Abstract
Visualizing data is key in effective data analysis: to perform initial investigations, to confirm or refuting data models, and to elucidate mathematical or algorithmic concepts. In this chapter, we explore different types of data graphs using the R programming language, which has excellent graphics functionality; we end the chapter with a description of Python’s matplotlib module—a popular Python tool for data visualization.
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Notes
- 1.
There are several different formal definitions for percentiles. Type help( quantile) for several competing definitions that R implements.
- 2.
Slope 1 corresponds to 45 degrees incline from left to right.
- 3.
A formal definition of the t-distribution appears in TAOD volume 1, Chapter 3.
References
L. Wilkinson. The Grammar of Graphics. Springer, second edition, 2005.
W. S. Cleveland. The Elements of Graphing Data. Hobart Press, 1985.
W. S. Cleveland. Visualizing Data. Hobart Press, 1993.
J. W. Tuckey. Exploratory Data Analysis. Addison Wesley, 1977.
D. Sarkar. Lattice: Multivariate Data Visualization with R. Springer, 2008.
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Lebanon, G., El-Geish, M. (2018). Visualizing Data in R and Python. In: Computing with Data. Springer, Cham. https://doi.org/10.1007/978-3-319-98149-9_8
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DOI: https://doi.org/10.1007/978-3-319-98149-9_8
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