Lab Homework 3 Jiashu Zhao
Michael Zhao
2025-01-24
Question #0: Finish all Lab Exercises
How many samples are there? How many variables are there?
Answer: samples are 10000, and variables are 55
glimpse(loans_full_schema)loans <- select(loans_full_schema,loan_amount,interest_rate,term,grade,state,annual_income,homeownership,debt_to_income)glimpse(loans)## Rows: 10,000
## Columns: 8
## $ loan_amount <int> 28000, 5000, 2000, 21600, 23000, 5000, 24000, 20000, 20…
## $ interest_rate <dbl> 14.07, 12.61, 17.09, 6.72, 14.07, 6.72, 13.59, 11.99, 1…
## $ term <dbl> 60, 36, 36, 36, 36, 36, 60, 60, 36, 36, 60, 60, 36, 60,…
## $ grade <fct> C, C, D, A, C, A, C, B, C, A, C, B, C, B, D, D, D, F, E…
## $ state <fct> NJ, HI, WI, PA, CA, KY, MI, AZ, NV, IL, IL, FL, SC, CO,…
## $ annual_income <dbl> 90000, 40000, 40000, 30000, 35000, 34000, 35000, 110000…
## $ homeownership <fct> MORTGAGE, RENT, RENT, RENT, RENT, OWN, MORTGAGE, MORTGA…
## $ debt_to_income <dbl> 18.01, 5.04, 21.15, 10.16, 57.96, 6.46, 23.66, 16.19, 3…How many distinct values are there for homeownership variable? Which value is the most common one?
Answer: There are four home ownerships variable which are “any”, “mortgage”, “own”, and “rent”. The most value is 4789 for “mortgage”
unique(loans$homeownership)## [1] MORTGAGE RENT OWN
## Levels: ANY MORTGAGE OWN RENTtable(loans$homeownership)##
## ANY MORTGAGE OWN RENT
## 0 0 4789 1353 3858How many distinct interest rates are there? Which value is the most common one?
Answer: There are 58 distinct interest rates, and the most common one is 9.93 for 390.
unique(loans$interest_rate)## [1] 14.07 12.61 17.09 6.72 13.59 11.99 6.71 15.04 9.92 9.43 19.03 28.72
