ANLY506_Assignment4_by_Team-2
Team-2 Saurav Chopra, Praveen Prakash Sanu, Kalrav Acharya, Satwik Chaudhary, Shantanu Lavania, Asti Chandrakar
March 12, 2018
## Observations: 2,215
## Variables: 147
## $ Êcommunityname <fct> BerkeleyHeightstownship, Marpletownship,...
## $ state <fct> NJ, PA, OR, NY, MN, MO, MA, IN, ND, TX, ...
## $ countyCode <fct> 39, 45, ?, 35, 7, ?, 21, ?, 17, ?, ?, ?,...
## $ communityCode <fct> 5320, 47616, ?, 29443, 5068, ?, 50250, ?...
## $ fold <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1...
## $ population <int> 11980, 23123, 29344, 16656, 11245, 14049...
## $ householdsize <dbl> 3.10, 2.82, 2.43, 2.40, 2.76, 2.45, 2.60...
## $ racepctblack <dbl> 1.37, 0.80, 0.74, 1.70, 0.53, 2.51, 1.60...
## $ racePctWhite <dbl> 91.78, 95.57, 94.33, 97.35, 89.16, 95.65...
## $ racePctAsian <dbl> 6.50, 3.44, 3.43, 0.50, 1.17, 0.90, 1.47...
## $ racePctHisp <dbl> 1.88, 0.85, 2.35, 0.70, 0.52, 0.95, 1.10...
## $ agePct12t21 <dbl> 12.47, 11.01, 11.36, 12.55, 24.46, 18.09...
## $ agePct12t29 <dbl> 21.44, 21.30, 25.88, 25.20, 40.53, 32.89...
## $ agePct16t24 <dbl> 10.93, 10.48, 11.01, 12.19, 28.69, 20.04...
## $ agePct65up <dbl> 11.33, 17.18, 10.28, 17.57, 12.65, 13.26...
## $ numbUrban <int> 11980, 23123, 29344, 0, 0, 140494, 28700...
## $ pctUrban <dbl> 100.00, 100.00, 100.00, 0.00, 0.00, 100....
## $ medIncome <int> 75122, 47917, 35669, 20580, 17390, 21577...
## $ pctWWage <dbl> 89.24, 78.99, 82.00, 68.15, 69.33, 75.78...
## $ pctWFarmSelf <dbl> 1.55, 1.11, 1.15, 0.24, 0.55, 1.00, 0.39...
## $ pctWInvInc <dbl> 70.20, 64.11, 55.73, 38.95, 42.82, 41.15...
## $ pctWSocSec <dbl> 23.62, 35.50, 22.25, 39.48, 32.16, 29.31...
## $ pctWPubAsst <dbl> 1.03, 2.75, 2.94, 11.71, 11.21, 7.12, 5....
## $ pctWRetire <dbl> 18.39, 22.85, 14.56, 18.33, 14.43, 14.09...
## $ medFamInc <int> 79584, 55323, 42112, 26501, 24018, 27705...
## $ perCapInc <int> 29711, 20148, 16946, 10810, 8483, 11878,...
## $ whitePerCap <int> 30233, 20191, 17103, 10909, 9009, 12029,...
## $ blackPerCap <int> 13600, 18137, 16644, 9984, 887, 7382, 17...
## $ indianPerCap <int> 5725, 0, 21606, 4941, 4425, 10264, 21482...
## $ AsianPerCap <int> 27101, 20074, 15528, 3541, 3352, 10753, ...
## $ OtherPerCap <fct> 5115, 5250, 5954, 2451, 3000, 7192, 2185...
## $ HispPerCap <int> 22838, 12222, 8405, 4391, 1328, 8104, 22...
## $ NumUnderPov <int> 227, 885, 1389, 2831, 2855, 23223, 1126,...
## $ PctPopUnderPov <dbl> 1.96, 3.98, 4.75, 17.23, 29.99, 17.78, 4...
## $ PctLess9thGrade <dbl> 5.81, 5.61, 2.80, 11.05, 12.15, 8.76, 4....
## $ PctNotHSGrad <dbl> 9.90, 13.72, 9.09, 33.68, 23.06, 23.03, ...
## $ PctBSorMore <dbl> 48.18, 29.89, 30.13, 10.81, 25.28, 20.66...
## $ PctUnemployed <dbl> 2.70, 2.43, 4.01, 9.86, 9.08, 5.72, 4.85...
## $ PctEmploy <dbl> 64.55, 61.96, 69.80, 54.74, 52.44, 59.02...
## $ PctEmplManu <dbl> 14.65, 12.26, 15.95, 31.22, 6.89, 14.31,...
## $ PctEmplProfServ <dbl> 28.82, 29.28, 21.52, 27.43, 36.54, 26.83...
## $ PctOccupManu <dbl> 5.49, 6.39, 8.79, 26.76, 10.94, 14.72, 8...
## $ PctOccupMgmtProf <dbl> 50.73, 37.64, 32.48, 22.71, 27.80, 23.42...
## $ MalePctDivorce <dbl> 3.67, 4.23, 10.10, 10.98, 7.51, 11.40, 5...
## $ MalePctNevMarr <dbl> 26.38, 27.99, 25.78, 28.15, 50.66, 33.32...
## $ FemalePctDiv <dbl> 5.22, 6.45, 14.76, 14.47, 11.64, 14.46, ...
## $ TotalPctDiv <dbl> 4.47, 5.42, 12.55, 12.91, 9.73, 13.04, 7...
## $ PersPerFam <dbl> 3.22, 3.11, 2.95, 2.98, 2.98, 2.89, 3.14...
## $ PctFam2Par <dbl> 91.43, 86.91, 78.54, 64.02, 58.59, 71.94...
## $ PctKids2Par <dbl> 90.17, 85.33, 78.85, 62.36, 55.20, 69.79...
## $ PctYoungKids2Par <dbl> 95.78, 96.82, 92.37, 65.38, 66.51, 79.76...
## $ PctTeen2Par <dbl> 95.81, 86.46, 75.72, 67.43, 79.17, 75.33...
## $ PctWorkMomYoungKids <dbl> 44.56, 51.14, 66.08, 59.59, 61.22, 62.96...
## $ PctWorkMom <dbl> 58.88, 62.43, 74.19, 70.27, 68.94, 70.52...
## $ NumKidsBornNeverMar <int> 31, 43, 164, 561, 402, 1511, 263, 2368, ...
## $ PctKidsBornNeverMar <dbl> 0.36, 0.24, 0.88, 3.84, 4.70, 1.58, 1.18...
## $ NumImmig <int> 1277, 1920, 1468, 339, 196, 2091, 2637, ...
## $ PctImmigRecent <dbl> 8.69, 5.21, 16.42, 13.86, 46.94, 21.33, ...
## $ PctImmigRec5 <dbl> 13.00, 8.65, 23.98, 13.86, 56.12, 30.56,...
## $ PctImmigRec8 <dbl> 20.99, 13.33, 32.08, 15.34, 67.86, 38.02...
## $ PctImmigRec10 <dbl> 30.93, 22.50, 35.63, 15.34, 69.90, 45.48...
## $ PctRecentImmig <dbl> 0.93, 0.43, 0.82, 0.28, 0.82, 0.32, 1.05...
## $ PctRecImmig5 <dbl> 1.39, 0.72, 1.20, 0.28, 0.98, 0.45, 1.49...
## $ PctRecImmig8 <dbl> 2.24, 1.11, 1.61, 0.31, 1.18, 0.57, 2.20...
## $ PctRecImmig10 <dbl> 3.30, 1.87, 1.78, 0.31, 1.22, 0.68, 2.55...
## $ PctSpeakEnglOnly <dbl> 85.68, 87.79, 93.11, 94.98, 94.64, 96.87...
## $ PctNotSpeakEnglWell <dbl> 1.37, 1.81, 1.14, 0.56, 0.39, 0.60, 0.60...
## $ PctLargHouseFam <dbl> 4.81, 4.25, 2.97, 3.93, 5.23, 3.08, 5.08...
## $ PctLargHouseOccup <dbl> 4.17, 3.34, 2.05, 2.56, 3.11, 1.92, 3.46...
## $ PersPerOccupHous <dbl> 2.99, 2.70, 2.42, 2.37, 2.35, 2.28, 2.55...
## $ PersPerOwnOccHous <dbl> 3.00, 2.83, 2.69, 2.51, 2.55, 2.37, 2.89...
## $ PersPerRentOccHous <dbl> 2.84, 1.96, 2.06, 2.20, 2.12, 2.16, 2.09...
## $ PctPersOwnOccup <dbl> 91.46, 89.03, 64.18, 58.18, 58.13, 57.81...
## $ PctPersDenseHous <dbl> 0.39, 1.01, 2.03, 1.21, 2.94, 2.11, 1.47...
