.13
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## 509 32 54.45
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## 511 48 63.61
## 512 33 43.13
## 513 31 48.52
## 514 20 58.22
## 515 29 51.75
## 516 27 57.68
## 517 24 56.07
## 518 37 48.52
## 519 25 44.21
## 520 41 57.68
## 521 27 89.89
## 522 21 58.95
## 523 22 72.41
## 524 32 98.63
## 525 25 52.83
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## 571 25 100.00
## 572 41 100.00
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## 574 28 57.55
## 575 25 54.57
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## 577 34 100.00
## 578 24 100.00
## 579 30 100.00
## 580 42 100.00
## 581 21 100.00
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## 765 28 59.55
## 766 30 61.99
## 767 30 49.22
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## 2648 25 100.00
## 2649 29 100.00
## 2650 25 96.11
## 2651 44 100.00
## 2652 47 100.00
## 2653 48 100.00
## 2654 45 100.00
## 2655 35 100.00
## 2656 31 100.00
## 2657 50 100.00
## 2658 33 100.00
## 2659 29 100.00
## 2660 48 68.80
## 2661 44 72.42
## 2662 25 66.73
## 2663 50 100.00
## 2664 23 100.00
## 2665 21 96.11
## 2666 41 100.00
## 2667 44 74.40
## 2668 43 76.00
## 2669 28 96.00
## 2670 43 86.40
## 2671 48 96.00
## 2672 38 82.40
## 2673 31 86.40
## 2674 26 67.20
## 2675 32 92.00
## 2676 44 67.20
## 2677 27 76.00
## 2678 43 73.60
## 2679 25 69.60
## 2680 22 80.80
## 2681 21 87.20
## 2682 48 75.20
## 2683 33 64.00
## 2684 34 100.00
## 2685 43 81.95
## 2686 44 100.00
## 2687 44 100.00
## 2688 32 94.34
## 2689 29 65.60
## 2690 77 92.00
## 2691 39 67.20
## 2692 39 81.14
## 2693 36 100.00
## 2694 42 91.15
## 2695 21 100.00
## 2696 50 88.15
## 2697 24 100.00
## 2698 44 92.16
## 2699 37 100.00
## 2700 27 92.16
## 2701 37 100.00
## 2702 38 100.00
## 2703 48 96.16
## 2704 30 100.00
## 2705 25 88.15
## 2706 40 86.15
## 2707 22 88.15
## 2708 34 100.00
## 2709 32 90.15
## 2710 31 86.15
## 2711 43 80.00
## 2712 31 89.38
## 2713 31 77.34
## 2714 34 96.34
## 2715 45 92.08
## 2716 48 100.00
## 2717 28 100.00
## 2718 22 100.00
## 2719 45 83.42
## 2720 30 85.41
## 2721 38 85.41
## 2722 20 100.00
## 2723 28 100.00
## 2724 24 100.00
## 2725 22 79.45
## 2726 35 93.35
## 2727 33 85.41
## 2728 31 95.34
## 2729 35 82.43
## 2730 35 90.37
## 2731 50 81.43
## 2732 26 100.00
## 2733 38 89.38
## 2734 45 100.00
## 2735 30 100.00
## 2736 37 91.37
## 2737 37 86.61
## 2738 36 71.89
## 2739 25 100.00
## 2740 37 100.00
## 2741 30 95.48
## 2742 36 100.00
## 2743 27 90.37
## 2744 48 61.44
## 2745 26 59.22
## 2746 26 85.13
## 2747 34 85.87
## 2748 44 85.87
## 2749 39 82.91
## 2750 45 76.25
## 2751 40 63.67
## 2752 42 70.33
## 2753 43 74.03
## 2754 34 72.55
## 2755 38 62.19
## 2756 35 71.07
## 2757 31 72.55
## 2758 32 64.41
## 2759 47 86.62
## 2760 39 68.11
## 2761 44 62.19
## 2762 39 85.87
## 2763 50 57.86
## 2764 22 75.51
## 2765 35 100.00
## 2766 45 55.62
## 2767 44 86.40
## 2768 36 87.36
## 2769 28 72.55
## 2770 43 62.19
## 2771 48 52.64
## 2772 28 48.17
## 2773 21 41.71
## 2774 37 50.65
## 2775 34 49.16
## 2776 40 41.71
## 2777 45 51.15
## 2778 28 52.14
## 2779 29 41.71
## 2780 48 44.69
## 2781 31 45.69
## 2782 32 57.61
## 2783 21 57.11
## 2784 32 58.60
## 2785 43 57.61
## 2786 21 45.19
## 2787 34 53.63
## 2788 34 43.70
## 2789 44 86.13
## 2790 27 76.31
## 2791 49 52.64
## 2792 23 95.20
## 2793 25 64.97
## 2794 50 87.15
## 2795 34 40.22
## 2796 20 56.12
## 2797 42 57.61
## 2798 25 56.78
## 2799 50 43.68
## 2800 32 64.97
## 2801 39 44.23
## 2802 50 60.06
## 2803 38 48.59
## 2804 40 50.23
## 2805 28 64.43
## 2806 42 50.23
## 2807 42 63.88
## 2808 36 63.34
## 2809 24 49.69
## 2810 23 65.52
## 2811 29 50.78
## 2812 37 45.86
## 2813 33 51.32
## 2814 32 60.06
## 2815 35 59.51
## 2816 40 55.69
## 2817 37 86.74
## 2818 42 97.16
## 2819 20 100.00
## 2820 29 100.00
## 2821 43 100.00
## 2822 34 62.24
## 2823 47 65.52don’t forget to assign it!
select_data <- select(sales_data, QUANTITYORDERED, PRICEEACH)- arrange() function order your data
arranged_asc_data <- arrange(select_data, PRICEEACH)
arranged_desc_data <- arrange(select_data, -PRICEEACH)- filter() function filter out data first argument is the dataset, second argument is the filter conditions
filtered_data <- filter(sales_data, STATE == 'NY')
filtered_data <- filter(sales_data, STATE == 'NY' & PRODUCTLINE == 'Classic Cars')If I want to ‘filter’ and ‘select’ data, I have to run the command twice
filtered_data <- filter(sales_data, STATE == 'NY' & PRODUCTLINE == 'Classic Cars')
