Final Draft
Loading libraries
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, unionlibrary(readr)
library(tidyr)
library(ggplot2)
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ tibble 3.1.4 ✓ stringr 1.4.0
## ✓ purrr 0.3.4 ✓ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()Introduction
Research Questions
- How does efficiency differentiate between the most expensive electric vehicles?
- How does efficiency differentiate between the least expensive electric vehicles?
- How does efficiency differentiate between mid-priced electric vehicles?
- How does price affect efficiency and range among Teslas?
- How does price affect efficiency and range among Volkswagons?
- How does price affect efficiency and range among Hyundai?
- How does price and performance compare among Tesla, Volkswagon and Hyundai?
Reading in new data
car_data <- read.csv("electric_cars.csv", header=TRUE, stringsAsFactors=FALSE)
head(car_data)## title topspeed_km.h range_km efficiency_Wh.km
## 1 Tesla Model 3 Long Range Dual Motor 233 455 154
## 2 Renault Megane E-Tech EV60 220hp 160 360 167
## 3 Tesla Model Y Long Range Dual Motor 217 410 171
## 4 Kia EV6 GT 260 395 196
## 5 Skoda Enyaq iV 80 160 420 183
## 6 Tesla Model 3 Standard Range Plus LFP 225 350 150
## fastcharge_speed_km.h price_de_euro price_nl_euro price_uk_pound
## 1 650 NA NA 48490
## 2 520 40000 40000 35000
## 3 590 59965 65010 54000
## 4 920 65990 63595 58295
## 5 510 43950 47780 39365
## 6 630 43560 49990 40990Observing variables
str(car_data)## 'data.frame': 185 obs. of 8 variables:
## $ title : chr "Tesla Model 3 Long Range Dual Motor" "Renault Megane E-Tech EV60 220hp" "Tesla Model Y Long Range Dual Motor" "Kia EV6 GT" ...
## $ topspeed_km.h : int 233 160 217 260 160 225 125 144 160 185 ...
## $ range_km : int 455 360 410 395 420 350 170 225 535 385 ...
## $ efficiency_Wh.km : int 154 167 171 196 183 150 158 164 168 189 ...
## $ fastcharge_speed_km.h: num 650 520 590 920 510 630 120 230 680 890 ...
## $ price_de_euro : num NA 40000 59965 65990 43950 ...
## $ price_nl_euro : num NA 40000 65010 63595 47780 ...
## $ price_uk_pound : num 48490 35000 54000 58295 39365 ...Data Wrangling
# Arranging the vehicles by price
top_price <- arrange(car_data, desc(`price_uk_pound`))
# Dataset for hgihest priced vehicles
top_price <- top_price %>% slice(1:10)
top_price## title topspeed_km.h range_km efficiency_Wh.km
## 1 Tesla Roadster 410 970 206
## 2 Porsche Taycan Turbo S Cross Turismo 250 380 220
## 3 Porsche Taycan Turbo S 260 390 215
## 4 Mercedes EQS AMG 53 4MATIC+ 250 565 191
## 5 Lucid Air Grand Touring 270 660 167
## 6 Tesla Model S Plaid 322 535 168
## 7 Porsche Taycan Turbo Cross Turismo 250 385 217
## 8 Porsche Taycan Turbo 260 400 209
## 9 Mercedes EQS 580 4MATIC 210 610 177
## 10 Tesla Model X Plaid 262 455 198
## fastcharge_speed_km.h price_de_euro price_nl_euro price_uk_pound
## 1 920 215000 215000 189000
## 2 790 187746 193200 139910
## 3 860 186336 191700 138830
## 4 740 175000 175000 135000
## 5 1380 140000 140000 125000
## 6 800 126990 131000 118980
## 7 800 154444 159300 116950
## 8 840 153016 157900 115860
## 9 800 135529 154949 115000
## 10 680 116990 121000 110980# Arranging vehicles by lowest price
low_price <- arrange(car_data, `price_uk_pound`)
low_price## title topspeed_km.h range_km
