2019-10-15-big-mtcars
John Blischak
2019-10-16
Last updated: 2019-10-17
Checks: 7 0
Knit directory: wflow-tidy-tuesday/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 6749e23 | John Blischak | 2019-10-17 | Add analysis with only internal combustion engines |
| html | 1473290 | John Blischak | 2019-10-17 | Build site. |
| Rmd | 8cd0d52 | John Blischak | 2019-10-17 | Explore Chrysler and Ford. Refactor. |
| html | da7ae23 | John Blischak | 2019-10-17 | Build site. |
| Rmd | 4aa8949 | John Blischak | 2019-10-17 | Report names of vans. Also add the data set. |
| html | a4d8491 | John Blischak | 2019-10-17 | Build site. |
| Rmd | 79e50d4 | John Blischak | 2019-10-17 | Big mtcars |
Introduction
Sooner rather than later I’m going to need to buy a new car. And let’s face it, it’ll probably be a van. What brand of van should I buy? I explore this below using this week’s #TidyTuesday data.
Conclusion: I should look at Chrysler vans for saving gas and producing fewer greenhouse gases.
Setup
library(tidyverse)
theme_set(theme_classic(base_size = 16))
library(stringr)Need to set guess_max to a high value so that the columns are correctly parsed. Using on the first 1000 rows, readr erroneously guesses that some columns are logical.
big_epa_cars <- readr::read_csv("data/big_epa_cars.csv", guess_max = 50000)What brand of van should I buy?
The EPA’s Greenhouse Gas Rating is a scale from 1 (worst) to 10 (best).
big_epa_cars %>%
count(VClass)# A tibble: 34 x 2
VClass n
<chr> <int>
1 Compact Cars 5936
2 Large Cars 2231
3 Midsize Cars 4972
4 Midsize Station Wagons 552
5 Midsize-Large Station Wagons 656
6 Minicompact Cars 1404
7 Minivan - 2WD 368
8 Minivan - 4WD 50
9 Small Pickup Trucks 538
10 Small Pickup Trucks 2WD 476
# … with 24 more rowssummary(big_epa_cars$year) Min. 1st Qu. Median Mean 3rd Qu. Max.
1984 1991 2003 2002 2012 2020 # https://www.epa.gov/greenvehicles/greenhouse-gas-rating
mean(big_epa_cars$ghgScore == -1) # NA[1] 0.7661229summary(big_epa_cars$ghgScore[big_epa_cars$ghgScore != -1]) Min. 1st Qu. Median Mean 3rd Qu. Max.
1.000 4.000 5.000 5.092 6.000 10.000 big_epa_cars %>%
filter(str_detect(VClass, "[Vv]an", ),
year > 2017) %>%
count(make, VClass)# A tibble: 7 x 3
make VClass n
<chr> <chr> <int>
1 Chrysler Minivan - 2WD 9
2 Dodge Minivan - 2WD 4
3 Ford Vans, Passenger Type 6
4 Honda Minivan - 2WD 5
5 Kia Minivan - 2WD 5
6 Toyota Minivan - 2WD 3
7 Toyota Minivan - 4WD 3vans <- big_epa_cars %>%
filter(str_detect(VClass, "[Vv]an"),
year > 2017,
ghgScore != -1) %>%
mutate(name = str_c(make, model, year, sep = "-"),
engine = if_else(str_detect(atvType, "[Hh]ybrid"),
"hybrid", "ICE", missing = "ICE")) %>%
select(name, make, model, year, VClass, youSaveSpend, ghgScore, engine)p <- ggplot(vans, aes(x = youSaveSpend, y = ghgScore)) +
geom_text(aes(label = make, color = engine), size = 5,
position = position_jitter(width = 1, height = 1, seed = NULL)) +
labs(x = "Gas savings over 5 years compared to average car",
y = "(worse <--) EPA Greenhouse Gas Rating (--> better)",
title = "What brand of van should I buy?") +
scale_color_brewer(name = "Engine",
labels = c("Hybrid", "Internal Combustion"),
palette = "Dark2") +
theme(legend.position = c(0.25, 0.75))
p
What are the 3 Chrysler vans at the top right of the graph?
