Getting Started

This data is taken from the Australian Road Deaths Database, which provides basic details of road transport crash fatalities in Australia as reported by the police each month to the State and Territory road safety authorities, obtained from: https://data.gov.au/dataset/ds-dga-5b530fb8-526e-4fbf-b0f6-aa24e84e4277/details?q=crash

Details provided in the database fall into two groups:

the circumstances of the crash, for example, date, location, crash type
some details regarding the persons killed, for example, age, gender and road user group.

The fatality data is updated every month. The heavy vehicle flags (for articulated truck, heavy rigid truck and bus involvement) are only updated each quarter, and are current to within two months. Information for heavy rigid truck involvement in crashes earlier than 2004 is incomplete.

Package Author’s Notes

Data was available at URL as at 12th December 2019. Data is imported into R, cleaned and transformed into a tidy format.

Indemnity Statement:

The Bureau of Infrastructure, Transport and Regional Economics has taken due care in preparing this information. However, noting that data have been provided by third parties, the Commonwealth gives no warranty as to the accuracy, reliability, fitness for purpose, or otherwise of the information.

This work is copyright and the data contained in this publication should not be reproduced or used in any form without acknowledgement.

Import data from the BITRE website into R

library(ozroaddeaths)
library(dplyr)
library(ggplot2)
library(lubridate)
#library(ggridges)

crashes <- oz_road_fatal_crash() 
fatalities <- oz_road_fatalities()

Variables available

Crashes

knitr::kable(head(crashes))

crash_id	n_fatalities	month	year	weekday	time	state	crash_type	bus	heavy_rigid_truck	articulated_truck	speed_limit	date	date_time
20233052	1	10	2023	Saturday	23:00:00	Qld	Single	No	No	No	70	2023-10-01	2023-10-01 23:00:00
20233053	1	10	2023	Sunday	16:00:00	Qld	Multiple	No	No	No	60	2023-10-01	2023-10-01 16:00:00
20231113	1	10	2023	Saturday	00:05:00	NSW	Single	No	No	No	100	2023-10-01	2023-10-01 00:05:00
20237008	1	10	2023	Friday	NA	NT	Single	No	No	No	NA	2023-10-01	2023-10-01 00:00:00
20234067	1	10	2023	Sunday	22:28:00	SA	Single	No	No	No	60	2023-10-01	2023-10-01 22:28:00
20235100	1	10	2023	Sunday	20:43:00	WA	Single	No	No	No	NA	2023-10-01	2023-10-01 20:43:00

Fatalities

knitr::kable(head(fatalities))

crash_id	month	year	weekday	time	state	crash_type	bus	heavy_rigid_truck	articulated_truck	speed_limit	road_user	gender	age	date	date_time
20237008	10	2023	Friday	NA	NT	Single	No	No	No	NA	Driver	Female	24	2023-10-01	2023-10-01 00:00:00
20234009	10	2023	Saturday	03:00:00	SA	Single	No	No	No	100	Driver	Male	22	2023-10-01	2023-10-01 03:00:00
20233087	10	2023	Saturday	03:00:00	Qld	Single	No	No	No	80	Driver	Male	19	2023-10-01	2023-10-01 03:00:00
20233149	10	2023	Sunday	03:00:00	Qld	Single	No	No	No	60	Passenger	Male	37	2023-10-01	2023-10-01 03:00:00
20233190	10	2023	Sunday	03:00:00	Qld	Multiple	No	No	No	100	Motorcycle rider	Male	35	2023-10-01	2023-10-01 03:00:00
20233052	10	2023	Saturday	23:00:00	Qld	Single	No	No	No	70	Driver	Female	32	2023-10-01	2023-10-01 23:00:00

Plot crashes by year

crash_plot <- ggplot(crashes,
                     aes(x = year)) +
  geom_line(stat = "count") +
  theme_minimal() +
  labs(title = "Annual number of fatal car accidents per year")

crash_plot

Plot crashes by year and state

crash_plot +
  scale_y_continuous(trans = "log2") +
  facet_wrap(~state) +
   labs(title = "Annual number of fatal car accidents per year and state",
        subtitle = "log2 scale" )

Fatalities by year

fatality_plot <- fatalities %>%
  mutate(year = lubridate::year(date_time)) %>%
  ggplot(aes(x =  year)) +
  geom_line(stat = "count") +
  theme_minimal() +
  ggtitle("Annual number of road fatalities")

fatality_plot

fatality_plot <- fatalities %>%
  filter(gender != "Unspecified") %>%
  mutate(year = lubridate::year(date_time)) %>%
  ggplot(aes(x = age, 
             fill = gender )) +
  geom_density() +
  facet_wrap(~gender) +
  theme_minimal() +
  ggtitle("Distribution of road fatalities by age 1989 to 2024")

fatality_plot

## Warning: Removed 98 rows containing non-finite outside the scale range
## (`stat_density()`).

## Warning: Groups with fewer than two data points have been dropped.

## Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf