This repository is an homage to the programming language R, open-source
geographic data and the art of map making. It provides code and examples to
render customizable stylized city maps using data from
OpenStreetMap. Take a look at the
tutorial for
a quick guide on how to get started.
Every three hours this repository creates and tweets a view of a random city.
You can find all city views created so far at the twitter handle
@rcityviews. Please do not hesitate to share
your own creations using the hashtag #rcityviews!
The functionality in this repository is implemented in the R package
rcityviews. This package is not available on CRAN but can be obtained via
GitHub by running the command below in R.
# install.packages("remotes") # Uncomment if you do not have the 'remotes' package installed
remotes::install_github("koenderks/rcityviews", dependencies = TRUE)After installation, you can load the package into the R session using the
following command.
library(rcityviews)First, you can search for a city name in the package database using the
list_cities() function. This function looks in the internal database and finds
any city name that contains the expression in match.
list_cities(match = "Ams")
#> name country lat long
#> 1356 Amstelveen The Netherlands 52.32 4.86
#> 1357 Amsterdam The Netherlands 52.37 4.89
#> 1358 Amstetten Austria 48.13 14.86
#> 25857 New Amsterdam Guyana 6.25 -57.53
#> 26031 Nieuw Amsterdam Suriname 5.91 -55.07If you cannot find your preferred city in the internal package database, you can
use the new_city() function to manually specify a location using its latitude
and longitude coordinates.
city <- new_city(name = "Lagos", country = "Portugal", lat = 37.10, long = -8.68)
#> Discovered the city of Lagos, Portugal at 37.1° / -8.68°!Second, once you have obtained the name of the city you want to view or have
specified a location of a city, you can use the cityview() function to create
a ggplot2 object. Use the zoom argument to zoom in on your city (e.g.,
zoom > 1, decreases computation time) or zoom out of your city (e.g.,
zoom < 0.5, increases computation time).
p <- cityview(name = "Amsterdam", zoom = 1) # or cityview(name = city)
# see ?cityview for more input parameters of this functionFinally, render times in R or RStudio can be very long for crowded spatial
images. It is therefore recommended to directly save the image in a
500mm x 500mm format. Typically, the ideal way to do this given a ggplot2
object named p is to execute the command below.
ggplot2::ggsave(filename = "Amsterdam.png", plot = p, height = 500, width = 500, units = "mm", dpi = 100)However, you can also do this instantly by providing a filename directly to the
cityview() function via its filename argument. To save rendering time, the
image is exported in an appropriate size and the function does not return a
ggplot2 object.
cityview(name = "Amsterdam", filename = "Amsterdam.png")
For personal (non-commercial) printing it is advised to use the option
license = FALSE and save the image to a .pdf or .svg file. Afterwards, the
image is best printed in a 500mm x 500mm format.
There are ten pre-specified themes that can be used to style the image. The
image above is created using theme = "vintage" (the default), but other
options for the theme argument include modern (top left), bright (top
middle), delftware (top right), comic (middle left), rouge (middle
middle), original (middle right), midearth (bottom left), batik (bottom
middle) and vice (bottom right).
In addition to the ten pre-specified themes, the package provides full flexibility to customize the theme by providing a named list. This is demonstrated in the code block below.
# For example: black, beige and white theme, streets only
myTheme <- list(
colors = list(
background = "#232323",
water = NA,
landuse = NA,
contours = NA,
streets = "#d7b174",
rails = c("#d7b174", "#232323"),
buildings = NA,
text = "#ffffff",
waterlines = NA
),
font = list(
family = "serif",
face = "bold",
scale = 1,
append = "\u2014"
),
size = list(
borders = list(
contours = 0.15,
water = 0.4,
canal = 0.5,
river = 0.6
),
streets = list(
path = 0.2,
residential = 0.3,
structure = 0.35,
tertiary = 0.4,
secondary = 0.5,
primary = 0.6,
motorway = 0.8,
rails = 0.65,
runway = 3
)
)
)
cityview(name = "Rio de Janeiro", zoom = 0.5, theme = myTheme, border = "square", filename = "Rio.png")
There are several types of borders that can be used to enclose the city. The
image above is created using border = "square", but other options for the
border argument include none (the default), circle (left), rhombus
(middle), square, hexagon, octagon, decagon and bbox (right).
There are three other arguments to the cityview() function that can be used to
tailor the image to your liking. First, the argument halftone allows you to
add a colored dotted dither to the image (e.g., halftone = "#ffffff", left).
Second, setting legend = TRUE adds a distance measurer and a compass to the
image (middle). Third, the argument places takes an integer and adds that
amount of names of towns, villages, suburbs, quarters and neighbourhoods to the
image (e.g., places = 10, right).
You can make your own images without having to code using an R Shiny
implementation of the package. A live version of the application can be found
here but it is also easily
accessible from within R by calling the function cityview_shiny().
The data is available under the Open Database License.
![github-actions[bot] avatar](https://avatars.githubusercontent.com/in/15368?v=4 "github-actions[bot]")
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent