The Easier Way to Create a Map of Norway Using {fhimaps}
August 24, 2021
Every now and then you discover a discover a much simpler solution to a problem you spent a lot of time solving. This recently happened to me on the topic of creating a map of Norway in R. In this post, I want to go through the process of what I learned.
Previously, I used a JSON file and the {geojsonio} package to create a
map of Norway and its fylker (counties) in particular. This was a very
flexible and robust way of going about this, but also quite cumbersome.
This method relies on a high-quality JSON file, meaning, a file that is
detailed enough to display everything nicely, but not too detailed that
it takes a ton of time and computing power to create a single plot.
While I’ll still use this method if I need to create a map for places
other than Norway, I think I’ve found a better and easier solution for
plotting Norway and the fylker and kommuner in the
{fhimaps} package.
The {fhimaps} package is created by the Norwegian Institute for Public
Health (Folkehelseinstuttet,
FHI).
It’s part of a series of packages (which FHI refer to as the
“fhiverse”), which includes a package containing basic FHI data
({fhidata}), one
with different disease spread models
({spread}) and a
few more. Here I’ll dive into the {fhimaps} package with some help
from the {fhidata} package. I’ll also use the {ggmap} package to
help with some other data and plotting. It’s perhaps important to note
that {ggmap} does contain a map of Norway as a whole, but not of the
fylker and kommuner (municipalities), hence the usefulness of the
{fhimaps} package, which contains both. I’ll also use {tidyverse}
and {ggtext} as I always do. I won’t load {fhimaps} with the
library() function, but will use the :: operator instead since it’ll
make it easier to navigate the different datasets included.
library(tidyverse)
library(ggtext)
library(ggmap)
So let’s have a look at what’s included. You’ll see that nearly all maps
come in either a data.table format or an sf format. Here I’ll use
only the data frames, since they’re a lot easier to work with. The maps
in sf format can be useful elsewhere, but I think for most purposes
it’s easier and more intuitive to work with data frames.
data(package = "fhimaps") %>%
pluck("results") %>%
as_tibble() %>%
select(Item,Title) %>%
print(n = 18)
# A tibble: 31 × 2
Item Title
<chr> <chr>
1 norway_lau2_map_b2019_default_dt Map of Norwegian municipalities (2019 borders)
2 norway_lau2_map_b2019_default_sf Map of Norwegian municipalities (2019 borders) in sf format
3 norway_lau2_map_b2019_insert_oslo_dt Maps of Norwegian counties and municipalities with inserts (2019 borders) in data.table format
4 norway_lau2_map_b2020_default_dt Map of Norwegian municipalities (2020 borders) in data.table format
5 norway_lau2_map_b2020_default_sf Maps of Norwegian municipalities (2020 borders) in sf format
6 norway_lau2_map_b2020_insert_oslo_dt Maps of Norwegian counties and municipalities with an insert for Oslo (2020 borders) in data.table format
7 norway_lau2_map_b2020_split_dt Split map of Norwegian municipalities (2020 borders) in data.table format
8 norway_lau2_position_geolabels_b2020_default_dt Map of Norwegian municipalities (2020 borders) in data.table format
9 norway_lau2_position_geolabels_b2020_insert_oslo_dt Maps of Norwegian counties and municipalities with an insert for Oslo (2020 borders) in data.table format
10 norway_nuts3_map_b2017_default_dt Map of Norwegian counties (2017 borders) in data.table format
11 norway_nuts3_map_b2017_default_sf Map of Norwegian counties (2017 borders) in sf format
12 norway_nuts3_map_b2017_insert_oslo_dt Map of Norwegian counties with inserts (2017 borders) in data.table format
13 norway_nuts3_map_b2019_default_dt Map of Norwegian counties (2019 borders)
14 norway_nuts3_map_b2019_default_sf Map of Norwegian counties (2019 borders) in sf format
15 norway_nuts3_map_b2019_insert_oslo_dt Maps of Norwegian counties and municipalities with inserts (2019 borders) in data.table format
16 norway_nuts3_map_b2020_default_dt Map of Norwegian counties (2020 borders) in data.table format
17 norway_nuts3_map_b2020_default_sf Map of Norwegian counties (2020 borders) in sf format
18 norway_nuts3_map_b2020_insert_oslo_dt Maps of Norwegian counties and municipalities with an insert for Oslo (2020 borders) in data.table format
# … with 13 more rows
A comprehensive version of this list is also included in the reference for this package.
So let’s have a look at one of those maps. For instance the one with the new fylker from 2020 with an inset of Oslo.
map_df <- fhimaps::norway_nuts3_map_b2020_insert_oslo_dt %>%
glimpse()
Rows: 4,493
Columns: 5
$ long <dbl> 5.823860, 5.969413, 6.183042, 6.302433, 6.538059, 6.693511, 6.657289, 6.767480, 7.096287, 7.110080, 7.214667, 7.098724, 7.048585, 6.991842, 6.962960, 7.022880, 6.882833, 6.932169, 6.865142, 6.824533, 6.847579, 6.939172, 6.881039, 6.903689, 6.560838, 6.608553, 6.518096, 6.572750, 6.622347, 6.436069, 6.497499, 6.614721, 6.587698, 6.631556, 6.560330, 6.382453, 6.324024, 6.350390, 6.239081, 6.039468, 5.996479, 5.875390, 5.666733, 5.489157, 5.601538, 5.576495, 5.633912, 5.686111, 5.565115, 5.581353, 5.769811, 5.741457, 5.786016, 5.851127, 5.893869, 5.979000, 5.965561, 6.175315, 6.281425, 6.116682, 6.086432, 6.108257, 6.072709, 5.995039, 6.036212, 6.090791, 5.949121, 5.889756, 5.977865, 6.174015, 6.022552, 6.192079, 6.562320, 6.360672, 6.157856, 6.113738, 6.273343, 6.289766, 6.217630, 6.226502, 6.186815, 6.201218, 6.176712, 6.102012, 6.118100, 6.085544, 6.020944, 6.212991, 6.210265, 6.270777, 6.598666, 6.270651, 6.368525, 6.312591, 6.218008, 6.119585, 6.014455, 5.977…
$ lat <dbl> 59.64576, 59.57897, 59.58607, 59.76743, 59.84285, 59.82295, 59.70174, 59.70049, 59.78278, 59.70060, 59.67269, 59.65129, 59.58683, 59.59703, 59.51645, 59.48120, 59.42930, 59.40027, 59.36840, 59.26988, 59.18267, 59.18192, 59.14459, 59.11340, 58.90995, 58.88007, 58.76741, 58.76528, 58.69668, 58.64933, 58.60992, 58.62509, 58.49277, 58.44801, 58.32740, 58.27794, 58.29827, 58.32613, 58.32347, 58.37897, 58.47691, 58.46442, 58.55017, 58.75369, 58.87953, 58.95905, 58.89528, 58.92810, 58.98060, 59.03644, 58.96494, 58.85185, 58.93869, 58.92934, 58.96797, 58.93705, 58.90389, 58.83516, 58.84096, 58.87579, 58.90089, 58.92352, 58.90352, 58.97113, 58.99728, 58.98579, 59.02345, 59.07371, 59.12151, 59.13993, 59.14356, 59.25835, 59.32107, 59.31982, 59.26062, 59.30281, 59.29761, 59.31837, 59.39100, 59.32360, 59.31884, 59.34543, 59.31699, 59.32179, 59.37375, 59.32646, 59.34218, 59.44488, 59.40925, 59.50584, 59.55886, 59.51752, 59.64394, 59.64117, 59.47384, 59.40548, 59.41009, 59.35…
$ order <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217…
$ group <fct> 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.1, 11.2, 11.2, 11.2, 11.2, 11.2, 11.2, 11.2, 11.2, 11.2, 11.2, 11.2, 11.2, 11.2, 11.3, 11.3, 11.3, 11.3, 11.3, 11.3, 11.4, 11.4, 11.…
$ location_code <chr> "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "county11", "co…
Immediately you can see that there’s a lot of rows, each representing a
point on the map. A data frame with a larger number of rows would be
more detailed (i.e. less straight lines, more detail in curvatures of
borders etc.). Let’s create a very simple map. We’ll use the
geom_polygon to turn our data frame into a map. The location of the
points are given in longitudes and latitudes (like x- and
y-coordinates), the group serves to make sure lines are drawn correctly
(try running the code below without group = group and see what
happens). The location_code denotes the county number (which isn’t
from 1 to 11, but instead uses some other standard format matching
numbers in other government datasets). Let’s see the simplest map:
ggplot(map_df, aes(x = long, y = lat, group = group, fill = location_code)) +
geom_polygon()
Now let’s convert the awkward county numbers to the actual names of the
fylker. The {fhidata} package has a data frame with codes and names
for all the kommuner, fylker, and even regions (Øst, Vest, Nord-Norge
etc.). We’re only interested in the fylker here, so we’ll select the
unique county codes and the corresponding county names.
