Can I tell you how tired I am of hearing people tell me that “bars are boring”? VERY TIRED.
A true pillar of data visualization, bars are used to display and compare values in health and healthcare data — values like number, frequency, distribution, or other measures (e.g. a statistical mean or median) for different discrete categories of data. Bars help us order data by ranking, or see and understand the distribution of a dataset, such as whether it is skewed to the left or right, or is normally distributed.
We can use bars to show deviation (difference or changes) from a baseline or benchmark, and they’re especially useful for displaying and comparing relative differences in data with wide-ranging absolute values.
Bars are highly adaptable and may be arranged either horizontally or vertically depending on overall layout, space constraints, and labeling requirements. When combined with other media such as points and lines, they help us see and understand important contextual information — to consider the “compared to what” and “so what?” questions essential to all great data analysis and data visualizations.
Bars are NOT boring. Only the unimaginative use of them is. Here are some possibilities for using bars effectively.
Bars are the go-to graph to show how often (number of times) something is observed|occurs (the frequency distribution) in a dataset. Remember that they may be oriented horizontally or vertically depending on factors such as labels and space on a page; and that the base of the bar must always start at zero on the X or Y axis scale to show the entirety of the value, and avoid exaggerating small differences.
A type of bar chart called a histogram is most commonly used to show the frequency distribution of data, organized into “bins,” of consecutive, non-overlapping intervals of a variable. Histograms are very useful in displaying the distribution of continuous intervals of data (ages, days, time, etc.), and determining whether the data is distributed relatively evenly, is skewed, or takes some other interesting shape, as in the following examples:
Displaying values in bar charts in rank order affords us a simple and elegant way to convey relative differences in health and healthcare data, such as “more vs. fewer people (per 1,000 population) diagnosed with HIV in different countries,” or “hospitals that had the highest vs. lowest rate of hospital-acquired infections,” or “which communities had the lowest vaccination rates.”
CHANGE OVER TIME
Time series data may be displayed using bars if it’s important to see the values in direct comparison to one another. Additionally, and because we often encounter seasonality in health and healthcare data, displaying it over time using a bar chart may be preferable for some results, as it helps us see not only chronological change, but also the distribution of results.
Sometimes, due to space constraints, or the number of time periods that need to be displayed, a horizontal bar chart is the best choice.
PROPORTIONS: PART-TO-WHOLE & DIFFERENT CATEGORIES OF DATA
Small Multiples Bar Chart is a helpful technique when we have three or more parts of the whole to display. By creating a series of bar charts, all with the same axes and scale, to show and compare different parts of the whole, we can make it far easier to see and compare those multiple parts. We are also able to view and compare directly the category of data being displayed in each column. Bar charts also extraordinarily well designed to display different categories of data across multiple dimensions.
DEVIATION (DIFFERENCE, VARIATION)
A bar graph may also be used to display a deviation; that is, how one or more sets of quantitative values differ from a reference set of values. They’re especially helpful when we need to compare differences or changes between groups that have a wide range of absolute values, such as departmental budgets vs. actual results, or different countries’ spending on healthcare services in one year and another. By using a deviation bar graph to display the relative differences, we can quickly identify which values are up or down, larger or smaller, and which ones may require further inquiry and analysis.
Displaying results in this way tells viewers how far over or under target or goal they are — that is, the real difference from target, their actual score displayed on the X or Y axis, and performance compared to other groups or in other time periods.
RANGES AND COMPARATIVE VALUES
A floating bar chart may be used to display the range of a category of data (minimum and maximum value, beginning and ending values such as start and stop times, comparative data values such as percentile results). Some examples of how we’ve used these floating bars include displaying operational metrics such as hospital surgical case starts and stop times in multiple operating rooms; patient visit times in a clinic office by the day of the week and time of day; or comparison percentile ranges for performance metrics like patient experience survey results.
When we need to display a wide range of values (500 to 10,000, say), we can place two bar charts with different axis ranges next to each other. One bar chart shows only the lower values with the longer bars truncated, while the other shows the full range of values. This simple technique allows us to display the smallest values and the largest values and makes clear to the viewer that the range is large. This technique also follows the best practice of starting both bar graphs at zero.
Bars truly are the workhorses of data visualization. Once you have a solid understanding of the fundamental ways they can be used, you will acquire much greater skill and confidence as you create a range of simple to complex displays. I’m confident that the more you work with them, the more you’ll come to agree that bars are anything but boring — rather, used correctly and imaginatively, they are both gratifying and beautiful in their ability to help us see and understand the important stories in our health and healthcare data.
Here’s wishing you a happy holiday season — and no more “Bar Humbug!”