## DATA SCIENCE IN A PANDEMIC
## Professor Dennis F.X. Mathaisel
## Original Script Created by: Thibaut FABACHER
## Script Modified by Dong Hyun (Veo) Chae under direction of Professor Mathaisel
## Data Source: John Hopkins University Dataset
## This script entails the data structure visualization
## referenced as Figure 12 in the publication.
#Packages Utilized
library(shiny)
library(leaflet)
library(RColorBrewer)
library(rgdal)
library(RCurl)
library(plotly)
library(viridis)
library(tidyverse)
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# THIS IS START OF THE SCRIPT #
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variable <-F
#loading the masterfile the original producer has created.
load(“C:/Users/dchae2/Desktop/Geographic Confirmed Cases/data/shapeFile.RData”)
#function that alters names of the regions to a official names.
dataCook<- function(data, pop, countries){
data$`Country/Region`<-as.character(data$`Country/Region`)
data$`Country/Region`[data$`Country/Region`==”Macau”]<- “Macao”
data$`Country/Region`[data$`Country/Region`==”Mainland China”]<- “China”
data$`Country/Region`[data$`Country/Region`==”South Korea”]<- “South Korea”
data$`Country/Region`[data$`Country/Region`==”North Macedonia”]<- “Macedonia”
data$`Country/Region`[data$`Country/Region`==”Czech Republic”]<- “Czechia”
data$`Country/Region`[data$`Country/Region`==”Dominican Republic”]<- “Dominican Rep.”
data$`Country/Region`[data$`Country/Region`==”UK”]<- “United Kingdom”
data$`Country/Region`[data$`Country/Region`==”Gibraltar”]<- “United Kingdom”
data$`Country/Region`[data$`Country/Region`==”US”]<- “United States”
data$`Country/Region`[data$`Country/Region`==”Saint Barthelemy”]<- “St-Barth√©lemy”
data$`Country/Region`[data$`Country/Region`==”Faroe Islands”]<- “Faeroe Is.”
data$`Country/Region`[data$`Country/Region`==”Bosnia and Herzegovina”]<- “Bosnia and Herz.”
data$`Country/Region`[data$`Country/Region`==”Vatican City”]<- “Vatican”
data$`Country/Region`[data$`Country/Region`==”Korea, South”]<- “South Korea”
data$`Country/Region`[data$`Country/Region`==”Republic of Ireland”]<- “Ireland”
data$`Country/Region`[data$`Country/Region`==”Taiwan*”]<-“Taiwan”
data$`Country/Region`[data$`Country/Region`==”Congo (Kinshasa)”]<-“Congo”
data$`Country/Region`[data$`Country/Region`==”Cote d’Ivoire”]<-“C√¥te d’Ivoire”
data$`Country/Region`[data$`Country/Region`==”Reunion”]<-“France”
data$`Country/Region`[data$`Country/Region`==”Martinique”]<-“France”
data$`Country/Region`[data$`Country/Region`==”French Guiana”]<-“France”
data$`Country/Region`[data$`Country/Region`==”Holy See”]<-“Vatican”
data$`Country/Region`[data$`Country/Region`==”Cayman Islands”]<-“Cayman Is.”
data$`Country/Region`[data$`Country/Region`==”Guadeloupe”]<-“France”
data$`Country/Region`[data$`Country/Region`==”Antigua and Barbuda”]<-“Antigua and Barb.”
data$`Country/Region`[data$`Country/Region`==”Curacao”]<-“Cura√ßao”
data$`Country/Region`[data$`Country/Region`==”Guadeloupe”]<-“France”
data$`Country/Region`[data$`Country/Region`==”occupied Palestinian territory”]<-“Palestine”
data$`Country/Region`[data$`Country/Region`==”Congo (Brazzaville)”]<-“Congo”
data$`Country/Region`[data$`Country/Region`==”Equatorial Guinea”]<-“Guinea”
data$`Country/Region`[data$`Country/Region`==”Central African Republic”]<-“Central African Rep.”
data$`Country/Region`[data$`Country/Region`==”South Sudan”]<-“S. Sudan”
data$`Country/Region`[data$`Country/Region`==”Eswatini”]<-“eSwatini”
data$`Country/Region`[data$`Country/Region`==”Western Sahara”]<-“W. Sahara”
data$`Country/Region`[data$`Country/Region`==”Burma”]<-“Myanmar”
data$`Country/Region`[data$`Country/Region`==”Sao Tome and Principe”]<-“S√£o Tom√© and Principe”
data$`Country/Region`[data$`Country/Region`==”Saint Vincent and the Grenadines”]<-“St. Vin. and Gren.”
data$`Country/Region`[data$`Country/Region`==”Saint Kitts and Nevis”]<-“St. Kitts and Nevis”
data$Pays<-as.character(unique(countries$NAME)[charmatch(data$`Country/Region`,unique(countries$NAME))])
print(data$`Country/Region`[is.na(data$Pays)])
dataPays<- data%>%dplyr::select(-`Province/State`, -Lat, -Long,-`Country/Region`)%>%group_by(Pays)%>%summarise_each(sum)
dataPays$Pays<-as.character(dataPays$Pays)
return(dataPays)
}
#loading the population size of each countries –> provided by the original creator.
population<- read.csv2(“C:/Users/dchae2/Desktop/Geographic Confirmed Cases/data/pop.csv”,stringsAsFactors = F)
population$pays<-as.character(unique(countries$NAME)[charmatch(population$Country,unique(countries$NAME))])
#The data source has been altered from using getURL –> actual CSV file. The CSV file is attached within the file
#This is the John’s Hopkins University Worldwide COVID-19 Dataset
data <- read.csv(“C:/Users/dchae2/Desktop/Geographic Confirmed Cases/data/time_series_covid19_confirmed_global.csv”, check.names = F)
dataCases<- dataCook(data, Pop, countries)
data <- read.csv(“C:/Users/dchae2/Desktop/Geographic Confirmed Cases/data/time_series_covid19_deaths_global.csv”, check.names = F)
dataDeaths<- dataCook(data, Pop, countries)
#Changing the format of variables according to its proper format: Date, Numeric and character.
#also filtering out the NAs
#merging the dataset into data (Confirmed) Cases and data (Confirmed) Deaths for each country
dataPays<-function(data=dataCases) return(data)
jour<-names(dataCases%>%select(contains( “/”)))
jourDate<- as.Date(jour, “%m/%d/%y”)
names(dataCases)[str_detect(names(dataCases), “/”)]<-format.Date(jourDate, “%m/%d/%y”)
names(dataDeaths)[str_detect(names(dataDeaths), “/”)]<-format.Date(jourDate, “%m/%d/%y”)
dataCases<-left_join(data.frame(Pays = countries$NAME%>%as.character(), Pop =countries$POP_EST%>%as.character()%>%as.numeric()),dataCases)
dataCases<-dataCases%>%filter(!is.na(Pays))
dataDeaths<-left_join(data.frame(Pays = countries$NAME%>%as.character(), Pop =countries$POP_EST%>%as.character()%>%as.numeric()),dataDeaths)
dataDeaths<-dataDeaths%>%filter(!is.na(Pays))
arrondi<- function(x) 10^(ceiling(log10(x)))
dataDeaths[,3]<-ifelse(is.na(dataDeaths[,3]),0,dataDeaths[,3])
dataCases[,3]<-ifelse(is.na(dataCases[,3]),0,dataCases[,3])
for(i in 4: dim(dataDeaths)[2]) dataDeaths[,i]<- ifelse(is.na(dataDeaths[,i]), dataDeaths[,i-1],dataDeaths[,i])
for(i in 4: dim(dataCases)[2]) dataCases[,i]<- ifelse(is.na(dataCases[,i]), dataCases[,i-1],dataCases[,i])
#Creating UI
ui <- bootstrapPage(
tags$style(type = “text/css”, “html, body {width:100%;height:100%}”,
HTML( “.panel-default {background-color: rgb(256, 256, 256,0.5);
padding : 10px;;}
.panel-title {background-color: rgb(256, 256, 256,0.8);
padding : 10px;
border-style: solid;
border-color: grey;}
.panel-credits {background-color: rgb(256, 256, 256,1);
padding : 15px;
border-style: solid;
border-color: black;}
.panel-mobile {background-color: rgb(256, 256, 256,0);
padding : 15px;
box-shadow: unset;}
“)
),
leafletOutput(“map”, width = “100%”, height = “93%”),
column(6,HTML(“<b><a href=’https://www.linkedin.com/in/thibaut-fabacher’>Thibaut FABACHER</a></b></br>
