R/dynamic_crash_summaries.Rmd

@@ -1,148 +0,0 @@
----
-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")
-```