Visualizing geo-spatial data with sf and plotly

Leveraging mapbox basemaps

Among the four mapping options, the one that excites me the most is plot_mapbox()³. The primary reason to use plot_mapbox() (or plot_geo()) over geom_sf() (or plot_ly()) is that these approaches include a base-map layer which updates dynamically on zoom and can quite helpful for providing geographic context. The base-map styling can be easily customized – especially for plot_mapbox() via the layout.mapbox.style attribute. In addition to a URL to a custom style, you can provide this attribute with a pre-packaged style name. To get a list of those pre-packaged styles, reach into the plotly.js plot schema():

styles <- schema()$layout$layoutAttributes$mapbox$style$values
styles

[1] "basic"             "streets"           "outdoors"          "light"            
[5] "dark"              "satellite"         "satellite-streets"

A nice feature to include with these maps is a dropdown to dynamically change the base-map styling. To do so, use the layout.updatemenus.buttons attribute to control the value of the layout.mapbox.style attribute. Since we wish to modify the layout.mapbox.style attribute, which is part of the plot’s layout, we’ll want each dropdown “button” to trigger a relayout event with the appropriate args. Here is one way to create a list of buttons, one for each of the default styles:

# generate plot.js buttons, one for every style 
style_buttons <- lapply(styles, function(s) {
  list(label = s, method = "relayout", args = list("mapbox.style", s))
})

With our list of style_buttons, we’re now ready to visualize something. Here I’ll leverage the trails data from the mapview package, which has a number of hiking trails of Franconia, Germany.

data(trails, package = 'mapview')

plot_mapbox(trails, color = I("black")) %>%
  layout(
    title = "Selected hiking trails in Franconia",
    mapbox = list(style = "satellite-streets"),
    updatemenus = list(list(y = 0.8, buttons = rev(style_buttons)))
  )

As a side note, you could also use shiny to modify the basemap layer without redrawing the entire plot, but the result here is self-contained HTML/SVG, which is much easier to share, host, scale, and maintain.

Linking views without shiny

In terms of linked brushing in self-contained HTML, plotly is definitely the most advanced crosstalk-compatible htmlwidget. You’ll be able to do more when linking plotly to plotly, but since persistent selection was recently added to DT, let’s demonstrate linking plotly with DT. To link views via crosstalk, you’ll want to supply a data frame of interest to the SharedData$new() method and route the resulting object to any plots that you want to link. By default, the row index (which, in this case, is a simple feature) is used to define the graphical queries, but you can also reference a (discrete) variable to achieve generalized selections (e.g., you could query all trails in a district via SharedData$new(trails, ~district)).⁴ Linking plotly with DT in this way gives us a pretty powerful way to identify simple features both directly and indirectly.

library(crosstalk)
tsd <- SharedData$new(trails)

bscols(
  plot_mapbox(tsd, text = ~FKN, hoverinfo = "text"),
  DT::datatable(tsd)
)

To provide a sneak peak into the power of the linking framework in plotly, let’s leverage a fairly recent feature: brushing of aggregated traces. This example demonstrates how to brush a histogram, but a similar approach could be used to brush other aggregated traces (e.g., add_histogram2d(), add_boxplot(), etc). In fact, one could replicate this example with add_bars() (instead of add_histogram()) by pre-computing bars heights and using a list-column key to assign multiple counties to each bar. In either case (add_bars() or add_histogram()), it is usually a good idea to set layout.barmode = "overlay" so that newly added bars don’t use the plotly.js default of dodging the existing bars. In the case where we let plotly.js dynamically compute aggregates (i.e., add_histogram()) it’s also a good idea to also define xbins (or ybins) so that binning of new bars use the same rules as the existing (i.e. initial) bars.

ncsd <- SharedData$new(nc)

bscols(
  plot_mapbox(ncsd) %>%
    highlight(dynamic = TRUE, persistent = TRUE),
  plot_ly(ncsd, x = ~AREA) %>% 
    add_histogram(xbins = list(start = 0, end = 0.3, size = 0.02)) %>%
    layout(barmode = "overlay") %>% 
    highlight("plotly_selected", persistent = TRUE)
)

If you’re interested in understanding the full power of the linking framework, my 2 day plotly for R workshop is the best way to learn it effectively. I also offer this workshop as an on-site training course, so please get in touch if you have any interest!

1. plot_ly() and geom_sf()

• Render in SVG or WebGL (toWebGL() makes SVG -> WebGL easy) 🔵
  • fill='toself' is still buggy in WebGL 🔴
• Make 3D visuals by adding a z attribute 🔵
  • With a bit of math, you can project any lat/lon data onto a sphere (i.e. globe) and color the globe’s surface according to some measure, like radiation 🔵
  • Unlike 2D lines, 3D lines support a color gradient 🔵
  • Using subplot() with 3D doesn’t currently rescale domains correctly 🔴
• Works with any coordinate system 🔵
  • Currently no way to update a graticule on zoom 🔴

2. plot_mapbox()

• Full customization of base-maps that provide geo-graphic context 🔵
• Simple features can be rendered as data or layout components 🔵
• Numerous trace rendering limitations including no marker.line hoveron='fill' (i.e., stroke and span won’t do anything) 🔴
• WebGL infrastructure allows one to render lots of graphical elements 🔵

3. plot_geo():

• Somewhat customization base-maps provide geo-graphic context 🔵
  • Compared to plot_mapbox(), base-map styling is quite limited 🔴
• Supports orthographic (3D) projections as well as a number of other 2D projections 🔵