<i>Groupe Methode en Recherche Clinique (Pr. MEYER) <a href=’http://www.chru-strasbourg.fr/’ target =’_blank’> CHRU STRASBOURG</a></br>Laboratoire de Biostatistique (Pr. SAULEAU)<a href=’https://icube.unistra.fr/’ target =’_blank’> ICUBE</a></i>”)),
column(2,br(), actionButton(“twitter_share”,
label = “Share”,
icon = icon(“twitter”))
),
column(2,br(),
checkboxInput(“plotEvolT”, “Show Evolution”,F)
),
column(2, br(),checkboxInput(“credits”, “Credits”, FALSE)),
absolutePanel(id = “input_date_control”,class = “panel panel-default”,bottom = 60, left = 10, draggable = F,
selectInput(“choices”, “Cases or Deaths ?”, choices = c(“Cases”,”Deaths”),selected = “Cases”),
uiOutput(“Slider”),
helpText(“The detail of each country can be obtained by clicking on it.”),
uiOutput(“selection”),
checkboxInput(“legend”, “Show legend”, TRUE)
),
absolutePanel(id = “mobile”,class = “panel panel-mobile”,top = 10, right = 10, HTML(“<a href=’https://thibautfabacher.shinyapps.io/covid-19-m/’>Mobile Version</a>”)),
uiOutput(“Credits”),
uiOutput(“mobile”),
uiOutput(“plotEvol”),
absolutePanel(id = “name”,class = “panel panel-title”,top = 10, left = 100, HTML(“<h1>COVID-19 outbreak</h1>”),draggable = T)
)
server <- function(input, output, session) {
dataPays<- reactive({
if(!is.null(input$choices)){
if(input$choices == “Cases”){
return( dataCases)
}else{
return(
dataDeaths)
}}
})
maxTotal<- reactive( max(dataPays()%>%select(-Pop)%>%select_if(is.numeric), na.rm = T)
)
maxTotalPrevalence<- reactive(max(dataPays()%>%select(-Pop)%>%select_if(is.numeric)%>%mutate_all(function(x) x/dataPays()$Pop*100000), na.rm = T)
)
Top5<-reactive( unique(c(dataPays()$Pays[order(dataPays()[,dim(dataPays())[2]]%>%unlist(),decreasing = T)][1:5]
,”France”)))
output$map <- renderLeaflet({
leaflet(data = countries) %>%
setView(0, 30, zoom = 3)
})
pal <- reactive(colorNumeric(c(“#FFFFFFFF” ,rev(inferno(256))), domain = c(0,log(arrondi(maxTotal())))))
pal2 <- reactive(colorNumeric(c(“#FFFFFFFF” ,rev(inferno(256))), domain = c(0,log(arrondi(maxTotalPrevalence())))))
observe({
casesDeath<- ifelse(input$choices == “Cases”,”Cases”,”Deaths”)
if (!is.null(input$day1)) {
indicator<-format.Date(input$day1, “%m/%d/%y”)
}else{
indicator = format.Date(max(jourDate), “%m/%d/%y”)
}
if (!is.null(input$day2)) {
indicator2<-format.Date(input$day2-c(1,0), “%m/%d/%y”)
}else{
indicator2 =format.Date(c(min(jourDate)-1,max(jourDate)), “%m/%d/%y”)
}
if(is.null(input$variable)){
}else{
variable<- input$variable
if(variable ==”Total cases/population”){
# nCases
countries2 <- merge(countries,
dataPays(),
by.x = “NAME”,
by.y = “Pays”,
sort = FALSE)
country_popup <- paste0(“<strong>Country: </strong>”,
countries2$NAME,
“<br><strong>”,
“Total cases/population :”,
” </strong>”,
round(countries2[[indicator]]/countries2$Pop*100000,2),” /100 000″)
leafletProxy(“map”, data = countries2)%>%
addPolygons(fillColor = pal2()(log((countries2[[indicator]]/countries2$Pop*100000)+1)),
layerId = ~NAME,
fillOpacity = 1,
color = “#BDBDC3”,
weight = 1,
popup = country_popup)
}else if(variable ==”Total cases”){
countries2 <- merge(countries,
dataPays(),
by.x = “NAME”,
by.y = “Pays”,
sort = FALSE)
country_popup <- paste0(“<strong>Country: </strong>”,
countries2$NAME,
“<br><strong>”,
“Total “,casesDeath,” :”,
” </strong>”,
round(countries2[[indicator]],2))
leafletProxy(“map”, data = countries2)%>%
addPolygons(fillColor = pal()(log((countries2[[indicator]])+1)),
