Ch. 1 - Data wrangling
The gapminder dataset
Loading the gapminder and dplyr packages
Understanding a data frame
The filter verb
Filtering for one year
Filtering for one country and one year
The arrange verb
Arranging observations by life expectancy
Filtering and arranging
The mutate verb
Using mutate to change or create a column
Combining filter, mutate, and arrange
Ch. 2 - Data visualization
Visualizing with ggplot2
Variable assignment
Comparing population and GDP per capita
Comparing population and life expectancy
Log scales
Putting the x-axis on a log scale
Putting the x- and y- axes on a log scale
Additional aesthetics
Adding color to a scatter plot
Adding size and color to a plot
Faceting
Creating a subgraph for each continent
Faceting by year
Ch. 3 - Grouping and summarizing
The summarize verb
Summarizing the median life expectancy
Summarizing the median life expectancy in 1957
Summarizing multiple variables in 1957
The group_by verb
Summarizing by year
Summarizing by continent
Summarizing by continent and year
Visualizing summarized data
Visualizing median life expectancy over time
Visualizing median GDP per capita per continent over time
Comparing median life expectancy and median GDP per continent in 2007
Ch. 4 - Types of visualizations
Line plots
Visualizing median GDP per capita over time
Visualizing median GDP per capita by continent over time
Bar plots
Visualizing median GDP per capita by continent
Visualizing GDP per capita by country in Oceania
Histograms
Visualizing population
Visualizing population with x-axis on a log scale
Boxplots
Comparing GDP per capita across continents
Adding a title to your graph
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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