Pro Data Visualization Using R and JavaScript : Analyze and Visualize Key Data on the Web

Use R 4, RStudio, Tidyverse, and Shiny to interrogate and analyze your data, and then use the D3 JavaScript library to format and display that data in an elegant, informative, and interactive way. You will learn how to gather data effectively, and also how to understand the philosophy and implementation of each type of chart, so as to be able to represent the results visually. With the popularity of the R language, the art and practice of creating data visualizations is no longer the preserve of mathematicians, statisticians, or cartographers. As technology leaders, we can gather metrics around what we do and use data visualizations to communicate that information. __Pro Data Visualization Using R and JavaScript__ combines the power of the R language with the simplicity and familiarity of JavaScript to display clear and informative data visualizations. Gathering and analyzing empirical data is the key to truly understanding anything. We can track operational metrics to quantify the health of our products in production. We can track quality metrics of our projects, and even use our data to identify bad code. Visualizing this data allows anyone to read our analysis and easily get a deep understanding of the story the data tells. This book makes the R language approachable, and promotes the idea of data gathering and analysis mostly using web interfaces. **What You Will Learn** * Carry out data visualization using R and JavaScript * Use RStudio for data visualization * Harness Tidyverse data pipelines Apply D3 and R Notebooks towards your data * Work with the R Plumber API generator, Shiny, and more **Who This Book Is For** Programmers and data scientists/analysts who have some prior experience with R and JavaScript. Table of Contents 5 About the Authors 10 About the Technical Reviewer 11 Acknowledgments 12 Chapter 1: Background 13 What Is Data Visualization? 15 Time Series Charts 16 Bar Charts 16 Histograms 17 Data Maps 18 Scatter Plots 19 History 20 Modern Landscape 24 Why Data Visualization? 26 Tools 27 Languages, Environments, and Libraries 27 Analysis Tools 29 Process Overview 32 Identify a Problem 32 Gather Data 32 Analyze Data 36 Visualize Data 42 Ethics of Data Visualization 43 Cite Sources 44 Be Aware of Visual Cues 44 Summary 44 Chapter 2: R Language Primer 45 Getting to Know the R Console 45 The Command Line 49 Command History 50 Accessing Documentation 51 Packages 52 Importing Data 55 Using Headers 56 Specifying a String Delimiter 57 Specifying Row Identifiers 57 Using Custom Column Names 58 Data Structures and Data Types 59 Data Frames 61 Matrices 63 Adding Lists 66 Looping Through Lists 67 Applying Functions to Lists 68 Functions 71 Summary 73 Chapter 3: A Deeper Dive into R 74 Object-Oriented Programming in R 74 S3 Classes 75 S4 Classes 79 Statistical Analysis with Descriptive Metrics in R 81 Median and Mean 85 Quartiles 85 Standard Deviation 86 RStudio IDE 87 R Markdown 89 RPubs 94 Summary 96 Chapter 4: Data Visualization with D3 97 Preliminary Concepts 97 HTML 98 CSS 100 SVG 101 JavaScript 104 History of D3 105 Using D3 106 Setting Up a Project 107 Using D3 107 Binding Data 109 Creating a Bar Chart 113 Loading External Data 121 Summary 124 Chapter 5: Visualizing Spatial Data from Access Logs 126 What Are Data Maps? 126 Access Logs 130 Parsing the Access Log 132 Read in the Access Log 133 Parse the Log File 134 Geolocation by IP 138 Output the Fields 141 Adding Control Logic 143 Creating a Data Map in R 145 Mapping Geographic Data 146 Adding Latitude and Longitude 150 Displaying Regional Data 152 Distributing the Visualization 155 Summary 160 Chapter 6: Visualizing Data over Time 161 Gathering Data 162 Data Analysis with R 163 Calculating the Bug Count 165 Examining the Severity of the Bugs 171 Adding Interactivity with D3 174 Reading in the Data 175 Drawing on the Page 177 Adding Interactivity 183 Summary 191 Chapter 7: Bar Charts 192 Standard Bar Chart 193 Stacked Bar Chart 195 Grouped Bar Chart 198 Visualizing and Analyzing Production Incidents 199 Plotting Data on a Bar Chart with R 204 Ordering Results 207 Creating a Stacked Bar Chart 209 Bar Charts in D3 210 Creating a Vertical Bar Chart 211 Creating a Stacked Bar Chart 215 Creating an Overlaid Visualization 219 Summary 224 Chapter 8: Correlation Analysis with Scatter Plots 225 Finding Relationships in Data 226 Introductory Concepts of Agile Development 230 Correlation Analysis 231 Creating a Scatter Plot 232 Creating a Bubble Chart 233 Visualizing Bugs 234 Visualizing Production Incidents 238 Interactive Scatter Plots in D3 241 Adding the Base HTML and JavaScript 241 Loading the Data 242 Adding Interactive Functionality 243 Adding Form Fields 245 Retrieving Form Data 246 Using the Visualization 247 Summary 251 Chapter 9: Visualizing the Balance of Delivery and Quality with Parallel Coordinates 252 What Are Parallel Coordinate Charts? 252 History of Parallel Coordinate Plots 256 Finding Balance 259 Creating a Parallel Coordinate Chart 259 Adding in Effort 262 Brushing Parallel Coordinate Charts with D3 265 Creating the Base Structure 265 Creating a Y-Axis for Each Column 266 Drawing the Lines 267 Fading the Lines 268 Creating the Axes 269 Summary 274 Index 275