## 1 Smart EQ fortwo coupe 130 100
## 2 Smart EQ forfour 130 95
## 3 Fiat 500e Hatchback 24 kWh 135 165
## 4 Volkswagen e-Up! 130 205
## 5 Smart EQ fortwo cabrio 130 95
## 6 Fiat 500e Hatchback 42 kWh 150 250
## 7 MG MG5 EV 185 295
## 8 Nissan Leaf 144 225
## 9 MG ZS EV 140 220
## 10 Mini Cooper SE 150 185
## 11 Mazda MX-30 140 170
## 12 MG MG5 EV Long Range 185 340
## 13 Fiat 500e Cabrio 150 245
## 14 Renault Zoe ZE50 R110 135 315
## 15 Volkswagen ID.3 Pure Performance 160 275
## 16 Opel Corsa-e 150 275
## 17 Peugeot e-208 150 275
## 18 Honda e 145 170
## 19 Hyundai Kona Electric 39 kWh 155 250
## 20 Volkswagen ID.3 Pro 160 350
## 21 CUPRA Born 110 kW - 55 kWh 160 275
## 22 Renault Zoe ZE50 R135 140 310
## 23 Volkswagen ID.3 Pro Performance 160 350
## 24 Honda e Advance 145 170
## 25 Nissan e-NV200 Evalia 123 170
## 26 Kia e-Niro 39 kWh 155 235
## 27 Peugeot e-Rifter Standard 50 kWh 135 200
## 28 Nissan Leaf e+ 157 325
## 29 CUPRA Born 150 kW - 62 kWh 160 350
## 30 Opel Mokka-e 150 255
## 31 Hyundai IONIQ Electric 165 250
## 32 Peugeot e-2008 SUV 150 250
## 33 Citroen e-C4 150 250
## 34 BMW i3 120 Ah 150 235
## 35 DS 3 Crossback E-Tense 150 250
## 36 CUPRA Born 170 kW - 62 kWh 160 345
## 37 Renault Kangoo Maxi ZE 33 130 160
## 38 Citroen e-SpaceTourer M 50 kWh 130 180
## 39 Skoda Enyaq iV 60 160 330
## 40 Volkswagen ID.4 Pure 160 285
## 41 BMW i3s 120 Ah 160 230
## 42 Kia e-Niro 64 kWh 167 370
## 43 Peugeot e-Rifter Long 50 kWh 135 195
## 44 Renault Megane E-Tech EV40 130hp 160 245
## 45 Hyundai Kona Electric 64 kWh 167 395
## 46 Renault Megane E-Tech EV60 220hp 160 360
## 47 Volkswagen ID.4 Pure Performance 160 285
## 48 Hyundai IONIQ 5 Standard Range 2WD 185 310
## 49 Kia e-Soul 64 kWh 167 370
## 50 Nissan Ariya 63kWh 160 335
## 51 Volkswagen ID.3 Pro S - 4 Seats 160 450
## 52 Tesla Cybertruck Single Motor 180 390
## 53 Skoda Enyaq iV 80 160 420
## 54 Polestar 2 Standard Range Single Motor 160 350
## 55 Nissan Ariya e-4ORCE 63kWh 200 325
## 56 Audi Q4 e-tron 35 160 280
## 57 Volkswagen ID.4 1st 160 410
## 58 Kia EV6 Standard Range 2WD 185 320
## 59 Tesla Model 3 Standard Range Plus LFP 225 350
## 60 Tesla Model 3 Standard Range Plus 225 350
## 61 Ford Mustang Mach-E SR RWD 180 345
## 62 Volkswagen ID.4 Pro Performance 160 410
## 63 Hyundai IONIQ 5 Long Range 2WD 185 385
## 64 CUPRA Born 170 kW - 82 kWh 160 450
## 65 Audi Q4 Sportback e-tron 35 160 295
## 66 Polestar 2 Long Range Single Motor 160 425
## 67 Skoda Enyaq iV Sportline 80x 160 400
## 68 Mercedes EQA 250 160 355
## 69 Lexus UX 300e 160 260
## 70 Audi Q4 e-tron 40 160 405
## 71 Nissan Ariya 87kWh 160 445
## 72 Hyundai IONIQ 5 Long Range AWD 185 375
## 73 Polestar 2 Long Range Dual Motor 205 395
## 74 Skoda Enyaq iV RS 180 395
## 75 Ford Mustang Mach-E SR AWD 180 330
## 76 Hyundai IONIQ 5 Project 45 185 370
## 77 Kia EV6 Long Range 2WD 185 420
## 78 Tesla Cybertruck Dual Motor 190 460
## 79 Nissan Ariya e-4ORCE 87kWh 200 420
## 80 Tesla Model 3 Long Range Dual Motor 233 455
## 81 Peugeot e-Traveller Standard 50 kWh 130 185
## 82 Opel Zafira-e Life L 50 kWh 130 175
## 83 Peugeot e-Traveller Long 50 kWh 130 185
## 84 Volvo XC40 Recharge Twin Pure Electric 180 340
## 85 Ford Mustang Mach-E ER RWD 180 440
## 86 Mercedes EQB 350 4MATIC 160 340
## 87 Byton M-Byte 72 kWh 2WD 190 325
## 88 Audi Q4 e-tron 50 quattro 180 385
## 89 BMW i4 eDrive40 190 475
## 90 Kia EV6 Long Range AWD 185 410
## 91 Nissan Ariya e-4ORCE 87kWh Performance 200 385
## 92 Audi Q4 Sportback e-tron 50 quattro 180 400
## 93 Tesla Model Y Long Range Dual Motor 217 410
## 94 Volkswagen ID.4 GTX 180 400
## 95 Ford Mustang Mach-E ER AWD 180 420
## 96 Volvo C40 Recharge 180 340
## 97 Byton M-Byte 95 kWh 2WD 190 400
## 98 Kia EV6 GT 260 395
## 99 BMW iX3 180 385
## 100 Tesla Model 3 Performance 261 470
## 101 Mercedes EQE 350 160 535
## 102 Tesla Model Y Performance 241 430
## 103 Byton M-Byte 95 kWh 4WD 190 390
## 104 Audi e-tron 50 quattro 190 280
## 105 BMW i4 M50 225 450
## 106 