Best Data Visualization Software for R

JMP, data analysis software Mac and Windows, combines powerful statistics with interactive visualization. It is simple to import and process data. Drag-and-drop interface, dynamically linked graphics, libraries of advanced analytics functionality, scripting language, and ways to share findings with others allow users to dig deeper into their data with greater ease. JMP was originally developed in 1980 to capture the new value of GUI for personal computers. JMP continues to add cutting-edge statistical methods to the software's functionality with every release. John Sall, the organization's founder, is still Chief Architect.

Dash & Dash Enterprise allow you to build and deploy analytic web applications using Python, R, or Julia. No JavaScript or DevOps are required. The world's most successful companies offer AI, ML and Python analytics at a fraction of the cost of full-stack development. Dash is the way they do it. Apps and dashboards that run advanced analytics such as NLP, forecasting and computer vision can be delivered. You can work in Python, R, or Julia. Reduce costs by migrating legacy per-seat license software to Dash Enterprise's unlimited end-user pricing model. You can deploy and update Dash apps faster without an IT or DevOps staff. You can create pixel-perfect web apps and dashboards without having to write any CSS. Kubernetes makes it easy to scale. High availability support for mission-critical Python apps

Aqua Data Studio is a tool for database administrators and database developers. It also helps data and business analysts manage data platforms and analyze data visually. It offers a unique combination, unlike its competitors, of a full-featured database editor and visual analytics. It also supports flexible import and export, advanced SQL queries optimization, extensive comparisons of databases and data, as well as powerful team collaboration.

EngineRoom is a powerful tool for process improvement. EngineRoom combines graphical tools, statistical tools, and modeling to organize and manage your projects. Analyze data, manage projects and map and model processes. EngineRoom's intuitive drag and drop interface allows you to avoid the steep learning curve associated with desktop data analysis software. EngineRoom has interactive, patented wizards that guide you through the process of analyzing your data and reporting your results. You can get started instantly without having to install anything on your client's desktop, manage complex licensing files or dedicate hard drive storage. Sign in to your account and you can access your projects from any browser. Your data and results will be saved the same way when you return to a project from anywhere.

Visplore makes the analysis of large, dirty time series data intuitive and extremely efficient. For process experts, R&D engineers, quality managers, industry consultants, and everyone who has spent a lot of time on the tedious preparation of complex measurement data. Knowing your data is the fundament of unlocking its value. Visplore offers ready-to-use tools to understand correlations, patterns, trends and much more, faster than ever. Cleansing and annotating make the difference between valuable and useless data. In Visplore, you deal with dirty data like outliers, anomalies and process changes as easily as using a drawing program. Integrations with Python, R, Matlab and many other sources makes workflow integration straightforward. And all of that at a performance that is still fun even with millions of data records, and allows for unexpectedly creative analyses.

ggplot2 allows you to create declarative graphics using The Grammar of Graphics. You provide the data and tell ggplot2 what graphical primitives you want to use. ggplot2 has been around for over 10 years and is used by thousands to create millions of plots. This means that ggplot2 itself is relatively stable. If we do make any changes, it will be to add functions or arguments to existing functions rather than to change the behavior of existing ones. If we do make changes to existing behavior, we will do so for compelling reasons. It is better to start with a systematic introduction to ggplot2 than to read individual documentation pages.