---
title: "Coronavirus"
output: 
  flexdashboard::flex_dashboard:
    social: menu
    source_code: embed
---

```{r setup, include=FALSE}
#------------------ Packages ------------------
library(flexdashboard)
#checks if there is data update on the Github version
coronavirus::update_dataset(silence = TRUE)
#remotes::install_github("RamiKrispin/coronavirus", force = TRUE)
library(coronavirus)
library(covid19italy)
data(coronavirus)
data(italy_total)
`%>%` <- magrittr::`%>%`
#------------------ Parameters ------------------
# Set colors
# https://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "coral"
death_color <- "slategray"
active_color <- "cornflowerblue"
recovered_color <- "mediumseagreen"
#------------------ Data ------------------
df <- coronavirus %>% 
  dplyr::group_by(country, type) %>%
  dplyr::summarise(total = sum(cases)) %>%
  tidyr::pivot_wider(names_from =  type, values_from = total) %>%
  dplyr::mutate(active = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
  dplyr::arrange(-confirmed) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(country = dplyr::if_else(country == "United Arab Emirates", "UAE", country)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
  dplyr::mutate(country = trimws(country)) %>%
  dplyr::mutate(country = factor(country, levels = country))
df_daily <- coronavirus %>% 
  dplyr::group_by(date, type) %>%
  dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
  tidyr::pivot_wider(names_from = type, values_from = total) %>%
  dplyr::arrange(date) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(active =  confirmed - death - recovered) %>%
  dplyr::mutate(confirmed_cum = cumsum(confirmed),
                death_cum = cumsum(death),
                recovered_cum = cumsum(recovered),
                active_cum = cumsum(active))
  
df1 <- coronavirus %>% dplyr::filter(date == max(date))
#------------trajectory plot data prep------------
df_china <- coronavirus %>% dplyr::filter(type == "confirmed", country == "China") %>%
  dplyr::group_by(date) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::ungroup() %>%
  dplyr::arrange(date) %>%
  dplyr::mutate(china = cumsum(cases)) %>%
  dplyr::filter(china > 100)  %>%
  dplyr::select(-cases, -date)
df_china$index <- 1:nrow(df_china)
df_uk <- coronavirus %>% dplyr::filter(type == "confirmed", country == "United Kingdom") %>%
  dplyr::group_by(date) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::ungroup() %>%
  dplyr::arrange(date) %>%
  dplyr::mutate(uk = cumsum(cases)) %>%
  dplyr::filter(uk > 100)  %>%
  dplyr::select(-cases, -date)
df_uk$index <- 1:nrow(df_uk)
df_fr <- coronavirus %>% dplyr::filter(type == "confirmed", country == "France") %>%
  dplyr::group_by(date) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::ungroup() %>%
  dplyr::arrange(date) %>%
  dplyr::mutate(france = cumsum(cases)) %>%
  dplyr::filter(france > 100)  %>%
  dplyr::select(-cases, -date)
df_fr$index <- 1:nrow(df_fr)
df_us <- coronavirus %>% dplyr::filter(type == "confirmed", country == "US") %>%
  dplyr::group_by(date) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::ungroup() %>%
  dplyr::arrange(date) %>%
  dplyr::mutate(us = cumsum(cases)) %>%
  dplyr::filter(us > 100)  %>%
  dplyr::select(-cases, -date)
df_us$index <- 1:nrow(df_us)
df_iran <- coronavirus %>% dplyr::filter(type == "confirmed", country == "Iran") %>%
  dplyr::group_by(date) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::ungroup() %>%
  dplyr::arrange(date) %>%
  dplyr::mutate(iran = cumsum(cases)) %>%
  dplyr::filter(iran > 100)  %>%
  dplyr::select(-cases, -date)
df_iran$index <- 1:nrow(df_iran)
df_sk <- coronavirus %>% dplyr::filter(type == "confirmed", country == "Korea, South") %>%
  dplyr::group_by(date) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::ungroup() %>%
  dplyr::arrange(date) %>%
  dplyr::mutate(sk = cumsum(cases)) %>%
  dplyr::filter(sk > 100)  %>%
  dplyr::select(-cases, -date)
df_sk$index <- 1:nrow(df_sk)
df_nz <- coronavirus %>% dplyr::filter(type == "confirmed", country == "New Zealand") %>%
  dplyr::group_by(date) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::ungroup() %>%
  dplyr::arrange(date) %>%
  dplyr::mutate(nz = cumsum(cases)) %>%
  dplyr::filter(nz > 100)  %>%
  dplyr::select(-cases, -date)
df_nz$index <- 1:nrow(df_nz)
df_spain <- coronavirus %>% dplyr::filter(type == "confirmed", country == "Spain") %>%
  dplyr::group_by(date) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::ungroup() %>%
  dplyr::arrange(date) %>%
  dplyr::mutate(spain = cumsum(cases)) %>%
  dplyr::filter(spain > 100)  %>%
  dplyr::select(-cases, -date)
df_spain$index <- 1:nrow(df_spain)
df_italy <- italy_total %>% dplyr::select(date, italy = total_currently_positive) %>%
  dplyr::filter(italy > 100) %>%
  dplyr::select(-date)
df_italy$index <- 1:nrow(df_italy)
df_trajectory <- df_china %>% 
  dplyr::left_join(df_italy, by = "index") %>%
  dplyr::left_join(df_iran, by = "index") %>%
  dplyr::left_join(df_sk, by = "index") %>%
  dplyr::left_join(df_us, by = "index") %>%
  dplyr::left_join(df_fr, by = "index") %>%
  dplyr::left_join(df_uk, by = "index") %>%
  dplyr::left_join(df_spain, by = "index") %>%
  dplyr::left_join(df_nz, by = "index")
```

