---
title: "Corona virus in Kenya"
author: "Carlvin.M."
output: 
  flexdashboard::flex_dashboard:
    theme: cosmo
    orientation: rows
    # social: ["facebook", "twitter", "linkedin"]
    source_code: embed
    vertical_layout: fill
---

```{r setup, include=FALSE}
#------------------ Packages ------------------
library(flexdashboard)
#checks if there is data update on the Github version
coronavirus::update_datasets(silence = TRUE)

library(coronavirus)
data(coronavirus)


`%>%` <- 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 ------------------
 coronavirus <- dplyr::add_row(coronavirus, Province.State = "Nairobi", Country.Region = "Kenya", Lat   = -0.0236, Long = 37.9062,date = "2020-03-12", cases = 0, type = "recovered")
 df <-  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
  ) %>%
  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 == "Kenya") %>%
  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(confirmed_cum = cumsum(confirmed),
                death_cum = cumsum(death),
                recovered_cum = cumsum(recovered)) %>%
  dplyr::mutate(active = (ifelse(is.na(confirmed), 0, confirmed) 
                - ifelse(is.na(death), 0, death) -  ifelse(is.na(recovered), 0, recovered)),
                active_cum = cumsum(active)) 


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

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
)
```




### active {.value-box}

```{r}
valueBox(value = paste(format(sum(df$unrecovered, na.rm = TRUE), big.mark = ","), " (",
                       round(100 * sum(df$unrecovered, 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)
```





### 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
)
```


Row
-----------------------------------------------------------------------
### **Cases Distribution by Type (Kenya)**

```{r daily_summary}

cases <- as.numeric(df[1,3:5])
type <- names(df[3:5])

piedata <- as.data.frame(cbind(cases,type))
plotly::plot_ly(piedata, labels=~type, values=~cases, type ='pie',
                 textposition = 'inside',
        textinfo = 'label+percent',
        insidetextfont = list(color = '#FFFFFF'),
        hoverinfo = 'text',
        # marker = list(colors = colors,
        #               line = list(color = '#FFFFFF', width = 1)),
                      #The 'pull' attribute can also be used to create space between the sectors
        showlegend = F)

```


### **Daily cumulative cases by type (Kenya)** 

```{r}
plotly::plot_ly(data = df_daily %>% dplyr::filter(date >= "2020-03-12")) %>%
  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::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("2020-03-13"),
    y = 1,
    text = paste("First case"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -10,
    ay = -90
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-03-26"),
    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("2020-03-18"),
  #   y = 14,
  #   text = paste(
  #     "New containment",
  #     "",
  #     "measures"
  #   ),
  #   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 cases (East African Countries)**
    
```{r}
daily_confirmed <- coronavirus %>%
  dplyr::filter(type == "confirmed") %>%
  dplyr::filter(date >= "2020-03-12") %>%
  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 = ~Kenya,
    size = ~Kenya,
    type = "scatter",
    mode = "lines+markers",
    name = "Kenya"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Uganda,
    size = ~Uganda,
    type = "scatter",
    #mode = "lines+markers",
    name = "Uganda"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Ethiopia,
    size = ~Ethiopia,
    type = "scatter",
    #mode = "lines+markers",
    name = "Ethiopia"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Tanzania,
    size = ~Tanzania,
    type = "scatter",
    #mode = "lines+markers",
    name = "Tanzania"
  ) %>%
  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
    )
  )
```



























































World 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 == "Kenya") %>%
  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(df2) {
    map_object <<- map_object %>%
      addCircleMarkers(
        data = cv_data_for_plot.split[[df2]],
        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[[df2]],
          feature.id = FALSE,
          row.numbers = FALSE,
          zcol = c("type", "cases", "Country.Region", "Province.State")
        ),
        group = df2,
        #                 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 Kenya**

This dashboard provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic for Kenya. This dashboard is built with R using the R Makrdown framework and was adapted from this [dashboard](https://ramikrispin.github.io/coronavirus_dashboard/){target="_blank"} by Rami Krispin.

**Code:**
 [GitHub](https://github.com/CarlvinJerry/COVID-19-Ke/blob/master/CovidKe.Rmd){target="_blank"}.

**Data:**
 [`{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"}.

[Reach out](https://beyondrawdata.rbind.io/#contact) for any comments or feedback.

**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")`.