Making maps using geofi-package

This vignettes gives an overview of different options for creating maps in R using the data from geofi-package. Vignette is divided in three sections: R-packages for static maps, static maps using ggplot2 and interactive maps. But we begin with the datasets we want to plot.

If you want more detailed explanation of how to plot sf-objects take a look at vignette 5. Plotting Simple Features.

Datasets

Lets start with latest municipality division from get_municipalities() with is a POLYGON data and with POINT data of municipality_central_localities that is shipped with the package.

library(geofi)
polygon <- get_municipalities(year = 2021, scale = 4500)
point <- geofi::municipality_central_localities
# municipality code into integer
point$municipality_code <- as.integer(point$kuntatunnus)

They both come in same CRS EPSG:3067 and can be plotted together without any further manipulation.

R-packages for static maps

There are two main technologies for creating static graphics in R: base and ggplot2. Both can be used to plot spatial data ie. to create maps. In addition, tmap : thematic maps in R is a great tool if you want to dig deeper into cartography in R.

base

library(sf)
plot(st_geometry(polygon["municipality_code"]))
plot(polygon["municipality_code"], add = TRUE, border="white")
plot(st_geometry(point["municipality_code"]), add = TRUE, color = "black")

ggplot2

library(ggplot2)
ggplot() + 
  geom_sf(data = polygon, aes(fill = municipality_code)) +
  geom_sf(data = point)

tmap

tmap is a versatile library for creating static thematic maps in R. It supports sf-class objects and is fully compatible with geospatial data available through geofi.

As I am only fluent in using ggplot2 the the more complex examples are using ggplot2-package.

Static maps using ggplot2

ggplot2-packages has three sf-class spesific functions: geom_sf plotting for points, lines and polygons, and geom_sf_text and geom_sf_label for labeling the maps. In the following examples we are using the Uusimaa region in Southern Finland.

library(dplyr)
polygon_uusimaa <- polygon %>% filter(maakunta_name_fi %in% "Uusimaa")
point_uusimaa <- point %>% filter(municipality_code %in% polygon_uusimaa$municipality_code)
ggplot() + 
  theme_light() +
  geom_sf(data = polygon_uusimaa, alpha = .3) + 
  geom_sf(data = point_uusimaa) + 
  geom_sf_text(data = point_uusimaa, aes(label = teksti))

Label overlapping

geom_sf_label or geom_sf_text cannot control the overlapping of labels which is a common issue when mapping objects of various shapes and sizes. With ggrepel you can solve the problem though it requires a bit of spatial data processing with sf-package.

ggplot() + 
  theme_light() +
  geom_sf(data = polygon_uusimaa, alpha = .3) + 
  geom_sf(data = point_uusimaa) + 
  ggrepel::geom_text_repel(data = point_uusimaa %>%
                        sf::st_set_geometry(NULL) %>%
                        bind_cols(point_uusimaa %>% 
                                    sf::st_centroid() %>% 
                                    sf::st_coordinates() %>% as_tibble()),
                     aes(label = teksti, x = X, y = Y))

Faceting

If want to present multiple variables of same regions you can use facets.

Facetting and combining maps

Facetting is a useful way to present data on multiple variables covering the same region. This is useful approach if you have, lets say, data on same indicator from two different time points and you want to have separate maps for separate times points, but have a shared scale. Below I create a random data for two year titled population and plot the data using facet_wrap()-function.

pop_data <- bind_rows(
  tibble(
    municipality_code = polygon$municipality_code
  ) %>% 
    mutate(population = rnorm(n = nrow(.), mean = 2000, sd = 250),
           time = 2020),
  tibble(
    municipality_code = polygon$municipality_code
  ) %>% 
    mutate(population = rnorm(n = nrow(.), mean = 2000, sd = 250),
           time = 2021)
  )
pop_data
#> # A tibble: 618 × 3
#>    municipality_code population  time
#>                <int>      <dbl> <dbl>
#>  1                 5      2043.  2020
#>  2                 9      1890.  2020
#>  3                10      2033.  2020
#>  4                16      2051.  2020
#>  5                18      1950.  2020
#>  6                19      1787.  2020
#>  7                20      2008.  2020
#>  8                35      2368.  2020
#>  9                43      2215.  2020
#> 10                46      2101.  2020
#> # ℹ 608 more rows

pop_map <- right_join(polygon, pop_data)

ggplot(pop_map, 
       aes(fill = population)) +
  geom_sf() +
  facet_grid(~time)

However, often the indicators you want to compare either have different values (shared scale not ideal), are aggregated differently or cover non-overlapping geographic region. The you may find patchwork useful as in the example below.

library(patchwork)
p_municipalities <- ggplot(polygon, aes(fill = municipality_code)) + 
  geom_sf() + 
  theme(legend.position = "top")
p_regions <- ggplot(polygon %>% count(maakunta_code), aes(fill = maakunta_code)) + 
  geom_sf() + 
  theme(legend.position = "top")
p_uusimaa <- ggplot(polygon_uusimaa, aes(fill = municipality_code)) + 
  geom_sf() + 
  theme(legend.position = "top")

(p_municipalities | p_regions) /
p_uusimaa + plot_layout(nrow = 2, heights = c(1,0.6)) +
  plot_annotation(title = "Combining multiple maps into a single (gg)plot")