CST-425 demo
TAsk Discription
Task 1: explore data Task 2: source the data Task 3: print a sample of raw data Task 4: print a plot of the sample data Task 5: clean the data Task 6: summarize the clean data *Task 7: plot the clean data
##Exploratory Analysis This is going to look at the 10 columns of the sample data. The data at this point is unclean so there is some possible false data points.
mydata = read.csv("~/CST-425/input.csv", header = TRUE)
print(head(mydata,10))## id name host_id host_name
## 1 2539 Clean & quiet apt home by the park 2787 John
## 2 2595 Skylit Midtown Castle 2845 Jennifer
## 3 3647 THE VILLAGE OF HARLEM....NEW YORK ! 4632 Elisabeth
## 4 3831 Cozy Entire Floor of Brownstone 4869 LisaRoxanne
## 5 5022 Entire Apt: Spacious Studio/Loft by central park 7192 Laura
## 6 5099 Large Cozy 1 BR Apartment In Midtown East 7322 Chris
## 7 5121 BlissArtsSpace! 7356 Garon
## 8 5178 Large Furnished Room Near B'way 8967 Shunichi
## 9 5203 Cozy Clean Guest Room - Family Apt 7490 MaryEllen
## 10 5238 Cute & Cozy Lower East Side 1 bdrm 7549 Ben
## neighbourhood_group neighbourhood latitude longitude room_type
## 1 Brooklyn Kensington 40.64749 -73.97237 Private room
## 2 Manhattan Midtown 40.75362 -73.98377 Entire home/apt
## 3 Manhattan Harlem 40.80902 -73.94190 Private room
## 4 Brooklyn Clinton Hill 40.68514 -73.95976 Entire home/apt
## 5 Manhattan East Harlem 40.79851 -73.94399 Entire home/apt
## 6 Manhattan Murray Hill 40.74767 -73.97500 Entire home/apt
## 7 Brooklyn Bedford-Stuyvesant 40.68688 -73.95596 Private room
## 8 Manhattan Hell's Kitchen 40.76489 -73.98493 Private room
## 9 Manhattan Upper West Side 40.80178 -73.96723 Private room
## 10 Manhattan Chinatown 40.71344 -73.99037 Entire home/apt
## price minimum_nights number_of_reviews last_review reviews_per_month
## 1 149 1 9 10/19/2018 0.21
## 2 225 1 45 5/21/2019 0.38
## 3 150 3 0 NA
## 4 89 1 270 7/5/2019 4.64
## 5 80 10 9 11/19/2018 0.10
## 6 200 3 74 6/22/2019 0.59
## 7 60 45 49 10/5/2017 0.40
## 8 79 2 430 6/24/2019 3.47
## 9 79 2 118 7/21/2017 0.99
## 10 150 1 160 6/9/2019 1.33
## calculated_host_listings_count availability_365
## 1 6 365
## 2 2 355
## 3 1 365
## 4 1 194
## 5 1 0
## 6 1 129
## 7 1 0
## 8 1 220
## 9 1 0
## 10 4 188##Summary of price
summary(mydata$price)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 69.0 106.0 152.7 175.0 10000.0##Ploting Data Plot below shows the areas and the prices in each area. Comparing the Bronx and Manhattan neighborhoods Manhattan has higher prices than the Bronx, which could mean that there are nicer places in Manhattan that can fetch more money than in the Bronx.
mydata_frame = data.frame(mydata$price,mydata$neighbourhood_group)
boxplot(mydata$price~mydata$neighbourhood_group, data = mydata_frame) 
##Analyzing Clean Data In looking over the data there are significant outliers between the Bronx and Manhattan neighborhoods.
