Assignment 1 — Electric Vehicle Population Data
Jaipreet Kaur
2025-09-07
Overview
This report completes the required tasks: 1. Import a web-sourced
dataset with both quantitative and categorical variables
2. Print descriptive statistics (numeric & categorical)
3. Transform at least one variable
4. Create one univariate plot and one scatterplot
5. Include a link to the dataset source
Dataset: Electric Vehicle Population Data (Washington State)
Source: _https://catalog.data.gov/dataset/electric-vehicle-population-data._
# Packages
library(readr)
library(dplyr)
library(ggplot2)
library(scales)1) Import the dataset
Below we read the CSV file named
Electric_Vehicle_Population_Data.csv.
If you keep the file in the same folder as this Rmd, the following line
will work as-is.
# If your file is in a different folder, update the path below.
csv_path <- "Electric_Vehicle_Population_Data.csv"
# Read the data
df <- read_csv(csv_path, show_col_types = FALSE)
# Quick look
dim(df)## [1] 257635 17colnames(df)## [1] "VIN (1-10)"
## [2] "County"
## [3] "City"
## [4] "State"
## [5] "Postal Code"
## [6] "Model Year"
## [7] "Make"
## [8] "Model"
## [9] "Electric Vehicle Type"
## [10] "Clean Alternative Fuel Vehicle (CAFV) Eligibility"
## [11] "Electric Range"
## [12] "Base MSRP"
## [13] "Legislative District"
## [14] "DOL Vehicle ID"
## [15] "Vehicle Location"
## [16] "Electric Utility"
## [17] "2020 Census Tract"head(df, 5)## # A tibble: 5 × 17
## `VIN (1-10)` County City State `Postal Code` `Model Year` Make Model
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 5YJ3E1EB5K Yakima Yakima WA 98901 2019 TESLA MODE…
## 2 1C4RJXU67R Kitsap Port Orch… WA 98367 2024 JEEP WRAN…
## 3 KNDCD3LD0N Snohomish Lynnwood WA 98036 2022 KIA NIRO
## 4 5UXKT0C37H King Auburn WA 98001 2017 BMW X5
## 5 1N4AZ0CP1D Skagit Mount Ver… WA 98273 2013 NISS… LEAF
## # ℹ 9 more variables: `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>,
## # `2020 Census Tract` <chr>Columns (abbrev.) - Quantitative:
Model Year, Electric Range,
Base MSRP
- Categorical: Make, Model,
Electric Vehicle Type,
Clean Alternative Fuel Vehicle (CAFV) Eligibility,
County, City
2) Descriptive statistics
Numeric summaries
# Choose numeric variables present in the dataset
numeric_vars <- c("Model Year", "Electric Range", "Base MSRP")
# Defensive: keep only columns that exist & are numeric
num_cols <- intersect(numeric_vars, names(df))
df %>%
select(all_of(num_cols)) %>%
summary()## Model Year Electric Range Base MSRP
## Min. :2000 Min. : 0.00 Min. : 0.0
## 1st Qu.:2020 1st Qu.: 0.00 1st Qu.: 0.0
## Median :2023 Median : 0.00 Median : 0.0
## Mean :2022 Mean : 43.13 Mean : 705.3
## 3rd Qu.:2024 3rd Qu.: 35.00 3rd Qu.: 0.0
## Max. :2026 Max. :337.00 Max. :845000.0
## NA's :3 NA's :3Note: In this dataset,
Base MSRPcan contain zeros that represent missing/unknown list prices.
We can treat 0 as missing for MSRP-related summaries/plots if needed.
df <- df %>% mutate(Base_MSRP_clean = ifelse(`Base MSRP` <= 0 | is.na(`Base MSRP`), NA_real_, `Base MSRP`))
summary(df$Base_MSRP_clean)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 31950 39995 57800 57174 69900 845000 254457Categorical summaries
cat_vars <- c("Make", "Model", "Electric Vehicle Type", "Clean Alternative Fuel Vehicle (CAFV) Eligibility")
# Show top categories for large-cardinality columns
lapply(cat_vars, function(v) {
if (v %in% names(df)) {
tab <- table(df[[v]], useNA = "ifany")
sort(tab, decreasing = TRUE)[1:10] # top 10 levels
} else {
paste("Column not found:", v)
}
})## [[1]]
##
## TESLA CHEVROLET NISSAN FORD KIA BMW TOYOTA HYUNDAI
## 107535 18602 16274 13750 12586 10656 10622 8638
## RIVIAN VOLVO
## 7816 6673
##
## [[2]]
##
## MODEL Y MODEL 3 LEAF MODEL S BOLT EV
## 53560 37807 13971 7911 7812
## MODEL X MUSTANG MACH-E ID.4 IONIQ 5 WRANGLER
## 6713 5597 5338 4833 4831
##
## [[3]]
##
## Battery Electric Vehicle (BEV) Plug-in Hybrid Electric Vehicle (PHEV)
## 205095 52540
## <NA> <NA>
##
## <NA> <NA>
##
## <NA> <NA>
##
## <NA> <NA>
##
##
## [[4]]
##
## Eligibility unknown as battery range has not been researched
## 157670
## Clean Alternative Fuel Vehicle Eligible
## 76157
## Not eligible due to low battery range
## 23808
## <NA>
##
## <NA>
##
## <NA>
##
## <NA>
##
## <NA>
##
## <NA>
##
## <NA>
## 3) Transform at least one variable
Here we add two example transformations (either one satisfies the
requirement): - is_Tesla: converts Make to a
binary indicator (1 if Tesla, else 0).
