#Exploring Numerical data
#load package
library(ggplot2)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
#dataset for 'cars'
cars=read.csv("C:/Users/ELCOT/Desktop/DSAC/R Programming/DataSet/cars.csv",header=T)
str(cars)
## 'data.frame': 428 obs. of 19 variables:
## $ name : chr "Chevrolet Aveo 4dr" "Chevrolet Aveo LS 4dr hatch" "Chevrolet Cavalier 2dr" "Chevrolet Cavalier 4dr" ...
## $ sports_car : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ suv : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ wagon : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ minivan : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ pickup : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ all_wheel : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ rear_wheel : logi FALSE FALSE FALSE FALSE FALSE FALSE ...
## $ msrp : int 11690 12585 14610 14810 16385 13670 15040 13270 13730 15460 ...
## $ dealer_cost: int 10965 11802 13697 13884 15357 12849 14086 12482 12906 14496 ...
## $ eng_size : num 1.6 1.6 2.2 2.2 2.2 2 2 2 2 2 ...
## $ ncyl : int 4 4 4 4 4 4 4 4 4 4 ...
## $ horsepwr : int 103 103 140 140 140 132 132 130 110 130 ...
## $ city_mpg : int 28 28 26 26 26 29 29 26 27 26 ...
## $ hwy_mpg : int 34 34 37 37 37 36 36 33 36 33 ...
## $ weight : int 2370 2348 2617 2676 2617 2581 2626 2612 2606 2606 ...
## $ wheel_base : int 98 98 104 104 104 105 105 103 103 103 ...
## $ length : int 167 153 183 183 183 174 174 168 168 168 ...
## $ width : int 66 66 69 68 69 67 67 67 67 67 ...
View(cars)
#Visualize Histogram for city_mpg and sub_graph wrt sport Utility Vechicle
cars %>% ggplot(aes(city_mpg))+geom_histogram()+facet_wrap(vars(suv))
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 14 rows containing non-finite values (stat_bin).
#Filter cars with 4,6,8 Cylinders
cars %>% filter(ncyl==c(4,6,8))
## Warning in ncyl == c(4, 6, 8): longer object length is not a multiple of shorter
## object length
## name sports_car suv wagon minivan
## 1 Chevrolet Aveo 4dr FALSE FALSE FALSE FALSE
## 2 Chevrolet Cavalier 4dr FALSE FALSE FALSE FALSE
## 3 Dodge Neon SXT 4dr FALSE FALSE FALSE FALSE
## 4 Ford Focus SE 4dr FALSE FALSE FALSE FALSE
## 5 Honda Civic HX 2dr FALSE FALSE FALSE FALSE
## 6 Hyundai Accent GL 4dr FALSE FALSE FALSE FALSE
## 7 Hyundai Elantra GT 4dr FALSE FALSE FALSE FALSE
## 8 Kia Rio 4dr manual FALSE FALSE FALSE FALSE
## 9 Kia Spectra GS 4dr hatch FALSE FALSE FALSE FALSE
## 10 Mini Cooper FALSE FALSE FALSE FALSE
## 11 Nissan Sentra 1.8 