Final
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?
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()Data
The dataset to be analyzing contians 185 different 2021 electric cars from various manufacturers. The meta-data attributed to each vehicle includes: top speed (km/h), range (km), efficiency (wh/km), fastcharge speed (km/h), and price in uero and pound. For this analysis I will mainly be focusing on vehicles from three of the top electric vehicle manufacturers, Tesla, Hyundai and Volkswagon. In order to evaluate performance of each, data will be drawn from range, efficiency and price.
-Data: Describe your dataset, and include the code that generated the data import and cleaning.
Reading in 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
The first steps of cleaning will be breaking down the data into three seperate dataframes: the most expensive vehicles, mid-priced vehicles and the least expensive vehicles.
# 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`.
As for the first visualization, I just wanted to get a sense of scope for the range of the all vehicles. As displayed, the range of most vehicles falls between 250 and 450 kilometers, with a few extreme high-end outliers.
# 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)")
Similar to my first visualization, I wanted to take a broad look at the efficiencies of all the models. This vizualization shows that most vehicles have efficiencies between 150 and 200 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))
This set of price based visualizations offer an explicit look at how efficiencies of specific vehicles compare at different price levels. I found the clearest way of visualisizing this data was through facet wrap, where the efficiencies of each model can be presented for comparison. The first thing that struck me about these three vizualizations are the discrepensies in price range at each level. At the least expensive level prices vary between 19,000 and 26,000 euros, a difference of 7,000 euros. Mid-priced vehicles range from 43,000 to 46,000, a differenence of just 3,000 euros. At the most expensive level, prices vary greatly from 111,000 to 189,000 eruos, an astounding 78,000 euros difference. This is all due to the Tesla Roadster, which is a supercar and one of the fastest in the world being compared to regular commerical vehicles. There are also discrepensies among efficiencies at all levels. Some vehicle efficiencies in the low and mid price perform better than their most expensive counterparts while costing tens of thousands of dollars less.
Brand Visualizations
In order to further narrow down the dataset I've specified my visualizations by brand, those being Tesla, Hyundai and Volkswagon. By plotting price vs efficiency and price vs range I can evaluate perfomance within and amongst each brand.
Tesla
# Pulling all Tesla rows and creating dataframe
tesla_data <- car_data[c(52,59,60,78,80,93,11,102,109,121,127,131,135,140,144),]
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
## 11 Mazda MX-30 140 170
## 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
## 11 176 180 34490 33990
## 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
## 11 26045
## 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 Mazda MX-30 140 170
## 2 Tesla Cybertruck Single Motor 180 390
## 3 Tesla Model 3 Standard Range Plus LFP 225 350
## 4 Tesla Model 3 Standard Range Plus 225 350
## 5 Tesla Cybertruck Dual Motor 190 460
## 6 Tesla Model 3 Long Range Dual Motor 233 455
## 7 Tesla Model Y Long Range Dual Motor 217 410
## 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
## 1 176 180 34490 33990
## 2 256 740 45000 45000
## 3 150 630 43560 49990
## 4 146 700 43560 49990
## 5 261 710 55000 56000
## 6 154 650 NA NA
## 7 171 590 59965 65010
## 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 26045
## 2 39000
## 3 40990
## 4 40990
## 5 48000
## 6 48490
## 7 54000
## 8 60000
## 9 68000
## 10 83980
## 11 90980
## 12 110980
## 13 118980
## 14 189000
## 15 NA# Facet wraping tesla price vs efficiency
tesla_data <- tesla_data
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(10,12,18,19,28,63,81,83,134,149),]
hyun_data## title topspeed_km.h range_km efficiency_Wh.km
## 10 Mini Cooper SE 150 185 156
## 12 MG MG5 EV Long Range 185 340 168
## 18 Honda e 145 170 168
## 19 Hyundai Kona Electric 39 kWh 155 250 157
## 28 Nissan Leaf e+ 157 325 172
## 63 Hyundai IONIQ 5 Long Range 2WD 185 385 189
## 81 Peugeot e-Traveller Standard 50 kWh 130 185 243
## 83 Peugeot e-Traveller Long 50 kWh 130 185 243
## 134 Porsche Taycan Turbo Cross Turismo 250 385 217
## 149 Renault Zoe ZE40 R110 135 255 161
## fastcharge_speed_km.h price_de_euro price_nl_euro price_uk_pound
## 10 260 32500 36200 26000
## 12 340 NA NA 26495
## 18 190 33850 35820 27660
## 19 210 35650 37000 27950
## 28 390 38350 41940 30445
## 63 890 45100 46500 41945
## 81 250 55900 NA 49065
## 83 250 56690 NA 49905
## 134 800 154444 159300 116950
## 149 230 29990 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 Mini Cooper SE 150 185 156
## 2 MG MG5 EV Long Range 185 340 168
## 3 Honda e 145 170 168
## 4 Hyundai Kona Electric 39 kWh 155 250 157
## 5 Nissan Leaf e+ 157 325 172
## 6 Hyundai IONIQ 5 Long Range 2WD 185 385 189
## 7 Peugeot e-Traveller Standard 50 kWh 130 185 243
## 8 Peugeot e-Traveller Long 50 kWh 130 185 243
## 9 Porsche Taycan Turbo Cross Turismo 250 385 217
## 10 Renault Zoe ZE40 R110 135 255 161
## fastcharge_speed_km.h price_de_euro price_nl_euro price_uk_pound
## 1 260 32500 36200 26000
## 2 340 NA NA 26495
## 3 190 33850 35820 27660
## 4 210 35650 37000 27950
## 5 390 38350 41940 30445
## 6 890 45100 46500 41945
## 7 250 55900 NA 49065
## 8 250 56690 NA 49905
## 9 800 154444 159300 116950
## 10 230 29990 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 1 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 1 rows containing missing values (geom_point).
