Dealing With Implicit Missing Data
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(....