R for Data Analysis in easy steps - R Programming essentials

by Mike McGrath

Overview

The R language is widely used by statisticians for data analysis, and the popularity of R programming has therefore increased substantially in recent years. The emerging Internet of Things (IoT) gathers increasing amounts of data that can be analyzed to gain useful insights into trends.

R for Data Analysis in easy steps has an easy-to-follow style that will appeal to anyone who wants to produce graphic visualizations to gain insights from gathered data.

R for Data Analysis in easy steps begins by explaining core programming principles of the R programming language, which stores data in “vectors” from which simple graphs can be plotted.

Next, the book describes how to create “matrices” to store and manipulate data from which graphs can be plotted to provide better insights. This book then demonstrates how to create “data frames” from imported data sets, and how to employ the “Grammar of Graphics” to produce advanced visualizations that can best illustrate useful insights from your data.

R for Data Analysis in easy steps contains separate chapters on the major features of the R programming language. There are complete example programs that demonstrate how to create Line graphs, Bar charts, Histograms, Scatter graphs, Box plots, and more. The code for each R script is listed, together with screenshots that illustrate the actual output when that script has been executed. The free, downloadable example R code is provided for clearer understanding.

By the end of this book you will have gained a sound understanding of R programming, and be able to write your own scripts that can be executed to produce graphic visualizations for data analysis. You need have no previous knowledge of any programming language, so it's ideal for the newcomer to computer programming.

Product Details

ISBN-13:	9781840787955
Publisher:	In Easy Steps Limited
Publication date:	03/13/2018
Series:	In Easy Steps
Pages:	192
Sales rank:	293,553
Product dimensions:	7.20(w) x 8.80(h) x 0.50(d)

About the Author

Mike McGrath now lives in South-east Europe, on the sun-kissed shores of the Aegean Sea. Mike gained his extensive knowledge of computer languages while working as a developer contracting to companies around the world. His interests include coins of ancient Greece, dining-out with friends, and the ongoing evolution of the world wide web.

Table of Contents

1 Getting started 7

Understanding data 8

Installing R 10

Installing RStudio 12

Exploring RStudio 14

Setting preferences 16

Creating an R Script 18

Summary 20

2 Storing values 21

Storing a single value 22

Adding comments 24

Recognizing data types 26

Storing multiple values 28

Storing mixed data types 30

Plotting stored values 32

Controlling objects 34

Getting help 36

Summary 38

3 Performing operations 39

Doing arithmetic 40

Making comparisons 42

Assessing logic 44

Operating on elements 46

Comparing elements 48

Recognizing precedence 50

Manipulating elements 52

Summary 54

4 Testing conditions 55

Seeking truth 56

Branching alternatives 58

Chaining branches 60

Switching branches 62

Looping while true 64

Performing for loops 66

Breaking from loops 68

Summary 70

5 Employing Functions 71

Doing mathematics 72

Manipulating strings 74

Producing sequences 75

Generating random numbers 78

Distributing patterns 80

Extracting statistics 82

Creating functions 84

Providing defaults 86

Summary 88

6 Building matrices 89

Building a matrix 90

Transposing data 92

Binding vectors 94

Naming rows and columns 96

Plotting matrices 98

Adding labels 100

Extracting matrix subsets 102

Maintaining dimensions 104

Summary 106

7 Constructing data frames 107

Constructing a data frame 108

Importing data sets 110

Examining data frames 112

Addressing frame data 114

Extracting frame subsets 116

Changing frame columns 118

Filtering data frames 120

Merging data frames 122

Adjusting factors 124

Summary 126

8 Producing quick plots 127

Installing packages 128

Scattering points 130

Smoothing lines 132

Portraying stature 134

Depicting groups 135

Adding labels 136

Drawing columns 138

Understanding histograms 140

Producing histograms 142

Understanding box plots 144

Producing box plots 145

Summary 148

9 Storytelling with data 149

Presenting data 150

Considering aesthetics 152

Using geometries 154

Showing statistics 156

Illustrating facets 158

Controlling coordinates 160

Designing themes 162

Summary 164

10 Plotting perfection 165

Loading the data 166

Retaining objects 168

Overriding labels 170

Adding a theme 172

Restoring the Workspace 174

Comparing boxes 175

Identifying extremes 176

Limiting focus 178

Zooming focus 179

Displaying facets 180

Exporting graphics 182

Presenting analyses 184

Summary 186

Index 187