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R Graphs Cookbook Second Edition - Second Edition

By : Jaynal Abedin, Hrishi V. Mittal

4 (2)

R Graphs Cookbook Second Edition

4 (2)

By: Jaynal Abedin, Hrishi V. Mittal

Overview of this book

Targeted at those with an existing familiarity with R programming, this practical guide will appeal directly to programmers interested in learning effective data visualization techniques with R and a wide-range of its associated libraries.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. R Graphics

1. R Graphics

Base graphics using the default package

Trellis graphs using lattice

Graphs inspired by Grammar of Graphics

2. Basic Graph Functions

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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

12. 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

13. Inspecting Large Datasets

Introduction

Multivariate continuous data visualization

Multivariate categorical data visualization

Visualizing mixed data

Zooming and filtering

14. Three-dimensional Visualizations

14. 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

15. 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

Multivariate continuous data visualization

In this recipe, we will visualize multivariate data with all continuous variables. The plot will look like a table of a bar plot but the important feature of this plot is that we can easily understand the relationship among variables.

Getting ready

Let's call the modified mtcars dataset that we created in the introduction section. Then, we will take only the continuous variables for this recipe:

# Taking subset with only continuous variables
con_dat <- modified_mtcars[c("mpg","disp","drat","wt","qsec")]

To produce multivariable visualization, we need to call the tabplot library. If this is not preinstalled, then users can install it using the following command and then load it:

# To install tabplot library
install.packages("tabplot")

# Loading the library
library(tabplot)

How to do it…

The primary command structure for this visualization is as follows:

tableplot(con_dat)

The resultant...

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