county_names <- fhidata::norway_locations_b2020 %>%
distinct(county_code, county_name)
print(county_names)
county_code county_name
1: county03 Oslo
2: county11 Rogaland
3: county15 Møre og Romsdal
4: county18 Nordland
5: county30 Viken
6: county34 Innlandet
7: county38 Vestfold og Telemark
8: county42 Agder
9: county46 Vestland
10: county50 Trøndelag
11: county54 Troms og Finnmark
Now let’s also create a nice color palette to give each fylke a nicer color than the default ggplot colors. We’ll create a named vector to match each fylke with a color from the batlow palette by Fabio Crameri.
county_colors <- setNames(normentR::norment_pal("batlow")(nrow(county_names)),
nm = county_names$county_name)
Let’s see what we can make now. We’ll add the county names to the large
data frame containing the longitudes and latitudes and then create a
plot again. I’ll also add some other style elements, such as a labels to
the x- and y-axes, circles instead of squares for the legend and a map
projection. For Norway especially I think a conic map projection works
well since the northern fylker are so massive and the southern fylker
are more dense, so adding a conic projection with a cone tangent of 40
degrees makes it a bit more perceptionally balanced (lat0 refers to
the cone tangent, the details are complicated but a higher cone tangent
results a greater distortion in favor of southern points).
map_df %>%
left_join(county_names, by = c("location_code" = "county_code")) %>%
ggplot(aes(x = long, y = lat, fill = county_name, group = group)) +
geom_polygon(key_glyph = "point") +
labs(x = NULL,
y = NULL,
fill = NULL) +
scale_x_continuous(labels = scales::label_number(suffix = "\u00b0W")) +
scale_y_continuous(labels = scales::label_number(suffix = "\u00b0N")) +
scale_fill_manual(values = county_colors,
guide = guide_legend(override.aes = list(shape = 21, size = 4))) +
coord_map(projection = "conic", lat0 = 40) +
theme_minimal() +
theme(legend.position = c(0.9,0.2),
legend.text = element_text(size = 5),
legend.key.height = unit(10,"pt"),
legend.background = element_rect(fill = "white", color = "transparent"))
Norway with Scandinavia
Sometimes it’s useful to plot Norway in geographical context. We can overlay Norway on a map of Scandinavia or Europe to create a more aesthetically pleasing figure that is less scientific but easier to read. For that we’ll first extract the longitude and latitude extremities of Norway, so we can easily center Norway on our new map.
str_glue("Range across longitude: {str_c(range(map_df$long), collapse = ', ')}")
str_glue("Range across latitude: {str_c(range(map_df$lat), collapse = ', ')}")
Range across longitude: 4.64197936086325, 31.0578692314387
Range across latitude: 57.9797576545731, 71.1848833506563
Let’s also combine the map with some actual data. The {fhidata}
package contains some simple data on vacciations in kommuner and fylker.
Let’s take the data from the different fylker in the last available year
for HPV vaccinations.
vax_data <- fhidata::norway_childhood_vax_b2020 %>%
filter(str_detect(location_code, "^county"),
year == max(year),
vax == "hpv")
Let’s create a map without the Oslo inset, combine it with the
vaccination data and plot it on top of a map of the world cropped to
just Scandinavia. So for this we load another data frame and use
left_join to merge the vaccination data into one data frame. The
{ggmap} package has a map of the entire world, which we’ll use. To
avoid awkward overlap, we’ll plot everything except Norway from that
world map (since we’ll have our own better map to use instead). We’ll
set the fill to proportion of the population vaccinated and set
similar style elements to make the figure look nicer. I used the
extremities we extracted earlier as a guideline, but we can play around
with the crop of the map to get something that works best.
fhimaps::norway_nuts3_map_b2020_default_dt %>%
left_join(vax_data, by = "location_code") %>%
ggplot(aes(x = long, y = lat, group = group)) +
geom_polygon(data = map_data("world") %>% filter(region != "Norway"),
fill = "grey80", color = "grey80", size = 1) +
geom_polygon(aes(fill = proportion), key_glyph = "point") +
labs(fill = "Proportion vaccinated against HPV") +
normentR::scale_fill_norment(palette = "bamako", reverse = TRUE,
limits = c(0.75,0.9), labels = scales::percent_format(accuracy = 1),
guide = guide_colorbar(title.position = "top", title.hjust = 0.5,
barwidth = 10, barheight = 0.5, ticks = FALSE)) +
coord_map(projection = "conic", lat0 = 60,
xlim = c(-8,40), ylim = c(57, 70)) +
theme_void() +
theme(plot.background = element_rect(fill = "#A2C0F4", color = "transparent"),
legend.direction = "horizontal",
legend.position = c(0.8, 0.1),
legend.title = element_text(size = 8),
legend.text = element_text(size = 6))
Geocoding
The {ggmap} package also has an incredibly useful function called
mutate_geocode() which transforms a string with an address or
description in character format to longitude and latitude. Since
{ggmap} uses the Google Maps API, it works similarly to typing in a
description in Google Maps. So an approximation of the location will
(most likely) get you the right result (e.g. with “Hospital
Lillehammer”). Note that mutate_geocode uses lon instead of long
as column name for longitude. Just to avoid confusion, I’ll rename the
column to long.
hospitals_df <- tibble(location = c("Ullevål Sykehus, Oslo","Haukeland universitetssjukehus, Bergen","St. Olav, Trondheim",
"Universitetssykehuset Nord-Norge, Tromsø","Stavanger Universitetssjukehus","Sørlandet Hospital Kristiansand", "Hospital Lillehammer")) %>%
mutate_geocode(location) %>%
rename(long = lon)
This is the list of coordinates it gave us:
print(hospitals_df)
# A tibble: 7 × 3
location long lat
<chr> <dbl> <dbl>
1 Ullevål Sykehus, Oslo 10.7 59.9
2 Haukeland universitetssjukehus, Bergen 5.36 60.4
3 St. Olav, Trondheim 10.4 63.4
4 Universitetssykehuset Nord-Norge, Tromsø 19.0 69.7
5 Stavanger Universitetssjukehus 5.73 59.0
6 Sørlandet Hospital Kristiansand 7.98 58.2
7 Hospital Lillehammer 10.5 61.1
Now let’s put these on top of the map. We’ll use the same map we used
earlier. We’ll add the locations using a simple geom_point. This time
I’ll also add a line from the inset to Oslo’s location on the map. The
xend and yend coordinates takes some trial-and-error but I think
it’s worth it. I’ll also add labels to each of the points with the help
of the {ggrepel} package to illustrate what the points stand for. I
suppose usually this is obvious from context, but I just wanted to show
how it can be done anyway.
set.seed(21)
map_df %>%
left_join(county_names, by = c("location_code" = "county_code")) %>%
ggplot(aes(x = long, y = lat, fill = county_name, group = group)) +
geom_polygon(key_glyph = "point") +
geom_segment(data = hospitals_df %>% filter(str_detect(location, "Oslo")),
aes(x = long, y = lat, xend = 19.5, yend = 62), inherit.aes = FALSE) +
geom_point(data = hospitals_df, aes(x = long, y = lat), inherit.aes = FALSE,
shape = 18, color = "firebrick", size = 4, show.legend = FALSE) +
ggrepel::geom_label_repel(data = hospitals_df, aes(x = long, y = lat, label = location),
size = 2, alpha = 0.75, label.size = 0, inherit.aes = FALSE) +
labs(x = NULL,
y = NULL,
fill = NULL) +
scale_fill_manual(values = county_colors,
guide = guide_legend(override.aes = list(size = 4, shape = 21,
color = "transparent"))) +
coord_map(projection = "conic", lat0 = 60) +
theme_void() +
theme(legend.position = c(0.2,0.7),
legend.text = element_text(size = 5),
legend.key.height = unit(10,"pt"))
Combine the map with other data
Let’s take it a step further and now look at how we can combine our map with data that didn’t come directly from the FHI. Instead I downloaded some data from the Norwegian Statistics Bureau (Statistisk sentralbyrå, SSB) on land use in the kommuner (link). This came in the form of a semi-colon separated .csv file.