Summary
=======================================================================
Column {data-width=100}
-----------------------------------------------------------------------

### death {.value-box}

```{r}
valueBox(value = paste(format(sum(df$death, na.rm = TRUE), big.mark = ","), " (",
                       round(100 * sum(df$death, na.rm = TRUE) / sum(df$confirmed), 1), 
                       "%)", sep = ""),
         caption = "Death Cases", 
         icon = "fas fa-heart-broken", 
         color = death_color)
```

### active {.value-box}

```{r}
valueBox(value = paste(format(sum(df$active, na.rm = TRUE), big.mark = ","), " (",
                       round(100 * sum(df$active, na.rm = TRUE) / sum(df$confirmed), 1), 
                       "%)", sep = ""), 
         caption = "Active Cases", #icon = "fas fa-ambulance", 
         color = active_color)
```

### recovered {.value-box}

```{r}
valueBox(value = paste(format(sum(df$recovered, na.rm = TRUE), big.mark = ","), " (",
                       round(100 * sum(df$recovered, na.rm = TRUE) / sum(df$confirmed), 1), 
                       "%)", sep = ""), 
         caption = "Recovered Cases", #icon = "fas fa-heartbeat", 
         color = recovered_color)
```


### Top Death Rates by Country (>10 deaths) {data-height=200}

```{r, fig.height=5}
df_summary <-coronavirus %>% 
  # dplyr::filter(Country.Region != "Others") %>%
  dplyr::group_by(country, type) %>%
  dplyr::summarise(total_cases = sum(cases)) %>%
  tidyr::pivot_wider(names_from = type, values_from = total_cases) %>%
  dplyr::arrange(- confirmed) %>%
  dplyr::filter(death >= 10) %>%
  dplyr::select(country = country
                , confirmed, death) %>%
  dplyr::mutate(death_rate = death / confirmed) %>%
  dplyr::select(country, death_rate) %>%
  dplyr::arrange(- death_rate)
df_summary %>%
  DT::datatable(rownames = FALSE,
            colnames = c("Country", "Death Rate"),
            options = list(pageLength = nrow(df_summary), dom = 'tip', style = "font-size: 75%; width: 75%")) %>%
  DT::formatPercentage("death_rate", 2)
```


