A Step-by-Step Guide to Creating Stacked Column Charts

Data visualization is becoming an increasingly critical skill for communicating insights from data analysis. Visual representations of data can convey complex information and patterns more efficiently than written text or tables of numbers alone. One popular yet powerful type of data visualization is the stacked column chart. In this beginner’s guide, I’ll walk through what stacked column charts are, when to use them, and provide a step-by-step tutorial for creating them in spreadsheet software.

 

Stacked column charts are ideal for showing how multiple categories or elements contribute to an overall total. By dividing a column vertically, we can illustrate the breakdown of a whole into its separate components. This allows viewers to instantly grasp part-to-whole relationships that would otherwise be buried in rows of data.

For example, stacked charts allow us to track monthly sales performance by product category or view a company’s quarterly revenue by region. As such, column stacking is extremely useful for financial reporting, business analytics, and other common applications. When leveraged properly, it brings an insightful perspective to dataset analysis.

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Understanding Stacked Column Charts

A stacked column or bar chart is similar to a basic vertical bar graph, except the bars are divided into colored segments representing different subgroups or categories. These categories are “stacked” on top of each other vertically to show their contribution to the whole column, which represents the aggregate total.

The key advantage of a stacked chart is it allows the viewer to immediately discern both individual and cumulative percentages or amounts in an intuitive format. Studying the segments makes it simple to spot the largest or smallest categories. We can also quickly analyze trends by subgroup within the same overall variable.

Situations where stacked column charts shine include:

  1. Comparing sub-category amounts to overall totals
  2. Tracking multiple time-based elements as a sum total
  3. Viewing market or demographic breakdowns
  4. Financial reporting and analysis
  5. Preparing your data

Before we begin building visualizations, it’s critical to have clean, well-organized source data. Our sample dataset tracks quarterly sales performance by product line—but this could easily be adapted for other needs.

The most essential step is structuring our spreadsheet to have one column for the primary category we want to graph (in this case, each quarter), and subsequent columns for the sub-categories (product lines) that will form each stacked element.

Example Structure

This shows the quarterly costs broken down into office categories.

Quarter Furniture Office Supplies Technology Total
Q1 $10,000 $5,000 $15,000 $30,000
Q2 $8,000 $6,000 $12,000 $26,000
Q3 $12,000 $7,000 $10,000 $29,000
Q4 $11,000 $8,000 $13,000 $32,000

With clean, well-formatted source data, we can start visualizing!

Step-by-Step Process to Creating a Stacked Column Chart in Excel

Many popular spreadsheet and data analysis programs allow users to generate stacked column or bar charts. The exact steps may vary slightly across platforms, but our example uses Excel due to its widespread adoption.

1. Create dataset in Excel and Click the Insert tab.

Within Excel, click the Insert tab on the command ribbon and select the Column chart option under Charts. This will insert a basic 2D column chart drawing from your existing data table.

2. Navigate to the “Charts” section and choose Stacked Column Chart

To convert the basic columns into a stacked format, find Chart Elements within the Chart Design tab. Click the dropdown arrow underneath this and hover over Change Chart Type. Here you will see options for Stacked Column Chart or 100% Stacked Column Chart.

The only difference is that 100% stacked columns adjust scale to always fill each column completely from bottom to top. Pick your preferred style for the use case.

3. Customize colors, labels, axes, and more

Now the column segments should be layered to illustrate contribution to the quarterly totals! From here, we can customize colors, labels, axes scales, and more to tailor the chart.

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Data Visualization Best Practices

Before finalizing any chart, keep these key best practices in mind:

  • Verify data accuracy – Fix any errors before visualizing to prevent distortion.
  • Pick easily distinguishable colors – Use basic color wheel principles to maximize contrast.
  • Emphasize the most important elements – Draw attention to key categories or data points.
  • Add concise, descriptive labels – Axis titles, legends, and data callouts should be clear without overwhelming the chart.

In our sales revenue example, we quickly identify that Technology revenue, shown in blue, is driving total sales growth. Meanwhile, the quarterly contributions of Furniture and Office Supplies remain relatively stable.

The length of each vertical sub-column shows absolute size, while color coding maps categories across time periods. If any unusual spikes in certain segments occur, they become visible. For example, if Technology suddenly dropped in Q3, it would raise questions about that quarter’s performance.

Advanced Tips and Tricks

  • For those ready to skill up beyond basics, stacked columns provide ample room for customization! Try…
    • Grouping columns for side-by-side category comparison rather than time sequence. Just be wary of overcrowding.
    • Call attention to key data points by annotating chart elements with arrows, shapes, and text boxes.
    • Build 3D graphic styles for added visual impact (though this risks distorting the perception of actual values).

Common Mistakes to Avoid

While stacked charts excel at proportional part-to-whole analysis, they can certainly be misused. Here are key mistakes:

  • Cramming too many data elements onto a single column makes it impossible to accurately read or label each color-coded section. Limit stacking to less than five segments when feasible.
  • Forgetting axis labels or legends defining color mapping. Without this context, interpretation becomes difficult as patterns won’t be adequately explained.
  • Using inconsistent y-axis scales across multiple charts. This distortion prevents accurate trend analysis. Values must align properly across chart series.

Finance & Investment Banking Use Cases for Stacked Column Charts

Here’s how investment bankers can utilize this chart for common tasks.

  • Comparing revenue contributions of different business segments over time. The segments can be stacked to show their proportion of total revenue. Useful to see where growth and declines are occurring.
  • Analyzing the composition of a portfolio over time – stacks can show percentage breakdowns of asset classes in the portfolio and how the composition changes.
  • Showing utilization of credit facilities – stacks could represent amounts of revolving credit, term loans, bonds, etc., that add up to the total credit limit or commitment. Good visualization for credit analysts.
  • Comparing expense categories over time – stacks can show a breakdown of total expenses into categories like compensation, technology, real estate, etc. A good way to spot increasing costs.
  • Analyzing sources of funding over time – stacks can show proportions from equity, debt, retained earnings, and other sources that sum to total funding. Useful in capital structure analysis.
  • Due diligence analysis – stacks can track items identified in due diligence by category over time, showing the composition of total items. Helps see trends in issues arising.

Conclusion

As this post demonstrated, stacked column and bar charts enable insightful part-to-whole data analysis – but must be carefully crafted to maximize their utility. By following key guidelines around proper color use, labeling, scaling, and alignment, we can produce intuitive stacked visualizations ready to boost data storytelling.

The data examples used here merely scratch the surface of use cases that span business and academia to journalism and personal tracking. Whether building executive reports or transforming raw survey results into an engrossing blog graphic, stacked charts belong in any analytics toolkit. I aimed to provide all the techniques needed to start comfortably using stacked columns not just in Excel, but expansive data visualization libraries!

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