## [13] 26.77 15.05 6.08 11.98 7.96 7.34 5.32 6.07 12.62 9.44 20.39 9.93
## [25] 21.45 10.42 18.06 22.91 30.79 17.47 5.31 7.97 14.08 19.42 10.91 16.02
## [37] 13.58 16.01 20.00 21.85 10.90 23.87 7.35 23.88 25.82 10.41 18.45 30.17
## [49] 24.85 25.81 24.84 30.75 29.69 26.30 22.90 6.00 30.65 30.94sort(table(loans$interest_rate))##
## 30.94 6 30.75 30.65 29.69 30.17 30.79 22.9 23.87 25.81 22.91 24.84 28.72
## 1 3 4 5 9 9 11 13 20 26 28 31 31
## 23.88 26.77 24.85 25.82 26.3 21.85 20.39 19.42 17.47 20 18.45 21.45 18.06
## 37 38 42 47 53 90 93 114 124 137 146 172 176
## 14.07 5.31 6.71 10.41 17.09 16.01 19.03 15.04 6.07 7.96 13.58 5.32 7.34
## 183 188 192 194 195 196 197 199 202 211 225 234 243
## 9.92 11.98 12.61 7.97 10.9 6.08 9.43 16.02 15.05 10.91 6.72 14.08 7.35
## 248 255 264 274 275 277 280 284 304 306 312 318 325
## 12.62 10.42 13.59 9.44 11.99 9.93
## 333 346 347 367 376 390Apply table function to the annual_income variable. Do you think the result is helpful or not?
table(loans$annual_income)##
## 0 1 3000 3120 3300 4000 4800 5000
## 23 1 2 1 1 1 1 2
## 5208 5235 5500 7200 7500 7800 8000 8500
## 1 1 1 1 1 1 1 1
## 9000 9600 9840 9972 9996 10000 10320 10500
## 4 2 1 1 1 11 1 2
## 10548 10596 10800 11000 11150 11352 11772 12000
## 1 1 4 3 1 1 1 9
## 12036 12250 12276 12300 12480 12696 12816 13000
## 1 1 1 1 1 1 1 6
## 13050 13136 13140 13164 13200 13390 13500 13692
## 1 1 1 1 2 1 1 1
## 13728 13800 13920 13930 14000 14200 14300 14364
## 1 1 1 1 5 1 1 1
## 14400 14500 14508 14560 14568 14616 14928 15000
## 4 1 1 1 1 1 1 27
## 15200 15250 15380 15500 15599 15600 15864 15972
## 1 1 1 1 1 2 1 1
## 15984 15996 16000 16200 16356 16500 16620 16700
## 1 1 7 2 1 1 1 1
## 16752 16848 17000 17200 17340 17453 17500 18000
## 1 1 14 1 1 1 1 19
## 18312 18348 18375 18400 18500 18624 18700 18708
## 1 1 1 1 1 1 1 1
## 18720 18800 18900 18960 19000 19200 19250 19582.09
## 1 1 1 1 12 2 1 1
## 19700 19719 20000 20076 20280 20376 20400 20562
## 1 1 46 1 1 1 2 1
## 20567 20600 20712 20800 21000 21049 21120 21320
## 1 1 1 1 11 1 1 1
## 21432 21444 21500 21600 21750 21800 21900 21918
## 1 1 1 3 1 1 1 1
## 22000 22104 22114 22212 22244 22362.75 22500 22560
## 20 1 1 1 1 1 1 1
## 22568 22644.7 22772.25 22800 22848 23000 23040 23077
## 1 1 1 4 1 24 1 1
## 23500 23666 23898 23920 24000 24402 24480 24482
## 3 1 1 1 41 1 1 1
## 24525 24538 24576 24720 25000 25100 25200 25236
## 1 1 1 1 92 1 4 1
## 25252 25320 25356 25400 25465 25500 25560 25600
## 1 1 1 1 1 5 1 1
## 25700 25800 25825 25875 25920 25992 26000 26127
## 1 1 1 1 1 1 33 1
## 26271 26304 26400 26448 26500 26551 26592 26600
## 1 1 1 1 3 1 1 1
## 26694 26895 27000 27200 27278 27354 27480 27500
## 1 1 21 1 1 1 1 3
## 27511 27550 27600 27610 27684 27703 27840 27872
## 1 1 1 1 1 1 1 1
## 27976 28000 28100 28201.32 28500 28560 28600 28711
## 1 48 1 1 3 1 1 1
## 28740 28750 28790 28800 28865 29000 29016 29120
## 1 1 1 3 1 19 1 2
## 29200 29250 