Some Graphs about COVID-19 in Italy

Table of Contents



This page contains various plots generated from that data using Org Mode and R: no fancy web services, just plain-old off-line generation. On top of being an interesting exercise on R and literate programming in Emacs, I use this page to get an idea of the evolution of the pandemic in Italy.

This page was created on <2020-03-28 Sat> and last updated on <2021-07-17 Sat>.

The source code available on the COVID-19 pages is distributed under the MIT License; the content is distributed under a Creative Commons - Attribution 4.0.

Get the data

First we get the data:

curl > data/dpc-covid19-ita-regioni.csv
curl > data/dpc-covid19-ita-andamento-nazionale.csv 
curl > data/dpc-covid19-ita-regioni-latest.csv

R Functions

This section contains the code for plotting data. The function my_plot plots different variables of an input dataframe over time, optionally filtering over region, which is the denomination of an Italian region.

The optional argument max defines the maximum value for the x-axis, while the optional Boolean arguments textlabels and filter control, respectively, whether text labels are printed on graphs and data has to be filtered by Region.

Finally, the optional arguments variables, graphtypes, and colors are vectors, defining, respectively, the variables to plot, the type of plot, and the colors used.

my_plot <- function(region, data, max=-1, textlabels=TRUE, filter=FALSE, 
                    variables  = c("totale_casi", "nuovi_positivi", "totale_positivi", "deceduti", "dimessi_guariti"),
                    graphtypes = c("l", "h", "l", "l", "l"),
                    colors     = c("red", "black", "orange", "slategrey", "forestgreen")) {
  par(cex=1.40, las=2)

  # if asked to filter, filter data according to region
  if (filter) {
    dataframe <- subset(data, denominazione_regione == region)
  else {
    dataframe <- data

  if (max == -1) {

       xlim=c(min(data$data), max(data$data)),
       main = region,

  axis.Date(1, at=dataframe$data, by="days", format="%b %d")

  # do the plots, now
  for (i in 1:length(variables)) {
    lines(x=dataframe$data, y=dataframe[, variables[i]],
    if (textlabels) {
      text(x=dataframe$data, y=dataframe[, variables[i]], 
           label=dataframe[, variables[i]], 

  values = sprintf("(%s)", dataframe[nrow(dataframe), variables])
  legend("topleft", legend=paste(variables, values), col=colors, lty=1, cex=1.6)
  grid(col = "lightgray")

Then we read the data from the CSV files of the Civil Protection repository:


# evolution over time, by Region
data = read.csv(file.path(PATH, "dpc-covid19-ita-regioni.csv"))
data$data <- as.Date(data$data)

# evolution over time at the National level
national = read.csv(file.path(PATH, "dpc-covid19-ita-andamento-nazionale.csv"))
national$data <- as.Date(national$data)

# latest regional data
latest = read.csv(file.path(PATH, "dpc-covid19-ita-regioni-latest.csv"))
latest$data <- as.Date(national$data)

We are now ready to print and plot the data.

This Week in Italy

cols = c(
labels = c(
  "In hospitals with symptoms", 
  "In ICUs",
  "Total hospitalized",
  "Quarantined at home", 
  "Active cases",
  "New cases",
  "Total number of cases"

Today = unlist(national[nrow(national), cols])
Yesterday = unlist(national[nrow(national) - 1, cols])
TwoDaysAgo = unlist(national[nrow(national) - 2, cols])
ThreeDaysAgo = unlist(national[nrow(national) - 3, cols])
FourDaysAgo = unlist(national[nrow(national) - 4, cols])
FiveDaysAgo = unlist(national[nrow(national) - 5, cols])

output_frame <- data.frame(labels, FiveDaysAgo, FourDaysAgo, ThreeDaysAgo, TwoDaysAgo, Yesterday, Today)
colnames(output_frame) <- rev(seq(Sys.Date(), by="-1 day", length.out=7))
colnames(output_frame)[1] <- "Label"
Label 2021-07-14 2021-07-15 2021-07-16 2021-07-17 2021-07-18 2021-07-19
In hospitals with symptoms 1128 1108 1089 1088 1111 1136
In ICUs 157 151 153 161 162 156
Total hospitalized 1285 1259 1242 1249 1273 1292
Quarantined at home 39364 40441 39658 41465 42218 44821
Active cases 40649 41700 40900 42714 43491 46113
New cases 1534 2153 2455 2898 3121 3127
Recovered 4105236 4106315 4109579 4110649 4112977 4113478
Deaths 127808 127831 127840 127851 127864 127867
Total number of cases 4273693 4275846 4278319 4281214 4284332 4287458

