Worldwide

Row

Countries

155

Confirmed

181546

Active Cases

96332 (53.1%)

Recovered

78088 (43%)

Deaths

7126 (3.9%)

Row

Active Cases - Worldwide

USA

Row

States & Territories

53

Confirmed

4632

Active

4530 (97.8%)

Recovered

17 (0.4%)

Deaths

85 (1.8%)

Row

Active Cases - USA

About

COVID-19 Virus Dashboard

This dasbhoard is designed to give a brief overview of the 2019 Novel Coronavirus COVID-19 pandemic.

Source

This dashboard makes use of the R package. The underlying data comes from Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus repository

Feedback

If you have any suggestions, comments, etc., please contact me on Twitter or file an issue.

---
title: "COVID-19"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
    source_code: embed
---

```{r setup, include=FALSE}
# Load libraries
library(flexdashboard)
library(covidvirus)
library(tidyverse)
library(plotly)
library(DT)

# Acquire Data
virus <- covidvirus::get_cases(wide=T)

# define colors
clr_active = 'orange'
clr_confirmed = 'blue'
clr_dead = 'darkred'
clr_recovered = 'forestgreen'
clr_total = 'blue'
clr_overall = 'purple'

# replace NAs with zeros
virus <- virus %>%
  mutate(
    confirmed = ifelse(is.na(confirmed), 0, confirmed),
    death = ifelse(is.na(death), 0, death),
    recovered = ifelse(is.na(recovered), 0, recovered),
    active = confirmed - death - recovered,
    country= case_when(
      country_region == 'United Arab Emirates' ~ 'UAE',
      country_region == 'United States' ~ 'USA',
      country_region == 'North Macedonia' ~ 'N. Macedonia',
      TRUE ~ country_region
    )
  )

# data summaries - country

country_totals <- virus %>%
  group_by(country) %>%
  summarize(
    confirmed = sum(confirmed),
    death = sum(death),
    recovered = sum(recovered),
    active = sum(active)
  ) %>%
  ungroup %>%
  arrange(desc(confirmed)) %>%
  mutate(country = factor(country, levels = country))

daily_totals <- virus %>%
  pivot_longer(
    cols = confirmed:active,
    names_to = 'type',
    values_to = 'cases'
  ) %>%
  group_by(date, type) %>%
  summarize(
    cases = sum(cases)
  ) %>%
  ungroup()


# data summaries - US

usdf <- virus %>%
  filter(country == 'USA') %>%
  select(-province_state, -city_county, -continent, -lat, -long, -country) %>%
  filter(!state %in% c('Diamond Princess', 'Grand Princess'))


us_totals <- usdf %>%
  group_by(state, state_name) %>%
  summarize(
    confirmed = sum(confirmed),
    death = sum(death),
    recovered = sum(recovered),
    active = sum(active)
  ) %>%
  ungroup %>%
  arrange(desc(confirmed)) %>%
  mutate(state = factor(state, levels = state))

us_daily <- usdf %>%
  pivot_longer(
    cols = confirmed:active,
    names_to = 'type',
    values_to = 'cases'
  ) %>%
  group_by(date, type) %>%
  summarize(
    cases = sum(cases)
  ) %>%
  ungroup()

```

Worldwide
==============

Row
-----------------------
### Countries

```{r vbox_world_cntrycount, echo = FALSE}
count_affected_countries <- country_totals %>%
  filter(confirmed > 0) %>%
  select(country, confirmed) %>%
  distinct() %>%
  count() %>%
  pull(n)

valueBox(count_affected_countries, color = clr_overall)
  
```

### Confirmed

```{r vbox_world_confirmed, echo = FALSE}
total_confirmed = sum(country_totals$confirmed)
valueBox(total_confirmed, icon = 'fa-user-md', color = clr_total)
```



### Active Cases
```{r vbox_world_active, echo = FALSE}
active_cases = sum(country_totals$active)
pct_active = round((active_cases / total_confirmed) * 100,1)
valueBox(paste0(active_cases," (",pct_active,"%)"), icon = 'fa-hospital', color = clr_active)
```