vans %>%
mutate(name = str_c(make, model, year, sep = "-")) %>%
filter(youSaveSpend > 0, ghgScore > 8) %>%
select(name, youSaveSpend, ghgScore)# A tibble: 3 x 3
name youSaveSpend ghgScore
<chr> <dbl> <dbl>
1 Chrysler-Pacifica Hybrid-2018 2500 10
2 Chrysler-Pacifica Hybrid-2019 2250 10
3 Chrysler-Pacifica Hybrid-2020 2250 10Oof. The cheapest model of a 2020 Chrysler Pacifica Hybrid costs ~$40K.
Ford sells both passenger vans and cargo vans. Could the poor performance of the Ford vans in these metrics be due to the fact that they are all cargo vans?
vans %>%
filter(make == "Ford") %>%
select(name, VClass, youSaveSpend, ghgScore)# A tibble: 6 x 4
name VClass youSaveSpend ghgScore
<chr> <chr> <dbl> <dbl>
1 Ford-Transit T150 Wagon-2018 Vans, Passenger Ty… -5250 2
2 Ford-Transit T150 Wagon FFV-20… Vans, Passenger Ty… -5250 2
3 Ford-Transit T150 Wagon-2018 Vans, Passenger Ty… -5250 2
4 Ford-Transit T150 Wagon-2019 Vans, Passenger Ty… -5250 2
5 Ford-Transit T150 Wagon FFV-20… Vans, Passenger Ty… -6000 2
6 Ford-Transit T150 Wagon-2019 Vans, Passenger Ty… -6000 2No, they are all passenger vans.
Limiting the analysis to internal combustion engines, there is a big tie between Chrysler, Honda, and Toyata (the graph has random jitter to be able to read the names):
p %+% (vans %>% filter(engine == "ICE")) + theme(legend.position = "none")
vans %>%
filter(engine == "ICE", youSaveSpend > -2000, ghgScore > 3) %>%
knitr::kable()| name | make | model | year | VClass | youSaveSpend | ghgScore | engine |
|---|---|---|---|---|---|---|---|
| Honda-Odyssey-2018 | Honda | Odyssey | 2018 | Minivan - 2WD | -1750 | 4 | ICE |
| Honda-Odyssey-2018 | Honda | Odyssey | 2018 | Minivan - 2WD | -1750 | 4 | ICE |
| Chrysler-Pacifica-2018 | Chrysler | Pacifica | 2018 | Minivan - 2WD | -1750 | 4 | ICE |
| Chrysler-Pacifica-2018 | Chrysler | Pacifica | 2018 | Minivan - 2WD | -1750 | 4 | ICE |
| Toyota-Sienna 2WD-2018 | Toyota | Sienna 2WD | 2018 | Minivan - 2WD | -1750 | 4 | ICE |
| Honda-Odyssey-2019 | Honda | Odyssey | 2019 | Minivan - 2WD | -1750 | 4 | ICE |
| Honda-Odyssey-2019 | Honda | Odyssey | 2019 | Minivan - 2WD | -1750 | 4 | ICE |
| Chrysler-Pacifica-2019 | Chrysler | Pacifica | 2019 | Minivan - 2WD | -1750 | 4 | ICE |
| Chrysler-Pacifica-2019 | Chrysler | Pacifica | 2019 | Minivan - 2WD | -1750 | 4 | ICE |
| Toyota-Sienna 2WD-2019 | Toyota | Sienna 2WD | 2019 | Minivan - 2WD | -1750 | 4 | ICE |
| Chrysler-Pacifica-2020 | Chrysler | Pacifica | 2020 | Minivan - 2WD | -1750 | 4 | ICE |
| Chrysler-Voyager-2020 | Chrysler | Voyager | 2020 | Minivan - 2WD | -1750 | 4 | ICE |
| Honda-Odyssey-2020 | Honda | Odyssey | 2020 | Minivan - 2WD | -1750 | 4 | ICE |
And here is a full table of all the vans that were included.