4dr FALSE FALSE FALSE FALSE
## 12 Saturn Ion1 4dr FALSE FALSE FALSE FALSE
## 13 Saturn lon2 quad coupe 2dr FALSE FALSE FALSE FALSE
## 14 Suzuki Aeno S 4dr FALSE FALSE FALSE FALSE
## 15 Suzuki Forenza EX 4dr FALSE FALSE FALSE FALSE
## 16 Toyota Corolla LE 4dr FALSE FALSE FALSE FALSE
## 17 Toyota Echo 4dr FALSE FALSE FALSE FALSE
## 18 Buick Century Custom 4dr FALSE FALSE FALSE FALSE
## 19 Chevrolet Malibu 4dr FALSE FALSE FALSE FALSE
## 20 Chevrolet Malibu LS 4dr FALSE FALSE FALSE FALSE
## 21 Chrysler PT Cruiser 4dr FALSE FALSE FALSE FALSE
## 22 Dodge Intrepid SE 4dr FALSE FALSE FALSE FALSE
## 23 Dodge Stratus SE 4dr FALSE FALSE FALSE FALSE
## 24 Honda Civic EX 4dr FALSE FALSE FALSE FALSE
## 25 Hyundai Sonata GLS 4dr FALSE FALSE FALSE FALSE
## 26 Mitsubishi Lancer OZ Rally 4dr auto FALSE FALSE FALSE FALSE
## 27 Oldsmobile Alero GX 2dr FALSE FALSE FALSE FALSE
## 28 Pontiac Grand Am GT 2dr FALSE FALSE FALSE FALSE
## 29 Pontiac Sunfire 1SC 2dr FALSE FALSE FALSE FALSE
## 30 Saturn L300-2 4dr FALSE FALSE FALSE FALSE
## 31 Subaru Legacy L 4dr FALSE FALSE FALSE FALSE
## 32 Suzuki Verona LX 4dr FALSE FALSE FALSE FALSE
## 33 Toyota Prius 4dr (gas/electric) FALSE FALSE FALSE FALSE
## 34 Volkswagen Jetta GLS TDI 4dr FALSE FALSE FALSE FALSE
## 35 Acura TSX 4dr FALSE FALSE FALSE FALSE
## 36 Buick Regal LS 4dr FALSE FALSE FALSE FALSE
## 37 Chevrolet Impala SS 4dr FALSE FALSE FALSE FALSE
## 38 Chrysler 300M 4dr FALSE FALSE FALSE FALSE
## 39 Ford Crown Victoria LX 4dr FALSE FALSE FALSE FALSE
## 40 Honda Accord EX V6 2dr FALSE FALSE FALSE FALSE
## 41 Infiniti G35 4dr FALSE FALSE FALSE FALSE
## 42 Kia Amanti 4dr FALSE FALSE FALSE FALSE
## 43 Mercury Grand Marquis LS Premium 4dr FALSE FALSE FALSE FALSE
## 44 Mitsubishi Diamante LS 4dr FALSE FALSE FALSE FALSE
## 45 Nissan Maxima SE 4dr FALSE FALSE FALSE FALSE
## 46 Pontiac Grand Prix GT2 4dr FALSE FALSE FALSE FALSE
## 47 Subaru Outback Limited Sedan 4dr FALSE FALSE FALSE FALSE
## 48 Subaru Outback H6 4dr FALSE FALSE FALSE FALSE
## 49 Toyota Camry Solara SLE V6 2dr FALSE FALSE FALSE FALSE
## 50 Volkswagen New Beetle GLS convertible 2dr FALSE FALSE FALSE FALSE
## 51 Audi A4 3.0 Quattro 4dr auto FALSE FALSE FALSE FALSE
## 52 BMW 325Ci 2dr FALSE FALSE FALSE FALSE
## 53 BMW 330i 4dr FALSE FALSE FALSE FALSE
## 54 BMW 525i 4dr FALSE FALSE FALSE FALSE
## 55 Cadillac CTS VVT 4dr FALSE FALSE FALSE FALSE
## 56 Jaguar X-Type 3.0 4dr FALSE FALSE FALSE FALSE
## 57 Lexus IS 300 4dr auto FALSE FALSE FALSE FALSE
## 58 Mercedes-Benz