## $ PctHousLess3BR <dbl> 11.06, 23.60, 47.46, 45.66, 55.64, 53.19...
## $ MedNumBR <int> 3, 3, 3, 3, 2, 2, 3, 2, 2, 2, 2, 2, 2, 3...
## $ HousVacant <int> 64, 240, 544, 669, 333, 5119, 566, 2051,...
## $ PctHousOccup <dbl> 98.37, 97.15, 95.68, 91.19, 92.45, 91.81...
## $ PctHousOwnOcc <dbl> 91.01, 84.88, 57.79, 54.89, 53.57, 55.50...
## $ PctVacantBoarded <dbl> 3.12, 0.00, 0.92, 2.54, 3.90, 2.09, 1.41...
## $ PctVacMore6Mos <dbl> 37.50, 18.33, 7.54, 57.85, 42.64, 26.22,...
## $ MedYrHousBuilt <int> 1959, 1958, 1976, 1939, 1958, 1966, 1956...
## $ PctHousNoPhone <dbl> 0.00, 0.31, 1.55, 7.00, 7.45, 6.13, 0.69...
## $ PctWOFullPlumb <dbl> 0.28, 0.14, 0.12, 0.87, 0.82, 0.31, 0.28...
## $ OwnOccLowQuart <int> 215900, 136300, 74700, 36400, 30600, 377...
## $ OwnOccMedVal <int> 262600, 164200, 90400, 49600, 43200, 539...
## $ OwnOccHiQuart <int> 326900, 199900, 112000, 66500, 59500, 73...
## $ OwnOccQrange <int> 111000, 63600, 37300, 30100, 28900, 3540...
## $ RentLowQ <int> 685, 467, 370, 195, 202, 215, 463, 186, ...
## $ RentMedian <int> 1001, 560, 428, 250, 283, 280, 669, 253,...
## $ RentHighQ <int> 1001, 672, 520, 309, 362, 349, 824, 325,...
## $ RentQrange <int> 316, 205, 150, 114, 160, 134, 361, 139, ...
## $ MedRent <int> 1001, 627, 484, 333, 332, 340, 736, 338,...
## $ MedRentPctHousInc <dbl> 23.8, 27.6, 24.1, 28.7, 32.2, 26.4, 24.4...
## $ MedOwnCostPctInc <dbl> 21.1, 20.7, 21.7, 20.6, 23.2, 17.3, 20.8...
## $ MedOwnCostPctIncNoMtg <dbl> 14.0, 12.5, 11.6, 14.5, 12.9, 11.7, 12.5...
## $ NumInShelters <int> 11, 0, 16, 0, 2, 327, 0, 21, 125, 43, 1,...
## $ NumStreet <int> 0, 0, 0, 0, 0, 4, 0, 0, 15, 4, 0, 49, 2,...
## $ PctForeignBorn <dbl> 10.66, 8.30, 5.00, 2.04, 1.74, 1.49, 9.1...
## $ PctBornSameState <dbl> 53.72, 77.17, 44.77, 88.71, 73.75, 64.35...
## $ PctSameHouse85 <dbl> 65.29, 71.27, 36.60, 56.70, 42.22, 42.29...
## $ PctSameCity85 <dbl> 78.09, 90.22, 61.26, 90.17, 60.34, 70.61...
## $ PctSameState85 <dbl> 89.14, 96.12, 82.85, 96.24, 89.02, 85.66...
## $ LemasSwornFT <fct> ?, ?, ?, ?, ?, ?, ?, ?, ?, 198, ?, ?, ?,...
## $ LemasSwFTPerPop <fct> ?, ?, ?, ?, ?, ?, ?, ?, ?, 183.53, ?, ?,...
## $ LemasSwFTFieldOps <fct> ?, ?, ?, ?, ?, ?, ?, ?, ?, 187, ?, ?, ?,...
## $ LemasSwFTFieldPerPop <fct> ?, ?, ?, ?, ?, ?, ?, ?, ?, 173.33, ?, ?,...
## $ LemasTotalReq <fct> ?, ?, ?, ?, ?, ?, ?, ?, ?, 73432, ?, ?, ...
## $ LemasTotReqPerPop <fct> ?, ?, ?, ?, ?, ?, ?, ?, ?, 68065.1, ?, ?...
## $ PolicReqPerOffic <fct> ?, ?, ?, ?, ?, ?, ?, ?, ?, 370.9, ?, ?, ...
## $ PolicPerPop <fct> ?, ?, ?, ?, ?, ?, ?, ?, ?, 183.5, ?, ?, ...
## $ RacialMatchCommPol <fct> ?, ?, ?, ?, ?, ?, ?, ?, ?, 89.32, ?, ?, ...
## $ PctPolicWhite <fct> ?, ?, ?, ?, ?, ?, ?, ?, ?, 78.28, ?, ?, ...
## $ PctPolicBlack <fct> ?, ?, ?, ?, ?, ?, ?, ?, ?, 11.11, ?, ?, ...
## $ PctPolicHisp <fct> ?, ?, ?, ?, ?, ?, ?, ?, ?, 10.61, ?, ?, ...
## $ PctPolicAsian <fct> ?, ?, ?, ?, ?, ?, ?, ?, ?, 0, ?, ?, ?, 0...
## $ PctPolicMinor <fct> ?, ?, ?, ?, ?, ?, ?, ?, ?, 21.72, ?, ?, ...
## $ OfficAssgnDrugUnits <fct> ?, ?, ?, ?, ?, ?, ?, ?, ?, 13, ?, ?, ?, ...
## $ NumKindsDrugsSeiz <fct> ?, ?, ?, ?, ?, ?, ?, ?, ?, 12, ?, ?, ?, ...
## $ PolicAveOTWorked <fct> ?, ?, ?, ?, ?, ?, ?, ?, ?, 60.2, ?, ?, ?...
## $ LandArea <dbl> 6.5, 10.6, 10.6, 5.2, 11.5, 70.4, 10.9, ...
## $ PopDens <dbl> 1845.9, 2186.7, 2780.9, 3217.7, 974.2, 1...
## $ PctUsePubTrans <dbl> 9.63, 3.84, 4.37, 3.31, 0.38, 0.97, 9.62...
## $ PolicCars <fct> ?, ?, ?, ?, ?, ?, ?, ?, ?, 100, ?, ?, ?,...
## $ PolicOperBudg <fct> ?, ?, ?, ?, ?, ?, ?, ?, ?, 9315474, ?, ?...
## $ LemasPctPolicOnPatr <fct> ?, ?, ?, ?, ?, ?, ?, ?, ?, 94.44, ?, ?, ...
## $ LemasGangUnitDeploy <fct> ?, ?, ?, ?, ?, ?, ?, ?, ?, 10, ?, ?, ?, ...
## $ LemasPctOfficDrugUn <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00...
## $ PolicBudgPerPop <fct> ?, ?, ?, ?, ?, ?, ?, ?, ?, 86346.3, ?, ?...
## $ murders <int> 0, 0, 3, 0, 0, 7, 0, 8, 0, 29, 1, 12, 3,...
## $ murdPerPop <dbl> 0.00, 0.00, 8.30, 0.00, 0.00, 4.63, 0.00...
## $ rapes <fct> 0, 1, 6, 10, ?, 77, 4, 34, 35, 141, 29, ...
## $ rapesPerPop <fct> 0, 4.25, 16.6, 57.86, ?, 50.98, 13.53, 5...
## $ robberies <fct> 1, 5, 56, 10, 4, 136, 9, 98, 16, 453, 71...
## $ robbbPerPop <fct> 8.2, 21.26, 154.95, 57.86, 32.04, 90.05,...
## $ assaults <fct> 4, 24, 14, 33, 14, 449, 54, 128, 41, 104...
## $ assaultPerPop <fct> 32.81, 102.05, 38.74, 190.93, 112.14, 29...
## $ burglaries <fct> 14, 57, 274, 225, 91, 2094, 110, 608, 42...
## $ burglPerPop <fct> 114.85, 242.37, 758.14, 1301.78, 728.93,...
## $ larcenies <fct> 138, 376, 1797, 716, 1060, 7690, 288, 22...
## $ larcPerPop <fct> 1132.08, 1598.78, 4972.19, 4142.56, 8490...
## $ autoTheft <fct> 16, 26, 136, 47, 91, 454, 144, 125, 206,...
## $ autoTheftPerPop <fct> 131.26, 110.55, 376.3, 271.93, 728.93, 3...
## $ arsons <fct> 2, 1, 22, ?, 5, 134, 17, 9, 8, 18, 6, 20...
## $ arsonsPerPop <fct> 16.41, 4.25, 60.87, ?, 40.05, 88.72, 57....
## $ ViolentCrimesPerPop <fct> 41.02, 127.56, 218.59, 306.64, ?, 442.95...
## $ nonViolPerPop <fct> 1394.59, 1955.95, 6167.51, ?, 9988.79, 6...## 'data.frame': 2215 obs. of 147 variables:
## $ communityname : Factor w/ 2018 levels "Aberdeencity",..: 151 1035 1781 665 141 1700 1272 41 566 1860 ...
## $ state : Factor w/ 48 levels "AK","AL","AR",..: 29 36 35 32 23 24 19 15 27 41 ...
