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

If you have a large dataset that includes a categorical variable, you can use the facets command to produce multiple graphs: one for each level of the categorical variable. In the following example, we will create a graph for each level of ETH (1, 2, and 3) using facets = ETH ~ . Note the tilde sign followed by the period. Enter this syntax:

qplot(HEIGHT, WEIGHT_1, data = T,  geom = "point",  main = "HEIGHT VS. WEIGHT BY ETHNICITY", xlab = "WEIGHT BEFORE TREATMENT (Kg)", ylab = "HEIGHT (cm)", facets = ETH ~ .)

Our facet plot is as follows:

Indeed, we have three scatterplots arranged vertically: one for each level of ETHNICITY. We can also create facet plots across the levels of two factor variables. In the following code, we will create a scatterplot of HEIGHT against WEIGHT_1 for each combination of SMOKE and EXERCISE in each facet, where the two levels of gender are represented by shape and color. Here is the required command:

qplot(HEIGHT, WEIGHT_1, data=T, shape=factor...

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