---
title: "COVID-19 Dashboard in Spain"
author: "Adolfo Sanchez Buron"
output:
  flexdashboard::flex_dashboard:
    orientation: rows
    source_code: embed
    vertical_layout: fill
  html_document:
    df_print: paged
---


```{r message=FALSE, warning=FALSE, paged.print=FALSE}
#------------------ Packages ------------------
library(flexdashboard)


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

coronavirus <- read.csv("https://raw.githubusercontent.com/RamiKrispin/coronavirus/master/csv/coronavirus.csv", stringsAsFactors = FALSE)
```



```{r message=FALSE, warning=FALSE, paged.print=FALSE}
#------------------ Parameters ------------------
# Set colors
# https://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "purple"
active_color <- "#1f77b4"
recovered_color <- "forestgreen"
death_color <- "red"
#------------------ Data ------------------
df <- coronavirus %>%
  # dplyr::filter(date == max(date)) %>%
  dplyr::filter(country == "Spain") %>%
  dplyr::group_by(country, type) %>%
  dplyr::summarise(total = sum(cases)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  # dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
  dplyr::mutate(unrecovered = confirmed - 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::filter(country == "Spain") %>%
  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(active = confirmed - death) %>%
  dplyr::mutate(
    confirmed_cum = cumsum(confirmed),
    death_cum = cumsum(death),
    recovered_cum = cumsum(recovered),
    active_cum = cumsum(active)
  )


df_tree <- coronavirus %>%
  dplyr::group_by(country, type) %>%
  dplyr::summarise(total = sum(cases), .groups = "drop") %>%
  dplyr::mutate(type = ifelse(type == "confirmed", "Confirmed", type),
                type = ifelse(type == "recovered", "Recovered", type),
                type = ifelse(type == "death", "Death", type)) %>%
  tidyr::pivot_wider(names_from = type, values_from = total) %>%
  dplyr::mutate(Active = Confirmed - Death - Recovered) %>%
  tidyr::pivot_longer(cols = -country, names_to = "type", values_to = "total")


df_world <- df_tree %>%
  dplyr::group_by(type) %>%
  dplyr::summarise(total = sum(total), .groups = "drop") %>%
  tidyr::pivot_wider(names_from = type, values_from = total)

names(df_world) <- tolower(names(df_world))
```

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

Row {data-width=400}
-----------------------------------------------------------------------

### confirmed {.value-box}

```{r}

valueBox(
  value = paste(format(sum(df$confirmed), big.mark = ","), "", sep = " "),
  caption = "Total confirmed cases",
  icon = "fas fa-user-md",
  color = confirmed_color
)
```



### 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 (death rate)",
  icon = "fas fa-heart-broken",
  color = death_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
)
```





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

### **Daily cumulative cases by type** (Spain only)
    
```{r}
plotly::plot_ly(data = df_daily) %>%
  plotly::add_trace(
    x = ~date,
    # y = ~active_cum,
    y = ~confirmed_cum,
    type = "scatter",
    mode = "lines+markers",
    # name = "Active",
    name = "Confirmed",
    line = list(color = active_color),
    marker = list(color = active_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_annotations(
  # x = as.Date("2020-03-11"),
  # y = 3,
  #     text = paste("First death"),
  #     xref = "x",
  #     yref = "y",
  #     arrowhead = 5,
  #     arrowhead = 3,
  #     arrowsize = 1,
  #     showarrow = TRUE,
  #     ax = -90,
  #     ay = -90
  #   ) %>%
  #   plotly::add_annotations(
  #     x = as.Date("2021-01-03"),
  #     y = 14,
  #     text = paste(
  #       "Lockdown"
  #     ),
  #     xref = "x",
  #     yref = "y",
  #     arrowhead = 5,
  #     arrowhead = 3,
  #     arrowsize = 1,
  #     showarrow = TRUE,
  #     ax = -10,
  #     ay = -90
  #   ) %>%
  plotly::layout(
    title = "",
    yaxis = list(title = "Cumulative number of cases"),
    xaxis = list(title = "Date"),
    legend = list(x = 0.1, y = 0.9),
    hovermode = "compare"
  )
```

Comparison
=======================================================================


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


### **Daily new confirmed cases**
    
```{r}
daily_confirmed <- coronavirus %>%
  dplyr::filter(type == "confirmed") %>%
  dplyr::filter(date >= "2020-02-29") %>%
  dplyr::mutate(country = country) %>%
  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 = ~Spain,
    type = "scatter",
    mode = "lines+markers",
    name = "Spain"
  ) %>%
   plotly::add_trace(
     x = ~date,
     y = ~France,
     type = "scatter",
     mode = "lines+markers",
     name = "France"
   ) %>%
  plotly::add_trace(
     x = ~date,
     y = ~Spain,
     type = "scatter",
     mode = "lines+markers",
    name = "United Kingdom"
   ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Italy,
    type = "scatter",
    mode = "lines+markers",
    name = "Italy"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Germany,
    type = "scatter",
    mode = "lines+markers",
    name = "Germany"
  ) %>%
  #plotly::add_trace(
    #x = ~date,
    #y = ~Netherlands,
    #type = "scatter",
    #mode = "lines+markers",
    #name = "Belgium"
  #) %>%
  plotly::layout(
    title = "",
    legend = list(x = 0.7, y = 0.9),
    yaxis = list(title = "New confirmed 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
    )
  )
```
 
### **Cases distribution by type**