Jaguar I-Pace EV400 200 380
## 107 Mercedes EQC 400 4MATIC 180 370
## 108 Ford Mustang Mach-E GT 200 410
## 109 Tesla Cybertruck Tri Motor 210 750
## 110 Audi e-tron Sportback 50 quattro 190 295
## 111 BMW iX xDrive40 200 350
## 112 Lucid Air Pure 200 540
## 113 Mercedes EQV 300 Long 160 320
## 114 Porsche Taycan 230 395
## 115 Audi e-tron 55 quattro 200 365
## 116 Porsche Taycan Plus 230 460
## 117 Porsche Taycan 4 Cross Turismo 220 405
## 118 Audi e-tron GT quattro 245 420
## 119 Audi e-tron Sportback 55 quattro 200 375
## 120 Porsche Taycan 4S 250 375
## 121 Tesla Model S Long Range 250 555
## 122 Audi e-tron S 55 quattro 210 320
## 123 Porsche Taycan 4S Cross Turismo 240 405
## 124 Porsche Taycan 4S Plus 250 435
## 125 Audi e-tron S Sportback 55 quattro 210 335
## 126 Lucid Air Touring 250 530
## 127 Tesla Model X Long Range 250 475
## 128 BMW iX xDrive50 200 505
## 129 Mercedes EQS 450+ 210 640
## 130 Audi e-tron GT RS 250 405
## 131 Tesla Model X Plaid 262 455
## 132 Mercedes EQS 580 4MATIC 210 610
## 133 Porsche Taycan Turbo 260 400
## 134 Porsche Taycan Turbo Cross Turismo 250 385
## 135 Tesla Model S Plaid 322 535
## 136 Lucid Air Grand Touring 270 660
## 137 Mercedes EQS AMG 53 4MATIC+ 250 565
## 138 Porsche Taycan Turbo S 260 390
## 139 Porsche Taycan Turbo S Cross Turismo 250 380
## 140 Tesla Roadster 410 970
## 141 Dacia Spring Electric 125 170
## 142 Hyundai Kona Electric 64 kWh 167 395
## 143 Aiways U5 150 335
## 144 Tesla Model 3 Long Range Dual Motor 233 490
## 145 MG Marvel R Performance 200 330
## 146 Skoda Enyaq iV 50 160 295
## 147 Lightyear One 150 575
## 148 Hyundai Kona Electric 39 kWh 155 250
## 149 Renault Zoe ZE40 R110 135 255
## 150 Renault Twingo Electric 135 130
## 151 Audi Q4 Sportback e-tron 40 160 425
## 152 Volkswagen ID.3 Pro S - 5 Seats 160 450
## 153 Opel Ampera-e 150 335
## 154 Volvo XC40 Recharge Pure Electric 160 315
## 155 Seres 3 155 270
## 156 Mercedes EQA 350 4MATIC 160 350
## 157 SEAT Mii Electric 130 205
## 158 Audi Q4 e-tron 45 quattro 180 385
## 159 MG MG5 Electric 180 340
## 160 Fiat 500e 3+1 150 245
## 161 Sono Sion 140 260
## 162 MG Marvel R 200 340
## 163 Mercedes EQA 300 4MATIC 160 350
## 164 JAC iEV7s 132 225
## 165 Hyundai IONIQ 5 Standard Range AWD 185 305
## 166 Kia e-Soul 64 kWh 167 370
## 167 Audi e-tron 55 quattro 200 365
## 168 Kia e-Soul 39 kWh 157 230
## 169 Mercedes EQV 300 Extra-Long 160 320
## 170 Toyota PROACE Verso M 75 kWh 130 250
## 171 Audi e-tron Sportback 55 quattro 200 375
## 172 Opel Zafira-e Life L 75 kWh 130 250
## 173 Toyota PROACE Verso L 75 kWh 130 250
## 174 Peugeot e-Traveller Long 75 kWh 130 270
## 175 Peugeot e-Traveller Standard 75 kWh 130 270
## 176 Citroen e-SpaceTourer M 75 kWh 130 250
## 177 Toyota PROACE Verso M 50 kWh 130 185
## 178 Citroen e-SpaceTourer XL 75 kWh 130 250
## 179 Peugeot e-Traveller Compact 50 kWh 130 185
## 180 Opel Zafira-e Life M 75 kWh 130 250
## 181 Toyota PROACE Verso L 50 kWh 130 180
## 182 Opel Zafira-e Life M 50 kWh 130 180
## 183 Citroen e-SpaceTourer XS 50 kWh 130 185
## 184 Opel Zafira-e Life S 50 kWh 130 185
## 185 Citroen e-SpaceTourer XL 50 kWh 130 175
## efficiency_Wh.km fastcharge_speed_km.h price_de_euro price_nl_euro
## 1 167 NA 18460 23995
## 2 176 NA 19120 23995
## 3 144 260 23560 24900
## 4 158 170 NA 25850
## 5 176 NA 21720 26995
## 6 149 420 27560 28600
## 7 165 340 NA NA
## 8 164 230 29990 34990
## 9 193 260 31990 30985
## 10 156 260 32500 36200
## 11 176 180 34490 33990
## 12 168 340 NA NA
## 13 152 410 30560 31600
## 14 165 230 31990 33990
## 15 164 410 NA 33490
## 16 164 370 29000 30599
## 17 164 370 30450 34900
## 18 168 190 33850 35820
## 19 157 