How can you ensure data accuracy in a stacked area chart?

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A stacked area chart is a type of data visualization that shows how different categories contribute to a total over time or across a range of values. It can be useful for comparing trends, proportions, and patterns among groups. However, to create an accurate and effective stacked area chart, you need to pay attention to some key aspects of data cleaning and preparation. In this article, you will learn how to ensure data accuracy in a stacked area chart by following these steps:

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Tianyu Xu

I help people acquire future-proof generative AI skills → Author | Speaker | Trainer | Consultant | Explorer
Tobiloba Adaramati

Data Analytics Consultant
Arpit Sarkar

????????? ?????????? @ ??? ???? ?? | ??????? ???????????| ??????x ???????????| ???? ?????-1??| Former SDET ??

1 Check the data source

The first step is to verify the reliability and validity of the data source that you are using for your stacked area chart. You should check the origin, quality, and currency of the data, as well as the methodology and assumptions behind it. If possible, use primary data sources that are directly collected from the field or the target population, rather than secondary or tertiary sources that may have been processed, aggregated, or interpreted by others. You should also check for any errors, inconsistencies, or outliers in the data that may affect the accuracy of your chart.

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Tianyu Xu

I help people acquire future-proof generative AI skills → Author | Speaker | Trainer | Consultant | Explorer
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Don't trust your own eyes, don't trust AI, and don't trust the visualization tool. Always verify the output. To ensure accuracy: 1. Examine the data tables linked to the visualizations. 2. Pick a few data points (e.g., from the stacked bar chart) and verify against the raw data. 3. Recreate the visualizations using another method (e.g., with Python) to quickly check for alignment.

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Tobiloba Adaramati

Data Analytics Consultant
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One thing I have always done with any dataset especially before visualization is to explore the data. Data exploration helps you just analyze the dataset without any predefined destination. Here you query the data for empty values, clean the data, search for outliers, discover the distribution of each variable using univariate analysis. Explore two different variables with each other through bicarbonate analysis. Discover the correlations that exist between the numerical data. All this helps give an idea of what’s good or bad about your data and the kind of story you should end up telling.

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2 Choose the right categories

The next step is to choose the right categories to represent in your stacked area chart. You should select categories that are relevant, meaningful, and mutually exclusive for your analysis and audience. You should also avoid having too many or too few categories, as this may make your chart cluttered or oversimplified. A good rule of thumb is to have between three and seven categories, depending on the complexity and diversity of your data. You should also label your categories clearly and consistently, using appropriate colors, legends, and annotations.

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Arpit Sarkar

????????? ?????????? @ ??? ???? ?? | ??????? ???????????| ??????x ???????????| ???? ?????-1??| Former SDET ??
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As is with the pie chart, the number of categories is critical in a stacked bar chart. While a stacked bar chart is used mainly for depicting ratio and composition, a business case with an initial 3-5 categories can progressively grow to a higher number, in which case it becomes difficult to independently discern each category. If this is a possibility, then use separate bars in sorted order instead of a stacked bar. Also ensure that the labels in a stacked bar don't overlap (if they do, then again separate bars is your fail-safe go to alternative) and legends are activated using distinguishable colours.

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3 Normalize the data

The third step is to normalize the data before plotting it in your stacked area chart. Normalization is the process of adjusting the data values to a common scale or range, such as percentages, ratios, or indexes. This can help you compare the relative contributions of each category to the total, as well as eliminate the effects of different units, magnitudes, or baselines. You can normalize your data using various methods, such as min-max normalization, z-score normalization, or standardization. You should choose the method that best suits your data type and purpose.

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4 Align the axes

The fourth step is to align the axes of your stacked area chart to ensure data accuracy and readability. You should align the horizontal axis (usually the time or value axis) with the zero point of the vertical axis (usually the percentage or ratio axis). This can help you avoid misleading or distorted impressions of the data trends and proportions, especially if you are using a logarithmic or nonlinear scale. You should also use consistent and appropriate intervals, ticks, and labels for your axes, as well as gridlines and reference lines if needed.

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5 Test the chart

The final step is to test the chart to see if it accurately and effectively communicates your data story and message. You should review the chart from various perspectives, such as your own, your audience's, and your data source's. Additionally, you should ask yourself questions such as: does the chart show the main trends and patterns in the data? Does it highlight the key differences and similarities among the categories? Does it convey the intended message and purpose of the analysis? Does it follow the best practices and principles of data visualization? Are there any errors, biases, or gaps in the data or presentation? If you find any issues or areas for improvement, you should revise the chart accordingly until you are satisfied with the result.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you ensure data accuracy in a stacked area chart?

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2

3

4

5

6

1 Check the data source

2 Choose the right categories

3 Normalize the data

4 Align the axes

5 Test the chart

6 Here’s what else to consider

Data Visualization

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How can you ensure data accuracy in a stacked area chart?

1

2

3

4

5

6

1 Check the data source

2 Choose the right categories

3 Normalize the data

4 Align the axes

5 Test the chart

6 Here’s what else to consider

Data Visualization

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