vans %>%
arrange(desc(youSaveSpend), desc(ghgScore)) %>%
knitr::kable()| name | make | model | year | VClass | youSaveSpend | ghgScore | engine |
|---|---|---|---|---|---|---|---|
| Chrysler-Pacifica Hybrid-2018 | Chrysler | Pacifica Hybrid | 2018 | Minivan - 2WD | 2500 | 10 | hybrid |
| Chrysler-Pacifica Hybrid-2019 | Chrysler | Pacifica Hybrid | 2019 | Minivan - 2WD | 2250 | 10 | hybrid |
| Chrysler-Pacifica Hybrid-2020 | Chrysler | Pacifica Hybrid | 2020 | Minivan - 2WD | 2250 | 10 | hybrid |
| Honda-Odyssey-2018 | Honda | Odyssey | 2018 | Minivan - 2WD | -1750 | 4 | ICE |
| Honda-Odyssey-2018 | Honda | Odyssey | 2018 | Minivan - 2WD | -1750 | 4 | ICE |
| Chrysler-Pacifica-2018 | Chrysler | Pacifica | 2018 | Minivan - 2WD | -1750 | 4 | ICE |
| Chrysler-Pacifica-2018 | Chrysler | Pacifica | 2018 | Minivan - 2WD | -1750 | 4 | ICE |
| Toyota-Sienna 2WD-2018 | Toyota | Sienna 2WD | 2018 | Minivan - 2WD | -1750 | 4 | ICE |
| Honda-Odyssey-2019 | Honda | Odyssey | 2019 | Minivan - 2WD | -1750 | 4 | ICE |
| Honda-Odyssey-2019 | Honda | Odyssey | 2019 | Minivan - 2WD | -1750 | 4 | ICE |
| Chrysler-Pacifica-2019 | Chrysler | Pacifica | 2019 | Minivan - 2WD | -1750 | 4 | ICE |
| Chrysler-Pacifica-2019 | Chrysler | Pacifica | 2019 | Minivan - 2WD | -1750 | 4 | ICE |
| Toyota-Sienna 2WD-2019 | Toyota | Sienna 2WD | 2019 | Minivan - 2WD | -1750 | 4 | ICE |
| Chrysler-Pacifica-2020 | Chrysler | Pacifica | 2020 | Minivan - 2WD | -1750 | 4 | ICE |
| Chrysler-Voyager-2020 | Chrysler | Voyager | 2020 | Minivan - 2WD | -1750 | 4 | ICE |
| Honda-Odyssey-2020 | Honda | Odyssey | 2020 | Minivan - 2WD | -1750 | 4 | ICE |
| Kia-Sedona SX-2018 | Kia | Sedona SX | 2018 | Minivan - 2WD | -2250 | 4 | ICE |
| Kia-Sedona-2019 | Kia | Sedona | 2019 | Minivan - 2WD | -2250 | 4 | ICE |
| Toyota-Sienna 2WD-2020 | Toyota | Sienna 2WD | 2020 | Minivan - 2WD | -2250 | 4 | ICE |
| Kia-Sedona-2020 | Kia | Sedona | 2020 | Minivan - 2WD | -2250 | 4 | ICE |
| Kia-Sedona-2018 | Kia | Sedona | 2018 | Minivan - 2WD | -2750 | 4 | ICE |
| Dodge-Grand Caravan-2018 | Dodge | Grand Caravan | 2018 | Minivan - 2WD | -2750 | 4 | ICE |
| Dodge-Grand Caravan-2018 | Dodge | Grand Caravan | 2018 | Minivan - 2WD | -2750 | 4 | ICE |
| Toyota-Sienna AWD-2018 | Toyota | Sienna AWD | 2018 | Minivan - 4WD | -2750 | 4 | ICE |
