index.Rmd

---
title: "Coronavirus in Malaysia"
author: "Kamarul Ariffin Mansor"
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)
library(readr)
urlfile1="https://raw.githubusercontent.com/RamiKrispin/coronavirus/master/csv/coronavirus.csv"
coronavirus<-read_csv(url(urlfile1))
# update_dataset()
# 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 <- coronavirus %>%
  # dplyr::filter(date == max(date)) %>%
  dplyr::filter(country == "Malaysia") %>%
  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 == "Malaysia") %>%
  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=400}
-----------------------------------------------------------------------

### Date Updated {.value-box}  

```{r}
valueBox(
  value = paste(max(coronavirus$date)),
  caption = "Date Updated",
  icon = "fas fa-calendar",
  color = "black"
)
```

### 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
-----------------------------------------------------------------------

### **Daily cumulative cases by type** (Malaysia 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 = 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_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_annotations(
    x = as.Date("2020-01-25"),
    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("2020-03-17"),
    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(
      "Lockdown"
    ),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -10,
    ay = -90
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-04-01"),
    y = 14,
    text = paste(
      "2nd"
    ),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -10,
    ay = -90
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-04-15"),
    y = 12,
    text = paste(
      "3rd (PKP)"
    ),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -10,
    ay = -90
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-05-04"),
    y = 10,
    text = paste(
      "4th (PKPB)"
    ),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -10,
    ay = -90
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-05-13"),
    y = 8,
    text = paste(
      "5th (PKPB)"
    ),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = 30,
    ay = -90
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-06-10"),
    y = 6,
    text = paste(
      "6th (PKPP)"
    ),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = 20,
    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 {data-width=400}
-----------------------------------------------------------------------


### Daily Cumulative Cases by Type

```{r}

# plotly::plot_ly(df_daily, x = ~date, y = ~active_cum, name = 'Active', type = 'scatter', mode = 'none', stackgroup = 'one', fillcolor = "#1f77b4") %>%
# plotly::add_trace(y = ~recovered_cum, name = 'Recovered', fillcolor = "green") %>%
# plotly::add_trace(y = ~death_cum, name = "Death", fillcolor = "red") %>%
#   plotly::layout(title = "",
#          xaxis = list(title = "",
#                       showgrid = FALSE),
#          yaxis = list(title = "Cumulative Number of Cases",
#                       showgrid = FALSE),
#          legend = list(x = 0.1, y = 0.9),
#                  hovermode = "compare")


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"),
                 legend = list(x = 0.1, y = 0.9),
                 hovermode = "compare")
```

### Recovery and Death Rates by Country

```{r}
df_summary <-coronavirus %>%
  # dplyr::filter(country != "Others") %>%
  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 >= 25) %>%
  dplyr::select(country = country, confirmed, recovered, death) %>%
  dplyr::mutate(recover_rate = recovered / confirmed,
         death_rate = death / confirmed)  
df_summary %>%
  DT::datatable(rownames = FALSE,
            colnames = c("Country", "Confirmed", "Recovered", "Death", "Recovery Rate", "Death Rate"),
            options = list(pageLength = nrow(df_summary), dom = 'tip')) %>%
  DT::formatPercentage("recover_rate", 2) %>%
  DT::formatPercentage("death_rate", 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

daily_confirmed %>%
  plotly::plot_ly() %>%
  plotly::add_trace(
    x = ~date,
    y = ~Malaysia,
    type = "scatter",
    mode = "lines+markers",
    name = "Malaysia"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Thailand,
    type = "scatter",
    mode = "lines+markers",
    name = "Thailand"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Indonesia,
    type = "scatter",
    mode = "lines+markers",
    name = "Indonesia"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Singapore,
    type = "scatter",
    mode = "lines+markers",
    name = "Singapore"
  ) %>%
  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_ASIA <- coronavirus %>%
  # dplyr::filter(date == max(date)) %>%
  dplyr::filter(country == "Malaysia" |
    country == "Thailand" |
    country == "Singapore" |
    country == "Indonesia" |
    country == "Brunei" |
    country == "Vietnam" |
    country == "Philippines" |
    country == "Cambodia") %>%
  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_ASIA,
  x = ~country,
  # y = ~unrecovered,
  y = ~ unrecovered,
  # text =  ~ confirmed,
  # textposition = 'auto',
  type = "bar",
  name = "Active",
  marker = list(color = active_color)
) %>%
  # plotly::add_trace(
  #   y = ~confirmed,
    # text =  ~ death,
    # textposition = 'auto',
  #   name = "Confirmed",
  #   marker = list(color = confirmed_color)
  # ) %>%
  plotly::add_trace(
    y = ~recovered,
    # text =  ~ death,
    # textposition = 'auto',
    name = "Recovered",
    marker = list(color = recovered_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
    )
  )
```