210 35650 37000
## 20 166 490 35460 36240
## 21 164 440 32700 33000
## 22 168 230 33990 35590
## 23 166 490 36960 37740
## 24 168 190 38000 39080
## 25 218 170 43433 45173
## 26 167 230 35290 35995
## 27 225 270 37590 NA
## 28 172 390 38350 41940
## 29 166 440 37220 37990
## 30 176 340 34110 34399
## 31 153 220 35350 37015
## 32 180 330 35450 40930
## 33 180 330 34640 33990
## 34 161 270 39000 39995
## 35 180 330 30040 39990
## 36 168 430 39000 39000
## 37 194 NA NA 38801
## 38 250 240 51440 53011
## 39 176 420 38850 40780
## 40 182 410 36950 40690
## 41 165 260 42600 43690
## 42 173 350 39090 38995
## 43 231 260 42590 NA
## 44 163 510 35000 35000
## 45 162 370 41850 41000
## 46 167 520 40000 40000
## 47 182 410 38450 42190
## 48 187 720 41900 43500
## 49 173 350 NA NA
## 50 188 450 45000 44000
## 51 171 550 42460 NA
## 52 256 740 45000 45000
## 53 183 510 43950 47780
## 54 174 430 46500 45900
## 55 194 440 50000 46000
## 56 184 390 41900 48295
## 57 188 500 NA NA
## 58 181 740 44990 44595
## 59 150 630 43560 49990
## 60 146 700 43560 49990
## 61 197 380 46900 50425
## 62 188 500 44450 47790
## 63 189 890 45100 46500
## 64 171 550 43000 43000
## 65 175 410 43900 50345
## 66 176 550 49500 49900
## 67 193 490 47000 50000
## 68 187 420 47541 49995
## 69 192 150 47550 39990
## 70 189 500 47500 52815
## 71 196 530 50000 52000
## 72 194 870 48900 54500
## 73 190 510 52500 53900
## 74 195 480 50000 55000
## 75 206 360 54000 58165
## 76 196 860 59550 58995
## 77 184 980 48990 52095
## 78 261 710 55000 56000
## 79 207 500 57500 55000
## 80 154 650 NA NA
## 81 243 250 55900 NA
## 82 257 230 54625 54196
## 83 243 250 56690 NA
## 84 221 440 59250 56495
## 85 200 430 54475 58575
## 86 196 400 60000 60000
## 87 222 420 53500 55000
## 88 199 470 53600 64815
## 89 170 660 58300 60697
## 90 189 950 52850 54595
## 91 226 460 65000 60000
## 92 192 490 55600 66865
## 93 171 590 59965 65010
## 94 193 490 50415 52190
## 95 210 410 62900 67640
## 96 221 440 62050 57995
## 97 238 480 62000 62500
## 98 196 920 65990 63595
## 99 192 520 67300 69000
## 100 162 790 58560 64990
## 101 168 680 70000 70000
## 102 177 720 66965 71010
## 103 244 460 64000 65000
## 104 231 470 69100 62700
## 105 179 630 69900 73496
## 106 223 360 77300 83072
## 107 216 440 66069 77935
## 108 215 400 NA 75490
## 109 267 710 75000 78000
## 110 219 490 71350 65100
## 111 203 470 77300 86972
## 112 157 1410 80000 80000
## 113 281 280 71388 74609
## 114 180 790 83520 87200
## 115 237 590 NA NA
## 116 182 960 89244 93244
## 117 207 850 93635 97399
## 118 202 840 99800 104895
## 119 231 600 NA NA
## 120 189 750 106487 110600
## 121 162 830 86990 91000
## 122 270 510 93800 104540
## 123 207 850 111842 116000
## 124 192 910 113008 116431
## 125 258 540 96050 106940
## 126 160 1390 95000 100000
## 127 189 710 95990 101000
## 128 208 620 98000 105472
## 129 168 840 106374 118891
## 130 210 810 138200 146295
## 131 198 680 116990 121000
## 132 177 800 135529 154949
## 133 209 840 153016 157900
## 134 217 800 154444 159300
## 135 168 800 126990 131000
## 136 167 1380 140000 140000
## 137 191 740 175000 175000
## 138 215 860 186336 191700
## 139 220 790 187746 193200
## 140 206 920 215000 215000
## 141 158 120 20490 17890
## 142 162 370 41850 41595
## 143 188 350 35993 39950
## 144 155 820 53560 57990
## 145 197 380 50000 50000
## 146 176 240 33800 35000
## 147 104 540 149000 149990
## 148 157 210 34850 36795
## 149 161 230 29990 NA
## 150 164 NA 24790 20690
## 151 180 520 49500 54865
## 152 171 550 42620 41990
## 153 173 210 42990 NA
## 154 213 400 NA 45995
## 155 193 390 NA 37995
## 156 190 420 56216 NA
## 157 158 170 24650 NA
## 158 199 