Data Visualization with R

5 Days. Instructor-led.

Data Visualization with R will teach you the many ways that raw and summary data can be turned into visualizations that convey meaningful insights. This course starts with basic graphs such as bar charts, scatter plots, and line charts, but progresses to less well-known visualizations such as tree maps, alluvial plots, radar charts, mosaic plots, effects plots, correlation plots, biplots, and the mapping of geographic data. Both static and interactive graphics are created and the use of color, shape, shading, grouping, annotation, and animations are covered in detail. This course starts with a default look and feel for graphs, and then teaches you how to create graphs with customized colors, fonts, legends, annotations, and organizational themes.

Learning Objectives

After completing this course, you will be able to:

• Understand and utilize a wide breadth of graph types, including newer and less well-known approaches.
• Connect each graph type to the characteristics of the data and the goals of the analysis.
• Build visualizations that describe complex relationships among many variables, progressing from simple graphs describing one variable.
• Create interactive web graphics using JavaScript libraries.
• Customize each graph type to meet your needs and those of your audiences.
• Produce publication-ready visualizations for print (in color or black and white) and the web.
• Apply best practices in data visualization.
• Draw from examples across a wide variety of fields to enhance your visualizations.

Audience

This course is aimed at those new to data analysis as well as the seasoned data scientist. It will particularly appeal to anyone who needs to describe data visually and wants to find and emulate the most appropriate method quickly. You should have some basic coding experience, but expertise in R is not required. Some of the later modules (e.g., visualizing statistical models) assume exposure to statistical inference at the level of analysis of variance and regression.

Course Outline

1) Data Preparation
a. Importing data
b. Cleaning data

2) Introduction to ggplot2
a. A worked example
b. Placing the data and mapping options
c. Graphs as objects

3) Univariate Graphs
a. Categorical
b. Quantitative

4) Bivariate Graphs
a. Categorical vs. Categorical
b. Quantitative vs. Quantitative
c. Categorical vs. Quantitative

5) Multivariate Graphs
a. Grouping
b. Faceting

6) Maps
a. Geocoding
b. Dot density maps
c. Choropleth maps
d. Going further

7) Time-dependent graphs
a. Time series
b. Dumbbell charts
c. Slope graphs
d. Area Charts
e. Stream graph

8) Statistical Models
a. Correlation plots
b. Linear Regression
c. Logistic regression
d. Survival plots
e. Mosaic plots

9) Other Graphs
a. 3-D Scatterplot
b. Bubble charts
c. Biplots
d. Alluvial diagrams
e. Heatmaps
f. Radar charts
g. Scatterplot matrix
h. Waterfall charts
i. Word clouds

10) Customizing Graphs
a. Axes
b. Colors
c. Points & Lines
d. Fonts
e. Legends
f. Labels
g. Annotations
h. Themes
i. Combining graphs

11) Saving Graphs
a. Via menus
b. Via code
c. File formats
d. External editing

12) Interactive Graphs
a. plotly
b. ggiraph
c. Other approaches

13) Advice / Best Practices
a. Labeling
b. Signal to noise ratio
c. Color choice
d. y-Axis scaling
e. Attribution
f. Going further