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

### **Daily new death cases in Malaysia**

```{r}
daily_confirmed <- coronavirus %>%
  dplyr::filter(type == "death") %>%
  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 = ~Malaysia,
    type = "scatter",
    mode = "lines+markers",
    line = list(color = death_color),
    marker = list(color = death_color),
    name = "Malaysia"
  ) %>%
  plotly::layout(
    title = "",
    # legend = list(x = 0.1, y = 0.9),
    yaxis = list(title = "Number of new death cases"),
    xaxis = list(title = "Date"),
    # paper_bgcolor = "black",
    # plot_bgcolor = "black",
    # font = list(color = 'white'),
    # line = list(color = death_color),
    # marker = list(color = death_color),
    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
library(leaflet)
library(leafpop)
library(purrr)
cv_data_for_plot <- coronavirus %>%
  # dplyr::filter(country == "Malaysia") %>%
  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)
  )
```


Full Data
=======================================================================

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

### **Full Data by Country**  
```{r}
df_summary <-coronavirus %>%
  # dplyr::filter(country != "Others") %>%
  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 >= 25) %>%
  dplyr::select(country = country, confirmed, recovered, death) %>%
  dplyr::mutate(recover_rate = recovered / confirmed,
         death_rate = death / confirmed)  
df_summary %>%
  # head(df_summary,20) %>%
  DT::datatable(rownames = FALSE,
            colnames = c("Country", "Confirmed", "Recovered", "Death", "Recovery Rate", "Death Rate"),
            extensions = 'Buttons',
            options = list(scrollX=TRUE, scrollY="450px",
                           pageLength = 25,
                           # lengthMenu = c(25,50,75,100),
                           # pagelength = nrow(50),
                           searching = TRUE,
                           # fixedColumns = TRUE, autoWidth = TRUE,
                           ordering = TRUE, dom = 'Bfrtip',
                           buttons = c('copy', 'csv', 'excel','pdf'))) %>%
  DT::formatPercentage("recover_rate", 2) %>%
  DT::formatPercentage("death_rate", 2)
```  


### **Full Data on Daily New Death Cases by Country**

```{r}
daily_confirmed <- coronavirus %>%
  dplyr::filter(type == "death") %>%
  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)

daily_confirmed %>%
  # head(df_summary,20) %>%
  DT::datatable(rownames = FALSE,
            #colnames = c("Country", "Confirmed", "Recovered", "Death", "Recovery Rate", "Death Rate"),
            extensions = 'Buttons',
            options = list(scrollX=TRUE, scrollY="450px",
                           pageLength = 25,
                           # lengthMenu = c(25,50,75,100),
                           # pagelength = nrow(50),
                           searching = FALSE,
                           # fixedColumns = TRUE, autoWidth = TRUE,
                           ordering = TRUE, dom = 'Bfrtip',
                           buttons = c('copy', 'csv', 'excel','pdf'))
  )
```
          

Row {data-height=50}
-----------------------------------------------------------------------  
### Note  
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")`.  

**To download the data, just click on the preferred formatted button on the top of this page**

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

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

This [Coronavirus dashboard: the case of Malaysia](https://ariff118.github.io/COVID-19_Malaysia/) provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic for Malaysia. 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**

The code behind this dashboard is available on [GitHub](https://github.com/ariff118/COVID-19_Malaysia){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 at 6PM (Asia/Pacific time).

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"}.

**Information and contact**

More information about this dashboard and how to replicate it for your own country can be found in this [article](https://www.statsandr.com/blog/how-to-create-a-simple-coronavirus-dashboard-specific-to-your-country-in-r/).

For any question or feedback, you can [contact me](https://ariff118.github.io/kamansor.github.io/contact.html/).

**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")`.