knitr::opts_chunk$set( collapse = TRUE, comment = "#>", message = FALSE, warning = FALSE, fig.height = 7, fig.width = 7, dpi = 75 )
Installation
geofi
can be installed from CRAN using
# install from CRAN install.packages("geofi") # Install development version from GitHub remotes::install_github("ropengov/geofi")
# Let's first create a function that checks if the suggested # packages are available check_namespaces <- function(pkgs){ return(all(unlist(sapply(pkgs, requireNamespace,quietly = TRUE)))) } apiacc <- geofi::check_api_access() pkginst <- check_namespaces(c("sf","dplyr","patchwork","leaflet","ggplot2")) apiacc_pkginst <- all(apiacc,pkginst)
This vignettes gives an overview of different options for creating maps in R using the data from geofi
-package. Vignette is divided in three sections: R-packages for static maps, static maps using ggplot2 and interactive maps. But we begin with the datasets we want to plot.
If you want more detailed explanation of how to plot sf
-objects take a look at vignette 5. Plotting Simple Features.
Lets start with latest municipality division from get_municipalities()
with is a POLYGON data and with POINT data of municipality_central_localities
that is shipped with the package.
library(geofi) polygon <- get_municipalities(year = 2021, scale = 4500) point <- geofi::municipality_central_localities # municipality code into integer point$municipality_code <- as.integer(point$kuntatunnus)
They both come in same CRS EPSG:3067
and can be plotted together without any further manipulation.
There are two main technologies for creating static graphics in R: base and ggplot2. Both can be used to plot spatial data ie. to create maps. In addition, tmap : thematic maps in R is a great tool if you want to dig deeper into cartography in R.
base
library(sf) plot(st_geometry(polygon["municipality_code"])) plot(polygon["municipality_code"], add = TRUE, border="white") plot(st_geometry(point["municipality_code"]), add = TRUE, color = "black")
ggplot2
library(ggplot2) ggplot() + geom_sf(data = polygon, aes(fill = municipality_code)) + geom_sf(data = point)
tmap
tmap
is a versatile library for creating static thematic maps in R. It supports sf
-class objects and is fully compatible with geospatial data available through geofi
.
As I am only fluent in using ggplot2
the the more complex examples are using ggplot2
-package.
ggplot2
-packages has three sf
-class spesific functions: geom_sf
plotting for points, lines and polygons, and geom_sf_text
and geom_sf_label
for labeling the maps. In the following examples we are using the Uusimaa region in Southern Finland.
library(dplyr) polygon_uusimaa <- polygon %>% filter(maakunta_name_fi %in% "Uusimaa") point_uusimaa <- point %>% filter(municipality_code %in% polygon_uusimaa$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))
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))
If want to present multiple variables of same regions you can use facets.
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
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")
Creating informative and aesthetically pleasing maps is always a challenge and there are no recipe for instant success. Colors are important and colorbrewer2.org can help with that. In ggplot2
you can use colorbrewer-palettes with scale_fill_brewer
, scale_fill_distiller
, scale_fill_fermenter
-functions.
You may want to get rid of the grid as well as axis-labels and -titles.
ggplot(polygon_uusimaa, aes(fill = municipality_code)) + geom_sf(color = alpha("white", 1/3)) + scale_fill_fermenter(palette = "YlGnBu") + theme_minimal() + theme(axis.text = element_blank(), axis.title = element_blank(), panel.grid = element_blank(), legend.position = "top" ) + labs(title = "Municipality code", fill = NULL)
As for interactive maps Leaflet is not the only option. For exploring you datasets in almost any CRS you should to try out mapview. As for larger datasets, you are probably safer with WebGL based mapdeck.
Again, I am most experienced with leaflet so the following example is using leaflet. Leaflet default projection is EPSG:3857
or WGS84
, also known as "Google Mercator" or "Web Mercator, and you have reproject your geofi
data to plot it using leaflet.
polygon_wgs84 <- sf::st_transform(x = polygon, crs = "+proj=longlat +datum=WGS84") point_wgs84 <- sf::st_transform(x = point, crs = "+proj=longlat +datum=WGS84") library(leaflet) # lets create a palette for polygon fill (municipality codes) pal <- leaflet::colorNumeric(palette = "Blues", domain = polygon_wgs84$municipality_code) # labels for localities labels <- sprintf( "<strong>%s</strong> (%s)", point_wgs84$teksti, point_wgs84$kuntatunnus ) %>% lapply(htmltools::HTML) # popup for polygons popup <- sprintf( "<strong>%s</strong> (%s)", polygon_wgs84$municipality_name_fi, polygon_wgs84$municipality_code ) %>% lapply(htmltools::HTML) EPSG3067 <- leaflet::leafletCRS(crsClass = "L.Proj.CRS", code = "EPSG:3067", proj4def = "+proj=utm +zone=35 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs", resolutions = 1.5^(25:15)) leaflet(polygon_wgs84, options = leafletOptions(worldCopyJump = F, crs = EPSG3067)) %>% addProviderTiles(provider = providers$CartoDB.Positron) %>% addPolygons(fillColor = ~pal(municipality_code), color = "black", weight = 1, opacity = 1, dashArray = "3", fillOpacity = 0.4, popup = popup, highlight = highlightOptions( weight = 2, color = "#666", dashArray = "", fillOpacity = 0.4, bringToFront = TRUE) ) %>% addMarkers(data = point_wgs84, label = labels, clusterOptions = markerClusterOptions(), labelOptions = labelOptions(opacity = .7, style = list("font-weight" = "normal", padding = "2px 4px"), textsize = "12px", direction = "auto"))
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