Introduction
Electric vehicles are becoming increasingly prevalent in our everyday life. Even though owning a Tesla is still generally perceived as a luxury only a lucky few can afford, the technologies which enable the transition from fossil fuels to renewable energy have marched steadily forward. Nonetheless, challenges to EV ownership have persisted. In this report I examine EV trends present in Washington State, which signed into law a bill setting a date of 2030 for all new cars registered in the state to be electric. I analyze relationships among several important variables and segment the Washington EV market into several zones with their distinct characteristics.
Data Overview
colnames(ev_data)
[1] "ID"
[2] "VIN (1-10)"
[3] "County"
[4] "City"
[5] "State"
[6] "ZIP Code"
[7] "Model Year"
[8] "Make"
[9] "Model"
[10] "Electric Vehicle Type"
[11] "Clean Alternative Fuel Vehicle (CAFV) Eligibility"
[12] "Electric Range"
[13] "Base MSRP"
[14] "Legislative District"
[15] "DOL Vehicle ID"
[16] "Vehicle Location"
[17] "Electric Utility"
[18] "Expected Price ($1k)" summary(ev_data)
ID VIN (1-10) County
Length:64353 Length:64353 Length:64353
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
City State ZIP Code Model Year
Length:64353 Length:64353 Min. : 745 Min. :1993
Class :character Class :character 1st Qu.:98052 1st Qu.:2017
Mode :character Mode :character Median :98121 Median :2018
Mean :98143 Mean :2018
3rd Qu.:98370 3rd Qu.:2021
Max. :99701 Max. :2022
NA's :6 NA's :7
Make Model Electric Vehicle Type
Length:64353 Length:64353 Length:64353
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
Clean Alternative Fuel Vehicle (CAFV) Eligibility Electric Range
Length:64353 Min. : 0.0
Class :character 1st Qu.: 14.0
Mode :character Median : 73.0
Mean :106.9
3rd Qu.:215.0
Max. :337.0
Base MSRP Legislative District DOL Vehicle ID
Min. : 0 Min. : 0.00 Min. : 4385
1st Qu.: 0 1st Qu.:19.00 1st Qu.:137286488
Median : 0 Median :34.00 Median :175377597
Mean : 2525 Mean :29.95 Mean :197290491
3rd Qu.: 0 3rd Qu.:43.00 3rd Qu.:229903894
Max. :845000 Max. :49.00 Max. :478934571
NA's :169
Vehicle Location Electric Utility Expected Price ($1k)
Length:64353 Length:64353 Min. : 0.00
Class :character Class :character 1st Qu.: 22.86
Mode :character Mode :character Median : 40.00
Mean : 45.45
3rd Qu.: 65.90
Max. :1100.00
NA's :13 VIN (1-10) - The 1st 10 characters of each vehicle’s
Vehicle Identification Number (VIN).
County - The county in which the registered owner
resides.
City - The city in which the registered owner
resides.
State- The state in which the registered owner
resides.
ZIP Code - The 5-digit zip code in which the registered
owner resides.
Model Year - The model year of the vehicle is
determined by decoding the Vehicle Identification Number (VIN).
Make - The manufacturer of the vehicle, determined by
decoding the Vehicle Identification Number (VIN).
Model- The model of the vehicle is determined by
decoding the Vehicle Identification Number (VIN).
Electric Vehicle Type - This distinguishes the vehicle
as all-electric or a plug-in hybrid.
Clean Alternative Fuel Vehicle (CAFV) Eligibility -
This categorizes vehicles as Clean Alternative Fuel Vehicles (CAFVs)
based on the fuel requirement and electric-only range requirement.
Electric Range - Describes how far a vehicle can travel
purely on its electric charge.
Base MSRP - This is the lowest Manufacturer’s Suggested
Retail Price (MSRP) for any trim level of the model in question.
Legislative District - The specific section of
Washington State that the vehicle’s owner resides in, as represented in
the state legislature.