29300 29388 29400 29415 29500 29744
## 1 1 1 1 1 1 2 1
## 29760 29881.32 29982 30000 30163 30205.5 30457 30504
## 1 1 1 168 1 1 1 1
## 30564 30600 30720 30784 30800 31000 31200 31250
## 1 1 2 1 1 32 6 1
## 31280.04 31300 31304 31360 31500 31536 31600 31656
## 1 1 1 1 6 1 2 1
## 31680 31700 31800 31812 31858 31942.63 31972 32000
## 1 1 1 1 1 1 1 77
## 32028 32112 32248.24 32300 32340 32344 32357 32500
## 1 1 1 1 1 1 1 5
## 32688 32760 32800 32844 32900 33000 33024 33040
## 1 1 1 2 1 46 1 1
## 33110 33134.4 33186 33195 33197 33250 33280 33300
## 1 1 1 1 1 1 2 1
## 33395 33427.94 33477 33480 33500 33600 33661.32 33701
## 1 1 1 1 3 4 1 1
## 33768 33800 33852 33918 33975 33996 33999 34000
## 1 1 1 1 1 1 1 45
## 34047 34113 34235 34320 34385 34415.16 34416 34451
## 1 1 1 2 1 1 1 1
## 34500 34521 34560 34600 34625.64 34700 34733.5 34771
## 4 1 3 1 1 1 1 1
## 34800 34840 34964 34982.21 35000 35200 35300 35360
## 1 1 1 1 182 1 1 1
## 35500 35568 35600 35790 35859 35860 36000 36025
## 2 1 2 1 1 1 73 1
## 36051 36144 36171 36224 36240 36250 36300 36320
## 1 1 1 1 1 1 1 1
## 36331 36400 36420 36433 36500 36536 36598 36609
## 1 4 2 1 2 1 1 1
## 36610 36780 36785 36800 36886.32 36900 37000 37020
## 1 1 1 1 1 1 46 1
## 37024 37183.56 37200 37260 37262 37400 37440 37447
## 1 1 1 1 1 1 2 1
## 37500 37540 37700 37770 37780 37840 38000 38043
## 5 1 1 1 1 2 70 1
## 38168 38200 38248 38272 38400 38411 38427 38450
## 1 1 1 1 7 1 1 1
## 38455 38464 38500 38550 38560 38600 38666 38678
## 1 1 3 1 1 2 1 1
## 38700 38900 38979 38990.47 39000 39053 39332.8 39362
## 2 1 1 1 47 1 1 1
## 39400 39442 39500 39520 39579 39600 39800 39804
## 1 1 2 1 1 2 1 1
## 39900 39910 39995 40000 40012 40059.96 40250 40280
## 1 1 1 247 1 1 1 1
## 40296 40320 40390 40400 40552 40600 40743.25 40758
## 1 1 1 1 1 1 1 1
## 40765 40800 40825 41000 41072 41200 41280 41364
## 1 2 1 35 1 1 1 1
## 41442 41500 41524 41600 41640.62 41787 41900 41969
## 1 3 1 6 1 1 1 1
## 42000 42204 42240 42321 42433 42500 42563 42585.6
## 121 1 1 1 1 5 1 1
## 42600 42614 42640 42655.99 42700 42733 42755 42780
## 1 1 2 1 1 1 1 1
## 42996 43000 43125 43188.48 43200 43260 43437 43492.8
## 1 53 1 1 2 1 1 1
## 43500 43600 43611 43640 43664 43678 43896 43900
## 3 1 1 1 1 1 1 1
## 43930 44000 44050 44088 44100 44184 44190 44200
## 1 37 1 1 1 1 1 3
## 44211 44240 44268 44346 44393 44500 44600 44640
## 1 1 1 1 1 7 1 1
## 44668 44775 44820 44865 44900 45000 45001 45198
## 1 1 1 1 1 204 1 1
## 45240 45300 45360 45384 45500 45576 45700 45782
## 1 1 1 1 2 1 1 1
## 45804 45930 46000 46080 46128 46219 46236 46280
## 1 1 40 1 1 1 1 1
## 46310 46328 46400 46422 46500 46556 46650 46680
## 1 1 1 1 6 1 1 1
## 46800 46996 47000 47028 47106 47128 47328 47418
## 2 1 32 1 1 1 1 1
## 47476 47500 47595 47600 47611.2 47700 47710.44 47757
## 1 4 1 1 1 3 1 1
## 47782 47800 47840 47960 48000 48028 48200 48222
## 1 2 2 1 116 1 2 1
## 48300 48370 48391 48425 48450 48500 48509 