大数据可视化:用R和Python与GUI工具分配

SK Ahammad Fahad, Abdulsamad E. Yahya
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引用次数: 17

摘要

海量的数据伴随着海量的知识。适当地利用持久信息可以帮助克服挑衅和支持建立进一步的复杂判断。数据可视化技术被科学地验证为一千倍的可靠性,而不是文本表示。早期的数据可视化系统遇到了一些困难,对于处理这种大数据量已经有了一些解决方案。数据科学使用两种不同的语言Python和R来毫不偏离地可视化大数据。在经营业务方面也有很多工具。本文重点介绍了Python和R的可视化技术。R的出现包括ggplot2、传单、晶格等非凡的可视化库,以克服庞大的体积的挑衅。Python有几个用于数据可视化的特定库。它们通常是Bokeh, Seaborn, Altair, ggplot和Pygal。此外,使用最现代,安全和强大的零编码GUI的附件来描述大数据可视化,以实际的决心进行真正的识别。数据可视化描述的方法和过程对于从大规模数据集中恢复特定知识具有重要意义。
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Big Data Visualization: Allotting by R and Python with GUI Tools
A tremendous amount of data comes with a vast amount of knowledge. Decent use of the persistent information can assist to overcome provocations and support to establish further sophisticated judgment. Data visualization techniques are authenticated scientifically as thousand times reliable rather than textual representation. The premature data visualization system met some difficulties and there has some solution for handle this kind of big quantity of data. Data science used two distinct languages Python and R to visualize big data undeviatingly. There also have a lot of tools in operating business. This paper is focused on the visualization technique of Python and R. R appears including the extraordinary visualization library alike ggplot2, leaflet, and lattice to defeat the provocation of the extensive volume. Python has several particular libraries for data visualization. Commonly they are Bokeh, Seaborn, Altair, ggplot and Pygal. Also, with most modern, secure and powerful zero coding GUI's accessories to describe big data visualization for genuine recognition with practical determination. Method and process of visual description of data are significant to recover specific knowledge from the large-scale dataset.
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