470 50900 58065
## 159 168 440 35000 35000
## 160 152 410 29560 30600
## 161 181 310 25500 26000
## 162 191 390 40000 40000
## 163 190 420 53538 NA
## 164 173 160 NA 32210
## 165 190 710 45700 NA
## 166 173 350 37790 36495
## 167 237 590 81500 71500
## 168 170 220 33990 33495
## 169 281 280 72281 75674
## 170 260 290 64530 58995
## 171 231 600 83750 73900
## 172 260 290 60625 63150
## 173 260 290 65385 60195
## 174 252 290 58230 NA
## 175 252 290 57440 NA
## 176 260 290 57440 62026
## 177 243 250 58530 NA
## 178 260 290 58230 63962
## 179 243 250 50880 NA
## 180 260 290 59800 62061
## 181 250 240 59385 NA
## 182 250 240 53800 53107
## 183 243 250 50880 NA
## 184 243 250 56700 NA
## 185 257 230 52230 54947
## price_uk_pound
## 1 19200
## 2 19795
## 3 20495
## 4 21055
## 5 21620
## 6 23995
## 7 25095
## 8 25995
## 9 25995
## 10 26000
## 11 26045
## 12 26495
## 13 26645
## 14 26795
## 15 27135
## 16 27140
## 17 27225
## 18 27660
## 19 27950
## 20 28435
## 21 28500
## 22 28795
## 23 29755
## 24 30160
## 25 30255
## 26 30345
## 27 30375
## 28 30445
## 29 30500
## 30 30540
## 31 30550
## 32 30730
## 33 30895
## 34 31305
## 35 31500
## 36 31500
## 37 31680
## 38 31995
## 39 32010
## 40 32150
## 41 32305
## 42 32445
## 43 32455
## 44 32500
## 45 32550
## 46 35000
## 47 36030
## 48 36995
## 49 37545
## 50 38000
## 51 38815
## 52 39000
## 53 39365
## 54 39900
## 55 40000
## 56 40750
## 57 40800
## 58 40985
## 59 40990
## 60 40990
## 61 41330
## 62 41570
## 63 41945
## 64 42000
## 65 42250
## 66 42900
## 67 42915
## 68 43495
## 69 43900
## 70 44990
## 71 45000
## 72 45145
## 73 45900
## 74 46000
## 75 46650
## 76 48000
## 77 48000
## 78 48000
## 79 48000
## 80 48490
## 81 49065
## 82 49465
## 83 49905
## 84 49950
## 85 49980
## 86 50000
## 87 50000
## 88 51370
## 89 51905
## 90 52000
## 91 52000
## 92 52870
## 93 54000
## 94 55540
## 95 57030
## 96 57400
## 97 57500
## 98 58295
## 99 59730
## 100 59990
## 101 60000
## 102 60000
## 103 60000
## 104 60600
## 105 63905
## 106 65195
## 107 65720
## 108 67225
## 109 68000
## 110 69100
## 111 69905
## 112 70000
## 113 70665
## 114 70690
## 115 71500
## 116 74739
## 117 79340
## 118 79900
## 119 79900
## 120 83580
## 121 83980
## 122 87000
## 123 87820
## 124 88193
## 125 88700
## 126 90000
## 127 90980
## 128 91905
## 129 95000
## 130 110950
## 131 110980
## 132 115000
## 133 115860
## 134 116950
## 135 118980
## 136 125000
## 137 135000
## 138 138830
## 139 139910
## 140 189000
## 141 NA
## 142 NA
## 143 NA
## 144 NA
## 145 NA
## 146 NA
## 147 NA
## 148 NA
## 149 NA
## 150 NA
## 151 NA
## 152 NA
## 153 NA
## 154 NA
## 155 NA
## 156 NA
## 157 NA
## 158 NA
## 159 NA
## 160 NA
## 161 NA
## 162 NA
## 163 NA
## 164 NA
## 165 NA
## 166 NA
## 167 NA
## 168 NA
## 169 NA
## 170 NA
## 171 NA
## 172 NA
## 173 NA
## 174 NA
## 175 NA
## 176 NA
## 177 NA
## 178 NA
## 179 NA
## 180 NA
## 181 NA
## 182 NA
## 183 NA
## 184 NA
## 185 NA# Isolating low price
low_price <- low_price %>% slice(1:10)# Re-arranging vehicles to find mid-priced
car_data <- arrange(car_data, `price_uk_pound`)
# calculating median price
median(car_data$price_uk_pound, na.rm = TRUE)## [1] 44995# Isolating mid_price
mid_price <- car_data %>% slice(66:74)
mid_price## title topspeed_km.h range_km efficiency_Wh.km
## 1 Polestar 2 Long Range Single Motor 160 425 176
## 2 Skoda Enyaq iV Sportline 80x 160 400 193
## 3 Mercedes EQA 250 160 355 187
## 4 Lexus UX 300e 160 260 192
## 5 Audi Q4 e-tron 40 160 405 189
## 6 Nissan Ariya 87kWh 160 445 196
## 7 Hyundai IONIQ 5 Long Range AWD 185 375 194
## 8 Polestar 2 Long Range Dual Motor 205 395 190
## 9 Skoda Enyaq iV RS 180 395 195
## fastcharge_speed_km.h price_de_euro price_nl_euro price_uk_pound
## 1 550 49500 49900 42900
## 2 490 47000 50000 42915
## 3 420 47541 49995 43495
## 4 150 47550 39990 43900
## 5 500 47500 52815 44990
## 6 530 50000 52000 45000
## 7 870 48900 54500 45145
## 8 510 52500 53900 45900
## 9 480 50000 55000 46000Visualizations
# Plotting
ggplot(car_data, aes(range_km)) +
