Wait for it...

I made this graphic for my own purposes, namely to keep an eye on the growth of confirmed covid-19 cases in the United States. The data are sourced from the Johns Hopkins Github Repo. The bulk of the work here is in the data processing, so please enjoy the fruits of my labor:

      let url = "https://raw.githubusercontent.com/CSSEGISandData/
                COVID-19/master/csse_covid_19_data/
                csse_covid_19_time_series/
                time_series_covid19_confirmed_global.csv"

      d3.csv(url, function (rawdata) {
  
        rawdata.forEach(function (d) {
          delete d['Province/State']
          delete d['Lat']
          delete d['Long']
        })
  
        var data = d3.nest()
          .key(function (d) { return d['Country/Region'] })
          .entries(rawdata);
  
        data = data.filter(function (d) { 
          return d.key == "Italy" || 
                 d.key == "US" || 
                 d.key == "Spain" 
                })
                
        data.forEach(function (d, i) {
          let parsedData = d.values
          parsedData.forEach(function (j) {
            delete j['Country/Region']
          })
  
          let keys = Object.keys(parsedData[0])
          let values = Object.values(parsedData[0])
  
          var i;
          for (i = 0; i < keys.length; i++) {
            d.values[i] = { date: parseTime2(keys[i]), cumsum: +values[i] 
            };
          }
        })
      })
      

If you're looking for R code to do the same thing, try this:

        library("dplyr")
        data = read.csv("https://raw.githubusercontent.com/CSSEGISandData/
        COVID-19/master/csse_covid_19_data/
        csse_covid_19_time_series/
        time_series_covid19_confirmed_global.csv")
        
        data = data %>%
          select(-Province.State, -Lat, -Long) %>%
          melt(id.vars = "Country.Region") 
        
        names(data) = c("country", "date", "cumsum")
        
        data = data %>%
          mutate(date = as.character(date)) %>%
          mutate(date = gsub("X", "", date)) %>%
          mutate(date = as.Date(date, format = "%m.%d.%y")) %>%
          group_by(country) %>%
          arrange(date) %>%
          unique() %>%
          ungroup() %>%
          group_by(country, date) %>%
          mutate(cumsum = sum(cumsum)) %>%
          unique()  %>%
          group_by(country) %>%
          mutate(nthday = seq(1, length(cumsum), 1)) %>%
          filter(!is.na(cumsum)) %>%
          mutate(lag = c(0, diff(cumsum))) %>%
          ungroup() %>%
          filter(country %in% c("US", "Italy", "Spain", "China"))