Last updated: 2019-10-16

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File Version Author Date Message
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))

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 rows
summary(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] 0.7661229
summary(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(stringr::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            3
big_epa_cars %>%
  filter(stringr::str_detect(VClass, "[Vv]an", ),
         year > 2017,
         ghgScore != -1) %>%
  mutate(label = stringr::str_c(make, model, year, sep = "-"),
         label = make) %>%
  ggplot(aes(x = youSaveSpend, y = ghgScore)) +
  geom_text(aes(label = label), 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?")


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          modelr_0.1.5        
[16] readxl_1.3.1         lifecycle_0.1.0      munsell_0.5.0       
[19] gtable_0.3.0         workflowr_1.4.0.9002 cellranger_1.1.0    
[22] rvest_0.3.4          evaluate_0.14        labeling_0.3        
[25] knitr_1.25           fansi_0.4.0          broom_0.5.2         
[28] Rcpp_1.0.2           scales_1.0.0         backports_1.1.5     
[31] jsonlite_1.6         fs_1.3.1             hms_0.5.1           
[34] digest_0.6.21        stringi_1.4.3        grid_3.6.1          
[37] rprojroot_1.2        cli_1.1.0            tools_3.6.1         
[40] magrittr_1.5         lazyeval_0.2.2       crayon_1.3.4        
[43] whisker_0.4          pkgconfig_2.0.3      zeallot_0.1.0       
[46] xml2_1.2.2           lubridate_1.7.4      assertthat_0.2.1    
[49] rmarkdown_1.15       httr_1.4.1           rstudioapi_0.10     
[52] R6_2.4.0             nlme_3.1-141         git2r_0.26.1.9000   
[55] compiler_3.6.1