R- Complete Data Visualization Solutions

R: Complete Data Visualization Solutions, available at $44.99, has an average rating of 3.9, with 71 lectures, 10 quizzes, based on 27 reviews, and has 248 subscribers.

You will learn about Create professional data visualizations and interactive reports Deepen your knowledge by adding bar-charts, scatterplots, and time series plots using ggplot2 Enhance the user experience using dynamic visualisation Test your coding limits by creating stunning interactive plots for the web Gain insight into how data scientists visualize data using some of the most popular R packages Understand how to apply useful data visualization techniques in R for real-world applications Build an assortment of interactive maps, reports, and more Make your visualizations interactive using R This course is ideal for individuals who are This Integrated Course is useful whether someone is a hobbyist, analyst, an aspiring or professional data scientist, or even learning data visualization for the first time. Those already familiar with the basics of R, but want to learn to utilize the full power of R’s data visualization capabilities will also find this Integrated Course a match for their needs. It is particularly useful for This Integrated Course is useful whether someone is a hobbyist, analyst, an aspiring or professional data scientist, or even learning data visualization for the first time. Those already familiar with the basics of R, but want to learn to utilize the full power of R’s data visualization capabilities will also find this Integrated Course a match for their needs.

Enroll now: R: Complete Data Visualization Solutions

Summary

Title: R: Complete Data Visualization Solutions

Price: $44.99

Average Rating: 3.9

Number of Lectures: 71

Number of Quizzes: 10

Number of Published Lectures: 71

Number of Published Quizzes: 10

Number of Curriculum Items: 81

Number of Published Curriculum Objects: 81

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Create professional data visualizations and interactive reports

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

Enhance the user experience using dynamic visualisation

Test your coding limits by creating stunning interactive plots for the web

Gain insight into how data scientists visualize data using some of the most popular R packages

Understand how to apply useful data visualization techniques in R for real-world applications

Build an assortment of interactive maps, reports, and more

Make your visualizations interactive using R

Who Should Attend

This Integrated Course is useful whether someone is a hobbyist, analyst, an aspiring or professional data scientist, or even learning data visualization for the first time. Those already familiar with the basics of R, but want to learn to utilize the full power of R’s data visualization capabilities will also find this Integrated Course a match for their needs.

Target Audiences

This Integrated Course is useful whether someone is a hobbyist, analyst, an aspiring or professional data scientist, or even learning data visualization for the first time. Those already familiar with the basics of R, but want to learn to utilize the full power of R’s data visualization capabilities will also find this Integrated Course a match for their needs.

If you are looking for that one course that includes everything about data visualization with R, this is it. Let’s get on this data visualization journey together.

This course is a blend of text, videos, code examples, and assessments, which together makes your learning journey all the more exciting and truly rewarding. It includes sections that form a sequential flow of concepts covering a focused learning path presented in a modular manner. This helps you learn a range of topics at your own speed and also move towards your goal of learning data visualization with R.

The R language is a powerful open source functional programming language. R is becoming the go-to tool for data scientists and analysts. Its growing popularity is due to its open source nature and extensive development community. R is increasingly being used by experienced data science professionals instead of Python and it will remain the top choice for data scientists in 2017. Large companies continue to use R for their data science needs and this course will make you ready for when these opportunities come your way.

This course has been prepared using extensive research and curation skills. Each section adds to the skills learned and helps us to achieve mastery of data visualization. Every section is modular and can be used as a standalone resource.?This course covers different visualization techniques in R and assorted R graphs, plots, maps, and reports. It is a practical and interactive way to learn about R graphics, all of which are discussed in an easy-to-grasp manner. This course has been designed to include topics on every possible data visualization?requirement from a data scientist and it does so in a step-by-step and practical manner.

We will start by focusing on “ggplot2” and show you how to create advanced figures for data exploration. Then, we will?move on to customizing the plots and then cover interactive plots. We will then cover time series plots, heat maps, dendograms. Following that, we will look at maps and how to make them interactive. We will then turn our attention to building an interactive report using the “ggvis” package and publishing reports and plots using Shiny. Finally, we will cover data in higher dimensions which will complete our extensive tour of the data visualization capabilities possible using R.

This course has been authored by some of the best in their fields:

Dr. Fabio Veronesi

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 has specialized in the application of spatial statistical techniques to environmental data.

Atmajitsinh Gohil

Atmajitsinh Gohil works as a senior consultant at a consultancy firm in New York City. 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.

