---
title: "Covid19 US"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    social: menu
    source_code: embed
    vertical_layout: fill
---

```{r setup, include=FALSE}
#------------------ Packages ------------------
library(flexdashboard)
library(coronavirus)
data(coronavirus)

`%>%` <- magrittr::`%>%`

#------------------ Data ------------------
df <- read.csv("https://covidtracking.com/api/states.csv", stringsAsFactors = FALSE) %>%
  dplyr::left_join(data.frame(state_name = state.name, state = state.abb, stringsAsFactors = FALSE),
                   by = "state") %>%
  dplyr::mutate(text = paste(state_name, "
", 
                             "Total Tested: ", total,"
",
                             "Positive: ", positive,"
",
                             "Negative: ", negative,"
",
                             "Pending: ", pending,"
",
                             "Death:", death))

```





Summary
=======================================================================



### Chart A

```{r}
l <- list(color = plotly::toRGB("white"), width = 2)
# specify some map projection/options
g <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = plotly::toRGB('white')
)


p1 <- plotly::plot_geo(df, locationmode = 'USA-states') %>%
  plotly::add_trace(
    z = ~positive, locations = ~state,
    text = ~ text,
    color = ~positive, colors = 'Reds') %>% 
  plotly::colorbar(title = "Number of Cases") %>%
  plotly::layout(title = 'US Number of Positive Cases',
    geo = g) 

p2 <-  coronavirus::coronavirus %>%
  dplyr::filter(Country.Region == "US") %>%
  dplyr::group_by(Province.State, type) %>%
  dplyr::summarise(total = sum(cases)) %>%
  dplyr::ungroup() %>%
  tidyr::pivot_wider(names_from = type,
                     values_from = total) %>%
  dplyr::mutate(active = confirmed - death - recovered) %>%
  dplyr::arrange(-confirmed) %>%
  dplyr::top_n(n = 20) %>%
  dplyr::mutate(state = factor(Province.State, levels = Province.State)) %>%
  plotly::plot_ly(y = ~state, 
                  x = ~ active, 
                  orientation = 'h',
                  text =  ~ active,
                  textposition = 'auto',
                  type = "bar", 
                  name = "Active",
                  marker = list(color = "#1f77b4")) %>%
  plotly::add_trace(x = ~ recovered,
                    text =  ~ recovered,
                    textposition = 'auto',
                    name = "Recovered",
                    marker = list(color = "forestgreen")) %>%
  plotly::add_trace(x = ~ death, 
                    text =  ~ death,
                    textposition = 'auto',
                    name = "Death",
                    marker = list(color = "red")) %>%
  plotly::layout(title = "Cases Distribution by State (Top 20)",
                 barmode = 'stack',
                 yaxis = list(title = "State"),
                 xaxis = list(title = "Number of Cases"),
                 hovermode = "compare",
                 legend = list(x = 0.65, y = 0.9),
                 margin =  list(
                   l = 20,
                   r = 10,
                   b = 10,
                   t = 30,
                   pad = 2
                 ))


plotly::subplot(p2, p1, nrows = 1)
```

















Map
=======================================================================

**Map**

```{r}
# map tab added by Art Steinmetz
library(leaflet)
library(leafpop)
library(purrr)
cv_data_for_plot <- coronavirus %>% 
  dplyr::filter(Country.Region == "US") %>% 
  dplyr::group_by(Province.State,Lat,Long,type) %>% 
  dplyr::summarise(cases = sum(cases)) %>% 
  dplyr::mutate(log_cases = 2 * log(cases)) %>% 
  dplyr::ungroup()
cv_data_for_plot.split <- cv_data_for_plot %>% split(cv_data_for_plot$type)
pal <- colorFactor(c("orange", "red","green"), domain = c("confirmed", "death","recovered"))
map_object <- leaflet() %>% addProviderTiles(providers$Stamen.Toner)
names(cv_data_for_plot.split) %>%
  purrr::walk( function(df) {
    map_object <<- map_object %>%
      addCircleMarkers(data=cv_data_for_plot.split[[df]],
                 lng=~Long, lat=~Lat,
#                 label=~as.character(cases),
                 color = ~pal(type),
                 stroke = FALSE,
                 fillOpacity = 0.8,
                 radius = ~log_cases,
                 popup =  leafpop::popupTable(cv_data_for_plot.split[[df]],
                                              feature.id = FALSE,
                                              row.numbers = FALSE,
                                              zcol=c("type","cases","Province.State")),
                 group = df,
#                 clusterOptions = markerClusterOptions(removeOutsideVisibleBounds = F),
                 labelOptions = labelOptions(noHide = F,
                                             direction = 'auto'))
  })
map_object %>%
  addLayersControl(
    overlayGroups = names(cv_data_for_plot.split),
    options = layersControlOptions(collapsed = FALSE) 
  )
```