## $ countyCode : Factor w/ 115 levels "?","1","101",..: 57 60 1 55 84 1 46 1 40 1 ...
## $ communityCode : Factor w/ 960 levels "?","100","1000",..: 511 426 1 215 473 1 468 1 177 1 ...
## $ fold : int 1 1 1 1 1 1 1 1 1 1 ...
## $ population : int 11980 23123 29344 16656 11245 140494 28700 59459 74111 103590 ...
## $ householdsize : num 3.1 2.82 2.43 2.4 2.76 2.45 2.6 2.45 2.46 2.62 ...
## $ racepctblack : num 1.37 0.8 0.74 1.7 0.53 ...
## $ racePctWhite : num 91.8 95.6 94.3 97.3 89.2 ...
## $ racePctAsian : num 6.5 3.44 3.43 0.5 1.17 0.9 1.47 0.4 1.25 0.92 ...
## $ racePctHisp : num 1.88 0.85 2.35 0.7 0.52 ...
## $ agePct12t21 : num 12.5 11 11.4 12.6 24.5 ...
## $ agePct12t29 : num 21.4 21.3 25.9 25.2 40.5 ...
## $ agePct16t24 : num 10.9 10.5 11 12.2 28.7 ...
## $ agePct65up : num 11.3 17.2 10.3 17.6 12.6 ...
## $ numbUrban : int 11980 23123 29344 0 0 140494 28700 59449 74115 103590 ...
## $ pctUrban : num 100 100 100 0 0 100 100 100 100 100 ...
## $ medIncome : int 75122 47917 35669 20580 17390 21577 42805 23221 25326 17852 ...
## $ pctWWage : num 89.2 79 82 68.2 69.3 ...
## $ pctWFarmSelf : num 1.55 1.11 1.15 0.24 0.55 1 0.39 0.67 2.93 0.86 ...
## $ pctWInvInc : num 70.2 64.1 55.7 39 42.8 ...
## $ pctWSocSec : num 23.6 35.5 22.2 39.5 32.2 ...
## $ pctWPubAsst : num 1.03 2.75 2.94 11.71 11.21 ...
## $ pctWRetire : num 18.4 22.9 14.6 18.3 14.4 ...
## $ medFamInc : int 79584 55323 42112 26501 24018 27705 50394 28901 34269 24058 ...
## $ perCapInc : int 29711 20148 16946 10810 8483 11878 18193 12161 13554 10195 ...
## $ whitePerCap : int 30233 20191 17103 10909 9009 12029 18276 12599 13727 12126 ...
## $ blackPerCap : int 13600 18137 16644 9984 887 7382 17342 9820 8852 5715 ...
## $ indianPerCap : int 5725 0 21606 4941 4425 10264 21482 6634 5344 11313 ...
## $ AsianPerCap : int 27101 20074 15528 3541 3352 10753 12639 8802 8011 5770 ...
## $ OtherPerCap : Factor w/ 1918 levels "?","0","10000",..: 1022 1049 1174 717 784 1418 681 1460 1068 1445 ...
## $ HispPerCap : int 22838 12222 8405 4391 1328 8104 22594 6187 5174 6984 ...
## $ NumUnderPov : int 227 885 1389 2831 2855 23223 1126 10320 9603 27767 ...
## $ PctPopUnderPov : num 1.96 3.98 4.75 17.23 29.99 ...
## $ PctLess9thGrade : num 5.81 5.61 2.8 11.05 12.15 ...
## $ PctNotHSGrad : num 9.9 13.72 9.09 33.68 23.06 ...
## $ PctBSorMore : num 48.2 29.9 30.1 10.8 25.3 ...
## $ PctUnemployed : num 2.7 2.43 4.01 9.86 9.08 5.72 4.85 8.19 4.18 8.39 ...
## $ PctEmploy : num 64.5 62 69.8 54.7 52.4 ...
## $ PctEmplManu : num 14.65 12.26 15.95 31.22 6.89 ...
## $ PctEmplProfServ : num 28.8 29.3 21.5 27.4 36.5 ...
## $ PctOccupManu : num 5.49 6.39 8.79 26.76 10.94 ...
## $ PctOccupMgmtProf : num 50.7 37.6 32.5 22.7 27.8 ...
## $ MalePctDivorce : num 3.67 4.23 10.1 10.98 7.51 ...
## $ MalePctNevMarr : num 26.4 28 25.8 28.1 50.7 ...
## $ FemalePctDiv : num 5.22 6.45 14.76 14.47 11.64 ...
## $ TotalPctDiv : num 4.47 5.42 12.55 12.91 9.73 ...
## $ PersPerFam : num 3.22 3.11 2.95 2.98 2.98 2.89 3.14 2.95 3 3.11 ...
## $ PctFam2Par : num 91.4 86.9 78.5 64 58.6 ...
## $ PctKids2Par : num 90.2 85.3 78.8 62.4 55.2 ...
## $ PctYoungKids2Par : num 95.8 96.8 92.4 65.4 66.5 ...
## $ PctTeen2Par : num 95.8 86.5 75.7 67.4 79.2 ...
## $ PctWorkMomYoungKids : num 44.6 51.1 66.1 59.6 61.2 ...
## $ PctWorkMom : num 58.9 62.4 74.2 70.3 68.9 ...
## $ NumKidsBornNeverMar : int 31 43 164 561 402 1511 263 2368 751 3537 ...
## $ PctKidsBornNeverMar : num 0.36 0.24 0.88 3.84 4.7 1.58 1.18 4.66 1.64 4.71 ...
## $ NumImmig : int 1277 1920 1468 339 196 2091 2637 517 1474 4793 ...
## $ PctImmigRecent : num 8.69 5.21 16.42 13.86 46.94 ...
## $ PctImmigRec5 : num 13 8.65 23.98 13.86 56.12 ...
## $ PctImmigRec8 : num 21 13.3 32.1 15.3 67.9 ...
## $ PctImmigRec10 : num 30.9 22.5 35.6 15.3 69.9 ...
## $ PctRecentImmig : num 0.93 0.43 0.82 0.28 0.82 0.32 1.05 0.11 0.47 0.72 ...
## $ PctRecImmig5 : num 1.39 0.72 1.2 0.28 0.98 0.45 1.49 0.2 0.67 1.07 ...
## $ PctRecImmig8 : num 2.24 1.11 1.61 0.31 1.18 0.57 2.2 0.25 0.93 1.63 ...
## $ PctRecImmig10 : num 3.3 1.87 1.78 0.31 1.22 0.68 2.55 0.29 1.07 2.31 ...
## $ PctSpeakEnglOnly : num 85.7 87.8 93.1 95 94.6 ...
## $ PctNotSpeakEnglWell : num 1.37 1.81 1.14 0.56 0.39 0.6 0.6 0.28 0.43 2.51 ...
## $ PctLargHouseFam : num 4.81 4.25 2.97 3.93 5.23 3.08 5.08 3.85 2.59 6.7 ...
## $ PctLargHouseOccup : num 4.17 3.34 2.05 2.56 3.11 1.92 3.46 2.55 1.54 4.1 ...
## $ PersPerOccupHous : num 2.99 2.7 2.42 2.37 2.35 2.28 2.55 2.36 2.32 2.45 ...
## $ PersPerOwnOccHous : num 3 2.83 2.69 2.51 2.55 2.37 2.89 2.42 2.77 2.47 ...
## $ PersPerRentOccHous : num 2.84 1.96 2.06 2.2 2.12 2.16 2.09 2.27 1.91 2.44 ...
## $ PctPersOwnOccup : num 91.5 89 64.2 58.2 58.1 ...
## $ PctPersDenseHous : num 0.39 1.01 2.03 1.21 2.94 2.11 1.47 1.9 1.67 6.14 ...
## $ PctHousLess3BR : num 11.1 23.6 47.5 45.7 55.6 ...
## $ MedNumBR : int 3 3 3 3 2 2 3 2 2 2 ...
## $ HousVacant : int 64 240 544 669 333 5119 566 2051 1562 5606 ...
## $ PctHousOccup : num 98.4 97.2 95.7 91.2 92.5 ...
## $ PctHousOwnOcc : num 91 84.9 57.8 54.9 53.6 ...
## $ PctVacantBoarded : num 3.12 0 0.92 2.54 3.9 2.09 1.41 6.39 0.45 5.64 ...
## $ PctVacMore6Mos : num 37.5 18.33 7.54 57.85 42.64 ...
## $ MedYrHousBuilt : int 1959 1958 1976 1939 1958 1966 1956 1954 1971 1960 ...
## $ PctHousNoPhone : num 0 0.31 1.55 7 7.45 ...