Yu-Wei, Chiu (David Chiu)

Yu-Wei, Chiu (David Chiu) is the founder of LargitData, a start-up company that mainly focuses on providing Big Data and machine learning products. In addition to being a start-up entrepreneur and data scientist, he specializes in using Spark and Hadoop to process big data and apply data mining techniques for data analysis. Yu-Wei is also a professional lecturer and has delivered lectures on big data and machine learning in R and Python, and given tech talks at a variety of conferences.

Course Curriculum

Chapter 1: Introducing Plotting in R

Lecture 1: Introduction

Lecture 2: Preview of R plotting functionalities

Lecture 3: Loading tables and CSV files

Lecture 4: Loading Excel files

Lecture 5: Exporting data

Chapter 2: Visualizing Data with ggplot2

Lecture 1: Creating basic plots with ggplot2

Lecture 2: Changing aesthetics mapping

Lecture 3: Introducing geometric objects

Lecture 4: Performing transformations

Lecture 5: Adjusting scales

Lecture 6: Faceting

Lecture 7: Adjusting themes

Lecture 8: Combining plots

Chapter 3: Scientific Plotting in ggplot2

Lecture 1: Creating histograms

Lecture 2: The importance of box plots

Lecture 3: Plotting bar charts

Lecture 4: Plotting multiple variables – scatterplots

Lecture 5: Dealing with time – time-series plots

Lecture 6: Handling uncertainty

Chapter 4: Customizing Plots

Lecture 1: Changing the theme

Lecture 2: Changing colors

Lecture 3: Modifying axis and labels

Lecture 4: Adding supplementary elements

Lecture 5: Adding text inside and outside of the plots

Lecture 6: Multi-plots

Chapter 5: Interactive Plots in rCharts

Lecture 1: Getting started with interactive plotting

Lecture 2: Creating interactive histograms and box plots

Lecture 3: Plotting interactive bar charts

Lecture 4: Creating interactive scatterplots

Lecture 5: Developing interactive time-series plots and saving

Chapter 6: Heat Maps and Dendrograms

Lecture 1: Constructing a simple dendrogram and modifying it with colors and labels

Lecture 2: Creating a heat map and modifying it with customized colors

Lecture 3: Generating an integrated dendrogram and a heat map

Lecture 4: Creating a three-dimensional heat map and a stereo map

Lecture 5: Constructing a tree map in R

Chapter 7: Maps

Lecture 1: Introducing regional maps

Lecture 2: Introducing choropleth maps

Lecture 3: A guide to contour maps

Lecture 4: Constructing maps with bubbles

Lecture 5: Integrating text with maps

Lecture 6: Introducing shapefiles

Lecture 7: Creating cartograms

Chapter 8: Interactive Maps

Lecture 1: Understanding interactive maps

Lecture 2: Plotting vector data on Google Maps

Lecture 3: Adding layers

Lecture 4: Plotting raster data on Google Maps

Lecture 5: Using Leaflet to plot on OpenStreetMaps

Chapter 9: Creating Global Economic Maps with Open Data

Lecture 1: Data available from the World Bank

Lecture 2: Importing Data from the World Bank

Lecture 3: Adding Geocoding Information

Lecture 4: Additional tricks

Chapter 10: Making Interactive Reports

Lecture 1: Creating R Markdown reports

Lecture 2: Embedding R code chunks

Lecture 3: Creating interactive graphics with ggvis

Lecture 4: Understanding the basic syntax and grammar of ggvis

Lecture 5: Controlling axes and legends and using scales

Lecture 6: Adding interactivity to a ggvis plot

Lecture 7: Creating an R Shiny document

Lecture 8: Publishing an R Shiny report

Chapter 11: Creating a Website with Shiny

Lecture 1: Getting started with Shiny

Lecture 2: Creating a simple website

Lecture 3: File input

Lecture 4: Conditional panels – UI

Lecture 5: Conditional panels – servers

Lecture 6: Deploying the site

Chapter 12: Data in Higher Dimensions

Lecture 1: Constructing a sunflower plot and a hexbin plot

Lecture 2: Generating interactive calendar maps

Lecture 3: Creating Chernoff faces in R

Lecture 4: Constructing a coxcomb plot in R

Lecture 5: Constructing network plots and radial plots

Lecture 6: Generating a very basic pyramid plot

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 1 votes

2 stars: 4 votes

3 stars: 7 votes

4 stars: 6 votes

5 stars: 9 votes

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!