---
title: "Mutation count"
date: "`r format(Sys.Date(), '%d.%m.%Y')`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Mutation count}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(SMMT)

```

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. 


```{r code, eval=FALSE, include=TRUE}

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:

```{r code2, eval=FALSE, include=TRUE}

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)

```