What is Data Visualization?
The data explosion in recent years has led to a corresponding need for businesses and organizations to communicate information in a way that’s accessible and engaging. Visual content is becoming an increasingly popular means for sharing data.
Data visualization relies on visual representations like graphs or charts to convey raw data. Presenting data in a visual manner makes it easier to understand and faster to process, even for those who aren’t mathematically inclined or trained in analytics. Each visual data representation tells a story about the data, which can lead to more informed business decisions and favorable outcomes.
There are many different kinds of data visualizations, such as maps, histograms, scatter plots, and pie charts. Those who know how to present information in visually engaging stories have the power to help make sense of past events, provide insights on current trends, and offer predictions for the future.
What is a Data Visualization Library?
Data visualization libraries are designed to help users break down complicated ideas and create visualizations that depict this information. Choosing a data visualization library plays an important role when working with large or complicated datasets, as it can affect the kinds of insights taken from the data. There are many options to choose from, so it’s important to learn about the specific features of each to decide which library is best for your data visualization needs.
This article will explore two popular libraries, Plotly and ggplot2, to see which one comes out ahead for data visualization purposes.
What is Plotly?
Plotly is a free, open-source library that offers statistical, analytics, and online graphing tools, as well as scientific graphing libraries. This library is often used for data visualization purposes, as it provides users with ample options for creating their own interactive visualizations. Plotly has an extensive collection of graphs: heatmaps, network graphs, 3D charts, histograms, and contour plots, among others.
Benefits & Drawbacks of Using Plotly for Data Visualization
Users around the world cite many benefits to working with Plotly for their data visualization needs. Here are just a few of the perks of using Plotly, as well as a few drawbacks, to this library:
Benefits
- Control over what is being plotted: Plotly is based on Pandas, which means that users can quickly execute complicated transformations on data before it’s plotted. Those working with Plotly can decide how many graphs to display simultaneously, as well as how many dropdown menus and interactive tools they wish to have displayed.
- Simple syntax: The syntax required to create plots in Plotly is very simple. Even users from non-technical backgrounds can design their own engaging plots with the help of Plotly GUI.
- Interactivity: Plotly’s visualization library offers users enhanced interactivity features, such as the ability to interact with graphs on display and zoom in and out, which make for a more engaging storytelling experience. In addition, these interactive tools are customizable, and users can add interactive features like sliders, dropdowns, or buttons when showing various graph perspectives.
- Support: Plotly provides users with an extensive support community. Their online community forum provides a means for Plotly users to interact with one another, as well as to ask questions.
- Compatibility: Plotly is compatible with many languages, such as Python, MATLAB, Julia, and R.
- Collaboration: Users can easily share their plots online with others.
- Large number of chart types offered: Not only can Plotly plot every Seaborn and Matplotlib chart, but it also has a robust chart and graph offering of its own, consisting of:
- Scientific charts like Radar Charts and Network Graphs
- Geological maps, including interactive, 3D Plots
- Statistical charts, such as Probability Tree Plots and Parallel Categories
- Financial charts like Funnels, Candlesticks, and Bullet Charts
Drawbacks
- Because the plots created by those working with the community version of Plotly are public, it’s possible for anyone to view them.
- Color options are limited in the community version of Plotly since there are fewer color palette options.
- The community version of Plotly places an upper limit on the API calls each day.
What is ggplot2?
ggplot2 is an open-source data visualization system designed to help users create graphics and data visualizations. This package implements Leland Wilkinson’s The Grammar of Graphics, which can be applied to data visualization in order to separate graphs into semantic components like layers and scales. When working with ggplot2, once the data has been provided, users decide how to map variables as well as which graphical primitives to apply, and it does the rest to create a data visualization.
Benefits & Drawbacks of Using GGplot for Data Visualization
Benefits
- Easily add complexity to data visualizations: ggplot2 allows users to incorporate various forms of complexity to their visualizations without additional hassle, and also remove them with ease if necessary.
- Allows users to use a single system for data visualizations: Instead of having to use several visualization platforms when working with data, those working with ggplot2 can use just this one system. It provides a means of creating layered plots and other complex visualizations.
- Ability to save plots as objects: When working with ggplot2, users are able to save plots as objects. This is particularly helpful for those who wish to create several versions of a plot and don’t wish to repeat lines of code.
- Aesthetics: ggplot2’s default colors and settings tend to appeal to users more than those of other systems. Elements like margins, points, axis titles, and tickmarks look better when using ggplot2.
Drawbacks
- Some output types are not handled well using ggplot2.
- When working with complex figures, the syntax in ggplot2 can be unwieldy.
- Ggplot2 has a different syntax from the rest of R.
Which Comes out Ahead for Data Visualization?
Aesthetically, many users consider ggplot2 to be better looking than Plotly, due to its margins and points. In terms of speed, ggplot2 tends to run much slower than Plotly. With regard to integration, both Plotly and ggplot2 can integrate with a variety of tools, like Python, MATLAB, Jupyter, and React. Both Plotly and ggplot2 are good options for both large and small businesses. Each has received relatively similar SmartScores (9.4 for Plotly and 9.3 for ggplot2). ggplot2 comes out slightly ahead of Plotly for user satisfaction, with a 96% satisfaction rate compared to 90% for Plotly.
Overall, both Plotly and ggplot2 provide great options for Data Analysts working with data visualizations. Yet, when deciding which one is a better match for your business, key features of each library, such as speed of use and overall layout, must be considered.
Start Learning Data Visualization with Hands-On Classes
Are you interested in learning more about how to create stunning and helpful data visualizations? If so, check out the more than six dozen live online data visualization courses available for students who want to study how to design their own data visualizations in the live online learning format. Topics include Tableau, Python, Excel, and Power BI, among others.
Noble Desktop’s data analytics classes are open to students with no prior coding experience. These full-time and part-time courses are taught by top New York Data Analysts and provide timely and hands-on training for those wishing to learn more about topics like Python, SQL, Excel, or data science, among others.
Noble Desktop’s Classes Near Me tool is designed for those who want to locate other data visualization courses in the area. More than 200 course options are available that provide training for those who are new to working with data, as well as those with prior experience who hope to perfect their data visualization skills.