Data Visualization with R

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Data Visualization with R

Authors: Emilee McWilliams, Martin Burger, Chase DeHan

One of the best ways to analyze data is visually. It takes a fraction of a second to create connections and see patterns. Data Visualization is one of the best ways to sum up... Read more

What You Will Learn

This skill teaches you the best ways to visualize your data with R. R offers a variety of packages aimed at visualizing data which this skill will cover in great detail.

Pre-requisites

  • Data Literacy
  • R for Data Analysts

Beginner

Create simple charts using base R graphics and create advanced charts using ggplot2.

Creating Visualizations Using Base R Graphics

by Emilee McWilliams

Dec 23, 2019 / 35m

35m

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Description

Creating great visuals can be time consuming when using multiple packages. In this course, Creating Visualizations Using Base R Graphics, you will gain the ability to create powerful visuals that fit your dataset and business goals. First, you will learn simple charts such as a bar plot. Next, you will discover more complex charts including an area graph. Finally, you will explore how to create statistical graphs for deeper data analysis. When you are finished with this course, you will have the skills and knowledge to produce and customize visualizations needed to present insights quickly.

Table of contents
  1. Course Overview
  2. Creating Bar Graphs in Base R
  3. Producing Line and Area Graphs in Base R
  4. Creating Statistical Graphs in Base R

Creating Visualizations in R Using ggplot2: R Playbook

by Emilee McWilliams

Nov 26, 2019 / 1h 10m

1h 10m

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Description

Creating great visuals can be time consuming without proper goals made. In this course, Creating Visualizations in R Using ggplot2: R Playbook, you will gain the ability to create powerful visuals that fit your dataset and business goals. First, you will learn simple charts for one variable. Next, you will discover more complex charts with two or more variables. Finally, you will explore how to create a map with geo spatial data. When you're finished with this course, you will have the skills and knowledge of visualizations needed to present insights to your audience.

Table of contents
  1. Course Overview
  2. Leveraging ggplot2 for Creating Graphs
  3. Showing the Best Plots for One or Two Variables Both Continuous
  4. Producing the Best Plots for Two Variables, One Discrete, and One Continuous
  5. Creating Graphs with Three or More Variables and Making a Map

Intermediate

Format the elements of ggplot2 plots to customize the visualization for a specific purpose as well as adding annotations to plots.

Formatting ggplot2 Visualization Elements in R

by Martin Burger

Nov 13, 2019 / 2h 1m

2h 1m

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Description

Do you want to learn how to fine tune ggplots so that they look even better? Ggplots are great, but their fine tuning can be tricky. They work on different principles than standard R Base plots. So, there is a learning curve to this package. In this course, Formatting ggplot2 Visualization Elements in R, you will learn how ggplots are modified piece by piece. First, you will learn the general setup of a ggplot and how each ggplot has a typical logic that has to be followed. Next, you will discover how different color palettes can be used in ggplot2 and the several tools available that offer color palettes. You will be looking at these tools and how they are applied in a ggplot, including the R Color Brewer and Color Space. After that, you will be shown how the axes and scales of a plot are modified, as well as how to get access to each piece. Finally, you will have the skills to modify the plot theme, which leads to the component of a ggplot that defines its appearance. When you are finished with this course, you will better understand the logic behind ggplot2 and you will be able to modify the plot theme, the axes, and the coloration.

Table of contents
  1. Course Overview
  2. Understanding a ggplot2 Setup
  3. Changing Default Color Settings
  4. Working on Plot Axes
  5. Using and Changing Plot Themes in ggplot2

Annotating ggplot2 Visualizations in R

by Martin Burger

Dec 11, 2019 / 2h 1m

2h 1m

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Description

Knowing how to annotate ggplots is the key in ensuring clear visualizations in R. In this course, Annotating ggplot2 Visualizations in R, you will learn how to add annotations and support elements so that anybody can understand your charts. First, you will learn how to work on ggplot legends which are crucial elements of charts that provide clarity on color or shape encoded variables. Next, you will discover how to use text based annotations which can be simple text snippets, but also whole phrases or even tables that are added to a plot. Then, you will delve into using interactive charts that show labels upon user interaction. Finally, you will explore other support elements like lines, smoothers, rectangles, or circles. When you are finished with this course, you will have the skills and knowledge of advanced ggplot2 needed to provide clarity to charts that would otherwise be tricky to read and interpret.

Table of contents
  1. Course Overview
  2. Understanding Plot Features
  3. Working on Plot Legends
  4. Using Text Based Annotations
  5. Adding Support Features to a Plot

Advanced

Create special-purpose plots using specific R packages.

Creating Special-purpose Plots: R Playbook

by Chase DeHan

Nov 26, 2019 / 1h 33m

1h 33m

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Description

There are many options for creating visualizations in R. In this course, Creating Special-purpose Plots: R Playbook, you will gain the ability to create a multitude of different plots you wouldn't typically be exposed to. First, you will learn how to create maps with the Leaflet library. Next, you will discover how to make correlograms with the Corrgram library. Finally, you will explore how to make 3D scatterplots in two different libraries: Lattice and Scatter3d . When you're finished with this course, you will have the skills and knowledge of niche visualizations needed to excel at creating visualizations for any application.

Table of contents
  1. Course Overview
  2. Summarizing with Dplyr
  3. Building Maps with Leaflet
  4. Visualizing Correlations with Corrgram
  5. Creating 3D Scatter Plots with Scatter3d
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