Ch. 1 - Data cleaning and summarizing with dplyr
The United Nations voting dataset
Filtering rows
Adding a year column
Adding a country column
Grouping and summarizing
Summarizing the full dataset
Summarizing by year
Summarizing by country
Sorting and filtering summarized data
Sorting by percentage of “yes” votes
Filtering summarized output
Ch. 2 - Data visualization with ggplot2
Visualization with ggplot2
Choosing an aesthetic
Plotting a line over time
Other ggplot2 layers
Visualizing by country
Summarizing by year and country
Plotting just the UK over time
Plotting multiple countries
Faceting
Faceting by country
Faceting with free y-axis
Choose your own countries
Ch. 3 - Tidy modeling with broom
Linear regression
Linear regression on the United States
Finding the slope of a linear regression
Finding the p-value of a linear regression
Tidying models with broom
Tidying a linear regression model
Combining models for multiple countries
Nesting for multiple models
Nesting a data frame
List columns
Unnesting
Fitting multiple models
Performing linear regression on each nested dataset
Tidy each linear regression model
Unnesting a data frame
Working with many tidy models
Filtering model terms
Filtering for significant countries
Sorting by slope
Ch. 4 - Joining and tidying
Joining datasets
Joining datasets with inner_join
Filtering the joined dataset
Visualizing colonialism votes
Tidy data
Tidy data observations
Using gather to tidy a dataset
Recoding the topics
Summarize by country, year, and topic
Visualizing trends in topics for one country
Tidy modeling by topic and country
Nesting by topic and country
Interpreting tidy models
Steepest trends by topic
Checking models visually
Conclusion
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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