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Visualizing Data in R 4 - Graphics Using the base, graphics, stats, and ggplot2 Packages (Paperback, 1st ed.)
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Visualizing Data in R 4 - Graphics Using the base, graphics, stats, and ggplot2 Packages (Paperback, 1st ed.)
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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. What You Will Learn 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() Who This Book Is For Those
in data science who use R. Some prior experience with R or data
science is recommended.
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