CHOROPLETH MAP OF US POPULATION DENSITY DOWNLOAD

The rnaturalearth package is better for obtaining any map data in the world via an API provided by (South 2017). A couple notable examples include rnaturalearth and USAboundaries. There are numerous packages for accessing geo-spatial data as simple features data structures. 14 The sf package itself does not really provide geo-spatial data – it provides the framework and utilities for storing and computing on geo-spatial data structures in an opinionated way. This allows each row to represent the real unit of observation/interest – whether it’s a polygon, multi-polygon, point, line, or even a collection of these features – and as a result, works seamlessly inside larger tidy workflows. The key idea behind sf is that it stores geo-spatial geometries in a list-column of a data frame. The sf R package is a modern approach to working with geo-spatial data structures based on tidy data principles (Pebesma 2018 Wickham 2014 b). It’s worth noting that the locationmode is currently limited to countries and US states, so if you need to a different geo-unit (e.g., counties, municipalities, etc), you should use the choroplethmapbox trace type and/or use a “custom” mapping approach as discussed in Section 4.2. 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?.Note that we need to limit the CPT range to leave out the very high density states like Monaco otherwise all others would get the same color. codes, vals = GMT.mk_codes_values(paises, paises, region='eu') Ĭreate a Categorical CPT that allows plotting the choropleth. Generate two vector with country codes (2 chars codes) and the population density for European countries.

CHOROPLETH MAP OF US POPULATION DENSITY DOWNLOAD

Load packages needed to download data and put into a DataFrame paises = CSV.File(HTTP.get('').body, delim=' ') |> DataFrame Įxtract the Europe countries from the DCW file eu = coast(DCW='=EU+z', dump=true) To facilitate this, some tools were added to the Julia wrapper.

GMT lets us plot choropleth maps but the process is not straightforward because we need to put the color information in the header of a multi-segment file.

A choropleth is a thematic map where areas are colored in proportion to a variable such as population density.

Choropleth map of us population density