Description
Course info
Rating
(75)
Level
Beginner
Updated
Mar 11, 2016
Duration
3h 1m
Description

R is a popular open-source programming language for data analysis. Its interactive programming environment and data visualization capabilities make R an ideal tool for creating a wide variety of data visualizations. In this course, you will learn how to answer questions about your data by creating data visualizations with R.

About the author
About the author

Matthew is a data science consultant, author, and international public speaker. He has over 17 years of professional experience working with tech startups to Fortune 500 companies. He is a Microsoft MVP, ASPInsider, and open-source software contributor.

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Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi, I'm Matthew Renze with Pluralsight, and welcome to Beginning Data Visualization with R. R is a very popular programming language for data analysis and data visualization. Its interactive programming capabilities, powerful data analysis tools, and data visualization capabilities make it many expert's first choice for creating professional data visualizations. In this course we'll learn how to create and interpret data visualizations using the three main plotting systems in R, that is the base graphic system, lattice, and ggplot2. In addition, we'll learn about best practices for creating professional data visualizations for a wide audience. As an overview of this course, first we'll learn the basics of creating data visualizations with the three a main plotting systems in R. Next we'll learn how to create and interpret data visualizations that involve either one or two categorical or numeric variables. Finally, we'll move down the basics and learn about more advanced data visualizations, best practices for creating professional data visualizations, and we'll look at a few alternatives to using R to create our data visualizations. By the end of this course you have the skills necessary to create and interpret a variety of data visualizations using all three of the main plotting systems in R. Before beginning the course you should be familiar with the basic syntax of R and the RStudio integrated development environment. If you do not meet these prerequisites please feel free to watch module one of my Exploratory Data Analysis with R course first. This module will cover all of the basics. So please join us today at Pluralsight and learn how to transform your data into actual insight with Beginning Data Visualization with R.

Visualizing One Numeric Variable
Hello again, and welcome to our next module on Data Visualization with R. I'm Matthew Renze with Pluralsight, and in this module we'll learn how to create and interpret data visualizations for a single numeric variable using R. As an overview of this module, first, we'll learn about data visualizations for quantitative univariate analysis, that is the analysis of a single numeric variable. Next, we'll learn how to create these data visualizations using the base graphic system. Then, we'll learn how to create these same data visualizations using lattice. Finally, we'll create these data visualizations using ggplot2. To help orient ourselves in relation to the rest of the course this module will cover quantitative univariate analysis, that is, the analysis of a single numeric variable. In addition, we'll be building upon what we learned in qualitative univariate analysis, and preparing ourselves for bivariate data analysis as well.

Visualizing Two Categorical Variables
Welcome back to Data Visualization with R. I'm Matthew Renze with Pluralsight, and in this module we'll learn how to create and interpret data visualizations for the relationship between two categorical variables using R. As an overview of this module, first, we'll learn about data visualizations for qualitative bivariate analysis, that is, the analysis of the relationship between two categorical variables. Next, we'll learn how to create these data visualizations using the base graphic system. Then, we'll learn how to create these same data visualizations using lattice. Finally, we'll create these data visualizations using ggplot2. To help orient ourselves in relation to the rest of the course this module will cover qualitative bivariate analysis, that is the analysis of two categorical variables. In addition we'll be expanding upon what we learn from univariate analysis and preparing ourselves for the remainder of bivariate analysis.

Visualizing Two Numeric Variables
Hello, and welcome back to Data Visualization with R. I'm Matthew Renze with Pluralsight, and in this module we'll learn how to create and interpret data visualization for the relationship between two numeric variables using R. As an overview of this module, first, we'll learn about data visualizations for quantitative bivariate analysis, that is the analysis of the relationship between two numeric variables. Next, we'll learn how to create these data visualizations using the base graphic system. Then, we'll learn how to create these same data visualizations using lattice. Finally, we'll create these data visualizations using ggplot2. To help orient yourselves in relation to the rest of the course this module will cover quantitative bivariate analysis, that is the analysis of the relationship between two numeric variables. In addition, we'll be expanding upon what we've learned so far and preparing ourselves for the last type of bivariate analysis.

Visualizing Both a Categorical and a Numeric Variable
Welcome back to Data Visualizations with R. I'm Matthew Renze with Pluralsight, and in this module we'll learn how create and interpret data visualizations for both a categorical variable and a numeric variable using R. As an overview of this module, first, we'll learn about data visualizations for bivariate analysis of both a qualitative and quantitative variable, that is the analysis of a numeric variable grouped by a categorical variable. Next, we'll learn how to create these data visualizations using the base graphic system. Then, we'll learn how to create these same data visualizations using lattice. Finally, we'll create these data visualizations using ggplot2. To help orient yourselves in relation to the rest of the course, this module will cover bivariate analysis of both a qualitative and a quantitative variable, that is the analysis of a numeric variable grouped by a categorical variable. In addition, we'll be wrapping up our study of the various types of univariate and bivariate analysis, and preparing to learn about more advanced types of data analysis that go beyond these five basic types.

Moving Beyond the Basics
Hello again, and welcome to our final module on Data Visualization with R. I'm Matthew Renze with Pluralsight, and in this module we'll move beyond the basic concepts of data visualization and take a look at a few advanced topics. As an overview of this module, first, we'll learn about a few more types of advanced data analysis in data visualizations. Next, we'll learn about best practices for creating professional data visualizations, then we'll see a demo of how to produce clean, professional data visualizations, next we'll learn about a few alternatives to using R to create our data visualizations, finally, we'll wrap up this module and the course as a whole.