---
title: "Getting Started"
author: "Adam Gruer (+Kristy Robledo)"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Getting Started}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r}
#| label: setup
#| include: false
#| fig-show: hide
knitr::opts_chunk$set(fig.width=8, fig.height=5) 
```


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

```{r}
#| label: load-library
#| message: false
#| warning: false
#| cache: true
library(ozroaddeaths)
library(dplyr)
library(ggplot2)
library(lubridate)
#library(ggridges)
```

```{r}
#| label: load-data

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

```

## Variables available

### Crashes

```{r}
#| results: asis
knitr::kable(head(crashes))
```

### Fatalities

```{r}
#| label: fatalities first look
#| results: asis
knitr::kable(head(fatalities))
```

### Plot crashes by year

```{r}
#| label: crash-plot-by-year
#| width: 10
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

```{r}
#| label: crash-plot-by-year-and-state
#| width: 10

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

```{r}
#| label: fatalities-plot-by-year
#| width: 10

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

```

```{r}
#| label: fatalities-plot-by-age
#| width: 10

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

```