48580
## 1 1 1 1 1 2 1 1
## 48589 48712 48750 48769 48996 49000 49200 49300
## 1 1 2 1 1 31 1 1
## 49343 49500 49583 49623 49642 49650 49920 49992
## 1 7 1 1 1 1 1 1
## 50000 50066 50120 50129 50160 50220 50256 50320
## 350 1 1 1 1 1 1 1
## 50400 50615 50622 50676 50731 50840 50912 50919
## 2 1 1 1 1 1 1 1
## 50960 50976 51000 51072 51200 51252 51500 51600
## 1 1 31 1 2 1 1 1
## 51632 51754 51800 51882 51883 51900 51921 52000
## 1 1 1 1 1 1 1 90
## 52140 52250 52278.85 52300 52325 52400 52420 52440
## 1 1 1 1 1 1 1 1
## 52464 52499.2 52500 52505.4 52537 52540 52600 52680
## 1 1 5 1 1 1 1 1
## 52755 52764 52780 52821.43 53000 53038.52 53045 53200
## 1 1 1 1 39 1 1 1
## 53232 53246 53400 53456 53500 53527.6 53574 53592
## 1 1 1 1 3 1 1 1
## 53654.88 53695 53700 53701 53774 53800 53900 53953
## 1 1 1 1 1 1 1 1
## 54000 54024 54032 54059.5 54100 54105 54127.1 54164
## 69 1 1 1 1 1 1 1
## 54200 54237 54250 54269 54300 54500 54600 54625
## 3 1 1 1 1 5 3 1
## 54750 54800 54840 54883 54902 54996 55000 55120
## 1 1 1 1 1 1 236 1
## 55160.04 55200 55213 55232 55296 55400 55412 55500
## 1 3 1 1 1 1 1 1
## 55523.24 55584 55700 55836 55944 56000 56160 56309
## 1 1 1 1 1 66 1 1
## 56368 56400 56419.68 56446 56450 56500 56736 56750
## 1 2 1 1 1 2 1 1
## 56800 56820 56891 56912 57000 57056.62 57200 57500
## 1 1 1 1 48 1 1 9
## 57506 57564 57600 57626 57820 57845 57920 58000
## 1 1 2 1 1 1 1 69
## 58142 58240 58250 58380 58500 58544 58559 58700
## 1 1 2 1 4 1 1 1
## 58734 58746 58756 58764 58800 58900 59000 59019
## 1 1 1 1 4 1 37 1
## 59054 59142 59182 59200 59300 59390 59500 59600
## 1 1 1 1 1 1 1 1
## 59700 59789 59819.97 59869 59871 59991 60000 60100
## 1 1 1 1 1 1 383 1
## 60198 60200 60372 60433 60495.2 60500 60600 60705
## 1 1 1 1 1 1 2 1
## 60800 60825 60960 61000 61175 61200 61300 61368
## 1 1 1 31 1 1 1 1
## 61400 61400.91 61500 61538 61582 61584 61766 61800
## 1 1 2 1 1 1 1 1
## 61846 61880 62000 62004 62100 62200 62300 62339
## 1 1 76 1 1 1 1 1
## 62400 62500 62628 62712 62800 62823 62914 62933.64
## 11 4 1 1 1 1 1 1
## 63000 63024 63071 63130 63215 63235 63288 63330
## 50 1 1 1 1 1 1 1
## 63360 63373 63446.53 63477.84 63500 63645 63695 63700
## 2 1 1 1 4 1 1 1
## 63780 63938 63950 64000 64106 64449 64600 64655
## 1 1 1 46 1 1 1 1
## 64660 64721 64800 64865 64874 64934 64992 65000
## 1 1 1 1 1 1 1 314
## 65244 65318.52 65488 65500 65700 65800 65895 65900
## 1 1 1 3 2 1 1 1
## 65918 65926 66000 66144 66150 66200 66300 66500
## 1 1 29 1 1 1 1 2
## 66800 66872 66900 66913.6 66920 66996 67000 67200
## 1 1 2 1 1 1 46 3
## 67400 67500 67600 67700 67745 67760 67764 67790
## 4 4 3 1 1 1 1 1
## 67800 67846 68000 68095 68186 68200 68250 68274
## 1 1 76 1 1 2 1 1
## 68353 68400 68450 68520 68575 68640 68700 68748
## 1 1 1 1 1 1 2 1
## 68750 68810 69000 69077 69108 69116 69131 69200
## 2 1 28 1 1 1 1 1
## 69351 69467 69700 69800 69854 69860 69900 69996
## 1 1 1 1 1 1 2 2
## 70000 70020 