---
title: "Covid_19 Tracker"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
    social: ["twitter", "facebook", "menu"]
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(knitr)
library(DT)
library(rpivotTable)
library(ggplot2)
library(plotly)
library(dplyr)
library(openintro)
library(highcharter)
library(ggvis)

library("tidyverse")
library('purrr')
library('readr')

options(stringsAsFactors = FALSE);
```

```{r}
url <- 'https://covid19.who.int/WHO-COVID-19-global-data.csv'
data <- read_csv(url)
dta <- data[complete.cases(data), ]
data <- data %>% arrange(Date_reported, Country_code)
```

```{r}
myColors <- c("blue", "#FFC125", "darkgreen", "darkorange")
```


Interactive Covid_19 Data Visualization
==============================================

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



### Total cases so far in US as of `r data$Date_reported[nrow(data)] `
```{r}
valueBox(filter(data, Country_code == "US") %>% select(New_cases) %>% sum(),
         icon = 'fa-building')
```

### Total Deaths in US as of `r data$Date_reported[nrow(data)] `

```{r}
valueBox(filter(data, Country_code == "US") %>% select(New_deaths) %>% sum(),
         icon = 'fa-building')
```



### Total Cases in INDIA as of `r data$Date_reported[nrow(data)] `
```{r}
valueBox(filter(data, Country_code == "IN") %>% select(New_cases) %>% sum(),
         icon = 'fa-building')
```

### Total Cases in BRAZIL as of `r data$Date_reported[nrow(data)] `
```{r}
valueBox(filter(data, Country_code == "BR") %>% select(New_cases) %>% sum(),
         icon = 'fa-building')
```



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


### Total cases by Countries with more than 100,000 cases as of `r data$Date_reported[nrow(data)] `

```{r}
p1 <- data %>% group_by(Country) %>% 
    summarize(Total=sum(New_cases)) %>%
    filter(Total > 100000) %>%
    plot_ly(x = ~Country,
            y = ~Total,
            color = rainbow(data %>% group_by(Country) %>% 
    summarize(Total=sum(New_cases)) %>%
    filter(Total > 100000) %>%count()),
            type = 'bar') %>%
layout(xaxis = list(title = "Total cases by Country"),
       yaxis = list(title = 'Total number of cases'))
p1
```

### Top Countries with more than 10,000 Total Deaths as of `r data$Date_reported[nrow(data)] `

```{r}
p1 <- data %>% group_by(Country) %>% 
    summarize(Total=sum(New_deaths)) %>%
    filter(Total > 10000) %>%
    plot_ly(x = ~Country,
            y = ~Total,
            color = rainbow(16),
            type = 'bar') %>%
layout(xaxis = list(title = "Total Deaths by Country"),
       yaxis = list(title = 'Total number of Deaths'))
p1
```


### Top Countries with more than 100 Average Daily Deaths  as of `r data$Date_reported[nrow(data)] `
```{r}
p3 <- data %>% group_by(Country) %>% 
    summarize(mean=mean(New_deaths)) %>%
    filter(mean > 100) %>%
    plot_ly(labels = ~Country,
            values = ~mean,
            marker = list(colors = myColors)) %>%
            add_pie(hole=0.3) %>%
layout(xaxis = list(zeroline = F,
                    showline = F,
                    showticklabels = F,
                    showgrid = F),
       yaxis = list(zeroline = F,
                    showline = F,
                    showticklabels = F,
                    showgrid = F))

p3
```


### Top Countries with Case Fatality Rate (CFR) at least more than 10% as of `r data$Date_reported[nrow(data)] `
```{r}
p4 <- data %>% group_by(Country) %>% 
    summarize(CFR=round(sum(New_deaths)*100/sum(New_cases)), digits = 2) %>%
    filter(CFR >=10) %>%
    plot_ly(labels = ~Country,
            values = ~CFR,
            marker = list(colors = myColors)) %>%
            add_pie(hole=0.3) %>%
layout(xaxis = list(zeroline = F,
                    showline = F,
                    showticklabels = F,
                    showgrid = F),
       yaxis = list(zeroline = F,
                    showline = F,
                    showticklabels = F,
                    showgrid = F))