Column {data-width=500, .tabset}
-----------------------------------------------------------------------

### Cases by Type (Top 20 Countries)

```{r daily_summary}
plotly::plot_ly(data = df[1:20,], 
                x = ~ country, 
                y = ~ active, 
                type = "bar", 
                name = "Active",
                marker = list(color = active_color)) %>%
  plotly::add_trace(y = ~ death, 
                    name = "Death",
                    marker = list(color = death_color)) %>%
  plotly::add_trace(y = ~ recovered, 
                    name = "Recovered",
                    marker = list(color = recovered_color)) %>%
  plotly::layout(barmode = 'stack',
                 yaxis = list(title = "Total Cases"),
#                       type = "log"),
                 xaxis = list(title = ""),
                 hovermode = "compare",
                  margin =  list(
                   # l = 60,
                   # r = 40,
                   b = 10,
                   t = 10,
                   pad = 2
                 ))
```

### Log Cases by Type (Top 20 Countries)

```{r daily_summary log}
plotly::plot_ly(data = df[1:20,], 
                x = ~ country, 
                y = ~ active, 
                type = "bar", 
                name = "Active",
                marker = list(color = active_color)) %>%
  plotly::add_trace(y = ~ death, 
                    name = "Death",
                    marker = list(color = death_color)) %>%
  plotly::add_trace(y = ~ recovered, 
                    name = "Recovered",
                    marker = list(color = recovered_color)) %>%
  plotly::layout(barmode = 'stack',
                 yaxis = list(title = "Total Cases",
                       type = "log"),
                 xaxis = list(title = ""),
                 hovermode = "compare",
                  margin =  list(
                   # l = 60,
                   # r = 40,
                   b = 10,
                   t = 10,
                   pad = 2
                 ))
```

Column {data-width=500, .tabset}
-----------------------------------------------------------------------


### Daily Cumulative Cases
    
```{r}
plotly::plot_ly(data = df_daily) %>%
  plotly::add_trace(x = ~ date,
                    y = ~ confirmed_cum,
                    type = "scatter",
                    mode = "lines+markers",
                    name = "Confirmed",
                    line = list(color = confirmed_color),
                    marker = list(color = confirmed_color)) %>%
  plotly::add_trace(x = ~ date,
                    y = ~ death_cum,
                    type = "scatter",
                    mode = 'lines+markers',
                    name = "Death",
                    line = list(color = death_color),
                    marker = list(color = death_color)) %>%
   plotly::add_trace(x = ~ date,
                    y = ~ active_cum,
                    type = "scatter",
                    mode = 'lines+markers',
                    name = "Active",
                    line = list(color = active_color),
                    marker = list(color = active_color)) %>%
     plotly::add_trace(x = ~ date,
                    y = ~ recovered_cum,
                    type = "scatter",
                    mode = 'lines+markers',
                    name = "Recovered",
                    line = list(color = recovered_color),
                    marker = list(color = recovered_color)) %>%
  plotly::layout(title = "",
                 yaxis = list(title = "Cumulative Number of Cases"),
                 xaxis = list(title = "Date"),
                 legend = list(x = 0.1, y = 0.9),
                 hovermode = "compare")
  
```

### Log Daily Cumulative Cases
    
```{r}
plotly::plot_ly(data = df_daily) %>%
  plotly::add_trace(x = ~ date,
                    y = ~ confirmed_cum,
                    type = "scatter",
                    mode = "lines+markers",
                    name = "Confirmed",
                    line = list(color = confirmed_color),
                    marker = list(color = confirmed_color)) %>%
  plotly::add_trace(x = ~ date,
                    y = ~ death_cum,
                    type = "scatter",
                    mode = 'lines+markers',
                    name = "Death",
                    line = list(color = death_color),
                    marker = list(color = death_color)) %>%
   plotly::add_trace(x = ~ date,
                    y = ~ active_cum,
                    type = "scatter",
                    mode = 'lines+markers',
                    name = "Active",
                    line = list(color = active_color),
                    marker = list(color = active_color)) %>%
     plotly::add_trace(x = ~ date,
                    y = ~ recovered_cum,
                    type = "scatter",
                    mode = 'lines+markers',
                    name = "Recovered",
                    line = list(color = recovered_color),
                    marker = list(color = recovered_color)) %>%
  plotly::layout(title = "",
                 yaxis = list(title = "Cumulative Number of Cases (log scaled)", type="log"),
                 xaxis = list(title = "Date"),
                 legend = list(x = 0.1, y = 0.9),
                 hovermode = "compare")
  