```{r}
df_EU <- coronavirus %>%
  # dplyr::filter(date == max(date)) %>%
  dplyr::filter(country == "Spain" |
    country == "France" |
    country == "Italy" |
    country == "United Kingdom") %>%
  dplyr::group_by(country, type) %>%
  dplyr::summarise(total = sum(cases)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  # dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
  dplyr::mutate(unrecovered = confirmed - 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))

plotly::plot_ly(
  data = df_EU,
  x = ~country,
  #y = ~unrecovered,
  y = ~ confirmed,
  # text =  ~ confirmed,
  # textposition = 'auto',
  type = "bar",
  name = "Confirmed",
  marker = list(color = active_color)
) %>%
  plotly::add_trace(
    y = ~death,
    # text =  ~ death,
    # textposition = 'auto',
    name = "Death",
    marker = list(color = death_color)
  ) %>%
  plotly::layout(
    barmode = "stack",
    yaxis = list(title = "Total cases"),
    xaxis = list(title = ""),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )
```


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

### **World map of cases** (*use + and - icons to zoom in/out*)

```{r}
# map tab added by Art Steinmetz
#install.packages("gdtools")
library(leaflet)
library(leafpop)
library(purrr)
cv_data_for_plot <- coronavirus %>%
  # dplyr::filter(country == "Belgium") %>%
  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$type)
pal <- colorFactor(c("orange", "red", "green"), domain = c("confirmed", "death", "recovered"))
map_object <- leaflet() %>% addProviderTiles(providers$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", "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)
  )
```



Distribution by Type
=======================================================================




```{r}

### Cases Distribution by Type (`r  max(df$date)`)

plotly::plot_ly(
  data = df_tree %>% dplyr::filter(type == "Confirmed"),
  type= "treemap",
  values = ~total,
  labels= ~ country,
  parents=  ~type,
  domain = list(column=0),
  name = "Confirmed",
  textinfo="label+value+percent parent"
) %>%
  plotly::add_trace(
    data = df_tree %>% dplyr::filter(type == "Active"),
    type= "treemap",
    values = ~total,
    labels= ~ country,
    parents=  ~type,
    domain = list(column=1),
    name = "Active",
    textinfo="label+value+percent parent"
  ) %>%
  plotly::add_trace(
    data = df_tree %>% dplyr::filter(type == "Recovered"),
    type= "treemap",
    values = ~total,
    labels= ~ country,
    parents=  ~type,
    domain = list(column=2),
    name = "Recovered",
    textinfo="label+value+percent parent"
  ) %>%
  plotly::add_trace(
    data = df_tree %>% dplyr::filter(type == "Death"),
    type= "treemap",
    values = ~total,
    labels= ~ country,
    parents=  ~type,
    domain = list(column=3),
    name = "Death",
    textinfo="label+value+percent parent"
  ) %>%
  plotly::layout(grid=list(columns=4, rows=1))
  

```



Daily Cases
=======================================================================



```{r}
plotly::plot_ly(data = df_daily,
                x = ~ date,
                y = ~ active_cum, 
                name = 'Active', 
                fillcolor = active_color,
                type = 'scatter',
                mode = 'none', 
                stackgroup = 'one') %>%
  plotly::add_trace(y = ~ recovered_cum,
                    name = "Recovered",
                    fillcolor = recovered_color) %>%
  plotly::add_trace(y = ~ death_cum,
                    name = "Death",
                    fillcolor = death_color) %>%
  plotly::layout(title = "",
                 yaxis = list(title = "Cumulative Number of Cases"),
                 xaxis = list(title = "Date",
                              type = "date"),
                 legend = list(x = 0.1, y = 0.9),
                 hovermode = "compare")
```



Recovery/Death
=======================================================================



```{r}
coronavirus %>% 
  
  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 >= 20000) %>%
  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(title = "Recovery / Death Ratio (Countries with More than 20,000 Cases)",
    yaxis = list(title = "Recovery Rate", ticksuffix = "%"),
                xaxis = list(title = "Death Rate", ticksuffix = "%", 
                             dtick = 1, 
                             tick0 = 0),
                hovermode = "compare")
```






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

**The Coronavirus Dashboard: The case of Spain**

This [Coronavirus dashboard: The case of Spain](https://rpubs.com/AdSan-R/COVID19_Spain) provides an overview of the 2020 Novel Coronavirus COVID-19 epidemic for Spain This dashboard is built with R using the R Makrdown framework and was adapted from these:

[dashboard](https://ramikrispin.github.io/coronavirus_dashboard/){target="_blank"} by Rami Krispin.

[dashboard](https://www.antoinesoetewey.com/files/coronavirus-dashboard.html){target="_blank"} by Antoine Soetewey.

**Code**

The code behind this dashboard is available on [GitHub](https://github.com/AdSan-R/COVID-Dashboard-Spain/blob/main/COVID19.Rmd){target="_blank"}.

**Data**

The input data for this dashboard is the dataset available from the [`{coronavirus}`](https://raw.githubusercontent.com/RamiKrispin/coronavirus/master/csv/coronavirus.csv){target="_blank"} R package. 

The data and dashboard are refreshed on a daily basis.

The raw data is pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus [repository](https://github.com/RamiKrispin/coronavirus-csv){target="_blank"}.

**Blog**

Go back to [Machine Learning 2 Projects](https://www.ml2projects.com/) (blog).
```