Last week, some examples of creating visualizations with htmlwidgets and R were presented. Fortunately, there are many more options available for creating nice visualizations. Tools and libraries exist for all your favorite languages. This post plans to provide a quick reference list of some of the possible options for creating data visualizations.
A full-purpose programming language, python has now also become a tool-of-choice for many in data science. Pandas and Scikit-learn provide many of necessary functions for data analysis and machine learning. Below is a list of some of the leading tools for creating visualizations in Python. See the following project for these examples, Python Visualization.
A very popular language for data science, originally built by/for statisticians but now very widely used.
A more recent newcomer, Julia is quickly gaining popularity among data scientists. Due to its young age, the Julia visualization tools are less mature, but they are advancing quickly. A sample project for Julia can be seen at, Simple Julia Plots.
Gadfly - A Julia library for visualizations. Inspired by ggplot2 for R. It is not really interactive, but it is a great start.
Escher - Beautiful, interactive web UIs in Julia. Escher is rather new, so it is definitely a project to watch. It uses gadfly for graphics.
Continue following the blog, (@senseplatform), and Facebook for more examples of creating wonderful visualizations with R, Python, Julia, and Sense.