Ordering box plots

StatViz Analytics has built a state-by-state cigarette consumption explorer for a public health client. The dataset shows US cigarette consumption per capita by state from 1985 to 1995.

How you order the box plots affects the kinds of questions that are easy to answer. Sorting alphabetically makes it easy to look up a specific state. Sorting by median consumption makes it easy to find the highest or lowest consumption. Sorting by inter-quartile range (IQR) — the spread of the "middle half" of the data — makes it easy to see which states had the most variation among typical smokers.

Use the dropdown to explore different sort orders, then determine which statement is false.

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Understanding Data Visualization

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Understanding Data Visualization

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In this chapter you’ll learn the value of visualizations, using real-world data on British monarchs, Australian salaries, Panamanian animals, and US cigarette consumption, to graphically represent the spread of a variable using histograms and box plots.

Exercise 1: A plot tells a thousand words Exercise 2: Motivating visualization Exercise 3: Continuous vs. categorical variables Exercise 4: Histograms Exercise 5: Interpreting histograms Exercise 6: Adjusting bin width Exercise 7: Box plots Exercise 8: Interpreting box plots Exercise 9: Ordering box plots

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You’ll learn how to interpret data plots and understand core data visualization concepts such as correlation, linear relationships, and log scales. Through interactive exercises, you’ll also learn how to explore the relationship between two continuous variables using scatter plots and line plots. You'll explore data on life expectancies, technology adoption, COVID-19 coronavirus cases, and Swiss juvenile offenders. Next you’ll be introduced to two other popular visualizations—bar plots and dot plots—often used to examine the relationship between categorical variables and continuous variables. Here, you'll explore famous athletes, health survey data, and the price of a Big Mac around the world.

Exercise 1: Scatter plots Exercise 2: Interpreting scatter plots Exercise 3: Trends with scatter plots Exercise 4: Line plots Exercise 5: Interpreting line plots Exercise 6: Logarithmic scales for line plots Exercise 7: Bar plots Exercise 8: Interpreting bar plots Exercise 9: Interpreting stacked bar plots Exercise 10: Dot plots Exercise 11: Interpreting dot plots Exercise 12: Sorting dot plots

It’s time to make your insights even more impactful. Discover how you can add color and shape to make your data visualizations clearer and easier to understand, especially when you find yourself working with more than two variables at the same time. You'll explore Los Angeles home prices, technology stock prices, math anxiety, the greatest hiphop songs, scotch whisky preferences, and fatty acids in olive oil.

Exercise 1: Higher dimensions Exercise 2: Another dimension for scatter plots Exercise 3: Another dimension for line plots Exercise 4: Using color Exercise 5: Eye-catching colors Exercise 6: Qualitative, sequential, diverging Exercise 7: Highlighting data Exercise 8: Plotting many variables at once Exercise 9: Interpreting pair plots Exercise 10: Interpreting correlation heatmaps

In this final chapter, you’ll learn how to identify and avoid the most common plot problems. For example, how can you avoid creating misleading or hard to interpret plots, and will your audience understand what it is you’re trying to tell them? All will be revealed! You'll explore wind directions, asthma incidence, and seats in the German Federal Council.

Exercise 1: Polar coordinates Exercise 2: Pie plots Exercise 3: Rose plots Exercise 4: Axes of evil Exercise 5: Bar plot axes Exercise 6: Dual axes Exercise 7: Sensory overload Exercise 8: Chartjunk Exercise 9: Multiple plots Exercise 10: Congratulations