boxplot.stats(mydata$price, coef =2)$out## [1] 800 500 400 475 500 500 390 575 500 650 599 395
## [13] 495 451 450 500 400 2000 500 429 399 438 700 400
## [25] 850 599 495 760 485 1300 402 800 450 499 500 3000
## [37] 549 500 395 1300 500 450 499 800 450 500 399 700
## [49] 500 400 1000 700 433 595 2000 550 900 399 425 495
## [61] 625 575 400 500 600 390 465 4000 500 400 400 495
## [73] 999 800 500 2500 890 399 400 650 420 950 575 550
## [85] 499 1000 499 1395 425 395 650 550 1000 750 499 1000
## [97] 595 390 399 1500 1899 399 465 495 800 450 5000 390
## [109] 600 1100 550 420 400 600 1200 440 439 555 400 450
## [121] 390 700 799 475 455 420 400 550 1700 525 600 399
## [133] 499 450 450 650 750 399 499 1999 480 450 1500 1000
## [145] 850 500 500 400 500 395 1000 800 599 400 700 575
## [157] 560 400 6000 900 900 850 2000 750 575 599 2000 850
## [169] 950 1500 675 900 395 1500 749 1000 4000 650 750 1000
## [181] 1200 420 395 5250 1500 1500 1000 1000 1500 1550 500 500
## [193] 400 750 425 1000 1250 1485 600 400 6500 395 2750 1500
## [205] 2500 3750 500 800 1600 750 1000 600 800 1000 900 425
## [217] 975 650 400 500 500 400 400 500 450 650 400 600
## [229] 395 399 550 595 450 2300 425 400 409 430 400 650
## [241] 450 5000 4500 8000 600 795 414 499 950 750 500 500
## [253] 425 429 450 400 425 500 785 1000 425 400 500 750
## [265] 400 495 550 600 399 500 500 545 400 395 390 400
## [277] 450 860 1000 450 540 1400 500 600 395 419 500 900
## [289] 500 1050 395 2695 1000 450 650 2000 985 425 699 550
## [301] 495 400 450 1500 400 450 450 399 1000 500 450 895
## [313] 600 600 550 525 500 1000 1000 9999 400 2400 600 2000
## [325] 600 500 450 549 760 1000 795 450 500 415 995 1763
## [337] 510 1450 400 495 600 499 825 700 500 524 750 450
## [349] 2000 2000 400 2000 475 2000 475 450 450 400 450 1200
## [361] 499 499 480 950 750 750 445 399 495 400 449 395
## [373] 400 499 395 680 900 1000 700 450 399 400 600 440
## [385] 1495 399 450 499 888 389 460 2000 700 450 900 800
## [397] 700 800 500 999 10000 600 499 850 500 400 500 398
## [409] 450 599 700 450 500 450 450 500 600 450 480 399
## [421] 650 650 1600 450 450 399 595 400 499 450 400 700
## [433] 400 700 1500 600 400 450 500 420 500 425 490 800
## [445] 500 400 400 1200 1100 500 400 400 1195 450 500 999
## [457] 395 400 500 429 700 469 600 399 400 500 414 450
## [469] 455 500 650 2000 425 800 800 500 495 400 450 1000
## [481] 500 2000 650 745 399 395 410 1170 699 1150 1000 500
## [493] 450 434 500 590 899 750 500 400 450 400 800 650
## [505] 465 425 400 450 465 500 450 531 525 655 450 500
## [517] 400 410 1200 9999 500 450 450 450 395 400 550 450
## [529] 390 399 450 425 499 1000 500 750 477 699 1200 800
## [541] 799 1100 995 500 600 850 425 400 500 400 500 485
## [553] 700 425 800 587 399 550 500 449 500 499 500 1731
## [565] 400 600 1002 500 400 450 450 1250 600 425 400 400
## [577] 1100 399 395 450 814 450 550 500 550 550 390 1800
## [589] 2000 700 1200 499 421 690 619 600 450 2100 1100 400
## [601] 999 500 450 750 495 625 400 490 450 400 425 650
## [613] 595 500 800 450 999 1000 500 999 400 500 500 409
## [625] 750 449 450 2500 525 450 436 399 500 935 500 650
## [637] 6000 1300 500 399 800 980 1000 1000 1000 400 900 399
## [649] 480 390 475 500 450 700 2000 399 575 400 1000 850
## [661] 400 400 700 559 499 600 509 400 650 2000 500 500
## [673] 600 450 450 1100 450 400 525 1000 400 408 700 499
## [685] 495 500 399 525 700 400 750 500 1000 3800 479 450
## [697] 10000 650 2350 500 400 822 1100 450 400 499 560 407
## [709] 700 650 425 489 425 450 2500 650 795 800 399 400
## [721] 499 999 440 750 672 3000 399 400 800 575 800 500
## [733] 415 400 500 584 750 399 475 894 643 699 