area_use <- read_delim("Areal.csv", delim = ";", skip = 2) %>%
janitor::clean_names()
print(area_use)
# A tibble: 356 × 20
region area_2021_residential_areas area_2021_recreational_facilities area_2021_built_up_areas_for_agriculture_and_fishing area_2021_industrial_commercial_and_service_areas area_2021_education_and_day_care_facilities area_2021_health_and_social_welfare_institutions area_2021_cultural_and_religious_activities area_2021_transport_telecommunications_and_technical_infrastructure area_2021_emergency_and_defence_services area_2021_green_areas_and_sports_facilities area_2021_unclassified_built_up_areas_and_related_land area_2021_agricultural_land area_2021_forest area_2021_open_firm_ground area_2021_wetland area_2021_bare_rock_gravel_and_blockfields area_2021_permanent_snow_and_glaciers area_2021_inland_waters area_2021_unclassified_undeveloped_areas
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 3001 Halden 9.61 1.85 2.51 2.89 0.29 0.21 0.22 11.4 0.03 0.84 1.51 61.6 480. 2.88 16.2 1.3 0 48.7 0
2 3002 Moss 10.4 1.86 1.59 3.92 0.32 0.21 0.14 7.7 0.26 1.57 1.42 33.6 60.0 4.09 0.21 0.48 0 10.1 0
3 3003 Sarpsborg 14.0 2.95 3.49 4.39 0.48 0.28 0.19 12.2 0.01 1.87 1.52 78.1 241. 8.22 1.7 1.55 0 33.5 0
4 3004 Fredrikstad 19.9 5.14 3.05 6.24 0.62 0.36 0.26 11.8 0.27 2.84 2.49 67.4 139. 8.91 0.23 15.4 0 8.59 0
5 3005 Drammen 18.4 1.17 1.26 4.59 0.63 0.35 0.23 11.6 0.01 3.41 2.54 26.0 226. 2.59 5 1.17 0 13.2 0
6 3006 Kongsberg 7.68 2.23 2.38 2.23 0.36 0.11 0.15 11.8 0.35 1.86 1.4 40.2 616. 15.4 32.1 18.8 0 39.6 0
7 3007 Ringerike 11 3.99 4.18 5.19 0.37 0.29 0.11 21.4 0.36 1.27 2.12 77.7 1191. 47.9 50.8 3.21 0 134. 0
8 3011 Hvaler 2.31 5.79 0.65 0.34 0.04 0.02 0.03 1.96 0 0.29 0.19 4.76 34.0 5.65 0.16 33.5 0 0.21 0
9 3012 Aremark 0.64 0.73 0.8 0.31 0.02 0.01 0.03 3.35 0 0.16 0.14 20.9 245. 0.94 7.24 0.02 0 38.6 0
10 3013 Marker 1.38 0.58 1.68 0.52 0.03 0.02 0.05 5.38 0.01 0.14 0.34 40.0 305. 1.71 10.5 0.04 0 46.0 0
# … with 346 more rows
You can see there’s 356 rows, each representing a different kommune in
Norway. The columns here represent the surface area (in km2)
with different designations (e.g. forest, health services, agriculture
etc.). All data here is from 2021. Now, kommunes have different sizes,
so I want to get the designations of interest as percentages of total
area in the kommune. Here I assumed that the sum of all designations is
equal to the total size of each kommune. I also want to extract the
kommune number, since we’ll use that to merge this data frame with the
map later. The kommune number needs to be 4 digits, so we need to add a
leading 0 in some instances. Then we’ll create the location_code
column which will match the location_code column in the data frame
from {fhimaps}. Then we’ll calculate the percentage land use for
different designations. Here I’m just interested in “bare rock, gravel,
and blockfields”, “wetland, “forest”, and “Open firm ground”.
area_use <- area_use %>%
mutate(total_area = rowSums(across(where(is.numeric))),
kommune_code = parse_number(region),
kommune_code = format(kommune_code, digits = 4),
kommune_code = str_replace_all(kommune_code, " ", "0"),
location_code = str_glue("municip{kommune_code}"),
perc_rocks = area_2021_bare_rock_gravel_and_blockfields / total_area,
perc_wetland = area_2021_wetland / total_area,
perc_forest = area_2021_forest / total_area,
perc_open_ground = area_2021_open_firm_ground / total_area) %>%
arrange(kommune_code) %>%
glimpse()
Rows: 356
Columns: 27
$ region <chr> "0301 Oslo municipality", "1101 Eigersund", "1103 Stavanger", "1106 Haugesund", "1108 Sandnes", "1111 Sokndal", "1112 Lund", "1114 Bjerkreim", "1119 H\xe5", "1120 Klepp", "1121 Time", "1122 Gjesdal", "1124 Sola", "1127 Randaberg", "1130 Strand", "1133 Hjelmeland", "1134 Suldal", "1135 Sauda", "1144 Kvits\xf8y", "1145 Bokn", "1146 Tysv\xe6r", "1149 Karm\xf8y", "1151 Utsira", "1160 Vindafjord", "1505 Kristiansund", "1506 Molde", "1507 \xc5lesund", "1511 Vanylven", "1514 Sande (M\U{803782f}g Romsdal)", "1515 Her\U{8039a0d}\U{803782f}g Romsdal)", "1516 Ulstein", "1517 Hareid", "1520 \xd8rsta", "1525 Stranda", "1528 Sykkylven", "1531 Sula", "1532 Giske", "1535 Vestnes", "1539 Rauma", "1547 Aukra", "1554 Aver\xf8y", "1557 Gjemnes", "1560 Tingvoll", "1563 Sunndal", "1566 Surnadal", "1573 Sm\xf8la", "1576 Aure", "1577 Volda", "1578 Fjord", "1579 Hustadvika", "1804 Bod\xf8", "1806 Narvik", "1811 Bindal", "1812 S\xf8m…
$ area_2021_residential_areas <dbl> 51.53, 3.58, 19.28, 7.30, 11.90, 1.15, 1.03, 0.70, 3.66, 3.45, 3.49, 2.23, 4.70, 1.71, 3.75, 0.81, 1.25, 1.54, 0.12, 0.34, 3.48, 12.53, 0.07, 2.98, 4.78, 7.88, 15.66, 1.22, 0.82, 3.00, 2.17, 1.50, 2.92, 1.30, 2.15, 2.69, 2.29, 2.86, 1.96, 1.86, 2.26, 0.83, 1.37, 2.07, 2.11, 0.88, 1.28, 2.53, 0.75, 4.79, 8.31, 5.79, 0.56, 0.65, 2.56, 0.63, 0.20, 0.86, 2.10, 0.85, 3.50, 0.56, 0.57, 0.71, 0.76, 1.69, 7.11, 0.88, 0.19, 0.63, 2.97, 1.04, 0.47, 1.82, 3.82, 0.75, 1.02, 0.77, 0.58, 0.25, 0.29, 0.64, 5.09, 2.41, 2.97, 1.29, 1.85, 3.32, 2.06, 0.45, 1.38, 9.61, 10.41, 13.98, 19.91, 18.35, 7.68, 11.00, 2.31, 0.64, 1.38, 13.26, 1.31, 2.95, 2.67, 1.93, 4.30, 11.79, 4.68, 3.60, 5.70, 22.83, 25.10, 7.36, 3.31, 3.23, 5.76, 15.38, 5.24, 1.92, 7.53, 7.49, 8.37, 4.31, 1.38, 2.35, 0.56, 1.50, 1.81, 0.83, 1.66, 1.98, 1.42, 1.09, 5.35, 6.29, 6.06, 1.11, 0.50, 0.95, 2.39, 3.30, 6.35, 7.93, 7.19, 10.47, 13.54, 3.07, 8.24, 2.52, 3.89…
