In filipwastberg/swemaps2: Swedish map data for ggplot and leaflet in R

knitr::opts_chunk$set(
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County & Municipality

Statistics Sweden (SCB) have a lot of interesting statistics that can be analyzed on a geographical level. Through their Database Statistikdatabasen you can download the data as Excel tables. Even better, you can use the R Open Gov package pxweb to query data from SCB.

By running pxweb_interactive() you can interactively choose which data to query. In this case we're going to use data from Statistics Sweden.

library(pxweb)

pxweb_interactive()

knitr::include_graphics("figures/pxwb_interactive.png")

When you have clicked through which data you want to query you can get the call to the database as R code. In the code below I have manipulated the code that was returned by pxweb to only query county codes that are in swemaps2::county.

The data queried here is population count with higher education.

library(pxweb)
library(janitor)
library(swemaps2)
library(tidyverse)

pxweb_query_list <- 
  list("Region"=county$ln_kod,
       "Kon"=c("1+2"),
       "ContentsCode"=c("BE0101U1"),
       "Tid"=c("2021"))

# Download data 
px_data <- 
  pxweb_get(url = "https://api.scb.se/OV0104/v1/doris/sv/ssd/BE/BE0101/BE0101C/BefArealTathetKon",
            query = pxweb_query_list)

# Convert to data.frame 
px_data_frame <- as.data.frame(px_data, column.name.type = "text", variable.value.type = "text")

population_per_kvm <- px_data_frame |> 
  clean_names()

Unfortunately, we do not get the region codes from SCB. But if we remove the " län from the region names we can join it by name:

population_per_kvm <- population_per_kvm %>% 
  mutate(region = str_replace(region, " län", "")) 

county_pop <- left_join(county, population_per_kvm, by = c("ln_namn" = "region"))

county_pop

We can now easily visualize this data on a Swedish map

library(scales)
ggplot(county_pop, aes(fill = log10(invanare_per_kvadratkilometer))) +
  geom_sf() +
  theme_swemap2() +
  scale_fill_viridis_c(option = "magma", labels = number) +
  labs(
    title = "",
    fill = "log10(population\nper square meter)",
    caption = "Source: SCB"
  )

We can also create an interactive map with simplevis

library(mapview)

mapView(county_pop, zcol = "invanare_per_kvadratkilometer")

DeSO & RegSO

Using pxweb we can query lots of statistics from SCB on DeSO level. For example, we might be interested in the proportion of children with low economic standard in Stockholm. Using pxweb_interactive() we get a good skeleton of code. Below I have only changed the code to only query regions that are in deso_sthlm.

deso <- load_deso()

deso_sthlm <- deso %>% 
  filter(kommunnamn == "Stockholm")

pxweb_query_list <- 
  list("Region"= deso_sthlm$deso,
       "Alder"=c("0-19"),
       "ContentsCode"=c("000006TA"),
       "Tid"=c("2019"))

# Download data 
px_data <- 
  pxweb_get(url = "https://api.scb.se/OV0104/v1/doris/sv/ssd/START/HE/HE0110/HE0110I/TabVX4InkDesoN1",
            query = pxweb_query_list)

# Convert to data.frame 
px_sthlm <- as.data.frame(px_data, column.name.type = "text", variable.value.type = "text") %>% 
  clean_names() %>% 
  as_tibble()

deso_sthlm <- deso_sthlm %>% 
  left_join(px_sthlm, by = c("deso" = "region"))

Finally, we can use this data to create a map:

ggplot(deso_sthlm , aes(fill = lag_ekonomisk_standard_procent)) +
  geom_sf() +
  scale_fill_viridis_c(option = "magma") + 
  theme_swemap2() +
  labs(
    title = "% Barn med Låg ekonomisk standard",
    subtitle = "Andel personer 0-19 år med låg ekonomisk standard per Demografiskt statistikområde\ni Stockholm stad",
    fill = "% Låg\nekonomisk\nstandard"
  )