70090 70200 70209 70260 70400 70480
## 273 1 1 1 1 1 1 1
## 70500 70540 70566 70866 70900 71000 71104 71240
## 3 1 1 1 1 41 1 1
## 71400 71422 71487 71500 72000 72100 72135 72150
## 1 1 1 3 110 1 1 1
## 72197.95 72200 72276 72320 72400 72465 72500 72550
## 1 1 1 1 1 1 3 1
## 72600 72612 72672 72720 72768 72800 72828 72900
## 1 1 1 1 1 3 1 1
## 72949 73000 73364 73400 73460 73500 73623 74000
## 1 29 1 1 1 1 1 27
## 74106 74200 74300 74400 74500 74600 74620 74644
## 1 1 1 1 2 2 1 1
## 74870 74895 75000 75200 75250 75312 75341 75400
## 1 1 260 1 2 1 1 1
## 75500 75610 75696 75800 75894 75999 76000 76012
## 1 1 1 1 1 1 36 1
## 76300 76500 76590 76760 76800 76892 77000 77017
## 1 4 1 1 1 1 26 1
## 77049.16 77100 77177.88 77400 77500 77856 77900 78000
## 1 1 1 1 3 1 1 60
## 78132.55 78500 78648 78762 78816 78900 78927 79000
## 1 2 1 1 1 1 1 19
## 79040 79043 79300 79312 79500 79547 79584 79700
## 2 1 1 1 5 1 1 2
## 79800 79982 79992 80000 80200 80400 80500 80833.44
## 1 1 1 248 1 2 2 1
## 80901 80969.94 81000 81015 81120 81200 81250 81257.8
## 1 1 23 1 1 1 1 1
## 81526 81750 81800 81900 82000 82048 82200 82416
## 1 1 1 1 60 1 1 1
## 82500 82756.8 82767.78 83000 83004 83043 83200 83207
## 6 1 1 30 1 1 1 1
## 83233 83500 83600 83740 83800 83819.67 84000 84240
## 1 1 1 2 1 1 34 1
## 84279.78 84362 84396 84400 84500 84646 84676 84800
## 1 1 1 1 3 1 1 1
## 85000 85175.04 85208.71 85410 85441 85451 85500 85600
## 141 1 1 1 1 1 4 1
## 85605 85750 85765 85800 85820 86000 86110 86500
## 1 1 1 1 1 37 1 2
## 86845 86850 87000 87040 87120 87125 87276 87400
## 1 1 24 1 1 1 1 2
## 87500 87600 87800 87812 88000 88116 88141 88251
## 9 1 1 1 29 1 1 1
## 88272 88300 88400 88500 88600 88932 88944 89000
## 1 1 1 1 1 1 1 26
## 89179 89206 89400 89500 89748 90000 90500 90540
## 1 1 1 1 1 204 2 1
## 90666 90720 91000 91100 91200 91275 91380 91690
## 1 1 22 1 2 1 1 1
## 91900 92000 92200 92290 92400 92465 92500 92600
## 1 45 1 1 1 1 1 1
## 92879 92942 93000 93006 93300 93500 93600 93700
## 1 1 18 1 1 5 2 1
## 93707 93844 94000 94500 94966 95000 95280 95500
## 1 1 29 1 1 96 1 2
## 95504 95543 95634 95731 95800 96000 96152 96355.33
## 1 1 1 1 1 36 1 1
## 96400 96500 96512 96774 96800 96821.31 96996 97000
## 1 2 1 1 2 1 1 21
## 97500 97598 97600 98000 98241 98281 98460 98560
## 3 1 1 37 1 1 1 1
## 98700 98728 99000 99097 99270 99985 1e+05 100100
## 1 1 18 1 1 1 221 1
## 100203 100500 100600 100609.6 100800 101000 101200 101400
## 1 1 1 1 1 22 1 1
## 101500 101576 101760 101794 101851 101920 102000 102004
## 1 1 1 1 1 1 23 1
## 102500 102900 102947 103000 103170 103294 103500 103960
## 2 1 1 24 1 1 3 1
## 104000 104176.66 104465 104500 104550 104660 105000 105832
## 27 1 1 1 1 1 66 1
## 106000 106080 106090 106300 106500 106686 106883 107000
## 23 1 1 1 2 1 1 19
## 107200 107611 108000 108254.9 108367 108407 109000 109500
## 1 1 26 1 1 1 18 1
## 109600 109688 109992 110000 110100 110500 111000 111230.9
