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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