Let’s look at the very first bar chart I ever made in Tableau.

While it isn’t the worst bar chart that has ever graced your eyes, it could definitely use improvement. Here are a few of the things you should consider,

Purpose

Bar charts are typically used to compare values of magnitude (size). This means that bar charts are most effectively used when comparing two or more values. In most cases, the values are different bars on the same graph. Alternatively, bars can be compared to a reference line. The purpose of this chart was purely to show the relative nutritional composition of a Large Big Mac Meal.

Then what purpose do each of these bars fulfill on this chart? Do we really need a dual axis ? The answer is No. The Energy bar ruins the scale of the chart, and a dual-axis was employed in an attempt to maintain the scale. However, since you cannot compare unrelated units (kJ vs grams), comparisons between these bars cannot be inferred. So, lets remove the Energy bar.

Colour

Colour can be used in two main ways:

  • Informational colour choice – used to add or highlight information. In the case below, the viewer’s attention is drawn to protein over carbohydrates or fat.

  • Stylistic colour choice – used to to improve aesthetics. In the case below, colour relates to a logical source of the component in the burger (orange=bun, brown=meat patty, yellow=cheese). Therefore, colour is used both to improve aesthetics but also helps the viewer relate to each nutritional component.

Labels

Labels are one of the easiest ways to convey specific information such as exact values on a bar chart. In this case, although the values of each bar are displayed on the label, the units are not. This makes the viewer have to look in two places for one piece of information. In addition, this label alignment decreases readability and renders the bar component of the bar chart useless. Lets change that.

Axis/Gridlines

In the current chart, the gridlines add unnecessary clutter. A gridline is most useful in charts with several bars of varying magnitudes that need to be compared to an axis tick. However, since there are only a few bars and we now have labels, neither the gridlines nor the axis ticks are necessary to estimate bar value.

Miscellaneous Changes

  • Improve title/subtitle for the chart to provide context
  • Increase the font size to match bar width

BEFORE…

AFTER

Notable Improvements

  • It is easier to understand the relative proportions of carbohydrates/ protein/ and fat in a Large Big Mac Meal
  • Colours are within a smaller palette and more congruous with likely component sources within a burger
  • Clutter has been removed
  • Accompanying text better explains the contents of the graph and provides context to the viewer

 

CONCLUSION

Despite how simple they are, bar charts are very easy to get wrong. Consider how each component of your bar chart affects how your viewer experiences and digests the information you provide to them.

Kevin Prescilla
Author: Kevin Prescilla

As a late-stage PhD candidate, Kevin’s appreciation for data analytics grew during his studies into poultry nutrition, or as he calls it, “chickens”. It was this appreciation which spurred his decision to change career paths and ultimately led him to apply to the Data School. In his spare time he enjoys powerlifting – ever challenging himself to beat his last max weight - as well as all kinds of gaming, from board to PC. If Kevin could go anywhere in the world, where would it be and why? Well, the answer is Antarctica, as he is fascinated with how people can live and survive down there (although some might argue because it’s the furthest place you can go on Earth from a chicken).