fillOpacity = 1,
layerId = ~NAME,
color = “#BDBDC3”,
weight = 1,
popup = country_popup)
}else if(variable ==”New cases over period”){
dataPaysSel<-dataPays()%>%select(Pays, Pop)
if(indicator2[1] == format.Date(min(jourDate)-1, “%m/%d/%y”)){
dataPaysSel$ncases<-dataPays()[,indicator2[2]]
}else{
dataPaysSel$ncases<-dataPays()[,indicator2[2]]-dataPays()[,indicator2[1]]
}
# nCases
countries2 <- merge(countries,
dataPaysSel,
by.x = “NAME”,
by.y = “Pays”,
sort = FALSE)
country_popup <- paste0(“<strong>Country: </strong>”,
countries2$NAME,
“<br><strong>”,
“New “,casesDeath,” over period :”,
” </strong>”,
countries2$ncases)
leafletProxy(“map”, data = countries2)%>%
addPolygons(fillColor = pal()(log(countries2$ncases+1)),
fillOpacity = 1,
color = “#BDBDC3”,
layerId = ~NAME,
weight = 1,
popup = country_popup)
}else{
dataPaysSel<-dataPays()%>%select(Pays, Pop)
if(indicator2[1] == format.Date(min(jourDate)-1, “%m/%d/%y”)){
dataPaysSel$ncases<-dataPays()[,indicator2[2]]
}else{
dataPaysSel$ncases<-dataPays()[,indicator2[2]]-dataPays()[,indicator2[1]]
}
# nCases
countries2 <- merge(countries,
dataPaysSel,
by.x = “NAME”,
by.y = “Pays”,
sort = FALSE)
country_popup <- paste0(“<strong>Country: </strong>”,
countries2$NAME,
“<br><strong>”,
“New “,casesDeath,” over period / population :”,
” </strong>”,
round(countries2$ncases/countries2$Pop*100000,2),” /100 000″)
leafletProxy(“map”, data = countries2)%>%
addPolygons(fillColor = pal2()(log(countries2$ncases/countries2$Pop*100000+1)),
fillOpacity = 1,
color = “#BDBDC3”,
layerId = ~NAME,
weight = 1,
popup = country_popup)
}
}
}
)
observe({
if(is.null(input$variable)){
}else{
variable<- input$variable
proxy <- leafletProxy(“map”, data = countries)
# Remove any existing legend, and only if the legend is
# enabled, create a new one.
proxy %>% clearControls()
if (input$legend) {
if(variable %in% c(“Total cases/population”,”New cases over period/population”)){
proxy %>% addLegend(position = “bottomright”,
pal = pal2(),opacity = 1,
bins = log(10^(seq(0,log10(arrondi(maxTotalPrevalence())),0.5))),
value = log(1:10^(log10(arrondi(maxTotalPrevalence())))),
data =log(1:10^(log10(arrondi(maxTotalPrevalence())))),
labFormat = labelFormat(transform = function(x) round(exp(x)) ,suffix = ” /100 000″)
)
}else{
proxy %>% addLegend(position = “bottomright”,
pal = pal(),opacity = 1,
bins = log(10^(0:log10(arrondi(maxTotal())))),
value = log(1:10^(log10(arrondi(maxTotal())))),
data = log(10^(0:log10(arrondi(maxTotal())))),
labFormat = labelFormat(transform = exp )
)
}
}
}
})
output$Slider<-renderUI({
if(is.null(input$variable)){
}else{
if(input$variable %in% c(“Total cases”, “Total cases/population”)){
sliderInput(“day1”, “Day”, min(jourDate), max(jourDate),
value = c(max(jourDate)),animate = T, step = 1
#min(jourDate),
)}else{
sliderInput(“day2”, “Day”, min(jourDate), max(jourDate),
value = c(max(jourDate)-7,max(jourDate)),animate = T, step = 1
#min(jourDate),
)
}
}
})
output$selection <- renderUI({
if(input$choices ==”Cases”){
radioButtons(“variable”, choices = c(“New cases over period”,
“New cases over period/population”,”Total cases”, ‘Total cases/population’ ),
label = “Indicator”)
}else{
radioButtons(“variable”, choices = list(“Deaths over period”=”New cases over period”,
“Deaths over period/population”=”New cases over period/population”,