### Recovered
```{r vbox_world_recovered, echo = FALSE}
total_recovered = sum(country_totals$recovered)
pct_recovered = round((total_recovered / total_confirmed) * 100,1)
valueBox(paste0(total_recovered," (",pct_recovered,"%)"), icon = 'fa-walking', color = clr_recovered)
```

### Deaths
```{r vbox_world_deaths, echo = FALSE}
total_deaths = sum(country_totals$death)
pct_dead = round((total_deaths / total_confirmed) * 100, 1)
valueBox(paste0(total_deaths," (", pct_dead, "%)"), icon = 'fa-dizzy', color = clr_dead)
```

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

### Active Cases - Worldwide

```{r map_world_choropleth, echo = FALSE}
# specify map projection/options
 g <- list(
   showframe = FALSE,
   showcoastlines = TRUE,
   projection = list(type = 'Robinson')
 )

country_totals2 <- country_totals %>%
  mutate(
    hover = paste0(country, '
', 'Confirmed cases: ', confirmed, '
', 'Deaths: ', death, '
', 'Recovered: ', recovered)
  )

 plot_geo(country_totals2) %>%
   add_trace(
     z = ~active, locations = ~country,
     text = ~hover,
     locationmode = 'country names',
     color = ~active,
     colors = 'Oranges'
   ) %>%
   colorbar(title = 'Active Cases', limits = c(0, max(country_totals$active)), len = 1) %>%
   layout(
     #title = 'Active Covid-19 Virus Cases Worldwide
(Hover for breakdown)',
     geo = g,
     autosize = T
   )
```



USA
=================================
Row
--------------
### States & Territories
```{r vbox_usa_statecount, echo = FALSE}
state_count = usdf %>% filter(confirmed > 0 & !state %in% c('Diamond Princess','Grand Princess') & !is.na(state)) %>% select(state) %>% distinct() %>% count()
valueBox(value = state_count, color = clr_overall)
```


### Confirmed

```{r vbox_usa_confirmed, echo = FALSE}
us_confirmed = sum(usdf$confirmed)
valueBox(us_confirmed, icon = 'fa-user-md', color = clr_total)
```


### Active

```{r vbox_usa_active, echo = FALSE}
us_active_cases = sum(usdf$active)
us_pct_active = round((us_active_cases / us_confirmed) * 100,1)
valueBox(paste0(us_active_cases," (",us_pct_active,"%)"), icon = 'fa-hospital', color = clr_active)
```


### Recovered

```{r vbox_usa_recovered, echo = FALSE}
us_recovered = sum(usdf$recovered)
us_pct_recovered = round((us_recovered / us_confirmed) * 100,1)
valueBox(paste0(us_recovered," (",us_pct_recovered,"%)"), icon = 'fa-walking', color = clr_recovered)
```


### Deaths

```{r vbox_usa_deaths, echo = FALSE}
us_deaths = sum(usdf$death)
us_pct_dead = round((us_deaths / us_confirmed) * 100, 1)
valueBox(paste0(us_deaths," (", us_pct_dead, "%)"), icon = 'fa-dizzy', color = clr_dead)
```

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

### Active Cases - USA

```{r map_us_choropleth, echo = FALSE}

us2 <- us_totals %>%
  mutate(
    hover = paste0(state_name, '
', 'confirmed cases: ', confirmed, '
', 'Deaths: ', death, '
', 'Recovered: ', recovered)
  )

max_cases <- us_totals %>%
  filter(!is.na(state)) %>%
  filter(!state %in% c('Guam','Puerto Rico','Virgin Islands')) %>%
  summarize(
    max_case_count = max(active)
  ) %>%
  pull(max_case_count)

g2 <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white')
)

fig <- plot_geo(us2, locationmode = 'USA-states') %>%
  add_trace(
    z = ~active, text = ~hover, locations = ~state,
    color = ~active, colors = 'Oranges'
  ) %>%
  layout(
    geo = g2
  ) %>%
  colorbar(title = "Active Cases", limits = c(0, max_cases) , len = 1)

config(fig, fillFrame = TRUE)
# bug fix solution: https://github.com/ropensci/plotly/issues/1546 ; see solution by cpsievert on Aug. 30, 2019