## 1 1 1 124 1 2 6 1
## 111275 111996 112000 112029 112500 112700 112798 112914
## 1 1 19 1 2 1 1 1
## 113000 113213.25 113409 113500 114000 114416 115000 115200
## 8 1 1 2 16 1 62 1
## 115800 115942 116000 116162 116365 116480 117000 117650
## 1 1 6 1 2 1 15 1
## 118000 118500 119000 119016 119216 119568 120000 120690
## 14 2 4 1 1 1 178 1
## 120700 120850 121000 121500 121800 122000 123000 123973
## 1 1 6 1 1 11 7 1
## 124000 124500 124730 124800 125000 125381 126000 126049
## 7 2 1 1 89 1 12 1
## 126200 126402 126700 127000 127200 127563 128000 128500
## 1 1 1 6 1 1 14 2
## 129000 129600 130000 130500 130800 131000 131004.7 132000
## 7 1 80 1 1 4 1 14
## 132200 132300 132323 132400 132454 132500 132900 133000
## 1 1 1 1 1 1 1 7
## 133500 134000 134315 134666.2 134676 135000 135400 135500
## 1 2 1 1 1 34 1 1
## 135865 136000 137000 137200 137500 138000 138900 139000
## 1 5 6 1 2 11 1 8
## 139200 139500 140000 141000 142000 142500 143000 143559
## 1 1 75 1 10 2 3 1
## 144000 145000 145262 146000 147000 148000 148600 148650
## 14 26 1 4 4 3 1 1
## 148692 150000 151000 151500 152000 153000 154000 154400
## 1 99 5 1 11 2 4 1
## 155000 155555 156000 157000 158000 159000 160000 160482
## 25 1 7 3 3 3 44 1
## 161000 161105 162000 163000 164000 164200 165000 165200
## 2 1 3 4 1 1 22 1
## 166000 166088 166400 166600 167000 168000 168500 169000
## 5 1 1 1 5 4 1 2
## 169880 170000 171000 172000 173180 175000 175500 175900
## 1 31 1 4 1 29 1 1
## 175933 176000 177000 179000 179443 180000 182000 182200
## 1 1 2 1 1 43 4 1
## 183000 185000 185220 186000 187000 187116 187500 188000
## 1 22 1 1 1 1 1 2
## 188500 189000 190000 191000 192000 193000 193783 194000
## 1 5 23 1 2 1 1 1
## 195000 196000 197000 197280 198000 198999 2e+05 204000
## 10 4 1 1 3 1 46 2
## 205000 206000 208000 208888 209000 210000 211000 212000
## 9 4 1 1 1 8 1 1
## 212500 214458 215000 216000 217000 220000 220500 223500
## 1 1 1 1 1 13 1 1
## 225000 230000 231000 232000 233000 235000 236000 240000
## 11 9 1 1 2 5 1 15
## 245000 249996 250000 252000 254000 255000 256000 258000
## 6 1 30 1 2 1 1 2
## 258240 260000 264000 265000 270000 271000 275000 278000
## 1 8 1 2 1 1 9 1
## 280000 281000 281125 285000 288000 289000 289139 290000
## 8 1 1 2 1 1 1 2
## 294000 299520 3e+05 301000 305000 312221 315000 316317
## 1 1 24 1 1 1 2 1
## 318000 319000 320000 325000 325500 330000 332000 340000
## 1 1 4 3 1 2 1 2
## 349000 350000 351950 352250 353000 355000 363000 375000
## 1 9 1 1 1 1 1 3
## 377000 388000 389000 4e+05 405000 410000 418000 420000
## 1 1 1 5 1 1 1 1
## 425000 430000 440000 450000 485000 498000 5e+05 520000
## 1 1 1 2 1 1 5 1
## 550000 6e+05 650000 7e+05 740000 750000 780000 793000
## 2 2 1 1 1 1 2 1
## 885000 910000 1020000 1050000 1200000 1600001 2300000
## 1 1 1 1 1 1 1Answer: It does not help a lot, since there are so many distinct annual incomes from the data base. In the table, it looks a little messy.
