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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.

Copyright

© Commonwealth of Australia, 2024

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

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