3. Mastering the qplot Function | R Graph Essentials

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Table Of Contents

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

Creating facet plots for histograms

As with scatterplots, we can create facet plots for histograms. This technique is useful when we have one or more categorical variables and wish to obtain histograms for each level or combination of levels. Let's create a histogram facet plot of weight before treatment for each level of treatment. First, we turn TREATMENT into a factor. To create facet plots across the levels of a categorical variable, first turn the variable into a factor using the following syntax:

TRC <- factor(TREATMENT)

Now, let's create the facet plot using the syntax facets = TREATMENT ~ .

qplot(WEIGHT_1, data = T,   geom = "histogram",  binwidth = 5, xlab = "WEIGHT", ylab = "FREQUENCY", fill = I("red"), facets = TREATMENT ~ .)

The facet plot is shown as follows:

We now have a histogram for each level of TREATMENT. Facet plots provide additional detail about the variability of the critical variables.

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