Big Data Visualization Tools: R vs. Python

Big Data Visualization Tools: R vs. Python
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Big Data Visualization Tools: R vs. Python
Big data visualization provides a coherent representation of patterns existing in data, allowing users to glean insights from huge data effectively. In essence, Big Data visualization entails presenting data in any graphical presentation which offers a sound way to interpret and understand. However, the representation goes beyond pie charts, histograms, and corporate graphs to more complex ones such as fever charts and heat maps, allowing decision-makers to discover unexpected and correlation patterns in data sets.
Data visualization tools offer visualizers of data with a solution to create visual representations in vast sets of data. Some of the common tools include Tableau, Bottom Line, Inforgram, D3, ChartBlocks, Datawrapper, Fusion Charts, R, Python, Chartlist.js Grafana, and Google Charts. Python and R are capable of generating attractive and complex statistical graphs to explore and gain insights from datasets (Siddiqui, Alkadri, & Khan, 2017). Both tools are well-founded to process data points amounting to millions in number.
Python is well known or its wide range of libraries that are capable of handling Big Data visualization. The most common libraries include seaborn and matplotlib. Python is virtually a general-purpose programming language. Python is renowned for its libraries, such as scikit-learn, scipy, NumPy, pandas, which are vital in handling data science tasks. Python is more advantageous compared to R in particular situations. Some of the significant benefits of Python include readability and maintenance. Python provides keen attention to the quality of code, thus making it easy to maintain updates. The programming language supports multiple models, such as structure and object-oriented programming paradigms (Fahad & Yahya, 2018). Besides its compatibility of running and completing code in any platform, Python offers robust libraries that are extensive for implementing any service or task. More importantly, Python is an open-source framework that contains free development tools that vastly reduce the development cost. Key disadvantages include its weakness in mobile computation, slow and prone to run-time errors.
On the other hand, R is an open-source programming language, and it is regarded as one of the best considering using statistical languages come with a price. Some of the advantages of R include its wide range of techniques such as classification, clustering, linear as well as non-linear modeling. Furthermore, R supports matric and vectors computation since its data structures have arrays, vectors, matrices, and lists. More so, it complies with other languages such as Java, C, and C++. R offers users a large community that influences its modification, which allows it to be portable and compatible with several platforms (Fahad & Yahya, 2018). Some of the disadvantages of R include its library dependencies, inconsistency, and its steep learning curve.
In conclusion, one should consider Python if they have no prior knowledge of programming. The syntax of Python is more analogous in comparison to R. R might be confusing to individuals who are new to programming as a result of its non-standardized kind of code. The critical question in context is, which is better between R and Python in developing interactive data visualizations? R offers better ways of handling datasets and continuous prototyping. R libraries such as Leaflet, widgets, HTML, and ggplot2 come in hand in creating interactive and beautiful data visualizations. Even though Python has made several developments, it still lags in the area.

References
Fahad, S. A., & Yahya, A. E. (2018, July). Big Data Visualization: Allotting by R and Python with GUI Tools. In 2018 International Conference on Smart Computing and Electronic Enterprise (ICSCEE) (pp. 1-8). IEEE.
Siddiqui, T., Alkadri, M., & Khan, N. A. (2017). Review of Programming Languages and Tools for Big Data Analytics. International Journal of Advanced Research in Computer Science, 8(5).