$ area_2021_recreational_facilities <dbl> 1.12, 1.20, 1.87, 0.21, 2.85, 0.26, 0.50, 0.67, 0.43, 0.13, 0.04, 0.66, 0.30, 0.14, 1.61, 1.02, 1.64, 0.92, 0.07, 0.18, 1.74, 0.80, 0.05, 1.31, 0.47, 1.62, 1.83, 0.31, 0.17, 0.35, 0.22, 0.13, 0.64, 0.72, 0.68, 0.15, 0.10, 0.63, 1.09, 0.11, 0.52, 0.67, 1.16, 0.67, 1.43, 0.50, 1.39, 0.63, 0.76, 0.80, 2.53, 3.45, 0.42, 0.28, 0.79, 0.44, 0.18, 0.58, 0.50, 0.50, 0.81, 0.71, 0.64, 0.38, 0.39, 0.93, 1.60, 0.85, 0.05, 0.55, 0.83, 1.76, 0.59, 1.06, 1.03, 0.61, 0.90, 0.59, 0.61, 0.05, 0.06, 0.15, 0.81, 0.67, 0.68, 0.75, 0.42, 0.66, 0.50, 0.10, 1.37, 1.85, 1.86, 2.95, 5.14, 1.17, 2.23, 3.99, 5.79, 0.73, 0.58, 1.42, 0.41, 0.29, 1.19, 0.42, 2.02, 0.52, 0.57, 4.12, 1.68, 0.83, 4.51, 1.47, 0.13, 1.02, 0.02, 0.69, 0.53, 0.06, 0.10, 0.66, 0.67, 0.30, 0.69, 1.00, 2.64, 3.65, 3.06, 2.30, 3.08, 7.11, 4.51, 1.56, 0.57, 0.53, 0.32, 3.26, 2.13, 3.73, 0.31, 1.26, 1.46, 0.74, 1.62, 0.86, 7.62, 1.36, 0.90, 0.86, 0.65, 0.82, 0.84, 0.7…
$ area_2021_built_up_areas_for_agriculture_and_fishing <dbl> 0.64, 1.26, 3.51, 0.33, 3.58, 0.54, 0.89, 1.22, 4.21, 4.36, 2.28, 1.03, 1.91, 1.07, 1.29, 1.41, 1.48, 0.39, 0.09, 0.27, 2.25, 2.12, 0.03, 3.02, 0.30, 3.22, 3.33, 1.26, 0.57, 0.78, 0.39, 0.60, 2.15, 0.93, 1.04, 0.29, 0.82, 1.51, 1.78, 0.33, 1.32, 1.27, 1.14, 1.28, 1.96, 0.81, 1.37, 2.14, 1.02, 3.73, 1.65, 1.21, 0.71, 0.89, 1.13, 0.42, 0.26, 0.18, 0.70, 1.00, 1.68, 0.49, 0.74, 0.62, 0.37, 1.27, 1.53, 0.26, 0.03, 0.25, 0.76, 0.33, 0.55, 1.26, 0.75, 0.62, 1.04, 0.29, 0.49, 0.07, 0.15, 0.24, 0.78, 0.67, 1.14, 0.32, 0.36, 1.35, 1.03, 0.12, 0.75, 2.51, 1.59, 3.49, 3.05, 1.26, 2.38, 4.18, 0.65, 0.80, 1.68, 8.58, 1.33, 3.52, 1.62, 1.41, 1.35, 1.49, 1.38, 0.73, 0.49, 1.09, 2.42, 3.81, 0.16, 1.08, 0.30, 4.10, 0.81, 0.60, 2.66, 4.79, 2.72, 1.97, 0.63, 1.01, 0.63, 1.34, 1.75, 0.84, 2.15, 1.15, 2.14, 0.81, 2.82, 2.39, 2.67, 1.53, 1.12, 1.44, 0.81, 1.53, 3.41, 1.95, 2.40, 3.56, 7.60, 2.30, 3.70, 2.11, 2.62, 2.44, 2.84, 4.2…
$ area_2021_industrial_commercial_and_service_areas <dbl> 10.37, 2.09, 4.86, 1.71, 5.89, 4.44, 0.24, 0.49, 1.75, 1.79, 1.39, 1.71, 2.97, 0.49, 1.93, 1.22, 1.78, 0.79, 0.02, 0.10, 3.02, 3.96, 0.01, 1.51, 2.06, 3.37, 4.50, 1.97, 0.33, 0.77, 0.85, 0.50, 1.38, 0.75, 0.90, 0.48, 0.51, 1.34, 1.58, 1.58, 1.12, 0.28, 0.46, 1.81, 1.15, 0.31, 1.23, 0.95, 0.85, 2.95, 2.88, 3.71, 0.24, 0.21, 1.91, 0.19, 0.08, 0.21, 1.57, 0.35, 2.27, 0.43, 0.46, 0.11, 0.45, 0.85, 9.87, 0.28, 0.07, 0.16, 1.17, 0.76, 0.16, 0.97, 1.24, 0.79, 0.29, 0.43, 0.23, 0.11, 0.15, 0.26, 1.29, 1.08, 0.76, 0.50, 0.52, 1.41, 1.21, 0.12, 0.51, 2.89, 3.92, 4.39, 6.24, 4.59, 2.23, 5.19, 0.34, 0.31, 0.52, 4.45, 0.33, 1.23, 1.28, 0.29, 2.61, 2.58, 1.38, 0.42, 0.53, 3.41, 4.44, 1.91, 0.19, 0.97, 1.51, 6.03, 1.57, 0.16, 5.18, 1.46, 1.78, 0.75, 0.27, 0.51, 0.39, 0.77, 1.03, 0.99, 1.26, 1.26, 0.94, 0.58, 1.77, 2.48, 3.10, 0.97, 0.43, 0.79, 0.69, 0.60, 2.23, 2.57, 2.28, 3.19, 3.99, 0.54, 1.60, 0.57, 1.20, 0.94, 1.08, 1.…
$ area_2021_education_and_day_care_facilities <dbl> 3.51, 0.17, 1.27, 0.37, 0.69, 0.02, 0.06, 0.04, 0.22, 0.23, 0.22, 0.14, 0.30, 0.14, 0.16, 0.06, 0.07, 0.06, 0.00, 0.02, 0.13, 0.45, 0.00, 0.12, 0.21, 0.40, 0.80, 0.05, 0.03, 0.11, 0.07, 0.03, 0.12, 0.06, 0.08, 0.10, 0.11, 0.12, 0.07, 0.09, 0.06, 0.06, 0.04, 0.10, 0.09, 0.04, 0.04, 0.17, 0.03, 0.19, 0.49, 0.26, 0.04, 0.02, 0.08, 0.02, 0.01, 0.01, 0.09, 0.03, 0.15, 0.02, 0.03, 0.02, 0.05, 0.06, 0.24, 0.04, 0.01, 0.04, 0.11, 0.05, 0.02, 0.06, 0.15, 0.03, 0.04, 0.03, 0.02, 0.01, 0.01, 0.04, 0.18, 0.11, 0.10, 0.03, 0.04, 0.12, 0.08, 0.01, 0.05, 0.29, 0.32, 0.48, 0.62, 0.63, 0.36, 0.37, 0.04, 0.02, 0.03, 0.45, 0.04, 0.09, 0.09, 0.06, 0.19, 0.47, 0.54, 0.11, 0.17, 1.08, 0.90, 0.18, 0.14, 0.10, 0.33, 0.72, 0.25, 0.06, 0.41, 0.28, 0.26, 0.15, 0.06, 0.11, 0.01, 0.03, 0.08, 0.02, 0.13, 0.07, 0.04, 0.03, 0.19, 0.16, 0.21, 0.05, 0.02, 0.03, 0.06, 0.12, 0.21, 0.33, 0.29, 0.33, 0.41, 0.09, 0.28, 0.05, 0.14, 0.08, 0.05, 0.1…
$ area_2021_health_and_social_welfare_institutions <dbl> 1.17, 0.04, 0.47, 0.14, 0.19, 0.01, 0.02, 0.00, 0.16, 0.07, 0.05, 0.02, 0.07, 0.02, 0.03, 0.02, 0.04, 0.03, 0.00, 0.00, 0.05, 0.16, 0.00, 0.06, 0.08, 0.15, 0.31, 0.03, 0.01, 0.05, 0.04, 0.02, 0.04, 0.02, 0.02, 0.03, 0.02, 0.07, 0.03, 0.05, 0.03, 0.02, 0.02, 0.04, 0.04, 0.02, 0.04, 0.07, 0.04, 0.08, 0.18, 0.10, 0.02, 0.02, 0.03, 0.01, 0.01, 0.01, 0.03, 0.02, 0.07, 0.00, 0.02, 0.01, 0.01, 0.02, 0.14, 0.03, 0.01, 0.01, 0.05, 0.02, 0.01, 0.05, 0.07, 0.03, 0.02, 0.03, 0.01, 0.00, 0.01, 0.01, 0.05, 0.04, 0.06, 0.02, 0.03, 0.06, 0.03, 0.01, 0.02, 0.21, 0.21, 0.28, 0.36, 0.35, 0.11, 0.29, 0.02, 0.01, 0.02, 0.23, 0.03, 0.04, 0.03, 0.03, 0.06, 0.16, 0.09, 0.04, 0.08, 0.38, 0.43, 0.08, 0.03, 0.03, 0.20, 0.25, 0.05, 0.01, 0.21, 0.07, 0.12, 0.04, 0.04, 0.06, 0.02, 0.03, 0.02, 0.01, 0.04, 0.03, 0.01, 0.02, 0.14, 0.09, 0.07, 0.01, 0.02, 0.02, 0.02, 0.07, 0.16, 0.15, 0.10, 0.17, 0.23, 0.04, 0.23, 0.07, 0.03, 0.04, 0.06, 0.0…
$ area_2021_cultural_and_religious_activities <dbl> 0.90, 0.06, 0.36, 0.11, 0.22, 0.03, 0.04, 0.02, 0.10, 0.07, 0.06, 0.10, 0.09, 0.04, 0.07, 0.03, 0.05, 0.02, 0.00, 0.01, 0.08, 0.25, 0.00, 0.07, 0.07, 0.18, 0.26, 0.04, 0.03, 0.07, 0.02, 0.02, 0.06, 0.03, 0.03, 0.03, 0.04, 0.05, 0.05, 0.03, 0.06, 0.04, 0.04, 0.05, 0.05, 0.05, 0.03, 0.05, 0.04, 0.12, 0.17, 0.15, 0.02, 0.03, 0.07, 0.03, 0.01, 0.02, 0.05, 0.02, 0.07, 0.01, 0.03, 0.03, 0.03, 0.05, 0.14, 0.04, 0.01, 0.03, 0.07, 0.05, 0.03, 0.04, 0.03, 0.02, 0.04, 0.02, 0.03, 0.00, 0.01, 0.01, 0.11, 0.08, 0.07, 0.03, 0.04, 0.05, 0.08, 0.02, 0.07, 0.22, 0.14, 0.19, 0.26, 0.23, 0.15, 0.11, 0.03, 0.03, 0.05, 0.22, 0.02, 0.07, 0.03, 0.02, 0.03, 0.10, 0.06, 0.05, 0.06, 0.18, 0.20, 0.08, 0.03, 0.03, 0.05, 0.19, 0.05, 0.02, 0.09, 0.07, 0.10, 0.04, 0.04, 0.02, 0.02, 0.05, 0.03, 0.02, 0.06, 0.07, 0.03, 0.02, 0.07, 0.10, 0.06, 0.03, 0.03, 0.03, 0.05, 0.03, 0.10, 0.17, 0.20, 0.14, 0.14, 0.04, 0.11, 0.04, 0.06, 0.07, 0.04, 0.0…