“Total deaths”=”Total cases”,
‘Total deaths/population’=’Total cases/population’ ),
label = “Indicator”)
}
})
output$plotEvol<-renderUI({
if (input$plotEvolT) {
tagList(absolutePanel(
id=”name”,
class=”panel panel-credits”,
top = 10,width = “700px”,
right = 10,draggable = F,
plotlyOutput(outputId = “evol”,width = “600px”),
actionButton(“reset”, “Reset Graph”),
actionButton(“clear”, “Clear all traces”)
))
}
})
output$evol <-renderPlotly({
if(input$variable %in% c(“Total cases/population”,”Total cases”)){
df_evo<- dataPays()%>%filter(Pays%in% trace$data)%>%pivot_longer(cols = -c(Pays,Pop),
values_to = “Cases”,names_to = “Date”)%>%
mutate(Date= lubridate::parse_date_time(Date, orders = c(“mdy”)))
if(input$variable==”Total cases/population”){
plot_ly(data = df_evo,x = ~Date, y = ~Cases/Pop*100000, color = ~Pays, type = “scatter”,mode = “lines”)%>%
layout(yaxis = list( title = paste(input$choices,”/ 100 000″)))
}else{
plot_ly(data = df_evo,x = ~Date, y = ~Cases, color = ~Pays, type = “scatter”,mode = “lines”)%>%
layout(yaxis = list( title = input$choices))
}
}else{
df_evo<- dataPays()%>%filter(Pays%in% trace$data)
for(i in dim( df_evo)[2]:4) df_evo[i]<- df_evo[i]- df_evo[i-1]
df_evo<- df_evo%>%pivot_longer(cols = -c(Pays,Pop),
values_to = “Cases”,names_to = “Date”)%>%
mutate(Date= lubridate::parse_date_time(Date, orders = c(“mdy”)))
if( input$variable==”New cases over period/population”){
plot_ly(data = df_evo,x = ~Date, y = ~Cases/Pop*100000, color = ~Pays, type = “scatter”,mode = “lines”)%>%
layout(yaxis = list( title = paste(input$choices,”/ 100 000/day”)))
}else{
plot_ly(data = df_evo,x = ~Date, y = ~Cases, color = ~Pays, type = “scatter”,mode = “lines”)%>%
layout(yaxis = list( title = paste(input$choices,”/day”)))
}
}
})
trace<- reactiveValues()
observe({trace$data<-Top5()
})
observeEvent(input$reset, {
for (i in 1: length(trace$data)){
plotlyProxy(“evol”, session) %>%
plotlyProxyInvoke(“deleteTraces”,list(0))
}
if(input$variable %in% c(“Total cases/population”,”Total cases”)){
df_evo<- dataPays()%>%filter(Pays%in% Top5())%>%pivot_longer(cols = -c(Pays,Pop),
values_to = “Cases”,names_to = “Date”)%>%
mutate(Date= lubridate::parse_date_time(Date, orders = c(“mdy”)))
if(input$variable==”Total cases/population”){
for (i in Top5()){
df_evoi<- df_evo%>%filter(Pays == i)
plotlyProxy(“evol”, session) %>%
plotlyProxyInvoke(“addTraces”,
list(x =df_evoi$Date ,
name =i ,
y = df_evoi$Cases/df_evoi$Pop*100000,
type = ‘scatter’,
mode = ‘lines’))
}
}else{
for (i in Top5()){
df_evoi<- df_evo%>%filter(Pays == i)
plotlyProxy(“evol”, session) %>%
plotlyProxyInvoke(“addTraces”,
list(x =df_evoi$Date ,
name =i ,
y = df_evoi$Cases,
type = ‘scatter’,
mode = ‘lines’))
}
}
}else{
df_evo<- dataPays()%>%filter(Pays%in% Top5())
for(i in dim(df_evo)[2]:4) df_evo[i]<-df_evo[i]-df_evo[i-1]
df_evo<-df_evo%>%pivot_longer(cols = -c(Pays,Pop),
values_to = “Cases”,names_to = “Date”)%>%
mutate(Date= lubridate::parse_date_time(Date, orders = c(“mdy”)))
if( input$variable==”New cases over period/population”){
for (i in Top5()){
df_evoi<- df_evo%>%filter(Pays == i)
plotlyProxy(“evol”, session) %>%
plotlyProxyInvoke(“addTraces”,
list(x =df_evoi$Date ,
name =i ,
y = df_evoi$Cases/df_evoi$Pop*100000,
type = ‘scatter’,
mode = ‘lines’))
}
}else{
for (i in Top5()){
df_evoi<- df_evo%>%filter(Pays == i)
