R Graphs Cookbook

R Graphs Cookbook

By : Hrishi V. Mittal

Buy this Book

R Graphs Cookbook

By: Hrishi V. Mittal

Buy this Book

Overview of this book

With more than two million users worldwide, R is one of the most popular open source projects. It is a free and robust statistical programming environment with very powerful graphical capabilities. Analyzing and visualizing data with R is a necessary skill for anyone doing any kind of statistical analysis, and this book will help you do just that in the easiest and most efficient way possible. Unlike other books on R, this book takes a practical, hands-on approach and you dive straight into creating graphs in R right from the very first page. You want to harness the power of this open source programming language to visually present and analyze your data in the best way possible – and this book will show you how. The R Graph Cookbook takes a practical approach to teaching how to create effective and useful graphs using R. This practical guide begins by teaching you how to make basic graphs in R and progresses through subsequent dedicated chapters about each graph type in depth. It will demystify a lot of difficult and confusing R functions and parameters and enable you to construct and modify data graphics to suit your analysis, presentation, and publication needs. You will learn all about making graphics such as scatter plots, line graphs, bar charts, pie charts, dot plots, heat maps, histograms and box plots. In addition, there are detailed recipes on making various combinations and advanced versions of these graphs. Dedicated chapters on polishing and finalizing graphs will enable you to produce professional-quality graphs for presentation and publication. With R Graph Cookbook in hand, making graphs in R has never been easier.

R Graphs Cookbook

Credits

About the Author

About the Reviewers

www.PacktPub.com

Preface

Free Chapter

Basic Graph Functions

Introduction

Creating 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

Labelling data points

Correlation matrix using pairs plot

Adding error bars

Using jitter to distinguish closely packed data points

Adding linear model lines

Adding non-linear model curves

Adding non-parametric model curves with lowess

Making three-dimensional scatter plots

How to make Quantile-Quantile plots

Displaying data density on axes

Making scatter plots with 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

Creating sparklines

Plotting functions of a variable in a dataset

Formatting time series data for plotting

Plotting date and time 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

Labelling 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 bin size and number of breaks

Adjusting histogram styles: bar colors, borders, and axes

Overlaying density line over a histogram

Multiple histograms along the diagonal of a pairs plot

Histograms in the margins of line and scatter plots

Creating 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 number of observations

Creating box plots with notches

Including or excluding outliers

Creating horizontal box plots

Changing 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 single Z variable with scale

Creating correlation heat maps

Summarizing multivariate data in a 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

Finalizing graphs for publications and presentations

Introduction

Exporting graphs in high resolution image formats: PNG, JPEG, BMP, TIFF

Exporting graphs in vector formats: SVG, PDF, 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

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Formatting time series data for plotting

Time series or trend charts are the most common form of line graphs. There are a lot of ways in R to plot such data, however it is important to first format the data in a suitable format that R can understand. In this recipe, we will look at some ways of formatting time series data using the base and some additional packages.

Getting ready

In addition to the basic R functions, we will also be using the zoo package in this recipe. So first we need to install it:

install.packages("zoo")

How to do it...

Let's use the dailysales.csv example dataset and format its date column:

sales<-read.csv("dailysales.csv")
d1<-as.Date(sales$date,"%d/%m/%y")
d2<-strptime(sales$date,"%d/%m/%y")
data.class(d1)
[1] "Date"
data.class(d2)
[1] "POSIXt"

How it works...

We have seen two different functions to convert a character vector into dates. If we did not convert the date column, R would not automatically recognize the values in the column as dates. Instead, the column...

R Graphs Cookbook

By : Hrishi V. Mittal

R Graphs Cookbook

By: Hrishi V. Mittal

Overview of this book

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Current Title:

R Graphs Cookbook

Formatting time series data for plotting

Getting ready

How to do it...

How it works...