```

UK vs NZ
=======================================================================
Column
-----------------------------------------------------------------------

### New Daily Cases (UK)

```{r}
daily_uk_cases <- coronavirus %>%
  dplyr::filter(country == "United Kingdom") %>%
  tidyr::pivot_wider(names_from=type, values_from=cases) %>%
  dplyr::arrange(country, date) %>%
  dplyr::group_by(country, province, date) %>%
  dplyr::mutate(active = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
  dplyr::summarise(confirmed = sum(confirmed), death = sum(death)
                   , recovered = sum(recovered), active=sum(active)) %>%
  dplyr::mutate(confirmed_cum = cumsum(confirmed), death_cum = cumsum(death),
                recovered_cum = cumsum(recovered), active_cum = cumsum(active)) %>%
  dplyr::mutate(case_fatality_rate = death_cum/confirmed_cum)
  
daily_nz_cases <- coronavirus %>%
  tidyr::pivot_wider(names_from=type, values_from=cases) %>%
  dplyr::arrange(country, date) %>%
  dplyr::group_by(country, date) %>%
  dplyr::mutate(active = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
  dplyr::summarise(confirmed = sum(confirmed), death = sum(death),
                   recovered = sum(recovered), active = sum(active)) %>%
  dplyr::mutate(confirmed_cum = cumsum(confirmed), death_cum = cumsum(death),
                recovered_cum = cumsum(recovered), active_cum = cumsum(active)) %>%
  dplyr::mutate(case_fatality_rate = death_cum/confirmed_cum) %>%
  dplyr::filter(country == "New Zealand")
plotly::plot_ly(data = daily_uk_cases) %>%
  plotly::add_trace(x = ~ date,
                    y = ~ recovered,
                    type = "bar",
                    #mode = "lines+markers",
                    name = "Recovered",
                    line = list(color = recovered_color),
                    marker = list(color = recovered_color)) %>%
    plotly::add_trace(x = ~ date,
                    y = ~ death,
                    type = "bar",
                    #mode = "lines+markers",
                    name = "Death",
                    line = list(color = death_color),
                    marker = list(color = death_color)) %>%
  plotly::add_trace(x = ~ date,
                    y = ~ active,
                    type = "bar",
                    #mode = "lines+markers",
                    name = "Active",
                    line = list(color = active_color),
                    marker = list(color = active_color)) %>%
  plotly::layout(title = "",
                 yaxis = list(title = "Daily New Cases"),
                 xaxis = list(title = "Date"),
                 legend = list(x = 0.1, y = 0.9),
                 hovermode = "compare", 
                 barmode = 'stack')
```

### Cumulative Cases (UK)

```{r}
plotly::plot_ly(data = daily_uk_cases) %>%
  plotly::add_trace(x = ~ date,
                    y = ~ confirmed_cum,
                    type = "scatter",
                    mode = "lines+markers",
                    name = "Confirmed",
                    line = list(color = confirmed_color),
                    marker = list(color = confirmed_color)) %>%
  plotly::add_trace(x = ~ date,
                    y = ~ death_cum,
                    type = "scatter",
                    mode = 'lines+markers',
                    name = "Death",
                    line = list(color = death_color),
                    marker = list(color = death_color)) %>%
   plotly::add_trace(x = ~ date,
                    y = ~ active_cum,
                    type = "scatter",
                    mode = 'lines+markers',
                    name = "Active",
                    line = list(color = active_color),
                    marker = list(color = active_color)) %>%
     plotly::add_trace(x = ~ date,
                    y = ~ recovered_cum,
                    type = "scatter",
                    mode = 'lines+markers',
                    name = "Recovered",
                    line = list(color = recovered_color),
                    marker = list(color = recovered_color)) %>%
  plotly::layout(title = "",
                 yaxis = list(title = "Cumulative Number of Cases"),
                 xaxis = list(title = "Date"),
                 legend = list(x = 0.1, y = 0.9),
                 hovermode = "compare")
```