C240 4dr FALSE FALSE FALSE FALSE
## 59 Mercedes-Benz C320 4dr FALSE FALSE FALSE FALSE
## 60 Pontiac Bonneville GXP 4dr FALSE FALSE FALSE FALSE
## 61 Saab 9-3 Arc Sport 4dr FALSE FALSE FALSE FALSE
## 62 Saab 9-5 Aero 4dr FALSE FALSE FALSE FALSE
## 63 Subaru Outback H-6 VDC 4dr FALSE FALSE FALSE FALSE
## 64 Acura 3.5 RL 4dr FALSE FALSE FALSE FALSE
## 65 Audi A4 3.0 Quattro convertible 2dr FALSE FALSE FALSE FALSE
## 66 Audi S4 Quattro 4dr FALSE FALSE FALSE FALSE
## 67 BMW 530i 4dr FALSE FALSE FALSE FALSE
## 68 BMW 545iA 4dr FALSE FALSE FALSE FALSE
## 69 Cadillac Seville SLS 4dr FALSE FALSE FALSE FALSE
## 70 Jaguar Vanden Plas 4dr FALSE FALSE FALSE FALSE
## 71 Lincoln LS V8 Sport 4dr FALSE FALSE FALSE FALSE
## 72 Lincoln Town Car Ultimate 4dr FALSE FALSE FALSE FALSE
## 73 Mercedes-Benz C32 AMG 4dr FALSE FALSE FALSE FALSE
## 74 Mercedes-Benz CL500 2dr FALSE FALSE FALSE FALSE
## 75 Mercedes-Benz CLK320 coupe 2dr (convertible) FALSE FALSE FALSE FALSE
## 76 Mercedes-Benz CLK500 coupe 2dr (convertible) FALSE FALSE FALSE FALSE
## 77 Mercedes-Benz S430 4dr FALSE FALSE FALSE FALSE
## 78 Volvo S80 T6 4dr FALSE FALSE FALSE FALSE
## 79 BMW M3 coupe 2dr TRUE FALSE FALSE FALSE
## 80 BMW Z4 convertible 3.0i 2dr TRUE FALSE FALSE FALSE
## 81 Cadillac XLR convertible 2dr TRUE FALSE FALSE FALSE
## 82 Ford Mustang 2dr (convertible) TRUE FALSE FALSE FALSE
## 83 Ford Mustang GT Premium convertible 2dr TRUE FALSE FALSE FALSE
## 84 Jaguar XKR coupe 2dr TRUE FALSE FALSE FALSE
## 85 Mazda MX-5 Miata LS convertible 2dr TRUE FALSE FALSE FALSE
## 86 Mercedes-Benz SLK230 convertible 2dr TRUE FALSE FALSE FALSE
## 87 Mercedes-Benz SLK32 AMG 2dr TRUE FALSE FALSE FALSE
## 88 Porsche 911 Targa coupe 2dr TRUE FALSE FALSE FALSE
## 89 Porsche Boxster S convertible 2dr TRUE FALSE FALSE FALSE
## 90 Subaru Impreza WRX STi 4dr TRUE FALSE FALSE FALSE
## 91 Chevrolet Suburban 1500 LT FALSE TRUE FALSE FALSE
## 92 GMC Envoy XUV SLE FALSE TRUE FALSE FALSE
## 93 CMC Yukon 1500 SLE FALSE TRUE FALSE FALSE
## 94 Toyota Sequoia SR5 FALSE TRUE FALSE FALSE
## 95 BMW X3 3.0i FALSE TRUE FALSE FALSE
## 96 BMW X5 4.4i FALSE TRUE FALSE FALSE
## 97 Buick Rendezvous CX FALSE TRUE FALSE FALSE
## 98 Honda Pilot LX FALSE TRUE FALSE FALSE
## 99 Kia Sorento LX FALSE TRUE FALSE FALSE
## 100 Land Rover Range Rover HSE FALSE TRUE FALSE FALSE
## 101 Mercedes-Benz ML500 FALSE TRUE FALSE FALSE
## 102 Mitsubishi Endeavor XLS FALSE TRUE FALSE FALSE
## 103 Pontiac Aztekt FALSE TRUE FALSE FALSE
## 104 Porsche Cayenne