Visualizing Data in R 4: Graphics Using the Base, Graphics, Stats, and Ggplot2 Packages

ISBN-13: 9781484268308 / Angielski / Miękka / 2021 / 401 str.

Margot Tollefson

Spis treści

1) Introduction: plot(), qplot(), and ggplot(), Plus Some

a) plot() – arguments, ancillary functions, and methods; par() and layout()

b) qplot() and ggplot() – aesthetics, geometries, and other useful functions

c) other plotting functions in graphics and stats

Part I.  An Overview of plot()

2) The plot() Function

a) what the function is and how the function works

b) will use method .xy for example

3) The Arguments to plot()

a) Type of plot, axis labels, plot titles, display format

b) Plotting characters, character size, fonts, colors, line styles and widths

4) Ancillary Functions to use with plot()

a) axis(), box(), clip(), grid(), legend(), mtext(), rug()

b) abline(), contour(), curve(), lines(), polypath()

c) arrows(), image(), points(), polygon(), rect(), segments(), symbols(), text()

d) axTicks(), identify(), locator(), pch(), strwidth(),

5) The Methods for plot()

a) What are methods?

b) Methods in the graphics package

c) Methods in the stats package

6) How to Use the Functions par() and layout()

a) What par() does

b) Arguments specific to par()

c) Multiple plots

Part II. A look at the ggplot2 Package

7) The Functions qplot(), ggplot(), and the Specialized Notation in ggplot2

a) Working with qplot()

b) The ggplot() function

c) Specialized notation

8) Themes

a) The theme() function

b) The element_*() functions

9) Aesthetics and Geometries

a) The aes() function

b) The geom_*() functions

10) Controlling the Appearance

a) The annotate_*() functions

b) The coord_*() functions

c) The facet_*() functions

d) The guide_*() functions

e) The position_*() functions

f) The scale_*() functions

g) The stat_*() functions

Appendix I.  Plots for Contingency Tables

Appendix II. Plots for Continuous Variables

Appendix III. Plots for Data with a Limited Number of Values

Appendix IV. Functions that Generate Multiple Plots

Appendix V. Plots for Time Series

Appendix VI. Miscellaneous Plots

O autorze

Margot Tollefson, PhD is a semi-retired freelance statistician, with her own consulting business, Vanward Statistics. She received her PhD in statistics from Iowa State University and has many years of experience applying R to statistical research problems. Dr. Tollefson has chosen to write this book because she often creates graphics using R and would like to share her knowledge and experience. Her professional blog is on WordPress at vanwardstat.  Social media: @vanstat 

Blurb

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()