## $ PctWOFullPlumb : num 0.28 0.14 0.12 0.87 0.82 0.31 0.28 0.49 0.19 0.33 ...
## $ OwnOccLowQuart : int 215900 136300 74700 36400 30600 37700 155100 26300 54500 28600 ...
## $ OwnOccMedVal : int 262600 164200 90400 49600 43200 53900 179000 37000 70300 43100 ...
## $ OwnOccHiQuart : int 326900 199900 112000 66500 59500 73100 215500 52400 93700 67400 ...
## $ OwnOccQrange : int 111000 63600 37300 30100 28900 35400 60400 26100 39200 38800 ...
## $ RentLowQ : int 685 467 370 195 202 215 463 186 241 192 ...
## $ RentMedian : int 1001 560 428 250 283 280 669 253 321 281 ...
## $ RentHighQ : int 1001 672 520 309 362 349 824 325 387 369 ...
## $ RentQrange : int 316 205 150 114 160 134 361 139 146 177 ...
## $ MedRent : int 1001 627 484 333 332 340 736 338 355 353 ...
## $ MedRentPctHousInc : num 23.8 27.6 24.1 28.7 32.2 26.4 24.4 26.3 25.2 29.6 ...
## $ MedOwnCostPctInc : num 21.1 20.7 21.7 20.6 23.2 17.3 20.8 15.1 20.7 19.4 ...
## $ MedOwnCostPctIncNoMtg: num 14 12.5 11.6 14.5 12.9 11.7 12.5 12.2 12.8 13 ...
## $ NumInShelters : int 11 0 16 0 2 327 0 21 125 43 ...
## $ NumStreet : int 0 0 0 0 0 4 0 0 15 4 ...
## $ PctForeignBorn : num 10.66 8.3 5 2.04 1.74 ...
## [list output truncated]## communityname state countyCode communityCode
## Auburncity : 5 CA : 279 ? :1221 ? :1224
## Greenvillecity : 5 NJ : 211 3 : 78 1000 : 3
## Jacksonvillecity: 5 TX : 162 17 : 63 21000 : 3
## Springfieldcity : 5 MA : 123 9 : 45 77200 : 3
## Albanycity : 4 OH : 111 1 : 40 79000 : 3
## Athenscity : 4 MI : 108 7 : 40 82000 : 3
## (Other) :2187 (Other):1221 (Other): 728 (Other): 976
## fold population householdsize racepctblack
## Min. : 1.000 Min. : 10005 Min. :1.600 Min. : 0.000
## 1st Qu.: 3.000 1st Qu.: 14366 1st Qu.:2.500 1st Qu.: 0.860
## Median : 5.000 Median : 22792 Median :2.660 Median : 2.870
## Mean : 5.494 Mean : 53118 Mean :2.707 Mean : 9.335
## 3rd Qu.: 8.000 3rd Qu.: 43024 3rd Qu.:2.850 3rd Qu.:11.145
## Max. :10.000 Max. :7322564 Max. :5.280 Max. :96.670
##
## racePctWhite racePctAsian racePctHisp agePct12t21
## Min. : 2.68 Min. : 0.03 Min. : 0.12 Min. : 4.58
## 1st Qu.:76.32 1st Qu.: 0.62 1st Qu.: 0.93 1st Qu.:12.25
## Median :90.35 Median : 1.23 Median : 2.18 Median :13.62
## Mean :83.98 Mean : 2.67 Mean : 7.95 Mean :14.45
## 3rd Qu.:96.22 3rd Qu.: 2.67 3rd Qu.: 7.81 3rd Qu.:15.36
## Max. :99.63 Max. :57.46 Max. :95.29 Max. :54.40
##
## agePct12t29 agePct16t24 agePct65up numbUrban
## Min. : 9.38 Min. : 4.64 Min. : 1.66 Min. : 0
## 1st Qu.:24.41 1st Qu.:11.32 1st Qu.: 8.75 1st Qu.: 0
## Median :26.78 Median :12.54 Median :11.73 Median : 18041
## Mean :27.64 Mean :13.98 Mean :11.84 Mean : 47735
## 3rd Qu.:29.20 3rd Qu.:14.35 3rd Qu.:14.41 3rd Qu.: 41918
## Max. :70.51 Max. :63.62 Max. :52.77 Max. :7322564
##
## pctUrban medIncome pctWWage pctWFarmSelf
## Min. : 0.00 Min. : 8866 Min. :31.68 Min. :0.0000
## 1st Qu.: 0.00 1st Qu.: 23817 1st Qu.:73.40 1st Qu.:0.4600
## Median :100.00 Median : 31441 Median :78.61 Median :0.6900
## Mean : 70.47 Mean : 33985 Mean :78.31 Mean :0.8818
## 3rd Qu.:100.00 3rd Qu.: 41481 3rd Qu.:84.03 3rd Qu.:1.1000
## Max. :100.00 Max. :123625 Max. :96.76 Max. :6.5300
##
## pctWInvInc pctWSocSec pctWPubAsst pctWRetire
## Min. : 5.81 Min. : 4.81 Min. : 0.180 Min. : 3.46
## 1st Qu.:34.68 1st Qu.:20.77 1st Qu.: 3.270 1st Qu.:12.88
## Median :42.88 Median :26.59 Median : 5.610 Median :15.65
## Mean :43.75 Mean :26.41 Mean : 6.801 Mean :15.97
## 3rd Qu.:52.74 3rd Qu.:31.71 3rd Qu.: 9.105 3rd Qu.:18.75
## Max. :89.04 Max. :76.39 Max. :44.820 Max. :45.51
##
## medFamInc perCapInc whitePerCap blackPerCap
## Min. : 10447 Min. : 5237 Min. : 5472 Min. : 0
## 1st Qu.: 29538 1st Qu.:11602 1st Qu.:12610 1st Qu.: 6742
## Median : 36678 Median :14101 Median :15073 Median : 9777
## Mean : 39857 Mean :15604 Mean :16568 Mean : 11542
## 3rd Qu.: 46999 3rd Qu.:17795 3rd Qu.:18610 3rd Qu.: 14526
## Max. :139008 Max. :63302 Max. :68850 Max. :212120
##
## indianPerCap AsianPerCap OtherPerCap HispPerCap
## Min. : 0 Min. : 0 0 : 140 Min. : 0
## 1st Qu.: 6345 1st Qu.: 8286 15000 : 6 1st Qu.: 7274
## Median : 9895 Median : 12250 10588 : 3 Median : 9721
## Mean : 12229 Mean : 14228 10870 : 3 Mean :11019
## 3rd Qu.: 14758 3rd Qu.: 17328 12000 : 3 3rd Qu.:13418
## Max. :480000 Max. :106165 13000 : 3 Max. :54648
## (Other):2057
## NumUnderPov PctPopUnderPov PctLess9thGrade PctNotHSGrad
## Min. : 78.0 Min. : 0.64 Min. : 0.200 Min. : 1.46
## 1st Qu.: 912.5 1st Qu.: 4.51 1st Qu.: 4.640 1st Qu.:13.92
## Median : 2142.0 Median : 9.33 Median : 7.740 Median :21.38
## Mean : 7590.9 Mean :11.62 Mean : 9.187 Mean :22.31
## 3rd Qu.: 4988.0 3rd Qu.:16.91 3rd Qu.:11.835 3rd Qu.:29.20
## Max. :1384994.0 Max. :58.00 Max. :49.890 Max. :73.66
##
## PctBSorMore PctUnemployed PctEmploy PctEmplManu
## Min. : 1.63 Min. : 1.320 Min. :24.82 Min. : 2.05
## 1st Qu.:14.10 1st Qu.: 4.045 1st Qu.:56.49 1st Qu.:12.21
## Median :19.65 Median : 5.450 Median :62.44 Median :17.30
## Mean :23.06 Mean : 6.045 Mean :62.02 Mean :18.23
## 3rd Qu.:29.05 3rd Qu.: 7.440 3rd Qu.:67.83 3rd Qu.:23.40
## Max. :79.18 Max. :31.230 Max. :84.67 Max. :50.03
##
## PctEmplProfServ PctOccupManu PctOccupMgmtProf MalePctDivorce
## Min. : 8.69 Min. : 1.37 Min. : 6.48 Min. : 2.130
## 1st Qu.:20.07 1st Qu.: 9.13 1st Qu.:21.91 1st Qu.: 7.110
## Median :23.39 Median :13.15 Median :26.24 Median : 