---
title: "COVID-19 in Bulgaria"
author: "Metodi Simeonov"
output:
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
#install.packages("htmltools")
#devtools::install_github("RamiKrispin/coronavirus", force = TRUE)
library(coronavirus)
data(coronavirus)

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

#------------------ Parameters ------------------
# Set colors - https://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "#4682B4"
active_color <- "#1f77b4"
recovered_color <- "deepskyblue"
death_color <- "#451208"

#------------------ Data ------------------
df <- coronavirus %>%
  dplyr::filter(country == "Bulgaria") %>%
  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(death), 0, death)) %>%
  dplyr::arrange(-confirmed) %>%
  dplyr::ungroup() %>%
  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 == "Bulgaria") %>%
  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) %>%
  dplyr::mutate(
    confirmed_cum = cumsum(confirmed),
    death_cum = cumsum(death),
    active_cum = cumsum(active)
  )

df1 <- coronavirus %>% dplyr::filter(date == max(date))

df_tree <- coronavirus %>%
  dplyr::filter(country == "Bulgaria" | country == "Romania" | country == "Greece" | country == "Serbia" | country == "North Macedonia") %>%
  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")

```

Summary Tab
=====================================
Row {data-width=350}
-----------------------------------------------------------------------

### Confirmed cases {.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
)

```

### Deaths {.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 = "fa-cross",
  color = death_color
)
```

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

### **Daily Cumulative Cases by Type** (Bulgaria only)

```{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 = 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("2020-03-13"),
    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"
  )

```

Row {.tabset}
-----------------------------------------------------------------------

### Cases Distribution by Type

```{r daily_summary}

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))
  
```

Comparison by Country
=======================================================================

Row {data-width=350}
-------------------------------------

### **Daily New Confirmed Cases**

```{r}

daily_confirmed <- coronavirus %>%
  dplyr::filter(type == "confirmed") %>%
  dplyr::filter(date >= "2020-03-07") %>%
  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 = ~Bulgaria,
    type = "scatter",
    mode = "lines+markers",
    name = "Bulgaria"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Romania,
    type = "scatter",
    mode = "lines+markers",
    name = "Romania"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~`North Macedonia`,
    type = "scatter",
    mode = "lines+markers",
    name = "N.Macedonia"
  ) %>%
    plotly::add_trace(
    x = ~date,
    y = ~Serbia,
    type = "scatter",
    mode = "lines+markers",
    name = "Serbia"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Greece,
    type = "scatter",
    mode = "lines+markers",
    name = "Greece"
  ) %>%
  plotly::layout(
    title = "",
    legend = list(x = 1, y = 0.9),
    yaxis = list(title = "New confirmed cases"),
    xaxis = list(title = "Date"),
    hovermode = "compare",
    margin = list(
      b = 10,
      t = 10,
      pad = 2
    )
  )

```

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

###  **Cases Distribution by Type**

```{r}

df_EU <- coronavirus %>%
  dplyr::filter(country == "Bulgaria" |
    country == "North Macedonia" |
    country == "Romania" |
    country == "Serbia" |
    country == "Greece") %>%
  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(death), 0, death)) %>%
  dplyr::arrange(confirmed) %>%
  dplyr::ungroup() %>%
  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 = ~ confirmed,
  type = "bar",
  name = "Confirmed",
  marker = list(color = active_color)
) %>%
  plotly::add_trace(
    y = ~death,
    name = "Death",
    marker = list(color = death_color)
  ) %>%
  plotly::layout(
    barmode = "stack",
    yaxis = list(title = "Total cases"),
    xaxis = list(title = ""),
    hovermode = "compare",
    margin = list(
      b = 10,
      t = 10,
      pad = 2
    )
  )

```

### **Recovery / Death Ratio**

```{r}

coronavirus %>%

  dplyr::filter(country == "Bulgaria" | country == "Romania" | country == "Greece" | country == "North Macedonia") %>%
  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::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")
```

World Map
=======================================================================

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

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

```{r}

# map tab added by Art Steinmetz

library(leaflet)
library(leafpop)
library(purrr)
cv_data_for_plot <- coronavirus %>%
  # dplyr::filter(country == "Bulgaria") %>%
  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$type)
pal <- colorFactor(c("#4682B4", "#451208", "#02c48d"), domain = c("confirmed", "death", "recovered"))
map_object <- leaflet() %>% addProviderTiles(providers$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 = 0.6,
        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)
  )

```

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

**The COVID-19 Dashboard**

This dashboard provides an overview of the Novel Coronavirus (COVID-19 / SARS-CoV-2) epidemic for Bulgaria and its surrounding countries. This dashboard is built with R using the R Makrdown and flexdashboard framework and was adapted from the dashboard of [Rami Krispin](https://ramikrispin.github.io){target="_blank"}, courtesy of [Antoine Soetewey](https://github.com/AntoineSoetewey){target="_blank"}.

**Code**

The code for the dashboard is available on [GitHub](https://github.com/Met0o/COVID){target="_blank"}.

**Packages**

* Dashboard interface - [flexdashboard](https://rmarkdown.rstudio.com/flexdashboard/)
* Visualization - [plotly](https://plot.ly/r/)
* Data manipulation - [dplyr](https://dplyr.tidyverse.org/), [tidyr](https://tidyr.tidyverse.org/), and [purrr](https://purrr.tidyverse.org/)
* Mapping - [leaflet](https://rstudio.github.io/leaflet/) and [leafpop](https://github.com/r-spatial/leafpop)

**Data**

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

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"}.