In etc5523-2020/COVID19bb: COVID-19 Global Analysis Shiny Application

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 9,
  fig.height = 7.5
)

Introduction

Aim of the app

This vignette is used to introduce the COVID-19 Global Analysis Shiny Application, the way to launch the app and the guidelines for it use. As for the shiny app, it is intended to be used as an intuitive and easy-to-understand visualization tool for COVID-19 research, users can observe the total distribution of confirmed cases, death cases and recovered cases around the world by selecting the type of the distribution they want to view, as well as exploring the trends of mortality, diagnosis recovered counts among provinces or states of different countries all over the world

Installation

First, we need to install the package COVID19bb from the github.

install.packages("devtools")
devtools::install_github("https://github.com/etc5523-2020/r-package-assessment-Yiwen-Zhang-259")

Launch the app

After installing the required packages, users can use launch function to launch the app.

library(COVID19bb)
library(tidyverse)
library(ggplot2)
library(shiny)
library(plotly)
library(leaflet)
library(DT)
library(highcharter)
library(dplyr)
library(shinythemes)

launch_app()

Guidelines for use

Exploring the Distribution of cases by type

COVID19bb package provides a highchart map to show the distribution of confirmed cases, recovered cases and the deaths cases of COVID-19. Meanwhile, it also contains the relevant dataset covid_19_data, which is from [World Health Organization]https://www.who.int/emergencies/diseases/novel-coronavirus-2019 and covers 222 countries or regions, from 2020-01-22 to 2020-09-24. Users can select the type pf the case (confirmed, recovered and deaths) and then get a world distribution map of COVID-19 to have a general view of this epidemic. Here is the example of distribution of confirmed cases.

covid_19_data_filter <- covid_19_data %>% filter(type == "confirmed")

    highchart() %>%
      hc_add_series_map(worldgeojson, covid_19_data_filter, value = 'count', joinBy = c('name','country_region'))  %>%
      #hc_colors(c("darkorange", "darkgray")) %>%
      hc_colorAxis(stops = color_stops()) %>%
      hc_title(text = "Countries with COVID-19 exposure") %>%
      hc_subtitle(text = 'with Total Cases -  Figures')

Exploration of case trend among regions over time

COVID19bb package provides a interactive line chart to show the COVID-19 case trend among regions over time. Meanwhile, it also contains the relevant dataset covid_19_all, which is from [COVID19 Daily Updates]https://www.kaggle.com/gpreda/coronavirus-2019ncov and covers 215 countries or regions, from 2020-01-22 to 2020-10-03. Users can select the name of the state or province in different countries and then get a interactive line chart, which consists of three lines (red for confirmed, green for recovered and black for deaths). Here Here is the example of Hubei province in China.

covid_19_all_4 <- covid_19_all %>% filter(province_state == "Hubei")

    ggplot(data = covid_19_all_4) +
      geom_line(data = covid_19_all_4, aes(x = date, y = recovered), color = "green") +
      geom_line(data = covid_19_all_4, aes(x = date, y = deaths), color = "red") +
      geom_line(data = covid_19_all_4, aes(x = date, y = confirmed), color = "yellow") +
      ggtitle("Comparing COVID-19 trends across regions globally over time") +
      xlab('Dates') +
      ylab('Cases') 

Exploring the specific case in table

COVID19bb package provides a interactive DT table to show the detailed information of COVID-19 case. Users can input the country name or the date, and even the type of the case, the results will show in a this table, in this way, users can get more specific data about the condition.

covid_19_all %>%
  head(20) %>% 
  kableExtra::kable(align = 'lrrrrrr',caption = "Detailed information on COVID-19 cases") %>% 
   kableExtra::kable_styling(
     font_size = 15,
     bootstrap_options = c("striped", "hover", "condensed")) 

Functions

There are two functions in this package:

• Function select_input() can help users select input object within the Shiny Application.
• Function distribution_map() can create a distribution map of desired objects.

etc5523-2020/COVID19bb documentation built on Jan. 9, 2022, 12:04 a.m.