9.150
## Mean :24.53 Mean :13.82 Mean :28.21 Mean : 9.128
## 3rd Qu.:27.59 3rd Qu.:17.66 3rd Qu.:32.81 3rd Qu.:11.050
## Max. :62.67 Max. :44.27 Max. :64.97 Max. :20.080
##
## MalePctNevMarr FemalePctDiv TotalPctDiv PersPerFam
## Min. :12.06 Min. : 3.35 Min. : 2.830 Min. :2.29
## 1st Qu.:25.45 1st Qu.: 9.86 1st Qu.: 8.575 1st Qu.:2.99
## Median :29.00 Median :12.52 Median :10.900 Median :3.10
## Mean :30.68 Mean :12.33 Mean :10.813 Mean :3.13
## 3rd Qu.:33.41 3rd Qu.:14.74 3rd Qu.:12.985 3rd Qu.:3.22
## Max. :76.60 Max. :23.92 Max. :22.230 Max. :4.64
##
## PctFam2Par PctKids2Par PctYoungKids2Par PctTeen2Par
## Min. :22.97 Min. :18.30 Min. : 8.70 Min. :20.20
## 1st Qu.:67.90 1st Qu.:63.99 1st Qu.: 74.78 1st Qu.:70.17
## Median :75.03 Median :72.53 Median : 83.99 Median :76.92
## Mean :74.06 Mean :71.23 Mean : 81.87 Mean :75.52
## 3rd Qu.:81.90 3rd Qu.:80.39 3rd Qu.: 91.67 3rd Qu.:82.77
## Max. :93.60 Max. :92.58 Max. :100.00 Max. :97.34
##
## PctWorkMomYoungKids PctWorkMom NumKidsBornNeverMar
## Min. :24.42 Min. :41.95 Min. : 0
## 1st Qu.:55.43 1st Qu.:64.90 1st Qu.: 147
## Median :60.71 Median :69.23 Median : 352
## Mean :60.54 Mean :68.85 Mean : 2141
## 3rd Qu.:65.98 3rd Qu.:73.50 3rd Qu.: 1032
## Max. :87.97 Max. :89.37 Max. :527557
##
## PctKidsBornNeverMar NumImmig PctImmigRecent PctImmigRec5
## Min. : 0.000 Min. : 20 Min. : 0.000 Min. : 0.00
## 1st Qu.: 1.070 1st Qu.: 400 1st Qu.: 6.695 1st Qu.:11.26
## Median : 2.040 Median : 1024 Median :12.260 Median :19.08
## Mean : 3.115 Mean : 6277 Mean :13.526 Mean :20.42
## 3rd Qu.: 3.910 3rd Qu.: 3302 3rd Qu.:17.950 3rd Qu.:27.45
## Max. :27.350 Max. :2082931 Max. :64.290 Max. :76.16
##
## PctImmigRec8 PctImmigRec10 PctRecentImmig PctRecImmig5
## Min. : 0.00 Min. : 0.00 Min. : 0.000 Min. : 0.000
## 1st Qu.:17.20 1st Qu.:22.73 1st Qu.: 0.170 1st Qu.: 0.280
## Median :26.72 Median :34.79 Median : 0.500 Median : 0.750
## Mean :27.54 Mean :34.73 Mean : 1.099 Mean : 1.697
## 3rd Qu.:36.49 3rd Qu.:46.19 3rd Qu.: 1.310 3rd Qu.: 2.015
## Max. :80.81 Max. :88.00 Max. :13.710 Max. :19.930
##
## PctRecImmig8 PctRecImmig10 PctSpeakEnglOnly PctNotSpeakEnglWell
## Min. : 0.000 Min. : 0.000 Min. : 6.15 Min. : 0.000
## 1st Qu.: 0.390 1st Qu.: 0.520 1st Qu.:84.38 1st Qu.: 0.510
## Median : 1.040 Median : 1.310 Median :92.18 Median : 0.920
## Mean : 2.308 Mean : 2.944 Mean :87.07 Mean : 2.406
## 3rd Qu.: 2.700 3rd Qu.: 3.455 3rd Qu.:95.45 3rd Qu.: 2.270
## Max. :25.340 Max. :32.630 Max. :98.98 Max. :38.330
##
## PctLargHouseFam PctLargHouseOccup PersPerOccupHous PersPerOwnOccHous
## Min. : 0.960 Min. : 0.440 Min. :1.580 Min. :1.61
## 1st Qu.: 3.390 1st Qu.: 2.370 1st Qu.:2.410 1st Qu.:2.55
## Median : 4.280 Median : 3.050 Median :2.570 Median :2.71
## Mean : 5.387 Mean : 3.916 Mean :2.616 Mean :2.74
## 3rd Qu.: 5.870 3rd Qu.: 4.210 3rd Qu.:2.770 3rd Qu.:2.90
## Max. :34.870 Max. :30.870 Max. :4.520 Max. :4.48
##
## PersPerRentOccHous PctPersOwnOccup PctPersDenseHous PctHousLess3BR
## Min. :1.550 Min. :13.93 Min. : 0.050 Min. : 3.06
## 1st Qu.:2.110 1st Qu.:57.28 1st Qu.: 1.290 1st Qu.:37.51
## Median :2.290 Median :65.91 Median : 2.340 Median :46.39
## Mean :2.367 Mean :66.37 Mean : 4.132 Mean :45.41
## 3rd Qu.:2.530 3rd Qu.:76.58 3rd Qu.: 4.730 3rd Qu.:53.52
## Max. :4.730 Max. :97.24 Max. :59.490 Max. :95.34
##
## MedNumBR HousVacant PctHousOccup PctHousOwnOcc
## Min. :1.000 Min. : 36.0 Min. :37.47 Min. :16.86
## 1st Qu.:2.000 1st Qu.: 304.5 1st Qu.:91.29 1st Qu.:54.82
## Median :3.000 Median : 558.0 Median :94.21 Median :62.83
## Mean :2.641 Mean : 1748.4 Mean :92.93 Mean :63.37
## 3rd Qu.:3.000 3rd Qu.: 1228.0 3rd Qu.:96.02 3rd Qu.:72.64
## Max. :4.000 Max. :172768.0 Max. :99.00 Max. :96.49
##
## PctVacantBoarded PctVacMore6Mos MedYrHousBuilt PctHousNoPhone
## Min. : 0.000 Min. : 3.12 Min. :1939 Min. : 0.000
## 1st Qu.: 0.720 1st Qu.:24.48 1st Qu.:1956 1st Qu.: 0.905
## Median : 1.660 Median :34.10 Median :1964 Median : 2.850
## Mean : 2.779 Mean :34.77 Mean :1963 Mean : 4.290
## 3rd Qu.: 3.430 3rd Qu.:43.97 3rd Qu.:1971 3rd Qu.: 6.805
## Max. :39.890 Max. :82.13 Max. :1987 Max. :23.880
##
## PctWOFullPlumb OwnOccLowQuart OwnOccMedVal OwnOccHiQuart
## Min. :0.0000 Min. : 14999 Min. : 19500 Min. : 28200
## 1st Qu.:0.1600 1st Qu.: 41500 1st Qu.: 56200 1st Qu.: 74300
## Median :0.3200 Median : 65500 Median : 82800 Median :106700
## Mean :0.4253 Mean : 88696 Mean :113098 Mean :145318
## 3rd Qu.:0.5550 3rd Qu.:121500 3rd Qu.:150600 3rd Qu.:188000
## Max. :5.3300 Max. :500001 Max. :500001 Max. :500001
##
## OwnOccQrange RentLowQ RentMedian RentHighQ
## Min. : 0 Min. : 99.0 Min. : 120.0 Min. : 182.0
## 1st Qu.: 32200 1st Qu.: 213.5 1st Qu.: 289.5 1st Qu.: 366.0
## Median : 43400 Median : 307.0 Median : 397.0 Median : 486.0
## Mean : 56623 Mean : 330.0 Mean : 428.5 Mean : 527.3
## 3rd Qu.: 65450 3rd Qu.: 421.0 3rd Qu.: 544.0 3rd Qu.: 659.5
## Max. :331000 Max. :1001.0 Max. :1001.0 Max. :1001.0
##
## RentQrange MedRent MedRentPctHousInc MedOwnCostPctInc
## Min. : 0.0 Min. : 192.0 Min. :14.9 Min. :14.00
## 1st Qu.:139.0 1st Qu.: 364.0 1st Qu.:24.3 1st Qu.:18.70
## Median :171.0 Median : 467.0 Median :26.1 Median :21.00
## Mean :197.3 Mean : 501.5 Mean :26.3 Mean :20.99
## 3rd Qu.:232.5 3rd Qu.: 615.0 3rd Qu.:28.0 3rd Qu.:23.10
## Max. :803.0 Max. :1001.0 Max. :35.1 