---
title: "Covid-19 Cases in Malawi" 
author: "Clinton Nkolokosa"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    source_code: embed
    vertical_layout: fill
---

```{r setup, include=FALSE}
#------------------ Packages ------------------
library(flexdashboard)
library(devtools)
#devtools::install_github("RamiKrispin/coronavirus", force = TRUE)
library(coronavirus)
library(leaflet)
library(leafpop)
library(purrr)
library(rgdal)
library(dplyr)
library(leaflet)
library(leaflet.extras)
library(htmltools)
library(tidyverse)
library(RColorBrewer)
library(scales)
library(lubridate)
library(gganimate)
library(gifski)

`%>%` <- magrittr::`%>%`
#------------------ Parameters ------------------
# Set colors
# https://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "purple"
active_color <- "blue"
recovered_color <- "forestgreen"
death_color <- "red"
#------------------ Data ------------------
df <- coronavirus %>%
  dplyr::filter(country == "Malawi") %>%
  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 == "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 == "Malawi") %>%
  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))

setwd("~/R/Covid visualisation/R")

d<-readOGR(dsn = getwd(),layer = "census_dist")
cd<-read.csv("Covid cases per district (copy).csv") %>% 
  mutate(active_cases= Confirmed-(Deaths+Recovered))
dist<-merge(d,cd,by.x="fid",by.y="Fid")

covid<-read_csv("time-series-19-covid-combined-16-oct.csv")

# Remove unnecessary Columns and Rename
covid <- covid %>% 
  select(Date,country=`Country/Region`, Confirmed, Recovered,Deaths)

# Create a useful data frame
covid_stat<- covid %>% 
  group_by(country) %>% 
  summarise(Death=max(Deaths),Confirmed =max(Confirmed), Recovered=max(Recovered)) %>% 
  mutate(Active_case = Confirmed - Recovered)


```



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

### **Districts with Covid-19 cases** | `r format(Sys.time(), "%A %B %d, %Y")` | Clinty 

```{r}

df <- coronavirus %>%
  dplyr::filter(country == "Malawi" |
    country == "Zambia" |
    country == "Tanzania" |
    country == "Mozambique") %>%
  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 == "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))

bar<-plotly::plot_ly(
  data = df,
  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::add_trace(
    y = ~recovered,
    name = "Recovered",
    marker = list(color = recovered_color)
  ) %>% 
  plotly::layout(
    barmode = "stack",
    yaxis = list(title = "Total cases"),
    xaxis = list(title = ""),
    hovermode = "compare",
    margin = list(
      b = 10,
      t = 10,
      pad = 2
    )
  )

# Map title using CSS styles and HTML

title<-tags$style(HTML(".map-title{
                  transform:translate(-10%,20%);position:fixed!important;
                  left:10%;text-align:left;padding-left:10px;
                  padding-right:10px;background:rgba(255,255,255,0.75);
                  font-weight:bold;font-size:15px}"))

leaflettitle<-tags$div(title, HTML("Matenda a Corona virus ndi opeweka"))

bins<-c(0,1,5,10,50,100,500,1000,1500,2000)
pal<-colorBin("YlOrRd",domain = dist,bins=bins)

labels<-paste("
Covid-19 cases
",
              "",cd$District,"
",
              "Confirmed:",cd$Confirmed,"
",
              "Deaths:",cd$Deaths,"
",
              "Recovered:",cd$Recovered,"
",
              "Active cases:",cd$active_cases,"
",
              sep = " ")

covid.map<-leaflet() %>%
  setView(lng = 33.7,lat =  -13.9, zoom= 6) %>% 
  addProviderTiles(providers$CartoDB)%>% 
  addPolygons(data = dist,
              group = "active_cases",
              weight = 1,
              smoothFactor = 1,
              color = "gray",
              fillOpacity = 1,
              fillColor =~pal(active_cases),
              label = lapply(labels,HTML)) %>% 
  addPolygons(data = dist,
              group = "Confirmed",
              weight = 1,
              smoothFactor = 1,
              color = "gray",
              fillOpacity = 1,
              fillColor =~pal(Confirmed),
              label = lapply(labels,HTML)) %>% 
  addPolygons(data = dist,
              group = "Deaths",
              weight = 1,
              smoothFactor = 1,
              color = "gray",
              fillOpacity = 1,
              fillColor =~pal(Deaths),
              label = lapply(labels,HTML)) %>% 
  addPolygons(data = dist,
              group = "Recovered",
              weight = 1,
              smoothFactor = 1,
              color = "gray",
              fillOpacity = 1,
              fillColor =~pal(Recovered),
              label = lapply(labels,HTML)) %>% 
  addLayersControl(baseGroups = c( "Active", "Confirmed","Deaths","Recovered"),
                   options = layersControlOptions(collapsed = FALSE)) %>% 
  addLegend(pal = pal,values = c(1:2000),
            opacity = 0.7,
            title = 'Covid-19 Cases',
            position = 'bottomright') %>% 
  addScaleBar(position= 'bottomleft') %>% 
  addControl(leaflettitle,position = "topleft",className = "map-title")

covid.map
```