462 749
## [745] 799 599 699 499 799 499 649 799 399 499 534 748
## [757] 748 534 748 748 850 400 450 550 1000 390 399 600
## [769] 425 2000 408 1500 500 534 534 534 534 534 748 748
## [781] 748 449 2000 487 450 499 1500 500 400 2000 1020 700
## [793] 900 950 400 500 899 700 599 799 1599 649 699 799
## [805] 500 500 699 400 799 800 1200 500 400 700 525 850
## [817] 425 585 799 450 900 2590 400 400 499 399 599 400
## [829] 450 599 999 550 895 899 550 500 1000 995 399 640
## [841] 800 459 400 400 900 600 489 600 450 545 650 500
## [853] 600 500 400 4000 600 500 450 2250 800 700 484 429
## [865] 395 5000 2800 499 510 700 2250 550 1000 490 400 850
## [877] 1475 450 400 615 419 545 2000 2900 450 400 475 430
## [889] 825 500 399 390 800 515 1500 1067 465 1599 389 400
## [901] 424 1000 578 1200 950 475 600 800 1000 399 450 650
## [913] 400 425 1500 500 390 399 550 450 400 389 900 400
## [925] 499 400 2500 398 550 2500 400 700 700 1000 550 400
## [937] 789 600 500 500 599 585 795 449 500 399 1795 690
## [949] 500 1095 500 389 450 899 399 800 600 800 400 1500
## [961] 600 615 800 409 600 1000 672 530 400 400 495 449
## [973] 799 389 699 389 400 499 5000 450 3900 496 1700 435
## [985] 422 525 435 450 400 700 650 695 425 1177 790 400
## [997] 400 1500 475 750 5000 525 573 1100 766 840 400 400
## [1009] 540 585 395 800 577 589 750 550 949 400 489 400
## [1021] 500 1000 485 500 2200 395 850 400 459 497 550 500
## [1033] 700 495 750 1000 499 500 600 400 400 400 1066 400
## [1045] 450 450 398 398 425 412 400 650 600 850 400 850
## [1057] 395 550 650 800 649 485 650 475 460 487 455 399
## [1069] 400 400 400 425 400 450 400 647 485 625 600 500
## [1081] 500 400 3000 400 720 400 3000 400 500 2010 3210 4160
## [1093] 1046 400 475 1000 400 500 450 450 499 499 1500 10000
## [1105] 399 400 550 800 450 490 416 545 395 800 450 1680
## [1117] 7703 3518 6419 2626 2103 980 1100 1500 600 480 450 400
## [1129] 399 495 525 850 495 1250 500 500 700 395 1000 2000
## [1141] 2500 8500 409 400 450 479 550 450 640 400 500 400
## [1153] 400 400 400 750 800 750 550 800 600 600 400 600
## [1165] 2990 1000 1000 600 620 399 649 412 412 412 650 500
## [1177] 750 1500 394 450 425 450 975 450 545 400 1000 974
## [1189] 400 400 415 700 400 400 650 599 400 450 550 450
## [1201] 600 1195 795 1200 400 1500 415 400 2500 399 398 450
## [1213] 500 650 699 475 450 450 400 390 600 799 399 410
## [1225] 450 1000 425 650 1000 500 460 1333 500 1100 499 500
## [1237] 398 400 525 649 499 425 1000 550 550 800 780 850
## [1249] 399 399 393 550 745 1200 750 425 595 400 499 650
## [1261] 449 400 450 599 1500 400 465 900 500 490 995 460
## [1273] 390 425 750 395 395 1250 999 750 450 700 1200 415
## [1285] 500 483 550 450 1700 699 699 699 699 699 699 699
## [1297] 699 699 737 737 737 737 737 737 737 737 699 699
## [1309] 699 737 737 550 395 600 650 495 470 435 525 550
## [1321] 400 1000 450 799 475 450 689 400 400 1100 3750 400
## [1333] 800 400 400 399 600 495 394 799 499 400 499 639
## [1345] 460 480 530 590 848 410 430 500 600 590 600 1500
## [1357] 449 800 750 389 785 500 575 425 400 403 471 1000
## [1369] 750 399 500 400 500 799 480 500 478 450 399 425
## [1381] 400 500 850 575 800 433 400 999 600 500 899 975
## [1393] 425 599 670 499 390 500 525 1500 625 499 700 1700
## [1405] 6500 600 399 1000 399 500 860 650 500 2545 399 500
## [1417] 485 500 1115 650 700 650 399 700 520 399 455 590
## [1429] 913 550 505 718 475 692 400 3600 750 400 400 450
## [1441] 600 600 450 5000 600 499 600 2000 800 500 400 460
## [1453] 550 1880 3000 495 450 500 400 714 675 675 675 675
## [1465] 675 956 675 675 675 675 675 714 714 714 956 714
## [1477] 714 714 675 675 714 714 714 714 714 3000 670 