Max. :32.70
##
## MedOwnCostPctIncNoMtg NumInShelters NumStreet
## Min. :10.10 Min. : 0.00 Min. : 0.00
## 1st Qu.:12.00 1st Qu.: 0.00 1st Qu.: 0.00
## Median :12.80 Median : 0.00 Median : 0.00
## Mean :13.01 Mean : 66.95 Mean : 17.82
## 3rd Qu.:13.70 3rd Qu.: 22.00 3rd Qu.: 1.00
## Max. :23.40 Max. :23383.00 Max. :10447.00
##
## PctForeignBorn PctBornSameState PctSameHouse85 PctSameCity85
## Min. : 0.18 Min. : 6.75 Min. :11.83 Min. :27.95
## 1st Qu.: 2.06 1st Qu.:50.11 1st Qu.:44.99 1st Qu.:72.06
## Median : 4.31 Median :64.49 Median :52.17 Median :79.49
## Mean : 7.34 Mean :61.54 Mean :51.54 Mean :77.41
## 3rd Qu.: 9.25 3rd Qu.:74.86 3rd Qu.:58.74 3rd Qu.:85.14
## Max. :60.40 Max. :93.14 Max. :78.56 Max. :96.59
##
## PctSameState85 LemasSwornFT LemasSwFTPerPop LemasSwFTFieldOps
## Min. :32.83 ? :1872 ? :1872 ? :1872
## 1st Qu.:85.20 100 : 6 104.59 : 1 94 : 8
## Median :90.03 105 : 5 104.93 : 1 123 : 6
## Mean :88.11 111 : 5 104.99 : 1 137 : 5
## 3rd Qu.:93.01 117 : 5 105.54 : 1 102 : 4
## Max. :99.90 91 : 5 106.01 : 1 105 : 4
## (Other): 317 (Other): 338 (Other): 316
## LemasSwFTFieldPerPop LemasTotalReq LemasTotReqPerPop PolicReqPerOffic
## ? :1872 ? :1872 ? :1872 ? :1872
## 183.22 : 2 100000 : 4 100078.4: 1 337 : 2
## 101.55 : 1 50000 : 4 100747.8: 1 367 : 2
## 101.9 : 1 150000 : 3 101139.5: 1 398.9 : 2
## 102.08 : 1 40000 : 3 101223.8: 1 405.3 : 2
## 102.22 : 1 107811 : 2 102017.4: 1 422.6 : 2
## (Other): 337 (Other): 327 (Other) : 338 (Other): 333
## PolicPerPop RacialMatchCommPol PctPolicWhite PctPolicBlack
## ? :1872 ? :1872 ? :1872 ? :1872
## 109.6 : 2 100 : 7 100 : 6 0 : 26
## 125.2 : 2 63.67 : 2 89.47 : 3 0.6 : 3
## 145.8 : 2 80.05 : 2 92.86 : 3 0.69 : 3
## 147.9 : 2 80.06 : 2 68.85 : 2 2.5 : 3
## 153 : 2 82.16 : 2 80 : 2 0.56 : 2
## (Other): 333 (Other): 328 (Other): 327 (Other): 306
## PctPolicHisp PctPolicAsian PctPolicMinor OfficAssgnDrugUnits
## ? :1872 ? :1872 ? :1872 ? :1872
## 0 : 79 0 : 203 0 : 8 6 : 36
## 0.74 : 4 0.28 : 4 0.6 : 2 8 : 24
## 0.32 : 3 0.32 : 3 0.69 : 2 12 : 20
## 0.48 : 3 0.38 : 3 0.71 : 2 4 : 20
## 4 : 3 0.42 : 3 0.76 : 2 7 : 18
## (Other): 251 (Other): 127 (Other): 327 (Other): 225
## NumKindsDrugsSeiz PolicAveOTWorked LandArea PopDens
## ? :1872 ? :1872 Min. : 0.90 Min. : 10
## 9 : 59 0 : 6 1st Qu.: 7.30 1st Qu.: 1182
## 10 : 46 117.3 : 3 Median : 13.70 Median : 2027
## 8 : 46 105.7 : 2 Mean : 27.42 Mean : 2784
## 7 : 37 107.6 : 2 3rd Qu.: 26.10 3rd Qu.: 3322
## 6 : 27 116.2 : 2 Max. :3569.80 Max. :44230
## (Other): 128 (Other): 328
## PctUsePubTrans PolicCars PolicOperBudg LemasPctPolicOnPatr
## Min. : 0.000 ? :1872 ? :1872 ? :1872
## 1st Qu.: 0.360 55 : 8 4700000 : 2 88.89 : 3
## Median : 1.220 36 : 6 8000000 : 2 90.48 : 3
## Mean : 3.041 40 : 6 10000000 : 1 93.07 : 3
## 3rd Qu.: 3.365 100 : 5 10001186 : 1 93.33 : 3
## Max. :54.330 105 : 5 100274208: 1 83.87 : 2
## (Other): 313 (Other) : 336 (Other): 329
## LemasGangUnitDeploy LemasPctOfficDrugUn PolicBudgPerPop
## ? :1872 Min. : 0.0000 ? :1872
## 0 : 141 1st Qu.: 0.0000 100841.5: 1
## 10: 92 Median : 0.0000 101393.7: 1
## 5 : 110 Mean : 0.9802 101405.1: 1
## 3rd Qu.: 0.0000 101501.3: 1
## Max. :48.4400 101771.3: 1
## (Other) : 338
## murders murdPerPop rapes rapesPerPop
## Min. : 0.000 Min. : 0.000 ? : 208 ? : 208
## 1st Qu.: 0.000 1st Qu.: 0.000 0 : 189 0 : 189
## Median : 1.000 Median : 2.170 1 : 180 10.77 : 3
## Mean : 7.765 Mean : 5.859 2 : 159 12.83 : 3
## 3rd Qu.: 3.000 3rd Qu.: 8.365 3 : 141 19.13 : 3
## Max. :1946.000 Max. :91.090 4 : 122 19.14 : 3
## (Other):1216 (Other):1806
## robberies robbbPerPop assaults assaultPerPop
## 1 : 114 0 : 86 12 : 40 0 : 18
## 3 : 102 14.47 : 3 9 : 39 ? : 13
## 2 : 96 21.86 : 3 7 : 38 105.27 : 2
## 0 : 86 28.72 : 3 4 : 35 123.3 : 2
## 4 : 86 101.77 : 2 5 : 33 138.45 : 2
## 5 : 77 105.1 : 2 6 : 33 141.21 : 2
## (Other):1654 (Other):2116 (Other):1997 (Other):2176
## burglaries burglPerPop larcenies larcPerPop
## 79 : 15 ? : 3 218 : 6 ? : 3
## 62 : 13 1440.25: 2 294 : 6 4631.1 : 2
## 64 : 12 1769.39: 2 357 : 6 10005.65: 1
## 108 : 11 281.58 : 2 467 : 6 1004.15 : 1
## 58 : 11 370.72 : 2 547 : 6 1005.91 : 1
## 63 : 11 500.23 : 2 207 : 5 1006.63 : 1
## (Other):2142 (Other):2202 (Other):2180 (Other) :2206
## autoTheft autoTheftPerPop arsons arsonsPerPop
## 16 : 28 ? : 3 0 : 323 0 : 323
## 32 : 28 213.62 : 3 1 : 236 ? : 91
## 19 : 26 105.17 : 2 2 : 155 17.53 : 4
## 26 : 26 107.63 : 2 3 : 151 31.75 : 4
## 31 : 26 109.15 : 2 4 : 114 4.25 : 4
## 10 : 25 122.09 : 2 5 : 96 63.69 : 4
## (Other):2056 (Other):2201 (Other):1140 (Other):1785
## ViolentCrimesPerPop nonViolPerPop
## ? : 221 ? : 97
## 223.06 : 3 2246.14: 2
## 103.33 : 2 2998.85: 2
## 105.23 : 2 4012.32: 2
## 115.14 : 2 4295.96: 2
## 144.9 : 2 5613.23: 2
## (Other):1983 (Other):2108Our dataset has 2215 rows of observations and 147 columns of variables To start cleaning we ran glimpse function that returned an overview of our dataset, including the variable list, variable types, and the first few values. Also variable Êcommunityname is renamed to communityname
## [1] communityname||state count(1)
## <0 rows> (or 0-length row.names)## [1] "numeric"144 duplicates were found in crime data set when looked up by community name. By concatenating community names with states, we could eliminate these redundent entries.