Volkswagon
# Pulling all volkswagon rows and creaing dataframe
volks_data <- car_data[c(17,27,47,57,58,73,88,95,103,125,133),]
volks_data## title topspeed_km.h range_km efficiency_Wh.km
## 17 Peugeot e-208 150 275 164
## 27 Peugeot e-Rifter Standard 50 kWh 135 200 225
## 47 Volkswagen ID.4 Pure Performance 160 285 182
## 57 Volkswagen ID.4 1st 160 410 188
## 58 Kia EV6 Standard Range 2WD 185 320 181
## 73 Polestar 2 Long Range Dual Motor 205 395 190
## 88 Audi Q4 e-tron 50 quattro 180 385 199
## 95 Ford Mustang Mach-E ER AWD 180 420 210
## 103 Byton M-Byte 95 kWh 4WD 190 390 244
## 125 Audi e-tron S Sportback 55 quattro 210 335 258
## 133 Porsche Taycan Turbo 260 400 209
## fastcharge_speed_km.h price_de_euro price_nl_euro price_uk_pound
## 17 370 30450 34900 27225
## 27 270 37590 NA 30375
## 47 410 38450 42190 36030
## 57 500 NA NA 40800
## 58 740 44990 44595 40985
## 73 510 52500 53900 45900
## 88 470 53600 64815 51370
## 95 410 62900 67640 57030
## 103 460 64000 65000 60000
## 125 540 96050 106940 88700
## 133 840 153016 157900 115860# 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 Peugeot e-208 150 275 164
## 2 Peugeot e-Rifter Standard 50 kWh 135 200 225
## 3 Volkswagen ID.4 Pure Performance 160 285 182
## 4 Volkswagen ID.4 1st 160 410 188
## 5 Kia EV6 Standard Range 2WD 185 320 181
## 6 Polestar 2 Long Range Dual Motor 205 395 190
## 7 Audi Q4 e-tron 50 quattro 180 385 199
## 8 Ford Mustang Mach-E ER AWD 180 420 210
## 9 Byton M-Byte 95 kWh 4WD 190 390 244
## 10 Audi e-tron S Sportback 55 quattro 210 335 258
## 11 Porsche Taycan Turbo 260 400 209
## fastcharge_speed_km.h price_de_euro price_nl_euro price_uk_pound
## 1 370 30450 34900 27225
## 2 270 37590 NA 30375
## 3 410 38450 42190 36030
## 4 500 NA NA 40800
## 5 740 44990 44595 40985
## 6 510 52500 53900 45900
## 7 470 53600 64815 51370
## 8 410 62900 67640 57030
## 9 460 64000 65000 60000
## 10 540 96050 106940 88700
## 11 840 153016 157900 115860# 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))
# 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))
There are some valuable pieces of information to be taken away from these visualizations. In general, Tesla outperforms both Hyundai and Volkswagon, especially in terms of range, most of which being above 400 km. However, Tesla efficiency levels are comprable to Hyundai and volkswagon, despite costing half the price in some cases.
