Heat maps, stacked area plots, mosaic plots, choropleths – oh my! There are so many different ways to visually convey relationships and patterns in data! In this workshop on data visualization literacy, you’ll learn to recognize many popular types of charts and how to glean insights from them. The Appendix contains some examples of data visualization as visual essays and it also includes links to resources for learning how to create your own.
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Graphical displays should:
– Edward R. Tufte, The Visual Display of Quantitative Information
Refer to levels of measurement for more information.
A pie chart and a bar chart (sometimes called a bar plot) are an easy way to visually compare values. The pie chart – where the slices represent proportions of the whole – is excellent for 2-4 categories, the table is great for 1-8 categories, and the bars’ heights work well for comparing more than 5 categories.
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In the past decade, a semi-alternative to the pie chart called waffle charts (or “square pie charts”) has gained popularity at representing relative sizes between groups. (See Women in IT – Squaring the Pie?.) Semi-alternative becauses waffles compare totals and pie charts compare percentages. As such, waffle charts are good for comparing relative sizes, but not at comparing relative %s.
Each square represents a certain number of units, which I think makes it easier to visually compare sizes of groups. For example, it is easier to compare 11 squares (2nd class passengers who survived) to 20 squares (1st class passengers who survived) than 1 pie slice to another pie slice that is 1.8 times bigger:
A histogram shows the distribution of a continuous variable by splitting it into bins and counting how many observations fall into each bin (left). Sometimes those counts are divided by the total number of observations to yield proportions/probabilities instead (right). Note that the histogram on the right also includes a probability density estimate.
An important factor to watch out for is the bin size, which – ideally – was carefully chosen by the creator of the visualization. Bins that are too wide will cause the distribution to appear wide, while bins that are too narrow will make the distribution appear to noisy:
For a deeper look at histograms, I encourage you to check out Exploring Histograms by Aran Lunzer and Amelia McNamara.
When you see one of these, they are used for comparing distributions of a continuous variable (such as sepal length of Iris flowers) between different groups (such as different species):
The density plot on the left is like a smooth histogram that doesn’t discretize the variable into bins. The violin plot on the left is a rotated version that makes it easier to perform the comparison because the densities (distributions) are not overlapping.
An alternative called ridgeline plot recently gained a lot of popularity for comparing distributions across groups because of how compact it was, which was especially useful when comparing many groups.
My personal preference is when a violin plot and a box plot are combined so you still see the distribution in case there are multiple peaks (modes – something you can’t see with just a box-and-whiskers plot – but you also see the summaries:
Scatter plots are the most popular and simplest way to investigate relationships between quantitative variables. You have one variable on the X axis and one variable on the Y axis. Each point represents a single unit from your dataset (e.g. a subject of an experiment):
Data scientists and analysts often use scatterplot matrices to look at many different relationships between pairs of variables simultaneously:
At first glance there is a lot going on in that particular matrix, but really there are three main components that we can focus on just one at a time:
Mosaic plots are used to visualize the relationships between two or more qualitative variables, and they are incredibly rare. While they are very useful once you learn how to read them, that step can be very difficult and so it is unsurprising that they don’t show up more. They’re often used by statisticians during exploratory data analysis to perform a visual check before performing a statistical test of independence.
We will use these to examine distribution of hair and eye colors in ~600 statistics students at University of Delaware reported by Snee, R. D. in The American Statistician journal in 1974:
We can extend a mosaic plot to include standardized residuals (also called studentized residuals) from a log-linear model. Cells representing negative residuals – meaning there are fewer observations than would have been expected under independence – are drawn as red with broken borders; positive residuals – meaning more observations than would be expected – are drawn in blue with solid borders.
We can also look at the proportions across all three variables:
What the third mosaic plot tells us:
A stacked area plot is a way to visualize changes in amounts (or proportions) over time.
Heatmaps are a graphical representation of matrices. For example, we can visualize a dataset of top 50 NBA players’ performance statistics from the 2008-09 season (obtained from RotoWire, formerly databaseBasketball):
Treemapping is a way to visualize hierarchical (nested) data as rectangles within other rectangles, with the area of the rectangle representing the proportion and sometimes a shade or color representing another variable. It is not dissimilar to a mosaic plot!
Choropleths are geographical maps that are colored and/or shaded according to some variable such as population density.
Network diagrams are for visualizing graphs (from graph theory) and networks (from network theory) where there are nodes (vertices) connected by links (edges). Their goal is to visually represent relationships between units. For example, using the Wikipedia Clickstream data from November 2017 we can start at the article on net neutrality and visualize a neighborhood of articles that are adjacent to the central one:
The darkness of the edges connecting the vertices represents how many clicks there were between the pairs of articles. We can see that there are more clicks between “net neutrality” and “digital rights” than between “net neutrality” and “human rights”, but way more clicks between “net neutrality” and “Wikipedia Zero”.
Let us revisit the pageviews data from earlier by utilizing a logarithmic axis:
It is possible (but rare) to encounter logarithmically scaled time axes, which are helpful when you have long tails caused by outliers:
Pair up with someone sitting next to you and pick one of the following 3 visualizations. You and your partner(s) should agree on the same one.
A different take on the Titanic data:
A different take on the violent crime rates data:
A different take on the Wikipedia pageviews data:
Some questions to verify that you understand the core concepts in data visualization: