Artificial intelligence era, data and algorithm and hardware resources is very important, a great company of related industries is becoming more and more attention in the data contains the value of the data collection and application is more important than ever before, even business similar companies do compete on price or at a loss for user active volume, are these all look for the data contains the value of We need to wait for data scientists to dig further, brush away the surface of the fog, and discover the deep hidden trade secrets or scientific research contained in big data. Data scientist has also become a hot job, with many IT professionals switching careers to this new field. The fly in the ointment is that as we mine data to find useful information, it becomes more and more difficult to present the processes that emerge and the results of implementation. Fortunately, there are a number of open source data visualization tools that can capture unique data from Spaces and tables and present information to users through the use of advanced graphics and charts.
So which tools are worth the time to explore or adopt? This article brings together five open source data visualization tools that take a declarative approach to working with complex data.
R Shiny
R Shiny is an open source software package that provides a Web framework for building data visualizations using the R language, through interactive charts and applications. This tool helps us turn analysis into stylish interactive Web visuals without the need for a deep understanding of HTML, CSS, or JavaScript. Similar to a spreadsheet, this reactive programming model makes it easy to manipulate data without waiting for the entire page to reload each time. With the advent of new retail, we’ve seen the retail industry constantly update data and look for platforms that can be successfully updated minute by minute.
Tableau Public
Tableau Public is a popular data visualization tool with the ability to display graphs, charts, maps, and more. The tool is also completely free. With up to 10 GB of storage and a drag-and-drop interface, users can collaborate with others on the team to see real-time updates. The “Public” part of Tableau means you can save your data to Public profiles where others can access your data, but if you’re not a highly open company where privacy is your number one concern, Tableau Public offers plenty of upside for business analysts and managers. The latest version is optimized for mobile devices, can connect to a variety of data sources beyond Excel, and can link directly to Google tables.
Datawrapper
Datawrapper is a great open source tool for complete data visualization and the ability to embed real-time and interactive charts. You simply upload the data to a CSV file, and online tools can build custom visuals, such as bar charts and line charts. Datawrapper is great for small businesses or presentations because it allows only 10,000 views per chart, but it may not be suitable for large businesses with a large number of customers. But most agreed that an easy-to-use interface and the ability to quickly present statistics in a straightforward way helped us.
Pivot
Pivot is an intuitive UI designed to enable exploratory analysis of event data while leveraging the much-touted drag-and-drop interface. Pivot sets a property centered on two operations: Filter and Split. Filter Narrows down the data view and is equivalent to the “WHERE” clause in SQL, WHERE Split is very similar to SQL’s “GROUP BY” function. Split, however, allows data to be Split across multiple dimensions — and so far, it’s seen great success with grocery price, promotion analysis, and optimization.
D3
D3 stands for Data-driven document and is a JavaScript library that binds arbitrary data to the Document Object Model (DOM) and then applies data-driven transformations to the document. Although D3 may be more appealing to programmers as the tool involves code creation, it is fascinating to see that D3 is able to build a really compelling set of charts, maps, charts, and so on into a web page. If you’re willing to put in some extra work, visual payments are worth it.
Regardless of industry, these tools are key to understanding the valuable data that continues to explode. These tools are easy to use and can be used to visualize patterns or highlight trends, but most importantly they don’t have to be free, which is one of the reasons the open source community is grateful for the ability of individuals to donate or contribute to open source tools, and I think it’s a sense of belonging for programmers.
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