Variations with respect to previous day

We now plot the variations in the last week, that is the difference between a day and the previous day. In many cases, the lower the number, the better. In other cases (e.g., Recovered), the higher, the better.

Diff4 = FourDaysAgo - FiveDaysAgo
Diff3 = ThreeDaysAgo - FourDaysAgo
Diff2 = TwoDaysAgo - ThreeDaysAgo 
Diff1 = Yesterday - TwoDaysAgo
Diff0 = Today - Yesterday

diff_frame <- data.frame(labels, Diff4, Diff3, Diff2, Diff1, Diff0)
labels Diff4 Diff3 Diff2 Diff1 Diff0
In hospitals with symptoms -20 -19 -1 23 25
In ICUs -6 2 8 1 -6
Total hospitalized -26 -17 7 24 19
Quarantined at home 1077 -783 1807 753 2603
Active cases 1051 -800 1814 777 2622
New cases 619 302 443 223 6
Recovered 1079 3264 1070 2328 501
Deaths 23 9 11 13 3
Total number of cases 2153 2473 2895 3118 3126

See also the historical series of new cases in Italy.

Situation in Italy

Overall Situation

Evolution over time.

my_plot("Italia", national, textlabels=FALSE)


Breakdown of Quarantine

It tells where people with COVID-19 are spending their quarantine, that is, a breakdown of the “yellow” line of the previous plot.

The blue line is the number of people hospedalized during the (first) lockdown. Now the capacity of the health system should be higher, but it seems something to look at (although the situation differs from region to region).

          max(national$isolamento_domiciliare), textlabels=FALSE, filter=FALSE, 
          variables=c("ricoverati_con_sintomi", "terapia_intensiva", "totale_ospedalizzati", "isolamento_domiciliare"),  
          graphtypes=c("l", "l", "l", "l", "h"),
          colors=c("#FECEAB", "#EC2049", "#E84A5F", "#A7226E"))
abline(h = national[38,]$totale_ospedalizzati, col="#330000", lwd=2, lty=3)
abline(v = as.Date("2020-10-25"), col="#330000", lwd=2, lty=3)


Focus on Trentino, Liguria, Veneto and Lombardia

Situation in Trentino

my_plot("P.A. Trento", data, filter=TRUE, textlabels=FALSE)


Situation in Liguria

my_plot("Liguria", data, filter=TRUE, textlabels=FALSE)


Situation in Veneto

my_plot("Veneto", data, filter=TRUE, textlabels=FALSE)


Situation in Lombardia

my_plot("Lombardia", data, filter=TRUE, textlabels=FALSE)


Situation by Region

Situation by Region

# how many rows and columns?
par(mfrow=c(11, 2))

max <- max(data$totale_casi)

regions <- c("Valle d'Aosta", "Piemonte", "Liguria", "Lombardia", "Veneto",
             "P.A. Trento", "P.A. Bolzano", "Friuli Venezia Giulia",
             "Emilia-Romagna", "Toscana", "Marche", "Umbria",
             "Lazio", "Abruzzo", "Molise", "Campania",
             "Puglia", "Basilicata", "Calabria", "Sicilia",
for (region in regions) {
    region, data, filter=TRUE, textlabels=FALSE,
          variables=c("totale_casi", "totale_positivi", "deceduti", "dimessi_guariti"),
          max = max,
          graphtypes=c("l", "l", "l", "l"),
          colors=c("red", "orange", "slategrey", "forestgreen"))