It also brings to the fore the poor state of the economies of central Asia, which is a feature not emphasized by any of the other visualizations. This does a much better job at allowing us to understand Africa.
We can improve our ability to distinguish between the countries with smaller GDP by changing to a multi-color scale and transforming the data, as shown below.
The choropleth below gives a very poor understanding of the distributions of GDPs, essentially splitting the world into three tiers: US, China, and others. However, it introduces a different problem. All these word clouds and alternatives to word clouds are created in Displayr which provides a more flexible and powerful alternative for word or data visualization than PowerPoint or Tableau.Ī choropleth solves the cartogram's overplotting problem. The rest of this post explores some better alternatives to word clouds. Unlike most charts, a word cloud gets better with the more things that it displays. While word clouds are often ridiculed, they do scale well. The size of each country in the cloud is in proportion to its GDP. A word cloud with phrases can be a useful addition or alternative to regular word clouds. This visualization below is a word cloud (or sometimes referred to as a phrase cloud), which shows the whole names of countries (i.e., phrases) rather than just words. The common option: A word (phrase) cloud. The visualization examples in this post use the GDP of 185 countries and are created using R. This post illustrates seven alternatives to word or phrase clouds that can be used to visualize data from long lists, each has its own trade-offs. While word clouds (sometimes known as phrase clouds) are often the popular choice, they are not always the best option. Creating a meaningful visualization from data with long lists can be challenging.
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