Last updated 10/2018MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 3.73 GB | Duration: 7h 28m

Advanced visualization techniques in R to visualize 2D and 3D interactive plots!

What you'll learn

Deepen your knowledge by adding bar-charts, scatterplots, and series plots using ggplot2.

Create a fully-featured website using Shiny with real- features such as adding and controlling functionalities.

Create simple and quick visualizations using the basic graphics tools in R.

Introduce users to basic R functions and data manipulation techniques while creating meaningful visualizations.

Add elements, text, animation, and colors to your plot to make sense of data.

Perform predictive modeling and create animated applications.

Requirements

Prior basic understanding of R programming is expected.

Description

Effective visualization helps you get better insights from your data, make better and more informed business decisions! R is one of the most widely used open source languages for data and graph analysis. It is platform-independent and allows users to load various packages as well as develop their own packages to interpret data better. R gives aspiring analysts and data scientists the ability to represent complex sets of data in an impressive way. So, if you're a data science professional and want to learn about the powerful data visualization techniques of R, then go for this Learning Path.This comprehensive 3-in-1 course follows a practical approach, where each recipe presents unique functions of plots, charts, and maps as well as visualization of 2D and 3D interactive plots in a step-by-step manner! You’ll b with generating various plots in R using the basic R plotting techniques. Utilize R packages to add context and meaning to your data. Finally, you'll design interactive visualizations and integrate them on your website or blog!By the end of the course, you’ll master the visualization capabilities of R to build interactive graphs, plots, and Pie charts as well as visualize 2D and 3D interactive plots.Contents and OverviewThis training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Learning R for Data Visualization, covers getting to grips with R’s most popular packages and functions to create interactive visualizations for the web. We start by importing data in R from popular formats such as CSV and Excel tables. Then you will learn how to create basic plots such as histograms, scatterplots, and more, with the default options, which guarantees stunning results. In the final part of the course, the Shiny package will be extensively discussed. This allows you to create fully-featured web pages directly from the R console, and Shiny also allows it to be uploaded to a live website where your peers and colleagues can browse it and you can share your work. You will see how to build a complete website to import and plot data, plus we will present a method to upload it for everybody to use. Finally, you will revise all the concepts you've learned while having some fun creating a complete website. By the end of the course, you will have an armor full of different visualization techniques, with the capacity to apply these abilities to real-world data sets.The second course, R Data Visualization - Basic Plots, Maps, and Pie Charts, covers mastering the visualization capabilities of R to build interactive graphs, plots, and Pie. We start - off with the basics of R plots and an introduction to heat maps and customizing them. After this, we gradually take you through creating interactive maps using the googleVis package. Finally, we generate choropleth maps and contouring maps, bubble plots, and pie charts.The third course, R Data Visualization - Word Clouds and 3D Plots, covers advanced visualization techniques in R to build word clouds, 3D plots, and more. We start off with the basics of R plots and an introduction to heat maps and customizing them. After this, we gradually take you through creating interactive maps using the googleVis package. Finally, we generate choropleth maps and contouring maps, bubble plots, and pie charts.By the end of the course, you’ll master the visualization capabilities of R to build interactive graphs, plots, and Pie charts as well as visualize 2D and 3D interactive plots. About the AuthorsFabio Veronesi obtained a Ph.D. in digital soil mapping from Cranfield University and then moved to ETH Zurich, where he has been working for the past three years as a postdoc. In his career, Dr. Veronesi worked at several topics related to environmental research: digital soil mapping, cartography, and shaded relief, renewable energy and transmission line siting. During this Dr. Veronesi specialized in the application of spatial statistical techniques to environmental data.Atmajit Singh Gohil works as a senior consultant at a consultancy firm in New York City. After graduating, he worked in the financial industry as a Fixed Income Analyst. He writes about data manipulation, data exploration, visualization, and basic R plotting functions on his blog. He has a master's degree in financial economics from the State University of New York (SUNY), Buffalo. He also graduated with a Master of Arts degree in economics from the University of Pune, India. He loves to read blogs on data visualization and loves to go out on hikes in his free .