```


Trends
===============================

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

### Daily Cumulative Cases by Type - Worldwide

```{r trends_worldwide, echo = FALSE}
dailies <- daily_totals %>%
  pivot_wider(
    names_from = type,
    values_from = cases,
    values_fill = list(cases = 0)
  ) %>%
  arrange(date) %>%
  mutate(
    total_active = cumsum(active),
    total_confirmed = cumsum(confirmed),
    total_dead = cumsum(death),
    total_recovered = cumsum(recovered)
  ) %>%
  select(-active, -confirmed, -death, -recovered)

plot_ly(data = dailies, x = ~date, y = ~total_active, name='Active', type = 'scatter', mode = 'lines+markers', line = list(color = clr_active), marker = list(color = clr_active)) %>%
  add_trace(y = ~total_dead, name = 'Dead', type = 'scatter', mode = 'lines+markers', line = list(color = clr_dead), marker = list(color = clr_dead)) %>%
  add_trace(y = ~total_recovered, name = 'Recovered', type = 'scatter', mode = 'lines+markers', line = list(color= clr_recovered), marker = list(color=clr_recovered)) %>%
  layout(yaxis = list(title = 'Cumulative Total Cases (log scale)', type = 'log'), xaxis = list(title = 'Date'))
```


### Daily Cumulative Cases by Type - USA

```{r trends_usa, echo=FALSE}
us_dailies <- us_daily %>%
  pivot_wider(
    names_from = type,
    values_from = cases,
    values_fill = list(cases = 0)
  ) %>%
  arrange(date) %>%
  mutate(
    total_active = cumsum(active),
    total_confirmed = cumsum(confirmed),
    total_dead = cumsum(death),
    total_recovered = cumsum(recovered)
  ) %>%
  select(-active, -confirmed, -death, -recovered)

plot_ly(data = us_dailies, x = ~date, y = ~total_active, name='Active', type = 'scatter', mode = 'lines+markers', line = list(color = clr_active), marker = list(color = clr_active)) %>%
  add_trace(y = ~total_dead, name = 'Dead', type = 'scatter', mode = 'lines+markers', line = list(color = clr_dead), marker = list(color = clr_dead)) %>%
  add_trace(y = ~total_recovered, name = 'Recovered', type = 'scatter', mode = 'lines+markers', line = list(color= clr_recovered), marker = list(color=clr_recovered)) %>%
  layout(yaxis = list(title = 'Cumulative Total Cases (log scale)', type = 'log'))
```

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

### Active, Recovery, & Death Rates by Country (minimum 50 confirmed cases)


```{r tbl_world, echo=FALSE}

country_rates <- country_totals %>%
  mutate(
    active_pct = round((active / confirmed)*100,1),
    dead_pct = round((death / confirmed) * 100, 1),
    recover_pct = round((recovered / confirmed) * 100, 1)
  ) %>%
  arrange(desc(confirmed)) %>%
  select(country = country, confirmed, active_pct, dead_pct, recover_pct) %>%
  filter(confirmed >= 50)

datatable(country_rates, 
          rownames = F, 
          colnames = c("Country", "Confirmed", "Active Rate", "Death Rate", "Recovery Rate"),
          options = list(dom = 'tip')
          )
```


### Active, Recovery, & Death Rates by State


```{r tbl_USA, echo=FALSE}

state_rates <- us_totals %>%
  mutate(
    active_pct = round((active / confirmed)*100,1),
    dead_pct = round((death / confirmed) * 100, 1),
    recover_pct = round((recovered / confirmed) * 100, 1)
  ) %>%
  arrange(desc(confirmed)) %>%
  select(state, confirmed, active_pct, dead_pct, recover_pct) %>%
  filter(confirmed > 0 & !is.na(state))

datatable(state_rates, 
          rownames = F, 
          colnames = c("State", "Confirmed", "Active Rate", "Death Rate", "Recovery Rate"),
          options = list(dom = 'tip')
          )
```


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

**COVID-19 Virus Dashboard**

This dasbhoard is designed to give a brief overview of the 2019 Novel Coronavirus COVID-19 pandemic.

**Source**

This dashboard makes use of the [](https://github.com/nikdata/covidvirus) R package. The underlying data comes from Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus [repository](https://github.com/CSSEGISandData/COVID-19)

**Feedback**

If you have any suggestions, comments, etc., please contact me on [Twitter](www.twitter.com/nikagarwal) or file an issue.