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---
title: "Covid-19"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    source_code: embed
    vertical_layout: fill
---

```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(coronavirus)
library(plotly)
library(viridis)
library(wbstats)
library(DT)
library(leaflet)

#devtools::install_github("RamiKrispin/coronavirus", force = TRUE)

data("coronavirus")

daily_df = 
  coronavirus %>% 
  group_by(date, type) %>% 
  summarize(n = sum(cases)) 

max_date = max(coronavirus$date) 

# pull in population
pop = wb(indicator = "SP.POP.TOTL", startdate = 2018, enddate = 2019) %>% 
  select(date, country, value) %>% 
  filter(date == "2018") %>% 
  mutate(country = recode(country, "United States" = "US", "Czech Republic" = "Czechia", "Korea, Rep." = "Korea, South", "Iran, Islamic Rep." = "Iran", "Russian Federation" = "Russia")) 
 #%>% rename("Country.Region" = "country")

rates = left_join(coronavirus, pop, by = "country") %>% 
  group_by(country, type) %>% 
  summarize(total = sum(cases), population = mean(value)) %>% 
  pivot_wider(names_from = type, values_from = total) %>% 
  filter(confirmed > 5000) %>% 
  mutate(case_rate = confirmed/population, 
         death_rate = death/confirmed) 

# US boxes
us_total = coronavirus %>% 
  select(country, type, cases) %>% 
  filter(type == "confirmed", country == "US") %>% 
  group_by(type) %>% 
  summarize(n = sum(cases))
us_total_color = "#1f77b4"

us_dead = coronavirus %>% 
  filter(type == "death",  country == "US") %>% 
  select(country, type, cases)  %>% 
  group_by(type) %>% 
  summarize(n = sum(cases))

us_new_cases = coronavirus %>% 
    filter(type == "confirmed",  country == "US", date == max_date) %>% 
    select(country, type, cases)  %>% 
    group_by(type) %>% 
    summarize(n = sum(cases))

us_new_deaths = coronavirus %>% 
    filter(type == "death",  country == "US", date == max_date) %>% 
    select(country, type, cases)  %>% 
    group_by(type) %>% 
    summarize(n = sum(cases))

# World boxes
world_total = coronavirus %>% 
  select(type, cases) %>% 
  filter(type == "confirmed") %>% 
  group_by(type) %>% 
  summarize(n = sum(cases))

world_total_color = "#38d6b7"

world_dead = coronavirus %>% 
  filter(type == "death") %>% 
  select(country, type, cases)  %>% 
  group_by(type) %>% 
  summarize(n = sum(cases))

world_dead_color = "#FF9754"

world_new_cases = coronavirus %>% 
    filter(type == "confirmed", date == max_date) %>% 
    select(country, type, cases)  %>% 
    group_by(type) %>% 
    summarize(n = sum(cases))

world_new_cases_color = "#6f727a"

world_new_deaths = coronavirus %>% 
    filter(type == "death", date == max_date) %>% 
    select(country, type, cases)  %>% 
    group_by(type) %>% 
    summarize(n = sum(cases))

world_new_deaths_color = "#FE595E"

```

Home
======================================================================
Row {data-height = 100}
-----------------------------------------------------------------------
### total {.value-box}
```{r}
valueBox(value = format(world_total$n, big.mark = ","), caption = "Confirmed Cases", color = world_total_color, icon = "fas fa-thermometer-half")
```

### new cases {.value-box}
```{r}
valueBox(value = format(world_new_cases$n, big.mark = ","), caption = "New Cases", color = world_new_cases_color, icon = "fas fa-thermometer-full")
```

### deaths {.value-box}
```{r}
valueBox(value = format(world_dead$n, big.mark = ","), caption = "Deaths", color = world_dead_color)
```


### new deaths {.value-box}
```{r}
valueBox(value = format(world_new_deaths$n, big.mark = ","), caption = "New Deaths", color = world_new_deaths_color)
```

Row
-----------------------------------------------------------------------

### Worldwide Cases by Day

```{r fig.height = 7.5}
daily_df %>% 
  plot_ly(x = ~date, 
          y = ~n, 
          type = "scatter", 
          color = ~type, 
          mode = "markers", 
          stackgroup = "one") %>% 
  layout(title = "",
        yaxis = list(title = "# Cases"),
        xaxis = list(title = "Date"),
        legend = list(x = 0.1, y = 0.9),
        hovermode = "compare")

```


Row
-----------------------------------------------------------------------

### Cases and Deaths by Country

```{r fig.height = 10}

rates %>% 
  plot_ly(y = ~ round(100*case_rate, 2), 
          x = ~ round(100*death_rate, 2), 
          size = ~ (confirmed), 
          color = ~ country, 
          type = "scatter", 
          mode = "markers", 
          marker = list(sizemode = "diameter", opacity = 0.5), 
          hoverinfo = "text", 
          text = ~paste("", country, "
 Confirmed Cases: ", paste(format(confirmed, big.mark = "," )),
                                            "
 Case Rate: ", paste(round(100 * case_rate, 2), "%", sep = ""),
                                            "
 Death Rate: ", paste(round(100 * death_rate, 2), "%", sep = ""))
        ) %>% 
  layout(yaxis = list(title = "Cases per Population", ticksuffix = "%"),
                xaxis = list(title = "Death Rate", ticksuffix = "%", 
                             dtick = 1, 
                             tick0 = 0),
                hovermode = "compare")
  

```

### Status Update for `r max_date`

```{r fig.height = 10}

rates %>% 
  select(-population) %>% 
  arrange(-confirmed) %>% 
  rename("Country" = "country", "Confirmed" = "confirmed", "Deaths" = "death", "Case Rate" = "case_rate", "Death Rate" = "death_rate", "Recovered" = "recovered") %>% 
  select(Country, Confirmed, Recovered, Deaths, everything()) %>% 
  datatable(options = list(dom = 't')) %>% 
    formatPercentage("Case Rate", 3) %>% 
    formatPercentage("Death Rate", 3)
  
```