$ area_2021_transport_telecommunications_and_technical_infrastructure <dbl> 36.75, 6.15, 14.50, 4.02, 12.64, 2.54, 3.20, 4.10, 6.42, 4.41, 3.95, 4.35, 8.82, 1.46, 4.51, 3.95, 6.63, 2.51, 0.15, 0.94, 7.28, 9.44, 0.12, 7.87, 4.19, 12.01, 13.70, 3.74, 1.53, 2.59, 1.83, 1.37, 5.54, 3.47, 2.81, 1.41, 2.66, 4.08, 6.33, 1.69, 3.39, 3.49, 4.13, 4.86, 5.55, 2.96, 5.99, 6.41, 3.14, 7.54, 14.10, 12.01, 2.70, 2.12, 4.95, 1.57, 0.76, 1.05, 3.15, 2.77, 9.05, 5.61, 5.24, 2.20, 1.72, 5.14, 14.53, 1.72, 0.18, 2.20, 4.31, 3.10, 2.17, 5.43, 4.62, 3.18, 4.21, 2.12, 3.65, 0.25, 0.34, 1.33, 5.77, 4.23, 4.79, 2.75, 2.37, 5.37, 9.81, 0.49, 5.11, 11.45, 7.70, 12.20, 11.85, 11.60, 11.76, 21.43, 1.96, 3.35, 5.38, 17.05, 1.92, 6.68, 4.01, 3.35, 5.02, 8.09, 4.36, 3.14, 2.57, 10.12, 13.79, 13.67, 1.66, 3.19, 3.11, 13.83, 4.23, 1.43, 18.48, 9.76, 9.49, 6.29, 3.57, 3.06, 5.72, 6.94, 6.32, 4.33, 7.75, 8.58, 8.10, 4.61, 7.66, 7.79, 7.17, 7.62, 4.54, 9.82, 3.21, 5.32, 16.13, 7.22, 9.64, 13.64, 23.25, 6.24, 13.03, 5.9…
$ area_2021_emergency_and_defence_services <dbl> 0.66, 0.01, 0.41, 0.02, 0.14, 0.01, 0.00, 0.07, 0.01, 0.00, 0.00, 0.06, 0.09, 0.00, 0.01, 0.00, 0.01, 0.00, 0.00, 0.00, 0.00, 0.02, 0.00, 0.02, 0.03, 0.04, 0.07, 0.02, 0.00, 0.00, 0.01, 0.01, 0.00, 0.00, 0.00, 0.02, 0.01, 0.00, 0.31, 0.01, 0.00, 0.00, 0.03, 0.01, 0.02, 0.02, 0.01, 0.03, 0.00, 0.04, 0.29, 0.34, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.10, 0.00, 0.00, 0.00, 0.00, 0.01, 0.02, 0.00, 0.00, 0.00, 0.01, 0.01, 0.00, 0.09, 0.04, 0.00, 0.00, 0.00, 0.64, 0.00, 0.01, 0.00, 0.01, 0.02, 0.03, 0.00, 0.00, 0.04, 0.26, 0.00, 0.00, 0.03, 0.26, 0.01, 0.27, 0.01, 0.35, 0.36, 0.00, 0.00, 0.01, 0.03, 0.00, 0.00, 0.24, 0.01, 0.00, 0.22, 0.01, 0.11, 0.01, 0.21, 0.04, 0.01, 0.05, 0.01, 0.03, 0.31, 0.00, 0.00, 0.85, 0.00, 0.03, 0.01, 0.00, 0.05, 0.00, 0.01, 0.00, 0.00, 0.03, 0.01, 0.00, 0.00, 0.01, 0.01, 0.03, 0.00, 0.01, 0.00, 0.00, 0.00, 0.12, 0.03, 0.37, 0.01, 0.01, 0.01, 0.01, 0.00, 0.01, 0.00, 0.00, 0.0…
$ area_2021_green_areas_and_sports_facilities <dbl> 9.78, 0.49, 2.81, 0.71, 2.14, 0.21, 0.10, 0.57, 0.81, 0.46, 0.66, 0.44, 1.53, 0.39, 0.37, 0.11, 0.27, 0.45, 0.03, 0.03, 0.57, 1.40, 0.02, 0.37, 0.53, 1.44, 2.03, 0.26, 0.18, 0.19, 0.20, 0.34, 0.59, 1.71, 0.38, 0.19, 0.35, 0.46, 0.60, 0.16, 0.21, 0.14, 0.25, 0.62, 0.61, 0.13, 0.17, 0.91, 0.42, 0.79, 2.12, 2.43, 0.14, 0.07, 0.29, 0.06, 0.03, 0.09, 0.30, 0.11, 0.83, 0.13, 0.12, 0.09, 0.13, 0.17, 1.75, 0.10, 0.02, 0.06, 0.38, 0.12, 0.09, 0.25, 0.61, 0.14, 0.16, 0.10, 0.11, 0.03, 0.02, 0.04, 0.53, 0.84, 0.25, 0.16, 0.17, 0.73, 0.66, 0.04, 0.14, 0.84, 1.57, 1.87, 2.84, 3.41, 1.86, 1.27, 0.29, 0.16, 0.14, 2.44, 0.09, 0.85, 0.21, 0.29, 0.68, 1.81, 0.56, 0.70, 0.25, 4.47, 2.71, 1.15, 0.35, 0.65, 1.44, 2.11, 1.96, 0.47, 1.75, 1.62, 0.58, 0.53, 0.31, 0.70, 0.12, 1.53, 1.19, 1.86, 0.85, 2.64, 0.32, 0.82, 0.73, 0.62, 1.49, 0.41, 0.16, 0.85, 0.29, 0.30, 1.49, 0.95, 1.03, 1.24, 1.98, 0.42, 1.25, 0.25, 0.36, 0.70, 0.35, 0.6…
$ area_2021_unclassified_built_up_areas_and_related_land <dbl> 8.32, 0.81, 3.21, 1.19, 2.55, 0.21, 0.18, 0.25, 0.84, 0.79, 0.97, 0.53, 1.23, 0.38, 0.82, 0.29, 0.37, 0.25, 0.03, 0.11, 1.28, 2.36, 0.03, 0.95, 0.86, 1.58, 3.43, 0.37, 0.30, 0.81, 0.39, 0.41, 0.80, 0.36, 0.52, 0.54, 0.71, 0.65, 0.63, 0.30, 0.54, 0.31, 0.43, 0.63, 0.69, 0.29, 0.39, 0.76, 0.38, 1.23, 1.81, 1.95, 0.19, 0.19, 0.52, 0.15, 0.07, 0.19, 0.46, 0.37, 0.79, 0.18, 0.12, 0.20, 0.09, 0.26, 0.91, 0.04, 0.01, 0.05, 0.31, 0.09, 0.02, 0.26, 0.92, 0.27, 0.27, 0.20, 0.23, 0.05, 0.13, 0.18, 0.87, 0.66, 0.61, 0.25, 0.40, 0.42, 0.21, 0.15, 0.36, 1.51, 1.42, 1.52, 2.49, 2.54, 1.40, 2.12, 0.19, 0.14, 0.34, 2.13, 0.23, 0.64, 0.34, 0.41, 0.91, 1.33, 0.74, 0.52, 0.64, 2.63, 4.14, 1.16, 0.46, 0.51, 0.78, 1.73, 0.70, 0.23, 1.48, 1.47, 0.97, 0.47, 0.30, 0.40, 0.18, 0.50, 0.24, 0.29, 0.25, 0.18, 0.59, 0.37, 0.89, 1.00, 1.38, 0.30, 0.21, 0.22, 0.42, 0.64, 1.33, 1.08, 1.70, 1.32, 2.81, 0.42, 1.23, 0.42, 0.67, 0.62, 0.42, 0.6…
$ area_2021_agricultural_land <dbl> 9.60, 52.14, 92.91, 9.14, 89.80, 16.75, 24.57, 60.74, 118.94, 74.25, 83.45, 55.25, 35.43, 14.72, 27.35, 40.64, 35.16, 8.31, 3.44, 9.54, 56.49, 58.49, 1.48, 80.91, 4.85, 46.89, 42.05, 26.10, 10.25, 7.50, 5.69, 4.94, 36.20, 17.96, 14.47, 2.78, 11.52, 22.83, 35.41, 10.75, 22.44, 26.19, 20.60, 24.71, 38.44, 18.89, 24.96, 33.79, 18.92, 79.39, 35.19, 24.16, 14.02, 31.15, 36.28, 17.06, 9.07, 11.67, 29.45, 24.87, 37.48, 9.65, 36.32, 20.95, 15.44, 24.72, 32.93, 11.08, 0.67, 10.22, 18.51, 14.81, 12.53, 14.04, 19.93, 8.49, 33.68, 6.69, 8.91, 1.40, 1.16, 5.15, 41.33, 12.06, 25.22, 18.79, 10.46, 28.30, 24.19, 0.58, 15.93, 61.55, 33.60, 78.12, 67.39, 26.01, 40.17, 77.73, 4.76, 20.86, 40.01, 235.68, 32.57, 109.71, 35.31, 34.96, 34.88, 38.93, 37.71, 15.30, 5.03, 14.39, 40.64, 104.14, 6.26, 30.36, 5.86, 117.74, 17.06, 26.21, 88.50, 141.56, 53.51, 51.83, 7.90, 22.27, 7.35, 17.59, 25.22, 22.12, 40.10, 20.54, 35.57, 10.35, 52.2…
$ area_2021_forest <dbl> 279.30, 83.02, 80.63, 20.73, 215.91, 60.98, 121.74, 113.52, 16.42, 5.08, 17.12, 97.32, 3.75, 1.49, 123.23, 296.76, 595.47, 118.94, 0.00, 5.40, 165.70, 39.31, 0.18, 275.45, 46.98, 489.57, 270.36, 68.09, 11.13, 13.03, 18.31, 21.31, 188.95, 223.66, 128.83, 23.32, 2.39, 189.39, 379.15, 11.97, 66.69, 199.19, 224.07, 332.08, 455.33, 8.69, 320.03, 270.09, 269.47, 196.36, 545.74, 835.81, 387.48, 69.51, 292.09, 13.65, 97.72, 4.14, 50.08, 124.01, 674.33, 705.00, 808.09, 37.38, 57.54, 531.95, 1007.77, 73.80, 0.00, 142.32, 198.73, 189.96, 308.67, 519.92, 300.21, 435.66, 385.29, 184.36, 149.49, 0.00, 0.02, 8.62, 60.81, 133.56, 213.64, 68.57, 93.44, 298.75, 162.99, 1.39, 637.75, 480.39, 60.03, 241.20, 138.60, 225.58, 616.43, 1190.86, 34.03, 245.44, 304.58, 451.14, 53.75, 282.96, 53.54, 192.11, 77.37, 124.24, 46.05, 54.27, 42.22, 120.58, 254.82, 857.40, 42.49, 150.51, 46.40, 235.65, 141.42, 47.59, 117.12, 418.69, 293.99, 2…
