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Data Visualization in R

Data Visualization in R

S. R. Mani Sekhar (M. S. Ramaiah Institute of Technology, India), Siddesh G. M. (M. S. Ramaiah Institute of Technology, India), and Sunilkumar S. Manvi (REVA University, India)
Copyright: © 2019 |Pages: 18
DOI: 10.4018/978-1-5225-4999-4.ch012
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Abstract

Data visualization helps the users to understand the relationships and associations between information. Visualization helps in minimizing the errors generated during decision making. Different visualization methods have been developed to unlock the valuable insight. These methods have been developed on the supposition that the information to be present is free from ambiguity. This chapter provides an overview of data visualization techniques in R programming. Various methods have been discussed with supported explanation and examples which in turn helps the reader to create their own visualization method. Later, four different case studies are presented to understand the importance and use of data visualization in real-world problems.
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History Of Data Visualization

This section provides an outline of the brief history of data visualization. As shown in Table 1 (Friendly, 2006), It consist of five columns, first and second columns displays the serial numbers and year. Column third “Named” display the historical names of that years. Column fourth shows the methods used in that year for visualizations. Thereafter column fifth displays the different examples incorporated using corresponding methods.

Table 1.
History of data visualization
S. No.Year /MilestoneEvent NameMethodologyPurpose / Example
1Pre -17th CenturyFirst maps and diagramsPosition of stars and celestial bodiesNavigation, exploration, and laying of towns etc
21600-1699Measurement and theoryTheories of error of measurement and estimation (Hald, 1990), probability theory (Pascal & Fermat), and political arithmetic (William Petty).Understanding the wealth of the state etc.
31700-1799Novel graphic formsGraphical forms:
• Geometric figure& cartograms
• Square and rectangle.
• topographic maps
• line graphs& bar charts(Playfair, 1786)
• pie chart and circle graph(Playfair, 1801)		Visual encoding for quantitative data like population or tax measure etc.
.
41800-1850Starts of modern graphicsBeginnings of modern graphics:
• stratigraphic geology (Smith, 1815)
• dot map(Snow, 1855)		System for reporting information, Map generations, and dot map used to display death due to cholera.
51850-1900Statistical graphicsStatistical theory, graph and maps (divided circle diagram, scales)
contour graphs (Vauthier, 1874)		Statistical atlas of the 9th census, anti-cyclonic system, Display of information in graph form
61900-1950Dark ageButterfly diagram (Maunder’s, 1904)To learn the dissimilarity ofsunspots over time
71950-1975Re- birth of data visualizationmultivariate data visualization (Andrews, 1972)
Two way table, stem-leaf plots, boxplots, hanging		Star plot representation, cluster representation& tree representation. Visualization of multi dimension data, animations & etc.
81975- presentInteractive and dynamic visualizationScatterplot matrix (Tukey & Tukey, 1981), parallel coordinates plot (Inselberg, 1985; Wegman, 1990).
spreadplots (Young, 1994a)		SAS, R Project and Lisp-Stat etc.

(Friendly, 2006)

Key Terms in this Chapter

Visualization: Provide a visual impression of the given data, for example, image, figure, diagram, etc.

Data: It is a collection of information.

Methodology: Hypothetical analysis of the methods.

Dataset: Collection of data or information.

Analysis: Helps in gaining the understanding of the given topic.

Syntax: Collection of rules.

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