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

Row {data-width=400}
-----------------------------------------------------------------------

### confirmed {.value-box}

```{r}
d <-df %>% 
  filter(country == "Malawi")
valueBox(
  value = paste(format(sum(d$confirmed), big.mark = ","), "", sep = " "),
  caption = "Total confirmed cases",
  icon = "fas fa-user-md",
  color = confirmed_color
)
```


### death {.value-box}

```{r}

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

### recovered {.value-box}

```{r}

valueBox(
  value = paste(format(sum(d$recovered, na.rm = TRUE), big.mark = ","), " ",sep = ""),
  caption = "Total recovered cases",
  color = recovered_color
)
```


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

### **Daily cumulative cases** | Malawi
    
```{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::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}
-------------------------------------


### **Cases distribution by type in districts**
    
```{r}

 cd %>% filter(District != "Zomba City",
               District != "Lilongwe",
               District != "Blantyre") %>% 
# cd %>% filter(District %in% c("Chitipa","Karonga","NkhataBay",
#                               "Rumphi","Mzimba",
#                             "Likoma","Kasungu","Nkhotakota","Ntchisi","Dowa",
#                               "Salima","Lilongwe City",
#                               "Mchinji","Dedza","Ntcheu",
#                               "Mangochi","Machinga","ZombaCity","Chiradzulu",
#                               "Blantyre City","Thyolo","Mulanje","Phalombe",
#                               "Chikwawa","Nsanje","Balaka",
#                               "Neno","Mwanza","Mzuzu City"))%>% 
  
  plotly::plot_ly(
  y = ~ District,
  x = ~ Confirmed,
  type = "bar",
  orientation = 'h',
  name = "Confirmed",
  marker = list(color = confirmed_color)
 ) %>%
  plotly::add_trace(
    x = ~ Deaths,
    name = "Death",
    marker = list(color = death_color)
  ) %>%
  plotly::add_trace(
    x = ~ Recovered,
    name = "Recovered",
    marker = list(color = recovered_color)
  ) %>% 
   plotly::add_trace(
    x = ~ active_cases,
    name = "Active",
    marker = list(color = active_color)
   ) %>% 
  plotly::layout(
    barmode = "stack",
    xaxis = list(title = "Total cases"),
    yaxis = list(title = ""),
    hovermode = "compare",
    margin = list(
      b = 10,
      t = 10,
      pad = 2
    )
  )
  
```
 
### **Cases distribution by type in neighboring countries **

```{r daily_summary}
df <- coronavirus %>%
  dplyr::filter(country == "Malawi" |
    country == "Zambia" |
    country == "Tanzania" |
    country == "Mozambique") %>%
  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 == "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,
  x = ~country,
  y = ~ confirmed,
  type = "bar",
  name = "Confirmed",
  marker = list(color = confirmed_color)
) %>%
  plotly::add_trace(
    y = ~death,
    name = "Death",
    marker = list(color = death_color)
  ) %>%
  plotly::add_trace(
    y = ~recovered,
    name = "Recovered",
    marker = list(color = recovered_color)
  ) %>% 
  plotly::layout(
    barmode = "stack",
    yaxis = list(title = "Total cases"),
    xaxis = list(title = ""),
    hovermode = "compare",
    margin = list(
      b = 10,
      t = 10,
      pad = 2
    )
  )
```


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

**The Covid-19 Dashboard for Malawi**

This [Covid-19 dashboard for Malawi](https://rpubs.com/Clinty) provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) pandemic. 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.


**Data**

The input data for this dashboard is 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"}.


**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")`.