$ area_2021_open_firm_ground <dbl> 7.55, 216.78, 23.07, 18.13, 521.59, 165.91, 188.31, 368.19, 78.49, 5.89, 46.87, 364.82, 6.49, 1.86, 62.20, 528.31, 818.84, 321.92, 1.60, 25.25, 143.15, 73.82, 3.35, 202.35, 14.90, 592.96, 200.22, 234.44, 61.52, 80.55, 57.77, 38.48, 258.42, 377.52, 125.74, 23.78, 14.61, 138.71, 567.72, 20.77, 60.66, 107.65, 50.78, 822.37, 659.92, 147.25, 223.17, 391.69, 461.82, 146.07, 542.35, 1400.98, 626.84, 64.46, 459.76, 106.97, 277.31, 38.91, 57.29, 207.80, 847.93, 856.49, 1252.48, 106.65, 83.16, 638.39, 2239.28, 131.28, 12.50, 334.86, 249.82, 330.56, 632.46, 1227.25, 508.36, 592.27, 364.43, 245.33, 45.55, 6.17, 11.80, 120.10, 233.13, 216.77, 211.34, 98.67, 132.98, 217.51, 180.95, 85.45, 750.60, 2.88, 4.09, 8.22, 8.91, 2.59, 15.40, 47.93, 5.65, 0.94, 1.71, 8.81, 0.75, 3.23, 3.57, 2.06, 3.52, 3.21, 2.28, 1.21, 0.83, 4.96, 5.08, 4.81, 0.97, 1.46, 1.05, 8.44, 1.41, 0.61, 4.15, 3.54, 4.24, 1.85, 0.81, 1.55, 159.30, 133.12, 8…
$ area_2021_wetland <dbl> 4.09, 13.50, 5.72, 3.30, 9.87, 9.99, 6.70, 15.98, 12.97, 0.55, 9.36, 8.41, 0.36, 0.09, 2.87, 22.54, 17.53, 8.40, 0.03, 2.08, 12.94, 11.33, 0.04, 18.28, 3.85, 50.85, 36.33, 14.66, 2.39, 5.29, 4.71, 6.02, 5.69, 12.75, 12.20, 0.51, 2.44, 17.23, 21.35, 10.32, 11.46, 27.59, 15.15, 18.43, 80.50, 72.38, 34.71, 13.88, 12.06, 54.52, 57.92, 54.50, 22.24, 6.47, 21.96, 9.90, 2.56, 1.59, 4.67, 20.62, 70.09, 111.61, 144.50, 9.09, 3.19, 63.69, 100.35, 5.10, 0.27, 10.66, 13.42, 11.42, 14.95, 45.82, 36.49, 30.26, 36.43, 13.63, 18.58, 0.09, 0.26, 4.30, 37.72, 28.76, 29.41, 30.02, 42.63, 75.73, 204.36, 0.21, 42.56, 16.21, 0.21, 1.70, 0.23, 5.00, 32.09, 50.78, 0.16, 7.24, 10.49, 7.46, 0.20, 7.96, 0.41, 1.58, 0.12, 0.95, 0.21, 0.11, 0.21, 0.67, 1.58, 53.65, 0.44, 1.81, 0.23, 2.98, 3.53, 2.68, 1.11, 20.15, 7.61, 9.28, 8.75, 2.17, 45.17, 74.40, 59.38, 32.05, 102.76, 84.30, 41.63, 11.29, 10.51, 7.72, 3.98, 22.37, 27.03, 175.26, 9.1…
$ area_2021_bare_rock_gravel_and_blockfields <dbl> 0.52, 4.57, 1.19, 0.74, 63.49, 3.90, 4.46, 7.11, 1.56, 0.69, 0.22, 19.28, 0.76, 0.09, 4.97, 51.53, 98.29, 41.34, 0.65, 0.28, 1.25, 2.42, 0.88, 1.66, 1.92, 213.87, 11.43, 11.10, 0.81, 2.56, 2.08, 1.00, 140.75, 184.30, 34.40, 0.56, 1.25, 18.40, 352.71, 0.37, 1.79, 2.28, 1.24, 426.30, 64.25, 6.12, 4.17, 105.12, 344.71, 7.66, 93.69, 733.08, 134.70, 15.27, 174.25, 9.95, 127.57, 3.82, 35.94, 66.14, 180.55, 176.39, 146.79, 7.58, 17.76, 104.75, 553.53, 31.44, 2.37, 134.21, 134.17, 62.76, 145.37, 254.45, 121.03, 357.95, 129.93, 51.39, 11.61, 1.04, 4.02, 27.89, 17.30, 56.54, 57.10, 12.56, 24.17, 62.47, 23.28, 20.99, 368.66, 1.30, 0.48, 1.55, 15.41, 1.17, 18.85, 3.21, 33.54, 0.02, 0.04, 0.11, 0.01, 0.01, 1.02, 0.02, 0.23, 0.07, 0.02, 0.13, 0.20, 0.25, 2.49, 0.04, 0.01, 0.08, 0.02, 0.14, 0.11, 0.01, 0.00, 0.01, 0.36, 0.06, 0.12, 0.52, 6.60, 3.13, 1.45, 128.67, 153.00, 154.92, 1.67, 0.55, 1.03, 0.26, 0.83, 1.55, 2.53, 5.…
$ area_2021_permanent_snow_and_glaciers <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 1.53, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 7.08, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 3.46, 17.42, 3.51, 0.00, 0.00, 0.00, 16.49, 0.00, 0.00, 0.00, 0.00, 9.38, 0.88, 0.00, 0.00, 1.93, 27.92, 0.00, 0.35, 111.75, 0.05, 0.00, 1.06, 0.00, 0.34, 0.00, 0.00, 0.00, 5.48, 13.66, 14.71, 0.00, 0.00, 55.69, 226.52, 0.00, 0.00, 48.25, 170.79, 4.37, 60.29, 8.74, 106.05, 34.59, 4.43, 0.97, 0.43, 0.00, 0.00, 0.00, 0.00, 0.35, 2.22, 0.00, 0.00, 0.08, 0.00, 0.00, 9.47, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.04, 0.21, 9.20, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00…
$ area_2021_inland_waters <dbl> 28.32, 46.55, 6.35, 4.45, 97.18, 28.00, 56.36, 76.87, 10.97, 11.17, 13.05, 61.60, 0.30, 0.62, 26.54, 119.38, 155.83, 40.67, 0.02, 2.65, 25.96, 11.01, 0.04, 23.52, 1.33, 70.25, 26.31, 21.47, 3.21, 1.79, 2.48, 5.64, 13.90, 22.89, 10.02, 1.65, 0.76, 6.31, 60.80, 0.27, 2.70, 11.64, 15.80, 67.95, 52.31, 12.28, 21.24, 45.74, 48.27, 15.55, 85.40, 240.41, 73.64, 4.04, 48.34, 2.29, 22.78, 0.85, 1.25, 15.87, 93.75, 123.20, 273.44, 6.49, 2.24, 159.13, 261.82, 7.73, 0.12, 26.57, 76.80, 43.39, 43.25, 134.66, 104.45, 171.62, 47.64, 20.43, 11.55, 0.59, 0.08, 9.71, 18.37, 20.13, 16.19, 12.03, 9.72, 25.93, 44.89, 8.59, 185.91, 48.71, 10.11, 33.47, 8.59, 13.19, 39.65, 134.27, 0.21, 38.57, 45.95, 38.33, 8.19, 14.64, 13.27, 18.02, 0.68, 7.02, 2.06, 1.17, 0.81, 4.20, 13.43, 93.88, 15.01, 37.54, 3.45, 46.32, 7.31, 1.15, 2.83, 29.16, 71.69, 17.73, 24.65, 57.91, 36.18, 40.90, 19.47, 43.18, 93.92, 201.74, 32.68, 35.43, 53.99, 40.18,…
$ area_2021_unclassified_undeveloped_areas <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.03, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.03, 0.00, 0.00, 0.02, 0.03, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02, 0.02, 0.02, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.02, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.0…
$ total_area <dbl> 454.13, 432.42, 262.42, 72.60, 1040.63, 294.95, 408.40, 650.54, 257.96, 113.39, 183.18, 617.95, 69.10, 24.71, 261.71, 1068.08, 1736.24, 546.54, 6.25, 47.20, 425.37, 229.87, 6.30, 620.45, 87.41, 1503.36, 632.62, 385.13, 93.28, 119.44, 97.23, 82.32, 661.61, 865.85, 337.78, 58.53, 40.59, 404.64, 1448.06, 60.66, 175.25, 381.65, 336.71, 1713.36, 1365.33, 271.62, 640.22, 876.89, 1190.60, 521.81, 1395.17, 3432.12, 1264.01, 195.38, 1046.07, 163.34, 538.96, 64.18, 187.63, 465.33, 1928.93, 2004.14, 2684.33, 192.51, 183.33, 1588.79, 4460.07, 264.67, 16.51, 711.07, 873.21, 664.60, 1221.63, 2216.19, 1209.82, 1637.30, 1009.82, 527.38, 252.72, 10.11, 18.52, 178.67, 424.15, 478.98, 566.58, 246.74, 319.60, 722.30, 656.59, 118.72, 2020.66, 642.45, 137.92, 405.62, 292.16, 317.68, 793.10, 1555.09, 89.97, 319.26, 412.91, 791.79, 101.18, 434.87, 118.83, 256.97, 133.97, 202.98, 102.70, 85.73, 61.48, 192.28, 376.72, 1144.80, 71.69,…
