---
title: "Coronavirus in Eswatini"
author: "Ermias Amene"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    #social: menu
    theme: cerulean #flatly #cerulean #cosmo #sandstone #united sandstone #yeti #bootstrap #cosmo
    # social: ["facebook", "twitter", "linkedin"]
    source_code: embed
    vertical_layout: fill
---






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

# max(coronavirus$date)

Inst_Load_Pkg <- function(pkg){
  new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
  if (length(new.pkg))
    install.packages(new.pkg, dependencies = TRUE)
  sapply(pkg, require, character.only = TRUE)
}

# usage
packages <- c("ggplot2",  "dplyr", "rmarkdown", "shiny", "tidyverse", 
             "plyr", "dplyr", "knitr", "sjlabelled",  "htmltools","plotly" ,
              "kableExtra","flexdashboard","leaflet","leafpop","purrr")
Inst_Load_Pkg(packages)

library(coronavirus)

#update_dataset()

data(coronavirus)

# A function for calculating 7 day moving average 
mav <- function(x, n=20){stats::filter(x,rep(1/n,n), sides=2)}

`%>%` <- magrittr::`%>%`
#------------------ Parameters ------------------
# Set colors
# https://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "#4169E1"
active_color <- "#4169E1"
recovered_color <-  "#DAA520"
death_color <- "#800000"
#------------------ Data ------------------
df <- coronavirus %>%
  # dplyr::filter(date == max(date)) %>%
  dplyr::filter(country == "Eswatini") %>%
  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 == "Eswatini") %>%
  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))
```

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

Row {data-width=50}
-----------------------------------------------------------------------

### confirmed {.value-box}

```{r}

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

### Recovered {.value-box}

```{r}

valueBox(
  value = paste(format(sum(df$recovered), big.mark = ","), "", sep = " "),
  caption = "Total recovered cases",
  icon = "far fa-laugh",
  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 = "Deaths",
  icon = "fas fa-heart-broken",
  color = death_color
)
```


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

Column {data-width=4000}
-------------------------------------

### **Daily cumulative cases by type** 
    
```{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 = 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_annotations(
    x = as.Date("2020-03-14"),
    y = 1,
    text = paste("First case"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -50,
    ay = -140
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-04-16"),
    y = 1,
    text = paste("First death"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -5,
    ay = -110
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-03-17"),
    y = 30,
    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"
  )
```

### **7 day moving average**
    
```{r}

coronavirus$trace_ma <-mav(coronavirus$cases)

coronavirusF <- coronavirus %>%
  dplyr::filter(date >= "2020-02-29") %>%
  dplyr::filter(country == "Eswatini")%>%
  dplyr::filter(type == "confirmed")
plot_ly(coronavirusF, x = ~date, y = ~trace_ma, 
               name = '7 Day Moving Average',
               type = 'scatter', mode = 'lines',
               line = list(color = confirmed_color))%>%
  
    plotly::layout(
      yaxis = list(title = "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
    )
  )
```


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
#trace_ma <-mav(coronavirus$cases)

daily_confirmed %>%
  plotly::plot_ly() %>%
  plotly::add_trace(
    x = ~date,
    y = ~Eswatini,
    type = "scatter",
    mode = "lines+markers",
    name = "Eswatini"
  ) %>%
  # plotly::add_trace(
  #   x = ~date,
  #   y = ~Kenya,
  #   type = "scatter",
  #   mode = "lines+markers",
  #   name = "Kenya"
  # ) %>%
  # plotly::add_trace(
  #   x = ~date,
  #   y = ~Moldova,
  #   type = "scatter",
  #   mode = "lines+markers",
  #   name = "Moldova"
  # ) %>%


  plotly::add_trace(
    x = ~date,
    y = ~Lesotho,
    type = "scatter",
    mode = "lines+markers",
    name = "Lesotho"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Moldova,
    type = "scatter",
    mode = "lines+markers",
    name = "Moldova"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Kenya,
    type = "scatter",
    mode = "lines+markers",
    name = "Kenya"
  ) %>%
  plotly::layout(
    title = "",
    legend = list(x = 0.1, 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 daily_summary}
df_EA <- coronavirus %>%
  # dplyr::filter(date == max(date)) %>%
  dplyr::filter(country == "Eswatini" |
                  country == "Lesotho" |
                   country == "Moldova" |
                   country == "Kenya") %>%
  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_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::add_trace(
    y = ~recovered,
    # text =  ~ death,
    # textposition = 'auto',
    name = "Recovered",
    marker = list(color = '#6B8E23')
  ) %>%
  
  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
=======================================================================

### **Map of Eswatini** (*No information is available for regional distribution of cases*)

```{r}

cv_data_for_plot <- coronavirus %>%
  # dplyr::filter(country == "Eswatini") %>%
  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$type)
pal <- colorFactor(c("orange", "red", "green"), domain = c("confirmed", "death", "recovered"))
map_object <- leaflet() %>% addProviderTiles(providers$Esri.NatGeoWorldMap)
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"
        )
      )%>%
      setView(lng=31.4659, lat=-26.5225, zoom = 7)
  })

map_object %>%
  addLayersControl(
    overlayGroups = names(cv_data_for_plot.split),
    options = layersControlOptions(collapsed = FALSE)
  )
```





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

**The Coronavirus Dashboard: the case of Eswatini**

This [Eswatini Coronavirus Dashboard](https://rpubs.com/ErmiasA/644480) provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic for Eswatini. 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 and modified for Eswatini by Ermias Amene (ermiaswm@gmail.com).


**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 (and add the function `update_dataset()` after loading the library for future updates):

```
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"}.


**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")`.