Example of using sapply and Lapply
## [1] 148## [1] 2215## 'data.frame': 2215 obs. of 148 variables:
## $ citystate : chr "BerkeleyHeightstownshipNJ" "MarpletownshipPA" "TigardcityOR" "GloversvillecityNY" ...
## $ communityname : Factor w/ 2018 levels "Aberdeencity",..: 151 1035 1781 665 141 1700 1272 41 566 1860 ...
## $ state : Factor w/ 48 levels "AK","AL","AR",..: 29 36 35 32 23 24 19 15 27 41 ...
## $ countyCode : chr "39" "45" "?" "35" ...
## $ communityCode : chr "5320" "47616" "?" "29443" ...
## $ fold : chr "1" "1" "1" "1" ...
## $ population : chr "11980" "23123" "29344" "16656" ...
## $ householdsize : chr "3.1" "2.82" "2.43" "2.4" ...
## $ racepctblack : chr "1.37" "0.8" "0.74" "1.7" ...
## $ racePctWhite : chr "91.78" "95.57" "94.33" "97.35" ...
## $ racePctAsian : chr "6.5" "3.44" "3.43" "0.5" ...
## $ racePctHisp : chr "1.88" "0.85" "2.35" "0.7" ...
## $ agePct12t21 : chr "12.47" "11.01" "11.36" "12.55" ...
## $ agePct12t29 : chr "21.44" "21.3" "25.88" "25.2" ...
## $ agePct16t24 : chr "10.93" "10.48" "11.01" "12.19" ...
## $ agePct65up : chr "11.33" "17.18" "10.28" "17.57" ...
## $ numbUrban : chr "11980" "23123" "29344" "0" ...
## $ pctUrban : chr "100" "100" "100" "0" ...
## $ medIncome : chr "75122" "47917" "35669" "20580" ...
## $ pctWWage : chr "89.24" "78.99" "82" "68.15" ...
## $ pctWFarmSelf : chr "1.55" "1.11" "1.15" "0.24" ...
## $ pctWInvInc : chr "70.2" "64.11" "55.73" "38.95" ...
## $ pctWSocSec : chr "23.62" "35.5" "22.25" "39.48" ...
## $ pctWPubAsst : chr "1.03" "2.75" "2.94" "11.71" ...
## $ pctWRetire : chr "18.39" "22.85" "14.56" "18.33" ...
## $ medFamInc : chr "79584" "55323" "42112" "26501" ...
## $ perCapInc : chr "29711" "20148" "16946" "10810" ...
## $ whitePerCap : chr "30233" "20191" "17103" "10909" ...
## $ blackPerCap : chr "13600" "18137" "16644" "9984" ...
## $ indianPerCap : chr "5725" "0" "21606" "4941" ...
## $ AsianPerCap : chr "27101" "20074" "15528" "3541" ...
## $ OtherPerCap : chr "5115" "5250" "5954" "2451" ...
## $ HispPerCap : chr "22838" "12222" "8405" "4391" ...
## $ NumUnderPov : chr "227" "885" "1389" "2831" ...
## $ PctPopUnderPov : chr "1.96" "3.98" "4.75" "17.23" ...
## $ PctLess9thGrade : chr "5.81" "5.61" "2.8" "11.05" ...
## $ PctNotHSGrad : chr "9.9" "13.72" "9.09" "33.68" ...
## $ PctBSorMore : chr "48.18" "29.89" "30.13" "10.81" ...
## $ PctUnemployed : chr "2.7" "2.43" "4.01" "9.86" ...
## $ PctEmploy : chr "64.55" "61.96" "69.8" "54.74" ...
## $ PctEmplManu : chr "14.65" "12.26" "15.95" "31.22" ...
## $ PctEmplProfServ : chr "28.82" "29.28" "21.52" "27.43" ...
## $ PctOccupManu : chr "5.49" "6.39" "8.79" "26.76" ...
## $ PctOccupMgmtProf : chr "50.73" "37.64" "32.48" "22.71" ...
## $ MalePctDivorce : chr "3.67" "4.23" "10.1" "10.98" ...
## $ MalePctNevMarr : chr "26.38" "27.99" "25.78" "28.15" ...
## $ FemalePctDiv : chr "5.22" "6.45" "14.76" "14.47" ...
## $ TotalPctDiv : chr "4.47" "5.42" "12.55" "12.91" ...
## $ PersPerFam : chr "3.22" "3.11" "2.95" "2.98" ...
## $ PctFam2Par : chr "91.43" "86.91" "78.54" "64.02" ...
## $ PctKids2Par : chr "90.17" "85.33" "78.85" "62.36" ...
## $ PctYoungKids2Par : chr "95.78" "96.82" "92.37" "65.38" ...
## $ PctTeen2Par : chr "95.81" "86.46" "75.72" "67.43" ...
## $ PctWorkMomYoungKids : chr "44.56" "51.14" "66.08" "59.59" ...
## $ PctWorkMom : chr "58.88" "62.43" "74.19" "70.27" ...
## $ NumKidsBornNeverMar : chr "31" "43" "164" "561" ...
## $ PctKidsBornNeverMar : chr "0.36" "0.24" "0.88" "3.84" ...
## $ NumImmig : chr "1277" "1920" "1468" "339" ...
## $ PctImmigRecent : chr "8.69" "5.21" "16.42" "13.86" ...
## $ PctImmigRec5 : chr "13" "8.65" "23.98" "13.86" ...
## $ PctImmigRec8 : chr "20.99" "13.33" "32.08" "15.34" ...
## $ PctImmigRec10 : chr "30.93" "22.5" "35.63" "15.34" ...
## $ PctRecentImmig : chr "0.93" "0.43" "0.82" "0.28" ...
## $ PctRecImmig5 : chr "1.39" "0.72" "1.2" "0.28" ...
## $ PctRecImmig8 : chr "2.24" "1.11" "1.61" "0.31" ...
## $ PctRecImmig10 : chr "3.3" "1.87" "1.78" "0.31" ...
## $ PctSpeakEnglOnly : chr "85.68" "87.79" "93.11" "94.98" ...
## $ PctNotSpeakEnglWell : chr "1.37" "1.81" "1.14" "0.56" ...
## $ PctLargHouseFam : chr "4.81" "4.25" "2.97" "3.93" ...
## $ PctLargHouseOccup : chr "4.17" "3.34" "2.05" "2.56" ...
## $ PersPerOccupHous : chr "2.99" "2.7" "2.42" "2.37" ...
## $ PersPerOwnOccHous : chr "3" "2.83" "2.69" "2.51" ...
## $ PersPerRentOccHous : chr "2.84" "1.96" "2.06" "2.2" ...
## $ PctPersOwnOccup : chr "91.46" "89.03" "64.18" "58.18" ...
## $ PctPersDenseHous : chr "0.39" "1.01" "2.03" "1.21" ...
## $ PctHousLess3BR : chr "11.06" "23.6" "47.46" "45.66" ...
## $ MedNumBR : chr "3" "3" "3" "3" ...
## $ HousVacant : chr "64" "240" "544" "669" ...
## $ PctHousOccup : chr "98.37" "97.15" "95.68" "91.19" ...
## $ PctHousOwnOcc : chr "91.01" "84.88" "57.79" "54.89" ...
## $ PctVacantBoarded : chr "3.12" "0" "0.92" "2.54" ...
## $ PctVacMore6Mos : chr "37.5" "18.33" "7.54" "57.85" ...
## $ MedYrHousBuilt : chr "1959" "1958" "1976" "1939" ...
## $ PctHousNoPhone : chr "0" "0.31" "1.55" "7" ...
## $ PctWOFullPlumb : chr "0.28" "0.14" "0.12" "0.87" ...
## $ OwnOccLowQuart : chr "215900" "136300" "74700" "36400" ...
## $ OwnOccMedVal : chr "262600" "164200" "90400" "49600" ...
## $ OwnOccHiQuart : chr "326900" "199900" "112000" "66500" ...
## $ OwnOccQrange : chr "111000" "63600" "37300" "30100" ...
## $ RentLowQ : chr "685" "467" "370" "195" ...
## $ RentMedian : chr "1001" "560" "428" "250" ...
## $ RentHighQ : chr "1001" "672" "520" "309" ...
## $ RentQrange : chr "316" "205" "150" "114" ...
## $ MedRent : chr "1001" "627" "484" "333" ...
## $ MedRentPctHousInc : chr "23.8" "27.6" "24.1" "28.7" ...
## $ MedOwnCostPctInc : chr "21.1" "20.7" "21.7" "20.6" ...
## $ MedOwnCostPctIncNoMtg: chr "14" "12.5" "11.6" "14.5" ...
## $ NumInShelters : chr "11" "0" "16" "0" ...
## $ NumStreet : chr "0" "0" "0" "0" ...
## [list output truncated]## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion
## Warning in lapply(X = X, FUN = FUN, ...): NAs introduced by coercion## 'data.frame': 2215 obs. of 148 variables:
## $ citystate : chr "BerkeleyHeightstownshipNJ" "MarpletownshipPA" "TigardcityOR" "GloversvillecityNY" ...
## $ communityname : Factor w/ 2018 levels "Aberdeencity",..: 151 1035 1781 665 141 1700 1272 41 566 1860 ...
## $ state : Factor w/ 48 levels "AK","AL","AR",..: 29 36 35 32 23 24 19 15 27 41 ...
