---
title: "Counts"
output: rmarkdown::html_vignette
fig.width: 8
fig.height: 5
vignette: >
  %\VignetteIndexEntry{Counts}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  markdown: 
    wrap: 80
---

## 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:

```{r, message=F}
library(rfars)
library(dplyr)
library(ggplot2)
```

Then pull a year of FARS data for Virginia:

```{r}
myFARS <- get_fars(years = 2021, states = "VA", proceed = T)
```

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

Here we count crashes:

```{r, results='asis'}
my_counts <- counts(
  myFARS,
  what = "crashes",
  interval = c("month")
  )
```

This returns the following dataframe:

```{r, results='asis'}
knitr::kable(my_counts, format = "html")
```

Which we can graph:

```{r}
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:

```{r}
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:

```{r}
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:

```{r}
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:

```{r}
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:

```{r}
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()