Column
-----------------------------------------------------------------------

### New Daily Cases (NZ)

```{r}
plotly::plot_ly(data = daily_nz_cases) %>%
  plotly::add_trace(x = ~ date,
                    y = ~ recovered,
                    type = "bar",
                    #mode = "lines+markers",
                    name = "Recovered",
                    line = list(color = recovered_color),
                    marker = list(color = recovered_color)) %>%
    plotly::add_trace(x = ~ date,
                    y = ~ death,
                    type = "bar",
                    #mode = "lines+markers",
                    name = "Death",
                    line = list(color = death_color),
                    marker = list(color = death_color)) %>%
  plotly::add_trace(x = ~ date,
                    y = ~ active,
                    type = "bar",
                    #mode = "lines+markers",
                    name = "Active",
                    line = list(color = active_color),
                    marker = list(color = active_color)) %>%
  plotly::layout(title = "",
                 yaxis = list(title = "Daily New Cases"),
                 xaxis = list(title = "Date"),
                 legend = list(x = 0.1, y = 0.9),
                 hovermode = "compare", 
                 barmode = 'stack')
```

### Cumulative Cases (NZ)

```{r}
plotly::plot_ly(data = daily_nz_cases) %>%
  plotly::add_trace(x = ~ date,
                    y = ~ confirmed_cum,
                    type = "scatter",
                    mode = "lines+markers",
                    name = "Confirmed",
                    line = list(color = confirmed_color),
                    marker = list(color = confirmed_color)) %>%
  plotly::add_trace(x = ~ date,
                    y = ~ death_cum,
                    type = "scatter",
                    mode = 'lines+markers',
                    name = "Death",
                    line = list(color = death_color),
                    marker = list(color = death_color)) %>%
   plotly::add_trace(x = ~ date,
                    y = ~ active_cum,
                    type = "scatter",
                    mode = 'lines+markers',
                    name = "Active",
                    line = list(color = active_color),
                    marker = list(color = active_color)) %>%
     plotly::add_trace(x = ~ date,
                    y = ~ recovered_cum,
                    type = "scatter",
                    mode = 'lines+markers',
                    name = "Recovered",
                    line = list(color = recovered_color),
                    marker = list(color = recovered_color)) %>%
  plotly::layout(title = "",
                 yaxis = list(title = "Cumulative Number of Cases"),
                 xaxis = list(title = "Date"),
                 legend = list(x = 0.1, y = 0.9),
                 hovermode = "compare")
```

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

**Map**

```{r}
# map tab added by Art Steinmetz
library(leaflet)
library(leafpop)
library(purrr)
cv_data_for_plot <- coronavirus %>% 
  dplyr::filter(cases > 0) %>% 
  dplyr::group_by(country,province,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$type)
pal <- colorFactor(c("coral", "slategray","mediumseagreen"), domain = c("confirmed", "death","recovered"))
map_object <- leaflet() %>% addProviderTiles(providers$CartoDB.Positron)
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","country","province")),
                 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) 
  )
```

