The quickest way to get a plot of your results is by using sf
and its
in-built plotting functionality. There are many packages within
R
that can be used to generate visualizations of geographic
data, including ggplot2
and tmap
.
The package maptiles
lets you add basemaps to your plots, giving your data real world
context.
The OS Data Hub APIs return GeoJSON, which can be used to make a
simple features data frame. This type of data frame works in a very
similar way to a regular data.frame
in R
, with
the addition of a list-like column of geometry and attributes for
geospatial data. The R
package sf
can be used
to create these objects. For more information on sf
see the
technical
documentation.
sf
allows you to quickly visualize the data returned by
your API query, using the coordinate reference system of the data to
accurately plot the spatial relationships. Plotting this way can be
useful to quickly check your API query results are looking like you were
expecting, as well as giving you options to produce highly polished
plots.
This example requests water features along a section of the Glastonbury Canal in Somerset from the ‘wtr-fts-water-1’ collection via the NGD Features API and then makes a simple plot to show what was returned.
library(osdatahub)
library(sf)
# Choose data
<- 'wtr-fts-water-1'
collection
# Define query extent
<- 342730
W <- 137700
S <- 347700
E <- 141642
N
<- 'EPSG:27700'
crs <- extent_from_bbox(c(W, S, E, N), crs = crs)
extent
# Query API
<- query_ngd(extent,
results collection = collection,
crs = crs,
max_results = 100000)
These results are in GeoJSON format. The sf
package can
convert this format into a data.frame
with the appropriate
geometry information. Alternatively, the osdatahub
package
for R
provides the option to return the query results as a
data.frame
object. Specify the returnType
argument as ‘sf’ in query_ngd()
.
<- st_read(results, crs = st_crs(crs), quiet = TRUE)
results_df #> Warning: st_crs<- : replacing crs does not reproject data; use st_transform for that
We can ignore the warning about setting the CRS in this case because we specified the API should return the features in EPSG:27700.
Now it is as simple as calling plot()
on the data frame,
including some optional styling parameters, to visualize the results.
The in-built plotting commands of sf
are detailed in this
vignette.
# Plot the query extent
plot(st_geometry(extent$polygon),
lty = 'dashed',
main = 'Water features',
axes = TRUE,
xlab = 'Eastings',
ylab = 'Northings')
# Plot the query results
plot(st_geometry(results_df),
col = 'purple',
add = TRUE)
mtext('Contains OS data © Crown copyright and database rights, 2023.', side = 1, line = 4)
This plot shows us the features returned form the API and plots them correctly in geogrpahic space. Once we’ve checked the results look sensible, it would be nice to see how the feaures relate to the rest of the geography of the area. To do this we might want to add a basemap, which we can do using maptiles and tmap.
maptiles
downloads and composes images from web map tile services. You can use
the OS Maps API with maptiles to get a variety of different basemaps.
Find out more about the OS Maps API here. tmap
is one of
the more sophisticated mapping and visualisation packages in
R
that is designed to work with sf
objects and
can display basemaps.
This first example demonstrates how to create a plot similar to the
basic sf
plot using tmap
.
library(maptiles)
library(tmap)
# Make the same plot as above, but using tmap
<- tm_shape(results_df) +
query_plot tm_fill(col = 'purple') +
tm_borders() +
tm_shape(extent$polygon) +
tm_borders(lty = 'dashed') +
tm_grid(labels.format = list(big.mark = ""),
lines = FALSE) +
tm_credits('Contains OS data © Crown copyright and database rights, 2023.',
position = c("LEFT", "BOTTOM"))
query_plot
To add a basemap we will retrieve the OS Maps tiles using a custom
source in maptiles
.
# Define the tile server parameters
<- list(src = 'OS Maps',
osmaps q = 'https://api.os.uk/maps/raster/v1/zxy/Light_3857/{z}/{x}/{y}.png?key=XXXXXX',
sub = '',
cit = 'Contains OS data © Crown copyright and database rights, 2023.')
# Download tiles and compose basemap raster
<- get_tiles(x = extent$polygon,
tile_maps provider = osmaps,
crop = FALSE,
cachedir = tempdir(),
apikey = get_os_key(),
verbose = FALSE)
# Add basemap to the tmap
<- tm_shape(tile_maps,
final_plot bbox = st_bbox(results_df)) +
tm_rgb() +
query_plot
final_plot
Note that maptiles
currently only supports tiles in
EPSG:3857 projection. The package attempts to re-project the basemap to
match the CRS of the query features. This can cause some distortion in
the basemap image. If this warping is not acceptable,
osdatahub
provides an alternative interface to query and
download the tiles in either ESPG:27700 or EPSG:3857. However, it
requires more advanced processing and some additional packages, such as
terra
.
# Download tiles
<- query_maps(extent_from_bbox(st_bbox(results_df),
res crs = 27700),
layer = 'Light_27700',
output_dir = tempdir())
# Convert tiles into georeferenced rasters
<- function(path, bbox, crs){
png2rast <- png::readPNG(path) * 255
img <- terra::rast(img)
img ::RGB(img) <- c(1,2,3)
terra::ext(img) <- bbox[c(1,3,2,4)]
terra::crs(img) <- crs
terra
return(img)
}
<- lapply(res, function(t){ png2rast(t$file_path, t$bbox, t$crs) })
imgList
# Combine tiles into basemap
<- do.call(terra::mosaic, imgList)
basemap
# Add basemap to the tmap
<- tm_shape(basemap,
base_plot bbox = results_df) +
tm_rgb() +
query_plot
base_plot