Explain the variables
The dataset contains information about Battery Electric Vehicles (BEVs) and Plug-in Hybrid Electric Vehicles (PHEVs). I plan to focus on visualizations and analyze relationships between EV features and target price.
# Convert data to tibble and list columns,summary
ev <- read_csv("electric_vehicles/train.csv") %>% as_tibble()
head(ev)
# A tibble: 6 × 18
ID `VIN (1-10)` County City State `ZIP Code` `Model Year` Make
<chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr>
1 EV331… 5YJ3E1EC6L Snoho… LYNN… WA 98037 2020 TESLA
2 EV402… JN1AZ0CP8B Skagit BELL… WA 98229 2011 NISS…
3 EV122… WBY1Z2C56F Pierce TACO… WA 98422 2015 BMW
4 EV557… 1G1RD6E44D King REDM… WA 98053 2013 CHEV…
5 EV287… 1G1FY6S05K Pierce PUYA… WA 98375 2019 CHEV…
6 EV498… KMHE24L10G Clark VANC… WA 98683 2016 HYUN…
# … with 10 more variables: Model <chr>,
# `Electric Vehicle Type` <chr>,
# `Clean Alternative Fuel Vehicle (CAFV) Eligibility` <chr>,
# `Electric Range` <dbl>, `Base MSRP` <dbl>,
# `Legislative District` <dbl>, `DOL Vehicle ID` <dbl>,
# `Vehicle Location` <chr>, `Electric Utility` <chr>,
# `Expected Price ($1k)` <dbl>colnames(ev)
[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)" sapply(ev, class)
ID
"character"
VIN (1-10)
"character"
County
"character"
City
"character"
State
"character"
ZIP Code
"numeric"
Model Year
"numeric"
Make
"character"
Model
"character"
Electric Vehicle Type
"character"
Clean Alternative Fuel Vehicle (CAFV) Eligibility
"character"
Electric Range
"numeric"
Base MSRP
"numeric"
Legislative District
"numeric"
DOL Vehicle ID
"numeric"
Vehicle Location
"character"
Electric Utility
"character"
Expected Price ($1k)
"numeric" sum.ev <- summary(ev)
sum.ev
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 Data exploration
# A tibble: 41,280 × 18
ID `VIN (1-10)` County City State `ZIP Code` `Model Year` Make
<chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr>
1 EV41… 5YJRE1A18A King NORT… WA 98045 2010 TESLA
2 EV48… 5YJRE1A16A King SEAT… WA 98101 2010 TESLA
3 EV42… 5YJRE1A32A Island FREE… WA 98249 2010 TESLA
4 EV56… 5YJRE1A32A King BELL… WA 98006 2010 TESLA
5 EV33… 5YJRE1A33A King SEAT… WA 98126 2010 TESLA
6 EV71… 5YJRE1A32A King KIRK… WA 98034 2010 TESLA
7 EV26… 5YJRE1A11A King SEAT… WA 98144 2010 TESLA
8 EV87… 5YJRE1A14A King SEAT… WA 98112 2010 TESLA
9 EV71… 5YJRE1A30A Snoho… EDMO… WA 98026 2010 TESLA
10 EV84… 5YJRE1A11A Snoho… MUKI… WA 98275 2010 TESLA
# … with 41,270 more rows, and 10 more variables: Model <chr>,
# `Electric Vehicle Type` <chr>,
# `Clean Alternative Fuel Vehicle (CAFV) Eligibility` <chr>,
# `Electric Range` <dbl>, `Base MSRP` <dbl>,
# `Legislative District` <dbl>, `DOL Vehicle ID` <dbl>,
# `Vehicle Location` <chr>, `Electric Utility` <chr>,
# `Expected Price ($1k)` <dbl>slice(ev, 20:40)
# A tibble: 21 × 18
ID `VIN (1-10)` County City State `ZIP Code` `Model Year` Make
<chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <chr>
1 EV27… WBY1Z4C57E Snoho… MUKI… WA 98275 2014 BMW
2 EV76… KNDCC3LG0L Pierce UNIV… WA 98467 2020 KIA
3 EV31… 5YJ3E1EB7M Pierce UNIV… WA 98466 2021 TESLA
4 EV69… 5YJ3E1EA7J King COVI… WA 98042 2018 TESLA
5 EV46… 5YJSA1E24G King CLYD… WA 98004 2016 TESLA
6 EV83… KNDJP3AE4J Clark VANC… WA 98683 2018 KIA
7 EV75… 5YJYGDEF6L King REDM… WA 98053 2020 TESLA
8 EV70… 5YJSA1E25G Snoho… STAN… WA 98292 2016 TESLA
9 EV67… KNDCC3LC4H King SAMM… WA 98074 2017 KIA
10 EV58… 5YJYGDEE7M King REDM… WA 98052 2021 TESLA
# … with 11 more rows, and 10 more variables: Model <chr>,
# `Electric Vehicle Type` <chr>,
# `Clean Alternative Fuel Vehicle (CAFV) Eligibility` <chr>,
# `Electric Range` <dbl>, `Base MSRP` <dbl>,
# `Legislative District` <dbl>, `DOL Vehicle ID` <dbl>,
# `Vehicle Location` <chr>, `Electric Utility` <chr>,
# `Expected Price ($1k)` <dbl>teslas <- filter(ev, `Make` == "TESLA" & `Model Year` > 2016)
ggplot(ev, aes(x=`Expected Price ($1k)`)) +
geom_histogram(aes(y=..density..), colour="black", fill="white") +
xlim(0,150)
ggplot(ev, aes(x=`Expected Price ($1k)`)) +
geom_histogram(colour="blue", fill="white") +
xlim(0,150)
ggplot(ev, aes(x=`Electric Range`))+
geom_histogram(colour="green", fill="white")