DOL Vehicle ID - Unique number assigned to each vehicle
by the Department of Licensing for identification purposes.
Vehicle Location - The center of the ZIP Code for the
registered vehicle.
Electric Utility - This is the electric power retail
service territory serving the address of the registered vehicle.
Expected Price - This is the expected price of the
vehicle.
# Returns the summary statistics of a particular column
print_stats <- function(x) {
print(paste("Min: ",as.character(min(x, na.rm = TRUE))))
print(paste("Max: ",as.character(max(x, na.rm = TRUE))))
print(paste("Median: ",as.character(median(x, na.rm = TRUE))))
print(paste("Standard Deviation: ",as.character(sd(x, na.rm = TRUE))))
print(paste("Mean: ",as.character(mean(x, na.rm = TRUE))))
print(paste("Interquartile Range",as.character(IQR(x, na.rm = TRUE))))
}
#Calculating the summary statistics for the electric range of electric vehicles
el_range_summary_stats <- print_stats(ev_data$`Electric Range`)
[1] "Min: 0"
[1] "Max: 337"
[1] "Median: 73"
[1] "Standard Deviation: 104.093918983571"
[1] "Mean: 106.948984507327"
[1] "Interquartile Range 201"#Calculating the summary statistics for the expected price of electric vehicles
expected_price_summary_stats <- print_stats(ev_data$`Expected Price ($1k)`)
[1] "Min: 0"
[1] "Max: 1100"
[1] "Median: 40"
[1] "Standard Deviation: 24.7282052168784"
[1] "Mean: 45.4479695679204"
[1] "Interquartile Range 43.043"The summary statistics tells us that, on average, the range of an electric vehicle in the data is just shy of 75 miles on a single charge. However, the standard deviation of 104 miles tells us that the data is quite a bit dispersed from the mean. The summary statistics of the expected price paint a somewhat different picture. The mean and median are very similar and the standard deviation is only 25000. The max of $1.1M tells us that there are some expensive outliers in the data.
# Calculate frequencies for the following categorical variables: EV Type, Make, Electric Utility and County
table(ev_data$`Electric Vehicle Type`)
Battery Electric Vehicle (BEV)
47869
Plug-in Hybrid Electric Vehicle (PHEV)
16484 prop.table(table(ev_data$`Electric Vehicle Type`))
Battery Electric Vehicle (BEV)
0.7438503
Plug-in Hybrid Electric Vehicle (PHEV)
0.2561497 table(ev_data$Make)
AUDI AZURE DYNAMICS BENTLEY
1265 3 1
BMW CADILLAC CHEVROLET
2707 69 6651
CHRYSLER DODGE FIAT
1091 1 561
FISKER FORD HONDA
11 3850 528
HYUNDAI JAGUAR JEEP
894 142 436
KIA LAND ROVER LINCOLN
3066 26 59
LUCID MOTORS MERCEDES-BENZ MINI
2 248 303
MITSUBISHI NISSAN POLESTAR
376 8678 164
PORSCHE RIVIAN SMART
455 26 192
SUBARU TESLA TH!NK
34 27903 1
TOYOTA VOLKSWAGEN VOLVO
2700 1217 688
WHEEGO ELECTRIC CARS
1 prop.table(table(ev_data$`Electric Utility`))
AVISTA CORP
1.933020e-03
AVISTA CORP||INLAND POWER & LIGHT COMPANY
1.571561e-05
BONNEVILLE POWER ADMINISTRATION
3.143122e-05
BONNEVILLE POWER ADMINISTRATION||AVISTA CORP||BIG BEND ELECTRIC COOP, INC
3.300278e-04
BONNEVILLE POWER ADMINISTRATION||AVISTA CORP||INLAND POWER & LIGHT COMPANY
