Data visualization tools for developers

Data visualization tools for developers
Data visualization tools for developers


To work directly with data, the most popular choice is the R statistical package. This is a very complex tool that is widely used in the scientific and academic world. It can handle all types of mathematical and statistical operations. Another popular tool in the world of science is the Matlab software, or its free open-code alternative Octave. Both can be used to generate data visualizations.

If we are interested in developing pure and simple software, a common choice is the Python programming language to build visualization applications. There is a wide variety of libraries available in this language: Numpy/Scipy, Pandas, Anaconda, MatPlotLib, etc. More information about these is available here. If we prefer the world of PHP to Python, we can opt for jpGraph


Finally, as regards data handling, a very popular choice is the Processing tool, an integrated open-code software development environment which has several years of software development at its back. It has its own Java-based development language.

If the preference is to focus on generating data visualizations and not so much creating, editing and handling data, there are many Javascript libraries available that allow data visualization in any Web browser. One of the best known is D3.js, which has become increasingly popular and accepted among developers. This tool allows the developer to generate truly complex visualizations, while its learning curve is also somewhat demanding.


Alternatives to D3 include Dygraphs, Rickshaw, jqPlot, JIT, Highcharts, Flot, jQuery Visualize and Raphaël, among which the latter is also quite popular and widely used.

Geovisualization software

The geovisualization component takes on added importance if we want to represent data based on their location. Currently, there is no lack of accessible solutions to present data visualizations on a map in a simple fashion.

One of the best known is CartoDB, mentioned in a previous post. This is a cloud platform that offers easy and user-friendly data storage in the cloud, the option of referencing the data geographically and generating visualizations. CartoDB documentation can be found here. We will be showing how to use the CartoDB API and geospatial database queries in a subsequent article.

Apart from CartoDB, another popular option are the tools developed by Mapbox, which can be used to prepare visualizations of map-based data that can be viewed on both Web browsers and iOS or Android mobile terminals.

If we are interested exclusively in the web part of map-based visualization development, the most popular solution may be Google Maps. However, it is not the most complete option. Currently, the degree of development of the OpenLayers and Leaflet libraries may make them the most appropriate choice, if we want to develop a visualization application of geolocated data ready to be accessed from web browsers or mobile devices. Not to mention that both CartDB and Mapbox provide data visualization javascript libraries as part of the tool suite for developers, named cartodb-js and mapboxjs, respectively.

There are several libraries that can still be used as alternatives to the tools mentioned above, without leaving the Javascript ecosystem, although they have not been updated for quite a long time. These are ModestMaps, developed by Stamen, Polymaps or Kartograph


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