Which is Your Favorite Chart Type?
Lines, bars, tables, gauges, pies, donuts, bubbles – there’s a chart type for every need! They help us understand data better. How many do you use in your reports and dashboards?
As per the theory, there are 4 ways you can present your data:
Comparison
Composition
Distribution
Relationship
Bar charts are good for comparisons, while line charts work better for trends. Scatter plot charts are good for relationships and distributions, but pie charts should be used only for simple compositions — never for comparisons or distributions.
There is even a chart selection diagram created by Dr.Andrew Abela that should help you to choose the right chart type if in doubt ;)
In this post, we will review the most popular chart types. According to usage statistics, the top 5 most often used chart types in eazyBI for Jira Cloud are bar charts, tables, line charts, pie charts, and gauge charts. Let’s review those chart types and the best practices for using them in data visualization.
Tables
In fact, this is a place we all start, right? Tables are a source for all the charts, but they can also be used for comparison, composition, or relationship analysis in situations when there are only a few data points. Creating a chart would not make much sense if the data can be easily interpreted from the table. You can use tables when:
You need to compare or look up individual values.
You require precise values.
Values involve multiple units of measure.
The data has to communicate quantitative information, but not trends.
Those two tables show different data presentations. Both tables show total values: the table on the left counts the number of issues by type and status, while the table on the right shows resolved issues distribution by weekdays over time (as a heat map). As you can see, visually representing the data in tables using symbols, colors, or conditional formatting is possible and even suggested.
Bar Charts
Bar charts are probably the most used chart type for comparison. You can compare values for different categories or compare value changes over a period of time for a single category. Bar charts can be horizontal or vertical (sometimes named as column charts), the main principles are the same for both types:
If the number of categories is small—up to five, but not more than seven—use bar charts for comparison. When the number of categories is greater than seven, use horizontal bar charts to improve the readability.
Set the time dimension on the horizontal axis if one of your data dimensions is time (years, quarters, months, days, hours, etc.).
In charts, time should always run from left to right, never from top to bottom.
The numerical axis must start at zero. Our eyes are very sensitive to the height of columns, and we can draw inaccurate conclusions when those bars are truncated.
Use stacked bar charts to show a composition. Do not use too many composition items (not more than three or four).
For easier comparison, sort your data in ascending or descending order by the value, not alphabetically.
If you have long category names, use horizontal bar charts, as they provide more space for long text.
Bar charts excellently present the comparison and composition over time or defined time intervals. Bar charts bring value by adding trendlines or cumulative lines to deepen the story. By using conditional formatting, similar or vice versa, contrasting colors are helpful to perceive the information more easily.
Line Charts
Line charts are among the most frequently used chart types. They are best suited for visualizing trends in data over a period of time.
Lines are used to present continuous data on an interval scale, where intervals are equal in size.
For line charts, the axis may not start from zero if the intended message of the chart is the rate of change or overall trend, not exact values or comparison. It’s best to start the axis with zero for wide audiences because some people may otherwise interpret the chart incorrectly.
In line charts, time should always run from left to right.
Do not skip values for consistent data intervals presenting trend information, for example, certain days with zero values. And, if you have time on x axis, use timeline - all dates, even with empty values, appears in this specific line chart.
The line chart on the left presents the correlation between closed and resolved issues over time. Notice color-filled areas between both values to show the positive or negative difference visually. The second example shows story point resolution progress during the selected sprint with cumulative burned value, trendline, and ideal burn-down guideline. The timeline chart is the most typical line chart group. Some lines representing real facts may need data points, while projection lines, like trends and guidelines, do not have them. Mixing different chart types (bars with lines) while keeping the same color schema improves the readability of data: bars represent daily burned story points, while the line is cumulative, and both are green.
Pie and Donut Charts
Who doesn’t love pies or donuts, right? Not in data visualization, though. These charts are among the most frequently used and also misused charts.
A pie chart typically represents numbers in percentages, used to visualize a part to whole relationship or a composition. Pie charts are not meant to compare individual sections to each other or to represent exact values (you should use a bar chart for that).
If you still feel sentimental about the old PowerPoint Pie charts and want to keep using them, there are some things to remember.
Ensure that all segments' total sum equals 100 percent (if percentages are displayed).
Use pie charts only if you have less than six categories unless there’s a clear winner you want to focus on.
Ideally, there should be only two categories, like men and women visiting your website, or only one category, like a market share of your company, compared to the whole market.
Don’t use a pie chart if the category values are almost identical or completely different.
Don’t use 3D or blow apart effects — they reduce comprehension and show incorrect proportions.
The first pie chart very well represents the proportion of “Closed” issues over the issues in other statuses. However, it is impossible to visually distinguish the difference between issues in “Idea” and “To Do” statuses. To do that, a table or bar chart might serve better. It is possible to compare values for a current and previous time period in a pie chart, as in the example on the left. In this case, priorities are grouped into two larger groups to keep the chart as simple and readable as possible.
Gauge Charts
Gauge charts are good for displaying KPIs (Key Performance Indicators). They typically display a single key value, comparing it to a color-coded performance level indicator, showing green for “good” and red for “trouble.”
Gauges are a great choice for:
Show progress toward a goal.
Represent a percentile measure, like a KPI.
Show the exact value and meaning of a single measure.
Display a single bit of information that can be quickly scanned and understood.
There are different ways you can present data in Gauge charts:
To show key performance indicators (KPIs) as numbers or color-coded gauges with progress towards defined limits.
To indicate the date when all the issues will be closed.
To get an overview of the project progress.
We’ve reviewed only the most frequently used chart types, however, there are a couple of common takeaways I would like to leave you with:
Keep the charts simple and clear - don’t add too much information to a single chart. If necessary, split data into two charts, use highlighting, simplify colors, or change chart type.
Consider using labels vs legend to keep the focus on the chart itself.
Keep the coloring consistent to ease the reading of it over the time.
If you'd like to compare the same value at different time periods, use the same color in a different intensity (from light to dark).
Remove any excess information, lines, colors, and text from a chart that does not add value. Take a look at the example here: the data-ink ratio.
If you’d like to dig a bit deeper in data visualization, please find a full article on how to pick the right chart type on our blog.