S FALSE TRUE FALSE FALSE
## 105 Saturn VUE FALSE TRUE FALSE FALSE
## 106 Suzuki XL-7 EX FALSE TRUE FALSE FALSE
## 107 Chevrolet Tracker FALSE TRUE FALSE FALSE
## 108 Honda CR-V LX FALSE TRUE FALSE FALSE
## 109 Jeep Liberty Sport FALSE TRUE FALSE FALSE
## 110 Jeep Wrangler Sahara convertible 2dr FALSE TRUE FALSE FALSE
## 111 Land Rover Discovery SE FALSE TRUE FALSE FALSE
## 112 Suzuki Vitara LX FALSE TRUE FALSE FALSE
## 113 Chrysler Pacifica FALSE FALSE TRUE FALSE
## 114 Infiniti FX35 FALSE FALSE TRUE FALSE
## 115 Infiniti FX45 FALSE FALSE TRUE FALSE
## 116 Kia Rio Cinco FALSE FALSE TRUE FALSE
## 117 Lexus IS 300 SportCross FALSE FALSE TRUE FALSE
## 118 Mitsubishi Lancer Sportback LS FALSE FALSE TRUE FALSE
## 119 Nissan Murano SL FALSE FALSE TRUE FALSE
## 120 Saab 9-5 Aero FALSE FALSE TRUE FALSE
## 121 Subaru Forester X FALSE FALSE TRUE FALSE
## 122 Toyota Matrix XR FALSE FALSE TRUE FALSE
## 123 Chevrolet Venture LS FALSE FALSE FALSE TRUE
## 124 GMC Safari SLE FALSE FALSE FALSE TRUE
## 125 Kia Sedona LX FALSE FALSE FALSE TRUE
## 126 Nissan Quest S FALSE FALSE FALSE TRUE
## 127 Pontiac Montana FALSE FALSE FALSE TRUE
## 128 Toyota Sienna XLE Limited FALSE FALSE FALSE TRUE
## 129 Cadillac Escalade EXT FALSE FALSE FALSE FALSE
## 130 Dodge Ram 1500 Regular Cab ST FALSE FALSE FALSE FALSE
## 131 Ford F-150 Regular Cab XL FALSE FALSE FALSE FALSE
## 132 Mazda B2300 SX Regular Cab FALSE FALSE FALSE FALSE
## 133 Mazda B4000 SE Cab Plus FALSE FALSE FALSE FALSE
## 134 Toyota Tundra Access Cab V6 SR5 FALSE FALSE FALSE FALSE
## pickup all_wheel rear_wheel msrp dealer_cost eng_size ncyl horsepwr
## 1 FALSE FALSE FALSE 11690 10965 1.6 4 103
## 2 FALSE FALSE FALSE 14810 13884 2.2 4 140
## 3 FALSE FALSE FALSE 15040 14086 2.0 4 132
## 4 FALSE FALSE FALSE 15460 14496 2.0 4 130
## 5 FALSE FALSE FALSE 14170 12996 1.7 4 117
## 6 FALSE FALSE FALSE 11839 11116 1.6 4 103
## 7 FALSE FALSE FALSE 15389 14207 2.0 4 138
## 8 FALSE FALSE FALSE 10280 9875 1.6 4 104
## 9 FALSE FALSE FALSE 13580 12830 1.8 4 124
## 10 FALSE FALSE FALSE 16999 15437 1.6 4 115
## 11 FALSE FALSE FALSE 12740 12205 1.8 4 126
## 12 FALSE FALSE FALSE 10995 10319 2.2 4 140
## 13 FALSE FALSE FALSE 14850 13904 2.2 4 140
## 14 FALSE FALSE FALSE 12884 12719 2.3 4 155
## 15 FALSE FALSE FALSE 15568 15378 2.0 4 119
## 16 FALSE FALSE FALSE 15295 13889 1.8 4 130
## 17 FALSE FALSE FALSE 11290 10642 1.5 4 108
## 18 FALSE FALSE FALSE 22180 20351 3.1 6 175
## 19 FALSE FALSE FALSE 18995 17434 2.2 4 145
## 20 FALSE