R Graphs Cookbook Second Edition

R Graphs Cookbook Second Edition

Buy this Book

R Graphs Cookbook Second Edition

Buy this Book

Overview of this book

R Graphs Cookbook

Credits

About the Authors

About the Reviewers

www.PacktPub.com

Preface

Free Chapter

R Graphics

Base graphics using the default package

Trellis graphs using lattice

Graphs inspired by Grammar of Graphics

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

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

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

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

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

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

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

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

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

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

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

Inspecting Large Datasets

Introduction

Multivariate continuous data visualization

Multivariate categorical data visualization

Visualizing mixed data

Zooming and filtering

Three-dimensional Visualizations

Introduction

Three-dimensional scatter plots

Three-dimensional scatter plots with a regression plane

Three-dimensional bar charts

Three-dimensional density plots

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

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Creating heat maps

Heat maps are colorful images that are very useful to summarize a large amount of data by highlighting hotspots or key trends in the data.

How to do it...

There are a few different ways to make heat maps in R. The simplest is to use the heatmap() function in the base library:

heatmap(as.matrix(mtcars), 
Rowv=NA, 
Colv=NA, 
col = heat.colors(256), 
scale="column",
margins=c(2,8),
main = "Car characteristics by Model")

How it works...

The example code has a lot of arguments, so it might look difficult at first sight. However, if we consider each argument in turn, we can understand how it works. The first argument to the heatmap() function is the dataset. We are using the built-in dataset mtcars, which holds data such as fuel efficiency (mpg), number of cylinders (cyl), weight (wt), and so on for different models of cars. The data needs to be in a matrix format, so we use the as.matrix() function. Rowv and Colv specify whether and how dendrograms should be displayed to the left...

R Graphs Cookbook Second Edition

R Graphs Cookbook Second Edition

Overview of this book

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Current Title:

R Graphs Cookbook Second Edition

Creating heat maps

How to do it...

How it works...