The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
Let’s use the SMMT package and the municipality inventory data to analyse the number of mutations.
By now, we know that municipalities change frequently in Switzerland. To get a better grasp, how often mutations occur, we can analyse the yearly frequency of these changes.
start_date <- seq.Date(as.Date("1960-01-01"), to = as.Date("2022-01-01"), by = "1 year")
res <- mutation_count(mutations = mutations_object$mutations,
start_date, start_date + lubridate::years(1),
territorial_changes_only = FALSE)
p <- ggplot(data = res, aes(start_date, number_of_mutations_in_period)) + geom_bar(stat = "identity")
print(p)This calculation also includes the administrative changes as well. Mutations that had a territorial effect can be calculated as follows:
res <- territorial_mutation_count(mutations = mutations_object$mutations,
start_date, start_date + lubridate::years(1))
p <- ggplot(data = res, aes(start_date, number_of_mutations_in_period)) + geom_bar(stat = "identity")
print(p)These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.