$ kommune_code <chr> "0301", "1101", "1103", "1106", "1108", "1111", "1112", "1114", "1119", "1120", "1121", "1122", "1124", "1127", "1130", "1133", "1134", "1135", "1144", "1145", "1146", "1149", "1151", "1160", "1505", "1506", "1507", "1511", "1514", "1515", "1516", "1517", "1520", "1525", "1528", "1531", "1532", "1535", "1539", "1547", "1554", "1557", "1560", "1563", "1566", "1573", "1576", "1577", "1578", "1579", "1804", "1806", "1811", "1812", "1813", "1815", "1816", "1818", "1820", "1822", "1824", "1825", "1826", "1827", "1828", "1832", "1833", "1834", "1835", "1836", "1837", "1838", "1839", "1840", "1841", "1845", "1848", "1851", "1853", "1856", "1857", "1859", "1860", "1865", "1866", "1867", "1868", "1870", "1871", "1874", "1875", "3001", "3002", "3003", "3004", "3005", "3006", "3007", "3011", "3012", "3013", "3014", "3015", "3016", "3017", "3018", "3019", "3020", "3021", "3022", "3023", "3024", "3025", "3026", "3027", "…
$ location_code <glue> "municip0301", "municip1101", "municip1103", "municip1106", "municip1108", "municip1111", "municip1112", "municip1114", "municip1119", "municip1120", "municip1121", "municip1122", "municip1124", "municip1127", "municip1130", "municip1133", "municip1134", "municip1135", "municip1144", "municip1145", "municip1146", "municip1149", "municip1151", "municip1160", "municip1505", "municip1506", "municip1507", "municip1511", "municip1514", "municip1515", "municip1516", "municip1517", "municip1520", "municip1525", "municip1528", "municip1531", "municip1532", "municip1535", "municip1539", "municip1547", "municip1554", "municip1557", "municip1560", "municip1563", "municip1566", "municip1573", "municip1576", "municip1577", "municip1578", "municip1579", "municip1804", "municip1806", "municip1811", "municip1812", "municip1813", "municip1815", "municip1816", "municip1818", "municip1820", "municip1822", "municip1824", "muni…
$ perc_rocks <dbl> 1.145047e-03, 1.056843e-02, 4.534715e-03, 1.019284e-02, 6.101112e-02, 1.322258e-02, 1.092067e-02, 1.092938e-02, 6.047449e-03, 6.085193e-03, 1.201004e-03, 3.119994e-02, 1.099855e-02, 3.642250e-03, 1.899049e-02, 4.824545e-02, 5.661084e-02, 7.563948e-02, 1.040000e-01, 5.932203e-03, 2.938618e-03, 1.052769e-02, 1.396825e-01, 2.675477e-03, 2.196545e-02, 1.422613e-01, 1.806772e-02, 2.882144e-02, 8.683533e-03, 2.143336e-02, 2.139257e-02, 1.214772e-02, 2.127386e-01, 2.128544e-01, 1.018414e-01, 9.567743e-03, 3.079576e-02, 4.547252e-02, 2.435742e-01, 6.099571e-03, 1.021398e-02, 5.974060e-03, 3.682694e-03, 2.488094e-01, 4.705822e-02, 2.253148e-02, 6.513386e-03, 1.198782e-01, 2.895263e-01, 1.467967e-02, 6.715311e-02, 2.135939e-01, 1.065656e-01, 7.815539e-02, 1.665759e-01, 6.091588e-02, 2.366966e-01, 5.952010e-02, 1.915472e-01, 1.421357e-01, 9.360112e-02, 8.801281e-02, 5.468404e-02, 3.937458e-02, 9.687449e-02, 6.593068e-0…
$ perc_wetland <dbl> 0.0090062317, 0.0312196476, 0.0217971191, 0.0454545455, 0.0094846391, 0.0338701475, 0.0164054848, 0.0245642082, 0.0502791130, 0.0048505159, 0.0510972814, 0.0136095153, 0.0052098408, 0.0036422501, 0.0109663368, 0.0211032881, 0.0100965304, 0.0153694149, 0.0048000000, 0.0440677966, 0.0304205750, 0.0492887284, 0.0063492063, 0.0294624869, 0.0440453037, 0.0338242337, 0.0574278398, 0.0380650689, 0.0256217839, 0.0442900201, 0.0484418389, 0.0731292517, 0.0086002328, 0.0147254143, 0.0361181834, 0.0087134803, 0.0601133284, 0.0425810597, 0.0147438642, 0.1701285856, 0.0653922967, 0.0722913664, 0.0449942087, 0.0107566419, 0.0589601049, 0.2664752227, 0.0542157383, 0.0158286672, 0.0101293465, 0.1044824745, 0.0415146541, 0.0158793982, 0.0175947975, 0.0331149555, 0.0209928590, 0.0606097710, 0.0047498887, 0.0247740729, 0.0248894100, 0.0443126383, 0.0363362071, 0.0556897223, 0.0538309373, 0.0472183263, 0.0174003164, 0.040087110…
$ perc_forest <dbl> 0.615022130, 0.191989270, 0.307255545, 0.285537190, 0.207480084, 0.206746906, 0.298090108, 0.174501184, 0.063653280, 0.044801129, 0.093459985, 0.157488470, 0.054269175, 0.060299474, 0.470864698, 0.277844356, 0.342965258, 0.217623596, 0.000000000, 0.114406780, 0.389543221, 0.171009701, 0.028571429, 0.443951970, 0.537467109, 0.325650543, 0.427365559, 0.176797445, 0.119318182, 0.109092431, 0.188316363, 0.258867833, 0.285591209, 0.258312641, 0.381402096, 0.398428157, 0.058881498, 0.468045670, 0.261833073, 0.197329377, 0.380542083, 0.521917988, 0.665468801, 0.193817995, 0.333494467, 0.031993226, 0.499875043, 0.308008986, 0.226331262, 0.376305552, 0.391163801, 0.243525867, 0.306548208, 0.355768246, 0.279226056, 0.083568018, 0.181312157, 0.064506077, 0.266908277, 0.266499044, 0.349587595, 0.351771832, 0.301039738, 0.194171731, 0.313860252, 0.334814544, 0.225953853, 0.278837798, 0.000000000, 0.200149071, 0.227585575…
$ perc_open_ground <dbl> 0.016625195, 0.501318163, 0.087912507, 0.249724518, 0.501225219, 0.562502119, 0.461092067, 0.565975958, 0.304271980, 0.051944616, 0.255868545, 0.590371389, 0.093921852, 0.075273169, 0.237667647, 0.494635233, 0.471616827, 0.589014528, 0.256000000, 0.534957627, 0.336530550, 0.321138035, 0.531746032, 0.326134257, 0.170461046, 0.394423159, 0.316493314, 0.608729520, 0.659519726, 0.674397187, 0.594158182, 0.467444121, 0.390592645, 0.436010856, 0.372254130, 0.406287374, 0.359940872, 0.342798537, 0.392055578, 0.342400264, 0.346134094, 0.282064719, 0.150812272, 0.479975020, 0.483341024, 0.542117664, 0.348583299, 0.446680884, 0.387888460, 0.279929476, 0.388733989, 0.408196683, 0.495913798, 0.329921179, 0.439511696, 0.654891637, 0.514527980, 0.606263634, 0.305334968, 0.446564803, 0.439585677, 0.427360364, 0.466589428, 0.553997195, 0.453608247, 0.401808924, 0.502072837, 0.496013904, 0.757116899, 0.470924100, 0.286093838…
Then the next step is very similar to what we’ve done before. We’ll use
left_join to merge the data frame containing the land use variables
with the data frame containing the map with the kommune borders. I want
to plot the four designations of interest in one figure, so I’ll
transform the plot to a long format using pivot_longer. Then I’ll