FALSE FALSE 20370 18639 3.5 6 200
## 21 FALSE FALSE FALSE 17985 16919 2.4 4 150
## 22 FALSE FALSE FALSE 22035 20502 2.7 6 200
## 23 FALSE FALSE FALSE 20220 18821 2.4 4 150
## 24 FALSE FALSE FALSE 17750 16265 1.7 4 127
## 25 FALSE FALSE FALSE 19339 17574 2.7 6 170
## 26 FALSE FALSE FALSE 17232 16196 2.0 4 120
## 27 FALSE FALSE FALSE 18825 17642 2.2 4 140
## 28 FALSE FALSE FALSE 22450 20595 3.4 6 175
## 29 FALSE FALSE FALSE 17735 16369 2.2 4 140
## 30 FALSE FALSE FALSE 21410 19801 3.0 6 182
## 31 FALSE TRUE FALSE 20445 18713 2.5 4 165
## 32 FALSE FALSE FALSE 17262 17053 2.5 6 155
## 33 FALSE FALSE FALSE 20510 18926 1.5 4 110
## 34 FALSE FALSE FALSE 21055 19638 1.9 4 100
## 35 FALSE FALSE FALSE 26990 24647 2.4 4 200
## 36 FALSE FALSE FALSE 24895 22835 3.8 6 200
## 37 FALSE FALSE FALSE 27995 25672 3.8 6 240
## 38 FALSE FALSE FALSE 29865 27797 3.5 6 250
## 39 FALSE FALSE TRUE 27370 25105 4.6 8 224
## 40 FALSE FALSE FALSE 26960 24304 3.0 6 240
## 41 FALSE FALSE TRUE 28495 26157 3.5 6 260
## 42 FALSE FALSE FALSE 26000 23764 3.5 6 195
## 43 FALSE FALSE TRUE 29595 27148 4.6 8 224
## 44 FALSE FALSE FALSE 29282 27250 3.5 6 205
## 45 FALSE FALSE FALSE 27490 25182 3.5 6 265
## 46 FALSE FALSE FALSE 24295 22284 3.8 6 200
## 47 FALSE TRUE FALSE 27145 24687 2.5 4 165
## 48 FALSE TRUE FALSE 29345 26660 3.0 6 212
## 49 FALSE FALSE FALSE 26510 23908 3.3 6 225
## 50 FALSE FALSE FALSE 23215 21689 2.0 4 115
## 51 FALSE TRUE FALSE 34480 31388 3.0 6 220
## 52 FALSE FALSE TRUE 30795 28245 2.5 6 184
## 53 FALSE FALSE TRUE 35495 32525 3.0 6 225
## 54 FALSE FALSE TRUE 39995 36620 2.5 6 184
## 55 FALSE FALSE TRUE 30835 28575 3.6 6 255
## 56 FALSE TRUE FALSE 33995 30995 3.0 6 227
## 57 FALSE FALSE TRUE 32415 28611 3.0 6 215
## 58 FALSE FALSE TRUE 32280 30071 2.6 6 168
## 59 FALSE FALSE TRUE 37630 35046 3.2 6 215
## 60 FALSE FALSE FALSE 35995 32997 4.6 8 275
## 61 FALSE FALSE FALSE 30860 29269 2.0 4 210
## 62 FALSE FALSE FALSE 39465 37721 2.3 4 250
## 63 FALSE TRUE FALSE 31545 28603 3.0 6 212
## 64 FALSE FALSE FALSE 43755 39014 3.5 6 225
## 65 FALSE TRUE FALSE 44240 40075 3.0 6 220
## 66 FALSE TRUE FALSE 48040 43556 4.2 8 340
## 67 FALSE FALSE TRUE 44995 41170 3.0 6 225
## 68 FALSE FALSE TRUE 54995 50270 4.4 8 325
## 69 FALSE FALSE FALSE 47955 43841 4.6 8 275
## 70 FALSE FALSE TRUE 68995 62846 4.2 8 294
## 71 FALSE FALSE TRUE 40095 36809 3.9 8 280
## 72 FALSE FALSE TRUE 44925 41217 4.6 8 239
## 73 FALSE FALSE TRUE 52120 48522 3.2 6 349
## 74 FALSE FALSE TRUE 94820 88324 5.0 