p4
```


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

### Map
```{r}
#Cumulative_deaths <- filter(data, Date_reported==data$Date_reported[nrow(data)]) 
highchart() %>% hc_title(text = "Total deaths by Country") %>%
                hc_subtitle(text = "source: WHO-COVID-19-global-data.csv" ) %>%
                hc_add_series_map(worldgeojson, filter(data, Date_reported==data$Date_reported[nrow(data)]),
                                  name = "Country",
                                  value = "Cumulative_deaths",
                                  joinBy = c("woename", "Country")) %>%
                hc_mapNavigation((enabled = T))
```


Data Table
===============================================================

```{r}
data_CFR <- data %>% mutate(CFR = round(Cumulative_deaths/Cumulative_cases, digits=2))
datatable(data_CFR, 
          caption = "WHO Covid_19 Data",
          rownames = T,
          filter = "top",
          options = list(pageLength = 25))

```



Pivot Table
====================================================================

```{r}
data_CFR <- data %>% mutate(Cumulative_CFR = Cumulative_deaths/Cumulative_cases)
rpivotTable(data_CFR,
             aggregatorName = "Last",
             rows = "Date_reported",
             cols = "Country",
             rendererName = "Heatmap")
```


Summary {data-orientation=columns}
=============================================================

Column
-------------------------------------------------------------



### Total number of Covid_19 cases in the world so far
```{r}

valueBox(filter(data, Date_reported==data$Date_reported[nrow(data)]) %>% select(Cumulative_cases) %>% sum(),
        icon = "fa-user")
```



### Total number of Deaths in the world so far
```{r}

valueBox(filter(data, Date_reported==data$Date_reported[nrow(data)]) %>% select(Cumulative_deaths) %>% sum(),
        icon = "fa-user")
```

### Total number of cases in WHO Western Pacific Region (WPRO)
```{r}

valueBox(filter(data, WHO_region=="WPRO") %>% select(New_cases) %>% sum(),
        icon = "fa-user")
```

### Total number of cases in WHO South East Asian Region (SEARO)
```{r}

valueBox(filter(data, WHO_region=="SEARO") %>% select(New_cases) %>% sum(),
        icon = "fa-user")
```

### Total number of cases in WHO American Region (AMRO)
```{r}

valueBox(filter(data, WHO_region=="AMRO") %>% select(New_cases) %>% sum(),
        icon = "fa-user")
```

### Total number of cases in WHO Europe Region (EURO)
```{r}

valueBox(filter(data, WHO_region=="EURO") %>% select(New_cases) %>% sum(),
        icon = "fa-user")
```

### Total number of cases in WHO African Region (AFRO)
```{r}

valueBox(filter(data, WHO_region=="AFRO") %>% select(New_cases) %>% sum(),
        icon = "fa-user")
```

### Total number of cases in WHO Eastern Mediterranean Region (EMRO)
```{r}

valueBox(filter(data, WHO_region=="EMRO") %>% select(New_cases) %>% sum(),
        icon = "fa-user")
```

### Total number of cases in WHO OtherRegion (Other)
```{r}

valueBox(filter(data, WHO_region=="Other") %>% select(New_cases) %>% sum(),
        icon = "fa-user")
```


Column
---------------------------------------------
Report

* Top 5 countries in terms of total number of cases: 
```{r}
filter(data, Date_reported == data$Date_reported[nrow(data)]) %>% top_n(5, Cumulative_cases)

```

* Top 5 countries in terms of total number of deaths: 
```{r}
filter(data, Date_reported == data$Date_reported[nrow(data)]) %>% top_n(5, Cumulative_deaths)

```

* Countries with more than 10% of Case Fatality Rate (CFR) are:
```{r}
data %>% group_by(Country) %>% 
    summarize(CFR=sum(New_deaths)*100/sum(New_cases)) %>%
    filter(CFR > 10)
```
About Report
==================================================================

Created By: Sanjeeva Reddy Dodlapati

Confidentiality: NOT AT ALL

Feel free to share it and use it

Please cite me as: Sanjeeva Reddy Dodlapati (url: https://rpubs.com/sdodlapa/663277)