Ch. 1 - Introduction
Introduction
Explore and Explain
Exploring ggplot2, part 1
Exploring ggplot2, part 2
Grammar of Graphics
Exploring ggplot2, part 3
Understanding Variables
ggplot2
Exploring ggplot2, part 4
Exploring ggplot2, part 5
Understanding the grammar, part 1
Understanding the grammar, part 2
Ch. 2 - Data
Objects and Layers
base package and ggplot2, part 1 - plot
base package and ggplot2, part 2 - lm
base package and ggplot2, part 3
ggplot2 compared to base package
Proper Data Format
Plotting the ggplot2 way
Tidy Data
Variables to visuals, part 1
Variables to visuals, part 1b
Variables to visuals, part 2
Variables to visuals, part 2b
Ch. 3 - Aesthetics
Visible Aesthetics
All about aesthetics, part 1
All about aesthetics, part 2
All about aesthetics, part 3
All about attributes, part 1
All about attributes, part 2
Going all out
Aesthetics for categorical and continuous variables
Modifying Aesthetics
Position
Setting a dummy aesthetic
Aesthetics Best Practices
Overplotting 1 - Point shape and transparency
Overplotting 2 - alpha with large datasets
Ch. 4 - Geometries
Scatter Plots
Scatter plots and jittering (1)
Scatter plots and jittering (2)
Bar Plots
Histograms
Position
Overlapping bar plots
Overlapping histograms
Bar plots with color ramp, part 1
Bar plots with color ramp, part 2
Overlapping histograms (2)
Line Plots - Time Series
Line plots
Periods of recession
Multiple time series, part 1
Multiple time series, part 2
Ch. 5 - qplot and wrap-up
qplot
Using qplot
Using aesthetics
Choosing geoms, part 1
Choosing geoms, part 2 - dotplot
Wrap-up
Chicken weight
Titanic
About Michael Mallari
Michael is a hybrid thinker and doer—a byproduct of being a StrengthsFinder “Learner” over time. With nearly 20 years of engineering, design, and product experience, he helps organizations identify market needs, mobilize internal and external resources, and deliver delightful digital customer experiences that align with business goals. He has been entrusted with problem-solving for brands—ranging from Fortune 500 companies to early-stage startups to not-for-profit organizations.
Michael earned his BS in Computer Science from New York Institute of Technology and his MBA from the University of Maryland, College Park. He is also a candidate to receive his MS in Applied Analytics from Columbia University.
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