## $ countyCode : num 39 45 NA 35 7 NA 21 NA 17 NA ...
## $ communityCode : num 5320 47616 NA 29443 5068 ...
## $ fold : num 1 1 1 1 1 1 1 1 1 1 ...
## $ population : num 11980 23123 29344 16656 11245 ...
## $ householdsize : num 3.1 2.82 2.43 2.4 2.76 2.45 2.6 2.45 2.46 2.62 ...
## $ racepctblack : num 1.37 0.8 0.74 1.7 0.53 ...
## $ racePctWhite : num 91.8 95.6 94.3 97.3 89.2 ...
## $ racePctAsian : num 6.5 3.44 3.43 0.5 1.17 0.9 1.47 0.4 1.25 0.92 ...
## $ racePctHisp : num 1.88 0.85 2.35 0.7 0.52 ...
## $ agePct12t21 : num 12.5 11 11.4 12.6 24.5 ...
## $ agePct12t29 : num 21.4 21.3 25.9 25.2 40.5 ...
## $ agePct16t24 : num 10.9 10.5 11 12.2 28.7 ...
## $ agePct65up : num 11.3 17.2 10.3 17.6 12.6 ...
## $ numbUrban : num 11980 23123 29344 0 0 ...
## $ pctUrban : num 100 100 100 0 0 100 100 100 100 100 ...
## $ medIncome : num 75122 47917 35669 20580 17390 ...
## $ pctWWage : num 89.2 79 82 68.2 69.3 ...
## $ pctWFarmSelf : num 1.55 1.11 1.15 0.24 0.55 1 0.39 0.67 2.93 0.86 ...
## $ pctWInvInc : num 70.2 64.1 55.7 39 42.8 ...
## $ pctWSocSec : num 23.6 35.5 22.2 39.5 32.2 ...
## $ pctWPubAsst : num 1.03 2.75 2.94 11.71 11.21 ...
## $ pctWRetire : num 18.4 22.9 14.6 18.3 14.4 ...
## $ medFamInc : num 79584 55323 42112 26501 24018 ...
## $ perCapInc : num 29711 20148 16946 10810 8483 ...
## $ whitePerCap : num 30233 20191 17103 10909 9009 ...
## $ blackPerCap : num 13600 18137 16644 9984 887 ...
## $ indianPerCap : num 5725 0 21606 4941 4425 ...
## $ AsianPerCap : num 27101 20074 15528 3541 3352 ...
## $ OtherPerCap : num 5115 5250 5954 2451 3000 ...
## $ HispPerCap : num 22838 12222 8405 4391 1328 ...
## $ NumUnderPov : num 227 885 1389 2831 2855 ...
## $ PctPopUnderPov : num 1.96 3.98 4.75 17.23 29.99 ...
## $ PctLess9thGrade : num 5.81 5.61 2.8 11.05 12.15 ...
## $ PctNotHSGrad : num 9.9 13.72 9.09 33.68 23.06 ...
## $ PctBSorMore : num 48.2 29.9 30.1 10.8 25.3 ...
## $ PctUnemployed : num 2.7 2.43 4.01 9.86 9.08 5.72 4.85 8.19 4.18 8.39 ...
## $ PctEmploy : num 64.5 62 69.8 54.7 52.4 ...
## $ PctEmplManu : num 14.65 12.26 15.95 31.22 6.89 ...
## $ PctEmplProfServ : num 28.8 29.3 21.5 27.4 36.5 ...
## $ PctOccupManu : num 5.49 6.39 8.79 26.76 10.94 ...
## $ PctOccupMgmtProf : num 50.7 37.6 32.5 22.7 27.8 ...
## $ MalePctDivorce : num 3.67 4.23 10.1 10.98 7.51 ...
## $ MalePctNevMarr : num 26.4 28 25.8 28.1 50.7 ...
## $ FemalePctDiv : num 5.22 6.45 14.76 14.47 11.64 ...
## $ TotalPctDiv : num 4.47 5.42 12.55 12.91 9.73 ...
## $ PersPerFam : num 3.22 3.11 2.95 2.98 2.98 2.89 3.14 2.95 3 3.11 ...
## $ PctFam2Par : num 91.4 86.9 78.5 64 58.6 ...
## $ PctKids2Par : num 90.2 85.3 78.8 62.4 55.2 ...
## $ PctYoungKids2Par : num 95.8 96.8 92.4 65.4 66.5 ...
## $ PctTeen2Par : num 95.8 86.5 75.7 67.4 79.2 ...
## $ PctWorkMomYoungKids : num 44.6 51.1 66.1 59.6 61.2 ...
## $ PctWorkMom : num 58.9 62.4 74.2 70.3 68.9 ...
## $ NumKidsBornNeverMar : num 31 43 164 561 402 ...
## $ PctKidsBornNeverMar : num 0.36 0.24 0.88 3.84 4.7 1.58 1.18 4.66 1.64 4.71 ...
## $ NumImmig : num 1277 1920 1468 339 196 ...
## $ PctImmigRecent : num 8.69 5.21 16.42 13.86 46.94 ...
## $ PctImmigRec5 : num 13 8.65 23.98 13.86 56.12 ...
## $ PctImmigRec8 : num 21 13.3 32.1 15.3 67.9 ...
## $ PctImmigRec10 : num 30.9 22.5 35.6 15.3 69.9 ...
## $ PctRecentImmig : num 0.93 0.43 0.82 0.28 0.82 0.32 1.05 0.11 0.47 0.72 ...
## $ PctRecImmig5 : num 1.39 0.72 1.2 0.28 0.98 0.45 1.49 0.2 0.67 1.07 ...
## $ PctRecImmig8 : num 2.24 1.11 1.61 0.31 1.18 0.57 2.2 0.25 0.93 1.63 ...
## $ PctRecImmig10 : num 3.3 1.87 1.78 0.31 1.22 0.68 2.55 0.29 1.07 2.31 ...
## $ PctSpeakEnglOnly : num 85.7 87.8 93.1 95 94.6 ...
## $ PctNotSpeakEnglWell : num 1.37 1.81 1.14 0.56 0.39 0.6 0.6 0.28 0.43 2.51 ...
## $ PctLargHouseFam : num 4.81 4.25 2.97 3.93 5.23 3.08 5.08 3.85 2.59 6.7 ...
## $ PctLargHouseOccup : num 4.17 3.34 2.05 2.56 3.11 1.92 3.46 2.55 1.54 4.1 ...
## $ PersPerOccupHous : num 2.99 2.7 2.42 2.37 2.35 2.28 2.55 2.36 2.32 2.45 ...
## $ PersPerOwnOccHous : num 3 2.83 2.69 2.51 2.55 2.37 2.89 2.42 2.77 2.47 ...
## $ PersPerRentOccHous : num 2.84 1.96 2.06 2.2 2.12 2.16 2.09 2.27 1.91 2.44 ...
## $ PctPersOwnOccup : num 91.5 89 64.2 58.2 58.1 ...
## $ PctPersDenseHous : num 0.39 1.01 2.03 1.21 2.94 2.11 1.47 1.9 1.67 6.14 ...
## $ PctHousLess3BR : num 11.1 23.6 47.5 45.7 55.6 ...
## $ MedNumBR : num 3 3 3 3 2 2 3 2 2 2 ...
## $ HousVacant : num 64 240 544 669 333 ...
## $ PctHousOccup : num 98.4 97.2 95.7 91.2 92.5 ...
## $ PctHousOwnOcc : num 91 84.9 57.8 54.9 53.6 ...
## $ PctVacantBoarded : num 3.12 0 0.92 2.54 3.9 2.09 1.41 6.39 0.45 5.64 ...
## $ PctVacMore6Mos : num 37.5 18.33 7.54 57.85 42.64 ...
## $ MedYrHousBuilt : num 1959 1958 1976 1939 1958 ...
## $ PctHousNoPhone : num 0 0.31 1.55 7 7.45 ...
## $ PctWOFullPlumb : num 0.28 0.14 0.12 0.87 0.82 0.31 0.28 0.49 0.19 0.33 ...
## $ OwnOccLowQuart : num 215900 136300 74700 36400 30600 ...
## $ OwnOccMedVal : num 262600 164200 90400 49600 43200 ...
## $ OwnOccHiQuart : num 326900 199900 112000 66500 59500 ...
## $ OwnOccQrange : num 111000 63600 37300 30100 28900 35400 60400 26100 39200 38800 ...
## $ RentLowQ : num 685 467 370 195 202 215 463 186 241 192 ...
## $ RentMedian : num 1001 560 428 250 283 ...
## $ RentHighQ : num 1001 672 520 309 362 ...
## $ RentQrange : num 316 205 150 114 160 134 361 139 146 177 ...
## $ MedRent : num 1001 627 484 333 332 ...
## $ MedRentPctHousInc : num 23.8 27.6 24.1 28.7 32.2 26.4 24.4 26.3 25.2 29.6 ...
## $ MedOwnCostPctInc : num 21.1 20.7 21.7 20.6 23.2 17.3 20.8 15.1 20.7 19.4 ...
## $ MedOwnCostPctIncNoMtg: num 14 12.5 11.6 14.5 12.9 11.7 12.5 12.2 12.8 13 ...
## $ NumInShelters : num 11 0 16 0 2 327 0 21 125 43 ...
## $ NumStreet : num 0 0 0 0 0 4 0 0 15 4 ...
## [list output truncated]## [1] NA## [1] 589.0789When we change factor to numeric, sometimes it creats hidden issues like errors. So we chose to change factors to characters and then change characters to numeric. From warning we noticed there were NAs introduced by coercion, as the ? symbols were replaced by NAs. We fixed these newly introduced NAs by using sqldf function/
Plots for Crime Dataset
We collected state coordinates for map data, and Converted state abbreviation into full names.We excluded DC from the state data and then appended crime data to the state map.