Dot Plot Chart
Written by Konrad Mattheis
Updated over a week ago


This article looks at the Dot Plot chart available in Astrato.

A Dot Plot chart has a unique offering; it allows users to see an aggregated view of the data. combined with a detailed view of the data.

A dot plot, also known as a strip plot, is a way to visualize the distribution of a single continuous variable or the relationship between two continuous variables. It is particularly useful when dealing with datasets with overlapping data points.

Here's what you can learn from a dot plot:

  1. Data Distribution: You can observe the distribution of data points along the continuous variable(s) represented on the plot. This helps in understanding the spread and concentration of data.

  2. Density: Dot plots can give insights into the density of data points at different continuous variable values. Denser regions indicate where most data points lie, while sparse regions indicate areas with fewer data points.

  3. Outliers: Outliers, or extreme values, can be identified on a dot plot as individual points that lie far away from the main cluster of data. These points can provide valuable information about anomalies in the dataset.

  4. Relationships: If you plot two continuous variables, you can observe any patterns or relationships between them. For example, if the points cluster in a particular pattern, it may indicate a positive or negative correlation between the variables.

  5. Variability: Dot plots can visually represent the variability or dispersion of data points. Widely spread-out points suggest high variability, while closely clustered points indicate low variability.

  6. Comparison: Dot plots can be used to compare distributions or relationships across different groups or categories within the dataset. This can help in identifying differences or similarities between groups.

  7. Overplotting: Dot plots help mitigate overplotting, which occurs when multiple data points have the same or similar values and overlap on a traditional scatter plot. By adding a small amount of random noise (jitter) to the data points, they are spread out, making it easier to visualize individual points.

Overall, Dot plots provide a quick and effective way to explore and understand a dataset's characteristics, especially when dealing with continuous variables or group comparisons.

Building a Dot Plot

  • Selecting Data

    A Dot plot is a chart using two dimensions and one measure.

    The first dimension is used for Grouping and usually has a limited number of unique values.

    The second dimension shows the individual entities inside the first dimension; this dimension usually has more unique values.

    The measure distributes the points representing the second dimension for each group.

  • In the below example, we show Customer LTV by product category. The distribution in the bars represents the customers; the coloring is by the customer country.


  • Styling a Dot Plot

    • Points and Bars Styling

      • Point size- set the point size to display inside the bars

      • Jitter points- when checked, points will be spread randomly inside the bar width to display a higher number of points and reduce overlapping points.

      • Bar padding- the distance between the individual bars

      • Bar radius- the radius in the corners of the bars.

      • Bar color- the color of the bars

      • Bar border

        • Border color- the color of the bars borders.

        • Border width- the width of the bars border line.

    • Color

      • Type of coloring- select one of the three options to color the points:

        • Single- all points will have the same color

        • Categorical- points will be colored by the value of the selected field

        • Gradient- coloring the points according to the selected measure values.

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