covid19

Today I want to test some ways to deal with implicit missing values: namely, creating grids with several commands and performing full joins on our data. Let’s use again COVID-19 vaccinations data in Italy, available from the official repo. Load Data url_vaccinations <- 'https://raw.githubusercontent.com/italia/covid19-opendata-vaccini/master/dati/somministrazioni-vaccini-latest.csv' read_csv(url_vaccinations, col_types = cols( # parse as dates data_somministrazione = "D", # parse as factors fornitore = "f", area = "f", fascia_anagrafica = "f" # the rest, let it be guessed )) %>% # remove 'categoria' from several column names rename_with( ~ stringr::str_remove(....

Now that we have wrangled the data a bit, we can proceed with some visualisations. We want to plot three things: How many vaccinations are administered daily. How many doses have been administered so far and their ratio. See how regions perform in terms of doses administered and doses received. Data Wrangling Let’s load the data: read_csv( 'https://raw.githubusercontent.com/orizzontipolitici/covid19-vaccine-data/main/data_ita/doses_by_date_ita.csv', ) -> doses_by_date read_csv( 'https://raw.githubusercontent.com/orizzontipolitici/covid19-vaccine-data/main/data_ita/vaccinations_by_area_ita.csv', col_types = cols(area = col_factor()) ) -> vaccinations_by_area These two datasets are incompatible: first, data grouped by area needs to be grouped to the Italian level....