8 302
## 75 FALSE FALSE TRUE 45707 41966 3.2 6 215
## 76 FALSE FALSE TRUE 52800 49104 5.0 8 302
## 77 FALSE FALSE TRUE 74320 69168 4.3 8 275
## 78 FALSE FALSE FALSE 45210 42573 2.9 6 268
## 79 FALSE FALSE TRUE 48195 44170 3.2 6 333
## 80 FALSE FALSE TRUE 41045 37575 3.0 6 225
## 81 FALSE FALSE TRUE 76200 70546 4.6 8 320
## 82 FALSE FALSE TRUE 18345 16943 3.8 6 193
## 83 FALSE FALSE TRUE 29380 26875 4.6 8 260
## 84 FALSE FALSE TRUE 81995 74676 4.2 8 390
## 85 FALSE FALSE TRUE 25193 23285 1.8 4 142
## 86 FALSE FALSE TRUE 40320 37548 2.3 4 192
## 87 FALSE FALSE TRUE 56170 52289 3.2 6 349
## 88 FALSE FALSE TRUE 76765 67128 3.6 6 315
## 89 FALSE FALSE TRUE 52365 45766 3.2 6 258
## 90 FALSE TRUE FALSE 31545 29130 2.5 4 300
## 91 FALSE FALSE FALSE 42735 37422 5.3 8 295
## 92 FALSE FALSE FALSE 31890 28922 4.2 6 275
## 93 FALSE FALSE FALSE 35725 31361 4.8 8 285
## 94 FALSE TRUE FALSE 35695 31827 4.7 8 240
## 95 FALSE TRUE FALSE 37000 33873 3.0 6 225
## 96 FALSE TRUE FALSE 52195 47720 4.4 8 325
## 97 FALSE FALSE FALSE 26545 24085 3.4 6 185
## 98 FALSE TRUE FALSE 27560 24843 3.5 6 240
## 99 FALSE FALSE FALSE 19635 18630 3.5 6 192
## 100 FALSE TRUE FALSE 72250 65807 4.4 8 282
## 101 FALSE TRUE FALSE 46470 43268 5.0 8 288
## 102 FALSE TRUE FALSE 30492 28330 3.8 6 215
## 103 FALSE FALSE FALSE 21595 19810 3.4 6 185
## 104 FALSE TRUE FALSE 56665 49865 4.5 8 340
## 105 FALSE TRUE FALSE 20585 19238 2.2 4 143
## 106 FALSE FALSE FALSE 23699 22307 2.7 6 185
## 107 FALSE FALSE FALSE 20255 19108 2.5 6 165
## 108 FALSE TRUE FALSE 19860 18419 2.4 4 160
## 109 FALSE TRUE FALSE 20130 18973 2.4 4 150
## 110 FALSE TRUE FALSE 25520 23275 4.0 6 190
## 111 FALSE TRUE FALSE 39250 35777 4.6 8 217
## 112 FALSE TRUE FALSE 17163 16949 2.5 6 165
## 113 FALSE FALSE TRUE 31230 28725 3.5 6 250
## 114 FALSE FALSE TRUE 34895 31756 3.5 6 280
## 115 FALSE TRUE FALSE 36395 33121 4.5 8 315
## 116 FALSE FALSE FALSE 11905 11410 1.6 4 104
## 117 FALSE FALSE TRUE 32455 28647 3.0 6 215
## 118 FALSE FALSE FALSE 17495 16295 2.4 4 160
## 119 FALSE FALSE TRUE 28739 27300 3.5 6 245
## 120 FALSE FALSE FALSE 40845 38376 2.3 4 250
## 121 FALSE TRUE FALSE 21445 19646 2.5 4 165
## 122 FALSE FALSE FALSE 16695 15156 1.8 4 130
## 123 FALSE FALSE FALSE 27020 24518 3.4 6 185
## 124 FALSE FALSE TRUE 25640 23215 4.3 6 190
## 125 FALSE FALSE FALSE 20615 19400 3.5 6 195
## 126 FALSE FALSE FALSE 24780 22958 3.5 6 240
## 127 FALSE FALSE FALSE 23845 21644 3.4 6 185
## 128 FALSE FALSE FALSE 28800 25690 3.3 6 230
## 