GGplot of COVID-19 cases using Italian Health Ministry data

Screenshot 2020-03-22 at 17.14.33.png

Data link

Order labels (this is according to the total number of cases on the 20th of March:
mydt$ord <- factor(mydt$denominazione_provincia, levels=c( 'Bergamo', 'Brescia', 'Milano', 'Cremona', 'Lodi', 'Pavia', 'Mantova', 'Monza e della Brianza', 'Piacenza', 'Sondrio', 'Varese', 'Lecco', 'In fase di definizione/aggiornamento'))

Graph code:
ggplot(mydt%>% filter(codice_regione =='03'))+ #select one region, in this case Lombardia
geom_bar(stat='identity', aes(x=data, fill=ord, y=totale_casi))+
theme(legend.title = element_blank())+
scale_fill_manual(values=c('#a6cee3', '#1f78b4','#b2df8a', '#33a02c', '#fb9a99', '#e31a1c', '#fdbf6f', '#ff7f00', '#cab2d6', '#6a3d9a', '#ffff99', '#b15928', '##000000', '#f0f0f0'), labels=c( 'Bergamo', 'Brescia', 'Milano', 'Cremona', 'Lodi', 'Pavia', 'Mantova', 'Monza', 'Piacenza', 'Sondrio', 'Varese', 'Lecco', ''))+
labs(x = '' , y = 'Total cases')+
theme(legend.title = element_blank())

Author: acarioli

is a researcher at the Geography and Environment department of the University of Southampton, WorldPop project team. She is also affiliated researcher at CED, UAB and Dondena Centre. Her interests include spatial econometrics and modeling, bayesian methods, machine learning processes, forecasting, micro-data simulation, and data visualization. Demo-traveler, Mac enthusiast, R zealot and Rladies member.