---
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)
library(visdat)

`%>%` <- 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")
covid <- read_csv("https://raw.githubusercontent.com/datasets/covid-19/master/data/time-series-19-covid-combined.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)

vis_miss(covid_stat)

```



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(0,5,10,50,100,500,1000,1500,2000,2500)
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(1:2500),
            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) has been developed to build awareness and pass the message to kick out Covid-19, and is actively supporting awareness campaigns led by World Health Organization, Malawi Ministry of Health, Public Health Institute of Malawi, UNICEF and many others. 

Through open-source GIS and statistical software, I am delighted to support health organizations to raise awareness on Covid-19 pandemic. This [dashboard](https://rpubs.com/Clinty) is helping Malawian residents access up-to-date, high resolution, district-level geospatial information on spread of the pandemic. 

I have no doubt that with this type of support coupled with campaigns promoting hand sanitizing, masking up, physical distancing, coughing etiquette and isolating if feeling unwell, together we will win.

By helping people to identify country-level risk of infection, this [dashboard](https://rpubs.com/Clinty) has demonstrated that geovisualization of global crises such as infectious diseases can be enlightening, empowering, and can help curb the spread of online misinformation.

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 my [`GitHub repository`](https://github.com/ClintyNkolokosa/Covid-19-analysis-and-visualisation){target="_blank"} . 


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")`.