129 TRUE TRUE FALSE 52975 48541 6.0 8 345
## 130 TRUE FALSE TRUE 20215 18076 3.7 6 215
## 131 TRUE FALSE TRUE 22010 19490 4.6 8 231
## 132 TRUE FALSE TRUE 14840 14070 2.3 4 143
## 133 TRUE TRUE FALSE 22350 20482 4.0 6 207
## 134 TRUE TRUE FALSE 25935 23520 3.4 6 190
## city_mpg hwy_mpg weight wheel_base length width
## 1 28 34 2370 98 167 66
## 2 26 37 2676 104 183 68
## 3 29 36 2626 105 174 67
## 4 26 33 2606 103 168 67
## 5 36 44 2500 103 175 67
## 6 29 33 2290 96 167 66
## 7 26 34 2635 103 178 68
## 8 26 33 2403 95 167 66
## 9 24 32 2686 101 178 68
## 10 28 37 2524 97 143 67
## 11 28 35 2513 100 178 67
## 12 26 35 2692 103 185 67
## 13 26 35 2751 103 185 68
## 14 25 31 2676 98 171 68
## 15 22 30 2756 102 177 68
## 16 32 40 2524 102 178 67
## 17 35 43 2055 93 163 65
## 18 20 30 3353 109 195 73
## 19 24 34 3174 106 188 70
## 20 22 30 3297 106 188 70
## 21 22 29 3101 103 169 67
## 22 21 29 3469 113 204 75
## 23 21 28 3175 108 191 71
## 24 32 37 2601 103 175 68
## 25 19 27 3217 106 187 72
## 26 NA NA 2744 102 181 67
## 27 24 32 2946 107 187 70
## 28 20 29 3118 107 186 70
## 29 24 33 2771 104 182 68
## 30 20 28 3197 107 190 69
## 31 21 28 3285 104 184 69
## 32 20 27 3380 106 188 72
## 33 59 51 2890 106 175 68
## 34 38 46 3003 99 172 68
## 35 22 29 3230 105 183 69
## 36 20 30 3461 109 196 73
## 37 18 28 3606 111 200 73
## 38 18 27 3581 113 198 74
## 39 17 25 4057 115 212 78
## 40 21 30 3294 105 188 71
## 41 18 26 3336 112 187 69
## 42 17 25 NA 110 196 73
## 43 17 25 4052 115 212 78
## 44 18 25 3549 107 194 70
## 45 20 28 3473 111 194 72
## 46 20 30 3484 111 198 74
## 47 20 27 3495 104 184 69
## 48 19 26 3610 104 184 69
## 49 20 29 3439 107 193 72
## 50 24 30 3082 99 161 68
## 51 18 25 3627 104 179 70
## 52 20 29 3197 107 177 69
## 53 20 30 3285 107 176 69
## 54 19 28 3428 114 191 73
## 55 18 25 3694 113 190 71
## 56 18 25 3516 107 184 70
## 57 18 24 3285 105 177 68
## 58 20 25 3360 107 178 68
## 59 20 26 3450 107 178 68
## 60 NA NA 3790 112 203 74
## 61 20 28 3175 105 183 69
## 62 21 29 3470 106 190 71
## 63 19 26 3630 104 184 69
## 64 18 24 3880 115 197 72
## 65 18 25 4013 105 180 70
## 66 14 20 3825 104 179 70
## 67 20 30 3472 114 191 73
## 68 18 26 3814 114 191 73
## 69 18 26 3992 112 201 75
## 70 18 28 3803 119 200 73
## 71 17 24 3768 115 194 73
## 72 17 25 4369 118 215 78
## 73 16 21 3540 107 178 68
## 74 16 24 4085 114 196 73
## 75 20 26 3770 107 183 69
## 76 17 22 3585 107 183 69
## 77 18 26 4160 122 203 73
## 78 19 26 3653 110 190 72
## 