ggplot(loans) +
geom_histogram(mapping = aes(x = interest_rate))
ggplot(data = loans) +
geom_histogram(mapping = aes(x = interest_rate), bins = 20)
ggplot(data = loans) +
geom_histogram(mapping = aes(x = interest_rate), bins = 10)
ggplot(data = loans) +
geom_histogram(mapping = aes(x = interest_rate), binwidth = 5)
ggplot(data = loans) +
geom_histogram(mapping = aes(x = interest_rate), binwidth = 5, boundary = 10)
ggplot(data = loans) +
geom_histogram(mapping = aes(x = interest_rate), binwidth = 1, center = 10)
ggplot(data = loans) +
geom_histogram(mapping = aes(x = interest_rate), binwidth = 1, center = 10) +
xlim(0, 40)Create a histogram of loan_amount. Customize your plot to give a graph that looks most reasonable to you.
ggplot(loans) +
geom_histogram(mapping = aes(x = loan_amount), binwidth = 3000, boundary = 10)
Create a histogram of annual_income. What is the issue with your graph?
ggplot(loans) +
geom_histogram(mapping = aes(x = annual_income))
Answer: Since there is an outlier about 2.3 million, so it stretches the whole graph to the right, and we don’t have enough data between the outlier and the whole body of data. The graph will look like plain on the right.
ggplot(loans, aes(x = loan_amount)) +
geom_density()
ggplot(loans, aes(x = loan_amount)) +
geom_histogram(aes(y = after_stat(density)),
boundary = 0, colour = "black", fill = "white") +
geom_density(linewidth = 1.2)
ggplot(loans, aes(x = loan_amount)) +
geom_histogram(aes(y = after_stat(density)), binwidth = 5000,
boundary = 0, colour = "black", fill = "white") +
geom_density(adjust = 30/8, linewidth = 1.2) #30/8 = 30bins/8bins
ggplot(loans) +
geom_histogram(mapping = aes(x = loan_amount, y = after_stat(count/sum(count))),
binwidth = 5000, boundary = 0, colour = "black", fill = "white")Create a histogram of variable debt_to_income in loans with the following requirements: 1. The plotting range of x is between 0 and 100 2. The binwidth is 2 3. Create a density plot on top of the histogram
ggplot(loans, aes(x = debt_to_income)) +
geom_histogram(aes(y = after_stat(density)),boundary = 0, binwidth = 2, colour = "black", fill = "white") +
geom_density(linewidth = 1.2) +
xlim(0,100)
The distribution of “debt_to_income” is likely right-skewed. This is
because most people are unlikely to have debt equal to their entire
income, as it would create excessive financial pressure when repaying
the debt.
For loans data, create a scatter plot of interest_rate vs debt_to_income with mapping color to grade. What can you learn from the graph?
ggplot(loans) +
geom_point(aes(x = debt_to_income, y = interest_rate, color = grade))
From the graph, we can see that as the grade increases from A to G, the
“debt_to_income” ratio decreases. This suggests that individuals with
lower grades tend to take out smaller loans. Additionally, as the grade
increases from A to G, the interest rate rises. This implies that
individuals with lower grades should borrow less to minimize interest
payments. For lenders, this serves as a form of risk hedging.
Question #1: Create a scatter plot of loan_amount vs interest_rate with a color grouping using term variable (please use factor(term) to convert it into a categorical variable). Save your plot to your local folder.
ggplot(loans) +
geom_point(aes(loan_amount,interest_rate,colour = factor(term)))
ggsave("my_plot.pdf")