11 libraries for creating data visualisations

Programming libraries are sets of code files that are used to develop software. Their purpose is to facilitate programming by providing common functionalities that have already been solved by other programmers.

Libraries are an essential component for developers to be able to program in a simple way, avoiding duplication of code and minimising errors. They also allow for greater agility by reducing development time and costs.

These advantages are reflected when using libraries to make visualisations using popular languages such as Python, R and JavaScript.

Python libraries

Python is one of the most widely used programming languages. It is an interpreted language (easy to read and write thanks to its similarity to the human language), multiplatform, free and open source. In this previous article you can find courses to learn more about it.

Given its popularity, it is not surprising that we can find many libraries on the web that make creating visualisations with this language easier, such as, for example:

Matplotlib

•  Description:

Matplotlib is a complete library for generating static, animated and interactive visualisations from data contained in lists or arrays in the Python programming language and its mathematical extension NumPy.

• Supporting materials:

The website contains examples of visualisations with source code to inspire new users, and various guides for both beginners and more advanced users. An external resources section is also available on the website, with links to books, articles, videos and tutorials produced by third parties.

Seaborn

•  Description:

Seaborn is a Python data visualisation library based on matplotlib. It provides a high-level interface to draw attractive and informative statistical graphs.

• Supporting materials:

Tutorials are available on their website, with information on the API and the different types of functions, as well as a gallery of examples. It is also advisable to take a look at this paper by The Journal of Open Source Software.

Bokeh

•  Description:

Bokeh is a library for interactive data visualisation in a web browser. Its functions range from the creation of simple graphs to the creation of interactive dashboards.

• Supporting materials:

Users can find detailed descriptions and examples describing the most common tasks in the guide. The guide includes the definition of basic concepts, working with geographic data or how to generate interactions, among others.

The website also has a gallery with examples, tutorials and a community section, where doubts can be raised and resolved.

Geoplotlib

• Description:

Geoplotlib is an open source Python library for visualising geographic data. It is a simple API that produces visualisations on top of OpenStreetMap tiles. It allows the creation of point maps, data density estimators, spatial graphics and shapefiles, among many other spatial visualisations.

• Supporting materials:

In Github you have available this user guide, which explains how to load data, create colour maps or add interactivity to layers, among others. Code examples are also available.

Libraries for R

R is also an interpreted language for statistical computing and the creation of graphical representations (you can learn more about it by following one of these courses). It has its own programming environment, R-Studio, and a very flexible and versatile set of tools that can be easily extended by installing libraries or packages - using its own terminology - such as those detailed below:

ggplot 2

•  Description:

Ggplot is one of the most popular and widely used libraries in R for the creation of interactive data visualisations. Its operation is based on the paradigm described in The Grammar of Graphics for the creation of visualisations with 3 layers of elements: data (data frame), the list of relationships between variables (aesthetics) and the geometric elements to be represented (geoms).

• Supporting materials:

On its website you can find various materials, such as this cheatsheet that summarises the main functionalities of ggplot2. This guide begins by explaining the general characteristics of the system, using scatter diagrams as an example, and then goes on to detail how to represent some of the most popular graphs. It also includes a number of FAQs that may be of help.

Lattice

•  Description:

Lattice is a data visualisation system inspired by Trellis or raster graphs, with a focus on multivariate data.  Lattice's user interface consists of several generic "high-level" functions, each designed to create a particular type of graph by default.

• Supporting materials:

In this manual you can find information about the different functionalities, although if you want to learn more about them, in this section of the web you can find several manuals such as R Graphics by Paul Murrell or Lattice by Deepayan Sarkar.

Esquisse

•  Description:

Esquise allows you to interactively explore data and create detailed visualisations with the ggplot2 package through a drag-and-drop interface. It includes a multitude of elements: scatter plots, line plots, box plots, multi-axis plots, sparklines, dendograms, 3D plots, etc.

• Supporting materials:

Documentation is available via this link, including information on installation and the various functions. Information is also available on the R website.

Leaflet

• Description:

Leaflet allows the creation of highly detailed, interactive and customised maps. It is based on the JavaScript library of the same name.

• Supporting materials:

On this website you have documentation on the various functionalities: how the widget works, markers, how to work with GeoJSON & TopoJSON, how to integrate with Shiny, etc.

Librerías para JavaScript

JavaScript is also an interpreted programming language, responsible for making web pages more interactive and dynamic. It is an object-oriented, prototype-based and dynamic language.

Some of the main libraries for JavaScript are:

D3.js

• Description:

D3.js is aimed at creating data visualisations and animations using web standards, such as SVG, Canvas and HTML. It is a very powerful and complex library.

• Supporting materials:

On Github you can find a gallery with examples of the various graphics and visualisations that can be obtained with this library, as well as various tutorials and information on specific techniques.

Chart.js

•  Description:

Chart.js is a JavaScript library that uses HTML5 canvas to create interactive charts. Specifically, it supports 9 chart types: bar, line, area, pie, bubble, radar, polar, scatter and mixed.

• Supporting materials:

On its own website you can find information on installation and configuration, and examples of the different types of graphics. There is also a section for developers with various documentation.

Other libraries

Plotly

• Description:

Plotly is a high-level graphics library, which allows the creation of more than 40 types of graphics, including 3D graphics, statistical graphics and SVG maps.  It is an Open Source library, but has paid versions.

Plotly is not tied to a single programming language, but allows integration with R, Python and JavaScript.

• Supporting materials:

It has a complete website where users can find guides, use cases by application areas, practical examples, webinars and a community section where knowledge can be shared.

 

Any user can contribute to any of these libraries by writing code, generating new documentation or reporting bugs, among others. In this way they are enriched and perfected, improving their results continuously.

Do you know of any other library you would like to recommend? Leave us a message in the comments or send us an email to dinamizacion@datos.gob.es.

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Content prepared by the datos.gob.es team.