---
title: "Coronavirus in East Africa"
author: "Drew Ddembe"
date: "`r Sys.Date()`" 
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    # social: ["facebook", "twitter", "linkedin"]
    source_code: embed
    vertical_layout: fill
---

```{r setup, include=FALSE}
#------------------ Packages ------------------
library(flexdashboard)
# install.packages("devtools")
devtools::install_github("RamiKrispin/coronavirus", force = TRUE)
library(coronavirus)
data(coronavirus)
#update_datasets()
# View(coronavirus)
# max(coronavirus$date)

`%>%` <- magrittr::`%>%`
#------------------ 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_kenya <- coronavirus %>%
   #dplyr::filter(date == max(date)) %>%
   dplyr::filter(Country.Region == "Kenya") %>%
   dplyr::group_by(Country.Region, type) %>%
   dplyr::summarise(total = sum(cases)) %>%
   tidyr::pivot_wider(
     names_from = type,
     values_from = total
   )
df_tanzania <- coronavirus %>%
   #dplyr::filter(date == max(date)) %>%
   dplyr::filter(Country.Region == "Tanzania") %>%
   dplyr::group_by(Country.Region, type) %>%
   dplyr::summarise(total = sum(cases)) %>%
   tidyr::pivot_wider(
     names_from = type,
     values_from = total
   )
df_rwanda <- coronavirus %>%
   #dplyr::filter(date == max(date)) %>%
   dplyr::filter(Country.Region == "Rwanda") %>%
   dplyr::group_by(Country.Region, type) %>%
   dplyr::summarise(total = sum(cases)) %>%
   tidyr::pivot_wider(
     names_from = type,
     values_from = total
   )
df_burundi <- coronavirus %>%
   #dplyr::filter(date == max(date)) %>%
   dplyr::filter(Country.Region == "Burundi") %>%
   dplyr::group_by(Country.Region, type) %>%
   dplyr::summarise(total = sum(cases)) %>%
   tidyr::pivot_wider(
     names_from = type,
     values_from = total
   )

df <- coronavirus %>%
  #dplyr::filter(date == max(date)) %>%
  dplyr::filter(Country.Region == "Uganda") %>%
  dplyr::group_by(Country.Region, 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.Region == "United Arab Emirates", "UAE", Country.Region)) %>%
  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.Region == "Uganda") %>%
  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)
  )


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


#------------trajectory plot data prep------------

df_ug <- coronavirus %>% dplyr::filter(type == "confirmed", Country.Region == "Uganda") %>%
  dplyr::group_by(date) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::ungroup() %>%
  dplyr::arrange(date) %>%
  dplyr::mutate(ug = cumsum(cases)) %>%
  dplyr::filter(ug > 1)  %>%
  dplyr::select(-cases, -date)
df_ug$index <- 1:nrow(df_ug)

df_tz <- coronavirus %>% dplyr::filter(type == "confirmed", Country.Region == "Tanzania") %>%
  dplyr::group_by(date) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::ungroup() %>%
  dplyr::arrange(date) %>%
  dplyr::mutate(tz = cumsum(cases)) %>%
  dplyr::filter(tz > 1)  %>%
  dplyr::select(-cases, -date)
df_tz$index <- 1:nrow(df_tz)

df_ke <- coronavirus %>% dplyr::filter(type == "confirmed", Country.Region == "Kenya") %>%
  dplyr::group_by(date) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::ungroup() %>%
  dplyr::arrange(date) %>%
  dplyr::mutate(ke = cumsum(cases)) %>%
  dplyr::filter(ke > 1)  %>%
  dplyr::select(-cases, -date)
df_ke$index <- 1:nrow(df_ke)

df_rw <- coronavirus %>% dplyr::filter(type == "confirmed", Country.Region == "Rwanda") %>%
  dplyr::group_by(date) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::ungroup() %>%
  dplyr::arrange(date) %>%
  dplyr::mutate(rw = cumsum(cases)) %>%
  dplyr::filter(rw > 1)  %>%
  dplyr::select(-cases, -date)
df_rw$index <- 1:nrow(df_rw)

df_bu <- coronavirus %>% dplyr::filter(type == "confirmed", Country.Region == "Burundi") %>%
  dplyr::group_by(date) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::ungroup() %>%
  dplyr::arrange(date) %>%
  dplyr::mutate(bu = cumsum(cases)) %>%
  dplyr::filter(bu > 1)  %>%
  dplyr::select(-cases, -date)
df_bu$index <- 1:nrow(df_bu)

df_trajectory <- df_ug %>% 
  dplyr::left_join(df_ke, by = "index") %>%
  dplyr::left_join(df_tz, by = "index") %>%
  dplyr::left_join(df_rw, by = "index") %>%
  dplyr::left_join(df_bu, by = "index")

