3. Mastering the qplot Function | R Graph Essentials

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R Graph Essentials

4.5 (4)

R Graph Essentials

4.5 (4)

Overview of this book

This book is targeted at R programmers who want to learn the graphing capabilities of R. This book will presume that you have working knowledge of R.

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. Base Graphics in R – One Step at a Time

1. Base Graphics in R – One Step at a Time

Learning basic graphics techniques

Creating and joining points

Creating scatterplots and line plots

Colors in R

Passing parameter values to titles and labels

Including a regression line

Graphing mathematical functions

R provides many options

Creating graphs with several curves

Customizing your axes

Creating axis labels

Creating multiple graphs on the same page

Saving your graphs

Including mathematical expressions on your plots

Summary

2. Advanced Functions in Base Graphics

2. Advanced Functions in Base Graphics

Reading datasets into R

Including a regression line and residuals

A medical dataset to create graphs

Creating complex multiple axes

Including a grid on your graph

Shading and coloring your graph

Creating bar charts

R's color palettes

Using smoothers on your graph

Creating scatterplot matrices

Summary

3. Mastering the qplot Function

3. Mastering the qplot Function

About qplot

The qplot syntax

Producing scatterplots using qplot

Subsetting your data before graphing

Mapping aesthetics to categorical variables

Controlling colors on your graph

Setting up graphs as objects

Creating facet plots

Creating line graphs using qplot

Creating multiple curves simultaneously

Including smoothed curves

Creating histograms with qplot

Creating facet plots for histograms

Creating kernel density plots

Creating bar charts

Creating boxplots

Creating graphs with dates

Summary

4. Creating Graphs with ggplot

4. Creating Graphs with ggplot

Getting started with ggplot

Mapping color, shape, and size to a variable

Creating attractive color schemes

Creating curves for each factor level

Creating histograms

Creating bar charts

Creating boxplots

Labeling points with text

Including regression lines

Summary

Index

Index

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Creating kernel density plots

Let's create a kernel density plot for patient height. The kernel density plot is essentially a smoothed version of a histogram. A full discussion of kernel density plots is beyond the scope of this book, but for many applications they provide a viable alternative to the histogram. We use a bin width of 5 cm, though we could try other bin widths. Enter the following syntax:

qplot( HEIGHT, data = T,   geom = "density",  binwidth = 5, xlab = "HEIGHT (cm)", ylab = "DENSITY")

The kernel density plot looks like this:

In a kernel density plot, the height of the curve gives an estimate of the probability density at the given value along the horizontal axis.

To shade underneath a density plot, you can use the polygon() command. In the following example, we will illustrate how this is done in base graphics. Here, we select a light green color and use the default smoothing (that is, we do not specify a bin width). Enter the following syntax:

plot(density(HEIGHT), xlab...

Visually different images

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