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Corporations and individuals around the world generate huge volumes of data that are stored and later processed to make it understandable to humans. However, this big data is only useful to companies when it is properly analyzed. Therefore, big data analytics is essential as it helps organizations and individuals establish patterns, identify market trends, and other crucial information for business growth. Additionally, technology is a necessary prerequisite for using big data because it offers storage and computational resources. R is one of the technologies used by companies to break down big data into understandable human form. This paper focuses on R technology, especially on data visualization techniques and applications.
Data Visualization Techniques in R
Big data analytic systems are designed to collect and process data from different sources. R is an open-source software used in big data mining and analysis. R is more of an interpreted programming language in which the commands are typed on the keyboard are directly executed. The data to be processed is stored in the computer’s active memory, from where it is accessed and visualized. Data visualization is described by Bikakis (2018) as the presentation of data in graphical or pictorial format.
The graphical representation of data helps the users to identify patterns and understand the correlations, which are essential in making decisions. Fahad and Yahya (2018) noted that there are nine distinct types of visualization methods in R. These methods include bar chart, box plot, heat map, correlogram, mosaic plots, map visualization, scatter plots, and histograms (Fahad & Yahya, 2018). The users select the visualization type depending on the objective or complexity of the data.
The main goal of data analytics is to help users make meaning of complex data through data visual graph representations. The first visual representation in R is a bar or line chart. Bar charts are essential when the user wants to show the variations in quantities over time (Fahad & Yahya, 2018). Box plots present the data in five sections, which start with the initial value at 0, followed by the first quarter at 25%, to the last point at 100%. Therefore, box plots are essential when you want to show the first percentile, median, third quartile, and the maximum value as well as reveal the outliers. Fahad and Yahya (2018) indicated that correlogram encourages users to visualize data in correlation matrices. As a result, the correlogram is used to display the correlation between variables in a dataset.
R has a heat map function designed to visualize the data in hierarchical clustering. The variables are represented in the XY axis, and the dimensions are populated using color concentration (Fahad & Yahya, 2018). Heat maps are useful in visualizing the clusters of samples because areas that matter most have higher concentrations of color. Histograms in R represent the frequencies of the variables in varying ranges (Fahad & Yahya, 2018). The values are broken into several sections within the same range. R works processes geographical data and utilizes other functions to create map visualizations. Maps are essential because they allow the readers to locate a building or an area of interest.
The mosaic plots are a multidimensional expansion that presents data for individual variables (Fahad & Yahya, 2018). For instance, the mosaic plots can include representing the number of males or females who participated in a research study. Finally, scatterplots are vital in describing the relationship between two variables. The scatterplots are similar to the correlogram, and they are useful in pointing out initial relations before conducting an in-depth statistical analysis.
Applications of Data Visualization
Big data analytics enables corporations to collect and analyze data useful in the decision-making process. Some of the applications of big data visualization include processing vast amounts of data and displaying it to help the decision-makers arrive at the right decision (Fahad & Yahya, 2018). Data visualization reveals trends about a dataset, which organizations can use to their advantage. For instance, big data analytics enables companies to collect information from different sources, which is then analyzed to learn consumer behaviors (Duan & Xiong, 2015). Big data analytics can analyze feedback left by the customers after purchases, and the company can use such analytics to improve their services delivery.
Bikakis (2018) noted that data visualization techniques are applied in the bioinformatics field to analyze large amounts of biological data. Visualization enables scientists to identify unique patterns in genes and biological tissues (Bonifacio et al., 2015). Data visualization is applied in the field of atmospheric sciences, especially in meteorology for weather predictions (Bikakis, 2018). Satellites and sensors collect a vast amount of data that is processed and visualized in real-time to identify climatic conditions such as tsunamis, floods, and hurricanes.
In conclusion, R plays a crucial role in big data analytics. Users apply different visualization techniques to understand the existing market trends and patterns that help steer the company in the right direction. Companies can boost their productivity and efficiency when they effectively integrate R in their systems for analytical purposes. Additionally, data visualizations techniques enable users to perform a series of analyses on data, which is not possible with other data analytics techniques.
Bikakis, N. (2018). Big data visualization tools. arXiv preprint arXiv:1801.08336.
Bonifacio, A., Beleites, C., & Sergo, V. (2015). Application of R-mode analysis to Raman maps: a different way of looking at vibrational hyperspectral data. Analytical and bioanalytical chemistry, 407(4), 1089-1095. Web.
Duan, L., & Xiong, Y. (2015). Big data analytics and business analytics. Journal of Management Analytics, 2(1), 1-21. Web.
Fahad, S. A., & Yahya, A. E. (2018). 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. Web.