R City Views

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.

• Installation
• Create your own in R
• Create your own in Shiny
• Acknowledgements

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!

Installation

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.

```r

install.packages("remotes") # Uncomment if 'remotes' package is not installed

remotes::install_github("koenderks/rcityviews", dependencies = TRUE) ```

After installation, you can load the package into the R session using the following command.

r library(rcityviews)

Create your own in R

Finding a city to map

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. It returns a data.frame, so you can select a specific city by its row index (i.e., [rowIndex, ]).

```r 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 -55.07

city <- list_cities(match = "Ams")[2, ] # Select Amsterdam ```

If you cannot find your preferred city in the internal package database but know its coordinates, you can use the new_city() function to manually specify the location using the latitude (lat)and longitude (long) arguments.

```r city <- new_city(name = "Lagos", country = "Portugal", lat = 37.10, long = -8.68)

> Discovered Lagos, Portugal at 37.1 / -8.68!

```

Alternatively, if you don't know the exact coordinates of your city, you can leave the lat and long arguments empty, and the new_city() function will automatically attempt to find the coordinates using geocoding.

```r city <- new_city(name = "Coober Pedy", country = "Australia")

> Discovered Coober Pedy, Australia at -29.0133682 / 134.7536164!

```

Creating the map

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). By default, cityview() is called with the cache = TRUE flag, which means that it will cache the map data so that you can quickly try out different themes (see below).

```r p <- cityview(name = "Amsterdam", zoom = 1) # or cityview(name = city)

see ?cityview for more input parameters of this function

```

Saving the map

Finally, 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.

r 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.

r 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.

Styling the map

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.

```r

For example: black, beige and white theme, streets only

myTheme <- list( colors = list( background = "#232323", water = "#232323", landuse = "#232323", contours = "#232323", streets = "#d7b174", rails = c("#d7b174", "#232323"), buildings = "#232323", text = "#ffffff", waterlines = "#232323" ), 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") ```

You can store the custom theme in the package cache for retrieval in a future R session with the city_themes() function. This is illustrated below for the myTheme list.

```r

Store the theme in the persistent cache

city_themes(name = "blackyellow", theme = myTheme)

Retreive the theme from the persistent cache (e.g., in a future R session)

city_themes(name = "blackyellow")

Remove the theme from the persistent cache

city_themes(name = "blackyellow", remove = TRUE) ```

To use a custom font in myTheme[["font"]][["family"]], simply donwload a .ttf file of the font from the web, save it as path/to/font/<font_name>.ttf and register the font via the code below. Then, use <font_name> for family.

r sysfonts::font_add("<font_name>", "path/to/font/<font_name>.ttf")

Enclosing the map

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).

Other display options

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).

Create your own in Shiny

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().

Acknowledgements

The data is available under the Open Database License.