[](https://github.com/ropensci/osmplotr/actions/workflows/R-CMD-check.yaml) [](https://app.codecov.io/gh/ropensci/osmplotr) [](https://www.repostatus.org/)  [](https://cran.r-project.org/package=osmplotr/) [](https://cran.r-project.org/package=osmplotr/) [](https://github.com/ropensci/software-review/issues/27) R package to produce visually impressive customisable images of OpenStreetMap (OSM) data downloaded internally from the [overpass api](http://overpass-api.de/). The above map was produced directly from `osmplotr` with no further modification. This `README` briefly demonstrates the following functionality: [1. Quick Introduction](#1%20intro) [2. Installation](#2%20installation) [3. A Simple Map](#3%20simple%20map) [4. Highlighting Selected Areas](#4%20highlighting%20areas) [5. Highlighting Clusters](#5%20highlighting%20clusters) [6. Highlighting Areas Bounded by Named Highways](#6%20highlighting%20with%20highways) [7. Data Surfaces](#7%20data%20surfaces) [8. Gallery](#8%20gallery) ------------------------------------------------------------------------ ## 1. Quick Introduction But first the easy steps to map making: 1. Specify the bounding box for the desired region ``` r bbox <- get_bbox (c (-0.15, 51.5, -0.10, 51.52)) ``` 2. Download the desired data—in this case, all building perimeters. ``` r dat_B <- extract_osm_objects (key = "building", bbox = bbox) ``` 3. Initiate an `osm_basemap` with desired background (`bg`) colour ``` r map <- osm_basemap (bbox = bbox, bg = "gray20") ``` 4. Overlay objects on plot in the desired colour. ``` r map <- add_osm_objects (map, dat_B, col = "gray40") ``` 5. Print the map to graphics device of choice ``` r print_osm_map (map) ``` ------------------------------------------------------------------------ ## 2. Installation First install the package ``` r install.packages ("osmplotr") ``` or the development version ``` r devtools::install_github ("ropensci/osmplotr") ``` And then load it in the usual way ``` r library (osmplotr) ``` ------------------------------------------------------------------------ ## 3. A Simple Map Simple maps can be made by overlaying different kinds of OSM data in different colours: ``` r dat_H <- extract_osm_objects (key = "highway", bbox = bbox) dat_P <- extract_osm_objects (key = "park", bbox = bbox) dat_G <- extract_osm_objects (key = "landuse", value = "grass", bbox = bbox) ``` ``` r map <- osm_basemap (bbox = bbox, bg = "gray20") map <- add_osm_objects (map, dat_B, col = "gray40") map <- add_osm_objects (map, dat_H, col = "gray80") map <- add_osm_objects (map, dat_P, col = "darkseagreen") map <- add_osm_objects (map, dat_G, col = "darkseagreen1") print_osm_map (map) ``` ------------------------------------------------------------------------ ## 4. Highlighting Selected Areas `osmplotr` is primarily intended as a data visualisation tool, particularly through enabling selected regions to be highlighted. Regions can be defined according to simple point boundaries: ``` r pts <- cbind ( c (-0.115, -0.13, -0.13, -0.115), c (51.505, 51.505, 51.515, 51.515) ) ``` OSM objects within the defined regions can then be highlighted with different colour schemes. `cols` defines colours for each group (with only one here), while `bg` defines the colour of the remaining, background area. ``` r map <- osm_basemap (bbox = bbox, bg = "gray20") map <- add_osm_groups (map, dat_B, groups = pts, cols = "orange", bg = "gray40") map <- add_osm_objects (map, london$dat_P, col = "darkseagreen1") map <- add_osm_groups ( map, london$dat_P, groups = pts, cols = "darkseagreen1", bg = "darkseagreen", boundary = 0 ) print_osm_map (map) ``` Note the `border = 0` argument on the last call divides the park polygons precisely along the border. The same map highlighted in dark-on-light: ``` r map <- osm_basemap (bbox = bbox, bg = "gray95") map <- add_osm_groups (map, dat_B, groups = pts, cols = "gray40", bg = "gray85") map <- add_osm_groups (map, dat_H, groups = pts, cols = "gray20", bg = "gray70") print_osm_map (map) ``` ------------------------------------------------------------------------ ## 5. Highlighting Clusters `add_osm_groups` also enables plotting an entire region as a group of spatially distinct clusters of defined colours. Groups can be defined by simple spatial points denoting their centres: ``` r set.seed (2) ngroups <- 12 x <- bbox [1, 1] + runif (ngroups) * diff (bbox [1, ]) y <- bbox [2, 1] + runif (ngroups) * diff (bbox [2, ]) groups <- as.list (data.frame (t (cbind (x, y)))) ``` Calling `add_osm_groups` with no `bg` argument forces all points lying outside those defined groups to be allocated to the nearest groups, and thus produces an inclusive grouping extending across an entire region. ``` r map <- osm_basemap (bbox = bbox, bg = "gray20") map <- add_osm_groups ( map, dat_B, groups = groups, cols = rainbow (length (groups)), border_width = 2 ) print_osm_map (map) ``` ------------------------------------------------------------------------ ## 6. Highlighting Areas Bounded by Named Highways An alternative way of defining highlighted groups is by naming the highways encircling desired regions. ``` r # These highways extend beyond the previous, smaller bbox bbox_big <- get_bbox (c (-0.15, 51.5, -0.10, 51.52)) highways <- c ( "Davies.St", "Berkeley.Sq", "Berkeley.St", "Piccadilly", "Regent.St", "Oxford.St" ) highways1 <- connect_highways (highways = highways, bbox = bbox_big) highways <- c ("Regent.St", "Oxford.St", "Shaftesbury") highways2 <- connect_highways (highways = highways, bbox = bbox_big) highways <- c ( "Piccadilly", "Shaftesbury.Ave", "Charing.Cross.R", "Saint.Martin", "Trafalgar.Sq", "Cockspur.St", "Pall.Mall", "St.James" ) highways3 <- connect_highways (highways = highways, bbox = bbox_big) highways <- c ( "Charing.Cross", "Duncannon.St", "Strand", "Aldwych", "Kingsway", "High.Holborn", "Shaftesbury.Ave" ) highways4 <- connect_highways (highways = highways, bbox = bbox_big) highways <- c ( "Kingsway", "Holborn", "Farringdon.St", "Strand", "Fleet.St", "Aldwych" ) highways5 <- connect_highways (highways = highways, bbox = bbox_big) groups <- list (highways1, highways2, highways3, highways4, highways5) ``` And then passing these lists of groups returned by `connect_highways` to `add_osm_groups`, this time with some Wes Anderson flair. ``` r map <- osm_basemap (bbox = bbox, bg = "gray20") library (wesanderson) cols <- wes_palette ("Darjeeling", 5) map <- add_osm_groups ( map, dat_B, groups = groups, boundary = 1, cols = cols, bg = "gray40", colmat = FALSE ) map <- add_osm_groups ( map, dat_H, groups = groups, boundary = 0, cols = cols, bg = "gray70", colmat = FALSE ) print_osm_map (map) ``` ------------------------------------------------------------------------ ## 7. Data Surfaces Finally, `osmplotr` contains a function `add_osm_surface` that spatially interpolates a given set of spatial data points and colours OSM objects according to a specified colour gradient. This is illustrated here with the `volcano` data projected onto the `bbox`. ``` r x <- seq (bbox [1, 1], bbox [1, 2], length.out = dim (volcano) [1]) y <- seq (bbox [2, 1], bbox [2, 2], length.out = dim (volcano) [2]) xy <- cbind (rep (x, dim (volcano) [2]), rep (y, each = dim (volcano) [1])) z <- as.numeric (volcano) dat <- data.frame (x = xy [, 1], y = xy [, 2], z = z) ``` ``` r map <- osm_basemap (bbox = bbox, bg = "gray20") cols <- gray (0:50 / 50) map <- add_osm_surface (map, dat_B, dat = dat, cols = cols) # Darken cols by ~20% map <- add_osm_surface ( map, dat_H, dat = dat, cols = adjust_colours (cols, -0.2) ) map <- add_colourbar (map, cols = cols, zlims = range (volcano)) map <- add_axes (map) print_osm_map (map) ``` ------------------------------------------------------------------------ ## 8. Gallery Got a nice `osmplotr` map? Please contribute in one of the following ways: 1. Fork repo, add link to `README.md/.Rmd`, and send pull request; or 2. Open issue with details; or 3. Send email to address in [`DESCRIPTION`](https://github.com/ropensci/osmplotr/blob/master/DESCRIPTION). ------------------------------------------------------------------------ See package vignettes ([basic maps](https://docs.ropensci.org/osmplotr/articles/basic-maps.html) and [data maps](https://docs.ropensci.org/osmplotr/articles/data-maps.html)) for a lot more detail and further capabilities of `osmplotr`. Please note that this project is released with a [Contributor Code of Conduct](https://ropensci.org/code-of-conduct/). By participating in this project you agree to abide by its terms. ------------------------------------------------------------------------ [](https://ropensci.org/)