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

Including smoothed curves

Let's create a scatterplot relating height and weight before treatment, along with both points and a smooth curve using geom = c("point","smooth"). In qplot, the default smoother is LOWESS, and the gray band represents a standard error confidence interval. LOWESS fits models to local subsets of the variables to produce a smoothed version of the data.

You can read further about LOWESS in various texts and online sources. For this example, we set up the graph as an object (Y) and plot it by entering the object name on the command line:

Y <- qplot(HEIGHT, WEIGHT_1, data = T, xlab = "HEIGHT (cm)", ylab = "WEIGHT BEFORE TREATMENT (kg)", geom = c( "point","smooth"))   
 
Y

Our graph now looks like this:

We have the smoothed curve and the confidence interval. Let's graph the same data, but map color to ethnicity. We add transparency in order to make the curves easy to interpret. The syntax is as follows:

Y <- qplot(HEIGHT, WEIGHT_1, data = T, xlab = "HEIGHT (cm)",...

CONTINUE READING

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