A complete guide to bar charts

A bar chart (aka bar graph, column chart) plots numeric values for levels of a categorical feature as bars. Levels are plotted on one chart axis, and values are plotted on the other axis. Each categorical value claims one bar, and the length of each bar corresponds to the bar’s value. Bars are plotted on a common baseline to allow for easy comparison of values.

This example bar chart depicts the number of purchases made on a site by different types of users. The categorical feature, user type, is plotted on the horizontal axis, and each bar’s height corresponds to the number of purchases made under each user type. We can see from this chart that while there are about three times as many purchases from new users who create user accounts than those that do not create user accounts (guests), both are dwarfed by the number of purchases made by repeating users.

A bar chart is used when you want to show a distribution of data points or perform a comparison of metric values across different subgroups of your data. From a bar chart, we can see which groups are highest or most common, and how other groups compare against the others. Since this is a fairly common task, bar charts are a fairly ubiquitous chart type.

The primary variable of a bar chart is its categorical variable. A categorical variable takes discrete values, which can be thought of as labels. Examples include state or country, industry type, website access method (desktop, mobile), and visitor type (free, basic, premium). Some categorical variables have ordered values, like dividing objects by size (small, medium, large). In addition, some non-categorical variables can be converted into groups, like aggregating temporal data based on date (eg. dividing by quarter into 20XX-Q1, 20XX-Q2, 20XX-Q3, 20XX-Q4, etc.) The important point for this primary variable is that the groups are distinct.

In contrast, the secondary variable will be numeric in nature. The secondary variable’s values determine the length of each bar. These values can come from a great variety of sources. In its simplest form, the values may be a simple frequency count or proportion for how much of the data is divided into each category – not an actual data feature at all. For example, the following plot counts pageviews over a period of six months. You can see from this visualization that there was a small peak in June and July before returning to the previous baseline.

Other times, the values may be an average, total, or some other summary measure computed separately for each group. In the following example, the height of each bar depicts the average transaction size by method of payment. Note that while the average payments are highest with checks, it would take a different plot to show how often customers actually use them.

Data rendered as a bar chart might come in a compact form like the above table, with one column for the categories and the second column for their values. Other times, data may come in its unaggregated form like the below table snippet, with the visualization tool automatically performing the aggregation at the time of visualization creation.

First and foremost, make sure that all of your bars are being plotted against a zero-value baseline. Not only does that baseline make it easier for readers to compare bar lengths, it also maintains the truthfulness of your data visualization. A bar chart with a non-zero baseline or some other gap in the axis scale can easily misrepresent the comparison between groups since the ratio in bar lengths will not match the ratio in actual bar values.

Another major no-no is to mess with the shape of the bars to be plotted. Some tools will allow for the rounding of the bar caps, rather than just have straight edges. This rounding means that it’s difficult for the reader to tell where to read the actual value: from the top of the semicircle, or somewhere in the middle? A little bit of rounding of the corners can be okay, but make sure each bar is flat enough to discern its true value and provide an easy comparison between bars.

Similarly, you should avoid including 3-d effects on your bars. As with heavy rounding, this can make it harder to know how to measure bar lengths, and as a bonus, might cause baselines to not be aligned (see the above point).

If you feel the need to use icons to depict value, then a better – though still not great – option is to use the pictogram chart type instead. In a pictogram chart, each category’s value is indicated by a series of icons, with each icon representing a certain quantity. In a certain sense, this is like changing the texture of its corresponding bar to a repeating image. One major caution with this chart type is that it can make values harder to read, since the reader needs to perform some mental mathematics to gauge the relative values of each category.

A common bar chart variation is whether or not the bar chart should be oriented vertically (with categories on the horizontal axis) or horizontally (with categories on the vertical axis). While the vertical bar chart is usually the default, it’s a good idea to use a horizontal bar chart when you are faced with long category labels. In a vertical chart, these labels might overlap, and would need to be rotated or shifted to remain legible; the horizontal orientation avoids this issue.

A common addition to bar charts are value annotations. While it is fairly easy for readers to compare bar lengths and gauge approximate values from a bar chart, exact values aren’t necessarily easy to state. Annotations can report these values where they are important, and are usually placed in the middle of the bar or at their ends.

Histograms are a close cousin to bar charts that depict frequency values. While a bar chart’s primary variable is categorical in nature, a histogram’s primary variable is continuous and numeric. The bars in a histogram are typically placed right next to each other to emphasize this continuous nature: bar charts usually have some space between bars to emphasize the categorical nature of the primary variable.

Most tools that can create visualizations, whether they be spreadsheets, programming libraries, or business intelligence tools, should be capable of creating basic vertical bar charts. Sometimes, options need to be checked or modified in order to follow best practices. However, for basic data exploration needs, any tool should be sufficient. Other variations like horizontal bars, error bars, and annotations may not always be possible. In particular, the lollipop chart variation is not normally considered a default chart type, and will usually require specialized tweaking with programmatic tools instead.