plotlyProxy(“evol”, session) %>%
plotlyProxyInvoke(“addTraces”,
list(x =df_evoi$Date ,
name =i ,
y = df_evoi$Cases,
type = ‘scatter’,
mode = ‘lines’))
}
}
}
trace$data<-Top5()
})
observeEvent(input$clear, {
for (i in 1: length(trace$data)){
plotlyProxy(“evol”, session) %>%
plotlyProxyInvoke(“deleteTraces”,list(0))
}
trace$data<- NULL
})
observeEvent(input$map_shape_click, {
country_Click<- input$map_shape_click$id
if (!country_Click%in%trace$data & input$plotEvolT){
trace$data<-c(trace$data,country_Click)
if(input$variable %in% c(“Total cases/population”,”Total cases”)){
df_click<- dataPays()%>%filter(Pays%in% country_Click)%>%pivot_longer(cols = -c(Pays,Pop),
values_to = “Cases”,names_to = “Date”)%>%
mutate(Date= lubridate::parse_date_time(Date, orders = c(“mdy”)))
if(input$variable==”Total cases/population”){
plotlyProxy(“evol”, session) %>%
plotlyProxyInvoke(“addTraces”,
list(x =df_click$Date ,
name =country_Click ,
y = df_click$Cases/df_click$Pop*100000,
type = ‘scatter’,
mode = ‘lines’))
}else{
plotlyProxy(“evol”, session) %>%
plotlyProxyInvoke(“addTraces”,
list(x =df_click$Date ,
name =country_Click ,
y = df_click$Cases,
type = ‘scatter’,
mode = ‘lines’))
}
}else{
df_click<- dataPays()%>%filter(Pays%in% country_Click)
for(i in dim( df_click)[2]:4) df_click[i]<- df_click[i]- df_click[i-1]
df_click<- df_click%>%pivot_longer(cols = -c(Pays,Pop),
values_to = “Cases”,names_to = “Date”)%>%
mutate(Date= lubridate::parse_date_time(Date, orders = c(“mdy”)))
if( input$variable==”New cases over period/population”){
plotlyProxy(“evol”, session) %>%
plotlyProxyInvoke(“addTraces”,
list(x =df_click$Date ,
name =country_Click ,
y = df_click$Cases/df_click$Pop*100000,
type = ‘scatter’,
mode = ‘lines’))
}else{
plotlyProxy(“evol”, session) %>%
plotlyProxyInvoke(“addTraces”,
list(x =df_click$Date ,
name =country_Click ,
y = df_click$Cases,
type = ‘scatter’,
mode = ‘lines’))
}
}
}
})
output$mobile<- renderUI({
if (!input$plotEvolT) {
absolutePanel(id = “mobile”,class = “panel panel-mobile”,top = 10, right = 10, HTML(“<a href=’https://thibautfabacher.shinyapps.io/covid-19-m/’>Mobile Version</a>”))
}
})
output$Credits <- renderUI({
if (input$credits) {
tagList(
absolutePanel(
id=”name”,
class=”panel panel-credits”,
top = “45%”,
left = “45%”,
HTML(
“<h1> Data Source : </h1>
<p> <li><a href=’https://coronavirus.jhu.edu/map.html’>Coronavirus COVID-19 Global Cases map Johns Hopkins University</a></li>
<li>COVID-19 Cases : <a href=’https://github.com/CSSEGISandData/COVID-19′ target=’_blank’>Github Johns Hopkins University</a></li>
<li>World population : <a href=’https://en.wikipedia.org/wiki/List_of_countries_and_dependencies_by_population’ target=’_blank’>Wikipedia</a></li>
<li>Shapefile : <a href=’https://www.naturalearthdata.com/downloads/50m-cultural-vectors/50m-admin-0-countries-2/’ target=’_blank’>Natural Earth Data</a></li>
<li> <a href =’https://github.com/DrFabach/Corona’ target=’_blank’>Code on Github </a></li>
<li> <a href = ‘https://www.r-project.org/’ target=’_blank’>The R Project for Statistical Computing</a></li>
<li> <a href = ‘https://shiny.rstudio.com/’ target=’_blank’>Shiny R package</a></li>
<li> <a href = ‘https://leafletjs.com/’ target=’_blank’>Leaflet </a></li>
</p>”
),
draggable = T
)
)
}
})
}
shinyApp(ui, server)
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