8. Visualizing Continuous Data | R Data Visualization Cookbook

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Table Of Contents

R Data Visualization Cookbook

By : Gohil

4.2 (6)

R Data Visualization Cookbook

4.2 (6)

By: Gohil

Overview of this book

If you are a data journalist, academician, student or freelance designer who wants to learn about data visualization, this book is for you. Basic knowledge of R programming is expected.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Sections

Conventions

Reader feedback

Customer support

Free Chapter

1. A Simple Guide to R

1. A Simple Guide to R

Installing packages and getting help in R

Data types in R

Special values in R

Matrices in R

Editing a matrix in R

Data frames in R

Editing a data frame in R

Importing data in R

Exporting data in R

Writing a function in R

Writing if else statements in R

Basic loops in R

Nested loops in R

The apply, lapply, sapply, and tapply functions

Using par to beautify a plot in R

Saving plots

2. Basic and Interactive Plots

2. Basic and Interactive Plots

Introduction

Introducing a scatter plot

Scatter plots with texts, labels, and lines

Connecting points in a scatter plot

Generating an interactive scatter plot

A simple bar plot

An interactive bar plot

A simple line plot

Line plot to tell an effective story

Generating an interactive Gantt/timeline chart in R

Merging histograms

Making an interactive bubble plot

Constructing a waterfall plot in R

3. Heat Maps and Dendrograms

3. Heat Maps and Dendrograms

Introduction

Constructing a simple dendrogram

Creating dendrograms with colors and labels

Creating a heat map

Generating a heat map with customized colors

Generating an integrated dendrogram and a heat map

Creating a three-dimensional heat map and a stereo map

Constructing a tree map in R

4. Maps

4. Maps

Introduction

Introducing regional maps

Introducing choropleth maps

A guide to contour maps

Constructing maps with bubbles

Integrating text with maps

Introducing shapefiles

Creating cartograms

5. The Pie Chart and Its Alternatives

5. The Pie Chart and Its Alternatives

Introduction

Generating a simple pie chart

Constructing pie charts with labels

Creating donut plots and interactive plots

Generating a slope chart

Constructing a fan plot

6. Adding the Third Dimension

6. Adding the Third Dimension

Introduction

Constructing a 3D scatter plot

Generating a 3D scatter plot with text

A simple 3D pie chart

A simple 3D histogram

Generating a 3D contour plot

Integrating a 3D contour and a surface plot

Animating a 3D surface plot

7. Data in Higher Dimensions

7. Data in Higher Dimensions

Introduction

Constructing a sunflower plot

Creating a hexbin plot

Generating interactive calendar maps

Creating Chernoff faces in R

Constructing a coxcomb plot in R

Constructing network plots

Constructing a radial plot

Generating a very basic pyramid plot

8. Visualizing Continuous Data

8. Visualizing Continuous Data

Introduction

Generating a candlestick plot

Generating interactive candlestick plots

Generating a decomposed time series

Plotting a regression line

Constructing a box and whiskers plot

Generating a violin plot

Generating a quantile-quantile plot (QQ plot)

Generating a density plot

Generating a simple correlation plot

9. Visualizing Text and XKCD-style Plots

9. Visualizing Text and XKCD-style Plots

Introduction

Generating a word cloud

Constructing a word cloud from a document

Generating a comparison cloud

Constructing a correlation plot and a phrase tree

Generating plots with custom fonts

Generating an XKCD-style plot

10. Creating Applications in R

10. Creating Applications in R

Introduction

Creating animated plots in R

Creating a presentation in R

A basic introduction to API and XML

Constructing a bar plot using XML in R

Creating a very simple shiny app in R

Index

Index

Generating a decomposed time series

A time series is dominated by seasonality as well as trend. The main objective of this section is to introduce the concept of decomposition. We can extract different elements from a time series such as trend or seasonality. The residual series is the series that is free from both, trend and seasonality.

R provides us with the tools to conduct time series analysis very efficiently. It is not possible to cover the topic of time series analysis in detail and I would highly recommend readers to refer to Introductory Time Series with R given in the See also section.

In this recipe, we will explore a simple additive decomposition model. The model is represented as follows:

Xt = Mt+St+Zt

In this notation, the variables at time t are as follows:

Xt: This is an observed series
Mt: This is the trend
St: This is the seasonal effect
Zt: This is the error term

It is also possible to use a multiplicative model and it is necessary that readers interested in decomposing the time...

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