## notice the first argument is filtered_data
select_data <- select(filtered_data, QUANTITYORDERED, PRICEEACH)
## notice the first argument is select_data
arrange_data <- arrange(select_data, PRICEEACH) It’s kind of tedious to have to run the command twice, so we will use a concept called piping (%>%) Piping is sending the output of one function into the input of another. The output will be the first argument of the next function The same command above can be written like this:
piped_data <- sales_data %>%
filter(STATE == 'NY' & PRODUCTLINE == 'Classic Cars') %>%
select(QUANTITYORDERED, PRICEEACH) %>%
arrange(PRICEEACH)- mutate() function create your own columns using mutate Same as above but using piping
mutated_data <- mutate(sales_data, discounted = 0.95 * SALES)
## same as above but using piping
mutated_data <- sales_data %>%
mutate(discounted = 0.95 * SALES)YOUR TURN!
sales_data <- read.csv('sales_data_sample.csv') # Reread the data in case you made any changes to it## Q1: what is the most common deal size (column name: DEALSIZE)?
summary(sales_data$DEALSIZE)## Large Medium Small
## 157 1384 1282## Q2: what is the average quantity ordered? (HINT: Can also use mean function)
mean(sales_data$QUANTITYORDERED)## [1] 35.09281summary(sales_data$QUANTITYORDERED)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 6.00 27.00 35.00 35.09 43.00 97.00## Q3: create a new dataset called q3_data with
## - a new column called MSRP_REV which is equal to the MSRP * QUANTITYORDERED
## - filtered to only have 'Large' sized deals
## - with only the selected columns ORDERNUMBER, QUANTITYORDERED, PRICEEACH, MSRP, SALES, MSRP_REV
## - ordered in descending order by SALES
q3_data <- sales_data %>%
mutate(MSRP_REV = MSRP * QUANTITYORDERED) %>%
filter(DEALSIZE == 'Large') %>%
select(ORDERNUMBER, QUANTITYORDERED, PRICEEACH, MSRP, SALES, MSRP_REV) %>%
arrange(-SALES)## 5. group_by() and summarise() function
## group_by() and summarise() will help us solve questions such as, what are the total sales by country?
grouped_data <- group_by(sales_data, COUNTRY)
summarised_data <- summarise(grouped_data, total_sales = sum(SALES))
## same as above, But using piping
summarised_data <- sales_data %>%
group_by(COUNTRY) %>%
summarise(total_sales = sum(SALES))## Instead of sum(), can also do max(), min(), mean(), n() for count, and others
## Q4: what is the average SALE by PRODUCTLINE?
summarised_data <- sales_data %>%
group_by(PRODUCTLINE) %>%
summarise(average_sales = mean(SALES))## create a simple dot plot
ggplot(sales_data, aes(x = QUANTITYORDERED, y = SALES)) +
geom_point() +
theme_classic()
## change color
ggplot(sales_data, aes(x = QUANTITYORDERED, y = SALES)) +
geom_point(aes(color = 'red')) +
theme_classic()
## add labels and remove legend
ggplot(sales_data, aes(x = QUANTITYORDERED, y = SALES)) +
geom_point(aes(color = 'red')) +
labs(title = 'Sales and Quantity Ordered',
y = 'Unit Price ($)',
x = 'Quantity Ordered (Units)') +
theme(legend.position="none") +
theme_classic()
## add regression line
ggplot(sales_data, aes(x = QUANTITYORDERED, y = SALES)) +
geom_point(aes(color = 'red')) +
labs(title = 'Sales and Quantity Ordered',
y = 'Unit Price ($)',
x = 'Quantity Ordered (Units)') +
theme(legend.position = "none")+
geom_smooth(method = "lm") +
theme_classic()
## bar charts
## will first create a grouped by and summarised dataset
status_data <- sales_data %>%
group_by(STATUS) %>%
summarise(total_count = n()) %>%
select(STATUS, total_count)## now we will create a bar chart
ggplot(status_data, aes(x = STATUS, y = total_count)) +
geom_bar(stat = 'identity', color = 'red', fill = 'blue') +
theme_classic()
## your turn!
## Q5: create a bar chart of the total sales by country with the following properties:
## - x axis label: Product Line
## - y axis label: Total Sales ($)
## - title: Sales by Product Line
## - Outline of bars: red
## - Fill of bars: pink
## Will first create a grouped by and summarised dataset
status_data <- sales_data %>%
group_by(PRODUCTLINE) %>%
summarise(total_sales = sum(SALES))## bar plot
ggplot(status_data, aes(x = PRODUCTLINE, y = total_sales)) +
geom_bar(stat = 'identity', color = 'red', fill = 'pink') +
labs(title = 'Sales by Product Line') +
theme_classic() +
coord_flip()
## Q6: using the ggplot2 cheat sheet, try constructing your own plot of choice!
## bar plot
ggplot(status_data, aes(x = PRODUCTLINE, y = total_sales)) +
geom_bar(stat = 'identity', color = 'red', fill = 'yellow') +
labs(title = 'Sales by Product Line') +
theme_classic() +
coord_polar()