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Chapter 11 Data Visualization in R (11.3 Adding details to plots (11.3.1…
Chapter 11
Data Visualization in R
11.1. A quick introduction to base R graphics
11.1.1. The world of data visualization
11.1.2 Creating an exploratory plot array
11.1.3 Creating an explanatory scatterplot
11.1.4 The plot() function is generic
11.1.5 A preview of some more and less useful techniques
11.1.6 Adding details to a plot using point shapes, color, and reference lines
11.1.7 Creating multiple plot arrays
11.1.8 Avoid pie charts
11.2 Different plot types
11.2.1 Characterizing a single variable
11.2.2 The hist() and truehist() functions
11.2.3 Density plots as smoothed histrograms
11.2.5 Visualizing relations between two variables
11.2.4 Using the qqPlot() function to see many details in data
11.2.6 The sunflowerplot() function for repeated numerical data
11.2.7 Useful options for the boxplot() function
11.2.9 Showing more complex relations between variables
11.2.8 Using mosaicplot() function
11.2.10 Using the bagplot() function
11.2.11 Plotting correlation matrices with corrplot() function
11.2.12 Building and plotting rpart() models
11.3 Adding details to plots
11.3.1 The plot() function and its options
11.3.2 Introduction to the par() function
11.3.3 Exploring the type option
11.3.4 The surprising utility of the type "n" option
11.3.5 Adding lines and points to plots
11.3.6 The lines() function and line types
11.3.7 The points() function and line types
11.3.8 Adding trend lines from linear regression models
11.3.9 Adding text to plots
11.3.10 Using the text() function to label plot features
11.3.11 Adjusting text position, size and font
11.3.12 Rotating text with the srt argument
11.3.13 Adding or modifying other plot details
11.3.14 Using the legend() function
11.3.15 Adding custom axes with the axis() function
11.3.16 Using the supsum() function to add smooth trend curves
11.4 How much is too much?
11.4.1 Managing visual complexity
11.4.2 Too much is too much
11.4.3 Deciding how many scatterplots is too many
11.4.4 How many words is too many?
11.4.5 Creating plot arrays with the mfrow parameter
11.4.6 The Anscombe quartet
11.4.7 The utility of common scaling and individual titles
11.4.8 Using multiple plots to give multiple views of dataset
11.4.9 Creating plot arrays with the layout() function
11.4.10 Constructing and displaying layout matrices
11.4.11 Creating a triangular arrays of pltos
11.4.12 Creating arrays with different sized plots
11.5 Advanced plot customization and beyond
11.5.6 Iliinsky and Steele's 12 recommended colors
11.5.7 Using color to enhance a bubbleplot
11.5.8 Using color to enhance stacked barplots
11.5.9 Other graphics system in R
11.5.5 Using color effectively
11.5.4 Saving plot results as files
11.5.3 Using the symbols() function to display relations between more than two variables
11.5.10 The tabplot package and grid graphics
11.5.2 Some plot functions also return useful information
11.5.11 A lattice graphics example
11.5.12 A ggplot2 graphics example
11.5.1 Creating and saving more complex plots