```

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

Row {data-width=160}
-----------------------------------------------------------------------

### confirmed {.value-box}

```{r}

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



### confirmed (Kenya) {.value-box}

```{r}

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

### confirmed (Tanzania) {.value-box}

```{r}

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

### confirmed (Rwanda) {.value-box}

```{r}

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

### confirmed (Burundi) {.value-box}

```{r}

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

Row {data-width=160}
-----------------------------------------------------------------------

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

### death {.value-box}

```{r}

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

### death {.value-box}

```{r}

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

### death {.value-box}

```{r}

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

### death {.value-box}

```{r}

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


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

### **Daily cumulative cases by type** (Uganda 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("2020-03-30"),
    y = 35,
    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.Region) %>%
  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 = ~Uganda,
    type = "scatter",
    mode = "lines+markers",
    name = "Uganda"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Tanzania,
    type = "scatter",
    mode = "lines+markers",
    name = "Tanzania"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Kenya,
    type = "scatter",
    mode = "lines+markers",
    name = "Kenya"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Rwanda,
    type = "scatter",
    mode = "lines+markers",
    name = "Rwanda"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Burundi,
    type = "scatter",
    mode = "lines+markers",
    name = "Burundi"
  ) %>%
  plotly::layout(
    title = "",
    legend = list(x = 0.1, y = 0.9),
    yaxis = list(title = "Number of 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 daily_summary}
df_EA <- coronavirus %>%
  # dplyr::filter(date == max(date)) %>%
  dplyr::filter(Country.Region == "Uganda" |
    Country.Region == "Tanzania" |
    Country.Region == "Kenya" |
    Country.Region == "Rwanda" |
    Country.Region == "Burundi") %>%
  dplyr::group_by(Country.Region, 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.Region == "United Arab Emirates", "UAE", Country.Region)) %>%
  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_EA,
  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
    )
  )
```

Trajectory
=======================================================================

Row {data-width=800}
-----------------------------------------------------------------------

### Trajectory Plot - East African Countries 

```{r}
plotly::plot_ly(data = df_trajectory) %>%
  plotly::add_lines(x = ~ index,
                    y = ~ ug,
                    name = "Uganda",  line = list(width = 2)) %>%
  plotly::add_lines(x = ~ index,
                    y = ~ ke,
                    line = list(color = "red", width = 2),
                    name = "Kenya") %>%
  plotly::add_lines(x = ~ index,
                    y = ~ tz,
                    name = "Tanzania",  line = list(width = 2)) %>%
    plotly::add_lines(x = ~ index,
                    y = ~ rw,
                    name = "Rwanda",  line = list(width = 2)) %>%
      plotly::add_lines(x = ~ index,
                    y = ~ bu,
                    name = "Burundi",  line = list(width = 2)) %>%
  #plotly::layout(yaxis = list(title = "Cumulative Positive Cases",type = 'scatter', mode = 'lines'),
  plotly::layout(yaxis = list(title = "Cumulative Positive Cases",type = 'log'),
                 xaxis = list(title = "Days since the total positive cases surpass 1"),
                 legend = list(x = 0.7, y = 0.3),
                 hovermode = "compare")
```

  

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

### **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.Region == "Uganda") %>%
  dplyr::filter(cases > 0) %>%
  dplyr::group_by(Country.Region, 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", "Country.Region", "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)
  )
```





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

**The Coronavirus Dashboard: the case of East Africa**

This Coronavirus dashboard: the case of East Africa started out with a focus on Ugana but has not been updated to include and compare with surrounding East African countries. It provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic for the East african countries of Uganda, Kenya, Tanzania, Rwanda and Burundi.

This dashboard is built with R using the R Markdown framework and was adapted from this [dashboard](https://ramikrispin.github.io/coronavirus_dashboard/){target="_blank"} by Rami Krispin.

**Code**

The code behind this dashboard is available on [GitHub](https://github.com/AntoineSoetewey/coronavirus_dashboard){target="_blank"}.

**Data**

The input data for this dashboard is the dataset available from the [`{coronavirus}`](https://github.com/RamiKrispin/coronavirus){target="_blank"} R package. Make sure to download the development version of the package to have the latest data:

```
install.packages("devtools")
devtools::install_github("RamiKrispin/coronavirus")
```

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"}.

**Contact**

For any question or feedback, you can [contact me](www.facebook.com/drew.ddembe). More information about this dashboard can be found in this [article](https://www.statsandr.com/blog/how-to-create-a-simple-coronavirus-dashboard-specific-to-your-country-in-r/).

**Update**

The data is as of `r format(max(coronavirus$date), "%A %B %d, %Y")` and the dashboard has been updated on `r format(Sys.time(), "%A %B %d, %Y")`.