Trends
=======================================================================


Column {data-width=400}
-------------------------------------
    
### New Cases - Top 15 Countries (`r  max(coronavirus$date)`)
    
```{r}
max_date <- max(coronavirus$date)
coronavirus %>% 
  dplyr::filter(type == "confirmed", date == max_date) %>%
  dplyr::group_by(country) %>%
  dplyr::summarise(total_cases = sum(cases)) %>%
  dplyr::arrange(-total_cases) %>%
  dplyr::mutate(country = factor(country, levels = country)) %>%
  dplyr::ungroup() %>%
  dplyr::top_n(n = 15, wt = total_cases) %>%
  plotly::plot_ly(x = ~ country,
                  y = ~ total_cases,
                  text = ~ total_cases,
                  textposition = 'auto',
                  type = "bar") %>%
  plotly::layout(yaxis = list(title = "Number of Cases"),
                 xaxis = list(title = ""),
                 margin =  list(
                   l = 10,
                   r = 10,
                   b = 10,
                   t = 10,
                   pad = 2
                 ))
```


### Daily New Cases - USA vs. Rest of the World
    
```{r}
daily_confirmed <- coronavirus %>%
  dplyr::filter(type == "confirmed") %>%
  dplyr::mutate(country = dplyr::if_else(country == "US", 
                                        "US", 
                                        "Rest of the World")) %>%
  dplyr::group_by(date, country) %>%
  dplyr::summarise(total = sum(cases)) %>% 
  dplyr::ungroup() %>%
  tidyr::pivot_wider(names_from = country, values_from = total) 
daily_death <- coronavirus %>%
  dplyr::filter(type == "death") %>%
  dplyr::mutate(country = dplyr::if_else(country == "US", 
                                         "US", 
                                         "Rest of the World")) %>%
  dplyr::group_by(date, country) %>%
  dplyr::summarise(total = sum(cases)) %>% 
  dplyr::ungroup() %>%
  tidyr::pivot_wider(names_from = country, values_from = total) 
#----------------------------------------
# Plotting the data
daily_confirmed %>%
  plotly::plot_ly() %>% 
  plotly::add_trace(x = ~ date, 
                    y = ~ US, 
                    type = "scatter", 
                    mode = "lines+markers",
                    name = "USA") %>% 
  plotly::add_trace(x = ~ date, 
                    y = ~ `Rest of the World`, 
                    type = "scatter", 
                    mode = "lines+markers",
                    name = "Rest of the World") %>% 
 
  plotly::layout(title = "",
                 legend = list(x = 0.1, y = 0.9),
                 yaxis = list(title = "Number of New Cases"),
                 xaxis = list(title = "Date"),
                 # paper_bgcolor = "black",
                 # plot_bgcolor = "black",
                 # font = list(color = 'white'),
                 hovermode = "compare",
                 margin =  list(
                   # l = 60,
                   # r = 40,
                   b = 10,
                   t = 10,
                   pad = 2
                 ))
```
   

Column {data-width=400}
-------------------------------------

### Log Trajectory Plot - Major Countries 

```{r}
plotly::plot_ly(data = df_trajectory) %>%
  plotly::add_lines(x = ~ index,
                    y = ~ china,
                    name = "China",  line = list(width = 2)) %>%
  plotly::add_lines(x = ~ index,
                    y = ~ italy,
                    line = list(color = "red", width = 2),
                    name = "Italy") %>%
  plotly::add_lines(x = ~ index,
                    y = ~ us,
                    name = "United States",  line = list(width = 2)) %>%
    plotly::add_lines(x = ~ index,
                    y = ~ uk,
                    name = "United Kingdom",  line = list(width = 2)) %>%
      plotly::add_lines(x = ~ index,
                    y = ~ france,
                    name = "France",  line = list(width = 2)) %>%
  plotly::add_lines(x = ~ index,
                    y = ~ iran,
                    name = "Iran",  line = list(color = "orange", width = 2)) %>%
  plotly::add_lines(x = ~ index,
                    y = ~ sk,
                    name = "South Korea",  line = list(width = 2)) %>%
  plotly::add_lines(x = ~ index,
                    y = ~ nz,
                    name = "New Zealand", line = list(width = 2)) %>%
  plotly::add_lines(x = ~ index,
                    y = ~ spain,
                    name = "Spain") %>%
  plotly::layout(yaxis = list(title = "Cumulative Positive Cases",type = "log"),
                 xaxis = list(title = "Days since the total positive cases surpass 100"),
                 legend = list(x = 0.7, y = 0.3),
                 hovermode = "compare")
```
   
