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R Graphs Cookbook Second Edition

Book Image

R Graphs Cookbook Second Edition

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R Graphs Cookbook Second Edition
Oct, 2014 | 368 pages
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Table of Contents (22 chapters)
R Graphs Cookbook
R Graphs Cookbook
Credits
Credits
About the Authors
About the Authors
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
R Graphics
R Graphics
Base graphics using the default package
Trellis graphs using lattice
Graphs inspired by Grammar of Graphics
2
Basic Graph Functions
Basic Graph Functions
Introduction
Creating basic scatter plots
Creating line graphs
Creating bar charts
Creating histograms and density plots
Creating box plots
Adjusting x and y axes' limits
Creating heat maps
Creating pairs plots
Creating multiple plot matrix layouts
Adding and formatting legends
Creating graphs with maps
Saving and exporting graphs
3
Beyond the Basics – Adjusting Key Parameters
Beyond the Basics – Adjusting Key Parameters
Introduction
Setting colors of points, lines, and bars
Setting plot background colors
Setting colors for text elements – axis annotations, labels, plot titles, and legends
Choosing color combinations and palettes
Setting fonts for annotations and titles
Choosing plotting point symbol styles and sizes
Choosing line styles and width
Choosing box styles
Adjusting axis annotations and tick marks
Formatting log axes
Setting graph margins and dimensions
4
Creating Scatter Plots
Creating Scatter Plots
Introduction
Grouping data points within a scatter plot
Highlighting grouped data points by size and symbol type
Labeling data points
Correlation matrix using pairs plots
Adding error bars
Using jitter to distinguish closely packed data points
Adding linear model lines
Adding nonlinear model curves
Adding nonparametric model curves with lowess
Creating three-dimensional scatter plots
Creating Quantile-Quantile plots
Displaying the data density on axes
Creating scatter plots with a smoothed density representation
5
Creating Line Graphs and Time Series Charts
Creating Line Graphs and Time Series Charts
Introduction
Adding customized legends for multiple-line graphs
Using margin labels instead of legends for multiple-line graphs
Adding horizontal and vertical grid lines
Adding marker lines at specific x and y values using abline
Creating sparklines
Plotting functions of a variable in a dataset
Formatting time series data for plotting
Plotting the date or time variable on the x axis
Annotating axis labels in different human-readable time formats
Adding vertical markers to indicate specific time events
Plotting data with varying time-averaging periods
Creating stock charts
6
Creating Bar, Dot, and Pie Charts
Creating Bar, Dot, and Pie Charts
Introduction
Creating bar charts with more than one factor variable
Creating stacked bar charts
Adjusting the orientation of bars – horizontal and vertical
Adjusting bar widths, spacing, colors, and borders
Displaying values on top of or next to the bars
Placing labels inside bars
Creating bar charts with vertical error bars
Modifying dot charts by grouping variables
Making better, readable pie charts with clockwise-ordered slices
Labeling a pie chart with percentage values for each slice
Adding a legend to a pie chart
7
Creating Histograms
Creating Histograms
Introduction
Visualizing distributions as count frequencies or probability densities
Setting the bin size and the number of breaks
Adjusting histogram styles – bar colors, borders, and axes
Overlaying a density line over a histogram
Multiple histograms along the diagonal of a pairs plot
Histograms in the margins of line and scatter plots
8
Box and Whisker Plots
Box and Whisker Plots
Introduction
Creating box plots with narrow boxes for a small number of variables
Grouping over a variable
Varying box widths by the number of observations
Creating box plots with notches
Including or excluding outliers
Creating horizontal box plots
Changing the box styling
Adjusting the extent of plot whiskers outside the box
Showing the number of observations
Splitting a variable at arbitrary values into subsets
9
Creating Heat Maps and Contour Plots
Creating Heat Maps and Contour Plots
Introduction
Creating heat maps of a single Z variable with a scale
Creating correlation heat maps
Summarizing multivariate data in a single heat map
Creating contour plots
Creating filled contour plots
Creating three-dimensional surface plots
Visualizing time series as calendar heat maps
10
Creating Maps
Creating Maps
Introduction
Plotting global data by countries on a world map
Creating graphs with regional maps
Plotting data on Google maps
Creating and reading KML data
Working with ESRI shapefiles
11
Data Visualization Using Lattice
Data Visualization Using Lattice
Introduction
Creating bar charts
Creating stacked bar charts
Creating bar charts to visualize cross-tabulation
Creating a conditional histogram
Visualizing distributions through a kernel-density plot
Creating a normal Q-Q plot
Visualizing an empirical Cumulative Distribution Function
Creating a boxplot
Creating a conditional scatter plot
12
Data Visualization Using ggplot2
Data Visualization Using ggplot2
Introduction
Creating bar charts
Creating multiple bar charts
Creating a bar chart with error bars
Visualizing the density of a numeric variable
Creating a box plot
Creating a layered plot with a scatter plot and fitted line
Creating a line chart
Graph annotation with ggplot
13
Inspecting Large Datasets
Inspecting Large Datasets
Introduction
Multivariate continuous data visualization
Multivariate categorical data visualization
Visualizing mixed data
Zooming and filtering
14
Three-dimensional Visualizations
Three-dimensional Visualizations
Introduction
Three-dimensional scatter plots
Three-dimensional scatter plots with a regression plane
Three-dimensional bar charts
Three-dimensional density plots
15
Finalizing Graphs for Publications and Presentations
Finalizing Graphs for Publications and Presentations
Introduction
Exporting graphs in high-resolution image formats – PNG, JPEG, BMP, and TIFF
Exporting graphs in vector formats – SVG, PDF, and PS
Adding mathematical and scientific notations (typesetting)
Adding text descriptions to graphs
Using graph templates
Choosing font families and styles under Windows, Mac OS X, and Linux
Choosing fonts for PostScripts and PDFs
Index
Index

Using jitter to distinguish closely packed data points

Sometimes, when working with large datasets, we might find that a lot of data points on a scatter plot overlap each other. In this recipe, we will learn how to distinguish between closely packed data points by adding a small amount of noise with the jitter() function.

Getting ready

All you need for the next recipe is to type it in the R prompt as we will use some base library functions to define a new error bar function. You can also save the recipe code as a script so that you can use it again later on.

How to do it...

First, let's create a graph that has a lot of overlapping points:

x <- rbinom(1000, 10, 0.25)
y <- rbinom(1000, 10, 0.25)
plot(x,y)

Now, let's add some noise to the data points to see whether there are overlapping points:

plot(jitter(x), jitter(y))

How it works...

In the first graph, we plotted 1,000 random data points generated with the rbinom() function. However, as you can see in the first graph, only a few data points...

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