| Dodge-Grand Caravan-2019 | Dodge | Grand Caravan | 2019 | Minivan - 2WD | -2750 | 4 | ICE |
| Toyota-Sienna AWD-2019 | Toyota | Sienna AWD | 2019 | Minivan - 4WD | -2750 | 4 | ICE |
| Dodge-Grand Caravan-2019 | Dodge | Grand Caravan | 2019 | Minivan - 2WD | -2750 | 4 | ICE |
| Toyota-Sienna AWD-2020 | Toyota | Sienna AWD | 2020 | Minivan - 4WD | -2750 | 4 | ICE |
| Kia-Sedona SXL-2018 | Kia | Sedona SXL | 2018 | Minivan - 2WD | -3250 | 3 | ICE |
| Ford-Transit T150 Wagon-2018 | Ford | Transit T150 Wagon | 2018 | Vans, Passenger Type | -5250 | 2 | ICE |
| Ford-Transit T150 Wagon FFV-2018 | Ford | Transit T150 Wagon FFV | 2018 | Vans, Passenger Type | -5250 | 2 | ICE |
| Ford-Transit T150 Wagon-2018 | Ford | Transit T150 Wagon | 2018 | Vans, Passenger Type | -5250 | 2 | ICE |
| Ford-Transit T150 Wagon-2019 | Ford | Transit T150 Wagon | 2019 | Vans, Passenger Type | -5250 | 2 | ICE |
| Ford-Transit T150 Wagon FFV-2019 | Ford | Transit T150 Wagon FFV | 2019 | Vans, Passenger Type | -6000 | 2 | ICE |
| Ford-Transit T150 Wagon-2019 | Ford | Transit T150 Wagon | 2019 | Vans, Passenger Type | -6000 | 2 | ICE |
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.3 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/atlas/libblas.so.3.10.3
LAPACK: /usr/lib/x86_64-linux-gnu/atlas/liblapack.so.3.10.3
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] forcats_0.4.0 stringr_1.4.0 dplyr_0.8.3 purrr_0.3.2
[5] readr_1.3.1 tidyr_1.0.0 tibble_2.1.3 ggplot2_3.2.1
[9] tidyverse_1.2.1
loaded via a namespace (and not attached):
[1] tidyselect_0.2.5 xfun_0.10 haven_2.1.1
[4] lattice_0.20-38 colorspace_1.4-1 vctrs_0.2.0
[7] generics_0.0.2 htmltools_0.4.0 yaml_2.2.0
[10] utf8_1.1.4 rlang_0.4.0 pillar_1.4.2
[13] glue_1.3.1 withr_2.1.2 RColorBrewer_1.1-2
[16] modelr_0.1.5 readxl_1.3.1 lifecycle_0.1.0
[19] munsell_0.5.0 gtable_0.3.0 workflowr_1.4.0.9001
[22] cellranger_1.1.0 rvest_0.3.4 evaluate_0.14
[25] labeling_0.3 knitr_1.25 fansi_0.4.0
[28] highr_0.8 broom_0.5.2 Rcpp_1.0.2
[31] scales_1.0.0 backports_1.1.5 jsonlite_1.6
[34] fs_1.3.1 hms_0.5.1 digest_0.6.21
[37] stringi_1.4.3 grid_3.6.1 rprojroot_1.2
[40] cli_1.1.0 tools_3.6.1 magrittr_1.5
[43] lazyeval_0.2.2 crayon_1.3.4 whisker_0.4
[46] pkgconfig_2.0.3 zeallot_0.1.0 xml2_1.2.2
[49] lubridate_1.7.4 assertthat_0.2.1 rmarkdown_1.15
[52] httr_1.4.1 rstudioapi_0.10 R6_2.4.0
[55] nlme_3.1-141 git2r_0.26.1.9000 compiler_3.6.1