---
title: "Dynamic Crash Summaries"
output:
  html_document:
    toc: true
    toc_depth: 5
    toc_float:
      collapsed: false
      smooth_scroll: true
editor_options:
  chunk_output_type: console
---

# Input Data & Configuration

## Libraries
```{r libs, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
date()
rm(list=ls())
library(tidyverse)
library(RColorBrewer)
library(leaflet)
library(htmltools)
library(htmlwidgets)
library(plotly)
Sys.setenv(LANG = "en-US.UTF-8")
```


## Load TOPS data
```{r loadTOPS, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
load(file = "data/TOPS/TOPS_data.Rda")
load(file = "data/TOPS/vuln_roles.Rda")
load(file = "data/TOPS/retrieve_date.Rda")
load(file = "data/TOPS/injury_severity.Rda")
injury_severity_pal <- colorFactor(palette = injury_severity$color, levels = injury_severity$InjSevName)

```

## Summarize data by county
```{r summarizeData, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}

county_summaries <- TOPS_data %>%
  group_by(CNTYNAME, year, ped_inj_name) %>%
  summarize(count = n())

```

## Make graphs
```{r makeGraphs, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}

#county_focus <- "MILWAUKEE"
county_focus <- unique(county_summaries %>% pull(CNTYNAME))

injury_focus <- c("Suspected Minor Injury", "Suspected Serious Injury", "Fatality")

wisconsin_crash_summary <- ggplotly(
ggplot(data = county_summaries %>% 
         filter(CNTYNAME %in% county_focus) %>% 
         filter(ped_inj_name %in% injury_focus) %>%
         group_by(year, ped_inj_name) %>%
         summarize(count = n()) %>%
         mutate(Year = year, "Injury severity" = ped_inj_name, "Number of crashes" = count)) +
  geom_col(aes(x = Year,
               y = `Number of crashes`,
               fill = `Injury severity`)) +
  scale_fill_manual(values = injury_severity_pal(injury_severity %>% filter(InjSevName %in% injury_focus) %>% pull(InjSevName))) +
  labs(title = "People walking and biking injured in car crashes in Wisconsin")
)

saveWidget(wisconsin_crash_summary, file = "figures/dynamic_crash_summaries/wisconsin_crash_summary.html", 
           selfcontained = TRUE,
           title = "Wisconsin Crash Summary")
```

```{r makeGraphsDropdown, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
# Create a list to store plotly objects for each county, including "All of Wisconsin"
county_plots <- list()

# Generate plot for "All of Wisconsin"
all_wisconsin_data <- county_summaries %>%
  filter(ped_inj_name %in% injury_focus) %>%
  group_by(year, ped_inj_name) %>%
  summarize(count = n()) %>%
  mutate(Year = year, "Injury severity" = ped_inj_name, "Number of crashes" = count)

p_all_wisconsin <- ggplot(all_wisconsin_data, aes(x = Year, y = `Number of crashes`, fill = `Injury severity`)) +
  geom_col() +
  scale_fill_manual(values = injury_severity_pal(injury_severity %>% filter(InjSevName %in% injury_focus) %>% pull(InjSevName))) +
  labs(title = "People walking and biking injured in car crashes in all of Wisconsin") +
  theme_minimal()

county_plots[["All of Wisconsin"]] <- ggplotly(p_all_wisconsin)

# Iterate over each unique county to create plotly objects
for (county in unique(county_summaries$CNTYNAME)) {
  plot_data <- county_summaries %>%
    filter(CNTYNAME == county) %>%
    filter(ped_inj_name %in% injury_focus) %>%
    mutate(Year = year, "Injury severity" = ped_inj_name, "Number of crashes" = count)
  
  p <- ggplot(plot_data, aes(x = Year, y = `Number of crashes`, fill = `Injury severity`)) +
    geom_col() +
    scale_fill_manual(values = injury_severity_pal(injury_severity %>% filter(InjSevName %in% injury_focus) %>% pull(InjSevName))) +
    labs(title = paste("People walking and biking injured in car crashes in", county, "County")) +
    theme_minimal()
  
  county_plots[[county]] <- ggplotly(p)
}

# Render HTML with JavaScript to switch between plots
html_output <- tags$html(
  tags$head(
    tags$title("County Crash Summary"),
    tags$script(
      HTML("
        function showPlot(county) {
          var plots = document.getElementsByClassName('county-plot');
          for (var i = 0; i < plots.length; i++) {
            plots[i].style.display = 'none';
          }
          document.getElementById(county).style.display = 'block';
        }
        window.onload = function() {
          showPlot('All of Wisconsin'); // Default to 'All of Wisconsin'
        }
      ")
    )
  ),
  tags$body(
    tags$h1("County Crash Summary"),
    tags$select(onchange = "showPlot(this.value)",
                lapply(names(county_plots), function(county) {
                  tags$option(value = county, county)
                })
    ),
    lapply(names(county_plots), function(county) {
      tags$div(id = county, class = "county-plot", style = "display:none;", county_plots[[county]])
    })
  )
)

# Save the output to an HTML file
save_html(html_output, file = "figures/dynamic_crash_summaries/county_crash_summaries.html")

# Open the file in a web browser
browseURL("figures/dynamic_crash_summaries/county_crash_summaries.html")
```