The data displayed by this {shinyapp} is taken from Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE). The raw data is available on {Github}. The data is updated on daily basis.

Packages needed for the app:

library(easypackages) # install.packages("easypackages")
libraries("lubridate", "tidyverse", "readr", "plotly")
urlfile="https://raw.githubusercontent.com/RamiKrispin/coronavirus/master/csv/coronavirus.csv"
coronavirus <- read_csv(url(urlfile))
coronavirus <- coronavirus %>%
mutate(month=as.factor(month(as.POSIXlt(coronavirus$date, format="%Y/%m/%d"))))
countries_list <- c(levels(as.factor(coronavirus$country)))
Months <- c(levels(coronavirus$month))

Overview of 2020

selectInput(inputId = "Ctr",
label = "Country of interest",
choices = countries_list, selected = "Germany")
checkboxGroupInput("type_dat", "Type",
choiceNames=c("Confirmed", "Death", "Recovered"),
choiceValues = c("confirmed", "death", "recovered"),
selected = "Confirmed")
dat_Ctr <- reactive(
coronavirus %>% filter( (country %in% input$Ctr) & (type %in% input$type_dat) )
%>% group_by(month, type) %>% summarise(cases=sum(cases))
)
renderPlotly({
validate(
need(input$type_dat, "")
)
dat_Ctr() %>% ggplot(aes(month, cases, color=type, group=type))+
geom_point()+ geom_line()+ theme_bw()+
theme(text = element_text(size=20))+
facet_wrap(~type, scales = "free", nrow = 2,
labeller = labeller(
type = c("confirmed" = "Confirmed", "death"="Death",
"recovered"="Recovered")) )+
theme(legend.position = "none")+
theme(axis.text.x = element_text(size = 12, face="bold"),
axis.text.y = element_text(size = 12, face="bold"))+
xlab("Month")+ ylab("Cases")
})

Year Overview

Overview of Months

selectInput(inputId = "Ctr_",
label = "Country of interest",
choices = countries_list, selected = "Germany")
checkboxGroupInput("type_dat_", "Type",
choiceNames=c("Confirmed", "Death", "Recovered"),
choiceValues = c("confirmed", "death", "recovered"),
selected = "Confirmed")
numericInput("obss", "Month", max(as.numeric(as.character(coronavirus$month))), min = 1, max = 12)
dat_Month <- reactive(
coronavirus %>%
filter( (country %in% input$Ctr_) & (month %in% as.character(input$obss)) & (type %in% input$type_dat_))%>% group_by(date, type) %>% summarise(cases=sum(cases), month=unique(month), type=unique(type)) )
renderPlotly({
validate(
need(input$obss, "")
)
validate(
need(input$type_dat_, "")
)
dat_Month() %>% ggplot(aes(date, cases, color=type))+
geom_point(aes(shape=month))+ geom_line()+
theme_bw()+
facet_wrap(~type, scales = "free", nrow = 2, labeller = labeller(
type = c("confirmed" = "Confirmed", "death"="Death", "recovered"="Recovered")) )+
theme(legend.position = "none")+
theme(axis.text.x = element_text(size = 12, face="bold"),
axis.text.y = element_text(size = 12, face="bold"))+
xlab("Month")+ ylab("Cases")
})

Overview of months