Why Should You Use a Stacked Chart?

If you’re looking for a way to compare two or more data sets effectively, a stacked chart is a great option. Keep reading to learn more about using a stacked chart and when it’s the best choice for your data visualization needs.

What is a stacked chart?

Stacked Chart

Before we explain why use a stacked chart, let’s define stacked charts first. A stacked chart is a graph in which data is displayed as bars, with each bar representing a different category and the total of all the bars for a given type represented by the height of the bar. The advantage of using a stacked chart is that it allows you to compare the relative sizes of different categories at a glance. For example, if you want to compare the market shares of two companies, you can use a stacked chart to display them side-by-side.

The central stack in a stacked chart is the vertical column representing the total value of all the data series in the chart. The main stack is always located on the right side of the chart, and it always has a height that corresponds to the total value of all the data series. The central stack can determine how much of the total value is represented by each data series. To interpret data from a stacked chart, start by looking at the overall trend. In most cases, you’ll want to focus on the direction of the most significant category first and then look at trends for smaller categories afterward.

For example, if you’re looking at market share analytical data, you would want to focus on whether or not one company’s market share is growing or shrinking compared to the other company’s market share. Once you’ve identified any trends, try to identify any outliers or points that don’t seem to fit with the rest of the data. Finally, always read any accompanying notes or labels explaining what specific values correspond to each bar on the graph.

How do you use a stacked chart for scientific analysis?

A stacked chart is a great way to visualize how different parts make up a whole. For scientific analysis, you can use a stacked chart to compare different data sets and see how they interact. In the example below, we are looking at how temperature affects the growth of bacteria. The y-axis shows the number of bacteria colonies after 24 hours, while the x-axis shows the temperature in degrees Celsius.

example chart
The first data shows that as the temperature increases, so does the number of bacteria colonies. The second data set shows that as the temperature decreases, so does the number of bacteria colonies. If we combine these two data sets into one chart, we can see that as the temperature increases or decreases, there is an overall trend regarding how it affects bacterial growth.

What are the different types of stacked charts you can create?

There are three types of stacked charts: column, bar, and area.

  • Column and bar charts are very similar, except that column charts use vertical bars to represent data while bar charts use horizontal bars.
  • Area charts are similar to column and bar charts, but instead of using bars, they use shaded areas to represent data. All three are great for data visualization, though. A stacked column chart is a graph that displays data as stacked columns.

Each column represents a different category, and each column’s height reflects that category’s value. The total size of the stack represents the total value for all types. To interpret a stacked column chart, first look at the full height of the pile. This indicates the total value for all categories. Then, look at each column to see how it contributes to the overall total. Finally, look at how each category’s value compares to the others.

All three stacked charts can be used to compare values between different categories. For example, you could use a column chart to compare sales figures for other products over time. This chart can also identify trends or patterns in one or both of the tracked measures. Or you could use an area chart to compare market shares for different companies in a particular industry.

How do you create a stacked chart?

Creating a stacked chart is a great way to compare and contrast data. To create a stacked chart, you’ll need two data sets you want to reach. Once you have your data set, you’ll need to create a chart. Excel has several different chart types, so select the completed chart that best suits your data. Once you have made your chart, you’ll need to choose the data you want to appear in the chart. To do this, click and drag across the cells you want to appear in the chart.

With your data selected, right-click on the chart and select “Format Data Series.” This will open the Format Data Series window. You’ll need to choose the “Stacked Column” chart type in this window.

Once you have selected the “Stacked Column” chart type, the Format Data Series window will change. You’ll need to choose the “Stacked” option in this window. This will tell Excel to stack the data sets on top of each other.

You can also change the colors of your data series in this window. To do this, click on the “Series Options” tab and select the color that you want to use. Once you have finished formatting your data, click “OK,” and your chart will be updated.

How to use a stacked chart to show percentage changes?

When you want to compare changes in proportions across different data sets, a stacked chart is the best way to go. This visualization type displays values as stacks of rectangles, with each rectangle representing a data point. The rectangle’s height corresponds to the value for that data point, and the color indicates which data set the value belongs to.

Stacked charts are especially useful when you want to see how one variable changes relative to another. For example, you could use a stacked chart to compare the percentage of men and women in different age groups who are unemployed. In this case, the x-axis would be the age group, and the y-axis would be the percentage of people who are unemployed.


A stacked chart is essential because it provides an overall view of how a specific metric is broken down into different categories. This can be helpful in understanding how a change in one category affects the overall metric.

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