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.
PxWeb-data
In this first example we join municipality level data from Statistics Finland municipality key figures
library(geofi)
muni <- get_municipalities(year = 2019)
libs <- c("pxweb","dplyr","tidyr","janitor","ggplot2")
if (check_namespaces(pkgs = libs)) {
library(pxweb)
pxweb_query_list <-
list("Alue 2020"=c("*"),
"Tiedot"=c("*"),
"Vuosi"=c("2019"))
px_raw <-
pxweb_get(url = "https://pxnet2.stat.fi/PXWeb/api/v1/en/Kuntien_avainluvut/2020/kuntien_avainluvut_2020_aikasarja.px",
query = pxweb_query_list)
library(dplyr)
library(tidyr)
library(janitor)
library(sf)
px_data <- as_tibble(
as.data.frame(px_raw,
column.name.type = "text",
variable.value.type = "text")
) %>% setNames(make_clean_names(names(.))) %>%
pivot_longer(names_to = "information", values_to = "municipal_key_figures", 3:ncol(.))
px_data
} else {
message("One or more of the following packages is not available: ",
paste(libs, collapse = ", "))
}
#> # A tibble: 12,800 × 4
#> region_2020 year information municipal_key_f…
#> <chr> <chr> <chr> <dbl>
#> 1 WHOLE COUNTRY 2019 degree_of_urbanisation_percent 86.4
#> 2 WHOLE COUNTRY 2019 population 5525292
#> 3 WHOLE COUNTRY 2019 population_change_from_the_previous_yea… 0.1
#> 4 WHOLE COUNTRY 2019 share_of_persons_aged_under_15_of_the_p… 15.8
#> 5 WHOLE COUNTRY 2019 share_of_persons_aged_15_to_64_of_the_p… 62
#> 6 WHOLE COUNTRY 2019 share_of_persons_aged_over_64_of_the_po… 22.3
#> 7 WHOLE COUNTRY 2019 share_of_swedish_speakers_of_the_popula… 5.2
#> 8 WHOLE COUNTRY 2019 share_of_foreign_citizens_of_the_popula… 4.8
#> 9 WHOLE COUNTRY 2019 excess_of_births_persons -8336
#> 10 WHOLE COUNTRY 2019 intermunicipal_migration_gain_loss_pers… 0
#> # … with 12,790 more rowsOnce we have the data in long format we can observe the region_2020-column.
if (check_namespaces(pkgs = libs)) {
count(px_data, region_2020)
} else {
message("One or more of the following packages is not available: ",
paste(libs, collapse = ", "))
}
#> # A tibble: 400 × 2
#> region_2020 n
#> <chr> <int>
#> 1 Akaa 32
#> 2 Alajärvi 32
#> 3 Alavieska 32
#> 4 Alavus 32
#> 5 Asikkala 32
#> 6 Askola 32
#> 7 Aura 32
#> 8 Brändö 32
#> 9 Central Finland 32
#> 10 Central Ostrobothnia 32
#> # … with 390 more rowsThis 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.
if (check_namespaces(pkgs = libs)) {
map_data <- right_join(muni,
px_data,
by = c("municipality_name_fi" = "region_2020"))
} else {
message("One or more of the following packages is not available: ",
paste(libs, collapse = ", "))
}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.
if (check_namespaces(pkgs = libs)) {
library(ggplot2)
map_data %>%
filter(grepl("swedish|foreign", information)) %>%
ggplot(aes(fill = municipal_key_figures)) +
geom_sf() +
facet_wrap(~information) +
theme(legend.position = "top")
} else {
message("One or more of the following packages is not available: ",
paste(libs, collapse = ", "))
}
