Introduction 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