Data & Intelligence

Data Visualization – How to Pick the Right Chart? – Part 2

Man using tablet to look at charts

So Many BI Charts, but which one to choose and when? This blog series will help you understand each chart’s uses and properties.

This is the second blog in a series about data visualization. Read the first blog in the series here.

10. Area Chart

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An area chart is essentially a line chart — good for trends and some comparisons. Area charts will fill up the area below the line, so the best use for this type of chart is for presenting accumulative value changes over time, like item stock, number of employees, or a savings account.
Do not use area charts to present fluctuating values, like the stock market or price changes.

11. Stacked Chart

 

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Stacked area charts are best used to show changes in composition over time. A good example would be the changes in market share among top players or revenue shares by product line over a period of time.
Stacked area charts might be colorful and fun, but you should use them cautiously because they can quickly become a mess. Don’t use them if you need an exact comparison, and don’t stack together more than three to five categories.

12. Scatter Chart

 

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Scatter charts are primarily used for correlation and distribution analysis. They are suitable for showing the relationship between two variables where one correlates to another (or doesn’t).
Scatter charts can also show the data distribution or clustering trends and help you spot anomalies or outliers.
An excellent example of scatter charts would be a chart showing marketing spending vs. revenue.

13. Bubble Chart

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A bubble chart is a great option if you need to add another dimension to a scatter plot chart. Scatter plots compare two values, but you can add bubble size as the third variable and thus enable comparison. If the bubbles are very similar in size, use labels.

Use Scatter and Bubble charts to:

  • Present relationships between two (scatter) or three (bubble) numerical variables.
  • Plot two or three sets of variables on one x-y coordinate plane.
  • Turn the horizontal axis into a logarithmic scale, thus showing the relationships between more widely distributed elements.
  • Present patterns in large sets of data, linear or non-linear trends, correlations, clusters, or outliers.
  • Compare a large number of data points without regard to time. The more data you include in a scatter chart, the better comparisons you can make.
  • Present relationships, but not exact values for comparisons.

14. Map Charts

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Map charts are good for giving your numbers a geographical context to quickly spot the best and worst-performing areas, trends, and outliers. If you have location data like coordinates, country names, state names or abbreviations, or addresses, you can plot any related data on a map.
Maps won’t be very good for comparing exact values because map charts are usually color-scaled, and humans are pretty bad at distinguishing shades of colors. Sometimes it’s better to use overlay bubbles or numbers if you need to convey exact numbers or enable comparison.

A good example would be website visitors by country, state, city, or product sales by state, region, or city.
But don’t use maps for absolutely everything that has a geographical dimension. Today, almost any data has a geographical dimension, but it doesn’t mean you should display it on a map.

When to use map charts?

  • If you want to display quantitative information on a map.
  • To present spatial relationships and patterns.
  • When a regional context for your data is essential.
  • To get an overview of the distribution across geographic locations.
  • Only if your data is standardized (that is, it has the same data format and scale for the whole set).

15. Gantt Charts

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Gantt charts are good for planning and scheduling projects. They are essentially project maps, illustrating what needs to be done, in what order, and by what deadline. You can visualize the total time a project should take, the resources involved, and the order and dependencies of tasks.

16. Gauge Charts
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Gauge charts are suitable for displaying KPIs (Key Performance Indicators). They typically display a single key-value, comparing it to a color-coded performance level indicator, typically showing green for “good” and red for “trouble.”

A Dashboard would be the most obvious place to use Gauge charts. There, all the KPIs will be in one place and give a quick “health check” for your project or company.
Gauges are a great choice to:

  • Show progress toward a goal.
  • Represent a percentile measure, like a KPI.
  • Show an exact value and meaning of a single measure.
  • Display a single bit of information that can be quickly scanned and understood.

The bad side of gauge charts is that they take up a lot of space and typically only show a single data point. If many gauge charts are compared against a single performance scale, a column chart with threshold indicators would be a more effective and compact option.

17. Multi Axes Charts
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Sometimes, a simple chart just cannot tell the whole story. If you want to show relationships and compare variables on vastly different scales, the best option might be to have multiple axes.
A multi-axis chart will let you plot data using two or more y-axes and one shared x-axis. But it comes at a cost. That is, the charts are much more challenging to read and understand. Multi-axes charts might be suitable for presenting common trends, correlations (or the lack thereof), and the relationships between several data sets. But multi-axes charts are not ideal for exact comparisons (because of different scales), and you should not use this type if you need to show precise values.

Use multi-axes charts if you want to:

  • Display a line chart and a column chart with the same X-axis.
  • Compare multiple measures with different value ranges.
  • Illustrate the relationships, correlation, or lack thereof between two or more measures in one visualization.
  • Save canvas space (if the chart does not become too complicated).

Data Visualization Do’s and Don’ts – A General Conclusion

  • Time axis. When using time in charts, set it on the horizontal axis. Time should run from left to right. Do not skip values (time periods), even if there are no values.
  • Proportional values. The numbers in a chart (displayed as bar, area, bubble, or another physically measured element in the chart) should be directly proportional to the numerical quantities presented.
  • Data-Ink Ratio. Remove any excess information, lines, colors, and text from a chart that does not add value.
  • Sorting. For column and bar charts, to enable easier comparison, sort your data in ascending or descending order by the value, not alphabetically. This also applies to pie charts.
  • Legend. You don’t need a legend if you have only one data category.
  • Labels. Use labels directly on the line, column, bar, pie, etc., whenever possible to avoid indirect look-up.
  • Inflation adjustment. When using monetary values in a long-term series, make sure to adjust for inflation.
  • Colors. In any chart, don’t use more than six colors.
  • Colors. For comparing the same value at different time periods, use the same color in a different intensity (from light to dark).
  • Colors. For different categories, use different colors. The most widely used colors are black, white, red, green, blue, and yellow.
  • Colors. Keep the same color palette or style for all charts in the series and the same axes and labels for similar charts to make your charts consistent and easy to compare.
  • Colors. Check how your charts would look when printed out in grayscale. If you cannot distinguish color differences, you should change the hue and saturation of colors.
  • Data Complexity. Don’t add too much information to a single chart. If necessary, split data into two charts, use highlighting, simplify colors, or change the chart type.

 

This is part 2 of this blog series on how to pick the right chart for data visualization. Stay tuned for more!

Happy reading & learning.

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Arpit Malviya

Arpit Malviya has worked at Perficient as a technical consultant. He has been working in the Power BI & Data Analysis field since 2019.

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