- log1p_range: log-transform of Electric Range
to reduce right skew.
df <- df %>%
mutate(
is_Tesla = ifelse(Make == "TESLA" | Make == "Tesla", 1L, 0L),
log1p_range = ifelse(is.na(`Electric Range`), NA_real_, log1p(`Electric Range`))
)
# Quick check
df %>% select(Make, `Electric Range`, is_Tesla, log1p_range) %>% head(10)## # A tibble: 10 × 4
## Make `Electric Range` is_Tesla log1p_range
## <chr> <dbl> <int> <dbl>
## 1 TESLA 220 1 5.40
## 2 JEEP 21 0 3.09
## 3 KIA 26 0 3.30
## 4 BMW 14 0 2.71
## 5 NISSAN 75 0 4.33
## 6 NISSAN 84 0 4.44
## 7 TESLA 210 1 5.35
## 8 TESLA 0 1 0
## 9 NISSAN 84 0 4.44
## 10 PORSCHE 14 0 2.714) Plots
A) Univariate plot (Histogram of Electric Range)
ggplot(df, aes(x = `Electric Range`)) +
geom_histogram(bins = 40) +
labs(
title = "Distribution of Electric Range",
x = "Electric Range (miles)",
y = "Count"
)## Warning: Removed 3 rows containing non-finite outside the scale range
## (`stat_bin()`).
B) Scatterplot: Model Year vs Electric Range, colored by Electric Vehicle Type
# Keep rows with both Model Year and Electric Range available
plot_df <- df %>%
filter(!is.na(`Model Year`), !is.na(`Electric Range`))
ggplot(plot_df, aes(x = `Model Year`, y = `Electric Range`, color = `Electric Vehicle Type`)) +
geom_point(alpha = 0.4) +
labs(
title = "Model Year vs. Electric Range",
x = "Model Year",
y = "Electric Range (miles)",
color = "EV Type"
)
5) Notes & Interpretation (brief)
- Electric Range histogram shows the typical driving
range distribution for vehicles in the dataset.
- Model Year vs Range: newer model years generally
trend toward higher ranges; BEVs typically offer higher range than
PHEVs.
- Transformations:
log1p_rangecan help normalize the distribution for modeling;is_Teslacan be used to compare Tesla vs non‑Tesla vehicles on summary stats or plots.
Appendix
sessionInfo()## R version 4.3.2 (2023-10-31 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 11 x64 (build 26100)
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## Matrix products: default
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## [2] LC_CTYPE=English_United States.utf8
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.utf8
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## time zone: America/New_York
## tzcode source: internal
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## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
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## other attached packages:
## [1] scales_1.3.0 ggplot2_3.5.0 dplyr_1.1.4 readr_2.1.5
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## loaded via a namespace (and not attached):
## [1] bit_4.0.5 gtable_0.3.4 jsonlite_1.8.8 highr_0.10
## [5] crayon_1.5.2 compiler_4.3.2 tidyselect_1.2.0 parallel_4.3.2
## [9] jquerylib_0.1.4 yaml_2.3.8 fastmap_1.1.1 R6_2.5.1
## [13] labeling_0.4.3 generics_0.1.3 knitr_1.45 tibble_3.2.1
## [17] munsell_0.5.0 bslib_0.6.1 pillar_1.9.0 tzdb_0.4.0
## [21] rlang_1.1.3 utf8_1.2.4 cachem_1.0.8 xfun_0.41
## [25] sass_0.4.8 bit64_4.0.5 cli_3.6.2 withr_3.0.0
## [29] magrittr_2.0.3 digest_0.6.34 grid_4.3.2 vroom_1.6.5
## [33] rstudioapi_0.15.0 hms_1.1.3 lifecycle_1.0.4 vctrs_0.6.5
## [37] evaluate_0.23 glue_1.7.0 farver_2.1.1 fansi_1.0.6
## [41] colorspace_2.1-0 rmarkdown_2.25 tools_4.3.2 pkgconfig_2.0.3
## [45] htmltools_0.5.7