### Recovery and Death Rates for Countries with at Least 5000 Cases

```{r}
coronavirus::coronavirus %>% 
  # dplyr::filter(Country.Region != "Others") %>%
  dplyr::group_by(country, type) %>%
  dplyr::summarise(total_cases = sum(cases)) %>%
  tidyr::pivot_wider(names_from = type, values_from = total_cases) %>%
  dplyr::arrange(- confirmed) %>%
  dplyr::filter(confirmed >= 5000) %>%
  dplyr::mutate(recover_rate = recovered / confirmed,
                death_rate = death / confirmed) %>% 
  dplyr::mutate(recover_rate = dplyr::if_else(is.na(recover_rate), 0, recover_rate),
                death_rate = dplyr::if_else(is.na(death_rate), 0, death_rate)) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(confirmed_normal = as.numeric(confirmed) / max(as.numeric(confirmed))) %>%
  plotly::plot_ly(y = ~ round(100 * recover_rate, 1),
                  x = ~ round(100 * death_rate, 1),
                  size = ~  log(confirmed),
                  sizes = c(5, 70),
                  type = 'scatter', mode = 'markers',
                  color = ~ country,
                  marker = list(sizemode = 'diameter' , opacity = 0.5),
                  hoverinfo = 'text',
                  text = ~paste("
", country, 
                                "
 Confirmed Cases: ", confirmed,
                                "
 Recovery Rate: ", paste(round(100 * recover_rate, 1), "%", sep = ""),
                                "
 Death Rate: ",  paste(round(100 * death_rate, 1), "%", sep = ""))
                 ) %>%
  plotly::layout(yaxis = list(title = "Recovery Rate", ticksuffix = "%"),
                xaxis = list(title = "Death Rate", ticksuffix = "%", 
                             dtick = 1, 
                             tick0 = 0),
                hovermode = "compare")
  
```   



About
=======================================================================

**The Coronavirus Dashboard**

This Coronavirus dashboard provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic. This dashboard is built with R using the Rmakrdown framework and can easily reproduce by others. The code behind the dashboard available [here](https://github.com/olivermills/covid19). The layout and most of the code was taken from Rami Krispin's [dashboard](https://github.com/RamiKrispin/coronavirus).

**Data**

The input data for this dashboard is the [coronavirus](https://github.com/RamiKrispin/coronavirus) R package (dev version). The data and dashboard is refreshed on a daily bases. The raw data pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus [repository](https://github.com/RamiKrispin/coronavirus-csv)

**Packages**

* Dashboard interface - the [flexdashboard](https://rmarkdown.rstudio.com/flexdashboard/) package. 
* Visualization - the [plotly](https://plot.ly/r/) package for the plots and [leaflet](https://rstudio.github.io/leaflet/) for the map
* Data manipulation - [dplyr](https://dplyr.tidyverse.org/), and [tidyr](https://tidyr.tidyverse.org/)

**Deployment and reproducibly**

The dashboard was deployed to Github docs. If you wish to deploy and/or modify the dashboard on your Github account, you can apply the following steps:

* Fork the dashboard [repository](https://github.com/RamiKrispin/coronavirus_dashboard), or
* Clone it and push it to your Github package
* Here some general guidance about deployment of flexdashboard on Github page - [link](https://github.com/pbatey/flexdashboard-example)

For any question or feedback, you can either open an [issue](https://github.com/RamiKrispin/coronavirus_dashboard/issues) or contact Rami Krispin on [Twitter](https://twitter.com/Rami_Krispin).

**Contribution** 

The **Map** tab was contributed by [Art Steinmetz](https://github.com/apsteinmetz) on this [pull request](https://github.com/RamiKrispin/coronavirus_dashboard/pull/1). Thanks Art!