1.436407e-02
BONNEVILLE POWER ADMINISTRATION||AVISTA CORP||PUD NO 1 OF ASOTIN COUNTY
4.557527e-04
BONNEVILLE POWER ADMINISTRATION||BENTON RURAL ELECTRIC ASSN
5.814776e-04
BONNEVILLE POWER ADMINISTRATION||BIG BEND ELECTRIC COOP, INC
1.728717e-04
BONNEVILLE POWER ADMINISTRATION||CITY OF CENTRALIA - (WA)|CITY OF TACOMA - (WA)
1.178671e-03
BONNEVILLE POWER ADMINISTRATION||CITY OF COULEE DAM - (WA)
3.143122e-05
BONNEVILLE POWER ADMINISTRATION||CITY OF ELLENSBURG - (WA)
6.914869e-04
BONNEVILLE POWER ADMINISTRATION||CITY OF MCCLEARY - (WA)
1.257249e-04
BONNEVILLE POWER ADMINISTRATION||CITY OF MILTON - (WA)|CITY OF TACOMA - (WA)
4.400371e-04
BONNEVILLE POWER ADMINISTRATION||CITY OF PORT ANGELES - (WA)
1.540130e-03
BONNEVILLE POWER ADMINISTRATION||CITY OF RICHLAND - (WA)
5.311876e-03
BONNEVILLE POWER ADMINISTRATION||CITY OF TACOMA - (WA)||BENTON RURAL ELECTRIC ASSN|PENINSULA LIGHT COMPANY
2.200185e-04
BONNEVILLE POWER ADMINISTRATION||CITY OF TACOMA - (WA)||ELMHURST MUTUAL POWER & LIGHT CO|PENINSULA LIGHT COMPANY
2.184470e-03
BONNEVILLE POWER ADMINISTRATION||CITY OF TACOMA - (WA)||LAKEVIEW LIGHT & POWER|PENINSULA LIGHT COMPANY
8.486430e-04
BONNEVILLE POWER ADMINISTRATION||CITY OF TACOMA - (WA)||OHOP MUTUAL LIGHT COMPANY, INC|PENINSULA LIGHT COMPANY
6.757712e-04
BONNEVILLE POWER ADMINISTRATION||CITY OF TACOMA - (WA)||PARKLAND LIGHT & WATER COMPANY|PENINSULA LIGHT COMPANY
6.286244e-04
BONNEVILLE POWER ADMINISTRATION||CITY OF TACOMA - (WA)||PENINSULA LIGHT COMPANY
4.555955e-02
BONNEVILLE POWER ADMINISTRATION||CITY OF TACOMA - (WA)||PUD NO 1 OF LEWIS COUNTY
2.215901e-03
BONNEVILLE POWER ADMINISTRATION||CITY OF TACOMA - (WA)||PUD NO 1 OF MASON COUNTY
6.914869e-04
BONNEVILLE POWER ADMINISTRATION||CITY OF TACOMA - (WA)||PUD NO 3 OF MASON COUNTY
4.211784e-03
BONNEVILLE POWER ADMINISTRATION||COLUMBIA RURAL ELEC ASSN, INC
1.571561e-04
BONNEVILLE POWER ADMINISTRATION||INLAND POWER & LIGHT COMPANY
2.215901e-03
BONNEVILLE POWER ADMINISTRATION||ORCAS POWER & LIGHT COOP
6.553410e-03
BONNEVILLE POWER ADMINISTRATION||PUD 1 OF SNOHOMISH COUNTY
8.722164e-03
BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF ASOTIN COUNTY||INLAND POWER & LIGHT COMPANY
3.143122e-05
BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF BENTON COUNTY
7.449199e-03
BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF CLALLAM COUNTY
5.374739e-03
BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF CLALLAM COUNTY|PUD NO 1 OF JEFFERSON COUNTY
1.571561e-05
BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF CLARK COUNTY - (WA)
5.921642e-02
BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF COWLITZ COUNTY
5.437601e-03
BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF FERRY COUNTY
1.414405e-04
BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF FRANKLIN COUNTY
2.970250e-03
BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF GRAYS HARBOR COUNTY
3.551728e-03
BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF KITTITAS COUNTY
9.429366e-05
BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF KLICKITAT COUNTY
1.492983e-03
BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF MASON COUNTY|PUD NO 1 OF JEFFERSON COUNTY
1.571561e-04
BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF SKAMANIA CO
1.021515e-03
BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF WAHKIAKUM COUNTY
3.143122e-04
BONNEVILLE POWER ADMINISTRATION||PUD NO 2 OF PACIFIC COUNTY
1.540130e-03
BONNEVILLE POWER ADMINISTRATION||PUGET SOUND ENERGY INC||PUD NO 1 OF JEFFERSON COUNTY
6.113372e-03
BONNEVILLE POWER ADMINISTRATION||TOWN OF EATONVILLE - (WA)|CITY OF TACOMA - (WA)
1.885873e-04
BONNEVILLE POWER ADMINISTRATION||TOWN OF RUSTON - (WA)|CITY OF TACOMA - (WA)||PENINSULA LIGHT COMPANY
2.357342e-04
BONNEVILLE POWER ADMINISTRATION||TOWN OF STEILACOOM|CITY OF TACOMA - (WA)||PENINSULA LIGHT COMPANY
1.052946e-03
BONNEVILLE POWER ADMINISTRATION||VERA IRRIGATION DISTRICT #15
2.027314e-03
CITY OF BLAINE - (WA)||PUD NO 1 OF WHATCOM COUNTY
1.257249e-03
CITY OF CHENEY - (WA)
2.357342e-04
CITY OF CHEWELAH
6.286244e-05
CITY OF SEATTLE - (WA)
1.571561e-05
CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)
1.966651e-01
CITY OF SUMAS - (WA)||PUD NO 1 OF WHATCOM COUNTY
4.714683e-05
CITY OF TACOMA - (WA)
4.714683e-05
CITY OF TACOMA - (WA)||TANNER ELECTRIC COOP
1.304396e-03
MODERN ELECTRIC WATER COMPANY
6.537694e-03
OKANOGAN COUNTY ELEC COOP, INC
7.386337e-04
PORTLAND GENERAL ELECTRIC CO
6.286244e-05
PUD NO 1 OF CHELAN COUNTY
5.940501e-03
PUD NO 1 OF DOUGLAS COUNTY
1.933020e-03
PUD NO 1 OF OKANOGAN COUNTY
5.343308e-04
PUD NO 1 OF PEND OREILLE COUNTY
2.985966e-04
PUD NO 1 OF WHATCOM COUNTY
3.614590e-04
PUD NO 2 OF GRANT COUNTY
2.703085e-03
PUGET SOUND ENERGY INC
2.010027e-01
PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)
3.544656e-01
PUGET SOUND ENERGY INC||PUD NO 1 OF WHATCOM COUNTY
2.527070e-02 table(ev_data$County)
Adams Alameda Albemarle
21 1 1
Alexandria City Allegheny Anchorage
3 1 1
Anne Arundel Asotin Baltimore
5 31 1
Bartow Bell Benton
1 1 812
Bexar Bradley Calvert
3 1 2
Camden Carroll Carson City
1 1 1
Charles Chaves Chelan
2 1 378
Chesapeake City Clallam Clark
2 441 3771
Columbia Coryell Cowlitz
7 1 346
Cumberland Danville City Davidson
2 1 1
Denton District Of Columbia Douglas
1 3 127
Dupage Duval El Paso
1 1 1
Erie Fairbanks North Star Fairfax
1 1 5
Ferry Flathead Franklin
16 1 201
Frederick Garfield Glacier
3 3 1
Goochland Grant Grays Harbor
2 172 234
Guadalupe Hamilton Harris
1 1 1
Harrison Hillsborough Honolulu
1 1 4
Howard Hudson Humacao
2 1 1
Island Jefferson Kern
773 400 2
King Kings Kitsap
33552 2 2302
Kittitas Klamath Klickitat
213 1 95
Lake Laramie Leavenworth
1 1 1
Lewis Liberty Lincoln
293 1 17
Los Angeles Maricopa Marin
4 1 1
Mason Mecklenburg Monterey
325 1 1
Montgomery Multnomah Muscogee
7 3 1
New Castle New London Newport
1 3 1
Norfolk City Nueces Okanogan
1 1 82
Orange Otero Ozaukee
4 1 1
Pacific Passaic Pend Oreille
98 1 19
Pennington Philadelphia Pierce
1 1 4825
Pinellas Placer Plaquemines
1 1 1
Platte Powhatan Prince Georges
