---
title: "Plotting API Results: sf, tmap, and maptiles"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Plotting API Results}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---



The quickest way to get a plot of your results is by using
[`sf`](https://cran.r-project.org/package=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`](https://cran.r-project.org/package=ggplot2) and
[`tmap`](https://cran.r-project.org/package=tmap). The package
[`maptiles`](https://cran.r-project.org/package=maptiles) lets you add basemaps
to your plots, giving your data real world context.

## Simple Features Data Frame

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](https://r-spatial.github.io/sf/).

`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.



```r
library(osdatahub)
library(sf)
```



```r
# Choose data
collection <- 'wtr-fts-water-1'

# Define query extent
W <- 342730
S <- 137700
E <- 347700
N <- 141642

crs <- 'EPSG:27700'
extent <- extent_from_bbox(c(W, S, E, N), crs = crs)

# Query API
results <- query_ngd(extent,
                     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()`.


```r
results_df <- st_read(results, crs = st_crs(crs), quiet = TRUE)
#> 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](https://r-spatial.github.io/sf/articles/sf5.html).



```r
# 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, 2025.', side = 1, line = 4)
```

<img src="fig-plotngd-1.png" style="display: block; margin: auto;" />

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 and tmap

[`maptiles`](https://github.com/riatelab/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](https://osdatahub.os.uk/docs/wmts/overview).
[`tmap`](https://r-tmap.github.io/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`.



```r
library(maptiles)
library(tmap)

# Make the same plot as above, but using tmap
query_plot <- tm_shape(results_df) +
  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, 2025.',
             position = c("LEFT", "BOTTOM"))

query_plot
```

<img src="fig-plotngdtm1-1.png" style="display: block; margin: auto;" />

To add a basemap we will retrieve the OS Maps tiles using a custom source in
`maptiles`.



```r
# Define the tile server parameters
osmaps <- list(src = 'OS Maps',
               q = 'https://api.os.uk/maps/raster/v1/zxy/Light_3857/{z}/{x}/{y}.png?key={apikey}',
               sub = '',
               cit = 'Contains OS data © Crown copyright and database rights, 2025.')

# Download tiles and compose basemap raster
tile_maps <- get_tiles(x = extent$polygon,
                       provider = osmaps,
                       crop = FALSE,
                       cachedir = tempdir(),
                       apikey = get_os_key(),
                       verbose = FALSE)

# Add basemap to the tmap
final_plot <- tm_shape(tile_maps,
                       bbox = st_bbox(results_df)) +
                tm_rgb() +
                query_plot

final_plot
```

<img src="fig-plotngdtm2-1.png" style="display: block; margin: auto;" />

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`.



```r
# Download tiles
res <- query_maps(extent_from_bbox(st_bbox(results_df),
                                   crs = 27700),
                  layer = 'Light_27700',
                  output_dir = tempdir())
#> Light_27700_6_161_345.png already exists. Set `overwrite` to TRUE.
#> Light_27700_6_162_345.png already exists. Set `overwrite` to TRUE.
#> Light_27700_6_163_345.png already exists. Set `overwrite` to TRUE.
#> Light_27700_6_161_344.png already exists. Set `overwrite` to TRUE.
#> Light_27700_6_162_344.png already exists. Set `overwrite` to TRUE.
#> Light_27700_6_163_344.png already exists. Set `overwrite` to TRUE.

# Convert tiles into georeferenced rasters
png2rast <- function(path, bbox, crs){
  img <- png::readPNG(path) * 255
  img <- terra::rast(img)
  terra::RGB(img) <- c(1,2,3)
  terra::ext(img) <- bbox[c(1,3,2,4)]
  terra::crs(img) <- crs

  return(img)
}

imgList <- lapply(res, function(t){ png2rast(t$file_path, t$bbox, t$crs) })

# Combine tiles into basemap
basemap <- do.call(terra::mosaic, imgList)

# Add basemap to the tmap
base_plot <- tm_shape(basemap,
                      bbox = results_df) +
                tm_rgb() +
                query_plot

base_plot
```

<img src="fig-plottiles-1.png" style="display: block; margin: auto;" />

