EquityAnalysisDashboard.Rmd

---
title: "San Mateo Extreme Heat Equity Analysis Dashboard"
runtime: shiny
output:
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
    source_code: embed
---

<style>
.datatables{
overflow: auto;
}
</style>
```{r global, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(htmltools)
library(sf)
library(leaflet)
library(mapboxapi)
smc_indicator_cbgs_2019 <- readRDS("Exheat_Spring/smc_indicator_cbgs_2019.rds")
smc_thresholds_cbgs_df <- readRDS("SanMateo/new_exheat_tm_30yr_thresholds.rds")
smc_thresholds_cbgs_df_max <- readRDS("SanMateo/new_exheat_tm_30yr_thresholds_max.rds")
```
Inputs {.sidebar}
-------------------------------------
**Directions** Select which indicators you would like to see compared with extreme heat. Select Threshold Temperature (degrees Fahrenheit). Select whether you want to use extreme heat projections summarized as a maximum or an average across the ten models. Wait a couple of seconds and the plot will update. Finally, select whether you want to compare different extreme heat ranges by actual population size or by proportional break down.
```{r, context="server"}
indicator_options = c("Race", "Income", "Ethnicity", "Age")
selectInput(
  "indicator",
  label = "Indicator:",
  choices = indicator_options,
  selected = indicator_options[3]
)
```
```{r, context="server"}
threshold_options = c("90", "95", "100", "105", "110")
selectInput(
  "threshold",
  label = "Temperature Threshold:",
  choices = threshold_options,
  selected = threshold_options[2]
)
```
```{r}
summary_options = c("Average of Climate Models","Maximum of Climate Models")
selectInput(
  "summary",
  label = "Statistic Type: ",
  choices = summary_options,
  selected = summary_options[1]
)
```
```{r}
plot_options = c("Actual","Proportional")
selectInput(
  "plot_option",
  label = "Plot Type: ",
  choices = plot_options,
  selected = plot_options[1]
)
```

Row {data-height=600}
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### {.no-padding}
```{r}
#plotOutput("plot")
```

```{r, context="server"}
renderPlot({
  variable <- input$indicator
  threshold <- input$threshold
  summary <- input$summary
  plot_option <- input$plot_option
  
  smc_selected_threshold <-
    smc_thresholds_cbgs_df %>%
    dplyr::filter(threshold_temp %in% c(paste0("above_",threshold))) %>%
    rename(
      weighted_exheat = weighted_tm_ave,
      geography_id = GEOID
    )
  
  if(summary == "Maximum of Climate Models") {
    smc_selected_threshold <-
      smc_thresholds_cbgs_df_max %>%
      dplyr::filter(threshold_temp %in% c(paste0("above_",threshold))) %>%
      rename(
        weighted_exheat = weighted_tm_max,
        geography_id = GEOID
      )
  }
  variable_exheat_cbgs <-
    smc_indicator_cbgs_2019 %>%
    dplyr::filter(indicator_name == variable) %>% 
    left_join(smc_selected_threshold, by = c("CBG" = "geography_id")) %>%
    dplyr::filter(!is.na(weighted_exheat))
  
  variable_exheat_quantiles <-
    as.data.frame(quantile(variable_exheat_cbgs$weighted_exheat, seq(0,1,length.out = 6),na.rm = T))
  colnames(variable_exheat_quantiles) <- "qt"
  
  variable_bin_names <-
    c(paste0(round(variable_exheat_quantiles[2,],2), " or less"), paste0(round(variable_exheat_quantiles[2,],2)," to ",round(variable_exheat_quantiles[3,],2)), paste0(round(variable_exheat_quantiles[3,],2)," to ",round(variable_exheat_quantiles[4,],2)), paste0(round(variable_exheat_quantiles[4,],2)," to ",round(variable_exheat_quantiles[5,],2)),paste0(round(variable_exheat_quantiles[5,],2), " to ",round(variable_exheat_quantiles[6,],2)))
  
  variable_exbins <-
    variable_exheat_cbgs %>%
    mutate(
      exheat_bin = case_when(
        weighted_exheat <= variable_exheat_quantiles[2,] ~ variable_bin_names[1],
        weighted_exheat > variable_exheat_quantiles[2,] & weighted_exheat <= variable_exheat_quantiles[3,] ~ variable_bin_names[2],
        weighted_exheat > variable_exheat_quantiles[3,] & weighted_exheat <= variable_exheat_quantiles[4,] ~ variable_bin_names[3],
        weighted_exheat > variable_exheat_quantiles[4,] & weighted_exheat <= variable_exheat_quantiles[5,] ~ variable_bin_names[4],
        weighted_exheat > variable_exheat_quantiles[5,] ~ variable_bin_names[5]
      )
    )
  
  position_choice = "stack"
  if(plot_option == "Proportional") {
    position_choice = "fill"
  }
  
  ggplot(
    variable_exbins %>%
      group_by(indicator, exheat_bin) %>%
      summarize(estimate = sum(estimate))
  ) + geom_bar(
    aes(
      x = exheat_bin %>% factor(levels = rev(variable_bin_names)),
      y = estimate,
      fill = indicator
    ),
    stat = "identity",
    position = position_choice
  ) +
    labs(
      x = paste0("Number of Days above ",threshold," degrees F"),
      y = "Population",
      title = paste0(variable," by extreme heat days (",threshold," degrees f)")
    ) +
    coord_flip() +
    theme(
      legend.position = "bottom",
      legend.direction = "vertical"
    )
})
```

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