Counts

Generating Counts

A first step in many transportation safety analyses involves counting the number of relevant crashes, fatalities, or people involved. counts() lets users specify what to count, where to count them (rural/urban and/or in specified states or regions), who to include, the interval over which to count (annually or monthly), and factors involved in the crashes. It returns a simple tibble that can be easily piped into ggplot() to quickly visualize counts.

First we load the required libraries:

library(rfars)
library(dplyr)
library(ggplot2)

## Warning: package 'ggplot2' was built under R version 4.3.3

Then pull a year of FARS data for Virginia:

myFARS <- get_fars(years = 2021, states = "VA", proceed = T)

## ✓ 2021 data downloaded
## Preparing raw data files...
## ✓ Accident file processed
## ✓ Vehicle file processed
## ✓ Person file processed
## ✓ Weather file(s) processed
## ✓ Crash risk factors file processed
## ✓ Vehicle-level files processed
## ✓ PBtype file processed
## ✓ SafetyEq file processed
## ✓ Person-level files processed
## ✓ Flat file constructed
## ✓ Multi_acc file constructed
## ✓ Multi_veh file constructed
## ✓ Multi_per file constructed
## ✓ SOE file constructed
## ✓ Prepared files saved in C:/Users/s87ja/AppData/Local/Temp/RtmpGyYhCh/FARS data/prepd/2021
## ✓ Codebook file saved in C:/Users/s87ja/AppData/Local/Temp/RtmpGyYhCh/FARS data/prepd/

Then we can use counts() to reduce the data to desired counts.

Here we count crashes:

my_counts <- counts(
  myFARS,
  what = "crashes",
  interval = c("month")
  )

This returns the following dataframe:

knitr::kable(my_counts, format = "html")

month	date	n	what	states	region	urb	who
April	2021-04-01	83	crashes	all	all	all	all
August	2021-08-01	77	crashes	all	all	all	all
December	2021-12-01	74	crashes	all	all	all	all
February	2021-02-01	47	crashes	all	all	all	all
January	2021-01-01	50	crashes	all	all	all	all
July	2021-07-01	100	crashes	all	all	all	all
June	2021-06-01	65	crashes	all	all	all	all
March	2021-03-01	57	crashes	all	all	all	all
May	2021-05-01	87	crashes	all	all	all	all
November	2021-11-01	79	crashes	all	all	all	all
October	2021-10-01	108	crashes	all	all	all	all
September	2021-09-01	79	crashes	all	all	all	all

Which we can graph:

my_counts %>%
  ggplot(aes(x=date, y=n, label=scales::comma(n))) + 
    geom_col() + 
    geom_label(vjust=1.2) +
    labs(x=NULL, y=NULL, title = "Fatal Crashes in Virginia")

We could alternatively count fatalities:

counts(
  myFARS,
  what = "fatalities",
  interval = c("month")
  ) %>%
  ggplot(aes(x=date, y=n, label=scales::comma(n))) + 
    geom_col() + 
    geom_label(vjust=1.2) +
    labs(x=NULL, y=NULL, title = "Fatalities in Virginia")

Or fatalities involving speeding:

counts(myFARS,
       what = "fatalities",
       interval = c("month"),
       involved = "speeding"
       ) %>%
  ggplot(aes(x=date, y=n, label=scales::comma(n))) + 
    geom_col() + 
    geom_label(vjust=1.2) +
    labs(x=NULL, y=NULL, title = "Speeding-Related Fatalities in Virginia")

Or fatalities involving speeding in rural areas:

counts(myFARS,
       what = "fatalities",
       where = list(urb="rural"),
       interval = c("month"),
       involved = "speeding"
       ) %>%
  ggplot(aes(x=date, y=n, label=scales::comma(n))) + 
    geom_col() + 
    geom_label(vjust=1.2) +
    labs(x=NULL, y=NULL, title = "Speeding-Related Fatalities in Rural Virginia")

We can use compare_counts() to quickly produce comparison graphs.

Here we compare speeding-related fatalities in rural and urban areas:

compare_counts(
  df = myFARS,
  interval = "month",
  involved = "speeding",
  what = "fatalities",
  where = list(urb="rural"),
  where2 = list(urb="urban")
  ) %>%
  ggplot(aes(x=date, y=n, label=scales::comma(n))) + 
    geom_col() + 
    geom_label(vjust=1.2) +
    facet_wrap(.~urb) +
    labs(x=NULL, y=NULL, title = "Speeding-Related Fatalities in Virginia", fill=NULL)

And here we compare speeding-related crashes to those related to distraction:

compare_counts(
  df = myFARS,
  interval = "month",
  involved = "speeding",
  involved2 = "distracted driver",
  what = "crashes",
  ) %>%
  ggplot(aes(x=date, y=n, label=scales::comma(n))) + 
    geom_col() + 
    geom_label(vjust=1.2) +
    facet_wrap(.~involved) +
    labs(x=NULL, y=NULL, title = "Speeding- and Distraction-Related Crashes in Virginia", fill=NULL)

See the documentation for more information on the available options. * counts() * compare_counts()