# Joining attribute data with geofi data

#### Markus Kainu

#### 2024-03-13

This vignettes provides few examples on how to join attribute data from common sources of attribute data. Here we are using data from THL Sotkanet and Paavo (Open data by postal code area).

Installation

`geofi` can be installed from CRAN using

    # install from CRAN
    install.packages("geofi")

    # Install development version from GitHub
    remotes::install_github("ropengov/geofi")

## Municipalities

Municipality data provided by `get_municipalities()`-function contains 77 indicators variables from each of 309 municipalities. Variables can be used either for aggregating data or as keys for joining attribute data.

### Population data from Sotkanet

In this first example we join municipality level indicators of Swedish-speaking population at year end from Sotkanet population data, Dataset is provided as part of geofi package as `geofi::sotkadata_swedish_speaking_pop`.

    library(geofi)
    muni <- get_municipalities(year = 2023)

    library(dplyr)
    sotkadata_swedish_speaking_pop <- geofi::sotkadata_swedish_speaking_pop

This is not obvious to all, but have the municipality names in Finnish among other regional breakdowns which allows us to combine the data with spatial data using `municipality_name_fi`-variable.

    map_data <- right_join(muni,
                           sotkadata_swedish_speaking_pop,
                           by = c("municipality_code" = "municipality_code"))

Now we can plot a map showing `Share of Swedish-speakers of the population, %` and `Share of foreign citizens of the population, %` on two panels sharing a scale.

    library(ggplot2)
    map_data %>%
      ggplot(aes(fill = primary.value)) +
      geom_sf() +
      labs(title = unique(sotkadata_swedish_speaking_pop$indicator.title.fi)) +
      theme(legend.position = "top")

## Zipcode level

You can download data from Paavo (Open data by postal code area) using `pxweb`-package. In this example we use dataset that can be downloaded preformatted in `csv` format directly from Statistics Finland. Population data is provided as part of geofi package as `geofi::statfi_zipcode_population`.

    statfi_zipcode_population <- geofi::statfi_zipcode_population

Before we can join the data, we must extract the numerical postal code from `postal_code_area`-variable.

    # Lets join with spatial data and plot the area of each zipcode
    zipcodes19 <- get_zipcodes(year = 2019)
    zipcodes_map <- left_join(zipcodes19,
                              statfi_zipcode_population)
    ggplot(zipcodes_map) +
      geom_sf(aes(fill = X2022),
              color  = alpha("white", 1/3)) +
      labs(title = "Total number of inhabitants, 2022",
           fill = NULL)