geom_histogram() +
labs(title = "Scope of Vehicle Range", x = "Range (km)")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
# Plotting the spread of Vehicle Efficiencies
ggplot(car_data, aes(title, efficiency_Wh.km)) + geom_point() +
labs(title = "Efficiency of Models", x = "Model" , y = "Efficiency (wh/km)")
Price Range Visualizations
# Most expensive price vs range facet wrap
ggplot(top_price, aes(efficiency_Wh.km, price_uk_pound)) + geom_point() +
theme_bw() +
labs(title = "Price vs Efficiency of Most Expensive Vehicles", y = "Price (UK pound)", x = "Efficiency (wh/km)") +
facet_wrap(vars(title))
# Least expensive price vs efficiency facet wrap
ggplot(low_price, aes(efficiency_Wh.km, price_uk_pound)) + geom_point() +
theme_bw() +
labs(title = "Price vs Efficiency of Least Expensive Vehicles", y = "Price (UK pound)", x = "Efficiency (wh/km)") +
facet_wrap(vars(title))
# Mid priced price vs efficiency facet wrap
ggplot(mid_price, aes(efficiency_Wh.km, price_uk_pound)) + geom_point() +
theme_bw() +
labs(title = "Price vs Efficiency Mid-Priced Vehicles", y = "Price (UK pound)", x = "Efficiency (wh/km)") +
facet_wrap(vars(title))
Brand Visualizations
Tesla
# Pulling all tesla rows and creating dataframe
tesla_data <- car_data[c(52,59,60,78,80,93,100,102,109,121,127,131,135,140,144),]
tesla_data## title topspeed_km.h range_km
## 52 Tesla Cybertruck Single Motor 180 390
## 59 Tesla Model 3 Standard Range Plus LFP 225 350
## 60 Tesla Model 3 Standard Range Plus 225 350
## 78 Tesla Cybertruck Dual Motor 190 460
## 80 Tesla Model 3 Long Range Dual Motor 233 455
## 93 Tesla Model Y Long Range Dual Motor 217 410
## 100 Tesla Model 3 Performance 261 470
## 102 Tesla Model Y Performance 241 430
## 109 Tesla Cybertruck Tri Motor 210 750
## 121 Tesla Model S Long Range 250 555
## 127 Tesla Model X Long Range 250 475
## 131 Tesla Model X Plaid 262 455
## 135 Tesla Model S Plaid 322 535
## 140 Tesla Roadster 410 970
## 144 Tesla Model 3 Long Range Dual Motor 233 490
## efficiency_Wh.km fastcharge_speed_km.h price_de_euro price_nl_euro
## 52 256 740 45000 45000
## 59 150 630 43560 49990
## 60 146 700 43560 49990
## 78 261 710 55000 56000
## 80 154 650 NA NA
## 93 171 590 59965 65010
## 100 162 790 58560 64990
## 102 177 720 66965 71010
## 109 267 710 75000 78000
## 121 162 830 86990 91000
## 127 189 710 95990 101000
## 131 198 680 116990 121000
## 135 168 800 126990 131000
## 140 206 920 215000 215000
## 144 155 820 53560 57990
## price_uk_pound
## 52 39000
## 59 40990
## 60 40990
## 78 48000
## 80 48490
## 93 54000
## 100 59990
## 102 60000
## 109 68000
## 121 83980
## 127 90980
## 131 110980
## 135 118980
## 140 189000
## 144 NA# Arranging in order of least expensive to most
tesla_data <- arrange(tesla_data, `price_uk_pound`)
tesla_data## title topspeed_km.h range_km
