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In one of our previous posts, we discussed data visualization and the techniques used both in regular projects and in Big Data analysis.
However, knowing the plot does not let you go beyond a theoretical understanding of what toll to apply for certain data. With the abundance of techniques, the data visualization world can overwhelm the newcomer. Here we have collected some best data visualization libraries and platforms.
Data visualization libraries
Though all of the most popular languages in Data Science have built-in functions to create standard plots, building a custom plot usually requires more efforts. To address the necessity to plot versatile formats and types of data. Some of the most effective libraries for popular Data Science languages include the following:
R
The R language provides numerous opportunities for data visualization — and around 12,500 packages in the CRAN repository of R packages. This means there are packages for practically any data visualization task regardless of the discipline. However, if we choose several that suit most of the task, we’d select the following:
ggplot2
ggplot2 is based on The Grammar of Graphics, a system for understanding graphics as composed of various layers that together create a complete plot. Its powerful model of graphics simplifies building complex multi-layered graphics. Besides, the flexibility it offers allows you, for example, to start building your plot with axes, then add points, then a line, a confidence interval, and so on.
ggplot2 is slower than base R and rather difficult to master, it pays huge dividends for any data scientist working in R.
Lattice
Lattice is a system of plotting inspired by Trellis graphics. It helps visualize multivariate data, creating tiled panels of plots to compare different values or subgroups of a given variable. Lattice is built using the grid package for its underlying implementation and it inherits many grid’s features. Therefore, the logic of Lattice should feel familiar to many R users making it easier to work with.
RGL
rgl package is used to create interactive 3D plots. Like Lattice, it’s inspired by the grid package, though it’s not compatible with it. RGL features a variety of 3D shapes to choose from, lighting effects, various “materials” for the objects, as well as the ability to make an animation.
Python
The Python Package Index has libraries for practically every data visualization need, however, the most popular ones offering the broadest range of functionalities are the following:
Matplotlib
Matplotlib is the first Python data visualization and the most widely-used library for generating simple and powerful visualizations in the Python community. The library allows building a wide range of graphs from histograms to heat plots to line plots. Matplotlib is the basis for many other libraries that are designed to work in conjunction with analysis. For instance, libraries like pandas and matplotlib are “wrappers” over Matplotlib allowing access to a number of Matplotlib’s methods with less code.
Seaborn
An example of a popular library, built on top of Matplotlib, is Seaborn. Seaborn’s default styles and colour palettes are much more sophisticated than Matplotlib. Beyond that, Seaborn is a higher-level library, so it is easier to generate certain kinds of plots, including heat maps, time series, and violin plots.
Bokeh
Similar to the ggplot library for R, Bokeh is based on The Grammar of Graphics. It supports streaming and real-time data. Unlike the majority of other data visualization libraries, Bokeh can create interactive, web-ready plots, which can easily output as JSON objects, HTML documents, or interactive web applications. Bokeh has three interfaces with varying degrees of control to accommodate different types of users from users wishing to create simple charts quickly to developers and engineers who wish to define every element of the chart.
Scala
Python and R remain the leading languages for rapid data analysis, however, Scala is becoming the key language in the development of functional products that work with big data, as the latter need stability, flexibility, high speed, scalability, etc
Vegas
Probably, the most functional Scala library for data visualization, Vegas allows plotting specifications such as filtering, transformations, and aggregations. It is similar in structure to Python’s Bokeh and Plotly. Vegas provides declarative visualization, so that the user can focus on specifying what needs to be done with the data, without having to worry about the code implementation.
Breeze-viz
Breeze-viz is based on the prominent Java charting library JFreeChart and has a MATLAB-like syntax. Although Breeze-viz has much fewer opportunities than MATLAB, matplotlib in Python, or R, it is still quite helpful in the process of developing and establishing new models.
Javascript
Javascript may not be among languages adopted for Data Science, but it offers vast opportunities for data visualization, and many libraries for other languages are actually wrappers for JS packages.
D3.js
D3 is called the mother of all visualization libraries since it is the basis for many libraries. Being the oldest library, it remains the most popular and extensive Javascript data visualization library. It uses web standards and is framework agnostic, working smoothly with any Javascript framework. D3 is built for manipulating documents based on data and bring data to life using HTML, SVG, and CSS. D3’s emphasis on web standards gives you the capabilities of modern browsers without coupling to a proprietary framework, combining visualization components and a data-driven approach to DOM manipulation.
Chart.js
Chart.js is a lightweight library that has fully responsive charts including Bar, Bubble, Doughnut, Line, PolarArea, Radar, Scatter. This is an open-source library based on HTML5. V.2 provides mixed chart-types, new chart axis types, and beautiful animations. Designs are simple and elegant with 8 basic chart types, and you can combine the library with moment.js for the time axis.
Online platforms for Data Visualization
At a certain time, especially at the beginning of a project, it is important to generate a lot of charts quickly, in order to explore the scope, depth and the texture of the data and find interesting stories to develop further. There are quite a lot of online platforms to generate data visualization.
Plotly
Plotly as an online platform for data visualization, that is, among all can be accessed from an R or Python Notebook. Plotly is an advanced, online data visualization program with a colourful design. Its forte is making interactive plots, but it offers some charts you won’t find in most packages, like contour plots, candlestick charts, and 3D charts. You can use the chart studio to create web-based reporting templates. You can also modify your own dashboards and interactive graphs for your collaborators to comprehend better.
Tableau
Tableau is a business intelligence system that has a new approach to data analysis. Tableau lets you create charts, graphs, maps and many other graphics. A big advantage of Tableau is the availability of several versions: desktop, server, and a cloud one. You can create and publish dashboards, share them with colleagues, and analyze using different methods. We recommend it because of its simplified drag-and-drop system, all day technical support, and flexible package fees.
Of course, it is just a small fraction of all platforms, tools and libraries available for you to visualize your data in the most effective and transparent way. The data itself, as well as the project goals — be it scientific analysis, business intelligence or creating a website that should incorporate some charts — will prompt you the approach, or, most usually a combination of approaches from quick online plotting to base functions and specialized packages.
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