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Visualizing data in R 4graphics using the base, graphics, stats, and ggplot2 packages /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Visualizing data in R 4by Margot Tollefson.
Reminder of title:	graphics using the base, graphics, stats, and ggplot2 packages /
Author:	Tollefson, Margot.
Published:	Berkeley, CA :Apress :2021.
Description:	xv, 401 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Information visualization.
Online resource:	https://doi.org/10.1007/978-1-4842-6831-5
ISBN:	9781484268315$q(electronic bk.)

Visualizing data in R 4graphics using the base, graphics, stats, and ggplot2 packages /
Tollefson, Margot.

Visualizing data in R 4graphics using the base, graphics, stats, and ggplot2 packages /[electronic resource] :by Margot Tollefson. - Berkeley, CA :Apress :2021. - xv, 401 p. :ill., digital ;24 cm.

Part I. An Overview of plot() -- 1. Introduction: plot(),qplot(), and ggplot(), Plus Some -- 2. The plot() Function -- 3. The Arguments to plot() -- 4. Ancillary Functions for plot() -- 5. The Methods for plot() -- 6, Graphics Devices and Laying Out Plots -- Part II. A look at the ggplot2 Package -- 7. Graphics with the ggplot2 Package: An Introduction -- 8. Working with the ggplot() Function: The Theme and the Aesthetics -- 9. The Geometry, Statistic, Annotate, and Border Functions -- 10. Formatting and Plot Management Tools -- Part III. Appendices -- A. Plots for Contingency Tables and Discrete Data -- B. Plots for Continuous Variables -- C. Functions That Plot Multiple Plots -- D. Smoothers -- E. Plots for Time Series -- F. Miscellaneous Plotting Functions.

Master the syntax for working with R's plotting functions in graphics and stats in this easy reference to formatting plots. The approach in Visualizing Data in R 4 toward the application of formatting in ggplot() will follow the structure of the formatting used by the plotting functions in graphics and stats. This book will take advantage of the new features added to R 4 where appropriate including a refreshed color palette for charts, Cairo graphics with more fonts/symbols, and improved performance from grid graphics including ggplot 2 rendering speed. Visualizing Data in R 4 starts with an introduction and then is split into two parts and six appendices. Part I covers the function plot() and the ancillary functions you can use with plot() You'll also see the functions par() and layout(), providing for multiple plots on a page. Part II goes over the basics of using the functions qplot() and ggplot() in the package ggplot2. The default plots generated by the functions qplot() and ggplot() give more sophisticated-looking plots than the default plots done by plot() and are easier to use, but the function plot() is more flexible. Both plot() and ggplot() allow for many layers to a plot. The six appendices will cover plots for contingency tables, plots for continuous variables, plots for data with a limited number of values, functions that generate multiple plots, plots for time series analysis, and some miscellaneous plots. Some of the functions that will be in the appendices include functions that generate histograms, bar charts, pie charts, box plots, and heatmaps. You will: Use R to create informative graphics Master plot(), qplot(), and ggplot() Discover the canned graphics functions in stats and graphics Format plots generated by plot() and ggplot()

ISBN: 9781484268315$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-6831-5doiSubjects--Topical Terms:

248886
Information visualization.

LC Class. No.: QA76.9.I52

Dewey Class. No.: 001.4226

Visualizing data in R 4graphics using the base, graphics, stats, and ggplot2 packages /
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245 1 0 $a Visualizing data in R 4 $h [electronic resource] : $b graphics using the base, graphics, stats, and ggplot2 packages / $c by Margot Tollefson.
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505 0 $a Part I. An Overview of plot() -- 1. Introduction: plot(),qplot(), and ggplot(), Plus Some -- 2. The plot() Function -- 3. The Arguments to plot() -- 4. Ancillary Functions for plot() -- 5. The Methods for plot() -- 6, Graphics Devices and Laying Out Plots -- Part II. A look at the ggplot2 Package -- 7. Graphics with the ggplot2 Package: An Introduction -- 8. Working with the ggplot() Function: The Theme and the Aesthetics -- 9. The Geometry, Statistic, Annotate, and Border Functions -- 10. Formatting and Plot Management Tools -- Part III. Appendices -- A. Plots for Contingency Tables and Discrete Data -- B. Plots for Continuous Variables -- C. Functions That Plot Multiple Plots -- D. Smoothers -- E. Plots for Time Series -- F. Miscellaneous Plotting Functions.
520 $a Master the syntax for working with R's plotting functions in graphics and stats in this easy reference to formatting plots. The approach in Visualizing Data in R 4 toward the application of formatting in ggplot() will follow the structure of the formatting used by the plotting functions in graphics and stats. This book will take advantage of the new features added to R 4 where appropriate including a refreshed color palette for charts, Cairo graphics with more fonts/symbols, and improved performance from grid graphics including ggplot 2 rendering speed. Visualizing Data in R 4 starts with an introduction and then is split into two parts and six appendices. Part I covers the function plot() and the ancillary functions you can use with plot() You'll also see the functions par() and layout(), providing for multiple plots on a page. Part II goes over the basics of using the functions qplot() and ggplot() in the package ggplot2. The default plots generated by the functions qplot() and ggplot() give more sophisticated-looking plots than the default plots done by plot() and are easier to use, but the function plot() is more flexible. Both plot() and ggplot() allow for many layers to a plot. The six appendices will cover plots for contingency tables, plots for continuous variables, plots for data with a limited number of values, functions that generate multiple plots, plots for time series analysis, and some miscellaneous plots. Some of the functions that will be in the appendices include functions that generate histograms, bar charts, pie charts, box plots, and heatmaps. You will: Use R to create informative graphics Master plot(), qplot(), and ggplot() Discover the canned graphics functions in stats and graphics Format plots generated by plot() and ggplot()
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