Visualizing Brands Together
# cleaning dataframes to present title, efficiency, range and price of 10 vehicles from each brand
volks_preff <- volks_data %>% select(title, efficiency_Wh.km, range_km, price_uk_pound)
volks_preff <- volks_preff %>% slice(1:10)
tesla_preff <- tesla_data %>% select(title, efficiency_Wh.km, range_km, price_uk_pound)
tesla_preff <- tesla_preff %>% slice(1:10)
hyun_preff <- hyun_data %>% select(title, efficiency_Wh.km, range_km, price_uk_pound)
hyun_preff <- hyun_preff %>% slice(1:10)# plotting price vs efficiency based on three dataframes
cars_preff <- ggplot(NULL, aes(price_uk_pound, efficiency_Wh.km)) +
geom_line(data = tesla_preff, col = "red") +
geom_line(data = hyun_preff, col = "blue") +
geom_line(data = volks_preff, col = "green") +
labs(x = "Price (Pound)",
y = "Efficiency (wh/km)",
color = "Legend") +
scale_color_manual(values = colors) +
labs(title = "Price vs Efficiency of Tesla, Hyundai and Volkswagon") +
scale_color_manual(labels = c("Tesla", "Hyundai", "Volkswagon"), values = c("red", "blue", "green"))## Scale for 'colour' is already present. Adding another scale for 'colour',
## which will replace the existing scale.cars_preff## Warning: Removed 1 row(s) containing missing values (geom_path).
Tesla = Red Hyundai = Blue Volkswagon = Green
# plotting price vs range based on three dataframes
cars_pran <- ggplot(NULL, aes(range_km, price_uk_pound)) +
geom_line(data = tesla_preff, col = "red") +
geom_line(data = hyun_preff, col = "blue") +
geom_line(data = volks_preff, col = "green") +
labs(x = "Range (km)",
y = "Price (pound)",
color = "Legend") +
scale_color_manual(values = colors) +
labs(title = "Price vs Range of Tesla, Hyundai and Volkswagon" )
cars_pran
Tesla = Red Hyundai = Blue Volkswagon = Green
My intention with this set of visualizations was to plot brand price vs performance head to head. While my previous visualizations effectively displayed the performance of specific vehicles in their own right, I needed a way to clearly present the different manufactures together at the same time. What I've taken away from these plots is that Hyudai and Volkswagon perform similarly steadily in terms of price and performance. They both produce lower to mid-price range vehicles that gradually perform better as they get more expensive. Tesla on the other hand dominates the most expensive price range, though their performance deviates more than the other brands as models get more expensive, specifically regarding efficiency that changes drastically.
Visualizing Range vs Efficiency Volkswagon and Tesla with labels
# Assigning title row
rownames(volks_preff) <- volks_preff$title
volks_preff## title
## Peugeot e-208 Peugeot e-208
## Peugeot e-Rifter Standard 50 kWh Peugeot e-Rifter Standard 50 kWh
## Volkswagen ID.4 Pure Performance Volkswagen ID.4 Pure Performance
## Volkswagen ID.4 1st Volkswagen ID.4 1st
## Kia EV6 Standard Range 2WD Kia EV6 Standard Range 2WD
## Polestar 2 Long Range Dual Motor Polestar 2 Long Range Dual Motor
## Audi Q4 e-tron 50 quattro Audi Q4 e-tron 50 quattro
## Ford Mustang Mach-E ER AWD Ford Mustang Mach-E ER AWD
## Byton M-Byte 95 kWh 4WD Byton M-Byte 95 kWh 4WD
## Audi e-tron S Sportback 55 quattro Audi e-tron S Sportback 55 quattro
## efficiency_Wh.km range_km price_uk_pound
## Peugeot e-208 164 275 27225
## Peugeot e-Rifter Standard 50 kWh 225 200 30375
## Volkswagen ID.4 Pure Performance 182 285 36030
## Volkswagen ID.4 1st 188 410 40800
## Kia EV6 Standard Range 2WD 181 320 40985
## Polestar 2 Long Range Dual Motor 190 395 45900
## Audi Q4 e-tron 50 quattro 199 385 51370
## Ford Mustang Mach-E ER AWD 210 420 57030
## Byton M-Byte 95 kWh 4WD 244 390 60000
## Audi e-tron S Sportback 55 quattro 258 335 88700# Plotting range vs efficiency with labels
ggplot(volks_preff, aes(x=efficiency_Wh.km, y=range_km)) +
geom_point() + # Show dots
geom_label(
label=rownames(volks_preff)) +
labs(x = "Efficiency (wh/km)",
y = "Range (km)",
color = "Legend") +
scale_color_manual(values = colors) +
labs(title = "Range vs Efficiency of Volkswagon" )
# Assigning title row
rownames(tesla_preff) <- tesla_preff$title
tesla_preff## title
## Mazda MX-30 Mazda MX-30
## Tesla Cybertruck Single Motor Tesla Cybertruck Single Motor
## Tesla Model 3 Standard Range Plus LFP Tesla Model 3 Standard Range Plus LFP
## Tesla Model 3 Standard Range Plus Tesla Model 3 Standard Range Plus
## Tesla Cybertruck Dual Motor Tesla Cybertruck Dual Motor
## Tesla Model 3 Long Range Dual Motor Tesla Model 3 Long Range Dual Motor
## Tesla Model Y Long Range Dual Motor Tesla Model Y Long Range Dual Motor
## Tesla Model Y Performance Tesla Model Y Performance
## Tesla Cybertruck Tri Motor Tesla Cybertruck Tri Motor
## Tesla Model S Long Range Tesla Model S Long Range
## efficiency_Wh.km range_km price_uk_pound
## Mazda MX-30 176 170 26045
## Tesla Cybertruck Single Motor 256 390 39000
## Tesla Model 3 Standard Range Plus LFP 150 350 40990
## Tesla Model 3 Standard Range Plus 146 350 40990
## Tesla Cybertruck Dual Motor 261 460 48000
## Tesla Model 3 Long Range Dual Motor 154 455 48490
## Tesla Model Y Long Range Dual Motor 171 410 54000
## Tesla Model Y Performance 177 430 60000
## Tesla Cybertruck Tri Motor 267 750 68000
## Tesla Model S Long Range 162 555 83980# Plotting range vs efficiency with labels
ggplot(tesla_preff, aes(x=efficiency_Wh.km, y=range_km)) +
