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R: Data Visualization with R – A Complete Guide!: 3-in-1

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R: Data Visualization with R – A Complete Guide!: 3-in-1, available at $19.99, has an average rating of 4.05, with 104 lectures, 2 quizzes, based on 20 reviews, and has 153 subscribers.

You will learn about Deepen your knowledge by adding bar-charts, scatterplots, and time series plots using ggplot2. Create a fully-featured website using Shiny with real-time 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. This course is ideal for individuals who are Data Analysts, Data Scientists or Data Journalist, who wants to learn about Data Visualization and represent complex sets of data in an impressive way. It is particularly useful for Data Analysts, Data Scientists or Data Journalist, who wants to learn about Data Visualization and represent complex sets of data in an impressive way.

Enroll now: R: Data Visualization with R – A Complete Guide!: 3-in-1

Summary

Title: R: Data Visualization with R – A Complete Guide!: 3-in-1

Price: $19.99

Average Rating: 4.05

Number of Lectures: 104

Number of Quizzes: 2

Number of Published Lectures: 104

Number of Published Quizzes: 1

Number of Curriculum Items: 106

Number of Published Curriculum Objects: 105

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

  • Deepen your knowledge by adding bar-charts, scatterplots, and time series plots using ggplot2.
  • Create a fully-featured website using Shiny with real-time 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.

Who Should Attend

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

Target Audiences

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

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 begin 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 Overview

This 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 Authors

  • Fabio Veronesiobtained 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 time Dr. Veronesi specialized in the application of spatial statistical techniques to environmental data.

  • Atmajit Singh Gohilworks 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 time.

Course Curriculum

Chapter 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 Time – Time-series Plots

Lecture 12: Handling Uncertainty

Lecture 13: Changing Theme

Lecture 14: Changing 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 Time-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

Chapter 2: R Data Visualization – Basic Plots, Maps, and Pie Charts

Lecture 1: The Course Overview

Lecture 2: Installing Packages and Getting Help in R

Lecture 3: Data Types and Special Values in R

Lecture 4: Matrices and Editing a Matrix in R

Lecture 5: Data Frames and Editing a Data Frame in R

Lecture 6: Importing and Exporting Data in R

Lecture 7: Writing a Function and if else Statement in R

Lecture 8: Basic and Nested Loops in R

Lecture 9: The apply, lapply, sapply, and tapply Functions

Lecture 10: Using and Saving par to Beautify a Plot in R

Lecture 11: Introducing a Scatter Plot with Texts, Labels, and Lines

Lecture 12: Connecting Points and Generating an Interactive Scatter Plot

Lecture 13: A Simple and Interactive Bar Plot

Lecture 14: Introduction to Line Plot and Its Effective Story

Lecture 15: Generating an Interactive Gantt/Timeline Chart in R

Lecture 16: Merging Histograms

Lecture 17: Making an Interactive Bubble Plot

Lecture 18: Constructing a Waterfall Plot in R

Lecture 19: Constructing Simple Dendrogram

Lecture 20: Creating Dendrograms with Colors and Labels

Lecture 21: Creating Heat Maps

Lecture 22: Generating a Heat Map with Customized Colors

Lecture 23: Generating an Integrated Dendrogram and a Heat Map

Lecture 24: Creating a Three-Dimensional Heat Map and Stereo Map

Lecture 25: Constructing A Tree Map in R

Lecture 26: Introducing Regional Maps

Lecture 27: Introducing Choropleth Maps

Lecture 28: A Guide to Contour Maps

Lecture 29: Constructing Maps with bubbles

Lecture 30: Integrating Text with Maps

Lecture 31: Introducing Shapefiles

Lecture 32: Creating Cartograms

Lecture 33: Generating a Simple Pie Chart

Lecture 34: Constructing Pie Charts with Labels

Lecture 35: Creating Donut Plots and Interactive Plots

Lecture 36: Generating a Slope Chart

Lecture 37: Constructing a Fan Plot

Chapter 3: R Data Visualization – Word Clouds and 3D Plots

Lecture 1: The Course Overview

Lecture 2: Constructing a 3D Scatter Plot

Lecture 3: Generating a 3D Scatter Plot with Text

Lecture 4: A Simple 3D Pie Chart

Lecture 5: A Simple 3D Histogram

Lecture 6: Generating a 3D Contour Plot

Lecture 7: Integrating a 3D Contour and a Surface Plot

Lecture 8: Animating a 3D Surface Plot

Lecture 9: Constructing a Sunflower Plot

Lecture 10: Creating a Hexbin Plot

Lecture 11: Generating Interactive Calendar Maps

Lecture 12: Creating Chernoff Faces in R

Lecture 13: Constructing a Coxcomb Plot in R

Lecture 14: Constructing Network Plots

Lecture 15: Constructing a Radial Plot

Lecture 16: Generating a Very Basic Pyramid Plot

Lecture 17: Generating a Candlestick Plot

Lecture 18: Generating Interactive Candlestick Plots

Lecture 19: Generating a Decomposed Time Series

Lecture 20: Plotting a Regression Line

Lecture 21: Constructing a Box and Whiskers Plot

Lecture 22: Generating a Violin Plot

Lecture 23: Generating a Quantile-Quantile Plot (QQ Plot)

Lecture 24: Generating a Density Plot

Lecture 25: Generating a Simple Correlation Plot

Lecture 26: Generating a Word Cloud

Lecture 27: Constructing a Word Cloud from a Document

Lecture 28: Generating a Comparison Cloud

Instructors

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Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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