---
title: Visualizations
output: 
 flexdashboard::flex_dashboard:
    source_code: embed
    orientation: columns
    theme: journal
    vertical_layout: fill
    navbar:
      - { title: "Home", href: "https://fionalav.github.io/p8105_final_project/", align: right }
      - { icon: fa-github fa-lg, href: "https://github.com/Fionalav/p8105_final_project", align: right }

---
    
```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(rvest)
library(httr)
library(janitor)
library(stringr)
library(readxl)
library(plotly)
library(dplyr)
library(viridisLite)
library(forecast)
library(flexdashboard)
library(fiftystater)
library(RColorBrewer)
library(broom)
library(knitr)
library(forcats)
```


```{r import_data, include=FALSE}
cod_data = read_csv("./data/NCHS_-_Potentially_Excess_Deaths_from_the_Five_Leading_Causes_of_Death.csv") %>%
  clean_names() %>%
  na.omit() %>%
  filter(!(state == "United States")) %>%
  separate(., percent_potentially_excess_deaths, into = c("percent_excess_death"), sep = "%") %>% 
  mutate(percent_excess_death = as.numeric(percent_excess_death), mortality = observed_deaths/population * 10000, mortality = as.numeric(mortality)) %>% 
  select(year, age_range, cause_of_death, state, locality, observed_deaths, population, expected_deaths, potentially_excess_deaths, percent_excess_death, mortality, hhs_region)
```


Column {.tabset .tabset-fade}
-----------------------------------------------------------------------

### Rural-urban Disparty of Mean Percent Excess Death Across Public Health Regions 

```{r}

plotly1 = cod_data %>%
  select(state, locality, percent_excess_death, hhs_region, cause_of_death) %>%
  filter(locality != "All") %>% 
  group_by(cause_of_death, locality, hhs_region) %>% 
  summarise(mean_ped = mean(percent_excess_death)) %>% 
  mutate(hhs_region = as.factor(hhs_region)) %>% 
  group_by(cause_of_death) %>% 
  mutate(mean_ped_order = mean(mean_ped)) %>% 
  ungroup(cause_of_death) %>% 
  mutate(cause_of_death = fct_reorder(cause_of_death,mean_ped_order))

plotly1 %>%
plot_ly(
    x = ~hhs_region, 
    y = ~mean_ped, 
    color = ~cause_of_death, 
    frame = ~locality, 
    text = ~mean_ped, 
    hoverinfo = "text",
    type = 'bar',
    mode = 'markers'
  ) %>% 
  layout(
         xaxis = list(title = "Pulic Health Regions"),
         yaxis = list(title = "Mean Percent Excess Death"))

```   

### U.S. Map with Mean Percent of Excess Death Rate Distribution
```{r}
map_cod_data = cod_data %>%
  filter(locality == "Metropolitan") %>%
  select(state, locality, percent_excess_death) %>% 
  group_by(state) %>% 
  summarise(mean_ped = mean(percent_excess_death)) %>% 
  dplyr::filter(!(state == "District of\nColumbia"))
  

map = as.tibble(fifty_states) %>%
  group_by(id) %>% 
  summarize(clong = mean(long), clat = mean(lat)) %>% 
  filter(!(id == "district of columbia"))

df <- cbind(map, state.abb, state.center, rate = unique(map_cod_data$mean_ped))

ggplot(df, aes(map_id = id)) + 
    geom_map(aes(fill = rate), map = fifty_states) + 
    expand_limits(x = fifty_states$long, y = fifty_states$lat) + 
    labs(x = "", y = "") +
    theme(panel.background = element_blank(), 
          axis.text.x = element_blank(), 
          axis.text.y = element_blank(), 
          axis.ticks = element_blank()) + 
    geom_text(aes(x = clong, y = clat, label = state.abb)) +
  scale_fill_gradient(low="gold", high="red")


```