Overview

Section 1: Learning R for Data Visualization

Lecture 1 The Course Overview

Lecture 2 Preview of R Plotting Functionalities

Lecture 3 Introducing the Dataset

Lecture 4 Loading Tables and CSV Files

Lecture 5 Loading Excel Files

Lecture 6 Exporting Data

Lecture 7 Creating Histograms

Lecture 8 The Importance of Box Plots

Lecture 9 Plotting Bar Charts

Lecture 10 Plotting Multiple Variables – Scatterplots

Lecture 11 Dealing with – -series Plots

Lecture 12 Handling Uncertainty

Lecture 13 Chag Theme

Lecture 14 Chag Colors

Lecture 15 Modifying Axis and Labels

Lecture 16 Adding Supplementary Elements

Lecture 17 Adding Text Inside and Outside of the Plot

Lecture 18 Multi-plots

Lecture 19 Exporting Plots as Images

Lecture 20 Adjusting the Page Size

Lecture 21 Getting Started with Interactive Plotting

Lecture 22 Creating Interactive Histograms and Box Plots

Lecture 23 Plotting Interactive Bar Charts

Lecture 24 Creating Interactive Scatterplots

Lecture 25 Developing Interactive -series Plots and Saving

Lecture 26 Getting Started with Shiny

Lecture 27 Creating a Simple Website

Lecture 28 File Input

Lecture 29 Conditional Panels – UI

Lecture 30 Conditional Panels – Servers

Lecture 31 Deploying the Site

Section 2: R Data Visualization - Basic Plots, Maps, and Pie Charts

Lecture 32 The Course Overview

Lecture 33 Installing Packages and Getting Help in R

Lecture 34 Data Types and Special Values in R

Lecture 35 Matrices and Editing a Matrix in R

Lecture 36 Data Frames and Editing a Data Frame in R

Lecture 37 Importing and Exporting Data in R

Lecture 38 Writing a Function and if else Statement in R

Lecture 39 Basic and Nested Loops in R

Lecture 40 The apply, lapply, sapply, and tapply Functions

Lecture 41 Using and Saving par to Beautify a Plot in R

Lecture 42 Introducing a Scatter Plot with Texts, Labels, and Lines

Lecture 43 Connecting Points and Generating an Interactive Scatter Plot

Lecture 44 A Simple and Interactive Bar Plot

Lecture 45 Introduction to Line Plot and Its Effective Story

Lecture 46 Generating an Interactive Gantt/line Chart in R

Lecture 47 Meg Histograms

Lecture 48 Making an Interactive Bubble Plot

Lecture 49 Constructing a Waterfall Plot in R

Lecture 50 Constructing Simple Dendrogram

Lecture 51 Creating Dendrograms with Colors and Labels

Lecture 52 Creating Heat Maps

Lecture 53 Generating a Heat Map with Customized Colors

Lecture 54 Generating an Integrated Dendrogram and a Heat Map

Lecture 55 Creating a Three-Dimensional Heat Map and Stereo Map

Lecture 56 Constructing A Tree Map in R

Lecture 57 Introducing Regional Maps

Lecture 58 Introducing Choropleth Maps

Lecture 59 A Guide to Contour Maps

Lecture 60 Constructing Maps with bubbles

Lecture 61 Integrating Text with Maps

Lecture 62 Introducing Shapefiles

Lecture 63 Creating Cartograms

Lecture 64 Generating a Simple Pie Chart

Lecture 65 Constructing Pie Charts with Labels

Lecture 66 Creating Donut Plots and Interactive Plots

Lecture 67 Generating a Slope Chart

Lecture 68 Constructing a Fan Plot

Section 3: R Data Visualization - Word Clouds and 3D Plots

Lecture 69 The Course Overview

Lecture 70 Constructing a 3D Scatter Plot

Lecture 71 Generating a 3D Scatter Plot with Text

Lecture 72 A Simple 3D Pie Chart

Lecture 73 A Simple 3D Histogram

Lecture 74 Generating a 3D Contour Plot

Lecture 75 Integrating a 3D Contour and a Surface Plot

Lecture 76 Animating a 3D Surface Plot

Lecture 77 Constructing a Sunflower Plot

Lecture 78 Creating a Hexbin Plot

Lecture 79 Generating Interactive Calendar Maps

Lecture 80 Creating Chernoff Faces in R

Lecture 81 Constructing a Coxcomb Plot in R

Lecture 82 Constructing Network Plots

Lecture 83 Constructing a Radial Plot

Lecture 84 Generating a Very Basic Pyramid Plot

Lecture 85 Generating a Candlestick Plot

Lecture 86 Generating Interactive Candlestick Plots

Lecture 87 Generating a Decomposed Series

Lecture 88 Plotting a Regression Line

Lecture 89 Constructing a Box and Whiskers Plot

Lecture 90 Generating a Violin Plot

Lecture 91 Generating a Quantile-Quantile Plot (QQ Plot)

Lecture 92 Generating a Density Plot

Lecture 93 Generating a Simple Correlation Plot

Lecture 94 Generating a Word Cloud

Lecture 95 Constructing a Word Cloud from a Document

Lecture 96 Generating a Comparison Cloud

Lecture 97 Constructing a Correlation Plot and a Phrase Tree

Lecture 98 Generating Plots with Custom Fonts

Lecture 99 Generating an XKCD-Style Plot

Lecture 100 Creating Animated Plots in R

Lecture 101 Creating a Presentation in R

Lecture 102 A basic Introduction to API and XML

Lecture 103 Constructing a Line Plot Using JSON in R

Lecture 104 Creating a Very Simple Shiny App in R

Data Analysts, Data Scientists or Data Journalist, who wants to learn about Data Visualization and represent complex sets of data in an impressive way.

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