## 1 Tesla Cybertruck Single Motor 180 390
## 2 Tesla Model 3 Standard Range Plus LFP 225 350
## 3 Tesla Model 3 Standard Range Plus 225 350
## 4 Tesla Cybertruck Dual Motor 190 460
## 5 Tesla Model 3 Long Range Dual Motor 233 455
## 6 Tesla Model Y Long Range Dual Motor 217 410
## 7 Tesla Model 3 Performance 261 470
## 8 Tesla Model Y Performance 241 430
## 9 Tesla Cybertruck Tri Motor 210 750
## 10 Tesla Model S Long Range 250 555
## 11 Tesla Model X Long Range 250 475
## 12 Tesla Model X Plaid 262 455
## 13 Tesla Model S Plaid 322 535
## 14 Tesla Roadster 410 970
## 15 Tesla Model 3 Long Range Dual Motor 233 490
## efficiency_Wh.km fastcharge_speed_km.h price_de_euro price_nl_euro
## 1 256 740 45000 45000
## 2 150 630 43560 49990
## 3 146 700 43560 49990
## 4 261 710 55000 56000
## 5 154 650 NA NA
## 6 171 590 59965 65010
## 7 162 790 58560 64990
## 8 177 720 66965 71010
## 9 267 710 75000 78000
## 10 162 830 86990 91000
## 11 189 710 95990 101000
## 12 198 680 116990 121000
## 13 168 800 126990 131000
## 14 206 920 215000 215000
## 15 155 820 53560 57990
## price_uk_pound
## 1 39000
## 2 40990
## 3 40990
## 4 48000
## 5 48490
## 6 54000
## 7 59990
## 8 60000
## 9 68000
## 10 83980
## 11 90980
## 12 110980
## 13 118980
## 14 189000
## 15 NA# Facet wraping tesla price vs efficiency
ggplot(tesla_data, aes(efficiency_Wh.km, price_uk_pound)) + geom_point() +
theme_bw() +
labs(title = "Telsa Price vs Efficiency", y = "Price (UK pound)", x = "Efficiency (wh/km)") +
facet_wrap(vars(title))## Warning: Removed 1 rows containing missing values (geom_point).
# Facet wraping tesla price vs range
ggplot(tesla_data, aes(range_km, price_uk_pound)) + geom_point() +
theme_bw() +
labs(title = "Telsa Price vs Range", y = "Price (UK pound)", x = "Range (km)") +
facet_wrap(vars(title))## Warning: Removed 1 rows containing missing values (geom_point).
Hyundai
# Pulling all hyundai rows and creaing dataframe
hyun_data <- car_data[c(19,31,45,48,63,72,76,142,148,165),]
hyun_data## title topspeed_km.h range_km efficiency_Wh.km
## 19 Hyundai Kona Electric 39 kWh 155 250 157
## 31 Hyundai IONIQ Electric 165 250 153
## 45 Hyundai Kona Electric 64 kWh 167 395 162
## 48 Hyundai IONIQ 5 Standard Range 2WD 185 310 187
## 63 Hyundai IONIQ 5 Long Range 2WD 185 385 189
## 72 Hyundai IONIQ 5 Long Range AWD 185 375 194
## 76 Hyundai IONIQ 5 Project 45 185 370 196
## 142 Hyundai Kona Electric 64 kWh 167 395 162
## 148 Hyundai Kona Electric 39 kWh 155 250 157
## 165 Hyundai IONIQ 5 Standard Range AWD 185 305 190
## fastcharge_speed_km.h price_de_euro price_nl_euro price_uk_pound
## 19 210 35650 37000 27950
## 31 220 35350 37015 30550
## 45 370 41850 41000 32550
## 48 720 41900 43500 36995
## 63 890 45100 46500 41945
## 72 870 48900 54500 45145
## 76 860 59550 58995 48000
## 142 370 41850 41595 NA
## 148 210 34850 36795 NA
## 165 710 45700 NA NA# Arranging in order of least expensive to most
hyun_data <- arrange(hyun_data, `price_uk_pound`)
hyun_data## title topspeed_km.h range_km efficiency_Wh.km
## 1 Hyundai Kona Electric 39 kWh 155 250 157
## 2 Hyundai IONIQ Electric 165 250 153
## 3 Hyundai Kona Electric 64 kWh 167 395 162
## 4 Hyundai IONIQ 5 Standard Range 2WD 185 310 187
## 5 Hyundai IONIQ 5 Long Range 2WD 185 385 189
## 6 Hyundai IONIQ 5 Long Range AWD 185 375 194
## 7 Hyundai IONIQ 5 Project 45 185 370 196
## 8 Hyundai Kona Electric 64 kWh 167 395 162
## 9 Hyundai Kona Electric 39 kWh 155 250 157
## 10 Hyundai IONIQ 5 Standard Range AWD 185 305 190
## fastcharge_speed_km.h price_de_euro price_nl_euro price_uk_pound
## 1 210 35650 37000 27950
## 2 220 35350 37015 30550
## 3 370 41850 41000 32550
## 4 720 41900 43500 36995
## 5 890 45100 46500 41945
## 6 870 48900 54500 45145
## 7 860 59550 58995 48000
## 8 370 41850 41595 NA
## 9 210 34850 36795 NA
## 10 710 45700 NA NA# Hyundai price vs efficiency facet wrap
ggplot(hyun_data, aes(efficiency_Wh.km, price_uk_pound)) + geom_point() +
theme_bw() +
labs(title = "Hyundai Price vs Efficiency", y = "Price (UK pound)", x = "Efficiency (wh/km)") +
facet_wrap(vars(title))## Warning: Removed 3 rows containing missing values (geom_point).