1 1 1
Prince William Queens Riley
3 1 1
Riverside Rock Island Sacramento
2 1 1
Saint Clair Saint Marys Salt Lake
1 1 1
San Bernardino San Diego San Juan
2 16 419
Santa Clara Santa Cruz Sarasota
2 1 1
Sarpy Skagit Skamania
1 779 65
Snohomish Spokane Stafford
6920 1579 1
Stevens Suffolk Thurston
70 2 2446
Tipton Ventura Virginia Beach City
1 2 1
Wahkiakum Wake Walla Walla
20 1 173
Washoe Whatcom Whitman
1 1714 95
Wichita Wilson Yakima
1 1 350
Yamhill
1 Battery electric vehicles outnumber Plug-in Hybrids in the dataset by almost 3 to 1. This is interesting observation, especially given that PHEVs tend to have smaller batteries and have lower upfront cost. This may indicate that the owners represented here are investing for the long haul with their EV purchase.
# Display EV type proportions per category
ev_data %>%
count(`Electric Vehicle Type`) %>%
mutate(perc = n / nrow(ev_data)) -> ev_type_percent
ggplot(data=ev_type_percent, aes(x=`Electric Vehicle Type`, y=perc)) +
geom_bar(stat="identity") +
scale_x_discrete(name ="Electric Vehicle Type Breakdown") +
scale_y_continuous(name ="Percent")
# Plot the summary statistics of electric range and expected price using a boxplot
# Examine the two variables individually as well as their relationship using a scatter plot
ggplot(data=ev_data, aes(x=`Electric Range`)) +
geom_boxplot(outlier.colour="black", outlier.shape=8,
outlier.size=2, notch=FALSE) +
scale_x_continuous(name ="Electric range (miles)", breaks=seq(0,350,50)) +
theme(axis.text.y = element_blank())
ggplot(data=ev_data, aes(x=`Expected Price ($1k)`)) +
geom_boxplot(outlier.colour="blue", outlier.shape=8,
outlier.size=2, notch=FALSE) +
scale_x_continuous(name ="Expected price (thousands)", limits=c(0,300), breaks=seq(0,300,50)) +
theme(axis.text.y = element_blank())
ggplot(data=ev_data, aes(x=`Electric Range`, y=`Expected Price ($1k)`)) +
geom_point(outlier.colour="blue", outlier.shape=8,
outlier.size=2, notch=FALSE) +
scale_x_continuous(name="Electric range (miles)",breaks=seq(0,350,50)) +
scale_y_continuous(name="Expected price (thousands)",limits=c(0,200),breaks=seq(0,200,50))
# Focus solely on Washington States as the dataset focuses predominantly on it
evs_under_30k <- ev_data %>% filter(State == 'WA' & `Expected Price ($1k)` < 30) %>% select(`ZIP Code`, Make, Model, `Electric Vehicle Type`, `Electric Range`, `Expected Price ($1k)`)
# Get stats for Teslas as a luxury brand
teslas <- ev_data %>% filter(Make == "TESLA") %>% select(`ZIP Code`, Make, Model, `Electric Vehicle Type`, `Electric Range`, `Expected Price ($1k)`)
# Get summary statistics for the electric range of EVs under 30k
evs_under_30k %>% summarize(mean.el_range = mean(`Electric Range`, na.rm = TRUE),
median.el_range = median(`Electric Range`, na.rm = TRUE),
sd.el_range = sd(`Electric Range`, na.rm = TRUE),
min.el_range = min(`Electric Range`, na.rm= TRUE),
max.el_range = max(`Electric Range`, na.rm= TRUE))
# A tibble: 1 × 5
mean.el_range median.el_range sd.el_range min.el_range max.el_range