79 16 24 3415 108 177 70
## 80 21 29 2998 98 161 70
## 81 17 25 3647 106 178 72
## 82 20 29 3290 101 183 73
## 83 17 25 3347 101 183 73
## 84 16 23 3865 102 187 71
## 85 23 28 2387 89 156 66
## 86 21 29 3055 95 158 68
## 87 17 22 3220 95 158 68
## 88 18 26 3119 93 175 70
## 89 18 26 2911 95 170 70
## 90 18 24 3263 100 174 69
## 91 14 18 4947 130 219 79
## 92 15 19 4945 129 208 75
## 93 16 19 5042 116 199 79
## 94 14 17 5270 118 204 78
## 95 16 23 4023 110 180 73
## 96 16 22 4824 111 184 74
## 97 19 26 4024 112 187 74
## 98 17 22 4387 106 188 77
## 99 16 19 4112 107 180 73
## 100 12 16 5379 113 195 76
## 101 14 17 4874 111 183 72
## 102 17 21 4134 109 190 74
## 103 19 26 3779 108 182 74
## 104 14 18 4950 112 188 76
## 105 21 26 3381 107 181 72
## 106 18 22 3682 110 187 70
## 107 19 22 2866 98 163 67
## 108 21 25 3258 103 179 70
## 109 20 24 3826 104 174 72
## 110 16 19 3575 93 150 67
## 111 12 16 4576 100 185 74
## 112 19 22 3020 98 163 67
## 113 17 23 4675 116 199 79
## 114 16 22 4056 112 189 76
## 115 15 19 4309 112 189 76
## 116 26 33 2447 95 167 66
## 117 18 24 3410 105 177 68
## 118 NA NA 3020 102 181 67
## 119 20 25 3801 111 188 74
## 120 19 29 3620 106 190 71
## 121 21 28 3090 99 175 68
## 122 29 36 2679 102 171 70
## 123 19 26 3699 112 187 72
## 124 16 20 4309 111 190 78
## 125 16 22 4802 115 194 75
## 126 19 26 4012 124 204 78
## 127 19 26 3803 112 187 72
## 128 19 27 4165 119 200 77
## 129 13 17 5879 130 NA NA
## 130 16 21 4542 121 NA NA
## 131 15 19 4788 126 NA NA
## 132 24 29 2960 112 NA NA
## 133 15 19 3571 126 NA NA
## 134 14 17 4435 128 NA NA
#create a box plot of city mpg by ncyl
cars %>% ggplot(aes(as.factor(ncyl),city_mpg))+geom_boxplot()
## Warning: Removed 14 rows containing non-finite values (stat_boxplot).
#create overlaid density plot for same data
cars %>% ggplot(aes(x=city_mpg,fill=as.factor(ncyl)))+geom_density(alpha=.3)
## Warning: Removed 14 rows containing non-finite values (stat_density).
## Warning: Groups with fewer than two data points have been dropped.
## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning -
## Inf
#create hist of horsepwr
cars %>% ggplot(aes(horsepwr))+geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
#create hist of horsepwr with binwidth of 3.
cars %>% ggplot(aes(horsepwr))+geom_histogram(binwidth = 3)
#construct boxplot of msrp
cars %>% ggplot(aes(msrp))+geom_boxplot()
#exclude outliers from data more than 10000
cars %>% filter(msrp>10000) %>% ggplot(aes(msrp))+geom_boxplot()