README.md

In kjhealy/farsdata: Vehicle Fatality Data

farsdata

The farsdata package contains data from the Fatality Analysis Reporting System (FARS) for fatal automobile crashes in the United States.

Installation

farsdata is a data package containing a dataset of PBE vaccination exemption rates in California Kindergartens in 2014-15. There are two ways to install it.

Install direct from GitHub

You can install the beta version of farsdata from GitHub with:

devtools::install_github("kjhealy/farsdata")

Installation using drat

While using install_github() works just fine, it would be nicer to be able to just type install.packages("farsdata") or update.packages("farsdata") in the ordinary way. We can do this using Dirk Eddelbuettel's drat package. Drat provides a convenient way to make R aware of package repositories other than CRAN.

First, install drat:

if (!require("drat")) {
    install.packages("drat")
    library("drat")
}

Then use drat to tell R about the repository where farsdata is hosted:

drat::addRepo("kjhealy")

You can now install farsdata:

install.packages("farsdata")

To ensure that the farsdata repository is always available, you can add the following line to your .Rprofile or .Rprofile.site file:

drat::addRepo("kjhealy")

With that in place you'll be able to do install.packages("farsdata") or update.packages("farsdata") and have everything work as you'd expect.

Note that the drat repository only contains data packages that are not on CRAN, so you will never be in danger of grabbing the wrong version of any other package.

Loading the data

The package works best with the tidyverse libraries and the simple features package for mapping.

library(tidyverse)
#> ── Attaching packages ─────────────────────────────────────── tidyverse 1.2.1 ──
#> ✔ ggplot2 3.2.1     ✔ purrr   0.3.3
#> ✔ tibble  2.1.3     ✔ dplyr   0.8.3
#> ✔ tidyr   1.0.0     ✔ stringr 1.4.0
#> ✔ readr   1.3.1     ✔ forcats 0.4.0
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter()  masks stats::filter()
#> ✖ purrr::is_null() masks testthat::is_null()
#> ✖ dplyr::lag()     masks stats::lag()
#> ✖ dplyr::matches() masks tidyr::matches(), testthat::matches()

Load the data:

library(farsdata)

Look at it:

vehicles
#> # A tibble: 945 x 5
#>    vehicle_type           year involving    yes    no
#>    <chr>                 <int> <chr>      <dbl> <dbl>
#>  1 Passenger Car          2004 distracted  2864 22818
#>  2 Light Truck - Pickup   2004 distracted  1365  9489
#>  3 Light Truck - Utility  2004 distracted   931  6903
#>  4 Light Truck - Van      2004 distracted   460  3227
#>  5 Light Truck - Other    2004 distracted    13    98
#>  6 Large Truck            2004 distracted   808  4094
#>  7 Motorcycle             2004 distracted   420  3701
#>  8 Bus                    2004 distracted    40   239
#>  9 Other/Unknown          2004 distracted    92  1167
#> 10 Passenger Car          2005 distracted  2604 22565
#> # … with 935 more rows

kjhealy/farsdata documentation built on Dec. 14, 2019, 7:25 p.m.