hurricane_normalization_app/dashboard.Rmd.old

---
title: "Hurricane Normalization App"
runtime: shiny
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
    navbar:
      - {title: "Home", icon: "fa-home", href: "#"}
      - {title: "About", icon: "fa-info-circle"}
    theme: 
      version: 4
      bootswatch: journal
---

```{r setup, include=FALSE}
library(flexdashboard)
library(shiny)
library(leaflet)
library(DT)
library(dplyr)
library(DBI)
library(tidyr)
library(ggplot2)
library(plotly)
library(viridis)
library(lubridate)
library(scales)
library(readr)
library(stringr)
library(kableExtra)
library(bslib)

setwd("/home/dylan/Personal/Projects/Hurricane Normalization/")

pop <- read.csv("Data/population_with_projections.csv", stringsAsFactors = F) %>% 
  pivot_longer(
    cols = starts_with("X"),
    names_to = "year",
    values_to = "pop",
  ) %>% 
  mutate(
    year = parse_number(year)
  )

housing <- read.csv("Data/housing_units.csv", stringsAsFactors = F) %>%
  pivot_longer(
    cols = starts_with("X"),
    names_to = "year",
    values_to = "housing"
  ) %>% 
  mutate(
    year = parse_number(year)
  )

counties <- pop %>% 
  left_join(housing %>% select(FIPS, year, housing), by = c("FIPS", "year")) %>% 
  pivot_longer(
    cols = c("pop", "housing"),
    names_to = "metric",
    values_to = "value"
  )

pop <- pop %>% 
  mutate(
    FIPS = ifelse(nchar(FIPS) == 4, paste0("0", FIPS), FIPS),
    state_fips = substr(FIPS, 1, 2),
    county_fips = substr(FIPS, 3, 5)
  ) %>% 
  rename(population = pop) %>% 
  select(!full_county_and_state:County & !county_state & !FIPS)

housing <- housing %>% 
  mutate(
    FIPS = ifelse(nchar(FIPS) == 4, paste0("0", FIPS), FIPS),
    state_fips = substr(FIPS, 1, 2),
    county_fips = substr(FIPS, 3, 5)
  ) %>% 
  rename(housing_units = housing) %>% 
  select(!County.Full:County & !County.State & !FIPS)

metrics.pop_and_housing <- pop %>% 
  left_join(housing, by = c("state_fips", "county_fips", "year"))

donna <- read.csv("R/dataScripts/restructured/hurdat2/gis_counties.csv") 

donna <- donna %>% 
  filter(HURDAT_C_1 == "AL051960") %>% 
  mutate(
    FIPS = as.character(FIPS)
  )

donna <- donna %>% 
  mutate(
    FIPS = case_when(
      str_length(donna$FIPS) == 4 ~ paste0("0", donna$FIPS),
      TRUE ~ donna$FIPS
    ),
    state_fips = substr(FIPS, 1, 2),
    county_fips = substr(FIPS, 3, 5)
  ) %>% 
  rename(year = Year) 

donna <- donna %>% 
  left_join(metrics.pop_and_housing, by = c("state_fips", "county_fips", "year")) 

donna <- donna %>% 
  select(Storm_Name, STATE_NAME, County_Name, state_fips, county_fips, population, housing_units)

donna <- donna %>% 
  mutate(Storm_Name = substr(Storm_Name, 1, 5))

donna <- donna %>% 
  mutate(
    FIPS = paste0(state_fips, county_fips)
  ) %>% 
  select(
    -state_fips, 
    -county_fips
  )

donna <- donna %>% 
  select(
    County = County_Name,
    Population = population,
    Housing = housing_units
  )


# 1-3 FL
# 4-10 NC
# 11-13 CT
# 14-16 NY

```


Dashboard
=============================================

Row {data-height=100}
----------------------------------------------
### {data-width=103}
```{r}
h6("Hurricane")
h3("Galveston 1900")
```

### {data-width=25}

```{r}
valueBox("#18", caption = "Rank by population")
```

### {data-width=25}

```{r}
valueBox("$58.11b", caption = "Loss by population", icon = "fa-user")
```

### {data-width=25}

```{r}
valueBox("#37", caption = "Rank by housing")
```

### {data-width=25}

```{r}
valueBox("$10.63b", caption = "Loss by housing", icon = "fa-house")
```

Row {data-height=400}
----------------------------------------------

### {data-width=50}

```{r}
donna %>% 
  kbl() %>%
  kable_styling(font_size = 16) %>% 
  pack_rows("Florida", 1, 3, label_row_css = "color: #fff") %>% 
  pack_rows("North Carolina", 4, 10, label_row_css = "color: #fff") %>% 
  pack_rows("Connecticut", 11, 13, label_row_css = "color: #fff") %>% 
  pack_rows("New York", 14, 16, label_row_css = "color: #fff") %>% 
  row_spec(1:16, color = "white")
```


### {data-width=50}

```{r}
output$worldMap <- renderLeaflet({
  leaflet() %>% 
    addTiles() %>% 
    setView(lng = 0, lat = 20, zoom = 2) %>% 
    addScaleBar(position = "bottomleft") %>% 
    addLayersControl(
      baseGroups = c("Default", "Satellite", "Terrain"),
      options = layersControlOptions(collapsed = FALSE)
    ) %>% 
    addProviderTiles(providers$Esri.WorldImagery, group = "Satellite") %>% 
    addProviderTiles(providers$Stamen.Terrain, group = "Terrain") %>% 
    hideGroup("Satellite") %>% 
    hideGroup("Terrain")
})

output$map <- renderLeaflet({
  leaflet() %>% 
    addProviderTiles("Stadia.AlidadeSmoothDark", option = providerTileOptions(minZoom = 2, maxZoom = 18)) %>% 
    setView(lng = 0, lat = 20, zoom = 2)
})

#leafletOutput("map", height="100%")

leafletOutput("worldMap", height = "100%")
```

Row {data-height=400}
---

### {data-width=50}

```{r}
lines <- metrics.pop_and_housing %>% 
  filter(state_fips == "01" & county_fips == "087" & year >= 1960)

output$lineChart <- renderPlot({
  ggplot(lines, aes())
})
```

### {data-width=50}
```{r eval=FALSE, include=FALSE}

```