By simply providing a z attribute, plotly_geo() objects will try to create a choropleth, but you’ll also need to provide locations and a locationmode. state data from the datasets package (R Core Team 2016). Comparatively speaking, choroplethmapbox is more powerful because you can fully specify the feature collection using GeoJSON, but the choropleth trace can be a bit easier to use if it fits your use case.įigure 4.6 shows the population density of the U.S. In addition to scatter traces, both of the integrated mapping solutions (i.e., plot_mapbox() and plot_geo()) have an optimized choropleth trace type (i.e., the choroplethmapbox and choropleth trace types). However, if you run into limitations with plotly’s mapping functionality, there is a very rich set of tools for interactive geospatial visualization in R, including but not limited to: leaflet, mapview, mapedit, tmap, and mapdeck (Robin Lovelace 2019). That said, there are benefits to using plotly-based maps since the mapping APIs are very similar to the rest of plotly, and you can leverage larger plotly ecosystem (e.g., linking views client side like Figure 16.23). It’s worth noting that plotly aims to be a general purpose visualization library, and thus, doesn’t aim to be the most fully featured geo-spatial visualization toolkit. Section 4.2 covers making sophisticated maps (e.g., cartograms) using the sf R package, but it’s also possible to make custom plotly maps via other tools for geo-computing (e.g., sp, ggmap, etc). On the other hand, the custom mapping approach offers complete control since you’re providing all the information necessary to render the geo-spatial object(s). The integrated approach is convenient if you need a quick map and don’t necessarily need sophisticated representations of geo-spatial objects. Currently there are two supported ways of making integrated maps: either via Mapbox or via an integrated d3.js powered basemap. Integrated maps leverage plotly.js’ built-in support for rendering a basemap layer.
Generally speaking the approaches fall under two categories: integrated or custom. There are numerous ways to make a map with plotly – each with it’s own strengths and weaknesses. 19.1 Assignment, subsetting, and iteration.17.2.8 Accumulating and managing event data.16.4.3 Statistical queries with ggplotly().1.3.5 Data visualization best practices.
1.1 Why interactive web graphics from R?.
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