The bixplot: A variation on the boxplot suited for bimodal data
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909010876170240 |
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| author | Montalcini, Camille M. Rousseeuw, Peter J. |
| author_facet | Montalcini, Camille M. Rousseeuw, Peter J. |
| contents | Boxplots and related visualization methods are widely used exploratory tools for taking a first look at collections of univariate variables. In this note an extension is provided that is specifically designed to detect and display bimodality and multimodality when the data warrant it. For this purpose a univariate clustering method is constructed that ensures contiguous clusters, meaning that no cluster has members inside another cluster, and such that each cluster contains at least a given number of unique members. The resulting bixplot display facilitates the identification and interpretation of potentially meaningful subgroups underlying the data. The bixplot also displays the individual data values, which can draw attention to isolated points. Implementations of the bixplot are available in both Python and R, and their many options are illustrated on several real datasets. For instance, an external variable can be visualized by color gradations inside the display. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_09276 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The bixplot: A variation on the boxplot suited for bimodal data Montalcini, Camille M. Rousseeuw, Peter J. Methodology Applications Boxplots and related visualization methods are widely used exploratory tools for taking a first look at collections of univariate variables. In this note an extension is provided that is specifically designed to detect and display bimodality and multimodality when the data warrant it. For this purpose a univariate clustering method is constructed that ensures contiguous clusters, meaning that no cluster has members inside another cluster, and such that each cluster contains at least a given number of unique members. The resulting bixplot display facilitates the identification and interpretation of potentially meaningful subgroups underlying the data. The bixplot also displays the individual data values, which can draw attention to isolated points. Implementations of the bixplot are available in both Python and R, and their many options are illustrated on several real datasets. For instance, an external variable can be visualized by color gradations inside the display. |
| title | The bixplot: A variation on the boxplot suited for bimodal data |
| topic | Methodology Applications |
| url | https://arxiv.org/abs/2510.09276 |