<dbl> <dbl> <dbl> <dbl> <dbl>
1 72.7 72 62.3 0 258teslas %>% summarize(mean.el_range = mean(`Electric Range`, na.rm = TRUE),
median.el_range = median(`Electric Range`, na.rm = TRUE),
sd.el_range = sd(`Electric Range`, na.rm = TRUE),
min.el_range = min(`Electric Range`, na.rm= TRUE),
max.el_range = max(`Electric Range`, na.rm= TRUE))
# A tibble: 1 × 5
mean.el_range median.el_range sd.el_range min.el_range max.el_range
<dbl> <dbl> <dbl> <dbl> <dbl>
1 153. 210 119. 0 337
Call:
lm(formula = `Expected Price ($1k)` ~ `Electric Range`, data = evs_under_30k)
Residuals:
Min 1Q Median 3Q Max
-20.5998 -2.5466 -0.5566 4.4120 9.3982
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 20.5998193 0.0481087 428.194 <2e-16 ***
`Electric Range` -0.0007397 0.0005025 -1.472 0.141
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.731 on 22808 degrees of freedom
Multiple R-squared: 9.501e-05, Adjusted R-squared: 5.117e-05
F-statistic: 2.167 on 1 and 22808 DF, p-value: 0.141
evs_under_30k %>%
ggplot(aes(`Electric Range`, `Expected Price ($1k)`)) +
geom_point()
pred <- predict(range.p1)
evs_under_30k %>% ggplot(mapping=aes(x=`Electric Range`)) +
geom_point(aes(y=`Expected Price ($1k)`)) +
geom_jitter(aes(y=pred), color="red")
The linear regression shows us that there is no statistically significant relationship between the Electric Range of an EVs under $30k and their expected price (p-value: 14%).
evs_rich_zips <- ev_data %>%
filter(City == "REDMOND" | City == 'SEATTLE' | City == 'BELLEVUE' | City == 'MEDINA') %>% #
select(`ZIP Code`, City, Make, Model, `Electric Vehicle Type`, `Electric Range`, `Expected Price ($1k)`)
range.p2 <- lm(`Expected Price ($1k)`~`Electric Range`, data=evs_rich_zips)
evs_rich_zips %>%
ggplot(aes(`Electric Range`, `Expected Price ($1k)`)) +
geom_point()
#Univariate barplot showing the tpye of EVs in Washington's most expensive cities
ggplot(data=evs_rich_zips, aes(x=`Electric Vehicle Type`)) +
geom_bar() +
theme_clean()
#Bivariate plot telling us which EV makes are present in each of the 4 wealthier cities
ggplot(data=evs_rich_zips, aes(x=City, y=Make)) +
geom_point()
#Bivariate plot giving an overview of the electric range of EVs in the high-end cities
ggplot(data=evs_rich_zips, aes(x=`Electric Range`,y=Make)) +
geom_point() +
scale_x_continuous(name="Electric range (miles)",breaks=seq(0,350,50))
Identifying limitations
This dataset focuses predominantly on EVs in Washington State. Source
While giving a high overview of the data, these visualizations may need
further breakdown to convey deeper meaning. For instance, performing
more thorough analysis on the zip codes or cities to provide a better
understanding of the specific models preferred by those populations may
offer an insight into that particular market and would allow a third
party (an EV marketing agency, car manufacturers, etc.) to achieve
better product placement. In the next iteration I will work on
segmenting the data into more easily digestible parts in order to
improve the visualizations.