geom_point() + # Show dots
geom_label(
label=rownames(tesla_preff)) +
labs(x = "Efficiency (wh/km)",
y = "Range (km)",
color = "Legend") +
scale_color_manual(values = colors) +
labs(title = "Range vs Efficiency of Tesla" )
The purpose of these two visualizations was to precisely identify specifically which Teslas and Volkswagons outperform other models. They are meant to build upon the earlier facet wraps, consolidating the information and displaying it in a distinct way. From Volkswagen there are a few vehicles that stand out, the Volkswagen ID.4 GTX, ID.4 Pro Performance and the 3.1 S Pro - 4 seater. From Tesla, the various Cybertrucks outperform other models by a large margin with the exception of the Model S Long Range.
Reflection
With electric vehicles becoming more and more prominent among car manufacturers and as states move towards banning the manufacturing of gas cars I sought to delve into the current market. The most challenging aspect of this project was trying to draw valuable information and visualizations from the fairly limited initial dataset. This meant a lot of data wrangling and narrowing to create more focused dataframes and visualizations through price range manfucacturer. I identified efficiency, range and price as the most relevant variables in terms of performance. Also, in my opinion, these are the most important variables when it comes to comparing electric vehicles to gas alternatives. In order to improve this project I would have like to have more data regarding the market sales of each vehicle and how they are rated among comsumers. This would allow me to ask more specific questions and judge the how each manufacturer fucntions within the market. While I find my project is insightful for those interested in researching the electric car market, I struggled to find ways of presenting powerful information to a general audience.
Conclusion
How does efficiency differentiate among the most expensive electric vehicles? - The efficiencies of the most expensive vehicles vary minutely, from around 190 to 220. However, there os not a strong correlation between price and efficiency, espeically since plently of less expensive vehicles have greater efficiencies.
How does efficiency differentiate among the least expensive electric vehicles? - The efficiencies and prices among the least expensive vehicels differentiate less than with the most expensive, most falling around 160 wh/km.
How does efficiency differentiate among mid-priced electric vehicles? - Similar to the least expensive, the prices and efficiencies of mid-priced vehicles don't deviate greatly, most falling around 190 wh/km. The price vs efficiency performances are comprable to the most expensiv vehicels.
How does price affect efficiency and range among Teslas - As Teslas get more expensive, efficiency and range tend to improve, with the exeption of Cybertrucks which are less expensive but with outstanding efficiencies.
How does price affect efficiency and range among Volkswagens? - As Volkswagen get more expensive, efficiency and range improve more steadily than Tesla, presenting a strong correlation between price, efficiency and range.
How does price affect efficiency and range among Hyundai? - Similar to Volkswagen, Hyundai models present a very strong correlation between price, efficiency and range, as performance improves in a positve linear way as price increases.
How does price and performance compare among Tesla, Volkswagen and Hyundai? - While price strongly implicates the performance of Volkswagen and Hyundai, Teslas present more inconsistencies with cheaper vehicles performing better than some of the most expensive. The price of Volkswagens and Hyudais correlate more with their perfomance.
Unanswered Questions - How do Tesla, Volkswagen and Hyundai compare to other manufacturers included in the dataset? - How does the performance of certain vehicles compare to their popularity among the public?
Bibliography
RStudio Team (2020). RStudio: Integrated Development for R. RStudio, PBC, Boston, MA URL http://www.rstudio.com/
Wickham, H., & Grolemund, G. (2016). R for data science: Visualize, model, transform, tidy, and import data. OReilly Media.
Yo Han Joo. (October, 2021). Electric Cars 2021, Version 1. Retrieved June, 2021 from https://www.kaggle.com/datasets/searoll/electric-cars-2021.