这个可视化的名称是什么?

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英文:

What is the name of this visualisation?

问题

我正在尝试弄清楚这种图的名称,它在顶部有条形图,底部有棒棒糖图,显示了感兴趣变量的不同类别之间的相互关系。

如果您还能提供任何关于这方面的R包建议,那将会非常棒!!

英文:

I am trying to figure out the name of this plot that has bar chart on top and lollipops on the bottom which show the inter-relationship of the different categories for the variable of interest.

If you could also suggest any R packages for this would be amazing!!

这个可视化的名称是什么?

答案1

得分: 3

这是一个UpSet图,是Venn图的替代品,用于展示多个类别/集合以及集合之间的共同个体数量或特定交集很难可视化的情况。行通常代表集合,而列或条形图表示交集的大小。如果一个集合不是交集的一部分,将显示浅灰色的圆圈或空白的白色单元格。因此,连接集合的垂直线表示集合之间的列关系。例如,在您的图中,考虑从左边的第一根柱子表示有670个个体是成年、男性、未生还和船员类别。同样,第二个柱子表示有387个个体是成年、男性、未生还和三等舱。在左侧,您可以看到每个集合中的个体数量,例如95.05%2092个个体是成年人。在R中有许多可以绘制UpSet图的包,例如UpSetRComplexUpset。希望对您有所帮助。

英文:

This is UpSet plot, an alternative to Venn diagram where you have many categories/sets and very difficult to visualize number of individuals common between sets or of particular intersections. The rows generally represent the sets and the columns or bars indicate intersection size. If a set is not part of the intersection, a light gray circle or just empty white cell is shown. So, the vertical line connecting sets indicates the column-wise relationships between sets. For example in your plot, considering the first bar from left indicates there are 670 individuals that are Adult, Male, not survived, and with crew class. Likewise, the second one indicates that there are 387 individuals that are Adult, Male, not survived and with third class. On the left side you see the number of individuals from each Set, of which for example 95.05% or 2092 individuals are adult. There are many packages in R that are capable of drawing UpSet plots. for example, UpSetR or ComplexUpset. Hope it could helps

答案2

得分: 2

An option could be using the gg_miss_upset function from the naniar package which makes it possible to visualize missing data patterns across your data like this:

library(naniar)
gg_miss_upset(riskfactors)

这个可视化的名称是什么?

<sup>Created on 2023-03-31 with reprex v2.0.2</sup>

英文:

An option could be using the gg_miss_upset function from the naniar package which makes it possible to visualize missing data patterns across your data like this:

library(naniar)
gg_miss_upset(riskfactors)

这个可视化的名称是什么?<!-- -->

<sup>Created on 2023-03-31 with reprex v2.0.2</sup>

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  • 本文由 发表于 2023年3月31日 19:42:33
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