# Hyundai price vs range facet wrap
ggplot(hyun_data, aes(range_km, price_uk_pound)) + geom_point() +
theme_bw() +
labs(title = "Hyundai Price vs Range", y = "Price (UK pound)", x = "Range (km)") +
facet_wrap(vars(title))## Warning: Removed 3 rows containing missing values (geom_point).
Volkswagon
# Pulling all volkswagon rows and creaing dataframe
volks_data <- car_data[c(4,15,20,23,40,47,51,57,62,94,152),]
volks_data## title topspeed_km.h range_km efficiency_Wh.km
## 4 Volkswagen e-Up! 130 205 158
## 15 Volkswagen ID.3 Pure Performance 160 275 164
## 20 Volkswagen ID.3 Pro 160 350 166
## 23 Volkswagen ID.3 Pro Performance 160 350 166
## 40 Volkswagen ID.4 Pure 160 285 182
## 47 Volkswagen ID.4 Pure Performance 160 285 182
## 51 Volkswagen ID.3 Pro S - 4 Seats 160 450 171
## 57 Volkswagen ID.4 1st 160 410 188
## 62 Volkswagen ID.4 Pro Performance 160 410 188
## 94 Volkswagen ID.4 GTX 180 400 193
## 152 Volkswagen ID.3 Pro S - 5 Seats 160 450 171
## fastcharge_speed_km.h price_de_euro price_nl_euro price_uk_pound
## 4 170 NA 25850 21055
## 15 410 NA 33490 27135
## 20 490 35460 36240 28435
## 23 490 36960 37740 29755
## 40 410 36950 40690 32150
## 47 410 38450 42190 36030
## 51 550 42460 NA 38815
## 57 500 NA NA 40800
## 62 500 44450 47790 41570
## 94 490 50415 52190 55540
## 152 550 42620 41990 NA# Arranging in order of least expensive to most
volks_data <- arrange(volks_data, `price_uk_pound`)
volks_data## title topspeed_km.h range_km efficiency_Wh.km
## 1 Volkswagen e-Up! 130 205 158
## 2 Volkswagen ID.3 Pure Performance 160 275 164
## 3 Volkswagen ID.3 Pro 160 350 166
## 4 Volkswagen ID.3 Pro Performance 160 350 166
## 5 Volkswagen ID.4 Pure 160 285 182
## 6 Volkswagen ID.4 Pure Performance 160 285 182
## 7 Volkswagen ID.3 Pro S - 4 Seats 160 450 171
## 8 Volkswagen ID.4 1st 160 410 188
## 9 Volkswagen ID.4 Pro Performance 160 410 188
## 10 Volkswagen ID.4 GTX 180 400 193
## 11 Volkswagen ID.3 Pro S - 5 Seats 160 450 171
## fastcharge_speed_km.h price_de_euro price_nl_euro price_uk_pound
## 1 170 NA 25850 21055
## 2 410 NA 33490 27135
## 3 490 35460 36240 28435
## 4 490 36960 37740 29755
## 5 410 36950 40690 32150
## 6 410 38450 42190 36030
## 7 550 42460 NA 38815
## 8 500 NA NA 40800
## 9 500 44450 47790 41570
## 10 490 50415 52190 55540
## 11 550 42620 41990 NA# Volkswagon price vs range facet wrap
ggplot(volks_data, aes(efficiency_Wh.km, price_uk_pound)) + geom_point() +
theme_bw() +
labs(title = "Volkswagon Price vs Efficiency", y = "Price (UK pound)", x = "Efficiency (wh/km)") +
facet_wrap(vars(title))## Warning: Removed 1 rows containing missing values (geom_point).
# Volkswagon price vs range facet wrap
ggplot(volks_data, aes(range_km, price_uk_pound)) + geom_point() +
theme_bw() +
labs(title = "Volkswagon Price vs Range", y = "Price (UK pound)", x = "Range (km)") +
facet_wrap(vars(title))## Warning: Removed 1 rows containing missing values (geom_point).
What is missing from your final project?
- Before I can continue with my analysis I need to find a way to visualize how price and performance (efficiency/range/top-speed) compare among Tesla, Hyundai and Volkswagon. Also, my goal is to find a way to overlay the price vs efficiency/range plots of these three brands to visualize how they correlate. The same can be done for the most expensive, mid-priced and least expensive plots. Before submition time I would like accomplish these visualization goals, allowing me to offer clear answers to my research questions.