create a new label with nicer descriptions of the designations, and then
the rest is similar to before. We’ll facet the plot based on the
designation:
fhimaps::norway_lau2_map_b2020_split_dt %>%
left_join(area_use, by = "location_code") %>%
pivot_longer(cols = starts_with("perc"), names_to = "land_type", values_to = "percentage") %>%
mutate(land_type_label = case_when(str_detect(land_type, "rocks") ~ "Bare rock, gravel and rockfields",
str_detect(land_type, "wetland") ~ "Wetland",
str_detect(land_type, "forest") ~ "Forest",
str_detect(land_type, "open_ground") ~ "Open firm ground")) %>%
ggplot(aes(x = long, y = lat, group = group, fill = percentage)) +
geom_polygon() +
labs(fill = "Percentage") +
normentR::scale_fill_norment(palette = "acton", labels = scales::label_percent(), limits = c(0,1),
guide = guide_colorbar(barheight = 0.5, barwidth = 12,
ticks = FALSE, direction = "horizontal",
title.position = "top", title.hjust = 0.5)) +
facet_wrap(~ land_type_label) +
coord_map(projection = "conic", lat0 = 60) +
theme_void() +
theme(legend.position = "bottom",
strip.text.x = element_textbox_simple(size = rel(1.25), halign = 0.5,
margin = margin(10,0,10,0, "pt")))
Oslo
The last thing I want to show is a map of Oslo! I haven’t been able to
find a JSON file of the bydeler (boroughs) of Oslo. {fhimaps} does
contain a detailed map of the bydeler at higher resolution than the
large map of the municipalities or counties. Now, these bydeler are
again coded and {fhidata} doesn’t contain a data frame with the
corresponding names, so we’ll have to find it ourselves. Luckily there’s
a Wikipedia page with Oslo’s bydeler which contains a table with the
bydel numbers, the names, and some data we can use for visualization.
We’ll extract the table from the website using {rvest}, do some data
wrangling and prepare it for merging into the data frame with the map. I
won’t go into the wrangling much here, we’re interested mainly in the
plotting of the data right now.
bydel_data <- "https://en.wikipedia.org/wiki/List_of_boroughs_of_Oslo" %>%
rvest::read_html() %>%
rvest::html_table() %>%
pluck(1) %>%
janitor::clean_names() %>%
mutate(inhabitants = str_remove_all(residents, "[[:blank:]]"),
inhabitants = as.numeric(inhabitants),
area = str_remove_all(area, "km2"),
area = str_replace_all(area, ",", "."),
area = str_squish(area),
area = as.numeric(area),
pop_density = inhabitants / area) %>%
arrange(number) %>%
mutate(bydel_nr = format(number, digits = 2),
bydel_nr = str_replace_all(bydel_nr, " ", "0"),
location_code = str_glue("wardoslo0301{bydel_nr}"))
print(bydel_data)
# A tibble: 15 × 8
borough residents area number inhabitants pop_density bydel_nr location_code
<chr> <chr> <dbl> <int> <dbl> <dbl> <chr> <glue>
1 Gamle Oslo 58 671 7.5 1 58671 7823. 01 wardoslo030101
2 Grünerløkka 62 423 4.8 2 62423 13005. 02 wardoslo030102
3 Sagene 45 089 3.1 3 45089 14545. 03 wardoslo030103
4 St. Hanshaugen 38 945 3.6 4 38945 10818. 04 wardoslo030104
5 Frogner 59 269 8.3 5 59269 7141. 05 wardoslo030105
6 Ullern 34 596 9.4 6 34596 3680. 06 wardoslo030106
7 Vestre Aker 50 157 16.6 7 50157 3022. 07 wardoslo030107
8 Nordre Aker 52 327 13.6 8 52327 3848. 08 wardoslo030108
9 Bjerke 33 422 7.7 9 33422 4341. 09 wardoslo030109
10 Grorud 27 707 8.2 10 27707 3379. 10 wardoslo030110
11 Stovner 33 316 8.2 11 33316 4063. 11 wardoslo030111
12 Alna 49 801 13.7 12 49801 3635. 12 wardoslo030112
13 Østensjø 50 806 12.2 13 50806 4164. 13 wardoslo030113
14 Nordstrand 52 459 16.9 14 52459 3104. 14 wardoslo030114
15 Søndre Nordstrand 39 066 18.4 15 39066 2123. 15 wardoslo030115
{fhimaps} also provides a very useful data frame containing the
geographical center or best location to put a label to avoid overlap and
make it as clear as possible which label corresponds to which bydel. So
we’ll merge those two together.
bydel_centres <- fhimaps::oslo_ward_position_geolabels_b2020_default_dt %>%
inner_join(bydel_data, by = "location_code")
Then we’ll create the final plot. This will be more-or-less identical to what we did before.
fhimaps::oslo_ward_map_b2020_default_dt %>%
left_join(bydel_data, by = "location_code") %>%
ggplot(aes(x = long, y = lat, group = group)) +
geom_polygon(aes(color = pop_density, fill = pop_density), size = 1.5) +
geom_label(data = bydel_centres, aes(label = borough, group = 1), alpha = 0.5, label.size = 0) +
labs(fill = "N<sup>o</sup> of inhabitants per km<sup>2</sup>") +
normentR::scale_color_norment(palette = "turku", limits = c(0,1.5e4), guide = "none") +
normentR::scale_fill_norment(palette = "turku", limits = c(0,1.5e4), labels = scales::number_format(),
guide = guide_colorbar(title.position = "top", title.hjust = 0.5,
barwidth = 15, barheight = 0.75, ticks = FALSE)) +
theme_void() +
theme(legend.position = "bottom",
legend.title = element_markdown(),
legend.direction = "horizontal")
BONUS
An example of when you might use the sf data format is in interactive
maps. Here’s a short example of what that might look like. Here we’ll
use the {leaflet} package (which is an R wrapper for the homonymous
JavaScript library) to create an interactive map where we can zoom and
hover on the different fylker. This won’t work on printed media, but it
might be nice to include in a digital format. You could possibly extract
the corresponding HTML and JavaScript code and embed it on a webpage
separately.
library(leaflet)
map_sf <- fhimaps::norway_nuts3_map_b2020_default_sf %>%
sf::st_as_sf() %>%
left_join(county_names, by = c("location_code" = "county_code"))
map_sf %>%
leaflet() %>%
addProviderTiles(providers$Esri.WorldStreetMap) %>%
addPolygons(
fillColor = unname(county_colors),
weight = 0.1,
opacity = 1,
fillOpacity = 0.75,
highlightOptions = highlightOptions(
color = "#333", bringToFront = TRUE,
weight = 2, opacity = 1)
)