1500
## [1489] 2999 399 499 499 499 399 399 399 399 418 1600 399
## [1501] 452 397 392 396 420 459 416 453 481 466 388 423
## [1513] 430 750 805 3000 725 443 392 800 699 595 800 398
## [1525] 400 390 450 550 1350 550 392 950 520 500 400 3200
## [1537] 500 500 500 575 600 500 600 600 550 599 432 450
## [1549] 444 399 590 450 499 515 500 400 750 600 1000 425
## [1561] 420 1350 443 1000 499 450 1075 550 3000 956 410 450
## [1573] 820 520 399 1999 400 649 399 890 500 1750 450 1000
## [1585] 478 400 450 399 399 500 1080 1299 9999 495 525 395
## [1597] 400 599 400 400 400 2500 1150 1050 750 600 1295 1200
## [1609] 700 400 495 519 699 799 450 450 575 450 1500 450
## [1621] 400 1978 1494 3512 600 890 800 500 699 799 1000 500
## [1633] 400 418 450 404 425 503 392 472 999 454 472 503
## [1645] 404 431 412 403 451 419 505 468 390 396 498 612
## [1657] 650 1200 999 999 999 500 650 400 488 425 1497 750
## [1669] 515 1250 420 500 500 2850 400 500 500 500 540 700
## [1681] 990 395 484 388 407 425 432 396 416 506 650 499
## [1693] 840 450 1315 650 400 450 489 488 400 400 446 7500
## [1705] 3613 400 443 520 388 400 2600 399 600 4100 399 700
## [1717] 396 700 794 500 395 1145 1306 400 525 450 595 560
## [1729] 5100 499 800 898 419 450 950 750 1000 799 600 470
## [1741] 395 442 949 400 439 505 425 425 425 800 400 499
## [1753] 419 700 600 395 600 472 474 495 398 1799 395 699
## [1765] 700 549 550 800 4200 420 390 475 420 400 450 500
## [1777] 700 500 790 616 408 6800 396 500 500 590 550 500
## [1789] 945 450 690 409 430 390 410 468 1000 500 499 650
## [1801] 470 470 539 499 426 699 577 499 699 500 699 500
## [1813] 595 2000 450 750 875 389 499 395 499 589 449 699
## [1825] 699 699 699 438 650 463 399 700 389 688 449 399
## [1837] 595 399 399 485 479 499 399 600 500 750 549 599
## [1849] 400 475 475 425 450 450 500 425 450 450 425 400
## [1861] 425 945 500 620 500 560 500 400 400 950 580 583
## [1873] 650 390 999 2500 420 400 690 400 449 500 585 499
## [1885] 460 1900 500 479 500 490 499 499 650 450 500 395
## [1897] 1250 799 659 650 650 399 475 499 1000 400 830 440
## [1909] 489 650 399 590 7500 450 2500 550 459 650 850 650
## [1921] 699 600 499 399 850 399 975 4200 895 499 447 447
## [1933] 600 795 995 695 1800 595 900 600 450 399 389 495
## [1945] 400 400 550 495 1099 400 428 650 599 650 700 400
## [1957] 549 479 395 400 600 395 390 500 500 500 650 425
## [1969] 445 1750 390 450 400 550 590 585 399 425 399 399
## [1981] 399 500 420 499 500 600 450 500 500 995 945 395
## [1993] 500 500 444 550 900 395 490 900 400 920 500 400
## [2005] 900 800 390 750 450 930 599 459 700 950 416 500
## [2017] 475 750 399 2995 480 500 550 999 500 4500 450 700
## [2029] 450 500 400 1200 400 400 489 650 400 400 500 489
## [2041] 450 500 500 450 450 575 600 800 475 900 400 450
## [2053] 850 1750 395 475 650 500 950 750 499 465 800 600
## [2065] 900 600 843 395 400 500 450 800 429 425 600 400
## [2077] 425 800 400 400 400 475 417 450 450 395 450 500
## [2089] 2000 400 400 1400 750 400 399 449 400 400 399 399
## [2101] 649 450 399 500 650 1100 399 750 500 600 1500 550
## [2113] 600 400 495 595 500 750 600 450 500 399 499 450
## [2125] 699 699 550 400 425 425 1600 800 420 599 800 399
## [2137] 650 6500 1050 1308 390 560 583 550 595 500 561 1000
## [2149] 999 2999 1999 600 750 600 400 400 450 450 520 450
## [2161] 1369 450 1749 466 421 400 400 430 450 400 750 396
## [2173] 393 550 499 700 475 800boxplot(mydata$price~mydata$neighbourhood_group, data = mydata_frame)
##conclusion Looking at the data, there was not much if at anything to clean. Analyzing two neighborhood groups from the graph above Manhattan has the a higher price range than the Bronx neighborhood.